Genetic signature for predicting a response to immune checkpoint inhibitor therapy

Abstract

The present invention relates to a method for predicting the response to cancer immunotherapy of a cancer patient and / or prognosing the survival of a cancer patient and / or quantifying tumor-associated macrophages expressing both HAVCR2 and VSIR in a tumor sample from a cancer patient and / or quantifying tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR in a tumor sample from a cancer patient by measuring the expression of selected genes. The present invention further relates to the use of the method of the invention for predicting the response to cancer immunotherapy of a cancer patient, in a method of treating cancer.

Description

[0001] GENETIC SIGNATURE FOR PREDICTING A RESPONSE TO IMMUNE CHECKPOINT INHIBITOR THERAPY

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to methods to predict the response to immunotherapy of a cancer patient and its use in a method of treating cancer.

[0004] BACKGROUND OF THE INVENTION

[0005] Immune -checkpoint signaling, involving interactions between immune-inhibitory receptors and their cognate ligands, is used by tumors to suppress anticancer immunity. Accordingly, immune-checkpoint blockers (ICBs) targeting T cell-associated ‘classical’ immune-inhibitory receptors (like PD-1) or their ligands (like PD-L1) have revolutionized treatment of various cancer patients. Regrettably, several cancer types or patient sub-groups remain unresponsive to PD-(L)1 blockade. Non -immunogenic or low antigenic tumors with sparse T cells that enrich tumor-associated macrophages (TAM) are particularly resistant to ICBs. Accordingly, some of the most dominant immunoresistance mechanisms include defects in antigen presentation / availability, TAM-based immunosuppression, dysregulated interferon (IFN)-y signaling, and / or, enrichment of alternative immune-inhibitory receptors.

[0006] Most studies targeting immune-inhibitory receptors concentrate on T cells, due to former’s established association with tumor-reactive CD8+T cell exhaustion. While previously this association was considered to be exclusive, it has been recently repeatedly extended to various myeloid cells, including TAM. However, the immunobiology of TAM-specific immune-inhibitory receptor signaling, enriched exclusively in tumors resistant to PD-(L)1 blockade, and its therapeutic or clinical impact remains unexplored. It is not clear which classical or alternative immune-inhibitory receptors are preferentially exploited by TAM to support immune subversion. There is thus a need to understand the underlying mechanism of immune subversion by TAM in order to better predict patient’ s response to immunotherapy and identify therapeutic routes addressing the need of nonresponder to ‘classical’ ICB. Here, using reverse translational approaches, the inventors have identified a unique niche of HAVCR2(+)VSIR(+) TAM (i.e., TAM expressing HAVCR2 and VSIR) that dominated the human and mouse immuno-resistant, CD8+T cell-depleted, tumors. The inventors have found that the enrichment of HAVCR2(+)VSIR(+) TAM within the tumors is predictive of shorter patient survival and resistance to unimodal ICB targeting PD-1, PD-L1, CTLA-4, HAVcr-2 and / or VISTA. Moreover the inventors have studied the impact of the HAVCR2(+)VSIR(+) TAM on patient’s response to therapy combining an immunogenic cell death (ICD) inducer with ICB (multimodal ICD therapy) and found that the enrichment of HAVCR2(+)VSIR(+) TAM is indicative of susceptibility to anti- HAVcr-2 and / or anti- VISTA multimodal ICD therapy. To leverage these mechanistic insights, a method to identify patient bearing tumors enriched in HAVCR2(+)VSIR(+) TAM has been developed by identifying a genetic signature associated with the HAVCR2(+)VSIR(+) TAM niche. This signature comprises genes, the expression of which is positively associated with the presence of HAVCR2(+)VSIR(+) TAM and may be used on easily accessible bulk protein and / or RNA extracts.

[0007] SUMMARY

[0008] The present invention relates to a method for predicting the response to cancer immunotherapy of a cancer patient and / or prognosing the survival of a cancer patient and / or quantifying tumor-associated macrophages expressing both HAVCR2 and VSIR in a tumor sample from a cancer patient and / or quantifying tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR in a tumor sample from a cancer patient, comprising the steps of: a. measuring in a tumor sample from said patient the expression of at least 10, preferably at least 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,

[0009] 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,

[0010] 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,

[0011] 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,

[0012] 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, 0AZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29. b. comparing the measured gene expression to a reference gene expression level corresponding to a cancer immunotherapy response group and / or to patient survival prognosis group and / or to an amount, or proportion, of HAVCR2 and VSIR-expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR- expressing tumor-associated macrophages group; and, c. assigning on the basis of said comparison said patient to a cancer immunotherapy response group and / or to a patient survival prognosis group and / or to an amount, or proportion, of HAVCR2 and VSIR- expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR-expressing tumor-associated macrophages group, thereby predicting the response to cancer immunotherapy of said cancer patient and / or prognosing the survival of said cancer patient and / or quantifying tumor-associated macrophages expressing both HAVCR2 and VSIR in a tumor sample from said cancer patient and / or quantifying tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR in a tumor sample from said cancer patient.

[0013] In one embodiment, said method is for predicting the response to cancer immunotherapy of a cancer patient and comprises the steps of: a. measuring in a tumor sample from said patient the expression of at least 10, preferably at least 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29. b. comparing the measured gene expression to a reference gene expression level corresponding to a cancer immunotherapy response group; and, c. assigning on the basis of said comparison said patient to a cancer immunotherapy response group, thereby predicting the response to cancer immunotherapy of said cancer patient.

[0014] In one embodiment, said method is for quantifying tumor-associated macrophages expressing both HAVCR2 and VSIR in a tumor sample from a cancer patient and / or quantifying tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR in a tumor sample from a cancer patient, comprising the steps of: a. measuring in a tumor sample from said patient the expression of at least 10, preferably at least 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29. b. comparing the measured gene expression to a reference gene expression level corresponding to an amount, or proportion, of HAVCR2 and VSIR- expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR-expressing tumor-associated macrophages group; and, c. assigning on the basis of said comparison said patient to an amount, or proportion, of HAVCR2 and VSIR-expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR-expressing tumor-associated macrophages group, thereby quantifying tumor-associated macrophages expressing both HAVCR2 and VSIR in a tumor sample from said cancer patient and / or quantifying tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR in a tumor sample from said cancer patient.

[0015] In one embodiment, the expression of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA- DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIO, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29 increases with the amount, or proportion, of tumor-associated macrophages expressing both HAVCR2 and VSIR and with the amount, or proportion of, tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR, present within the tumor sample.

[0016] In one embodiment, said measuring step (a) comprises measuring in a tumor sample from said patient the expression of at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA- DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIO, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

[0017] In one embodiment, said measuring step (a) comprises measuring in a tumor sample from said patient the expression of at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA- DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S1OOA11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

[0018] In one embodiment, said measuring step (a) comprises measuring in a tumor sample from said patient the expression of VSIR, HAVCR2 and CSF1R.

[0019] In one embodiment, said measuring step (a) comprises measuring in a tumor sample from said patient the expression of VSIR, HAVCR2, CSF1R, HLA-DMA, ARHGDIB, ITGB2, AIF1, CAPZB, SYNGR2, and BST2

[0020] In one embodiment, said measuring step (a) comprises measuring in a tumor sample from said patient the expression of the following genes: VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

[0021] In one embodiment, said cancer immunotherapy comprises administration of a blocking agent targeting the human protein, Programmed cell death protein 1 (PD-1), Programmed cell death 1 ligand 1 (PD-L1), Cytotoxic T-lymphocyte protein 4 (CTLA-4), Hepatitis A virus cellular receptor 2 (HAVcr-2) and / or V-type immunoglobulin domain-containing suppressor of T-cell activation (VISTA). In one embodiment, said cancer immunotherapy comprises administration of a blocking agent targeting the human protein PD-1, PD-L1, CTLA-4, HAVcr-2 and / or VISTA in combination with an immunogenic cell death (ICD)-inducing therapy.

[0022] In one embodiment, said cancer immunotherapy comprises administration of a blocking agent targeting the human proteins HAVcr-2 and / or VISTA in combination with ICD- inducing therapy.

[0023] In one embodiment, said immunogenic cell death (ICD)-inducing therapy comprises administration of paclitaxel, docetaxel, Epothilones A to F, anthracy clines, in particular doxorubicin, epirubicin, idarubicin and mitoxantrone, cyclophosphamide, oxaliplatin, bortezomib, carfilzomib, PARP inhibitors, therapeutic oncolytic virus, LTX-315, LTX- 401, crizotinib, cetuximab, dinaciclib, ibrutinib, spautin-1, bleomycin, LB-100, shikonin, and / or capsaicin; and / or treatment with ionizing radiation, extracorporeal photochemotherapy, hypericin-based photodynamic therapy and / or near-infrared photoimmunotherapy .

[0024] In one embodiment, said measuring is done by measuring RNA and / or protein level in bulk RNA and / or bulk protein extract(s).

[0025] In one embodiment, said cancer is selected form the group consisting of lung cancer, colorectal cancer, skin cancer, uterine cancer, kidney cancer, liver cancer, lymphoma, head and neck cancer, stomach cancer, breast cancer, pancreas cancer, bladder cancer, brain cancer, adrenal glands cancer, cervical cancer, bile duct cancer, esophageal cancer, mesothelioma, ovarian cancer, extra-adrenal paragangliomas, prostate cancer, sarcoma, testicular cancer, thyroid cancer and eye cancer.

[0026] In one embodiment, said cancer is selected form the group consisting of lung cancer, colorectal cancer, skin cancer, uterine cancer, kidney cancer, liver cancer, lymphoma, head and neck cancer, stomach cancer, breast cancer and pancreas cancer.

[0027] DETAILED DESCRIPTION

[0028] When describing the invention, the terms used are to be construed in accordance with the following definitions, unless a context dictates otherwise. As used herein, the singular forms "a", "an", and "the" include both singular and plural referents unless the context clearly dictates otherwise. By way of example, "a compound" means one compound or more than one compound.

[0029] “bulk RNA and / or bulk protein extract” is used herein in reference to RNA or protein extract on pooled cell populations (e.g. tissue sample) and is thus defined in contrast with transcriptome or proteome analyses done on single cells.

[0030] “Cancer immunotherapy”, “checkpoint inhibitor therapy”, “immune checkpoint inhibitor therapy”, “immune checkpoint blockade (ICB)”, “ICB” or “immune checkpoint blockade therapy” are used herein interchangeably in reference to therapy comprising the administration of a blocking agent (or combination thereof), generally an antibody, that blocks, or inhibits, an inhibitory immune checkpoint protein to avoid dampening of the immune response, classically T-cell -mediated response, against cancer cells. The terms also include administration of such a blocking agent, in combination with another therapeutic agent. Example of inhibitory immune checkpoint protein relevant in the context of the invention includes, without being limited to, the following human proteins: Programmed cell death protein 1 (PD-1, UniProtKB reference Q15116), Programmed cell death 1 ligand 1 (PD-L1, UniProtKB reference Q9NZQ7), Cytotoxic T-lymphocyte protein 4 (CTLA-4, UniProtKB reference P16410), Hepatitis A virus cellular receptor 2 (HAVcr-2, UniProtKB reference Q8TDQ0, alternatively known as T-cell immunoglobulin mucin receptor 3 or TIM-3) and V-type immunoglobulin domaincontaining suppressor of T-cell activation (VISTA, UniProtKB reference Q9H7M9, encoded by the VSIR gene). Cancer immunotherapy may be referred to by the combination of the term “anti” in front of the name of the inhibitory immune checkpoint protein. For example, anti-PD-1 cancer immunotherapy designate the administration of a PD-1 blocking agent, such as a PD-1 blocking antibody.

[0031] Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure, in one or more embodiments.

[0032] The terms “expression” or “expressing” when used herein in reference to a gene refers to transcription and / or translation of said gene. The measure of the expression of a gene hence refers to the measure of the quantity of (m)RNA and / or protein produced by said gene.

[0033] The terms “Immunogenic Cell Death-inducing therapy” or “ICD-inducing therapy” are used interchangeably in reference a therapy that induces ICD, a form of regulated cell death that is sufficient to activate an adaptive immune response in immunocompetent syngeneic hosts. Methods to identify ICD and thus ICD-inducing therapy are well-known in the art, for example by following the “Consensus guidelines for the definition, detection and interpretation of immunogenic cell death” published by Galluzzi et al. (J Immunother Cancer. 2020; 8(1): e000337). ICD is notably characterized by secretion and / or exposure on the dying cell surface of damage-associated molecular patterns (DAMP) that are necessary for the recruitment and maturation of antigen-presenting cells, including, without being limited to, ANXA1, CALR, adenosine triphosphate, CCL2, CXCL1, CXCL10, cytosolic RNA, cytosolic DNA, ERp57, Extracellular DNA, F-actin, HMGB1, HSP70, HSP90, TFAM and Type I IFN. It is thus within the reach of the skilled artisan to identify therapies inducing ICD. Example of such therapies include, without being limited to therapy comprising (i) administration of chemotherapeutics such as paclitaxel (CAS number 33069-62-4), docetaxel (CAS number 114977-28-5), Epothilones A to F (CAS numbers 152044-53-6, 152044-54-7, 186692-73-9, 189453-10-9, 201049-37-8 and 208518-52-9) anthracyclines, in particular doxorubicin (CAS numbers 23214-92-8 and 25316-40-9), epirubicin (CAS number 56420-45-2), idarubicin (CAS number 58957-92- 9) and mitoxantrone (CAS number 65271-80-9), cyclophosphamide (CAS number 50- 18-0), oxaliplatin (CAS number 61825-94-3), bortezomib (CAS number 179324-69-7) and carfilzomib (CAS number 868540-17-4) and PARP inhibitors; (ii) administration of therapeutic oncolytic viruses; (iii) administration of oncolytic compounds, in particular LTX-315 (Haug et al., Journal of Medicinal Chemistry (2016), 59(7), 2918-2927) and LTX-401 (Ausbacher et al., Biochim Biophys Acta 2012; 1818: 2917-2925); (iv) administration of targeted anticancer therapy agents such as crizotinib (CAS number 877399-52-5), cetuximab (CAS number 205923-56-4), dinaciclib (CAS number 779353- 01-4) and ibrutinib (CAS number 936563-96-1) (v) administration of spautin-1 (CAS number 1262888-28-7) , bleomycin (CAS numbers 11056-06-7 and 9041-93-4), LB-100 (CAS number 1632032-53-1), shikonin (CAS number 517-89-5) and capsaicin (CAS number 404-86-4); and other therapies such as ionizing radiation, extracorporeal photochemotherapy, hypericin-based photodynamic therapy (PDT) and near-infrared photoimmunotherapy .

[0034] A “therapeutically effective amount” means herein an amount that is sufficient to achieve the effect for which it is indicated, herein the treatment of cancer. The amount of the therapeutic agent(s) to be administered can be determined by standard procedures well known by those of ordinary skill in the art. Physiological data of the patient (e.g. age, size, and weight), the routes of administration and the disease to be treated have to be taken into account to determine the appropriate dosage. The amount may also vary according to other aspect of a treatment protocol (e.g. administration of other medicaments and the like).

[0035] The present invention relates to a method for (i) predicting the response to cancer immunotherapy of a cancer patient and / or (ii) prognosing the survival of a cancer patient and / or (iii) quantifying tumor-associated macrophages expressing both HAVCR2 and VSIR in a tumor sample from a cancer patient and / or (iv) quantifying tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR in a tumor sample from a cancer patient.

[0036] In one embodiment, the cancer immunotherapy comprises administration of a blocking agent, preferably of a blocking antibody, targeting an inhibitory immune checkpoint protein. In one embodiment, the cancer immunotherapy comprises administration of a blocking agent, preferably of a blocking antibody, targeting the human protein, Programmed cell death protein 1 (PD-1), Programmed cell death 1 ligand 1 (PD-L1), Cytotoxic T-lymphocyte protein 4 (CTLA-4), Hepatitis A virus cellular receptor 2 (HAVcr-2) and / or V-type immunoglobulin domain-containing suppressor of T-cell activation (VISTA).

[0037] In one embodiment, the cancer immunotherapy comprises administration of a blocking agent, preferably of a blocking antibody, targeting the human protein, PD-1, PD-L1, CTLA-4, HAVcr-2 and / or VISTA in combination with an Immunogenic Cell Death (ICD) -inducing therapy. In one embodiment, the cancer immunotherapy comprises administration of a blocking agent, preferably of a blocking antibody, targeting the human protein, PD-1, PD-L1, HAVcr-2 and / or VISTA in combination with an ICD)-inducing therapy. In one embodiment, the cancer immunotherapy comprises administration of a blocking agent, preferably of a blocking antibody, targeting the human protein, HAVcr- 2 and / or VISTA in combination with an ICD -inducing therapy.

[0038] In one embodiment, the immunogenic cell death (ICD)-inducing therapy comprises administration of paclitaxel, docetaxel, epothilones A to F, anthracy clines, in particular doxorubicin, epirubicin, idarubicin and mitoxantrone, cyclophosphamide, oxaliplatin, bortezomib, carfilzomib, PARP inhibitors, therapeutic oncolytic viruses, LTX-315, LTX- 401, crizotinib, cetuximab, dinaciclib, ibrutinib, spautin-1, bleomycin, LB-100, shikonin, and / or capsaicin; and / or treatment with ionizing radiation, extracorporeal photochemotherapy, hypericin-based photodynamic therapy and / or near-infrared photoimmunotherapy. In one embodiment, the immunogenic cell death (ICD)-inducing therapy comprises administration of paclitaxel, docetaxel, epothilones A to F, anthracyclines, in particular doxorubicin, epirubicin, idarubicin and mitoxantrone, cyclophosphamide, oxaliplatin, bortezomib, carfilzomib, crizotinib, cetuximab, dinaciclib, ibrutinib, spautin-1, bleomycin, LB-100, shikonin, and / or capsaicin; and / or treatment with ionizing radiation, extracorporeal photochemotherapy, hypericin-based photodynamic therapy and / or near-infrared photoimmunotherapy. In one embodiment, the immunogenic cell death (ICD) -inducing therapy comprises administration of paclitaxel.

[0039] In one embodiment, the method of the invention comprise a step of measuring in a tumor sample from said patient the expression of at least 1, 2, 3, 4, 5, 6, 7, 8 or 9, preferably at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 or 29, more preferably at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or 49, even more preferably at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA- DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

[0040] VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA- DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29 correspond herein to the human genes’ HGNC symbols as defined in the HGNC Database, HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom www.genenames.org. Data retrieved July, 2023. The corresponding HGNC ID as well as alternative names used in the literature are indicated in Table 1. In one embodiment, the method of the invention comprise a step of measuring in a tumor sample from said patient the expression of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA- DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

[0041] In one embodiment, the measuring step of the invention comprises measuring the expression of VSIR, HAVCR2, CD68, FCGRT, C1QC, C1QB, C1QA, CSF1R and FCER1G .In one embodiment, the method of the invention comprise a step of measuring in a tumor sample from said patient the expression of VSIR, HAVCR2, CD68, FCGRT, C1QC, C1QB, C1QA, CSF1R and FCER1G and optionally, of at least 1, 2, 3, 4, 5, 6, 7, 8 or 9, preferably at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 or 29, more preferably at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or 49, even more preferably at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76 or 77 genes selected from the group consisting of TYROBP, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, HLA- DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, ITM2B, PFN1, HLA-B, HLA- DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, PPT1, CTSH, CALM2, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

[0042] In one embodiment, the measuring step of the invention comprises measuring the expression of VSIR, HAVCR2 and CSF1R. In one embodiment, the method of the invention comprise a step of measuring in a tumor sample from said patient the expression of VSIR, HAVCR2 and CSF1R and optionally, of at least 1, 2, 3, 4, 5, 6, 7, 8 or 9, preferably at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 or 29, more preferably at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or 49, even more preferably at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82 or 83 genes selected from the group consisting of TYROBP, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA- DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIO, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

[0043] In one embodiment, the measuring step of the invention comprises measuring the expression of VSIR, HAVCR2, CSF1R, HLA-DMA, ARHGDIB, ITGB2, AIF1, CAPZB, SYNGR2, and BST2. In one embodiment, the method of the invention comprise a step of measuring in a tumor sample from said patient the expression of VSIR, HAVCR2, CSF1R, HLA-DMA, ARHGDIB, ITGB2, AIF1, CAPZB, SYNGR2, and BST2, and, optionally, of at least 1, 2, 3, 4, 5, 6, 7, 8 or 9, preferably at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 or 29, more preferably at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or 49, even more preferably at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, or 76 genes selected from the group consisting of TYROBP, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA- A, HLA-DMB, HLA-DPA1, GPX1, CD68, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, CYBA, TMSB10, CTSB, HLA-DQB1, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, C1QA, PPT1, CTSH, CALM2, YWHAH, RPL28, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, RPS3, NACA, PRDX1, LAIR1, PPIA and ERP29.

[0044] In one embodiment, the measuring step of the invention comprises measuring the expression of EEF1A1, RPSA, GPX1, ITM2B, ACTG1, RPL13A, ARPC3, ACTB, PPIA, PTMA, TMSB4X and CTSC. In one embodiment, the method of the invention comprise a step of measuring in a tumor sample from said patient the expression of EEF1A1, RPSA, GPX1, ITM2B, ACTG1, RPL13A, ARPC3, ACTB, PPIA, PTMA, TMSB4X and CTSC, and optionally, of at least 1, 2, 3, 4, 5, 6, 7, 8 or 9, preferably at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 or 29, more preferably at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or 49, even more preferably at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, or 74 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, CD68, HLA-DMA, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, CAPG, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, TYMP, CLIC1, ATP6V0E1, CTSD, HEXB, SERF2, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, and ERP29.

[0045] The skilled artisan is familiar with techniques allowing the measure of gene expression. The step of measuring of gene expression in the tumor sample from the cancer patient may be done by measuring RNA and / or protein level. Techniques to measure RNA level include, without being limited to quantitative RT-PCR, RNA sequencing, DNA microarrays northern blot, in-situ hybridization and the like. Techniques to measure protein level include, without being limited to, immunoassays such as ELISA, western blot and immunohistochemistry, mass spectrometry-based assays and the like. The inventors have found that the method of the invention can, despite relating to the detection of a distinct tumor-associated macrophages population within the tumor sample, be applied on pooled tumor cell population. This is advantageous in particular to simplify sample preparation.

[0046] In one embodiment, the step of measuring of gene expression in the tumor sample from the cancer patient is done by measuring RNA and / or protein level in bulk RNA and / or bulk protein extract(s).

[0047] The method of the invention comprises a step of comparing the measured gene expression (in the tumor sample from the cancer patient) to a reference gene expression level.

[0048] As used herein, the term “reference gene expression level” refers to a measure of gene expression for a given control subjects or for a given population of control subject, of know outcome in respect to response to cancer immunotherapy and / or survival prognosis and / or of know amount, or proportion, of tumor-associated macrophages expressing both HAVCR2 and VSIR and / or of know amount, or proportion, of tumor-associated macrophages expressing both CSF1R, HAVCR2 and VSIR. It is within the reach of the skilled artisan to select for the reference gene expression level the most appropriate data set representation, such as for example and without limitation, raw values, means, medians, or any form of mathematical or graphical representation. It is to be understood in the context of the comparison step of the invention, that the reference gene expression level comprises information on the level of expression of the same gene that are considered in the measuring step. Therefore, embodiments relating to selection of genes considered in the measuring step may apply to the determination of the reference gene expression level.

[0049] In one embodiment, the reference gene expression level corresponds to a cancer immunotherapy response group and / or to patient survival prognosis group and / or to an amount, or proportion, of HAVCR2 and VSIR-expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR-expressing tumor-associated macrophages group. Example of response to cancer immunotherapy include, without being limited to, clinical responses such as duration of survival, time to disease progression, response rate (e.g. complete or partial response), length of progression-free survival, treatment of the cancer, inhibition of tumor growth, occurrence of relapse or metastasis.

[0050] The inventors have found that (i) expression of the genes in Table 1 is higher in patients that do not respond to cancer immunotherapy, in particular to anti-PD-1, anti-PD-Ll, anti- CTLA-4, anti-HAVcr-2 and / or anti-VISTA therapy when said cancer immunotherapy is not combined with an Immunogenic Cell Death-inducing therapy than in responders to said therapy; (ii) expression of the genes in Table 1 is higher in patients that do respond to anti-VISTA and / or anti-HAVcr-2 therapy combined with an Immunogenic Cell Deathinducing therapy than in non-responder to said therapy (iii) expression of the genes in Table 1 increases with the amount of HAVCR2(+)VSIR(+) tumor associated macrophages present within the tumor sample; (iv) expression of the genes in Table 1 increases with the amount of HAVCR2(+)VSIR(+)CSFR1(+) tumor associated macrophages (i.e., TAM expressing HAVCR2, VSIR and CSFR1) present within the tumor sample and (v) expression of the genes in Table 1 anti correlates with patient survival prognosis.

[0051] Based on the above, it is within the reach of the skilled artisan to select the control subject(s) to establish a reference gene expression level corresponding to a cancer immunotherapy response group and / or to patient survival prognosis group and / or to an amount, or proportion, of HAVCR2 and VSIR-expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR-expressing tumor-associated macrophages group, notably accounting for parameters such as for instance, and without limitation, the targeted sensitivity or specificity or the number of groups to be defined. It is likewise within the reach of the skilled artisan having performed such a selection, to interpret the comparison of a measured gene expression levels in a cancer patient with the reference expression level to assign, the cancer patient to an a cancer immunotherapy response group and / or to patient survival prognosis group and / or to an amount, or proportion, of HAVCR2 and VSIR-expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR- expressing tumor-associated macrophages group. In one embodiment, the cancer immunotherapy response group corresponds to responders to said cancer immunotherapy. It is to be understood in the context of the invention that a responder to cancer immunotherapy refers to a cancer patient showing a positive outcome (e.g., longer survival, treatment, remission or slowing-down of the progression, of the cancer) following said cancer immunotherapy. In one embodiment, the cancer immunotherapy response group corresponds to responders to a cancer immunotherapy comprising administration of a blocking agent, preferably a blocking antibody, targeting the human proteins HAVcr-2 and / or VISTA in combination with a immunogenic cell death inducer.

[0052] The method of the invention comprises a step of assigning on the basis of the comparison between the measured gene expression level and the reference gene expression level, the cancer patient to a cancer immunotherapy response group and / or to patient survival prognosis group and / or to an amount, or proportion, of HAVCR2 and VSIR-expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR-expressing tumor-associated macrophages group. This step thereby allows i) predicting the response to cancer immunotherapy of a cancer patient and / or (ii) prognosing the survival of a cancer patient and / or (iii) quantifying tumor-associated macrophages expressing both HAVCR2 and VSIR in a tumor sample from a cancer patient and / or (iv) quantifying tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR in a tumor sample from a cancer patient.

[0053] In the context of the invention, the term “patient(s)” refers to human subject(s). The term “cancer patient(s)” refers to patient(s) affected with cancer.

[0054] In one embodiment, the cancer is selected from the group consisting of lung cancer (in particular, lung adenocarcinoma , lung squamous cell carcinoma and non-small cell lung cancer), colorectal cancer (in particular colon adenocarcinoma, rectum adenocarcinoma and colorectal adenocarcinoma), skin cancer (in particular skin subcutaneous melanoma and basal cell carcinoma), uterine cancer (in particular, uterine corpus endometrial carcinoma, uterine carcinosarcoma and endometrial adenocarcinoma), kidney cancer (in particular kidney renal papillary cell carcinoma and renal clear cell carcinoma), liver cancer (in particular, hepatocellular carcinoma), lymphoma, head and neck cancer (in particular, head and neck squamous cell carcinoma and nasopharyngeal cancer), stomach cancer (in particular stomach adenocarcinoma), breast cancer (in particular breast invasive carcinoma), pancreas cancer (in particular pancreatic adenocarcinoma), bladder cancer (in particular, bladder urothelial carcinoma), brain cancer (in particular, glioblastoma, glioblastoma multiforme and brain lower grade glioma), adrenal glands cancer (in particular, adrenocortical carcinoma, pheochromocytoma, adrenal paraganglioma), cervical cancer (in particular, cervical squamous cell carcinoma and endocervical adenocarcinoma), bile duct cancer (in particular cholangiocarcinoma), esophageal cancer (in particular esophageal carcinoma), mesothelioma, ovarian cancer (in particular ovarian serous cystadenocarcinoma), extra-adrenal paraganglioma, prostate cancer (in particular prostate adenocarcinoma), sarcoma, testicular cancer (in particular testicular germ cell tumors), thyroid cancer and eye cancer (in particular uveal melanoma).

[0055] In one embodiment, the cancer is selected from the group consisting of lung cancer (in particular, lung adenocarcinoma and non-small cell lung cancer), colorectal cancer (in particular colorectal adenocarcinoma), skin cancer (in particular, skin subcutaneous melanoma and basal cell carcinoma), uterine cancer (in particular endometrial adenocarcinoma), kidney cancer (in particular renal clear cell carcinoma), liver cancer (in particular hepatocellular carcinoma), lymphoma, head and neck cancer (in particular nasopharyngeal cancer), stomach cancer (in particular stomach adenocarcinoma), breast cancer (in particular breast invasive carcinoma), pancreas cancer (pancreatic adenocarcinoma), bladder cancer (bladder urothelial carcinoma) and brain cancer (in particular glioblastoma.

[0056] In one embodiment, the cancer is selected from the group consisting of lung cancer (in particular, lung adenocarcinoma and non-small cell lung cancer), colorectal cancer (in particular colorectal adenocarcinoma), skin cancer (in particular, skin subcutaneous melanoma and basal cell carcinoma), uterine cancer (in particular endometrial adenocarcinoma), kidney cancer (in particular renal clear cell carcinoma), liver cancer (in particular hepatocellular carcinoma), lymphoma, head and neck cancer (in particular nasopharyngeal cancer), stomach cancer (in particular stomach adenocarcinoma), breast cancer (in particular breast invasive carcinoma) and pancreas cancer (pancreatic adenocarcinoma).

[0057] In one embodiment, the cancer is selected from the group consisting of colorectal cancer, skin cancer (in particular skin subcutaneous melanoma), uterine cancer (in particular endometrial adenocarcinoma), kidney cancer (in particular renal clear cell carcinoma), stomach cancer (in particular stomach adenocarcinoma) breast cancer (in particular breast invasive carcinoma) and pancreas cancer (pancreatic adenocarcinoma).

[0058] In one embodiment, the cancer is lung cancer. In one embodiment, the cancer is lung adenocarcinoma or non-small cell lung cancer.

[0059] In the context of the invention, the sample is a tumor sample. In one embodiment, the sample is a cancerous tumor sample. Such a sample can be a solid sample (e.g., solid biopsy, or part of a surgically excised or removed tumor) or a fluid sample (e.g. a liquid biopsy, such as non-invasive liquid biopsy or non-invasive sample). In particular, the (cancerous) tumor sample can be a sample comprising cell-free genetic material shed from the tumor, and / or can be a sample comprising circulating tumor cells

[0060] In one embodiment, the method of the invention comprises a step of providing a (tumor or cancerous tumor) sample from the cancer patient.

[0061] In one embodiment, the sample was previously taken from the cancer patient. In one embodiment, the method of the invention does not comprise a step of collecting the sample from the subject (In other words, the method of the invention is an in vitro method).

[0062] In one embodiment, the method of the invention is for predicting the response to cancer immunotherapy of a cancer patient and comprises the steps of: a. measuring in a tumor sample from said patient the expression of at least at least 1, 2, 3, 4, 5, 6, 7, 8 or 9, preferably at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 or 29, more preferably at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or 49, even more preferably at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29. b. comparing the measured gene expression to a reference gene expression level corresponding to a cancer immunotherapy response group; and, c. assigning on the basis of said comparison said patient to a cancer immunotherapy response group, thereby predicting the response to cancer immunotherapy of a cancer patient.

[0063] In one embodiment, the method of the invention is for prognosing the survival of a cancer patient and comprises the steps of: a. measuring in a tumor sample from said patient the expression of at least at least 1, 2, 3, 4, 5, 6, 7, 8 or 9, preferably at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 or 29, more preferably at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or 49, even more preferably at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29. b. comparing the measured gene expression to a reference gene expression level corresponding to a patient survival prognosis group; and, c. assigning on the basis of said comparison said patient to a patient survival prognosis group, thereby prognosing the survival of a cancer patient.

[0064] In one embodiment, the method of the invention is for prognosing the survival of a cancer patient and comprises the steps of: a. measuring in a tumor sample from said patient the expression of at least at least 1, 2, 3, 4, 5, 6, 7, 8 or 9, preferably at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 or 29, more preferably at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or 49, even more preferably at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29. b. comparing the measured gene expression to a reference gene expression level corresponding to a patient survival prognosis group; and, c. assigning on the basis of said comparison said patient to a patient survival prognosis group, thereby prognosing the survival of a cancer patient.

[0065] The present invention further relates to a method of treating cancer in a cancer patient comprising the steps of: a. predicting the response to cancer immunotherapy of said patient using the method of the invention for predicting the response to cancer immunotherapy; and, b. administering (a therapeutically effective amount of) said cancer immunotherapy to said patient, preferably administering said cancer immunotherapy to said patient identified as a responder to said cancer immunotherapy at step (a).

[0066] In one embodiment, the method of treatment of the invention comprises the steps of: a. predicting the response to cancer immunotherapy of said patient using the method of the invention for predicting the response to cancer immunotherapy; b. administering (a therapeutically effective amount of) said cancer immunotherapy to said patient, preferably administering (a therapeutically effective amount of) said cancer immunotherapy to said patient identified a responder to said cancer immunotherapy at step (a), wherein said cancer immunotherapy comprises administration of (a therapeutically effective amount of) a blocking agent, preferably of a blocking antibody, targeting the human protein, HAVcr-2 and / or VISTA in combination with an Immunogenic Cell Death (ICD) -inducing therapy.

[0067] The present invention further also relates to a method of treating cancer in a cancer patient comprising the steps of: a. quantifying using the method of the invention tumor-associated macrophages expressing both HAVCR2 and VSIR, and / or tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR, in a tumor sample from said cancer patient; and, b. administering (a therapeutically effective amount of) ICB therapy to said patient, preferably administering ICB therapy to said patient when macrophages expressing both HAVCR2 and VSIR and / or macrophages expressing CSF1R, HAVCR2 and VSIR are detected in said tumor sample.

[0068] In one embodiment, the method of treatment of the invention comprises the steps of: a. quantifying using the method of the invention tumor-associated macrophages expressing both HAVCR2 and VSIR, and / or tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR, in a tumor sample from said cancer patient; and, b. administering (a therapeutically effective amount of) ICB therapy to said patient, preferably administering ICB therapy to said patient when macrophages expressing both HAVCR2 and VSIR and / or macrophages expressing CSF1R, HAVCR2 and VSIR are detected in said tumor sample, wherein said ICB therapy comprises administration of a (therapeutically effective amount of) blocking agent, preferably of a blocking antibody, targeting the human protein, HAVcr-2 and / or VISTA, preferably in combination with an Immunogenic Cell Death (ICD) -inducing therapy.

[0069] BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a radar plot showing the spearman correlation of immune-deconvoluted (Quantiseq) macrophage-fractions with the scRNAseq-validated HAVCR2(+)VSIR(+)TAM signature in TCGA datasets consisting of six pan-cancer immune-landscape classes (Cl wound healing, n=2067; C2 IFNy-dominant, n=2424, C3 inflammatory, n=2349; C4 lymphocyte depleted, n=1142, C5 immunologically quiet, n=387; C6 TGFp response dominant, n=180).

[0070] Figure 2 is Kaplan-Meier curve of cancer patients spanning 5 cancer-types (skin cutaneous melanoma, bladder cancer, kidney renal clear cell cancer, glioblastoma, stomach adenocarcinoma) (n=779), where tumors were transcriptome profiled before anti-PD(L)l and / or anti-CTLA4 antibody treatments. Kaplan-Meier curve shows overall survival of HAVCR2(+)VSIR(+) TAM signature™™ (n=585, gray line) vs. HAVCR2(+)VSIR(+) TAM signatureLOWpatients (n=194, black line) (statistical auto cut-off for expression, log-rank test). Hazard Ratio=1.27, p-value=0.03.

[0071] Figure 3 is a Violin plot of the flow cytometry analysis of CD45+cell fraction obtained from subcutaneous wild- type LLC and MC38 tumors on day 24 after tumor cell injection showing the percentage of TIM3+VISTA+tumor-associated macrophages / TAMs (CDl lb+F4 / 80+) (n=5; Mann-Whitney test).

[0072] Figure 4 Tumor volume curve of wild-type LLC tumor-bearing mice treated with 250pg anti-PDl antibody (a-PDl), 250pg anti-PDLl antibody (a-PDLl), 250pg anti-CTLA4 antibody (a-CTLA4) or PBS on day 11, 14 and 18 after tumor injection. (n=4).

[0073] Figure 5 Tumor volume curve of wild-type MC38 tumor-bearing mice treated with 250pg anti-PDl antibody (a-PDl), 250pg anti-PDLl antibody (a-PDLl), 250pg anti- CTLA4 antibody (a-CTLA4) or PBS on day 11, 14 and 18 after tumor injection. (n=4; area under curve from day 17; Mann-Whitney test, p-values indicate comparison to PBS).

[0074] Figure 6 Tumor volume curve of wild-type LLC tumor-bearing mice treated with anti 250pg -TIM3 antibody (aTIM3), 250pg anti-VISTA antibody (a- VISTA) or PBS on day 11, 14 and 18 after tumor injection. (n=4). Figure 7 Tumor volume curve of wild-type MC38 tumor-bearing mice treated with anti 250pg -TIM3 antibody (aTIM3), 250pg anti-VISTA antibody (a- VISTA) or PBS on day

[0075] 11, 14 and 18 after tumor injection. (n=4).

[0076] Figure 8 Tumor volume curve of wild-type LLC tumor-bearing mice treated with 250|ig anti-PDl antibody (a-PDl) and / or combination with 250pg anti-TIM3 antibody (a- TIM3) or 250pg anti-VISTA antibody (a- VISTA) or PBS on day 11, 14 and 18 after tumor injection. (n=4).

[0077] Figure 9 Tumor volume curve of wild-type MC38 tumor-bearing mice treated with 250pg anti-PDl antibody (a-PDl) and / or combination with 250pg anti-TIM3 antibody (a-TIM3) or 250pg anti-VISTA antibody (a- VISTA) or PBS on day 11, 14 and 18 after tumor injection. (n=4; area under curve from dayl7; Mann-Whitney test, p-values indicate comparison to PBS).

[0078] Figure 10 Tumor volume curve of wild-type LLC tumor-bearing mice treated with 250pg anti-TIM3 antibody (a-TIM3) or 250pg anti-VISTA antibody (a- VISTA) on day 11, 14 and 18 after tumor injection in combination with 8mg / kg cisplatin (CDDP) or PBS on day 10, 13 and 17 after tumor injection. (n=5; area under curve from day 17; Mann- Whitney test, p-values indicate comparison to PBS).

[0079] Figure 11 Tumor volume curve of wild-type MC38 tumor-bearing mice treated with 250pg anti-TIM3 antibody (a-TIM3) or 250pg anti-VISTA antibody (a- VISTA) on day 11, 14 and 18 after tumor injection in combination with 8mg / kg cisplatin (CDDP) or PBS on day 10, 13 and 17 after tumor injection. (n=5; area under curve from dayl7; Mann- Whitney test, p-values indicate comparison to PBS).

[0080] Figure 12 Tumor volume curve of wild-type LLC tumor-bearing mice treated with 250pg anti-TIM3 antibody (a-TIM3) or 250pg anti-VISTA antibody (a- VISTA) on day 11, 14 and 18 after tumor injection in combination with 8mg / kg paclitaxel (PTX) or PBS on day 10, 13 and 17 after tumor injection. (n=4; area under curve from dayl7; Mann-Whitney test, p-values indicate comparison to PBS). Figure 13 Tumor volume curve of wild-type MC38 tumor-bearing mice treated with 250pg anti-TIM3 antibody (a-TIM3) or 250pg anti- VISTA antibody (a- VISTA) on day 11, 14 and 18 after tumor injection in combination with 8mg / kg paclitaxel (PTX) or PBS on day 10, 13 and 17 after tumor injection. (n=4).

[0081] Figure 14 Tumor volume curve of wild-type LLC tumor bearing mice treated with 8 mg / kg paclitaxel (PTX) or PBS on day 10, 13 and 17 after tumor injection and / or combination with 250 pg anti-TIM3 antibody (a-TIM3) and / or 250 pg anti- VISTA antibody (a- VISTA) on day 11, 14 and 18 after tumor injection (n=4; area under curve from day 17; One-way ANOVA, p-values indicate comparison to PBS).

[0082] EXAMPLES

[0083] The present invention is further illustrated by the following examples.

[0084] Materials and Methods

[0085] Cell lines

[0086] LLC (Ref. CRL-1642 from ATCC and MC38 (Ref. ENH204-FP from Kerafast, cell lines were cultured at 37°C under 5% CO2 split when 90% confluency was reached through enzymatic dissociation (Trypsin). Cells were maintained in DMEM media containing 2 mM L-glutamine, 3.7 g / L sodium bicarbonate, 4.5 g / L glucose and 1.0 mM sodium pyruvate with 10% heat-inactivated fetal bovine serum (30 min at 56°C; FBS), penicillin 100 U / ml streptomycin 100 pg / ml. All cell lines were tested for Mycoplasma every month.

[0087] Mice

[0088] Wild type C57BL / 6j were obtained from the KU Leuven breeding facility. All subcutaneous tumor experiments were done using 7- to 12-week-old female / male mice, maintained in the conventional mouse facility. Mouse Experiments were approved by the animal ethics committee at KU Leuven (project Pl 14 / 2019 and pl 95 / 2020) following the European directive 2010 / 63 / EU as amended by the Regulation (EU) 2019 / 1010 and the Flemish government decree of 17 February 2017.

[0089] Analysis of single-cell RNA-sequencing (scRNAseq) datasets

[0090] Single-cell datasets covering melanoma patients responding (or not) to immunotherapy - Programmed cell death protein 1 (PD-1) / Cytotoxic T-lymphocyte protein 4 (CTLA-4) blockade - [1], pan-immune dataset from 4 different cancers [2], pan -myeloid dataset from 8 different cancers [3], colorectal cancer (CRC) dataset with microsatellite instable (MSI) and microsatellite stable (MSS) CRC patients [4] and lung cancer patients treated with multi-modal combination of cytotoxic therapy and immunotherapy [5], were accessed using a standardized and uniformized workflow of BBrowser_v_3 [6], using built-in qualitative filters. The pre-existing scRNAseq dataset of subcutaneous murine Lewis lung carcinoma (LLC)-tumors [7] was uploaded into the same workflow and further analyzed to maintain uniformity. This workflow was used for generating density plots, or dot plots, as applicable. Cellular annotations either pre-existed on the level of author-derived annotations or were based on automated annotations available within the above work-flow. We used the HAVCR2(+)VSIR(+) Tumor Associated Macrophages (TAM) within the ColoRectal Cancer (CRC) scRNAseq dataset to perform differential gene-enrichment (DGE) analyses as well as co-dependent pathway enrichment analyses (REACTOME for human, Gene Ontology Biological Process for mouse), using the Venice non-parametric analyses approach (https: / / github.com / bioturing / signac). This was specifically done by comparing scRNAseq profiles of HAVCR2(+)VSIR(+) TAM (HAVCR2 and VSIR expression > 0) vs. HAVCR2(-)VSIR(-) TAM (HAVCR2 and VSIR expression = 0) against each other. Of note, in some datasets, an alternative name for VSIR was C10orf54. The DGE analyses also allowed us to extract a signature for HAVCR2(+)VSIR(+)TAM which included all positively enriched genes in favor of this subset above the statistical cut-off of -logl0(p-value)=100. With this cut-off, we delineated the signature to the gene indicated in Table 1. VSIR, HAVCR2, CD68, FCGRT, C1QC, C1QB, C1QA, CSF1R and FCER1G therein are major TAM genes. Table 1: List of positively enriched genes in HAVCR2(+)VSIR(+) TAM.

[0091] *HGNC Database, HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom www.genenames.org. Data retrieved July, 2023.

[0092] The full scRNAseq profile of HAVCR2(+)VSIR(+) TAM (HAVCR2 and VSIR expression > 0) was used to drive the automated Cell Ontology analyses (i.e., Cell Search algorithm) to interrogate the 300 scRNAseq datasets within the curated BioTuring database as available on 17th February 2021 [6], The hits were filtered at a Jaccard’s index threshold of 0.7. This analyses delineated the following dataset hits for macrophage node: Basal Cell Carcinoma (GSE123814), Colorectal Cancer (GSE144735), Liver Cancer (GSE125449), Lung (COVID-19; GSE145926), Lung Cancer (E-MT AB-6149), Lung Cancer (GSE140819), Pancreatic Cancer (GSA: CRA001160), Testis (Normal; GSE134144); and the following datasets for monocyte node: Fetal Liver (E-MTAB- 7407), Lung (PRJEB31843), Lung (Fibrosis; GSE128033), Lung (Fibrosis; GSE121611), Lung (Fibrosis; GSE135893), Lung Cancer (GSE131907), Lung (SSc-ILD; GSE128169), Melanoma (GSE123139), Nasopharyngeal Cancer (GSE150430), PBMCs (sepsis; SCP548), PBMCs (SC2018), Respiratory Tract (EGAS00001004082), Spleen (PRJEB31843).

[0093] Immuno-oncology clinical trials analyses

[0094] Patient survival analyses in immuno-oncology clinical trials was carried out using the HAVCR2(+)VSIR(+)TAMs signature indicated in Table 1. We accessed bulk tumor transcriptomic data from 779 cancer patients with pre-treatment / baseline samples profiled before anti- PD-1, anti- Programmed cell death 1 ligand 1 (PD-L1) or anti- CTLA-4 immunotherapy using an existing computational workflow (KMPlot[8] and [9]) along with overall survival values for these patients (follow up threshold = all, survival time = days). These analyses entailed creation of Kaplan-Meier curves, at auto select best cut- off (14657.99 gene expression value, within the range of 5985 - 88189) with univariate

[0095] Cox regression and log-rank p value analyses.

[0096] Transcriptomic analysis in The Cancer Genome Atlas (TCGA)

[0097] Bulk transcriptomics count data from the TCGA Toil-recompute project

[0010] were downloaded from UCSC Xena

[0011] , Survival information was obtained from the same data hub. Immune classifications, delineating distinct immune subtypes in TCGA (Cl to C6; see results for more details), were obtained from the supplementary files associated with the “The immune landscape of cancer” study

[0012] , which lists calls for 11081 samples. Pre-calculated immune deconvolutions for TCGA were downloaded from TIMER(19) 2.0, which contains a total of 11071 samples. In this study, we only used the Quantiseq

[0013] calls. The dataset was subset to only include primary tumors, recurrent tumors, metastatic tumors, as well as “additional new primary” or “additional metastatic” samples, resulting in a data matrix containing 9611 samples.

[0098] Spearman correlations between a TIM3-VISTA metagene and the corresponding Quantiseq-deconvoluted macrophage population, were calculated per immune subtype (C1-C6) with scipy

[0014] 1.6.2. Samples for which no immune subtype or deconvoluted fraction was available, were ignored.

[0099] Survival analysis in TCGA

[0100] The TCGA dataset described above, was used for a prognostic analysis, in which the effect of the expression of the HAVCR2 - VSIR metagene (mean expression of all genes in signature of Table 1) was associated with the overall survival, using a Cox regression as implemented in lifelines

[0015] 0.26.3 with default settings. Overall, our analysis included 8516 patients with survival and metagene expression spanning 30 cancer types: Adrenocortical carcinoma (ACC): 76, Bladder Urothelial Carcinoma (BLCA): 395, Breast invasive carcinoma (BRCA): 1085, Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC): 301, Cholangiocarcinoma (CHOL): 35, Colon adenocarcinoma (COAD): 280, Esophageal carcinoma (ESCA): 171, Glioblastoma multiforme (GBM): 157, Head and Neck squamous cell carcinoma (HNSC): 513, Kidney Chromophobe (KICH): 65, Kidney renal clear cell carcinoma (KIRC): 513, Kidney renal papillary cell carcinoma (KIRP): 277, Liver hepatocellular carcinoma (LIHC): 361, Brain Lower Grade Glioma (LGG): 519, Lung adenocarcinoma (LU AD): 446, Lung squamous cell carcinoma (LUSC): 447, Mesothelioma (MESO): 82, Ovarian serous cystadenocarcinoma (OV): 272, Pancreatic adenocarcinoma (PAAD): 152, Pheochromocytoma and Paraganglioma (PCPG): 180, Prostate adenocarcinoma (PRAD): 403, Rectum adenocarcinoma (READ): 87, Sarcoma (SARC): 226, Skin Cutaneous Melanoma (SKCM): 104, Stomach adenocarcinoma (STAD): 383, Testicular Germ Cell Tumors (TGCT): 136, Thyroid carcinoma (THCA): 507, Uterine Corpus Endometrial Carcinoma (UCEC): 177, Uterine Carcinosarcoma (UCS): 57, Uveal Melanoma (UVM): 79. We noticed an imbalance in SKCM towards the primary tumor samples (104 out of 469 SKCM used) as primarily metastatic samples did not have a definite immune subtype assigned by Thorsson and co-workers

[0012] , However, this only affected a small proportion of the total pan cancer dataset.

[0101] Mouse experiments

[0102] Seven to twelve-week-old female / male C57BL / 6J mice were subcutaneously (s.c) injected with IxlO6LLC, MC38 cells. In the case of chemotherapy, mice were treated with 8mg / kg of Cisplatin (Sigma #p4394) or Paclitaxel (Sigma #T1912) on day 10, 13 and 17 via intraperitoneal (i.p.) injections. When applicable, mice were treated or cotreated with 250 g of anti-mouse PD-L1 (clone MIH5; Polpharma Biologies), antimouse CTLA-4 (clone 4F10; Polpharma Biologies), anti- mPD-l(RMPl-14; Polpharma Biologies), anti-mouse HAVcr-2 (clone B8.2C12; BioXCell), or anti- mouse VISTA (clone 13F3; BioXCell), on day 11, 14 and 18 via i.p. injections. Mice were monitored and weighed every other day and tumor volume was determined by height x width x length.

[0103] Murine tumor infiltrating leukocytes (TILs) isolation

[0104] Tumors were isolated at day 24 after tumor injection. A single cell suspension was made, using the tumor dissociation kit (Miltenyi #130-096-730). TILs were isolated through magnetic bead separation via CD45 (Miltenyi #130-110-618). Isolated TILs were maintained in RPMI supplemented with 100 u / mL penicillin, 100 pg / L streptomycin, 2.5% Hepes Ph7.5, 10% heat-inactivated fetal bovine serum (FBS). Following fluorochrome-conjugated antibody clones were used: F4 / 80 (clone T45-2342), CDl lb (clone ICRF44), HAVcr-2 (cloneB8.2C12) and VISTA (cloneMH5A).

[0105] Flow cytometry

[0106] Before staining procedure, FC receptor of all samples were blocked using TruStain FcX (Biolegend #101320) for 15min. Cells were further stained with the indicated antibodies above, diluted in 0.5% BSA, for lh and fixed with cytofix (BD Bioscience #554655). After fixation, cells were maintained in 0.5% BSA. Flow cytometry was performed on FACSCanto (BD Bioscience) or the ID7000 (SONY). Cell doublets were excluded based on FSC-A / FSC-H. Flow cytometry data was analysed using Flow Jo.

[0107] Evaluation of gene prognostic value

[0108] Patient prognostic analyses in immuno-oncology clinical trials from the CRI iATLAS (https: / / cri-iatlas.org / ) was carried out using the HAVCR2(+)VSIR(+)TAMs signature indicated in Table 1. We accessed bulk tumor transcriptomic data from 971 cancer patients with pre-treatment / baseline samples profiled before anti- PD-1, antiProgrammed cell death 1 ligand 1 (PD-L1) or anti- CTLA-4 immunotherapy using an existing computational workflow (KMPlot[8] and [9]) along with overall survival values for these patients (follow up threshold = all, survival time = days). These analyses entailed creation of overall survival (OS), at auto cut-off; anti-PDl / PD-Ll / CTLA4; pre-treatment; patients = 59-971 with p value analyses.

[0109] The scRNAseq atlas generated in

[0025] was used to obtain the minimal gene list. Talk2Data’s co-expression function was used to calculate the absolute Jaccard scores amongst genes of HAVCR2(+)VSIR(+)TAMs signature indicated in Table 1, guided by VSIR and CSF1R. VSIR and CSF1R had the highest Jaccard index (0.53) and were therefore used to guide the search for the minimal gene list. The Jaccard Index between two genes gl and g2 is calculated as follows: Jaccard Index (gl, g2) = (Number of cells that express both gl and g2) / (Number of cells that express at least one of the two genes).

[0110] Results and conclusions We wished to extract the dominant immune-inhibitory receptor(s) enriched in the myeloid compartment of Immune Checkpoint Blocker-nonresponsive or non-immunogenic human tumors. Hence, we assembled a comprehensive computational data-framework

[0016] ,

[0017] integrating an array of existing patient cohorts with single -cell (sc)-RNAseq data (supervised exploratory analyses across: 114 patients, 11 tumor types and 36 distinct scRNAseq cohorts; unsupervised validation analyses across: -300 human scRNAseq cohorts, >15 million single-cells, >60 organ / disease contexts including cancer).

[0111] Firstly, we compared the expression of nine major immune-inhibitory receptor coding genes, mostly linked to tumor-reactive CD8+T cell exhaustion

[0017]

[0018] and a broad Tumor Associated Macrophages (TAM) marker (CSF1R), in T cells versus myeloid cells from melanoma patients [1], These patients were either responsive or non-responsive to anti- PD-l / anti- CTLA-4 Immune Checkpoint Blockers (ICB) [1], T cells from ICB- nonresponsive patients showed increased expression of multiple immune-inhibitory receptor genes as shown in Table 2.

[0112] Table 2: scRNAseq data for CD8+T-cells from skin cutaneous melanoma (SKCM) patients (n=32) responding or not responding to PD1 / CTLA4 blockade. Proportion of cells (number of cells expressing the gene to total amount of cells - %) and expression levels (Z-score) of indicated genes. however, the myeloid cells from these patients showed a dominant enrichment of specifically two alternative immune-inhibitory receptor genes i.e., HAVCR2 and VSIR along with CSF1R as shown in Table 3. Table 3: scRNAseq data for myeloid cells from skin cutaneous melanoma (SKCM) patients (n=32) responding or not responding to PD1 / CTLA4 blockade. Proportion of cells (number of cells expressing the gene to total amount of cells - %) and expression levels (Z-score) of indicated genes. This heightened (co-)expression of HAVCR2 and VSIR was unique for TAM compartment since it was not seen in other broad T or B cell subsets and dendritic cells (DCs) as shown in Table 4.

[0113] Table 4: scRNAseq data for indicated immune cell subsets from skin cutaneous melanoma (SKCM) patients (n=32) treated with anti-PDl or anti-CTLA4 . Proportion of cells (number of cells expressing the gene to total amount of cells - %) and expression levels (Z-score) of indicated genes. Next, to verify whether TAM-specific co-enrichment of HAVCR2 / VSIR was generalizable to other tumor types beyond melanoma, we used a multi -cancer and multi- immune cells scRNAseq dataset [2], A strong HAVCR2(+)VSIR(+) phenotype associated mainly with TAM and was nearly absent in CD4+T cells, B cells, monocytes, or NK cells (Table 5).

[0114] Table 5: scRNAseq data from 14 treatment-naive patients across four different types of cancer (lung adenocarcinoma, endometrial adenocarcinoma, colorectal adenocarcinoma and renal cell carcinoma, clear cell). Number of HAVCR2(+)VSIR(+) cells in indicated immune cells types. TRM= tissue resident memory. Threshold of 0.5 HAVCR2 and VSIR gene expression was taken.

[0115] While interesting, in above studies the sub-population diversity of TAMs, DCs or monocytes was not sufficiently resolved. To ameliorate this, we utilized a larger multicancer and multi-myeloid cells scRNAseq meta-dataset with consensus sub-population annotations [3]. Herein, the HAVCR2HIGHVSIRHIGHphenotype was dominantly associated with CSF1RHIGHTAM ontogeny rather than other subsets of TAM, DCs or monocytes as shown in Table 6.

[0116] Table 6: 33 scRNAseq datasets including 8 cancer types (non-small cell lung cancer, hepatocellular carcinoma, lymphoma, head and neck, colorectal cancer, stomach, breast, and pancreas) (n=45). Proportion of cells (number of cells expressing the gene to total amount of cells - %) and expression levels (Z-score) of indicated genes.

[0117]

[0118] Above results called for more ‘controlled’ analyses of how HAVCR2(+)VSIR(+)TAM associate with pro-inflammatory vs. anti-inflammatory features, in immunogenic vs. non- immunogenic tumors. To address this in a unified pathology, we used scRNAseq dataset of colorectal cancer (CRC) patients, since CRC tumors exhibit either immunogenic (i.e., microsatellite instable or MSI) or non-immunogenic (i.e., microsatellite stable or MSS) pathologies [4], Herein, data of normal adjacent tissue-associated macrophages was also interrogated. Remarkably, HAVCR2(+)VSIR(+) phenotype was strongly associated with anti-inflammatory SPP1(+)TAM in primarily MSS-CRC , rather than pro -inflammatory TAMs or MSI-CRC (Table 7). Table 7: scRNAseq data from 23 primary colorectal cancer (CRC) patients (4 microsatellite instable high (MSI-H) and 19 microsatellite stable (MSS)) including 10 matched normal adjacent tissues. Number of HAVCR2(+)VSIR(+) cells in indicated immune cells types in anti-inflammatory SPP1+ macrophages and pro -inflammatory macrophages. Threshold of 0.5 gene expression of HAVCR2 and VSIR was taken. A differential pathway-enrichment analysis between HAVCR2(+)VSIR(+) vs. HAVCR2(-)VSIR(-)TAM showed that HAVCR2(+)VSIR(+)TAM enriched for phagocytosis, pattern-recognition receptor (PRR) signaling relevant for danger sensing (e.g., nucleic acids), and anti-inflammatory signaling (Table 8).

[0119] Table 8: aggregated scores for representative pathway terms based on a broader differential pathway-enrichment analysis between HAVCR2(+)VSIR(+)TAM vs. HAVCR2NEGVSIRNEGTAMS in CRC patient dataset from Table 7.

[0120] Due to the supervised nature of above analyses with heterogenous datasets, we felt it was necessary to validate our observations with an unbiased approach. Hence, we used an automated cell ontology analyses that used above HAVCR2(+)VSIR(+)TAM signature as a guide to find similar immune populations in a massive database of -300 publicly- available scRNAseq datasets spanning >60 normal or diseased tissues [6]. Strikingly, the alignment between HAVCR2(+)VSIR(+)TAM signature and macrophages was a dominant characteristic of multiple human tumors (especially of epithelial -origin), rather than other healthy / diseased tissues.

[0121] These comprehensive single-cell analyses indicated that a unique HAVCR2(+)VSIR(+)TAM niche is preferentially enriched in immuno-resistant human tumors and allowed us to extract a signature for HAVCR2(+)VSIR(+)TAM which included all genes positively enriched when HAVCR2(+)VSIR(+)TAM are present in the tumor (Table 1). To determine how HAVCR2(+)VSIR(+) TAM embed in the human pan-cancer immune- landscape and how they prognostically or predictively associate with patient survival, we pursued a high-powered bulk-tumor transcriptome and high resolution scRNAseq mapping. An immunogenomics analysis with The Cancer Genome Atlas (TCGA) datasets had previously established six pan-cancer immune-landscape classes (Cl-to-C6; for class-labels, see radar plot in figure 1

[0012] , We analyzed the correlation of immune- deconvoluted macrophage-fractions, from above classes, with our scRNAseq-validated HAVCR2(+)VSIR(+)TAM signature in 9611 patients (of which 8549 with survival information and macrophage quantification) across 30 cancer types. Interestingly, the more non -immunogenic or immuno-resistant C4 / C5-tumours showed the highest correlation between HAVCR2(+)VSIR(+)TAM signature and macrophages, followed by mixed inflammatory C3-tumours (figure 1). In terms of prognostic impact, HAVCR2(+)VSIR(+)TAM signature associated with increased hazard ratio (HR>1) implying shorter overall survival (OS) in all the pan-cancer immune-landscape classes, except the immunogenic IFNy-dominant tumors (Table 9).

[0122] Table 9: Hazard ratios (HR) for the impact of HAVCR2(+)VSIR(+) tumor associated macrophage / TAM signature on overall survival of cancer patients in TCGA datasets consisting of six pan-cancer immune-landscape classes (Cl, n=2067; C2, n=2424, C3, n=2349; C4, n=1142, C5, n=387; C6, n=180).

[0123] Similarly, in an integrated multi-cancer dataset of 779 patients (spanning 5 cancer types), whose tumors were transcriptome-profiled before PD-1, PD-L1 and / or CTLA-4 blockade, OS of patients with an high expression of HAVCR2(+)VSIR(+) TAM signature was significantly shorter than those with low expression (figure 2). This highlighted a pan-cancer association of HAVCR2(+)VSIR(+)TAM signature with non-immunogenic human tumors and its ability to predict shorter patient OS in prognostic as well as unimodal ICB -response predictive settings. To address the functional relevance of the HAVCR2(+)VSIR(+) TAM niche, we selected following representative, epithelial-origin, tumor models syngeneic for C57BL / 6 mice: non-immunogenic LLC (macrophageHIGHIFNy-signallingLOW) versus immunogenic MC38 (macrophageLOWIFNy-signallingHIGH). as predicted by our human data, immunoresistant LLC -tumors indeed enriched significantly more Havcr2(+)Vsir(+) TAM than MC38-tumors (figure 3) and LLC -tumors were resistant to PD-1 / PD-L1 / CTLA-4 blockade (figure 4), whereas MC38-tumors were significantly susceptible (figure 5).

[0124] We pursued therapeutic blockade of HAVcr-2 or VISTA via antibodies to blunt the Havcr2(+)Vsir(+) TAM. Unexpectedly, both LLC (figure 6) and MC38 (figure 7) tumors were resistant to Havcr2 or Vsir blockade. Even HAVcr-2 or VISTA and PD-1 coblockade, a common strategy being pursued in ongoing clinical trials

[0020] , either did not affect the growth of LLC-tumours (figure 8) or didn’t out-perform PD1 blockade alone for MC38-tumours (figure 9). Thus, despite being immuno-resistant and strongly enriching Havcr2(+)Vsir(+) TAM, LLC -tumors were resistant to unimodal HAVcr-2 or VISTA blockade.

[0125] Above data indicated that a blockade-like strategy was perhaps not sufficient to overcome Havcr2(+)Vsir(+) TAM. This made us wonder whether their functional re-polarization toward pro-inflammatory activity, based on immunogenic engagement, offered better therapeutic opportunities. Human and mouse tumoral scRNAseq data redundantly indicated the affinity of Havcr2(+)Vsir(+) TAM for phagocytic and danger signaling-like engagements. To co-provide these, we pursued combinatorial treatment with an immunogenic cell death (ICD)-inducing chemotherapy widely used in immunotherapy trials i.e., paclitaxel (PTX)

[0021] ,

[0022] , Apoptotic ICD is well-established to propagate danger signaling and create immunogenic phagocytic interface

[0022] ,

[0023] . Herein, we took along a well-established non-ICD inducing chemotherapy as negative control i.e., cisplatin (CDDP)

[0024] , in vivo LLC and MC38 tumors were susceptible to CDDP (figures 10 and 11) but almost completely resistant to PTX (figures 12 and 13). Remarkably, in LLC -tumors, HAVcr-2 or VISTA blockade synergized with only PTX (but not CDDP) to cause significant tumor regression (figures 10 and 12). Such synergism was absent in MC38-tumours (figures 11 and 13). Of note, the triple combination of PTX-treatment with HAVcr-2 and VISTA co-blockade was not significantly different from doublet combinations thereby indicating that HAVcr-2 or VISTA performed redundant, rather than separate, immune functions (figure 14). Thus, HAVcr-2 or VISTA blockade synergizes with ICD-inducing chemotherapy to re-polarize Havcr2(+)Vsir(+) TAM toward pro -inflammatory phenotype, which associated with regression of non- immunogenic LLC -tumors.

[0126] We accessed tumor transcriptomic data from cancer patients with pre- / on-treatment samples profiled relative to anti-PDl, anti-PD-Ll or anti-CTLA4 immunotherapy, along with overall survival (OS). Here, we wanted to see the size of the gene signature enabling the prediction the negative prognostic impact of HAVCR2+VSIR+ CSF1R+ TAMs. We used the mean expression of all 86 genes of our HAVCR2+VSIR+ tumor associated macrophage / TAM signature and the gene expression of each single gene to calculate Hazard ratios (HR) for the impact on overall survival (OS). Notably, EEF1A1, RPSA, GPX1, ITM2B, ACTG1, RPL13A, ARPC3, ACTB, PPIA, PTMA, TMSB4X and CTSC had an increased hazard ratio (HR>1) implying shorter overall survival (OS) and negative prognostic impact (Table 10).

[0127] Table 10: Gene expression of all 86 genes of HAVCR2+VSIR+ tumor associated macrophage / TAM signature and their Hazard ratio (HR) for the impact on overall survival (OS) and p-value.

[0128] Next, we used a pan-cancer atlas to compare our macrophage signature across multiple macrophage subsets. Here, we wanted to see how many of the 86 genes of our HAVCR2+VSIR+ tumor associated macrophage / TAM signature were necessary to detect HAVCR2+VSIR+ CSF1R+ TAMs. We used VSIR and CSF1R to guide this search. Based on this analysis we found that a signature of 10 genes (See Table 11 VSIR, HAVCR2, CSF1R and HLA-DMA, ARHGDIB, ITGB2, AIF1, CAPZB, SYNGR2 and BST2) enabled to delineate our patient subgroup with HAVCR2+VSIR+ CSF1R+ TAMs.

[0129] Table 11: Jaccard index per gene for VSIR, HAVCR2 and CSF1R.

[0130] Altogether these results show that the enrichment of HAVCR2(+)VSIR(+) TAM within the tumors and thus the associated high expression of the genes indicated in Table 1, is predictive of

[0131] (i) shorter patient survival,

[0132] (ii) resistance to unimodal ICB (targeting PD-1, PD-L1, CTLA-4, HAVcr-2 and / or VISTA without being combined with an ICD inducer)

[0133] (iii) sensitivity to VISTA or HAVcr-2 multimodal ICD therapy (z.e. therapy combining HAVcr-2 and / or VISTA blockade with an ICD inducer).

[0134] References

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[0136] 2. T. D. Wu et al., Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature. 579, 274-278 (2020).

[0137] 3. K. Mulder et al., Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease. Immunity. 54, 1883-1900. e5 (2021).

[0138] 4. H.-O. Lee et al., Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. Nat. Genet. 52, 594-603 (2020).

[0139] 5. A. Maynard et al., Therapy -Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing. Cell. 182, 1232-125 Le22 (2020).

[0140] 6. T. Le et al., BBrowser: Making single-cell data easily accessible. BioRxiv (2020), doi: 10.1101 / 2020.12.11.414136. A. Murgaski et al., Efficacy of CD40 agonists is mediated by distinct cdc subsets and subverted by suppressive macrophages. Cancer Res. 82, 3785-3801 (2022). A. Lanczky, B. Gyorffy, Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J. Med. Internet Res. 23, e27633 (2021). S. A. Kovacs, B. Gyorffy, Transcriptomic datasets of cancer patients treated with immune-checkpoint inhibitors: a systematic review. J. Transl. Med. 20, 249 (2022). J. Vivian et al., Toil enables reproducible, open source, big biomedical data analyses. Nat. BiotechnoL 35, 314-316 (2017). M. J. Goldman et al., Visualizing and interpreting cancer genomics data via the Xena platform. Nat. BiotechnoL 38, 675-678 (2020). V. Thorsson et al., The immune landscape of cancer. Immunity. 48, 812-830. el4 (2018). F. Finotello et al., Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 11, 34 (2019). SciPy - Citing SciPy, (available at https: / / scipy.org / citing-scipy / ). Citing lifelines — lifelines 0.27.4 documentation, (available at https: / / lifelines.readthedocs.io / en / stable / Citing%201ifelines.html). J. Sprooten et al., Peripherally -driven myeloid NFkB and IFN / ISG responses predict malignancy risk, survival, and immunotherapy regime in ovarian cancer. J. Immunother. Cancer. 9 (2021), doi:10.1136 / jitc-2021-003609. S. Naulaerts et al., BioRxiv, in press, doi:10.1101 / 2021.11.22.468617. A. Mantovani, P. Allavena, F. Marchesi, C. Garlanda, Macrophages as tools and targets in cancer therapy. Nat. Rev. Drug Discov. 21, 799-820 (2022). P. Liu et al., Crizotinib-induced immunogenic cell death in non-small cell lung cancer. Nat. Commun. 10, 1486 (2019). G. Curigliano et al., Phase I / Ib Clinical Trial of Sabatolimab, an Anti-TIM-3 Antibody, Alone and in Combination with Spartalizumab, an Anti-PD-1 Antibody, in Advanced Solid Tumors. Clin. Cancer Res. 27, 3620-3629 (2021). 21. 1. Vanmeerbeek et al., Trial watch: chemotherapy-induced immunogenic cell death in immuno-oncology. Oncoimmunology. 9, 1703449 (2020).

[0141] 22. G. Kroemer, C. Galassi, L. Zitvogel, L. Galluzzi, Immunogenic cell stress and death. Nat. Immunol. 23, 487-500 (2022). 23. A. D. Garg, E. Romano, N. Rufo, P. Agostinis, Immunogenic versus tolerogenic phagocytosis during anticancer therapy: mechanisms and clinical translation. Cell Death Differ. 23, 938-951 (2016).

[0142] 24. A. D. Garg et al., Pathogen response-like recruitment and activation of neutrophils by sterile immunogenic dying cells drives neutrophil-mediated residual cell killing. Cell Death Differ. 24, 832-843 (2017).

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Claims

CLAIMS1. A method for quantifying tumor-associated macrophages expressing both HAVCR2 and VSIR in a tumor sample from a cancer patient and / or quantifying tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR in a tumor sample from a cancer patient, comprising the steps of: a. measuring in a tumor sample from said patient the expression of at least 10, preferably at least 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29. b. comparing the measured gene expression to a reference gene expression level corresponding to an amount, or proportion, of HAVCR2 and VSIR- expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR-expressing tumor-associated macrophages group; and,c. assigning on the basis of said comparison said patient to an amount, or proportion, of HAVCR2 and VSIR-expressing tumor-associated macrophages group and / or to an amount, or proportion of, CSF1R, HAVCR2 and VSIR-expressing tumor-associated macrophages group, thereby quantifying tumor-associated macrophages expressing both HAVCR2 and VSIR in a tumor sample from said cancer patient and / or quantifying tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR in a tumor sample from said cancer patient.

2. The method according to claim 1, wherein the expression of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29 increases with the amount, or proportion, of tumor- associated macrophages expressing both HAVCR2 and VSIR and with the amount, or proportion of, tumor-associated macrophages expressing CSF1R, HAVCR2 and VSIR, present within the tumor sample.

3. The method according to claim 1 or 2, wherein said measuring step (a) comprises measuring in a tumor sample from said patient the expression of at least 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting ofVSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA-E, CFL1, ARPC1B, FCGRT,ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S1OOA11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

4. The method according to any one of claims 1 to 3, wherein said measuring step (a) comprises measuring in a tumor sample from said patient the expression of at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 or 86 genes selected from the group consisting of VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, OAZ1, HLA- E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

5. The method according to any one of claims 1 to 4, wherein said measuring step (a) comprises measuring in a tumor sample from said patient the expression of VSIR, HAVCR2 and CSF1R.

6. The method according to any one of claims 1 to 5, where said measuring step (a) comprises measuring in a tumor sample from said patient the expression of VSIR, HAVCR2, CSF1R, HLA-DMA, ARHGDIB, ITGB2, AIF1, CAPZB, SYNGR2, and BST2.

7. The method according to claim any one of claims 1 to 6, wherein said measuring step (a) comprises measuring in a tumor sample from said patient the expression of the following genes: VSIR, TYROBP, HAVCR2, B2M, GRN, LAPTM5, CD74, HLA-C, PSAP, NPC2, TMEM176B, VAMP8, HLA-A, HLA-DMB, HLA-DPA1, GPX1, CD68, HLA-DMA, TMSB4X, 0AZ1, HLA-E, CFL1, ARPC1B, FCGRT, ITM2B, C1QC, PFN1, HLA-B, HLA-DPB1, HLA-DRA, CST3, ARHGDIB, CYBA, TMSB10, CTSB, HLA-DQB1, ITGB2, ACTG1, CAPG, ARPC3, S100A11, RPL15, CTSS, C1QB, ATP6V0B, CTSA, RPS2, YBX1, CTSC, PTMA, SRP14, AIF1, C1QA, PPT1, CTSH, CALM2, CSF1R, YWHAH, RPL28, CAPZB, ACTB, TYMP, CLIC1, ATP6V0E1, CTSD, RPL13A, HEXB, SERF2, EEF1A1, FCER1G, ACP5, TMEM176A, RPS19, PLD3, CORO1B, RPSA, RPL27A, PYCARD, SYNGR2, RPS3, BST2, NACA, PRDX1, LAIR1, PPIA and ERP29.

8. The method according to any one of claims 1 to 7, wherein said measuring is done by measuring RNA and / or protein level in bulk RNA and / or bulk protein extract(s).

9. The method according to any one of claims 1 to 8, wherein said cancer is selected form the group consisting of lung cancer, colorectal cancer, skin cancer, uterine cancer, kidney cancer, liver cancer, lymphoma, head and neck cancer, stomach cancer, breast cancer, pancreas cancer, bladder cancer, brain cancer, adrenal glands cancer, cervical cancer, bile duct cancer, esophageal cancer, mesothelioma, ovarian cancer, extra-adrenal paragangliomas, prostate cancer, sarcoma, testicular cancer, thyroid cancer and eye cancer.

10. The method according to any one of claims 1 to 9, wherein said cancer is selected form the group consisting of lung cancer, colorectal cancer, skin cancer, uterine cancer, kidney cancer, liver cancer, lymphoma, head and neck cancer, stomach cancer, breast cancer and pancreas cancer.

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