Dr Marie Kostine
email: marie.kostine@chu-bordeaux.fr

Eleonora Mauric

Alice Tison

Thomas Barnetche

Aurélien Barre

Macha Nikolski

Léa Rouxel

Caroline Dutriaux

Léa Dousset

Sorilla

Sorilla Prey

Marie Beylot-Barry

Julien Seneschal

Rémi Veillon

Charlotte Vergnenegre

Amaury Daste

Charlotte Domblides

Baptiste Sionneau

Marine Gross-Goupil

Alain Ravaud

Edouard Forcade

Thierry Schaeverbeke

Baseline co-medications may alter the anti-tumoural effect of checkpoint inhibitors as well as the risk of immune-related adverse events

Keywords: co-medications, immunotherapy, immune checkpoint, anti PD-1, patient outcomes, immune-related adverse events, cancer

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Immune checkpoint inhibitors (ICI) are revolutionizing the treatment of advanced cancers [START_REF] Pardoll | The blockade of immune checkpoints in cancer immunotherapy[END_REF][START_REF] Ribas | Cancer therapy: Tumours switch to resist[END_REF]. Several studies have demonstrated that the gut microbiota composition may be an important factor explaining why not all patients respond adequately to ICI [START_REF] Vétizou | Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota[END_REF][START_REF] Sivan | Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy[END_REF][START_REF] Routy | Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors[END_REF]. Moreover, it has been shown that microbiota composition can also be related to the occurrence of immune related adverse events frequently observed under ICI [START_REF] Chaput | Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab[END_REF][START_REF] Wang | Fecal microbiota transplantation for refractory immune checkpoint inhibitor-associated colitis[END_REF]. Progression-free survival (PFS) and overall survival (OS) were significantly shorter in lung, kidney and bladder cancer patients who received antibiotics for routine reasons one or two months before or soon after starting an anti PD-1 drug, suggesting that simply avoiding antibiotics while starting PD-1 inhibitors could boost patient responses from 25% to 40% [START_REF] Routy | Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors[END_REF]. Then, such observation has been reproduced by several groups [START_REF] Wilson | The effect of antibiotics on clinical outcomes in immune-checkpoint blockade: a systematic review and meta-analysis of observational studies[END_REF] and led to the concept that manipulations of the gut microbiota could serve to enhance the efficacy of immune checkpoint inhibitors in cancer treatment [START_REF] Pitt | Fine-Tuning Cancer Immunotherapy: Optimizing the Gut Microbiome[END_REF][START_REF] Jobin | Precision medicine using microbiota[END_REF]. If antibiotic treatments obviously alter the gut microbiota, strongly modifying its composition, reducing its microbial diversity and promoting colonization by pathogens, non-antibiotic drugs may also have an important impact on this microbiota [START_REF] Keeney | Effects of antibiotics on human microbiota and subsequent disease[END_REF][START_REF] Walsh | Drug-gut microbiota interactions: implications for neuropharmacology[END_REF]. In this way, anti-diabetics, proton pump inhibitors (PPIs), aspirin, nonsteroidal anti-inflammatory drugs (NSAIDS), steroids, immunomodulators, psychotropic drugs and analgesics such as opioids have been associated with significant changes in microbiome composition [START_REF] Maier | Systematically investigating the impact of medication on the gut microbiome[END_REF][START_REF] Imhann | Proton pump inhibitors affect the gut microbiome[END_REF]. Thus, as many patients suffering from advanced cancer are exposed to such drugs, our aim was to investigate the possible influence of these co-medications on the anti-tumor effect and safety of ICI.

METHODS

Study design

This was a retrospective cohort study conducted in Bordeaux University Hospital, including patients treated with ICI.

Patients

All adult patients who received ICI from May 2015 to September 2017 were included in the study.

Study objectives

The primary objective was to estimate the impact of co-medications on OS after ICI onset.

Secondary objectives were to evaluate their impact on 1) PFS, 2) best tumor response, collected and defined by the Response Evaluation Criteria In Solid Tumors (RECIST) 1.1 criteria as read by a radiologist on serial CT imaging [START_REF] Eisenhauer | New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)[END_REF], and 3) immune-related adverse events (irAEs) occurrence.

Data extraction

Data were extracted from the medical file records of each patient included.

Co-medications given to the patient within one month before and after the first administration of ICI were assessed according to a predefined list of medications known to impact gut microbiota: antibiotics (including class, duration, route of administration, therapeutic or prophylactic use and hospitalization), glucocorticoids (GCs) (including dose, route of administration and indication), metformin and other oral antidiabetic drugs, insulin, PPIs, aspirin, NSAIDs, statins, angiotensin-converting enzyme (ACE) inhibitors/angiotensin II receptor blockers (ARBs), immunomodulators, psychotropic drugs, hormone replacement therapies and morphine.

Known prognostic factors for melanoma (ECOG performance status, BRAF status, LDH and stage ≥ M1c), lung cancer (ECOG performance status, brain metastasis, EGFR, smoking status, histology) and for renal cell carcinoma (IMDC risk score) were collected.

OS and PFS were assessed from the start of ICI until September 2019 or last follow-up.

Statistical analysis

Qualitative data were analyzed between groups using Chi-2 test or Fisher's exact test.

Patients were considered as responders when complete response, partial response or stable disease was obtained, and as non-responders in case of progressive disease. Survival data were analyzed with univariable Cox regression and presented using Kaplan-Meier's graphics.

When patients received sequential ICI therapies, we considered the first ICI. Analyses were computed on STATA 13.1 software (StataCorp LP, College Station, TX) with a statistically significant threshold of 0.05. Graphs were constructed using GraphPad Prism software version 6.

Considering the large number of factors susceptible to influence the treatment response, we performed a factor analysis of mixed data (FAMD), which is a principal component method balancing the influence of all the quantitative and qualitative variables in the construction phase of the dimensions of variability. The aim of using this method is to find similarities between individuals, the relationships between variables and to link the study of the individuals with that of the variables. This method reduces the dimensionality of the data and provides the subspace that best represents the dataset. According to their response to ICIs, we first categorized patients as poor, intermediate or long survival patients. Then, we considered separately the main types of cancer (melanoma, lung and renal), in order to include their respective prognostic biomarkers. Imputations methods for missing data were not used since they are based on mean values while our dataset contained mainly categorical variables. All samples containing at least one missing value have been discarded. This FAMD was performed by using the R package FactoMineR (version 2.4).

RESULTS

Patients

635 patients were included. Baseline characteristics and prevalence of co-medications are shown in Table 1. Only co-medications with prevalence ≥3% were then analyzed. Patient characteristics according to co-medications of interest are described in Table S1. Overall, median OS was 16 months, median PFS was 5 months, and 399/635 (63%) patients died.

Tumor response occurred in 387/569 patients (68%), including complete response (n=37; 7%), partial response (n=156; 27%) or stable disease (n=194; 34%), while progressive disease was observed in 182 patients (32%).

Impact of co-medications on survival and tumor response

Antibiotics

At the time of CPI initiation, 150 patients (24%) were given antibiotics, oral (n=97), intravenous (n=42) or topical (n=3). For 22 patients (15%), antibiotics were prescribed for prophylactic use. The most prescribed class was beta-lactam (n=103, 69%), followed by sulfonamide (n=25, 17%), quinolone (n=20, 13%), macrolide (n=18, 12%), aminoglycoside (n=11, 7%) and tetracycline (n=8, 5%). The majority of patients received one class of antibiotics (n=96, 64%), 41 patients (27%) receiving two and 13 patients (9%) ≥ 3 classes of antibiotics. Hospitalization was required for 58 patients (40%). Median OS was shorter for patients receiving antibiotics compared to patients who did not (8 versus 23 months; p<0.001; HR, 1.66; 95%CI, 1.34-2.07) (Figure 1A and Table 2). Similarly, median PFS was negatively impacted in patients receiving antibiotics (4 versus 7 months, p=0.04; HR, 1.27; 95%CI, 1.01-1.59) (Table S2). Tumor response occurred in 57% of patients who received antibiotics compared to 71% of patients without antibiotics (p=0.004) (Figure 2).

Glucocorticoids (GCs)

When starting ICI treatment, 195 patients (31%) were receiving GCs, 165 patients (26%) had systemic GCs and 30 patients (5%) topical GCs. Systemic GCs were prescribed for brain metastases (n=61, 39%), other metastases (n=35, 22%), associated chemotherapy (n=16, 10%), pain (n=12, 8%), poor performance status (n=6, 4%), or various non-oncological indications (n=28, 18%). Baseline systemic GCs use was associated with significant decreased of both OS (median 5 versus 26 months; p<0.001; HR, 1.46; 95%CI, 1.26-1.69) and PFS (median 3 versus 7 months; p=0.005; HR, 1.24; 95%CI, 1.07-1.44) (Figure 1B, Table 2). In order to assess whether different steroids dosages may differently impact patient outcomes, we stratified patients into 4 groups: no GC, ≤10mg/day, >10mg/day but ≤20mg/day and >20mg/day of prednisone equivalent. While low GCs dosage ≤10mg/day did not significantly impact OS (p=0.5; HR, 1.12; 95%CI, 0.79-1.59), higher dosages were associated with poorer outcome (p<0.001; HR, 2.47; 95%CI, 1.57-3.88 between 10 and 20mg/day and p<0.001; HR, 2.97; 95%CI, 2.33-3.78 when >20mg/day) (Figure 3A and Table 2). Similarly, PFS was significantly impacted with baseline dosage >10mg/day (Table S2). Tumor response was less frequently observed for patients receiving GC (50% versus 79%, p=0.0001) (Figure

2).

Of note, 30 patients (4.7%) had inhaled or topical steroids, and we did not observe significant difference neither in OS nor PFS compared to patients without GC (median 18 versus 26 months; p=0.6 and 6.5 versus 7 months; p=0.35 respectively) (Figure S1).

Then, we aimed to evaluate whether the use of GCs given not at ICI onset but later for irAE management may also impact survival. While median OS of patients treated with GCs at the start of ICI was 5 months, it was 48 months in patients treated with GCs later for irAE management (p<0.0001)(Figure 3B). Median PFS of patients with baseline GCs was 3 months but 9 months if GCs were used later for irAE management (p=0.004).

PPIs

239 patients (38%) were receiving PPIs. Most prescribed PPIs were esomeprazole (n=141, 59%), at 20mg/day (60%) or higher dose (40%), and omeprazole (n=58, 24%) at 20mg/day (82%) or higher dosage (18%). Pantoprazole was prescribed in 23 patients at 20mg/day (40%) or 40mg/day (60%), lansoprazole in 8 patients and rabeprazole in 7 patients. Comparing patients receiving PPIs or not, median OS was 9 versus 26.5 months (p<0.001; HR, 1.70; 95%CI, 1.40-2.08) (Figure 1C and Table 2). PFS was also significantly reduced for patients with baseline PPIs (3.5 versus 8 months; p=0.002; HR 1.37; 95%CI, 1.12-1.66) (Table S2).

Tumor response was less frequently observed in patients receiving such co-medication (61% versus 72%, p=0.007) (Figure 2).

Statins

Statin was prescribed to 133 patients (21%). OS, PFS and tumor response were similar between patients receiving statin or not (Figure 1D and2, Table 2 andS2). Morphine 130 patients (20%) were given morphine. Significant shorter median OS and PFS were observed in this group of patients compared to patients not treated with morphine (respectively 4 versus 25.5 months, p<0.001; HR, 2.69; 95%CI, 2.15-3.38 and 3 versus 6.5 months, p<0.001; HR, 1.58; 95%CI, 1.23-2.03) (Figure S2A, Table 2 andS2). Morphine treatment was also associated with a decreased rate of tumor response (51% versus 71%, p=0.0001) (Figure 2). NSAIDs 63 patients (10%) received oral NSAIDs. Median OS was 10 months for these patients versus 20 months for patients not receiving NSAIDs (p=0.05; HR, 1.36; 95%CI, 0.99-1.85) (Figure S2B and Table 2). Median PFS was similar in both groups (6 months, p=0.4; HR, 1.15; 95%CI, 0.84-1.59) (Table S2). Tumor response rate was similar in both groups (72% versus 68%, p=0.5) (Figure 2). Aspirin 109 (17%) patients were prescribed aspirin, with a median OS of 19.5 months versus 22 months (p=0.005; HR, 1.42; 95%CI, 1.11-1.82), and a median PFS of 7 months versus 6 months for patients without aspirin (p=0.8; HR 1.03; 95%CI, 0.80-1.33). Tumor response rate was similar in both groups (67% versus 68%, p=0.8) (Figure 1E and2, Table 2 andS2).

ACE inhibitors / ARBs

OS, PFS and tumor response were not altered with baseline co-medication with ACE inhibitors and/or ARBs (Figure 1F and2, Table 2 andS2).

Psychotropic drugs

An important proportion of patients (41%) were treated with baseline psychotropic drugs, which significantly impacted OS (median 9 versus 28 months; p<0.001; HR, 1.82; 95%CI, 1.49-2.22), PFS (median 4 versus 7 months; p=0.002; HR, 1.36; 95%CI, 1.12-1.64) and tumor response (60% versus 73%, p=0.002) (Figure 1G and2, Table 2 andS2).

Metformin, other oral antidiabetic drugs and insulin

Both OS and PFS were not impacted with baseline metformin regimen (median 20 months; p=0.9; HR, 0.98; 95%CI, 0.64-1.50 and median 5 versus 6 months; p=0.9; HR, 0.9; 95%CI, 0.64-1.44 respectively) (Figure 1H, Table 2 andS2) and tumor response rate was 74% versus 68% (p=0.4) (Figure 2). Similar results were found with other oral antidiabetic drugs (Figure

S2C and 2, Table 2 and S2).

Baseline co-medication with insulin (n=23) associated with significant decreased in OS (median 6.5 versus 20 months; p=0.043; HR, 1.68; 95%CI, 1.01-2.77) (Figure S2D andTable 2)

but not in PFS (median 3.5 versus 6 months; p=0.8; HR, 1.08; 95%CI, 0.63-1.84) (Table S2) nor with tumor response (67% versus 68%; p=0.9) (Figure 2).

Combination of treatments with significant impact on survival

Prescription of PPIs, GCs and antibiotics that were shown to impact patient outcomes are frequently combined. Therefore, we assessed the impact of each medication alone and in combination (Figure 3C). Median OS was 4.5 months for patients receiving combination of PPIs and GCs versus 9 months when GCs were given alone (p=0.1), 17 months when PPIs alone (p<0.0001) and 32 months when none of these treatment were used (p<0.0001).

Median PFS was 3 months for the combination PPIs and GCs, similar to GCs alone (p=0.8), versus 5 months with PPIs alone (p=0.2) and 8.5 months when none of these co-medications were given (p<0.0001).

Median OS of patients receiving the combination of PPIs and antibiotics was 5.5 months versus 12.5 months when antibiotics were given alone (p=0.3), 11 months when PPIs alone (p=0.1) and 32 months with none of them (p<0.0001). Same results were observed for PFS with a median of 3.25 months in the group combination PPIs and antibiotics, versus 4 months if antibiotics alone (p=0.9), 3.5 months if PPIs alone (p=0.9) and 8,5 months if none of these co-medications (p=0.02).

Impact of co-medications on irAE occurence

Overall, 276/632 patients (43.7%) experienced irAEs, either one irAE (n=163), two irAEs (n=79) or ≥ 3 irAEs (n=34), with a median exposure time of 52 days for the first irAE.

Most common irAEs were cutaneous (n=160, 25%), digestive/hepatic (n=79, 12.5%), endocrine (n=66, 10%) and rheumatic (n=49, 8%), followed by pulmonary (n=21, 3%), neurological (n=13, 2%), ocular (n=10, 1.5%), renal (n=7, 1%), hematological (n=1) and cardiac (n=1) irAEs. Notably, colitis occured in 38 patients (6%).

Baseline co-medication with antibiotics, GCs, PPIs, morphine, NSAIDs, aspirin and psychotropic drugs was associated with decreased occurrence of irAE (Table 3). Notably, irAE occured in 50% of patients (197/393) without PPIs while only in 33% of patients (79/239) receiving PPIs (p<0.001). However, there was no difference in colitis occurrence according to PPIs treatment (6% in both groups). All but NSAIDs and aspirin were also associated with altered overall survival and tumor response. While insulin negatively impacted overall survival, it was not associated with change in irAE occurrence.

Influence of all variables through a principal component analysis

Factor analysis of mixed data was applied separately for melanoma, lung cancer and renal carcinoma patients, including demographics, prognostic biomarkers, co-medications, oncological outcomes and irAEs occurrence (Figure 4 and Supplementary FAMD data).

Variable that contributed the most to the first dimension (x-axis) was survival, in all cancer types. Age was the main contributor to the second dimension (y-axis) for melanoma patients, as well as for lung cancer patients together with GCs, and it was tumor progression and IMDC for renal carcinoma patients. Position and colour of each factor on the plot represented its contribution value. Globally, compared to known prognostic factors, baseline use of GCs, antibiotics, PPIs and psychotropic drugs contributed to worse oncological outcome while irAE occurrence and GCs use for irAE contributed to better outcome, independently in all cancer types.

DISCUSSION

First, our results confirm the detrimental impact on oncological outcomes of antibiotics and GCs at a dosage ≥ 10 mg/day when given within one month before or after ICI onset [START_REF] Routy | Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors[END_REF][START_REF] Elkrief | Antibiotics are associated with decreased progression-free survival of advanced melanoma patients treated with immune checkpoint inhibitors[END_REF][START_REF] Arbour | Impact of Baseline Steroids on Efficacy of Programmed Cell Death-1 and Programmed Death-Ligand 1 Blockade in Patients With Non-Small-Cell Lung Cancer[END_REF][START_REF] Scott | Brief report: Early use of systemic corticosteroids in patients with advanced NSCLC treated with nivolumab[END_REF]. Moreover, we show that other co-medications may significantly alter the antitumoral response of ICI, such as PPIs, psychotropic drugs, morphine, aspirin and insulin, while others seem to have no impact, such as statins, ACEs and/or ARBs, NSAIDs, and oral anti-diabetic drugs.

These results suggest some practical advice in a patient candidate to ICIs. First, antibiotic treatment should be limited to documented infections. Regarding baseline glucocorticoids use, the cut-off of 10mg/day should be respected, considering the deleterious effect of higher dosage. Moreover, due to the lack of impact of inhaled or topical GCs, local routes should be preferred. Conversely, our study brings reassuring data regarding the use of GCs for the management of irAE(s), which did not alter ICI efficacy, confirming previous reports [START_REF] Garant | Concomitant use of corticosteroids and immune checkpoint inhibitors in patients with hematologic or solid neoplasms: A systematic review[END_REF]. Another message concerns the use of PPIs, considering that they are most often given for prevention, gastric discomfort and clearly over-prescribed. Such prescription should therefore be (re)-evaluated properly at the time of ICI initiation, in order to optimize patient outcomes. Between our data collection and publication of the manuscript, other groups have reported similar findings on PPIs, even with the development of drug-based prognostic score including exposure to GC, antibiotics and PPIs [START_REF] Cortellini | Integrated analysis of concomitant medications and oncological outcomes from PD-1/PD-L1 checkpoint inhibitors in clinical practice[END_REF][START_REF] Buti | Effect of concomitant medications with immune-modulatory properties on the outcomes of patients with advanced cancer treated with immune checkpoint inhibitors: development and validation of a novel prognostic index[END_REF]. Our data on drugs combination is in accordance with such score, revealing the cumulative negative effect of multiple drugs exposure. Of note, the negative impact of concomitant administration of PPIs has been also outlined with other drugs than ICI such as pazopanib in soft-tissue sarcoma patients [START_REF] Mir | Impact of Concomitant Administration of Gastric Acid-Suppressive Agents and Pazopanib on Outcomes in Soft-Tissue Sarcoma Patients Treated within the EORTC 62043/62072 Trials[END_REF].

On another hand, we found that some co-medications such as statins or ACE inhibitors/ARBs, clearly not impacted anti-tumoral efficacy of ICI, in accordance to Cortellini et al [START_REF] Cortellini | Integrated analysis of concomitant medications and oncological outcomes from PD-1/PD-L1 checkpoint inhibitors in clinical practice[END_REF], while statins have been associated with improved clinical activity of ICI [START_REF] Cantini | Highintensity statins are associated with improved clinical activity of PD-1 inhibitors in malignant pleural mesothelioma and advanced non-small cell lung cancer patients[END_REF][START_REF] Omori | Statins improve survival in patients previously treated with nivolumab for advanced non-small cell lung cancer: An observational study[END_REF] and ACE inhibitors with impaired outcomes [START_REF] Medjebar | Angiotensinconverting enzyme (ACE) inhibitor prescription affects non-small-cell lung cancer (NSCLC) patients response to PD-1/PD-L1 immune checkpoint blockers[END_REF]. Psychotropic drugs are an heterogeneous group of medications that should be evaluated separately. Synergistic effect of metformin in combination with ICI has been previously suggested but not observed neither in our study nor in those of Omori et al [START_REF] Omori | Statins improve survival in patients previously treated with nivolumab for advanced non-small cell lung cancer: An observational study[END_REF][START_REF] Afzal | Efficacy of metformin in combination with immune checkpoint inhibitors (anti-PD-1/anti-CTLA-4) in metastatic malignant melanoma[END_REF]. Insulin strongly associated with poorer outcomes but on a small number of patients and further studies with particular interest in antidiabetic agents should be conducted before any recommendation.

The observational nature of our study does not allow any causal conclusion. Since our first aim was to evaluate the impact of co-medications known to modify gut microbiota, we do not know whether their effect on cancer outcomes is thoroughly mediated by changes in microbiota or other immunomodulatory properties. Microbiota is now recognized as a major intrinsic regulator of immune response and its composition influences response to ICIs in both humans and mice, mainly through the induction of IFNγ+ CD8 T cells and NKT cells [START_REF] Belkaid | Role of the microbiota in immunity and inflammation[END_REF][START_REF] Ma | Gut microbiome-mediated bile acid metabolism regulates liver cancer via NKT cells[END_REF][START_REF] Tanoue | A defined commensal consortium elicits CD8 T cells and anti-cancer immunity[END_REF]. This pharmacometabolomic concept holds the immense potential of gut manipulation to improve ICI efficacy. The first in-human studies of fecal microbiota transplant (FMT) before anti PD-1 rechallenge in refractory melanoma patients were recently published, inducing rapid changes in gut microbiota with clinical response in some patients [START_REF] Baruch | Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients[END_REF][START_REF] Davar | Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients[END_REF].

They represent a proof-of-concept that manipulation of gut microbiota may overcome resistance to anti PD-1 treatment, and other clinical trials are ongoing (NCT04130763, NCT04729322, NCT03341143). Probiotic therapy and high-fiber, plant-based diet are also studied, as more acceptable ways to induce favorable changes in gut microbiota to enhance ICI efficacy [START_REF] Si | Lactobacillus rhamnosus GG induces cGAS/STING-dependent type I interferon and improves response to immune checkpoint blockade[END_REF][START_REF] Lee | Role of the gut microbiome for cancer patients receiving immunotherapy: Dietary and treatment implications[END_REF]. Finally, introducing or withdrawing co-medications according to their positive or negative impact on ICI's response may represent another feasible option in clinical practice, therefore subject of active research highlighted by the growing number of publications during the last two years. The positive influence of some co-medications has been highlighted, as shown for dual antiplatelet therapy by aspirin and clopidogrel enhancing responsiveness to PD-1 blockade [START_REF] Riesenberg | Cutting Edge: Targeting Thrombocytes to Rewire Anticancer Immunity in the Tumor Microenvironment and Potentiate Efficacy of PD-1 Blockade[END_REF]. Since only few patients were receiving clopidogrel in our cohort, we could not perform such analyses.

All the treatments that altered the anti-tumoral response to ICI were interestingly also associated with a decreased incidence of irAEs. Since there is a growing literature on the relationship between irAE occurrence and tumor response [START_REF] Toi | Association of Immune-Related Adverse Events with Clinical Benefit in Patients with Advanced Non-Small-Cell Lung Cancer Treated with Nivolumab[END_REF][START_REF] Bertrand | Immune related adverse events associated with anti-CTLA-4 antibodies: systematic review and metaanalysis[END_REF][START_REF] Haratani | Association of Immune-Related Adverse Events With Nivolumab Efficacy in Non-Small-Cell Lung Cancer[END_REF][START_REF] Sato | Correlation between immune-related adverse events and efficacy in non-small cell lung cancer treated with nivolumab[END_REF][START_REF] Judd | Immune-Related Adverse Events as a Biomarker in Non-Melanoma Patients Treated with Programmed Cell Death 1 Inhibitors[END_REF], our data are in accordance with such idea and suggest that microbiota may also play a role in irAE development [START_REF] Andrews | Gut microbiota signatures are associated with toxicity to combined CTLA-4 and PD-1 blockade[END_REF].

This study acknowledges several limitations beyond its retrospective and monocentric design, with reporting bias and missing data on baseline comedications, specific prognostic factors and cancer outcomes. We noticed differences between cancer types, with a negative impact of several co-medications only observed in melanoma patients highly represented in our cohort. Therefore focusing on one cancer type with a multicentric design or unplanned pooled post hoc analyses of clinical trials are of interest [START_REF] Cortellini | Differential influence of antibiotic therapy and other medications on oncological outcomes of patients with non-small cell lung cancer treated with first-line pembrolizumab versus cytotoxic chemotherapy[END_REF][START_REF] Chalabi | Efficacy of chemotherapy and atezolizumab in patients with non-small-cell lung cancer receiving antibiotics and proton pump inhibitors: pooled post hoc analyses of the OAK and POPLAR trials[END_REF]. One may also consider that currently ICI are more often prescribed as first-line treatment, notably in lung cancer patients (only 9 out of 150 patients in our cohort), and that previous systemic treatments may represent an additional bias in the interpretation of our results.

Finally, considering that the identification of prognostic factors of ICI response and improving its efficacy remains a major challenge, baseline co-medications should be carefully assessed at the time of ICI initiation and clinicians aware of their possible deleterious effect.

Figure 1 .

 1 Figure 1. Overall survival of patients treated with checkpoint inhibitors on the basis of

Figure 2 .

 2 Figure 2. Overall response rate defined by the Response Evaluation Criteria In Solid Tumors

Figure 3 .

 3 Figure 3. Overall survival of patients treated with checkpoint inhibitors according to baseline

Figure 4 .

 4 Figure 4. Squared loading plot with visualization of categorical variables in melanoma

Table 1 . Baseline characteristics of patients

 1

	n (%)

ACE: angiotensin-converting enzyme; ARBs: angiotensin receptor blockers; ACE: angiotensin-converting enzyme; ARB: angiotensin receptor blocker; CI: confidence interval; d: day; NSAIDs: non-steroidal anti-inflammatory drugs

Table 2 . Univariable Cox regression analysis of Overall Survival

 2

	All cancers (n=635)	Melanoma (n=293)	Lung (n=150)	Renal (n=83)

Table 3 . irAE occurrence according to baseline co-medication

 3

The authors received no financial support for the research, authorship and/advisory boards for BMS and MSD France.

AR reports being a member of Golbal, European and/or French Advisory Board in GU tumors and/or immunotherapy for Pfizer, Novartis, BMS, Roche, Astra Zeneca and MSD, and received travel support from Pfizer, BMS, Roche, Astra Zeneca and MSD. SP reports consulting for BMS and travel support from MSD. RV reports being one of the investigators for a clinical trial from BMS, consulting and advisory boards for BMS and MSD and travel support from BMS and MSD. All the others authors declared no conflict of interest for this work. The study was approved by the local ethics committee (CE-GR-2017/007).