Philémon

Caroline Lilith

Coline, Sarantis Camille Charlotte

Alix Ares

Marine, Omar Carolina Antonio

Alex Sam

Félix Caro

Paula Michea

Floriane Noël

Eve Zakine

Urszula Czerwinska

Philemon Sirven

Omar Abouzid

Christel Goudot

Alix Scholer-Dahirel

Anne Vincent- Salomon

Fabien Reyal

Sebastian Amigorena

Maude Guillot-Delost

Elodie Segura

Vassili Soumelis
email: vassili.soumelis@curie.fr

Irit Carmi-Levy

Antonio Cappuccio

Maximilien Grandclaudon

Coline Trichot

Fanny Coffin

Philippe Hupé

Down CXCR1 CCR2, CSF1R, FZD2, GFRA2, IFNGR1, IL17RA, IL17RB, LTBR, PLXNB2, TNFRSF6B Up IL20RB, ITGAV, PDGFRA, NFRSF1B CCR5, FZD5, ITGA9, LILRB1 Mertk Calcrl

Clement Dumont

Alix Jacquier

Jerome Verine

Floriane Noel

Annabelle Goujon

Ching-Lien Wu

Tzu-Min Hung

François Desgrandchamps

Stephane Culine

Edgardo D Carosella

Nathalie Rouas-Freiss

Joel Lemaoult
email: joel.lemaoult@cea.fr

Adjustment of dendritic cells to the breast-cancer microenvironment is subset-specific

Keywords: AREG, C3, CCL1, CCL20, CCL3, CCL4, CD58, CD70, CD80, CLCF1, CSF1, CSF2, CSF3, EDN1, GAST, GUCA2A, HBEGF, ICOSLG, IFNB1, IGHG1, IL12A, IL12B, IL19, IL23A, IL36G, IL36RN, IL6, INHBA, JAG1, OSM, OSTN, PDGFB, RSPO4, SEMA4D, SEMA7A, TGFA, TNF, TNFSF10, TNFSF15, TNFSF18, TNFSF4, TNFSF9, TSLP Receptor , CCR7, CCRL2, CD40, GALR2, IL15RA, LMBR1L, NRP2, PLAUR, PRLR, TNFRSF18, TNFRSF4, TNFRSF9 Key Points ANC, Absolute neutrophil count, ALC, Absolute lymphocyte count, AMC, absolute monocyte count, CMML, chronic myelomonocytic leukemia, BM, bone marrow, CPSS: CMML-specific prognostic scoring system, NS, non significant Immune checkpoints, HLA-G, ILT2, PD-1, infiltrating T cells

Dans un premier temps, je tiens à remercier les membres de mon jury de thèse : mes deux rapporteurs Dr Jenny Valladeau-Guilemond et Dr Antonio Rausell,

famille. Ils ont été un soutien inébranlable. Ils ont toujours cru en moi, et m'ont appris à ne jamais abandoner. Je suis heureuse et fière de leur présenter ce travail.

5

Synthèse

Les systèmes vivants sont des systèmes ouverts qui échangent constamment des informations et de l'énergie-matière avec leur environnement. La communication, qui peut être définie comme un échange d'informations entre deux systèmes ou soussystèmes, est donc un élément essentiel de la vie. Il permet la coordination efficace des processus homéostatiques et l'adaptation à un environnement en constante évolution, y compris la réponse aux menaces internes ou externes ou les processus autocuratifs. Les organismes multicellulaires sont structurés de manière hiérarchique, les cellules étant souvent considérées comme des unités fondamentales : les cellules s'organisent pour former des tissus, un ensemble de tissus forme des organes, qui forment eux-mêmes des organismes. Par conséquent, la communication intercellulaire est à la base de l'organisation d'ordre supérieur observée dans les tissus, les organes et les organismes. Il est essentiel de coordonner la fonction de divers types cellulaires impliqués dans des processus biologiques complexes, tels que l'embryogenèse, la formation et le renouvellement des tissus, la régulation hormonale, la réponse au stress, une réaction immunitaire efficace aux agents pathogènes microbiens et le remodelage tissulaire au cours d'une inflammation et de la cicatrisation. La dérégulation dans la communication intercellulaire peut entraîner une pathologie due à l'échec des processus homéostatiques et/ou à une adaptation défectueuse face aux menaces environnementales. Comment les cellules s'adaptent-elles à un microenvironnement spécifique en fonction de la communication intercellulaire ? Est-ce qu'elles gardent leur identité ou adoptent un comportement spécifique ? Ces questions sont particulièrement pertinentes quand on étudie le système immunitaire dans le contexte de l'inflammation et du cancer. Les interactions entre les cellules cancéreuses et le microenvironnement tumoral (TME) jouent un rôle crucial dans le développement et la progression de la tumeur. Le TME est un système hétérogène constitué de nombreuses protéines et cellules de types différents qui interagissent au sein d'un réseau complexe. En particulier, de nombreux types de cellules immunitaires sont recrutés et participent à la réponse anti-tumorale, mais également à l'inflammation et à l'immunosuppression favorisant la tumeur. Il a été démontré que les cellules dendritiques (DCs) dans le TME étaient liées à la fois à la progression de la tumeur et à la prévention. Les DCs jouent un rôle essentiel dans le déclenchement des réponses immunitaires adaptatives. Chez l'homme, différentes sous-populations de DCs ont été identifiées. Des études antérieures ont montré que les DCs infiltrent des tumeurs solides, notamment le cancer du sein. Cependant, l'impact du TME sur le comportement des sous-ensembles de DCs infiltrant des tumeurs humaines est mal connu.

Dans le premier chapitre de la thèse, j'ai présenté les concepts généraux de la communication et du micro-environnement, puis j'ai décrit le cancer du sein et son microenvironnement, qui représentent un réseau complexe de cellules qui interagissent dans un contexte inflammatoire. Par la suite, j'ai présenté l'hétérogénéité des cellules présentatrices d'antigènes (APCs) qui infiltrent le TME du sein et leurs communications dans ce contexte. Enfin, je me suis concentrée sur les défis techniques et méthodologiques liés à l'étude de la communication cellulaire et sur les outils bioinformatiques que nous pouvons utiliser pour surveiller les communications intercellulaires.

Le deuxième chapitre de la thèse pose les hypothèses et objectifs de mon travail. Ce projet part de l'hypothèse que le microenvironnement tumoral module les réseaux de communication intra et intercellulaires formés par les APCs. Ces modifications auraient des conséquences sur l'interaction entre la tumeur et le système immunitaire de l'hôte et a fortiori sur sur le développement de la tumeur. Malheureusement, dans la littérature, il existe plusieurs limites concernant l'étude de APCs qui sont des cellules rares dans un contexte tissulaire. Les sous-populations d'APCs ont été caractérisées dans différents tissus (e.g. le sang, la rate, la peau), des maladies (e.g. cancers, maladies auto-immunes), des organismes (e.g. souris, humains). Cependant, dans le cancer du sein, seuls les macrophages et les cDC2 ont été étudiés [Ojalvo, Whittaker, et al. 2010; Wargo et al. 2016] et aucune comparaison n'a été faite entre le tissu tumoral (T) et le tissu non malin appelé juxtatumoral (J), ni entre différents sous-types de cancer du sein. L'objectif général de ma thèse est de comprendre l'impact du microenvironnement tumoral sur les sous-populations de DCs par une analyse systémique. Dans la première partie de mon travail de thèse, je cherchais à identifier les sous-populations de DCs dans le microenvironnement de la tumeur du sein. Plus précisément, le projet s'est concentré sur l'identification et la caractérisation des fonctions biologiques de sous-populations de DCs isolées de tumeurs du sein de deux sous-types différents: Luminal (LBC) et Triple-Négatif (TNBC), dont le pronostic est le plus sombre. Les APCs étant des cellules rares dans le TME du sein, nous avons voulu définir les sous-ensembles de APCs infiltrant les tumeurs à une résolution supérieure à celle décrite dans la littérature. En utilisant la technologie de séquençage ARN, nous avons généré le profil moléculaire de ces cellules et avons voulu en déduire les fonctions biologiques. Dans un premier temps, l'objectif de cette étude était de décrypter comment le TME modulait le profil de transcription des sous-populations dAPCs, en comparant les profils de transcription des APCs dans les tumeurs et les juxtatumeurs et en liant la variation de l'expression des gènes aux fonctions biologiques. Dans un deuxième temps, nous avons évalué l'impact de l'hétérogénéité de la tumeur mammaire sur les DCs et les monocytes/macrophages (Monomacs). Pour ce faire, nous avons comparé le profil de transcription des sous-types d'APCs, isolés de deux types de cancer du sein, LBC et TNBC. Enfin, comme nous avions étudié le microenvironnement de la tumeur du sein et son impact potentiel sur le réseau de communication des sous-populations d'APCs, nous nous sommes demandé quel était le lien entre la caractérisation de chaque population spécifique d'APCs et le résultat clinique. Existe-t-il des différences au niveau de la population entre les résultats cliniques et les différents TME, en fonction du sous-type de cancer du sein ? À partir de l'extraction de listes de gènes spécifiques caractérisant chaque population dAPCs identifiée dans le TME du sein, nous avons cherché à relier les signatures des sous-types cellulaires dans les différents contextes au pronostic des patients. Dans une seconde partie, nous avons étudié les communications cellulaires afin de comprendre comment les cellules intègrent les signaux provenant de leur environnement. Pour ce faire, nous avons cherché à créer un score de communication simple basé sur des profils transcriptomiques de cellules. Ce score pourrait être appliqué aux données de Il est intéressant de noter que la signature pDC était liée à une meilleure survie sans maladie dans les patients LBC, mais pas chez les patients TNBC, ce qui implique que le résultat associé à la signature pDC dépend du contexte. En conclusion, nous avons constaté que la reprogrammation transcriptionnelle d'APC infiltrant une tumeur est spécifique à un sous-type, ce qui suggère une interaction complexe entre l'ontogénie et l'empreinte tissulaire dans le conditionnement de la diversité des DCs dans le TME. Les signatures que nous avons générées sont particulièrement pertinentes pour l'identification de l'activation de voies biologiques et de nouveaux biomarqueurs dans les sous-types d'APCs.

Les résultats de la seconde partie de mon étude sont présentés sous la forme d'un manuscrit qui sera bientôt finalisé pour soumission. Il est intitulé "ńICELLNET:

Reconstruction des réseaux de communication intercellulaires à l'aide de profils transcriptomiquesż". Pour ce travail collaboratif, j'ai été impliqué dans le développement d'une approche systémique basée sur la transcriptomique pour reconstruire des réseaux de communications intercellulaires. En effet, la communication intercellulaire est essentielle pour transférer des informations entre des cellules dotées de fonctions et de capacités de détection différentes. La communication intercellulaire coordonne les activités de divers types de cellules nécessaires aux processus complexes tels que l'embryogenèse, le remodelage tissulaire au cours de l'inflammation et la cicatrisation des plaies, ainsi que les réponses immunitaires. Actuellement, il n'existe pas de méthode systématique pour reconstruire la communication intercellulaire de manière qualitative et quantitative. Dans cette étude, nous avons développé ICELLNET, un outil intégrant des informations sur les interactions ligand/récepteur, ainsi que des données d'expression génique spécifiques à une cellule et représentant des aspects quantitatifs et qualitatifs de la communication cellule à cellule sous forme de cartes de connectivité. ICELLNET peut être automatiquement appliqué à n'importe quel profil transcriptomique au niveau de la population cellulaire afin d'estimer et de quantifier sa communication avec plus de 12 autres types de cellules. Nous avons appliqué cette méthode aux cellules tumorales, aux cellules immunitaires innées et adaptatives (e.g., DC, cellules T, cellules B, NK), aux cellules épithéliales et stromales. En analysant un ensemble de données original de cellules dendritiques humaines générées de novo, nous avons identifié et validé expérimentalement l'IL-10 en tant que régulateur majeur de la connectivité intercellulaire des DCs au niveau systémique. Notre approche visant à évaluer la connectivité cellulaire peut constituer un outil précieux pour évaluer l'impact d'un contexte spécifique sur la communication entre cellules, en particulier dans un microenvironnement inflammatoire tel que le cancer. Dans les perspectives futures, les applications d'ICELLNET pourraient apporter des informations biologiques importantes et aider à orienter les manipulations pharmacologiques.

Dans la section discussion générale, je confronte mes résultats aux connaissances actuelles et expose les perspectives futures de ce travail. Dans un premier temps, j'ai discuté de la pertinence de caractériser les sous-populations d'APCs dans le cancer du sein et du positionnement de ce travail par rapport à la littérature. J'ai examiné l'impact de l 'hétérogénéité du cancer sur les communications cellulaires. En ce qui concerne les résultats biologiques que j'ai obtenus, j'ai discuté de la signature interféron trouvée dans les TNBC. De plus, j'ai souhaité examiner la pertinence d'utiliser des données transcriptomiques pour étudier la communication intercellulaire et l'impact du microenvironnement sur le comportement cellulaire. J'ai inclu des perspectives futures sur l'intérêt d'utiliser, dans ce domaine, une nouvelle technologie basée sur le séquençage d'ARN en cellule unique. Enfin, j'ai discuté de l'intérêt et de la complexité de la compréhension de la communication intercellulaire et des futurs développements pouvant être réalisés pour améliorer l'outil ICELLNET.

Enfin, en annexe, j'ai inclu deux manuscrits en préparation pour lesquels j'ai collaboré. Le premier décrit des îlots de DCs plasmacytoïdes dans la leucémie myélomonocytaire chronique. Le second est une étude de l'inhibition d'une population de lymphocytes T CD8+ cytotoxiques par le point de contrôle immunitaire HLA-G.

Preamble

Living systems are open systems constantly exchanging information and energymatter with their environment. Communication, which can be defined as an information exchange between two systems or subsystems, is thus an essential part of life. It allows the efficient coordination of homeostatic processes, and the adaptation to an ever-changing environment including internal or external threat response or self-curative processes.

Multicellular organisms are structured in a hierarchical manner, with cells often being viewed as fundamental units: cells get organized to form tissues, multiple tissues form organs, which themselves form organisms. Hence, cell-cell communication is at the basis of the higher-order organisation observed in tissues, organs, and organisms. It is critical to coordinate the function of diverse cell types involved in complex biological processes, such as embryogenesis, tissue formation and renewal, hormonal regulation, response to stress, efficient immune reaction to microbial pathogens, and tissue remodelling during inflammation and wound healing. Dysregulation in cell-to-cell communication can lead to pathology through the failure of homeostatic processes, and/or the defective adaptation to environmental threats.

How cells adapt to a specific microenvironment depending on cell-to-cell communication? Do they keep their identity or adopt a specific behavior? These questions are particularly relevant when studying the immune system in the context of inflammation and cancer. Interactions between cancer cells and the tumor microenvironment (TME) play a critical role in tumor development and progression. The TME is a heterogeneous system, which consists of numerous proteins and cells of different type interacting within a complex network. In particular, many immune cell types are recruited and participate in anti-tumor response, but also in tumor-promoting inflammation and immunosuppression. It has been shown that dendritic cells (DCs) within the TME were related to both tumor progression and prevention. DCs play a critical role in triggering adaptive immune responses. In human, different subsets of DCs have been identified. Previous studies reported that DCs infiltrate solid tumors, and particularly breast cancer. However, little is known about the impact of the TME on the behavior of DC subsets infiltrating human tumors.

As a framework of my study, I will introduce the general concepts of communication and microenvironment, then I will focus on breast cancer and its microenvironment which represent a complex network of cells that interact in an inflammatory context. Subsequently, I will introduce the heterogeneity of Antigen Presenting Cells (APCs) that infiltrate breast TME, and their communications in this context.

Finally, I will focus on the technical and methodological challenges of studying cellular communication and the bioinformatic tools we can use to monitor cell-to-cell communications.

The results will be presented in two sections. In a first part, I will present our published results showing that APCs adjust to the breast TME in a subsetspecific manner. In a second part, I will present a manuscript in preparation on the development and application of a communication score based on cell transcriptomic profiles.

In the general discussion section, I will confront my results to the current knowledge and expose future perspectives of this work.

Finally, in the appendix, I will include two manuscripts in preparation for which I collaborated. The first one describe plasmacytoid DC islands in chronic myelomonocytic leukemia. The second one is a study of the inhibition of a cytotoxic population of CD8 + T cells by the immune checkpoint HLA-G.

A General definition of communication

Communication is an important concept at the level of human being and it is simply defined by the transmission of a message or an information between entities or groups. The players of communication are represented by a transmitter of the message and a recipient. They can be two individuals, groups of individuals, entities or societies. A government communicating information to the population is one illustration of communication between two entities. Communication is also defined as a process by which information is exchanged between individuals through a common system of symbols, signs, or behavior. In this definition, the emphasis is put on the use of the same system of symbols. Two individuals can discuss and exchange information using the same language. In order for the receiving individual to understand and interpret the information, he has to be able to decode it (Figure 1.1). Coding and decoding processes of symbol systems can make communication more complex.

In cryptology, a lot of methods and algorithms to encrypt data or messages coexist.

A key to decode the message is required, in order to be understandable by the entity receiving the information. Coding and decoding messages are used to create a specific communication between two entities. One example of tools used to decipher crucial communications during World War II was the ancestor of computer created by Pr. Alan Turing, a british mathematician. His device enabled to decode messages encrypted by Nazis from the enigma machine and is considered as the ancestor of computer science. The methods to communicate between human beings are numerous and have evolved through time, from cave painting to the internet nowadays.

Major forms of communication use writing (e.g. books, letters) or speaking (e.g. direct speaking, phone, radio). Another interesting form of communication implies representation, images as painting, sculpture or even sign language. Evolution of technologies and science helped to develop different ways of communication. In particular, the comprehension and use of waves allowed us to convey messages by sonar, radio, television and phone. At the level of the cell, we observe the same phenomenon of communication.

One cell can communicate with an another by sending chemicals signals that will be sensed and processed by a receiving cell to trigger a specific response or mechanism (Figure 1.2). Cells can sense a great diversity of signals from the extracellular environment, such as growth factors, cytokines, danger signals, cell-to-cell contact and extracellular vesicles [Niel, D'Angelo, and Raposo 2018]. This signals are hundreds of distinct molecules, the majority being proteins and forming the words of the cell communication language. In cell biology, different types of signaling are described depending on the distance between the sending and the receiving cells. Paracrine signaling for short distance signaling, endocrine signaling for long distance signaling, autocrine signaling, and direct signaling across gap junctions are the four types of signaling used by cells in multicellular organisms. To sense and process the information, cells require decoding mechanisms. To detect the chemical signals, the receiving cell express specific receptors localized at their membrane or inside the cytoplasm or the nucleus. Once the ligand is attached to the receptor, the message is transduced following complex signaling pathways inside the cell (Figure 1.2). Figure 1.2: Message transduction at the cellular level, interaction between ligand and receptor. From Introduction to cell signaling, https://www.khanacademy.org.

Interest of communication

Communication is essential to human life in many ways. First of all, the use of communication can derive from a need for interacting and coordinating peoples actions in order to survive and grow. As Aristotle said, "Man is, by nature, a social animal" [Aristotle 2018]. Following this concept, humans are born to live in cities, and better exploit their potential via social interactions. Communities thrive around communication of a diversity of information between people, enabling to organize groups of individuals with rights and rules. Communication promotes social interactions which are key to human evolution. Social interactions and cooperation enhance the development of intelligence not only in humans but also in other species [McNally, Brown, and Jackson 2012]. Communication plays a role in the enhancement and expansion of societies, especially via education. Communication of emotions via art or entertainment is important for the psychological development of humans helping to avoid stress and anxiety and improve productivity and stability. In Africa, a study showed that communication between members of a community promotes active citizen participation and initiatives to the development of the communities [Adedokun, Adeyemo, and Olorunsola 2010]. Throughout time, communication methods have evolved to improve the efficiency to convey information. We witness a fast evolution of communication technologies, and nowadays, thanks to the new digital technologies, the world is interconnected [E. Williams 2011]. This evolution of communication gave an easier and faster access to information, knowledge, and a faster transmission of information between individuals. More people are connected even if they are far away from each other thanks to the phone, internet, and social media replacing mailing post and telegraphy. Fast access to information and connection between individuals with different culture, origin, and experience enhance sharing and improvement of the world's knowledge that can be then applied in various disciplines such as agro-industries, politics, entertainment, economy or justice. In science, one of the most important parts of the work is to communicate about the research and results to spread knowledge and information by means of conferences, publications, and posters.

In cell biology, communication is essential to development, growth, survival, maintenance, and defense of the individual cell but also for the development of multicellular organisms [Niklas and S. A. Newman 2013]. Depending on the sensed signal, different responses are initiated by the receiving cells and impact their fate.

Cell-to-cell interactions are crucial in the coordination of organism development and several signaling pathways are involved in and are responsible for most of the animal development: Hedgehog (Hh), wingless-related (Wnt), transforming growth factor-β (TGF-β), receptor tyrosine kinase (RTK), Notch, Janus kinase (JAK), signal transducer and activator of transcription (STAT) and nuclear hormone pathways [Barolo and Posakony 2002]. In the developed organism, intercellular communication coordinates the activities of multiple cell types required for complex processes such as immune response, growth, and homeostasis. When cells are damaged, they are also able to sense intracellular signals such as DNA in the cytosol and trigger mechanisms of cell death (e.g. apoptosis, autophagy). Another interest of communication be-tween cells is the complex signaling network to enable cell migrations which are critical for immune cell trafficking, wound healing, and stem cell homing, among other processes. Immune cell crosstalks play a role in establishing central tolerance and preventing autoimmunity. Indeed, in the thymus, when CD4 + CD8 + T cells recognize the complex formed by an external peptide and the major histocompatibility complex (MHC) molecule presented on cortex thymic epithelial cells, they receive critical survival signals and differentiate into CD4 + and CD8 + T cells. Then, they undergo a step of negative selection in the medulla where the autoreactive T cells which recognize self-antigens presented by DCs are eliminated [Takaba and Takayanagi 2017].

Cell-to-cell communication is essential to trigger an immune response and depends on the stimuli that activate immune cells. An impair in cell-to-cell communication can lead to the development of severe pathology. For instance, a lack of a specific receptor such as interferon-gamma receptor (IFNGR) in macrophages cause a rupture of communication. Cells do not receive the immune defense signals anymore which induce an increase sensibility to mycobacteria infection [Newport et al. 1996].

Factors impacting communication

Considering the diversity of communication methods, messages and responses, it becomes evident that independent factors impact interactions between individuals.

In sociology, different theories point out the cultural context as a major factor influencing communication and the efficacy of the message transmission. The theory introduced by anthropologist Edward T. Hall exposed that two types of culture, "low" and "high" contexts, are opposed and play a role in communication [Hall 1976]. "Low context" is defined by an explicit communication whereas "high context" is characterized by implicit communication, with the use of ambiguity where facial expression and the way of speaking can change the meaning of words. Therefore, low-context individuals, who are not used to read between lines, are less able to fully understand the messages transmitted by high-context individuals. This can be nefast for social interactions and a fortiori for the development of the society especially in diplomatic exchanges. A second interesting hypothesis by Sapir and Whorf shows that culture significantly affects how people think and communicate. More precisely, the language which is one of the bases of a culture and brings together people strongly affects the way of thinking. Ciaccio and Bormann studied the influence of color terms on the behavior of Italian and German speakers [Ciaccio and Bormann 2013]. They demonstrated that the judgments of colors boundaries was influenced by the language which validate the hypothesis of Sapir and Whorf.

Network representation of communication

The organization of multiple entities through communication is a complex system that researchers try to understand. To study complex system such as cell-to-cell For my thesis work I was interested in studying communication processes in one particular network which is breast cancer microenvironment.

Human Breast Cancer

Factors of incidence

Breast cancer is the second most common cancer worldwide with nearly 1.7 million new cases in 2012 and is the first cause of mortality by cancer among women (http: //globocan.iarc.fr/Default.aspx). In the literature, many factors are known to have an incidence on the risk to develop breast cancer. Some mutations, particularly in BRCA1/2, EGFR, and p53 genes result in an increased risk of occurrence of breast cancer [M.-C. King et al. 2003; Malkin et al. 1990; Sun et al. 2017]. However, it concerns only a small proportion of tumors, less than 30% of breast cancers. On the other hand, exposure to endogenous hormones (estrogen) increases the risk of breast cancer occurrence [Travis and Key 2003]. During the last decades, many groups have pointed out the higher risk of developing breast cancer induced by using exogenous hormones such as hormone replacement therapies (HTR). Moreover, the relative risk of breast cancer in current users increases with increasing duration of use of HRT [Li et al. 2003]. Additionally, environmental signals play a role in modifying the incidence of breast cancer. Danaei et al. have studied the impact of several environmental factors (e.g. cigarette smoke, diet, obesity) on the incidence of cancers worldwide. They showed that alcohol use, overweight and obesity, and physical inactivity have a joint incidence on 21% of all breast cancer deaths worldwide [Danaei et al. 2005].

The diversity of factors involved in the appearance of breast cancer is a first observation of the complexity of this disease. Another important layer is the heterogeneity of breast cancer subtypes.

Breast cancer subtypes and inter-tumor heterogeneity 1.2.2.1 Classification

Breast cancer has been suggested to be a heterogeneous disease, and multiple classifications exist to better characterize this disease and improve treatments and care of the patients. The first classification of breast cancer relies on the histopathological status of the disease. It is divided into more than 20 types with the most important being invasive ductal carcinomas (IDCs), not otherwise specified (NOS), and invasive lobular carcinoma (ILC). The grade of the disease can also be taken into ac- • Luminal A breast cancer is hormone-receptor positive (estrogen-receptor and/or progesterone-receptor positive), but negative for HER2 and have low level of Ki67 immunoreactivity. It is also characterized by a genomic stability.

• Luminal B breast cancer is hormone-receptor positive as Luminal A but is characterized by less genomic stability with some amplification (HER2), deletions and mutations (P53). It can be either HER2-positive or negative with high levels of Ki67 immunoreactivity.

• HER2enriched breast cancer has amplification of ERBB2 and many other genes. It is defined by positive expression of HER2 and no expression of the hormone receptors (ER, PR).

• Triple-Negative (TNBC) or Basallike breast cancer is defined based on the absence of expression of hormone receptors (ER, PR) and HER2. TNBC have a high genomic instability.

• Normal Breastlike group is similar normal breast epithelium in transcriptomic analyses.

• Claudinlow breast cancer is characterized by low expression of cell-to-cell communication proteins (claudins), no/low markers of luminal differentiation and a high expression of epithelial to mesenchymal transition (EMT) markers, immune response genes and cancer stem-cell markers. These tumors are only high grade and are less frequent (12-14% of cancers) These breast cancer classifications highlight the heterogeneity of the disease, at multiple layers: localisation, grade, molecular profile. Additionally, they have been linked to distinct clinical outcome. Both inter-tumoral and intra-tumoral heterogeneity make breast cancer a complex disease. Tumor cells evolve in a specific microenvironment (including nontumoral cells) that display specific signaling that can be hijacked by the tumor to promote its progression and survival [Poli, Fagnocchi, and Zippo 2018].

Diversity of behavior and outcome

Intra-tumor heterogeneity

Tumor microenvironment

TME is a complex network composed of cancer cells, stromal cells, endothelial cells, immune cells as well as components of the extracellular matrix (ECM). As described above, the TME shows high level of spatiotemporal heterogeneity which is partly due to alterations of the microenvironment. In normal breast, epithelial and stromal

Inflammatory environment

At the beginning of cancer studies, the immune system was not considered as play-

Antigen presenting cells

Antigen presenting cells (APCs) are key players of the immune system communication/social interactions and infiltrate the tumor microenvironment. Professional APCs include dendritic cells (DCs), B cells, and macrophages [Parkin and Cohen 2001]. These peculiar cells are the sentinels of the body and have an extremely important role as messenger of the immune system. They patrol many tissues and are able to trigger the adaptive immune response by presenting exogenous antigens through MHC class II molecules. This complex is then presented to T cells that recognize antigens via their TCR. These interactions lead to activation of T cells.

Here, we will focus on monocytes, macrophages and DCs which are mononuclear phagocytes distinguished on the basis of their morphology, function and origin.

Monocytes and macrophages

Monocytes are present in all vertebrates. In humans, these cells represent 10% of the nucleated cells in the blood. They arise from myeloid precursor cells in primary lymphoid organs. Two main human monocyte subpopulations are defined as CD14 + and CD14 low CD16 + monocytes. The first category can be further subdivided into distinct populations of CD14 + CD16 + and CD14 + CD16 -monocytes that have differential capacities to secrete key inflammatory cytokines upon in vitro stimulation

Dendritic cells

Notion of subset

First described as a largely homogeneous population distributed throughout the body, dendritic cells (DC) are, in fact, composed of distinct subtypes each specialized to respond to particular pathogens and to interact with specific subsets of T cells.

Classical DCs

In humans, the two subsets of classical DCs (cDCs) can be characterized in part by expression of BDCA-1 (CD1c) and BDCA-3 (CD141) in peripheral blood (Fig6 a-b). Haniffa et al also found these two subsets in other peripheral tissues such as liver, skin and lung [Haniffa, Shin, et al. 2012

Plasmacytoid pre-DC

In 1997, Grouard et al and Olweus et al. discovered a Lin -HLA-DR + plasmacytoid cell in human peripheral blood and lymphoid tissues, such as spleen and lymph nodes [Grouard et al. 1997; Olweus et al. 1997]. These cells were distinct from known myeloid DCs. At that time, they were named plasmacytoid T cells or plasmacytoid monocytes due to their expression of CD4, CD45RA, and their round shape and morphology resembling plasma cells [Grouard et al. 1997; Olweus et al. 1997].

However they did not express any T cell antigen, CD3, neither the myeloid antigens CD11b, CD11c, CD13, and CD33 while they have a high MHC-II expression when put in culture with monocytes. Upon culture with IL-3 and CD40L, plasmacytoid cells were shown to differentiate into cells with a mature DC morphology with dentrites [Cella et al. 1999 ulation is CD123 + resembling pDC but also express myeloid cDC antigens including CD11c, CD33 (SIGLEC3) and CX3CR1, AXL and SIGLEC6 (CD327). Though they were labelled AS DCs [Villani et al. 2017]. A new DC subset, called DC4, was described as distinct from nonclassical monocytes transcriptomic profile [Villani et al. 2017] and closer to a dendritic cell subset, although it resembles SLAN + cells which are a controversial population called DCs or CD16 + nonclassical monocyte in literature [Collin and Bigley 2018]. Their transcriptional profile is reminiscent of SLAN + cells with lower CD11b, CD14 and CD36 but higher expression of CD16.

Inflammatory DC (Monocyte-derived inflammatory DC)

In inflammatory skin context such as eczema [Wollenberg et al. 1996] or psoriasis [Wollenberg et al. 1996

Plasticity of APC

Antigen presenting cells are composed of a variety of cell populations that are heterogenous. These subsets of cells have distinct origin and functions but importantly, one key feature is their plasticity regarding various stimuli and environments.

Whether it is macrophages, monocytes or each subset of DCs, we can observe different spectra of activation and Th polarizations in response to environmental stimuli.

As described previously, macrophages are extremely plastic cells, they exhibit a huge functional diversity and can undergo M1 or M2 activation depending on the disease and tissue as it was shown in vitro [Sica and Mantovani 2012]. Concerning DCs, the ability of cDC1 and cDC2 to activate and polarize T cells into different Th profiles has been shown to depend on the activator they encounter. The type of pathogen sensed by DCs can induce a differential gene expression profile which impact their functions [Huang et al. 2001]. Alculumbre et al recently demonstrated that pDCs are plastic cells that can differentiate into two subsets with distinct phenotypes, morphology and functions [Alculumbre et al. 2018]. Those subsets appear only after activation with specific factors such as influenza virus infection.

If the type of stimuli sensed by APCs shapes their phenotype and functions toward a specific and adapted immune response, it may probably be the case of the tumor microenvironment. As we introduce previously, breast cancer is an heterogenous disease fashioned by the interaction of malignant and various non-malignant cells forming a peculiar cellular microenvironment. Since APCs are plastic cells, we can wonder whether the breast TME modulates the APC phenotype and how it can impact the communication and signaling between these key players of the immune system and cells in this specific network.

Communication in TME

One key feature of cellular communication is the expression of ligands and receptors by the cells. The interaction between these molecules convey a message to the cell and induce specific responses. APCs express at their membrane surface various receptors implicated in sensing pathogens and danger signals.

How can we study the communication between cells in the TME? 1.4.1 Challenges

The tumor microenvironment is a complex network which can be decomposed in multiple layers of information: tissue specificity, cellular infiltration, cell plasticity, soluble factors present in the environment, genetic and epigenetic modifications. This network evolves in time and space. To reconstruct cell-to-cell signaling, a general problem arises that all reconstructed signals hypotheses are generated from partial information due to technical limitations including the experimental design used, the access to biological material, the number of parameters monitored. Taking into account the combinatorial aspect of communication and response to signal, this make a huge network analyse with infinite combinations not necessarily validated experimentally. Simplification of the network is a first step to understand communication between cells in the TME. It appears to represent a challenge to study how the TME acts on cell-to-cell interactions, especially between APCs, and how it affects their functions in this specific context.

One way to understand the communication between two entities, is to study the expression of the messages (ligands) and if they can be deciphered (by receptors, downstream pathways) under different conditions. The modulation of messages expression in the environment can be diverse: up-regulation (higher expression), down-regulation (lower expression), blockade, inhibition, or activation. To monitor the expression of specific proteins localized at the surface of cells, one possibility is the use of fluorescence flow cytometry. Fluorescence-activated cell sorting (FACS) is commonly used for identifying cell population such as human DCs [Guilliams et al. 2016]. FACS enables to identify population via fluorescent antibodies targeting specific surface markers but also other proteins such as surface receptors, immune checkpoints, chemokines or cytokines. Advances through cytometry by time-of-light now enable 30 to 40 antigens to be analyzed simultaneously [Guilliams et al. 2016].

Other techniques based on protein targeting via antibodies can be used to monitor expression of ligands and receptors by cells. This includes bead-based (e.g. Cytometric bead array) and electrochemiluminescence systems (e.g. Luminex). However, the major limitation of all of these techniques is that they allow to monitor only several proteins at a time and the amount biological material required to perform these experiments. In recent years, development of high throughput sequencing techniques and bioinformatic tools enabled biologists to generate molecular profiles of cells. Breast cancer subtypes were identified using transcriptomics data [Curtis et al. 2012]. Dendritic cells were also characterized at the molecular level in blood and tissues, in human and mouse [Ginhoux and Guilliams 2016

Bioinformatics to study cell-to-cell communication 1.4.2.1 Transcriptomic profiles, information providers

The transcriptome refers to the ensemble of messenger RNA (mRNA) molecules transcribed from expressed genes in an organism. It also describe the group of mRNA transcripts produced in a particular cell or tissue type. The transcriptome actively changes, depending on many factors, including stage of development and environmental conditions. The study of transcriptomes can be use to dissect signaling information and compare gene expression differences between two environments such as TME and healthy tissue. From the analysis of a transcriptome, we can reveal genes differentially regulated between at least two conditions. Therefore, we can derive a gene signature which combines several genes with specific patterns of expression characterizing cells in one condition (e.g. subsets of cell, disease state, tissue localization) or genes corresponding to a biological process (e.g. signaling pathways, response to a stimulus, cellular function). These signatures can have different applications. In clinics, gene signatures can be used to perform either prediction of disease outcome (i.e. predictive signature), prediction of the effect of a treatment (i.e. prognostic signature) or classification of disease phenotypes and severity (i.e. diagnostic). Gene signatures characterizing cell populations can be used to deconvolute bulk expression datasets to estimate the proportion of cell infiltration in a specific context. Several methods are based on this concept such as CIBERSORT [A. M. Newman et al. 2015]. In the manuscript, subset-specific gene signatures refer to genes that have an up-regulated expression in a subpopulation of cells compared to all other populations of the study. It can also refer to a list of genes linked to a biological function or concept (e.g. signaling pathway, costimulatory molecules) and with a differential expression pattern between conditions of the study.

Two different techniques enable generation of transcriptomic profiles of organisms: gene-expression profiling microarrays and RNA-sequencing (RNA-seq). Microarrays can be used to measure the expression of thousands of genes at the same time, as well as to provide gene expression profiles, which describe changes in the transcriptome in response to a particular condition or treatment [Liotta and Petricoin 2000; Mills et al. 2001]. mRNA molecules are purified from both experimental and reference samples. A step of reverse transcription converts mRNAs into complementary DNA (cDNA), and each sample is labeled with a fluorescent probe of a different color.

Then, the cDNAs of the samples are bound to the microarray slide via hybridization. Following hybridization, the microarray is scanned to measure the expression of each gene printed on the slide (Figure 1 ing from various tissue origin (e.g. blood, skin, lymph nodes, spleen), species (e.g. human, mouse), and experimental conditions (healthy, disease, activation via diverse molecules). Hence, these resources are helpful to study gene expression linked to communication pathways in a variety of conditions. Regarding APCs and especially DCs, several gene expression datasets of human DC subsets are available (Figure 1.9). However, majority of the datasets were generated from blood or skin DCs but not from tumor microenvironment-infiltrating cells.

Tools to study communication

Nowadays, biostatistic and bioinformatic tools are essential to analyse large scale data and draw hypothesis on biological processes. From cell transcriptional profiles it is possible to extract a lot of information regarding the expression of genes coding for proteins involved in diverse cellular pathways. Additionally, the differential gene expression analysis of these profiles is helpful to decipher how an environment impacts the transcriptional profile of specific cell populations. Different algorithms In parallel, if we consider that gene regulation is part of a specific intra-cellular communication network, one step of transcriptomic analysis is functional network inference to reconstruct genes networks in each cell population studied. The ARACNe algorithm was developed by Manolin to this purpose [Margolin et al. 2006]. It is based on mutual information which measures the degree of statistical dependency between two variables. It enables the identification of candidate interactions by estimating pairwise gene expression profile mutual information. When linked to functional inference, this helps to understand the gene expression and regulation inside cells which correspond to the processing of the message, coding and decoding, as well as the response.

63

This project started from the hypothesis that the tumor microenvironment modulates the intra-and intercellular communication network formed by APCs. These modifications would have consequences on the interaction between the tumor and the host immune system and a fortiori on the tumor development. Unfortunately, several limits exist in the literature concerning the study of rare APCs in tissue context. APC subpopulations have been characterized in different tissues (e.g. blood, spleen, skin), diseases (e.g. cancer, autoimmune diseases), organisms (e.g. mouse, human). However, in breast cancer, only macrophages and cDC2 have been studied [Ojalvo, Whittaker, et al. 2010; Wargo et al. 2016] and no comparison has been made either between tumoral tissue (T) and non-malignant tissue which we call juxtatumor (J), or between different breast cancer subtypes. Our general objective was to understand the impact of the breast tumor microenvironment on DCs subsets using system-level analysis.

First objective: identify subsets of DCs and infer their biological functions in breast cancer using RNA-seq transcriptional profiles.

In the first part of my thesis work, I aimed at identifying DC subpopulations in the breast tumor microenvironment. More precisely, the project focused on the identification and characterization of biological functions of DCs subpopulations isolated from breast tumors of two different subtypes: Luminal (LBC) and Triple-Negative (TNBC) which is of worst prognosis. Since APCs are rare cells in the breast TME, we wanted to define tumor-infiltrating APCs subsets at a higher resolution than what has been done in the literature. Using RNA-seq technology, we generated the molecular profile of these cells and wanted to infer the biological functions. In a first step, the goal of this study was to decipher how the TME modulate the transcriptional profile of APC subsets by comparing APC transcriptional profiles in tumors and juxtatumors and linking the variation of gene expressions to biological functions. In a second step, we assessed the impact of the breast tumor heterogene-ity on DCs and Monomacs. To do so, we compared the transcriptional profile of APCs subset isolated from two types of breast cancer, LBC, and TNBC. Finally, since we had studied the breast tumor microenvironment and its potential impact on APC subsets communication network, we wondered what was the link between the characterization of each specific APC populations and the clinical outcome. Are there any differences at the population level between clinical outcome, and in different TME, depending on the breast cancer subtype? From the extraction of specific gene lists that characterize each population of APCs identified in breast TME, we aimed to link the subset-and context-specific signatures to the patient outcome.

Second objective: reconstruct intercellular communication networks

In a second part we studied the cellular communications in order to understand how cells integrate signals from their environment. To do so, we aimed at creating a simple communication score based on cell transcriptomic profiles. This score could be applied to microarray data as well as RNA-seq data. It will be part of a tool including a manually curated database of ligand and receptors interactions and a collection of transcriptional profiles of primary cells publicly available in BioGPS [Mabbott et al. 2013].

Chapter 3

Results

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Article 1: Adjustment of dendritic cells to the breast-cancer microenvironment is subset specific

The TME is composed of a wide variety of cell types that influence tumor progression from non-cancerous (juxtatumoral) tissue of the same patients, we identified tumorspecific gene signatures for each APC subset that were linked to distinct biological functions such as cell migration in pDCs. Furthermore, we observed substantial differences between the APC profiles in TNBC and LBC unveiling the impact of tumor microenvironment and not only the tissue imprint or the ontogeny on the behavior of APCs. Interestingly, the pDC signature was linked to a better diseasefree survival in LBC but not in TNBC patients, which implicates that the outcome associated with the pDC signature is context-dependent.

In conclusion, we found that transcriptional reprogramming of tumor-infiltrating APCs is subset-specific, suggesting a complex interplay between ontogeny and tissue imprinting in conditioning DC diversity in the TME. The signatures we generated are particularly relevant for the identification of biological pathway activation and novel biomarkers in APC subsets.

Link to the article: https://www.nature.com/articles/s41590-018-0145-8 Finally, we defined transcriptional signatures specific for tumor DC subsets with a prognostic effect on their respective breast-cancer subtype. We conclude that the adjustment of DCs to the tumor microenvironment is subset specific and can be used to predict disease outcome. Our work also provides a resource for the identification of potential targets and biomarkers that might improve antitumor therapies.

The functions and transcriptional profiles of dendritic cells (DCs) result from the interplay between ontogeny and tissue imprinting. How tumors shape human

Dendritic cells (DCs) are antigen-presenting cells (APCs) specialized in triggering adaptive

immune responses through T cell activation 1 . Different subsets of DCs were defined based on their ontogeny, phenotype and anatomical location 2,3 . Advances in high throughput technologies have improved DCs classification, by identifying novel subset-specific markers and molecular signatures 4 . At steady state, studies in mice and human suggest that ontogeny is a predominant factor in defining DC subsets identity 5,6,7,8 . For instance, studies on plasmacytoid pre-DCs (pDCs) 9 , conventional DC1 (CD141 + DC) and cDC2 (CD1c + DC) from human blood and tonsils, revealed that pDCs clustered first by ontogeny independently of their tissue of origin 10 . Instead, cDC1 and cDC2 were more sensitive to tissue localization as tonsil cDC1 clustered closer to tonsil cDC2 rather than blood cDC1 10 . Tissue imprinting also influence DCs function. Gut but not spleen DC induce T cell homing back to the gut through a retinoic acid-CCR9-and α4β7-dependent mechanism 11 . This suggests a complex interplay between ontogeny and tissue imprinting, the relative contribution of which remains a matter of debate.

During inflammation, complex signals must be integrated by various DC subsets, which may change their function and molecular features 12,13,14,15,16,17 . DC subset diversity itself is also modified by inflammation, with the appearance of monocyte-derived inflammatory DCs, which are absent in homeostatic conditions 18 . In humans, inflammatory DCs were characterized in psoriatic skin 19,20 , ovarian cancer ascites and rheumatoid arthritis synovial fluid 21 . DCs infiltrate most cancer types. They have a protective role in anti-tumor immunity through the expression of co-stimulatory molecules and inflammatory cytokines, and by inducing T cell activation 22,23 . Conversely, DCs also promote immunosuppression by secreting anti-inflammatory cytokines 24,25,26,27 or by expressing negative immune checkpoint molecules, which are currently targeted by promising anti-tumor therapies 28,29 . DC plasticity to various tumor microenvironments (i.e tissue imprinting), as well as specialized ontogenydriven DC functions, may contribute to such a molecular and functional heterogeneity.

In this study, we performed a systematic comparative transcriptomic study of DC subsets in human primary breast cancer and matched non-involved juxta-tumor tissue. We found that transcriptional reprogramming of tumor-infiltrating DCs was DC subset-specific, suggesting a complex interplay between ontogeny and tissue imprint in conditioning DC diversity in the tumor microenvironment. Our results also provide high-quality large-scale datasets of primary tumor-infiltrating DC, which constitute a valuable resource to the biomedical community.

Results

Phenotypically distinct APCs infiltrate human breast cancer

DCs infiltrating breast cancer tissues were identified by multicolor flow cytometry based on previous human DC subset studies 20 . Because this was the first in depth characterization of DC subsets in human breast cancer, we performed preliminary analyses to validate our strategy. After standard gating to eliminate debris, doublets and dead cells, we selected CD45 + cells to efficiently exclude CD45 -cells, which are mainly tumor cells and fibroblasts (Supplementary Fig. 1a). We used a lineage (Lin) panel to exclude CD3 + T cells, CD19 + B cells and CD56 + cells (Supplementary Fig. 1a). CD14 expression was analyzed independently of the lineage channel to efficiently identify CD14 + DC, which were reported in cancer 20,21,30,31,32 . In Lin -cells, we next gated on CD11c + HLA-DR hi cells to exclude CD11c + HLA-DR -/lo myeloid-derived suppressor cells (MDSC) 33 . HLA-DR + CD123 + pDCs were identified in the CD11c -gate (Supplementary Fig. 1a).

In the Lin -CD45 + gate we identified four distinct CD11c + cell populations defined by their CD1c and CD14 expression (Fig 1a). Based on a recent standardized nomenclature for blood DC subsets 34 , CD1c + CD14 -cells matched the cDC2 definition, CD1c -CD14 -cells contained cDC1, and CD1c -CD14 + cells were monocyte/macrophages (hereafter MonoMacs) (Fig. 1a).

We also identified a CD1c + CD14 + cell population that co-expressed monocytes and macrophage markers such as CD14, CD64, CD163 and cDC2 markers like CD1c, CD206 and FcεRI (Fig. 1b and Supplementary Fig. 1b). Because these CD1c + CD14 + cells were phenotypically distinct from MonoMacs, and because they were not systematically distinguished in previous studies 34 , we refer to them hereafter as CD14 + DCs. CD56 + CD14 + cells were reported as interferon-producing killer dendritic cells (IKDC) in the context of cancer 35 , later shown to correspond to activated NK cells 36 . A similar CD56 + CD14 + phenotype was previously described on a fraction of healthy blood monocytes 37 . We detected CD56 + CD14 + cells in breast cancer samples (18% of CD45 + CD3 -CD19 -live cells) (Supplementary Fig. 1c). Because of their controversial nature, we excluded them using CD56 in our lineage cocktail (Supplementary Fig. 1c).

Clec9A could not be used to identify cDC1, as it was degraded during enzymatic digestion of the tissue (Supplementary Fig 1d). CD141 (BDCA3) was promiscuously expressed by all DCs, including pDCs and MonoMacs (Fig 1b). However, CD141 hi cells were found only in the CD1c -CD14 -population (Fig. 1b), hence they were highly enriched in cDC1. Because CD141 hi cells were too few (<100 cells/sample) and rare (5-50% of CD141 hi from CD1c - CD14 -cells in only half of the patients) to enable further separation into subsets, we designated the CD1c -CD14 -cell subset as "cDC1-enriched" (cDC1e) and used it for further molecular characterization. MonoMacs, CD11c + HLADR -/lo , CD14 + DC, cDC2 and cDC1e did not express CD16 (Fig. 1b and data not shown). CD32B, described on a non-inflammatory subset of cDC2 in blood 38 , was highly expressed by MonoMacs, CD14 + DC and cDC2, but not cDC1e. AXL, which is expressed by blood DC precursors and cDC2, was mainly expressed by cDC2, CD14 + DCs and MonoMacs in breast tumors (Fig 1b). This indicates a clear discrepancy in DC markers between blood and breast tissue.

To examine the morphology of tumor APCs, we sorted and analyzed them for cytological features. pDCs presented a typical plasmacytoid morphology 9 , while cDC2, cDC1e and CD14 + DCs had a dendritic morphology with high nuclear-to-cytoplasmic ratio, and, We quantified the distinct APC subsets across 22 luminal breast cancer (LBC) samples.

MonoMacs were the most abundant (median of 6.1% of CD45 + cells) followed by CD14 + DC, and pDC (0.5% and 0.3% among CD45 + cells, respectively). cDC1e and cDC2 were the less numerous APC (0.2% of CD45 + cells) (Fig. 1d). This phenotypic analysis identified and quantified 5 APC populations infiltrating human breast cancer: MonoMac, cDC2, CD14 + DC, pDC and cDC1e.

Tumor-infiltrating DC are enriched in human blood DC signatures

Because the number of APCs from primary breast cancer samples obtained after sorting was very low (range: 2-12,000 cells), we adapted a protocol aimed to obtain robust RNA sequencing (RNA-seq) transcriptomes from rare cell populations (Supplementary Fig. 1e).

We only analyzed cell populations with more than 100 events. We generated RNA-seq profiles of pDC, cDC2, cDC1e, CD14 + DC and MonoMac from 13 LBC patients (Supplementary Table 1), with 44 transcriptomes passing all quality controls (Supplementary Table 2 and Methods). In average, 60.5% of reads were mapped to the reference transcriptome across all samples. After filtering and normalization of the RNA-seq raw data, we obtained an average of 14,417 expressed genes.

To verify the identity of each of the subsets at the RNA level in relation to the flow cytometric analysis, we checked the expression of various subset-specific and shared DC markers (Fig. 1e). As expected, pDCs had high expression of IL3RA, CLEC4C and TLR9; cDC2 had high expression of CD1A, CD1B and FCER1A (FcεRI); CLEC9A, XCR1 and BATF3, all markers of cDC1, were preferentially expressed in cDC1e; MonoMacs had high expression of CD14, MERTK and TLR4; and CD14 + DC shared the expression of FCER1A and CD14 with cDC2 and MonoMacs, respectively (Fig. 1e). Gene set enrichment analyses using public datasets indicated that breast cancer cDC2, cDC1e, and MonoMac were enriched in blood cDC2, cDC1 (CD141 hi ), and CD14 + dermal mononuclear phagocytes (DMP) and MonoMac genes, respectively (Fig. 1f). Hence, robust transcriptional profiles confirmed the identity of the main DC subsets and MonoMacs infiltrating breast cancer.

Tumor-infiltrating DC harbor subset-specific signatures

We performed differential analysis between pDC, cDC2, cDC1e, CD14 + DC and MonoMac, and identified 5,132 differentially expressed genes (DEG) in at least one subset compared to all other APC (P< 0.05) (Fig. 2a). We then applied a post-hoc test, to extract the upregulated genes for each APC, that we defined as subset-specific signatures. From a total of 662 subsetspecific genes 490 corresponded to pDC, 88 to cDC1e, 40 to MonoMacs and 4 to cDC2. We found no genes specific to CD14 + DC (Fig. 2b).

Among the 10 most significant DEG, the oncogene TCL1A and the anti-apoptotic ZFAT, were found in the pDC signature; the glutamate receptor GRIP, and the cytokines CCL22 and IFNL1 (IL-29) in the cDC2 signature; the plasma membrane proteins IL1RL1 (IL33R or ST2), and XCR1 in the cDC1e signature and ASAH1 and ME1, two RNA encoding for fatty acid biosynthesis enzymes, in the MonoMac signature (Supplementary Table 3).

We then inferred functions linked to each subset-specific signature (Methods; Fig. 2d). From a total of 29 pathways (False Discovery Rate (FDR) <0.05), the most significantly enriched in the pDC gene-network was "anatomical structure involved in morphogenesis" (FDR = 2,7x10 -07 ), including EPHB1, VEGFB and VASH2 (Fig. 2e,f). Two pathways were enriched in cDC1e network, both linked to hematopoiesis, which included KIT, IL9R, CSF1 (M-CSF) and ITGA2B (Fig. 2e,f). "PI3K signaling" was the only pathway enriched in the MonoMac signature (IGF1, SEPP1, HTR2B) (Fig. 2e,f). Thus, subset-specific genes were identified for LBC-infiltrating pDC, cDC2, cDC1e and MonoMac. Importantly, no pathway directly linked to immune function was differentially enriched in any of those subsets.

DC plasticity to the tumor microenvironment is subset-specific

To determine how tumor-infiltrating APC adapt to their microenvironment, we analyzed matched juxta-tumoral (non-malignant) tissue from 8 donors. pDC, cDC2, cDC1e, CD14 + DC and MonoMac populations described in the tumors were also identified in the juxta-tumoral tissue, but with decreased frequency among the CD45 + cells compared to the tumor, which was statistically significant for pDC (P = 0.078) and cDC1e (P = 0.039) (Fig. 3a and Supplementary Fig. 2a). We generated transcriptional profiles for each APC subset in the juxta-tumoral tissue following the RNA-seq workflow used for the tumor DC subsets and the transcriptomes were generated in parallel, ran in the same batch as their tumor counterpart and matched for each patient (Supplementary Fig. 2b). We compared tumor and juxta-tumor transcriptome for each APC subset (Supplementary Fig. 2b). We identified 607 DEG for pDC, 348 DEG for CD14 + DC, 236 DEG for MonoMacs, 45 DEG for cDC1e, and 22 DEG for cDC2 resulting in a total of 1,258 DEG (FDR<0.05; Log2 fold change (FC) >1) that were kept for further analysis (Fig. 3b). DEG from all DC subsets were increased in the tumor as compared to the juxta-tumor (Fig. 3b). We identified 7 genes with highest significance (FDR= 1,72x10-17-4,1x10 -10 ) in CD14 + DC compared to DEG from other APC subsets, which included the secretoglobulin, TFF1 and TFF3, which have a function in mucosal healing. Conversely, DEG from MonoMacs were mostly upregulated in juxta-tumor (195 DEG) as compared to tumor (41 DEG) samples. Among the genes most significantly upregulated in juxta-tumor MonoMacs was CD163L, which is associated with M2 polarization (Fig. 3b).

Among the top five most increased transcripts in the tumor APCs compared to juxta-tumor, we detected CD5 in pDCs (Fig. 3c) and the secretoglobulins SCGB2A2 and SCGB1D2 in cDC2. SCGB2A2 was also found in the top 5 DEG of CD14 + DC and pDC in the tumor versus juxta-tumor comparison (Fig. 3c and Supplementary Fig. 2b). TNFRSF13B (also named TACI), a TNF receptor superfamily protein, was among top 5 DEG upregulated in tumor compared to juxta-tumor cDC1e, whereas the chemokine CCL7 was highly upregulated in tumor compared to juxta-tumor MonoMacs (Fig. 3b). AGR2 was among the top upregulated genes in tumor compared to juxta-tumor cDC2, CD14 + DC and MonoMacs (Fig. 3b).

We next analyzed whether the DEG between tumor and juxta-tumor APCs were shared across subsets. Strikingly, most of the genes were differentially expressed exclusively in one (1074 genes) or two (184 genes) subsets (Fig. 3d). Only 21 DEG were shared with two other subsets, and none with three or four (Fig. 3d,e). This indicated that the tumor-induced transcriptional reprogramming of APC is subset-specific.

The differential expression of SCGB2A2 a gene previously associated to mammary epithelial tumor cells 39,40 , raised questions regarding its tumor-versus immune cell-specificity 41 . We excluded the possibility of a contamination by tumor cell mRNA based on our stringent gating strategy (Supplementary Fig. 1 and Fig. 1a), and on the observation that epithelialspecific mRNA, such as EPCAM, were not detected among DEG in tumor pDCs (Supplementary Fig. 2b). Considering that SCGB2A2 was detected in a transcriptome analysis of blood pDC from healthy donors 42 , these observations suggest that pDC might express SCGB2A2 mRNA endogenously at steady state and in inflammatory conditions. In conclusion, we showed that DCs adapt to the tumor microenvironment in a subset-specific manner.

Immune pathways are absent from APC tumor-emerging genes of APC

For each APC, we analyzed the functions linked to tumor-emerging genes (DEG upregulated), meaning enriched in tumor, as compared to juxta-tumor APC. Pathway enrichment analysis identified "actomyosin structure organization", and "proteinaceous extracellular matrix", in pDC, "receptor protein tyrosine kinase signaling" in CD14 + DC, and "kinetochore" in MonoMacs (Fig. 4a). Major genes driving enriched pathway included the growth factor CTGF in pDC, AGR2 in CD14 + DC, and the mitotic checkpoint BUB1 in MonoMac (Fig. 4b). Because we did not identify any immune function enrichment with this unbiased approach, we specifically investigated the expression of immune checkpoints important in anti-tumor immunity 28,29 . Out of 19 positive and 15 negative immune checkpoints (Methods), we found the following that were differentially expressed in tumor as compared to juxta-tumor APC: TNFRSF14 (HVEM) in pDC, VTCN1 (B7-H4) and CEACAM6 in cDC2 and CD14 + DC, and CEACAM6 in MonoMacs (Fig. 4c). In conclusion, tumor-emerging genes from LBC APC are poorly linked to immune functions.

Transcriptomics profile of tumor APC depends on breast cancer subtype

In order to evaluate the impact of tumor type on DC transcriptional profile, we generated the transcriptomes of pDC, cDC2 and CD14 + DC from four TNBC samples and of cDC1e and MonoMac from four TNBC samples (Supplementary Fig. 3 and Supplementary Table 1).

Principal component analysis of tumor DC transcriptional profiles using the 500 most variant genes indicated that DCs clustered based on cancer subtype rather than by DC subset (Fig. 5a), suggesting a differential tumor imprint on DC. pDC separated from the other APC subsets in both cancer types (Fig. 5a). To identify the genes upregulated in TNBC compared to LBC for each DC subset, we performed differential analysis (FDR<0.05, LogFC>1).

MonoMacs had the highest number of DEG (2,930 genes), followed by CD14 + DC (2,662 genes) and pDC (1,434 genes) (Fig. 5b). cDC1e (605 genes) and cDC2 (521 genes) were the less impacted by the tumor type (Fig. 5b). The majority of DEG (65% of up-regulated genes in TNBC compared to LBC) were exclusively upregulated in one DC subset (Fig. 5c). Four DEG (IFNL1, IFNB1 and ISG2 and ISG15), all associated to the IFN pathway, were upregulated in TNBC compared to LBC (Fig. 5d). These data indicate that two different types of cancer had a major impact on the transcriptome of infiltrating DC and MonoMac.

TNBC promotes a shared immune-related signature in DC

PDC had the highest number of enriched pathways (166) in comparison to others APC (Fig. 6a). MonoMac, cDC2 and CD14 + DC shared 49%, 36% and 29% of their enriched pathways with at least another subset, respectively (Fig. 6a). In contrast, cDC1e shared only 6% of their enriched pathways with other subsets (Fig. 6a). These results suggest that enriched pathways in TNBC APCs were mostly subset-specific, indicating a functional specialization for each subset.

We then focused on the pathways that were commonly enriched in APCs in TNBC. We identified 38 pathways, including those linked to immune-related functions, that were shared with at least another APC subset (Fig. 6b and Supplementary Fig. 4a). In particular, "chemokine activity", "cytokine activity", "cytokine receptor binding" and "IL-10 signaling" were shared between cDC2 and CD14 + DC (Supplementary Fig. 4a). Type 1 IFN related pathways, such as "IFNα/β signaling" and "negative regulation of viral life cycle" were commonly enriched in all DC subsets (Fig. 6b). From all type 1 IFN related pathways, we selected the significantly enriched genes, including IFNB1, ISG15 and ISG20 and classified them into distinct metagenes according to their contribution to IFN production or the IFN response (Supplementary Fig. 4b). Because both metagenes were strongly correlated across all TNBC samples (Fig. 6c) we pooled them into a single "IFN pathways" metagene, which was increased in all APCs in TNBC compared to APC in LBC (Fig. 6d). As a control, the "ECM organization pathway" metagene (Supplementary Fig. 4c) was significantly increased only in TNBC MonoMacs (Fig. 6d). We also analyzed the expression of a "costimulatory" metagene (Supplementary Fig. 4d) that was significantly increased only in TNBC cDC2 (Fig. 6d) and highly correlated with IFN pathways metagene (r=0,72, P=5,33x10 -11 ) (Fig. 6e).

When analyzing the dependency of individual checkpoint genes with the IFN pathways metagene, we found that genes such as CD48 (SLAMF2) in pDC, CD80 in cDC2, and SLAMF1 in cDC1e highly correlated with IFN pathways (Fig. 6f). In contrast, TNFSF4 in pDC, TIMD4 in cDC2, and CD70 in CD14 + DC were not correlated with the IFN pathway metagenes (Fig. 6f). This reveals two groups of checkpoint molecules that are differentially associated to the IFN pathways (Supplementary Fig. 4e). Thus, the APC transcriptomes in TNBC strongly differs from that of APC in LBC, with a common IFN pathway upregulated in all TNBC APC, revealing a specific contribution of TNBC to APC reprograming.

Subset-specific signatures of tumor APC predict breast cancer survival

In order to assess whether the APC subset-specific signatures may have a prognostic impact, we took advantage of the publicly available dataset from whole breast cancer transcriptome METABRIC, which includes patient survival clinical annotation 43 . Because of the differences in the APC transcriptional profiles, we separately investigated LBC and TNBC datasets. We calculated a Z-score for each APC subset-specific signature 44 (Supplementary Fig. 5a). We found that high pDC, cDC2 and cDC1e scores significantly predicted disease-free survival in LBC (P = 0.0018, 0.0183, and 0.0111, and Hazard Ratio (HR) of 1.45, 1.32, and 1.35, respectively) (Fig. 7a). On the contrary, a high MonoMac score was linked to bad prognosis in LBC (P = 0.005; HR= 0.72), and TNBC (P = 0.0079; HR: 0.58) (Fig. 7a). A high cDC1e score was linked to good prognosis in TNBC (P = 0.0083; HR: 1.72), with an increased significance than for LBC (Fig. 7a). pDC and cDC2 scores had no prognostic value in TNBC (Fig. 7a), suggesting various signatures may have a different clinical impact according to the DC subset and the breast cancer type. A CD103 + DC gene signature was reported to correlate with good prognosis in several tumor types, including breast cancer 44 . Using the METABRIC dataset, we found that the CD103 + DC gene signature score had significant impact on LBC survival (HR: 1.58; P<0.01) (Fig. 7b), but not on TNBC prognosis (Fig. 7b). We then assessed the prognostic value of blood pDC signature 7 . Blood pDC signature score had no significant impact on LBC nor TNBC survival outcome (Fig. 7c). Hence, prognostic significance was most efficiently reached in a given tumor using DC signatures generated from the same tumor type. Last, no prognostic value associated to the common IFN signature was found TNBC patients (Fig. 7d), showing that subset-specific signatures harbored more prognostic information than a shared signature.

We then determined whether subset-specific signatures could be independently associated to survival when integrated with the Nottingham Prognostic Index (NPI), a reference clinical score determining survival 45 . We observed that all significant scores in univariate analysis were kept in the multivariate analysis in LBC: pDC (P = 0.0072; HR=1.37), cDC2 (P = 0.0041; HR=1.27), cDC1e (P = 0.0041; HR=1.39), MonoMac (P = 0.025; HR=0.77) and in TNBC: cDC1e (P = 0.0058; HR=1.76), MonoMac (P = 0.049; HR=0.67) (Table 1), indicating that subset-specific APC signatures in LBC and TNBC were independent prognostic factors associated to disease-free survival. These results demonstrate the relevance of generating subset and breast cancer type-specific signatures to predict clinical outcome.

Discussion

Here we have used DC-specific markers to identify resident DC populations (cDC2, cDC1, pDC), MonoMacs and subsets that share many features with previously described inflammatory DCs (CD14 + DCs) 2,21 to provide a broad and systematic coverage of currently identified APC subsets in two types of breast cancer (LBC and TNBC).

Our analysis revealed pDC as the most distinct APC subtype, as reported before in various tissues and species 5,6,7,10,46,47 . We propose that part of such pDC-specific signature is determined by ontogeny, as supported by a number of genes identified in the pDC signatures independently of the tissue type, such as CLEC4C, GZMB, and TCF4 6,7,10 . Other pDC signature genes such as the basal membrane laminins LAMA4, LAMB1 and LAMC1, not previously associated to a pDC-specific signature 6,7 , might be attributed to tissue imprint, or a combined effect between ontogeny and tissue-driven factors. Contrary to pDC, CD14 + DC and cDC2 had a very close similarity to other subsets. Comparative analyses of DC subsets across multiple studies may uncover conserved, ontogeny-determined signatures, as opposed to more plastic and environmental-driven transcriptional modifications.

In high-throughput studies of tumor-infiltrating APC in the mouse 48,49,50 , only two had performed a comparison of tumor and non-tumor tissue 48,50 , but have focused on a single APC population: CD11b + DC 53 or macrophages 48,50 , and did not systematically compare diverse APC in regards to their adaptation to a tumor context. Here, by systematically comparing the tumor and the juxta-tumor non-involved tissue for each APC subset transcriptome, we uncovered emergent features in tumor-infiltrating as compared to the nontumor tissue APC. This imprinting was different for distinct APC subsets, both qualitatively and quantitatively, indicating that in breast cancer there is no unique signature that could be attributed to tissue imprinting, as was previously suggested in other anatomical sites 6,7,8 . We propose that the effect of the tissue microenvironment on innate immune cells should be considered and interpreted in close interaction with subset-specific molecular features.

Recently, cDC1 was proposed as the main APC subset driving antitumor response in mice tumor models in a type 1 IFN-dependent manner 44,51,52 . In our study, cDC1e expressing XCR1 and CLEC9A, as well as other cDC1-specific markers, had no increase in genes related to DC activation or antigen presentation, as compared to the other APC signatures, neither in LBC nor in TNBC. Moreover, all human APC transcriptome from TNBC, and not only cDC1e, were enriched IFN response and IFN production genes indicating that, at least in human breast cancer, all DC can upregulate an IFN signature. Further experiments are needed to determine whether cDC1 is key to antitumor immune responses in humans.

Tumors have been segregated based on their low versus high immune infiltrate ("cold" versus "hot" tumors) 53 . The first was characterized by a low T cell infiltration, and an increase in angiogenic and extracellular matrix factors 54,55 . The second had higher T cell infiltrates and increased chemokine and type 1 IFN expression 51,54,55 . Both tumor types were associated with distinct mechanisms of immune escape 53,54,55 . The breast cancer subtypes investigated here, TNBC and LBC, have high and poor immune infiltrate, respectively 56 . LBC DCs, and especially LBC pDCs, were enriched in "vascular wound healing" and "extracellular matrix" pathways, whereas TNBC DC subsets were enriched in immune signatures, including IFN pathways. Hence, our findings identify DCs as another level of immune-based stratification of tumors. This could serve to study the differential contribution of DC subsets in mechanisms of immune escape across different tumor types.

TNBC is a rare and more aggressive breast cancer subtype 57 . Clinical trials using checkpoints blockers are ongoing in TNBC with promising results 14,58 . Hence, there is a major interest in precisely characterizing the immune compartment in these patients. Here, we provide a detailed analysis of APC subsets in TNBC. In particular, cDC1e but not pDC or cDC2-specific signatures were predictors of survival in TNBC in contrast to LBC. Hence, our data can be exploited to identify TNBC-specific prognostic signatures, as well as promising targets to better direct immune checkpoint-targeting therapies.

Overall, our study provides a detailed and comprehensive molecular profiling of tumorinfiltrating DC subsets and MonoMac in human cancer, which may serve as a reference dataset to increase biological knowledge on DC in disease context. Our findings shed light on the rules dictating DC diversity and adaptation to complex microenvironments, such as in cancer, through transcriptional reprograming. Our data will help to dissect the individual contribution of DC subsets to anti-tumor immunity, and provide a valuable resource to identify potential targets and biomarkers to better direct cancer immunotherapies.

ONLINE METHODS

Human samples and patient characteristics

Fresh samples of tumor and juxta-tumor (exempt of malignant tumor cells) tissues of untreated breast cancer patients were obtained from Hôpital de l'Institut Curie (Paris) in accordance with Institut Curie ethical guidelines. Luminal and triple-negative breast cancer types were included in the study according to the hormonal receptor status. Patient characteristics are summarized in Supplementary Table 2.

Single cell suspension from human samples

Tumor and juxta-tumor tissue were cut into small pieces and digested in CO 2 -independent medium (Gibco) containing 5% FBS (HyClone) 2mg/mL collagenase I (C0130, Sigma), 2mg/mL hyaluronidase (H3506, Sigma) and 25 µg/mL DNAse (Roche) by three round of 15 min incubation in agitation at 37°C. The samples were filtered on a cell strainer 40µm (Fischer Scientific) and diluted in PBS 1X (Gibco) supplemented with 1% decomplemented human serum (BioWest), and EDTA 2 mM (Gibco). After centrifugation, cells were resuspended in the same medium and counted before being assessed by flow cytometry or sorted.

Antibodies and cell sorting

For phenotypical characterization, single cell suspension was stained with the following anti-

Morphological analysis

Sorted cells were subjected to cytospin and colored with May-Grunwald/Giemsa staining.

Pictures were taken with a ProgRes SpeedXT core 5 Microscope Camera (JENOPTIK) on a Leica DM 4000 B microscope.

RNA sequencing

General RNA-seq workflow was summarized in Supplementary Fig.

RNA-seq data pre-processing

Reads were mapped to the human genome reference (hg19/GRCh37) using Tophat2 software version 2.0.6 60 . Gene expression values were quantified as read counts using HTSeq-count 61 .

We filtered out genes with less than five read counts in at least 25% of samples, and normalized the raw data using RUVg method (RUVSeq R package) 62 . This method identifies technical noise based on negative control genes that should be affected by unwanted variations but not affected by biological effects of interest. We selected the 5,000 less variant genes as negative control genes. From the 82 samples sequenced, only two were excluded from this study, corresponding to tumor and juxta-tumor pDC. These sampled were expressing low levels of pDC-specific markers, and high expression of macrophage markers.

For exploratory analyses, we performed Principal Component Analysis (PCA) of the 500 most variant genes, based on inter-quartile range method (IQR) (EMA R package) 63 , of APC transcriptomes from LBC and TNBC tumor samples. Data were log2-transformed, centered and scaled. PCA was performed using the FactoMineR R package. Z-score of log2transformed gene expression, scaled by gene, were represented in a heatmap color.

Geneset enrichment analysis

We selected APC specific genesets from literature 59 and perform enrichment analysis on our dataset selected LBC-T samples. To do so, we used BubbleMap module of the BubbleGUM software which perform GSEA analyses with multiple testing correction 64 .

Statistical analysis

Significant differences in the APC frequency from total live cells or the CD45 + cells, were performed using ANOVA, followed by a post-hoc test. For paired samples in the tumor versus juxta-tumor comparison of APC we performed a Wilcoxon test, by using the GraphPad Prism 6.0.

To generate subset-specific signature of APC for each condition, we performed one-way ANOVA differential analysis test on the Log2 expression data of the five APC. We kept only the genes differentially expressed between at least two subsets (P <0.05). We then performed a Tukey post-hoc test to select genes exclusively expressed in one subset compared to all the others, (P <0.05). Those upregulated genes were defined as the subset-specific signature.

To identify genes that vary between tumor and juxta-tumor, for each APC separately, we performed pairwise comparison of gene expression matched samples using the generalized linear model (GLM) likelihood ratio test of EdgeR R package [START_REF] Robinson | a Bioconductor package for differential expression analysis of digital gene expression data[END_REF] . Only DEG with FDR < 0.05 and Log2 FC > 1 were considered as differentially expressed. The same analysis was applied to find differentially expressed genes between TNBC and LBC samples for each subset.

Metagenes expression was defined as the median expression in Log2 of the genes of interest in each sample. Differential expression analysis of metagenes was done using non-paired Wilcoxon test. Correlations were assessed using Pearson correlation test, and a threshold of P < 0.05.

All RNA-seq statistical analyses were performed using R software (Version 3.2.3).

Regulatory network and functional inference

We extracted the gene expression matrix for each subset, and each comparison. The conditions were as followed: 1) all subsets versus all other subsets in LBC, and 2) in TNBC.

3) tumor versus juxta-tumor in LBC, and 4) TNBC versus LBC, for each subset separately.

We then load the matrix on cytoscape software version 3.4.0. One analysis per subset was performed. Network inference was performed using ARACNe application, which is based on mutual information theory [START_REF] Margolin | ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context[END_REF][START_REF] Basso | Reverse engineering of regulatory networks in human B cells[END_REF]

Checkpoint expression analysis

The presence of the following immune checkpoints was analyzed among DEG in tumor versus juxta-tumor samples, for each subset. Positive checkpoint genes included: CD40, CD70, CD80, CD83, TNFSF9, also named 4-1BBL, ICOSL, SEMA4A, TIMD4, C10orf54 known as VISTA or B7-H5, TNFRSF13C, also named BAFFR, TNFSF13, also named APRIL, TNFSF13, also named as HVEML, CD84, CD48, TNSF4, also named OX40L, and PVR, also named as CD155. Negative immune checkpoint genes included: CD274, also named PD-L1, CD276, also named B7-H3, PDCD1LG2, also named PD-L2, BTLA,

LGALS1, LGALS3, LGALS9, CD279, also known as PD1, CEACAM6, and CD209, also named DC-SIGN.

Clinical outcome of subset-specific signature score in public breast cancer dataset

METABRIC is a public dataset 43 of transcriptomic data of breast tumor samples with clinical data associated. From this dataset, we selected samples from LBC (n=1043) and TNBC (n=259) according to the expression of receptors ER, PR and HER2. To study the clinical outcome of patients we considered the ones with the label "d-d.s" and "a" in the "last follow up status" variable. Similar to previous report 44 , we calculated a Z-score of APC subsetspecific signatures that we generated from our breast cancer RNA-seq data, as follows:

= = - ( )
To assess predictive value of the CD103 + DC reported in Broz et al. 44 , we applied the same Zscore, based on CD103 + DC signature as the "signature UP" and CD103 -DC signature as the "signature DOWN". CD103 + DC and CD103 -DC signatures contained 9 and 16 genes respectively 44 .

To assess predictive value of the pDC signature reported in Haniffa et al. 7 , we applied the same Z-score, based on pDC up-regulated genes as the "signature UP" and pDC downregulated genes as the "signature DOWN". pDC UP and pDC DOWN signatures contained 440 and 524 genes respectively.

To assess predictive value of the IFN signature found in TNBC-APC, we performed a Z-score on Log2 mean expression of: IFNL1, IFNB1, ISG15, and ISG20. We performed univariate cox analysis to assess the link between subset-specific signature ratio expression, and diseasefree survival. We divided the subset-specific Z-score ratio expression in two groups: "high" or "low", according to the median value. Kaplan-Meier curves were generated using survminer R package. Multivariate cox analysis was performed to link subset-specific signatures and the clinical prognostic parameter, Nottingham Prognostic Index (NPI) 45 , to disease-free survival.

Life Science Reporting Summary

Further information on Online Methods are available in the Nature Research Reporting Summary. Supplementary Figure 6 a, Extended list of enriched pathways and corresponding GO term from genes upregulated in TNBC versus LBC that were shared with 2 or 3 subsets, as indicated. b, Genes included in the IFN pathway metagene separated in IFN production and IFN response that were used for the analyses in Fig. 6 c-f. c, Genes included in the costimulatory metagene used for the analysis in Supplementary Table S1

Supplementary table S2: Sample description for RNA-seq analysis

ICELLNET: Reconstruction of intercellular communication networks using transcriptomic profiles

The second results will be presented as a manuscript that will be soon finalized for submission. It is entitled "ICELLNET: Reconstruction of intercellular communication networks using transcriptomic profiles". For this collaborative work, I was involved in the development of a systematic transcriptomic-based approach for cell communication network reconstruction. Indeed, cell-to-cell communication is essential to transfer information between cells with different functions and sensing capabilities. Intercellular communication coordinates the activities of diverse cell types required for complex processes such as embryogenesis, tissue remodelling during inflammation and wound healing, and immune responses. Currently, there is no systematic method to reconstruct cell-to-cell communication in a qualitative and quantitative manner.

In this study, we developed ICELLNET, a tool integrating prior information on ligand/receptor interactions, and cell-specific gene expression data and representing quantitative and qualitative aspects of cell-to-cell communication as connectivity maps. ICELLNET can be automatically applied to any cell population level transcriptomic profile in order to estimate and quantify its communication with over 12 other cell types. We applied this method to tumor cells, innate and adaptive immune cells (e.g. DCs, T cells, B cells, NK), epithelial, and stromal cells. By analyzing an original de novo generated dataset of human dendritic cells, we identified and experimentally validated IL-10 as a major regulator of the systems-level DC intercellular connectivity.

Our approach to assess cell connectivity may provide a valuable tool to evaluate the impact of a specific context on cell-to-cell communication, especially in inflammatory microenvironment such as cancer. In future perspectives, ICELL-NET applications could lead to important biological insight and helping to direct pharmacological manipulation.

Introduction

Cell-to-cell communication is essential to transfer information between cells with different functions and sensing capabilities. Intercellular communication is critical to coordinate the function of diverse cell types involved in complex biological processes, such as embryogenesis, tissue regeneration, and immune responses. For example, this allows innate immune cells, such as dendritic cells (DCs), to alert neighbouring cells after having sensed a threat through specialized innate receptors (1, 2). Numerous studies have established important cross-talks between distinct types of immune cells. However, inflammatory networks involve numerous cell-cell communications, which collectively determine the nature and outcome of the response (3, 4). Few attempts were made to reconstruct systems level immune inter-cellular networks, using literature-based approaches to enumerate possible connections between different cell types (5, 6). In such networks, nodes are cell-types, and edges correspond to ligand/receptor interactions forming a communication channel between two cell-types. This structure introduces three fundamental quantitative dimensions, which collectively shape the global functional output of a given cell network: 1) the number of different connections one cell type can form concurrently with other cell types, 2) the intensity of the communication between two cell types, 3) the efficiency of the communication reflected by information-induced modifications in the state or function of a target cell.

Currently, there is no systematic method to reconstruct cell-to-cell communication in a qualitative and quantitative manner. Given the multiplicity of possible communications between a given cell type and other cells, we reckoned that large-scale datasets could provide a valuable source of information in order to estimate cell communication.

In this study, we developed ICELLNET, a systematic transcriptomic-based approach for cell communication network reconstruction. This method can be automatically applied to any cell population level transcriptomic profile in order to estimate and quantify its communication with over 15 other cell types. We applied this method to tumor cells and various types of immune cells. By analyzing an original de novo generated dataset of human dendritic cells, we identified and experimentally validated IL-10 as a major regulator of the systems level DC intercellular connectivity.

4

Results

Development of ICELLNET, a transcriptomic-based communication score

We developed a quantitative and qualitative bioinformatics approach by integrating prior information on ligand/receptor interactions, and cell-specific expression data (Figure 1A).

We developed an automatized tool in R script, ICELLNET, to perform the score computation and network reconstruction that we can apply to any cell type transcriptomic profiles. In a first step, we manually curated a database of ligand-receptor interactions containing 244 entries (Supplementary Table S2B). The quantification of intercellular communication consisted of scoring the intensity of each ligand/receptor interaction between two cell types with known expression profiles. Whenever needed, we took into account the requirements for multiple ligand units, or receptor chains, using logical rules. The score of an individual ligand/receptor interaction was computed as the product of their expression levels respectively by the source and by the target cell. These individual scores were then combined into a global metric assessing the overall exchange of information between the cell types of interest (Figure 1A). As putative cellular targets, we selected 12 cell types known to be present in an inflamed tissue microenvironment (Figure 1B). Cell-specific gene expression data was obtained using a database from human primary cells (12, 13). As cells of interest, we selected

IL-10 controls an intercellular communication module in LPS-activated dendritic cells

After using a tumor cell model to test the connectivity map reconstruction, we wanted to assess if ICELLNET tool would allow us to characterize cellular communication using the immune system as a model. Particularly, we were interested in studying communication of resting and perturbed immune cells. To explore the role of autocrine loops, we cultured LPSactivated human monocyte-derived DCs in the presence or absence of blocking antibodies (Abs) to the TNF and IL-10 receptors (aTNFR and aIL10R). No effect on cell viability was observed (Figure S1). The most prominent effect of LPS on DC hallmark maturation markers was observed at the mRNA level in the time frame of 4 to 8 hours following activation (11).

We performed large-scale microarray analysis after 4 and 8 hours of culture of DC with LPS, with and without blocking Abs to TNF and IL-10 receptors (Figure 2A). To identify expression patterns determined specifically by each loop, we scored every differentially expressed gene according to its ability to separate the experimental condition LPS+aTNFR or LPS+aIL10R, respectively, from all of the other conditions considered as a single statistical group. At 4 hours, we could detect relatively small numbers of genes with expression patterns specific for the condition LPS+aTNFR or LPS+aIL10R (Figure 2B). At 8 hours, while only 77 genes exhibited significant separability when the TNF loop was blocked, blocking the IL-10 loop led to a transcriptional signature comprising 1432 genes (Figure 2B andC). These quantitative differences led us to focus on the IL-10 loop signature at 8 hours. Some of the genes in this signature (ARHGAP22, CSF2, CD163L1 and MLXIP for example) showed a remarkably large separability score (Figure 2B andC). By using various pathway analysis resources, we found that the TNF loop signature is enriched in functions involving the activation of different receptors (GPCR, rhodopsin-like and P2Y) (Figure 3). Applying the same methods to the IL-10 loop signature, a highly significant enrichment was found in annotation terms related to cytokine-cytokine receptor interactions, and positive regulation of cell communication (Figure 2D andE). These results were robust to changes in the empirical threshold used to define the IL10 loop signature, consistent with a robust biological signal

(supplementary table 1E-H).

We then screened the IL-10 and TNF loop signatures to systematically identify extracellular molecules mediating cell communication through ligand/receptor interactions. We were able to extract 47 ligands and 23 receptors from the IL-10 loop signature, while only 3 ligands and 5 receptors from the TNF loop signature (Figure 2F).

6

Despite extensive studies of both TNF and IL-10 in the context of innate immunity, their different contribution to DC intercellular communication could not be predicted a priori at this systems level. It was particularly striking that communication was controlled specifically by IL-10, although TNF has strong pro-inflammatory actions (7) which could have suggested a decreased cell communication in the absence of this cytokine.

The IL-10 loop signature comprised a variety of cytokines, chemokines, growth factors, and membrane ligands (table 1). Although some of the communication molecules in the loop signature were known to depend on IL-10 (for example CD80, GM-CSF and GCSF), many of them had weak or no prior association to IL-10 function (e.g. CLCF1 and TNFSF15), and would not have been captured by a strategy exclusively based on prior knowledge. We selected four important immunoregulatory molecules from the IL-6-and IL-12-families, and further validated expression at the protein level in 24h culture DC supernatants using cytometric bead array (CBA) and ELISA (Figure 2G). Interestingly, exogenous IL-10 downregulated several targets that were instead up-regulated by blocking the IL-10 loop (Figure S3).

Systems level reconstruction of dendritic cell intercellular communication networks

After establishing the possibility of increased DC communication in the absence of the IL-10 loop, we set out to identify the putative cellular partners in the local inflammatory microenvironment. We applied ICELLNET to reconstruct the intercellular networks between DCs and the putative target cells (Figure 3A and Figure S4). Focusing on individual ligand/receptor interactions connecting DCs with the putative target cells, we observed that certain ligands, such as TNF, could potentially act on many cellular partners (Figure 3B), consistent with a pleiotropic effect (14). However, other interactions pointed to crosstalk between DCs and specific targets, for example IL19 and IL36G with keratinocytes; TNFSF18 with NK cells; CD70 with T and B cells (Figure 3B). The connectivity maps, generated for

Experimental validation of multiple IL-10-dependent communication channels

To assess communication efficiency, i.e how increased connectivity translates into functional changes in target cells, we turned to experimental validation of predicted communication channels using immunological assays adapted to output response of each cell type. Due to its physiopathological relevance, we first investigated the DC-T cell axis through co-culture experiments of T cells with DCs treated by LPS with or without TNFR and IL10R blocking antibodies (Figure 3-figure supplement 1). We found that naive CD4 T cells, when cocultured with LPS-DC in the absence of the IL-10 loop, globally increased and shifted their pattern of cytokine secretion, as compared to LPS-DC, while blocking the TNF loop had almost no effect (Figure 4A). Similar results were obtained with memory T cells (Figure 4B).

Since the IL10/IL10R pathway could have a direct effect on T helper cells during the differentiation process, we verified that the observed T helper polarization was indeed due to the IL-10 loop blockade in the DCs, and not to a direct effect on T cells (Figure S5B). Indeed, remaining IL10R blocking antibodies after the DCs washes could have act directly on T cells during DC-T co-culture. By adding IL10R antibodies during DC-T co-culture (not only to during DC activation) we could show that IL10R antibodies in our setting would not have any direct effect on T cell polarization.

Among the factors best explaining the secretion profile of T cells determined by

LPS+aIL10R-DCs, we observed a remarkable emergence of Th17 cytokines (Figure 4C), in line with recent murine studies (15-17). Strikingly, IL-9 was also increased (Figure 4C), and produced by a T cell population distinct from Th17 cells producing IL-17A alone or coexpressed with IL-9 and IFN-g (Figure 4D). This provides the first demonstration that LPS-activated DCs, in the absence of an IL-10 loop, determine a Th17 and Th9 polarization in human, both of which participate in host defense and autoimmunity (18, 19).

Through a paired DC/T dataset, we detected correlations between specific DC outputs from the IL-10 loop signature and specific T helper cytokines (Figure 4E and Figure S6). IL-9 secretion from T cells strongly correlated with pro-inflammatory cytokines produced by DCs such as IL-12p70, again illustrating the link between communication molecules made by DC, and modifications in T cell behaviour.

We then aimed at validating the model-based hypothesis of an increased communication between DC and multiple cell types. We considered three additional types of target cells: The DC-pDC communication channel was also controlled by IL-10, since LPS+aIL10R-DC supernatants activated pDCs (as assessed by CD86, HLA-DR, and ICOSL surface expression), in comparison to LPS-DCs (Figure 5B). DC-induced activation of pDC and keratinocytes was not due to the presence of residual aIL10R (Figure S5C andD). DC-pDC crosstalk was suggested to be important in antiviral (22), antibacterial (23), and antitumor (24) immune responses. Through our systems approach, we now show that IL-10 controls DC-pDC connectivity.

Neutrophils contribute to DC migration to infection sites and to their subsequent activation (25, 26). Reciprocally, it was proposed that DCs can promote neutrophil survival (27).

LPS-DC supernatant induced only a mild activation of neutrophils (as evaluated by rapid upregulation of CD11b with concomitant downregulation of CD62L), while LPS+aIL10R-DC supernatants led to a strong activation of neutrophils (Figure 5C), establishing an IL-10 loop control of DC-neutrophils communication.

For all the above-mentioned communication channel, we aimed at getting further mechanistic insight. First, we performed control experiments using exogenous LPS that formally excluded a direct effect of LPS at the concentrations found in the DC supernatants (Figure S5A). We then considered ligand-receptor interactions showing high intensity, and thus more likely to mediate cellular crosstalk as observed with the LPS+aIL10R-DC supernatants

Discussion

In this study, we demonstrated that a single molecule, IL-10, was able to control intercellular communication between DC and multiple immune and non-immune cells. DC are central to immune responses in health and disease, and have the ability to orchestrate and/or modulate the function of many cell types, including CD4 and CD8 T cells (2, 28), NK cells (29), γδ T cells (30), neutrophils (27), as well as other DC subsets (22, 24). Our findings reveal that these multiple connections may be collectively regulated by one molecule, in a coordinated manner. This indicates a level of regulation that could not be captured by conventional methods isolating pair-wise cell cross-talks, and calls for systems approaches. Previous research in this direction showed, for example, that systems approaches can be successfully applied to reconstruct the global cell cross-talk in the stem cell niche (31). In our study, we add an essential component, in the form of perturbations on purified cell cultures, in order to address mechanisms regulating the connectivity of immune cells.

One key element of systems approaches to intercellular communication is our a priori knowledge of the possible ligand-receptor interactions triggering a transduction process. Such information can be retrieved, for example, through automatic literature mining (31). However, this method makes it difficult to control the publication quality, and may fail to capture the requirements for complex interactions involving hetero dimeric receptors. In our work, the 10 information on the relevant ligand-receptor interactions was curated manually, which allowed taking into account the quality of publications, as well as up-to-date knowledge on the different chains of heterodimeric and heterotrimeric receptors.

Once the molecular mediators of possible cell-to-cell interactions were identified, we turned to assessing their cell-specific expression. To this end, we identified BioGPS as a particularly suitable resource, because it integrates transcriptional profiles of over twenty human primary cell types generated with the same Affymetrix platform (12). While previous applications of BioGPS allowed identifying specific tissue-related genes (32, 33), we show as an original use of this resource the possibility to simulate cell cross-talks in diverse microenvironments. The fact that BioGPS includes transcriptional profiles for both steady state and activated cells indicates that a predicted communication channel would not be specific (and restricted) to a given activation cell state. Our ability to provide functional validation of many of the predicted cellular cross-talks indicates the robustness of the method, and warrants application to other cellular types.

After retrieving a set of ligand-receptor interactions and cell-specific transcriptomics expression, we faced the problem of quantifying the intensity of communication between any pair of cell types. To score individual ligand/receptor interactions, we used the product of their expression values consistent the law of mass action, commonly assumed in biochemical models (34). Such individual scores give rise to a complex multigraph with potentially hundreds of edges connecting any two cell types. To reduce this complexity, we introduced a global score summing up the intensity of all the individual channels. This greatly simplifies the interpretation and visualization of intercellular networks, but also introduces some arbitrariness when choosing to combine the individual scores. Notwithstanding, all the predicted cellular targets could be experimentally verified, which led us to gain new insight on the role of TNF and IL-10 auto-regulatory loops during bacterial activation of DC.

Exogenous TNF functional effect on dendritic cells has been described by many. It was found to induce maturation (35) and more specifically -induce and increase surface costimulatory molecules such as CD40, CD80, CD86, CD83 and HLA-DR (35-37). Exogenous IL-10, however, was found to have an opposite effect on the expression of these costimulatory molecules and led to their downregulation (8) (38). Contrasting effects on DC development were also described when comparing TNF to another anti-inflammatory cytokine, TGF beta (39). Taken together, one might expect to find contradicting effects of the TNF and IL-10 endogenous loops on DC, with an opposite directionality of gene regulation, including communication molecules. Our data, however, uncover a very different scenario. Distinct and non-overlapping set of genes were controlled by either loop, and the intercellular communication function was regulated almost exclusively by the IL-10 loop.

We identified IL-10 as a molecular switch able to regulate the connectivity of DC with 12 other cell types, and thereby to modify their activation and functional states. IL-10 was already shown to regulate DC-derived inflammatory cytokines and chemokines, in particular IL-12 (8, 40). Through our systems approach, we identified a large number of communication molecules not previously associated to IL-10 function. Most importantly, we could demonstrate that endogenous DC-derived IL-10 governs the global connectivity of DC with multiple cell types, subsequently affecting their activation state, which brings new insight into how IL-10 regulates inflammation. We propose that the intensity and efficiency of communication may constitute a novel paradigm underlying the regulation of inflammatory processes, with increased intercellular connectivity being associated to enhanced inflammation. This warrants further studies in disease settings, in vivo and ex vivo, in order to precisely define the physiopathological relevance to specific inflammatory disorders.

Interestingly, IL-10 functions as an auto-regulatory switch controlling the structure and intensity of communication within cell networks while it was not predicted to be a direct effector on other cell types (Supplementary table S3A-L). On the contrary, TNF was predicted and validated to be a direct effector in most communication channels, leading to activation of target cells, while in the context of a feedforward loop it did not play a major role in determining the intensity of the global communication network. Thus, IL-10 and TNF act at different hierarchical levels to regulate cell-cell communication: IL-10 as an upstream molecular switch, TNF as a downstream effector communication molecule. This may have implications to understand the impact of IL-10 and TNF targeting in inflammation.

Our study revealed that connectivity within cell networks could be controlled by a single molecule. This predicts that, within the inflammatory microenvironment, the impact of targeted therapies to soluble mediators or surface receptors may be much broader than anticipated, due to a global re-programming of intercellular communication. Our systems and quantitative approach to cell connectivity may provide a valuable tool to evaluate such impact. Future studies should prove useful in identifying other regulators of immune cell connectivity in various physiopathological contexts, leading to important biological insight and helping to direct pharmacological manipulation.

Methods

Purification of Peripheral blood mononuclear cells (PBMCs) from adult blood

Fresh blood samples were collected from healthy donors and obtained from Hôpital Crozatier Établissement Français du Sang (EFS), Paris, France, in conformity with Institut Curie ethical guidelines. In agreement with EFS rules, all informed consent and consent to publish were obtained. PBMCs were isolated by centrifugation on a Ficoll gradient (Ficoll-Paque PLUS, GE Healthcare Life Sciences).

Monocyte-derived dendritic cells generation and activation

Monocytes were selected from PBMCs using antibody-coated magnetic beads and magnetic columns according to manufacturer's instructions (CD14 MicroBeads, MiltenyiBiotec). To generate immature DCs, CD14+ cells were cultured for 5 days with IL-4 (50 ng/mL) and GM-CSF (10 ng/mL) in RPMI 1640 Medium, GlutaMAX (Life Technologies) with 10% FCS.

Monocyte-derived

Gene expression profiling

Monocyte-derived DCs were pre-treated with blocking Abs as described above for one hour and then cultured with medium or LPS (100 ng/mL, Invivogen) for additional 4 or 8 hours.

Total RNA was extracted using the RNeasy micro kit (Qiagen). Samples were then amplified and labelled according to the protocol recommended by Affymetrix for hybridization to Human Genome U133 Plus 2.0 arrays.

The gene expression profiles generated for this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE89342 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89342).

Purification of naive CD4+ T lymphocytes.

CD4 + T lymphocytes were purified from PBMCs by immunomagnetic depletion with the human CD4 + T cell Isolation KitII (MiltenyiBiotec), followed by staining with allophycocyanin-anti CD4 (VIT4 ; MiltenyiBiotec), phycoerythrin-anti-CD45RA (BD), fluoresceinisothiocyanate-anti-CD45RO (BD Bioscience) and phycoerythrin-7-anti-CD25 (BD bioscience). Naive CD4 + T cells sorting of CD4 + CD45RA + CD45RO -CD25 -had a purity of over 99% with a FACSAria (BD Bioscience).

DC-T cells Coculture assays.

To analyze T cell polarization, 24 hours activated DC and T cells were incubated in 96 well plates at a DC/T ratio 1:5 in Xvivo15 medium (Lonza). After 6 days, T cells were resuspended in fresh Xvivo15 medium at a concentration of 1 million cells per ml and restimulated with anti-CD3/CD28 beads (life Technologies) at a ratio bead/cell 1:1.

Supernatants of T cells were collected after 24 hours of restimulation. The following cytokines were measured in naive culture supernatants by CBA (BD Bioscience) according to the manufacturer's instructions: IL-2, IL-3, IL-4, IL-9, IL-10, IL-17A, IL-17F and IFN-g.

Additional cytokines were measured in memory T cells supernatant: IL-5, IL-13 TNF and GM-CSF.

Cytokines producing cells were analyzed by intracellular staining after addition of brefeldinA (10ug/mL) during the last 3 hours of the 5 hours restimulation in PMA and ionomycine respectively 100ng/mL and 500ng/ml. Cells were stained 30 minutes with the yellow live dead kit (Invitrogen). Finally, cells were fixed and permeabilized using the Staining Buffer Set (eBioscience) and stained with anti-IL9, anti-IFNg, and anti-IL17A (ebioscience), and analyzed by flow cytometry (BD Fortessa).

Measurement of surface molecules expression by plasmacytoid dendritic cells

In order to enrich plasmacytoid dendritic cells (pDCs), cells expressing CD3, CD9, CD14, CD16, CD19, CD34, CD56, CD66b and glycophorin A were depleted from PBMCs using magnetic sorting (Human Pan-DC Pre-Enrichment Kit, StemCell Technologies), pDCs were then sorted on a FACS Vantage instrument (BD Biosciences). pDCs were cultured for 24 hours at 37°C and 5% CO 2 with medium RPMI 1640 Medium, GlutaMAX (Life Technologies) with 10% FCS, GM-CSF (10 ng/mL) used as a positive control or DC supernatants. Cells were stained for 15 min at 4°C using a FITC-anti-CD86 (BD), an APC-anti-ICOSL (R&D Systems) and Alexa-Fluor-700-anti-HLA-DR (Biolegend) or with the corresponding isotypes. Cells were analyzed on a LSR II instrument (BD Biosciences).

Measurement of adhesion molecules expression at the Neutrophil surface

Whole-blood samples were obtained from healthy donors from Hôpital Crozatier

Real-time quantitative RT-PCR

The keratinocyte cell line HaCaT was cultured in DMEM (Gibco) supplemented with 10%

FBS and 1% penicillin/streptomycin. Cells were cultured with medium, LPS (100 ng/ml), or with DC supernatant diluted 1:10 for 4h. Total RNA was extracted by RNeasy Mini kit (Qiagen). RNA was then transcribed to cDNA using Superscript II reverse transcriptase based on the manufacture's protocol (Invitrogen). The Taqman method was used for real-time PCR with primers from Life technologies. The expression of mRNA was normalized to the geometrical mean of 3 house-keeping genes: β-actin, GAPDH and RPL34. HaCaT cells were negative for Mycoplasma contamination, standardized and regular tests were performed by PCR for mycoplasma detection.

Statistical analysis of gene expression data

Expression data were normalized with Plier. Transcriptomics analysis was performed in a Matlab environment. For independent filtering, we used the function geneverfilter, which calculates the variance of each probe across the samples and identifies the ones with low variance. Probes with variance less than 40 th percentile were filtered out because poorly informative. Differential analysis was performed using an ANOVA test (function anova1) at 4 hours and 8 hours. P-values were adjusted for multiple testing using the Benjamini-Hochberg correction using the function mafdr. Adjusted p-values <5% were considered significant (see

supplementary

Reconstruction of inter-cellular networks

To reconstruct the inter-cellular communication networks, we systematically extracted a list of ligands and receptors contained in the genes whose expression pattern was specific for the condition LPS, LPS+αIL10R, or LPS+ αTNFR (see the section above). Surveying the literature for any potential interactions, we manually curated a ligand-receptor database using STRING (http://string-db.org/) and Ingenuity (http://www.ingenuity.com/) online tools to verify protein-protein interactions. Logical rules were applied to address requirement for multiple chains as well as multiple ligand subunits (http://www.genome.jp/keggbin/show_pathway?hsa04060).

The database of ligand-receptor interactions is contained in the supplementary table 2B. To get the cell-specific expression level of the receptors of the ligands of interest, we used a database of transcriptional data from human primary cells (12) (13) and a dataset of SUM149 inflammatory breast cancer cell line transcriptional profile from literature (Wang et al. Cancer

Res 2013). All the cell-specific transcriptional profiles used in the analysis were generated with the U133 Plus 2.0 Array, which limits the platform-related bias. If multiple probes corresponded to the same receptor, we selected the optimal probe based on the Jetset optimality condition (42). The results are contained in supplementary table 2C-E. To score the intensity of a particular ligand-receptor interaction between DC and a given target cell, we considered the product of the expression of the ligand in DC and of the cognate receptor in the target cells. Formally, if ! " # is the average expression level of ligand i by DC in the experimental condition j, and $ % # is the average expression of the corresponding receptor by 16 cell type k, the intensity & ",% # of the corresponding interaction was quantified by & ",% # = ! " # • $ % # .

For interactions requiring multiple components of the ligand and/or of the receptor, we considered a geometric average of the receptor components. For example, if a given interaction corresponding to ligand i required two chains of the receptor, the score was computed as ! " # • $ % #,, . $ % #,-, where $ % #,, and $ % #,-are the expression levels of the two receptor chains in cell type k. To assign a global score . ",% to the communication between DC in the condition j and cell type k, a composite score was defined by summing up the intensity of all the possible ligand-receptor interactions, i.e., . ",% = & ",% # / #0, , N being the total number of interactions. Four DC experimental conditions were considered: Medium (j=0), LPS (j=1), blocking TNF loop (j=2), blocking IL10 loop. To emphasize comparisons . ",% across the four conditions, the global scores . ",% were normalized to the Medium condition (j=0). Thus, the final scores . 1,% used to measure the communication intensity between DC in the condition j and the target cell k were computed using the following formula . 1,% = . ",% /. 3,% .=

Statistical analysis of DC-T cell protein data

All analyses have been generated with R 3.1. For principal component analysis (PCA) of the T cell secretion profile, a data matrix was formed whose rows corresponded to conditions and columns to the different cytokines (each column was scaled using zscore). PCA was done using the function princomp. When appropriate, a paired student t-test was performed.

Significant differences were considered with p<0.05. The correlation heatmap based on Spearman was generated on the logged data. Correlations with p values<0.05 were considered as significant.

Calculation of the activation score of target cells

To compute a global activation score of keratinocytes, neutrophils and pDC, each activation marker output was first normalized in the range 0-1, 0 being to the untreated condition and 1 being to the maximum value observed in all the conditions. An average of the normalized outputs corresponding to the same cell type was then considered. All of the measured factors, with the exception of CD62L in neutrophils, were positively correlated with cell activation. In order to make CD62L consistent with the other factors, we considered the reciprocal of its value. The numerical results are in the supplementary table 3M-N. First, I will discuss the relevance of characterizing APC subsets in breast cancer and how this work is positioned in relation to the literature. I will consider how cancer heterogeneity can impact cellular communications. Regarding the biological results I obtained, I will discuss the interferon signature found in TNBC.

L 1 R 1 L 1 R 1' L 2 R 2 … … L n R n

Communication

Additionally, I want to review the relevance of using transcriptomic data to study the intercellular communication and the microenvironmental impact on cellular behavior. I will include future perspectives on the use of a new technology that is single-cell RNA-seq to this field. Finally, I will discuss the interest and the complexity of understanding cell-to-cell communication and future developments that can be done to improve the ICELLNET tool. The subset-specific signatures that we generated could be used in deconvolution tools, to mine publicly available transcriptomic datasets of bulk tissues. This enable to infer the immune infiltration and especially APC infiltration in breast cancer datasets as well as in other inflammatory contexts. These signatures could also be used to identify clusters of cells in single-cell RNA-seq (scRNA-seq) experiments.

Breast tumor-infiltrating APC subsets characterization

APCs are plastic and able to adapt to the inflammatory environment, such as during pathogen infection [Huang et al. 2001; Soumelis et al. 2015]. They can adapt according to a specific tissue-imprinting [Mora, Bono, et al. 2003]. At steady-state, however, the ontogeny is described as the predominant driver of DC subsets definition [Heidkamp et al. 2016; Miller et al. 2012]. When we compared LBC tumor-and juxta-tumor-infiltrating APC subsets, we observed that they display subset-specific transcriptional programs. This implies that APCs are able to integrate signals from the tumor and adapt their functions in a subset-dependent manner, highlighting the complex interplay between intrinsic origin of the cells and plasticity to their environment (i.e. tumor imprinting). This is a new vision of APC subset adaptation in a peculiar inflammatory microenvironment and it would be interesting to compare these results to APCs in other anatomical location under inflammatory conditions including invaded lymph nodes, other cancer types (e.g. lung cancer, head and neck cancer, lymphoma). These potential studies would shed new lights on the impact of tumor imprinting versus tissue imprinting and ontogeny on APC subset functional specialization.

Heterogeneity of tumors and impact on cellular communication

In the literature, the concept of "hot" versus "cold" tumors discriminates tumors according to the level of immune infiltration [Wargo et al. 2016]. Hot tumors present a greater T cell infiltration, chemokine and interferon expression, than cold tumors which display lack of T cell infiltration, poor chemokine expression and minimal presence of defined immune inhibitory pathways [Gajewski, H. Schreiber, and Fu 2013].

LBCs display poor immune-infiltration on contrary to TNBCs [Stanton, Adams, and Disis 2016]. Comparing LBC tumor-and juxtatumor-infiltrating APC subsets transcriptional profiles, we did not reveal differential gene expression or functions related to immune signaling. In LBC tumor tissue, DCs do not integrate signals activating immune responses or inducing an immunosuppressive phenotype. Targeting DCs to activate them and induce anti-tumor response could have beneficial therapeutic aftermaths in LBC. On the other hand, we observed that all TNBCinfiltrating APC subsets up-regulate genes related to interferon pathways as compared to LBC-infiltrating APCs. At the light of our results and literature concepts, we can hypothesize that the heterogeneity of T cells infiltration across breast cancer subtypes modify the TME and its signaling network. Immune cell communication network is a component of the TME which has to be evaluated in order to better understand mechanisms of immune escape. As a future perspective, we want to compare APCs infiltrating cancers of a different anatomical location to find clues on the link between immune infiltration, signaling and APC subset-specific functions. We want to further study the adaptation of APC subsets in other cancer types to better understand the contributions of DCs and Monomacs to immune escape mechanisms.

In the lab, we already generated transcriptional profiles of head and neck (HNSCC) tumor-infiltrating APC subsets, using the same strategy as in the breast. Analysis the three different cancer datasets (i.e. LBC, TNBC and HNSCC) would be a first step to evaluate how the tumor type influence the transcriptional reprogramming of APC subsets. It would also be insightful to transpose these results in another type of inflammatory disease such as autoimmune disease in order to see if APCs contribute to the inflammatory environment as in TNBC or if they are quite passive in term of immune-related functions as in LBC.

Myeloid cells and interferon, a potential therapeutic axis in TNBC?

One interesting biological feature we observed in TNBC-infiltrating APC transcriptome is the common interferon signature, which include genes related to interferon production and response with an increased expression of IFNλ (type III interferon) and IFNβ (type I interferon) in TNBC-infiltrating cDC2 and cDC1e (data not shown). Finding interferon signature and correlation with checkpoint expression in myeloid cells gives hint for developing targeted therapies. However, further experiments and validation must be performed. Previously in the lab, Ghirelli et al. [Ghirelli et al. 2015] showed that IFNγ and IFNα were undetectable in breast cancer cell line supernatants. However, it would be relevant to check the level of interferons in supernatants of fresh human breast cancer samples, taking into account the complexity of the TME, and comparing LBC and TNBC. We could also consider performing a co-culture of blood DCs activated with interferon (type I and/or type III) or tumor-infiltrating DCs and naive CD8 + T cells. Then, we could study cytokines expression, T cell activation profile and DC profile using transcriptomic data analysis for instance.

Relevance of using transcriptomic data

We can argue that transcriptional profiles do not provide all information needed to assess the cell-to-cell communication. It only reveals what happen at the transcript level and not at the protein level which is the final message sensed by cells. However, it gives huge hint to infer the cellular functions impacted by a specific context and enable to study more than one molecule at a time. Using transcriptomic data of communication molecules, we were able to i) characterize tumor-infiltrating APC subsets and study their adaptation to breast TME, ii) derive subset-specific signature to assess the clinical outcome of patients, and iii) derive a communication score and find clues of cell-to-cell communication in response to specific stimuli that was experimentally validated. We can also monitor proteins to study intercellular communication but depending on the experimental setting we used, the number of parameters can become a limitation. Recently, a study described the social network architecture of immune cells and their altered communication associated with pathology using quantitative proteomics [Rieckmann et al. 2017]. From the scRNAseq analysis, we can derive a specific gene signature for this small population. Mining public databases of breast cancer profiles with clinical outcome, as I performed in the first part of my thesis project, we can assess the correlation between this cluster of DCs and clinical outcome. These results would give cues on the relevance of targeting this population for the development of DC-targeted cancer therapy.

Single-cell RNA-seq technology

Complexity of intercellular communication, a challenge to study

Understanding cell-to-cell communication, and its underlying mechanisms that drive the development of cancer is essential. From a cell-to-cell communication view, mechanisms of tumor immune escape are poorly described due to the complexity and dynamic of the system: multiple cell types with distinct phenotypes and functions, multiple signals in this environment which impact cell functions and intercellular communication. To reduce this complexity, we introduced a global score summing up the intensity of all the individual channels. This greatly simplifies the interpretation and visualization of intercellular networks, but also introduces some arbitrariness when choosing to combine the individual scores. Moreover, the database of communication molecules was manually curated, despite the robustness and validity of the information it provides, the resource is focusing on immune interaction and is not exhaustive. It could be completed by adding molecules implicated in other communication modules such as cellular migration signaling. To follow up on characterizing APC subsets in the breast TME, we could also apply our ICELL-NET score focusing only on immune checkpoint expression. We could generate in silico prediction of checkpoint activation or inhibition to assess the changes on the connectivity maps. In cancer, it can give clues on which checkpoint(s) could be an interesting target for immunotherapy development. In my thesis work, I applied the ICELLNET tool to datasets of cells from in vitro generated context. We hypothesize that the cellular communication network of stemming from various tissues (e.g. blood, skin, brain) and diseases (e.g. tumors, autoimmune disorders, pathogen infection) will harbour different communication patterns. As a long term future perspective, the integration of dynamical aspects of communications in the network reconstruction would give a more realistic view of cell-to-cell communication in the human body. Cells are not all frozen in one location but are able to migrate throughout the body. The interactions between cells then, appear to be part of a dynamic process in space and time which is not taken into account in most of cellto-cell communication studies . Finally, the use of scRNA-seq data can be helpful to

Introduction

Plasmacytoid dendritic cells (pDC) are bone-marrow derived cells whose development relies mostly on Fms-like tyrosine kinase 3 ligand (Flt-3L) 1 and the master transcription factor TCF4. 2 Mature pDC lack most of the lineage surface markers for B, T, NK cells and monocytes but express HLA-DR, CD123 (Interleukin-3 receptor alpha, IL-3R ), CD303 (BDCA2) and CD304 (BDCA4/Neuropilin-1). 3 These cells are the most important source of type I interferons (IFN-I) following recognition of viruses or nucleic acids through Toll-Like Receptor-7 (TLR7) and TLR9. 4 They can also capture, process, and present or cross-present antigens, 5 bridging innate and adaptive immune response. 6 pDC infiltrate a variety of human neoplasms. 7 In most cases, these tumor-associated pDC are defective in IFN-I production and exert a suppressive or tolerogenic function, primarily by inducing IL-10 producing regulatory T cells. [8][9][10][11] Paradoxically, a decrease in the number of circulating pDC can also be associated with disease progression. 12,13 Chronic myelomonocytic leukemia (CMML) is a myeloid malignancy that arises from the age-related accumulation of somatic mutations in a hematopoietic stem or progenitor cell (HSPC). 14 This disease associates cellular dysplasia with proliferative features including monocytosis. 15 Although not specific, the high incidence of TET2, SRSF2, ASXL1 and signaling mutations (NRAS, KRAS and CBL) is characteristic of this disease. [16][17] Myeloid progenitors commonly demonstrate hypersensitivity to granulocyte macrophage-colony stimulating factor (GM-CSF). [18][19] Median overall survival of CMML patients ranges between 15 and 30 months. Approximately 25% of these patients die from disease transformation into acute myeloid leukemia (AML). 20 Allogeneic stem cell transplantation is a potentially curative therapeutic option, 21 while cytoreductive drugs and hypomethylating agents have limited impact on longterm outcome. 22 In the 1980s, pathologists identified the presence of irregular islands of CD123positive cells in the bone marrow and tissues of a fraction of patients with acute and chronic myeloid neoplasms, with a strong predominance in CMML. [23][24][25][26] These cells were initially described as plasmacytoid T cells because of their plasma cell-like morphology and the expression of CD4, 27 then as plasmacytoid monocytes because of the expression of monocyte markers 28 . Their precise identity, the mechanisms promoting their generation, and their impact on disease evolution had not been explored thus far.

We show that CD123 + cells infiltrating the bone marrow of CMML patients are bona fide pDC according to the most recent classification. [29][30] The emergence of pDC islands is selectively observed in Ras-mutated CMML in which stem and progenitor cell differentiation into pDC has become hypersensitive to FLT3L. An increase in the number of pDC, which produce type I IFN and IL-8 upon TLR stimulation, correlates with an increased risk of leukemic transformation, bringing novel insights into CMML physiopathology.

Patients and methods

Cell collection. The two cohorts were approved by institutional review boards, disease diagnoses fulfilled the WHO 2016 classification criteria, 15 cytogenetic risk was evaluated according to the CMML-specific prognostic scoring system (CPSS), 31 and samples were collected with informed content. The French cohort characteristics are in Table 1. Cell collection and sorting procedures are in supplemental methods. The US cohort characteristics are in Table S1. Biopsies were obtained at diagnosis. Cytogenetic information was available on 198 (98%), while bone marrow mononucleated cell (BMNC) DNA was available on 167 (83%) patients and subjected to a 29 gene panel targeted capture assay. 32

Flow cytometry

BMNC and PBMC were incubated for 15 min at room temperature with Fc blocking reagent (Miltenyi Biotech) before being stained for 45-60 min at 4°C with antibodies.

Subsequent intracellular staining was obtained by cell fixation with Perm/Fix (BD Biosciences) for 20 min at room temperature and Perm/Wash washing before incubation with antibodies for 60 min at 4°C. Flow analysis was performed on a BD LSRFortessa X-20 with BD FACSDiva software (BD biosciences). pDC (Figure S1 and supplemental methods) were quantified as the fraction of PBMC or BMMC, whose count was refined using a CD33 vs side structure (SSC) plot, which enables elimination of residual dysplastic immature myeloid cells. We used Kaluza (Beckman Coulter, Brea, California, USA) and ModFit LT (BD biosciences) softwares for standard flow and CFSE experiments analyses, respectively. Antibodies and cell sorting methods are in supplemental methods.

Cell morphology and immunohistochemistry

Sorted pDC were analyzed on May-Grunwald Giemsa stained cytospins. For ultrastructural studies, they were fixed in 1.6 % glutaraldehyde (v/v in 0.1 M phosphate buffer) and post fixed with 2 % osmium tetroxide (w/v in 0.1 M phosphate buffer). Following dehydration through a graded ethanol series, they were embedded in Epon™ 812 and ultrathin sections were stained with standard uranyl acetate and lead citrate. Images were taken using a Tecnai 12 electron microscope (FEI, Eindhoven, The Netherlands). Immunohistochemistry was performed on formalinfixed and decalcified paraffin-embedded BM biopsies (details in supplemental methods).

pDC generation and stimulation

BMNC and PBMC (2.10 6 cells/mL) were incubated for 3 hours at 37°C with TLR agonists. After washing with cold PBS (Gibco), cells were stained extracellularly, fixed, permeabilized and stained intracellularly. Analysis of intracellular cytokines is in supplemental methods. To generate pDC, CD34 + cells were cultured in X-vivo 15 (Lonza, Amboise, France) supplemented with insulin 10ng/mL, liposomes 20ng/mL, thrombopoietin (TPO 50ng/mL), Stem Cell Factor (SCF 50ng/mL), Fms-Like Tyrosine Kinase 3 ligand (FLT3L 100ng/mL), and Interleukin-3 (IL-3 20ng/mL) before flow cytometry detection and analysis of generated pDC (see supplemental methods)

Cytokine measurement in bone marrow supernatants

Fresh bone marrow samples were centrifuged at 150 G for 10 min. Supernatants were collected and frozen at -80°C until analysis using mesoscale (Meso Scale Diagnostics, Rockville, Maryland, USA) technology with two panels, including a 10plex (IFN , IL1R , MIF, FLT3-L, GM-CSF, CXCL12, VEGF, TNF , IL10 et IL17a) and a 9-plex (: IL1b, IL6, IL8, IL4, IL2R , IFN , M-CSF, MIP-1 (CCL4), TPO) panels.

Exome and transcriptome analyses

Whole exome sequencing was performed on DNA collected from sorted bone marrow T-cells (CD3 + ), monocytes (CD14 + ) and pDC (Lin -HLA-DR + CD123 high CD11c -BDCA4 + ). Total RNA was isolated from sorted cells with Single Cell RNA Purification Kit (Norgen Biotek Corp, Canada). Detailed methods are in supplemental material.

Statistical analyses

Given the number of samples, we used nonparametric tests, including Mann Whitney test to compare continuous variables, Fischer exact to compare categorical variables and Kendall's correlation test to compare ordinal variables. The Kaplan-Meier method was used to evaluate survival data from diagnosis to death or last news. Cumulative incidence of AML transformation was computed considering death as a competing risk and univariate and multivariate analyses performed with Fine & Gray's proportional subhazards model. Multivariate analysis was performed on all variables with significant impact in univariate analyses, followed by backward stepwise selection. All statistical analyses were two-sided, retaining p<0.05 as statistically significant and were realized with STATA or Prism 7.

Results

CD123-positive cells infiltrate hematopoietic tissues in a fraction of CMML patients

CD123-positive cells that form irregular nodules in the bone marrow of a fraction of CMML patients (Figure 1A) were suggested to be pDC. To further explore the presence of pDC in bone marrow aspirate and peripheral blood, we set up a multiparametric flow cytometry assay that detects lineage-negative (CD3 -, CD14 -, CD15 -, CD16 -, CD19 -, CD24 -), CD33-negative and CD11c-negative mononucleated cells expressing CD45, CD123, HLA-DR, BDCA-2, BDCA-4 and CD4 (Figure 1B and S1). Compared to age-matched healthy donor controls (24 bone marrow and 34 peripheral blood), an increased fraction of these cells was detected in mononucleated cells collected from the bone marrow of 32/159 (20%) and the peripheral blood of 22/198 (11%) CMML patients, respectively (Figure 1C, 1D andtable 1). The cut-off value defining an increased fraction of pDC in mononucleated cells (mean + 2SD in age-matched control samples) was calculated to be 1.2% in the bone marrow and 0.6% in the peripheral blood, respectively (Figure 1C, 1D andS2).

Analysis of 106 matched bone marrow and peripheral blood samples demonstrated that the fraction of pDC in mononucleated cells was always higher in bone marrow than in peripheral blood (median %pDC 0.32 [0.04-0.81] in bone marrow vs 0.10 [0.02-0.26] in peripheral blood, p<0.0001, Wilcoxon signed rank test, Figure S2A).

Importantly, in patients whose pDC number was below the cut-off value (pDC-poor CMML patients), the fraction of pDC was significantly lower than in healthy donor tissues (p=0.0002 and p=0.004 in bone marrow and peripheral blood, respectively,

Figure S2B & S2C).

Comparison of cell surface marker staining index only detected a slightly lower expression of BDCA-2 in pDC-poor CMML samples (Figure S2D to S2G). A significant correlation was observed between the fraction of pDC measured in matched blood and bone marrow samples collected from 106 individual patients (linear regression, R 2 =0.75, p<0.001; Figure 1E). This translated into a good agreement between pDC bone marrow and peripheral blood infiltration (91.5%, Cohen's kappa coefficient=0.66). Of these 106 cases, the 11 (10%) patients with an excess of pDC in peripheral blood had pDC excess in bone marrow, whereas 9 of the 20 patients with a pDC excess in bone marrow had a normal count in peripheral blood.

CD123-positive cells that infiltrate CMML bone marrow are bona fide pDC.

To further validate the presence of pDC in CMML patients using a rigorous definition, we sorted CD45 + , Lin -, HLA-DR + , CD123 + , CD11c -, BDCA-4 + cells from CMML patient bone marrow and performed Giemsa staining demonstrating a typical plasma cell-like morphology that included a round or oval shape, an eccentric nucleus, basophilic cytoplasm, and a pale Golgi zone known as the arcoplasm (Figure 2A).

Electron microscopic analysis of these cells showed a well-developed rough endoplasmic reticulum (RER) in an electron-dense cytoplasm (Figure 2B) congruent with pDC. In some cases, we also noticed cytoplasmic hyaline inclusions made of aggregates of filaments (Figure 2B, lower panels, arrow). Flow cytometry analyses indicated that a small fraction of these cells (always lower than 16%) expressed AXL and, among CD33 -AXL -cells, a small fraction (25.4 +/-17.7%, n = 17) expressed CD2 (Figure 2C and Figure S2H andS2I), two markers that were recently demonstrated to define independent cell populations. 29,30 We then explored the ability of CMML pDC to produce IFN by intracellular flow cytometry analysis of mononucleated cells treated with brefeldin A, which induces the cytoplasmic retention of synthesized cytokines. With this method, IFN was detected in the cytoplasm of a fraction of bone marrow (Figure 2D) and peripheral blood (Figure 2E) pDC stimulated for 6 hours with a TLR7 agonist (the guanosine analog loxoribine, 2 mM) or a TLR9 agonist (CpG ODN2395, 1 µM). We also stimulated sorted pDC with either lipopolysaccharides (LPS, 1 µg/ml), or loxoribine (2 mM), or CpG ODN2395 (1 µM), or IL-3 (10 ng/ml) for 24 hours. Those collected from pDCrich CMML patients secreted various amounts of IFN (Figure 2F), tumor necrosis factor (TNF) (Figure 2G), IL-6 (Figure 2H) and IL-8 (Figure 2I) in response to CpG ODN2395. All of them produced IL-8 in response to loxoribine (Figure 2I). None of these samples had any response to LPS (Figure 2F-I), compatible with the lack of TLR-4 expression in human pDC. 33 Since pDC accumulation in solid tumors has been associated with an expansion of regulatory T cells (Tregs), we measured the fraction of CD3 + , CD4 + , CD25 high , CD127 low Tregs (Figure S1) in bone marrow and peripheral blood mononucleated cells of CMML patients with and without excess pDC, and in healthy donors. The fraction of Tregs among T cells was significantly higher in the bone marrow and peripheral blood of CMML patients compared to healthy donors (Figure 2J and2K).

The fraction of TRegs among T cells was also significantly higher in the bone marrow (Figure 2J) and peripheral blood (Figure 2K) of pDC-rich bone marrow patients. A significant correlation between pDC infiltration and the fraction of Tregs was measured in bone marrow and peripheral blood, respectively (Figure 2J and2K).

CMML pDC are close to healthy donor pDC

RNA-sequencing was performed in pDC sorted from pDC-rich (n=11) and poor (n=5) CMML bone marrow samples as well as pDC-rich (n=3) and pDC poor (n-4) CMML peripheral blood samples. Gene expression in these cells were similar to that observed in pDC sorted from age-matched healthy donors (n=7) (Figure 3A). More specifically, genes that are known to be highly expressed in healthy donor pDC, including HLA-DR, CD123, CLEC4C (BDCA2), TLR9, TLR7, NRP1 (BDCA4), IRF7, LILRA4 (ILT7) and TCF4 (E2.2) genes were also highly expressed in CMMLassociated pDC. CMML derived pDC expressed lower levels of CD5, CD2, and SIGLEC6 genes that characterize the recently described "AS DC" (AXL + SIGLEC-6 + Dendritic Cell) population and low levels of lineage specific genes (Figure 3A). 29 Of note, while AXL mRNA was expressed in all groups tested, low levels of the protein were detected by flow analysis (Figure 3C). Principal component analysis performed on the 500 most variable genes across pDC from different sample origin, did not distinguish pDC sorted from pDC-rich and -poor CMML bone marrow samples and from healthy donor bone marrow (Figure 3B).

We also performed differential gene expression analysis. To eliminate any batch effect, we focused on a series of simultaneously analyzed bone marrow pDC collected from pDC-rich CMML (n=6), pDC-poor CMML (n=4) and healthy donor (n=3) bone marrow samples. We detected 74 differentially expressed genes (DEG) between pDC-poor and pDC-rich CMML, 13 DEG between pDC-rich CMML and healthy-donor pDC, and 144 DEG between pDC-poor CMML and healthy-donor pDC.

Unsupervised hierarchical clustering indicated that these genes could discriminate the three sample categories (Figure 3C). Gene Ontology enrichment analysis performed using over-representation test 34,35 demonstrated a trend toward enrichment in type I interferon signaling pathway, response to type I interferon and cellular defense response in pDC from pDC-rich compared to pDC-poor CMML.

pDC bone marrow infiltration correlates with increased leukemic transformation

Since pDC infiltration has been associated with a poor outcome in diverse solid tumors, we hypothesized that pDC-rich CMML had an inferior prognostic outlook in comparison to pDC-poor CMML. The follow-up was not sufficient to analyze the outcome of CMML patients whose pDC infiltration was prospectively evaluated by flow cytometry at diagnosis. Therefore, we performed this prognostic analysis on an independent retrospective cohort of 202 patients in which pDC were detected by immunohistopathology (Table S1). Bone marrow was considered as "pDC-rich" when > 5% of the bone marrow cellularity per field had CD123 + and TCL1 + cells. pDC infiltration measured in the peripheral blood of 56 of these patients by flow cytometry was in good accordance with immunohistochemistry analyses, i.e. pDC over 0.6% of peripheral blood mononucleated cells were detected only in patients with pDC > 5% in the bone marrow by immunohistochemistry (Figure 4A). pDC enrichment of CMML bone marrow was not statistically associated with overall survival outcomes, even when higher percentages of pDC were used as cut-offs for being considered "pDCrich" CMML (>10%, >25% and >50%) (not shown). However, pDC-rich CMML according to bone marrow immunohistochemistry was associated with a significantly higher cumulative incidence of leukemic transformation, considering death as a competing risk (Figure 4B, standardized hazard ratio 2.59 [95% confidence interval (CI) 1.21-5.51]; P=0.014). Importantly, an increased bone marrow infiltration with pDC remained an independent prognostic factor in multivariate analysis (sHR 3.3 [95% CI: 1.47-7.]; p=0.004), together with peripheral blood blast cell count and immature myeloid cell fraction (Table S2).

Bone marrow infiltrating pDC are detected in Ras-mutated CMML

Having demonstrated that CD123 high cells that accumulate in the bone marrow of 20% of patients with CMML are bona-fide pDC, we wanted to determine if their accumulation was related to specific genetic events. Using a panel of 38 genes analyzed by NGS, we first identified similar gene alterations and variant allele frequencies in sorted peripheral blood monocytes and bone marrow pDC of two CMML patients, validating that pDC were part of the leukemic clone (not shown). In the cohort of patients analyzed by flow cytometry (French cohort), we noticed that CMML with pDC-rich bone marrow demonstrated a significantly higher incidence of NRAS + KRAS + CBL mutations in their sorted monocytes (Table 1). We validated this observation in the above-mentioned, independent cohort of 202 patients analyzed by immunohistochemistry (Mayo Clinic cohort), in which we observed a significant association between the presence of NRAS and CBL mutations analyzed in bulk bone marrow mononucleated cells and bone marrow enrichment in pDC (Table S1).

We then sorted bone marrow pDC, peripheral blood CD14 + monocytes, and CD3 + T cells from 10 CMML patients, one patient developing an FLT3-ITD + AML as a result of CMML transformation (sample #2202) and a patient with atypical CML, another MDS/MPN (#1996). All 12 cases displayed marrow pDC enrichment, thus enabling for the sorting of a sufficient amount of pDC to perform whole exome sequencing.

Whole exome sequencing of each sorted cell population was performed (Figure 5A and Table S2). In every CMML patient, we detected at least one (7 cases) and sometimes two or more (5 cases) somatic mutations in genes encoding proteins of the Ras pathway. These heterogeneous mutations included variants in NRAS, KRAS, NF1, CBL, PTPN11 and MAP2K1 genes (Figure 5A). A significant correlation was observed between variant allele frequencies measured in monocytes and in pDC (linear regression, r 2 =0.74, p<0.0001, Figure 5B). Nevertheless, some clonal heterogeneity could be detected, e.g. in sample #2048, NRAS G13D identified in sorted pDC was not detected in sorted monocytes (Figure 5C) whereas, of the three mutations of the Ras pathway detected in sample #1829, NRAS A59D was almost exclusively identified in sorted monocytes (Figure 5D). Patient #2202 developed FLT3-ITD-driven secondary AML with NRAS G12V being mostly detected in sorted residual monocytes (Figure S3A). Finally, in one bone marrow sample (#2387), we were able to sort progenitor populations. 36,37 This sample was collected from the same patient as sample # 2062 with a 10.4 months interval between the two samples collection. Analysis of somatic mutations in sorted monocytes detected the loss of a KRAS G60V subclone. Five other somatic mutations showed a similar variant allele frequency in every cell compartment (Figure S3B).

CD34 + cells from pDC-rich CMML are hypersensitive to FLT-3L

We then analyzed the ability of CD34 + cells from CMML patients to generate pDC in vitro by culturing these cells in the presence of SCF, TPO, IL-3 and FLT3L for 30 days. 38,39 From day 15 to day 25, CD34 + cells from pDC-rich CMML demonstrated a significantly increased ability to generate pDC in culture when compared to CD34 + cells from pDC-poor CMML, as shown by serial flow analysis of CD34 + cells-derived pDC (Figure 6A and6B). Morphological analysis of the generated cells using conventional microscopy (Figure 6C, left panel) and electron microscopy (Figure 6C, right panel) confirmed pDC features. Analysis of somatic variants detected the same abnormalities with similar allele frequencies in sorted fresh pDC and pDC generated in culture from CD34 + cells (Figure 6D). Under these culture conditions, CD34 + cells from pDC-rich CMML also produced a greater number of pDC as compared to cord blood CD34 + cells, usually tested for pDC generation in vitro (Figure 6E &6F). We repeated the experiments in the absence or presence of increasing amount of FLT-3L, demonstrating the ability of CD34 + cells from pDC-rich CMML to produce pDC in the absence of FLT-3L and to produce more pDC in response to low concentrations of FLT-3L (Figure 6G). Interestingly, the level of FLT-3L measured in the supernatant of bone marrow was observed to be significantly lower in CMML patients, and this decreased FLT-3L level was more significant in pDC-rich CMML (Figure 6H).

Discussion

While the number of bone marrow pDC is decreased in a majority of CMML patients in comparison to age-matched healthy controls, an increased number of pDC is detected in the bone marrow of about 20% of these patients. All the studied patients with bone marrow pDC excess demonstrated genetic alterations of the oncogenic Ras pathway and their bone marrow CD34 + cells were hypersensitive to FLT3-L when induced to differentiate into pDC. The accumulation of leukemic pDC also correlated with a higher rate of regulatory T cells in the bone marrow and peripheral blood and a higher risk of AML transformation.

The presence of CD123 high cell islands in the bone marrow of a fraction of patients has long been identified in CMML and other myeloid neoplasms. 25,40,41 This pathologic finding is distinct from blastic plasmacytoid dendritic cell neoplasms (BPDCN), a rare clonal proliferation of pDC precursors that affects elderly people and involves alterations in MYC, RB1 and IKAROS gene family members. 41 Because of their plasmacytoid morphology, CD123 high cells were considered as pDC but a definitive proof of their identity was missing. Recent analyses have suggested that CD123 high cell population was more complex than anticipated. 29,30 Flow analysis combined with conventional and electron microscopy, gene expression analyses and cytokine production profiling in response to TLR9 and TLR7 agonists, establish these cells as authentic pDC that can be distinguished from CD123 high "AS DC" (AXL + , SIGLEC6 + ) or "pre-DC" cells. 42 pDC are the main type I IFN-producing cells. 43 IFN has demonstrated antineoplastic effects through the activation of pDC, cytotoxic T-cells and NK cells while having context-dependent effects of CD4 T cells 44 and therapeutic benefits of IFN was demonstrated in myeloproliferative neoplasms. 45 In multiple myeloma, in which bone marrow pDC mediate immune deficiency and promote plasma cell growth and drug resistance, CpG oligodeoxynucleotides could restore pDC immune function and abrogate pDC-induced plasma cell growth. 46 We show that CMML-associated pDC could produce IFN after stimulation with a TLR9 agonist, although at lower level than peripheral blood pDC from young healthy donors. CMML is a disease associated with ageing 17 and pDC from age-matched healthy donors also produced lower levels of IFN than pDC from younger healthy donors. The heterogeneous level of IFN produced by CMML pDC could indicate the differential amplification of pDC subpopulations that diversely react to a given individual stimulus under control of a TNF autocrine and/or paracrine communication loop. 47 Therefore, pDC stimulation may not necessarily be sufficient to restore pDC immune function and generate therapeutically active levels of IFN .

An alternative approach would be to inhibit the effect of cytokines produced by pDC.

Cytokines produced by mature cells of the leukemic clone modulate normal and leukemic progenitor differentiation in a mouse model of myeloproliferative neoplasm, 48 a regulatory loop demonstrated to be a potential therapeutic target in chronic myeloid leukemia. 49 The heterogeneous production of IFN by CMML associated pDC contrasts with IL-8 production, especially when these cells are stimulated with TLR7 agonists. While the primary function of this chemokine is the attraction and degranulation of neutrophils, 50 IL-8 can also promote the survival and self-renewal of hematopoietic stem cells. 51 Elevated IL-8 secretion has been detected in AML and MDS 52,53 and inhibition of its receptor CXCR2 could selectively inhibit the proliferation of MDS/AML cells, 54 indicating IL-8 as a potential therapeutic target in CMML patients with an excess of pDC.

Flt3-L and GM-CSF have a concerted effect on myeloid cell homeostasis 55 with Flt3-L supporting the development of pDC through TCF4 and IRF8 and GM-CSF antagonizing this effect through STAT5 activation. 56 Hypersensitivity to GM-CSF is a common feature of CMML myeloid progenitors, 19 which could account for the decreased number of pDC in the bone marrow of most CMML patients. The contrasting amplification of pDC detected in 20% of these patients suggests that pDC development escapes the inhibitory effect of GM-CSF, which may be related to additional genetic alterations of signaling pathways, including Ras pathway alterations and FLT3-ITD, promoting pDC development through enhanced progenitor sensitivity to FLT3-L. This is in contrast with a significantly decreased level of FLT-3L in the bone marrow environment (this paper) and the peripheral blood of CMML patients, 57 which appears to be unique among myeloid malignancies. 58 High risk MDS clones expand in a tolerant and immunosuppressive environment that involves CD4 + Treg expansion and myeloid-derived suppressive cells. [59][60][61] Similarly, IFN--deficient pDC that accumulate in aggressive human tumors promote the expansion of disease-associated Tregs, which contribute to tumor immune tolerance and poor clinical outcome. 62 In CMML, the correlated expansion of pDs and CD4 + Tregs may contribute to the higher risk of progression into acute leukemia.

Whatever their biological effects, therapeutic targeting of pDC, e.g. through the use of an IL-3R -targeted monoclonal antibody, 63 could deserve to be tested in CMML patients with clonal pDC expansion. Gene expression (logCPM) A.

Figures legends

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) 9 9 " @ J 9 @ < C K H ) 9 9 " @ J 9 @ B D D < ) 9 9 " @ J F D D L @ B D D < ) 9 9 " @ J F D D L @ < C K H (Immunoglobulin-like transcript 2, product of the gene LILRB1) and on monocytes/macrophages through receptors ILT2 and ILT4 (5), as well as indirect immunosuppressive effects through induction of regulatory T cells (6) or myeloid-derived suppressor cells (MDSCs) (7). HLA-G is frequently neo-expressed in immunotherapysensitive cancer types such as ccRCC (3,8-10), melanoma (11-13) or non-small cell lung cancer (14,15), especially in advanced-stage disease, and is associated with impaired prognosis (13-15). These elements strongly suggest a role in tumor escape from immune surveillance, which was confirmed by animal studies (7,16). We recently showed that TILs expressing ILT2 were present in the tumor microenvironment (TME) of ccRCC (3).

ILT2 is an inhibitory receptor with high affinity for HLA-G and lower affinity for classical MHC class I ligands (17). In the peripheral blood, ILT2 is expressed by a subset of CD8 + T cells: from about 20% in younger healthy subjects (18-21), the proportion of ILT2-positive CD8 + T cells may rise to over 50% with age and chronic viral infections (22,23). Previous reports have associated ILT2 expression by peripheral blood CD8 + T cells with a differentiated phenotype (CD28 -CD27 -CD57 + , CCR7 -CD45RA + ) (21-25) and perforin expression (21,25).

No characterization of CD8 + ILT2 + TILs has ever been published. Furthermore, the impact of ILT2 on effector T cell functions remains unclear (21,23,26).

Here, using transcriptomics and flow cytometry, we characterize peripheral blood and tumorinfiltrating ILT2 + CD8 + T cells as a differentiated cytotoxic population distinct from PD-1 expressing, exhausted T cells. Using ex vivo assays, we demonstrate that their effector functions are directly inhibited by target-expressed HLA-G through ILT2. Finally, we discuss HLA-G tumor expression as a mechanism of resistance to current cancer immunotherapy.

METHODS

Patients

T cells used for our experiments were isolated from cancer patients as well as control patients. Cancer patients were either patients who underwent transurethral resection for nonmuscle-invasive bladder cancer (NMIBC patients) or patients who underwent nephrectomy for localized ccRCC (ccRCC patients) at our center (Urology Department, Saint-Louis Hospital, Paris, France); control patients were patients aged over 40 with no personal history of cancer, and admitted for planned, non-carcinologic, surgery. Patients provided written informed consent before sampling.

Peripheral blood cells

Blood sampling was performed upon admission to the Urology department prior to surgery.

After sampling, peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll gradient (Ficoll-Paque, LifeSciences) as per the manufacturer's instructions and stored at -150°C.

Cell sorting and transcriptomics analysis of peripheral blood CD8 + T cells

For the sorting of CD8 + ILT2 -and CD8 + ILT2 + subpopulations, PBMC were labelled with antibodies specific for CD3, CD4, CD8, CD19 and ILT2. The CD8 subpopulations were then sorted according to ILT2 expression on a BD FacsAria II cell sorter, then immediately lyzed in RNA WIZ reagent (Ambion) and total RNA was extracted using the RNeasy micro kit (Qiagen). Samples were amplified and labelled according to the manufacturer's protocol for hybridization to Affymetrix Human Gene 2.0 ST arrays. Sample preparation, hybridization, washing, staining, scanning and quality control were performed by the Institut Curie Genomics core facility, Paris, France.

Regulatory network and functional inference

We extracted the expression matrix of the differentially expressed genes between ILT2 + CD8 + and ILT2ˉCD8 + T cells. We then imported the matrix on Cytoscape software version 3.5.1.

Analysis was performed in parallel for the ILT2 + and the ILT2ˉ up-regulated genes expression matrix. Network inference was performed using ARACNe application. After selecting genes of the output network from ARACNe, we utilized the ClueGO and CluePedia Applications to determine pathway enrichment. Public datasets of Gene Ontology (GO) -Biological process-GOA, -ImmuneSystemProcess-GOA, -Molecular Function-GOA, KEGG, Reactome, and WikiPathways were used. Only pathways with a "Bonferroni step down" corrected p-value below 0.05 were kept.

Tumor-infiltrating lymphocytes

Tumor-infiltrating lymphocytes (TILs) were extracted from ccRCC specimens. Fresh tumor samples were selected on nephrectomy specimens by a pathologist, rinsed with phosphatebuffered saline (PBS) and placed in RPMI culture medium for 30 minutes before being manually dissociated. Extemporaneous counting and phenotyping of TILs was performed using a MACSQuant 10 flow cytometer (Miltenyi biotec) and cells were then stored at -150°C for further phenotyping and functional assays. To rule out contamination with peripheral blood cells, a simultaneous flow cytometry analysis of PBMCs was performed.

Immunohistochemistry

Formalin-fixed paraffin-embedded (FFPE) samples from the same ccRCC specimens were analyzed by immunohistochemistry for tumor expression of HLA-G (clone 4H84) and PD-L1 (clone E1L3N) as previously described (3). Percentages of PD-L1 positivity in tumor cells and tumor-infiltrating cells were reported and HLA-G expression was estimated by the percentage of tumor cells positive for membrane staining.

Flow cytometry analysis

The following antibodies were used for cell surface staining and analysis: from Miltenyi

Cell lines

For functional studies, the monocytic cell line THP-1 (ATCC) transduced or not to express membrane-bound HLA-G1 (THP1-HLA-G1) was used as target cells. Cell-surface expression of HLA-G on THP-1-HLA-G1 cells was confirmed by flow cytometry, using a PE-conjugated anti-HLA-G mAb (clone MEM-G9, Exbio).

Peripheral blood T cell degranulation and IFNγ secretion assays

THP1 or THP1-HLA-G1 cells were placed in a 96-well culture plate in RPMI culture medium (Sigma) supplemented with 10% fetal calf serum (Sigma), L-glutamine, gentamicine and amphothericin B (Gibco), and containing 50ng/mL phorbol 12-myristate 13-acetate (PMA, Sigma). Cells were cultured with PMA for 72h in order to obtain a confluent macrophage layer (mTHP1/mTHP1-HLA-G1). Then, cells were coated for 15 minutes with αCD3 mAb (clone OKT3, Orthoclone). Coating concentrations of OKT3 for CD107a and interferongamma (IFNγ) secretion assays were 20 and 10ng/mL, respectively. PBMCs from NMIBC patients were incubated for 20 minutes at 37°C with 20µg/mL of a blocking anti-ILT2 antibody (clone GHI/75, BioLegend) or a control antibody. PBMCs were then added to the OKT3coated mTHP1/mTHP1-HLA-G1 target cells in culture medium supplemented with monensin and brefeldin A (Protein Transport Inhibitor Cocktail, eBioscience) in the presence of PEconjugated anti-CD107a antibody (clone H4A3, BioLegend) or isotype control.

After 4h of co-incubation at 37°C, cells were washed and stained for flow cytometry analysis.

For IFNγ secretion assays intracellular staining was then performed using the Inside Stain kit (Miltenyi biotec) according to the manufacturer's instructions.

The cytolytic degranulation and IFNγ secretion of CD3 + CD8 + T cell subsets were evaluated using the percentage of CD107a-and IFNγ-positive cells respectively. CD8 + T cell subsets were defined by the expression of ILT2 and CD57. Because of interference between the anti-ILT2 mAb GHI/75 used for blocking and the anti-ILT2 mAb HP-F1 used for staining we used the CD57 high subset, which was constantly made up of more than 75% ILT2-positive T cells in selected patients as opposed to the CD57 -subset, as a surrogate population for ILT2positive T cells in blocking experiments.

TIL effector functions assays

THP1 or THP1-HLA-G1 cells were differentiated into macrophage target cells as described above. TILs were incubated with the anti-ILT2 mAb or isotype control and added to the target cells in culture medium supplemented with monensin and brefeldin A (Protein Transport Inhibitor Cocktail, eBioscience) and PE-conjugated anti-CD107a mAb or control isotype as described above. TILs stimulated by PMA and ionomycin (Cell stimulation cocktail, eBioscience) served as positive controls and TILs incubated with non-OKT3-coated target cells served as negative controls. After a 4-hour co-incubation, cells were washed and stained for flow cytometry analysis as described above. Results from a preliminary phenotypical characterization of TILs served to select the best surrogate markers for the CD8 + ILT2 + TIL population in each sample (either CD57 high or CD45RA + TILs).

Statistical analyses

For transcriptomic experiments, gene expression data were normalized using RMA algorithm on custom Brainarray CDF. We selected the 500 most variant genes by IQR (27) to perform unsupervised analyses. Differential subgroups were identified by hierarchical clustering using

Pearson correlation metric and Ward distance. Differentially expressed genes between two groups were defined using limma R-package (p≤0.05 adjusted with Benjamini & Hochberg and |log fold-change|> 1.5). All these analyses were performed with R software environment.

For flow cytometry phenotyping, comparisons between T cell subsets were made using ttests paired by sample.

For functional studies, differences in terms of cytolytic degranulation or IFNγ production between different T cell subsets from the same PBMC or TIL samples were evaluated with ttests paired by sample. The impact of HLA-G expression by the target cells and of ILT2 blocking on a specific T cell subset was evaluated using unpaired t-tests.

RESULTS

Transcriptomic profiling of the CD8 + ILT2 + T cell subpopulation

First, we performed transcriptomic analysis to identify key features of ILT2 positive subpopulation. Peripheral blood CD8 + T cells from 4 NMIBC patients were sorted with respect to ILT2 cell-surface expression and transcriptome analysis was performed on the CD8 + ILT2 + vs CD8 + ILT2 -T cell subpopulations. Hierarchical clustering demonstrated that these two cell populations could easily be distinguished (Figure 1A). 71 genes were specifically upregulated in the CD8 + ILT2 + T cell population while 113 upregulated genes characterized their CD8 + ILT2 -counterparts (Figure 1B). A list of these genes is provided in Supplementary Table 1. Functional network inference revealed that the genes upregulated in the CD8 + ILT2 + population belonged to effector function-and effector function regulationrelated categories (Figure 1C), centered around NK cell-type categories (natural killer cellmediated cytotoxicity / natural killer cell-mediated immunity) and immune regulation (immune response-inhibiting cell surface receptor signaling pathways / regulation of alpha-beta T cell activation / immunoregulation interactions between a lymphoid and a non-lymphoid cell).

Comparatively, the functional network inference of down-regulated genes in ILT2-positive vs ILT2-negative CD8 + T cell populations was less restricted, but nevertheless centered around function-related categories (e.g. Cytokine-cytokine receptor interaction / regulation of cell-cell adhesion, positive regulation of chemotaxis), and differentiation (T cell differentiation / myeloid leukocyte differentiation, binding of TCF/LEF:CTNNB1 to target gene promoters).

The aim of this analysis was originally to characterize the CD8 + ILT2 + T cell subpopulation using phenotyping in the context of an anti-tumor function. Thus, we next focused on cellsurface-expressed markers and function or differentiation-related genes. Out of the 184 genes found to be differently expressed between CD8 + ILT2 + and CD8 + ILT2 -subsets, 46 matched these criteria, including 19 upregulated and 27 down-regulated in CD8 + ILT2 + T cells (Figure 1D). As suggested by the functional inference network data, upregulated genes in the CD8 + ILT2 + population were mostly involved in NK and/or cytotoxic T cell functions. Cytotoxic T/NK-related genes were TBX21 (T-bet), GNLY (granulysin), GZMB and GZMH (granzymes These data clearly indicated that ILT2-positive CD8+ T cells were likely to be antigenexperienced T cells with a high cytotoxic function, and expressing NK surface molecules (activating and inhibitory).

ILT2 cell-surface expression is a feature of differentiated cytotoxic CD8 + T cells

Frozen PBMCs from 4 NMIBC patients, 4 ccRCC patients and 2 control patients were used for phenotypical validation of transcriptomics findings using flow cytometry. No differences were observed across these various clinical settings regarding the phenotype of CD8 + ILT2 + T cells.

In accordance with transcriptomics findings, flow cytometry showed significantly lower expression of surface markers CD28, CD27 and CD127 on ILT2-positive CD8 + T cells (Figure 2A-B). These cells also frequently expressed CD57 and virtually all ILT2-positive cells expressed KLRG1.

Conversely, the proportion of ILT2-positive cells was significantly higher in CD28-negative, CD57-positive or KLRG1-positive subpopulations (Figure 3A). Most NKp80-positive and perforin-positive CD8+ T cells also expressed ILT2 . Progressive acquisition of ILT2 during CD8 + T cell differentiation was apparent through the rising proportion of ILT2-positive cells between the CD27-high, CD27-low and CD27-negative subpopulations, in that order. ILT2positive cells were constantly CCR7-negative (Figure 3B-C), consistent with antigenexperienced effector-memory CD8 + T cells (28); ILT2 expression was most frequent in the CCR7 -CD45RA + subset of CD8 + T cells (TEMRA or effector T cells).

CD8 + ILT2 + T cells display enhanced cytotoxic functions selectively impaired by targetexpressed HLA-G

After having characterized their phenotype, we studied the cytotoxicity of CD8 + ILT2 + T cells through the assessment of IFNγ secretion and cytolytic degranulation of peripheral blood T cells after ex vivo co-incubation with αCD3-coated target cells.

As the previous results let figure, ILT2-positive CD8 + T cells constantly showed higher degranulation levels than their ILT2-negative counterparts (Figure 4A-B). Expression of HLA-G1 by the target cells significantly reduced degranulation levels of CD8 + ILT2 + T cells by a mean 33% (range 18% to 47%) whereas CD8 + ILT2 -cells were unaffected (Figure 4B). In restoration experiments, when ILT2 staining was impossible because of previous ILT2 blocking, this could also be observed in surrogate T cell subpopulations defined by CD57 expression: expression of HLA-G1 by the target cells reduced degranulation levels in the ILT2-enriched CD8 + CD57 high subpopulation by a mean 34% (range 21% to 43%), whereas ILT2-negative CD8 + CD57 -T cell subpopulation was unaffected (Figure 4C). ILT2 blocking significantly increased the degranulation levels of CD8 + CD57 high T cells in the presence of HLA-G1, reversing HLA-G1-associated inhibition by a mean 86% (range 63% to 112%) whereas CD8 + CD57 -T cells were unaffected.

Similarly, CD8 + ILT2 + T cells showed higher IFNγ secretion than their ILT2-negative counterparts (Figure 4D-E), which was reduced by a mean 30% (range 13% to 56%) in the presence of HLA-G1 whereas CD8 + ILT2 -cells were unaffected (Figure 4E). Expression of HLA-G1 by the target cells reduced IFNγ secretion by CD8 + CD57 high T cells by a mean 40% (range 24% to 56%), CD8 + CD57 -T cells were typically unaffected (Figure 4F). ILT2 blocking significantly restored IFNγ secretion by CD8 + CD57 high T cells in the presence of HLA-G1 by a mean 113% (range 57% to 177%) whereas CD8 + CD57 -T cells were unaffected.

ILT2 is expressed by tumor-infiltrating CD8 + cytotoxic effectors in ccRCC

After studying the peripheral blood T cells of patients, tumor-infiltrating cells from 8 ccRCC specimens were investigated. Expression of immune checkpoint ligands HLA-G and PD-L1 in these tumors as well as the distribution of PD-1 + and ILT2 + TILs are summarized in Table 1 and Figure 5. Blood contamination was deemed minimal in all TIL samples, as shown by clear-cut phenotypical discrepancies between PBMCs and TILs such as the absence of specific subpopulations in the tumor, eg. CD8 + CCR7 + T cells (Supplementary Figure 1).

As we previously reported (3), the proportions of CD8 + PD-1 + and CD8 + ILT2 + TILs varied among specimens. Most strikingly, we observed that PD-1 expression and ILT2 expression by tumor-infiltrating CD8 + T cells defined two subpopulations that were mutually exclusive (Figure 6A). CD8 + ILT2 + TILs harbored a phenotype similar to that of their peripheral blood counterparts, expressing high levels of CD57 and being strictly KLRG1 + CD28 -CD27 -(Figure 6B) and most of them displayed an effector phenotype (CCR7 -CD45RA + ; Figure 6C). Perforin expression was again a specific feature of ILT2-positive cells (Figure 6B). Conversely, CD8 + PD-1 + TILs displayed a less mature phenotype with negative-to-low expression of CD57, CD28 and CD27 and no expression of KLRG1 or perforin (Figure 6B); they typically pertained of the effector-memory phenotype (CCR7 -CD45RA -; Figure 6C). In accordance with previous large-scale studies (29), expression of exhaustion-associated markers Tim-3, CD38, CD69 and 4-1BB/CD137 was only seen on PD-1-positive cells and never on ILT2positive cells (Figure 6B). Conversely, most cells expressing CD11b were ILT2-positive (Figure 6B).

HLA-G specifically inhibits the effector functions of tumor-infiltrating CD8 + effectors through ILT2

Finally, to compare with the results obtained on PBMCs, the effector functions of tumorinfiltrating CD8 + T cells were investigated. Again, cytolytic degranulation levels after ex vivo co-incubation with αCD3-coated target cells were higher in CD8 + ILT2 + TILs than in CD8 + PD-1 + TILs (Figure 7A). HLA-G1 expression by the target cells reduced the degranulation levels of CD8 + ILT2 + TILs by a mean 32% (range 17% to 44%), whereas no such effect was observed with CD8 + PD-1 + TILs. When considering ILT2-enriched surrogate TIL subsets selected for each individual sample (namely CD8 + PD-1 -CD45RA + or CD8 + PD-1 -CD57 high cells), expression of HLA-G1 by the target cells reduced degranulation levels by a mean 27%

(range 25% to 28%). Interestingly, while this was not apparent in our preliminary assays with PBMCs, ILT2 blocking in the absence of HLA-G1 resulted in an increase in the degranulation of these surrogate ILT2 + TILs, consistent with the removal of a HLA-G-independent lowerlevel inhibition due to the engagement of ILT2 by target-expressed classical MHC class I molecules. In any case, ILT2 blocking fully counteracted HLA-G1-mediated inhibition, as

shown by a mean 197% reversion (range 127% to 318%).

As with cytolytic degranulation, IFNγ secretion by CD8 + ILT2 + TILs was higher than that of CD8 + PD-1 + TILs (Figure 7B). HLA-G1 expression by the target cells reduced the degranulation levels of CD8 + ILT2 + TILs by a mean 34% (range 28% to 42%) and of surrogate ILT2 + populations by a mean 34% (range 19% to 46%). Again, ILT2 blocking resulted in an increase in IFNγ secretion by surrogate ILT2 + TIL subsets in the absence of HLA-G1 as well as in full reversion of HLA-G1-mediated inhibition (mean 243% reversion, range 222% to 256%).

DISCUSSION

Since we recently described for the first time the presence of CD8 + ILT2 + TILs in the stroma of ccRCC (3), their functional nature was a crucial question. The phenotypical and functional studies described here definitely associate ILT2 + TILs and their peripheral-blood counterparts with late differentiation and strong cytotoxic capacity. A most striking finding was the clear dichotomy between ILT2 + and PD-1 + TILs in the tumor microenvironment, which was especially relevant considering that the latter are the prime target of current cancer immunotherapy despite being less cytotoxic and sometimes less numerous than the former.

Our transcriptomics and flow cytometry analyses provide a definitive characterization of both peripheral blood and tumor-infiltrating CD8 + ILT2 + T cells as late-differentiated (CD28 -CD27 - CD57 + ) T cells in accordance with previous reports (19-22), ILT2 expression being a specific feature of effector-memory (CCR7 -) cells, even more prevalent in terminally differentiated (CCR7 -CD45RA + ) TEMRA/effector T cells (30). The CD8 + ILT2 + T cell subpopulation overlaps with those defined by trans-lineage cell-surface markers of cytotoxicity KLRG1 (31) and NKp80 (32) and indeed these cells display high expression of perforin and granzyme B at the transcript and protein levels. Consistent with these cytotoxic and innate-like phenotypical traits, CD8 + ILT2 + T cells exhibit the highest degranulation levels upon CD3 engagement, appearing to be "ready-to-kill" effector cells. Interestingly, the cytotoxic functions of CD8 + T cells expressing the costimulatory receptor NKp80 have also been shown to be triggered without engagement of the TCR/CD3 complex (33) through NK-like costimulation. Whether such T cell-borne antigen-independent cytotoxicity is a component of antitumor immunity, possibly kept in check through ILT2, is of the highest interest.

We chose to characterize CD8 + ILT2 + T cells as a cytotoxic population characterized by its differentiation stage, rather than antigen specificity, by performing CD3-mediated polyclonal activation of uncultured PBMCs or TILs ex vivo, since the culture and cloning of T cells strongly alters their properties and phenotype, including ILT2 expression (21). This allowed us to observe the immediate effects of HLA-G on its native T cell targets. Indeed, the historical demonstration that HLA-G could inhibit T cell cytotoxicity was made using a cultured virus-specific cytotoxic T lymphoid (CTL) clone, with no regard to ILT2 expression, leaving doubts as to its relevance in vivo (34). Furthermore, previous studies of the impact of ILT2 on T cell functions did not test its engagement by HLA-G (21,23,26), and we believe that in such conditions the blocking of ILT2 could only lift the lower-level inhibition consecutive to its engagement by classical MHC class I molecules (17). Of note, we only inconsistently observed such an effect with uncultured PBMCs, as already reported ( 23), but we did observe it with TILs here. We hypothesize that TILs within the tumor microenvironment, while having been exposed to prolonged stimulation, had also been rendered more sensitive to classical MHC class I-mediated inhibition. This would reunite our observations with the enhancement of in vitro cytotoxicity through ILT2 blocking which was observed in functional assays that used T cells expanded through repeated in vitro stimulation (21,25). In any case, our experiments clearly evidenced superior ILT2-mediated inhibition of T cell cytotoxicity when HLA-G was expressed by the target cells.

Apart from a single observation of circulating melanoma-specific CD8 + ILT2 + T cells (19), a possible role for CD8 + ILT2 + T cells in antitumor response has been overlooked. We previously observed that CD8 + ILT2 + T cells were abundant in the microenvironment of ccRCC (3), and we show here that they are effective cytotoxic effectors, functionally and phenotypically distinct from exhausted T cells, and readily inhibited by target-borne HLA-G.

Although HLA-G expression by normal adult tissue is extremely restricted, it is frequently neo-expressed in immunotherapy-sensitive cancer types (35), including ccRCC (3,10). Our findings suggest that tumor-expressed HLA-G, through ILT2, could be a major inhibitory checkpoint for the effector functions of the naturally-occurring CD8 + ILT2 + cytotoxic T cells in the tumor microenvironment. Apart from our own observations, the relevance of this phenomenon may be inferred from a mass cytometry study of 77 ccRCC cases by Chevrier Interestingly, these clusters segregate together and may be more represented than PD-1-

positive clusters in about one-third of ccRCC cases: it still remains to be clarified whether this constitutes an "effector-infiltrated" subset of ccRCCs in which targeting HLA-G may be of particular interest.

Therapeutic targeting of the PD-1/PD-L1 checkpoint is the backbone of modern cancer immunotherapy. In metastatic renal cell carcinoma, anti-PD-1 monotherapy yields clinical responses in about one-fourth of patients (1), despite frequent PD-1 expression on TILs. We show here that naturally-occurring CD8 + PD-1 + TILs display an incompletely differentiated phenotype, less mature than that of CD8 + ILT2 + TILs, and a much lower expression of perforin. We postulate that efficient PD-1 blockade and release from exhaustion may allow CD8 + PD-1 + TILs to undergo proliferation and full differentiation towards ILT2-positive effectors, as suggested by the study by Choueiri et al. (36) in which transcriptomics analyses were performed on ccRCC biopsy specimens from patients before and on treatment with Nivolumab. Their data showed that anti-PD-1 treatment led to higher expression of CD3 and CD8 transcripts, suggestive of CD8 + T cell proliferation, as well as higher expression of PRF1, GZMB or IFNG, suggestive of full cytotoxic differentiation. Among other genes overexpressed under PD-1 blockade were KLRG1 and LILRB1, the ILT2 gene. Regarding KLRG1, Choueiri et al. suggested that PD-1 blockade may lead to NK cell proliferation since KLRG1 is primarily known to be expressed by NK cells; LILRB1, which they classify as a myeloid cell gene, is not discussed. When completed by our present observations, this can be interpreted as revealing the expansion on PD-1 blockade of differentiated cytotoxic CD8 + KLRG1 + ILT2 + TILS such as one can also observe in untreated patients. In this perspective, we hypothesize that HLA-G expression by cancer cells could be a mechanism of resistance to PD-1/PD-L1 blockade in ccRCC patients, since these newly-generated effectors would eventually be inhibited by tumor-borne HLA-G. Studies are ongoing at our center to confirm this hypothesis and may plead in favor of combined PD-1/PD-L1 and HLA-G/ILT2 blockade in selected patients.

In conclusion, ILT2 expression is a key feature of differentiated cytotoxic CD8 + T cells, rendering them susceptible to HLA-G-mediated inhibition. ILT2-positive, HLA-G-sensitive, effector T cells are present in the tumor microenvironment of ccRCC, a solid cancer type in which HLA-G is frequently neo-expressed. This suggests that direct effector inhibition through the HLA-G/ILT2 checkpoint could be a mechanism of tumor escape from immune surveillance as well as of resistance to current checkpoint blockade therapy. This strengthens the rationale for targeting HLA-G concomitantly with other immune checkpoints in selected cancer patients.

Table 1. Immune checkpoint expression in ccRCC

Expression of immune checkpoint molecules in ccRCC samples used for this study. Expression of immune checkpoint ligands PD-L1 and HLA-G were determined using immunohistochemistry. Distribution of TIL subsets expressing immune checkpoint receptors PD-1 and ILT2 were determined using flow cytometry. A range of values denotes spatial heterogeneity when several tumor zones were studied. Title: Systems level analysis of immune cell subsets and intercellular communication networks in human breast cancer Keywords: Systems biology, immunology, transcriptome, anti-tumor immunity, dendritic cells Abstract: Cell-to-cell communication is at the basis of the higher order organization observed in tissues, organs, and organism. Understanding cell-to-cell communication, and its underlying mechanisms that drive the development of cancer is essential. Breast tumor microenvironment (TME) is composed of a great cellular diversity, such as endothelial, stromal or immune cells that can influence tumor progression as well as its response to treatment. Among the different immune cell populations, dendritic cells (DCs) subsets integrate signals from their microenvironment and are subsequently essential in orchestrating specific immune response through T cell activation. However, the differential function of these subsets, and their interactions within the TME remain poorly described. My main objective was to understand the impact of the breast TME on DC subsets using systems-level analysis. We used RNA sequencing to systematically analyze the transcriptomes of tumor-infiltrating plasmacytoid pre-DCs, cell populations enriched for type 1 classical DCs, type 2 classical DCs, CD14+DCs, and monocytes-macrophages from human primary luminal breast cancer and triple-negative breast cancer. We found that transcriptional reprogramming of tumorinfiltrating antigen-presenting cells is subset-specific. These results suggest a complex interplay between ontogeny and tissue imprinting in conditioning DC diversity and function in cancer. As a second objective, I aimed at studying the cellular communications in order to understand how cells integrate signals from their environment. I developed ICELLNET, a tool to reconstruct intercellular communication networks. This original quantitative method, integrating ligand-receptor interactions and cell type specific gene expression, can be automatically applied to any cell population level transcriptomic profile opening perspectives of application in several disease contexts and biology fields.

  puces à ADN ainsi qu'aux données de séquençage ARN. Il fera partie d'un outil comprenant une base de données sur les interactions des ligands et des récepteurs organisée et triée manuellement et un ensemble de profils de transcription des cellules primaires accessibles au public dans BioGPS [ cite mabbott e xpression 2 013]. Dans le troisième chapitre, les résultats de ma thèse sont présentés en deux parties. Dans une première partie, je présente les résultats publiés en montrant que les APCs s'adaptent au TME du sein dune manière spécifique selon la souspopulation. Dans une seconde partie, je présente le manuscrit en préparation décrivant le développement et l'application d'un score de communication basé sur les profils transcriptomiques. Le TME est composé d'une grande variété de types de cellules qui influencent la progression de la tumeur et l'évasion immunitaire. Les DCs sont des APCs qui peuvent s'infiltrer dans la plupart des types de cancer. Ils peuvent jouer un rôle protecteur dans l'immunité antitumorale mais, inversement, ils peuvent également favoriser l'immunosuppression [DeNardo, Barreto, et al. 2009; Faget et al. 2012; Ghirelli et al. 2015]. L'influence du TME sur la diversité et la plasticité de ces APCs reste peu explorée. Dans le cadre de ma thèse, j'ai analysé les profils de séquençage à grande échelle des APCs infiltrant des tumeurs dans 8 échantillons de cancer du sein luminal (LBC) et 4 triples-négatifs (TNBC), en étroite collaboration avec Paula Michea, chercheuse post-doctorante au laboratoire. Sur la base d'analyses précédemment effectuées au sein du laboratoire et d'études publiées sur des sous-types de DCs humaines sur d'autres tissus, tels que le sang périphérique ou la peau, nous avons étudié quatre sous-populations de DCs et les macrophages au niveau phénotypique et transciptomique dans le cancer du sein. En comparant les transcriptomes de ces APCs provenant d'échantillons tumoraux et de tissus non cancéreux (juxtatumoraux) des mêmes patients, nous avons identifié des signatures géniques spécifiques à la tumeur pour chaque sous-population d'APCs, liées à des fonctions biologiques distinctes telles que la migration cellulaire chez les pDC. De plus, nous avons observé des différences substantielles entre les profils des APCs infiltrant les TNBC et les LBC, révélant ldu microenvironnement tumoral et pas seulement l'empreinte tissulaire ou l'ontogenèse sur le comportement des APC.

  pDC: plasmacytoid Dendritic Cell cDC: classical Dendritic Cell Monomacs: Monocytes/macrophages TLR: Toll-like Receptor PAMP: Pathogen-Associated Molecular Pattern PRR: Pattern Recognition Receptor CLR: C-type Lectin Receptor TAM: Tumor-Associated Macrophage DNA: Desoxyribonucleic Acid mRNA: messenger Ribonucleic Acid cDNA: complementary DNA RNA-seq: RNA sequencing scRNA-seq: single-cell RNA sequencing DEG: Differentially Expressed Gene TGF-β: transforming growth factor-β NF-κB: nuclear factor βB

Figure 1 . 1 :

 11 Figure 1.1: Schema representing a communication model. From National Communication Association (www.natcom.org).

  Environmental factors (e.g. pollution) or physical factors (e.g. the intensity of a signal, the speed of the transmission) can affect the effectiveness of communication or alter the signal ending in a communication break. The clarity of the message is important for the comprehension between individuals. A study has revealed the nefaste impact of traffic noise on communication between frogs. The noises were masking the perception of acoustic communication signals preventing male frogs from communicating efficiently with female and it leads to a decrease of reproduction [Bee and Swanson 2007]. Personal history and previous communications can drive the way of thinking and interpreting information facilitating or complexifying coding and decoding processes. Internet and social media increase communication between people by simplifying interactions, increasing speed of connection between people all around the world and allowing the spread of all kind of information. But it raises questions on the quality of communication: is the information trustable since it is easy to spread any kind of information? Due to the multiplicity of connections and exchanges, are the communication effective? This questions highlight the complexity of communication networks which are impacted by several factors in a positive or negative manner. Looking at the cellular level, cell signaling can be impaired by factors acting directly on cells, altering the transmission or the reception of messages. Therapeutic agents can be used as receptor blockade mechanisms mimicking the ligand but without carrying the message that would have induced a response from the sensing cell. Communication can also be altered by genetic mutation destabilizing gene expression and response to stimuli. Stimuli such as Ultraviolet radiation (UV) provoke genetic mutation that can alter the expression of key genes, inducing skin cancer [Seebode, Lehmann, and Emmert 2016]. Mutated cells use a communication system different from normal cells, notably they release new signals to proliferate and survive. Cells can communicate and answer to stimuli differently according to their type and origin. If cells exhibit a plastic phenotype, they can sense external stimuli such as communication signals and adapt their future communication within the cellular environment (e.g. stress, UV, cigarette smoke, diet, culture medium). Given different stimuli and environments, a cell can differentiate into several states. A stimulus or a combination of stimuli sensed by one cell type can impact its communication with other cell types inducing various responses. For example, DCs have been identified as the main drivers of T helper (Th) polarization in 1999 [Rissoan et al. 1999]. However Th cells integrate numerous signals to specify their phenotypes [Zygmunt and Veldhoen 2011]. A large number of Th subsets have be defined based on the diversity cytokines patterns produced by Th cells [Raphael et al. 2015]. These results reveal the intrinsic complexity of the Th differentiation process as a central communication system integrating multiple signals coming from DCs and producing a large diversity of Th responses [Grandclaudon et al. n.d.]. The environment is a major factor impacting the signaling. Inflammation is triggered when innate immune cells detect infection or tissue injury. Changes occur in the inflamed environment such as the presence of cytokines impacting communication and behavior of non-immune cells. This peculiar microenvironment will be further described in section 1.4.2.

1 1 . 1 . 4 . 2

 11142 communication, networks are powerful tools to use [M. E. J. Newman 2003]. In mathematics, a network or graph is a set of nodes that are connected together by connections called edges or links. Two types of networks are distinguishable: directed and undirected. The first one is characterized by links indicating the direction in which the information circulates. If all edges are bidirectional, or undirected, the network is an undirected network. Representation of networks is often used in different fields of application. As examples we can cite connections between individuals on social media, the internet, financial networks or biological networks. In the case of a communication network, nodes describe entities communicating and edges monitor the transmission of messages. Putting communication into the perspective of a network enables to organize knowledge on cell interactions into a systemic view. Cell-to-cell communication networks comprise both intra-and intercellular processes. Several studies focusing on intracellular communication networks are found in the literature and describe metabolic networks [Jeong et al. 2000], gene-regulatory networks [Thompson, Regev, and Roy 2015], or networks of proteinprotein interactions [Hooda and Kim 2012]. These networks can model the signal transduction processes inside the cell and the response induced by the message. Intercellular networks model the interactions between different cell types. However, compared with intracellular signal transduction networks, the functions and engineering principles of cell-to-cell communication networks are less understood. Many studies have addressed cross-talks between a given pair of cell types [Ferlazzo and Morandi 2014; Haan, Arens, and Zelm 2014; Hivroz et al. 2012]. Most of the time, communication process is considered a linear signaling cascade, such as immune cascades [Ghirelli et al. 2015; Y.-J. Liu et al. 2007] involving the exchange of one information signal at each step. Some studies have focused their purpose on specific cases of communication such as the cytokines interleukin-2 (IL-2) [Fuhrmann et al. 2018], interferon-gamma (IFNγ) [Helmstetter et al. 2015], or tumor necrosis factor alpha( (TNF-α) [Paszek et al. 2010; Tay et al. 2010]. This view has several limitations: 1) it does not consider the possibility that one given cell type could communicate with multiple cell types concomitantly within the same microenvironment [Bindea et al. 2013; C. Q. F. Wang et al. 2013], 2) it does not consider the multiplicity of information signals possibly sent by one cell to another, 3) it does not integrate the complex and constant rewiring and cell state modifications in the system following exchange of information, 4) it provides limited mechanistic insight into the complexity of multicellular pathophysiological processes, 5) as a consequence, it is very limited in predicting the effects of physiological or pharmacological perturbations in higher order multicellular systems. To study cell-to-cell communication network, it is important to define and characterize the microenvironment of cells to model their interaction and behavior. In-deed, the microenvironment impacts cell communication as culture impacts human communication. Diversity of cellular microenvironments at the physiological state: role of the tissue type Within an organism, each cell exists in the context of a complex extracellular microenvironment. Different types of tissues across the human body have been defined such as nervous tissue, muscle tissue, epithelial tissue and connective tissue. Within a given tissue, microenvironmental factors and extracellular matrix proteins cooperate to provide both the biochemical signals and structural constraints that are required to influence intracellular programs of gene expression and further the cellular behaviors in the tissue in question. Various cell populations are described having tissue-dependent functions creating a specific cellular environment. This is the case for certain populations of immune cells. Studies have shown that T-cell primed by tissue-specific dendritic cells (DCs) can change their specific functions if they are re-primed by other tissue-specific DCs [Mora and Andrian 2006]. Natural killer cells (NKs) are a type of lymphocyte that identify infected or transformed cells through a complex range of activating and inhibitory receptors that regulate direct and indirect killing mechanisms. They migrate from peripheral blood to peripheral organs through cytokines-mediated signals. However, studies have highlighted the existence of tissue-specific subpopulations of NKs [Shi et al. 2011]. Tissue-specific NK cells are found in different tissues across the body. Studies suggest that subpopulations of tissue-specific NK cells may undergo phenotypic changes under inuence of the microenvironment, but also differentiate in situ from tissue-resident hematopoietic progenitor cells [Lysakova-Devine and O'Farrelly 2014]. Macrophages are immune cells present in most tissues in vertebrates. They are best known for their phagocytic role in immunity, but they can also function as an important source of growth factors for other cell types within tissues. Tissue-resident macrophages are heterogeneous populations in terms of phenotype and function. According to the location they re-side, tissue-resident macrophages display specific functions which are important for normal tissue homeostasis [Ginhoux and Guilliams 2016; Gosselin et al. 2014; Okabe and Medzhitov 2016]. Similarly, signaling factors derived from tissue environments play key roles in promoting the ontology and phenotype of the residing macrophage populations [Okabe and Medzhitov 2016]. Physiology versus pathology In addition to the specificity of tissue microenvironment, one key factor to think of when studying cellular environment is the physiological or pathological context. Steady state and inflammation have a different impact on communication between cells creating a specific microenvironment. Inflammation is a state of the microenvironment due to the establishment of an adaptive immune response after pathogen infection, external injuries or an effect of chemicals or radiations. Inflammation reflects a complicated, multifactorial, and multidimensional process, in which acute and chronic inflammation are differentiated. Acute inflammation is a short-term process occurring in response to tissue injury appearing within minutes or hours. It is characterized by five main signs: pain, redness, loss of function, swelling and heat. Inflammation follows several steps independently of the stimulus initiating the immune response. First, cell surface pattern receptors recognize detrimental stimuli that lead to activation of inflammatory pathways such as NFκB or MAPK pathways. Then, inflammatory markers, inflammatory cytokines, proteins, or enzymes, are released and inflammatory cells are recruited in the microenvironment [L. Chen et al. 2017]. The last step is the resolution of the issue by tissue repair and remodeling by monocytes. This is made possible by the switching from pro-inflammatory to antiinflammatory signals in the inflammatory environment, promoting the recruitment of monocytes and inhibiting recruitment of neutrophils [Medzhitov 2008]. In the case of infection, if the acute inflammatory response fails to eliminate the pathogen, the inflammatory process persists with the presence of macrophages and T cells in the tissue and a chronic inflammatory state occurs [Medzhitov 2008]. The chronic inflammatory process that plays a central role in some of the most challenging diseases, including cancers, rheumatoid arthritis, heart diseases, diabetes, asthma, and even Alzheimers. Complex genetic and environmental interactions contribute to the development of chronic inflammatory diseases. Autoimmunity is characterized by dysregulation of the adaptive immune system as well as the pathogenic role of innate immunity and is associated with several chronic inflammatory diseases. Studies have shown the importance of microbiota in the development of autoimmunity [Yurkovetskiy, Pickard, and Chervonsky 2015] but also the genetic impact of several autoimmune diseases [Zenewicz et al. 2010]. Chronic inflammation is thought to promote cancer development. Today, between 5% and 10% of cancer cases are thought to be triggered by mutation and up to 15% by inflammation; the origin of the 80% left is still unknown [Brücher and Jamall 2014]. Cancer is a complex and heterogeneous disease affecting several cell populations in many localization and tissues. The tumor microenvironment (TME) is a complex network not only composed of malignant cells but also stromal cells. Communications among tumor and stromal cells create a distinct cellular environment that plays a significant role in tumor development and progression. In solid tumors, fibroblasts in the TME secreting chemokines and growth factors contribute to tumor growth and affect the extracellular matrix environment that helps tumor to progress [Allen and Jones 2011]. Studies have shown the impact of metabolism in TME, particularly hypoxia that induce angiogenesis, and invasion [Allen and Jones 2011]. Since it is an inflamed environment, we can find immune cells infiltrating the TME. Leukocyte infiltration of solid tumors was first described in the 1800s by Virchow. Proinflammatory cytokines, chemokines, and adhesion molecules, which regulate the recruitment of leukocytes, are frequently observed in the TME. Some leukocytes including cytotoxic T cells and NK cells have a pro-inflammatory and anti-tumor role [DeNardo, Andreu, and Coussens 2010; Gavin P. Dunn, Old, and R. D. Schreiber 2004] whereas other leukocytes such as regulatory T cells and macrophages play an anti-inflammatory and pro-tumoral role promoting cancer immune evasion and cancer progression [DeNardo, Andreu, and Coussens 2010].

  count in the classification of breast cancer. Several scores measure the disease state such as Eston-Ellis grade, Nottingham prognostic index, or tumor, lymph nodes and metastasis status (TNM). They are based on measurement of the tumor growth and development, or the lymph node invasion status [Sinn and Kreipe 2013; Viale 2012]. Based on the molecular and transcriptional profile of breast cancers, different subtypes have been identified and correlated with clinical outcome [Koboldt et al. 2012; Prat, Pineda, et al. 2015; Viale 2012]. Six breast cancer subtypes have been established based on expression of hormone receptors (HR) which are estrogen receptor (ER) and progesterone receptor (PR), expression of HER2 (human epidermal growth factor 2), and Ki-67 protein immunoreactivity:

  Tumor complexity is due to the heterogeneity of the disease which impacts the clinical behavior and outcome of the patients [Koren and Bentires-Alj 2015]. The molecular subtypes of breast cancer correlate with different clinical outcomes and response to treatment [Prat, J. S. Parker, et al. 2010; Prat, Pineda, et al. 2015; Troester et al. 2004]. Troester et al. compared basal and luminal BC cell lines and showed that molecular subtypes of BC have a subtype-specific response to chemotherapies which was validated by in vivo data [Troester et al. 2004]. PAM50, a 50-gene qPCR assay, has been identified as a predictive marker of pathological complete response (pCR) regarding chemotherapy response. This predictive marker was shown to reflect the intrinsic molecular classification and its correlation to clinical outcome [Y.-R. Liu et al. 2016; Prat, Pineda, et al. 2015]. Luminal A cancers are low-grade, tend to grow slowly and have the best prognosis and long-term survival while luminal B cancers prognosis is slightly worse. This difference of prognosis was suggested to be due to a variation in response to estrogen therapy between luminal A and B [Rivenbark, OConnor, and Coleman 2013; Sørlie et al. 2003]. Triple-Negative or basal-like breast cancers are more aggressive with high rates of cell proliferation and have poor clinical outcomes. As they do not express hormone receptors neither HER2, herceptin and hormone therapies cannot be used. Patients with claudin-low breast cancer have poor recurrence-free and overall survival outcomes, this cancer not being responsive to chemotherapy treatments [Prat, J. S. Parker, et al. 2010]. HER2-enriched breast cancers are associated with a poor clinical outcome. As they are ER negative, they are not treated with anti-estrogen receptor therapies. However, survival of HER2 + breast cancer (HER2enriched, Luminal B) improved thanks to herceptin-targeted therapy, in addition to adjuvant chemotherapy [Cortés et al. 2012; Mukai 2010]. Although the molecular classification of breast cancer help to characterize the disease and defined adapted therapies, the patient outcomes are disparate. The observed variation in treatment efficacy has been connected to heterogeneity in the cellular composition of individual tumors and significant heterogeneity in immune composition is observed across subtypes as well as patients [Dushyanthen et al. 2015; García-Teijido et al. 2016]. This highlights the importance of taking into account the molecular subtypes as well as the intra-tumoral heterogeneity when studying breast cancer networks and communications.

  Two distinct but complementary theories describe the origin of tumor cells heterogeneity, the cancer stem cell (CSC) hypothesis[Meacham and Morrison 2013] and the clonal evolution and selection model [Greaves and Maley 2012]. CSCs originate from single cells possessing specific characteristics regarding cell plasticity. Those cells undergo tumor-reprogramming processes via multiple molecular alterations through a specific hierarchy and have indefinite self-renew potential that drive tumor growth. These mechanisms drive temporal intra-tumor heterogeneity. The clonal evolution/selection model is based on clonal expansion by natural selection and adaptation to tissue microenvironments. The factors contributing to clonal expansion promotes certain cellular characteristics allowing cancer cell proliferation in hypoxia environments. Depending on the local microenvironment, the clonal expansion wont be promoting the same clones, contributing to spatial heterogeneity. In the majority of the cases intra-tumor heterogeneity is clonal-based, however it has been shown in the literature that some areas of the tumor can be morphologically distinct with different repertoires of genetic aberrations [Greaves and Maley 2012; Martelotto et al. 2014]. Intra-tumoral heterogeneity is a complex interplay between CSCs genetic and epigenetic mutations and clonal evolution promoting develop-ment and evolution of breast cancer to metastasis. Several studies have revealed genetic differences between primary breast tumors and their metastases [Bonsing et al. 2000; Kuukasjärvi et al. 1997; Pandis et al. 1998; Torres et al. 2007; C. Wu et al. 2009]. Genetic and epigenetic modifications can be caused by external factors such as cigarette smoke, UV lights, chemotherapy agents and/or the microenvironment during the development and growth of the tumor contributing to the temporal heterogeneity of breast cancers [Martelotto et al. 2014]. Studying intra-tumor heterogeneity could have clinical benefits since we observe treatment failures due to therapeutic selection of cancer cells harboring resistance mechanisms [Turner and Reis-Filho 2012].

  cells communications are essential to inhibit tumor growth and proliferation [Quail and Joyce 2013]. However, in breast cancer, communication between cancer cells and non-malignant cells infiltrating the TME promotes heterogeneity, growth and proliferation of the disease [Quail and Joyce 2013]. Understand the composition of the tumor microenvironment and what are the interactions that promote development and resistance of the disease could help define better therapies. The TME is not only impacted by the presence of tumor cells, but it is also involved in the development of the disease, in different ways. Specific changes happen in the breast tumor microenvironment that regulate progression to invasion and metastasis, for instance increase of fibroblast proliferation and ECM remodeling [Bonnans, Chou, and Werb 2014]. Stromal cells can create a permissive microenvironment for tu-morigenesis.[Mao et al. 2013]. We also observe cell-to-cell signaling changes. Genes encoding for secreted proteins and cell surface receptors are found differentially expressed in epithelial and stromal cells during breast tumor progression [Allinen et al. 2004]. Paracrine signaling takes place through secretion of soluble factors by cancer cells, fibroblasts and other cells of the TME. Allinen et al. performed a molecular characterisation of breast cancer microenvironment. They compared normal epithelial and stromal cells to cancer epithelial cells and infiltrating stromal cells. High expression of CXCL12 and CXCL14 by myoepithelial and myofibroblast were found in the TME. These chemokines are involved in cell proliferation, differentiation, migration, and invasion of breast cancer cell lines. Several signaling pathways involved in the interplay between tumor infiltrating cells and cancer cells promote tumor growth, metastatic spread or even drug resistance. TGF-β signaling in breast TME plays an important part in tumorigenesis. It has implication in angiogenesis, recruitment of endothelial cells, monocytes and macrophages, and activation of fibroblasts. TGF-β also suppress T cell immunosurveillance and cytotoxic activity [Scheel et al. 2011; Taylor, Lee, and Schiemann 2011]. Breast tumor microenvironment represents a social network where cells produce and interpret a diversity of signals promoting cancer cells progression. Figure 1.3 represent in a schematic view these interactions between the cells of the breast environment. Cancer cells cross-talk with endothelial cells, fibroblasts and immune cells such as macrophages and T cells, using specific signaling including TGF-β, growth factors and inflammatory cytokines.

Figure 1 . 3 :

 13 Figure 1.3: Schematic representation showing the role of stromal cells in microenvironment and breast cancer progression. The tumor microenvironment is a dynamic composite of cells broadly categorized as multiple components of non-stromal and stromal cells, where tumor cells thrive. Stromal cells promote tumor growth, invasion, and metastasis by secreting multiple cytokines, chemokines, growth factors, etc. Moreover, tumor cells also affect the phenotype of stromal cells. From Mao et al. 2013.

  ing a role in cancer development neither on the clinical outcome of patients. In breast microenvironment, immune cells play a role of immunosurveillance, by killing potential cancer cells before they became a cancer. However, the immunosurveillance of immune cells put a selective pressure on cancer cells that develop resistance mechanisms and escape immune surveillance or generate an immunosuppressive environment [Gavin P. Dunn, Bruce, et al. 2002]. This implies changes in the cell-to-cell interaction network formed by the cells of the TME. Only recently, immune evasion has been recognized as a hallmark of BC which is enabled by three major characteristics being (epi)genetic modifications and clonal selection of cancer cells, and tumor-promoting inflammation [Hanahan and Weinberg 2011]. Now, it is well known that the TME is composed of different immune cell populations such as T and B lymphocytes, natural killers (NK), and myeloid cells including macrophages, myeloid derived suppressor cells (MDSCs), and dendritic cells (DCs). Cellular crosstalk between different leukocyte subsets infiltrating the breast cancer TME induces either pro-or antitumor functions driving immune-mediated anti-or pro-tumor activities in the microenvironment [D. S. Chen and Mellman 2013; DeNardo, Andreu, and Coussens 2010]. Distinct populations of tumor-infiltrating lymphoid and myeloid cells have been linked to different prognosis in BC patients [Kroemer et al. 2015]. While breast tumor infiltration by CD8 + T cells was associated with patient survival and response to therapy [DeNardo, Brennan, et al. 2011; Mahmoud et al. 2011; Seo et al. 2013], regulatory CD4 + FOXP3 + T cells support pro-tumor immunity and are associated with a poor prognosis in some cases of breast carcinoma [Ibrahim et al. 2014; Yeong et al. 2017; Zhou et al. 2017]. Myeloid cells localized in pre-and malignant tissues release amount of cytokines, soluble factors and other inflammatory molecules. These signals contributing to tissue remodelling, angiogenesis, and suppression of anti-tumor immunity [Stockmann et al. 2014]. If MDSCs have been characterized as regulator of the immune system [Gabrilovich and Nagaraj 2009], they also play a role in cancer development and metastasis. MDSCs and cancer cells interaction via IL-6/STAT3 and NOTCH signaling induce CSCs development [Peng et al. 2016]. Cancer cells also secrete molecules influencing the microenvironment towards pro-tumoral and pro-inflammatory environment. Ghirelli et al described the activation of pDC via GM-CSF and IL-6 secretion by breast tumor cells that was linked to a worse prognosis [Ghirelli et al. 2015]. Over the past years, new therapies have been developed targeting the immune system in cancer. As described by the concept of hot versus cold tumor, the diversity of TILs infiltration levels in tumors may impact the efficacy of immunotherapies [L. Chen et al. 2017; Spranger 2016; Wargo et al. 2016]. In breast cancer, level of TILs is variable across BC subtypes and high levels of TILs are correlated with increased expression of the checkpoint molecule programmed cell death 1 ligand (PD-L1). Immunotherapy treatments rely on therapeutic antibodies targeting immune checkpoint molecules that have costimulatory or co-inhibitory functions. Clinical trials on TNBC show some positive results. For instance, monotherapies targeting programmed cell death 1 (PD-1) and one of its ligand CD274 (PD-L1) which have an inhibitory interaction in metastatic TNBC and showed between 4.7% and 23% of overall response rate (Figure 1.4)[Tan 2018]. Despite some treatment successes, the response seen in patients is limited, especially in other subtypes such as luminal, drawing attention to the need of better understanding the immune components of the TME.

Figure 1 . 4 :

 14 Figure 1.4: Clinical trials of checkpoint inhibitors as monotherapy in metastatic breast cancer. From Tan 2018.

[

  Sánchez-Torres et al. 2001]. Monocytes and their progeny display various physiological processes including both DC-like and macrophage-like activities. They are able to promote angiogenesis and arteriogenesis [Ginhoux and Jung 2014]. Upon inflammation, in tissues, monocytes can differentiate into tissue-resident macrophages and especially in cancer they can give rise to tumor associated macrophages [Wynn, Chawla, and Pollard 2013]. Macrophages are myeloid immune cells that are characterized by avid phagocytosis. They are found in all tissues and have functions on various mechanisms such as development, tissue homeostasis, wound healing and tissue repair through immune responses to pathogens [Wynn, Chawla, and Pollard 2013]. Tissue macrophages have two distinct origins even though the majority of macrophages that reside in healthy tissues are established prenatally and show self-maintenance properties [Hashimoto et al. 2013]. As exposed before, macrophages can also derive from tissue-infiltrating monocytes [McGovern et al. 2014; Wynn, Chawla, and Pollard 2013]. In inflammatory condition, studies have delineated the activation of macrophages in response to various signals which exhibit distinct phenotypes called M1 and M2 [Gordon 2003; Sica and Mantovani 2012]. M1 macrophages express high levels of proinflammatory cytokines, produce reactive nitrogen and oxygen intermediates, promote Th1 response, and are able to kill microbes and tumor cells. In contrast, M2 macrophages promotes tissue remodeling and cancer progression. They are also characterized by immunoregulatory functions and efficient phagocytic activity. M1-M2 macrophages are also distinct in their chemokine expression profiles [Sica and Mantovani 2012].Tumor-associated macrophages secrete growth factors in the TME, promote breast cancer progression and correlate with poor prognosis[Mao et al. 2013].

  DCs were first described in mice by R. Steinman and Z. Cohn in 1973 [Steinman and Z. A. Cohn 1973]. These peculiar cells are essential sentinels and messenger between the innate and adaptive immune system. DCs are bone marrow-derived cells present in blood, lymphoid organs and tissues. When patrolling the body, the "immature" DCs can sense pathogen-associated and danger-associated signals and capture antigens. They are specialized antigen-presenting cells: they uptake antigens with high efficiency via different mechanisms including phagocytosis, micropinosis and endocytosis and present them through MHC class II molecules. DCs are able to present antigenic peptides complexed to MHC class I molecules to CD8 + T cells which will differentiate into cytotoxic killer cells capable of eliminating infected cells, damaged cells and even tumor cells [J. Banchereau and Steinman 1998]. Upon activation, DCs migrate to lymphoid organs to initiate adaptive immune response by interacting with T and B cells.

4 (

 4 Heterogeneity of the DCs have been described by generating the transcriptional profiles of mouse and human leukocytes (ImmGen) [Collin, McGovern, and Haniffa 2013]. Several subsets of DCs were characterized based on their ontogeny, phenotypic and functional specialization. All human DCs express high levels of MHC class II (HLA-DR) and lack typical lineage markers CD3 (T cell), CD19/20 (B cell) and CD56 (NK cell). The different subpopulations of DCs are defined as HLA-DR + lineage -cells. Several positive DC lineage markers identifies DCs as either "myeloid" or "plasmacytoid" and exclude monocytes expressing CD14 and CD16 markers [Haniffa, Collin, and Ginhoux 2013]. Two types of "classical" or "myeloid" DCs from "plasmacytoid" DCs can be distinguished across all mammalian species by looking at the differential expression of interferon regulatory factors 8 and IRF8 and IRF4) [Collin and Bigley 2018]. These three subsets derive from common myeloid progenitors (Figure 1.5). Each subset of DCs can be identified by the expression of surface markers and the secretion of various cytokines [Collin and Bigley 2018] (Figure 1.6).

Figure 1 . 5 :

 15 Figure 1.5: Monocytes and DCs classification. From Gardner and Ruffell 2016.

Figure 1 . 6 :

 16 Figure 1.6: Human dendritic cell subsets. Features of the principal human dendritic cell (DC) subsets. Diagrams of the main surface markers, pathogen sensors and responses of (a) classical DC1 (cDC1) and (b) cDC2 ; (c) plasmacytoid DC (pDC). Data are principally drawn from observations on freshly isolated blood DC and do not capture the variety of responses possible following inflammatory activation. From Collin and Bigley 2018.

1 . 3 . 2 . 4

 1324 ; Colonna, Trinchieri, and Y.-J. Liu 2004; O'Doherty et al. 1994; Siegal et al. 1999]. As they were able to induce Th1 or Th2 responses after activation by various stimuli, these cells were called plasmacytoid dendritic cells (pDCs). In 1999, Siegal et al. and Cella et al. definitively identified the pDCs of peripheral blood and secondary lymphoid organs as being the same population as natural interferon-producing cells previously described and responsible for type I interferon production in peripheral blood in response to most viruses [Cella et al. 1999; O'Doherty et al. 1994; Siegal et al. 1999]. pDCs play a role in viral infections and against bacterial and fungal pathogens. Upon recognition of nucleic acids from pathogens, pDCs produce massive amounts of type I and some quantities of type III interferons and acquire the capacity to present antigen. Production of type I interferons by human pDCs impact various cell types of the immune system including NK cells, DCs, T cells and even B cells. Indeed, it activates NK cells cytolytic activity against infected cells, and it promotes differentiation, maturation and immunostimulatory functions of DCs. Combined with IL-6 expression by pDCs, it induces B cells differentiation into plasma cells and production of immunoglobulin and induces early T cell activation markers, long-term T cell survival, IFNλproduction and Th1 differentiation [Colonna, Trinchieri, and Y.-J. Liu 2004]. Emerging subsets of DC Recently, the evolution of RNA-sequencing techniques and single-cell isolation and analysis helped in defining emerging subpopulations of DCs. Two single-cell RNAseq studies shed light on the heterogeneity of DCs subpopulations in blood. See et al. characterized a new subset of DC precursors called ["early pre-DC"], expressing CD33, CD45RA and CD123 markers [See et al. 2017]. Those cells present myeloid DC characteristics of inferior type I interferon production, higher IL-12 production and greater CD4 + T cell allo-stimulatory capacity. In parallel, Villani et al. have characterized an other DC subpopulation with similar characteristics. This new pop-

  ; Zaba, Krueger, and Lowes 2009] and inflammatory fluids including tumor ascites, one specific subpopulation of monocyte-derived DC (Mo-DC) was identified and called inflammatory DCs (infDCs). They present a DC morphology and phenotype with expression of CD11c, CD1c, FceR1, CD206, IRF4 cells and MHC class II expression (HLA-DR) suggesting they have the ability to activate T cells. Nonetheless, infDCs do not express CD16 and CD163 but they express CD14 at their surface at a lower level than macrophages [Segura and Amigorena 2013]. This peculiar cells can stimulate antigen-specific naive CD4 + T cells during pathogen infection and induce Th differentiation [León, López-Bravo, and Ardavín 2007; Nakano et al. 2009; Segura, Touzot, et al. 2013]. infDCs can express key cytokines and chemokines that are crucial for T cell polarization [Plantinga et al. 2013; H. A. Schreiber et al. 2013]. infDCs are also able to migrate from the site of infection to draining lymph nodes in a CCR7-dependent manner [Segura and Amigorena 2013].

  pDCs express Toll-Like Receptors (TLRs) to sense pathogens. DCs are able to sense pathogenassociated molecular patterns (PAMP) via Pattern Recognition Receptors (PRRs), TLRs and C-type Lectin Receptors (CLRs) such as CLEC9A in DC1 (Figure 1.6). They also express various ligands inducing immune responses such as cytokines and chemokines. The sensing of various stimuli impacts the differentiation of cells and their functions [Dalod et al. 2014; Huang et al. 2001; Y. J. Liu 2001; Pulendran, Palucka, and Jacques Banchereau 2001; Soumelis et al. 2015; Stagg and Allard 2013]. For instance, in response to various signals, macrophages may undergo classical M1 activation via stimulation by TLR ligands and IFNγ or alternatively M2 activation via stimulation by IL-4 and IL-13. Among those ligands and receptors, one category is particularly interesting. It is immune checkpoint molecules which are regulators of immune activation. They play a key role in maintaining immune homeostasis and preventing autoimmunity. In cancer, immune checkpoint mechanisms are often activated to suppress the nascent anti-tumor immune response. It has been shown that cancer cells can express inhibitory checkpoints suppressing T cell activation [Ott, Hodi, and Robert 2013](Figure 1.7). In recent years, immune checkpoint mechanisms became central targets of anti-cancer immunotherapies.

Figure 1 . 7 :

 17 Figure 1.7: Immune checkpoint interactions between T cells, APCs and cancer cells in the tumor microenvironment. From Ott, Hodi, and Robert 2013.

  .8). Standardized protocols are established to analyze the data even though many bioinformatic tools are available [Selvaraj and Natarajan 2011].

Figure 1 . 8 :

 18 Figure 1.8: Gene-expression profiling microarray protocol. Adapted from Ortuño et al. 2011.

Figure 1 . 9 :

 19 Figure 1.9:Table of human DCs and monocytes transcriptomic profile datasets available in the literature.

Figure 1 .

 1 Figure 1.10: RNA-seq experiment protocol (From Z. Wang, Gerstein, and Snyder 2009).

  and tools are dedicated to differential expression analyses regarding the technology used to generate the transcriptomic profiles. Tools such as limma[Ritchie et al. 2015] are widely used to analyze gene-expression profiling microarray data. It is less unanimous for RNA-seq data analysis. RNA-seq data can be represented as read count matrices, with a non-normal distribution. The different methods of differential expression analysis fit models to the data following either a poisson distribution or a negative-binomial distribution which is more accurate for RNA-seq data [Dillies et al. 2013; Risso et al. 2014]. Tools such as edgeR or DESeq2 in R software have then become gold-standard methods for differential gene expression analysis of RNA-seq data [Conesa et al. 2016; Love, Huber, and Anders 2014; Robinson, McCarthy, and Smyth 2010]. They implement negative-binomial model fitting with variance estimation to perform differential testing of gene expression. These methods enable the detection of differentially expressed genes (DEGs) between several conditions (e.g. disease state versus healthy state or between cell types). From the DEG list, we can perform functional enrichment tests to infer which biological functions are affected by one condition versus another and whether these functions are up-or down-regulated for each condition. A great majority of functions, processes and signaling pathways are gathered in databases as Gene Ontology (GO), KEGG, DAVID, and Reactom [Ashburner et al. 2000; Croft et al. 2011; Dennis et al. 2003; Kanehisa and Goto 2000]. In the TME, a few studies have analyzed macrophages and dendritic cells profiles [Ojalvo, W. King, et al. 2009; Pyfferoen et al. 2017]. Ojalvo et al. compared invasive and general TAMs purified from mice with carcinomas [Ojalvo, W. King, et al. 2009]. Using gene-expression profiling arrays, they identified 1457 differentially regulated transcripts between the two populations of TAMs. They also showed that invasive TAMs present genes enriched in Wnt signaling pathway. These results highlight the role of transcriptomic analyses in deciphering cellular communication and functions in specific context such as cancer.

  and immune evasion. DCs are APCs that can infiltrate most cancer types. They can have a protective role in anti-tumor immunity but conversely, they can also promote immunosuppression [DeNardo, Barreto, et al. 2009; Faget et al. 2012; Ghirelli et al. 2015]. The influence of the TME on the diversity and plasticity of these APCs remains poorly explored. During my thesis, I analyzed large-scale RNA sequencing profiles of tumor-infiltrating APCs in 8 luminal (LBC) and 4 triple-negative (TNBC) breast cancer samples, in close collaboration with Paula Michea, post-doctoral fellow in the lab. Based on previous analysis performed in the lab and on the basis of published studies of human DC subsets on other tissues such as peripheral blood or skin [Bronte et al. 2016; Guilliams et al. 2016; Zaba, Krueger, and Lowes 2009], we studied four DCs subsets and macrophages at the phenotypic and transcriptional level in breast cancer. By comparing the transcriptomes of those APCs from tumor sample and

  DCs is unknown. Here we used RNA-based next generation sequencing to systematically analyze the transcriptomes of plasmacytoid pre-DC (pDC), cDC1-enriched cells, cDC2, CD14 + DC, and monocyte/macrophages from human primary luminal breast cancer (LBC) and triple-negative breast cancer (TNBC). By comparing tumor tissue with noninvaded tissue from the same patients, we found that 85% of the genes upregulated in DCs in LBC were specific to each DC subset. However, all DC subsets in TNBC commonly showed enrichment for the interferon pathway, but those in LBC did not.

  compared to pDCs, a less basophilic cytoplasm (Fig 1c). MonoMacs presented an acidophilic cytoplasm with abundant vacuoles (Fig 1c), as commonly observed in this population.

Figure 1

 1 Figure 1 Phenotypic and molecular characterization of innate APC infiltrating breast cancer tissue. a, Representative flow cytometry contour plots showing the gate strategy to distinguish DC subsets from MonoMac, and their frequency in breast cancer samples. b,

Figure 2

 2 Figure 2 Subset-specific signatures defining tumor APC. a, Pie chart showing the

Figure 3

 3 Figure 3 Tumor-emergent genes from innate APC are subset-specific. a, Frequency of APC subsets as determined by flow cytometry, among CD45 + or total live cells, between

Figure 4

 4 Figure 4 Absence of immune function among enriched pathways from tumorupregulated genes. a, Functional network inference results for pDC, CD14 + DC and

Figure 5

 5 Figure 5 Transcriptional profile of innate APC subset is breast-cancer subtype dependent. a, Principal Component Analysis (PCA) showing the clustering of transcriptional

Figure 6

 6 Figure 6 Type-1 IFN pathway is upregulated in all TNBC-APC subsets. a, Functional

Figure 7

 7 Figure 7 Subset-specific signatures are linked to distinct disease-free survival depending on the subset and breast cancer type. a, Kaplan-Meier plots indicating the probability of

  human antibodies: CD3-Alexa700 (557943; clone: UCHT1) CD19-Alexa700 (557921; clone: HIB19), CD56-Alexa700 (557919; clone: B159) or -BUV737 (564448; clone: NCAM16.2 ), CD163-BV786 (741003; clone: GHI/61), CD11c-PECy5 (551077; clone:B-ly6) or -PE-CF594 (562393; clone:B-ly6), CD123-BV650 (563405; clone: 7G3), HLA-DR-BUV395 (564040; clone: G46-6), and CD45 APC-Cy7 (557833; clone: 2D1) from BD. CD14-Qdot605 (Q10013; clone: TüK4) from Life Technologies. CD14-BV605 (301833; clone:M5E2), CD16-BV510 (302047 clone: 3G8), CD123-PE-Cy7 (306010; clone: 6h6), CD1c-PE (331506; clone: L161), and HLA-DR BV711 (307643; clone: L243) from Biolegend. CD1c-PerCP-eFluor710 (46-0015-42; clone: L161), FceR1-APC (17-5899-42; clone: AER-37) from eBioscience. AXL-AlexaFluor488 (FAB154G; clone: 108724), and CD32B-APC (FAB1330A; clone: 190723) from R&D. CD141-PE (130-098-841; clone: AD5-14H12) from Miltenyi Biotec. For DC sorting, we used the following antibodies: CD45-BV570 (304033; clone: HI30) from Biolegend, CD14-FITC (555527; clone: 10.1) from BD, and HLA-DR-APCeFluor780 (47-9956-42; clone: LN3) from eBioscience instead of the corresponding marker. Single cell suspension was of tumor-digested cells were sorted in a BD FACS Aria III upgrade using the purity mode, a 100µm nozzle loop, and at low pressure (20psi). DC subsets were sorted in eppendorf tubes containing RPMI+5% FBS (HyClone) for morphological analysis. Once the morphology for each subset validated, and because of the low numbers of tumor-infiltrating APC, we directly sorted tumor-APC in TCL buffer (Qiagen) supplemented with 1% of β-Mercaptoethanol (SIGMA) for RNA-seq experiments.

  . The parameters used in ARACNe were, Mutual information Algorithm Type: Variable Bandwith. We used a transcription factor (TF) list for Hub/TF Definition from the dataset Fantom 68 . Mutual information Threshold was 0.5. We next, utilized the ClueGO Application 69 (to determine pathway enrichment in each network. Public datasets only from "Experimental evidence" of Gene Ontology (GO) -Biological process-GOA, -Cellular Component-GOA, -ImmuneSystemProcess-GOA, -Molecular Function-GOA, (updated date: 15.01.2017), InterPro dB: Protein Domains (updated date: 03.11.2015), Reactome (updated date: 20.01.2017), and WikiPathways (updated date: 20.01.2017) were used. Go Term Fusion option was selected. Only pathways with a "Benjamini-Hochberg" (BH) adjusted p-val below 0.05 were kept.

1 3 5

 135 Figure 1

Fig. 6 dFig. 6 f. Supplementary Figure 7 a,

 67 Fig. 6 d,e. d, Heat map indicating the correlation coefficient between the indicated costimulatory gene, and the IFN pathway metagene for each APC subset from LBC and TNBC, as indicated. e, GO term associated to the ECM organization metagene used for the analyses in Fig. 6 f.

  from literature a dataset of SUM149 human inflammatory breast cancer cell line transfected with siRNA targeting tazarotene-induced gene 1 (TIG1), a potential tumorigenic gene identified in inflammatory breast cancer(Wang et al. Cancer Res 2013). By taking into account all the individual ligand/receptor interactions, we developed a visualization tool by reconstructing the intercellular communication networks. In these directed graphs, nodes represent cell types, the width of the edges connecting two cell types is proportional to a global measure of the intensity of the communication between them and the arrows indicate the direction of communication from ligand to receptor (Figure1Aand see methods). Such connectivity maps enable to visualize the communication between cell types in a quantitative and qualitative manner. Generated for the two conditions (siTIG1 versus control), they demonstrated that SUM149 cells have a higher communication score with neutrophils, macrophages and monocytes. On the contrary, the lowest communication score is toward B cells. We can also observe an increase of communication score towards B cells in the siTIG1 condition compared to control (Figure1B).

  every DC experimental condition, demonstrated an increase of the global communication score in all 12 channels, when comparing LPS-activated DC to resting (medium) DC (Figure3Cand FigureS4B). Importantly, these maps revealed that blocking the IL-10 loop determined the largest amplification of DC communication with all 12 cellular targets, while the blocking of TNF loop in LPS-activated DC had a minor effect on the global communication score (Figure3Cand figureS4B). Supplementary table 3A-L details the top contributing communication molecules in each DC-target cell channel. Quantification of in-flow communication, as obtained by reversing the directionality of cell-to-cell interactions, showed that communication towards LPS-activated DC was also increased with respect to resting (Med) cells (FigureS4C and D). However, we observed a trend of decreased in-flow communication for LPS+aIL-10R-DC, relative to LPS-DC or LPS+aTNFR-DC (FigureS4Cand D), indicating that IL-10 specifically controls out-flow communication of DCs.

  keratinocytes, plasmacytoid DCs (pDC) and neutrophils. Similar to T cells, these cell types play key roles in the inflammatory microenvironment and had an increased global communication score. Target cells were cultured with DC-derived supernatants, and their activation assessed by qRT-PCR or FACS. LPS-DC supernatant induced marginal keratinocyte activation, as assessed by the expression of TNF, IL-1β and this was not affected by aTNFR (Figure 5A). However, blocking the IL-10 loop dramatically increased both factors (Figure 5A), validating a potent DC to keratinocyte communication controlled by IL-10. This extends DC-induced keratinocyte activation (20, 21) to the context of bacterial infection.

(

  supplementary table 3A-L). We blocked, in each DC communication channel, 4 of ligands, known as potential activators of the target cell type: GCSF, GM-CSF, IL-6 and TNF for neutrophils, IL-19, IL-36 gamma, OSM and TNF for keratinocytes, and G-CSF, GM-CSF, TNF and IL-12 for pDCs. Importantly, blocking TNF alone in the LPS+aIL10R-DC supernatant was sufficient to inhibit keratinocyte, pDC and neutrophil activation (Figure5A-C). By comparing the predicted communication intensities with a global score describing the activation level of keratinocytes, pDC and neutrophils, we observed a qualitative agreement (Figure5D), demonstrating increased communication efficiency. In all cases, the maximal activation of the target cells was determined by the condition LPS + aIL10R.

  DCs were pre-treated for one hour with mouse IgG1 (20 µg/mL, R&D Systems), mouse anti-IL10R blocking antibody (10 µg/mL, R&D Systems) or mouse anti-TNFα Receptors 1 and 2 (10 µg/mL, R&D Systems) (see Figure 1-Figure Supplement 4B) and then cultured with medium or LPS (100 ng/mL, LPS-EB Ultrapure, activates TLR4 only, Invivogen) for 24 hours. DCs from donors which responded to (a) LPS and (b) IL-10R blocking antibody, as evaluated by maturation markers, were included in this study. The following cytokines were measured in culture supernatants by CBA (BD Bioscience): IL-6, IL-12p70 and OSM. IL-23 was measured using ELISA (eBioscience).

Établissement

  Français du Sang (EFS), Paris, France, in conformity with Institut Curie ethical guidelines. Blood samples were stimulated for an hour at 37°C with medium, LPS (100 ng/mL) used as a positive control or DC supernatants. Cells were stained at 4°C for 15 min with an APC-anti-Human-CD62L (clone DREG-56, BD Pharmingen), a BV650-anti-Human-CD11b (BioLegend) and a PE-anti-Human-CD15 (MiltenyiBiotec) or with the corresponding isotypes. Erythrocytes were lysed with 1X BD Pharm Lyse Solution (BD Pharmingen), white cells were resuspended in PBS supplemented with 1% human serum and 2 mM EDTA and analyzed on a LSR Fortessa instrument (BD Biosciences).

  each interaction and each target cell in the experimental condition of IL10R blocking are provided in supplementary table 3A-L. The generation of the inflow connectivity maps was done by reversing the role of DC and their cellular targets. See supplementary figure 5. Global communication scores for both inflow and outflow connectivity maps are contained in supplementary table 3M-N.

Fig. 1 :

 1 Fig. 1: Development of a communication score based on transcriptomic profiles. (A) Pipeline used to create the intercellular communication score and network reconstruction. (B) Connectivity maps describing outgoing communication from SUM149 cells to primary cells in the conditions: siRNA control and siTIG1. The width of the edges corresponds to a global score combining the intensity of all the individual ligand/receptor interactions. A scale ranging from 400000 to 1600000, corresponding to minimum and maximum communication scores, is shown in the legend.

Fig. 2 :

 2 Fig. 2: IL-10R blocking activates a cell-to-cell communication module in LPS-stimulated DCs. (A) Depicted are the 4 experimental conditions for which transcriptomics was generated (n = 6). (B) Distribution of the separability score corresponding to the conditions LPS+aTNFR and LPS+aIL10R after 4 and 8 hours of cell culture. Genes with separability score ³ 4 were included in each condition's signature. Bottom: separability criterion used to define the signatures. (C) Expression pattern of 4 example genes with high separability score from the IL-10 loop signature (means± SEM, n=6). (D) Top 3 KEGG and REACTOME annotation terms over-represented in the IL-10 loop signature, together with the number of hits and the enrichment significance. (E) log-transformed enrichment significance of the overlap between the TNF and IL10 loop signatures, with the Gene Ontology term "positive regulation of cell communication". (F) Gene products corresponding to ligands (white) and receptors (black) counted in each loop signature and plotted according to regulation directionality: upregulated (Up) or downregulated (Down). (G) Protein levels of IL-6, OSM, IL-23 and IL-12p70 (means ± SEM), demonstrating increased secretion in LPS+aIL-10R DC supernatant.

Fig. 3 :

 3 Fig. 3: IL-10 loop controls DC intercellular connectivity. (A) Flowchart illustrating the strategy used for intercellular networks reconstruction. (B) Expression values of 8 example ligands in DCs (means ± SEM) side-by-side with the expression of their cognate receptor in 12 different cell types from four compartments: epithelium, stroma, innate and adaptive immune cells. Color code indicates different compartments. Box plots show cell-specific expression of the receptors in control and stimulated conditions, as provided by the BioGPS database (supplementary table 2C-E) (C) Connectivity maps describing outgoing communication from DCs to putative target cells in the conditions: Med, LPS, LPS+aTNFR and LPS+aIL-10R. The width of the edges corresponds to a global score combining the

Fig. 4 :

 4 Fig. 4: IL-10 but not TNF loop dictates T helper polarization by LPS-DC. (A-B) Supernatants of CD4+ naive (A) and memory (B) T cells, co-cultured with the indicated DCs, were analyzed for the presence of T helper cytokines by CBA: IL-2, IL-3, IL-4, IL-9, IL-10, IL-17A, IL-17F and IFN-g (A) and all the above in addition to IL-5, IL-13 TNF and GM-CSF (B). Results are shown in a 2D PCA. Dots represent mean of 9 (A) or 6 (B) independent coculture experiments. (C) Histogram representation (means ± SEM, n = 16) of 4 cytokines present in the supernatant of naive (white bars, left axis) or memory (black bars, right axis) supernatant. (D) CD4+ naive T cells were analyzed for IL-17A, IL-9 and IFNg production using intracellular staining FACS. Percentage of positive producers is given. Shown is one representative out of 3 independent experiments. (E)The matrix plot presents the significant (p value < 0.05) Spearman correlation values between DC soluble factors and T helpersecreted cytokines (9 independent co-culture experiments).

Fig. 5 :

 5 Fig. 5: IL-10 loop controls DC communication with keratinocytes, neutrophils and pDCs. (A) RT-PCR analysis of the expression of TNF and IL-1b mRNA in HaCat cells incubated with medium, LPS or with supernatant (diluted 1:10) of the indicated DCs for 4h. Blocking antibodies for the cytokines IL-19, IL-36g, OSM and TNF were added to LPS+aIL-10R-DC supernatant for 1h incubation before culturing with HaCat cells. Data represent mean ± SEM, n=4, * p<0.05. (B-C) Expression of maturation markers CD86, HLA-DR and ICOSL (B) or DC11b and CD62L (C) analyzed by flow cytometry with surface staining on pDCs (n=12) cultured with supernatant (diluted 1:10) of the indicated DC for 24h (b) and neutrophils (n=9) cultured with supernatant (diluted 1:100) of the indicated DC for 1h.Blocking antibodies for the cytokines GCSF, GM-CSF, TNF and IL-12 (for pDC) or IL-6 (neutrophils) were added to LPS+aIL-10R-DC supernatant for 1h incubation before culture.Each biological replicate comprised independent DC donor paired to independent pDCs / neutrophils donor. Data represent mean ± SEM, * p<0.05; ** p<0.01; *** p<0.001 (paired ttest). (D) For each target cell, we reduced the different activation markers to a single parameter normalized between 0 (Ø) and 1 (max) in the rectangles. The value 0 corresponds to the activation level induced by supernatants from untreated DC, while 1 corresponds to the maximum activation level from all the observed conditions. These experimentally validated

  Figure 1

  At the interface of innate and adaptive immunity, APCs are essential cells in triggering immune responses. Monocytes, macrophages and DCs present a diversity of cell types defined by their distinct phenotypes, functions and tissue localization [Collin, McGovern, and Haniffa 2013; Mildner and Jung 2014]. Several subsets have been characterized in tissues and inflammatory conditions [Mantovani et al. 2008; Segura and Amigorena 2013]. In the context of cancer, APCs and particularly DC subsets have not been described with a level of detail as we proposed in this work. In breast TME, we identified and characterized four subsets of DCs and a mix of monocytes and macrophages. pDC subset appears to be the most distinct subset as described in other tissues [Hanahan and Weinberg 2011; Heidkamp et al. 2016; Lindstedt, Lundberg, and Borrebaeck 2005] while CD14+ DCs, apparented to inflammatory DCs [Segura, Touzot, et al. 2013], were close to cDC2 and Monomacs.

  Interferons are cytokines produced by immune cells, such as dendritic cells and macrophages, following activation of innate sensors of pathogen infection, and lymphocytes. pDC are the "professional" interferon-producing cells since they constitutively express IRF7, an interferon regulatory factor. Myeloid or classical DC (mDC or cDC) also produce type I interferon, and Lauterbach et al. have shown that BDCA3 + DCs from PBMCs stimulated with poly-IC are the main producers of type III interferon (IFNλ)[Lauterbach et al. 2010]. IFNγ is a potent proinflammatory cytokine secreted by CD4 + Th1 lymphocytes, CD8 + cytotoxic lymphocytes and NK cells. In the context of cancer, some findings highlighted the important role and functions of type I interferons (IFNα and IFNβ) in antitumor immunity [Gough et al. 2012]. Endogenous interferon has also been shown to modulate the antitumor immune response [Gavin P Dunn et al. 2005]. In the context of TNBC tumors that fail to respond to chemotherapy, Doherty et al. showed that treatment with IFNβ represses thier cancer stem cell properties and could be used as a therapy in highly aggressive TNBC tumors [Doherty et al. 2017]. Regarding type III interferon, it can target tumor cells directly to inhibit proliferation, alter the cell cycle and induce apoptosis, as well as activate antitumor immunity [Stiff and Carson 2015]. Tumor cells, similarly to infiltrating innate immune cells and lymphocytes, are capable of producing type I interferon. Bidwell et al. described a novel immuneevasion mechanism whereby tumor cells suppressed their intrinsic secretion of type I interferon in order to metastasize successfully [Bidwell et al. 2012]. In mice, type I interferons can activate CD8 + T cells and induce CTL activity [Diamond et al. 2011; Fuertes et al. 2011]. However, it has been shown that CD8 + T cells can produce IFNγ which promotes tolerogenic DCs [Jurgens et al. 2009; Mojic, Takeda, and Hayakawa 2017]. It would be interesting to perform further experiments to study the communication between interferon-producing DCs and CD8 + T cells, to see if there is a feedback loop in interferon signaling in favor of tumor escape or antitumor immunity. There is a certain interest of using interferon in cancer therapies, as it is seen in available patents and clinical trials using interferon DC-vaccine to activate T cell and induce a proper immune response [Baek et al. 2015; B. S. Parker, Rautela, and Hertzog 2016; Santini et al. 2000]. Trials of interferon therapies in solid malignancies such as melanoma, renal cell carcinoma and Kaposi sarcoma have met with varied success. The source, inducer, subtype, dose, duration and stability of the endogenous or exogenous IFN also have a major impact on outcome; as does the requirement for IFNα/β receptor (IFNAR) expression. The presence of IFNAR on T cells should be assessed to verify if type I interferon production by myeloid cells is efficient to induce activation of CD8 + T cells in the TME. As suggested by Parker and collegues, the use of IFN therapy in combination with PD1targeted therapies could hold particular promise in the aggressive TNBC [B. S. Parker, Rautela, and Hertzog 2016]. This subtype of breast cancer expresses high levels of PDL1 and they have demonstrated an important role for IFN signaling in metastatic breast cancer, hence such a combination may hold great promise in a breast cancer subtype that currently has limited and untargeted treatment options. However, interferon therapies reach dose-limiting toxicities.

  Using single-cell RNA-seq (scRNA-seq), recent studies identified new subsets of DC in peripheral blood and proposed a new classification [See et al. 2017; Villani et al. 2017]. Single-cell technology is, indeed, a helpful tool to decipher cellular heterogeneity in tissues. In my thesis work, I analyzed APC transcriptomic profiles at the population-level, with a priori knowledge on the subset present in the breast TME. To go further on describing the heterogeneity of APCs in the context of breast cancer, we generated scRNA-seq profiles of immune cells from one sample of LBC. As preliminary results, I identified a cluster of DC which does not express genes coding for lineage markers classically used to identify DCs [See et al. 2017; Villani et al. 2017] but express genes coding for maturation markers (e.g. CCR7) and checkpoint molecules (e.g. IDO) (Figure 4.1). In the literature, mature DCs have been characterized at the periphery of tumor but not in the tumor bed of carcinoma cells whereas tumor-infiltrating DCs exhibit an immature phenotype [Bell et al. 1999; Janco et al. 2015]. Upon maturation, DCs increase their efficacy to present processed antigens and consequently improve their capability to activate T cells [Dudek et al. 2013] whereas immature DCs found in tumors exhibit a tolerogenic phenotype, expressing PD-L1 and suppressing T cell activation [Krempski et al. 2011]. Complementary studies including experimental validation are required to define phenotype and functions of this population. If it corresponds to mature DCs, we will have to investigate why this particular LBC sample is infiltrated by mature DCs and we will need to experimentally validate the presence of this DC subset in breast cancer, for instance by FACS or processing and analysis of public single-cell datasets of immune cells infiltrating breast cancers [Azizi et al. 2018; Yin et al. 2018].

Figure 4 . 1 :

 41 Figure 4.1: Characterization of breast tumor-infiltrating DC subsets using scRNAseq (preliminary results). a: tSNE representation of DCs (n= 253 cells) purified from one LBC sample. Color code and ellipses indicate clusters identified by graph-based clustering, five top discriminator genes are listed next to each cluster. b: Violin plot representation of lineage markers expression in each cluster. Each dot represent a unique cell. Color code indicate the same clusters as in (a).

  decipher intercellular communication between individual cells but it is a challenging project in term of methodological development and biological interpretation [Rodda et al. 2018; Thurley, L. F. Wu, and Altschuler 2018; Yin et al. 2018].

Figure 1 .

 1 Figure 1. Identification of CD123 high cells in bone marrow and peripheral blood of CMML patients. A. Histological and immunohistochemical analysis of bone marrow trephine biopsy sections of CMML patients. Two representative cases are shown (magnification x 20). Upper panel: CD123 + ,TCL1 + cell-rich CMML; Lower panel: CD123 + ,TCL1 + cell-poor CMML. Left column: hematoxylin/eosin staining; middle column: CD123 staining; right column: TCL1 staining. B. Multiparameter flow cytometry analysis of putative pDC in bone marrow aspirate and peripheral blood samples collected from CMML patients and age-matched healthy controls. Mononuclear cells were identified among CD45 + cells using Side Scatter (SSC) and CD33 staining. Putative pDC were identified among mononucleated cells as HLA-DR + Lineage (CD3, CD14, CD15, CD16, CD19, CD24) -, CD33 -, CD11c -, CD123 + , BDCA-2 + , BDCA-4 + , CD4 + cells. C,D. pDC richness was quantified as percentage of pDC among mononuclear cells in bone marrow (BMNC; Controls = 24, CMML = 159) (C) and peripheral blood (PBMC; Controls = 34, CMML = 198) (D). E. Linear regression of pDC in peripheral blood, expressed as the fraction of PBMC, versus pDC in bone marrow, expressed as the fraction of BMNC, in 106 CMML patients with matched samples (R 2 0.75; p<0.0001).

Figure 2 .

 2 Figure 2. Characteristics of CD123 high cells in CMML patients and age-matched controls. A-C. The fraction of pDC in mononuclear cells was determined in bone marrow samples collected from 159 CMML patients and 24 healthy donors (controls) and peripheral blood samples collected from 198 CMML patients and 34 healthy donors (controls). A. Fraction of pDC among mononuclear cells in peripheral blood and bone marrow of CMML patients (**** P<0.0001, Wilcoxon signed rank test). B. Fraction of pDC among bone marrow mononucleated cells collected from healthy donors and CMML patients, separating pDC-poor (<1.2% MNC) from pDC-rich CMML samples, showing a significant decrease in pDC fraction in pDC-poor CMML compared to healthy donors (**** P<0.0001, Mann Whitney test). C. Fraction of pDC among peripheral blood mononucleated cells collected from healthy donors and CMML patients, separating pDC-poor (<0.6% MNC) from pDC-rich CMML samples, showing a significant decrease in pDC fraction in pDC-poor CMML compared to healthy donors (**P<0.01, Mann Whitney test)). D-G. Staining index of indicated cell surface markers in indicated bone marrow samples. The only detected difference

Figure 3 .

 3 Figure 3. Gene expression analysis confirms that CD123 + cells are bona fide plasmacytoid dendritic cells. A. Heat-map of gene expression measured by RNA sequencing in sorted pDC from healthy donors (n=7), pDC-rich (n = 11) and pDCpoor (n = 5) bone marrow CMML samples, and pDC-rich (n = 3) and pDC-poor (n = 4) peripheral blood CMML samples, distinguishing genes highly expressed in typical pDC from those defining "AS DCs" and other cell lineages. B. Principal component analysis of gene expression in pDC sorted from healthy donors (blue), pDC-poor CMML (green) and pDC-rich CMML (red) bone marrow samples (LogCPM gene expression). C. Unsupervised hierarchical clustering of bone marrow pDC, based on differentially expressed genes (DEG) as identified by RNA sequencing in pDC sorted from a cohort of 4 healthy donors, 6 pDC-rich CMML and 3 pDC-poor CMML bone marrow samples (DEG between pDC-poor and pDC-rich CMML = 74; DEG between pDC-rich CMML and healthy-donor pDC =13; DEG between pDC-poor CMML and healthy-donor pDC =144).

Figure 4 .

 4 Figure 4. pDC bone marrow infiltration increases the risk of acute leukemia transformation. A. Relationship between immunohistochemistry analysis of bone marrow CD123 + , TCL1 + cells (pDC rich > 5% of bone marrow cells) and flow cytometry measurement of pDC fraction in peripheral blood samples (pDC rich > 0.6% of mononucleated cells) analyzed in 56 patients. P<0.001; Mann Whitney test. Agreement in 92% of cases, Kohen's kappa 0.75. B. Cumulative incidence of acute myeloid leukemia transformation in 202 CMML patients according to pDC infiltration, as defined by immunohistochemistry analysis of bone marrow biopsies (pDC-rich > 5% CD123 + TCL1 + cells), considering death as a competing risk (Hazard ratio 2.59 [95% confidence interval (CI) 1.21-5.51]; P=0.014).

Figure 5 .

 5 Figure 5. Bone marrow infiltrating pDC are observed in Ras-pathway mutated CMML. A. Whole exome sequencing was performed in monocytes, T cells and pDC

Figure 6 .

 6 Figure 6. CD34 + cells from pDC-rich CMML are hypersensitive to FLT-3L. A-C. CD34 + cells from pDC-rich CMML, pDC-poor CMML and cord blood were cultured in the presence of SCF, TPO, Flt3-L and IL-3 for indicated times before flow analysis of generated cells. A,B. Flow cytometry detection of pDC at day 30 of culture, based on HLA-DR, CD123 and BDCA4 expression; A, CD34 + cells from pDC-rich CMML bone marrow; B, CD34 + cells from pDC-poor CMML bone marrow. One representative of 10 independent experiments is shown. C. Generated pDC were sorted and examined by conventional (upper panel) and electronic (lower panel) microscopy. D. Somatic variants were detected by NGS in sorted bone marrow CD34 + cells, sorted bone marrow pDC and pDC generated by ex vivo differentiation of CD34 + cells at day 30. E. Time dependent generation of pDC by ex vivo culture of CD34 + cells collected from 5 pDC-rich (in red) and 8 pDC-poor (in green) CMML bone marrow samples (mean +/-SEM; * P<0.05, Mann-Whitney test); F. Fraction of pDC generated at day 25 by ex vivo culture of CD34 + cells sorted from the 5 pDC-rich (red) and 8 pDC-poor (green) CMML bone marrow samples (shown on panel E) compared to 5 cord blood CD34 + cell samples; (mean +/-SEM; * P<0.05, Mann-Whitney test); G. Fraction of pDC in cells generated by CD34 + cells sorted from 2 pDC-rich CMML bone marrow (in red) and 5 cord blood samples (in blue) and cultured for 30 days as above with indicated concentrations of Flt3-L. F. Flt3-L level was measured in bone marrow supernatant of 28 pDC-rich CMML patients (red) and 78 pDC-rich CMML patients

Figure 3 .

 3 Figure 3.

Figure 4

 4 Figure 4.

  Biotec: CD3-PerCP, CD3-VioBlue, CD4-PE-Vio770, CD4-VioBright-FITC, CD4-APC-Vio770, CD45RA-VioGreen, CD8-APC-Vio770, CD8-PerCP-Vio770, CD8-VioGreen, CD11b-FITC, CD38-PE-Vio770, CD45RA-VioGreen, CD57-VioBlue, CD127-PE-Vio770, CCR7-APC, IFNγ-FITC, KLRG1-FITC, NKp80-APC, Perforin-VioBlue, Granzyme B-PE, PD-1 PE-Vio770; from Beckman Coulter: CD27-PE, CD62L-FITC, CD69-PE, CD127-PE; from BD Pharmingen: CD28-FITC, CD137-PE; from eBioscience: ILT2-PE or ILT2-APC (Clone HP-F1), Tim-3-FITC; from Biolegend: PD-1-BV421. Intracellular staining was performed using the Inside Stain kit (Miltenyi biotec), according to the manufacturer's instructions. Acquisition was made on a MACSQuant 10 flow cytometer (Miltenyi biotec); analysis was performed using the MACSQuantify software (Miltenyi biotec) and Flowjo software.

B

  and H), ITGAM (CD11b), and FCRL6. Genes usually associated with NK cells were NCAM1 (CD56), cytotoxicity-triggering receptors NCR1 (NKp46), KLRF1 (NKp80) and CD244, lectin-like receptors KLRK1, KLRC3 and KLRC4 (NKG2D, NKG2E and NKG2F), and immunoglobulin-like receptors KIR2DL1, KIR2DL3, KIR3DL2. Downregulated genes in the CD8 + ILT2 + population were more diverse, but two categories stood out: genes involved in T cell stimulation/costimulation (CD28, CD28H (TMIGD2), CD27, TNFRSF8, CD40LG, TESPA1), and genes associated with T-cell differentiation (CD28, CD27, CCR7, CCR4, SELL, IL7R, TCF7).

  et al., who described 22 TIL clusters as well as their frequent associations(29). Based on our flow cytometry data, we postulate that ILT2-positive effector CD8 + TILs may be found in several of their PD-1-negative clusters, namely the T-11 (CD11b-positive), T-14 (CD45RApositive) and T-4 (PD-1-negative with no other positive discriminatory marker) clusters.

Figure 1 .

 1 Figure 1. Characterization of peripheral blood ILT2-positive CD8 + T cells using

Figure 2 .

 2 Figure 2. Phenotypical characterization of peripheral blood ILT2-positive CD8 + T cells.

Figure 3 .

 3 Figure 3. ILT2 cell-surface expression is a feature of differentiated cytotoxic CD8 + T

Figure 5 :

 5 Figure 5: Representative examples of HLA-G staining in tumor tissue specimens from

Figure 6 .

 6 Figure 6. ILT2-positive tumor-infiltrating T cells are differentiated cytolytic effectors

Figure 7 .

 7 Figure 7. Target-borne HLA-G1 inhibits the effector functions of ILT2-positive tumor-

Figure 1 DumontFigure S1 .

 1S1 Figure 1

Titre:

  Analyse systémique des sous-populations immunitaires et réseaux de communication intercellulaires dans les tumeurs du sein humaines Mots clés : Biologie des systèmes, immunologie, transcriptome, immunité antitumorale, cellules dendritiques Résumé : La communication intercellulaire est à la base de l'organisation d'ordre supérieur observée dans les tissus, les organes et l'organisme. Comprendre la communication intercellulaire et ses mécanismes sous-jacents qui sont impliqués dans le cancer est essentiel. Le microenvironnement des tumeurs du sein est composé d'une grande diversité cellulaire, telle que les cellules endothéliales, stromales ou immunitaires, qui peuvent influencer la progression tumorale ainsi que la réponse au traitement. Parmi les différentes populations de cellules immunitaires, les sous-populations de cellules dendritiques (DCs) intègrent les signaux du microenvironnement puis joue un rôle critique en orchestrant le développement d'une réponse immunitaire spécifique par activation des lymphocytes T. Cependant, les différentes fonctions de ces sous-populations et leurs interactions au sein du microenvironnement tumoral restent mal décrites. L'objectif principal de ma thèse a été de comprendre l'impact du microenvironnement tumoral du sein sur les sous-populations de DCs par analyse systémique. Nous avons utilisé le séquençage de l'ARN pour analyser systématiquement les transcriptomes des pré-DC plasmacytoïdes infiltrant les tumeurs, les populations cellulaires enrichies pour les DC classiques de type 1, les DC classiques de type 2, les DC CD14+ et les monocytes-macrophages chez des patientes atteintes de cancer primitif du sein luminal et cancer du sein triple négatif. Nous avons constaté que la reprogrammation transcriptionnelle des cellules présentatrices d'antigène infiltrant la tumeur est spécifique à un sous-ensemble. Ces résultats suggèrent une interaction complexe entre l'ontogenèse et l'empreinte tissulaire dans le conditionnement de la diversité des DCs et de leur fonction dans le cancer. En second lieu, j'ai cherché à étudier les communications intercellulaires afin de comprendre comment les cellules intègrent les signaux de leur environnement. Nous avons développé ICELLNET, un outil pour reconstruire les réseaux de communication intercellulaires. Cette méthode quantitative originale, intégrant les interactions ligand-récepteur et l'expression génique spécifique à un type cellulaire, peut être appliquée automatiquement à tous profils transcriptomiques de population cellulaire, que ce soit dans divers contextes pathologiques ou d'autres domaines de la biologie.

Table 1 : Multivariate Cox regression of predictors of disease-free survival influencing the disease-free survival of breast cancer patients.

 1

		LBC		TNBC
	Variables	HR	p-value	HR	p-value
	pDC signature and NPI
	NPI > 5.4	1	-	-	-
	NPI ≤ 5.4	0.31	7.5e-13	-	-
	pDC signature ratio High	1	-	-	-
	pDC signature ratio Low	1.37	0.0072	-	-
	cDC2 signature and NPI
	NPI > 5.4	1	-	-	-
	NPI ≤ 5.4	0.3	1.8e-13	-	-
	cDC2 signature ratio High	1	-	-	-
	cDC2 signature ratio Low	1.27	0.041	-	-
	cDC1e signature and NPI
	NPI > 5.4	1	-	1	-
	NPI ≤ 5.4	0.29	7.6e-14	0.27	1.1e-09
	cDC1e signature ratio High	1	-	1	-
	cDC1e signature ratio Low	1.39	0.0041	1.76	0.0058
	MMAC signature and NPI
	NPI > 5.4	1	-	1	-
	NPI ≤ 5.4	0.31	5.9e-13	0.28	3.9e-09
	MMAC signature ratio High	1	-	1	-
	MMAC signature ratio Low	0.77	0.025	0.67	0.049

NPI, Nottigham Prognostic Index; HR, Hazard Ratio; p-value < 0.05 marked in bold font shows statistical significant.

: Clinical information of the patients included in RNA-seq analysis

	DEMOGRAPHY	Groups	N	%
	Female		17	100
	Age	< 60		23.5
		61-70		29.4
		> 71		47.1
	EXTENSION
	Size (mm)	< 20		29.4
		21-40		41.2
		> 41		29.4
	Lymph Nodes (LN) involvement	LN+	11	64.7
		LN-		35.3
	HISTOLOGICAL SUBTYPE
	Invasive Ductal			53
	Invasive Lobular			23.5
	Mixed ductal/lobular			17.6
	Papillary			5.9
	Elston Ellis (EE) GRADE
	I			0
	II			35.3
	III		11	64.7
	MOLECULAR SUBGROUP
	Triple negative, TN (hormone receptor and HER2 negative)			23.5
	Luminal B, LB (hormone receptor and HER2 positive or negative)		13	76.5

Supplementary Table 3: Top 10 subset-specific DEG from LBC samples

	pDC	cDC2	cDC1e	MonoMac
	TCL1A	CCL22	SIGLEC17P	ADAP2
	NLRP7	CLEC17A	CLNK	MS4A7
	ZFAT	GRIP1	MS4A2	NXF3
	FAM129C	IFNL1	CLEC9A	LINC01094
	CUX2		CATSPER1	ASAH1
	GZMB		SLC45A3	DAB2
	SMIM6		XCR1	IGSF21
	ZDHHC17		LAX1	ME1
	CLEC4C		SH3RF2	PLA2G15
	CRYM		IL1RL1	DLEU7

  To detect genes whose expression pattern was specific for the conditions LPS, LPS+αIL10R, or LPS+ αTNFR, we used the function rankfeature. This function returns a separability score based on binary classification, which measures how well each gene separates a given experimental condition from all the others based on its expression profile. By inspecting the distribution of the separability score over all differentially expressed genes, we determined an empirical cutoff of 4. Genes with separability score larger than this

	cut off (supplementary table 1C-E) were further analyzed for functional interpretation using
	the Molecular Signature Database (41) (supplementary table 1F-H). The following
	databases were considered: KEGG, REACTOME and BIOCARTA. To compute the
	enrichment of the TNF and IL10 loop signatures in genes annotated in the GO term "positive
	regulation of cell communication" (GO:0010647) (supplementary table 2A), we performed a
	standard hypergeometric test.

table 1A-B).

Table 1 : Communication factors in signatures by separability.

 1 Communication factors (ligands and receptors) identified in the signatures corresponding to the DC conditions LPS,

	LPS+aTNFR, LPS+aIL10R.

Table 1 : Communication factors in signatures by separability

 1

	Factor	Direction	LPS	LPS+αTNFR	LPS+αIL10R
		Down	EFNB1	ICOSLG	EFNA4, PLAU, SLIT1, WNT5B
	Ligand
		Up	PDGFA, PVR	PLAU, VCAN

Table I .

 I Specifically upregulated and downregulated genes in CD8+ILT2+ T cells vs CD8+ILT2-T cells

	Supplementary Figures			Dumont et al.
		340547_at 105379362_at	5.65E-04 1.82E-02	7.77 1.63	VSIG1 LOC105379362 uncharacterized LOC105379362 V-set and immunoglobulin domain containing 1
		11123_at 196264_at	8.78E-04 1.83E-02	7.37 1.59	RCAN3 MPZL3	RCAN family member 3 myelin protein zero like 3
		Probeset 360_at 7273_at	adjusted P Value Fold difference 8.94E-04 7.25 1.84E-02 1.58	SYMBOL AQP3 TTN	GENENAME aquaporin 3 (Gill blood group) titin
		10859_at 1236_at 6959_at	2.09E-04 9.63E-04 1.97E-02	8.81 7.08 1.41	LILRB1 CCR7 TRBV21OR9-2 T cell receptor beta variable 21/OR9-2 (pseudogene) leukocyte immunoglobulin like receptor B1 C-C motif chemokine receptor 7
		353345_at 1803_at 959_at	9.81E-04 9.63E-04 1.97E-02	6.69 7.02 1.41	GPR141 DPP4 CD40LG	G protein-coupled receptor 141 dipeptidyl peptidase 4 CD40 ligand
		162966_at 126259_at 57124_at	1.25E-03 9.77E-04 1.99E-02	6.29 6.92 1.40	ZNF600 TMIGD2 CD248	zinc finger protein 600 transmembrane and immunoglobulin domain containing 2 CD248 molecule
		353189_at 145864_at 9934_at	1.63E-03 9.77E-04 1.99E-02	5.79 6.75 1.38	SLCO4C1 HAPLN3 P2RY14	solute carrier organic anion transporter family member 4C1 hyaluronan and proteoglycan link protein 3 purinergic receptor P2Y14
		59084_at 4609_at 8821_at	1.63E-03 9.77E-04 2.03E-02	5.76 6.82 1.34	ENPP5 MYC INPP4B	ectonucleotide pyrophosphatase/phosphodiesterase 5 (putative) MYC proto-oncogene, bHLH transcription factor inositol polyphosphate-4-phosphatase type II B
		10578_at 944_at 641518_at	1.75E-03 9.77E-04 2.36E-02	5.64 6.76 1.05	GNLY TNFSF8 LEF1-AS1	granulysin TNF superfamily member 8 LEF1 antisense RNA 1
		3684_at 399665_at 1263_at	1.75E-03 1.14E-03 2.39E-02	5.64 6.53 1.03	ITGAM FAM102A PLK3	integrin subunit alpha M family with sequence similarity 102 member A polo like kinase 3
		64097_at 5820_at 220158_at	2.13E-03 1.15E-03 2.44E-02	5.33 6.47 0.97	EPB41L4A PVT1 GTSCR1	erythrocyte membrane protein band 4.1 like 4A Pvt1 oncogene (non-protein coding) Gilles de la Tourette syndrome chromosome region, candidate 1
		81553_at 105370652_at 28715_at	2.13E-03 1.19E-03 2.44E-02	5.35 6.40 0.97	FAM49A LINC02295 TRAJ40	family with sequence similarity 49 member A long intergenic non-protein coding RNA 2295 T cell receptor alpha joining 40
		3804_at 256987_at 28751_at	2.24E-03 1.25E-03 2.45E-02	5.24 6.28 0.96	KIR2DL3 SERINC5 TRAJ4	killer cell immunoglobulin like receptor, two Ig domains and long cytoplasmic tail 3 serine incorporator 5 T cell receptor alpha joining 4
		9788_at 114804_at 28738_at	2.36E-03 1.37E-03 2.51E-02	5.15 6.05 0.92	MTSS1 RNF157 TRAJ17	MTSS1, I-BAR domain containing ring finger protein 157 T cell receptor alpha joining 17
		81563_at 120425_at 939_at	2.99E-03 1.37E-03 2.60E-02	4.93 6.10 0.85	C1orf21 JAML CD27	chromosome 1 open reading frame 21 junction adhesion molecule like CD27 molecule
		102724104_at 3572_at 8609_at	3.61E-03 1.37E-03 2.77E-02	4.74 6.10 0.76	LOC102724104 uncharacterized LOC102724104 IL6ST interleukin 6 signal transducer KLF7 Kruppel like factor 7
		10875_at 4753_at 387748_at	3.91E-03 1.37E-03 2.83E-02	4.65 5.99 0.73	FGL2 NELL2 OR56B1	fibrinogen like 2 neural EGFL like 2 olfactory receptor family 56 subfamily B member 1
		284367_at 60468_at 5324_at	3.94E-03 1.37E-03 2.94E-02	4.60 6.02 0.65	SIGLEC17P BACH2 PLAG1	sialic acid binding Ig like lectin 17, pseudogene BTB domain and CNC homolog 2 PLAG1 zinc finger
		30009_at 9057_at 131450_at	3.97E-03 1.37E-03 2.98E-02	4.57 6.05 0.62	TBX21 SLC7A6 CD200R1	T-box 21 solute carrier family 7 member 6 CD200 receptor 1
		23314_at 8718_at 57282_at	4.01E-03 1.41E-03 3.04E-02	4.48 5.94 0.59	SATB2 TNFRSF25 SLC4A10	SATB homeobox 2 TNF receptor superfamily member 25 solute carrier family 4 member 10
		53637_at 6920_at 107986485_at	4.01E-03 1.75E-03 3.13E-02	4.45 5.62 0.55	S1PR5 TCEA3 LOC107986485 uncharacterized LOC107986485 sphingosine-1-phosphate receptor 5 transcription elongation factor A3
		5874_at 196_at 27018_at	4.01E-03 1.92E-03 3.30E-02	4.46 5.52 0.44	RAB27B AHR BEX3	RAB27B, member RAS oncogene family aryl hydrocarbon receptor brain expressed X-linked 3
		4646_at 7294_at 28680_at	4.75E-03 2.09E-03 3.41E-02	4.16 5.42 0.39	MYO6 TXK TRAV8-6	myosin VI TXK tyrosine kinase T cell receptor alpha variable 8-6
		445347_at 3655_at 105375547_at	5.02E-03 2.13E-03 3.45E-02	4.09 5.34 0.37	TARP ITGA6 LOC105375547 uncharacterized LOC105375547 TCR gamma alternate reading frame protein integrin subunit alpha 6
		11098_at 100750325_at 105376892_at	5.62E-03 2.24E-03 3.60E-02	3.95 5.26 0.32	PRSS23 RCAN3AS LOC105376892 uncharacterized LOC105376892 protease, serine 23 RCAN3 antisense
		79899_at 8728_at 28659_at	5.62E-03 2.27E-03 3.64E-02	3.94 5.21 0.30	PRR5L ADAM19 TRAV24	proline rich 5 like ADAM metallopeptidase domain 19 T cell receptor alpha variable 24
		105370660_at 5578_at 107984947_at	5.67E-03 3.97E-03 3.88E-02	3.91 4.55 0.20	LOC105370660 uncharacterized LOC105370660 PRKCA protein kinase C alpha LOC107984947 uncharacterized LOC107984947
		79180_at 1880_at 2776_at	5.78E-03 4.01E-03 3.90E-02	3.85 4.44 0.19	EFHD2 GPR183 GNAQ	EF-hand domain family member D2 G protein-coupled receptor 183 G protein subunit alpha q
		4067_at 6402_at 149233_at	5.91E-03 4.01E-03 3.98E-02	3.73 4.51 0.13	LYN SELL IL23R	LYN proto-oncogene, Src family tyrosine kinase selectin L interleukin 23 receptor
		124221_at 9805_at 28984_at	6.52E-03 4.01E-03 4.07E-02	3.59 4.46 0.07	PRSS30P SCRN1 RGCC	protease, serine, 30 pseudogene secernin 1 regulator of cell cycle
		3002_at 28567_at 6285_at	6.64E-03 4.06E-03 4.18E-02	3.55 4.41 0.02	GZMB TRBV20-1 S100B	granzyme B T cell receptor beta variable 20-1 S100 calcium binding protein B
	Downregulated in CD8+ILT2+ T cells Upregulated in CD8+ILT2+ T cells	51314_at 3823_at 5782_at 105376387_at 4684_at 9832_at 105369656_at 59352_at 9839_at 343413_at 57458_at 100507195_at 3574_at 114879_at 401124_at 10417_at 11314_at 9437_at 51744_at 100528032_at 313_at 692229_at 9331_at 5911_at 3904_at 5351_at 5341_at 80310_at 117157_at 79815_at 55026_at 51176_at 55061_at 90139_at 3570_at 100506915_at 2053_at 51301_at 101929623_at 28685_at 5924_at 104326191_at 4747_at 105377225_at 4118_at 101927777_at 130367_at 80824_at 3003_at 55824_at 203328_at 23406_at 22866_at 101954266_at 28674_at 6322_at 1606_at 129293_at 351_at 51522_at 55423_at 814_at 10207_at 28595_at	6.85E-03 6.87E-03 7.66E-03 8.15E-03 8.15E-03 8.41E-03 8.98E-03 9.05E-03 9.26E-03 9.91E-03 9.91E-03 9.93E-03 1.00E-02 1.12E-02 1.12E-02 1.16E-02 1.23E-02 1.27E-02 1.31E-02 1.33E-02 1.33E-02 1.33E-02 1.37E-02 1.47E-02 1.61E-02 1.64E-02 1.85E-02 1.97E-02 2.03E-02 2.04E-02 2.18E-02 4.33E-03 4.65E-03 4.69E-03 5.25E-03 5.67E-03 5.67E-03 5.81E-03 5.84E-03 5.84E-03 5.84E-03 5.89E-03 6.52E-03 6.85E-03 6.85E-03 6.87E-03 6.87E-03 6.87E-03 7.49E-03 7.73E-03 7.98E-03 8.26E-03 8.57E-03 8.80E-03 9.05E-03 9.91E-03 1.07E-02 1.11E-02 1.12E-02 1.16E-02 1.20E-02 4.18E-02 4.65E-03 4.33E-02	3.49 3.46 3.29 3.17 3.16 3.08 2.99 2.93 2.90 2.78 2.77 2.75 2.73 2.54 2.53 2.49 2.37 2.30 2.22 2.15 2.17 2.16 2.11 1.99 1.83 1.78 1.57 1.42 1.34 1.33 1.22 4.32 4.21 4.18 4.03 3.88 3.90 3.83 3.77 3.79 3.78 3.74 3.60 3.49 3.48 3.41 3.44 3.43 3.32 3.26 3.21 3.13 3.05 3.02 2.94 2.78 2.65 2.59 2.54 2.49 2.42 0.02 4.21 -0.05	NME8 KLRC3 PTPN12 LOC105376387 uncharacterized LOC105376387 NME/NM23 family member 8 killer cell lectin like receptor C3 protein tyrosine phosphatase, non-receptor type 12 NCAM1 neural cell adhesion molecule 1 JAKMIP2 janus kinase and microtubule interacting protein 2 LOC105369656 uncharacterized LOC105369656 LGR6 leucine rich repeat containing G protein-coupled receptor 6 ZEB2 zinc finger E-box binding homeobox 2 FCRL6 Fc receptor like 6 TMCC3 transmembrane and coiled-coil domain family 3 LINC02384 long intergenic non-protein coding RNA 2384 IL7 interleukin 7 OSBPL5 oxysterol binding protein like 5 DTHD1 death domain containing 1 SPON2 spondin 2 CD300A CD300a molecule NCR1 natural cytotoxicity triggering receptor 1 CD244 CD244 molecule KLRC4-KLRK1 KLRC4-KLRK1 readthrough AOAH acyloxyacyl hydrolase SNORD105 small nucleolar RNA, C/D box 105 B4GALT6 beta-1,4-galactosyltransferase 6 RAP2A RAP2A, member of RAS oncogene family LAIR2 leukocyte associated immunoglobulin like receptor 2 PLOD1 procollagen-lysine,2-oxoglutarate 5-dioxygenase 1 PLEK pleckstrin PDGFD platelet derived growth factor D SH2D1B SH2 domain containing 1B NIPAL2 NIPA like domain containing 2 TMEM255A transmembrane protein 255A LEF1 lymphoid enhancer binding factor 1 SUSD4 sushi domain containing 4 TSPAN18 tetraspanin 18 IL6R interleukin 6 receptor CHRM3-AS2 CHRM3 antisense RNA 2 EPHX2 epoxide hydrolase 2 GCNT4 glucosaminyl (N-acetyl) transferase 4, core 2 LINC01215 long intergenic non-protein coding RNA 1215 TRAV8-1 T cell receptor alpha variable 8-1 RASGRF2 Ras protein specific guanine nucleotide releasing factor 2 LINC01336 long intergenic non-protein coding RNA 1336 NEFL neurofilament light LOC105377225 uncharacterized LOC105377225 MAL mal, T-cell differentiation protein SATB1-AS1 SATB1 antisense RNA 1 SGPP2 sphingosine-1-phosphate phosphatase 2 DUSP16 dual specificity phosphatase 16 GZMK granzyme K PAG1 phosphoprotein membrane anchor with glycosphingolipid microdomains 1 SUSD3 sushi domain containing 3 COTL1 coactosin like F-actin binding protein 1 CNKSR2 connector enhancer of kinase suppressor of Ras 2 RNVU1-14 RNA, variant U1 small nuclear 14 TRAV12-1 T cell receptor alpha variable 12-1 SCML1 sex comb on midleg like 1 (Drosophila) DGKA diacylglycerol kinase alpha TRABD2A TraB domain containing 2A APP amyloid beta precursor protein TMEM14C transmembrane protein 14C SIRPG signal regulatory protein gamma CAMK4 calcium/calmodulin dependent protein kinase IV PATJ PATJ, crumbs cell polarity complex component TRBV7-3 T cell receptor beta variable 7-3
		221895_at 199_at	2.20E-02 1.23E-02	1.21 2.38	JAZF1 AIF1	JAZF zinc finger 1 allograft inflammatory factor 1
		3802_at 9840_at	2.25E-02 1.23E-02	1.18 2.36	KIR2DL1 TESPA1	killer cell immunoglobulin like receptor, two Ig domains and long cytoplasmic tail 1 thymocyte expressed, positive selection associated 1
		23209_at 5217_at	2.36E-02 1.25E-02	1.05 2.33	MLC1 PFN2	megalencephalic leukoencephalopathy with subcortical cysts 1 profilin 2
		9289_at 6812_at	2.50E-02 1.27E-02	0.93 2.30	ADGRG1 STXBP1	adhesion G protein-coupled receptor G1 syntaxin binding protein 1
		387895_at 7074_at	2.53E-02 1.27E-02	0.89 2.29	LINC00944 TIAM1	long intergenic non-protein coding RNA 944 T-cell lymphoma invasion and metastasis 1
		84131_at 28594_at	2.77E-02 1.27E-02	0.77 2.27	CEP78 TRBV7-4	centrosomal protein 78 T cell receptor beta variable 7-4 (gene/pseudogene)
		3812_at 3575_at	3.20E-02 1.33E-02	0.50 2.19	KIR3DL2 IL7R	killer cell immunoglobulin like receptor, three Ig domains and long cytoplasmic tail 2 interleukin 7 receptor
		107984889_at 101927596_at	3.34E-02 1.39E-02	0.43 2.07	LOC107984889 uncharacterized LOC107984889 LOC101927596 uncharacterized LOC101927596
		51348_at 54674_at	3.66E-02 1.39E-02	0.29 2.08	KLRF1 LRRN3	killer cell lectin like receptor F1 leucine rich repeat neuronal 3
		23603_at 101927613_at	3.83E-02 1.46E-02	0.22 2.01	CORO1C LOC101927613 uncharacterized LOC101927613 coronin 1C
		90011_at 28692_at	3.98E-02 1.46E-02	0.13 2.01	KIR3DX1 TRAV1-2	killer cell immunoglobulin like receptor, three Ig domains X1 T cell receptor alpha variable 1-2
		2999_at 1233_at	4.93E-02 1.47E-02	-0.27 1.98	GZMH CCR4	granzyme H C-C motif chemokine receptor 4
		105373204_at 6932_at	2.09E-04 1.51E-02	8.95 1.94	LOC105373204 uncharacterized LOC105373204 TCF7 transcription factor 7 (T-cell specific, HMG-box)
		4050_at 23508_at	2.56E-04 1.59E-02	8.51 1.86	LTB TTC9	lymphotoxin beta tetratricopeptide repeat domain 9
		940_at 106481624_at	2.77E-04 1.64E-02	8.31 1.78	CD28 RNU1-106P RNA, U1 small nuclear 106, pseudogene CD28 molecule

Supplementary

Université Paris-Saclay

Espace Technologique / Immeuble Discovery Route de l'Orme aux Merisiers RD 128 / 91190 Saint-Aubin, France

Acknowledgements

ACKNOWLEDGMENTS

We thank the Institut Curie Cytometry Core facility for cell sorting. We thank INSERM U932, particularly C. Laurent and A.S. Hamy-Petit for bioinformatics advice, and S. Alculumbre and P. Vargas for helpful discussions. F. Noël was supported by a fellowship from the French Ministry of Research. This work was supported by funding from INSERM (BIO2012-02, BIO2014-08, HTE2016), Fondation pour la Recherche Médicale, ANR-10-IDEX-0001-02 PSL* and ANR-11-LABX-0043, European Research Council (IT-DC 281987) and CIC IGR-Curie 1428, INCA EMERG-15-ICR-1, la Ligue contre le cancer (labellisation EL2016.LNCC/VaS). High-throughput sequencing has been performed by the ICGex NGS platform of the Institut Curie supported by grants ANR-10-EQPX-03 (Equipex) and ANR-10-INBS-09-08 (France Génomique Consortium), InCA from ANR ("Investissements d'Avenir" program), by the Canceropole Ile-de-France and by the SiRIC-Curie program -SiRIC Grant "INCa-DGOS-4654".

Acknowledgements

We wish to thank Franck Perez, Sebastian Amigorena, Yong-Jun Liu and Fivos Soumelis for insightful comments and discussions, the Institut Curie Flow Cytometry facility (Z. Maciorowsky), the Institut Curie Affymetrix facility (D. Gentien). This work was supported by funding from the Institut Curie, Agence Nationale pour la Recherche (ANR), Fondation pour la Recherche Médicale (FRM), the European Research Council (ERC starting grant 281987), ANR-10-IDEX-0001-02 PSL, ANR-11-LABX-0043, CIC IGR-Curie 1428 for V.S. EMBO and Institut Curie post-doctoral fellowships to ICL. ANRS and ARC fellowships to MG. Acknowledgements: This work was supported by grants from the Ligue Nationale Contre le Cancer (Equipe Labellisée), the National Cancer Institute (INCa PL-BIO and PRT-K calls), the Molecular Medicine in Oncology program supported by the Agence Nationale de la Recherche, and the SIRIC SOCRATE program. NL was supported by a grant from the Ligue Nationale Contre le Cancer, MD by the ITMO Cancer (Plan cancer 2014-2019). Part of high-throughput sequencing was performed by the genomic platform of the Institut Curie, which is supported by grants ANR-10-EQPX-03 and ANR10-INBS-09-08 from the Agence Nationale de la Recherche (Investissements d'Avenir) and by Cancéropole Ile de France. We are grateful to Sylvain Baulande and Patricia Legoix-Ne from the genomic platform of Curie Institute and Karine Bailly from the Cochin Institute cytometry and immunobiology facility for their technical support, and to Abdelkrim Achibet from the orthopedic surgery department from the hospital of Le Mans for providing us with bone marrow controls.

merci à Maude pour sa bienveillance au quotidien, pour nos discussions scientifiques (

ACCESSION CODES

RNAseq data that support the findings of this study have been deposited in NCBI Sequence Read Archive (SRA) with the accession code PRJNA380940 (https://www.ncbi.nlm.nih.gov/bioproject/?term=380940)

Data availability

RNAseq data that support the findings of this study have been deposited in NCBI Sequence Read Archive (SRA) with the accession code PRJNA380940 (https://www.ncbi.nlm.nih.gov/bioproject/?term=380940)

List of abbreviations

Supplementary Tables Legends:

Supplementary Table 1: (A,B

Supplementary Table 2: (A)

Supplementary Table 3: (A-L)

Scores corresponding to each interaction and each target cell in the experimental condition of IL10R blocking. The name of each target cell from A to L is indicated as the first entry of each table. (M-N) Global communication scores for both outflow (M) and inflow (N) connectivity maps. The inflow connectivity maps was done by reversing the role of DC and their cellular targets.

Annexes 197

Progenitor hypersensivity to FLT3L generates plasmacytoid dendritic cell islands in chronic myelomonocytic leukemia Nolwenn Lucas, 1,2,3 Matthieu Duchmann, 1 Philippe Rameau, 4 Floriane Noël, 5 Paula Michea, 5 Véronique Saada, 3 Olivier Kosmider, 6,7 Gérard Pierron, 4 Martin E Fernadez-Zapico, 8,9 Matthew T. Howard, 8,9 Rebecca L. King, 8,9 Sandrine Niyongere, 10 M'boyba Khadija Diop, 1,4 Pierre Fenaux, 11 Raphael Itzykson, 11 Christophe Willekens, 1,3 Vincent Ribrag, 1,3 Michaela Fontenay, 6,7 Eric Padron, 10 Vassili Soumelis, 5 Nathalie Droin, 1,4 Mrinal M Patnaik, 12* Eric Solary, 1,2,3* Running head: Clonal PDC in chronic myelomonocytic leukemia

E.

D.

pDCs (% of BMNC) VAF in pDCs (%)

VAF in monocytes (%) Using transcriptomics and flow cytometry, we characterized both peripheral blood and tumorinfiltrating CD8 + ILT2 + T cells from cancer patients as late-differentiated CD27 -CD28 -CD57 + cytotoxic effectors. We observed a clear dichotomy between CD8 + ILT2 + and CD8 + PD-1 + TIL subsets. These two quantitatively matched populations barely overlapped phenotypically and were easily distinguished by their exclusive expression of sets of surface molecules that included checkpoint molecules, and activatory and inhibitory receptors. Furthermore, CD8 + ILT2 + TILs displayed a more mature phenotype and higher expression of cytotoxic molecules. In ex vivo functional experiments with both peripheral blood T cells and TILs, CD8 + ILT2 + T cells displayed significantly higher cytotoxicity and IFNγ production than their ILT2 neg (PBMC) and PD-1 + (TILs) counterparts. HLA-G expression by target cells specifically inhibited CD8 + ILT2 + T cell cytotoxicity but not that of their CD8 + ILT2 neg (PBMC) or CD8 + PD-1 + (TILs) counterparts, an effect counteracted by blocking the HLA-G:ILT2 interaction.