Capucine Baldini

François-Xavier Danlos

Andreea Varga

Matthieu Texier

Heloise Halse

Severine Mouraud

Lydie Cassard

Stéphane Champiat

Nicolas Signolle

Perrine Vuagnat

Patricia Martin-Romano

Jean-Marie Mi- Chot

Rastislav Bahleda

Anas Gazzah

Lisa Boselli

Delphine Bredel

Jonathan Grivel

Chifaou Mohamed-Djalim

Guillaume Escriou

Laetitia Grynszpan

Amelie E Bigorgne

Saloomeh Rafie

Alae Abbassi

Vincent Ribrag

Sophie Postel-Vinay

Antoine Hollebecque

Sandrine Susini

Siham Farhane

Aurelien Parpaleix

Salim Laghouati

Lau- Rence Zitvogel

Julien Adam

Nathalie Chaput

Jean-Charles C Soria

Christophe Massard

Aurelien Marabelle
email: aurelien.marabelle@gustaveroussy.fr

Diane Letourneur

Franc ¸ois-Xavier Danlos

Aure ´lien Marabelle

Y Loriot

J P Guégan

F X Danlos

B Besse

D Planchard

C Robert

C Even

M Khettab

L Tselika

L Friboulet

F André

I Nafia

F Le Loarer

A Bessede

A Italiano
email: antoine.italiano@gustaveroussy.fr

; A Marabelle

; Fx

Danlos

; B Besse

; N Chaput

; C Massard

; D Planchard

; C Robert

; C Even

; M Khettab

; L Tselika

; L Friboulet

; F André

; I Nafia

; F Le Loarer

; A Bessede

Pr Antoine

François-Xavier Danlos

Claudia Grajeda-Iglesias

Sylvère Durand

Allan Sauvat

Mathilde Roumier

Delphine Cantin

Emeline Colomba

Julien Rohmer

Fanny Pommeret

Safety, Recommended Dose and Efficacy Biomarkers for Nintedanib in combination with Pembrolizumab in Patients with Advanced Cancers

Keywords: Phase I, dose escalation, immunotherapy, anti PD-1, anti-angiogenic pembrolizumab, nintedanib, angiogenesis, PD-1 blockade, platinum-based chemotherapy refractory, unresectable pleural mesothelioma, aneuploidy, inflammation, monocyte

durable au traitement semblaient présenter moins de délétion homozygote de CDKN2A que les patients sans bénéfice ; ce qui pourrait être associé à l'augmentation d'expression des gènes transcrit par E2F et la diminution de ceux induits par les interférons de type 1. Une analyse protéique par fluorescence in situ par hybridation a permis d'étudier les délétions du segment 21 du bras court du chromosome 9 (9p21) ; responsable des délétions des gènes de CDKN2A et des interférons de type 1. Des délétions homozygotes de 9p21 ont été observés chez plusieurs patients (4/18 [22%]), mais uniquement chez ceux sans bénéfice au traitement. Par ailleurs, un score estimant la quantité globale d'altérations du nombres de copies de gènes à partir des données d'exomes tumoraux a montré que les patients sans bénéfice aux traitements en présentent significativement plus que les patients avec un bénéfice durable. Ces altérations étaient positivement corrélées aux taux plasmatiques d'IL6 et d'IL8 circulants et au taux de CCL2 tumoral soluble. Ces cytokines et chémokines étaient plus élevés chez les patients ne bénéficiant pas des traitements et associées à des proportions de polynucléaires neutrophiles et de monocytes inflammatoires circulants. Ces analyses ont permis d'identifier des caractéristiques individuelles et tumorales associées à l'efficacité ou la résistance aux traitements administrés à des patients atteints de cancer et particulièrement de mésothéliomes pleuraux dans le cadre d'un essai de phase 1. Ces observations sont intéressantes parce qu'elles permettent de souligner des éléments importants associées à l'inefficacité des traitements. Cependant, le faible effectif de patients inclus dans ces études et le nombre limité d'échantillons analysés imposent d'interpréter ces résultats avec précautions: des travaux complémentaires de confirmation sont indispensables pour valider ou infirmer les résultats observés.

Malignant Mesothelioma immune microenvironment

Human mesothelioma are inflammatory tumors highly infiltrated by T lymphocytes, regulatory T cells and macrophages and less by dendritic cells or B cells (66). Higher PD-L1 expression on tumor cells, higher infiltration by CD8 + T lymphocytes and macrophages were observed in sarcomatoid and biphasic tumors and associated with resistance to platinumbased chemotherapy and shorter overall survival (67-70). A study highlighted that tumors with expression of PD-L1 and sarcomatoid/biphasic tumors presented with higher immune infiltration, and higher infiltration by CD3 + T cells (71).

Among CD4 + T cells, FOXP3 + regulatory cells and PD1 + CD4 + T cells were higher in patients with PDL1 + tumors cells, but was associated to lower production of interferon-gamma (IFNγ) by CD8 + T cells and worse prognostic (71,72). Genome wide copy number analyses from MM tumor samples highlighted that in epithelioid histological subtype, higher rate of genomic alteration was associated with lower overall survival but did not correlate with PD-L1 expression, which suggested that tumor cells developed different evasion mechanisms (73).

Index of Figures

L'essai clinique PEMBIB (NCT02856425) est un essai thérapeutique de phase 1 évaluant l'association d'un anticorps monoclonal anti-Programmed-Death 1 (PD-1) avec le nintedanib, un traitement anti-angiogénique inhibiteur de tyrosine kinases. L'objectif de ce travail de thèse était d'identifier des facteurs biologiques sanguins et tumoraux associés à la survenue d'une réponse antitumorale associée aux traitements par pembrolizumab et nintedanib, administrés à des patients atteints de cancer métastatique ou avancé dans cette étude.

Les patients inclus dans l'étude PEMBIB ont été traités les 7 premiers jours par une monothérapie de nintedanib, puis le traitement par pembrolizumab était administré par voie intra-veineuse toutes les 3 semaines. Les patients étudiés dans ce travail de thèse ont été inclus dans deux cohortes différentes de l'essai clinique. Les premiers patients ont été traités dans la cohorte d'escalade de dose, dédiée à la définition de la dose maximale toléré de nintedanib en association au pembrolizumab, utilisé à la dose unique de 200mg en perfusion intra-veineuse toutes les 3 semaines. La première posologie de nintedanib était de 150mg matin et soir, et le palier supérieur était de 200mg matin et soir. Au-delà, les patients ont été traités dans différentes cohortes, dites d'extension, conçues pour évaluer la tolérance et l'efficacité de la combinaison dans différentes indications. Les patients inclus dans la seconde cohorte analysée dans ce travail étaient atteints de mésothéliome pleural non résécable réfractaire à une chimiothérapie, associant un sel de platine et du pemetrexed. Ces patients n'avaient pas été antérieurement traités par une immunothérapie. Des prélèvements sanguins et tumoraux ont été prospectivement collectés pour la réalisation d'analyses ancillaires. Les prélèvements sanguins ont été réalisés avant le début de traitement par nintedanib (J-7), avant la première perfusion de pembrolizumab (C1J1), 8 jours après la première perfusion de pembrolizumab (C1J8) et avant les 2 ème et 5 ème cycle de traitement (C2J1 et C5J1). Des biopsies tumorales ont été réalisés pendant la période de screening (préalablement à l'administration des traitements) et avant le 2 ème cycle de traitement.

12 patients ont été traités dans la cohorte d'escalade de dose. La dose maximale administrés de 200mg matin et soir de nintedanib a entrainé la survenue de plusieurs toxicités limitant la dose (essentiellement des toxicités hépatiques).

La dose retenue pour les cohortes d'extensions a été de 150mg matin et soir en association au pembrolizumab. Des réponses antitumorales objectives ont été observés chez 3 patients, atteints d'un carcinome thymique, carcinome cervical et carcinome indifférencié du nasopharynx. Les deux patients atteints de mésothéliomes avancés dans cette cohorte ont présenté une progression de leur maladie cancéreuse, sans bénéfice aux traitements. L'analyse des prélèvements sanguins et tumoraux avant le début des traitements ont suggérés que les patients avec un bénéfice durable (absence de progression tumorale avant 6 mois de traitement) présentaient des taux plasmatiques de CXCL10, CCL22 et Tie2 plus élevés que les patients n'en bénéficiant pas. Après l'initiation des traitements, les patients avec un bénéfice présentaient dans les biopsies réalisées avant la deuxième perfusion de pembrolizumab, un infiltrat immunitaire caractérisé par un ratio lymphocytaire T CD3+ / macrophage CD68+, une infiltration de lymphocyte T FOXP3+ et une expression de PDL-1 sur les cellules tumorales plus importantes que ceux ne bénéficiant pas du traitement. 30 patients ont été inclus dans la cohorte dédiés aux mésothéliomes pleuraux non résécables. Parmi ceux-ci, une patiente est décédée d'une toxicité liée à la combinaison thérapeutique, caractérisée par une atteinte cardiaque et des thromboses veineuses et artérielles. Considérant un bénéfice clinique durable par l'absence de survenue de progression tumorale avant 6 mois, 14 patients (46,6%) ont été considéré avec un bénéfice clinique durable. Parmi eux, 7/29 (24,1%) ont eu une réponse partielle antitumorale objective. Le phénotypage immunitaire des cellules sanguines par cytométrie de flux réalisé avant l'initiation des traitements a montré que les patients bénéficiant des traitements présentaient des proportions de lymphocytes T CD4 de type Th1 et de lymphocytes T CD8+ exprimant l'intégrines CD49a plus importantes que les patients ne bénéficiant pas des traitements. Considérant les taux plasmatiques de chémokines, il n'y avait pas de différence entre les patients considérant les taux de CXCL9, CXCL10 ou CXCL13, antérieurement associée à l'efficacité des anti-PD-1, et après la première perfusion de pembrolizumab, les concentrations plasmatiques de ces chémokines ainsi que celles de PD-1 et PD-L1 solubles ont augmentés significativement, de façon pharmacodynamique, chez tous les patients qu'ils aient bénéficié ou non des traitements. Au niveau intra-tumoral, les analyses de l'infiltration immunitaire par immunohistochimie et par déconvolution à partir de données de séquençage complet des ARN messagers (RNA-seq) n'ont pas montrés de différences entre les patients avec un bénéfice ou non aux traitements, considérant les lymphocytes T et notamment les lymphocytes T CD8+. En revanche, l'expression de PD-L1 évaluée en IHC sur les cellules tumorales était significativement plus élevée chez les patients avec un bénéfice durable. L'infiltration de cellules myéloïdes dendritiques estimée par déconvolution des données RNA-seq était significativement plus élevée chez les patients ayant un bénéfice clinique durable aux traitements. L'analyse des expressions différentielles de gènes issues des données RNA-seq des biopsies tumorales réalisées avant l'initiation des traitements montre que des voies oncogéniques (E2F et cKIT), la prolifération cellulaire (phase G2M du cycle cellulaire) et la différentiation épithélio-mésenchymateuse étaient amplifiées dans les tumeurs des patients sans bénéfice au traitement.

A contrario, la transcription des gènes induits par les interférons gamma et de type 1 était diminuée chez ces mêmes patients. L'analyse des exomes tumoraux a permis d'identifier des délétions et des mutations récurrentes chez ces patients, notamment des gènes de BAP1, EP300, NF2, SETD2, CDKN2A ou encore TP53. Les patients avec un bénéfice

Introduction

Background

Immune checkpoint blockade is undergoing extensive investigation in oncology, and has become a new standard of care in many cancer types, in many clinical situations (1,2). Unlike standard chemotherapy or targeted agents, which act mainly on tumor cells, immune checkpoint blockade act by restoring the immune system's ability to eradicate tumors (3). Results of clinical trials evaluating anti-PD-(L)1 monotherapies have shown meaningful and to promote durable tumor response rates, with overall survival benefits compared to conventional treatments. Remarkable activity has been demonstrated in Renal Cell Carcinoma (RCC), Non-Small Cell Lung Cancer (NSCLC), Head & Neck Squamous Cell Carcinoma (HNSCC), Gastric Cancer (GC), Urothelial Cancer (UC), Triple Negative Breast Cancer (TNBC), non-Hodgkin and Hodgkin Lymphoma (4)(5)(6)(7)(8)(9)(10)(11). Thus, trials in many tumor types have validated the relevancy of blocking the PD-1 / PD-L1 pathway in order to unleash specific anti-tumoral CD8 + T cells to eliminate cancer cells and control metastatic disease (12)(13)(14). However, a majority of patients still do not respond to immune checkpoint blockade monotherapy. Clinical and fundamental research is required to develop new immunotherapeutic strategies and overcome such primary resistance to immunotherapies in order to improve cancer patients survival (15)(16)(17)(18).

One main approach to overcome primary resistance to immune checkpoint blockade should be to promote a normalization of angiogenesis in the tumor and limit the pleiotropic pro-tumoral effects of angiogenic factors (19). Angiogenesis is involved in tumor growth and in the development of metastases (20). Tumors induce blood vessel growth by secreting many angiogenic factors. Vascular Endothelial Growth Factor (VEGF) and its high affinity receptor VEGFR-2 are crucial for the formation of new tumor vessels. In addition, there is preclinical evidence that fibroblast growth factor (FGF) and platelet-derived growth factor (PDGF) and their associated receptors substantially contribute to such tumor angiogenesis (21,22). The VEGF -VEGFR-2 axis, besides promoting angiogenesis, may also be involved in stimulating growth of tumor cells themselves via an autocrine loop (23,24). This is suggested by in vitro data and immunohistochemical studies in NSCLC, ovarian carcinoma (OC), colorectal cancer, and also mesothelioma (20). Therefore, suppression of neo angiogenesis via inhibition of the VEGF / VEGFR pathway has become a useful strategy for the treatment of solid cancers (25).

A substantial number of clinical trials with different VEGF and VEGFR inhibitors, in various type of cancers, have demonstrated that this approach can convey clinical benefit, as monotherapy in RCC and HCC as well as in combination therapy with standard chemotherapeutic drugs in NSCLC, CRC, Ovarian Cancer (26)(27)(28)(29)(30).

Unresectable pleural mesothelioma is a rare cancer evolved to pleural leaflets, primary induced by asbestos exposure and for which immune checkpoint blockade and anti-angiogenics drugs, in different clinical settings, have shown antitumoral efficacy. In this thesis, I will present the biomarkers identified to be associated with the efficacy or resistance to the association of pembrolizumab (an anti-PD-1 monoclonal antibodies) with nintedanib (an anti-angiogenic tyrosine kinase inhibitor); first in an escalation cohort of patients with several cancer type, then in an expansion cohort dedicated to patients with pleural unresectable mesothelioma.

Malignant Mesothelioma, a disease with angiogenesis and immune dysregulation Malignant Mesothelioma pathophysiology

Malignant mesotheliomas are infrequent and severe cancers arising from mesothelial cells (31). Mesotheliomas have been mainly associated to the environmental and professional exposure to asbestos (32). Our current understanding of the pathophysiology of the disease is that non-degradable asbestos mineral fibers are deposited into mesothelial cells and phagocyted by macrophages, inducing a chronic inflammation in situ which could led to the development of malignant mesotheliomas after a long period of clinical latency (33)(34)(35). More specifically, mesothelial cells exposed to asbestos undergo an oxidative stress with the generation of reactive oxygen species (ROS) which can lead to DNA mutagenesis and chromosomal damage (33). Just like a damage associated molecular pattern (DAMP) molecule, asbestos fibers can stimulate the NOD-like receptor family pyrin domain containing 3 (NLRP3, or Nalp3) which induces the production of pro-inflammatory cytokines IL1β, IL18 and macrophages recruiting chemokines such as C-C chemokine ligand 2 (CCL2) (36)(37)(38). The necrosis of mesothelial cells induced by asbestos fibers leads to the release of high-mobility group box 1 protein (HMGB1) which activates macrophages and stimulates their production of tumor necrosis factor (TNF) (39). Those pro-inflammatory cytokines together with the accumulation of DNA damages contribute to the mesothelial cells transformation and their increased survival, leading to an oncogenesis process (40)(41)(42)(43).

Malignant Mesothelioma molecular subtypes

Malignant Mesotheliomas have been classified in three major histologic subtypes with increasing aggressiveness: epithelioid, biphasic and sarcomatoid (median survival of 9-12 months for the first two types and 4 months for the sarcomatoid type) (44,45). International guidelines for the classification of malignant mesotheliomas have recently evolved, integrating new molecular data characterizing mesothelioma cells and taking into account the tumor microenvironment

Efficacy of immunotherapies in Mesothelioma

Over the last decades, few therapeutic innovations have significantly benefited to the treatment and outcome of MMs (Figure 1). The advent of immune checkpoint blockade therapy in oncology has recently also revolutionized the standard of care in mesothelioma (1). Indeed, in a randomized phase 3 clinical trial, the combination of anti PD-1 (nivolumab) and anti-CTLA-4 (ipilimumab) monoclonal antibodies improved the median overall survival by 4 months compared to conventional platinum-based chemotherapy with pemetrexed in patients with untreated unresectable pleural mesothelioma (74). Interestingly, the nivolumab + ipilimumab combination immunotherapy generated a similar objective response rate than chemotherapy (n=120/303, 40%; 95% confidence interval [IC] 34-45 and n=129/302, 43%; 95% CI 37.1-45.4, respectively) (74). However, only immune checkpoint blockade therapy obtained complete responses in 5/303 (2%) patients and showed durable remissions with a 2-year duration of response rate of 32% (95% CI [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] compared to 8% (95% CI 3-15) with chemotherapy. Overall, those results demonstrated that immune modulation by targeting T-cell checkpoints was superior to conventional cytotoxic chemotherapies for untreated pleural mesothelioma (3). (74)(75)(76).

THREE PHASE 3 CLINICAL TRIALS BROUGHT IMPROVEMENT OF PATIENT'S OVERALL SURVIVAL IN LAST DECADES

However, the value of immune checkpoint blockade for patients with unresectable pleural MM who are refractory to platinum-based chemotherapy with pemetrexed has been less successful. Although superior in terms of ORR (22% for pembrolizumab vs 6% for chemotherapy by blinded independent radiological review), anti PD-1 monotherapy could not increase OS and neither progression free survival (PFS) compared to single-agent chemotherapy in a randomized phase 3 clinical trial (77). Interestingly, open-label phase 2 clinical trials suggested that PD-1/PD-L1 blockade monotherapy would be less efficient than combination with anti CTLA-4 monoclonal antibody (78)(79)(80)(81)(82). The MAPS2 trial, a non-comparative randomized phase 2 trial for patients with advanced pleural MM refractory to pemetrexed and platinum-based chemotherapy illustrated that disease control rate at 12 weeks tended to be higher with combination than monotherapy (27/54 Pts -50%; 95% IC and 24/54 Pts -44%; 95% IC , respectively) (83). Moreover, tumor hyper progression phenomena occurred in 6/59 patients (10%) treated with nivolumab and 2/55 patients (4%) treated with nivolumab and ipilimumab (83).

The FDA has now approved the nivolumab + ipilimumab combination as the standard of care in first line for the treatment of unresectable pleural MM. With this approval in first line and the disappointing results obtained by anti-PD1 monotherapies in second line for platinum-based chemotherapy refractory diseases, there are limited options for patients relapsing or refractory to either checkpoint blockade of chemotherapies. Therefore, novel therapeutic strategies are eagerly needed for patients with MM in situations of primary or secondary resistance to immune checkpoint blockade. Several ongoing clinical trials are evaluating therapeutics combination with immune checkpoint blockade but only few allowed previous treatment by anti PD-1 or anti CTLA-4 monoclonal antibodies (Table 1). Genomic and epigenetic drivers of malignant mesothelioma development promote inflammatory microenvironment in mesothelioma.

Interestingly, in the above mentioned randomized phase 3 trial of first line ipilimumab + nivolumab in MM, the therapeutic benefit of immune checkpoint blockade was higher in patients with sarcomatoid/biphasic MM compared to epithelioid MM, and in diseases which expressed more than 1% of PD-L1 on tumor cells (74). Those results highlight the fact that a better understanding of the histological, molecular and microenvironment of MMs should determine the features associated with resistance to immunotherapy. Genomic, transcriptomic, and epigenetic characterization of MM in the last decades brought important knowledge on MM physiopathology (Figure 2). Cytogenetic analyses have shown that MM are characterized by frequent deletions of chromosomal segments, such as 3p, 9p and 22q or present with chromosomal monosomies, leading to the loss of several tumor suppressor genes (84)(85)(86). The most common cytogenetic abnormality in MM is the homozygous deletion of 9p21 which leads to complete loss of CDKN2A gene (87,88). The CDKN2A protein is a powerful inhibitor of the formation of active cyclin D / cyclindependent kinases (CDK) 4 and 6 (89,90). CDKN2A deletion therefore leads to a strong inhibition of the retinoblastoma protein (RB1) by CDK4/6 proteins, and an upregulation of the transcription of genes that are targeted by E2 factors (E2F) (Figure 2). Loss of regulation by CDKN2A leads to chronic activation of the CDK4/6-E2F pathway by the mitogen activated protein kinase (MAPK) signaling pathway, secondary to the multiple stimuli from the tumor microenvironment (VEGF, PDGF, FGF, TNF or IL1). Over stimulation of transcription of the E2F target genes leads to cell proliferation (transition of phase G1 to S in cellular cycle, then S to G2 by FOXM1 target transcription) and resistance to apoptosis (Figure 11).

Neurofibromin 2 (NF2) is a regulator of the Hippo pathway, an evolutionarily conserved signaling pathway which regulates organ size and maintains tissue homeostasis (91). NF2 is another tumor suppressor gene frequently lost by mutations or deletion of the chromosomic region 22q (84,92). The NF2 gene encodes for a protein called Merlin, which controls the "Hippo" pathway. This pathway includes the main cytosolic serine/threonine kinases, mammalian STE20-like protein kinase 1 (MST1/STK4) and MST2/STK3, and the large tumour suppressor 1 (LATS1) and LATS2, which are associated with adaptor proteins (93). These proteins limit the nuclear translocation of the oncoproteins Yes-associated protein (YAP) by phosphorylation and the transcriptional activities of the transcriptional co-activator with PDZ-binding motif (TAZ) (94). Merlin regulates the Hippo pathway through interactions with LATS1/2 and its deletion leads to cell stemness properties, survival and proliferation; notably by interactions between YAP/TAZ, CDK4/RB1 and TP53 (95,96). Signals received from the tumor micro-environment amplify this pathway through activation of the MAPK and TGF/SMADs pathways and a loss of cellular mechano-transduction induced by epithelial to mesenchymal transition (97).

BAP1 loss is another main genomic alteration associated with MM development. To note, germline mutation of BAP1 have been identified as a hereditary syndrome responsible for familial MM, uveal melanoma and renal carcinoma (98,99). BAP1 is a ubiquitin carboxy-terminal hydrolase, promoting deubiquitylation of itself and other enzymes, and has important function to prevent DNA damages, genomic instability and also regulate gene transcription to control cell survival and proliferation [START_REF] Costello | A community effort to assess and improve drug sensitivity prediction algorithms[END_REF][START_REF] Manica | Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders[END_REF]. BAP1 loss can be obtained by deletion of 3p21 region or can be inactivated by somatic non-synonymous mutations and post-translational deregulation (86,[START_REF] Mo ¨sch | Machine learning for cancer immunotherapies based on epitope recognition by T cell receptors[END_REF][START_REF] Jamieson | Gene-expression profiling to predict responsiveness to immunotherapy[END_REF]. Its loss can lead to an accumulation of DNA double strand breaks by destabilization of its binding with BARD1 [START_REF] Mo ¨sch | Machine learning for cancer immunotherapies based on epitope recognition by T cell receptors[END_REF][START_REF] Tuxen | Copenhagen prospective personalized oncology (CoPPO)dclinical utility of using molecular profiling to select patients to phase I trials[END_REF]. BAP1 participates to the preservation of genome stability, to DNA synthesis and to genes transcription by interaction and deubiquitylation of chromatin remodeling complexes [START_REF] Rodon | Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial[END_REF][START_REF] Tanjo | Practical guide for managing large-scale human genome data in research[END_REF][START_REF] Alpern | BRB-seq: ultra-affordable highthroughput transcriptomics enabled by bulk RNA barcoding and sequencing[END_REF]. BAP1 also interacts with ASXL1 to form a complex which leads to deubiquitinate the histone H2A lysine 119, and to inhibit the polycomb repressive complexe (PRC) 1/2 activity [START_REF] Mai | Low-cost, low-bias and lowinput RNA-seq with high experimental verifiability based on semiconductor sequencing[END_REF]. Loss of BAP1 in mice is associated with elevated enhancer of zeste 2 polycomb respressive complex 2 subunit (EZH2), increase of trimethylated histone H3 lysine 27 (H3K27me3) and repression of PRC2 targets transcription [START_REF] Stoeckius | Simultaneous epitope and transcriptome measurement in single cells[END_REF]. Interestingly, CDKN2A gene transcription was shown to be abolished by H3K27 methylation and EZH2 activity and, as a negative retro control, EZH2 activity is promoted by RB1/E2F pathway [START_REF] Marx | Method of the Year: spatially resolved transcriptomics[END_REF][START_REF] Larsson | Spatially resolved transcriptomics adds a new dimension to genomics[END_REF].

Whole exome sequencing analyses of MM tumor samples have identified other mutations, particularly in TP53, SETD2, SETDB1 or DDX3X which are also implicated in cellular cycle, histone methylation and alternative splicing of RNA [START_REF] Jamieson | Gene-expression profiling to predict responsiveness to immunotherapy[END_REF]112,[START_REF] Liu | Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension[END_REF]. Characterization of human MM tumor samples by transcriptomic analyses have shown that the deletions and mutations previously described are associated with dysregulation of concordant pathways, such as Hippo, histone methylation and p53 ones [START_REF] Jamieson | Gene-expression profiling to predict responsiveness to immunotherapy[END_REF] and are correlated with specific immune infiltrates in the tumor microenvironment and impact MM survival [START_REF] Kharchenko | Bayesian approach to single-cell differential expression analysis[END_REF]. This knowledge on pathophysiological mechanisms implicated in malignant mesothelioma development suggested that immune checkpoint blockade and anti-angiogenic drugs could be an efficient therapeutic strategy to treat patients with these diseases.

Neo-angiogenesis and hypoxia are hallmarks of cancer and targets of distinct therapeutic interventions.

Neo-angiogenesis as a hallmark of cancers.

Neo-angiogenesis and abnormal vasculature development in the tumor microenvironment have been identified since decades as a major hallmark of cancer development [START_REF] Sompairac | Independent component analysis for unraveling the complexity of cancer omics datasets[END_REF]. Blood vessels in tumors present with an aberrant morphology, characterized by excessive branching, abundant and abnormal ends, discontinuous endothelial cells lining, and defective basement membrane and pericyte coverage [START_REF] Nazarov | Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients[END_REF][START_REF] Maaten L Van Der | Visualizing Data using t-SNE[END_REF]. These features are associated with impaired vascular maturation, poor vessel functionality and incoherent tumor perfusion [START_REF] Becht | Dimensionality reduction for visualizing single-cell data using UMAP[END_REF]. During tumor progression, angiogenesis is almost always activated leading to persistent sprouting of new vessels that help to sustain expansion of tumor growths [START_REF] Qiu | Reversed graph embedding resolves complex single-cell trajectories[END_REF]. Angiogenesis is promoted by signaling proteins that bind to stimulatory or inhibitory cell surface receptors displayed by vascular endothelial cells, which are induced by hypoxia in tumor microenvironment [START_REF] Chen | Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM[END_REF]. The wellknown angiogenesis inducer is vascular endothelial growth factor-A (VEGF-A) [START_REF] Wolf | PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells[END_REF]. The VEGF-A gene encodes ligands that are involved in orchestrating new blood vessel growth during embryonic and postnatal development, and then in homeostatic survival of endothelial cells, as well as in physiological and pathological situations in the adult [START_REF] Sun | Cancer progression modeling using static sample data[END_REF]. VEGF signals through three receptors of tyrosine kinases (VEGFR-1-3), which are regulated at multiple levels. And VEGF gene expression can by upregulated both by hypoxia and by oncogenic stress signaling [START_REF] Aibar | SCENIC: single-cell regulatory network inference and clustering[END_REF].

Pro-angiogenic factors can be also produced in the tumor microenvironment by immune cells.

Cancer cells are an important source of VEGF-A and other pro-angiogenic mediators, but immune cells recruited in the tumor microenvironment, such as macrophages, polymorphonuclear cells, NK and B cells, can also be a source of proangiogenic factors. Particularly, tumor associated macrophages (TAM) secrete growth factors and inflammatory cytokines that support angiogenesis by promoting endothelial cells survival, activation and proliferation [START_REF] Aubin-Frankowski | Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference[END_REF]. TAMs are an important source of VEGFA in tumors [START_REF] Specht | LEAP: Constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering[END_REF]. TAM-derived VEGF-A also enhances vascular permeability, thereby facilitating cancer cell intravasation and metastasis [START_REF] Chan | Gene regulatory network inference from single-cell data using multivariate information measures[END_REF]. Pro-angiogenic factors produced by TAMs include also Placental growth factor (PlGF) and VEGF-C, tumor necrosis factor (TNF), interleukin-1β, (IL-1β), IL-6, CXC-chemokine ligand 8 (CXCL8; also known as IL-8) and fibroblast growth factor 2 (FGF2) [START_REF] Xu Rui | Clustering[END_REF]. A recent subpopulation of circulating blood monocytes can be distinguished from other circulating monocytes by their expression of the angiopoietin receptor TIE2 (also known as TEK) [START_REF] Blondel | Fast unfolding of communities in large networks[END_REF]. Tie2 expressing monocytes (TEM) have been identified in the peripheral blood of both humans and mice and are distinct from the previously described circulating endothelial cells or endothelial progenitors' cells that also express TIE2 [START_REF] Traag | From Louvain to Leiden: guaranteeing well-connected communities[END_REF]. Macrophages expressing Tie2 are found in human tumors and have also been identified in mice [START_REF] Levine | Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis[END_REF]. Unlike peripheral blood monocytes, TEM do not express chemokine (C-C motif) receptor 2 (CCR2) and are unlikely to respond to CCL2, suggesting that TEM are recruited to tumors by a mechanism different to that of monocytes that are destined to differentiate into TAM [START_REF] Blondel | Fast unfolding of communities in large networks[END_REF]. The monocyte attracting chemokines CCL3, CCL5 and CCL8 are also upregulated in tumors, and these could cause recruitment of TEM through chemokine receptors CCR1 or CCR5. However, TEM could also be recruited by the TIE2 ligand, angiopoietin 2, a cytokine abundantly expressed by both hypoxic tumor cells and endothelial cells in tumor blood vessels [START_REF] Stuart | Comprehensive integration of single-cell data[END_REF]. Angiopoietin 2 may not only help to recruit TEM into tumors but also regulate their release of angiogenesis-modulating, inflammatory and immunosuppressive cytokines [START_REF] Regev | The human cell atlas white paper[END_REF][START_REF] Newman | Robust enumeration of cell subsets from tissue expression profiles[END_REF].

Angiogenic factors and hypoxia lead to immunosuppression in the tumor area and inhibit the antitumor response.

Circulating immune cells have to interact with endothelial cells from tumor blood vessels in order to infiltrate tumors and develop an efficient anti-tumor immune response [START_REF] Li | TIMER2.0 for analysis of tumor-infiltrating immune cells[END_REF][START_REF] Aran | xCell: digitally portraying the tissue cellular heterogeneity landscape[END_REF]. Leukocytes can adhere to the endothelium and transmigrate across the vessel wall thanks to specific interactions between membrane molecules [START_REF] Yoshihara | Inferring tumour purity and stromal and immune cell admixture from expression data[END_REF]. Angiogenic molecules present in the tumor microenvironment, such as VEGF-A, can control the trafficking of immune cells to the tumor by altering the expression of such adhesion molecules, including the integrin ligands intercellular adhesion molecule 1 (ICAM1) and the vascular cell adhesion protein 1 (VCAM1), on endothelial cells and immune cells [START_REF] Shen-Orr | Cell type-specific gene expression differences in complex tissues[END_REF]. Endothelial cells in tumors can also express the immune-checkpoint protein programmed cell death 1 ligand 1 (PD-L1), which binds to PD-1 expressed on T cells and thereby suppresses their anticancer activity [START_REF] Baron | A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure[END_REF]. In addition, tumors can secrete soluble factors, such as chemokines (CCL2, CCL28, CXCL8 and CXCL12), angiopoietin 2, VEGF, PlGF, and adenosine, which promote the recruitment of immunosuppressive cells (cf review 1 in annexes and ( 139)). These cells include immature dendritic cells (DC), regulatory T cells (Treg), myeloid-derived suppressor cells (MDSCs), and TAM with pro-tumor immunosuppressive phenotypes [START_REF] Monaco | RNA-seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types[END_REF]. In turn, through the production of VEGF, angiopoietin 2, IL-10, and TGFβ, these leukocytes collaborate with tumor cells and endothelial cells to promote angiogenesis and both local and systemic immunosuppression [START_REF] Sturm | Comprehensive evaluation of transcriptomebased cell-type quantification methods for immuno-oncology[END_REF].

VEGF is also well known to induce immunosuppression [START_REF] Petitprez | The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression[END_REF]. VEGF inhibits DC maturation and antigen presentation, limits T cell activation and anti-tumor immune responses [START_REF] Chen | Inference of immune cell composition on the expression profiles of mouse tissue[END_REF][START_REF] Repsilber | Biomarker discovery in heterogeneous tissue samples[END_REF]. VEGF directly inhibits CD8 + T cells trafficking, proliferation, and effector functions (145). Moreover, angiopoietin 2 has been shown to promote immunosuppression in tumors. Angiopoietin 2 leads to the upregulation of adhesion molecules by endothelial cells, increases tumor infiltration by MDSCs or TEM and suppress anti-tumor function of such monocytes derived cells in the tumor microenvironment [START_REF] Regev | The human cell atlas white paper[END_REF][START_REF] Gaujoux | Semi-supervised Nonnegative Matrix Factorization for gene expression deconvolution: a case study[END_REF].

VEGF-A promotes immunosuppressive function of Antigen Presenting Cells (APC)

Mature DC are key elements of the anti-tumor immune response as they can present tumor-specific antigens on class 1 major histocompatibility complex (MHC) molecules and trigger efficiently CD8 + T-cells cytotoxic immune responses [START_REF] Xie | A deep autoencoder model for gene expression prediction[END_REF]. However most tumor infiltrative DCs are immature and impaired in their functions; they promote tumor tolerance rather than tumor immunity [START_REF] Dincer | Adversarial deconfounding autoencoder for learning robust gene expression embeddings[END_REF]. VEGF-A affects DC maturation in vitro [START_REF] Chen | Inference of immune cell composition on the expression profiles of mouse tissue[END_REF]149). A study revealed that VEGF-A can alter the differentiation of monocytes into DC (150). This effect was reversed by an anti-VEGF-A monoclonal antibody (bevacizumab) or by sorafenib, an anti-angiogenic molecule targeting different receptors (VEGFR, PDGFR, and RAF kinases) (150). Accordingly, the proportion of circulating immature DCs in patients with cancer is correlated with the stage of the disease and the level of circulating VEGF-A, and is partially corrected by surgery or anti-VEGF treatment [START_REF] Dincer | Adversarial deconfounding autoencoder for learning robust gene expression embeddings[END_REF]151).

VEGF-A promotes the recruitment and activation of regulatory T-cells (Tregs)

VEGF-A favors the immature phenotype of tumor associated myeloid cells or APC which in turn sustain the development of Tregs, key actors of the tumor immune tolerance. This phenomenon is induced via TGF-β either directly or indirectly (152,153). Moreover, it has been demonstrated that VEGF-A could also directly induce Treg proliferation in patients with cancers in a VEGFR-2 dependent manner (154-156). Higher infiltration of colorectal cancer microenvironment by VEGFR-2 + Tregs has been correlated with a worst outcome after surgical resection of localized colorectal cancer (157).

Bevacizumab, as well as sunitinib, an anti-angiogenic tyrosine kinase inhibitor, were shown to decrease in vivo in a murine model the regulatory T cells infiltration of tumors , mostly mediated by an inhibition of Treg proliferation (155).

A similar decrease of circulating Treg proliferation was induced by bevacizumab in patients treated for metastatic colorectal carcinoma in combination with 5-fluorouracil and oxaliplatin (FOLFOX) (155).

VEGF-A limits the functions of anti-tumor cytotoxic CD8 + T-cells.

Intra-tumoral hypoxia induces VEGF-A, which promotes intrinsic abilities of tumor cells to resist to cytotoxic T lymphocyte mediated lysis (158). VEGF-A, together with immunosuppressive cytokines, can also induce the expression of Fasligand on endothelial cells surface, which induces CD8 + T cells apoptosis and promotes Treg infiltration into tumors (159). Indeed, anti-angiogenic drugs (and notably bevacizumab) have been reported to increase T cell trafficking into the tu-mor bed. Specifically, bevacizumab has been shown to affect DC priming of T cells through the stimulation of DC maturation and inhibition of myeloid derived suppressor cells (160). Moreover, VEGF-A had direct effects on human VEGFR-2 + CD8 + T cells through NFAT and TOX transcriptional activities and induces T cell exhaustion, with expression of checkpoint molecules such as PD-1, T-cell immunoglobulin mucin-3 (Tim3) or Lymphocyte Activation Gene 3 (LAG3) (145,161). Proliferation of CD8 + T cells after in vitro stimulation is limited by VEGF-A through VEGFR-2 expression (162,163).

Development of anti-angiogenic drugs in medical oncology.

Treatments blocking angiogenic pathways in cancers are powerful drugs theoretically acting directly on tumor cells growth and indirectly through inhibition of neo-angiogenesis and increasing immune cells infiltration in the tumor microenvironment. Those treatments have been developed to prevent the interactions between extra-cellular soluble or membranous VEGF with their receptors VEGFR-1/2 and to limit the expression of VEGF/VEGFR intracellular pathways (164). Several agents, including bevacizumab, aflibercept, and ramucirumab, that target the VEGF/VEGFR interactions have been developed and are now approved across several indications. Tyrosine kinase inhibitors targeting the VEGF/VEGFR pathways are numerous and present the particularity to not be exclusively specific of this pathway, and to inhibit other intracellular pathways, such as VEGFR-3, PDGFR-1, Kit, Flt3 and less frequently FGFR, Tie2 or RAF. Although they all aim at inhibiting the angiogenesis, those drugs are not strictly equivalent as described below.

Bevacizumab

Bevacizumab is a humanized monoclonal antibody that targets VEGF-A to prevent its interaction with VEGFR-1 and -2.

It was the first targeted antiangiogenic approved for use in oncology (165). Currently approved as combination therapy for first-and second-line treatment of metastatic colorectal cancer (mCRC) and metastatic renal cell carcinoma (mRCC) and for first-line therapy of unresectable, locally advanced, recurrent, or metastatic non-small cell lung cancer (NSCLC).

Although an important advance in treatment, the monotherapy of bevacizumab provides only a modest survival benefit, with inconsistent effects in different tumor types. Responses to bevacizumab are often transient, and patients could experience disease progression as an adaptive response to ongoing therapy or following treatment withdrawal (166,167). Early studies with bevacizumab across a variety of cancer types have established a set of adverse events associated with anti-angiogenic treatments, with the most documented toxicity being hypertension (168). The most common adverse events of bevacizumab are epistaxis, headaches, hypertension and proteinuria (169).

Aflibercept

Aflibercept is a fusion protein that consists of VEGF-binding portions from the extracellular domains of human VEGFR-1 and -2 fused to the Fc portion of human immunoglobulin G1 (IgG1) (170). Aflibercept functions as a decoy receptor by neutralizing the available VEGF-A and -B and PlGF and making the ligands unavailable to bind and activate VEGFRs.

The ability of aflibercept to bind multiple VEGF ligands should provide a different blockade of angiogenesis compared to bevacizumab. Indicated for patients with mCRC that is resistant to or that has progressed following an oxaliplatincontaining regimen, the approval of aflibercept was based on findings from a phase III trial that when combined with folinic acid, fluorouracil, and irinotecan it significantly improved OS relative to control arm (171). Adverse events of aflibercept were very similar to those seen with bevacizumab.

Ramucirumab

Ramucirumab is a fully humanized IgG1 monoclonal antibody targeting the extracellular domain of VEGFR-2 (172).

Ramucirumab had been evaluated as monotherapy and in combination in previously treated patients with advanced gastric cancer, NSCLC and breast cancer (173-175). Furthermore, ramucirumab was well tolerated, with most adverse events occurring at a similar frequency in the ramucirumab and placebo arms.

Tyrosine kinases inhibitors.

Tyrosine kinases exert their functions by transducing extracellular signals into the cells and play a key role in the intracellular homeostasis (176). Receptors of tyrosine kinases have an extracellular domain which can bind specific ligands, a single trans-membrane helix, and an intracellular region which contains a protein tyrosine kinase domain (177). The intracellular domain, also called kinase domain, presents with a N-terminal lobe, an adenosine triphosphate (ATP) binding cleft and a C-terminal lobe. Ligands binding to the extracellular domain induces dimerization and allows autophosphorylation of the intracellular domains and activation of the receptor's tyrosine kinase (177). Receptors of tyrosine kinases catalyze the transfer of the phosphates of ATP to the hydroxyl group of tyrosine residues on target proteins (178). VEGF, FGF, PDGF, EGF or insulins receptors are part of the tyrosine kinase receptors family.

VEGF-A mediates its effects by binding to its two high-affinity receptors: VEGFR-1 and VEGFR-2. There are at least four members in the PDGF family (PDGF-A, PDGF-B, PDGF-C, and PDGF-D) and they act through PDGFR-α and PDGFR-β (179). Meanwhile, bFGF, belonging to the FGF family, also contributes to angiogenesis (180). Therefore, small tyrosine kinase inhibitory molecules have been developed as anti-angiogenics drugs and have antitumoral efficiency in patients with several cancer types (Table 2). These drugs have different inhibition constant (Ki) for kinases, as resumed in Factors associated with the resistance of tumor to anti-angiogenic drugs.

Tumor cells adapt to VEGF blockade and hypoxia by redundancy in angiogenic signaling and activation of compensatory signaling pathways of angiogenesis, summarized in Figure 4 (reproduced from (18), with the authorization of Dr Mihaela Aldea). Mechanisms of resistance partly implicate redundancy between proangiogenic pathways, strong interaction with stromal cells such as cancer-associated fibroblast and macrophages, pro-inflammatory cytokines induced by secondary hypoxia and stimulation of reactive pathways such as indication of MAP Kinase by increased of apelin. Immune checkpoint blockade in onco-immunology.

The immune system, through all its orchestrated and integrated effectors, from innate sensing and response to damage signals from cancer cells up to the adequate adaptive cytotoxic and humoral T and B cells responses, is closely implicated in tumor control and growth [START_REF] Monaco | RNA-seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types[END_REF]. By using preclinical studies, the concept of "cancer immunoediting" has described distinct steps leading to efficient immune control of tumor cells vs immune responses facilitating tumor development and metastatic spreading of cancers (191). Several therapeutic strategies dedicated to increase immune responses against tumors were developed to treat patients with advanced cancers. William Coley is considered as a pioneer through intra-tumoral injections of bacteria and bacteria's toxins in patients with unresectable sarcomas (192). Clinical trials evaluating type 1 interferons and IL-2 administrations in patients with advanced melanoma, renal cell carcinoma and hepatocellular carcinoma showed efficiency in patients but their uses had not been generalized to all fields of medical oncology mostly because of their low therapeutic index (i.e. high toxicities and low efficacy) (193)(194)(195)(196). Moreover, a better understanding of immune responses from cytotoxic CD8 + T lymphocytes against class 1 major histocompatibility complex (MHC) restricted tumor associated antigens, such as MAGEA1, gp100, Wt1 or Hsp7, led to the development of anti-tumoral vaccination strategies, but again with modest results (197)(198)(199)(200). However, these therapeutic strategies did not allow to overcome and explain the Hellström paradox: why the cellular infiltrates in tu- Clinical development of immune checkpoint blockade for patients with cancers.

First, anti CTLA-4 antibody therapy has been shown to induce durable regression of tumors in animal models (213).

The first translation of this therapeutic strategy into humans with ipilimumab, an IgG1 anti-CTLA-4 antibody, was tested in cancer patients in 2000. It led to tumor regressions and were unfrequently associated with adverse events related to tissue specific inflammation (214,215). The most common of these toxicities included colitis, hepatitis, and dermatitis, which could be frequently controlled by corticosteroids. The clinical activity of CTLA-4 blockade was most apparent in patients with advanced metastatic melanoma, with a 15% rate of objective radiographic response that has been durable in some patients for >10 years since stopping therapy (7,8).

Nivolumab was the first anti PD-1 checkpoint blocker administered into a patient in a phase 1 clinical trial in 2006 (216). Among the 16 patients included, six (37.5%) had objective tumor responses, including patients with melanoma, renal cell carcinoma, and non-small cell lung cancer (NSCLC) (216). The anti-PD-1 antibody pembrolizumab clinical development began in April 2011 (the antibody was initially called lambrolizumab). The phase 1 clinical trial of pembrolizumab, for patients with melanoma and NSCMC was one of the larger ever conducted in oncology (217,218). FDA approvals of PD-1-blocking antibodies for the treatment of patients with refractory melanoma and advanced NSCLC happened in 2014 and in 2015, respectively. The first anti-PD-L1 antibody approved was atezolizumab for urothelial cancers in 2016, followed by avelumab for Merkel cell carcinoma in 2017.

As opposed to chemotherapies and tumor-targeted therapies, objective tumor responses with those immune-targeted therapies are durable. It was hoped that such immunotherapy could induce long-lasting responses, because of the ability of T cells to maintain memory to their target, and a polyclonal response that the cancer should have trouble escaping. However, a significant proportion (~50%) of patients with partial responses to anti-PD(L)1 monotherapies eventually have their disease relapsing (219).

Anti PD-1 blockade monotherapy has a relatively favorable toxicity profile, with toxicities requiring medical intervention (grades 3 to 4) in the range of 10 to 15% in most series (220). The most common immune related adverse events are fatigue, diarrhea, rash, and pruritus. In a smaller percentage of patients, toxicities are more serious and include several endocrinopathies, in which the immune system infiltrates a hormone producing gland, leading to permanent dysfunction that requires lifelong substitutive hormonal therapy, such as thyroid disorders (10 to 15%), hypophysitis, adrenal gland disorders (1 to 3%), and type 1 diabetes (1%). Serious visceral organ inflammatory toxicities are uncommon (~1%) but can affect any organ, such as lung or heart, leading to life threatening adverse events (221).

Mechanisms of action of anti PD-1 immune checkpoint blockade.

It is believed that patients respond to single-agent anti-PD-1 or anti-PD-L1 therapy because of a preexisting antitumor Characteristics associated with outcomes of patients treated by immune checkpoint blockade.

Between the two main immune checkpoint blockade classes, anti CTLA-4 and anti PD-1/PD-L1 monoclonal antibodies, most efforts were done to identify tumoral and individual features associated with efficiency of treatment with PD-(L)1 blockers. There are several characteristics associated with the pharmacodynamics of the treatment, the tumor contexture (cancer cells and their stromal microenvironment), and the features of the host.

Immune checkpoint blockers do not have similar pharmacodynamic effects.

Pharmacodynamic effects of immune checkpoint blockade are different between anti CTLA-4 and anti-PD-1 monoclonal antibodies. Different doses of anti CTLA-4 antibodies were used in clinical trials, in monotherapy or in combination with anti PD-1 antibodies. There was a correlation between the concentrations of ipilimumab, its toxicity and its therapeutic efficacy (236,237). On the contrary, the anti-PD1 immunotherapies nivolumab and pembrolizumab efficiencies

were not related to their concentration (238). These differences may be due to the biochemical structures of those drugs and their constant fragment of Ig (Fc) isotype and their construct: ipilimumab is an IgG1k, nivolumab is an IgG4 and pembrolizumab is an IgG4k. The IgG1 sequence of ipilimumab is able to link to all Fc gamma receptors (FcGR). The activity of ipilimumab seems to be dependent of linkage of cells with FcGR, such as dendritic cells or macrophages in tumors (239,240 High tumor mutational burden is associated with better efficiency of anti PD-1 antibodies.

Recognition of cancer cells by the immune system is believed to be related to the increased tumor mutational burden (TMB) in cancers (222,251). However, not all mutations seem to have the necessary qualities to give rise to robust targets of an antitumor immune responses (252). The processing and presentation by MHC molecules of neoepitopes that result from mutations shapes the landscape of neoantigens recognized by antitumor T cells (253,254). A low preexisting T cell infiltration in tumors may be due to the low immunogenicity of such somatic mutations, therefore not favoring T-cell responses against neoantigens (251). The oncogenic conditions leading to high TMB are associated with a better antitumoral efficiency for patients treated by PD-1 blockade (255). Examples of oncogenic processes leading to high TMB Cancers include:

-Cancers with Micro Satellite-Instability (MSI) secondary to mismatch-repair deficiency (MMrD); those tumors present with a high frequency of insertions and/or deletions (indels) (256);

-Cancers with mutations in the DNA polymerase epsilon (POLE); those tumors are associated with an ultra-mutator phenotype, notably in endometrial tumors (257);

-Cancers with chronic ultraviolet light-induced point mutations such as desmoplastic melanoma (258);

In these cases, response rates are around 50 to 90%. TMB is considered high when the number of mutations per megabase is higher than 10. This threshold is part of the labeling for the approval by the FDA of pembrolizumab for TMB high cancers (259). However, this threshold might not be optimal for all cancers and there is no consensus on the method to measure TMB precisely. Efforts to homogenize the measurement of TMB is mandatory to be really used in real life clinical practice (260).

Preexisting tumor cells expression of PD-L1 is associated with increased efficacy of PD-1 blockade.

The value of PD-L1 expression on tumor and immune cells to predict the efficacy of anti-PD1 was early identified after the fundamental discovery of the molecule (211). It was early studied as a potential markers associated with efficacy of PD-1/PD-L1 blockade (216). As each companies developing an anti PD-1 developed a companion test for immunohistochemistry (IHC) study of PDL1 expression, an effort had been done to homogenize clinical practice (261). In phase 3 clinical trials, the expression of PD-L1 on tumor cells or on both tumor and immune cells in stroma was associated overall with better anti tumoral responses and overall survival of patients receiving anti PD-1 in monotherapy (9,262).

However, there are subtle differences across tumor histologies (e.g PD-L1 has predictive value mostly when expressed on cancer cells but not immune cells in NSCLC and vice versa in bladder cancers).

Preexisting tumor infiltration by coordinated adaptive immune response is associated with a better efficacy of PD-1 blockade.

The infiltration of the tumor microenvironment by immune cells and particularly lymphocytes has been known for decades (201). The prognostic value of such infiltration is not similar for each tumor type. In colorectal cancer, the immunoscore computing the number of CD3+ and CD8+ cells in the tumor and invasive margins has a better prediction of the patients' prognostic than conventional TNM classification (263,264). However, CD8 + T cells infiltration and PD-L1 expression on tumor cells are higher in sarcomatoid mesothelioma compared to epithelioid subtypes of mesothelioma, but the prognostic of patients is lower (69)(70)(71)265). Considering the expected mode of action of PD-1 blockade, its efficacy should be associated with higher infiltration by T cells in the tumor microenvironment. Some cancers display a low level of T cell infiltration into the tumor and other tumors may have higher T cell infiltration but exhausted T cells. Signature methods from transcriptomic analyses of melanoma biopsies from patients treated by PD-1 blockade comfort this idea (266). Moreover, analyses of pre-treatment and on-treatment biopsies samples of patients treated by PD-1 blockade highlighted that preexisting T cell infiltration in tumor and increasing of T cell infiltration and production of chemokines recruiting such immune cells in tumors, were higher in patients who respond to treatment (12,14).

Also, special structures gathering immune cells in the tumor microenvironment and known as tertiary lymphoid structures (TLS) have been described in several tumor types (263,267). These structures correspond to the development in non-lymphoid tissues of coordinated structures centered on follicular dendritic cells, appearance of high endothelial venules and lymphoid structures including DC, CD4 + T cells and B cells (268). The degree of organization of such TLS could be different from one tumor to another, between patients but also in a same patient. The presence of TLS and B cells in the tumor microenvironment of melanoma, renal cell carcinoma and sarcoma has been recently associated with better responses to PD-1 blockade (232-234). The presence of mature TLS was recently associated with improved objective response rates, progression-free survival and overall survival, independently from PD-L1 expression and CD8+ T cell density (269).

The intestinal microbiota influences the efficacy of immune checkpoint blockade.

Studies of gut dysbiosis in mice highlighted the contribution of commensal bacteria on CTLA-4 and PD-L1 blockade effects (270,271). The diversity and composition of the intestinal microbiota seem to play a key role on response to cancer immunotherapy and might have a prognostic value in patients (272-274). Antibiotic-related dysbiosis seem to impair immune checkpoint blockade efficacy in patients across various cancers (275). These studies have highlighted a strong interaction between specific immunogenic bacteria and systemic anti-tumor immune responses. In NSCLC patients, specific CD4 + and CD8 + T cells memory responses toward Akkermansia muciniphila predicted a longer progression-free survival (272). Whereas in melanoma, an enrichment in Faecalibacterium was associated with a higher frequency of cytotoxic CD8 + T cell infiltration in the tumor bed (274). Moreover, the intestinal microbiota composition was recently associated with the occurrence of immune related adverse events upon combination of CTLA-4 and PD-1 blockades (276).

Primary and acquired resistance to immune checkpoint blockade.

Primary resistance to immune checkpoint blockade is unfortunately frequent and has been reported to be driven by genomic and nongenomic mechanisms (18). Defects or alterations in the processes orchestrating tumor-host-microenvironment relationships could be divided into three broad categories, which relate to the cancer cell-intrinsic biology, the impact of this cancer cell on the phenotype of the TME, and the biology of the host. Different aspects of these categories are summarized in Figure 5 (reproduced from (18), with the agreement of Dr Mihaela Aldea). Primary resistance is defined as progressive disease as the best response to an anti-PD1 therapy (i.e. at first clinical or radiological assessment). It includes the paradoxical phenomenon of hyperprogression where an acceleration of the cancer is seen upon initiation of the blockade of the PD1/PD-L1 axis (277). Secondary or acquired resistance is defined as a cancer progression occurring after an initial response to PD1/PD-L1 blockade therapy. About 50% of patients with an initial RECIST partial tumor response will eventually die of their cancer reprogression (219).

Rationale for anti-PD1 & anti-angiogenic combinations

Both anti-angiogenic treatments and anti-PD1 immune checkpoint blockers have shown monotherapy efficacy in solid tumors. Moreover, comprehensive pre-clinical rationale and clinical results support the hypothesis that anti-VEGF therapy could synergize with checkpoint targeted immunotherapy (19). Fundamental research in the field of cancer angiogenesis has highlighted that an important feature of pro-tumoral abilities of VEGF-A is also to limit the control of cancer cells by the immune system [START_REF] Sompairac | Independent component analysis for unraveling the complexity of cancer omics datasets[END_REF]. Mechanisms of tumor resistance to both monotherapy strategies could be therefore overcome by the other one.

Anti-VEGF-A anti-angiogenic therapy synergizes with anti-PD-1 immunotherapy

Anti-angiogenic treatment might increase the efficacy of PD-1 blockade, as observed in a murine model, highlighting that such combination could induce both normalization of micro vessel architecture and adaptive immune response in tumors (278). Importantly, VEGFR2 blockade did not seem to interfere with T-cell infiltration and immunological activation induced by PD-1 blockade (278). Simultaneous blockade of PD-1/PD-L1 and VEGF/VEGFR pathways has been shown

to induce a synergistic anti-tumor effect in several mouse models (279,280). In patients with untreated metastatic renal cell carcinoma, the association of atezolizumab (an anti PD-L1 monoclonal antibody) with bevacizumab has confirmed that the addition of an anti-angiogenic treatment to immune checkpoint blockade increased immune susceptibility of cancer cells to adaptive immune response (through correction of class 1 MHC expression) and effector CD8 + T cells infiltration (281,282); such as recently in a phase 1 trial, for patients with unresectable peritoneal mesothelioma (283).

Rationale for the association of pembrolizumab with nintedanib.

Both compounds nintedanib, a pan anti-angiogenic TKI, and pembrolizumab, an anti-PD1 monoclonal antibody are being investigated and developed in patients with refractory advanced cancers. Both drugs have proven monotherapy efficacy and the combination of them might be at least additional if not synergistic. Recently, nintedanib was shown to increase CD8+ T cells infiltrates in a mouse model of melanoma and to be synergistic with an anti-PD-1 monoclonal antibody to limit tumor growth (284).

Nintedanib: an anti-angiogenic tyrosine kinases inhibitor

Nintedanib is a potent small molecule belonging to the receptor TKI family. It has inhibitory properties against vascular endothelial growth factor receptor (VEGFR) 1-3, platelet derived growth factor receptor (PDGFR) α/β, and fibroblast growth factor receptor (FGFR) 1-3 (187). At the molecular level, nintedanib inhibits the signaling cascade which mediated angiogenesis by binding to the adenosine 5' triphosphate (ATP) binding pocket of the receptor kinase domain, thus interfering with cross-activation via autophosphorylation of the target receptor homodimers (187). Besides the inhibition of neo angiogenesis, tumor regression may also be achieved by inducing apoptosis of endothelial cells from tumor blood vessel [START_REF] Wolf | PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells[END_REF]. Inhibition of receptor kinases may also interfere with autocrine and paracrine stimulation of tumor angiogenesis via activation loops involving VEGF, PDGF, and FGF. Nintedanib had been evaluated in phase 2 and 3 clinical trials for patients with advanced NSCLC or RCC in combination with chemotherapies. LUME-Lung 1 was an international, randomized, double-blind, phase III trial assessing the efficacy and safety of docetaxel plus nintedanib as second line therapy for non-small-cell lung cancer (NSCLC) (7). persistent diarrhea (305). Hepatitis and cholangitis can occur with anti-PD-1 therapy and their incidence could maybe increase when used in combination with anti-angiogenic drugs such as nintedanib (306). Thromboses are not frequently associated with immune checkpoint blockade but several case reports have described such adverse events (307-310).

Therefore, an association with an anti-angiogenic drug must lead to pay caution to these events. Even if major clinical adverse events observed after pembrolizumab are distinct from those induced by nintedanib, some overlap may occur, notably diarrhea, nausea, and liver enzyme increase. The incidence of them needs to be carefully monitored.

Conclusion

Although considerable progress has occurred in the field of drug development in oncology, most patients with advanced tumors eventually succumb to their disease (311,312). There is a high need for efficient therapeutic strategies to improve the outcome of patients with advanced cancer. A combination therapy associating pembrolizumab with nintedanib may have the potential to provide significant benefit for patients with solid tumors, by slowing tumor progression and metastasis, restoring normal function of endothelial cells, and promoting anti-tumoral immune response.

Ancillary analyses from blood and tumor samples of patients included in such trial should bring important knowledge on real life factors associated with primary resistance to treatment, or efficiency (313).

Thesis objectives

Ancillary analyses of samples from patients included in early development clinical trials can help to discriminate factors associated with tolerability and efficacy of the experimental treatments. These analyses can bring information, which can help future drug developments and build stratification strategies for the treatment of patients with cancers.

The main scientific objective of this thesis work was to identify tumor and blood biomarkers associated with the efficacy or resistance to the combination of pembrolizumab and nintedanib.

This main objective was associated with the following personal correlative objectives:

1. Learn how translational research projects could be built into early phase clinical trials protocols.

2. Become operational with the major techniques used in modern immunology such as multi-color flow cytometry, multiplexed immuno-assays and single cell RNA-seq analysis.

3. Be confronted with the technical challenges of working with fresh, fixed or frozen patient samples collected from blood and tumor.

4.

Collaborate with platforms and other research teams in order to perform adequate specialized analyzes (e.g IHC, RNA-seq, WES).

5. Acquire the technical and computational skills (e.g coding with R) which are necessary to compile and analyze big datasets from the results of ancillary analyses, notably when it comes to correlations with clinical outcomes in order to identify biological features associated with primary resistance and/or treatment efficacy.

6. Confront the results obtained from the ancillary analyses to the current scientific knowledge in order to come up with new hypotheses deserving new clinical and translational scientific explorations.

7. Become proficient in the writing of clinico-scientific publications and research grants.

Introduction

Angiogenesis is a key mechanism in tumour growth and development of metastases (20). Tumours induce blood vessel growth (angiogenesis) by secreting growth factors such as the Vascular Endothelial Growth Factor (VEGF), fibroblast growth factor (FGF) and platelet-derived growth factor (PDGF). VEGF and its high affinity receptor VEGFR-2 are crucial for the formation of new tumour vessels (314). In addition, there is preclinical evidence that FGF, PDGF and their associated receptor tyrosine kinases substantially contribute to tumour angiogenesis. The VEGF/VEGFR-2 axis may also generate an autocrine loop which stimulates growth of tumour cells (315). Therefore, suppression of neo-angiogenesis via inhibition of VEGFR-2 is a promising and efficient strategy for the treatment of solid tumors (25).

Immune checkpoint blockade is a new treatment strategy undergoing extensive investigation in multiple malignancies.

Unlike standard chemotherapy or targeted therapy, the immune checkpoint blockade restores the immune system's capacity to eradicate tumours (316). Antibodies targeting the Programmed Death-1 receptor (PD-1) and its ligand (PD- Although the combination of anti-angiogenics together with immune checkpoint blockade is becoming an attractive combination for the treatment of many cancers (323), the safety and activity of such combination is depending on the anti-angiogenic molecule tested. For instance, combinations of sunitinib and pazopanib with the anti-PD1 nivolumab were deemed too toxic with more than 70% grade 3/4 treatment related adverse events (324). Therefore, novel antiangiogenics should be tested in dedicated clinical trials to establish the dose, regimen, safety and activity when used in combination with anti-PD1 immunotherapies (325). Nintedanib is an oral tyrosine kinase receptor inhibitor (TKI) of PDG-FRα/β, FGFR1-3, and VEGFR1-3 that has been approved in 2014 by the EMA in combination with docetaxel for the treatment of second line advanced NSCLC.

We aimed to determine for the first time in humans the safety and efficacy of nintedanib, in combination with pembrolizumab, a humanized IgG4 anti-PD1 monoclonal antibody immunotherapy in patients with advanced solid tumors (NCT02856425).

Patients and methods

Study design

In this monocentric phase Ib dose escalation cohort (NCT02856425), we evaluated escalating doses of nintedanib (Dose level 1 (DL1) = 150 mg bid, DL2 = 200 mg bid) in combination with IV flat dose of pembrolizumab over 30 minutes (200 mg Q3W). Two dose-levels below the approved regimen of nintedanib (DL-1 & DL-2) could also be tested in case of unacceptable toxicities upon combination with pembrolizumab (Figure 6A). Patients received a one-week lead-in course of nintedanib monotherapy prior starting pembrolizumab. The sample size of the dose escalation cohort was conducted according to the rolling 6 design (326). Up to 6 patients evaluable for dose-limiting toxicity (DLT) could be exposed to a dose level. As soon as 3 patients were evaluable for DLT at a given dose level, dose escalation or de-escalation was permitted upon review of the safety data. Patients underwent tumor biopsies prior to the beginning of nintedanib and prior to the second injection of pembrolizumab in order to monitor the pharmacodynamic effects of the treatment and to identify biomarkers predictive of efficacy (Figure 6B).

Outcomes

The primary objective was to establish the maximal tolerated dose (MTD) of nintedanib in combination with pembrolizumab based on the assessment of DLT occurrence during the first 4 weeks (28 days since C1D1). Secondary objectives were to determine the tolerability and safety of the Recommended Phase 2 Dose (RP2D) of nintedanib combined with pembrolizumab and to evaluate first anti-tumor activity of this combination in expansion cohorts. The aim of the ancillary studies was to identify predictive biomarkers of efficacy.

Immuno-Histochemistry staining

Multiple chromogenic and one fluorescent multiplex Immuno-Histo-Chemistry (IHC) stainings were performed on each biopsy. The Ventana Discovery Ultra platform was used for both PD-L1 and ICAM1 (DAB) single chromogenic staining, for FOXP3 (Purple)/CD31 (DAB) multiplex and CD3 (Purple)/CD8 (DAB) multiplex chromogenic staining and for a panel combining DC-LAMP (OPAL 620), CD68 (OPAL 570), IDO1 (OPAL 540), CD163 (OPAL 520) and DAPI nuclear staining for the identification of myeloid cells. The multispectral images were captured using the Vectra® microscope tunable filter that shines 35 incremental wave lengths of light from 300 nm to 750 nm. High-powered images were selected by a pathologist, matched to a Hematoxylin and Eosin Staining (HES) to validate tumor-associated tissue. The spectral library was synthesized using the Inform® v2.2 software.

Immune monitoring -Fresh blood immune phenotype.

For each patient, heparinized blood samples (30-40 mL) at day -7 (baseline), C1D1 and C5D1 were collected whenever possible for monitoring circulating immune populations by flow cytometry. Fresh whole blood phenotyping of T-cell migration, T-cell polarization, T-cell activation, Treg function and myeloid cells was performed using 5 specific panels, as previously described (329). Stained cells were acquired using a Gallios Cytometer (Beckman Coulter) and analyzed using Kaluza software (Beckman Coulter).

Cytokine, chemokine and soluble angiogenic factors measurements.

Plasma samples were centrifuged for 15 min at 1,000 g, diluted 1:4, then monitored using the Angiogenesis Panel 1 Statistical analysis and illustrations.

Clinical statistical analysis had been done using the SAS® statistical software version 9.4 (Cary, North Carolina, USA).

Calculations and statistical tests for ancillary analyses were performed using R v3. Median follow up of the patients was 23.7 months (95% Confidence interval: [5.55; 40.5]). The best objective response was PR for 3 patients (25%), SD for 4 patients (33%) and a primary PD for 5 patients (42%) (Summarized in waterfall plot, Figure 7A). Two patients with stable disease did not develop a tumor progression during follow up (Figure 7B).

Best responses were achieved at cycle 3 in most cases (58%). Five patients were alive at the end of the follow up pe- Preexisting immune & angiogenic characteristics were associated with antitumoral response upon immune checkpoint blockade in combination with antiangiogenic therapy.

Patients with durable clinical benefit (DCB) defined as objective partial response or stable disease for at least 6 months after the beginning of treatment, presented higher CXCL10, CCL22/Macrophage-Derived Chemokine (MDC) and soluble Tie2 plasma levels before initiation of treatment than patients without DCB (Figure 8A). Flow cytometry analyses on fresh whole blood highlighted that CD4 + PD1 + OX40 + and CD4 + α4β7 + among total CD4 + T cells were more present in patients with DCB than patients without DCB (Figure 8B). IHC analyses on pre-treatment tumor biopsies highlighted that patient who developed DCB tended to have higher immune infiltration characterized by higher percentage of PDL1 + tumor cells, and higher densities of CD3 + T cells, FOXP3 + cells and DCLAMP + dendritic cells than patients without DCB after treatment (Figure 8C). During the first week of the clinical trial, patients received nintedanib monotherapy. The comparison of soluble factors in plasma and phenotype of fresh blood circulating lymphocytes between day -7 and C1D1 allowed the identification of changes associated with DCB to treatment which could be induced by the lead-in nintedanib monotherapy. Placental Growth Factor (PlGF) and VEGF-D plasma levels increased significantly between day -7 and C1D1 only in patients without DCB (Figure 9A; Supplementary Figure 1), although other soluble angiogenic factors levels remained stable. In parallel, plasma levels of CCL3 / MIP-1α (Macrophage Inflammatory Protein 1-Alpha), another ligand for CCR4 with CCL22/MDC, tended to increase in patients with DCB and was significantly higher before pembrolizumab infusion in patients with DCB compared to patients without DCB (Figure 9B). Also, patients who benefited from treatment had a higher percentage of circulating conventional CCR4 + CXCR3 + helper memory T cells and Tregs than patients without DCB (Figure 9C). Early inflammatory changes occurring after immune checkpoint blockade were associated with resistance to treatment and progressive disease.

Early inflammatory changes were observed in plasma of patients without DCB as opposed to patients with DCB. Before the second pembrolizumab infusion, patients without DCB presented an increasing plasmatic rate of TNF, compared to paired pre-pembrolizumab plasma (Figure 10A), and also for CCL3, CCL4, IL-18, IL-10, IL-22 and VEGF-D (Supplementary Figure 2). Patients without DCB also had higher plasmatic rate of inflammatory cytokines such as IL-6, IL-27 and CXCL8

than patients with DCB at C2D1 (Figure 10B). IHC performed on tumor biopsies done before C2D1, showed that percentages of PDL1 + tumor cells, ratio of CD3 + on CD163 + cells and density of FOXP3 + cells were significantly higher in patients with DCB than without DCB (Figure 10C). Tumor densities of CD68 + and CD163 + cells were not different in C2D1 biopsies (Supplementary Figure 3); neither those of DC-LAMP + , IDO1 + , ICAM1 + or CD31 + cells (data not shown).

Discussion

This phase 1b dose escalation cohort showed that the toxicity of the nintedanib and pembrolizumab combination was manageable and consistent with the safety profile of each drug and did not seem to generate higher toxicity than the cumulative impact of each compound. The RP2D for the combination was defined as nintedanib 150mg bid and flat dose of pembrolizumab 200mg IV and is currently being investigated in multiple expansion cohorts The main toxicity of this combination therapy was liver enzymes increase, which is consistent with other combination of immune checkpoint inhibitors and antiangiogenic TKIs. The incidence of drug-related grade 3 liver enzymes elevation was 33% for alanine aminotransferase and 25% for aspartate aminotransferase in our study. Other combinations of anti PD-1 monoclonal antibody and antiangiogenic TKI reported variable liver toxicity: nivolumab/sunitinib, nivolumab/pazopanib, pembrolizumab/pazopanib and pembrolizumab/axitinib reported 18%, 20%, 60-70% and 8% of grade 3-4 elevated alanine aminotransferase, respectively (6,324,330). In the dose escalation trial of nintedanib monotherapy, the DLTs were grade 3 or 4 liver enzyme elevations (2-5%), all reversible upon drug interruption (287). The adverse events reported in this study seem to be mostly related to nintedanib, but programmed death 1 blockade could possibly enhance the on-target off-tumor side effects of the TKIs. The observed levels of immune related adverse events such as colitis (8%), nephritis (8%) and thyroiditis (25%) were in line with the expected irAE associated with pembrolizumab monotherapy. Thromboses were observed in two patients: one with superior mesenteric artery thrombus and both with thrombus on catheter in superior vena cava. These adverse events could be related to the combination of antiangiogenic TKI with immune checkpoint blockade and not to nintedanib alone. These two patients with thromboses obtained a durable clinical benefit (SD and PR, respectively during at least 6 months). Overall, the antitumor activity of the combination tested was promising with an ORR of 25% in this all-comer cohort and will be confirmed in the expansion cohorts of the study.

Baseline ancillary analyses highlighted those patients who developed DCB to this anti-angiogenic / ICB combination presented with both preexisting immune and angiogenic features. Higher expression of CXCL10 and immune infiltrate in biopsies were previously associated with ICB monotherapy efficiency in different tumor types (12,331). It was interesting to observe that soluble Tie2 was higher in plasma of patients with DCB. Tie2 is the receptor of angiopoietin-2, expressed at surface of endothelial cells but also circulating monocytes (332). Studies highlighted that Tie2 production and membrane shedding are increased by hypoxia and induced by VEGF (333). Here, Tie2 plasmatic rate could be a witness of hypoxia occurring in the tumor of patients who benefited from the treatment.

After seven days of nintedanib monotherapy, quick changes were observed in patients who benefited from the treatment. Although those could be spontaneous changes favored by different tumor changes, their rapid evolution suggest that they could be induced by nintedanib. Particularly, the increase of plasmatic CCL3/MIP-1α in patients with DCB could be a consequence of tumor hypoxia on chemokine production (334). At baseline, a higher Tie2 plasmatic rate, along with higher proportions of circulating Tregs, and conventional helper memory T cells which co-expressed CCR4 and CXCR3 (receptors of CCL22/MDC, CCL3/MIP-1α and CXCL10 respectively) were also predictive of DCB. Blood Tregs constitutively express CCR4 on their surface (335). CCL22 production by tumor cells was identified as a mechanism of immune escape in breast cancer, through CCR4 + Treg recruitment in the tumor microenvironment (TME), induced by IFNγ exposure (336,337). These observations led to the development of CCR4 antagonists as antitumoral immunotherapy (338,339). Our results suggest that Treg recruitment in the TME was associated with a higher infiltration of immune cells, particularly DC-LAMP + dendritic cells and T cells, associated with a pre-existing anti-tumoral immune response, sensitive to immune checkpoint blockade.

However, patients without DCB developed others significant changes after one week of nintedanib monotherapy. They seemed to present an increasing plasma rate of PlGF as of patients with DCB. This soluble angiogenic factor was recently implicated in interactions between angiogenesis, Th17 polarization of CD4 + T cells and autoimmunity (340). This observation should be associated with changes induced further by anti PD-1 blockade. Patients with tumor progression and resistance to treatment presented then with an increasing plasma rate of IL17A and TNF after the first infusion of pembrolizumab, in comparison to patients with DCB. Patients without DCB had also a higher plasma rate of IL-6, IL-8/CXCL8

and IL-27 at C2D1 than patients who had benefited from treatment. Increased rates of both IL-6 and IL-8/CXCL8 have been described to be associated with resistance to anti-PD1 blockade monotherapy (341,342). Of note, the baseline plasma rates of these cytokines were not significantly different between patients with or without DCB outcome.

Conclusion

Nintedanib 150mg bid is the RP2D for combination with pembrolizumab and is being currently investigated in multiple expansion cohorts. Early immune biomarkers in the tumor and in the blood were associated with the efficacy of the nintedanib + pembrolizumab combination therapy. Our biological findings at baseline and during the early phases of treatment, associated significantly with the clinical benefit to treatment or to primary tumor progression, suggesting that patients could be selected on their tumor biology rather than their cancer histology in order to benefit from such anti-angiogenic and immune targeted therapies. Abstract.

Background: the PEMBIB study (NCT02856425) is a multi-cohort Phase Ib trial evaluating the safety and efficacy of the combination of an anti-angiogenic tyrosine kinase inhibitor (nintedanib) with an anti-PD1 immunotherapy (pembrolizumab). Here we report the results from an expansion cohort dedicated to patients with unresectable malignant mesothelioma, refractory to a first line platinum-based chemotherapy. Ancillary analyses were performed to identify biomarkers associated with efficacy or primary resistance. antibodies has recently shown important synergistic results in RCC and HCC (318-320). Indeed, anti-VEGF therapies may enhance anti PD-1/PD-L1 efficacy by reversing VEGF-mediated immunosuppression and promote T-cell infiltration in tumors (19). Both anti-angiogenic and immune checkpoint blockers have shown activity in advanced pleural mesothelioma. Bevacizumab, an IgG1 monoclonal antibody targeting VEGFA, in association with platinum-based regimen chemotherapy, increases the overall survival of patients with untreated unresectable pleural mesothelioma, compared to chemotherapy alone (75). Moreover, the combination of both anti PD-1 and anti CTLA-4 monoclonal antibodies significantly improves the survival of patients with untreated unresectable pleural mesothelioma, compared to standard of care chemotherapy (74).

Methods

We aimed to determine the safety and efficacy of nintedanib, an oral triple receptor tyrosine kinase inhibitor (TKI) of PDGFRα/β, FGFR1-3, and VEGFR1-3, in combination with pembrolizumab, a humanized IgG4 anti-PD1 monoclonal antibody in patients with unresectable mesothelioma naïve to immunotherapy, previously treated by at least one line of platinum-based chemotherapy regimen (NCT02856425).

Patients and methods

Study design

In this expansion cohort of the phase Ib clinical trial PEMBIB (NCT02856425), we evaluated the association of nintedanib

Patients

Eligible patients had advanced pleural mesothelioma who progressed after at least one line of standard therapy, naïve to immune checkpoint blockade and nintedanib. Additional inclusion criteria included age ≥ 18 years, Eastern Cooperative Oncology Group (ECOG) performance status of 0-1, adequate organ function, measurable disease according to RE-CIST v1.1 criteria, and written informed consent. Key exclusion criteria were radiographic evidence of cavitary tumors, local invasion of major blood vessels and/or at risk for perforation, history of clinically significant hemoptysis within the past 3 months, history of clinically significant hemorrhagic or thromboembolic event in the past 6 months, history of significant cardiovascular diseases, prior treatment with nintedanib and anti PD-(L)1 agents, concurrent steroid medication, history of autoimmune and inflammatory disease. This study was conducted in compliance with the Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects.

Procedures

Screening procedures were performed up to 21 days (D-28) before Day -7 (start of nintedanib). Patients continued treatment until disease progression, undue toxicity, withdrawal of consent or for a maximum duration of 24 months.

Adverse events were graded using National Cancer Institute (NCI) CTCAE Version 4.03. Tumor responses were evaluated every 6 weeks based on Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 (327,328).

Outcomes

The objectives of the trial were to determine the tolerability and safety of oral nintedanib 150mg BID combined with IV pembrolizumab 200mg Q3W and to evaluate the first efficacy signals with RECIST 1.1 best objective response rate (BOR), progression free survival (PFS), overall survival (OS) of this combination in a dedicated cohort of advanced MM.

The aim of the ancillary studies was to identify predictive biomarkers of efficacy or resistance to this combination therapy.

Analyses of tumor infiltrating immune cells from biopsies by flow cytometry

Fine-needle biopsy samples from tumoral lesions were immediately placed into 1ml of NaCl 0.9%. After a minimum of 30 minutes of incubation, the supernatant of fresh tumor biopsies in 0,9% NaCl was collected and frozen at -80°C and biopsies were mechanically dissociated with the top of a 2ml syringe plunger in a wet 70µm filter placed at the top of a

Immune monitoring -Fresh blood immune phenotype

For every patient, heparinized blood samples (30-40 mL) at day -7 (baseline), C1D1 and C5D1 were collected whenever possible for monitoring circulating immune populations by flow cytometry. Fresh whole blood phenotyping of T-cell migration, T-cell polarization, T-cell activation, Treg function and myeloid cells was performed using 5 specific panels, as previously described (329). Stained cells were acquired using a Gallios Cytometer (Beckman Coulter) and analyzed using Kaluza software (Beckman Coulter).

Cytokine, chemokine and soluble angiogenic factors measurements

Serum IL6 quantification was performed with Siemens Atellica IM1600 and Atellica IM Interleukin-6 kit (Siemens Healthineers, Saint-Denis, France) validated by Gustave Roussy accredited diagnostic laboratory. Quantification method validation was performed according to ISO15189 recommendation. The quantification range covers from 2.7

(limit of quantification) to 5500 pg/ml. Any quantification batches included 3 levels of internal quality control analysis.

Frozen plasma and frozen tumor biopsy supernatants were subsequently thawed, centrifuged for 15 min at 1,000 g, Raw data from Meso Scale Discovery's kit were analyzed with MSD's Discovery Workbench 4.0.

then

Immuno-Histo-Chemistry staining

Immuno-Histo-Chemistry (IHC) chromogenic staining were performed on formalin fixed paraffin embedded (FFPE) tumor biopsies. PD-L1 (DAB) single chromogenic staining were performed using the Ventana Benchmark Ultra platform.

CD3 (Purple)/CD8 (DAB) multiplex and CD20 (DAB) chromogenic staining were performed using the Ventana Discovery Ultra platform. Images were selected by a Pathologist, Hematoxylin and Eosin (HES) stained slides were used to validate that FFPE tumor locks were tumor-associated tissue.

Whole-transcriptome RNA-seq

Integrity (RNA Integrity Score≥7.0) of RNA extracted from frozen tumoral biopsies was checked on the Agilent 2100 Bioanalyzer (Agilent) and quantity was determined using NanoDrop (Thermofisher Scientific). SureSelect Automated Strand Specific RNA Library Preparation Kit was used according to the manufacturer's instructions with the Bravo Platform. Briefly, 50 to 200ng of total RNA sample was used for poly-A mRNA selection using oligo (dT) beads and subjected to thermal mRNA fragmentation. The fragmented mRNA samples were used for cDNA synthesis and were further converted into double stranded DNA using the reagents supplied in the kit, and the resulting dsDNA was used for library preparation. The final libraries were bar-coded, purified, pooled together in equal concentrations and processed for paired-end 2x100 sequencing on Novaseq-6000 sequencer (Illumina) at Gustave Roussy.

Bulk Tumor RNA-seq analyses

The QC and analysis pipeline was based on Love et al, powered by SnakeMake (346,347). Quality controls were performed on raw FastQ files with FastQC v0.11.9. Reads trimming for low 3' terminal base quality and removing of adapter sequences was performed using fastp v0.20.1. Sample contamination was assessed with FastqScreen v0.14.0. Quality reports were gathered with MultiQC v1.9 (348). Abundance estimation was performed with Salmon v1.4.0, using 100 bootstraps and the GenCode v34 annotations, corresponding to the GRCh38 genome build (349). Aggregation was performed with the tximport package, and differential gene analysis was performed with DESeq2, with a formula design taking care of the sample effect when sample pairs (Screening/C2J1) were taken into consideration (350). Deconvolution of immune cell fractions from bulk RNA sequencing data was done with immunedeconv package (351). Gene set enrichment and overall representation analyses (GSEA/ORA) were performed with the clusterProfiler package v4.0.2, against the MsigDb collections, the Disease Ontology, and the KEGG, CellMarker and MeSH databases (352). For quality control, the variance stabilizing transformation (vst) normalization from DESeq2 was applied on raw counts. Of note, RNA-seq data from baseline biopsies without tumor cells were not integrated in the analyses, and the data from baseline pleural biopsy of patient #23, who had a peritoneal progression before 6 months, was considered in the "DCB" group because of a primary and persistent complete response of pleural lesions (Supplementary figure 4).

Tumoral and germline whole exome sequencing (WES) 50 to 200 ng of genomic DNA was extracted from frozen tumor biopsies with the Covaris E220 system (LGC Genomics / Kbioscience). DNA fragments were end-repaired, extended with an 'A' base on the 3′ end, ligated with paired-end adaptors with the Bravo Platform (Agilent) and amplified (ten cycles). Exome-containing adaptor-ligated libraries were hybridized for 40 h with biotinylated oligo RNA baits using SureSelect Clinical Research 2 (Agilent), and enriched with streptavidin-conjugated magnetic beads. The final libraries were indexed, pooled and sequenced using the onboard cluster method, as paired-end sequencing (2x100 bp reads) on Illumina NovaSeq-6000 sequencer at Gustave Roussy.

Whole exome sequencing analyses

Identification of mutations and individual somatic copy number alteration were done with following methods. Reads were mapped using BWA-MEM (v0.7.12) software (353) to the GRCh37 human reference genome and then we used the standard GATK best practice pipeline (354) to process the samples and call somatic genetic variants. PCR duplicates were removed and base quality score recalibrated using MarkDuplicates and BaseRecalibrator tools which a part of the GATK package (355). Somatic SNVs and INDELs were called and filtered using GATK tools Mutect2, FilterMutectCalls and FilterByOrientationBias and annotated with oncotator (356). SCNAs calling was done with FACETS software using only regions with at least 20 reads coverage in both tumor and normal samples (357). Quality controls of FASTQ and mapping were done with FASTQC, samtools (v1.9), GATK HSmetrics and multiqc (348,358,359). The processing steps were combined in a pipeline built with snakemake (360). Somatic mutations with PASS flag from GATK Mutect2 were additionally filtered to have at least 1 supporting reads from each strand and 3 reads in total. We used then MAF annotator to find oncogenic mutations from OncoKB database and visualized them as an oncoplot with the maftools R package (361).

Tumor mutational burden of the samples was calculated in accordance with the guidelines proposed by the "Friends of Cancer Research TMB Harmonization Project" (260).

Identification of copy-number anomalies were performed using EaCoN v0.3.6 (https://github.com/gustaveroussy/EaCoN) on R v3.6.2. Per-patient paired samples were analyzed using tumoral samples as test, and genomic DNA from PBMCs used as reference, for each pair. GATK-recalibrated BAM files were transformed to the mpileup format using Rsamtools v2.8.0, ignoring replicates and secondary alignments (362). Log2ratio (L2R) data, mpileup profiles were binned to windows of 50 nt in median (depending on the capture BED information), and bins with a total depth < 20 were discarded. Using a pre-generated track of GC% content in bins, those with a value <20% or > 80 were identified as outliers. L2R depths was computed for each bin, and linearly imputed for GC% outliers. L2R was then normalized for GC% using a local regression. To generate the BAF data, any non-reference sequences in the mpileups were identified and their depth quantified (363). SNP variants supported by less than 3 reads and/or for which the total depth was below 20 were discarded. All SNP variants in the test sample with a reference frequency below 33% were discarded. The bivariate (L2R and BAF) data were then segmented, evaluated for their allele-specific absolute copynumber, as well as ploidy and tumor cellularity, using ASCAT v2.5.2.

Single cells RNA-seq analyses of peripheral blood mononuclear cells (PBMC)

Frozen PBMC from pre-treatment (time point "day -7") samples of patients (n=10) and healthy controls (n=2) were Cells were suspended at 1.10 6 cells/mL in PBS.

For the Rhapsody experiment, all the process was done by following manufacturer's (BD Biosciences) protocol. 7,239 cells were captured in a single run with 12 barcoded samples pooled together. The sample was processed according to BD mRNA targeted and sample tag library preparation with the BD Rhapsody Immune response (Human) amplification kit. Samples were then subjected to an indexed paired-end sequencing run of 2x151 cycles on an Illumina HiSeq 4000 system (Illumina, San Diego, CA, USA) with 20% PhiX spike in. Targeted transcriptomics Fastq files were processed via the standard Rhapsody analysis pipeline (BD Biosciences)) per the manufacturer's recommendations. First, R1 and R2 reads are filtered for high-quality reads, dropping reads too short (less than 66 bases for R1 and 64 bases for R2) or have a base quality score of less than 20. R1 reads are annotated to identify cell label sequences and unique molecular identifiers (UMIs), and R2 reads are mapped to the respective reference sequences using Bowtie2. Finally, all passing R1 and R2 reads are combined and annotated to the respective molecules. For quality control of the reads, recursive substation error correction (RSEC) and distribution-based error correction (DBEC) were applied, which manufacturer-developed algorithms are correcting for PCR and sequencing errors. For determining putative cells (which will contain many more reads than noise cell labels), a filtering algorithm takes the number of DBEC-corrected reads into account, calculating the minimum second derivative along with the cumulative reads as the cut-off point. Finally, the expression matrix was obtained from the DBEC-adjusted molecule counts in a CSV format. A cell is determined as a singlet if the minimum read count of a single sample tag is above the threshold of 75%. A cell is classified as a multiplet if the cell exceeds the threshold for more than one sample tag. A cell that does not meet the threshold is labelled as undetermined. Both multiplets and undetermined cells were excluded from the analysis as described below.

For downstream analysis, Seurat V4 was used (364). Counts were normalized, and PCA analysis was performed. Using these variable features, a UMAP was generated. Clustering was performed using SNN nearest-neighbour analysis. Differentially expressed genes were identified by a threshold of FDR below 0.05 and logFC below -0.25 or above 0.25.

Analyses of peripheral blood mononuclear cells by flow cytometry

Frozen PBMC from pre-treatment (time points "day -7" [n=19]) samples of patients and healthy controls (n=3) were thawed in "Medium" (see above). Cells were suspended at 1.10

Statistical analysis and illustrations

Clinical statistical analysis has been done using the SAS® statistical software version 9.4 (Cary, North Carolina, USA).

Calculations and statistical tests for ancillary analyses were performed using R v3.4. Wilcoxon-Mann-Whitney test was used to assess differences between two patient's groups. Data representation and analyses was performed with software R v3.3.3 using tidyverse, dplyr, ggplot2, ggpubr, complexheatmap, survival and atable packages. Figure's aesthetics were worked with Affinity Designer® (v1.9.2.1035).

Results

Tolerability of pembrolizumab with nintedanib for patients with advanced pleural mesothelioma, naive to immunotherapy and refractory to platinum-based chemotherapy.

32 patients were enrolled between October 10 th , 2017, and April 11 th , 2019, in the mesothelioma expansion cohort of the trial: two patients were screen failed because of exclusion criteria and 30 patients were included in the trial. Patients' characteristics are described in Table 6. They were mainly male (n=20/30, 67%), with a median age of 69 years old (standard-deviation [SD] 11 years). The enrolled MM were advanced tumors involving the pleura, with metastatic peritoneal carcinosis in 2/30 cases (6.7%) and were all refractory to platinum-regimen based chemotherapy. The number of previous lines of treatment were 1, 2 and ≥3 for 23/30 (77%), 5/30 (17%) and 2/30 (6.7%) of the patients, respectively.

Previous treatment with bevacizumab, associated with platinum and pemetrexed, was reported for 12/30 (40%). Of note, a patient (#40) was treated efficiently in fourth line by an EZH2 inhibitor for a year before developing a tumor progression and be enrolled in this trial.

Dose modifications and/or scheduling modifications occurred in 12/29 (41%), and 4/29 (14%) patients because of adverse events associated with nintedanib and pembrolizumab, respectively. Adverse events were reported in Table 7.

The most frequent adverse events (AE) (grades 1-3) related to the combination were diarrhea, fatigue, nausea and liver enzymes elevation. Twelve (40%) and 3/30 (10%) patients developed grade 3/4/5 treatment-and immune-related adverse events respectively (colitis with pneumonitis [n=1] and myocarditis [n=2], grade 3, 4 and 5, respectively Pre-existing immune status of the tumor and its host dictates treatment resistance without impacting the pharmacodynamics of anti-angiogenic and anti-PD-1 therapies.

To determine factors associated with the efficacy of the combination therapy, we defined durable clinical benefit (DCB)

as the absence of tumor progression before 6 months of treatment. Fourteen (46.7%) and 16 (53.3%) patients had been considered with and without DCB, respectively. Baseline serum levels of interleukin-6 (IL-6), measured retrospectively at accredited diagnostic laboratory, were significantly higher in patients without DCB (Supplementary table 1).

There were no differences between tumor biopsies from patients with or without DCB in terms of biopsy size or percentage of tumor cells (Supplementary figure 6A). Also, there were no differences in terms of T cells, B cells neither tertiary lymphoid structure infiltrating baseline tumor biopsies according to IHC scoring (Supplementary table 2). However, tumor cells of patients with DCB had significantly higher expression of PD-L1 than patients without DCB (Figure 12A). Looking at tumor infiltrating T cells by flow cytometry on fresh tumor biopsies at baseline (Supplementary figure 7), we found that patients with DCB had higher percentages of CD45 + immune cells and CD3 + T cells among total cells and more CD8 + T cells among CD3 + T cells than patients without DCB (Figure 12B). Because of limited subsets of cells (the mean number of CD3 + T cells in biopsies were 2234 cells (SD [4715]) and 435 cells (SD [546]) for patients with and without DCB, respectively) the comparison of costimulatory molecules expressed by t-cells, such as PD-1 or CTLA-4, could not be done rigorously (as illustrated in Supplementary figure 7B). Prospective immuno-monitoring of fresh blood circulating T lymphocytes by flow cytometry allowed to observe that patients with DCB presented at screening with higher percentages of type 1 helper T cells (Th1) among helper CD4 + T cells, and CD49a + effector memory CD8 + T cells than patients without DCB (Figure 12C).

DNA and RNA extracted from frozen tumor biopsies collected at baseline brought information about the molecular differences between tumors sensitive or not to our treatment. Recent molecular deconvolution from transcriptomic analyses of human mesothelioma described gradients of "epithelioid signature" (E-score) and "sarcomatoid signature" (S-score) ( 114)). Here, there was no difference for E-score between biopsies of patients with or without DCB. Mean Sscore tended to be higher in biopsies of patients without DCB (Supplementary figure 3B). Whole Exome Sequencing done on DNA from tumor biopsies, considering only those with tumor cells (n=13), losses of tumor suppressor genes by mutations and/or copy number alterations (CNA) were identified in BAP1 (67%), EP300 (67%), NF2 (61%), SETD2 (61%), CDKN2A (56%), CREBBP (44%), TP53 (39%), MGA (22%) and DDX3X (17%) genes (Figure 12D; Supplementary figure 8).

There were no significant differences between patients with or without DCB considering mutations and CNA (data not shown). Median tumor mutational burden was 0.7 mutations per megabase (0.48 to 2.11). Deconvolution of immune tumoral infiltration from transcriptomic data highlighted that biopsy from patients without DCB and tumoral progression, presented with lower immune infiltrates, higher percentages of tumor cells, and tended to cluster together (Figure 12D). Of note the levels of expression of myeloid dendritic cells score in biopsies at screening were higher in tumors from patients with DCB than without DCB (Figure 12E).

Measurement of plasmatic angiogenic factors did not identify clear pre-therapeutic difference between patients with or without DCB (Supplementary figure 9). The first 7 days of treatment by nintedanib monotherapy leaded to a significant decrease of plasmatic rates of angiopoietin-2 in patients with or without DCB (Supplementary figure 10). Also, several chemokines increased their plasma concentrations upon the first pembrolizumab infusion (Figure 12F). CXCL9, CXCL10, CXCL13, CCL19, soluble PD-1 and soluble PD-L1 significantly increased their plasma concentrations one week after the first pembrolizumab infusion in all patients, whatever their clinical course (Figure 12F & 12G; Supplementary figure 11). However, plasma concentration of CCL3 (ligand of CCR1/4/5), CCL8 (ligand of CCR1/2/3) CCL17 (ligand of CCR4) and CCL23 (ligand of CCR1) increased significantly between day 1 and day 8 only in patients without DCB (Supplementary figure 11). Interestingly, according to transcriptomic deconvolution analysis, these pharmacodynamics effects of the treatment were accompanied by an increase in immune infiltrations in tumor biopsies between screening and C2D1 irrespective of clinical benefit (Supplementary figure 12A), but higher immune infiltrate estimation tended to cluster data from patients with DCB (Supplementary figure 12B). Moreover, flow cytometry analyses of cycle 2 biopsies did not find differences in terms of global CD45 + immune cells, neither in CD3 + T cells, but the percentage of CD4 + among CD3 + T cells and CD4 + CD25 + cells among CD4 + T cells were higher in patients without DCB, suggesting a greater representation of regulatory T cells in these tumors upon treatment (Figure 12H). Treatment-sensitive mesotheliomas have lower expressions of RB/E2F, MYC and epithelial-mesenchymal transition (EMT) pathways than refractory tumors

To better determine the tumoral characteristics associated with sensitivity to treatment, we performed an unsupervised analysis of genes differentially expressed in screening tumor biopsies by RNA-seq (n=13). This analysis found 115 genes differentially expressed between tumors of patients with and without DCB (log 2-fold change [log2FC] +/-3 and adjusted p-value [padj] < 0.01 with Benjamini-Hochberg method) (Figure 13A). Of note, CCL11 (eotaxin) expression was significantly higher (log2FC = 5.3, padj = 0.0004) and CXCL8 expression significantly lower (log2FC = -1.93, padj = 0.045) in biopsies of patients with DCB before treatment initiation.

Gene set enrichment analysis (GSEA), with the "Hallmark" panels, identified 11 gene sets which were significantly differentially expressed in biopsies of patients with DCB (Figure 13B). Seven gene sets were upregulated and 4 were repressed in tumor biopsies of patients with DCB. Genes significantly upregulated pertained to pathways related to oxidative phosphorylation (OXPHOS) metabolism, interferon alpha (IFNα) pathway, interferon gamma (IFNγ) pathway and "allograft rejection" signature, suggesting an ongoing adaptive immune response in the tumor (Figure 13C). Significantly suppressed pathways in tumor from patients with DCB (therefore relatively enriched in tumors primary resistant to treatment), were genes from the epithelial to mesenchymal transition (EMT) pathway, G2M checkpoint, E2F and MYC targets (Figure 13D). Those three E2F, MYC and G2M pathways shared several genes downstream expression (Supplementary figure 13). Somatic copy number alteration is increased in patients with primary resistance to treatment and correlates with higher tumor and plasma levels of chemokines leading to mobilization of inflammatory innate immune cells Somatic copy number alterations (SCNA), or aneuploidy, estimated by tumoral and constitutive paired WES analyzes, were higher in biopsies from patients without DCB than in patients who benefited from the treatment (Figure 14A). Wilcoxon rank-sum test) (Figure 14D). CCL2 was the only chemokine from tumor biopsy supernatants with significant differences in concentration between patient groups. In blood, the pro-inflammatory cytokines CXCL8 (IL8) were significantly higher before treatment in patients without DCB (Figure 14E). In parallel, prospective immuno-monitoring of circulating immune cells highlighted that percentages of CD15 + polymorphonuclear cells (PMN) among total CD45 + were significantly higher in fresh blood of patients without DCB (Figure 14F).

Single cell (n=7239 cells) expression analyses of 400 genes from pre-treatment PBMC of 10 patients (5 patients with DCB and 5 without DCB) and 2 healthy controls were performed to identify preexisting differences between circulating immune cells in patients with or without DCB. Thirteen clusters of cells were identified (Figure 14G). The comparison between healthy controls' PBMC (n=1471 cells) to patients with DCB (n=3847 cells) or no DCB (n=1921 cells) allowed to identify a cluster of cells (Monocyte #1) which was almost not present in controls but only in MM patients (Figure 14H).

Top genes those expression defined this cluster were CD14, alarmins S100A12 and S100A9, scavenger receptor CD163, pattern recognition receptor lectins CLEC4E and FCN1, nicotinamide transferase NAMPT, chemokines CXCL8, glutathione transferase MGST1 and VEGFA (Supplementary figure 14). Median percentage of cells from cluster Monocyte #1 among total cells were 3.9% (SD 3.2), 21.6% (SD 21.1) and 39.6% (SD 22.9) in healthy subjects, patients with and without DCB respectively (Kruskal-Wallis test, p=0.12) (Figure 14I).

Analyses by flow cytometry of thawed cells from healthy controls and pre-treatment patients' PBMC samples were done to study monocyte's surface proteins presentation, products of these genes (Figure 14J). It confirmed that the median percentage of intermediate monocytes among total monocytes were significantly higher in patients than in healthy controls (6.95% [SD 3.1] and 3.95% [SD 1.6], respectively; p=0.028, Wilcoxon rank-sum test) (Figure 14K; cf gating strategy in Supplementary figure 15). Patients without DCB had significantly higher percentage of intermediate monocytes CD163 + CCR2 -among those HLA-DR int , than patient with DCB (6% [SD 5.8] and 0.8% [SD 0.7], respectively; p=0.013, Wilcoxon rank-sum test) (Figure 14K). In cycle 2 biopsies, TREM1 expression was higher in biopsies from patients without than with DCB (Figure 14L). Discussion.

This expansion cohort of the phase 1b PEMBIB clinical trial, dedicated to patients with advanced mesothelioma naïve to immunotherapy and refractory to first line of platinum-based chemotherapy, showed that the association of pembrolizumab 200mg Q3W with nintedanib 150mg BID has antitumor activities, with manageable toxicities. The disease control rate (DCR) at 12 weeks was 68.4% (95%IC [43.4:87.4]). This is higher than those observed in phase 1 and phase 2 clinical trials which evaluated anti-PD1/PD-L1 monoclonal antibodies monotherapies (79,80,300,365,366), and similar to those with combination of anti PD-1/PD-L1 with anti CTLA-4 monoclonal antibodies (81)(82)(83), for patients with advanced mesothelioma refractory to platinum-based chemotherapy.

Our study brought knowledge on mesothelioma characteristics associated with the primary resistance to anti PD-1 monoclonal antibodies combined with an antiangiogenic TKI. First, we observed that after one anti PD-1 infusion, global immune and T cells infiltrates increased in tumors from all patients, but those who did not benefit from treatment presented with higher percentage of CD4 + cells among T cells and CD25 + cells among CD4 + T cells. There results suggest that PD-1 blockade can lead to increase amounts of regulatory T cells in the tumor microenvironment and therefore have pro-tumoral effect. In the TME, PD-1 blockade has been described to release Tregs PD-1 + immunosuppressive and pro-tumoral abilities (367). These PD-1 + Tregs expressed high levels of CTLA-4, had important proliferative abilities and had been associated with hyper progressive disease upon anti PD-1 infusion (368,369). Thus, a combination of an Fc competent anti CTLA-4 with an anti PD-1 monoclonal antibodies, for patients with tumors infiltrated by such Tregs, could deplete those Tregs and limit this detrimental phenomenon (241,242). A phase 3 clinical trial (CheckMate743) evaluating the association of an anti PD-1 with an anti CTLA-4 monoclonal antibodies versus standard of care chemotherapy, for untreated unresectable malignant pleural mesothelioma, has shown a significant higher overall survival in patients receiving this combination of immune checkpoint blockade and might therefore support this hypothesis (74).

Our exome analyses did not show significant enrichment of loss of tumor suppressor genes among patients who did not benefit from treatment. However, our transcriptomic analyses highlighted that CDK/E2F and MYC pathways such as G2M checkpoints were enriched in refractory MM tumors. Their upregulation is classically promoted by loss of tumor genes suppressor such as CDKN2A and RB, which are frequently deleted or inactivated in malignant mesothelioma, leading to the deregulation of cyclin D4/6 (CDK4/6) activity, FOXM1 transcriptional activity and cell proliferation.

Recent studies have described in different tumor types that those mutations and their downstream pathways lead to immunosuppressive cancer cells abilities and limit immune infiltrations in tumors (370-376). In mesothelioma such as in other metastatic cancers, TP53 and CDKN2A are somatic genes frequently altered [START_REF] Jamieson | Gene-expression profiling to predict responsiveness to immunotherapy[END_REF]377) Comparison of results from the dose escalation and the mesothelioma cohorts.

Patients treated with nintedanib and pembrolizumab in both the dose escalation and the mesothelioma cohorts presented with several features which may be important to understand the differences observed between those sets of ancillary analyses. First, these cohorts enrolled patients with different cancer histologies (in dose escalation cohort, they were only two patients with unresectable mesothelioma, those one with a primary peritoneal disease). And the genomic abnormalities associated with cancer types can influence the biology of these tumors and their response to our studied treatments. Second, all the patients did not receive the same dose of nintedanib (in the dose escalation part, patients 7 to 13 received nintedanib at 200mg bid, and all others were at 150mg bid). Although pembrolizumab has not a dose effect pharmacodynamics profile (and all the patients were treated at the approved dose of 200mg Q3W IV), for nintedanib there is a clear dose-efficacy-toxicity relationship which can affect our ancillary biological readouts. For instance, liver toxicities were more frequent when nintedanib was used at 200mg bid instead of 150mg bid with pembrolizumab. But, in the dose escalation cohort, 3/12 patients (25%) presented with an objective partial response and two of them received nintedanib at 150mg bid. These patients were treated for a cervical cancer, an UCNT and a thymic carcinoma, which are very different diseases. The first two are diseases associated with chronic viral infections (HPV and EBV, respectively) and thymic carcinoma is developed from epithelial cells in a primary lymphoid organ. PD-1 blockade was shown to be effective in cancers with oncogenesis promoted by chronic viral infections, such as HNSCC, HCC, cervical carcinoma and maybe UCNT (396). Conversely, mesothelioma are diseases that do not present the usual factors associated with efficacy of immune checkpoint blockade: their mutational burden is typically quite low, and their PD-L1 expression by tumor cells is sparce (except for sarcomatoid subtypes, which were a minority of our patients). These major differences could partly explain the differences observed between both studies.

Differences observed between both studies may also be due to the fact that analyses were not all following the same methods. For example, biopsies in the dose escalation cohort were analyzed with IHC (including a multiplex panel dedicated to analyses of myeloid cells) whereas in the mesothelioma cohort, biopsies were processed for flow cytometry, but had less complete IHC analyses, bulk RNA-seq and WES. Also, the quantification of the immune infiltration by IHC was not evaluated the same way in both cohorts: quantitatively in the escalation cohort (number of cells per HPF or density of cells per mm 2 ) instead of semi-quantitative scores in the mesothelioma cohort. Another difference concerned post treatment timepoints for measurement of soluble factors in plasma: in escalation cohort, soluble factors were measured at cycle 2 day 1 whereas in the mesothelioma cohort the analysis were performed at cycle 1 day 8.

Those differences could also be explained by the fact that those ancillary analysis were not done at the same time and 106 in a same run. Batch effects could indeed be an important bias that need to be considered for each experiment and requires being cautious to compare results from different experiments.

The pre-treatment observations on tumor biopsies and circulating factors were not exactly similar between both cohorts. For the first 12 patients, even if it was not significant, the immune infiltration estimated by IHC tended to be higher in the tumor microenvironment of patients with benefit to treatment (for CD3+ T cells, FOXP3+ T cells and DC-LAMP+ DC) as opposed to the patients in the mesothelioma cohort (Figures 8C; Supplementary Table 2). This point highlights that, in this cohort, the global immune infiltrate, including immunosuppressive regulatory CD4 + T cells, infiltrate more tumors from patients who will benefit to PD-1 blockade combined with anti-angiogenic. The observations that patients with tumor infiltration by Tregs, high plasma rates of CCL22, high percentage of circulating Tregs and CCR4 + CXCR3 + memory CD4 + T helper cells were benefiting from the therapy were surprising because these immune effectors are precisely associated with an immunosuppressive pro-tumoral microenvironment in humans (Figures 8A, 9C). This could be explained by the fact that a preexisting immune response, secondary to T cells recognition of type 2 MHC complex with tumor associated antigens, viral associated antigens or neoantigens make those types of tumor to respond to the treatment. Then, the administration of both pembrolizumab and nintedanib may unleash a pre-existing anti-tumor immune response. In addition, if such factors that are typically associated with bad outcomes under conventional therapies become biomarkers of efficacy with those novel treatments they will turn from prognostic to predictive factors.

In the mesothelioma cohort, there was no difference between patients with and without benefit to treatment considering CD8 + , CD3 + CD8 -, CD20 + cells infiltration and TLS structure (Supplementary table 2). Some patients who presented with primary progressive disease had TLS in their tumor microenvironment. This observation highlights that no single factor can simply predict the efficacy of a treatment and that there are other factors limiting their beneficial effect. But flow cytometry analyses of fresh mesothelioma biopsies showed that tumors from patients with clinical benefit had higher CD45 + , CD3 + and CD8 + among CD3 + cells than patients without benefit to treatment (Figure 12B).

This could be concordant with literature but discordant with IHC analyses of paired biopsy samples.

The baseline plasmatic factors, which were identified to differentiate patients with or without clinical benefit in both cohorts, were not identical. First, the plasma rate of CXCL10 was higher in patients with benefits to treatment in the dose escalation cohort (Figure 8A). It was also the case in the mesothelioma cohort, but after pembrolizumab infusion, increasing rates of plasma CXCL10 were observed in all patients, independently of their response to treatment (Supplementary figure 10; Figure 12F/G). These observations suggest that in some tumor types, the release of CXCL10 by cells in the tumor microenvironment may favor the infiltration of CXCR3 + T cells, and notably specific antitumoral T cells, and not in others. This could explain partly the sensitivity of some tumors to PD-1 blockade and not for others.

Also, by disrupting the PD-1/PD-L1 interaction systemically, the PD-1 blockade might let exhausted T cells to produce IFNγ, which in turn could induce the production of CXCL10 by targeted cells and notably macrophages, independently of the sensitivity of the tumor to the treatment. Alternatively, the fact that all patients upregulated CXCL10 could suggest that it is a pharmacodynamics effect of PD1 blockade, and that all patients respond to pembrolizumab and nintedanib but that other downstream mechanisms of resistance limit the efficacy of the treatment in a subgroup of patients.

Common results in both cohorts of the study.

Overall, the clinical results tended to be similar in both cohorts. First, besides conventional immune related adverse events, one patient in the dose escalation cohort and two in the mesothelioma cohort developed unusual vascular adverse events, with mesenteric arterial occlusion in two patients, cardiac involvement with myocarditis in two patients, and one of them died early after the first infusion of pembrolizumab. These were worrying toxicities, rare, and have been related to the treatment. However, the other patients did not present unmanageable adverse events upon combination.

Secondary, even if the disease control rate at 12 weeks in the mesothelioma cohort tended to be slightly higher than in phase 1 clinical trials of PD-1 blockade monotherapies for patients with unresectable mesothelioma refractory to platinum-based chemotherapy, the addition of nintedanib seemed to increase modestly the antitumoral response.

PD-1/PD-L1 blockade are now tested in combination with anti-angiogenic TKIs in multiple cancer indications. Early results support that such combination can increase the response rates compared to those obtained with anti-PD1 therapy alone. However, still a majority of patients (>50%) do not respond to such combination treatment (397-399). Antiangiogenic TKI do not have similar capacities to inhibit kinases pathways: constant of inhibition can be higher or lower depending on the drug, and some of them can inhibit pathways, which are not targeted by others (e.g c-Met, Tie2, and others) (Table 2; Figure 3). But maybe, the ideal anti-angiogenic drug for such combination would be a monoclonal antibody. Indeed, the results of a recent phase 1 clinical trial of combination of atezolizumab with bevacizumab for patients with unresectable peritoneal mesothelioma were very impressive, with an objective response rate of 40% (8/20; 95%CI:19.1-64.0) (283).

Several aspects of the observations brought by our ancillary analyses can be considered similar in both cohort studies.

With different methods, CD8+ T cells infiltrates in tumors were more abundant in pretreatment biopsies in patients with benefit to treatment. Presence of dendritic cells identified with IHC trough DC-LAMP expression and by expression of some genes using deconvolution of bulk RNA-seq data (MCP-Counter) were higher in tumor infiltrates of patients with benefit to treatment.

We found higher rates of plasma/serum inflammatory cytokines and chemokines, notably IL-6 and CXCL8, in patients without benefit to treatment in the mesothelioma cohort. This was not observed at baseline in the dose escalation cohort (Supplementary figure 9; Figure 14E). These observations become to be highly prevalent in the scientific and medical literature, with analyses of large cohort of plasma samples from patients included in phase 3 clinical trials evaluating PD-1 blockade to treat metastatic melanoma or NSCLC (329,330). As presented in our introduction, high levels of IL-6 and CXCL8 have been described as factors associated with tumor resistance to immune checkpoint blockade and anti-angiogenic drugs, when used in monotherapy (384). Combinations of both therapeutic strategies did not allow to overcome these barriers to antitumor efficacy, in our study. However, as opposed to the mesothelioma cohort where concentration of IL6 and CXCL8 stay at the same rate during treatment, patients without benefit to treatment had increasing rates of TNF and higher rates of IL6 and CXCL8 at cycle 2 than patients with benefit to treatment in the dose escalation cohort (Figure 10). This could be due to a sampling bias (rates may be higher before initiation of treatment and not only at cycle 2), or secondary to cancer progression or this could be induced by the treatment.

In the dose escalation cohort, after pembrolizumab infusion, plasma rates of CCL3, CCL4, IL18, and IL10 increased significantly only in patients without benefit to treatment between cycle 1 day 1 and cycle 2 day 1 (Supplementary figure 2). In the mesothelioma cohort, plasma rate of CCL3, CCL8, CCL17, CCL19 and CCL23 increased also significantly only in patients without benefit to treatment between cycle 1 day 1 and cycle 1 day 8 (Supplementary figure 11). These observations may be compared to those published in relation to the phenomena of tumor hyper-progression (385). A deleterious effect of our treatment on specific cancer subtypes cannot be eliminated. The role played by these chemokines in the biology of the cancers that we treated remain to be determined.

The pathophysiology of the cytokines belonging to the IL-6 family in cancer development is pleiotropic (400). We recently observed that another cytokines of the gp130 family, the leukemia inhibitory factor (LIF,) was also a strong negative predictor of antitumor responses to PD-1 blockade in different tumor types (401) (Appendix 2). These cytokines have direct effects on tumor survival, through the induction of cellular growth by amplification of several intracellular kinase pathways (402). They also have effects on innate and adaptive immune cells, promoting immunosuppressive Treg polarization of CD4 + T cells and impacting the function of monocytes / macrophages toward an immunosuppressive profile (403). IL-6 and cytokines from the gp130 family can be secreted by immune cells, fibroblasts and tumor cells and can shape the tumor microenvironment (404). Interestingly, patients with higher levels of plasma LIF presented with lower immune infiltrates in their tumors (401). Also elevated plasma rate of IL-6 is associated with patients cachexia and systemic manifestations associated with cancer (405-408). Targeting the IL6 pathway with an anti-IL6R could be interesting for such patients with high baseline levels of IL6. However, our experience in Covid-19 patients treated by tocilizumab have suggested that baseline levels of TNF are also important to predict the value of such strategy (409). Hence, blocking TNF could be beneficial for a larger group of patients. Interestingly, this approach of checkpoint blockade plus anti-TNF has recently proved to be safe (410). Also, the targeting of IL-6 cytokines might be insufficient because of the redundancy with other factors, such as LIF, and because of an association with other inflammatory pathways, such as CXCL8 and neutrophils which also can limit the efficacy of PD-1 blockade and anti-angiogenic drugs. Thus, the understanding of mechanisms leading to the production of such cytokines by tumor cells, directly or indirectly, appear essential. Some cancers and molecular alterations were known to produce and promote IL-6 family cytokines, such as pancreatic ductal adenocarcinoma with KRAS mutation (411-414). Senescent associated secretory phenotype was described as the ability of cells with such oncogenic alterations to produce inflammatory cytokines chemokines such as CXCL8 and CCL2, even in different types of diseases (415-417). Last but not least, the link between the level somatic copy number alterations and those pro-tumoral pro-inflammatory profiles remain elusive. More clinical observations and pre-clinical in vitro experiments are needed to decipher if such oncogenic stress directly result into higher secretions of pro-inflammatory cytokines or if those cytokines are secreted by the tumor stroma in reaction to the recognition of these altered-self cancer cells by immune cells in order to protect them.

Perspectives for the next steps: Understanding the genomic alterations of unresectable malignant mesothelioma to overcome the resistance to immune checkpoints blockade.

Impacts of genomic abnormalities on the Immune Microenvironment of Malignant Mesothelioma tumors.

The deletions of tumor suppressor genes identified in mesothelioma might shape the tumor microenvironment and hamper the efficacy of immune checkpoint blockade in patients with advanced malignant mesothelioma (MM). Indeed, or IL-8) and CCL2 in the tumor microenvironment (422,423). Interestingly, higher rates of CXCL8 in plasma from cancer patients has been recently associated with resistance to immune checkpoint blockade, and was associated to the recruitment of such polymorphonuclear MDSCs in tumors (342). Also, murine models showed that that the production of CCL2 in breast tumors led to the recruitment of inflammatory monocytes in the tumor microenvironment which favored tumor growth and metastasis spreading (387).

CDKN2A
Overexpression of EZH2 in tumor cells, through BAP1 loss or upon E2F promotion, leads to H3K27 trimethylation and gene silencing of pathways that are important to coordinate an efficient anti-tumor immunity (191). For instance, a murine model showed that in human ovarian cancer cells, trimethylation of H3K27 and DNA methylation (induced by DNMT1) in the promoter regions of CXCL9 and CXCL10 genes led to the downregulation of their expression, a decrease in CD8 + T cells tumor infiltration and in sustained tumor growth (424). Accordingly, in a murine model of melanoma, the presence of TNF in the tumor microenvironment and tumor infiltration by T lymphocytes induced an overexpression of EZH2 in tumor cells, which mediated the silencing of class 1 major histocompatibility complex (MHC-1), CXCL9 and CXCL10 genes (425). In both models, Ezh2 blockade restored immune cell infiltrates in tumors, decreased tumor growth and allowed to overcome a mechanism of resistance to anti-tumor immunity.

Biologically driven drug development to overcome immunotherapy resistance in malignant mesothelioma

Targeted therapies to alter immunosuppressive oncogenic pathways

As opposed to oncogenic drivers, loss of tumor suppressor genes has been difficult to target in the past by pharmacological means. But nowadays, novel therapies allow to target those alterations (426). In the context of patients with advanced MM refractory to immune checkpoint blockades, treatments developed to counterbalance BAP1, CDKN2A or NF2 somatic loss could be of interest (Figure 2). Such drugs are tested in biomarker selected MM patients using FISH to identify CDKN2A homozygous deletion, IHC for BAP1 deletion or maybe both for NF2 deletion and Hippo pathway alterations. Particularly in the mesothelioma cohort of PEMBIB trial, using of FISH for CDKN2A (9p21) must be interested because of E2F target gene set was enriched in patients who did not benefit to treatment, in parallel type 1 IFN gene set were decreased in these patients (genes of type 1 IFN are on the same chromosomal region of CDKN2A and are lost by the deletion of 9p21) and all patients who did not benefit to treatment had at least heterozygous deletion of 9p21 instead of patients with benefit (Figure 13B; Supplementary figure 8).

To note, ongoing many early clinical trials investigating targeted therapies and cellular immunotherapies in patients with unresectable mesothelioma do not exclude those previously treated by immune checkpoint blockade (Table 8). However, the genomic characterization of human MM highlighted that mesothelioma cells present frequently with several genomic alterations, loss of tumor suppressors and that those alterations are all interconnected [START_REF] Jamieson | Gene-expression profiling to predict responsiveness to immunotherapy[END_REF][START_REF] Liu | Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension[END_REF]. For instance, loss of FAT1 or NF2 genes have been shown to increase CDK6 expression via the Hippo pathway and to reduce the sensitivity to CDK4/6 inhibitors in a breast tumor model (446). Thus, the alteration of a second tumor suppressor pathway could inhibit the therapeutic intervention against the first targeted pathway. Recent studies have highlighted that human advanced MM present with genomic and epigenetic heterogeneity and sequentially acquired molecular events through cancer cell clonal evolution (386). In this work, BAP1 loss (or 3p21 deletion) were early acquired by MM cancer cells during their phylogenetic evolution, whereas NF2 alterations (or 22q losses), leading to Hippo pathway inactivation, where predominantly late events, positively selected. In this series of human MM, higher number of copy number alterations was associated to high neutrophil to lymphocyte ratios in the blood of patients, a feature strongly associated to resistance to immune checkpoint blockade (447,448). Also, patients with the most genomically unstable MM were presenting with higher CD8+ T-cell infiltration in their tumors and tumors with higher neoantigen burden had HLA loss of heterozygosity, consistent with clonal selection toward immune escape (386,449).

Targeted therapies to modulate the detrimental inflammation and angiogenesis in the microenvironment of mesothelioma tumors

Inflammatory and angiogenic molecules induced by the loss of suppressor tumor genes could be specifically targeted to enhance mesothelioma immunotherapy efficacy (Figure 15). Mesothelioma tumor cells can induce, directly or through the recruitment of inflammatory cells, pro-inflammatory cytokines such TNF, IL1β, IL6 and VEGF-A in the tumor milieu.

Monoclonal antibodies and small inhibitory molecules targeting those pathways are already available in the clinic (410,450,451). Tumor cells can also secrete chemokines such as CCL2 or CXCL8/IL8 which attract Tregs and MDSCs derived from circulating monocytes and neutrophils; those chemokines could also be therapeutically antagonized

Conclusion

The anti PD- Abstract The use of antagonistic immune checkpointetargeted monoclonal antibodies has profoundly modified the standard of care and significantly increased the survival for many cancers. However, many patients still do not respond to those treatments. Biomarkers predictive for efficacy or failure of such immunotherapies would allow developing treatment stratification strategies which could further increase the survival rates of patients with cancer.

Chemokines are a subset of the immune cell messenger molecules known as cytokines. Chemokines are key chemoattractant molecules which are essential for the homing of immune cells, notably within tumours. Therefore, they are good candidates for providing predictive biomarkers of the clinical response to checkpoint blockade immunotherapies. In this review, we summarise the recent advances in our understanding of the role of chemokines and how chemokine concentrations may set the tone for the efficacy of immune checkpointetargeted immunotherapies. ª 2020 Elsevier Ltd. All rights reserved.

Introduction

Cancer is one of the leading causes of mortality worldwide. More than 9.5 million people died from cancer in 2018 [1]. Over the last decade, immunotherapy has taken a significant place within the anticancer armamentarium beside surgery, radiotherapy, cytotoxic chemotherapies, anti-angiogenics and oncogenic tyrosine kinase inhibitors [2]. For the first time, monoclonal antibodies (mAbs) have been designed to target the immune system rather than the tumour itself to boost the anticancer response naturally occurring in patients with cancer. The tumour microenvironment is composed not only of cancer cells but also of stromal cells and immune cell infiltrates such as T cells, B cells, macrophages and dendritic cells (DCs); all these cells can secrete activating or inhibitory messenger molecules, chemoattractants and growth factors of the immune system known as cytokines [3]. The immune response seems to be inefficient in its fight against tumours because of a dysregulated expression of immune checkpoints such as programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) and cytotoxic T-lymphocyteeassociated protein 4 (CTLA-4) [4,5]. In physiological conditions, these checkpoints can inhibit inflammatory responses and protect organisms against autoimmune diseases. However, PD-1/PD-L1 and CTLA-4 can be overexpressed in the tumour microenvironment and therefore hamper the antitumour immune response. Among the commercially available immunotherapies, immune checkpointetargeted antibodies are blocking PD-1/PD-L1 and CTLA-4. Durable tumour responses can be obtained in up to 40% of the patients in antiePD-1 monotherapies and 55% in combination with antieCTLA-4 [6,7]. Interestingly, the levels of tumour-infiltrating immune cells have been shown to be more accurate predictors of the clinical outcome than the tumour stage [9,10]. Chemokines are the chemotactic cytokines which are essential for the homing of immune cells in specific tissue microenvironment, notably within tumours. Therefore, circulating chemokine concentrations could correlate with the immune profile of a tumour and could be monitored more easily than tumour-infiltrating immune cells during immunotherapy. In this review, we summarise the current knowledge of how chemokines could participate to the cancer spreading or the homing Fig. 1. Structure of chemokine receptors. Chemokine receptors are structured into an extracellular N-terminal domain, an intracellular Cterminal domain and seven transmembrane helices. In conventional receptors, the C-terminal domain is coupled with a G-protein and contains either two adjacent cysteines (CC), two cysteines separated by another amino acid (CXC), one single cysteine (XC) or two cysteines separated by three amino acids (CX 3 C). The N-terminal domain determines the affinity of the chemokine receptor for its ligands, while the C-terminal domain triggers the intracellular pathway in response to the binding of the ligand. Chemokine receptors can be biologically functional as monomers, homodimers and heterodimers. An example of each case is provided in the table [11][12][13][14].

of immune cells into tumours and how their monitoring could ameliorate immune checkpoint blockade therapy.

1.1. Chemokines are a family of immune-related molecules 1.1.1. Molecular nature of chemokines Chemokines are small secreted proteins of 8e14 kDa from the cytokine superfamily [15]. They are expressed by a broad variety of immune cell types from the myeloid and lymphoid lineages but can also be expressed by stromal and cancer cells of the tumour microenvironment [16]. They are classified in four subfamilies relatively to the position of one or two conserved cysteines ('C') in the N-terminal domain of each of these proteins: CC, CXC, CX 3 C and XC (where 'X' stands for an alternate amino acid) ( [17], Fig. 1). Their chemotactic properties allow for the recruitment of leukocytes to the site of inflammation. 48 chemokines and 23 chemokine receptors, among which 5 are atypical receptors, have been described so far in the human species ([12], Table 1). Conventional chemokine receptors are heptahelical G-proteinecoupled molecules that activate intracellular pathways. Atypical receptors do not seem to transduce intracellular signals but seem to contribute to the regulation of ligand colocalisation [17].

Chemokine-receptor interaction

The chemokine network is redundant, as different ligands can bind to the same receptor and vice versa ( [17], Table 1). The ligand-receptor interaction is notably modulated by the nature of the receptor, its glycosylation, the concentration of the ligand and the number of receptors expressed at the cell surface. Moreover, chemokine receptors can form homodimers or heterodimers [17,18]. For example, the CXC motif chemokine receptors 3 and 4 (CXCR3 and CXCR4) can form heterodimers [19]. Agonistic ligands can induce conformational changes of their receptors, leading to the intracellular activation of various pathways including the Janus kinase/signal transducer and activator of transcription (STAT) and mitogen-activated protein kinase pathways [12].

Biological role of the chemokine network

Chemokines are mostly known to form a gradient that can chemoattract leukocytes to the site of damage or infection. Their biological activity relies on the nature of the immune cells that express the chemokine receptor. The human chemokine receptors are activated by agonistic ligands and inhibited by antagonistic ligands. They display various roles in immunity depending on the type of cells expressing the corresponding chemokine receptors.

In bold are main ligands responsible for the biological activity of the receptor and in italic are atypical chemokine receptors. DC: dendritic cell.

For example, CXCR5 is involved in adaptive immunity by activating B cells [12]. More and more studies demonstrate that chemokines are involved in other processes such as cell proliferation and differentiation, apoptosis and embryonic development [12].

1.2. Cancer cells can hijack the chemokine network 1.2.1. Chemokines are reknown to participate to the anticancer immune response Chemokines counter cancer spreading using various processes. Chemokines can be secreted by either sentinel cells that perform tissue immunosurveillance, stromal cells or cancer cells. They form a gradient from the tumour site to the blood and lymph vessels, thereby recruiting leukocytes expressing the corresponding chemokine receptors. For example, CXCL9/10/11 could chemoattract natural killer cells and T cells to the tumour site [20]. Once activated, DCs can present tumour antigens to naive and memory T cells leading to their priming and their migration throughout the blood, lymphatic vessels and tumours. Eventually, cancer cells can be destroyed by such primed cytotoxic natural killers and CD8þ T lymphocytes ( [21], Fig. 2A). Similarly, CXCL13 produced by T follicular helper cells (T FH ) could chemoattract B cells and favour their activation in tertiary lymphoid structures (TLSs) [22]. Such B cells could act as antigen-presenting cells or differenciate into plasma cells to produce antibodies and cytokines targeted against the tumour [10].

Chemokines can also have direct inhibitory effects on cancer cells

Interestingly, chemokines can also directly target cancer cells. First, they can inhibit cancer cell proliferation. For example, in hepatocellular carcinoma (HCC), CeC motif chemokine ligand 14 reduces avian myelocytomatosis virus oncogene cellular homologue expression via the Wnt/b-catenin pathway [23], and in vitro CXCL2 promotes apoptosis and alters the extracellular signalregulated kinase 1/2 pathway, resulting in cell cycle arrest in cancer cells ( [24], Fig. 2A). Second, chemokines can impair metastasis. For example, the CCL28-CCR10 axis increases the expression of E-cadherin in oral squamous cell carcinoma, favouring cell-to-cell adhesion and reducing epithelialmesenchymal transition (EMT) [25]. Interestingly, CCL28 and CXCL2 are downregulated in the tumour microenvironment compared with healthy tissues, suggesting that there is an escape mechanism from cancer cells [8,9]. Epigenetic processes could be involved in such escape, as suggested by a recent article showing that the CXCL2 gene is over methylated in HCC cell lines [26].

Chemokines can also favour the tumour immunosuppressive microenvironment

Cancer cells can secrete diverse molecules which impact the tumour tissue microenvironment and counteract the antitumour immune response. Chemokines can participate to this immunosuppression, either indirectly by recruiting and promoting the proliferation of regulatory T cells (Tregs) or directly by impairing the function of cytotoxic T cells (Fig. 2B).

Chemokines can recruit Tregs that inhibit the antitumour effect of cytotoxic T cells by producing antiinflammatory cytokines such as interleukin 10 (IL-10) and tumour growth factor b (TGF-b). In HCC, histological studies suggest that CCL22 expressed by stromal cells attracts CCR4-expressing immunosuppressive Tregs [27]. The same phenomenon was observed in ovarian and breast cancers [28e30].

In a mouse model, CXCL4 produced by colon cancer cells have been shown to induce Tregs proliferation and to inhibit cytotoxic T-cell proliferation [31]. Moreover, CXCL4 can promote cytotoxic T-cell apoptosis [31]. These processes were not observed anymore in CXCR3À/À mice showing that CXCL4 favours tumour immunosuppressive microenvironment via CXCR3 [31]. In addition, the CXCL4-CXCR3 interaction has been shown to reduce the cytotoxic T-cell response and therefore allow for cancer spreading [31].

Chemokines can also manipulate the PD-1/PD-L1 axis. This mechanism will be detailed in The immune checkpointetargeted therapy paradigm of this review. In brief, PD-1 expressed by T cells interacts with PDL-1 mainly expressed by antigen-presenting cells, leading to T-cell inactivation [4]. CXCL4 produced by cancer cells upregulates PD-1 expression on CXCR3þ cytotoxic T cells and downregulates it on CXCR3þ Tregs [31]. A bioinformatics study has shown that the interaction between the ligands CXCL9/10/11 and the CXCR3 receptor can upregulate PD-L1 expression in gastric cancer cells, via the STAT and phosphoinositide 3kinase (PI3K)/AKT8 virus oncogene cellular homologue (Akt) pathways [32]. Therefore, CXCL9/10/11 via CXCR3 signalling may both favour the expression of PD-1 in cytotoxic T cells and PD-L1 in cancer cells, thereby hindering the anticancer immune response.

Chemokines can also directly support cancer cell growth

Growing evidence shows that chemokines can directly bind cancer cells and increase their proliferation. CXCR5 is expressed by different types of cancer cells and its activation by CXCL13 can lead to cancer cell proliferation ( [15], Fig. 2B). The downstream pathways comprise PI3K/Akt and extracellular signalregulated kinase in prostate and clear cell renal cell carcinoma (RCC). Interestingly, in nonesmall-cell lung cancer (NSCLC), T cells with high PD-1 expression secrete more CXCL13 [15,33]. Therefore, the induction of PD-1 in T cells by cancer cells [31] can not only favour tumour survival but also promote its growth.

Chemokines can support tumour angiogenesis and lymphangiogenesis

The formation of blood (angiogenesis) and lymph vessels (lymphangiogenesis) is crucial for cancer metastasis. Tumour cells are known to produce vascular endothelial growth factors (VEGFs) to promote angiogenesis and/ or lymphangiogenesis (Fig. 2C). In vitro stimulation of osteosarcoma cells with CCL3 has shown that CCL3 can induce VEGF-A expression [34]. In this work, VEGF-A production was highly reduced in presence of CCR5 antagonists or mAbs, showing that CCL3 activates VEGF-A production via CCR5 engagement. VEGF-A and CCL3/CCR5-mediated angiogenesis was also assessed in a mouse model using CCL3-shRNA that downregulates CCL3. CCL3 was overexpressed in osteosarcoma tumours; however, the type of cells expressing CCL3 was not investigated in this work.

CCR5 is also involved in lymphangiogenesis. Oral squamous cell carcinoma has been shown to preferentially spread metastasis via lymph vessels [35]. Using in vitro studies, these authors have demonstrated that the CCL4-CCR5 axis can activate the VEGF-C pathway. Interestingly, in vivo, nude mice grafted with cancer cells expressing CCL4-shRNA displayed less lymph vessels compared with the control without knockdown. Hence, the same receptor seemed to be involved in both angiogenesis and lymphangiogenesis depending on the type of ligand. Altogether, the CCR5-VEGF pathway might be an interesting therapeutic target to counter cancer metastasis.

Chemokines can favour cancer cells metastasis

To metastasise, cancer cells need to dissociate with the primary tumour to reach vessels, circulate and then stick again to an extracellular matrix to form new distant tumours. Chemokines can play a role in all those steps (Fig. 2D). First, cancer cells can dissociate from the tumour during the EMT [15]. Matrix metalloproteases (MMPs) favour this process by degrading proteins of the extracellular matrix. In breast cancer cells expressing CXCR5, CXCL13 induces the expression of mesenchymal markers such as N-cadherin and MMP9 and downregulates epithelial markers such as E-cadherin [15]. Two main oncogenic pathways seem to be involved in the cancer chemokine expression: the Rous sarcoma oncogene cellular homologue (Src) kinase family and the PI3K/Akt pathway [36,37]. Indeed, human prostate cancer cells expressing CXCR1 and 2 have been shown to present enhanced migration and downregulation of Ecadherin upon CXCL1 stimulation [36]. Analysis of the phosphorylation state of members of the Src pathway demonstrated that this pathway was upregulated in that process. Moreover, the migration of cancer cells was abolished upon their exposure to an inhibitor of the Src pathway. These results suggest that the binding of CXCL1 to its receptors CXCR1 and 2 could lead to the activation of the Src pathway and then engage cancer cells to an EMT. Another study using similar methods in tongue squamous cell carcinoma demonstrated that the CXCL9-CXCR3 axis was associated to the promotion of EMT via the Akt pathway stimulation [37]. It remains unknown if both CXCR5 and CXCR3 pathways are activated simultaneously by chemokines or if they are independent ways of triggering an EMT.

After EMT, cancer cells can spread throughout the organism using vasculature and can eventually form metastases. We have previously shown that chemokines can favour metastasis by promoting the formation of blood or lymph vessels [34,35]. Once in the vasculature, chemokine gradients may guide cancer cells to the tissues where they preferentially metastasise. For instance, in human breast cancers, the lung and brain are the most frequent sites for metastases. Interestingly, breast cancer cells injected intravenously in a mouse model formed spontaneously tumours in the mammary glands, the organs where CCL8 was the most abundant [38]. When the experiment was performed with neutralising antibodies against CCL8, less breast tumours were formed. The same tendency was observed in the lung [38].

1.3. Chemokines as biomarkers for the efficacy of the immune checkpoint blockade therapy 1.3.1. The immune checkpointetargeted therapy paradigm Immune checkpoint blockade therapy consists in using intravenous administrations of antagonistic mAbs against PD-1 (aPD-1), PD-L1 (aPD-L1) or CTLA-4 (aCTLA-4) to block the corresponding coinhibitory signalling to the T-cell receptor (Fig. 3). aPD-1 and aPD-L1 mAbs prevent the PD-1/PD-L1 interaction and allow for reactivation of the Tcellemediated antitumour response [39]. Two nonmutually exclusive hypotheses would explain the efficacy/toxicity profile of aCTLA-4 mAbs. One classical and historical mechanism of action relies on the ability of aCTLA-4 mAbs to prevent the interaction of CTLA- 4 with CD80 and CD86 [40]. The second mechanism relies on the ability of such antibody-derived cell cytotoxicity (ADCC) competent mAbs to deplete CTLA-4epositive cells, that is, mainly Tregs (mouse and humans aCTLA-4 are ADCC competent and therefore not purely antagonistic) [41e47]. However, this second mechanism of action remains currently controversial [48e50].

Chemokines as pharmacodynamic vs predictive markers of efficacy

Pharmacodynamic (PD) markers are molecules or cells whose presence/expression/concentration is correlated with the bioactivity of a drug and its mechanism of action but independently from the question of its clinical efficacy. They provide information on the drug's bioavailability in tissues and on the target engagement of the drug.

A higher level of baseline Cxcl9 gene expression prior treatment by atezolizumab (initially called MPDL3280A, an Fc-silent IgG1 aPD-L1 mAb) has been associated to objective tumour responses in multiple tumour types including NSCLC, RCC and melanoma ( [51], Table 2). However, the circulating levels of CXCL9 (aka MIG) and CXCL10 (aka IP10) proteins in the blood seem to increase [52] or decrease [53] eventually in all patients upon aPD-1 therapy independently from the tumour response. This would suggest that changes in CXCL9 and CXCL10 levels upon treatment might behave as a good PD marker for aPD-1 therapy, but the higher absolute values at baseline would be associated to a higher chance of aPDL-1 efficacy in patients with cancer.

Indeed, responders have been associated with a high expression of CXCR3 ligands in the tumour before immunotherapy, before aCTLA-4 [54], aPD-1 [55] and aPD-L1 [56] treatment. High levels of CXCL9 in tumours before aPD-L1 treatment were associated with response in melanoma and RCC but not in NSCLC [51]. In addition, in metastatic melanoma, responders displayed higher gene expression of CCL5, CCR5, CXCR6 and CXCL9/10/11 before aPD-1 treatment [55]. The predictive power of this set of genes for diagnosis was assessed in an independent cohort of metastatic melanoma, as well as head and neck squamous cell carcinoma, and gastric cancer. Along with other immune genes, this set was sufficient to discriminate between responders and non-responders, suggesting that mechanisms of response following aPD-1 are shared among different types of cancer.

An immune-active tumour microenvironment may enhance the effect of immune checkpoint inhibitors, Gene expression Tumour Pembrolizumab (aPD-1) Higher at baseline in responders [55] Higher at baseline in non-responders [63] The gene and protein expression of chemokine ligands and receptors have been associated with the effect of the immune checkpoint blockade therapy, dependently or independently of the clinical response. IHC: immunohistochemistry, aCTLA-4: antibody against CTLA-4, aPD-1: antibody against PD-1, aPD-L1: antibody against PD-L1.

likely via T-helper type 1 cells [54]. CXCR3 and its ligands have long been thought to enhance immunotherapy efficacy by recruiting T cells at the tumour site. However, a recent study in mice questions this dogma [57]. There were no differences in CD8þ T-cell populations within tumours between wild-type and Cxcr3À/ À mice before aPD-1 treatment. Intratumoural CD8þ T-cell populations of wild-type mice expanded after treatment, whereas those of Cxcr3À/À mice did not change. Therefore, CXCR3 does not seem to be involved in the recruitment of CD8þ T cells to the tumour site in the context of aPD-1 treatment but rather favours their proliferation within the tumour. These chemokines have been shown to be produced by myeloid cells within tumour tissues in a neuroblastoma mice model [58]. CXCL9, 10 and 11 guide T cells expressing CXCR3 to the tumour site ( [12,58], Fig. 2A), and those results are consistent with the observed increased T-cell infiltration after treatment.

In addition, increase. in CXCL11 (also called ITAC or IP-9) has been seen upon aPD-L1 therapy but did not track significantly with response or progression ( [51], Fig. 4)

Most chemokine studies have been initially performed on melanoma, NSCLC and RCC, and it is possible that the response to aPD(L)-1 therapy might vary with the cancer histology. Indeed, a trial of pembrolizumab in HCC did not find any correlation between baseline circulating CXCL9, CCL4 and CCL5 between responders and non-responders, but the baseline levels of TGF-b were significantly associated to disease progression on treatment [52].

Intratumoural levels of chemokine proteins have also been associated with clinical responses. In NSCLC, intratumoural CD8þ T cells expressing high levels of PD-1 constitutively secrete CXCL13 [33]. A chemotaxis assay showed that CXCR5þ cells and especially CD4þ T cells migrate through a CXCL13 gradient, suggesting that CXCL13 mediates the recruitment of immune cells to the tumour microenvironment. Because CD8þ T cells with high PD-1 expression are associated with good prognosis for aPD-1 therapy, CXCL13 could behave as a biomarker for responders as well (Fig. 5).

A recent study by Chalabi et al. [59] testing the neoadjuvant combination of nivolumab (aPD-1) and ipilimumab (aCTLA-4) in mismatch repair deficient (dMMR) or mismatch repair proficient (pMMR) colon cancers showed that CXCL13 gene expression was upregulated upon treatment by both dMMR and pMMR tumours but at a much higher level in dMMR (2e3 fold higher) and increased at the same level of magnitude in responding vs non-responding patients. This would suggest that the increase of CXCL13 gene expression behaves as a PD marker of the PD-1/CTLA-4 blockade and not as a biomarker of response. However, the baseline level of CXCL13 and CXCR5 gene and protein expression in tumours correlates with the existence of TLSs and has been shown to correlate with a higher chance of response to aPD-1 in melanoma and sarcoma [10,60,61]. Interestingly, many genes included in the various TLS gene signature identified so far in the literature include predominantly chemokine genes, namely Ccl19, Ccl21, Cxcl13, Ccr7 and Cxcr5 [62]. More specifically, CXCL13 has been associated to a strong correlation with baseline TLS in sarcoma [60]. CXCL13 seems to be predominantly expressed by CD8þ T cells, whereas its receptor CXCR5 by B cells and CD4þ T cells (mostly T FH ) in the TLS [61]. An important chemokine that seems to be differentially expressed in tumour infiltrative B cells is CXCR3. An increased percentage of CXCR3þ switched memory B cells has been found in melanoma tumours responding to aPD-1 compared with non-responding tumours [10].

On the other hand, some chemokine patterns of expression seem to correlate with non-responders. High tumoural levels of CXCR6 [63] and CX3CL1 [51] before treatment are promising markers of resistance to aPD-1 and aPDL-1, respectively. Some articles have also demonstrated that CX3CL1 enhances the metastatic activity of CX3CR1-expressing cancer cells [64,65]; however, this role has never been investigated in patients after immunotherapy. Whether CX3CL1 hinders immunotherapy efficacy and how are yet to be shown because this chemokine is also known for its antitumour activity via the chemoattraction of immune cells [51].

Interestingly, the absolute increase in the serum level of CXCL5 upon nivolumab (aPD-1) treatment has been suggested to be associated to the occurrence of immunerelated adverse events [66].

Chemokines associated with resistance to immune checkpoint blockade

Liver kinase B1 (LKB1) mutations and phosphatase and tensin homologue (PTEN) loss have been described as mutations of resistance to aPD(L)-1 therapies in NSCLC and melanoma [67e69]. In a mouse model, the dual loss of both Lkb1 and Pten in tumours led to an activation of the AKT and mechanistic target of rapamycin (mTOR) pathways, pathways classically associated to cancer cell proliferation and tumourigenesis [70]. The co-deletion of these two genes in cancer cells was also associated with the upregulation of expression of specific chemokine genes in mouse tumours. Compared with Kras G12D mutated tumour cells, LKB1/PTEN double negative EpCAMþcellesorted cancer cells expressed very high levels of the chemokines CXCL3 and CXCL5, high PD-L1 expression and the bronchoalveolar fluid contained elevated CXCL1, CXCL2, CXCL5 and CXCL7.

Immunotherapy using immune checkpointetargeted antibodies can also lead to a paradoxical acceleration of the disease, an atypical pattern of progression known as hyperprogression [71]. Interestingly, a lower frequency of effector/memory T-cell subsets that are CCR7-and Fig. 5. Impact of baseline chemokine gene expression on the efficacy of immune checkpoint blockade therapy. The concentration of CCL4 and 5, CXCL9, 10, 11 and 13 before aPD(L)-1 and aCTLA-4 immunotherapy has been retrospectively shown to be higher in the tumour of responding patients. Moreover, CX3CL1 was more expressed in the tumour microenvironment of patients whose disease progressed and worsened after aPD-L1 therapy, whereas CCR5 and CXCR5 were more expressed in tumours from responders before aPD-1 and aCTLA-4 therapy. aCTLA-4: antibody against CTLA-4, aPD-1: antibody against PD-1, aPD-L1: antibody against PD-L1, CTLA-4: cytotoxic T-lymphocyteeassociated protein 4, DC: dendritic cell, EMT: epithelial-mesenchymal transition, PD-1: programmed cell death 1, PD-L1: programmed cell death ligand 1, Treg: regulator T cell.

CD45RA-among the total CD8þ T cells has been associated with hyperprogression and inferior survival rate than with classical progression in NSCLC [72]. This would suggest a potential role for the CCL19/21-CCR7 axis in CD8þ T cells to prevent that phenomenon.

Chemokines as therapeutic targets

So far, few strategies can be described to identify patients that are likely to respond to immune checkpoint blockade immunotherapy. It becomes increasingly clear that a better response is observed when immune checkpointetargeted antibodies reactivate a pre-existing immunity [51,54]. High intratumoural gene levels or protein expressions of PD-1, PD-L1 and CTLA-4 have been associated to a better outcome for aPD-1 [33], aPD-L1 [51] and aCTLA-4 [73] therapy, respectively.

Some key chemokines appear to play an important role in the antitumour response and the sensitivity to immune checkpoint blockade therapy. Therefore, activators or inhibitors of chemokines could be envisioned as anticancer therapies. Clinical trials have shown promising antitumour effects. In a phase Ib study, 33 patients with metastatic breast cancer were treated three times a day with oral tablets containing 1200 mg of a CXCR1 and 2 antagonist ([74], NCT02001974). Grade III treatment-related adverse events were rare, and 29.6% of patients responded to the treatment. An anti-CXCR4 antibody was even more promising ([75], NCT01359657). Forty six patients with myeloma received 10 mg/kg/dose intravenously on a weekly basis and 72.4% had a clinical benefit. However, grade IV neutropenia was observed for 13 patients. Moreover, anti-CXCR4 therapy combined with chemotherapy led to a higher response rate with acceptable toxicity [75].

Chemokine antagonists might also synergise with immune checkpoint inhibitors to improve their efficacy. Only few clinical trials have combined chemokine antagonists with immunotherapy, with no blatant results. In a phase I/II study in pancreatic adenocarcinoma and small cell lung cancer, 41 patients received intravenously either 200 or 400 mg of an anti-CXCR4 every week in combination with 3 mg/kg of aPD-1 administered every two weeks. All participants presented adverse events, and the trial was terminated because of its inefficiency ( [76], Identifier: NCT02472977). Results of the phase I/ II study of an anti-CCR4 in hepatocellular carcinoma ( [77], Identifier: NCT02705105) or an anti-CCR5 in colorectal cancer ( [78], Identifier: NCT03274804) with aPD-1 are yet to be published.

Conclusion

Chemokines, similar to most cytokines, have a pleiotropic effect and therefore can be involved in both the anticancer immune response but also in the biology of tumour spreading, by favouring tumour growth, immunosuppression and metastasis. They are likely to be the effectors of the reactivation of the immune response after immune checkpoint blockade therapy. However, the expression of some chemokine receptors or the release of some chemokine ligands such as CXCL5 by cancer cells could be the source of some immune-related adverse events. The level of concentration of the chemokines CXCL9 and CXCL10 before treatment can be correlated with the clinical response according to retrospective studies. Identifying chemokines associated with responders for each immunotherapy may permit to choose the most beneficial treatment for a patient and to increase the survival rate.

Combining immunotherapy with chemotherapy, radiotherapy or chemokine antagonists may increase further the response rate by targeting different pathways and thereby countering cancer resistance. Besides, identifying immune profiles favourable to cancer could permit the diagnosis of cancer at early stages, perhaps before tumours become detectable, and lead to preventive treatments.

INTRODUCTION

The discovery of immune inhibitory checkpoints has revolutionized the systemic approach of the treatment of cancer. Blocking the interaction between the Programmed cell death 1 (PD-1) receptor and its primary ligand Programmed death-ligand 1 (PD-L1) has demonstrated remarkable anti-cancer activity and has led to the recent approval of anti-PD-1/PD-L1 drugs in several solid tumors 1 . However, most patients receiving anti-PD-1/PD-L1 monoclonal antibodies do not derive clinical benefit. Therefore, there is a crucial need to identify reliable predictive biomarkers of response to anti-PD-1/PD-L1 agents, both to develop precision medicine in cancer immunotherapy and to better understand mechanisms of sensitivity and resistance.

PD-L1 expression status as assessed by immunohistochemistry, tumor mutational burden and microsatellite instability status are so far the sole companion diagnostic markers approved to guide for anti-PD(L)1 therapy 2-4 . However, all of them and particularly PD-L1 expression are imperfect predictors of response to immunecheckpoint inhibition as demonstrated by the discordant results reported by multiple studies 2 .

While tumor tissue profiling is important for biomarker discovery, this approach has several limitations including limited accessibility and temporal and spatial heterogeneity. Hence, identification of biomarkers than can be readily evaluable through peripheral blood sampling is crucial to allow the easiest implementation in routine clinical practice. To the best of our knowledge, we report here the first large analysis, including discovery and validation cohorts, of plasma proteome from cancer patients treated with immune checkpoint blockers (ICB).

J o u r n a l P r e -p r o o f

METHODS

Patients (Figure 1)

This study was based on the analysis of two prospective cohorts of advanced cancer patients treated with ICB at Gustave Roussy (Villejuif, France) (Discovery: MATCH-R 5 , NCT02517892; Validation cohort: PREMIS, NCT03984318). The inclusion criteria were age ≥ 18 years, histologically proven malignant tumor, unresectable and/or metastatic disease, at least one tumor evaluation by imaging after immunotherapy onset, and, for the MATCH-R study, availability of paraffin-embedded tumor material obtained before immunotherapy onset. Patients treated with combinations of ICB and chemotherapy were excluded from the analysis. Institutional ethics review board approval and patient informed consents were obtained for both studies.

Treatments and evaluation

All patients were treated either with anti-PD(L)1 monotherapies or anti-PD(L)1 based combination therapies. Patients were treated by immunotherapy either within clinical trials, or in the context of EMA-approved indications, or within early access programs. The best response to treatment was evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST) 6 or iRECIST depending on the protocol in which patients were treated. Routine follow-up and treatment beyond progression therapeutic options were similar within the two cohorts. Durable clinical benefit (DCB) was defined as the proportion of patients achieving objective response or stable disease lasting ≥ 12 months. Progression-free survival (PFS) was defined as the time from the start of treatment until disease progression, death, or last patient contact.

Overall survival (OS) was defined as the time from the start of treatment until death or last patient contact.

J o u r n a l P r e -p r o o f

Plasma proteome analysis

Proteome analysis has been performed as previously described 7 thanks to the Olink Proximity Extension Assay (PEA) (Olink Proteomics AB, Uppsala, Sweden). In brief, pairs of oligonucleotide-labeled antibody probes bind to their targeted protein, and if the two probes are brought in proximity the oligonucleotides will hybridize in a pair-wise manner. The addition of a DNA polymerase leads to a proximity-dependent DNA polymerization event, generating a unique target sequence analyzed through either Next Generation Sequencing or Real-Time PCR.

Analysis of baseline samples from the discovery cohort has been performed using the Olink® Explore 1536 library consisting of 1472 proteins and 48 controls assays divided into four 384-plex panels focused on inflammation, oncology, cardiometabolic and neurology proteins. Sequencing was performed on a NovaSeq 6000 system using two S1 flow cells with 2 × 50 base read lengths. Counts of known sequences are thereafter translated into normalized protein expression (NPX) units through a QC and normalization process developed and provided by Olink.

Plasma samples from the validation cohort were assessed using the Olink® Target 96 Inflammation panel (Olink Proteomics AB, Uppsala, Sweden) according to the manufacturer's instructions 8 . In that case, the resulting DNA sequence was subsequently detected and quantified using a microfluidic real-time PCR instrument (Biomark HD, Fluidigm). Data were quality controlled and normalized using an internal extension control and an inter-plate control, to adjust for intra-and inter-run variation. The final assay read-out is presented in Normalized Protein eXpression (NPX) values, which is an arbitrary unit on a log2-scale where a high value corresponds to a higher protein expression. All J o u r n a l P r e -p r o o f assay validation data (detection limits, intra-and inter-assay precision data, etc.) are available on manufacturer's website (www.olink.com).

Immunohistochemistry stainings

All staining were carried out on 3,5 micrometers paraffin slides using a Ventana Discovery Ultra platform (Ventana, Roche Diagnostics). Double immunohistochemistry was performed on all cases with i) CD3 (2GV6, Ventana) combined with CD20 (L26, Ventana) and ii) CD8 (C8/144B, Dako) combined to PD-L1 (QR1, Diagomics).

Stainings were performed with the protocol RUO discovery universal according to the manufacturer's recommendations with the detection kits OmniMap anti-Rb HRP (760-4311, Ventana) and OmniMap anti-Ms HRP (760-4310, Ventana).

Tumor TLS assessment

All cases were reviewed blindly by a pathologist for the presence of TLS according to the hematoxylin eosin saffron (HES) and the multiplexed immunohistochemistry on serial sections as previously described 9 . TLS were defined as lymphoid aggregates of B lymphocytes (admixed with a variable proportion of plasma cells and T lymphocytes in most cases). Only TLS made up of more than 50 cells and located either among the tumor cells or at the invasive margin (defined as fibrous tissue distant of less than 1mm from tumor cells) were considered. When the TLS status was assessed on lymphoid organs (namely lymph nodes, spleen, tonsils), TLS were only taken into account when admixed to tumor cells and if distant from the residual parenchyma, to exclude pre-existing lymphoid follicles.

Tumor PD-L1 scoring

For all tumors, the PD-L1 status was determined with TPS (tumor positive score) following guidelines. Only viable tumor cells displaying partial or complete staining for J o u r n a l P r e -p r o o f PD-L1 membrane expression were considered relative to the total number of tumor cells. Positive immune cells and neoplastic cells showing only cytoplasmic staining were excluded 10 .

Semi-automated and quantitative analysis of T-cell infiltrate

Density of CD8+ T cells within the tumor lesion was obtained by image analysis after slides digitization on a multispectral slide-imaging platform (Vectra Polaris, Akoya Bioscience). Using Inform software (Akoya Bioscience, version 2.4.1), tissue segmentation and cell phenotyping were performed and allowed for CD8+ T cells detection within the tumor lesion previously annotated by an expert pathologist.

Combining CD8+ T cell detection and calculation of the tumor lesion surface, density of CD8+ lymphocytes was obtained for each sample.

RNAseq analysis

RNA sequencing was performed as previously described 11 . Reads were aligned to the hg38 human genome assembly using Rsubread (version 2.2.6) without prior trimming 12 . Counts were then summarized at the gene level using FeatureCounts and normalized using Deseq2. Relative abundance of immune cell types was estimated using the ConcensusTME 13 on the CIBERSORT 14 and Bindea 15 gene sets.

Statistical analysis

The cutoff date for statistical analysis of baseline demographic data and clinical outcome was 11/30/2020. Descriptive statistics were used to describe the distribution of variables in the population. Survival rates were estimated using the Kaplan-Meier method. Differences between groups were evaluated by chi-square test or Fisher's exact test for categorical variables and Student's test for continuous variables.

J o u r n a l P r e -p r o o f

Receiver operating characteristic (ROC) curve analysis was performed using the ROCit R package. Prognostic factors were planned to be identified by univariate and multivariate analyses using a Cox regression model. Variables tested in univariate analysis included age, gender, tumor type, number of metastatic sites, presence of liver metastasis, performance status (PS), number of previous lines of treatment, and LIF plasma levels. Variables associated with PFS and OS with a P-value <0.05 in the univariate analysis were planned to be included in the multivariate analysis. Analyses were carried out using SPSS 20.0 statistical software (IPSS Inc., Chicago, IL). All statistical tests were two-sided, and P < 0.05 indicated statistical significance.

J o u r n a l P r e -p r o o f

RESULTS

Unbiased proteomic analysis identifies baseline serum level of Leukemia Inhibitory Factor (LIF) is associated with poor clinical outcome in cancer patients treated with immune-checkpoint blockers

To detect potential peripheral biomarkers of efficacy of ICB, we implemented a proteomics analysis based on the Proximity Extension Assay (PEA) technology and the use of Olink® Explore 1536 panel 7 (1472 proteins and 48 controls) on plasma samples, collected before anti-PD(L)1-based immunotherapy onset, from 95 patients enrolled prospectively in the MATCH-R study (NCT02517892, Discovery cohort)patient's characteristics are described in Table 1. Proteomic analysis allowed for the detection and quantification of 1463 unique proteins in all plasma samples. We then explored the correlation for each marker -classified as High and Low according to their respective median value -with progression-free survival. Among several cytokines (Supplementary Figure 2) already known to be associated with clinical outcome in cancer patients treated with immunotherapy such as IL6, CXCL8 (IL8) or CXCL1 (Supp. Fig. 2) 16-17 , Leukemia Inhibitory Factor (LIF) was the most significantly associated with outcome (Fig. 2a). The median follow-up was 26.4 months. The median PFS of LIF Low patients was 7.4 months (95% CI, 2.9-11.9) versus 1.7 months (95% CI, 1.3-2.1) in the LIF High group, p<0.0001 (Fig. 2b). The 6-month, 1-year, and 2-year PFS rates were 55.9%, 41.5%, and 16.2% in LIF Low group and 17%, 6.4% and 0% in the LIF High group, respectively. At the time of analysis, 69 patients (72.6%) had died and 26 (27.4%) were still alive. The median overall survival (OS) was 21.7 months (95% CI, 12-31.4) in the LIF Low group versus 4.3 months (95% CI, 3.4-5.1) in the LIF High group, p<0.0001 (Fig. 2b). The 6-month, 1-year, and 2-year OS rates were 81.1%, 67.8%, and 47.2% in the LIF Low group and 40.4%, 29%, and 10.6% in the LIF High J o u r n a l P r e -p r o o f group, respectively. Overall, LIF plasma levels were significantly lower in patients with durable clinical benefit in comparison with other patients (Fig. 2c). Indeed, in patients classified as plasma LIF High , the durable clinical benefit rate was 6.4% versus 41.7% in LIF Low patients (NPX value below the median), p<0.0001 (Fig. 2d). Also, to analyze the performance of baseline LIF level to predict the clinical benefit, we performed a univariate time-dependent ROC (Receiver Operating Characteristics) curve analysis and found an AUC (Area Under Curve) at 0,735 thus confirming its strong predictive value (Supp. Fig. 1).

Leukemia Inhibitory Factor (LIF) predicts outcome in cancer patients treated with immune-checkpoint blockers independently of PD-L1 expression status

We then performed an exploratory analysis investigating association of LIF level with clinical outcome according to PD-L1 expression score (Fig. 3a) and CD8+ T-cell infiltration density (Fig. 3d) -as assessed by multiplexed immunohistochemistry -in a sub-cohort of 59 patients with available matched-tumor tissue. The PD-L1 tumor proportion score (TPS) was ≥ 1% in 20 patients (33.9%) and < 1% in 39 patients (66,1%). Peripheral level of LIF was similar in patients with PD-L1-positive and negative tumors (Fig. 3b). The proportion of PD-L1-positive tumors was similar among tumors with a high level (46.1%) and a low level of circulating LIF (55%) (data not shown). Regardless of the PD-L1 expression status, and despite the limited size of the sub-cohort, we observed that patients with tumors characterized by a low level of circulating LIF had better outcome. Indeed, among patients with a PD-L1 TPS < 1%, the median PFS was 7 months (95% CI, 2.8-11.1) in the LIF Low group versus 1.5 months (95% CI, 0.9-2) in the LIF High group; overall log-rank test p=0.001 (PFS).

Among patients with a PD-L1 TPS ≥ 1%, the median PFS was 6.3 months (95% CI, J o u r n a l P r e -p r o o f 0-13.5) in the LIF Low group versus 2.2 months (95% CI, 0.6-3.7) in the LIF High group, overall log-rank test p=0.106 (PFS) (Fig. 3c).

We then quantified the density of CD8+ T cells within the tumor lesion and considered highly infiltrated tumor when density was above the threshold value of 262.7/mm² (corresponding to the 75th percentile). Interestingly, CD8-infiltrated tumors were characterized by a lower level of peripheral LIF (Fig. 3e, p=0,02). Also, whatever the CD8 infiltration density of the tumor, circulating LIF level was significantly associated with an improved PFS in the low CD8+ T-cell density group (p=0,016), and a trend was observed in the high CD8+ T-cell density subgroup (p=0,062) (Fig. 3f). The lack of statistical significance in the high CD8+ T-cell density subgroup may be related to the low sample size.

Leukemia Inhibitory Factor (LIF) serum levels are associated with specific tumor microenvironment features and the presence of tertiary lymphoid structures

We then investigated whether circulating LIF level was correlated with the intratumor immune landscape through RNAseq expression data deconvolution with Bindea (Fig. 4a) or CIBERSORT (Supp. Fig. 3) algorithms. A significant inverse correlation between LIF and B cells (Fig. 4a andb) as well as with follicular helper T cells (Fig. 4a) was observed. These two cell types are major components of the so called tertiary lymphoid structures (TLS) 17 , and we therefore decided to assess the presence of TLS in tumor samples by using multiplexed-immunohistochemistry (Fig. 4c) as previously described 9 . We observed the presence of TLS in 22 cases (37.3%). The proportion of TLS positive cases was significantly higher in the LIF Low group than in the LIF High group; 50% vs 24.1%, p=0.04 (Fig. 4d).

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Baseline serum levels of LIF predict outcome independently of other prognostic factors in a validation cohort of cancer patients treated with immune-checkpoint blockers

To confirm the robustness of the predictive value of peripheral LIF level, plasma samples collected from 292 patients enrolled in the PREMIS study (NCT03984318)serving as a validation cohortcytokines, including LIF, were measured using the Olink Target 96 inflammation panel. This assay relies on a qPCR readout which was found to be highly similar and correlated with the Olink® Explore 1536 panel 18 . We found improved objective response rate (32.2% vs 16.4%, p=0.002), durable clinical benefit rate (34.2% vs 17.8%, p=0.001) (Fig. 5c), PFS (5.1 vs 2.6 months, p<0.0001) (Fig. 5a), and OS (not reached vs 8.5 months, p<0.001) (Fig. 5b), in the LIF Low group compared with the LIF High group. AUC of the ROC curve analysis was evaluated at 0.622 (Supp. Fig. 4) thus confirming the predictive value of LIF in an independent validation cohort. On multivariate analysis, LIF plasma levels remained independently associated with both PFS and OS (table 2).

To confirm that our results were representative of all cancer types, we performed one additional analysis by stratifying patients included in the PREMIS study according to tumor type: non-small-cell lung cancer (NSCLC) or non-NSCLC cases. We observed in each stratum significantly higher objective response rate, durable clinical benefit rate, PFS and OS indicating that the predictive value of circulating LIF level was not solely driven by the NSCLC histology (Supp. Fig. 5a and5b)

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DISCUSSION

In the wake of genomics, the study of proteins is now emerging as the new frontier for understanding real-time human biology. Protein biomarker discovery enables identification of signatures with pathophysiological importance, bridging the gap between genomes and phenotypes. This type of data may have a deep impact on improving future healthcare, particularly with respect to precision medicine, but progress has been hampered by the lack of technologies that can provide reliable specificity, high throughput, good precision, and high sensitivity. Here, we used a Proximity Extension Assay (PEA) technology, a unique method where each biomarker is addressed by a matched pair of antibodies, coupled to unique, partially complementary oligonucleotides, and measured by next generation sequencing 7 . This enables a high level of multiplexing while maintaining high-level data quality. To the best of our knowledge, we report here the largest study implementing a comprehensive analysis of the plasma proteome to identify predictive biomarker of efficacy in cancer patients treated with ICB. In comparison with traditional biomarkers such as PD-L1 expression status, circulating biomarkers offer a promising alternative to address the pitfalls associated with analysis of tumor tissue such as temporal and spatial tumor heterogeneity.

Thanks to a robust methodology, we were able to identify, starting from a Discovery cohort, LIF as a predictive factor of objective response rate, PFS and OS in cancer patients treated with ICB. To strengthen this finding, these results have been validated using samples from an independent and large validation cohort. In addition, analysis of the lung adenocarcinoma cohort of the TCGA database (Broad GDAC 1/28/2016) demonstrated that LIF was not associated with prognosis of lung cancer patients thus J o u r n a l P r e -p r o o f highlighting its specific predictive value for patients treated with anti-PD(L)1-based ICB (data not shown).

LIF is a pleiotropic cytokine involved in many physiological and pathological processes (reviewed in ref 19) and is highly expressed in a subset of tumors across multiple tumor types where it has been shown to be associated with poor prognosis. As recently shown by single-cell studies, LIF is mainly expressed by tumor cells 20 . However, the mechanisms involving this cytokine in cancer progression are not well understood.

One of the first demonstration of the role of LIF in immunity was reported by Gao et al showing that LIF promotes self-tolerance by stimulating the Treg differentiation and inhibiting T helper type 17 cell differentiation 21 . Moreover, LIF favors the acquisition of an M2 phenotype by macrophages and the recruitment of myeloid-derived suppressor cells into the tumor microenvironment 22-23 , all these mechanisms participating in the anti-tumor immune evasion. LIF has also been shown to regulate the maturation of dendritic cells (DCs), leading to the development of tolerogenic DCs, which contribute to an immunosuppressive microenvironment 24 . Interestingly, LIF neutralization was associated with strong inhibition of tumor growth in several preclinical models 25,26 . A recent study has also shown that LIF blockade is associated with an increased production of CXCL9 by macrophages and a concomitant decrease in CD206, CD163 and CCL2 26 . In our study, while baseline plasma LIF was associated with an intratumoral expression of LIF, no correlation was found for either CCL2, CD206 or CXCL9 (Supp. Fig. 6)the same results were observed by analyzing LIF gene expression in tumor samples (data not shown). In addition, we highlighted that plasma LIF was positively associated with circulating IL6 and CCL2 (Supp. Fig. 7).

We therefore assessed whether the peripheral level of LIF was associated with specific tumor microenvironment features. By using both transcriptomic and multiplexed-IHC J o u r n a l P r e -p r o o f analysis, we found that low levels of LIF were strongly associated with the presence of follicular helper T (Tfh) and B cells in the context of TLS. TLS can be likened to microsecondary lymphoid organs. TLS have been identified in several solid tumor types and are associated with better survival when present in the tumor microenvironment 27-30 .

Higher densities of TLS were associated with an increased density of tumor-infiltrating CD8+ T lymphocytes 31-32 and with an activated and cytotoxic immune signature 30 . We have recently reported that the presence of TLS is highly predictive of improved outcomes in cancer patients treated with immune checkpoint inhibitors 11 . Preclinical data have suggested that LIF blockade promotes CD8 + T cell infiltration in several tumors models 26 . In our study, we bring, for the first time, evidence suggesting that low level of LIF is associated with the presence of TLS, which could in turn favor antitumor T-cell immunity induction. The combination of anti-LIF and anti-PD1 antibodies has also been shown to be synergistic in pre-clinical tumor models 26 .

Recently, the results of a phase I study investigating the safety and efficacy of MSC-1, a first-in-class humanized IgG1 monoclonal antibody that potently and selectively inhibits LIF, have been reported 33 . Eligible patients had advanced relapsed/refractory solid tumors and received treatment with MSC-1 intravenously (75mg-1500 mg) once every 3-weeks as a single agent until disease progression. Single agent MSC-1 was well tolerated with no dose limiting toxicities observed during the first cycle of treatment. Preliminary signs of activity were observed with disease stabilization in 9 patients out of 41. Interestingly, analysis of paired biopsies (before treatment onset and on treatment) showed increase CD8 T-cell infiltration in a subset of samples.

Our results indicate that LIF could represent a key factor in resistance to cancer immunotherapy and thus suggest that targeting LIF axis may represent a promising J o u r n a l P r e -p r o o f approach to improve efficacy of ICB in cancer patients, and particularly in patients characterized by a high plasma level of LIF.

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J o u r n a l P r e -p r o o f FIGURES LEGENDS Figure 1. Flow chart depicting the identification strategy of a biomarker associated to resistance to anti-PD(L)1 immunotherapy within a discovery cohort and its assessment in an additional validation cohort.

Pre-treatment (Pre-Tx) plasma samples and matched tumor biopsies were collected before anti-PD1/PDL1 antibodies-based treatment in cancer patients (see Table 1 for patient details). Plasma samples (n=95 patients) were processed for a comprehensive proteomic analysis allowing the simultaneous detection of 1463 proteins. Tumor biopsies were exploited for i) RNA-Sequencing for tumor immune gene expression profile (n=52 patients) and for ii) immunohistochemistry in order to assess tumor PDL1 expression (TPS score), CD8 T-cells density and the presence of Tertiary Lymphoid Structures (TLS) (n=59 patients). Computed data were then tested for their association with clinical data including clinical outcome. Durable Clinical Benefit (DCB) was considered for patients deriving complete or partial response but also a stable disease with a progression free survival (PFS) > 12months. Non Durable Benefit (NDB) was considered for patients with a progressive disease or a stable disease with a PFS ≤ 12 months. The best selected biomarker was investigated in an independent validation cohort of 292 patients (see Table 1 for patients' details) receiving PD1 / PDL1 blockade antibodies.

Figure 3. LIF is a predictive biomarker independently from PDL1 expression status and tumoral CD8 infiltration level. A PDL1 expression was assessed by immunohistochemistry (PDL1 stained in purple)

. Illustrations here depict tumor cases with negative (TPS<1%) and positive (TPS ≥ 1%) PDL1 expression. B Representation of plasmatic LIF level in patients according to their PDL1 TPS score (TPS<1 vs TPS ≥1). p value was calculated using Wilcoxon Rank sum test. C PFS probability according to LIF level (High vs Low) in patients negative (TPS<1, n=39) or positive (TP≥1, n=20) for tumoral PDL1 expression. D CD8+ T cell infiltration was assessed through immunohistochemistry staining (CD8 stained in brown). Illustrations highlight tumor cases with low and high CD8 infiltration level. E Plasmatic LIF level in patients according to their CD8 infiltration level. p value was calculated using Wilcoxon Rank sum test. E PFS probability according to LIF level (High vs Low) in patients classified as CD8 Low (n=44) or CD8 High (n=15).

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Figure 5. Baseline circulating LIF level is predictive of outcome of cancer patients treated with anti-PD(L)1 immunotherapyresults from an independent validation cohort. A Probability of PFS

for LIF High (median survival = 2,57 mo.) and LIF Low (median survival = 5,07 mo.) patients in the Validation Cohort (n = 292). B Probability of OS for LIF High (median survival = 8,53 mo.) and LIF Low (median survival = NA) patients in the same patients cohort. C Proportion of patients who experienced DCB or NCB according to their baseline plasmatic level of LIF classified as High (above median value) and Low (below median value). P value was calculated through Chi-squared test. J o u r n a l P r e -p r o o f As a result, COVID-19 is mobilizing an unprecedented technological and scientific effort to diagnose, comprehend, and adequately treat the disease. Indeed, contagion by SARS-CoV-2 provokes a silent or pauci-symptomatic infection in at least 80% of patients, not requiring any treatment 2,3 . However, a substantial fraction of patients with pre-existing and often age-associated medical conditions (obesity, diabetes, hypertension, cardiomyopathy, hematological cancers, and general frailty) develop SARS, requiring hospitalization, oxygen supply, and for the most severe cases mechanical ventilation in the intensive care unit 1,4,5 . Nonetheless, there is a substantial 'gray zone', meaning that physically fit and relatively young patients without known pre-existing pathologies may succumb to SARS-CoV-2, calling for the identification of biomarkers that predict COVID-19 severity and help management of patients 1,4,5 .

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Beyond genomic studies (to find COVID-19 susceptibility genes) 6 , single-cell transcriptomics performed on circulating leukocytes (to identify inflammatory/ immune cell subsets involved in, and predictive of, COVID-19 pathogenesis) 7,8 and plasma proteomics (to pinpoint relevant cytokines) 9,10 , metabolomics offers a functional, 'post-genomic' characterization of biochemical circuitries influenced by COVID-19 and its treatment. Indeed, a few studies have used mass spectrometric metabolomics to identify COVID-19-induced alterations in circulating metabolites, focusing on the correlation of such parameters with clinical presentation 10 , circulating interleukin (IL)-6 concentrations 11 or male sex 12 . Additional studies have revealed a metabolomic signature of COVID-19 infection in circulating exosomes 13 and in the saliva 14 .

Here, we report the results of two metabolomic studies, a first one, non-interventional, in which we correlate shifts in circulating metabolites with the severity stage of COVID-19 patients and a second study, interventional, in which we focus on patients with a uniformly moderate clinical presentation to identify metabolites whose alteration predicts clinical evolution. We identified anthranilic acid, a product of the kynurenine pathway, as a potentially prognostic biomarker of the evolution of COVID-19.

Results

COVID-19 stage-dependents shifts in the plasma metabolome

Targeted and untargeted metabolomics were performed using gas chromatography-mass spectrometry (GC-MS) and ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) on plasma samples retrieved from a total of 72 patients with PCR-verified diagnosis of SARS-CoV-2 infection and compared to 27 ambulatory patients with flu-like symptoms, negative for SARS-CoV-2. Patients with COVID-19 were staged according to their clinical characteristics into mild (confinement at home, no complementary exams), moderate (standard hospitalization with a radiological diagnosis of pneumonitis, oxygen therapy <9 L/min), and critical (intensive care unit, oxygen therapy >9 L/min) cases. Clustering of mass spectrometry-detectable peaks revealed stage-associated shifts in the metabolome (Supplemental Fig. 1) that become clearly visible upon statistical filtering at p < 0.05 (Wilcoxon rank-sum test) and application of a false discovery rate of 0.05 following the Benjamini-Hochberg procedure to identify metabolites which were stringently different between critical and mild COVID-19 patients. Thus, 77 metabolites exhibited stage-dependent alterations in their plasma concentration (Fig. 1A). Among these metabolites, 57 were increased in critical care patients. Random forest classification model was built to rank the metabolites the upregulation or downregulation of which distinguished critical from mild patients (Fig. 1B). Among 30 metabolites, 29 were higher (and 1 lower) in critical than in mild cases, indicating a preponderance of upregulation (p < 0.000044, χ 2 analysis).

As described in the literature 3,4 , critical COVID-19 patients were more overweighted, obese, diabetic, and hypertensive than mild COVID-19 and controls patients (Supplemental Table 1). Linear regression was used to control the differences in mean metabolites concentrations between critical and mild COVID-19 patients after adjustment for such comorbidities (Supplemental Table 2).

Specific changes associated with COVID-19 severity stages

A number of simple sugars including arabinose and ribose (and its reduction product ribitol), sugar alcohols (arabitol, erythritol and xylitol), the disaccharide maltose (which is undistinguishable from trehalose), and the trisaccharide raffinose were increased in critical cases (Figs. 1A and 2A and Supplemental Fig. 1A). Moreover, a series of amino acids were elevated in critical care patients: arginine, aspartic acid, glutamic acid, phenylalanine, and tyrosine. In addition, the methylated derivative of lysine, trimethyl-lysine, the methionine derivative (and methyl group donor) S-adenosylmethionine, and the dipeptide leucylproline were elevated (Figs. 1A and2B), perhaps resulting from increased proteolysis. In contrast, desaminotyrosine was reduced in critical care patients (Figs. 1A and2B), likely reflecting the use of antibiotics that inhibit the generation of this bacterial metabolite in the gut 15 . One of the few amino acids that decreased with disease severity is arginine, contrasting with an increase in ornithine, spermine, spermidine, and their mono-or diacetylated derivatives (Figs. 1A and3A), suggesting enhanced polyamine synthesis from arginine. Moreover, tryptophan tended to diminish, while its immunosuppressive metabolite kynurenine increased in critical care patients as compared to mild cases. The kynurenine metabolite anthranilic acid was higher in critical as compared to moderate and mild COVID-19 patients (Figs. 1A and3B). Of note, the elevation of anthranilic acid has not been found in another study that actually claimed that anthranilic acid decreased in COVID-19 patients as compared to controls 11 . Indeed, we found that another molecule that shared the same neutral monoisotopic mass (137.04768 Da) as anthranilic acid and that decreased in COVID-19 patients (annotated and validated as trigonelline, Supplemental Table 3), perhaps explaining the difference in the results. Since we compared the gas chromatographic retention time of the derivatized analytes to standards (Supplemental Fig. 2), we conclude that anthranilic acid is indeed increased in severe COVID-19. We also found that 3-hydroxy-DL-kynurenine, which his produced from kynurenine by the enzyme kynurenine 3-hydroxylase, and 5-hydroxy-DL-tryptophan, which is produced from tryptophan by the enzyme tryptophan 5-monooxygenase, were increased, correlating with the severity of COVID-19 (Fig. 3B).

Bacterial breakdown products of tryptophan, such as indole, indole-acetamide, indole-3-acrylic acid, and methyl-3-indole-acetate were significantly reduced in critical care patients (Figs. 1 and3B). Other important metabolic changes affected free fatty acids (arachidonic acid) or carnitine esters, phospholipids, the immunomodulator spingosine-1-phosphate, the secondary bile acid deoxycholic acid, as well as the niacin metabolite trigonelline, that all diminish with disease severity, contrasting with markers of reduced renal clearance (creatine, urea) that increase (Fig. 1A). Altogether, a specific pattern of stage-dependent alterations in the metabolome emerges.

Prognostic alterations in the circulating metabolome

The aforementioned results indicate that the progression of COVID-19 is associated with major metabolic shifts, yet do not allow to identify prognostic biomarkers. For this, we recruited a group of 25 patients that were hospitalized in standard conditions (not in the ICU) and were relatively homogeneous in their clinical presentation (Fig. 4A and Supplemental Tables 4 and5). After the initial determination of their circulating metabolome and the quantitation of serum cytokines (n = 21), these patients received standard of care treatments plus tocilizumab. Unfavorable evolution of the COVID-19 (9 out of 21 patients) was defined as a clinical deterioration with WHO progression scale >5, transfer to ICU, mechanical ventilation, or death 16 . Inspection of the global metabolomic profiles did not revealed any major shift that would distinguish the favorable versus unfavorable evolution of COVID-19 neither at baseline nor at day 7 (Supplemental Fig. 3 and Supplemental Tables 6 and7). Only 10 metabolites were significantly different between patients that demonstrated favorable versus unfavorable evolution (Fig. 4A). However, they allowed a good discrimination between groups (PC1 45.9%) according to their abundance variation (Fig. 4B and Supplemental Fig. 5). To find out the variable importance of these metabolites, we used a random forest classification model. Although the model was limited because of the reduced number of individuals in the study, it showed that, among the most significant metabolites, the upregulation of anthranilic acid coupled to the diminution of S-adenosylmethionine and proline stood out as parameters that allowed to distinguish the unfavorable and favorable evolution of COVID-19 patients respectively (Fig. 4C). Dimethylglycine, ß-hydroxypyruvate, N1-acetylspermidine, hypotaurine, and valine were significantly lower in patients with an unfavorable evolution, while 3-methylhistidine and O-phosphoethanolamine were higher, but these changes had a lower impact according to the random forest classification (Fig. 4B).

Prognostic immunometabolic correlations

None of the 10 cytokines measured at baseline did exhibit significant differences between the patients with favorable and unfavorable clinical evolution (Supplemental Fig. 4), in line with the similar clinical presentation of the patients. At difference with patients that exhibited an unfavorable evolution, patients who ameliorated their condition exhibited an increase in total lymphocyte counts (Fig. 5A), a decrease in the inflammatory cytokine interleukin (IL)-18 (IL18) (Fig. 5B), a reduction in the immunosuppressive factor IL10 (Fig. 5C) and an increase in circulating tryptophan levels (Fig. 5D). Correlation plots revealed a median correlation (all values positive) among cytokines of 0.1573, between cytokines and metabolites of 0.2116, and among metabolites of 0.3702 (which was significantly higher than the intragroup correlation and the correlation among cytokines, p < 0.0001, Mann-Whitney test), suggesting a more robust coordination of metabolic as compared to inflammatory pathways (Fig. 6A). IL8 correlated with N1-acetylspermidine and hypotaurine, tumor necrosis factor-α (TNFα) with O-phosphoethanolamine (Fig. 6A), and anthranilic acid with both IL10 and IL18 (Fig. 6B) at baseline, before the initiation of the treatment. This latter correlation appears particularly intriguing because anthranilic acid is ranked as the best negative Fig. 4 Patients with unfavorable clinical evolution after tocilizumab, infused for worsening pulmonary involvement of COVID-19, had pretreatment metabolomics differences compared to patients with favorable outcome. A Heatmap illustrating pre-tocilizumab metabolite abundance in COVID-19 patients evaluable for clinical evolution after treatment (n = 21). Significant metabolites were identified by Wilcoxon ranksum test between patients with favorable and unfavorable evolution after tocilizumab infusion. BMI Body Mass Index, WHO World Health Organization, O2 oxygenotherapy, ICU intensive care unit, OTI orotracheal intubation. B Principal component analysis biplot, showing the contribution of the most significant metabolites (p < 0.05) to the discrimination (PC1 45.9%) between patients with favorable and unfavorable evolution after tocilizumab infusion. C Random forest classification model was built using main metabolites altered (p < 0.05) in baseline samples from COVID-19 patients with favorable and unfavorable evolution after tocilizumab treatment as a predicting tool. The variable importance (as the mean decrease of the Gini index) for building the model is reported in a dot plot, with dots substituted by an up-pointing black triangle to indicate metabolites increased in patients who showed unfavorable vs favorable evolution, and by a gray down-pointing triangle in the opposite case. The confusion matrix (indicating model accuracy) is depicted. OOB out-of-bag error.

prognostic marker (Fig. 4B) and both IL10 and IL18 remain elevated in the context of an unfavorable evolution (Fig. 5).

Discussion

The present study has been designed to unravel COVID-19 stage-dependent and prognostic alterations in the circulating metabolome. Strong shifts across multiple classes of metabolites were observed among different stages of COVID-19, from mild through moderate to critical disease. These shifts reflect in part iatrogenic effects such as the apparent improvement of the nutritional state (with higher levels of circulating sugars but lower levels of free fatty acids and ketone bodies, which would be indicative of acute undernutrition) in the critical stage and the reduction of bacterial metabolites (such as the tyrosine metabolite desaminotyrosine and the tryptophane metabolites indole, indole-3-acetamide, indole-3acrylic acid, and methyl-3-indole-acetate), likely resulting from the administration of broad-spectrum antibiotics. Other metabolomic shifts may reflect proteolysis (with an increase in free amino acids and amino acid derivatives), as well as ongoing organ failure affecting the kidney (enhanced acetyl polyamine, creatine, and urate levels) and the liver (reduced primary bile acid production).

Most intriguingly, however, COVID-19 appears to be associated with metabolic signs of immunosuppression, as indicated by the increase of kynurenic acid and anthranilic acid. Tryptophan was diminished in mild and critical COVID-19 patients compared to uninfected controls, suggesting a disease-associated activation of tryptophanconsuming indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO) that produce the kynurenic acid precursor kynurenine 17 . Anthranilate is a downstream metabolite of kynurenine 18 , with marked immunosuppressive effects 19 . Previous work has identified an activation of the kynurenine pathway (though without an elevation of anthranilate) in COVID-19 patients, correlating with an elevation of IL6 levels 11 , which in turn are associated with poor prognosis 20-23 .

Small, but specific differences were observed in a cohort of patients that demonstrated a similar clinical stage at presentation, but dissimilar evolution during hospitalization. Some metabolites that apparently were not COVID-19 stage-associated were different between patients that demonstrated a favorable or unfavorable evolution. This applies to dimethylglycine, 3-methylhistidine and O-phosphoethanolamine, proline, and valine. Some metabolites exhibited a behavior that can be classified as 'paradoxical'. Thus, N1-acetylspermidine, S-adenosylmethionine, and hypotaurine that are highest among severe COVID-19 patients are associated with favorable prognosis, perhaps because their production reflects an attempt to attenuate the pathogenesis of COVID-19. Indeed, in preclinical models, the N1-acetylspermidine precursor, spermidine, has marked anti-inflammatory and immunostimulatory effects 24-27 . Administration of S-adenosylmethionine attenuates the cytokine storm induced by bacterial sepsis 28 and mediates immunostimulatory effects in a cancer model 29 . Clinical trials have demonstrated that taurine, the downstream metabolite of hypotaurine, decreases serum markers of inflammation including C-reactive protein 30 , which is a negative prognostic marker of COVID-19 31 .

In sharp contrast to these 'paradoxical' associations, one metabolite exhibited a 'concordant' behavior. This applies to anthranilic acid, the concentration of which increases with disease severity and which also predicts unfavorable prognosis. This observation places the kynurenine pathway in the limelight of this study. Larger prospective studies are required to validate the conjecture that metabolomic profiling and specific measurement of selected metabolites including anthranilic acid may predict the fate of COVID-19 patients. Circulating anthranilic acid levels reportedly correlate with hyperleptinemia in schizophrenia 32 and are increased in the plasma of patients with type-1 (but not type-2) diabetes 33 and subgroups of patients with chronic liver disease 34 , calling for additional investigations of possible confounding factors. Irrespective of these considerations, it might be worthwhile to explore the experimental treatment of COVID-19 with IDO and TDO inhibitors that are in clinical development 35-38 . Such inhibitors have been generated as immune checkpoint inhibitors for the treatment of cancer, but have not yet received regulatory approval. The fact that high levels of anthranilic acid predict the maintenance of high levels of IL10 and IL18 suggests (but does not prove) that the kynurenine pathway has an immunomodulatory impact on COVID-19 pathogenesis. However, this speculation should be tested by treating anthranilic acid-high COVID-19 patients with IDO/TDO or other kynurenine pathway inhibitors within a dedicated Phase 2 clinical trial.

Methods

Patients

All patients were recruited by different hospitals of the Assistance Publique Hôpitaux de Paris (AP-HP) network or at Foch Hospital or Gustave Roussy. The noninterventional study was approved by institutional review boards (IRB) of Cochin-Port Royal (Paris, France) hospital and the ethical committee of Cochin-Port Royal Hospital (CLEP Decision N: AAA-2020-08023), and conformed to the principles outlined in the Declaration of Helsinki. Controls (n = 29) were symptomatic patients who were seen at the Hôtel-Dieu screening unit and were negative for SARS-CoV-2 RT-PCR on pharyngeal swab. Mild COVID-19 patients (n = 23) were defined by having limited clinical symptoms (fever, cough, diarrhea, myalgia, and anosmia/ageusia) that did not require CT scan or hospitalization. Moderate cases (n = 21) were defined as symptomatic patients with dyspnea and radiological findings of pneumonia on thoracic CT scan, requiring hospitalization and a maximum of 9 L/min of oxygen. Critical patients (n = 28) were those hospitalized in the ICU with respiratory distress requiring 10 L/min of oxygen or more, without or with endotracheal intubation and mechanical ventilation. "Comorbidities" variable for adjustment was considered for patients with obesity or diabetes or chronic kidney disease and hypertension.

The interventional study was approved by the Foch IRB (approval number IRB00012437) and was registered on the National Institute of Health data platform INDS (no 4710280420). Patients received tocilizumab, in an off-label use setting, to treat severe COVID-19, at Gustave Roussy and Foch Hospital, over the period of March 20 and 5 April 2020. Inclusions criteria were: (i) Patients who received at least one dose of tocilizumab, as treatment of COVID-19. (ii) ≥18 years, informed, and not opposed for retrospective use of their anonymized health care files. (iii) Diagnosis of COVID-19 confirmed by RT-PCR test, with respiratory symptoms, shortness of breath and requirement of oxygen therapy and pulmonary images compatible with COVID-19. (iv) Patients at risk of developing respiratory distress due to COVID-19, with worsening of oxygen therapy supplementation equal or more than 4 L/min and requirement by increase of >50% of the need for supplemental oxygen therapy in the last 24 h before first dose of tocilizumab. Exclusion criteria for evaluable population for the response to tocilizumab were: (i) Patients placed under mechanical ventilation with intubation due to the COVID-19 before the first dose of tocilizumab treatment. (ii) Respiratory failure related to other cause than COVID-19 at tocilizumab initiation. (iii) Patients, who have previously received anti-IL6 receptor therapy in the last 3 weeks before tocilizumab initiation. (iii) Alanine transaminase/aspartate transaminase (ALT/ AST) >5 times the upper limit of normal at timing of first dose of tocilizumab. (iv) Absolute neutrophil count <1.0 × 10 9 or platelets <50 × 10 9 at timing of first dose of tocilizumab. Tocilizumab was given intravenously at 8 mg/kg and could be repeated once in the following 48 h if necessary. All patients were followed until day 30 after the first dose of tocilizumab. Patients' sera were collected and stored before and after treatment. Favorable clinical evolution after tocilizumab infusion was retained in patients evaluable for the outcome and fulfilling the following three criteria. Criterion 1: on day 14 post first dose of tocilizumab, the patient has a WHO progression scale ≤ 5 16 . Criterion 2: between days 1 and 14 after the first dose of tocilizumab, the patient is alive and did not need to have at any time recourse to invasive mechanical ventilation (orotracheal intubation) and without any new intention of "non-realization of resuscitation or ventilation". Criterion 3: the respiratory symptoms related to COVID-19 clinically significantly improved with decrease in oxygen requirements after first dose of tocilizumab and the WHO scale did not deteriorate after the administration of the first dose of tocilizumab.

Sampling

Human peripheral blood from the first cohort was collected into sterile dry vacutainer tubes with 3.2% buffered sodium citrate solution. Samples were centrifuged twice (2500 × g/20 min), and plasma was collected. Regarding the samples from the interventional study, human peripheral blood was collected into sterile dry vacutainer tubes and centrifuged (1500-2000 × g/15 min) for serum collection. Fifty microliters of sample were mixed with 500 µL of a cold solvent mixture (meOH/ water, 9/1, -20 °C, with a cocktail of internal standards), vortexed and centrifuged (10 min at 1500 × g, 4 °C) for metabolite extraction and protein precipitation. The supernatants were collected, split in 4 fractions, and treated according to the protocols described previously 39 . Briefly, 2 fractions of 120 µL each (1st and 2nd fractions, respectively) of sample extract were transferred to an injection amber glass vial (with fused-in insert) and evaporated to dryness (Techne DB3, Staffordshire, UK) at 40 °C. The 1st dried fraction was solubilized in 50 µL of methoxyamine (CAS 593-56-6; 20 mg/mL in pyridine, Sigma-Aldrich), and left to incubate overnight, at room temperature and protected from light. The next day, derivatization was carried out by adding 80 µL of MSTFA (CAS 24589-78-4; Supelco), followed by 30 min-incubation at 40 °C. Derivatized samples were immediately used for GC/MS injection and analysis. The 2nd dried fraction was recovered with 100 µL of ultra-pure water and kept at -80 °C until injection and analysis by UHPLC/MS. The 3rd fraction consisted of 40 µL of sample extract transferred to an injection amber glass vial (with fused-in insert) for derivatization and SCFA analysis. Sample derivatization was carried out by adding 20 µL of 3-NPH (200 mM in meOH; CAS 636-95-3; Sigma-Aldrich) and 20 µL of EDC (150 mM in meOH; CAS 25952-53-8; Sigma-Aldrich) to the sample. Immediately after incubation (1 h/ 40 °C), 80 µL of water were added, and the derivatized samples were used for UHPLC/MS injection and analysis. Finally, the 4th fraction together with the sample pellet were reextracted with 80 µL of 2% SSA (in meOH), vortexed and centrifuged (10 min at 15,000 × g, 4 °C). The supernatant (200 µl) was transferred to an injection polypropylene vial (with fused-in insert) and evaporated to dryness (Techne DB3, Staffordshire, UK) at 40 °C. Dried samples were recovered with 200 µl of ultra-pure water and kept at -80 °C until injection and analysis by UHPLC/MS for polyamines detection.

Cytokine measurements

Serum samples were monitored using the V-plex Proinflammatory panel 1 Human kit (Meso Scale Discovery, ref: K15049D) according to the manufacturer's instructions, for the measurement of IFNγ, IL1β, IL2, IL4, IL6, IL8, IL10, IL12p70, IL13, and TNFα. Soluble Calprotectin (diluted 1:100), IFNα2a and IL18 were analyzed using a R-plex Human Calprotectin Antibody Set (Meso Scale Discovery, ref: F21YB), the ultra-sensitive assay S-plex Human IFNa2a kit (Meso Scale Discovery, ref: K151P3S) and the U-plex Human IL18 assay (Meso Scale Discovery, ref: K151VJK), respectively, following manufacturer's instructions. Acquisitions and analyses of soluble cytokines and calprotectin were performed on a MESO QuickPlex SQ120 reader and the MSD's Discovery Workbench 4.0. Each serum sample was assayed twice with the average value taken as the result.

Widely targeted analysis of intracellular metabolites GC/MS

Derivatized samples for GC/MS analysis (1st fraction) were injected (1 µL) into a gas chromatograph (Agilent 7890B; Agilent Technologies, Waldbronn, Germany) coupled to a triple quadrupole mass spectrometer (QQQ/ MS; 7000C Agilent Technologies, Waldbronn, Germany), equipped with a high sensitivity electronic impact source (EI) operating in positive mode. Injection was performed in splitless mode. Front inlet temperature was kept at 250 °C, transfer line and ion-source temperature were 250 °C and 230 °C, respectively. Septum purge flow was fixed at 3 mL/min, purge flow to split vent operated at 80 mL/min during 1 min and gas saver mode was set to 15 mL/min after 5 min. Helium gas flowed through column (HP-5MS, 30 m × 0.25 mm, i.d. 0.25 mm, d.f. J&WScientific, Agilent Technologies Inc.) at 1 mL/min. Column temperature was held at 60 °C for 1 min, raised to 210 °C (10 °C/min), then to 230 °C (5 °C/min), to finally reach 325 °C (15 °C/min), and hold at during 5 min. Collision gas was nitrogen.

UHPLC/MS

Targeted UHPLC/MS analyses were performed on a RRLC 1260 system (Agilent Technologies, Waldbronn, Germany), with an autosampler kept at 4 °C, and a pelletier oven for rigorous control of the column temperature. The UHPLC was coupled to a QQQ/MS 6410 (Agilent Technologies) equipped with an electrospray source, using nitrogen as collision gas. For bile acids detection, 10 µL from samples recovered in water (2nd fraction) were injected into a Poroshell 120 EC-C8 (100 mm × 2.1 mm particle size 2.7 µm; Agilent technologies) column protected by a guard column (XDB-C18, 5 mm × 2.1 mm particle size 1.8 μm). Mobile phase consisted of 0.2% formic acid (A) and ACN/IPA (1/1; v/v) (B) freshly made. Flow rate was set to 0.3 mL/min, and gradient as follow: 30% B during 1.5 min; increased to 60% B over 9 min; and finally to 98% B for 2 minutes (column washing), followed by 2 min of column equilibration at 30% B (initial conditions). After each injection, needle was washed twice with IPA and thrice with water. The QQQ/ MS was operated in negative mode. Gas temperature and flow were set to 325 °C and 12 L/min, respectively. Capillary voltage was set to 4.5 kV.

Derivatized samples for SCFA detection (3rd fraction) were injected (10 μL) into a Zorbax Eclipse XBD C18 (100 mm × 2.1 mm particle size 1.8 µm; Agilent technologies) column, protected by a guard column (XDB-C18, 5 mm × 2.1 mm particle size 1.8 μm). Column oven maintained at 50 °C during analysis. Mobile phase consisted of 0.01% formic acid (A) and ACN (0.01% formic acid) (B). Flow rate was set to 0.4 mL/min, and gradient as follow: 20% B during 6 min; increased to 45% B over 7 min; and finally to 95% B for 5 minutes (column washing), followed by column equilibration phase at 20% B, during 4 min. The QQQ/MS was operated in negative mode. Gas temperature was set to 350 °C with a gas flow of 12 L/min. Capillary voltage was set to 4.0 kV.

Polyamines were detected in the 4th fraction after injection of 10 μL of sample were into a Kinetex C18 (150 mm × 2.1 mm particle size 2.6 µm; Phenomenex) column protected by a guard column C18 (5 mm × 2.1 mm, particle size 1.8 μm). Column oven maintained at 40 °C during analysis. The gradient mobile phase consisted of 0.1% HFBA (Sigma-Aldrich) (A) and ACN (0.1% HFBA) (B) freshly made. The flow rate was set to 0.2 ml/min, and gradient as follow: from 5% (initial conditions) to 40% B in 10 min; then 90% B maintained 2.5 min, and finally equilibration to initial conditions, 5% B, for 4 min. The QQQ/ MS was operated in positive mode. The gas temperature was set to 350 °C with a gas flow of 12 l/min. The capillary voltage was set to 3.5 kV. At the end of each UHPLC/MS batch analysis, column was rinsed with 0.3 mL/min of ultra-pure water (A) and ACN (B) as follow: 10% B during 20 min, to 90% B in 20 min, and maintained during 20 min before shutdown. MRM scan mode was used for targeted analysis in both GC and UHPLC/MS. Peak detection and integration were performed using the Agilent Mass Hunter quantitative software (B.07.01).

Pseudo-targeted analysis of intracellular metabolites

The profiling analysis was performed with a Dionex Ultimate 3000 UHPLC system (Thermo Scientific) coupled to an Orbitrap mass spectrometer (q-Exactive, Thermo Fisher Scientific) equipped with an electrospray source operating in both positive and negative mode, and acquired samples in full scan analysis mode, from 100 to 1200 m/z. LC separation was performed on reversed phase (Zorbax Sb-Aq 100 ×2.1 mm × 1.8 µm particle size), with mobile phases: 0.2% acetic acid (A) and ACN (B). Column oven was kept at 40 °C. Ten microliters of aqueous sample (2nd fraction) were injected for metabolite separation with a gradient starting from 2% B, increased to 95% B in 22 min, and maintained during 2 min for column rinsing, followed by column equilibration at 2% B for 4 min. Flow rate was set to 0.3 mL/min. The q-Exactive parameters were: sheath gas flow rate 55 au, auxiliary gas flow rate 15 au, spray voltage 3.3 kV, capillary temperature 300 °C, and S-Lens RF level 55V. The mass spectrometer was calibrated with sodium acetate solution dedicated to low mass calibration. Data were treated by the quantitative node of Thermo Xcalibur TM (version 2.2) in a pseudotargeted approach with a home-based metabolites list.

Untargeted analysis of intracellular metabolites

Raw data files obtained by the previously described pseudo-targeted analysis were also used to perform unbiased profiling analysis, using the Thermo Compound Discoverer (3.1.). After sample injection and data acquisition, raw data files were processed following a customized node-based workflow for identifying unknown compounds in metabolomics. Spectra selection and retention time alignment were performed, followed by removal of background noise and baseline correction. Next, the processing workflow found chromatographic peaks for unknown compounds (molecular weight, MW, x retention time, RT) extracting all relevant spectral and chromatographic information, to predict the elemental composition of the unknowns. All data was exported to R software (version 3.4) for data representation.

Statistical analysis

All targeted and pseudo-targeted treated data were merged and cleaned with a dedicated R (version 3.4) package (@Github/Kroemerlab/GRMeta). Calculations and statistical tests were performed using R v3.4. Wilcoxon-Mann-Whitney test was used to assess differences in concentration between two different groups. When indicated, the false discovery rate (FDR, p > 0.05) was controlled using the Benjamini-Hochberg procedure. Data representation was performed with softwares R v3.6 and Rstudio v1.2.1335 using tidyverse, dplyr, ggplot2, ggpubr, complexheatmap, and corrplot packages. Principal component analysis biplot was built using FactoMineR and factoextra packages, after selection of the metabolites significantly different (p < 0.05) between clinical evolution groups ("unfavorable" and "favorable"), at baseline. Data were scaled unit variance before the analysis.

Determination of most discriminating metabolites with Random Forest Classification Model

Selected metabolites were thereafter used for training a random forest classification model using the R caret package. This machine learning tool allowed to classify the relative importance of metabolites for distinguishing COVID-19 stage (here classified in a binary fashion as «critical» and «mild») and clinical evolution ("unfavorable" and "favorable"), by computing the mean decrease of the Gini index (an entropy-like measure of the impurity) over the random forest nodes that were split on them. [2,3]. In a recent issue of Nature Medicine, Del Valle et al. reported the plasma levels of IL-6, IL-8, tumour necrosis factor (TNF)-a and IL-1b in patients hospitalised for COVID-19 [7]. They highlighted in a comprehensive multivariate analysis that high plasma levels of IL-6 and TNF-a remained significantly associated with a poor prognosis of patients with COVID-19 [7]. They proposed to measure IL-6 and TNF-a to stratify patients in further prospective clinical trials treating COVID-19 patients [7]. Targeting interleukin-6 (IL6) e one of the more proinflammatory cytokine e with the anti-IL6 receptor (anti-IL6R) have shown encouraging, although controversial, preliminary efficacy in the treatment of severe COVID-19 patients, while definitive results of clinical trials are still pending (COVACTA trial [NCT04320615];

CORIMUNO-TOCI trial [NCT04331808]; RECOV-ERY trial [NCT04381936] [4e6].

In order to better understand the effects of the anti-IL6R tocilizumab (Roche, Switzerland) on Covid19, we measured the serum cytokines levels before and during the tocilizumab therapy of 25 consecutive patients hospitalised for severe respiratory symptoms of COVID-19 at Gustave Roussy and Foch hospitals in the Paris area in France. Patients received tocilizumab (one infusion 8 mg/ kg, intravenously, repeated once 12 h later at the same dose in case of absence of clinical response) in an off-label use between 20th March and 20th April 2020 (i.e. at the time of the first epidemic peak in France). All patients were treated because of worsening of their respiratory symptoms and their increase in oxygen therapy requirements 4 L/mn. Cytokine titration was performed on serum samples collected for routine biological assessments. Patients and healthy donors were informed of those analyses, and this project was run in compliance with the French law on human research (Loi Jarde ´, hors RIPH, conformite ´MR004). The measurements of serum IL-6 and TNF-a were done at two time points: before tocilizumab infusion 'baseline' and 7 days after tocilizumab. Serum IL-6 and TNF-a titrations were performed with the Meso Scale Discovery â VPlex proinflammatory assay (Meso Scale Diagnostics, USA). The main outcomes were mortality 30 days after treatment and favourable clinical response to tocilizumab. Clinical response was defined by improvement of respiratory symptoms related to COVID-19 and decrease in oxygen requirements and a WHO clinical progression scale as between those with or without response to tocilizumab (19.4 [9.6e45.1] pg/mL and 27.5 [5.1e56.7] pg/mL respectively, p Z 0.471, Wilcoxon test). However, the baseline serum TNF-a level was significantly higher in patients who eventually died of COVID-19 within 30 days post-tocilizumab treatment (4.7 [2.5e6.4] pg/mL] and 2.4 [0.9e3.6] pg/mL respectively, p Z 0.015, Wilcoxon test). Additionally, 7 days after treatment, serum TNF-a increased from 3.5 [1.7e6.4] pg/mL to 12.1 [2.6e36.1] pg/mL in patients who did not favourably respond to tocilizumab, whereas patients favourably responding to tocilizumab were keeping their serum TNF-a at the same levels than healthy controls (Fig. 1).

Huang and et al. reported that COVID-19 patients in the intensive care unit had higher plasma concentrations of TNF-a than non-ICU patients [2]. Del Valle and et al. reported serum IL-6 and TNF-alpha as significant predictors of survival [4]. Furthermore, a pivotal role of calprotectin, an inflammation protein upstream of tumour necrosis factor-alpha (TNF-a), has been recently demonstrated in severe forms of COVID-19 [9]. Our results confirm TNF-a as a biomarker of severity in a cohort of patients treated with tocilizumab and support that serum levels of TNF-a should help to stratify patients according to their severity in further prospective clinical trials. These data reinforce Feldmann and et al. 1. TNF-a levels were measured in serum collected from Healthy controls and COVID-19 patients before 'baseline' and 7 days after tocilizumab. Data shown are those of all the patients treated with tocilizumab and who were able to provide a pre-treatment (baseline) and post-treatment serum (up-to-date serum þ7). Of the 25 patients treated with tocilizumab, pre-treatment and post-treatment serum analyses were available for 18 patients represented according to their outcome; mortality before day 30 (left panel) and response to tocilizumab (right panel). Baseline TNF-a serum levels were significantly higher at baseline in patients who eventually died of Covid19 (left panel). Post-tocilizumab TNF-a serum levels were significantly increasing in patients who did not favourably respond to tocilizumab (right panel).

view [1] supporting further investigations of TNF-a blocking therapies as potential drugs for patients with a severe and life-threatening form of COVID-19 and provide a putative rationale for tocilizumab resistance.

INTRODUCTION

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel beta-coronavirus that has caused a worldwide pandemic of the human respiratory illness COVID-19, resulting in a severe threat to public health and safety worldwide. Because of age, gender, cancer-associated risk factors, metabolic syndrome, and side effects induced by their specific therapies (such as cardiomyopathy, systemic immunosuppression, and cellular senescence), cancer patients appear more vulnerable to severe infection than individuals without cancer [1]. Indeed, a high hospitalization and mortality rates of SARS-CoV-2 infection were heralded in patients with malignancy in several studies across distinct geographical sites [2][3][4][5]. Cancer types, performance status, and stage are additional risk factors for severe COVID-19 in this patient population. Patients with hematological, lung and breast cancer have been reported to be more susceptible to hospitalization or death due to COVID-19 as compared to patients with other malignancies [3,[5][6][7][8]. Patients diagnosed with metastatic cancers are more vulnerable to severe forms of COVID-19 than individuals with localized malignancies [9]. Recent (<3 months) cancer treatments including surgery, chemotherapy, and immunotherapy independently contribute to worsening the prognosis of COVID-19 among patients with the malignant disease [2,5,7,[9][10][11].

Here, we explored several independent cohorts of cancer patients diagnosed with COVID-19 (1063 patients, 58% with cancer) during the first surge of the pandemic to analyze the dynamics between host (blood immunology, metabolism, metagenomics) and viral parameters and validated the most clinically relevant findings in the second surge of the pandemic. We concluded that virus-induced or -associated lymphopenia that coincided with T-cell exhaustion, abnormalities in polyamine and biliary salt pathways and circulation of Enterobacteriaceae and Micrococcaceae bacterial DNA, is a dismal prognosis factor in cancer patients, likely participating in the vicious circle of immunosuppression-associated chronic virus shedding.

RESULTS

Prolonged viral shedding and higher viral loads in cancer patients compared with cancer-free COVID-19 + patients

To explore the clinical significance of viral and/or immunological parameters in cancer patients, we gathered the data from electronic clinical files from various cancer centers or general hospitals across France and Canada, in order to monitor the magnitude and duration of virus RNA shedding in nasopharyngeal swabs according to cancer (versus healthcare workers (HCW) or COVID-19 + cancer-free individuals), tumor types (hematological versus solid malignancies) and staging (localized, locally advanced, metastatic diseases) (Fig. 1A). First, we conducted a prospective epidemiological study named Cancer_FR1_Translational Research (TR) at Gustave Roussy, Villejuif, France, during the first surge of the COVID-19 pandemic (from April 10, 2020 to May 11, 2020, NCT04341207) to evaluate the prevalence and severity of COVID-19 in all adult patients under treatment or recently treated for solid tumors or hematological malignancies (Fig. S1 and Table S1). Our secondary endpoint was the identification of viral, immunological, metabolic, and metagenomics blood predictors of severe complications among cancer patients. Clinical characteristics were collected from electronic medical records (Table S1). Nasopharyngeal samples were serially collected at every hospital visit motivated by the cancer management or any symptomatology related to seasonal flu or COVID-19 and transferred to the virology laboratory for SARS-CoV-2-specific quantitative reverse transcription-PCR (RT-qPCR) testing. Out of 473 patients enrolled in Cancer_FR1_TR, 53 (11%) were diagnosed with COVID-19 by RT-qPCR, and this diagnosis was corroborated by a specific serology in 87% of cases (Fig. S1A). Among the 52 patients evaluable for translational research, 37% were males, 60% suffered from at least one of the comorbidities associated with coronavirus pandemic, such as hypertension (58%) or obesity (21%) (Table S1). Seventyseven percent had an ECOG performance status of 0-1 at the time of nasopharyngeal sampling. Twenty-one percent of COVID-19positive cancer patients did not report any symptom of infection, 61% required hospital admission (for any cause or because of COVID-19 aggravation within 28 days after diagnosis) and 11% a transfer to intensive care unit (ICU), culminating with cancer death in 7% of the cases (from an undetermined cause, no systematic necropsy) (Fig. S1B-G and Table S1). Among patients with cancer diagnosed with COVID-19, 20% were followed up for hematological (as opposed to solid) malignancies and developed more severe symptoms of infection (Fig. S1B, G and Table S1). In the Cancer_FR1_TR study, 33%, 21%, and 46% presented with localized, locally advanced, and metastatic disease, respectively, that were equally susceptible to severe COVID-19 (Fig. S1F-G).

Given that cycle threshold (Ct) values of the first RT-qPCR test may be correlated with the clinical characteristics of the patients [12,13], we performed a longitudinal follow-up of Ct values by RT-qPCR. We targeted several genes coding for the envelope, the nucleocapsid and/or the replication-transcription complex (RdRP, Orf1a, subgenomic RNA of the SARS-CoV-2 [14,15]) to assess the duration of the nasopharyngeal SARS-CoV-2 RNA shedding, starting at COVID-19 diagnosis for up to 6 months as per-protocol indications (Fig. S2A). The duration of viral shedding was defined as the number of days from the first positive to the first negative RT-qPCR, after longitudinal monitoring with an interval inferior to 40 days, to reduce bias in viral shedding estimation. This time lapse of 40 days corresponded to the median of SARS-CoV-2 virus carriage in the cancer population (Fig. 1B, C and Table S1). In parallel, a similar and systematic COVID-19 protocol with longitudinal RT-qPCR testing was applied to healthcare workers (HCW) at Gustave Roussy. Healthcare workers had a mean age of 35 years (range: 19-61), were mostly females (male versus female: 13% versus 87%), and presented with one or two comorbidities in 27% and 4%, respectively, thereby significantly diverging from the cancer population diagnosed with COVID-19. Starting from 50 COVID-19-positive cancer patients and 100 HCW, we conducted RT-qPCR in 210 and 200 nasopharyngeal swabs, respectively (Fig. S2). However, applying the exclusion criteria detailed in Fig. S2, we could compare the median length of SARS-CoV-2 RNA detection in 35 cancer patients (Cancer_FR1_TR) and 45 HCW using 168 and 118 samples, respectively. Patients with cancer exhibited prolonged nasopharyngeal RNA virus shedding (Fig. 1B, median of 40 days (range: 6-137) for patients with cancer compared to 21 days (range: 7-53) for HCW, Fig. 1C, log-rank test P value < 0.0001). This difference persisted after adjusting for age, gender, and comorbidities (Cox multivariate analysis, adjusted hazard ratio (95% confidence interval) = 2.88 [1.42;5/85], P = 0.00291, Fig. 1C). To further validate the differences observed in the duration of viral RNA shedding between Cancer_FR1_TR and HCW, we analyzed another cohort of patients diagnosed with COVID-19 in a general hospital from Southern France and paired-in a case-control study-175 cancer patients (with a history of cancer or currently treated with cancer (Table S1)) with 350 cancer-free individuals based on age, gender, comorbidities, and COVID-19 severity (FR2_Case-Control, Cancer and Non-Cancer) (Fig. 1A and Table S1). Here again, there was a prolonged length of RT-qPCR positivity in cancer individuals compared with cancer-free COVID-19 patients (8 days versus 6 days, log-rank test P value, P = 0.03), taking into account that >70% were treated with hydroxychloroquine and azithromycin, a combination regimen reducing viral shedding [16]. Moreover, the proportion of patients with a viral shedding above 16 days (corresponding to the 90th percentile of the viral shedding in cancer-free patients) was higher in cancer patients (Fig. 1D, P < 0.0015). A second independent validation was achieved in the third series of 66 patients with cancer extracted from a cohort of 252 cancer individuals living in Canada and diagnosed with COVID-19 (Cancer_CA), for whom a longitudinal SARS-CoV-2-specific RT-qPCR (using Orf1 and E gene probe sets [17]) follow-up had been carried out [18] (Fig. 1A and Table S1). Here again, we observed that 26% of cancer patients were still PCR positive after 40 days from diagnosis by RT-qPCR (Fig. 1E). Such a long-term PCR detection of viral RNA could indicate stable subgenomic RNA contained within double-membrane vesicles or the presence of a replicative mucosal viral strain. Hence, we confirmed in three independent series of cancer patients prolongation of RNA virus shedding previously described in case reports in hematological or immunocompromised patients [19][20][21][22].

Hence, we focused on the differential characteristics of cancer patients presenting with long-term viral RNA shedding (LVS), defined by a positive RT-qPCR duration ≥40 days (median of RT-qPCR duration in Cancer_FR1_TR (Fig. 1C)), compared to those experiencing Short term Viral RNA Shedding (SVS), defined by a positive RT-qPCR duration <40 days henceforth (Table S1). The increased susceptibility to develop a LVS was independent of initial symptomatology, observed in 33% of Canadian (CA) to 40% of French (FR1_TR) asymptomatic and 27% (CA) to 56% (FR1_TR) of symptomatic cancer patients (Fig. 1F). There was a higher propensity to LVS in hematological malignancies compared to solid cancers (86% versus 43%, respectively (P = 0.04, Fig. 1G and Table S1) and in advanced disease (P = 0.011) in FR1_TR cohort (Fig. 1H and Table S1) but less so, in the CA cohort. Importantly, the LVS phenotype was associated with an increased risk to develop a moderate form of COVID-19 (defined by thoracic CT scan, hospitalization, and oxygen requirement <9 L/min) in Cancer_FR1_TR (P = 0.032) (Fig. 1I). This trend was confirmed in the third series of French patients from the clinical routine (CR) managed outside the translational ancillary study at Gustave Roussy (called henceforth "Cancer_FR1_CR"; Table S1 and Fig. S3), where 20% of cancer patients were diagnosed with LVS and exhibited more severe COVID-19 infections (Fig. 1I, P = 0.011). Again, the hospitalization rates and transfer to intensive care units were increased in LVS compared with SVS patients in Can-cer_FR1_TR (P = 0.0018, Table S1) and Cancer_FR2, respectively (P = 0.02, Table S1). Finally, the FR2 and Canadian series of LVS cancer patients also tended to exhibit more severe manifestations of COVID-19 compared with SVS Canadian cancer patients (Fig. 1I, bottom).

Of note, the duration of viral RNA shedding correlated with "viral load", i.e., Ct values at diagnosis, in that cancer patient with LVS experienced lower Ct values at diagnosis than SVS cancer patients in most cohorts for which the data were available (Fig. 1J). Importantly, cancer patients doomed to develop LVS presented with lower Ct values at diagnosis than those prone to become SVS in Cancer_FR1 and Cancer_FR2 cohorts (Fig. 1K). Of note, Ct values at disease onset were significantly anticorrelated with duration of viral RNA shedding in cancer patients using either N or Orf1ab/ RdRP gene-specific probe sets (data not shown).

The redundancy analysis (RDA) is an extension of the principal component analysis (PCA) aimed at identifying viral components which depend on other known covariates such as clinical parameters. RDA revealed that, within 30 days from diagnosis, 18% of the variance of the biological parameters are explained by ten components adjusted for the major clinical parameters for COVID-19 in Cancer_FR1_TR (Fig. 1L). These components were mainly influenced by the virus shedding (SVS versus COVID-19negative, P = 0.037; LVS versus COVID-19-negative, P = 0.0010), COVID severity (mild versus COVID-19-negative, P = 0.0030; moderate versus COVID-19-negative, P = 0.0574; severe versus COVID-19-negative, P not computable), age (P = 0.0514), hematological rather than solid malignancy (hematological versus solid, P = 0.001), metastatic status (P = 0.0059), and Ct values at diagnosis (>25 versus < 25, P = 0.0738). As outlined in Table S1, LVS patients tended to be older (66 versus 56 years old, P = 0.08), more metastatic (72% versus 29%, P = 0.01), and experienced increased hospitalization rates (83% versus 23%, P < 0.001) than SVS cancer patients in the Cancer_FR1_TR cohort.

Immunological hallmarks of long-term virus carriers at diagnosis

Intrigued by these findings, we addressed the question as to whether and how prolonged viral RNA shedding would impact on Cancer_FR1_TR patients with respect to COVID-19-related immunological alterations previously reported for cancer-free infected individuals [23][24][25][26][27][28][29]. More than 80 phenotypic markers were quantified on circulating leukocytes by means of highdimensional spectral flow cytometry, complemented by multiplex ELISAs to detect serum chemokines, cytokines, and growth factors. These parameters were recorded within or after the first 20 days of inclusion in the Cancer_FR1_TR protocol, for 25 COVID-19 + cancer patients that were divided into LVS versus SVS subgroups, in comparison to 43 COVID-19-negative cancer patients ("controls" or "Ctls") matched for age, gender, comorbidities, cancer types, and tumor extension (Table S2). Asymptomatic individuals and cancer patients enrolled at the recovery phase of COVID-19 (meaning that they became PCR-negative) were analyzed separately. Within the first 20 days from diagnosis, LVS presented increased proportions of monocytes among circulating leukocytes (Fig. S4A, left panel), and a parallel drop in CD169 -HLA-DR + within conventional monocytes (Fig. S4A, middle panel) and in nonconventional monocytes (CD16 + CD14 low/-, Fig. S4A, right panel) compared to SVS, cancer controls, asymptomatic or recovered patients, as reported [23,30]. Polymorphonuclear cells (PMN) tended to increase in LVS, specifically immature CD101 +/-CD10 +/-CD16 - neutrophils, compared with SVS, convalescent, and controls (Fig. 2A,B, upper and lower panels and Fig. S4B).

Importantly, the most significant phenotypic traits distinguishing LVS from SVS featured among the reported hallmarks of severe COVID-19 in cancer-free subjects [23][24][25][26][27][28][29] (Fig. 2A). In accordance with the reported defects in germinal center formation in secondary lymphoid organs of severe COVID-19 [28], LVS cancer patients exhibited increased recirculation of activated CXCR5 + PD-1 high CD4 + follicular T-helper cells (TFH) expressing ICOS and CD38 (Fig. 2C, left panel), as well as a marked rise in plasmablasts (defined as CD19 low CD27 hi CD38 hi ) at the expense of transitional B (CD24 + CD38 hi CD19 + ) and doublenegative B cells (IgD -CD27 -CD19 + ) (Fig. 2C, right panel, Fig. S4C and Fig. 2D). As indicated in the Volcano plot in Fig. 2A, immature PMN and double-negative B cells were among the most significant immunological features, positively and negatively predicting LVS, respectively (Fig. 2B, bottom panel and Fig. 2D, right panel). LVS coincided with the prolonged systemic release of, and exposure to, type 1 IFN above levels measured in SVS, controls, and recovered individuals (Fig. 2E). Type 1 IFN levels anticorrelated with titers of neutralizing anti-S1 RBD antibodies (Fig. 2F). This landscape of immune profiling was corroborated by nonsupervised hierarchical clustering of innate and cognate Fig. 1 Prolonged duration of SARS-CoV-2 RNA shedding correlated with high viral load and COVID-19 severity in patients with cancer. A Graphical schema of cohorts and patients' accrual. B Proportion of patients with cancer from translational research (TR) (Cancer_FR1_TR, n = 35, magenta area) or healthcare workers (HCW, n = 45, blue area) by days of RT-qPCR positivity. Vertical dashed line at 40 days represents the 95th percentile of HCW and the median of positivity of patients with cancer. C Kaplan-Meier curves of time to negative RT-qPCR in HCW (n = 45, blue dotted lines) and patients with cancer (Cancer_FR1_TR, n = 35, magenta continuous lines). D COVID-19 + cancer-bearing or history of cancer (+) and cancer-free (-) individuals from FR2 treated with hydroxychloroquine +/azithromycin: number (percentages) of patients with RT-qPCR positivity beyond 16 days (90th percentile of the cancer-free population of FR2). E Number (percentages) of HCW, Cancer_FR1 patients (Cancer_FR1_TR), or Canadian patients with cancer (Cancer_CA) with short, intermediate (grouped in short-term viral RNA shedding, SVS), and prolonged (long-term viral RNA shedding, LVS) viral RNA shedding (E), according to the presence/absence of viral symptoms (symptomatic, Sym, vs asymptomatic, Asym) (F), diagnosis of hematological (H) versus solid (S) malignancy (G), and cancer staging (localized (L), locally advanced (LA), metastatic (M)) (H). I Number (percentages) of Cancer_FR1 patients (from translational research and clinical routine), Cancer_FR2 patients (Cancer_FR2) or Canadian patients with cancer (Cancer_CA) divided in SVS and LVS and regarding their respective COVID-19 severity. J Spearman correlation between Cycle threshold (Ct) for the RT-qPCR amplification of genes encoding proteins of SARS-CoV-2 replication-transcription complex at diagnosis and duration of RT-qPCR positivity for Cancer_FR1 (from translational research and clinical routine), each dot representing one sample/patient. K Ct values for the RT-qPCR amplification of genes encoding proteins of SARS-CoV-2 replication-transcription complex in nasopharyngeal swabs performed at diagnosis in SVS versus LVS in Cancer_FR1_TR and CR and Cancer_FR2, and dynamics over time from day 0 up to day 80 after inclusion in SVS (n = 33 samples, n = 28 patients, orange dots) versus LVS (57 samples, n = 17 patients, purple dots) in Cancer_FR1 (from translational research and clinical routine). L Redundancy statistical analysis (RDA) of cancer and viral related-clinical factors accounting for the variance of SARS-CoV-2 viral shedding status. Clinical components were influenced by the virus shedding (SVS versus COVID-19-negative, P = 0.037; LVS versus COVID-19 negative, P = 0.0010), COVID severity (mild versus COVID-19-negative, P = 0.0030; moderate versus COVID-19-negative, P = 0.0574; severe versus COVID-19-negative, P not computable), age (P = 0.0514), hematological rather than solid malignancy (hematological versus solid, P = 0.001), metastatic status (P = 0.0059), and Ct values at diagnosis (≥25 versus < 25, P = 0.0738). Chi-square tests with *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. immunotypes and serum cytokine concentrations analyzed within 30 days from diagnosis. This method allowed to segregate a small cluster of individuals characterized by low Ct values (<25), and moderate/severe complications of COVID-19, which included metastatic cancer carriers with LVS or SVS (Fig. S5). This cluster was separated from the others by typical signs of viral infection, including abundant circulating CD38 + HLA-DR + CD8 + T cells, plasmablasts, activated TFH cells, and high serum IFNα2a levels (Fig. S5). Likewise, while many inflammatory cytokines, chemokines, or alarmins (such as IFNγ, CXCL10, IL-4, IL-6, and

<---------------SVS | LVS ---------------> -log10(p) IgD -CD27 -in CD19 + IFNγ/INFα2a CD24 + IgD -CD27 -in CD19 + CXCL5 CCL11/CXCL10 CCL22 TNFα CCL11 GM-CSF CCL26 PD-1 + in non-naïve CD8 +
PD-1 + in Eomes hi TCF7 hi non-naïve CD8 + GzB + PD-1 + in non-naïve CD8 + CD101 -/+ CD10 -/+ in CD16 -Neutrophils GzB + PD-1 + in Eomes hi TCF7 hi non-naïve CD8 + CD38 + HLA-DR + in non-naïve CD8 + Plasmablasts (CD19 lo CD27 hi CD38 hi ) CD101 -/+ CD10 -/+ CD16 -in Neutrophils calprotectin) were elevated in symptomatic COVID-19 individuals compared with controls, asymptomatic, and recovered patients, none of them could predict LVS, except a drop in the IFNγ/IFNα2a and CCL11/CXCL10 ratios whose significance remains unclear (P = 0.016 and P = 0.0019, respectively) (Fig. S4D-I). Interestingly, innate and cognate immunotypes performed in convalescent patients and controls segregated at random in the non-supervised hierarchical clustering (Fig. S6).

Altogether, the high-dimensional flow cytometry of blood immune subsets indicated that LVS cancer patients harbored the immunological hallmarks of severe COVID-19 at diagnosis.

Virus-associated lymphopenia predicted shorter overall survival in the first and second surge of the pandemic Lymphocyte loss is a feature of severe COVID-19 in noncancer patients [24,27]. The "FR2" cohort was a case-control study with 175 cancer patients paired with 350 cancer-free individuals based on age, gender, comorbidities, and COVID-19 severity. As observed in Fig. 1J for cancer patients, there was an anticorrelation between Ct values at diagnosis and the duration of viral RNA shedding in cancer-free patients (r = -0.6, P < 0.0001) (Fig. S7A). Not surprisingly, blood absolute lymphocyte counts (ALC) at diagnosis anticorrelated with the duration of PCR positivity in Cancer_FR1_TR and Cancer_FR1_CR cohorts (Fig. 3A). However, although the ALC before the COVID-19 pandemic (blood drawn from December 2019 to mid-March 2020) were already somewhat lower in LVS than in SVS cancer patients, the ALC during the outbreak dramatically dropped in cancer patients doomed to develop LVS (in both Cancer_FR1_TR and Can-cer_FR1_CR cohorts), more so than in individuals prone to SVS (Fig. 3B, left panel). The extent in ALC reduction was more severe in patients presenting LVS than SVS (Fig. 3C). Of note, ALC recovered in both patient groups regardless of the LVS/SVS status. It supports that reduced ALC at COVID-19 diagnosis is induced by the virus rather than by cancer (Fig. 3B, left panel). In accord with the finding that LVS correlates with high viral load at symptom onset (Fig. 1J,K), higher viral loads at diagnosis were associated with a pronounced COVID-19-associated lymphopenia (Fig. 3B, right panel). Contrary to what we observed in cancer patients, there was no correlation between ALC at diagnosis, and duration of RT-qPCR in cancer-free individuals (r = 0.05, P = 0.3) (Fig. S7B). Comparing ALC at diagnosis to ALC post-hospitalization, we concluded that cancer-free patients presenting with a high viral load (Ct<25) did not harbor lymphopenia at diagnosis or during the acute phase (P = 0.11) (Fig. S7C) in contrast with what we observed in cancer patients. So, virus-induced lymphocyte loss occurs in a fraction of individuals with cancer and is detrimental for the prognosis. This phenomenon may be ascribed to cancerassociated chronic inflammation or co-medications.

We next assessed the clinical significance of the interaction between Ct values, ALC, and cancer patient survival in 110 cancer patients with COVID-19 (Discovery cohort (first surge of the pandemic) including 84 patients from Cancer_FR1 treated at Gustave Roussy and 26 patients from Cancer_ FR3 treated at Léon Bérard Cancer Center in Lyon, France) (Fig. 3D and Table 1). Cox logistic regression analyses and Kaplan-Meier survival curves were performed after stratification of the patients according to both, Ct and ALC values at diagnosis. The cutoff for the Ct value was 25 and corresponded to the median of the whole cohort FR1 + FR3, which coincided with the threshold at which live virus particles can be isolated in 70% of the cases [31]. The cut-off value for ALC was the median found in patients with high viral load (Ct <25) at diagnosis (ALC = 800/mm 3 ). ALC combined with Ct values predicted cancer-related overall survival in univariate analyses across all cancer stages (local, locally advanced, or metastatic) (Fig. 3D and Table 1). While patients presenting with ALC > 800/ mm 3 and low viral load (Ct >25) exhibited prolonged survival, a dismal prognosis affected 21% of them (23/110) who presented both deep lymphopenia (ALC < 800/mm 3 ) and high viral loads (Ct <25) at diagnosis (Fig. 3D) culminating in 40% deaths at 3 months. All four groups were comparable in terms of age, gender, comorbidities, cancer type, or staging (Table 1). Multivariate Cox analysis stratified for the cohort origin and adjusted for age (hazard ratio (95% confidence interval) = 1.042 [1.013; 1.072], P = 0.0043), ECOG performance status (4.547 [1.845; 11.206], P = 0.0001), gender (1.668 [0.775; 3.588], P = 0.1907), and metastatic status and hematological malignancies (2.747 [1.090; 6.923], P = 0.0322) confirmed a continuous decrease of risk with the increase of the Ct value (0.841 [0.776; 0.911], P = 0.00002) and the increase of the ALC (0.282 [0.119; 0.672], P = 0.0043) (Fig. 3E). Of note, treatment retardation could not explain the high mortality of patients presenting with a high viral load and low ALC (Table 1).

We confirmed these predictors (ALC < 800 & Ct <25) of poor survival during the second surge of the pandemic (between May 5, 2020 to November 25, 2020) in 116 new COVID-19 cancer patients ("Validation", Cancer_FR1 and Cancer_FR3, Fig. 1A). Here again, the subset of patients with ALC < 800 & Ct <25 (n = 38/116, 32.7%) exhibited the most reduced overall survival compared to Fig. 2 Immunotypes associated with prolonged viral RNA shedding in patients with cancer. A Volcano plot of the differential cellular and soluble immune parameters contrasting short-term viral RNA shedding (SVS) versus long-term viral RNA shedding (LVS) during the first 20 days of symptoms. Volcano plot was generated computing for each immune factor: (i) the log2 of fold change among the mean relative percentages after normalization in SVS versus LVS (x axis); (ii) the log10 of P values deriving from Wilcoxon test calculated on relative percentages in absolute values (y axis). Black and red dots are considered nonsignificant (P < 0.05) or significant (P > 0.05), respectively. B-F Temporal changes and correlation of blood leukocyte parameters measured by high-dimensional spectral flow cytometry (B-D) and soluble factors IFNα2a and anti-SARS-CoV-2 IgG (E, F) in various phases of COVID-19 presentation (no virus infection (Ctls, gray dots), asymptomatic viral infection (Asym, light blue dots), symptomatic viral infection examined in the first 20 days (≤20 d) or after 20 days (>20 d) of symptoms with those experiencing short-term viral RNA shedding (SVS, orange dots) or long-term viral RNA shedding (LVS, purple dots) and RT-qPCRnegative COVID-19 patients in the convalescent phase (recovery, green dots, or circled dots). Box plots display a group of numerical data through their 3rd and 1st quartiles (box), mean (central band), minimum and maximum (whiskers). Each dot represents one sample, each patient being drawn one to three times. Statistical analyses used one-way ANOVA with Kenward-Roger method to take into account the number of specimen/patient: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. B-D Percentages of neutrophils that do not express either CD101 and/or CD10 and lost CD16 within the gate of CD45 + CD56 -CD3 -CD19 -CD15 + cells (B, upper panel). Spearman correlation between the percentage of immature neutrophils (CD10 +/-CD101 +/-CD16 -) measured within the first 20 days of symptoms with the duration of SARS-CoV-2 RT-qPCR positivity (B, lower panel). C, D Percentages of CD38 + ICOS + among CXCR5 + PD-1 + non-naive CD4 + (C, left panel), plasmablasts defined as CD19 low CD38 high CD27 + within the CD19 + gate (C, right panel), double-negative IgD -CD27 -among CD19 + cells (D, left panel) and their Spearman correlation when measured within the first 20 days of symptoms with the duration of SARS-CoV-2 RT-qPCR positivity (D, right panel). E Ultrasensitive electrochemiluminescence assay to monitor the serum concentrations of IFNα2a (E, left panel) in a kinetic fashion (E, right panel). Each line and dot represent one patient and one sample, respectively, and the dashed line represents the median value of controls. F Spearman correlation between the serum IFNα2a values measured in symptomatic patients with IgG titers against SARS-CoV-2 S1 RBD considered as continuous variables (F, left panel). The raw data are represented in the right panel at both time points for each group of patients.

the other groups with >40% deaths at 50 days (Fig. 3F). Of note, the reduced survival rate in the subset of patients defined by ALC < 800 & Ct <25 was not a peculiarity of hematological malignancies (characterized by therapy-induced B cell depletion) since it was also observed in patients with solid neoplasia (Fig. S7D andS7E).

In conclusion, it appears that uncontrolled viral infection capable of compromising the number and function of circulating lymphocytes predicts the lethal outcome of patients with malignant disease.

Immunological, metabolic, and metagenomic parameters associated with virus-induced lymphocyte loss

Multiple and non-exclusive mechanisms could account for virusassociated lymphopenia [25,27,[32][33][34][35]. To further investigate this deleterious virus-induced lymphocyte loss, we searched for the most robust correlates between ALC and immunological, metabolic, or pathogenic cues in the Cancer_FR_TR cohort as well as noncancer COVID-19 patients that we previously reported [23].

First, the Spearman correlation matrix of the main immunological and serum markers monitored at the peak of disease (within the first 20 days of disease onset) indicated close interconnections between lymphocyte proportions and their subsets within leukocytes (Fig. 4A). Lymphopenia, which is a prominent feature of COVID-19 and a hallmark of severe infection, distinguished LVS from SVS or asymptomatic individuals (Fig. S8A,B), as exemplified for the proportion of B lymphocytes among total CD45 + leukocytes after 20 days of symptoms. As reported [27], the transitional differentiation of naive into effector/memory T cells co-expressing CD38 + HLA-DR + among CD8 + T cells is a hallmark of COVID-19 that persisted in LVS compared to controls and SVS (P = 0.002 and P = 0.012) (Fig. S8C,D). In particular, the most compelling LVS-associated T-cell subpopulation that expanded in the context of lymphopenia was the non-naive (non-CD45RA + CD27 + ) CD8 + T subset expressing an activation/exhaustion phenotype characterized by early and sustained expression of PD-1 (Fig. 4B), Eomes, Granzyme B, TCF-1 including the proapoptotic marker CD95-L (FasL) (Fig. 4C,D, left panel). There was no difference in T-bet + (effector) expression within Eomes + PD-1 + non-naive CD8 + over the different time courses and compared with controls (6.2 ± 0.74% (mean ± SEM) (data shown). However, COVID-19 + patients (both asymptomatic and symptomatic ones) exhibited higher proportions of cells co-expressing TOX and Eomes within PD-1 + non-naive CD8 + compared with patients at recovery or controls (Fig. S8E, left panel). Interestingly, a subset of these exhausted PD-1 + CD8 + T cells was proliferating while 1 for patient characteristics. E Multivariate Cox regression analysis stratified for the cohort and adjusted for age, ECOG status, gender, and metastatic and/or hematological status of cancer patients from the Discovery (1st surge) cohort (Cancer_FR1 + Cancer_FR3). F Kaplan-Meier curve and Cox regression analysis of overall survival of cancer patients from Validation (2nd surge) cohort (Cancer_FR1 + Cancer_FR3), all stages included, according to ALC and Ct value at diagnosis. Refer to Table 1 for patient characteristics.

Table 1. Clinical characteristics of Cancer_FR1 and Cancer_FR3 patients from discovery and validation cohorts presenting cycle threshold below (Ct<25) or above 25 (Ct>25) and with (<800/mm 3 ) or without (>800/mm 3 ) lymphopenia at diagnosis (refer Fig. 3D-F undergoing apoptosis during the acute phase compared with patients at recovery (Fig. S8E, right panel). All of these data tend to indicate that circulating PD-1-expressing CD8 + T cells are rather exhausted than activated with a trend toward apoptosis that could participate in the lymphopenia described in COVID-19 + cancer patients. The abundance of these non-naive exhausted PD-1 + CD8 + Tc1 cells positively correlated with the duration of SARS-CoV-2-specific RT-qPCR positivity (Fig. 4B, bottom panel and Fig. 4D, right panel) and may explain, at least partly, the reduced fitness and half-life of peripheral lymphocytes. Second, we performed the serum metabolome determined by untargeted and targeted mass spectrometry-based metabolomics analyzing more than 221 metabolites in 31 cancer patients from Cancer_FR1_TR, as well as in a previously described cohort of 66 cancer-free COVID-19 + patients for validation [23]. The nonsupervised hierarchical clustering of the serum metabolome clearly contrasted LVS from LVS patients (Fig. S9). The Volcano plot aimed at identifying significant differences between LVS and SVS patients pointed out the biliary salt metabolic pathway segregating SVS from LVS serum (Fig. 5A), previously described to have biological significance for lymphocyte fitness and maintenance [32][33][34][35]. Secondary biliary acids (such as the murideoxycholic acid (muri-DOC) (Fig. 5B, left panel) and the DOC (Fig. 5C)) were decreased in LVS compared with SVS and controls and correlated with lower ALC in cancer patients (Fig. 5B, right panels) or severe COVID-19 (Fig. 5D). Similarly, two other derivatives of DOC (hyo-DOC, urso-DOC) were decreased in LVS (compared to controls and SVS, Fig. S10A,B, left panels) and were associated with lymphocyte loss (Fig. S10A and S10B, right panels).

Another metabolic pathway pertaining to polyamines with high biological significance for age-related immunosenescence [36][37][38] was also strongly associated with the duration of RT-qPCR positivity, ALC, and disease severity (Fig. 5E-G and Fig. S9). In particular, the N1, N8 diacetylspermidine that anticorrelated with ALC (Fig. 5F, right panel) increased in the serum of LVS patients (but not SVS, Fig. 5F, left panel), in accordance with its marked rise in severe COVID-19 in cancer-free individuals (Fig. 5G, left panel) where high levels coincided with the lymphocyte drop (Fig. 5G, right panel). Of note, the tryptophane/kynurenine or lactic acid metabolites were not relevant in our study (Fig. 5A and Fig. S9).

Third, endotoxemia was shown to correlate with the cytokine storm during COVID-19 [25] and might cause activation-induced lymphocyte cell death. Assuming that the gut permeability could be altered during COVID-19-associated intestinal dysbiosis [39], we studied the circulating microbial populations associated with whole leukocytes by sequencing blood rDNA using nextgeneration sequencing of V3-V4 variable regions of the 16S rRNA bacterial gene as previously described [40]. Although we failed to observe significant quantitative differences in blood bacterial load between SVS (n = 14) and LVS (n = 15) patients, the linear discriminant analysis effect size indicated significant taxonomic differences in the bacteria family members between the two groups (Fig. 6A,B). The DNA from Enterobacteriaceae (mainly composed of Escherichia Shigella genus) was overrepresented in leukocytes of LVS compared with SVS patients (Fig. 6A,B,C, left panel). The circulating Enterobacteriaceae-related DNA markedly anticorrelated with CCL22 (a hallmark of SVS, Fig. 2A), but was strongly associated with the increase of exhausted CD8 + T lymphocytes (Fig. 6D,E). There was a trend for an increase in the relative abundance of Micrococcaceae in the blood leukocytes of LVS that was confirmed in cancer patients with dismal prognosis (ALC < 800 & Ct <25) (Fig. 6F,G,H).

Overall, we conclude that virus-associated lymphopenia may result in complementary or coordinated orthogonal disorders.

DISCUSSION

To interrogate viral-host interactions during the COVID-19 pandemic in cancer patients, we studied 1106 patients, among them 59% were cancer bearers (FR1 + FR2 + FR3 + CA), and 1063 COVID-19-positive (Fig. 1A). We used high-dimensional flow cytometry to perform deep immune profiling of innate, B and T cells, and measurements of 51 soluble markers, with temporal analysis of immune changes during infection in one cohort that was further explored by blood metabolomics and metagenomics. This longitudinal immune analysis was linked to virologic and oncological data (Figs. S5 andS6). Using this approach, we made several intriguing observations. First, 51%, 20%, and 26% of cancer patients in FR1_TR, FR1_CR, and CA, respectively, still shed SARS-CoV-2 RNA after day 40 from symptoms onset (versus 2% in HCW), correlating with high viral loads (Ct values <25) at diagnosis. Indeed, isolation of replication-competent viral strains between 10 and 20 days after symptom onset has been documented in some persons with severe COVID-19, mostly in immunocompromised cases [41]. However, ~90% of their specimens no longer yielded replication-competent viruses after day 15 from symptom onset [42,43]. Prolonged shedding of influenza, parainfluenza, rhinovirus, seasonal coronavirus, and the respiratory syncytial virus has previously been detected in immunosuppressed patients [44][45][46][47][48]. Cancer dissemination, cancer therapies, and virus-induced lymphopenia might cause an immunodeficiency that eventually jeopardizes virus clearance. The proposed mechanisms by which lymphopenia occurs in COVID-19 (often shared with cancer dissemination) [49] include virus-induced atrophy of secondary lymphoid organs [50][51][52], the disappearance of germinal centers [28], the direct pro-apoptotic activity of the virus related to ACE2-dependent or ACE2-independent entry into lymphocytes [53], T-cell demise consecutive to activation and exhaustion [54,55], senescence [1,56], and antiproliferative effects of lactic acid [57]. However, in our study, we found that lymphocyte loss was correlated with a decrease of secondary biliary salts in LVS patients, most likely associated with increased gut permeability that leads to bacterial translocation, as we observed increased circulating DNA for Micrococcaceae and Enterobacteriaceae family members. Moreover, the transformation of spermidine into N1, N8 diacetylspermidine was linked to decreased ALC, in accordance with the role of spermidine in preventing aging-related loss of lymphocyte fitness [36][37][38].

Second, prolonged viral RNA carriage was associated with signs of immunopathology (exacerbated T-cell responses, extrafollicular TFH, and plasmablast recirculation, exhausted PD-1 + Tc1 cells, sustained serum type 1 IFN levels), likely maintaining a positive feedback loop for the expression of the interferon-signaling genes product ACE2 [58] and pro-inflammatory interactions between airway epithelia and immune cells [29].

Third, prolonged SARS-CoV-2 RNA shedding after day 40 might precede the aggravation of both COVID-19 and malignant disease. Indeed, virus and/or cancer-induced lymphopenia and T-cell exhaustion may jointly enfeeble tumor immunosurveillance [59]. Interestingly, SARS-CoV-2 virus-induced immunopathology was accompanied by increased blood levels of IL-8 (Fig. S4G) and VEGF [26], which are well-known pro-angiogenic and pro-tumorigenic growth factors, predicting failure to cancer immunotherapy [60]. Of note, patients with high initial viral loads or LVS tended to accumulate poor prognosis-related parameters than SVS or patients with higher Ct values in both cohorts (Table 1 & Table S1), being older (66 versus 56 years old, P = 0.08), more metastatic at diagnosis of infection (72% versus 29%, P = 0.011), and increased hospitalization rates (83% versus 23%, P = 0.001). As a result, virusinduced lymphopenia markedly predicted early death of patients, within the first 2-3 months post-COVID-19 diagnosis in the first and second surge of the pandemic (in more than 200 patients) and call for caution to administer chemotherapy or steroids at the acute phase of the viral infection that exacerbate immunosuppression.

These observations call for a careful follow-up of cancer patients, in particular those bearing hematological and metastatic malignancies, during the second wave of COVID-19. Given the non-consensual efficacy of vaccines against influenza virus in vulnerable individuals suffering from cancer-, virus-, and ageassociated lymphopenia [49,61], passive immunization of high affinity neutralizing monoclonal antibodies against SARS-CoV-2 at COVID-19 onset might be envisaged. This could be combined with therapeutic stimulation of lymphopoiesis (for instance with rIL-7, G-CSF, inhibitors of indoleamine 2,3 deoxygenase), to achieve immunological tonus that is compatible with anticancer treatments [62][63][64]. Clinical trials are underway to evaluate rIL-7 against COVID-19, but may benefit from patient stratification based on Ct values, duration of viral RNA shedding, and ALC [65,65,66].

MATERIALS AND METHODS

All cohorts (refer to Supplementary Material, Table 1).

Cohorts for the duration of viral PCR positivity

Cancer_FR1_Translational Research (TR) (ONCOVID) clinical trial and regulatory approvals for translational research. Principles: Gustave Roussy Cancer Center sponsored the "ONCOVID" trial and collaborated with the academic authors on the design of the trial and on the collection, analysis, and interpretation of the data. Sanofi provided trial drugs. The trial was conducted in accordance with Good Clinical Practice guidelines and the provisions of the Declaration of Helsinki. All patients provided written informed consent. Protocol approval was obtained from an independent ethics committee (ethics protocol number EudraCT No: 2020-001250-21). The protocol is available with the full text of this article at https:// clinicaltrials.gov/ct2/show/NCT04341207. Patients: ONCOVID eligible patients were adults fitted for, or under, or recently treated by chemotherapy and/or immune-checkpoint blockade for the treatment of solid tumors or hematological malignancies (please refer to Table 1 and Table S1). Patients diagnosed for COVID-19 from April 10, 2020 to May 4, 2020 were included in the Discovery cohort and patients from May 5, 2020 to November 25, 2020 were included in the Validation cohort. Trial design: Cancer patients were screened for SARS-CoV-2 virus carriage by nasopharyngeal sampling at every hospital visit. The presence of SARS-CoV-2 RNA was detected by RT-qPCR assay in a BSL-2 laboratory. Asymptomatic and symptomatic patients (i.e., presenting with fever (t°> 38 °C) and/or cough and/or shortness of breath and/or headache and/or fatigue and/or runny nose and/or sore throat, anosmy/agueusia) with a positive SARS-CoV-2 RT-qPCR test, shifted to the interventional phase (tailored experimental approach with hydroxychloroquine and azithromycin therapy in symptomatic SARS-CoV-2-positive subjects). Asymptomatic or symptomatic patients with negative SARS-CoV-2 RT-qPCR test continued their standard of care anticancer treatments. Repeated RT-qPCR for SARS-CoV-2 on nasopharyngeal swabs and blood samples were performed to monitor the status for SARS-CoV-2 and the immune response, respectively, in COVID-19-positive and negative patients. The COVID-19 severity was defined based on oxygen, imaging, and hospitalization criteria. Patients with mild COVID-19 disease had limited clinical symptoms not requiring scan or hospitalization; patients with a moderate COVID-19 disease were Data Protection Regulation (GDPR) and the French Data Protection Authority's recommendation about Data Protection in clinical researches. Gustave Roussy Data Protection Officer (DPO) has evaluated this project and sent to the principal investigator a formalized operational action plan about data protection compliance: patient's information, security measures, good practices about pseudonymization, etc. All of the DPO's recommendations have been applied by the research team. Healthcare workers diagnosed for COVID-19 between 24 March, 2020 and 24 April, 2020 were included. Results of RT-qPCR, cycle threshold, age, gender, and number of comorbidities were collected. Data from healthcare workers who refused to participate and/or with cancer were excluded. In agreement with MR004 in France, we reported the series to the national information science and liberties commission.

Second series of patients with cancer (Cancer_FR2). CASE-CONTROL study:

All comers spontaneously presenting at a general hospital for infectious diseases (IHU Méditerranée Infections, Marseille, FR) (Table S1) from February 27, 2020 to December 15, 2020 composed of 996 COVID-19 patients. We performed a case-control study at a 1:2 paired ratio where the 175 cancer patients (with a currently treated cancer or history of cancer) were matched with 350 cancer-free individuals on age, gender, comorbidities relevant for COVID-19. Of note, >75% received hydroxychloroquine and >96% received azithromycin (Table S1) [16,67]. This study was approved by the IHU Méditerranée Infections review board committee (Méditerranée Infection N°: 2020-021).

Third series of cancer patients from Canada (Cancer_CA). We used 66 individuals from the clinical cohort previously reported [18] for whom data were available (Table S1). This study was conducted across eight Canadian institutions in Quebec and British Columbia and was approved by the institutional ethics committee at each site (Ethics number: MP-02-2020-8911 and H20-00892).

Fifth series of cancer patients, Cancer_FR1_Clinical Routine (CR). We used the clinical cohort previously reported [2] (Table S1). In accordance with the French regulations, there was no requirement for ethical approval to be sought for this observational study, based on medical files. Patients diagnosed for COVID-19 from March 14, 2020 to April 29, 2020 were included in the Discovery cohort and from April 29, 20 to November 25, 2020 in the Validation cohort. This study was also declared to the Gustave Roussy Cancer Centre's DPO and registered on the website of the French Healthcare Data Institute (declaration number: MR4911200520).

Cohorts for the ALC and Ct value predictors: first surge and the second surge of the pandemic Cancer_FR1_Translational Research (TR) (ONCOVID) clinical trial and regulatory approvals for translational research. Among the 52 patients diagnosed for COVID-19 during the first surge (from April 10, 2020 to May 4, 2020), absolute lymphocyte count (ALC) and cycle threshold (Ct) were available for 34 patients whom were included in this cohort.

Then, among the 18 patients included in ONCOVID during the second surge (from May 5, 2020 to November 25, 2020), absolute lymphocyte count (ALC) and cycle threshold (Ct) were available for nine patients who were included in this cohort Cancer patients referred to the clinical routine (Cancer_FR1_CR). In accordance with the French regulations, there was no requirement for ethical approval to be sought for this observational study, based on medical files. Among the 178 patients diagnosed for COVID-19 during the first surge (March 14, 2020 to April 29, 2020), ALC and Ct were available for 50 patients who were included in this cohort. Then, among 170 patients with cancer diagnosed for COVID-19 during the second surge (from May 5, 2020 to November 25, 2020), ALC and cycle threshold Ct were available for 82 patients who were included in this cohort.

Cancer patients referred to the Centre Léon Bérard, Lyon, France (Cancer_FR3). The PRE-ONCOVID-19 study was approved by the Institutional review board of the Centre Leon Bérard on March 12, 2020 (ET20-069). We used a subset of 25 patients included during the first surge from March 5, 2020 to May 4, 2020 with available ALC and Ct values. We used 26 patients included during the second surge from October 1, 2020 to December 5, 2020 with available data.

Patients from each cohort were classified using the same criteria. RT-PCR for subgenomic RNA (sgRNA) for SARS-CoV-2

RT-qPCR analysis

We used the protocol previously described by Wölfel et al. [15]. Briefly, the oligonucleotide sequence of the leader-specific primer was as follows: sgLeadSARSCoV2-F; CGATCTCTTGTAGATCTGTTCTC, and the oligonucleotide sequence of the E primer was as follows: E_Sarbeco_R; ATATTGCAG-CAGTACGCACACA. Briefly, 5 uL of RNA (>21 ng) were used for the sgRNA RT-PCR assay with Superscript III one-step RT-PCR system with Platinum Taq Polymerase (Invitrogen, Darmstadt, Germany) with 400 nM concentration of each primer. Thermal cycling was set up as described. Finally, RT-PCR products for sgRNAs were analyzed on agarose gel 2%.

Fig. 5 Lymphopenia and prolonged viral shedding are associated with perturbations of the polyamine and biliary acid pathways. A Volcano plot identifying statistically different serum metabolites between patients experiencing short-term viral RNA shedding (SVS) and those experiencing long-term viral RNA shedding (LVS) in Cancer_FR1_TR cohort. Metabolites significantly different between both groups are in red and annotated (P < 0.05, FC > 0.5). B Levels of murideoxycholic acid according to the duration of viral shedding in Cancer_FR1_TR (left panel) and Spearman correlation with absolute lymphocyte count (ALC) (right panel). The color code corresponds to the category of cycle threshold (Ct) and ALC at diagnosis. C, D Serum concentrations of deoxycholic acid according to the duration of viral shedding in Cancer_FR1_TR (C) and the severity of COVID-19 infection in cancer-free individuals (D). E Waterfall plot of Spearman's correlation coefficient (r s ) between ALC and 221 metabolites in the serum of patients diagnosed positive for COVID-19. F N1, N8 diacetylspermidine relative abundance in controls, SVS and LVS patients in the Cancer_FR1 cohort, that is negatively correlated with the ALC. The color code corresponds to the category of cycle threshold (Ct) and ALC at diagnosis. G Levels of N1, N8 diacetylspermidine in noncancer COVID-19 patients according to the clinical severity compared to COVID-19-negative controls (Ctls) (P < 0.0001) (G, left panel), that are negatively correlated with the absolute lymphocyte count (ALC) (G, right panel). Box plots display a group of numerical data through their 3rd and 1st quartiles (box), mean (central band), minimum and maximum (whiskers). Each dot represents one sample, each patient being drawn once for cancer-free individuals and one to two times for cancer patients. Statistical analyses used one-way ANOVA with Kenward-Roger method to take into account the number of specimen/patient (B, left panel, C-E, left panel): *P < 0.05, **P < 0.01), non-parametric unpaired Wilcoxon test (Mann-Whitney) for each two-group comparison: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Cell Death & Differentiation

Evaluation of SARS-CoV-2 RNA shedding

The duration of viral shedding was defined as the number of days from the first positive to the first negative RT-qPCR, after longitudinal monitoring. In order to prevent an overvaluation of this duration, we considered in this analysis only patients with an interval below 40 days between the last positive RT-qPCR and the first negative RT-qPCR. Six patients had one negative RT-qPCR followed by positive RT-qPCR. We extend the duration to the second negative RT-qPCR for three patients with a cycle threshold below 35 for the gene coding replication-transcription complex and within 6 days after the first negative result. Absolute lymphocyte count (ALC)

The absolute lymphocyte count was measured for the clinical routine using the Sysmex XN (Sysmex, Belgium). Values "PRE" were collected between 210 and 12 days before the symptom onset of COVID-19, values at diagnosis of the infection were collected between -4 and +7 days of the disease diagnosis by RT-qPCR, values "POST" were collected at the recovery time or later, meaning between 0 and 123 days after the first negative RT-qPCR. For the interpretation, the cut-off value for ALC was the median found in patients with high viral load at diagnosis (ALC = 800/mm 3 ). In parallel, we considered this value as relevant according to the common terminology criteria for adverse events where grades of lymphopenia were assigned as follows: grade 1 ALC < lower limit of normal to 800/mm 3 , grade 2 ALC < 800-500/mm 3 , and grade 3 ALC < 500-200/mm 3 .

Blood tests

Sampling. Blood samples were drawn from patients enrolled in ONCOVID at Gustave Roussy Cancer Campus (Villejuif, France). Whole human peripheral blood was collected into sterile vacutainer tubes.

Spectral flow cytometry. One hundred and twenty-one whole-blood samples from 88 patients (Supplementary Material Fig. 1) were mixed at a 1:1 ratio with Whole Blood Cell Stabilizer (Cytodelics), incubated at room temperature for 10 min and transferred to -80 °C freezer to await analysis. These samples were secondarily thawed in a water bath set to +37 °C. Cells were fixed at a ratio 1:1 with Fixation Buffer (Cytodelics, ratio 1:1) and incubated for 10 min at room temperature. Red blood cells were lysed by the addition of 2 mL of Lysis Buffer (Cytodelics, ratio 1:4) at room temperature for 10 min. White blood cells were washed with 2 mL of Wash Buffer (Cytodelics, ratio 1:5). Cells were resuspended in 100 µL extracellular antibody cocktail and incubated at room temperature for 15 min, then washed in Flow Cytometry Buffer (PBS containing 2% of fetal bovine serum and 2 mM EDTA). For intracellular labeling, a step of permeabilization was performed using 200 µL of eBioscience Foxp3 kit (ThermoFischer); cells were then incubated for 40 min at +4 °C, washed in Perm Buffer (ThermoFischer) and resuspended in an intracellular antibody cocktail. After incubation, cells were washed in Flow Cytometry Buffer and resuspended to proceed to the acquisition. All antibodies used are listed in Supplemental Material Table 2. Samples were acquired on CyTEK Aurora flow cytometer (Cytek Biosciences)(Cytek Biosciences) (T cell, B cell and myeloid cell/global panels) or BD LSR Fortessa X20 Flow cytometer (BD Biosciences-US)(apoptosis and exhaustion panel).

Data analysis

16S rDNA metagenomic profiling DNA from 100 µL of whole blood (from 5 mL EDTA sampling tube) was isolated and amplified in a strictly controlled environment at Vaiomer SAS (Labège, France) using a stringent contamination-aware approach, as discussed previously [40,[68][69][70]. The microbial populations based on rDNA present in whole blood were determined using next-generation sequencing of V3-V4 variable regions of the 16S rRNA bacterial gene as previously described [69]. For each sample, a sequencing library was generated by the addition of sequencing adapters. The joint pair length was set to encompass a 467 base pairs amplicon (using Escherichia coli 16S as a reference) with a 2 × 300 paired-end MiSeq kit V3 (Illumina, San Diego, CA, USA). The detection of the sequencing fragments was performed using the MiSeq Illumina® technology. Targeted metagenomic sequences from microbiota were analyzed using the bioinformatic pipeline from the FROGS guideline [71]. Briefly, the cleaning was done by removing amplicons without the two PCR primers (10% of mismatches were authorized), amplicons with at least one ambiguous nucleotide ("N"), amplicons identified as chimera (with vsearch v1.9.5), and amplicons with a strong similarity (coverage and identity ≥80%) with the phiX (library used as a control for Illumina sequencing runs). Clustering was produced in two passes of the swarm algorithm v2.1.6. The first pass was a clustering with an aggregation distance equal to 1. The second pass was a clustering with an aggregation distance equal to 3. Taxonomic assignment of amplicons into operational taxonomic units (OTUs) was produced by Blast+ v2.2.30+ with the Silva 134 Parc databank. To assess if the richness of microbiota was adequately captured by metagenomic sequencing, a rarefaction analysis was performed. To ensure a low background signal from bacterial contamination of reagents and consumables, two types of negative controls consisting of molecular grade water were added in an empty tube separately at the DNA extraction step and at the PCR steps and amplified and sequenced at the same time as the extracted DNA of the blood samples. The controls confirm that bacterial contamination was well contained in our pipeline and had a negligible impact on the taxonomic profiles of the samples of this study as published before [40,[68][69][70]. One sample has been excluded from the analyses for the aberrant profile.

Repurposing of Anticancer Drugs Expands Possibilities for Antiviral and Anti-Inflammatory Discovery in COVID-19

Mihaela Aldea 1 , Jean-Marie Michot 2 , Francois-Xavier Danlos 3,4 , Antoni Ribas 5 , and Jean-Charles Soria Significance: Targeting anti-inflammatory pathways with JAK/STAT inhibitors or anticytokine therapies aiming to curb COVID-19-related cytokine storm, using antiangiogenic drugs to reduce vascular abnormalities or immune-checkpoint inhibitors to improve antiviral defenses, could be of value in COVID-19. However, conflicting data on drug efficacy point to the need for better patient selection and biomarker studies.

iNtRODUctiON

A huge international effort has been made in the last 50 years to highlight cancer's mechanisms of proliferation and dissemination (1). Beyond chemotherapy, new drugs such as targeted therapy, immunotherapy, epigenetic modifiers, and more recently cellular therapies have been approved and have succeeded in improving lifetime expectancy and quality of life of patients living with cancer (2). Apart from their antitumor effects, anticancer drugs also increased knowledge about physiologic pathways in healthy tissues. Outside of cancer indication, some anticancer agents were then developed in extended indications. For example, JAK inhibitors, first approved to treat myeloproliferative neoplasia with activating JAK2 mutations, have now extended indications in rheumatologic diseases and are under development for several dysimmune diseases.

At the end of 2019, the world was destabilized by the emergence of a new coronavirus called SARS-CoV-2 and the coronavirus disease 2019 (COVID-19) pandemic (3). International relationships were greatly affected by the virus dissemination and government interventions to limit its spread (4,5). A huge effort by the scientific and biomedical community has sought to understand the pathophysiology and clinical manifestations of COVID-19. The full RNA sequence of the new coronavirus was identified with unprecedented speed, and insights into how it interacts with human cells quickly followed (6)(7)(8). Concomitantly, clinical studies worldwide revealed the different clinical aspects of severe COVID-19 manifestations characterized by brutal cytokine release syndrome (CRS) and visceral inflammatory involvement, particularly pneumonitis (9).

The main risk factors for developing severe COVID-19 are age, male sex, obesity, cardiovascular comorbidities, and diabetes (9,10). Laboratory science has shown that severe COVID-19 infection is driven mainly by immunopathologic inflammatory pathways mediated by IL6, IL8, tumor necrosis factor alpha (TNFα), and IFNγ (8). Large clinical trials have since evaluated antiviral drugs such as remdesivir (11), anticytokine approaches with anti-IL6 (12) and dexamethasone (13), and passive immunotherapy such as patient convalescent plasmatherapy or recombinant cocktail antibodies against anti-spike antibody (14).

COVID-19 Pathologic Mechanisms

COVID-19 is a human infectious and inflammatory disease related to SARS-CoV-2 infection. A large number of individuals infected with SARS-CoV- syndrome and acquired immunity directed against the new betacoronavirus (15). However, about 10% to 15% of patients progress to severe pneumonia and respiratory distress and eventually require admission in the intensive care unit (ICU). Nearly 25% to 30% of patients admitted to the ICU will ultimately die. After an incubation phase and 7 to 10 days of symptoms, a sudden viral pneumonia characterized by profound hypoxemia and interstitial lung disease may occur, related not only to virus infection but also to cytokine storm and immunothrombosis phenomenon (15).

Angiotensin-converting enzyme 2 (ACE2) and the transmembrane serine protease TMPRSS2, expressed by human alveolar and endothelial cells, participate in the virus cell internalization and activate the spike (S) protein of SARS-CoV-2 into the human cells (16). Obesity is associated with ACE2 and TMPRSS2 overexpression at the surface of endothelial cells through dysregulation of the leptin pathway (17). Also, TMPRSS2 is an androgen-regulated protein, which could explain male sex predisposition to develop severe forms of COVID-19 (18). Primum movens to severe COVID-19 seems to be the immunothrombosis phenomenon, characterized by small blood vessel thrombosis associated with microangiopathy and inflammatory infiltration in alveolar capillary, which leads to alveolar edema and associated systemic cytokine storm (19,20). Patients with severe COVID-19 develop an inflammatory state characterized by increased concentrations of plasmatic cytokines, chemokines, and alarmins related to activation and recruitment of inflammatory cells, such as IL1β, IL6, IL8, TNF, granulocyte-colony stimulating factor, chemokine ligand 2, and calprotectin (S100A8 and S100A9; refs. 21,22). The most severe COVID-19 infections correlate with low type 1 IFN concentration in blood and transcriptomic signature in peripheral blood mononuclear cells (23). Defects in type 1 IFN can be related to functional genetic polymorphisms or autoimmunity directed against type 1 IFN (24, 25). Besides soluble inflammatory and immune factors, patients who develop the most severe forms of COVID-19 present with pathologic hematologic and immunologic features, such as increased neutrophilia, monocytopenia (with loss of nonconventional monocytes), and lymphocytopenia (22,26). Patients with severe COVID-19 have profound lymphocytopenia with low, but activated and exhausted, CD4 + and CD8 + T cells such as CD19 + B cells and particularly early plasmablasts, which do not succeed in establishing effective antiviral immunity (27)(28)(29). Finally, patients with severe COVID-19 are also characterized in their blood and lungs by an emergency myelopoiesis with immature neutrophils and monocytes with deleterious proinflammatory abilities and immunosuppressive function, which can limit the development of effective adaptive immunity (22).

Patients living with cancer are exposed to a significantly higher risk of severe SARS-CoV-2 infection and higher risk of death (30). Particularly, patients with hematologic malignancies have a higher mortality rate related to COVID-19 than patients with solid tumors (31,32). Among patients with cancer, patients with more recent, disseminated, and symptomatic diseases undergoing chemotherapy are generally those with the most severe infections (33). The explanation for these increased risks remains unclear, such as the amplitude of viral load that could independently predict mortality in both patient populations, which certainly reflects that immunosuppression related to cancer predisposes to a more intense viral replication (34). These data provide a basis for priority for COVID-19 vaccination in patients living with cancer (35).

Administration of anticancer treatments is also an important factor that influences the risks of infectivity and severity of COVID-19 (36,37). Although chemotherapy was shown to be associated with a higher risk of COVID-19 worsening, interestingly, patients treated with immune-checkpoint immunotherapies or targeted anticancer therapies were not at higher risk (33,34,(36)(37)(38). Other studies suggested that patients treated for cancer and receiving antiandrogenic treatments or antiangiogenic drugs could have improved outcomes (39)(40)(41). As these treatments do not seem to increase COVID-19 severity and might improve outcomes for patients, the discussion of repurposing anticancer drugs for COVID-19 began (Fig. 1). Anticancer drugs currently undergoing investigation in clinical trials for the treatment of COVID-19 are depicted in Table 1.

RePURPOsiNG DRUGs FROM ONcOLOGY

Anti-Inflammatory Drugs

Dexamethasone

Corticosteroids, in particular dexamethasone, are drugs commonly used to treat hematologic malignancies such as multiple myeloma, non-Hodgkin lymphoma, or acute lymphoblastic leukemia. For the treatment of severe COVID-19 with viral pneumonia, the British Recovery study has shown that dexamethasone improves the overall survival of patients needing respiratory support (either invasive mechanical ventilation or oxygen alone; ref. 13). Dexamethasone then became the standard of care for patients hospitalized with COVID-19 with viral pneumonia requiring oxygen support.

Other anti-inflammatory drugs targeting new molecular pathways may be tested for patients with severe COVID-19. Biological studies have shown that calprotectin is one of the key molecules implicated in the inflammatory cascade leading to severe forms of COVID-19 (22), suggesting calprotectin as a potential new therapeutic target.

Anti-Cytokine IL6

In hematology oncology, CRS is known as a frequent complication associated with chimeric antigen receptor-T (CAR-T) cells or bispecific antitumor antibodies. CRS is characterized by fever, hypotension, and eventually hypoxemia or biological abnormalities including coagulopathy (42), related to a sudden release of cytokines by the immune system when the latter encounters the tumor antigen under the effect of anticancer treatment (43). CRS is dominated by high levels of IL6 in serum, which led to anti-IL6 receptor therapies being proposed to control CRS (43). The respiratory distress and high fever during COVID-19 viral pneumonia may share similarities with CRS (44).

The cytokine storm induced by the SARS-CoV-2 virus is becoming better understood. Cytokines such as IL6, IL10, and TNFα, and the overactivation of the systemic complement pathways, namely substrates C5b-9 and C4d, are correlated with the severity of COVID-19 (45). This systemic inflammation and complement activation are also associated with the 46). Dysregulated neutrophil extracellular traps in the endothelium of patients also contribute to immunothrombosis in patients with severe COVID-19, by propagating inflammation and microvascular thrombosis (46). Several anticytokine therapeutics aiming to curb COVID-19-related cytokine storm have been investigated. Retrospective studies and randomized clinical trials evaluating the most used anti-IL6 receptor blocker tocilizumab antibody suggested a therapeutic effect of the drug (47-49), while other clinical trials failed to complete their efficacy endpoints (50)(51)(52). Overall, the results of additional clinical trials with longer follow-up of enrolled patients are needed to better understand the magnitude of the therapeutic effect of anti-IL6 receptor therapy in patients with COVID-19. A more precise, and probably more selective, definition of the patient population with COVID-19 who may or may not benefit from anti-IL6 receptor therapy remains to be clarified in future studies.

Targeting the BTK Signaling Pathway

Bruton tyrosine kinase (BTK) inhibitors are approved therapies for treating lymphoid blood disorders such as chronic lymphocytic leukemia (CLL), mantle cell lymphoma (MCL), or Waldenstrom disease (53). The SARS-CoV-2 virus can generate an overactivation of immune cells, including both B and T cells, leading to a "cytokine storm," thus contributing to acute lung injury and respiratory distress (21). BTK pathway effector molecules contribute to the cytokine storm and are potential targets for the drug therapy of COVID-19 (41). An observational study evaluating the use of acalabrutinib in 19 patients with COVID-19 hospitalized and requiring oxygen supplementation suggested potential efficacy. However, the BTK inhibition hypothesis must be interpreted with caution, because of the concomitant use of dexamethasone in initial studies. Definitive results of a randomized controlled clinical trial investigating acalabrutinib for patients with COVID-19 (CALAVI phase II trial: NCT04380688 and NCT04346199) are pending, but a recent press communication reported that the study did not reach its primary endpoint as compared with best supportive care alone. randomized trials with a JAK inhibitor alone versus placebo for the treatment of COVID-19 are still pending (Table 1), while one trial was terminated early for futility (NCT04377620). In light of the hypercoagulability state of COVID-19, vigilance is recommended given the potentially increased risk of thrombosis reported with some JAK inhibitors (60).

Implications of JAK/STAT Inhibitors to

Antiangiogenics

The immunothrombosis phenomenon is associated with upregulation of macrophages, complement substrates, platelet activation, thrombosis, and proinflammatory markers (61,62). Moreover, proangiogenic factors, such as VEGF and angiopoetin 2, are crucial factors implicated in vascular permeability and pulmonary edema of patients with COVID-19 (63). In an autopsy study performed on lungs from people who died from viral infections, the amount of new vessel growth associated with COVID-19 was 2.7 times higher compared with that found in patients with H1N1 influenza virus. Pathologic findings in patients with COVID-19 indicate severe endothelial injury, disruptive cell membranes, and widespread thrombosis (20). In severe forms of COVID-19, some researchers suggest antiangiogenic drugs may suppress pulmonary edema by inhibiting proangiogenic factors and by promoting a vascular normalization. In a single-arm trial investigating bevacizumab plus standard of care in 26 patients with severe COVID-19 infection (NCT04275414), a single dose of 7.5 mg/kg of bevacizumab was associated with rapid improvement in PaO2/FiO2 ratios, improved oxygen support status in 92% of patients by day 28 (versus 62% in the external comparison cohort treated with standard of care only), and significant radiologic reduction of pneumonia lesions within seven days. Of note, no drug-related serious adverse effects were reported (64). However, in this study, patients were excluded if they had received full-dose anticoagulant within 10 days before enrollment or had thrombosis within 6 months before enrollment, criteria that apply to a significant proportion of patients with cancer. Other ongoing clinical trials are investigating bevacizumab, and results are pending (Table 1). A clinical trial (NCT04342897) evaluating the effect of targeting angiopoietin 2 in patients with COVID-19 was terminated early for futility. Further trials evaluating antiangiogenics should carefully assess the risk of thrombosis and probably offer concomitant anticoagulation treatments to control this risk of thrombosis.

Immune-Checkpoint Blockade

An effective immune response against viral infections depends on the activation of host CD8 + T cells expected to eliminate cells containing the SARS-CoV-2 virus (65,66). In patients with severe COVID-19, there is a reduced number of CD4 + and CD8 + T cells, while the surviving T cells exhibit an exhausted phenotype, with a higher level of PD-1 expression (67,68). The reduced T-cell counts (total T cells < 800/μL, CD8 + cells < 300/μL and CD4 + cells < 400/μL) in the blood of patients with COVID-19 were negatively correlated with the levels of TNFα, IL6, and IL10 (68). In an observational clinical study of 113 patients with cancer and laboratory confirmed COVID-19 while on treatment with immune-checkpoint inhibitors (ICI) without chemo-therapy, the mortality by COVID-19 in patients under ICI did not seem to be higher compared with rates reported in the general cancer population (69). Another observational study conducted in 178 patients with cancer managed for COVID-19 did not identify increased risk of clinical worsening or death in patients treated with ICI for cancer. Conversely, patients receiving cytotoxic chemotherapy had an increased risk of clinical worsening and death in the univariable analysis and a trend toward a higher risk of death in the multivariable analysis, after adjusting for ECOG performance status and cancer status (33). Similarly, chemotherapy negatively affected survival outcomes in patients with thoracic cancer and COVID-19 in the TERAVOLT cohort, as opposed to immunotherapy and targeted therapy (70,71). However, in another observational study including 423 patients with cancer with symptomatic COVID-19, treatment with ICI (N = 31) was a predictor for hospitalization and severe disease, while treatment with chemotherapy was not (72). Overall, these data are limited and their conflicting interpretation imposes caution about potential interest in repurposing ICI for COVID-19. ICI might have a dual effect: it might enhance T cell-mediated viral clearance in the early phase, but it has been suggested to also facilitate late-inflammatory states by promoting T regulatory cell activation and the exacerbation of the cytokine storm (73,74). The sum of these data suggests that future trials evaluating the potential interest of ICI for COVID-19 should select the patient population to be treated, most likely considering treatment in the early phase of the disease and without a cytokine storm.

Antiandrogenic Treatment

SARS-CoV-2 virus harnesses the TMPRSS2 receptors to enter within the host human cell, and these receptors are regulated by the androgen receptor (16). The TMPRSS2 gene is strongly upregulated in prostate cancer cells, and it has a testosterone-activated response element, suggesting potential antiandrogenic treatment in patients with COVID-19. In a cohort of men with prostate cancer, COVID-19 infection was less likely to be reported in patients treated with androgen deprivation therapy (ADT) as compared with those without (ADT 4/5,273 cases versus 114/37, odds ratio 4.05, 95% confidence interval 1.55-10.59, P = 0.00043; ref. 39). However, ADT effect seems to be modest, as the number of patients needed to treat with ADT for the prevention of one case of COVID-19 was 434. This minimal potential therapeutic effect makes ADT a less appealing option in the case of patients without prostate cancer, also considering the associated side effects of ADT (75). Moreover, preliminary data from multicenter registries do not support a decrease in COVID-related mortality with antiandrogenic drugs (76). Ongoing trials testing bicalutamide, enzalutamide, or GnRH antagonists are ongoing (Table 1). Also, camostat mesylate, a direct TMPRSS2 inhibitor, is currently being investigated in several clinical trials for COVID-19 as monotherapy or in combination with bicalutamide, with the aim of reducing the SARS-CoV-2 viral burden and forestalling complications of COVID-19 (NCT0435328, NCT04583592, NCT04608266, NCT04524663, and NCT04652765

The Targeting of Host-Interacting Proteins and Kinases Dysregulated during Infection

Recent preclinical studies on SARS-CoV-2-infected cells found important interactions between human proteins and SARS-CoV-2, and a dramatic rewiring of phosphorylation on host and viral proteins, highlighting how the virus uses the host's posttranslational regulatory systems to induce rapid signaling changes (77)(78)(79). The mapping of proteomic changes to pharmacologic modulators identified promising target-drug pairs that might trigger robust antiviral effects (77,78). For instance, eEF1A was identified in SARS-CoV virions (80,81), and plitidepsin, an eEF1A inhibitor approved in Australia for the treatment of multiple myeloma, was shown to have a potent anti-SARS-CoV-2 antiviral activity in preclinical studies (IC 90 = 0.88 nmol/L; ref. 81). Plitidepsin was 27.5-fold more potent than remdesivir in vitro, while having limited toxicity (81). These data indicated promising therapeutic repurposing for plitidepsin as antiviral therapy for COVID-19, and phase II/III trials are pending.

Another target is the virus-induced upregulation of casein kinase 2 (CK2), which might facilitate cell-to-cell spread by driving actin polymerization. CK2 inhibition with silmitasertib, currently being tested in recurrent medulloblastoma (NCT03904862), showed robust antiviral activity in SARS-CoV-2-infected cells (77). Phosphatidylinositol-3-Phosphate/ Phosphatidylinositol 5-Kinase (PIKfyve) is a protein that resides in early endosomes, being involved in endomembrane homeostasis. Its inhibition has the potential to inhibit viral entry, making it a promising target for the treatment of early COVID-19 infection. In vitro experiments showed that apilimod, a specific PIKfyve kinase inhibitor investigated in early-phase clinical trials for the treatment of non-Hodgkin lymphoma, successfully inhibited viral replication during entry (82). Apilimod is currently being evaluated for its impact on SARS-CoV-2 viral load in patients with confirmed COVID-19 infection treated in an outpatient setting (NCT04446377). SARS-CoV-2 also increases the phosphorylation of cyclin-dependent kinase 2 (CDK2) and promotes cell-cycle arrest in the S-G 2 phase, which may facilitate viral replication (77,83). Accordingly, strong antiviral activity for the CDK inhibitor dinaciclib was observed across two SARS-CoV-2-infected cell lines (77). Other examples of promising target-drug pairs include AXL and gilteritinib (approved by the FDA for relapsed or refractory acute myeloid leukemia with FLT3 mutations) or p38 and p38 inhibitors, such as ralimetinib (undergoing development in ovarian cancer; ref. 77).

cONcLUsiON

A better understanding of molecular mechanisms associated with COVID-19 as well as clinical observations from patients with COVID-19 concomitantly treated for cancer generates various hypotheses concerning potential anticancer drug repositioning. Although strong clinical evidence is still lacking, the sum of these data suggests anticancer drugs could be regarded as potent antiviral therapies, with both direct antiviral effects and indirect effects by blocking signaling pathways such as JAK/STAT or abnormal angiogenesis. Clinical trials for hypothesis testing of anticancer drugs are an encouraging strategy for discovering new possible therapies for COVID-19.

KEYWORDS

Immunotherapy;

Single-cell RNA-seq; Bulk RNA-seq; Tumour microenvironment; Cancer;

Abstract The rising interest for precise characterization of the tumour immune contexture has recently brought forward the high potential of RNA sequencing (RNA-seq) in identifying molecular mechanisms engaged in the response to immunotherapy. In this review, we provide an overview of the major principles of single-cell and conventional (bulk) RNA-seq applied to onco-immunology. We describe standard preprocessing and statistical analyses of data obtained from such techniques and highlight some computational challenges relative to the sequencing of individual cells. We notably provide examples of gene expression analyses such

Introduction

Tumour samples always contain heterogeneous cell populations that comprisedbeyond malignant cellsdstromal and immune tumour-associated cells (Fig. 1) [1]. After a long period of focusing on the tumour's genetic alterations, we have slightly entered an era in which the behaviour of non-tumoural cells becomes as meaningful as the tumour itself. Immunotherapy guidance for firstline treatment and beyond still lacks reliable biomarkers, yet accumulating evidence point towards the importance of studying a tumour tissue as a whole to better capture its potential of response to immunecheckpoint blockers: exhaustive composition, density, functional state, organization and interaction between cellular subtypes, including tumour cells [2,3].

Tumour immune infiltrates can associate CD8þ cytotoxic T cells, CD4þ helper cells (T H 1 cells), dendritic cells, CD25þ/FOXP3þ regulatory T cells, myeloidderived suppressor cells (MDSCs), natural-killer cells, B lymphocytes, tumour-associated macrophages, among others. Some of those promote immune escape of the tumour (e.g. MDSCs), some elicit antitumoural actions (e.g. dendritic cells), others can either promote or prevent immune escape depending on their activation status (e.g. CD8þ T cells) [4,5]. For example, the presence and proportion of M1 macrophages, B cells and T H 1 lymphocytes within the immune infiltrate almost always correlates with good prognosis, whereas the presence of regulatory T cells or M2 macrophages usually associates with bad prognosis [3]. Even though their cellular behavior may be influenced by the tumour, it is expected that those non-tumoural immune cells share the same DNA sequence, which is specific to the host (with the exceptions of rearranged immunoglobulin and TCR loci). However, they significantly vary phenotypically and harbor separate mRNA expression profiles, which makes them identifiable by gene expression analysis.

Among various techniques deployed to assess gene expression [6], RNA sequencing (RNA-seq) can provide qualitative (RNA sequence) and quantitative (RNA abundance) analyses of either targeted mRNA transcripts or the complete transcriptome of a particular tissue [7e9]. Two methods of RNA-seq are henceforth commonly considered for onco-immunology studies: standard bulk RNA-seq and single-cell RNA-seq (scRNA-seq). Standard bulk RNA-seq started replacing microarray techniques in the late 2000s and is now widely used in translational research. For each transcript, it provides an average expression level in the sample, which may comprise different cell types [10]. Conversely, scRNA-seq is a relatively new technology that measures the gene expression levels for each transcript within each individual cell of the sample and allows a representation of the distribution of this expression in each subpopulation of cells [11,12]. Therefore, scRNA-seq rather aims to study a particular cell or cell type behavior in the specific context of its microenvironment, while bulk RNA-seq was not primarily designed for the precise characterization of a tumour composition [13].

At first glance, one could believe that scRNA-seq represents the most suited method to draw a complete picture of a tumour and thus, may be a method of choice to study the immune tumour-related features that could be predictive of response to immunotherapy. Yet, the initial substantial excitement regarding scRNA-seq theoretical promises has historically faced technical limitations [13]. In addition, it is now technically possible to extrapolate the cellular composition of a tumour sample from bulk analysis, which at least partially alleviates the conceptual frontier between bulk and scRNA-seq full potentials.

Here, we give an overview of some standard methods used for processing single-cell or bulk RNA-seq data. We describe how scRNA-seq and bulk methods can share several computational analysis concepts (reads quality control, mapping or quasi-mapping, differential expression analysis, dimensionality reduction, clustering, etc.)dsometimes with the same objectives, sometimes with very distinct outputsdbearing in mind that for every task, scRNA-seq analysis will often imply additional dimensionalities to estimate one complete gene expression profile per individual cell.

We notably emphasise the statistical approaches that can be used to study the tumour microenvironment compositiondincluding from bulk analysisdin a qualitative and quantitative manner. We also describe machine learning tools that aim at predicting survival or tumour response following treatment with immunecheckpoint blockers. Finally, we depict the recent evolution in the clinical development field of oncoimmunology that increasingly integrates RNA-seq in clinical trial designs, reflecting the growing interest in this approach for multiple purposes.

2. Single-cell and bulk RNA-seq processing: basic clues to appreciate the differences 2.1. Collecting, storing, and processing RNA Unlike DNA, which is a stable structure, RNA is highly sensitive to oxidation and hydrolytic cleavage of its phosphodiester bonds by nucleases, and therefore requires conscientious storage to prevent rapid Fig. 1. Graphical abstract. Clinical positioning of RNA sequencing to personalise onco-immunotherapy treatments from the analysis of a patient's tumour biopsy. The tumour and its microenvironment can be analysed using gene expression signatures, deconvolution and modeling methods to predict tumour sensitivity to therapies.

degradation [14]. Bulk RNA-seq can be performed on human tumour samples obtained from surgical resection or biopsies, preserved in formalin-fixed and paraffin embedded (FFPE) or freshly frozen [15e17]. Thus, all the samples can be collected in due course and processed at the same time.

Additional facts to consider for scRNA-seq On the other hand, scRNA-seq analysis requires viable cells suspension to allow robust cell isolation, which contraindicates any fixation of freezing steps. ScRNA-seq therefore requires processing the samples quite promptlydwithin a few daysdafter the time of collection. Another important point to consider is that during that process, the cellular viability of each subpopulation of cells can be differently affected, leading to some cell types being over-or under-represented in the final dataset (source of interpretation bias). Therefore, before planning a scRNA-seq analysis, several questions need to be addressed, including: (i) which and how many cell types are to be analysed (for example, only tumour cells, all stromal immune cells or only a subset of immune cells such as memory CD4þ T cells or sparse population of a subset of dendritic cells), (ii) which method is used to prepare cell suspension (tissue dissociation, selected cells enrichment and cells conservation), (iii) which single-cell technology (dropletor plate-based; short or long reads; Unique Molecular Identifiers or not) and (iv) which library and other RNA-seq parameters to use [18e21].

Sequencing and basic bias correction

The following steps of single-cell and bulk RNA-seq usually comprise reverse transcription, reads sequencing, mapping or quasi-mapping, data format transformation, count table generation and inference, such as the exploration of gene expression and/or transcript fusions (Fig. 2).

Additional facts to consider for scRNA-seq As compared to bulk, scRNA-seq comprises additional steps, including: (i) keeping information of unique molecular identifiers (UMI) to further identify mRNA molecules, and barcodes to identify which mRNA belongs to which single cell, and (ii) reduce experimental biases often triggered by the sequencing of single cells (Table 1). Those biasesdmainly known as batch effect and dropout effectdexist for both single-cell and bulk RNA-seq, but are exacerbated by the processing of single cells.

Batch effect encompasses various causes of variation in gene expression estimation, related to technical bias or other independent factors such as variations in sampling, library preparation and sequencing platform, or variations inherent to the origin of the sample (different tumour types). Hence, a frequent challenge is to analyse combined data from various tumour types or from various centers. Batch effect can be corrected a posteriori with computational methods, such as Mutual Nearest Neighbors [22] and Canonical Correlation Analysis [23]. Several tools using these and other methods exist, such as ComBat-seq [24], RUVseq [25] and svaseq [26] for bulk RNA-seq, and kBET [27], Harmony [28], Conos [29] and Seurat [30] for scRNAseq.

ScRNA-seq, much more than bulk RNA-seq, generates sparse matrices filled with many zeros, corresponding either to a biological feature (truly silent gene) or to a technical artifact, called "dropout effect" and referring to a statistical observation called zero-inflated distribution. The "dropout effect" refers to the limited sampling of RNA content in single-cell wet protocols (falsely silent gene). Indeed, the expression of only 10% of total genes tends to be effectively measured in scRNA-seq experiments [31,32]. Zero-inflated scRNA-seq data can be processed by imputation methods, in order to reduce the impact of the dropout effects, before using pipelines designed for bulk RNA-seq [33,34]. Although deep learning has contributed to significant improvement on this task, the imputation step is rarely performed due to its highly data-dependent efficiency [34,35].

Letting the data speak using statistical analyses of gene expression

Once RNA-seq data are ripe for further analysis, statistical methods are applied to answer different biological questions. Selecting the most appropriate requires bearing in mind the objective of the analysis, the magnitude of the above-cited batch effect and potential statistical biases.

Expression values from bulk RNA-seq can generally be approximated by Poisson, negative-binomial or lognormal distributions and from scRNA-seq by zeroinflated distribution. This limits the use of statistics based on a Gaussian distribution. In practice, simple (logþ1)-transformation and expression matrix scaling are often performed, negative-binomial model (adjusted by a regression of bias) [36] can be used, and an increasing number of models now intend to handle zeroinflation in scRNA-seq [37,38]. In the next sections, we review the most common methods used, in a nonexhaustive manner. To illustrate the most common outputs of RNA-seq analysis (Fig. 3), such as volcano plots, we used the published data set from Hugo et al. [39], which comprises bulk RNA-seq data of patients with metastatic melanoma obtained prior to treatment with immune-checkpoint blockers, together with the clinical information of whether or not they responded to treatment. This data set was primarily used to build an immune signature associated with the response to immune-checkpoint blockers.

Additional facts to consider for scRNA-seq

Expression values from bulk RNA-seq can generally be approximated by Poisson, negative-binomial or lognormal distributions, whereas expression values from scRNA-seq follows a zero-inflated distribution. This limits the use of statistics based on a Gaussian distribution. In practice, simple (logþ1)-transformation and expression matrix scaling are often performed, although negative-binomial model (adjusted by a regression of bias) [36] can be used, and an increasing number of models now intend to handle zero-inflation in scRNA-seq [37,38].

3.2.

Statistics for high-dimensional data: why do we "adjust" the p-value? RNA-seq data often display a greater number of variables than observations (i.e. more genes than samples analysed). This high-dimensionality implies a high risk of false discovery. For example, if a statistical test is Fig. 2. Processing pipelines of bulk and single-cell RNA-seq experiments. RNA transcripts are reverse-transcribed into complementary DNA (cDNA), the cDNA second strand is synthesised and then amplified, before being sequenced by a next-generation sequencing (NGS) machine. In the case of scRNA-seq, the reverse transcription step includes the incorporation of UMI to identify mRNA molecules, and barcodes to identify single cells. RNA-seq generates 100 nucleotide long reads. A FASTQ file stores all the reads of one sample, in an arbitrary order, together with one quality score per sequenced nucleotide. Mapping (or alignment) refers to finding the position of a read on a reference genome or transcriptome. Quasi-mapping (or pseudo-mapping) aligns reads to a pre-indexed transcriptome rather than to a reference genome and is thus faster. A SAM file stores the information relevant to each read. This information, compressed in binary, is known as a BAM file. Expression estimation relates to counting every read using various normalization methods, and producing gene-or transcript-based quantification fitted for further analysis. Other analyses concerns detection of transcript fusions, splicing variants, or estimate genomic variations, which we do not detail in this review. performed iteratively for each individual gene, and considered significant if p-value <0.05, the user will potentially obtain five false-positive results every 100 tested genes. Thus, for 20,000 tested genes, the falsepositive signals may reach up to 1000 genes, so the pvalues must be adjusted according to the number of tests performed. Multiple methods have been proposed to control this "multiple testing error" as Bonferroni or Benjamini and Hochberg's false discovery rate [40e42].

Differential expression analysis

Differential expression (DE) analysis aims at identifying significant gene expression variations between two or more experimental groups. For example, DE analysis can be used to uncover genes that are the most differentially expressed in RNA-seq of patients who responded to immune-checkpoint blockers compared to RNAseq of patients who did not [25]. DESeq2 [43] and EdgeR [44] are two popular R packages for DE analysis from gene expression data.

Outputs of DE are fold-change of expressions between the different groups, associated with a corrected p-value. The results can be represented using a volcano plot (Fig. 3A) or a heatmap (Fig. 3B), which highlight the genes that are significantly overexpressed or underexpressed between two or several groups. Volcano plots display the magnitude of both the difference (xaxis) and its statistical meaning (y-axis), while heatmaps use colour gradients.

Special aspects of differential expression for scRNAseq analysis

In scRNA-seq, DE analysis can be used to compare subpopulations of cells or several treatment conditions within a peculiar cell type [45]. Some methods have been developed specifically for scRNA-seq, although their performances are often similar to naive methods or methods originally developed for bulk RNA-seq [46].

Dimensionality reduction

Dimension reduction consists in projecting data from a large space (with many dimensionsdfor example, each gene being a dimension) into a smaller space (with fewer dimensionsdfor example, by focusing on genes of interest). Basically, it consists in drastically "summarising" a data set to make it more understandable, ideally while retaining most of its intrinsic properties of interest.

Dimension reduction can be carried out using two ways through either feature selection or feature engineering. Eventually, one can apply both approaches sequentially or even iteratively. Feature selection consists in selecting automatically the most important genes for the analysis (for example, the most differentially expressed genes between two conditions, or variable selection with lasso regression, further detailed in Box 1). Feature engineering, or projection, aims at separating samples according to relevant properties in the data. It can consist in either linear methodsdmeaning that it preserves all differences between samplesdsuch as Principal Component Analysis (PCA) [47] or Independent Component Analysis (ICA), or non-linear methods such as t-distributed Stochastic Neighbor Embedding (t-SNE) or Uniform Manifold Approximation and Projection (UMAP) (Box 1).

In our example, we used PCA (the most famous linear feature engineering technique) on DE genes (adding feature selection), which reduced the gene list and clearly outlined the two groups of patients, Table 1 Comparison of bulk and single-cell RNA-seq for clinical translation, specifically in onco-immunology. The advantages and limitations of combining both techniques were also explored. responding or not to immune-checkpoint blockers (Fig. 3C). An example of application of non-linear dimensionality reduction is also shown in Fig. 3D, using the UMAP method.

RNA-sequencing

Special aspect of dimension reduction for scRNA-seq analysis: trajectory inference

In addition, a rapidly advancing field called "cellular trajectory inference", which uses single cell data, can be considered as a special type of non-linear dimensionality reduction, with the goal to study cellular dynamic processes such as cell cycle, cell differentiation or cell activation. This methoddwhich cannot be applied to bulk datadis based on the assumption that cells that differentiate display a continuous spectrum of states because individual cells will differentiate in an unsynchronised manner (each cell is a snapshot of differentiation time). Tools are therefore used to order the cells along a trajectory based on similarities in their expression patterns, where the "pseudotime" is the unit allowing representing each cell's transcriptional progression toward the terminal state of its trajectory. There are over 75 existing tools for trajectory inference; most of which can be found in the dynverse wrapper [48]. More details regarding trajectory inference are depicted in Box 2.

Clustering

Clustering defines a way to simplify and interpret gene expression data by classifying (or, partitioning) the samples (for bulk RNA-seq) or cells (for scRNA-seq), into groups with gene expression profiles more similar within the group rather than between the groups. Clustering belongs to the family of unsupervised machine learning methods: groups are inferred by the data themselves, their distribution and their multidimensional geometry (and not by predefined classes), among other possible characteristics. Interpretation of the resulting clusters often requires other variables to correlate with, such as phenotypic (type of cancer, toxic exposures, clinical outcome or other continuous data), genetic (for e.g. cluster of gene co-expression with HER2 status in breast cancers), cell types/states (for single-cell data) or technical bias to correct (batch effect). More details are provided in Box 3.

In our RNA-seq example, we have clustered samples based on differentially expressed genes and visualised their relation to response to immunotherapy (Fig. 3B, clustering on the left). A second clustering was performed on the same differentially expressed genes in order to identify co/anti-regulated modules (Fig. 3B, clustering on the top).

Box 1. To go deeper into dimensionality reduction

Feature selection can consist in the selection of genes from DE analysis by setting thresholds of fold-change and false discovery rate. A reduced gene list comprising only the most differently expressed genes can then be visualised, for example using a heatmap (Fig. 3B). Many other approaches of feature selection exist. For example, in the field of supervised machine learning, regularised and/or sparse linear regression (e.g. based on lasso) forces certain coefficients to zero, resulting in genes not used by the model. Non-zero genes are therefore selected as important for the predictions. Unsupervised methods of feature selection can be used by selecting the most fluctuating genes (from estimation of the variance), genes whose dispersion strongly deviates from what would be expected based on their mean expression level (so-called overdispersed genes) [START_REF] Kharchenko | Bayesian approach to single-cell differential expression analysis[END_REF], or genes strongly contributing to the distinction between data clusters or linear components (see below). The feature engineering approach to dimensionality reduction consists in transforming the initial data variables, such as gene expression, into a relatively small number of linear or non-linear combinations. In the case of linearly constructed features, they are frequently referred to as components or meta-genes. The oldest and most widely used linear method for dimensionality reduction is Principal Component Analysis (PCA) [47], which constructs orthogonal sets of vectors in the data space, corresponding to the directions of maximal variance. The classical PCA is a deterministic method while some of its later modifications are probabilistic, e.g. in the case of large single-cell data sets. Independent Component Analysis (ICA) is another popular linear method which is usually applied on top of PCA and creates the most statistically independent features as possible [START_REF] Sompairac | Independent component analysis for unraveling the complexity of cancer omics datasets[END_REF]. Both PCA and ICA components can be interpreted either by considering the weights associated to genes (by applying functional enrichment tests) or to samples (confronting with their annotations). In these analyses, ICA components are systematically characterised by improved biological or clinical interpretation [START_REF] Sompairac | Independent component analysis for unraveling the complexity of cancer omics datasets[END_REF]. Thus, individual independent components can emerge from the presence of distinct cell types within a tumour sample: as such, ICA can be used as an unsupervised cell type deconvolution technique [START_REF] Nazarov | Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients[END_REF]. T-distributed Stochastic Neighbor Embedding (t-SNE) [START_REF] Maaten L Van Der | Visualizing Data using t-SNE[END_REF], and Uniform Manifold Approximation and Projection (UMAP) [START_REF] Becht | Dimensionality reduction for visualizing single-cell data using UMAP[END_REF] are non-linear dimensionality reduction methods, based on constructing a pairwise distance similarity graph between data points and representing its structure as faithfully as possible in the reduced 2D or 3D space. t-SNE and UMAP are non-deterministic methods and the resulting axes cannot be directly interpreted. As a matter of fact, t-SNE and UMAP projections can be rotated without their meaning being changed. They can perform significantly better than PCA in grouping on the map points that are close in the large dimensional space, while PCA is more faithful in representing large distances between data points. Compared to t-SNE, the UMAP algorithm better preserves the global structure of the data. Both methods successfully deal with large data sets and are therefore relevant for visualizing scRNA-seq datasets and the results of their analyses. Moderate dimensionality reduction by applying PCA (with 10e50 dimensions being retained) is usually advised before application of t-SNE, which reduces the computational cost and de-noises the single cell profiles to some extent. By contrast, this step is not always mandatory in the case of UMAP, which can be directly applied to large dimensional data.

Special aspects of clustering for scRNA-seq analysis

Clustering is a crucial step in single cell data analyses with the objective to characterise the cellular heterogeneity by defining cell types and states. Clustering (or like other dimension reduction methods such as PCA, t-SNE or UMAP) can also be used for data analysis and quality control. For example, clustering of a scRNA-seq gene expression matrix may output clusters corresponding to the origin of each cell types identified. In this case, it might be (at least in part) sufficient to characterise the cellular heterogeneity within a sample, for example, to appreciate the immune infiltrate composition. Other algorithms such as deconvolution or mixture model-based approach adapted to single-cell data can help adjusting on confounding factors [33,49].

Describing the cancer immune contexture from bulk RNA-sequencing

As we just mentioned, when applied to scRNA-seq data, clustering methods might be sufficient to identify the various immune subpopulations that compose a tumour sample. Thus, the scRNA-seq technique is probably the most suited for this purpose. However, it is also possible to describe the immune contexture from bulk RNA-seq, and multiple approaches have become available to extrapolate the qualitative and relatively quantitative compositions of tumour-infiltrating immune cells from bulk data [50,51].

Those methods often rely on prior knowledge of biological processes or cell type specificities. Such information is usually obtained from independent data sets that were established upon purified cell cultures or single-cell experiments.

Enrichment analysis and gene lists applied to immune infiltrate description

Enrichment analysis provides higher level information compared to gene expression-based analysis. Basically, it enables to identify known biological processes that may be upregulated or downregulated within a sample or a population of cells (cluster). Enrichment analysis Box 2. To go deeper into trajectory inference Trajectory inference is applicable in the case when a population of cells represents a snapshot of an actively developing dynamic process, such as differentiation. In this case, the geometrical structure of the data is recapitulated as a bouquet of diverging and branching trajectories, where branching points represent important cell fate decisions. Each individual cell can be attributed to one or several trajectories and characterised by the value of pseudotime, which reflects the accumulated number of molecular changes as the total path length along the trajectory measured from some root state. The relation between pseudotime and physical time can be highly nontrivial. Dozens of trajectory inference methods such as Monocle [START_REF] Qiu | Reversed graph embedding resolves complex single-cell trajectories[END_REF], STREAM [START_REF] Chen | Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM[END_REF], PAGA [START_REF] Wolf | PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells[END_REF] have been suggested and systematically benchmarked [48]. Interestingly, trajectory-based analysis can be applied to bulk tumoural transcriptomic profiles, too [START_REF] Sun | Cancer progression modeling using static sample data[END_REF]. The usefulness of trajectory inference approaches in oncoimmunology is yet to be fully demonstrated. The trajectory inference is often accompanied by the dynamic network inference whose goal is to discover the gene regulatory networks that drive transitions from one cell type or state to another. Dynamic network inference analysis uses cell ordering from trajectory analysis and co-occurring or correlated genes. Several tools exist (SCENIC [START_REF] Aibar | SCENIC: single-cell regulatory network inference and clustering[END_REF], GRISLI [START_REF] Aubin-Frankowski | Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference[END_REF], LEAP [START_REF] Specht | LEAP: Constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering[END_REF] and PIDC [START_REF] Chan | Gene regulatory network inference from single-cell data using multivariate information measures[END_REF], for example), but the quality of results strongly depends on the data set.

Box 3. To go deeper into clustering

Clustering based on the transcriptome is attractive given the wide and unbiased nature of the output. However, clustering algorithms contain a crucial parameter that directly or indirectly defines the number of generated clustersdand thus, the resolution or granularity of the analysis. This parameter has a huge impact on downstream conclusions, although it is usually difficult to define before the interpretation step. Thus, underestimating the number of clusters leads to merging important cellular phenotypes together, while overestimating it might make it impossible to identify statistically significant features distinguishing cell states. Therefore, a pragmatic strategy frequently used is to generate several data partitions into clusters at several scales, and develop a multiscale data analysis strategy. The most commonly used algorithms for clustering bulk transcriptomic profiles are hierarchical clustering, kmeans and density-based clustering such as DBSCAN [START_REF] Xu Rui | Clustering[END_REF], and graph-based clustering algorithms also exist such as general-purpose Louvain [START_REF] Blondel | Fast unfolding of communities in large networks[END_REF] and Leiden [START_REF] Traag | From Louvain to Leiden: guaranteeing well-connected communities[END_REF]. PhenoGraph [START_REF] Levine | Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis[END_REF] approach gained popularity in the single-cell data analysis field. These algorithms are based on finding tight communities in point neighborhood graphs which makes them highly scalable and sensitive to relatively fine-grained dissimilarities between cellular transcriptomes [START_REF] Stuart | Comprehensive integration of single-cell data[END_REF]. Clustering methods have notably sparked several atlas projects such as the Human Cell Atlas [START_REF] Regev | The human cell atlas white paper[END_REF]. Using clustering, it is also possible to identify the genes that drive the most the differences between clustersdnamed "marker genes"dthat further reduce the information and help interpretation [50]. In the case of bulk RNA-seq, clustering can output clusters corresponding to tumour types, or to different sequencing techniques. In this case, such analysis can be used as a quality control. If technical (or undesired biological) factors correlate with clusters, additional correction of the batch effect can be considered.

can therefore also be used to identify and quantify cell type-specific signals, for example, to depict whether an identified cluster within a bulk sample is more likely to be CD8þ T cells, macrophages or tumour cells.

Enrichment analysis requires a-priori defined sets of genes that are specific to cell types or biological processes. These gene sets are often valid across many biological conditions [52]. For example, ImmuneSigDB is a collection of ~5000 gene lists derived from ~400 immunological studies, which can be completed with other sources of gene lists [53e56]. Depending on the question in hand, the user may also choose other gene set collections such as MsigDB (Molecular Signatures Database) [57] and KEGG (Kyoto Encyclopedia of Genes and Genomes containing pathways) [58].

Scoring methods are often used to design higher-level variables from the gene expression related to gene sets. Among many scoring methods, Gene Set Enrichment Analysis (GSEA) is the most widely used [57]. GSEAbased approaches rank genes according to their expression variation and then evaluate the enrichment of a particular cell type in a sample, using a semiquantitative score. For example, GSEA on bulk RNA-seq data was used to characterise the immune phenotypes of tumour-infiltrating lymphocytes enriched in 598 colorectal cancers [59]. It was also used to build the Cancer Immunome Atlas (https://tcia.at/home) from more than 9000 samples covering 20 different solid cancers [60]. Enrichment analyses are still under development and recent R packages, such as GAGE [61] and topGO [62], that provide access to several methods in a user-friendly manner.

Deconvolution methods

Deconvolution refers to breaking data up into its various composing elements. Basically, in RNA-seq analysis, the goal of deconvolution is to estimate the relative fractions of individual cell types from a bulk analysis, as a surrogate of what scRNA-seq analysis can produce [50,63]. More than 50 deconvolution algorithms for bulk RNA-seq data are published, with various methods explored, either supervised (the algorithm learns to predict labels from biological measurements) or unsupervised (from the unlabeled data, the algorithm extracts novel features and patterns). Some deconvolution methods assume that each gene expression in a heterogeneous sample is a linear combination of the expression levels of this gene across all the cell types within the sample, weighted by the relative cell fractions [63]. More details are provided in Box 4.

In an alternative approach, scRNA-seq can help deconvolution models applied to bulk RNA-seq: a model can be trained to predict proportions of the different cell types quantified from scRNA-seq, from bulk RNA-seq [64]. This method is notably used by CIBERSORTx [65], an improved version of CIBERSORT, which allows a research of cell signatures, and notably, signatures associated with response to immunotherapy. The joint use of single-cell and bulk RNA-seq for the analysis of a particular tissue or disease can therefore maximise the accuracy and the thoroughness of the final analysis (Table 1).

Clinical considerations, applications and current limitations of RNA-seq

In the era of cancer immunotherapy, RNA-seq technologies can help addressing major biological questions, such as how does the immune system evolve along with cancer progression, how drugs impact antitumour immunity and how clinicians can anticipate patients' outcomes and treatment effect, and so forth (Fig. 1). Using a rigorously selected pipeline (from wet to dry lab) among the aforementioned overall methods, one could theoretically answer a wide variety of biological questions from analysis of either bulk or single cell data, albeit with fluctuating performances. With this in mind, we reviewed in the next sections the actual landscape of bulk and scRNA-seq usages in translational and clinical practice.

Predictive biomarkers for immune-checkpoint blockers

Programmed death-ligand 1 (PD-L1) tumoural expression was the first biomarker approved as a companion test for the prescription of the immune-checkpoint inhibitors targeted toward PD-1, such as pembrolizumab. It remains, however, an imperfect biomarker for several Box 4. To go deeper into deconvolution methods Recent popular supervised deconvolution algorithms include CIBERSORT [START_REF] Newman | Robust enumeration of cell subsets from tissue expression profiles[END_REF], MCP-counter [56], TIMER2.0 [START_REF] Li | TIMER2.0 for analysis of tumor-infiltrating immune cells[END_REF], xCell [START_REF] Aran | xCell: digitally portraying the tissue cellular heterogeneity landscape[END_REF], ESTIMATE [START_REF] Yoshihara | Inferring tumour purity and stromal and immune cell admixture from expression data[END_REF], csSAM [START_REF] Shen-Orr | Cell type-specific gene expression differences in complex tissues[END_REF], BSEQ-sc [START_REF] Baron | A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure[END_REF], EPIC [START_REF] Racle | Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data[END_REF] and ABIS [START_REF] Monaco | RNA-seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types[END_REF], among other tools, which focuses on immune-infiltration quantification. A global approach of immune deconvolution could integrate several of these tools to estimate immune cell fractions from bulk RNA-seq data, as proposed by immunedeconv [START_REF] Sturm | Comprehensive evaluation of transcriptomebased cell-type quantification methods for immuno-oncology[END_REF]. Although most of these tools only work for human data, some of them, such as mMCP counter, also offer a version for murine data [START_REF] Petitprez | The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression[END_REF] (adapted from MCP-counter) and ImmuCC [START_REF] Chen | Inference of immune cell composition on the expression profiles of mouse tissue[END_REF] (derived from CIBERSORT). Unsupervised deconvolution algorithms, often based on matrix factorisation methods [116,144e146], work without predefined reference cell type signatures. Recent unsupervised methods have used neural networks in the form of auto-encoders, where the thinnest hidden layer of the neural network is extracted to represent a lower dimension of the data [START_REF] Xie | A deep autoencoder model for gene expression prediction[END_REF][START_REF] Dincer | Adversarial deconfounding autoencoder for learning robust gene expression embeddings[END_REF].

reasons, including high rates of false negatives and false positives [66,67], technical issues despite the standardization of immunohistochemistry assays [68,69] and the absence of consensus regarding the relevant staining threshold that should define a PD-L1epositive or PD-L1enegative tumour [70]. Additionally, tumour mutational load (i.e. the number of mutations per megabase) has been shown to be related to immune-checkpoint blockers efficacy, notably in microsatellite instabilityhigh (MSI-H) tumours [71e78]. In addition to PD-L1 expression, MSI status and tumour mutational burden, an increasing number of immune gene expression signatures are emerging across cancer types, together with methods that aim to estimate neoantigen presentation level, often with the intent to better characterise tumour immune response [39,53,79e81]. Notably, each of these approaches could be addressed using RNA-seq methods. For example, PD-L1 mRNA expression has been shown to correlate both with immunohistochemistry assessment of PD-L1 expression and clinical prediction of response to immune-checkpoint blockade [82,83].

The broad effect of most immunotherapies, including immune-checkpoint blockers, is tempered by the modest proportion of patients who derive a prolonged benefit from it. Up to 30% of patients with commonly sensitive solid tumours (such as melanoma or lung cancer) may in fact be primary refractory to immune-checkpoint blockers [84]. Secondary resistanceda mechanism whereby patient initially responding to a treatment cease to do sodadditionally affects up to 20% of patients with melanomas treated in first-line and 30% of patients with lung adenocarcinomas [85]. Finally, about 20% of patients harbour prolonged responses [86]. The type and quantity of immune cells found within the tumour microenvironment influence tumour development and can impact the prognosis of patients [2,87]. The immune infiltrate composition was also shown to be predictive of clinical response to immune-checkpoint blockers [2,88e90]. However, no perfect predictive biomarker has been identified so far and emerging immune signatures often lack validation on independent cohorts, such as in our example. Marker combinations may improve clinical utility. For example, a highly immunogenic tumourdwith high PD-L1 expression, CD8þ T cell and dendritic cell infiltrationdis more likely to respond to immune-checkpoint blockers than a non-immunogenic tumour [91].

Machine learning for prediction modeling

Machine learning is a branch of artificial intelligence that exploits a large number of statistical techniques to allow mathematical functions to "learn" from experience acquired from similar examples (training). Deep learning is a branch of machine learning stacking several statistical models together to increase nonlinearity between input data and predictions. Machine learning methods are increasingly used in cancer diagnosis, prognosis and treatment guidance; especially with RNA-seq data as inputs to feed linear models (such as shallow Cox regression) or nonlinear models (such as neural networks). Recent adaptations of neural networks have also integrated methods to learn on survival data (comprising censored values), for example by using a Cox-loss function [92,93].

Prognostic models

Some machine learning approaches have led to clinical applications such as estimating patients' outcome from gene expression values in breast cancers [92,94,95]. Nevertheless, overfitting and poor generalization inherent of high-dimensional data with a small number of examples have limited the success of such methods so far [96,97]. Several teams have developed pan-cancer prognosis predictors by feeding deep learning models with RNA-seq data from the TCGA project [92,93,98,99]. In these studies, concordance indexes (C-indexes)dwhich provide a global assessment of the model's ability to predict survivaldranged from 0.59 to 0.75, which correspond to modest performance values (0.50 corresponding to random prediction, and 1 to perfect prediction). Limiting overfitting during models training is a very active field of research in machine learning. Several methods exist, that are beyond the spectrum of this review, except for automatic input variable selection such as lasso penalization, which are close to dimension reduction technics detailed above and in Box 1.

Prediction of treatment effect

Evaluation of the effect of treatments has been mostly explored in cell lines [START_REF] Costello | A community effort to assess and improve drug sensitivity prediction algorithms[END_REF]. An early study showed that gene expression data were the most efficient data type to predict drug sensitivity, across multi-omics data [START_REF] Costello | A community effort to assess and improve drug sensitivity prediction algorithms[END_REF]. Recently, a deep learning-based model based on prior knowledge showed impressive results for the prediction of tumour sensitivity to anticancer compounds [START_REF] Manica | Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders[END_REF]. Additionally, Hwang et al. recently reported that they were able to accurately predict the response to anti-PD1 treatment in patients with non-small cell lung cancer (with sensitivity, specificity and accuracy of 0.89, 1.0 and 0.95, respectively), by applying a random forest classifier using immune gene signature scores obtained from single-sample GSEA (ssGSEA) [81]. Altogether, an increasing number of gene expression signatures related to response to immunotherapy are emerging from deep and machine learning analyses of RNA-seq data [START_REF] Mo ¨sch | Machine learning for cancer immunotherapies based on epitope recognition by T cell receptors[END_REF][START_REF] Jamieson | Gene-expression profiling to predict responsiveness to immunotherapy[END_REF].

RNA-seq in clinical trials in onco-immunology

Recently, an increasing number of clinical trials have been integrating RNA-seq in their design with various objectives; either biological description of the effect of treatments or with the intent to treat patients (Fig. 4). This illustrates the growing interest of clinicians in whole transcriptome profiling. Examples of interventional studies using RNA-seq to guide treatment decisions concerned children with high-grade gliomas (NCT03739372), or adult patients with biliary tract cancers (NCT04318834) or soft-tissue sarcomas (NCT03784014, NCT03375437). Recent evaluations of the use of RNA-seq to orient patients with refractory cancers to targeted treatments demonstrated the feasibility of such an approach in routine; however, it failed to improve patients' outcomes so far [START_REF] Tuxen | Copenhagen prospective personalized oncology (CoPPO)dclinical utility of using molecular profiling to select patients to phase I trials[END_REF][START_REF] Rodon | Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial[END_REF]. Other studies used RNA-seq to evaluate the immune transcriptome profiles of tumours receiving immunecheckpoint blockers, some using immune gene expression signatures (for example in trials NCT03978624, NCT04326257 and NCT03673787).

Studies using scRNA-seq often investigate the changes in tumour immune cell population rates in response to treatments. For example, this has been done in patients receiving endocrine therapy and a CDK4/6 inhibitor for advanced or metastatic breast cancer (NCT04352777), and in patients receiving pembrolizumab with or without chemotherapy for nonsmall cell lung cancer (NCT04061590).

Yet, the feasibility of the translation of RNA-seq methods into the clinical routine raises many questions, such as the lack of gold-standard technical and computational methods. For example, if we consider five types of sequencing libraries, times three different mapping techniques, four imputations, seven normalization processes and four differential expression testings, this leads to 1680 ways to differentially interpret one data set. This is largely underestimated if we take into account every parameter that could be manually set at each step. Therefore, standardised pipelines need to be developed to allow reproducibility between centers. Awareness is increasing regarding this aspect, and efforts are being made in this direction [START_REF] Tanjo | Practical guide for managing large-scale human genome data in research[END_REF].

Cost

Another important component for evaluating the implementation of RNA-seq into the routine clinical care is cost. The budget for performing RNA-seq depends on many factors, including sequencing depth (average number or reads per transcript), type of library used (rRNA-depleted, poly(A)-enriched RNA, whole RNA sequencing, small RNA sequencing, etc.), type of sequencing (single-end or paired-end), sample size and quality (number of cells to sequence for single-cell), and, if appropriate, cell isolation method (droplet-based, micro-fluidics and so forth). Bulk RNA-seq usually requires at least 100e2000 ng of total good-quality RNA. This corresponds to 2e10 mg of fresh-frozen tissue or 2e4 FFPE slides; although a few companies propose ultra-low input sequencing from 10 pg of high-quality total RNA, at higher costs. Regarding all these parameters, in 2020, a "typical" experiment of bulk RNA-seq may cost between 150V and 1500V per sample; while scRNA-seq still scales between 1000V and 9000V per sample (Table 1). These costs do not include bioinformatics services.

Low-cost alternatives are exponentially emerging and intend to reduce the average expense for a whole transcriptome profiling to less than 30V per sample, mostly by using specific libraries and extensively optimised sequencing techniques [35,[START_REF] Alpern | BRB-seq: ultra-affordable highthroughput transcriptomics enabled by bulk RNA barcoding and sequencing[END_REF][START_REF] Mai | Low-cost, low-bias and lowinput RNA-seq with high experimental verifiability based on semiconductor sequencing[END_REF]. Additionally, targeted RNA-seq represents an attractive cheaper alternative in case specific transcripts of interest are known beforehand, for example, once a particular immune signature has been built from whole transcriptome analyses and is related to a specific treatment effect. For example, restricting the systematic sequencing to a panel of 500 relevant transcripts could reduce the cost to 10e15V per sample.

Conclusion

After almost a decade of intensive clinical and translational research in the field on onco-immunology, major questions remain, and notably: can we, should wedand how todanticipate resistance phenomenon upon immune blockade? Those questions are still pending, yet RNA-seq technologies are on a clear positive path regarding technical feasibility, affordability, the vast number of computational tools associated with an active user community engaged in offering free-access to user-friendly tools. RNA-seq is expected to become a cornerstone of personalised care in onco-immunology. The increasing availability and popularity of innovative techniques further scales up the challenges connected to the analysis of molecular data. Bringing scRNA-seq to the clinic may be challenging, although an increasing number of translational studies should contribute to define its position in cancer treatment personalization.

ScRNA-seq analysis provides expressions of the genes within each cell studied, although recent technological advances allow carrying out several measurements on the same cells. For example, for lymphocytes, the complete variable sequences of the TCR/Ig of each cell can be obtained (Single Cell Immune Profiling). Similarly, the presence of target proteins on the surface of cells can be assessed by CITEseq [START_REF] Stoeckius | Simultaneous epitope and transcriptome measurement in single cells[END_REF]. Yet, the major innovation remains the association between gene expression and the location of cell groups in a tissue section, so called the "spatiallyresolved transcriptomics" [START_REF] Marx | Method of the Year: spatially resolved transcriptomics[END_REF][START_REF] Larsson | Spatially resolved transcriptomics adds a new dimension to genomics[END_REF]. In addition, the single-cell technologies are not restricted to transcriptomics. Single-cell ATAC-seq is well established, and single-cell proteomics are actively under development. Great efforts are being made to integrate "multiomics" from one cell.

Despite great improvement of the techniques, important issues regarding application of the computational and machine learning-based methods for the analysis of both bulk and scRNA-seq data are emerging. Mainly, it affects reproducibility and interpretability of their results and thus, limits their translationality to the clinic. The most technically advanced approaches, such as artificial neural network-based methods, even if they are very tempting to use, frequently produce results with limited generalization performance. Also, poor human interpretability keeps on being a limitation, even if no human can interpret clinically the data from a full cancer transcriptome, and while many methods are developed to decipher neural networks decisions. Also, gaining insights into these methods should improve the way we may use it in the clinic. In this context, clinical guidelines for the redaction of clinical trials protocols (SPIRIT-AI) [112] and final reports (CONSORT-AI) [START_REF] Liu | Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension[END_REF] of interventions involving artificial intelligence have very recently evolved to take account of these advances.

Finally, single-cell and bulk RNA-seq would both benefit from being developed as complementary techniques (Table 1). In the context of immune therapy, clinical translation of RNA-seq will require consolidating the robustness of predictive features associated with response/resistance to immune-checkpoint blockers. Ultimately, defining standardised analysis pipelines among thousands available will be essential and should benefit to reproducibility and gradual generalization of the practice.

Figure 1 .

 1 Figure 1. Key stages of successful drug development for patients with unresectable malignant pleural mesothelioma. ..........................................................................................................................................................................................

Figure 2 .

 2 Figure 2. Main molecular pathways and alterations involved in malignant mesothelioma pathophysiology. ................

Figure 3 .

 3 Figure 3. Comparative spectrum of kinase inhibition for kinases of interest in angiogenesis, for all approved antiangiogenic protein kinase inhibitors, reproduced from (164)....................................................................................

Figure 4 .

 4 Figure 4. Potential mechanisms of resistance to antiangiogenic drugs and proposed ways of overcoming resistance (reproduced from (18)) .....................................................................................................................................................

Figure 5 .

 5 Figure 5. Most commonly described mechanisms driving resistance to immunotherapy (reproduced from (18)). ........

Figure 6 .

 6 Figure 6. Decision tree for the nintedanib dose escalation (6A) and the collection of biological samples for ancillary analyses (6B). ....................................................................................................................................................................

Figure 7 .

 7 Figure 7. Cancer outcomes in the dose escalation cohort of the PEMBIB trial: Waterfall Plot (7A), Spider Plot (7B) and Swimmer plot (7C). ...........................................................................................................................................................

Figure 8 .

 8 Figure 8. Increased tumor immune cell infiltration in patients with durable clinical benefit before initiation of the nintedanib and pembrolizumab association (Dose Escalation). .......................................................................................

Figure 9 .

 9 Figure 9. Increased of circulating soluble PlGF, CCL3 and increase in specific T cells subsets during lead-in nintedanib monotherapy associated to clinical outcomes (dose escalation). ....................................................................................

Figure 10 .

 10 Figure 10. After pembrolizumab infusion, in association with nintedanib treatment, circulating and tumor microenvironment changes were associated with disctinct clinical outcomes (Dose escalation). ..................................

Figure 11 .

 11 Figure 11. Evolution of patients during treatment with nintedanib and pembrolizumab association (Mesothelioma Cohort). 11A. Waterfall plot. 11B. Spider plot of sum of target lesions during follow-up. 11C. Swimmer plot. ..............

Figure 12 .

 12 Figure 12. Patients with benefit to pembrolizumab and nintedanib had tumoral and systemic immune characteristics compared with patients primary resistant to treatment (Mesothelioma cohort). ..........................................................

Figure 13 .

 13 Figure 13. Differential expression genes (DEG) between screening tumor biopsies highlighted low immune signature and pro tumoral pathways molecular enrichment in patients with primary resistance to treatment (Mesothelioma cohort). .............................................................................................................................................................................

Figure 14 .

 14 Figure 14. Aneuploidy was higher in tumor from patients who did not benefit to the treatment and was positively associated with chemokines recruiting inflammatory suppressive innate immune cells in tumors (Mesothelioma cohort). .............................................................................................................................................................................

Figure 15 . 1 AKT

 151 Figure 15. Tumor inflammatory and angiogenic features associated with resistance to immune checkpoint blockade and potential therapeutic interventions for the treatment of patients with mesothelioma. ........................................

FIGURE 1 .

 1 FIGURE 1. KEY STAGES OF SUCCESSFUL DRUG DEVELOPMENT FOR PATIENTS WITH UNRESECTABLE MALIGNANT PLEURAL MESOTHELI-OMA.

FIGURE 2 .

 2 FIGURE 2. MAIN MOLECULAR PATHWAYS AND ALTERATIONS INVOLVED IN MALIGNANT MESOTHELIOMA PATHOPHYSIOLOGY. RED STARS: PRINCIPAL GENES INVOLVED BY MUTATIONS, DELETION OR EPIGENETIC SILENCING IN MESOTHELIOMA.

  Figure 3 (reproduced from (164), with the authorization of Dr Paul Gougis). These different effects and target inhibitions for each drug should led to differential effects on tumor growth, angiogenesis but also on immune infiltrates of the tumor microenvironment.

  ABBREVIATIONS: RCC = RENAL CELL CARCINOMA; HCC = HEPATOCELLULAR CARCINOMA; DTC = DIFFERENTIATED THYROID CARCI-NOMA; GIST = GASTRO-INTESTINAL STROMAL TUMOR; PNET = PANCREATIC NEUROENDOCRINE TUMORS; STS = SOFT TISSUE SAR-COMA; MTC = MEDULLARY THYROID CARCINOMA; IPF = IDIOPATHIC PULMONARY FIBROSIS; NSCLC = NON-SMALL CELL LUNG CAN-CER; GC = GASTRIC CANCER; CRC = COLORECTAL CANCER.

FIGURE 3 .

 3 FIGURE 3. COMPARATIVE SPECTRUM OF KINASE INHIBITION FOR KINASES OF INTEREST IN ANGIOGENESIS, FOR ALL APPROVED ANTI-ANGIOGENIC PROTEIN KINASE INHIBITORS, REPRODUCED FROM (164).

FIGURE 4 .

 4 FIGURE 4. POTENTIAL MECHANISMS OF RESISTANCE TO ANTIANGIOGENIC DRUGS AND PROPOSED THERAPEUTIC WAYS TO OVER-COME RESISTANCE (REPRODUCED FROM (18)) ABBREVIATIONS: CHR, CHROMOSOME; CRC, COLORECTAL CANCER; EC, ENDOTHELIAL CELL; EPC, ENDOTHELIAL PROGENITOR CELL; FGF, FIBROBLAST GROWTH FACTOR; HGF, HEPATOCYTE GROWTH FACTOR; IL, INTERLEUKIN; MAPKI, MAPK INHIBITORS; M2-TAM, M2 TUMOR-ASSOCIATED MACROPHAGES; MDSC, MYELOID-DERIVED SUPPRESSOR CELLS; TKI, TYROSINE KINASE INHIBI-TORS.

  mors and humoral immune responses were inefficient to control tumor growth in patients (201)? The discovery of immune checkpoints such as Cytotoxic T Lymphocyte Associated protein 4 (CTLA-4) and Programmed Death-1 (PD-1) brought stones to the building and allowed the development of a new therapy field in oncology.Discovery of immune checkpoints: CTLA-4 and PD-1.The CTLA-4 molecule, belonging to the immunoglobulin family, was discovered in 1987 by the team of Prof. Pierre Golstein at the Marseille-Luminy immunology center (202). The inhibitory properties of CTLA-4 were demonstrated by the scientific teams of Prof. Gordon Freeman, James Allison and Jeffrey Bluestone in the early 1990s (203-205). This costimulatory molecule is expressed on the surface of T lymphocytes after their activation and constitutionally on the surface of regulatory CD4 T lymphocytes. After T cell receptor (TCR) engagement and a costimulatory signal through CD28, CTLA-4 translocate to the cell surface, where it outcompetes CD28 for binding to critical costimulatory molecules (CD80, CD86) and mediates inhibitory signaling into the T cell, resulting in arrest of both proliferation and activation (206).The PD-1 receptor is a powerful negative regulator of T cell effector function when engaged by its ligands programmed cell death ligand 1 (PD-L1) and PD-L2, expressed on the surface of cells within a tumor (207,208). Its inhibitory function is mediated by the tyrosine phosphatase SHP-2, which dephosphorylates signaling molecules downstream of the TCR (209). PD-1 has two ligands, PD-L1 (also known as CD274 or B7-H1), which is expressed by numerous cells upon exposure to cytokines, such as interferon gamma, and PD-L2 which has more restricted expression in antigen-presenting cells (209). PD-L1 expression in the tumor microenvironment results in PD-1-mediated T cell exhaustion, inhibiting the antitumor cytotoxic T cell response and is frequently observed in human tumors, independently of histological tumor types(210,211). Exposure of T cells to class 1 MHC / tumor antigen complexes results in reactive PD-L1 expression by target cells, and continuous PD-1 signaling in T cells induces an epigenetic program of T cell exhaustion (212). PD-1 is therefore a negative regulator of preexisting immune responses, which becomes relevant in the biology of immune tolerance to cancer. In the first year of 21 st century, Prof James Allison theorized that the blocking of immune checkpoints with antibodies might allow tumor immunotherapy (206).

T

  cell response: infiltrating T cells engage their TCR through recognition of a tumor antigen, triggering expression of PD-1 on T cells and release of IFN-g, resulting in reactive expression of PD-L1 by cancer-resident cells (222). PD-1 blockade can promote tumor rejection through reactivation of CD8 + T cells, leading to both increased functional activity and frequency (208). Blockade of the PD-1 signaling axis prevents PD-1-mediated attenuation of proximal TCR signaling, allowing for restoration of activity of exhausted CD8 effectors (223). Clinical evidence supports a model in which blockade of the PD-1 signaling axis is most effective in tumors in which an endogenous T-cell response has already been elicited but is suppressed through PD-1 engagement by its ligands PD-L1 and PD-L2(12,14). Phenotyping of peripheral blood from patients treated with anti-PD-1 antibodies showed an expansion of PD-1 + CD8 + T cells after treatment infusion(224,225). A study from a neoadjuvant trial of nivolumab in patients with NSCLC showed that anti-PD-1 therapy enhances neoantigen-specific T-cell responses (226). It is likely that only specific T-cell populations mediate responses to checkpoint blockade therapy. But not all tumor infiltrating T cells in tumor are specific of tumor antigens and certainly only subsets of such reactivated T cells led to an antitumoral responses(227,228). Although PD-1 blockade primarily leads to the expansion of CD8 + T cells, CD4 T cells are required for effective responses (229). In addition to facilitating T-cell memory formation, it is possible that CD4 helper T cells may also enhance antitumor immunity by increasing CD8 + T-cell and antibody entry into peripheral tissue sites, as observed in viral infections(230,231). Recent observations highlighted that coordination in tertiary lymphoid structures of such CD8 + T cells with dendritic cells, CD4 + T cells and B cells in tumor stroma is associated with better outcomes of patients treated with PD-1 blockade for different cancer types (232-234). In another way, blockade of PD-L1 may also derive part of its efficacy from ADCC: Fc receptor binding is important for the efficacy of anti-PD-L1, but not anti-PD-1, for tumor regression in mouse models (235).

FIGURE 5 .

 5 FIGURE 5. MOST COMMONLY DESCRIBED MECHANISMS DRIVING RESISTANCE TO IMMUNOTHERAPY (REPRODUCED FROM (18)). A, NEOANTIGEN LOSS. B, DEFECTS OF THE ANTIGEN PROCESSING MACHINERY. C, ABNORMAL IFNΓ SIGNALING. D, ABERRANT GE-NOMIC SIGNALING. E, COINHIBITORY CHECKPOINTS. F, IMMUNOSUPPRESSIVE TME. CRC, COLORECTAL CANCER; IL, INTERLEUKIN; IFNY, INTERFERON GAMMA; MDSC, MYELOID-DERIVED SUPPRESSOR CELLS; MHC I, MAJOR HISTOCOMPATIBILITY COMPLEX I; M2-TAM, TUMOR-ASSOCIATED MACROPHAGES TYPE 2; MSS, MICROSATELLITE-STABLE; MUT, MUTATION; TREG, REGULATORY T CELLS.

  L1) have been extensively investigated and are still in active development across malignancies and their different lines of treatment (317). Combining anti-angiogenic drugs and anti-PD-(L)1 therapy has recently shown important synergistic results in RCC and HCC (318-320). Indeed, anti-VEGF therapies may enhance anti-PD-(L)1 efficacy by reversing VEGFmediated immunosuppression and promoting T-cell infiltration in tumours (145,159,281,282,321,322).

(

  human) (Meso Scale Discovery, ref: K151P3S-1), Chemokines Panel 1, Proinflammatory Panel 1, Cytokine Panel 1 (Meso Scale Discovery, ref: K151A9H-1), the ultra-sensitive assay S-plex Human IFNα2a kit (Meso Scale Discovery, ref: K151P3S-1), Human PD-1 and PD-L1 antibody sets (Meso Scale Discovery, ref: F214A-3 & F214C-3, respectively) following manufacturer's instructions. Acquisitions and analyses were performed on a MESO™ QuickPlex SQ120 reader and the MSD's Discovery Workbench 4.0. Each plasma sample was assayed twice with the average value taken as the result.

FIGURE 7 .

 7 FIGURE 7. CANCER OUTCOMES IN THE DOSE ESCALATION COHORT OF THE PEMBIB TRIAL: WATERFALL PLOT (7A), SPIDER PLOT (7B) AND SWIMMER PLOT (7C).

FIGURE 8 .

 8 FIGURE 8. INCREASED TUMOR IMMUNE CELL INFILTRATION IN PATIENTS WITH DURABLE CLINICAL BENEFIT BEFORE INITIATION OF THE NINTEDANIB AND PEMBROLIZUMAB ASSOCIATION (DOSE ESCALATION). 8A. CXCL10, CCL22 AND SOLUBLE TIE PLASMA LEVELS WERE SIGNIFICANTLY HIGHER IN PATIENTS WITH DCB (1464PG/ML [RANGE: 409.8-4290.5], 1032.8PG/ML [RANGE: 810.5-1479.9] AND 3796.6PG/ML [RANGE: 2710.9-4665.6], RESPECTIVELY) THAN PATIENTS WITHOUT DCB (334.7PG/ML [RANGE: 81.1-767.6], 745.2PG/ML [RANGE: 396.8-1088.5] AND 2896.5PG/ML [RANGE: 2230.4-4132.7]) (WILCOXON RANK SUM TEST; P=0.03, P=0.03 AND P=0.04, RESPECTIVELY). 8B. THE PERCENTAGE OF Α4Β7+ CD4+ AND PD1+ OX40+ CD4+ CELLS AMONG TOTAL CIRCULATING CD4+ T CELLS WAS SIGNIFICANTLY HIGHER IN PATIENTS WITH DCB (28.7% [RANGE: 22.4-42.6] AND 43.1% [RANGE: 25.6-78.9], RESPECTIVELY) THAN WITHOUT DCB (17.7% [RANGE: 13.1-23.9] AND 25.9% [RANGE: 14.5-55.4]) (WILCOXON RANK SUM TEST; P=0.024 AND P=0.03, RESPECTIVELY). 8C. PATIENTS WITH PR HAD PD-L1 EXPRESSION ON TUMOR CELLS AND IMMUNE INFILTRATION IN TUMOR STROMA, CHARACTERIZED BY IHC (WIL-

FIGURE 9 .

 9 FIGURE 9. INCREASED OF CIRCULATING SOLUBLE PLGF, CCL3 AND INCREASE IN SPECIFIC T CELLS SUBSETS DURING LEAD-IN NINTEDANIB MONOTHERAPY ASSOCIATED TO CLINICAL OUTCOMES (DOSE ESCALATION).

  9A. PLASMA RATE OF PLACENTAL GROWTH FACTOR (PLGF) INCREASED IN PATIENTS WITHOUT DCB BETWEEN D-7 (13.4PG/ML [RANGE: 8.4-17.9]) AND C1D1 (18.8PG/ML [RANGE: 10.8-30.3]) (PAIRED WILCOXON SIGNED RANK TEST; P=0.015). 9B. PLASMA RATE OF CCL3 WERE HIGHER IN PATIENTS WITH DCB (19.8PG/ML [RANGE: 11.7-26.1]) THAN WITHOUT DCB (12.9PG/ML [RANGE: 9.9-17.8]) AFTER 7 DAYS OF NINTEDANIB MONOTHERAPY (NON-PARAMETRIC WILCOXON RANK SUM TEST; P=0.03). 9C. PERCENTAGE OF BLOOD CCR4 + CXCR3 + T CELLS AMONG EFFECTOR MEMORY CD4+ T CELLS AND CD25 HIGH CD127 LOW TREGS AMONG TOTAL CD4+ T LYMPHOCYTES WERE HIGHER AT C1D1 IN PATIENTS WITH DCB (25.3% [RANGE: 21.2-35.8] AND 10% [RANGE: 6.4-15.4], RESPECTIVELY) THAN THOSE WITHOUT DCB (17.9% [RANGE: 14.4-23.8] AND 5.8% [RANGE: 4.1-8.5], RESPECTIVELY) (NON-PARAMETRIC WILCOXON RANK SUM TEST; P=0.017 AND P=0.024, RESPECTIVELY).

FIGURE 10 .

 10 FIGURE 10. AFTER PEMBROLIZUMAB INFUSION, IN ASSOCIATION WITH NINTEDANIB TREATMENT, CIRCULATING AND TUMOR MI-CROENVIRONMENT CHANGES WERE ASSOCIATED WITH DISCTINCT CLINICAL OUTCOMES (DOSE ESCALATION).

SUPPLEMENTARY FIGURE 3 .

 3 ILLUSTRATION OF MULTIPLEX CHROMOGENIC STAINING DEDICATED TO MYELOID CELLS.3A. REPRESENTATIVE IMAGE DISPLAYS A 500UMX669UM IMAGE AFTER MULTISPECTRAL IMAGING AND SPECTRAL UNMIXING (MERGED IMAGE). 3B. ALL MARKERS. 3C. CD68 (PSEUDOCOLOURED WHITE). 3D. DAPI NUCLEAR MARKER (PSEUDOCOLOURED BLUE). 3E. CD163 (PSEUDOCOLOURED GREEN). 3F. DC-LAMP (PSEUDOCOLOURED MAGENTA). 3G. IDO1 (PSEUDOCOLOURED YELLOW). 3H. CD163 MEASUREMENT WAS UNDERTAKENUSING INFORM V2.2 SOFTWARE SEGMENTATION BY PIXEL-BASED THRESH-OLD. 3I. CD163 + DENSITY IN BIOPSIES OF PATIENTS WITH DCB OR WITHOUT DCB AT BASELINE AND C2D1 WERE NOT SIGNIFICANTLY DIFFERENT (WILCOXON RANK-SUM TEST).

  Targeting the tumor microenvironment (TME) with immune checkpoint blockers or antiangiogenic drugs, has shown significant activity in several metastatic cancers. Angiogenesis contributes to tumor growth and the development of metastases but the modulation of neo-angiogenesis via inhibition of the VEGF/VEGFR pathway has shown anti-tumor activity in several human solid cancers (345). Also, blocking immune checkpoints with antagonistic monoclonal antibodies targeting Programmed death-1 receptor (PD-1) and its ligand (PD-L1) have been extensively investigated in the recent past and are still in active development across malignancies(1). Combining anti-angiogenic drugs and anti-PD-(L)1

(

  150 mg BID) in combination with IV flat dose of pembrolizumab 200 mg over 30 minutes Q3W. The posology of nintedanib was first determined in a dose escalation cohort, which showed that nintedanib at 150mg BID was better tolerated than 200mg BID, and subsequently selected for the expansion cohorts. Of note, patients received a one-week lead-in course of nintedanib monotherapy prior to starting pembrolizumab. The protocol was first approved by the Agence Nationale de Sécurité du Médicament (ANSM) on June 24 th 2016 (Ref #160371A-12). The protocol was also approved by the Ethical Committee (Comité de Protection des Personnes Ile de France 1) on Jul 12 th 2016 (Ref #2016mai-14236ND). The trial was first posted on clinicaltrials.gov on Aug 4 th 2016 (NCT02856425).

  50ml centrifuge tube. Isolated cells were washed by centrifugation and re-suspended in NaCl 0.9% for cell surface staining protocol. Cells were stained with anti-CD3/BUV395 (clone UCHT1; ref. 563546, BD Biosciences), anti CD4/BUV496 (clone SK3, ref. 564651, BD Biosciences), anti CD45/BUV805 (clone HI30, ref. 612891, BD Biosciences), anti PD1/BV421 (clone MIH4, ref. 564323, BD Biosciences), anti OX40/BV650 (clone ACT35, ref. 563658, BD Biosciences), anti CD39/FITC (clone TU66, ref. 561444, BD Biosciences), anti HLA-DR/PerCP5.5 (clone G46-6, ref. 551764, BD Biosciences), anti CTLA4/PE (clone BNI3, ref. 555853, BD Biosciences), anti 41BB/PECF594 (clone 4B4-1, ref. 309826, BD Biosciences), anti CD25/PECy7 (clone B1.49.9, ref. A52882, Beckmann Coulter), anti TIGIT/APC (clone MBSA43, ref. 17-9500-41, Biolegend), anti HLA-ABC/AF700 (clone W6/32, ref. 311438, Biolegend), anti CD8/APC-H7 (clone SK1, ref. 560179, BD Biosciences) and Zombie Aqua TM Fixable Viability (ref. 423101, Biolegend). CTLA-4 was first stained at 37°C for 20min before others surface antibodies were added and incubated at 4°C for 15 min. Then, cells were washed two times and acquired on an 18-colors flow cytometer BD Fortessa X20 (BD Biosciences). Data were acquired in FCS 3.0 format and analyzed with KALUZA software version 2.1.

  titrated using Bio-Plex Pro TM Human Chemokine Panel (40-Plex, ref. 171AK99MR2, Bio-rad), Angiogenesis Panel 1 (human) (ref. K151P3S-1, Meso Scale Discovery), Human PD-1 and PD-L1 antibody sets (ref. F214A-3 & F214C-3, Meso Scale Discovery) following manufacturer's instructions. Each plasma sample was run twice with the average value of the doublet taken as the result. Acquisitions were done on Bio-Plex 200TM System and MESO™ QuickPlex SQ120 readers.

  565560, BD Biosciences) and 7AAD (ref. 559925, BD Biosciences), at room temperature during 15 minutes. Then, cells were washed two times and acquired on flow cytometer BD Fortessa X20 (BD Biosciences). Data were acquired in FCS 3.0 format and analyzed with KALUZA software version 2.1.

FIGURE 11 .

 11 FIGURE 11. EVOLUTION OF PATIENTS DURING TREATMENT WITH NINTEDANIB AND PEMBROLIZUMAB ASSOCIATION (MESOTHELI-OMA COHORT). 11A. WATERFALL PLOT. 11B. SPIDER PLOT OF SUM OF TARGET LESIONS DURING FOLLOW-UP. 11C. SWIMMER PLOT.

FIGURE 12 .

 12 FIGURE 12. PATIENTS WITH BENEFIT TO PEMBROLIZUMAB AND NINTEDANIB HAD TUMORAL AND SYSTEMIC IMMUNE CHARACTERIS-TICS COMPARED WITH PATIENTS PRIMARY RESISTANT TO TREATMENT (MESOTHELIOMA COHORT). 12A. PD-L1 (CLONE SP263) STAINING OF TUMOR CELLS BY IMMUNE-HISTO-CHEMISTRY ON SCREENING BIOPSIES (N=20). 12B. EVAL-UATION OF TUMOR IMMUNE INFILTRATE AFTER FRESH TISSUE DISSOCIATION BY FLOW CYTOMETRY (N=22). 12C. ANALYSES OF FRESH BLOOD SAMPLES BY FLOW CYTOMETRY TO PHENOTYPE CIRCULATING T LYMPHOCYTES BEFORE TREATMENT INITIATION, BOXPLOT REPRE-SENTATION OF PERCENTAGE OF CXCR3 + CCR4 -CCR6 -CELLS AMONG HELPER MEMORY CD4+ T CELLS AND CD49A + AMONG EFFECTOR MEMORY CD8 + T CELLS (N=25). 12D. HEATMAP REPRESENTATION OF UNSUPERVISED CLUSTERING OF SCREENING TUMOR THROUGH IMMUNE INFILTRATION ESTIMATED BY DECONVOLUTION OF BULK RNA-SEQ DATA (MCP COUNTER) WITH CLINICAL AND MOLECULAR ANNOTATIONS (N=20). 12E. ESTIMATION OF MYELOID DENDRITIC CELLS AND T CELLS INFILTRATION IN TUMOR BY IMMUNE DECONVO-LUTION (MCP COUNTER) FROM RNA-SEQ IN SCREENING BIOPSIES FOR PATIENTS WITHOUT AND WITH DCB (N=20). 12F. VOLCANO PLOT REPRESENTATION OF DIFFERENTIAL CONCENTRATION OF PLASMA CHEMOKINES AND SOLUBLE FACTORS MEASURED FOR ALL PA-TIENTS BETWEEN CYCLE 1 DAY 8 AND DAY 1 BLOOD SAMPLES (N=25). 12G. EVOLUTION OF PAIRED PLASMA CXCL9 AND CXCL13 CONCENTRATION BETWEEN CYCLE 1 DAY 1 AND DAY 8 BLOOD SAMPLES FOR PATIENTS WITHOUT AND WITH DCB (N=25). 12H. COM-PARISON OF T CELLS INFILTRATE BETWEEN PATIENTS WITH AND WITHOUT DCB AT CYCLE 2 TUMOR BIOPSIES WITH FLOW CYTOMETRY (N=22). ALL TESTS WERE WILCOXON RANK-SUM TEST (NOT PAIRED SAMPLES) AND PAIRED WILCOXON SIGNED RANK TEST (PAIRED SAMPLES) (REPRESENTATION OF P-VALUE: NS >0.05, * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001, **** ≤ 0.0001).

FIGURE 13 .

 13 FIGURE 13. DIFFERENTIAL EXPRESSION GENES (DEG) BETWEEN SCREENING TUMOR BIOPSIES HIGHLIGHTED LOW IMMUNE SIGNA-TURE AND PRO TUMORAL PATHWAYS MOLECULAR ENRICHMENT IN PATIENTS WITH PRIMARY RESISTANCE TO TREATMENT (MESO-THELIOMA COHORT). 13A. VOLCANO PLOT REPRESENTATION OF DEG BETWEEN SCREENING TUMOR BIOPSIES FROM PATIENTS WITH OR WITHOUT DCB (LOG-2 FOLD CHANGE ≤ -3 OR ≥ 1 WITH P-VALUE ≤ 0.01 ADJUSTED WITH BENJAMINI-HOCHBERG METHOD [WALD'S TEST]) (N=13). 13B. DOT PLOT REPRESENTATION OF GENE SET ENRICHMENT ANALYSES ("HALLMARK" PANEL) BETWEEN SCREENING TUMOR BIOPSIES FROM PATIENTS WITH OR WITHOUT DCB (N=13), CONSIDERING ALL GENES DIFFERENTIALLY EXPRESSED (P-VALUE ≤ 0.05 ADJUSTED WITH BENJAMINI-HOCHBERG METHOD [WALD'S TEST]; N=515). GSEA PLOT REPRESENTATIONS OF PATHWAYS SIGNIFICANTLY ENRICHED OR SUPPRESSED IN TUMOR BIOPSIES FROM PATIENTS WITH DCB COMPARED TO THOSE WITHOUT DCB (13C AND 13D, RESPECTIVELY).

  SCNA score from all WES data (n=18) correlated positively with serum concentration of IL6 measured from baseline samples and with the concentration of soluble CCL2, present in the supernatant of fresh tumor biopsies done prior treatment initiation (Figure14B, 14C). Median CCL2 concentrations in those tumor supernatants were significantly higher in biopsies of patients without DCB (63.4pg/mL [SD 200.4] versus 18.1pg/mL [SD 48.4], respectively; p=0.027,

FIGURE 14 .

 14 FIGURE 14. ANEUPLOIDY WAS HIGHER IN TUMOR FROM PATIENTS WHO DID NOT BENEFIT TO THE TREATMENT AND WAS POSITIVELY ASSOCIATED WITH CHEMOKINES RECRUITING INFLAMMATORY SUPPRESSIVE INNATE IMMUNE CELLS IN TUMORS (MESOTHELIOMA COHORT). 14A. SOMATIC COPY NUMBER ALTERATION SCORE BETWEEN PATIENTS WITHOUT OR WITH DCB (N=18). 14B. CORRELATION PLOT TO ILLUSTRATE ASSOCIATION BETWEEN SCNA SCORE AND CONCENTRATION OF SERUM IL6 FROM SAMPLES DONE BEFORE TREATMENT INITIATION (DAY -7)(N=18). 14C. CORRELATION PLOT TO ILLUSTRATE ASSOCIATION BETWEEN SCNA SCORE AND CONCENTRATION OF SOLUBLE CCL2 IN SCREENING BIOPSY'S SECRETUM (N=18). 14D. CONCENTRATION OF SOLUBLE CCL2 IN SCREENING BIOPSY'S SECRETUM BETWEEN PATIENTS WITHOUT OR WITH DCB (N=25). 14E. CONCENTRATION OF PLASMATIC CXCL8 (DAY -7) BETWEEN PATIENTS WITHOUT OR WITH DCB (N=25). 14F. PERCENTAGES OF CD15 + POLYMORPHONUCLEAR (PMN) CELLS AMONG TOTAL CD45 + CELLS IN PRE-TREATMENT FRESH BLOOD SAMPLES (DAY -7) (N=25). 14G. REPRESENTATION BY UNIFORM MANIFOLD AP-PROXIMATION AND PROJECTION (UMAP) OF CONCATENATED CELLS FROM ALL PRE-TREATMENT PBMC OF PATIENTS AND HEALTHY CONTROLS CLUSTERED BY DIFFERENTIAL EXPRESSION OF 400 GENES (N=12). 14H. CLUSTERING OF CONCATENATED SINGLE CELLS RNA-SEQ FROM PRE-TREATMENT PBMC OF HEALTHY CONTROLS (N=2), PATIENTS WITH DCB (N=5) AND WITHOUT DCB (N=5). 14I. BARPLOT REPRESENTING PERCENTAGE OF CELLS FROM EACH CLUSTER FOR HEALTY CONTROLS, PATIENTS WITH OR WITHOUT DCB. 14J. ILLUSTRATION OF SELECTED GENES EXPRESSION AT SINGLE CELLS LEVELS WITH UMAP REPRESENTATION OF ALL CONCATE-NATED CELLS. 14K. PERCENTAGE OF CIRCULATING INTERMEDIATE MONOCYTES AMONG TOTAL MONOCYTES FROM THAWED PBMC COLLECTED IN HEALTHY CONTROLS (N=3) OR PATIENTS BEFORE INITIATION OF TREATMENT (DAY -7) (N=17). 14L. PERCENTAGE OF CIRCULATING CD163 + CCR2 -CELLS AMONG INTERMEDIATE MONOCYTES HLA-DR INT MONOCYTES FROM THAWED PBMC COLLECTED IN HEALTHY CONTROLS (N=3) OR PATIENTS BEFORE INITIATION OF TREATMENT (DAY -7) (N=17). 14M. COMPARISON OF TREM1 NORMALIZED GENE EXPRESSION BETWEEN PATIENTS WITHOUT AND WITH DCB AT CYCLE 2 TUMOR BIOPSIES (N=21). EXCEPT IF DE-TAILED, ALL TESTS WERE WILCOXON RANK-SUM TEST (NOT PAIRED SAMPLES) AND PAIRED WILCOXON SIGNED RANK TEST (PAIRED SAMPLES) (REPRESENTATION OF P-VALUE: NS >0.05, * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001, **** ≤ 0.0001).

  SUPP. 6A. SURFACE AREA AND PERCENTAGE OF TUMOR CELLS IN BIOPSIES, INCLUDING BIOPSIES WITHOUT ANY TUMOR CELLS (N=21). SUPP 6B. COMPARISON OF MESOTHELIOMA MOLECULAR SUBTYPE ESTIMATED BY DECONVOLUTION FROM RNA-SEQ WITH WISP ALGORITHM (E-SCORE, S-SCORE AND NORMAL-SCORE), INCLUDING BIOPSIES WITHOUT ANY TUMOR CELLS (N=20). SUPP. 6C. SURFACE AREA AND PERCENTAGE OF TUMOR CELLS IN BIOPSIES, EXCLUDING BIOPSIES WITHOUT ANY TUMOR CELLS (N=17). SUPP 6D. COMPARISON OF MESOTHELIOMA MOLECULAR SUBTYPE ESTIMATED BY DECONVOLUTION FROM RNA-SEQ WITH WISP ALGORITHM (E-SCORE, S-SCORE AND NORMAL-SCORE), EXCLUDING BIOPSIES WITHOUT ANY TUMOR CELLS (N=16). ALL TESTS WERE WILCOXON RANK-SUM TEST (NOT PAIRED SAMPLES) (REPRESENTATION OF P-VALUE: NS >0.05). SUPPLEMENTARY FIGURE 7. GATING STRATEGY OF FLOW CYTOMETRY ANALYSES OF T CELLS INFILTRATION FROM TUMOR BIOPSIES (MESOTHELIOMA COHORT). SUPP. 7A. EXAMPLE OF A PATIENT WITH HIGH IMMUNE INFILTRATE IN TUMOR. SUPP. 7B. EXAMPLE OF A PATIENT WITH LOW IM-MUNE INFILTRATE IN TUMOR. SUPPLEMENTARY FIGURE 10. PLASMA SOLUBLE FACTORS BETWEEN DAY -7 AND CYCLE 1 DAY 1 DURING THE LEAD-IN TREATMENT BY NINTEDANIB MONOTHERAPY (MESOTHELIOMA COHORT). TESTS WERE PAIRED WILCOXON SIGNED RANK TEST (PAIRED SAMPLES) (REPRESENTATION OF P-VALUE: NS >0.05, * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001, **** ≤ 0.0001). SUPPLEMENTARY FIGURE 11. PLASMA SOLUBLE FACTORS BETWEEN CYCLE 1 DAY 1 AND DAY 8 AFTER THE FIRST INFUSION OF PEMBROLIZUMAB (MESOTHELIOMA COHORT). TESTS WERE PAIRED WILCOXON SIGNED RANK TEST (PAIRED SAMPLES) (REPRESENTATION OF P-VALUE: NS >0.05, * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001, **** ≤ 0.0001). SUPPLEMENTARY FIGURE 12. DYNAMIC OF IMMUNE INFILTRATION IN TUMOR AFTER PEMBROLIZUMAB INFUSION WITH NINTEDANIB (MESOTHELIOMA COHORT). 12A. EVOLUTION OF IMMUNE SUBSETS INFILTRATION IN TUMOR BETWEEN SCREENING AND PRE CYCLE 2 BIOPSIES CONSIDERING PA-TIENTS WITH AND WITHOUT DCB, BY IMMUNE DECONVOLUTION OF RNA-SEQ (MCP COUNTER). 12B. REPRESENTATION OF UNSU-PERVISED CLUSTERING OF PRE CYCLE 2 DAY 1 TUMOR THROUGH IMMUNE INFILTRATION ESTIMATED BY DECONVOLUTION OF BULK RNA-SEQ DATA (MCP COUNTER) WITH CLINICAL AND MOLECULAR ANNOTATIONS (N=21). TESTS WERE PAIRED WILCOXON SIGNED RANK TEST (PAIRED SAMPLES) (REPRESENTATION OF P-VALUE: NS >0.05, * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001, **** ≤ 0.0001). SUPPLEMENTARY FIGURE 13. ILLUSTRATION OF HALLMARKS GSEA NETWORKS FROM DIFFERENTIALLY EXPRESSED GENES IDENTI-FIED BETWEEN BASELINE TUMOR BIOPSIES OF PATIENTS WITH AND WITHOUT DCB (N=13) (MESOTHELIOMA COHORT). SUPPLEMENTARY FIGURE 14. HEATMAP ILLUSTRATING UNSUPERVISED CLUSTERING OF CELLS FROM THAWED PBMC THROUGH TOP 10 DIFFERENTIALLY EXPRESSED GENES (MESOTHELIOMA COHORT).

  genomic alterations are associated with lower immune infiltrations in several tumor types and worse outcome upon anti-PD(L)1 immunotherapy for patients with metastatic urothelial carcinoma (418). In murine models of solid cancers, inhibition of CDK4/6 induces an inhibition of tumor cells proliferation and promotes T-cells infiltration in tumors through tumoral expression of endogenous retroviral elements and inhibition of Treg immunosuppressive functions in the tumor stroma, both mediated by control of DNMT1 which is a target of E2F (378). Also, murine models have shown that the hyper activation of the Hippo-YAP pathway induced the recruitment of myeloid derived suppressor cells (MDSC) and immunosuppressive macrophages in the tumor microenvironment and promote tumor growth, notably by production of specific chemokines (419-421). As a consequences of disinhibition of growth factor pathways, cAMP response element binding protein (CREB1) is overexpressed in MM cells and drives to the production of CXCL8 (aka interleukin-8

CDK4/ 6

 6 inhibitors have been shown to decrease tumor cell proliferation, to promote immunogenic cell death through release of factors such as high mobility group box 1 (HMGB1) by tumor cells, to induce T cell infiltration in tumors and to synergize with PD-1 blockade to limit tumor growth (379,433). In a phase 1 clinical trial, patients received anti-aromatase and pembrolizumab with or without palbociclib for positive hormonal receptor breast cancer. The addition of palbociclib induced changed upon treatment in the phenotypes of circulating dendritic cells and monocytes (434).An Hippo pathway alteration by NF2 loss leads to YAP/TAZ hyperactivation, to cell survival, proliferation, and to the recruitment of immunosuppressive cells in the tumor microenvironment (435). Therapeutic strategies to inhibit YAP/TAZ signaling is challenging, but recent drugs were shown to directly inhibit their dependent transcription functions through direct inhibition of binding between TEAD and YAP/TAZ, notably in MM cell line with NF2 loss (436,437). Focal Adhesion Kinase (FAK) inhibition in mouse models limited tumor growth of mesothelioma with loss of NF2 (438). A phase 2 evaluating a maintenance therapy by defactinib, a FAK inhibitor, versus placebo after a first line of chemotherapy in patients with unresectable mesothelioma, did not shown an improvement of PFS nor OS by defactinib (439).Otherwise, YAP/TAZ interact with the bromodomain-containing protein 4 (BRD4) to recruit RNA polymerase II and regulate downstream genes expression (440). The bromodomain and extra terminal motif (BET) inhibitor JQ1, an established BRD4 inhibitor, was shown to inhibit YAP/TAZ signaling in cell lines with Hippo pathway alterations (441,442). BET inhibition have shown anti-tumoral activities through the modulation of immune infiltrates in the tumor microenvironment by intrinsic tumor effects and regulation of immune cell functions in murine models (443-445).

(

  452,453). MDSCs secrete molecules and express soluble factors, such as arginine and leukocyte immunoglobulin-like receptor B4 (LILRB4), which are druggable targets currently tested in oncology clinical trials (454,455). Finally, yet importantly, the anti-angiogenic monoclonal antibody bevacizumab are an important therapeutic options for patients with MM. In a phase 3 clinical trial, the addition of bevacizumab, an anti VEGF-A IgG1 monoclonal antibody, to first line platinum-based chemotherapy with pemetrexed have shown to improve the overall survival of patients with unresectable pleural MM(75). Atezolizumab and bevacizumab showed impressive results for patients with unresectable peritoneal mesothelioma (283). Although bevacizumab is not part of the standard of care, antiangiogenic drugs are efficient 115 in MM. Interestingly, ongoing trials are currently evaluating the combination of anti-PD1 (pembrolizumab) with the antiangiogenic TKIs lenvatinib and nintedanib (PEMMELA/NCT04287829 and PEMBIB/NCT02856425 respectively).

FIGURE 15 .

 15 FIGURE 15. TUMOR INFLAMMATORY AND ANGIOGENIC FEATURES ASSOCIATED WITH RESISTANCE TO IMMUNE CHECKPOINT BLOCK-ADE AND POTENTIAL THERAPEUTIC INTERVENTIONS FOR THE TREATMENT OF PATIENTS WITH MESOTHELIOMA. LEFT PANEL: SCHEMATIC SITUATION ASSOCIATED WITH EFFICIENCY OF IMMUNE CHECKPOINT BLOCKADE; RIGHT PANEL: SCHEMATIC ILLUSTRATION OF MESOTHELIOMA MOLECULAR AND STROMAL CHARACTERISTICS ASSOCIATED WITH RESISTANCE TO ICB.

Fig. 2 .

 2 Fig. 2. Chemokines participate in the tumour microenvironment and can favour cancer spreading. Chemokines can either promote or inhibit cancer spreading, depending on the type of cancer and on the chemokines and receptors involved. (A) Gradients of chemokines participate in the immune response by chemoattracting natural killer cells and cytotoxic T cells that lyse cancer cells, and chemokines can directly enhance apoptosis and inhibit tumour proliferation. (B) Chemokines can enhance tumour proliferation and suppress the immune response by inactivating cytotoxic T cells via their apoptosis, PD-1/PD-L1 upregulation and Tregs chemoattraction. (C) Chemokines can favour angiogenesis and lymphangiogenesis by producing VEGFs that attracts VEGFR-expressing endothelial cells. (D) Chemokines can activate the production of MMP that degrade components of the extracellular matrix and promote EMT. Gradients of chemokines can guide tumour migration to form metastasis. EMT: epithelial-mesenchymal transition, MMP: matrix metalloprotease, PD-1: programmed cell death 1, PD-L1: programmed cell death ligand 1, Treg: regulator T cell, VEGF: vascular endothelial growth factor, VEGFR: vascular endothelial growth factor receptor.

Fig. 3 .

 3 Fig. 3. Immune checkpointetargeted antibodies reactivate the immune response within the tumour microenvironment. The T-cell receptor (TCR) of tumour-specific cytotoxic T cells recognises the antigen presented on the major histocompatibility complex I (MHC I) of cancer cells. The tumour microenvironment is immunosuppressive. Tregs produce cytokines such as IL-10 and TGF-b that inhibit cytotoxic Tcell activation, and cancer cells overexpress PD-L1 that binds to PD-1 exposed at the cytotoxic T-cell membrane. This interaction leads to the inactivation of the latter. Immune checkpoint blockade therapy using aPD-1 or aPD-L1 antibodies prevent PD-1/PD-L1 interaction and restore cytotoxic T-cell function. aCTLA-4 antibodies can either promote T-cell proliferation within the lymph nodes (not shown on this figure) or directly deplete tumour-specific Tregs. aPD-L1: antibody against PD-L1, aCTLA-4: antibody against CTLA-4, aPD-1: antibody against PD-1, CTLA-4: cytotoxic T-lymphocyteeassociated protein 4, ICB: immune checkpoint blockade, MHCI: major histocompatibility complex 1, PD-1: programmed cell death 1, PD-L1: programmed cell death ligand 1, TCR: T-cell receptor, Treg: regulator T cell, TGF-b: tumour growth factor b, IL-10: interleukin 10.

Fig. 4 .

 4 Fig. 4. Expected effects of immune checkpoint inhibitors. Within the tumour microenvironment, aPD-1 and aPD-L1 can reactivate cytotoxic T cells that kill cancer cells and thus reduce the number of cells doing EMT. aCTLA-4 depletes Tregs and thereby reduces immunosuppression. Besides, aPD-1 boosts the production of CXCL11 by myeloid cells and favours the recruitment of T cells to the tumour site, whereas aPD-1 and aCTLA-4 boost the production of CXCL13 by CD8þ T cells in the TLS and favour the recruitment of B cells. aCTLA-4: antibody against CTLA-4, aPD-1: antibody against PD-1, aPD-L1: antibody against PD-L1, CTLA-4: cytotoxic Tlymphocyteeassociated protein 4, EMT: epithelial-mesenchymal transition, ICB: immune checkpoint blockade, PD-1: programmed cell death 1, PD-L1: programmed cell death ligand 1, TLS: tertiary lymphoid structure, Treg: regulator T cell.

31 .

 31 Behr DS, Peitsch WK, Hametner C, et al. (2014) Prognostic value of immune cell infiltration, tertiary lymphoid structures and PD-L1 expression in Merkel cell carcinomas. Int J Clin Exp Pathol 7:7610-7621. 32. Caro GD, Bergomas F, Grizzi F, et al. (2014) Occurrence of tertiary lymphoid tissue is associated with T-cell infiltration and predicts better prognosis in earlystage colorectal cancers. Clin Cancer Res 20:2147-2158. 33. Schram A, Borazanci E, Brana I, Vieito Villar M, Garralda E, Spreafico A, Oliva M, Lakhani N, Wasserman R, Hoffmann K, Hallett R, Anido J, Maetzel D, Giblin P, Moran E, Kelly A, Seoane J, Von Hoff D, Siu L, Tabernero J. Phase 1 dose escalation of MSC-1, a humanized anti-LIF monoclonal antibody, in patients with advanced solid tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT147.

Figure 2 .

 2 Figure 2. Baseline plasmatic LIF level predicts response to PD1 / PDL1 axis blockade. A Display of the Log Rank p-values for progression-free survival (PFS) (y axis) and of the delta median PFS (y axis) associated with each plasmatic marker. Median value of each plasmatic marker was used to categorize patients with High or Low status. Each dot represents one marker. B Kaplan Meier curves of progression-free survival (left) and overall survival (right) according to baseline plasmatic LIF levels. C Quantification of baseline plasmatic LIF in NDB (n= 72, blue) and DCB (n= 23, red) patients. p value was calculated using Wilcoxon Rank sum test. D Proportion of patients who experienced durable clinical benefit (DCB) or non-clinical benefit (NCB) according to their baseline plasmatic level of LIF classified as High (above median value) and Low (below median value).

Figure 4 .

 4 Figure 4. Peripheral LIF level is associated with an intratumoral B cell signature and presence of Tertiary Lymphoid Structures. A Correlation of immune cell lineages -obtained through RNAsequencing and data deconvolution with Bindea algorithm -and LIF plasma level. Dot size depicts the correlation coefficient while the color is indicative of positive (red) or negative (blue) correlation. The Xaxis represents the transformed Log10 pearson p-value. B Histogram representation of B cell score (in relative units, RU) according to baseline plasmatic level of LIF classified as High or Low (median value used as a cut-off). p value was calculated using a Wilcoxon Rank sum test. C Representative histological images from a patient with squamous cell lung carcinoma showing presence of TLS highlighted through both Hematoxylin Eosin Saffron (HES) staining and double staining of CD3-CD20 (CD3 in brown, CD20 in purple). Left image has been captured at a low magnificationscale bar indicates 400µm sizewhile the images on the right has been obtained through slide digitization at a higher magnification; scale bar indicates 100µm. Black arrow indicates tumor cells that juxtapose TLS. D Proportion of patients with presence or absence of intratumoral TLS according to baseline LIF plasma level. p value was calculated through Chi-squared test.

Figure 5 .

 5 Figure 5. Baseline circulating LIF level is predictive of outcome of cancer patients treated with anti-PD(L)1 immunotherapyresults from an independent validation cohort. A Probability of PFS for LIF High (median survival = 2,57 mo.) and LIF Low (median survival = 5,07 mo.) patients in the Validation Cohort (n = 292). B Probability of OS for LIF High (median survival = 8,53 mo.) and LIF Low (median survival = NA) patients in the same patients cohort. C Proportion of patients who experienced DCB or NCB according to their baseline plasmatic level of LIF classified as High (above median value) and Low (below median value). P value was calculated through Chi-squared test. J o u r n a l P r e -p r o o f

Figure 4 .

 4 Figure 4. Peripheral LIF level is associated with an intratumoral B cell signature and presence of Tertiary Lymphoid Structures. A Correlation of immune cell lineages -obtained through RNAsequencing and data deconvolution with Bindea algorithm -and LIF plasma level. Dot size depicts the correlation coefficient while the color is indicative of positive (red) or negative (blue) correlation. The Xaxis represents the transformed Log10 pearson p-value. B Histogram representation of B cell score (in relative units, RU) according to baseline plasmatic level of LIF classified as High or Low (median value used as a cut-off). p value was calculated using a Wilcoxon Rank sum test. C Representative histological images from a patient with squamous cell lung carcinoma showing presence of TLS highlighted through both Hematoxylin Eosin Saffron (HES) staining and double staining of CD3-CD20 (CD3 in brown, CD20 in purple). Left image has been captured at a low magnificationscale bar indicates 400µm sizewhile the images on the right has been obtained through slide digitization at a higher magnification; scale bar indicates 100µm. Black arrow indicates tumor cells that juxtapose TLS. D Proportion of patients with presence or absence of intratumoral TLS according to baseline LIF plasma level. p value was calculated through Chi-squared test. J o u r n a l P r e -p r o o f

Fig. 1

 1 Fig. 1 Profound metabolomics alterations associated with COVID-19 clinical severity. A Heatmap illustrating the changes in metabolite abundance in the plasma from control (n = 27), mild (n = 23), moderate (n = 21), and critical (n = 28) COVID-19 patients. Significant metabolites were identified by Wilcoxon rank-sum test and the false discovery rate (FDR) controlled with Benjamini-Hochberg procedure between patients with critical and mild COVID-19. Hierarchical clustering (Euclidean distance, ward linkage method) of the metabolite abundance is shown. PCaes, total abundance of the different phosphatidylcholines identified in the cohort plasma samples. B, Random forest classification model was built using main metabolites altered (p < 0.05) between critical and mild COVID-19 patients as a predicting tool. The variables importance (as the mean decrease of the Gini index) for building the model is reported in a dot plot, with dots substituted by an up-pointing triangle to indicate metabolites increased in critical vs mild COVID-19 patients, and by a down-pointing triangle in the opposite case (B), the confusion matrix (indicating model accuracy) is depicted below. OOB out-of-bag error.

Fig. 2

 2 Fig. 2 Effects of COVID-19 on circulating sugars and amino acids. Modified carbohydrates (A) and amino acids (B) were profoundly altered in patients with the most severe COVID-19. Data in A and B were analyzed by non-parametric unpaired Wilcoxon test (Mann-Whitney) for each two-group comparison. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Fig. 3

 3 Fig. 3 Effecs of COVID-19 on polyamines, tryptophan derivatives and selected amino acids. Polyamines, arginine (A) and tryptophane (B) pathways alterations in critically ill COVID-19 patients were representative of an immunosuppressive metabolomic state. Data in A and B were analyzed by non-parametric unpaired Wilcoxon test (Mann-Whitney) for each two-group comparison. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Fig. 5

 5 Fig. 5 COVID-19 Patients with unfavorable outcome after tocilizumab infusion did not improved lymphopenia, inflammatory, immunosuppressive, and metabolomic abnormalities instead of patients who evolved towards clinical improvement. Patients with paired baseline and post treatment (day 7 ± 3) serum samples are represented (n = 18). The measured parameters include total lymphocyte counts (A) as well as the concentrations of IL18 (B), IL10 (C) and tryptophan (D). Wilcoxon signed-rank test was used to compare paired baseline and post treatment measures and Wilcoxon rank-sum test to compare baseline or day 7 (±3) measures between patients with response or no response. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Fig. 6

 6 Fig. 6 Correlations between cytokines and metabolites before tocilizumab infusion in patients with worsening COVID-19 highlighted that dysregulated metabolomic and immunologic pathways were closely related to clinical worsening of patients developing critical COVID-19. A Correlation between cytokines and most significant metabolites at baseline was analyzed by Pearson correlation. *p < 0.05, **p < 0.01, ***p < 0.001. B Pearson correlations between IL10 and IL18 with anthranilic acid serum levels.

  Letter to the Editor High levels of TNFa in patients with COVID-19 refractory to tocilizumab F.X. Danlos a , F. Ackermann b , J. Rohmer b , M. Roumier b , A. Marabelle a,c, *, J.M. Michot c a INSERM U1015, Gustave Roussy Villejuif, France b Hoˆpital Foch, Suresnes, France c De´partement D'Innovation The´rapeutique et D'Essais Pre´coces (DITEP), Gustave Roussy, Universite´Paris Saclay, Villejuif, France Received 25 January 2021; accepted 29 January 2021 Available online 20 March 2021 Recently, Feldmann et al. in Lancet suggested investigating TNF alpha blocker therapies for patients with COVID-19 at high risk of developing a life-threatening form [1]. The cytokine release syndrome and immunothrombosis induced by Sars-CoV2 mostly explain the severity of coronavirus disease 2019 (COVID-19)

  [8] 5, following tocilizumab therapy. Patients were mainly men (22/25), median age was 56 [32e77] years old, median body mass index was 27.6 [22.7e39.0] and 13 (25%) patients had at least one comorbidity (cancer [n Z 6, 24%], high blood pressure [n Z 5, 20%], obesity [n Z 4, 16%] or diabetes [n Z 4, 16%]). Before tocilizumab, the median [range] of oxygen therapy was 9 [4e15] L/min. The median time between first COVID19 symptoms and tocilizumab infusion was 10 days (3e19). Fourteen patients received one dose of tocilizumab and the 11 other patients were treated with two doses. During follow-up, 16 (64%) patients achieved favourable clinical response to tocilizumab. Four (16%) patients died of COVID-19 in the first 30 days. Before tocilizumab, median C-reactive protein was 186 [26e396] mg/l, procalcitonin 0.15 [0.05e21.27] ng/mL, ferritin 1313 [232e4148] mg/L, d-dimer 858 [232e20,412] mg/L and fibrinogen 6.8 [2.2e8.9] g/L. The baseline serum IL-6 level did not significantly differ between patients who eventually died and those who survived (21.4 [5.1e38.2] pg/mL and 23.0 [5.4e56.7] pg/mL respectively, p Z 0.652, Wilcoxon test)

Fig.

  Fig.1. TNF-a levels were measured in serum collected from Healthy controls and COVID-19 patients before 'baseline' and 7 days after tocilizumab. Data shown are those of all the patients treated with tocilizumab and who were able to provide a pre-treatment (baseline) and post-treatment serum (up-to-date serum þ7). Of the 25 patients treated with tocilizumab, pre-treatment and post-treatment serum analyses were available for 18 patients represented according to their outcome; mortality before day 30 (left panel) and response to tocilizumab (right panel). Baseline TNF-a serum levels were significantly higher at baseline in patients who eventually died of Covid19 (left panel). Post-tocilizumab TNF-a serum levels were significantly increasing in patients who did not favourably respond to tocilizumab (right panel).

Fig. 3

 3 Fig. 3 Lymphopenia and high viral load are dismal prognosis factors for overall survival in cancer patients in the first and second surge of the pandemic. A Spearman correlation between the absolute lymphocyte counts (ALC) of Cancer_FR1 (from translational research and clinical routine), with the duration of SARS-CoV-2 RT-qPCR positivity (only evaluable patients for both factors, n = 69 patients). B, C ALC of Cancer_FR1 (from translational research and clinical routine) in SVS (n = 37 patients) versus LVS (n = 22 patients) subsets (B, left panel) or SARS-CoV-2-cycle threshold (Ct) >25 (n = 21 patients) versus Ct <25 (n = 29 patients) (B, right panel) monitored during the COVID-19 pandemic ("PER", between -4 and +7 days of the disease diagnosis by RT-qPCR), between 210 and 12 days before the symptom onset of COVID-19 ("PRE") or within the recovery period (between 0 and 123 days after negative RT-qPCR) ("POST") at Gustave Roussy, with the calculation of the reduction between "PRE" and during COVID-19 (C). One patient defined as an outlier (at 215%) by ROUT method was excluded from the LVS group for the analysis. Each line and dot represents one patient and one sample. Statistical analyses used one-way ANOVA (paired and unpaired) with Kenward-Roger method taking into account the number of specimen/patient (B): *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, and Mann-Whitney (C): **P < 0.01. D Kaplan-Meier curve and Cox regression analysis of overall survival of cancer patients from the Discovery (1st surge) cohort (Cancer_FR1 + Cancer_FR3), all stages included, according to ALC and Ct value at diagnosis. Refer to Table1for patient characteristics. E Multivariate Cox regression analysis stratified for the cohort and adjusted for age, ECOG status, gender, and metastatic and/or hematological status of cancer patients from the Discovery (1st surge) cohort (Cancer_FR1 + Cancer_FR3). F Kaplan-Meier curve and Cox regression analysis of overall survival of cancer patients from Validation (2nd surge) cohort (Cancer_FR1 + Cancer_FR3), all stages included, according to ALC and Ct value at diagnosis. Refer to Table1for patient characteristics.

Fig. 4

 4 Fig. 4 Prolonged viral shedding is associated with T-cell exhaustion. A Spearman correlation matrix focusing on the most significant immune variables and serum analytes monitored within the first 20 days of symptoms in patients diagnosed with COVID-19 in the Cancer_FR1_TR cohort. Stars indicate significant values (P < 0.05) for positive (red) or negative (blue) correlations. B Percentages of PD-1 expressing cells within the non-naive CD8 + CD3 + population (B, upper panel), monitoring in various phases of COVID-19 presentation (no virus infection (Ctls, gray dots), asymptomatic viral infection (Asym, light blue dots), symptomatic viral infection examined in the first 20 days (≤20 d) or after 20 days (>20 d) of symptoms with those experiencing short-term viral RNA shedding (SVS, orange dots) or long-term viral RNA shedding (LVS, purple dots) and RT-qPCR-negative COVID-19 patients in the convalescent phase (recovery, green dots or circled dots) among Cancer_FR1_TR (B, middle panel) and Spearman correlation with the duration of SARS-CoV-2 RT-qPCR positivity measured within the first 20 days of symptoms (B, lower panel). C Percentages of subsets co-expressing PD-1 and Granzyme B (C, left panel) or Granzyme B and FasL (C, right panel) in non-naive CD8 + . D Percentage of PD-1 + and Granzyme B + within the non-naive CD8 + expressing Eomes high TCF-1 high gate (D, left panel) and Spearman correlation between this ratio measured within the first 20 days of symptoms with the duration of SARS-CoV-2 RT-qPCR positivity (D, right panel). Box plots display a group of numerical data through their 3rd and 1st quartiles (box), mean (central band), minimum, and maximum (whiskers). Each dot represents one sample, each patient being drawn one to three times. Statistical analyses used one-way ANOVA with Kenward-Roger method to take into account the number of specimen/patients: *P < 0.05, **P < 0.01, ***P < 0.001. Each line and dot represents one patient and one sample, respectively (B, middle panel).

abstRact

  In 2020, the COVID-19 pandemic led to an unprecedented destabilization of the world's health and economic systems. The rapid spread and life-threatening consequences of COVID-19 have imposed testing of repurposed drugs, by investigating interventions already used in other indications, including anticancer drugs. The contours of anticancer drug repurposing have been shaped by similarities between the pathogenesis of COVID-19 and malignancies, including abnormal inflammatory and immunologic responses. In this review, we discuss the salient positive and negative points of repurposing anticancer drugs to advance treatments for COVID-19.

Figure 1 .

 1 Figure 1. Anticancer drugs proposed for drug repurposing in COVID-19 infection and presented according to their potential therapeutic targets. In mild to moderate COVID-19, anti-PD-1 reinforces the T-cell immune system and may improve viral clearance. Antiandrogen drugs block the TMPRSS2 receptor used by the virus for entry into the cell. In severe COVID-19, JAK/STAT or BTK inhibitors mitigate the inflammatory signaling cascade into the cell and help control the cytokine storm. Anticytokines (anti-IL6 receptor) directly reduce the cytokine storm by blocking the IL6 receptor on the cell surface. Antiangiogenic drugs could limit vascular abnormalities induced by the virus in endothelium. Other anticancer drugs, such as plitidepsin and apilimod, have antiviral activity against SARS-CoV-2 by targeting the host protein eEF1A or by affecting endosomal homeostasis, respectively.

Fig. 3 .

 3 Fig.3. Illustration of popular methods used to analyse bulk and single-cell RNA-seq in the field of immune-oncology. For (A) to (C), we used the public GSE78220 data set of bulk RNA-seq performed on 38 melanoma patients before treatment with immune-checkpoint inhibitors (either pembrolizumab or nivolumab) described in the study byHugo et al., Cell (2016) [25]. (A) Volcano plot for differential expression analysis comparing responder and non-responder patients. Gene names are represented in colours for differentially expressed genes with absolute fold change >0.3 and p-values < 0.01. (B) Heatmap organised by hierarchical clustering of the log-transformed gene expression for the differentially expressed genes (defined in A). The row side bar represents responder and non-responder patients. (C) PCA of the differentially expressed genes separates responder and non-responder patients in the two first dimensions of the PCA (Dim1 and Dim2). (D) UMAP representation of the single-cell RNA-seq analysis of the KUL01-T sample retrieved from the publicly available GSE144735 project (Ho et al., Nat Genet (2020)). The sample comprised 1.922 cells analysed and pre-filtered and 51.292 genes or genomic loci. Expression values have been normalised by an improved negative binomial model. Dimensionality reduction was performed using PCA and then UMAP on the first 30 dimensions, using R Seurat version 3.2.0. The UMAP plot displays the cells (1 point Z 1 cell). UMAP efficiently depict the clusters of immune cell types. A gene list of 1149 genes related to activation of invasion and metastasis processes were retrieved (gene list from Zhang et al., Front Genet (2020)). Colouring intensity represents the sum of the expressions in the list of genes. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 .

 4 Fig. 4. Evolution of the number of opening clinical studies that integrated conventional (A) or single-cell (B) RNA-seq analyses in their designs between 2010 and 2019, according to clinicaltrials.gov. Only studies related to cancer were considered. Non-interventional studies are shown in red, interventional studies are shown in black. Dotted lines correspond to estimations based on the number of clinical trials fulfilling the criteria that opened between January 1, 2020 and July 1, 2020. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

TABLE 1 .

 1 ONGOING CLINICAL TRIALS WITH IMMUNE CHECKPOINT BLOCKADE (ICB) COMBINATION THERAPIES FOR MALIGNANT MESOTHELIOMA.

		Treatment(s)	Phase	Type of treatment	Post-ICB	NCT
	Chemotherapy with ICB	PCT + atezolizumab +/-bevacizumab PCT with durvalumab Gemcitabine with atezolizumab	3 3 2	First line First Line _	N/A N/A Allowed	NCT03762018 NCT04334759 NCT04480372
		Ramucirumab with nivolumab	2	mAb anti VEGFR2	Excluded	NCT04287829
	Anti-angiogenic	Bevacizumab with atezolizumab	2	mAb anti VEGF-A	N/S	NCT03654833
	drug with ICB	Lenvatinib with pembrolizumab	2	Tyrosine kinase inhibitor	Excluded	NCT03502746
		Nintedanib with pembrolizumab	1	Tyrosine kinase inhibitor	Excluded	NCT02856425
		Niraparib with dostarlimab	2	PARP inhibitor	Allowed	NCT04940637
	Targeted therapy	Niraparib with dostarlimab	2	PARP inhibitor	N/S	NCT03654833
	with ICB	Bemcentinib with pembrolizumab	2	AXL inhibitor	N/S	NCT03654833
		Defactinib with pembrolizumab	½	FAK inhibitor	Excluded	NCT02758587
	Anti mesothelin therapy with ICB	LMB-100 + ipilimumab Anetumab ADC + pembrolizumab iCasp9M28z T-cell + pembrolizumab	1 ½ ½	Immunotoxin anti mesothelin ADC anti mesothelin Autologous T-cell antimesothelin	Allowed Excluded N/S	NCT04840615 NCT03126630 NCT02414269
		CRS-207 with pembrolizumab	2	Mesothelin L. monocytogenes	Excluded	NCT03175172
		THOR-707 with pembrolizumab	2	IL-2 PEGylated	Allowed	NCT04914897
		SO-C101 with pembrolizumab	1	IL-15 superagonist RLI	Allowed	NCT04234113
	Immune stimula-	DC with pembrolizumab	1	Autologous DC + IL-2	Excluded	NCT03546426
	tory drugs with ICB	Galinpepimut-S with nivolumab	1	Targeted Cancer Vaccine	Excluded	NCT04040231
		UV1 with nivolumab + ipilimumab	2	UV1 vaccine + IL-2	Excluded	NCT04300244
		MTG201 with nivolumab	2	Adenovirus	Excluded	NCT04013334
		Naptumomab ADC with durvalumab	1	Fab anti 5T4 x SEA/E-120	Allowed	NCT03983954
		NGM707 with pembrolizumab	½	Antibody anti ILT2/ILT4	Allowed	NCT04913337
	ICB Combinations	MK-4830 with pembrolizumab Sym024 with Sym021 (anti PD-1)	1 1	Antibody anti ILT4 Antibody anti CD73	Allowed Excluded	NCT03564691 NCT04672434
		XmAb 20717	1	bispecific Ab aPD-1 x aCTLA-4	Excluded	NCT03517488

  ADCC) is one key mechanism of action of anti CTLA-4 antibodies(241,242). Nevertheless, presence of Treg in human tumor is not yet associated with efficacy of anti CTLA-4 antibodies and their mechanism of action remain controversial (243,244). The IgG4 isotype of anti PD-1 monoclonal antibodies have a low affinity to FcGR, except with FcGRI (CD64), and should not promote ADCC. A preclinical study showed that linkage to CD64 could led to anti PD-1 clearance by macrophages in tumors (245). The anti-PD-L1 antibodies atezolizumab and durvalumab are IgG1 antibodies with mutated Fc regions in order to have a low affinity for FcGR and avoid ADCC/ADCP (246). However avelumab has a non-modified IgG1 sequence and can trigger ADCC (247-249). This feature negatively impacts the half life of avelumab and increases its infusion related reactions but do not alter its objective response rate compared to other anti-PD-L1 (250).

). Preclinical studies highlighted that depletion of Tregs constitutively expressing CTLA-4 by antibody dependent cellular cytotoxicity (

TABLE 3 .

 3 In vitro, target receptors are all inhibited by nintedanib at low nanomolar concentrations (cf Table3) (187). In mouse models, nintedanib showed good antitumor efficacy at doses of 50 -100 mg/kg, leading to a substantial delay of tumor growth or even complete tumor-stasis in xenografts of a broad range of differing human tumor types (285). Histological examination of treated tumors showed a marked reduction of tumor vessel density of approximately 80% (285).

	Kinases	IC50 (nM)*
	VEGFR-1	34 +/-15
	VEGFR-2	21 +/-13
	VEGFR-3	13 +/-10
	FGFR-1	69 +/-70
	FGFR-2	37 +/-2
	FGFR-3	108 +/-41
	FGFR-4	610 +/-117
	PDGFR-α	59 +/-71
	PDGFR-β	65 +/-7
	EGFR	>50000
	CDK4	>10000
	Lck	16 +/-16

IN VITRO KINASE INHIBITION PROFILE OF NINTEDANIB.

*Assays performed with ATP concentration at the respective Michaelis constant (Km). This table is a reproduction from one published in Roth and al. Journal of Medicinal Chemistry, 2015 (187). mined at 250 mg bid (i.e twice daily dosing) with a manageable safety profile in advanced cancer patients (287). Based on the overall safety profile, the recommended phase 2 dose (RP2D) for nintedanib as monotherapy is 200 mg bid (287).

The predominant adverse events were nausea, diarrhea, vomiting, abdominal pain, and fatigue of mostly low to moderate severity. Dose limiting toxicities (DLT) were mainly limited to reversible hepatic enzyme elevations (AST, ALT, γGT) which increased dose dependently. Most cases occurring at doses of 250 mg and above, and a very low incidence at doses below 200 mg and were reversible after discontinuation of nintedanib treatment (287). All adverse events observed after administration of single doses of nintedanib to healthy volunteers were only of Common Terminology Criteria for Adverse Events (CTCAE) grade 1 severity and fully reversible (286).

Based on phase I trials combining nintedanib with pemetrexed, docetaxel, paclitaxel/carboplatin, or FOLFOX, the recommended dose of nintedanib in combination is 200 mg bid (171,288,289). The pattern of the treatment related adverse events of nintedanib in combinations with chemotherapies was comparable to the adverse event profile of the phase I monotherapy trial. Nintedanib is being investigated in several cancer indications and has been approved by the EMA for the treatment of second line non-squamous NSCLC in combination with docetaxel (290). Additionally, nintedanib is approved for idiopathic pulmonary fibrosis (IPF) in United States of America and European Union (291).

  , phase III study of nintedanib plus pemetrexed versus placebo plus pemetrexed in patients with advanced nonsquamous NSCLC after failure of first line chemotherapy (292). No safety issues were identified. Even though the study was stopped prematurely, the primary endpoint of this Phase III trial was met; treatment with nintedanib plus

	Safety issues regarding concomitant blockade of VEGF and PD-1 pathways.
	It is of interest to investigate whether the combination of these two treatments is tolerable. Nintedanib has a favorable
	safety profile, with the main adverse events being diarrhea and liver enzyme elevations. During treatment with pem-
	brolizumab, such as with other anti-PD-1 agents, a unique set of toxicities occur, called immune related adverse events
	(irAEs) (220). Most reported irAE of pembrolizumab are rash, pruritus, vitiligo, hypothyroidism, arthralgia, diarrhea, and
	pemetrexed resulted in a significant prolongation of PFS compared with placebo plus pemetrexed but there was no
	pneumonitis. Most of the time, an efficient management of irAE includes immune checkpoint blockade therapy suspen-
	improvement in OS in nintedanib-treated patients. Nintedanib 200 mg bid in monotherapy or combination with chemo-
	sion, associated sometimes to anti-inflammatory corticosteroids (303). But some irAE can be severe, refractory to cor-
	therapies had an acceptable and manageable safety profile, with no new or unexpected safety findings.
	ticosteroids and deadly (221). Among them myocarditis are infrequent events but leading to death of 50% of patients
	(61/122 patients) treated by immune checkpoint blockade reported in the VigiBase (WHO's global database of individual
	Pembrolizumab: a humanized anti PD-1 monoclonal IgG4-kappa isotype antibody case safety reports) (304). These irAEs include colitis, characterized by a mild to moderate but occasionally severe,
	Pembrolizumab is a highly selective, humanized monoclonal IgG4-kappa isotype antibody against PD-1 (208). The vari-
	able region sequences of a very-high-affinity mouse anti-human PD-1 antibody were grafted into a human IgG4 immu-
	noglobulin with a stabilizing S228P Fc alteration (293). The IgG4 immunoglobulin subtype does not promote ADCC or
	activate complement, thus should avoid cytotoxic effects of the antibody when it binds to the T cells that it is intended
	to activate (293). The first dose escalation in a phase 1 study involving patients with solid tumors showed that pembroli-
	zumab was safe at the dose levels tested (1 mg per kilogram of body weight, 3 mg per kilogram, and 10 mg per kilogram,
	administered every 2 weeks) without reaching a maximum tolerated dose (13,294). In addition, clinical responses were
	observed at all dose levels (13,294). The efficacy of pembrolizumab in melanoma has been subsequently established in
	a randomized dose comparison Phase 1 trial which demonstrated that pembrolizumab had the same efficacy at 2 and
	10mg/kg Q3W with an ORR of 21% in both cohorts (295). Pembrolizumab has also demonstrated superior efficacy to
	either chemotherapy or ipilimumab (anti-CTLA-4) immunotherapy in metastatic melanoma (296). Pembrolizumab has
	demonstrated activity with durable tumor responses in NSCLC (217), Hodgkin lymphoma (297), Gastric cancers (298),
	OC (299), CRC with mismatch repair deficiency (MMRd) (256), mesothelioma (300) and urothelial cancer (301). Anti-PD-
	1 monoclonal antibody has also shown anti-tumor activity in renal cell carcinoma (10), and hepatocellular carcinoma
	(302). Pembrolizumab is now approved in the United States of America by the Food & Drug Administration (FDA) and
	the European Commission granted marketing authorizations for the treatment of patients with many cancer locally
	advanced or unresectable (2).

LUME-Lung 1 met its primary endpoint by showing a statistically significant improvement of progression free survival (PFS) for all patients regardless of histology for nintedanib in combination with docetaxel. A significant improvement in overall survival (OS) was demonstrated in patients with adenocarcinoma. LUME-Lung 2 was a multicenter, randomized, doubleblind

  The protocol was first approved by the Agence Nationale de Sécurité du Médicament (ANSM) on June 24 th , 2016 (Ref #160371A-12) and by the Ethical Committee (Comité de Protection des Personnes Ile-de-France 1) on Jul 12 th , 2016 (Ref #2016-mai-14236ND). The trial was first posted on clinicaltrials.gov on Aug 4 th 2016 (NCT02856425).

	Patients
	Eligible patients had advanced, metastatic cancer which progressed after at least one line of standard therapy or were
	intolerant to standard therapy, naive to immune checkpoint blockade and nintedanib. Additional inclusion criteria in-
	cluded age ≥ 18 years, Eastern Cooperative Oncology Group (ECOG) performance status of 0-1, adequate organ function,
	measurable disease according to RECIST v1.1, and written informed consent. Key exclusion criteria were radiographic
	evidence of cavitary tumors, local invasion of major blood vessels and/or at risk for perforation, history of clinically
	significant hemoptysis within the past 3 months, history of clinically significant hemorrhagic or thromboembolic event
	in the past 6 months, history of significant cardiovascular diseases, prior treatment with anti PD-(L)1 agents, concurrent
	steroid medication, history of autoimmune and inflammatory disease. This study was conducted in compliance with the
	Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects.
	Procedures
	Screening procedures were performed up to 21 days (D-28) before Day -7 (start of nintedanib). Patients continued
	treatment until disease progression, undue toxicity, or withdrawal of consent for a maximum duration of 24 months.
	Adverse events were graded using National Cancer Institute (NCI) CTCAE Version 4.03. The occurrence of a non-hema-
	tological toxicity ≥ CTCAE Grade 3, a hematological toxicity ≥ CTCAE Grade 4, or an inability to resume nintedanib dosing
	within 7 days of stopping due to treatment related toxicity during the first 4 weeks were considered as a DLT, if judged
	by the Investigator to be possibly, probably, or related to study drug administration. Tumor evaluation was performed
	every 6 weeks based on Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and immune-related RECIST (irRECIST)
	(327,328). Details about screening exams, Nintedanib dose modification criteria, and other patient management rules
	are detailed in the protocol of the trial provided in the supplementary information files.

FIGURE 6. DECISION TREE FOR THE NINTEDANIB DOSE ESCALATION (6A) AND THE COLLECTION OF BIOLOGICAL SAMPLES FOR ANCIL-LARY ANALYSES (6B).

TABLE 4 .

 4 CLINICAL CHARACTERISTICS OF PATIENTS ENROLLED IN THE DOSE ESCALATION COHORT OF THE PEMBIB PHASE 1B TRIAL.

	4. Wilcoxon-Mann-Whitney test was

  MM) is a peculiar cancer, developed from pleural linings, upon chronic airways inhalation of environmental silicate mineral such as asbestos (343). Chronic exposure to asbestos microparticles leads to pleural inflammation, recruitment of inflammatory macrophages, development of an immunosuppressive pro tu-

	Introduction
	Unresectable malignant mesothelioma (
	: Patients with unresectable mesothelioma relapsing after at least one line of platinum-based chemotherapy
	were treated with a combination of oral nintedanib (150mg BID) & IV pembrolizumab (200mg Q3W), with a 7 days
	nintedanib lead-in preceding pembrolizumab initiation. Immune cells, cytokines, chemokines & soluble factors were
	prospectively measured in baseline and on-treatment fresh tumor biopsies & blood samples by flow cytometry and
	multiplexed immuno-assays. RNA-sequencing and whole exome sequencing were run on tumor samples, single cell
	RNAseq on blood samples.
	Results: 30 patients received at least one infusion of pembrolizumab combined with oral nintedanib. Median age of
	patients was 68 years old (38-85) and 86% had epithelioid mesothelioma. The most frequent adverse events (AE)
	(grades 1-3) related to the combination were liver enzymes increase, fatigue, nausea, and diarrhea. Four (13.3%) pa-
	tients developed grade 3-5 immune-related AE. Patients died of cancer progression (n=14), myocarditis with
	thrombo-embolic event (n=1) and COVID-19 (n=1). Median follow-up was 14.8 months (95%CI [9.70-18.2]). Best objec-
	tive response rates (BOR) were Partial Response (PR; n=7/29; 24.1%), Stable Disease (SD; n=17/29; 58.6%) and Pro-
	gressive Disease (PD; n=5/29; 17.2%). Disease Control Rate (DCR; defined as PR + SD) was 68.4% and 46.6% at 12 and
	24 weeks, respectively.
	Patients with durable clinical benefit (DCB), without progressive disease before 6 months, had significantly higher per-
	centages of PDL1 expression on tumor cells and higher CD8+ T cells infiltrate in tumor biopsies, evaluated by flow cy-
	tometry at screening. Upon treatment, soluble plasma rate of CXCL9 and CXCL13 as well as tumor immune infiltrates
	estimated by deconvolution of tumor biopsies RNA-seq increased in all patients independently from clinical benefit.
	Transcriptomic estimates of NK cells, T cells and myeloid dendritic cells infiltrates in baseline and C2D1 tumors biopsies
	were higher in patients with DCB. Pre & on-treatment IL6 and IL8 levels in tumor biopsies supernatant & blood plasma
	were higher among patients with early progression. Gene Set Enrichment Analyses on RNA-seq from screening tumor
	biopsies found an enrichment in E2F, MYC and KRAS gene pathways and lower expression of type 1 interferon signature
	in patients with early progressive disease than those with clinical benefit. Aneuploidy, or tumor somatic copy number

moral microenvironment, constitution of pathological neoangiogenesis with hypoxia and eventually pleural cells malignant transformation (344).

TABLE 6

 6

	Total (n=30)

). Two patients developed arterial thrombosis (acute coronaropathy [n=2] and mesenteric ischemia [n=1]). Patients died because of cancer progression (n=14/30, 46.7%), myocarditis with mesenteric thrombosis (n=1/30, 3.3%) and COVID-19 (n=1/30, 3.3%). . BASELINE CHARACTERISTICS OF PATIENTS INCLUDED IN THE EXPANSION COHORT DEDICATED TO NAÏVE TO IMMUNOTHER-APY, REFRACTORY TO PLATINUM BASED CHEMOTHERAPY ADVANCED MESOTHELIOMA.

TABLE 7 .

 7 LISTING OF ADVERSE EVENTS ASSOCIATED WITH TREATMENTS OR NOT OCCURING DURING THE TRIAL (MESOTHELIOMA

	COHORT).

Antitumoral activity of pembrolizumab with nintedanib for patients with advanced pleural mesothelioma.

Median follow-up was 14.8 months (95% confidence interval [95%CI]

[9.70:18.2]

). Patient's median progression free survival (PFS) was 6.2 months (95%CI [3:8.7]) (Figure

11A

). One patient could not be evaluated for tumoral response because of early death related to the above-mentioned grade 5 adverse event. Best Objective Responses (BOR) were Partial Response (PR, n=7/29; 24.1%), Stable Disease (SD, n=17/29; 58.6%) and Progressive Disease (n=5/29; 17.2%) (Figure

11B

). Disease Control Rates (DCR) (defined as PR + SD) were 68.4% (95%IC

[43.4:87.4]

) and 46.6% at 3 and 6 months, respectively. At database lock, two patients (7%) ended treatment because they completed the 2-year treatment per protocol but 23/30 (79%) had to stop because of cancer progression (Figure

11C

). Median PFS according to best objective responses were respectively 7.1 months (95% CI [3:9.3]) and 12.9 months (95% CI [1.7: not reached]) for SD and PR patients, respectively (Supplementary figure

4

).

TABLE 2 .

 2 . CDKN2A homozygous deletion has been associated with low immune infiltrate in TME, and CDK4/6 inhibitors have been shown to increase tumor immune infiltrations and to enhance the efficacy of immune checkpoint blockade (378-381). MYC pathway upregulation is able to induce an immunosuppressive microenvironment through an upregulation of immune checkpoint expressions by cancer cells and by the recruitment of immunosuppressive immune cells in the tumor stroma(382,383). Moreover, cancer cells proliferation and stemness abilities have been shown to be associated with tumoral immune suppression (384).Our estimation of aneuploidy from WES, even with a limited number of patients, allowed to highlight that SCNA was higher in pre-treatment tumor biopsies from patients who did not benefit from our treatment. Interestingly, SCNA was positively correlated with the concentration of IL6 and CCL2, respectively in the blood and secretome of tumor effects of anti-angiogenics between patients with benefit or not to treatment. Similar decreasing concentrations of angiopoietin-2 in the plasma upon nintedanib should limit the recruitment of inflammatory circulating macrophages expressing Tie2 for every patients receiving the treatment (Supplementary figure10) (128,393-395); but these effects seem insufficient in refractory patients. STUDY OF IMMUNE INFILTRATION IN SCREENING TUMOR BIOPSIES BY IHC. THE TEST USED TO COMPARE NOMINAL MEASUREMENTS (SEMI QUANTITATIVE SCORES) WAS Χ² TEST.

	The disease control rate at 12 weeks observed in this cohort, close to those observed in early trials evaluating the anti
	PD-1 + anti CTLA-4 combination, suggest that nintedanib could have a significant effect in combination with pembroli-
	zumab. Of note, bevacizumab proved an antitumoral effect only in association with platinum-based chemotherapy
	(75,390). Mesothelioma are highly hypoxic tumors and mesothelioma cells are sensitive to VEGF as a growth factor

biopsies. An association between aneuploidy, low immune tumoral infiltrate and systemic inflammation has been recently observed in different tumor types

(385,386)

. In mesothelioma, a disease with sub clonal evolutions, and in particular, with the late clonal acquisition of deletion of the NF2 genes in addition to BAP1 and CDKN2A, has been associated with this inflammation phenotype (386). The disease evolution of our patient #23 during the treatment, with a dissociated response between his pleural and peritoneal lesions, suggested that an homozygous deletion of CDKN2A could have occurred in his peritoneal cancer cells, hereby promoting an immunosuppressive inflammatory phenotype and a peritoneal progression (Figure

12D

; Supplementary figure

8

). The mechanisms leading to the secretion of such chemokines was not determined, but these molecules are known to recruit potential immunosuppressive neutrophils, inflammatory monocytes and recruit macrophages with a high potential to promote tumor metastatic spreading and immune evasion, directly or indirectly, notably through VEGF production and interaction with Tregs in the tumor stroma (342,387-389). Of note, we observed a heterogeneity among circulating monocytes of MM patients, notably among intermediate monocytes which deserve additional analyzes.

(391). Antiangiogenic drugs should improve tumor endothelial cells and T cells anergy induced by VEGF, and favor tumor infiltrates by efficient anti tumoral cytotoxic T cells (315,392). However, our ancillary analyses did not bring clear information about differential that patients with refractory diseases had, before the initiation of treatment, higher aneuploidy, positively associated with inflammatory patterns and recruitment of immunosuppressive inflammatory innate cells in their tumors. The loss of tumor suppressor genes, such as CDKN2A was enriched in malignant pleural mesothelioma, leading to molecular pathway upregulations with pro tumoral inflammatory features. Early identification of patients with such somatic genomic alterations could help to propose new therapeutic associations and treatment stratification strategies in MM.

SUPPLEMENTARY TABLE 1. BASELINE CHARACTERISTICS OF PATIENTS INCLUDED OF THE EXPANSION

COHORT DEDICATED TO NAÏVE OF IMMUNOTHERAPY, REFRACTORY TO PLATINUM-BASED CHEMOTHERAPY, UNRESECTABLE PLEURAL MESOTHELIOMA CONSIDERING PATIENTS WITH AND WITHOUT DCB. TESTS TO COMPARE NOMINAL AND ORDINAL MEASUREMENT WERE Χ² TEST AND WILCOXON RANK-SUM TEST, RESPECTIVELY. SUPPLEMENTARY FIGURE 4. ILLUSTRATION OF DISSOCIATED RESPONSE BETWEEN PLEURAL AND PERITONEAL LESIONS OF PATIENT #23 BEFORE 6 MONTHS OF TREATMENT. SUPPLEMENTARY FIGURE 5. KAPLAN MEIER SURVIVAL CURVE ILLUSTRATING SIGNIFICANTLY DIFFERENT OUTCOMES OF PATIENTS ACCORDING TO THE BEST OBJECTIVE RESPONSE (MESOTHELIOMA COHORT). LEFT. PROGRESSION FREE SURVIVAL ACCORDING TO THE BEST OBJECTIVE RESPONSE (RECISTV1.1 DISTINCTION). SUPP. RIGHT. OVERALL SURVIVAL ACCORDING TO BEST OBJECTIVE RESPONSE (RECISTV1.1 DISTINCTION).

TABLE 8 .

 8 ONGOING EARLY CLINICAL TRIALS WITH MONOTHERAPIES FOR MALIGNANT MESOTHELIOMA.For instance, Poly ADP Ribose Polymerase (PARP) inhibitors have been shown to generate chromatin fragments which activate cyclic GMP-AMP synthase/stimulator of IFN genes (cGAS/STING) pathway, and induce the production of CCL5 and the expression of PD-L1 by tumor cells in cell lines with homologous reparation deficiencies (427,428). Also, as discussed previously, EZH2 inhibition could sensitize tumor cells to specific immune cells. Moreover, it was shown that anti CTLA-4 administration increased EZH2 expression in peripheral T cells from treated patients with cancer receiving ipilimumab[START_REF] Messina | 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy?[END_REF]. EZH2 blockade in Tregs decreased their immunosuppressive functions, increased tumor infiltration by functional effector CD8+ T cells and inhibited tumor growth in a mice model (430).Considering MM with BAP1 loss, two phase 2 clinical trials evaluating PARP inhibitor and EZH2 inhibition have been done in patients with MM refractory to platinum-based chemotherapy. In the basket trial MiST1 (for Mesothelioma Stratified Therapy), dedicated to patients with relapsed MM (NCT03654833), treatment by rucaparib monotherapy brought a disease control rate at 12 weeks to 58% (n=15/26; 95% CI 37-77) and was well tolerated (431). Moreover, a phase 2 clinical trial evaluating an EZH2 inhibitor monotherapy for patients with refractory MM reported 51% of patients achieving disease control at 12 weeks (432).

		Treatment(s)	Phase	Type of treatment	Post-ICB	NCT
		Olaparib	2	PARP inhibitor	Allowed	NCT04515836
		Niraparib	2	PARP inhibitor	Allowed	NCT03207347
		CPI-0209	½	EZH2 inhibitor	Allowed	NCT04104776
		VT3989	2	TEAD inhibitor	Allowed	NCT04665206
	Tumor-targeted small molecule therapy	Defactinib APG-2449 Vandetanib Tivantinib	1 2 2 2	FAK inhibitor FAK, ALK, ROS1 inhibitor VEGFR, EGFR, RET inhibitor MET inhibitor	Allowed Allowed Allowed Allowed	NCT01870609 NCT03917043 NCT00597116 NCT01861301
		Dovitinib	2	FGFRs, VEGFR, (…) inhibitor	Allowed	NCT03975387
		ASTX295	1/2	MDM2 antagonist	Allowed	NCT04430842
		IOA-244	1	PI3Kδ Inhibitor	Allowed	NCT04328844
		PF-06952229	1	TGFbR1 inhibitor	Allowed	NCT03685591
		Autologous CAR-T/TCR-T Cell	½	CAR-T-cell anti mesothelin	Allowed	NCT03638206
		Gavo-cel (TC-210)	½	CAR-T-cell anti mesothelin	Allowed	NCT03907852
		huCART-meso cells	1	CAR-T-cell anti mesothelin	Excluded	NCT03054298
	Tumor-targeted Cel-lular Immunother-apy	M28z1XXPD1DNR αPD1-MSLN-CAR T Cells Adoptive Transfer of TIL NeoTIL	1 1 2 1	CAR-T-cell anti mesothelin CAR-T-cell anti mesothelin Tumor infiltrated lymphocytes Tu-mor infiltrated lymphocytes	Allowed Allowed Allowed Allowed	NCT04577326 NCT04489862 NCT03935893 NCT04643574
		Allogenic Natural Killer Cells	1	Cord Blood-derived NK	Allowed	NCT03420963
		Autologous Dendritic Cell Vaccination	½	DC-WT1 vaccination	Allowed	NCT02649829
		MesoPher	2/3	DC vaccination	Allowed	NCT03610360
		LMB-100 with Tofacitinib	1	anti mesothelin ADC + JAKi	Allowed	NCT04034238
	Tumor-targeted protein engineered therapy	HPN536 INBRX-109 SCB-313 SGN-CD228A	½ 1 1 1	Trispecific and MSLN/ALB/CD3 DR5 agonistic antibody TRAIL-Trimer Fusion Protein anti-CD228 ADC (MMAE)	Allowed Allowed Allowed Allowed	NCT03872206 NCT03715933 NCT03443674 NCT04042480
		Brentuximab Vedotin	2	anti-CD30 ADC (MMAE)	Allowed	NCT03007030

  1 and anti CTLA-4 immune checkpoint blockade combination has been approved by the FDA to treat patients with unresectable pleural MM, independently of molecular subtypes, genomic or epigenetic alterations, or tumor microenvironment features. Because primary and secondary resistances to those immunotherapies are frequent in MM, novel therapeutic strategies are eagerly awaited for this deadly cancer. The recent discoveries in the pathophysiology of MM and our better understanding of the mechanisms of resistance to immune checkpoint blockade provide now opportunities of patient stratification and biologically driven drug development in future clinical trials.

	KEYWORDS
	Chemokine;
	Immune checkpoint
	blockade;
	Biomarker;
	PD-1;
	PD-L1;
	CTLA-4

Table 1

 1 The human chemokine network: receptors and ligands.

	Receptors conventional	Chemokines agonist	Antagonist	Main immune functions [12]
	or 'atypical'
	CCR1	CCL 2, 3, 5, 7, 8, 13, 14, 15, 23	CCL 4, 18	DC and monocytes chemotaxis
	CCR2	CCL 2, 7, 8, 11, 13, 16	CCL 24, 26	T-helper 1 differentiation, DC and monocytes chemotaxis
	CCR3	CCL 2, 5, 8, 11, 13, 15, 24, 26, 28	CCL 18	T-helper 2 immunity, mast cells and eosinophils chemotaxis
	CCR4	CCL 17, 22		T-helper 2 immunity
	CCR5	CCL 2, 3, 4, 5, 8, 11, 13, 14	CCL 7	DC chemotaxis, production of TNF-a
	CCR6	CCL 20		DC chemotaxis, T-cell migration
	CCR7	CCL 19, 21		DC, monocytes and neutrophils chemotaxis,
				lymphocytes chemotaxis in lymphoid tissues
	CCR8	CCL 21		Monocytes chemotaxis, thymopoiesis
	CCR9	CCL 25		Recruitment of leukocytes to the gut
	CCR10	CCL 27, 28		Recruitment of T cells to the skin
	CXCR1	CXCL 1, 6, 8		DC and neutrophils chemotaxis
	CXCR2	CXCL 1, 2, 3, 5, 6, 7, 8		DC and neutrophils chemotaxis
	CXCR3-A, B, Alt	CCL 5, 13, 19, 20
		CXCL 9, 10, 11, 12a	CCL 7,11	T-helper 1 immunity
	CXCR4	CXCL 12a, 12b, 12g, 12d, 12ε		Lymphopoiesis, haematopoiesis
	CXCR5	CXCL 13		B-cell activation
	CXCR6	CXCL 16		T-cell adhesion and migration
	CX3CR1	CX3CL 1		Monocytes chemotaxis, microglial cells activation
				and migration
	XCR1	XCL 1, 2		Treg cells differentiation in the thymus
	ACKR1	CCL 2, 5, 7, 11, 14, 17
		CXCL 5, 6, 8, 11
	ACKR2	CCL 2, 3, 4, 5, 7, 8, 11, 13, 14, 17, 22		Regulation of chemokine abundance
	ACKR3	CXCL 11, 12a
	ACKR4	CCL 19, 21, 25
	CCRL2	CCL 19

Table 2

 2 Critical chemokines in immune checkpoint blockade therapy.

	Chemokine	Assay	Material	Therapy	Outcome	Reference
	CCL4	Gene expression	Tumour	Ipilimumab (aCTLA-4)	Higher at baseline in responders	[54]
	CCL5	Gene expression	Tumour	Ipilimumab (aCTLA-4)	Higher at baseline in responders	[54]
				Pembrolizumab (aPD-1)		[55]
	CXCL5	Protein assay	Serum	Nivolumab (aPD-1)	Higher absolute increase in patients who	[66]
					developed adverse events upon treatment
	CXCL9	Gene expression	Tumour	Atezolizumab (aPD-L1)	Higher at baseline in responders	[51]
				Durvalumab (aPD-L1)		[56]
				Ipilimumab (aCTLA-4)		[54]
				Pembrolizumab (aPD-1)		[55]
		Protein assay	Plasma	Pembrolizumab (aPD-1)	Increase in all patients upon treatment	[52]
			Serum	Nivolumab (aPD-1)	Decrease in all patients upon treatment	[53]
	CXCL10	Gene expression	Tumour	Ipilimumab (aCTLA-4)	Higher at baseline in responders	[54]
				Pembrolizumab (aPD-1)		[55]
		Protein assay	Serum	Nivolumab (aPD-1)	Decrease in all patients upon treatment	[53]
	CXCL11	Gene expression	Tumour	Ipilimumab (aCTLA-4)	Higher at baseline in responders	[54]
				Pembrolizumab (aPD-1)		[55]
		Gene expression	Tumour	Ipilimumab (aCTLA-4)	Higher increase in responders upon treatment	[54]
		Protein assay	Plasma	Atezolizumab (aPD-L1)	Increase in all patients upon treatment	[51]
	CXCL13	Gene expression	Tumour	Ipilimumab (aCTLA-4)	Increase in all patients upon treatment	[59]
				þ nivolumab (aPD-1)
				Pembrolizumab (aPD-1)	Higher in responders	[60]
	CX3CL1	Gene expression	Tumour	Atezolizumab (aPD-L1)	Higher at baseline in non-responders	[51]
	CCR5	Gene expression	Tumour	Pembrolizumab (aPD-1)	Higher at baseline in responders	[55]
	CXCR5	IHC	Tumour	Pembrolizumab (aPD-1)	Higher in responders	[60]
				Ipilimumab (aCTLA-4)		[61]
	CXCR6

TABLE 1 . Patient characteristics Discovery cohort (n=95)

 1

	Age

1 prostate carcinoma, biliary tract cancer, thyroid cancer, prostate carcinoma, uterine carcinoma, 2 cervix carcinoma, colorectal cancer, gastric cancer, head and neck cancer, renal cancer, soft-tissue sarcoma, triple negative breast carcinoma J o u r n a l P r e -p r o o f

TABLE 2 . Multivariate Analysis for Progression-Free Survival and Overall Survival Progression-Free Survival

 2

	Independent Variables		Hazard ratio	P value
	ECOG CODE	≤1	0.43 95% CI [0.29-0.65]	<0.001
		≥2	1
	Liver metastasis	Yes	1	0.042
		No	0.67 95% CI [0.46-0.98]
	Number_of_previous lines of treatment	≤1	0.61 95% CI [0.44-0.86]	0.004
		≥2	1
	LIF plasma levels	High	1.51 95% CI [1.1-2.1]	0.013
		Low	1
	Overall Survival
		B	Hazard ratio	P value
	ECOG CODE	≤1	0.21 95% CI [0.13-0.35]	<0.001
		≥2	1
	Liver metastasis	Yes	1	0.008
		No	0.54 95% CI [0.34-0.85]
	Number_of_previous lines of treatment	≤1	0.61 95% CI [0.40-0.94]	0.027
		≥2	1
	LIF plasma levels	High	1.78 95% CI |1.14-2.77]	0.01
		Low	1

  ).

	Discovery cohort
	Cancer patients' characteristics	Cancer_FR1_TR +	Ct > 25 &	Ct > 25 &	Ct < 25 &	Ct < 25 &
		Cancer_FR1_CR +	ALC > 800	ALC < 800	ALC > 800 (n	ALC < 800
		Cancer_FR3	(n = 36)	(n = 22)	= 29)	(n = 23)
		(n = 110)

Table 1 continued

 1

	Validation cohort
	Cancer patients' characteristics	Cancer_FR1_CR +	Ct > 25 &	Ct > 25 &	Ct < 25 &	Ct < 25 &
		Cancer_FR3	ALC > 800	ALC < 800	ALC > 800 (n	ALC < 800
		(n = 116)	(n = 22)	(n = 27)	= 29)	(n = 38)

  2,4

	Review
	1 Department of Medical Oncology, Gustave Roussy, Villejuif, France. 2 Drug
	Development Department, Gustave Roussy, Villejuif, France. 3 INSERM
	U1015, Gustave Roussy, Villejuif, France. 4 Paris Saclay University, Saint-
	Aubin, France. 5 Division of Hematology/Oncology, Department of Medi-
	cine, University of California, Los Angles, Los Angeles, California.
	Corresponding Author: Jean-Charles Soria, Gustave Roussy Cancer Cam-
	pus, 114 Rue Edouard Vaillant, Villejuif 94805, France. Phone: 33-0-14211-
	4016; E-mail: Jean-Charles.Soria@gustaveroussy.fr
	Cancer Discov 2021;11:1336-44
	doi: 10.1158/2159-8290.CD-21-0144
	©2021 American Association for Cancer Research.

  2 do not develop symptoms or develop mild manifestations characteristic of flu-like

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  phenomenon of immunothrombosis, a hypercoagulopathy

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Treat COVID-19 JAK

  is a family of intracellular, non-receptor tyrosine kinases that transduce signals transferred by the cytokines Published OnlineFirst April 12, 2021; DOI: 10.1158/2159-8290.CD-21-0144

	Anticancer Drug Repurposing in COVID-19
	Downloaded from	cancerdiscovery.aacrjournals.org	on September 7, 2021. © 2021 American Association for
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REVIEW

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table 1 . Examples of cancer drugs currently tested for COVID-19

 1 Published OnlineFirst April 12, 2021; DOI: 10.1158/2159-8290.CD-21-0144

			MoA related to Covid
	Targeted pathway Drug	pathogenesis	COVID-19 severity	Clinical trial
	Anticytokines
	IL6	Tocilizumab	Prevents the "cytokine storm"	Severe	NCT04377659
					Phase II
				Severe	NCT04363853
					Phase II
				Requiring hospitalization	NCT04317092 (TOCIVID-19)
				Requiring hospitalization	NCT04372186 (EMPACTA)
					Phase III
	IL8	BMS-986253	Prevents the "cytokine storm"	Severe	NCT04347226
					Phase II
	JAK inhibitors	Pacritinib (JAK2i)	Blocks multiple, proinflam-	Severe	NCT04404361 (PRE-VENT)
			matory cytokines; antiviral		Phase III
			effects by impeding cellular
			viral endocytosis
		Baricitinib		Moderate and severe	NCT04373044, Phase II
		(JAK1/2i) + anti-
		viral therapy
		Baricitinib		Moderate and severe	NCT04346147, Phase II
					NCT04390464, Phase IV
		Ruxolitinib (JAK1/2i)		Severe	NCT04331665
	BTK	Ibrutinib	Abrogation of pulmonary	Requiring hospitalization	NCT04439006
			inflammatory cytokines,		Phase II
			lung injury	Severe	NCT04375397 (iNSPIRE)
					Phase II
		Acalabrutinib		Requiring hospitalization	NCT04346199 (CALAVI)
					Phase II
		TL-895		Requiring hospitalization	NCT04419623
					Phase I/II
	Antiangiogenics	Bevacizumab	Inhibits VEGF, a key factor to	Severe	NCT04305106 (BEST-RCT)
			increase vascular perme-	Severe	NCT04275414 (BEST-CP)
			ability and induce pulmonary		Phase II
			edema caused by inflamma-	Requiring more than 3 L of	NCT04344782 (CORIMMUNO-
			tory exudation in COVID-19	oxygen	BEVA)
			infection		Phase II
	Androgen blockade Bicalutamide	Blocks TMPRSS2	Mild/moderate COVID-19	NCT04374279 (RECOVER)
				requiring hospitalization	Phase II
				Symptomatic	NCT04509999
					Phase III
		Degarelix	Suppresses androgens, which	Veterans hospitalized for	NCT04397718 (HITCH)
			might regulate TMPRSS2	COVID-19 (severity 3, 4, 5	Phase II
			expression in lung tissue	on the influenza scale)
		Enzalutamide	Antiandrogen inhibiting the	High-risk male COVID-19 pa-	NCT04456049
			expression of androgen-	tients, mild symptoms, not	Phase II
			regulated proteins, such as	requiring hospitalization
			TMPRSS2	Mild/severe COVID-19	NCT04475601 (COVIDENZA)
				requiring hospitalization	Phase II
	Other
	Proinflammatory	IFNα	Abrogates type I IFN deficiency	Severe	NCT04534725 (C-SMART)
			in severe COVID-19
		IMM-101	Stimulates dendritic cells	Prevention of severe
				COVID-19

NCT04442048 (COV-IMMUNO) (continued) Cancer Research. on September 7, 2021. © 2021 American Association for cancerdiscovery.aacrjournals.org Downloaded from

Abbreviations: ALI, acute lung injury; ARDS, acute respiratory distress syndrome; CK2, casein kinase 2; eEF1A, Eukaryotic translation elongation factor 1 alpha 1; GM-CSF, granulocyte macrophage-colony stimulating factor; HSP, heat shock protein; IFN, interferon; MoA, mechanism of action; MoAb, monoclonal antibody; NFkB, nuclear factor kB; NK, natural killer; SK2, sphingosine kinase-2; TMPRSS2, transmembrane protease, serine 2; VEGF, vascular endothelial growth factor.

table 1 . Examples of cancer drugs currently tested for COVID-19 (Continued)

 1 Published OnlineFirst April 12, 2021; DOI: 10.1158/2159-8290.CD-21-0144

	Anticancer Drug Repurposing in COVID-19

via the JAK-STAT signaling pathway. The JAK-STAT pathway is overactivated by activating mutations in JAK2 hematologic malignancies (54) such as myeloproliferative syndromes including myelofibrosis or polycythemia vera. The JAK-STAT pathway was recently highlighted as an important inflammatory signaling pathway in inflammatory diseases, and JAK inhibitors are approved drugs in rheumatoid arthritis. JAK inhibitors are expected to reduce cytokine storms (55); therefore, they were suggested as a promising therapy to mitigate the inflammatory cascade generated by COVID-19

(56)

. Preclinical studies in humans suggest that JAK inhibitors may restrict the expression of the ACE2 receptor-necessary for the entry of the SARS-CoV-2 virus-into human cells

(57, 58)

. A clinical study of 601 patients suggests that baricitinib, a JAK1/2 inhibitor, could improve the outcome of patients with severe COVID-19, primarily by reducing the rampant immune inflammation

(58)

. A double-blind, randomized, placebo-controlled trial evaluating baricitinib plus remdesivir in hospitalized adults with COVID-19 concluded that baricitinib plus remdesivir was superior to remdesivir alone in reducing recovery time and accelerating improvement in clinical status among patients with COVID-19

(59)

. Data from Cancer Research. on September 7, 2021. © 2021 American Association for cancerdiscovery.aacrjournals.org Downloaded from REVIEW JUNE 2021 CANCER DISCOVERY | 1341

  ).

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		Cancer Research.

COXON RANK SUM TEST; NON-SIGNIFICANT).

Téléchargé pour Anonymous User (n/a) à Gustave Roussy à partir de ClinicalKey.fr par Elsevier sur septembre 06, 2021. Pour un usage personnel seulement. Aucune autre utilisation n´est autorisée. Copyright ©2021. Elsevier Inc. Tous droits réservés.

D. Letourneur et al. / European Journal of Cancer 137 (2020) 260e271

Official journal of the Cell Death Differentiation Association

Cell Death & Differentiation

Acknowledgements

All figures were created with BioRender.com. The authors would like to deeply thank Dr. De ´borah PRE-VOT (E ´cole Normale Supe ´rieure de Lyon) for her critical review of the manuscript.

Acknowledgements

We thanks Mme Chifaou Mohamed-Djalim (INSERM U1015, Gustave Roussy), Dr Elise Sourdeau (Service d'accueil des urgences, AP-HP, Hôtel Dieu), Dr Tali-Anne Szwebel (Service de Médecine Interne, AP-HP, Hôpital Cochin), and Dr Nathalie Marin (Service de Médecine Intensive et Réanimation, AP-HP, Hôpital Cochin). This study has been supported by CARE network (directed by X. Mariette, Kremlin Bicêtre, AP-HP).

Acknowledgements

We would like to thank the following collaborators for their effort during this Covid19 crisis and their contribution to the generation, collection and analysis of our dataset: Dr Laurence Albiges, Dr Fanny Pommeret, Dr Emeline Colomba, Dr Lisa de Rosa, Pr Laurence Zitvogel, Pr Guido Kroemer, Dr Marc Vasse, Dr Christophe Willekens, Dr Antoine Hollebecque, Delphine Bredel, Dr Thomas Hueso, Severine Mouraud, Chifaou Mohamed-Djalim, Dr Giulia Baciarello, Prof Benjamin Besse, Prof Fabrice Barlesi, Prof Fabrice Andre ´, Prof Jean-Charles Soria. This work could have been done thanks to the funding support of Fondation Philanthropia, Gustave Roussy Cancer Center and Fondation Gustave Roussy.

ACKNOWLEDGEMENTS

We are thankful to the Spectral flow cytometry facility team of Gustave Roussy. We thank the ET-EXTRA team (Biological Resource Center (NF 96-600) and the microbiology team for technical help. We thank Dr. Aude Jary for helping to set up the subgenomic RNA analysis. We thank the staff from the health and safety of Gustave Roussy Cancer Campus for helping to set up the translational research studies. We are thankful to the Genalyte for their supportive help.

Acknowledgments

We kindly thank the AACR COVID-19 and Cancer Task Force members for their suggestions for this manuscript. The figure is an unpublished original work, created by Mihaela Aldea with BioRender.com, for the express purpose of publication in this Cancer Discovery article. Received February 1, 2021; revised March 22, 2021; accepted April 12, 2021; published first April 12, 2021.

Acknowledgment

The authors would like to thank colleagues involved in RNA-seq analysis and molecular analysis of tumour immunity for inspiring talks on this topic, including: Guillaume Meurice, Urszula Czerwinska, Nicolas Sompairac, Laurence Zitvogel, Christophe Massard, Antoine Hollebecque.

Received grants and personal fees from Janssen, during the conduct of the study; personal fees and non-financial support from Astellas, grants and personal fees from Sanofi, personal fees and non-financial support from Roche, personal fees and nonfinancial support from AstraZeneca, grants, personal fees and non-financial support from MSD, Received research grants from Mersu, Bristol-Myers Squibb, Boehringer Ingelheim,

FUNDING STATEMENT

Funding G.K. is supported by the Ligue contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR) -Projets blancs; AMMICa US23/CNRS UMS3655; Association pour la recherche sur le cancer (ARC); Association "Ruban Rose"; Cancéropôle Ile-de-France; Chancelerie des universités de Paris (Legs Poix), Fondation pour la Recherche Médicale (FRM); a donation by Elior; European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR); Gustave Roussy Odyssea, the European Union Horizon 2020 Project Oncobiome; Fondation Carrefour; High-end Foreign Expert Program in China (GDW20171100085), Institut National du Cancer (INCa); Inserm (HTE); Institut Universitaire de France; LeDucq Foundation; the LabEx Immuno-Oncology (ANR-18-IDEX-0001); the RHU Torino Lumière; the Seerave Foundation; the SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); and the SIRIC Cancer Research and Personalized Medicine (CARPEM). This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001. LZ, LD, and the ONCOVID effort have sponsored philantropists (Ralph Lauren, Agnès b. Izipizi, Malakoff Humanis). F.X.D. have been supported by Fondation Philanthropia.

FUNDING A-GG was supported by Fondation pour la Recherche Médicale (FRM). LD has received support by the Philanthropia Fondation Gustave Roussy. The Gustave Roussy sponsored clinical study on COVID-19 (ONCOVID; NCT NCT04341207 has been supported by the Fondation Gustave Roussy, the Dassault family, Malakoff Humanis, Agnès b., Izipizi, Ralph Lauren and Sanofi). LZ and GK were supported by RHU Torino Lumière (ANR-16-RHUS-0008), ONCOBIOME H2020 network, the Seerave Foundation, the Ligue contre le Cancer (équipe labelisée); Agence Nationale de la Recherche (ANR)-Projets blancs; ANR under the frame of E-Rare-2, the ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC); Cancéropôle Ile-de-France; FRM; a donation by Elior; the European Research Council (ERC); Fondation Carrefour; High-end Foreign Expert Program in China (GDW20171100085 and GDW20181100051), Institut National du Cancer (INCa); Inserm (HTE); Institut Universitaire de France; LeDucq Foundation; the LabEx Immuno-Oncology; the SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); CARE network (directed by Prof. Mariette, Kremlin Bicêtre AP-HP), and the SIRIC Cancer Research and Personalized Medicine (CARPEM). GI was supported by Italian Ministry of Health (grants Ricerca CorrenteLinea 1, 1 "Infezioni Emergenti e Riemergenti", projects COVID-2020-12371675 and COVID-2020-12371817). MMy and GG were supported by ANR Flash COVID-19 program and ARS-CoV-2 Program of the Faculty of Medicine from Sorbonne University ICOViD programs (PI: GG).

Funding

Publication of this review has been funded by ARC foundation for cancer research: Fondation ARC pour la recherche clinique -9 rue Guy Mo ˆquet 94803 Villejuif -France. Grant number SIGNIT201901302.

Index of Supplementary Figures. Supplementary Figure Supplementary Table 1 General discussion.

Index of Supplementary Tables

Many people are implicated to carry out clinical trials, and several years elapse between the preparation of a protocol and the analysis of data resulting from these experiments. The rationales of such trials are built on preclinical knowledges and results of previous trials evaluating therapeutic strategies. Ancillary analyses planned to study the effect of treatments on patients and conversely the patient's characteristics that are affecting the effects of treatments are analyzed through the prism of prior knowledge. Thus, clinical and scientific observations are necessarily guided by pre-conceived ideas. The opportunity of using high throughput tools and unsupervised algorithms, for example on bulk RNA-seq from tumor biopsies and correlate them to the clinical evolution of the patients have allowed us to identify factors that were not previously anticipated to be associated with outcomes to anti-angiogenic & immunotherapy combinations. Such observations can help to carry out additional analyzes, guided by these first results. Also, such results could show us that simple analyses, such as prospective flow cytometry characterization of immune infiltrate in fresh tumors, could bring valuable information which could be simply implemented in clinical practice (if these results can be standardized, replicated, and validated in additional cohorts of patients).

In our work, the number of patients treated remains relatively low, and the number of patient's samples analyzed for each of the experiments is even more limited, because of the difficulties inherent to the conditions of collection and storage, the sometimes-poor quality of the tissues, and the biases related to the techniques and analyzes. Thus, results of ancillary analyses observed from these early trials should be considered with caution, such as clinical results, and need to be reproduced and confirmed in other conditions. However, the limited number of patients is counterbalanced by the number of techniques used here to explore both the blood and the tumor of patients at baseline and upon treatment. Such broad analysis could not be performed on large scale clinical studies. Hence, these observations are important because they provide, at an early stage in the development of a therapeutic strategy, elements associated with the resistance and efficacy of the interventions, in real clinical situations. They enable to anticipate and imagine therapeutic options, which could overcome the resistance of those treatments in patients.

Conflict of interest statement

The funding of research on chemokines in the laboratory of A.M. is partly funded by academic grants from Boehringer Ingelheim, Fondation MSD Avenir and CIC BT Inserm 1428. The other authors declare no conflicts of interest.

ABSTRACT

Background: Immune checkpoint blockers (ICBs) are now widely used in oncology.

However, most patients do not derive benefit from these agents. Therefore, there is a crucial need to identify novel and reliable biomarkers of resistance to such treatments in order to prescribe potentially toxic and costly treatments only to patients with expected therapeutic benefits. In the wake of genomics, the study of proteins is now emerging as the new frontier for understanding real-time human biology.

Methods:

We analyzed the proteome of plasma samples, collected before treatment onset, from two independent prospective cohorts of cancer patients treated with ICB (Discovery cohort n= 95, validation cohort n= 292). We then investigated the correlation between protein plasma levels, clinical benefit rate, progression-free survival (PFS) and overall survival (OS) by Cox proportional hazards models.

Results: By using an unbiased proteomics approach, we show that, in both Discovery and Validation cohorts, elevated baseline serum level of Leukemia Inhibitory Factor (LIF) is associated with a poor clinical outcome in cancer patients treated with ICB, independently of other prognostic factors. We also demonstrated that circulating level of LIF is inversely correlated with the presence of Tertiary Lymphoid Structures (TLS) in the tumor microenvironment.

Conclusion:

This novel clinical dataset brings strong evidence for the role of LIF as a potential suppressor of anti-tumor immunity and suggest that targeting LIF or its pathway may represent a promising approach to improve efficacy of cancer immunotherapy in combination with ICB.

DATA SHARING STATEMENT

Individual participant data that underlie the results reported in this article will be available after deidentification beginning 24 months and ending 48 months following article publication to researchers who provide a methodologically sound proposal.

Requests should be sent to the corresponding author. Conflict of interest G.K. has been holding research contracts with Bayer Healthcare, Daiichi Sankyo, Genentech, Glaxo Smyth Kline, Institut Mérieux, Kaleido, Lytix Pharma, Nucana, Oncolinx, PharmaMar, Samsara, Sotio, and Vasculox. G.K. is on the Board of Directors of the Bristol Myers Squibb Foundation France. G.K. is a scientific co-founder of everImmune, Samsara Therapeutics, and Therafast Bio. L.Z. is a founder of everImmune.

Ethical approval

The non-interventional study was approved by institutional review boards (IRB) of Cochin-Port Royal (Paris, France) hospital and the ethical committee of Cochin-Port Royal Hospital (CLEP Decision N: AAA-2020-08023). The Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1038/s41419-021-03540-y.

Conflict of interest statement

The authors declare the following financial interests/ personal relationships which may be considered as potential competing interests: Over the last 5 years, AM and JMM have participated as principal investigators and sub-investigators of oncology clinical trials sponsored by manufacturers of anti-IL6 and anti-IL6R therapies Roche/Genentech, Sanofi/Regeneron, J&J/ Janssen. AM has received honoraria from Roche / Genentech, J&J and Sanofi for consulting in the field of cancer immunotherapy. AM has received grants for oncology research from Sanofi. Gustave Roussy has received funding, grants and drug supply from Roche / Genentech, Sanofi / Regeneron, Janssen for running clinical trials or research projects in oncology. FXD, FA, JR and MR have no conflict of interest to disclose.

ARTICLE

Serum tests

Serums from 120 samples corresponding to 88 patients (Supplementary Material Fig. 1) were collected from whole blood after centrifugation at 600 × g for 10 min at room temperature and transferred to -80 °C freezer to await analysis. Serology: anti-SARS-CoV-2 immunoglobulins. Serum was collected from whole blood after centrifugation at 600 × g for 10 min at room temperature and transferred to -80 °C freezer to await analysis.

Multiplex

Cell Death & Differentiation

Spike S1S2, Spike S2, and Spike S1 with in a multiplex format based on photonic ring resonance technology [72]. This system detects and measures with good reproducibility changes in resonance when antibodies bind to their respective antigens in the chip. The instrument automates the assay. Briefly, 10 µl of each serum sample were added to a sample well plate array containing required diluents and buffers. The plate and chip are loaded into the instrument. First, the chip is equilibrated with the diluent buffer to get baseline resonance. The serum sample is then charged over the chip to bind specific antibodies to antigens present on the chip. Next, chip is washed to remove low-affinity binders. Finally, specific antibodies of patients are detected with anti-IgG or -IgA or -IgM secondary antibodies.

Metabolomics analysis. Samples were prepared as previously described [73]. Briefly, serum samples were mixed with ice-cold extraction mixture (methanol/water, 9/1, v/v, with a mixture of internal standards), then centrifugated. Supernatants were collected for widely-targeted analysis of intracellular metabolites. GC/MS analysis: GC-MS/MS method was performed on a 7890B gas chromatography (Agilent Technologies, Waldbronn, Germany) coupled to a triple quadrupole 7000 C (Agilent Technologies, Waldbronn, Germany) equipped with a high sensitivity electronic impact source (EI) operating in positive mode. Targeted analysis of bile acids: Targeted analysis was performed on a RRLC 1260 system (Agilent Technologies, Waldbronn, Germany) coupled to a QTRAP 6500 + (Sciex) equipped with an electrospray source operating in negative mode. Gas temperature was set to 450 °C, with ion source gas 1 and 2 set to 30 and 70, respectively. Targeted analysis of polyamines: Targeted analysis was performed on a RRLC 1260 system (Agilent Technologies, Waldbronn, Germany) coupled to a QQQ 6410 (Agilent Technologies) equipped with an electrospray source operating in positive mode. The gas temperature was set to 350 °C with a gas flow of 12 l/min. The capillary voltage was set to 3.5 kV. Targeted analysis of SCFA: Targeted analysis was performed on a RRLC 1260 system (Agilent Technologies, Waldbronn, Germany) coupled to a QQQ 6410 (Agilent Technologies) equipped with an electrospray source operating in negative mode. Gas temperature was set to 350 °C with a gas flow of 12 L/min. The capillary voltage was set to 4.0 kV. Pseudotargeted analysis of intracellular metabolites: The profiling experiment was performed with a Dionex Ultimate 3000 UHPLC system (Thermo Scientific) coupled to a Q-Exactive (Thermo Scientific) equipped with an electrospray source operating in both positive and negative mode and full scan mode from 100 to 1200 m/z. The Q-Exactive parameters were: sheath gas flow rate 55 au, auxiliary gas flow rate 15 au, spray voltage 3.3 kV, capillary temperature 300 °C, S-Lens RF level 55 V. The mass spectrometer was calibrated with sodium acetate solution dedicated to low mass calibration.

Data analysis

Spectral flow cytometry. Fcs files were exported and analyzed using FlowJo software using the gating strategy showed in Supplementary Material, Fig. 2. Briefly, gates on CD45 + , CD3 + , or CD19 + from the myeloid, T cell and B panels, respectively, were exported in an fcs file. All exported gates from one panel were used to generate an UMAP [74]. As shown on Supplementary Material Figs. 3 and4, we used relative expression and manual gating strategy. For patients treated by anti-PD-1 monoclonal antibody, the gates including PD-1 were excluded of the analysis. For patients treated by anti-CD38 monoclonal antibody, the gates including CD38 were excluded of the analysis.

Representation of the results. Data representation was performed with software R v3.3.3 using tidyverse, dplyr, ggplot2, ggpubr, pheatmap, corrplot or Hmisc packages, or GraphPad Prism 7.

Statistical analyses

Calculations and statistical tests were performed either with R v3.3.3 or Prism 7 (GraphPad, San Diego, CA, USA). Unless stated, P values are twosided with 95% confidence intervals for the reported statistic of interest. Individual data points representing the measurement from one patient are systematically calculated from the corresponding distribution. Biological parameters associated to statistically significant differences between groups were considered for the data visualization described below. Group comparison was performed using one-way ANOVA with the lmer function of the lme4 R package. The p-values were computed with the Kenward-Roger method, available in the lmertest R package. Spearman correlations were computed using Hmisc and Pheatmap R package. Hierarchical clustering of the patient's factors was performed using the hclust R package. The redundancy analysis (RDA) was performed using the vegan R package to explore the association between the clinical variables and the biological parameter correlation latent structure. The RDA performs variance decomposition such as principal component analysis, but including additional supervised components depending on the explanatory variables (e.g., clinical factors). The association of the clinical factors with the biological parameter correlation latent structure was tested using a permutation test. Kaplan-Meier methodology was used to estimate the probability of overall survival as well as to visualize the median time of SARS-CoV-2 RNA shedding for each group (HCW and Cancer). One-way ANOVA (paired and unpaired) with Kenward-Roger method was used to calculate P value between ALC among groups of viral RNA shedding and Ct values. Chi-Square, Fischer test were used `to calculate the differences in proportion between groups. Comparing two groups, Mann-Whitney test was used. Univariate analyses were performed with the Cox regression model. P < 0.05 was considered significant. Multivariate Cox analysis was performed using the survival R package stratified for the cohort and adjusted for the age, ECOG performance status, gender and metastatic status and hematological malignancy.

AUTHOR CONTRIBUTIONS

LD, LZ, AM, and FB conceived and designed the clinical trial. A-GG, LD, and LZ conceived and designed the translational research trial. AG, F-XD, ST, CM, FP, EC, LA, ASt, BG, MMer, FS, AM, and LD included patients in the clinical trial. A-GG, AD, F-XD, MP, MMaz, ASi, GD carried out all the experiments. A-GG, AD, F-XD, ASi, and GD performed all the biological analyses. DD performed the non-supervised analysis and gave advices for statistical analyses. LD and CA-C-S collected and analyzed the clinical data. LT and AF provided the information from the HCW series. AE, SK, and BR analyzed and provided the clinical data from the Canadian series. BLS, DR, SG, and SC analyzed and provided the clinical data from the IHU series. GG, CB, and NL analyzed CT scan. EG and FGr performed the RT-qPCR. A-GG, CA-C-S, and MMaz collected the biological data (ALC and Ct values). CP collected the samples. MMaz, YH, EP, CF, PL, GF, CT, IL, and J-EF prepared the biological samples and provided some help for the experiments. A-GG, AD, LZ, and LD interpreted data. performed the viral cultures. MMy and GGo performed the dosages of antibodies. A-GG, F-XD, CG-D, SD, NN, FAp, and GK performed the metabolomic experiments and analyses. BL, AR, and AD performed metagenomic analyses of blood microbiota. All the authors advised for the interpretation of the data. OK gave advices on the design of the figures. LZ and LD wrote the manuscript, with all authors contributing to the writing and providing feedback.

CONFLICT OF INTEREST

LZ and GK are cofounders of everImmune, a biotech company devoted to the use of commensal microbes for the treatment of cancers. AG and AM as part of the Drug Development Department (DITEP) are Principal/sub-Investigator of Clinical Trials for Abbvie, Adaptimmune, Aduro Biotech, Agios Pharmaceuticals, Amgen, Argen-X Bvba, Arno Therapeutics, Astex Pharmaceuticals, Astra Zeneca, Astra Zeneca Ab, Aveo, Bayer Healthcare Ag, Bbb Technologies Bv, Beigene, Bioalliance Pharma, Biontech Ag, Blueprint Medicines, Boehringer Ingelheim, Boston Pharmaceuticals, Bristol-Myers Squibb, Bristol-Myers Squibb International Corporation, Ca, Celgene Corporation, Cephalon, Chugai Pharmaceutical Co., Clovis Oncology, Cullinan-Apollo, Daiichi-Sankyo, Debiopharm S.A., Eisai, Eisai Limited, Eli Lilly, Exelixis, Forma Tharapeutics, Gamamabs, Genentech, Gilead Sciences, Glaxosmithkline, Glenmark Pharmaceuticals, H3 Biomedicine, Hoffmann-La Roche Ag, Incyte Corporation, Innate Pharma, Institut De Recherche Pierre Fabre, Iris Servier, Janssen Cilag, Janssen Research Foundation, Kura Oncology, Kyowa Kirin Pharm. Dev., Lilly France, Loxo Oncology, Lytix Biopharma As, Medimmune, Menarini Ricerche, Merck Kgaa, Merck Sharp & Dohme Chibret, Merrimack Pharmaceuticals, Merus, Millennium Pharmaceuticals, Molecular Partners Ag, Nanobiotix, Nektar Therapeutics, Nerviano Medical Sciences, Novartis Pharma, Octimet Oncology Nv, Oncoethix, Oncomed, Oncopeptides, Onyx Therapeutics, Orion Pharma, Oryzon Genomics, Ose Pharma, Pfizer, Pharma Mar, Philogen S.P.A., Pierre Fabre Medicament, Plexxikon, Rigontec Gmbh, Roche, Sanofi Aventis, Sierra Oncology, Sotio A.S, Syros Pharmaceuticals, Taiho Pharma, Tesaro, Tioma Therapeutics, Wyeth Pharmaceuticals France, Xencor, Y's Therapeutics, Research Grants from Astrazeneca, BMS, Boehringer Ingelheim, Janssen Cilag, Merck, Novartis, Pfizer, Roche, Sanofi. Non-financial support (drug supplied) from Astrazeneca, Bayer, BMS, Boringher Ingelheim, Johnson & Johnson, Lilly, Medimmune, Merck, NH TherAGuiX, Pfizer, Roche. NL reports to be a Speaker at Jazz Pharmaceutical E.D. reports grants and personal fees from ROCHE GENENTECH, grants from SERVIER, grants from ASTRAZENECA, grants and personal fees from MERCK SERONO, grants from BMS, grants from MSD, outside the submitted work. OK is a cofounder of Samsara Therapeutics. F.B. reports personal fees from Astra Zeneca, Bayer, Bristol-Myers Squibb, Boehringer Ingelheim, Eli Lilly Oncology,ß. Hoffmann-La Roche Ltd, Novartis, Merck, MSD, Pierre Fabre, Pfizer and Takeda, outside the submitted work. J-CS was a full-time employee of Astra Zeneca between September 2017 and December 2019, he reports consultancy: Relay Therapeutics, Gritstone Oncology and shares: Gritstone, Astra Zeneca, Daiichi-Sankyo, outside the submitted work. LA reports consulting fees compensated to institution for Pfizer, Novartis, Bristol Myer Squibb, Ipsen, Roche, MSD, Astra Zeneca, Merck, Amgen, Astellas, Exelixis, Corvus Pharmaceuticals, Peloton Therapeutics, outside the submitted work. FS reports consulting fees from AMGEN, Roche, Chugai, Mylan, Mundi Pharma, Leo Pharma, Pierre Fabre Oncology, Helsinn, MSD, Pfizer, BMS, outside the submitted work.

ETHICAL APPROVAL AND CONSENT TO PARTICIPATE

The ONCOVID trial was conducted in accordance with Good Clinical Practice guidelines and the provisions of the Declaration of Helsinki. All patients provided written informed consent. Protocol approval was obtained from an independent ethics committee (ethics protocol number EudraCT No: 2020-001250-21). The protocol is available with the full text of this article at https://clinicaltrials.gov/ct2/ show/NCT04341207. The part of the research including healthcare workers was conducted in compliance with General Data Protection Regulation (GDPR) and the French Data Protection Authority's recommendation about Data Protection in clinical researches. Gustave Roussy Data Protection Officer (DPO) has evaluated this project and sent to the principal investigator a formalized operational action plan about data protection compliance. The Cancer_FR2 cohort used individuals from a clinical study approved by the IHU Méditerranée Infections review board committee (Méditerranée Infection No.: 2020-021). The Cancer_CA cohort used individuals from a clinical study approved by the institutional ethics committee at each site (Ethics number: MP-02-2020-8911 and H20-00892). The Cancer_FR1_CR was also declared to the Gustave

ADDITIONAL INFORMATION

Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41418-021-00817-9.

Correspondence and requests for materials should be addressed to L.Z. or L.D.

Reprints and permission information is available at http://www.nature.com/ reprints Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Authors' Disclosures

J.-M. Michot reports being Principal/sub-Investigator of Clinical Trials for AbbVie, Aduro, Agios, Amgen, Argen-x, Astex, AstraZeneca, Aveo Pharmaceuticals, Bayer, Beigene, Blueprint, BMS, Boehringer Ingelheim, Celgene, Chugai, Clovis, Daiichi Sankyo, Debiopharm, Eisai, Eos, Exelixis, Forma, Gamamabs, Genentech, Gortec, GSK, H3 Biomedicine, Incyte, Innate Pharma, Janssen, Kura Oncology, Kyowa, Lilly, Loxo, Lysarc, Lytix Biopharma, Medimmune, Menarini, Merus, MSD, Nanobiotix, Nektar Therapeutics, Novartis, Octimet, Oncoethix, Oncopeptides AB, Orion, Pfizer, Pharmamar, Pierre Fabre, Roche, Sanofi, Seattle Genetics, Servier, Sierra Oncology, Taiho, Takeda, Tesaro, and Xencor. A. Ribas En oncologie, l'efficacité des immunothérapies par anticorps monoclonaux anti Programmed Death 1 (PD1) reste insuffisante pour de nombreux patients. L'association de ces traitements avec une molécule anti-angiogénique pourrait permettre d'en améliorer leur efficacité, par des effets directs antitumoraux et en contribuant au développement d'une réponse immunitaire anti-tumorale. Title: Identification of biomarkers associated with the combination of immunotherapy and anti-angiogenic drugs in the treatment of mesothelioma. Translational analyses of the PEMBIB clinical trial combining the anti-PD1 monoclonal antibody pembrolizumab and the anti-angiogenic tyrosine kinase inhibitor nintedanib.

Keywords : Immune checkpoint blockade, anti-angiogenic drug, cancer, translational research.

In oncology, the efficacy of monoclonal antiprogrammed death 1 (PD1) antibody immunotherapies remains insufficient for many patients. The association of these treatments with an anti-angiogenic molecule could improve their efficacy, by direct anti-tumor effects and by contributing to the development of an anti-tumor immune response.

In oncology, the efficacy of monoclonal antiprogrammed death 1 (PD1) antibody immunotherapies remains insufficient for many patients. The association of these treatments with an anti-angiogenic molecule could improve their efficacy, by direct anti-tumor effects and by contributing to the development of an anti-tumor immune response.