A novel method for identifying HERV-derived epitopes.

Abstract

The present invention relates to a method for identifying HERV-derived T cell epitopes associated with cancer, and to peptides comprising or consisting of the epitopes identified by the method, expression vectors encoding the peptides, cytotoxic T lymphocytes (CTLs) of subjects treated with the peptides or vectors, and modified T cells expressing a T cell receptor that recognizes the peptides. The present invention also relates to the use of the peptides, expression vectors, CTLs, or modified T cells as vaccines or medicaments, in particular the use of the peptides, expression vectors, CTLs, or modified T cells for use in the prevention or treatment of cancer in a subject in need thereof.

Description

[Technical field]

[0001] The present invention relates to a method for identifying HERV-derived T cell epitopes associated with cancer, and to peptides comprising or consisting of the epitopes identified by the method, expression vectors encoding the peptides, cytotoxic T lymphocytes (CTLs) of subjects treated with the peptides or vectors, and modified T cells expressing a T cell receptor that recognizes the peptides. The present invention also relates to the use of said peptides, expression vectors, CTLs, or modified T cells as a vaccine or medicament, in particular the use of said peptides, expression vectors, CTLs, or modified T cells for the prevention or treatment of cancer in a subject in need thereof. [Background technology]

[0002] Adaptive T cell immune responses in cancer depend on the recognition of tumor epitopes that are specifically expressed by tumor cells. The role of neoantigens generated by specific nonsynonymous mutations in the tumor genome has been extensively studied in the past decade, and many clinical trials testing neoantigen combinations in personalized cancer vaccines have been launched with promising preliminary results. However, determining the optimal neoepitope combination for each patient remains challenging. Moreover, many tumors are characterized by low or moderate tumor mutation burden. Therefore, uncovering other families of tumor antigens, such as those derived from splice variants, fusion proteins, or endogenous retroelements and possibly shared between different cancer subtypes, is crucial for the development of readily accessible therapies for solid tumors.

[0003] Human endogenous retroviruses (HERVs) represent 8% of the human genome. Most HERV genes are nonfunctional due to DNA recombination, mutations, and deletions, but some produce functional proteins, including group-specific antigens (Gag), polymerase with reverse transcriptase (Pol), and envelope (Env) surface units. Most HERVs are silenced by epigenetic mechanisms in normal cells. However, HERVs were reported to be potential pathogens in carcinogenesis due to their involvement in insertional mutagenesis, chromosomal aberrations, or LTR-induced oncogene activation. Thus, HERVs may be an interesting source of common tumor antigens.

[0004] Among the prior art, WO 2020 / 049169 describes a method that allows the identification of epitopes derived from HERVs and capable of inducing a specific CD8+ T cell response. However, the method developed in this patent application was applied to one cancer type, namely triple-negative breast cancer (TNBC), which has a limited number of HERVs. Thus, there remains a need to develop new approaches that are efficient in inducing CD8+ T cell responses and that can identify antigens derived from HERVs that are common among multiple cancer subtypes.

[0005] In this application, the inventors have developed a new approach to address this need. In particular, the inventors have developed a method that relies on a selection step that allows the determination of a limited number of epitopes among a large number of HERV candidates that are efficient and shared by multiple cancer subtypes. Summary of the Invention

[0006] The present invention relates to a method for identifying human endogenous retrovirus (HERV)-derived T cell epitopes associated with at least one cancer, the method comprising: (a) identifying at least one cancer-associated HERV; (b) selecting T cell epitopes among the HERVs identified in the previous step; and The method further comprises: (i) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response, the selecting step being between steps (a) and (b); and / or (ii) evaluating the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in the tumor sample, said evaluating step being carried out after step (b). At least one, preferably two of the above.

[0007] In one embodiment, steps (a) and (b) and steps (i) and / or (ii) are in silico steps. In one embodiment, step (ii) is an in vitro step.

[0008] The present invention relates to an in silico method for identifying human endogenous retrovirus (HERV) derived T cell epitopes associated with cancer, the method comprising: (a) identifying and selecting HERVs associated with cancer; (b) selecting common T cell epitopes among the HERVs identified in the previous step; and The method further comprises: (i) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response, the selecting step being between steps (a) and (b); and / or (ii) evaluating the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in the tumor sample, said evaluating step being carried out after step (b). At least one, preferably two of the above.

[0009] The present invention relates to an in silico method for identifying human endogenous retrovirus (HERV) derived T cell epitopes associated with cancer, preferably associated with at least one cancer, the method comprising: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting T cell epitopes among the HERVs identified in the previous step; and The method further comprises: (i) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response, the selecting step being between steps (a) and (b); and / or (ii) evaluating the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in the tumor sample, said evaluating step being carried out after step (b). At least one, preferably two of the above.

[0010] In one embodiment, step (a) comprises comparing HERV expression in tumor and normal samples.

[0011] In one embodiment, step (b) comprises aligning the sequences of the HERVs identified in the previous step with the HERV proteins.

[0012] In one embodiment, step (b) further comprises predicting binding of a sequence sharing at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or more identity with the HERV protein to an MHC class I molecule.

[0013] In one embodiment, the association of cancer-associated HERVs with the cytotoxic T cell response in step (i) is assessed by the association of each HERV with at least one CD4 or CD8 T cell signature, the association of each HERV with a functional signature, which is either an interferon (IFN)-γ signature or cytolytic activity, and the absence of expression of each HERV in normal purified T cells or NK cells, preferably this association is assessed by a machine learning based approach.

[0014] In one embodiment, the method further comprises, after step (b) or before step (ii), selecting an epitope among the most shared epitopes in the cancer-associated HERVs identified in step (a).

[0015] In one embodiment, the method further comprises, after step (b) or after step (ii), aligning T cell epitopes from HERVs with the human proteome.

[0016] The present invention also relates to a peptide comprising or consisting of an epitope identified by the above-described method.

[0017] The present invention also relates to a peptide comprising or consisting of an epitope having a sequence selected in the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), ALMIVSMV (SEQ ID NO:14), MLLAALMIV (SEQ ID NO:15) and YIDDILCAA (SEQ ID NO:16). The present invention also relates to a peptide comprising or consisting of a sequence selected in the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), ALMIVSMV (SEQ ID NO:14), MLLAALMIV (SEQ ID NO:15) and YIDDILCAA (SEQ ID NO:16).

[0018] The present invention also relates to a peptide comprising or consisting of an epitope having a sequence selected in the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), MLLAALMIV (SEQ ID NO:15) and YIDDILCAA (SEQ ID NO:16). The present invention also relates to a peptide comprising or consisting of a sequence selected in the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), MLLAALMIV (SEQ ID NO:15) and YIDDILCAA (SEQ ID NO:16).

[0019] The present invention also relates to expression vectors directing the expression of one or more peptide(s) as described above.

[0020] The present invention also relates to the cytotoxic T lymphocytes of a subject treated with one or more peptide(s) or one or more expression vector(s) described above.

[0021] The present invention also relates to modified T cells expressing a T cell receptor that recognizes a peptide as described above.

[0022] The present invention also relates to one or more peptide(s), one or more expression vector(s), one or more cytotoxic T lymphocyte(s), or one or more modified T cell(s) described above for use as a vaccine or medicament.

[0023] The present invention also relates to one or more peptide(s), one or more expression vector(s), one or more cytotoxic T lymphocyte(s), or one or more modified T cell(s) described above for use in preventing or treating cancer in a subject in need thereof.

[0024] In one embodiment, the cancer is selected from the group comprising or consisting of breast cancer, including triple negative breast cancer, ovarian cancer, melanoma, sarcoma, teratocarcinoma, bladder cancer, lung cancer, including non-small cell lung cancer and small cell lung cancer, head and neck cancer, colorectal cancer, glioblastoma, leukemia, lymphoma, and other solid tumors and hematological malignancies.

[0025] definition In the present invention, the following terms have the following meanings:

[0026] "Epitope" refers to a portion of an antigen that is capable of stimulating an immune response.

[0027] "Human endogenous retrovirus (HERV)" refers to a retrovirus that has integrated into the germline and has lost its infectious ability but retained translocation capability.

[0028] "Peptide" refers to a linear polymer of amino acids of at least two amino acids linked together by peptide bonds. Amino acid residues in peptides are abbreviated as follows: phenylalanine is Phe or F, leucine is Leu or L, isoleucine is Ile or I, methionine is Met or M, valine is Val or V, serine is Ser or S, proline is Pro or P, threonine is Thr or T, alanine is Ala or A, tyrosine is Tyr or Y, histidine is His or H, glutamine is Gln or Q, asparagine is Asn or N, lysine is Lys or K, aspartic acid is Asp or D, glutamic acid is GIu or E, cysteine ​​is Cys or C, tryptophan is Trp or W, arginine is Arg or R, and glycine is Gly or G.

[0029] "Prevent", "preventing" and "prevention" refer to prophylactic and preventative measures, the purpose of which is to reduce the likelihood that a subject will develop a pathological condition or disorder over a given period of time. Such a reduction may be reflected, for example, in a delay in the onset of at least one symptom of the pathological condition or disorder in the subject.

[0030] "Protective immune response" refers to a cytotoxic T lymphocyte (CTL) and / or helper T lymphocyte (HTL) response to an antigen derived from an infectious agent or tumor antigen, which prevents or at least partially blocks disease symptoms or progression. The immune response may also include an antibody response promoted by stimulation of helper T cells.

[0031] "Subject" refers to a mammal, preferably a human. In one embodiment, the subject may be a "patient", i.e., a warm-blooded animal, more preferably a human, who is waiting to receive medical care or is receiving medical care, or has been, is, or will be the object of medical treatment, or is being monitored for the onset of disease. The term "mammal" herein refers to any mammal, including humans, domestic animals, and zoo, sport, or pet animals, such as dogs, cats, cows, horses, sheep, pigs, goats, rabbits, and the like. Preferably, the mammal is a primate, more preferably a human.

[0032] A "therapeutically effective amount" refers to a level or amount of one or more peptide(s), one or more expression vector(s), one or more CTL(s), or one or more engineered T cell(s) described herein to (1) delay or prevent the onset of a disease, disorder, or condition; (2) slow or halt the progression, worsening, or deterioration of one or more symptoms of a disease, disorder, or condition; (3) bring about an improvement in the symptoms of a disease, disorder, or condition; (4) reduce the severity or incidence of a disease, disorder, or condition; or (5) cure a disease, disorder, or condition, without causing significant negative or deleterious side effects to the target. A therapeutically effective amount may be administered prior to the onset of a disease, disorder, or condition for a prophylactic or preventative effect. Alternatively, or in addition, a therapeutically effective amount may be administered after the initiation of a disease, disorder, or condition for a therapeutic effect.

[0033] "Treating" or "treatment" or "palliation" refers to therapeutic treatment, the purpose of which is to delay (alleviate) the targeted disease state or disorder. A subject or mammal is successfully "treated" for cancer if, after receiving a therapeutic dose of one or more peptide(s), one or more expression vector(s), one or more cytotoxic T lymphocyte(s), or one or more modified T cell(s) according to the invention, the patient shows an observable and / or measurable reduction or absence of one or more of the following: a reduction in the number of cancer cells (or tumor size); a reduction in the percentage of all cells that are cancerous; and / or some degree of alleviation of one or more of the symptoms associated with a particular disease or condition; a reduction in morbidity and mortality, and an improvement in quality of life issues. The above parameters for assessing successful treatment and improvement in disease are easily measurable by routine procedures familiar to physicians.

[0034] "Vaccine" refers to a compound that, when administered to a patient, is capable of inducing a humoral and / or cellular immune response, which immune response is protective.

[0035] By "vector" or "expression vector" is meant a vehicle by which a DNA or RNA sequence (e.g., a foreign gene) can be introduced into a host cell in order to transform the host and promote expression (e.g., transcription and translation) of the introduced sequence. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] The present invention relates to methods for identifying T cell epitopes derived from human endogenous retroviruses (HERVs) that are associated with cancer, preferably associated with at least one cancer.

[0037] In one embodiment, all steps of the method are performed in silico, making the method of the invention an in silico method.

[0038] The method of the invention in particular makes it possible to select a limited number of HERV-derived T cell epitopes that are specifically overexpressed by tumor cells and that are likely to be the most immunogenic among the large number of HERV candidates.

[0039] In one embodiment, the method allows for the selection of shared epitopes, i.e. epitopes shared in multiple patients with the same cancer, and / or epitopes shared between different cancer types, and / or epitopes shared between cancer-associated HERVs. In one embodiment, the epitope(s) identified by the method of the invention are shared by multiple patients with the same cancer. In one embodiment, the epitope(s) identified by the method of the invention are shared between different cancer types. In one embodiment, the epitope(s) identified by the method of the invention are shared between cancer-associated HERVs.

[0040] In one embodiment, the method comprises at least 2, 3, 4, 5, or more of the steps described below.

[0041] In one embodiment, the method includes identifying HERVs in the tumor sample and the normal sample. The HERVs may be identified from a HERV database.

[0042] Methods for identifying HERVs in a sample are well known to those skilled in the art and include, but are not limited to, the use of reference tools such as Hervquant (CC Smith, et al., Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma. Journal of Clinical Investigation. 128, 4804-4820 (2018)) or Telescope (Bendall et al., Telescope: Characterization of the retrotranscriptome by accurate estimation of transposable element expression, PLoS Comput Biol. 2019 Sep 30; 15(9): e1006453).

[0043] Further, HERV databases are described in the literature, see, for example, L. Vargiu et al., "Classification and characterization of human endogenous retroviruses; mosaic forms are common. Retrovirology. 13, 7 (2016)." The HERV database may be obtained from the Genbank database.

[0044] In one embodiment, a normal sample refers to a sample obtained from a subject not afflicted with cancer, or a sample obtained at a site surrounding a tumor from a subject afflicted with cancer.

[0045] In one embodiment, a tumor sample refers to a sample from tumor tissue in a subject afflicted with cancer.

[0046] In one embodiment, the above-mentioned method comprises a step of selecting a HERV associated with cancer, preferably associated with at least one cancer, which step in particular allows the selection of HERVs that are differentially expressed in tumors and in normal samples.

[0047] In one embodiment, the selection of HERVs associated with cancer comprises comparing HERV expression in tumor and normal samples. This comparison may be performed at the RNA level, meaning that the expression of RNA sequences is compared for each HERV between tumor and normal samples.

[0048] RNA sequences can be obtained from RNA sequencing databases and / or by extraction from fresh tissue.

[0049] In one embodiment, a HERV is associated with cancer, preferably associated with at least one cancer, if the HERV is expressed more than 2-fold, 2.5-fold, 3-fold, 3.5-fold, 4-fold, 4.5-fold, 5-fold, or more in a tumor sample than in a normal sample. In one embodiment, a HERV is associated with cancer, preferably associated with at least one cancer, if the HERV is expressed more than 2-fold in a tumor sample than in a normal sample, and not more than 2-fold in any normal tissue compared to its matched tumor.

[0050] In one embodiment, the method described above comprises a step of selecting HERVs associated with a cytotoxic T cell response, which in particular allows the selection of HERVs capable of inducing an immune response.

[0051] In one embodiment, the association of HERVs with cytotoxic T cell responses is assessed by phenotypic criteria, hi one embodiment, the association of HERVs with cytotoxic T cell responses is assessed by the association of each HERV with at least one CD4 or CD8 T cell signature.

[0052] In one embodiment, the association of HERVs with cytotoxic T cell responses is assessed by determining the association of each HERV with a transcriptomic signature associated with at least one of the CD4 or CD8 T cell phenotypes. In one embodiment, this step makes it possible to select HERVs associated with transcripts suggestive of the presence of cells with a CD4 or CD8 T cell phenotype in the tumor sample.

[0053] Examples of methods for assigning the phenotype of CD4 or CD8 T cells are well known to those of skill in the art and include, for example, the Xcell method (D. Aran et al., xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 220 (2017)), and the MCP counter.

[0054] As used here, XCell is a gene signature-based method that performs cell type enrichment analysis from gene expression data for 64 immune and stromal cell types. The xCell signature was validated using extensive in-silico simulations and cytometric immunophenotyping. The xCell R package for generating cell type scores and R scripts for the development of xCell are available at https: / / github.com / dviraran / xCell and will be deposited at Zenodo (assigned DOI http: / / doi.org / 10.5281 / zenodo.1004662).

[0055] In one embodiment, the signature for assigning a CD4 or CD8 T cell phenotype is based on the signature of Xcell.

[0056] In one embodiment, the signature for assigning a CD4 T cell phenotype, in particular a CD4 memory T cell phenotype, comprises or consists of the following genes: CD6, CD28, GPR183, EIF3E, ITK, PKP4, PREPL, DNAJA2, PTGES3, CD2AP and ICOS.

[0057] In one embodiment, the signature for assigning a CD4 T cell phenotype, in particular a CD4 naive T cell phenotype, comprises or consists of the following genes: CD27, CLC, CTSW, DNAJB1, RBL2, HAUS3, ANKRD55, ZNF394 and CHMP7.

[0058] In one embodiment, a signature for assigning a CD4 T cell phenotype comprises or consists of the following genes: APBB1, CD28, CTLA4, ITK, PLCL1, SNPH, PPWD1, PHF3, ICOS, TRAT1, RAPGEF6, NOL9 and CHMP7.

[0059] In one embodiment, the signature for assigning a CD4 T cell phenotype, in particular a CD4 central memory T cell phenotype (CD4 Tcm), comprises or consists of the following genes: CCR4, CCR8, CTLA4, DAB1, ERN1, GPR15, DNAJB1, KRT1, POU6F1, TPO, TRADD, DLEC1, ICOS, TRAT1, FXYD7, FBXL8, SIRPG, ANKRD55 and OBSCN.

[0060] In one embodiment, the signature for assigning a CD4 T cell phenotype, in particular a CD4 effector memory T cell phenotype (CD4 Tem), comprises or consists of the following genes: RPN2, SLAMF1, SPTAN1, TRADD, MYO16, MCF2L2, ESYT1, TRAPPC2L, ARHGAP15 and RIC8A.

[0061] In one embodiment, the signature for assigning a CD8 T cell phenotype, in particular a CD8 naive T cell phenotype, comprises or consists of the following genes: CD8A, CD8B, EEF1D, GPR15, MYL1, NDUFA4, NDUFS5, PSG11, SKI, SON, HIST1H3A, BUD31, GPR52, ZNHIT3, CGRRF1, RRP8, NGDN, C19orf53, SETD2, MED31, SS18L2, CDK5RAP1, DDX24, PCIF1, MS4A5, HAUS3, LIN28A and TNKS2.

[0062] In one embodiment, a signature for assigning a CD8 T cell phenotype comprises or consists of the following genes: CASP8, CD8A, CD8B, GZMK, PTGDR, SLC1A7, TSPAN32, KLRG1, NPRL2, GIMAP4, CRTAM and ZNF611.

[0063] In one embodiment, a signature for assigning a CD8 T cell phenotype, in particular a CD8 central memory T cell phenotype (CD8 Tcm), comprises or consists of the following genes: CASP8, CD27, TNFSF8, GZMK, NCK1, GPR171, CRTAM, PARP11 and TMEM30B.

[0064] In one embodiment, the signature for assigning a CD8 T cell phenotype, in particular a CD8 effector memory T cell phenotype (CD8 Tem), comprises or consists of the following genes: DHX8, GZMH, GZMK, LAG3, ZAP70, COLQ, RGS9, CXCR6, PVRIG and PYHIN1.

[0065] In one embodiment, the signature for assigning a CD4 or CD8 T cell phenotype is based on the signature of the MCP counter.

[0066] In one embodiment, a signature for assigning a CD4 or CD8 T cell phenotype comprises or consists of the following genes: CD28, CD3D, CD3G, CD5, CD6, CHRM3-AS2, CTLA4, FLT3LG, ICOS, MAL, MGC40069, PBX4, SIRPG, THEMIS, TNFRSF25, TRAT1, CD8B, CD8A, EOMES, FGFBP2, GNLY, KLRC3, K LRC4, KLRD1, BANK1, CD19, CD22, CD79A, CR2, FCRL2, IGKC, MS4A1, PAX5, CD160, KIR2DL1, KIR2DL3, KIR2DL4, KIR3DL1, KIR3DS1, NCR1, PTGDR, SH2D1B, ADAP2, CSF1R, FPR3, KYNU, PLA2G7, RASSF4, TFEC, CD1A, CD1B, CD1E, CLEC10A, CLIC2, WFDC21P, CA4, CEACAM3, CXCR1, CXCR2, CYP4F3, FCGR3B, HAL, KCNJ15, MEGF9, SLC25A37, STE AP4, TECPR2, TLE3, TNFRSF10C, VNN3, ACVRL1, APLN, BCL6B, BMP6, BMX, CDH5, CLEC14A, CXorf36, EDN1, ELTD1 , EMCN, ESAM, ESM1, FAM124B, HECW2, HHIP, KDR, MMRN1, MMRN2, MYCT1, PALMD, PEAR1, PGF, PLXNA2, PTPRB, ROB O4, SDPR, SHANK3, SHE, TEK, TIE1, VEPH1, VWF, COL1A1, COL3A1, COL6A1, COL6A2, DCN, GREM1, PAMR1 and TAGLN.

[0067] In one embodiment, the association of HERVs with cytotoxic T cell responses is assessed by determining the association of each HERV with a functional signature, in one embodiment, the functional signature is an interferon (IFN)-γ signature or cytolytic activity.

[0068] In one embodiment, the IFN-γ signature is based on genes that are upregulated in response to IFN-γ. In one embodiment, the association of HERVs with cytotoxic T cell responses is assessed by determining the association of each HERV with a transcriptomic signature associated with an IFN-γ response.

[0069] In one embodiment, the IFN-γ signature is determined based on a database. For example, an IFN-γ signature database can be accessed via a molecular signature database.

[0070] In one embodiment, the IFN-γ signature includes at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 40, 60, 80, 100, 120, 140, 160, 180, or 200, or all of the following genes: ADAR, APOL6, ARID5B, ARL4A, AUTS2, B2M, BANK1, BATF2, BPGM, BST2, BTG1, C1R, C1S, CASP1, CASP3, CASP4, CASP7, CASP8, CCL2, CCL5, CCL7, CD274, CD38, CD40, CD69, CD74, CD86, CCL9, CCL10, CCL11, CCL12, CCL13, CCL14, CCL15, CCL16, CCL17, CCL18, CCL19, CCL20, CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28, CCL29, CCL30, CCL31, CCL32, CCL33, CCL34, CCL35, CCL36, CCL37, CCL38, CCL39 ... DKN1A, CFB, CFH, CIITA, CMKLR1, CMPK2, CSF2RB, CXCL10, CXCL11, CXCL9, DDX58, DDX60, DHX58, EIF2AK2, EIF4E3, EPSTI1, FAS, FCGR1A, FGL2, FPR1, FTSJ D2, GBP4, GBP6, GCH1, GPR18, GZMA, HERC6, HIF1A, HLA-A, HLA-B, HLA-DMA, HLA-DQA1, HLA-DRB1, HLA-G, ICAM1, IDO1, IFI27, IFI30, IFI35, IFI44, IFI44 L, IFIH1, IFIT1, IFIT2, IFIT3, IFITM2, IFITM3, IFNAR2, IL10RA, IL15, IL15RA, IL18BP, IL2RB, IL4R, IL6, IL7, IRF1, IRF2, IRF4, IRF5, IRF7, IRF8, IRF 9, ISG15, ISG20, ISOC1, ITGB7, JAK2, KLRK1, LAP3, LATS2, LCP2, LGALS3BP, LY6E, LYSMD2, 1-Mar, METTL7B, MT2A, MTHFD2, MVP, MX1, MX2, MYD88, NAMPT, N COA3, NFKB1, NFKBIA, NLRC5, NMI, NOD1, NUP93, OAS2, OAS3, OASL, OGFR, P2RY14, PARP12, PARP14, PDE4B, PELI1, PFKP, PIM1, PLA2G4A, PLSCR1, PML, PNP, PNPT1, PRIC285, PSMA2, PSMA3, PSMB10, PSMB2, PSMB8, PSMB9, PSME1, PSME2, ​​PTGS2, PTPN1, PTPN2, PTPN6, RAPGEF6, RBCK1, RIPK1, RIPK2, RNF213, RNF31,RSAD2, RTP4, SAMD9L, SAMHD1, SECTM1, SELP, SERPING1, SLAMF7, SLC25A28, SOCS1, SOCS3, SOD2, SP110, SPPL2A, SRI, SSPN, ST3GAL5, ST8SIA4, STAT1, STAT2, STAT3, STAT4, TAP1, TAPB P, TDRD7, TNFAIP2, TNFAIP3, TNFAIP6, TNFSF10, TOR1B, TRAFD1, TRIM14, TRIM21, TRIM25, TRIM26, TXNIP, UBE2L6, UPP1, USP18, VAMP5, VAMP8, VCAM1, WARS, XAF1, XCL1, ZBP1 and ZNFX1. ,

[0071] In one embodiment, the IFN-γ signature comprises all of the genes described above. In one embodiment, the IFN-γ signature is HALLMARK_INTERFERON_GAMMA_RESPONSE (https: / / www.gsea msigdb.org / gsea / msigdb / cards / HALLMARK_INTERFERON_GAMMA_RESPONSE.html).

[0072] In one embodiment, the IFN-γ signature is calculated by calculating an enrichment score based on the IFN-γ signature database for each sample per cancer type.

[0073] In one embodiment, cytolytic activity is calculated by assessing the expression levels of Granzyme A (GZMA) and Perforin (PRF1). In one embodiment, cytolytic activity is calculated by calculating the geometric mean of expression of Granzyme A (GZMA) and Perforin (PRF1) levels.

[0074] In one embodiment, the expression levels of Granzyme A (GZMA) and Perforin (PRF1) are calculated at the transcriptome level. In one embodiment, the expression levels of Granzyme A (GZMA) and Perforin (PRF1) are calculated by RNA-seq.

[0075] As used herein, RNA-Seq (short for RNA sequencing) is a sequencing technology that uses next-generation sequencing (NGS) to reveal the presence and quantity of specific RNAs in a biological sample at a particular time.

[0076] RNA-seq data can be found in databases such as those at the NCBI Gene Expression Omnibus (GEO) portal.

[0077] In one embodiment, the expression levels of Granzyme A (GZMA) and Perforin (PRF1) are calculated at the proteome level.

[0078] In one embodiment, the association of HERVs with cytotoxic T cell responses is assessed by the absence of expression of HERVs in normal purified T cells or NK cells. This step further allows for the selection of HERVs that are expressed in neoplastic cells and not in T cells or NK cells.

[0079] Examples of HERVs expressed in T cells or NK cells are known to those of skill in the art and include, but are not limited to, SEQ ID NO:33, SEQ ID NO:34, SEQ ID NO:35, SEQ ID NO:36, SEQ ID NO:37, SEQ ID NO:38, SEQ ID NO:39, SEQ ID NO:40 and SEQ ID NO:41.

[0080] In one embodiment, the association between HERVs and cytotoxic T cell responses is assessed by calculation of one, two or three of the parameters (or multiple) described above.

[0081] In one embodiment, the association of HERVs with cytotoxic T cell responses is assessed by (i) the association of each HERV with at least one CD4 or CD8 T cell signature, (ii) the association of each HERV with a functional signature, either an IFN-γ signature or cytolytic activity, and / or (iii) the absence of expression of each HERV in normal purified T cells or NK cells.

[0082] In one embodiment, the association between HERVs and one, two or three parameters (multiple) described above is evaluated by a machine learning based approach. In one embodiment, the machine learning based approach is used to test the association for each cancer type independently. As used herein, machine learning refers to artificial intelligence, where an algorithm builds a model based on training data.

[0083] In one embodiment, the association between the HERV and one of the above parameters is calculated by regression, preferably L1 penalized regression (LASSO). In particular, a model can be built for each cancer type, and each HERV with a positive coefficient in the model is considered as a parameter.

[0084] In one embodiment, the method comprises the step of selecting a T cell epitope, in particular a shared T cell epitope.

[0085] This step in particular allows for the selection of epitopes that i) have regions in common with known HERV proteins, thereby reducing the risk of selecting untranslated sequences, and i) are strong binders to MHC class I molecules, allowing their presentation by MHC class I molecules. This step may further allow for the selection of epitopes that are iii) most shared among HERVs, such as HERVs associated with cancer.

[0086] In one embodiment, the method comprises selecting a T cell epitope, hi one embodiment, selecting a T cell epitope comprises determining a putative epitope.

[0087] In one embodiment, the step of selecting a T cell epitope comprises translating the HERV sequence into 1, 2, 3, 4, 5, or 6 possible frames to identify open reading frames (ORFs) of at least 10, 11, 12, 13, 14, 15, or more amino acids to obtain putative epitopes.

[0088] In one embodiment, the step of selecting a T cell epitope comprises predicting binding of the putative epitope to an MHC class I molecule.

[0089] Analytical tools for predicting binding of sequences to MHC molecules are well known to those skilled in the art and include, for example, MHCflurry (TJO'Donnell et al., MHCflurry: Open-Source Class I MHC Binding Affinity Prediction. Cell Systems. 7, 129-132. e4 (2018)) or NetMCH I & II.

[0090] In one embodiment, the MCH class I molecule is an HLA molecule.In one embodiment, the MCH class I molecule comprises or is selected from the group consisting of HLA-A molecule, HLA-B molecule and HLA-C molecule.In one embodiment, the MCH class I molecule is an HLA-A molecule, such as HLA-A2 molecule.

[0091] In one embodiment, only T cell epitopes predicted to be capable of binding to MHC class I molecules are selected, in one embodiment, the epitopes are selected if their sequences are predicted to be strong binders of MHC class I molecules as defined above.

[0092] In one embodiment, the step of selecting the T cell epitope comprises aligning the epitope with sequences of known HERV proteins to reduce the risk of selecting untranslated sequences.

[0093] In one embodiment, the HERV protein is an envelope (Env) protein. In one embodiment, the HERV protein is a group-specific antigen (Gag) protein. In one embodiment, the HERV protein is a polymerase (Pol). In one embodiment, the HERV protein is a protease (Pro). In one embodiment, the HERV protein is a Rec protein. In one embodiment, the HERV protein is an accessory protein.

[0094] In one embodiment, the HERV protein is a HERV-K / HML-2 protein. In one embodiment, the HERV protein is a HERV-K / HML-2 Gag protein. In one embodiment, the HERV protein is a HERV-K / HML-2 Pol protein.

[0095] Examples of HERV proteins include, but are not limited to, HERV-K10Gag protein (SEQ ID NO: 20), HERV-K113Gag protein (SEQ ID NO: 21), HERV-K21Gag protein (SEQ ID NO: 22), HERV-K113Pol protein (SEQ ID NO: 23), HERV-K9Pol protein (SEQ ID NO: 24), HERV-K6Pol protein (SEQ ID NO: 25), and HERV-K113Env protein (SEQ ID NO: 26). SEQ ID NO:20 MGQTKSKIKSKYASYLSFIKILLKRGGVKVSTKNLIKLFQIIEQFCPWFPEQGTSDLKDWKRIGKELKQAGRKGNIIPLTVWNDWAIIKAALEPFQTEEDSISVSDAPGSCLIDCNENTRKKSQKETESLHCEYVAEPVMAQSTQNVDYNQLQEVIYPETLKLEGK GPELMGPSESKPRGTSPLPAGQVLVRLQPQKQVKENKTQPQVAYQYWPLAELQYRPPPESQYGYPGMPPAPQGRAPYHQPPTRRLNPMAPPSRQGSELHEIIDKSRKEGDTEAWQFPVTLEPMPPGEGAQEGEPPTVEARYKSFSIKMLKDMKEGVKQYGPNSPYMR TLLDSIAYGHRLIPYDWEILAKSSLSPSQFLQFKTWWIDGVQEQVRRNRAANPPVNIDADQLLGIGQNWSTISQQALMQNEAIEQVRAICLRAWEKIQDPGSTCPSFNTVRQGSKEPYPDFVARLQDVAQKSIADEKAGKVIVELMAYENANPECQSAIKPLKGKV PAGSDVISEYVKACDGIGGAMHKAMLMAQAITGVVLGGQVRTFGGKCYNCGQIGHLKKNCPVLNKQNITIQATTTGREPPDLCPRCKKGKHWASQCRSKFDKNGQPLSGNEQRGQPQAPQQTGAFPIQPFVPQGFQGQQPPLSQVFQGISQLPQYNNCPSPQAAVQQ SEQ ID NO:21 MGQTKSKIKSKYASYLSFIKILLKRGGVKVSTKNLIKLFQIIEQFCPWFPEQGTLDLKDWKRIGKELKQAGRKGNIIPLTVWNDWAIIKAALEPFQTEEDSVSVSDAPGSCIIDCNEKTRKKSQKETESLHCEYVAEPVMAQSTQNADYNQLQEVIYPETLKLEGK GPELMGPSESKPRGTSPLPAGQVPVTLQPQKQVKENKTQPPVAYQYWPPAELQYQPPPESQYGYPGMPPAPQGRAPYPQPPTRRLNPTAPPSRQGSELHEIIDKSRKEGDTEAWQFPVTLELMPPGEGAQEGEPPTVEARYKSFSIKMLKDMKEGVKQYGPNSPYMR TLLDSIAHGHRLIPYDWEILAKSSLSPSQFLQFKTWWIDGVQEQVRRNRAANNPPVNIDADQLLGIGQNWSTISQQALMQNEAIEQVRAICLRAWEKIQDPGSTCPSFNTVRQGSKEPYPDFVARLQDVAQKSIADEKARKVIVELMAYENANPECQSAIKPLKGKV PAGSDVISEYVKACDMGGAMHKAMLMAQAITGVVLGGQVRTFGGKCYNCGQIGHLKKNCPVLNKQNITIQATTTGREPPDLCPRCKKGKHWASQCRSKFDKNGQPLSGNEQRGQPQAPQQTGAFPIQPFVPQGFQGQQPPLSQVFQGISQLPQYNNCPPPQAAVQQ SEQ ID NO:22 MGQTKSKIKSKYASYLSFIKILLKRGGVKVSTKNLIKLFQIIEQFCPWFPEQGTLDLKDWKRIGKELKQAGRKGNIIPLTVWNDWAIIKAALEPFQTEEDSISVSDAPGSCIIDCNENTRKKSQKETEGLHCEYAAEPVMAQSTQNVDYNQLQEVIYPETLKLEGK GPELVGPSESKPRGTSPLPAGQVPVTLQPQTQVKENKTQPPVAYQYWPPAELQYRPPPESQYGYPGMPPAPQGRAPYPQPPTRRLNPTAPPSRQGSELHEIIDKSKEGDTEAWQFPVMLEPMPPGEGAQEGEPPTVEARYKSFSIKMLKDMKEGVKQYGPNSPYMR TLLDSIAHGHRLIPYDWEILAKSSLLPSQFLQFKTWWIDGVQEQVQRNRAANPPVNIDADQLLGIGQNWSTISQQALMQNEAIEQVRAICLRAWEKIQDPGSTCPSFNTVRQSSKEPYPDFVARLQDVAQKSIADEKARKVIVELMAYENANPECQSAIKPLKGKV PAGSDVISEYVKACDGIGGAMHKAMLMAQAITGVVLGGQVRTFGGKCYNCGQIGHLKKNCPVLNKQNITIQATTTGREPPDLCPRCKKGKHWASQCRSKFDKNGQPLSGNEQRGQPQAPQQTGAFPIQPFVPQGFQGQQPPLSQVFQGISQLPQYNNCPPPQAAVQQ SEQ ID NO:23 NKSRKRRNRVSFLGAATVEPPKPIPLTWKTEKPVWVNQWPLPKQKLEALHLLANEQLEKGHIEPSFSPWNSPVFVIQKKSGKWRMLTDLRAVNAVIQPMGPLQPGLPSPAMIPKDWPLI IIDLKDCFFTIPLAEQDCEKFAFTIPAINNKEPATRFQWKVLPQGMLNSPTICQTFVGRALQPVRDKFSDCYIIHYIDDILCAAETKDKLIDCYTFLQAEVANAGLAIASDKIQTSTPFH YLGMQIENRKIKPQKIEIRKDTLKTLNDFQKLLGDINWIRPTLGIPTYVMSNLFSILRGDSDLNSKRMLTPETTKEIKLVEEKIQSAQINRIDPLAPLRLLIFATAHSPIGIIIQNTDL VEWSFLPHSTVKTFTLYLDQIATLIGQTRLRIIKLCGNDPDKIVVPLTKEQVRQAFINSGAWQIGLANFVGIIDNHYPKTKIFQFLKLTTWILPKITRREPLENALTVFTDGSSNGKAAY TGLKERVIKTPYQSAQRAELVAVITVLQDFDQPINIISDSAYVVQATRDVETALIKYSMDDQLNQLFNLLQQTVRKRNFPFYITHIRAHTNLPGPLTKANEQADLLVSSALIKAQELHA LTHVNAAGLKNKFDVTWKQAKDIVQHCTQCQVLHLPTQEAGVNPRGLCPNALWQMDVTHVPSFGRLSYVHVTVDTYSHFIWATCQTGESTSHVKKHLLSCFAVMGVPEKIKTDNGPGYCS KAFQKFLSQWKISHTTGIPYNSQGQAIVERTNRTLKTQLVKQKEGGDSKECTTPQMQLNLAPYTLNFLNIYRNQTTTSAEQHLTGKKNSPHEGKLIWWKDNKNKTWEIGKVITWGRGFA CVSPGENQLPVWMPTRHLKFYNEPIGDAKKSTSAETETPQSSTVDSQDEQNGDVRRTDEVAIHQEGRAADLGTTKEADAVSYKISREHKGDTNPREYAACSLDDCINGGKSPYACRSSCS SEQ ID NO:24 SEQ ID NO:25 NKSRKRRNRESLLGAATVEPPKPIPLTWKTEKPVWVNQWPLPKQKLEALHLLANEQLEKGHIEPSFSPWNSPVFVIQKKSGKWRMLTDLRAVNAVIQPMGPLQPGLPSPAMIPKDWPLI IIDLKDCFFTIPLAEQDCEKFAFTIPAINNKEPATRFQWKVLPQGMLNSPTICQTFVGRALQPVREKFSDCYIIHCIDDILCAAETKDKLIDCYTFLQAEVANAGLAIASDKIQTSTPFH YLGMQIENRKIKPQKIEIRKDTLKTLNDFQKLLGDINWIRPTLGIPTYAMSNLFSILRGDSDLNSKRMLTPEATKEIKLVEEKIQSAQINRIDPLAPLQLLIFATAHSPTGIIIQNTDL VEWSFLPHSTVKTFTLYLDQIATLIGQTRLRIIKLCGNDPDKIVVPLTKEQVRQAFINSGAWKIGLANFVGIIDNHYPKTKIFQFLKLTTWILPKITRREPLENALTVFTDGSSNGKAAY TGPKERVIKTPYQSAQRAELVAVITVLQDFDQPINIISDSAYVVQATRDVETALIKYSMDDQLNQLFNLLQQTVRKRNFPFYITHIRAHTNLPGPLTKANEQADLLVSSALIKAQELHA LTHVNAAGLKNKFDVTWKQAKDIVQHCTQCQVLHLPTQEAGVNPRGLCPNALWQMDVTHVPSFGRLSYVHVTVDTYSHFIWATCQTGESTSHVKKHLLSCFAVMGVPEKIKTDNGPGYCS KAFQKFLSQWKISHTTGIPYNSQGQAIVERTNRTLKTQLVKQKEGGDSKECTTPQMQLNLALYTLNFLNIYRNQTTTSAEQHLTGKKNSPHEGKLIWWKDNKNKTWEIGKVITWGRGFA CVSPGENQLPVWIPTRHLKFYNEPIRDAKKSTSAETETSQSSTVDSQDEQNGDVRRTDEVAIHQEGRAANLGTTKEADAVSYKISREHKGDTNPREYAACSLDDCINGGKSPYACRSSCS SEQ ID NO:26 MNPSEMQRKAPPRRRRHRNRAPLTHKMNKMVTSEEEQMKLPSTKKAEPPTWAQLKKLTQLATKYLENTKVTQTPESMLLAALMIVSMVVSLPPMPAGAAAANYTYWAYVPFPPLIRAVTWMDNPIEIYVNDSVWVPGPTDDCCPAKPEEEGMMINISIGYRYPPICLGRAPGCLMP AVQNWLVEVVPTVSPISRFTYHMVSGMSLRPRVNYLQDFSYQRSLKFRPKGKPCPKEIPKESKNTEVLVWEECVANSAVILQNNEFGTLIDWAPRGQFYHNCSGQTQSCPSAQVSPAVDSDLTESLDKHKHKKLQSFYPWEWGEKGISTARPKIISPVSGPEHPELWRLTVASHHI RIWSGNQTLETRDRKPFYTIDLNSSLTVPLQSCVKPPYMLVVGNIVIKPDSQTITCENCRLLTCIDSTFNWQHRILLVRAREGVWIPVSMDRPWEASPSVHILTEVLKGVLNRSKRFIFTLIAVIMGLIAVTATAAVAGVALHSSVQSVNFVNDWQNNSTRLWNSQSSIDQKLAN QINDLRQTVIWMGDRLMSLEHRFQLQCDWNTSDFCITPQIYNESEHHWDMVRCHLQGREDNLTLDISKLKEQIFEASKAHLNLVPGTEAIAGVADGLANLNTVTWVKTIGSTTIINLILILVCLFCLLLVYRCTQQLRRDDSDHRERAMMTMVVLSKRKGGNVGKSKRDQIVTVSV

[0096] In one embodiment, an epitope is selected if its sequence shares at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or more identity with at least one HERV protein as defined above. In one embodiment, a sequence is selected if said sequence shares at least 90% identity with at least one HERV protein as defined above.

[0097] In one embodiment, an epitope is selected if its sequence shares at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or more identity with a HERV protein defined above and / or if said epitope is predicted to bind to an MHC class I molecule as described above.

[0098] In one embodiment, the above-mentioned method comprises a step of selecting an epitope among the most shared epitopes in the HERVs identified in one of the above-mentioned steps, which step in particular makes it possible to select an epitope among the most shared epitopes in HERVs, such as HERVs associated with cancer.

[0099] This step may result in the selection of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more epitopes among the most shared epitopes in the HERVs identified in one of the previous steps, such as HERVs associated with cancer.

[0100] In one embodiment, the above-mentioned method comprises a step of evaluating the expression of an epitope identified by one of the above-mentioned steps at the protein or peptide level in a tumor sample, which step in particular makes it possible to verify that the selected epitope is translated or expressed in the tumor sample.

[0101] In one embodiment, the expression of said epitopes at the protein or peptide level is calculated in silico by using one or more proteome database(s).

[0102] Proteome databases can be obtained from tandem mass spectrometry (MS / MS) data. As used herein, tandem mass spectrometry (TANDEM MS), also referred to as MS / MS, refers to a mass spectrometry technique that uses two or more mass analyzers. In tandem, precursor ions are mass selected by the first mass analyzer and focused into a collision region where they are then fragmented into product ions, which are then characterized by the second mass analyzer.

[0103] Analytical tools for evaluating the expression of proteins or peptides from databases are well known to those of skill in the art and include, for example, pepquery (B. Wen et al., PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations. Genome Res. 29, 485-493 (2019)).

[0104] In one embodiment, the expression of said epitopes at the protein or peptide level is calculated in vitro by immunopeptidemic analysis, which makes it possible to evaluate the presence of epitopes associated with HLA molecules of the tumor.

[0105] In one embodiment, an epitope is selected where said epitope shows evidence of translation in the tumor sample and / or evidence of translation and association with an HLA molecule in the tumor sample.

[0106] In one embodiment, the method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer-associated HERV; (b) selecting T cell epitopes among the HERVs identified in the previous step; and The method further comprises: (i) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response, the selecting step being between steps (a) and (b); and / or (ii) evaluating the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in the tumor sample, said evaluating step being subsequent to step (b); At least one, preferably two of the above.

[0107] In one embodiment, steps (a) and (b) and steps (i) and / or (ii) are in silico steps. In one embodiment, step (ii) is an in vitro step.

[0108] In one embodiment, step (a) comprises identifying and selecting a HERV associated with cancer, preferably associated with at least one cancer.

[0109] In one embodiment, step (b) comprises selecting a common T cell epitope.

[0110] In one embodiment, the method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer-associated HERV; (b) selecting T cell epitopes among the HERVs identified in the previous step; and The method further comprises: (i) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response, the selecting step being between steps (a) and (b); and / or (ii) evaluating the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in the tumor sample, said evaluating step being subsequent to step (b); At least one, preferably two of: Steps (a), (b), (i) and (ii) are carried out in silico.

[0111] Thus, in one embodiment, the in silico method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer-associated HERV; (b) selecting T cell epitopes among the HERVs identified in the previous step; and The method further comprises: (i) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response, the selecting step being between steps (a) and (b); and / or (ii) evaluating the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in the tumor sample, said evaluating step being subsequent to step (b); At least one, preferably two of the above.

[0112] In one embodiment, the method further comprises, after step (b) or before step (ii), selecting an epitope among the most shared epitopes in the cancer-associated HERVs identified in step (a).

[0113] In one embodiment, the in silico method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting a T cell epitope among the cancer-associated HERVs identified in step (a); (c) assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in the tumor sample; Includes.

[0114] In one embodiment, the in silico method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response; (c) selecting a T cell epitope among the HERVs identified in step (b); (d) assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (c) in the tumor sample; Includes.

[0115] In one embodiment, the in silico method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting a T cell epitope among the cancer-associated HERVs identified in step (a); (c) selecting an epitope among the most shared epitopes in the cancer-associated HERVs identified in step (a); (d) assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (c) in the tumor sample; Includes.

[0116] In one embodiment, the in silico method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response; (c) selecting a T cell epitope among the HERVs identified in step (b); (d) selecting an epitope among the most shared epitopes in the cancer-associated HERVs identified in step (a); (e) assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (d) in the tumor sample; Includes.

[0117] In one embodiment, the in silico method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response; (c) selecting T cell epitopes among the cancer-associated HERVs identified in step (a); Includes.

[0118] In one embodiment, the in silico method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response; (c) selecting a T cell epitope among the cancer-associated HERVs identified in step (b); (d) selecting an epitope among the most shared epitopes in the cancer-associated HERVs identified in step (a); Includes.

[0119] In one embodiment, the steps are as described above.

[0120] In one embodiment, the in silico method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response, selecting, wherein the association of the HERVs with the cytotoxic T cell response is assessed by (i) association of each HERV with at least one CD4 or CD8 T cell signature, (ii) association of each HERV with a functional signature, which is either an interferon (IFN)-γ signature or cytolytic activity, and / or (iii) absence of expression of each HERV in normal purified T cells or NK cells; (c) selecting T cell epitopes among the HERVs identified in step (b), Selecting a T cell epitope - determining a putative epitope; - predicting the binding of putative epitopes to MHC class I molecules; - optionally aligning the epitopes with sequences of known HERV proteins; a selecting step including: (d) optionally selecting an epitope among the most shared epitopes in cancer-associated HERVs; (e) optionally assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (c) or (d) in the tumor sample; Includes.

[0121] In one embodiment, the method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer-associated HERV; (b) selecting T cell epitopes among the HERVs identified in the previous step; and The method further comprises: (i) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response, the selecting step being between steps (a) and (b); and / or (ii) evaluating the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in the tumor sample, said evaluating step being subsequent to step (b); At least one, preferably two of: Steps (a), (b) and (i) are carried out in silico and step (ii) is carried out in vitro.

[0122] In one embodiment, step (a) comprises identifying and selecting a HERV associated with cancer, preferably associated with at least one cancer.

[0123] In one embodiment, step (b) comprises selecting a common T cell epitope.

[0124] In one embodiment, the method further comprises, after step (b) or before step (ii), selecting an epitope among the most shared epitopes in the cancer-associated HERVs identified in step (a).

[0125] In one embodiment, the method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting a T cell epitope among the cancer-associated HERVs identified in step (a); (c) assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in the tumor sample; Including, Steps (a) and (b) are carried out in silico, and step (c) is carried out in vitro.

[0126] In one embodiment, the method described above comprises the steps of: (c) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (d) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response; (c) selecting a T cell epitope among the HERVs identified in step (b); (d) assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (c) in the tumor sample; Including, Steps (a), (b) and (c) are carried out in silico and step (d) is carried out in vitro.

[0127] In one embodiment, the method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting a T cell epitope among the cancer-associated HERVs identified in step (a); (c) selecting an epitope among the most shared epitopes in the cancer-associated HERVs identified in step (a); (d) assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (c) in the tumor sample; Including, Steps (a), (b) and (c) are carried out in silico and step (d) is carried out in vitro.

[0128] In one embodiment, the method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response; (c) selecting a T cell epitope among the HERVs identified in step (b); (d) selecting an epitope among the most shared epitopes in the cancer-associated HERVs identified in step (a); (e) assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (d) in the tumor sample; Including, Steps (a), (b), (c) and (d) are performed in silico and step (e) is performed in vitro.

[0129] In one embodiment, the steps are as described above.

[0130] In one embodiment, the method described above comprises the steps of: (a) identifying a HERV associated with cancer, preferably at least one cancer associated HERV; (b) selecting, among the cancer-associated HERVs identified in step (a), a HERV associated with a cytotoxic T cell response, selecting, wherein the association of the HERVs with the cytotoxic T cell response is assessed by (i) association of each HERV with at least one CD4 or CD8 T cell signature, (ii) association of each HERV with a functional signature, which is either an interferon (IFN)-γ signature or cytolytic activity, and / or (iii) absence of expression of each HERV in normal purified T cells or NK cells; (c) selecting T cell epitopes among the HERVs identified in step (b), Selecting a T cell epitope - determining a putative epitope; - predicting the binding of putative epitopes to MHC class I molecules; - optionally aligning the epitopes with sequences of known HERV proteins; a selecting step including: (d) optionally selecting an epitope among the most shared epitopes in cancer-associated HERVs; (e) optionally assessing the expression at the protein or peptide level of the HERV-derived T-cell epitopes identified in step (c) or (d) in the tumor sample; Including, Steps (a), (b), (c) and (d) are performed in silico and step (e) is performed in vitro.

[0131] In one embodiment, steps (d) and (e), if present, are carried out in parallel. In one embodiment, the step of aligning epitopes with sequences of known HERV proteins and steps (d) and / or (e), if present, are carried out in parallel.

[0132] In one embodiment, the above-mentioned method further comprises a step of aligning the selected epitopes with the human proteome, which step in particular makes it possible to verify that the selected epitopes do not match any self-proteins.

[0133] In one embodiment, the step of aligning the selected epitopes with the human proteome is performed in parallel with the step of aligning the epitopes with sequences of known HERV proteins, if present, step (d) and / or step (e).

[0134] In one embodiment, the above method is combined with in vitro validation of the selected epitopes. In particular, the above method may be combined with one or more of the step(s) described below.

[0135] In one embodiment, the in vitro validation comprises calculating the induction of a T cell response.

[0136] In one embodiment, the induction of T cell responses is assessed by measuring the induction of CD8+ T cells specific to the selected epitope. An example of a method for assessing the induction of CD8+ T cells includes, for example, an in vitro priming assay using the selected epitope (see, for example, the Examples section).

[0137] In one embodiment, the induction of T cell response is evaluated by measuring the production of IFN-γ or granzyme B in the presence of epitope stimulated cells (see, for example, the Examples section). Exemplary methods for measuring the production of IFN-γ or granzyme B include, for example, flow cytometry or Fluorospot assay.

[0138] In one embodiment, the induction of a T cell response is assessed by measuring a degranulation marker, such as CD107a (see, for example, the Examples section).

[0139] In one embodiment, the in vitro validation includes calculating the affinity of the selected epitope-specific CD8+ T cells with the MCH molecule. Examples of methods for evaluating such affinity include, but are not limited to, 3D modeling (see, for example, the Examples section).

[0140] In one embodiment, the in vitro validation comprises calculating the functionality of CD8+ T cells specific for the selected epitope. Examples of methods for measuring the functionality of CD8+ T cells include, but are not limited to, immune cell killing assays and analysis of morphological signs of T cell activation by microscopy (see, for example, the Examples section).

[0141] In one embodiment, the in vitro validation comprises evaluating the presence of T cells specific for the selected epitope in tumor tissue of a subject suffering from cancer (see, for example, the Examples section). The tumor tissue may be obtained by biopsy.

[0142] The present invention also relates to a peptide comprising or consisting of an epitope identified by the above method.

[0143] The present invention relates to a peptide comprising or consisting of an epitope having a sequence selected in the group comprising or consisting of KLLGDINWI (SEQ ID NO:1), FIFTLIAVI (SEQ ID NO:2), RMLTDLRAV (SEQ ID NO:3), FLSLYFVSV (SEQ ID NO:4), TLIAVIMGL (SEQ ID NO:5), YLDQIATLI (SEQ ID NO:6), FLQAEVANA (SEQ ID NO:7), WMGDRLMSL (SEQ ID NO:8), ALHSSVQSV (SEQ ID NO:9), ILTEVLKGV (SEQ ID NO:10), LMAQAITGV (SEQ ID NO:11), RLSYVHVTV (SEQ ID NO:12), VLQDFDQPI (SEQ ID NO:13), ALMIVSMV (SEQ ID NO:14), MLLAALMIV (SEQ ID NO:15), YIDDILCAA (SEQ ID NO:16), YIIHYIDDI (SEQ ID NO:17), YIWCPTWRL (SEQ ID NO:18), and YIWCPTWSL (SEQ ID NO:19). The present invention relates to peptides comprising or consisting of SEQ ID NO:1), FIFTLIAVI (SEQ ID NO:2), RMLTDLRAV (SEQ ID NO:3), FLSLYFVSV (SEQ ID NO:4), TLIAVIMGL (SEQ ID NO:5), YLDQIATLI (SEQ ID NO:6), FLQAEVANA (SEQ ID NO:7), WMGDRLMSL (SEQ ID NO:8), ALHSSVQSV (SEQ ID NO:9), ILTEVLKGV (SEQ ID NO:10), LMAQAITGV (SEQ ID NO:11), RLSYVHVTV (SEQ ID NO:12), VLQDFDQPI (SEQ ID NO:13), ALMIVSMV (SEQ ID NO:14), MLLAALMIV (SEQ ID NO:15), YIDDILCAA (SEQ ID NO:16), YIIHYIDDI (SEQ ID NO:17), YIWCPTWRL (SEQ ID NO:18), and YIWCPTWSL (SEQ ID NO:19).

[0144] In one embodiment, the peptide comprises or consists of a sequence selected in the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), ALHSSVQSV (SEQ ID NO:9), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), ALMIVSMV (SEQ ID NO:14), MLLAALMIV (SEQ ID NO:15), YIDDILCAA (SEQ ID NO:16). In one embodiment, the peptide comprises or consists of an epitope having a sequence selected in the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), ALHSSVQSV (SEQ ID NO:9), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), ALMIVSMV (SEQ ID NO:14), MLLAALMIV (SEQ ID NO:15), YIDDILCAA (SEQ ID NO:16).

[0145] In one embodiment, the peptide comprises or consists of an epitope having a sequence selected in the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), ALMIVSMV (SEQ ID NO:14), MLLAALMIV (SEQ ID NO:15), YIDDILCAA (SEQ ID NO:16). In one embodiment, the peptide comprises or consists of a sequence selected in the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), ALMIVSMV (SEQ ID NO:14), MLLAALMIV (SEQ ID NO:15), YIDDILCAA (SEQ ID NO:16).

[0146] In one embodiment, the peptide comprises or consists of an epitope having a sequence selected from the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), ALMIVSMVV (SEQ ID NO:14), YIDDILCAA (SEQ ID NO:16). In one embodiment, the peptide comprises or consists of a sequence selected from the group comprising or consisting of RMLTDLRAV (SEQ ID NO:3), LMAQAITGV (SEQ ID NO:11), VLQDFDQPI (SEQ ID NO:13), ALMIVSMVV (SEQ ID NO:14), YIDDILCAA (SEQ ID NO:16).

[0147] In one embodiment, the peptide comprises or consists of an epitope of sequence KLLGDINWI (SEQ ID NO:1). In one embodiment, the peptide comprises or consists of an epitope of sequence FIFTLIAVI (SEQ ID NO:2). In one embodiment, the peptide comprises or consists of an epitope of sequence RMLTDLRAV (SEQ ID NO:3). In one embodiment, the peptide comprises or consists of an epitope of sequence FLSLYFVSV (SEQ ID NO:4). In one embodiment, the peptide comprises or consists of an epitope of sequence TLIAVIMGL (SEQ ID NO:5). In one embodiment, the peptide comprises or consists of an epitope of sequence YLDQIATLI (SEQ ID NO:6). In one embodiment, the peptide comprises or consists of an epitope of sequence FLQAEVANA (SEQ ID NO:7). In one embodiment, the peptide comprises or consists of an epitope of sequence WMGDRLMSL (SEQ ID NO:8). In one embodiment, the peptide comprises or consists of an epitope of the sequence ALHSSVQSV (SEQ ID NO: 9). In one embodiment, the peptide comprises or consists of an epitope of the sequence ILTEVLKGV (SEQ ID NO: 10). In one embodiment, the peptide comprises or consists of an epitope of the sequence LMAQAITGV (SEQ ID NO: 11). In one embodiment, the peptide comprises or consists of an epitope of the sequence RLSYVHVTV (SEQ ID NO: 12). In one embodiment, the peptide comprises or consists of an epitope of the sequence VLQDFDQPI (SEQ ID NO: 13). In one embodiment, the peptide comprises or consists of an epitope of the sequence ALMIVSMVV (SEQ ID NO: 14). In one embodiment, the peptide comprises or consists of an epitope of the sequence MLLAALMIV (SEQ ID NO: 15). In one embodiment, the peptide comprises or consists of an epitope of the sequence YIDDILCAA (SEQ ID NO: 16). In one embodiment, the peptide comprises or consists of an epitope of the sequence YIIHYIDDI (SEQ ID NO: 17).In one embodiment, the peptide comprises or consists of an epitope of the sequence YIWCPTWRL (SEQ ID NO: 18). In one embodiment, the peptide comprises or consists of an epitope of the sequence YIWCPTWSL (SEQ ID NO: 19).

[0148] In one embodiment, the peptide comprises or consists of KLLGDINWI (SEQ ID NO:1). In one embodiment, the peptide comprises or consists of FIFTLIAVI (SEQ ID NO:2). In one embodiment, the peptide comprises or consists of RMLTDLRAV (SEQ ID NO:3). In one embodiment, the peptide comprises or consists of FLSLYFVSV (SEQ ID NO:4). In one embodiment, the peptide comprises or consists of TLIAVIMGL (SEQ ID NO:5). In one embodiment, the peptide comprises or consists of YLDQIATLI (SEQ ID NO:6). In one embodiment, the peptide comprises or consists of FLQAEVANA (SEQ ID NO:7). In one embodiment, the peptide comprises or consists of WMGDRLMSL (SEQ ID NO:8). In one embodiment, the peptide comprises or consists of ALHSSVQSV (SEQ ID NO:9). In one embodiment, the peptide comprises or consists of ILTEVLKGV (SEQ ID NO:10). In one embodiment, the peptide comprises or consists of LMAQAITGV (SEQ ID NO:11). In one embodiment, the peptide comprises or consists of RLSYVHVTV (SEQ ID NO: 12). In one embodiment, the peptide comprises or consists of VLQDFDQPI (SEQ ID NO: 13). In one embodiment, the peptide comprises or consists of ALMIVSMVV (SEQ ID NO: 14). In one embodiment, the peptide comprises or consists of MLLAALMIV (SEQ ID NO: 15). In one embodiment, the peptide comprises or consists of YIDDILCAA (SEQ ID NO: 16). In one embodiment, the peptide comprises or consists of YIIHYIDDI (SEQ ID NO: 17). In one embodiment, the peptide comprises or consists of YIWCPTWRL (SEQ ID NO: 18). In one embodiment, the peptide comprises or consists of YIWCPTWSL (SEQ ID NO: 19).

[0149] The peptides may be made by any technique known to those of skill in the art, including expression of a protein, polypeptide, or peptide by standard molecular biology techniques, isolation of the protein or peptide from a natural source, or chemical synthesis of the protein or peptide. Synthetic peptides are generally a maximum of about 35 residues in length, which is approximately the upper length limit of automated peptide synthesis machines, such as those available from Applied Biosystems (Foster City, Calif.). Longer peptides may also be prepared, for example, by recombinant means.

[0150] The present invention also relates to an expression vector directing the expression of one or more peptide(s) as described above.The present invention also relates to an expression vector comprising a nucleic acid sequence encoding one or more peptide(s) as described above.

[0151] The vector may in particular be an RNA vector, a DNA vector or a plasmid, a viral vector, or a bacterial vector. Depending on the nature of the vector, there may or may not be integration of the expression cassette into the host cell genome, which is well known to those skilled in the art. The expression vector or expression cassette may further comprise elements necessary for the in vivo expression of the nucleic acid (polynucleotide) in a subject. For example, it may consist of an initiation codon (ATG), a stop codon, and a promoter, as well as a polyadenylation sequence for certain vectors, such as plasmids and viral vectors other than poxviruses. The ATG may be located 5' of the reading frame, and the stop codon may be located 3'. As is well known, there may be other elements that allow the control of expression, such as enhancer sequences, stabilizing sequences, and signal sequences that allow the secretion of the peptide.

[0152] As for RNA vectors, the vectors can use, for example, non-replicating mRNA or self-amplifying RNA derived from viruses. Conventional mRNA-based vectors can code for peptides of interest and contain 5' and 3' untranslated regions (UTRs). Self-amplifying RNA can code for not only peptides of interest but also viral replication mechanisms that allow intracellular RNA amplification and abundant protein expression.

[0153] Examples of viral vectors include, but are not limited to, lentiviruses and retroviruses.

[0154] In one embodiment, the method for obtaining a peptide as described above comprises the steps of: - introducing into a competent host cell, in vitro or ex vivo, the vector described above, and - culturing in vitro or ex vivo a host cell transformed with said expression vector under conditions suitable for the expression of the peptide; - optionally selecting cells which express and / or secrete said peptide; - recovering the expressed peptide, Includes.

[0155] The present invention also relates to the cytotoxic T lymphocytes (CTLs) of a subject treated with one or more peptide(s) as described above.

[0156] The present invention also relates to cytotoxic T lymphocytes (CTLs) of a subject treated with one or more expression vector(s) as described above.

[0157] The present invention also relates to a T cell receptor (TCR) that recognizes such a peptide.

[0158] The present invention also relates to modified T cells expressing a TCR that recognizes a peptide as described above.

[0159] The process of preparing these T cells is known to those skilled in the art and can be as follows: (i) TCR α and β chains are isolated from T cells that recognize the peptide as described above and inserted into a vector; (ii) T cells isolated from the peripheral blood of a patient or donor are modified with such vectors to encode the desired TCR αβ sequence; (iii) these modified T cells are then expanded in vitro and administered to the patient in sufficient numbers for therapy. Of note, the TCR sequence can be modified for optimization of TCR affinity.

[0160] The present invention also relates to one or more peptide(s), one or more expression vector(s), one or more CTL(s), or one or more modified T cell(s) described above for use as a vaccine.

[0161] The present invention also relates to one or more peptide(s), one or more expression vector(s), one or more CTL(s), or one or more modified T cell(s) described above for use as a medicament.

[0162] The present invention also relates to one or more peptide(s), one or more expression vector(s), one or more CTL(s), or one or more modified T cell(s) described above for use in treating or preventing cancer in a subject in need thereof.

[0163] The present invention also relates to the use of one or more peptide(s), one or more expression vector(s), one or more CTL(s), or one or more modified T cell(s) described above in the manufacture of a medicament for treating or preventing cancer in a subject in need thereof.

[0164] The present invention also relates to a method for treating or preventing cancer in a subject in need thereof, the method comprising administration to said subject of one or more peptide(s), one or more expression vector(s), one or more CTL(s), or one or more modified T cell(s) described above.

[0165] In one embodiment, the cancer is selected from the group including or consisting of breast cancer, including triple-negative breast cancer (TNBC), ovarian cancer, melanoma, sarcoma, teratocarcinoma, bladder cancer, lung cancer, such as non-small cell lung cancer and small cell lung cancer, head and neck cancer, colorectal cancer, glioblastoma, leukemia, lymphoma, and other solid tumors and hematological malignancies.

[0166] In one embodiment, the cancer is selected from the group including or consisting of breast cancer, including triple-negative breast cancer (TNBC), ovarian cancer, melanoma, sarcoma, lung cancer, such as non-small cell lung cancer and small cell lung cancer, head and neck cancer, glioblastoma, leukemia, lymphoma, and other solid tumors and hematological malignancies.

[0167] In one embodiment, the cancer is breast cancer, preferably TNBC.

[0168] In one embodiment, the one or more peptide(s), one or more expression vector(s), one or more CTL(s), or one or more modified T cell(s) described above induce an immune response, such as a T cell response, preferably an immune response against a tumor-associated epitope.

[0169] Those skilled in the art will know various assays for determining whether an immune response against a tumor-associated epitope has occurred. Various B-lymphocyte and T-lymphocyte assays are well known, such as cytotoxic T-lymphocyte (CTL) assays such as ELISA, chromium release assays, proliferation assays using peripheral blood lymphocytes (PBL), tetramer assays, and cytokine production assays.

[0170] Thus, the present invention also relates to a method for inducing an immune response in a subject in need thereof, the method comprising the administration to said subject of one or more peptide(s), one or more expression vector(s), one or more CTL(s) or one or more modified T cell(s) as described above.

[0171] In one embodiment, the one or more peptide(s), one or more expression vector(s), one or more CTL(s), or one or more modified T cell(s) described above are administered in a therapeutically effective amount.

[0172] However, it will be understood that the total daily usage of one or more peptide(s), one or more expression vector(s), one or more CTL(s), or one or more engineered T cell(s) described above will be determined by the attending physician within the scope of sound medical judgment.

[0173] The specific dose for any particular subject will vary depending on a variety of factors, including the condition being treated and the severity of the condition; the activity of the particular compound used; the particular composition used; the age, weight, general condition, sex, and diet of the patient; the time of administration, route of administration, and rate of excretion of the particular compound used; the duration of treatment; drugs used in combination or concomitantly with the particular compound used; and similar factors well known in the medical arts.

[0174] For use in administering to a subject, one or more peptides, one or more expression vectors, one or more CTLs, or one or more modified T cell(s) are formulated for administration to a subject. The one or more peptides, one or more expression vectors, one or more CTLs, or one or more modified T cell(s) described above may be administered parenterally, by inhalation spray, rectally, nasally, or via an implanted reservoir. The term administration as used herein includes subcutaneous, intravenous, intramuscular, intra-articular, intra-synovial, intrasternal, intrathecal, intrahepatic, intralesional, and intracranial injection or infusion techniques. [Brief description of the drawings]

[0175] [Figure 1] A combination of a diagram and one histogram showing pan-cancer identification of HERVs associated with CTL responses. Figure 1A: Venn diagram showing the total number of HERVs overexpressed in tumors and their normal counterparts (peritumoral tissues) and the total number of HERVs overexpressed in peritumor normal tissues and their tumor counterparts. HERVs that are overexpressed in at least one tumor and never in any of the peritumor tissues are considered cancer-associated. Figure 1B: Venn diagram of the selection criteria for HERVs (cyt-HERVs) to be annotated as associated with CTL responses. Each HERV must be associated with both phenotypic (CD8 or CD4 T cell signature) and functional (cytolytic activity (granzyme B and perforin 1) or IFN-γ signature) criteria (A and B) and not overexpressed in normal purified T / NK cells (A and B but not C). Figure 1C: Venn diagram showing the association of cancer-associated HERVs with the CTL response criteria defined in A. A total of 192 HERVs are annotated as cyt-HERVs. Figure 1D: Percentage of cancer-associated HERVs annotated as cyt-HERVs per cancer subtype. Cyt-HERVs are represented in light grey. CTL: cytotoxic T cell response, Cyt-HERVs: HERVs associated with CTL response in cancer: TNBC: triple negative breast cancer. [Diagram 2] A combination of one histogram, one scheme and one table showing the selection of common HLA-A2 epitopes derived from Gag and PolHERV-K / HML-2. Figure 2A: Flowchart of peptide selection from cyt-HERV sequences. Figure 2B: Bar graph of the top 25 most shared peptides predicted as strong HLA-A*02 binders among 192 cyt-HERVs. Selected peptides (P1-P6) are marked with an asterisk. Figure 2C: Characterization of the six selected HLA-A2 epitopes. 9-mer peptides were selected according to their predicted HLA-A*02 affinity (considering strong binders with percentile rank ≤ 0.5) and the number of HERVs containing their sequences. [Diagram 3] A combination of a diagram and two histograms showing that common CD8+ T cell epitopes derived from conserved Gag and PolHERV-K / HML-2 motifs are expressed in TNBC. Figure 3A: Venn diagram of the total number of cyt-HERVs overexpressed in each subtype of breast cancer in the TCGA database. Figure 3B: Shows the average expression of 54 cyt-HERVs overexpressed in TCGA basal subtypes, the independent database of Varley et al. and medullary thymic epithelial cells (mTECs). Figure 3C: Shows the expression of 18 peptide-containing CAHs in breast cancer basal (Hs578t and MDA-MB-231) and luminal A (MCF7 and T47D) cell lines analyzed by Riboseq. [Figure 4] A combination of schemes and plots showing that HERV-derived epitopes induce polyfunctional CD8+ T cell responses. Figure 4A: Schematic of the in vitro priming protocol. Figure 4B: Summary of results obtained with PBMCs from 11 HLA-A2 positive HDs (HD1-HD11, one donor per lineage). Figure 4C: Plots of IFN-γ (left panel), IFN-γ and TNF-α (middle panel), or IFN-γ and CD107a (right panel) staining gated on CD8+ T cells. PBMCs were stimulated with peptide (here P6, top line), no peptide (middle line), or CMVpp65 peptide (bottom line). [Diagram 5] A combined table and figure showing the visualization of CDR loops and their interactions with peptides. Figure 5A: Production frequency of TCRα and TCRβ CDR3 sequences of top clones specific for each peptide (P1, P2, P4, P6) and the corresponding isolated V, D, and J alleles. Figure 5B: Predicted binding affinity (Predict.Δg) expressed in kcal / mol. Mean values ​​are reported. Asterisks indicate statistical tests significant at the 5% level (Welch two-sample t-test). Figure 5C: Ranking of modeled HERV-specific TCR-pMHC and reference TCR-pMHC complexes obtained from crystallographic data available in the Protein Data Bank according to their predicted binding affinity. CDR: complementarity determining region, TCR: T cell receptor, MHC: major histocompatibility complex, pMHC: peptide-MHC, TRA: alpha chain of TCR, TRB: beta chain of TCR. [Figure 6] A combination of graphs showing that HERV-specific T-cell clones are functional, recognizing and killing tumor cells. Figure 6A: Functional avidity of CD8+ T-cell clones calculated as a nonlinear fit of normalized IFN-γ production. N9-V1 and N9-V2: CMV-specific T-cell clones (see Methods). EC50 is represented for each clone by dashed lines and interpolation on the X-axis. Figure 6B: Quantification of cell death is represented as increase in fluorescence intensity from baseline (Y-axis) in function of time (hours, X-axis). Figure 6C: Specific tumor cell lysis at 48 hours. The average percentage of technical triplicates is plotted for each condition (data are representative of at least two independent experiments). [Figure 7]A combination of graphs and photographs showing the presence of HERV-specific T cells among tumor-infiltrating T cells. Figure 7A: Overall survival according to 18-HERV score in TCGA HLA-A2 TNBC patients (n=65). Patients were divided into three groups according to tertiles of the score: high expression (n=22), intermediate expression (n=21), and low expression (n=22). Figure 7B: 60x images of TNBC organoids co-cultured with CMV, P1, or P6-specific CD8+ T cell clones (top to bottom) acquired at different time points using Nanolive technology. T cells are indicated by white arrows.

[0176] Working Example The present invention is further illustrated by the following examples.

[0177] material and method Dataset For RNA-seq data, raw fastq files were cloned using the accession numbers GSE58135 for the independent breast cancer dataset from Varley et al. (KEVarley et al., Recurrent read-through fusion transcripts in breast cancer. Breast Cancer Res. Treat. 146, 287-297 (2014)), GSE74246 for the sorted PBMC dataset (MRCorces et al., Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nature Genetics. 48, 1193-1203 (2016)), and GSE127825 and GSE127826 for the six mTEC samples (J.-D.Larouche et al., Widespread and tissue-specific expression of endogenous retroelements in human somatic tissues. Genome Med. 12, 1-16 (2020)) and accessed through the NCBI Gene Expression Omnibus (GEO) portal. TCGA pancancer raw fastq files were accessed through the Genomic Data Commons (GDC) portal (https: / / portal.gdc.cancer.gov / ). Cell line data were accessed through the Broad Institute Cancer Cell Line Encyclopedia (CCLE) portal (https: / / portals.broadinstitute.org / ccle).

[0178] Quantification of HERV expression HERV expression was assessed using the HervQuant pipeline (CC Smith et al., Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma. Journal of Clinical Investigation. 128, 4804-4820 (2018)). Briefly, RNAseq reads were mapped to the hg19 reference transcriptome (L. Vargiu et al., Classification and characterization of human endogenous retroviruses; mosaic forms are common. Retrovirology. 13, 7 (2016)), compiled with annotation of 3,173 HERV sequences, using STAR v2.7.3a (A. Dobin et al., STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 29, 15-21 (2013)). Multimaps ≤ 10 and mismatches ≤ 7 were allowed, as in the original publication. BAM output was filtered against HERV sequence-mapped reads using SAMtools v1.4 (H. Li et al., 1000 Genome Project Data Processing Subgroup, The Sequence Alignment / Map format and SAMtools. Bioinformatics. 25, 2078-2079 (2009)) and then quantified using Salmon v0.7.2 (R. Patro et al., Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference. Nat Methods. 14, 417-419 (2017)). Raw counts were normalized to counts per million total reads and then transformed to log2+1.

[0179] Quality check / sample filtering We regrouped 9,718 samples from 32 different cancer types, including only primary solid tumor samples (TCGA code 01), of which 9,492 were analyzable for HERV expression. A quality check led to the complete removal of ESCA and STAD samples due to highly skewed HERV distribution, leading to a final analysis of 8,893 samples from 29 different cancer types.

[0180] Immune signatures and genetic alterations Phenotypic immune signatures were calculated using the Xcell method (D. Aran, et al., xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 220 (2017)). For TCGA Pancancer samples, Xcell signatures were downloaded directly from the Xcell website (https: / / xcell.ucsf.edu / xCell_TCGA_RSEM.txt). For the GSM1401648 dataset, signatures were calculated for the entire dataset and immune signatures were subsequently filtered. Interferon gamma (IFN-γ) signatures were calculated by single-sample gene set mutation analysis (GSVA) ​​(S. Hanzelmann, et al., GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 14, 7 (2013)) based on the HALLMARK_INTERFERON_GAMMA_RESPONSE signature from the Molecular Signature Database (http: / / software.broadinstitute.org / gsea / msigdb / index.jsp). Enrichment scores were calculated for each sample per cancer type. Cytolytic activity (CYT_score) was calculated as the geometric mean of granzyme B (GRZB) and perforin (PRF1) expression as previously described (MS Rooney et al., Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 160, 48-61 (2015)). TCGA Pancancer gene alterations were obtained from Thorsson V. et al. (V. Thorsson et al., The Immune Landscape of Cancer. Immunity. 48, 812-830. e14 (2018)).

[0181] Cancer-related and cyt-HERV annotation To define cancer specificity, differential HERV expression was performed between tumor samples and their respective normal peritumoral matched tissues. Only TCGA studies with at least 10 peritumoral samples were included (n=14 different cancer types). Differential analysis of HERV expression was performed for each TCGA cancer type separately. Any HERVs expressed more than 2-fold in any normal tissue compared to its matched tumor were filtered out, and the remaining HERs overexpressed more than 2-fold in at least one cancer compared to its normal counterpart (peritumoral tissue) were considered as cancer-associated.

[0182] To be annotated as potentially immunogenic, each cancer-associated HERV had to be associated with at least one phenotypic (A) and one functional (B) criterion and not overexpressed by T / NK cells (C). Phenotypic criteria included association with either CD4+ or CD8+ T cell signatures as defined by the Xcell method. Functional criteria included association with either IFN-γ or cytolytic activity, defined by the geometric mean of granzyme A (GZMA) and perforin (PRF1) expression. Normal PBMC expression was assessed in an independent dataset of sorted PBMCs from healthy donors (MRCorces et al., Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nature Genetics. 48, 1193-1203 (2016)). HERV expression was compared independently in T and NK cells versus the rest of the PBMCs.

[0183] L1 Penalized Regression (LASSO) Associations were calculated by Lasso regression using the glmnet and c060 packages (JH Friedman et al., Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software. 33, 1-22 (2010), M. Sill et al., c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models. Journal of Statistical Software. 62, 1-22 (2014)). Gaussian distributions were considered for the CYT score and IFN-γ signature, and Poisson distributions for the Xcell signature. HERVs were analyzed as log2(CPM+1) and do not require further standardization. For each cancer type, a model was built based on the optimal parameters found in a 10-fold cross-validation. Each HERV with a positive coefficient in the final model (based on the lambda parameter that minimizes the mean squared error) was considered to be associated with the variable.

[0184] Epitope screening Open reading frame (ORF) detection was performed using sixpack from EMBOSS v6.6.0.0 (F. Madeira et al., The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 47, W636-W641 (2019)). Detected ORFs of more than 10 amino acids were then aligned to known HML-2 (HERV-K) Gag, Pro, Pol, Env, Rec and Np9 proteins referenced in UniProt (UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506-D515 (2019)). Blast with optimal parameters for retroviruses was used (word size = 3, configuration-based statistics, no "low complexity region" filter). Conserved sequences aligned with Gag and Pol proteins with >90% identity and e-value <0.05 were then screened for predicted HLA-A*02 strong binders using MHCflurry v1.3 (TJO'Donnell et al., MHCflurry: Open-Source Class I MHC Binding Affinity Prediction. Cell Systems. 7, 129-132. e4 (2018)). Peptides with rank <=0.5 percentile were considered to be strong binders. The human proteome was downloaded in Uniprot (ID: UP000005640) to verify that there were no matches prior to peptide synthesis and in vitro validation.

[0185] Cumulative Expression Score A pi score was defined for each HERV and each tissue comparison (TNBC vs. peritumoural tissue) as the product of the log2 fold change of expression and the log10 of the inverse p-value according to the method proposed by Xiao et al. (Y. Xiao et al., A novel significance score for gene selection and ranking. Bioinformatics. 30, 801-807 (2014)). A cumulative expression score was calculated by summing the pi scores of all HERVs (including CAH and other HERVs) that contained the epitope sequence.

[0186] Analysis of peptidome-proteome datasets Raw MS / MS datasets were downloaded from the CPTAC (N.J. Edwards et al., The CPTAC Data Portal: A Resource for Cancer Proteomics Research. J Proteome Res. 14, 2707-2713 (2015)) for breast cancer research (Cancer Genome Atlas Network, Comprehensive molecular portraits of human breast tumours. Nature. 490, 61-70 (2012); K. Krug et al., Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targeted Therapy. Cell. 183, 1436-1456. e31 (2020)). The acquired MS / MS spectra were converted to MGF format using msconvert from proteowizard (R. Adusumilli, P. Mallick, Data Conversion with ProteoWizard msConvert. Methods Mol Biol. 1550, 339-368 (2017)). The list of peptides was then analyzed using Pepquery (standalone version) (v.1.6.2.0) (B. Wen et al., PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations. Genome Res. 29, 485-493 (2019)). The command line used was: java-Xmx10G-jarpepquery.jar-fixMod6,62,108-varMod117-maxVar3-c1-tol10-toluppm-minScore12-e1-um-hcTRUE-n1000-itol0.05-m1cpu12-pep${peptides_list}-db${Reference_database}-ms${MS_database}-o${output_directory}.For the second dataset, the command line fixmod and varMod were adjusted as follows: -fixmod6,103,157 -varMod101,117.

[0187] Riboseq analysis Ribosome profiling data were obtained from a previously published study (F. Loayza-Puch et al., Tumour-specific proline vulnerability uncovered by differential ribosome codon reading. Nature. 530, 490-494 (2016)). Raw fastq files were preprocessed as described in the original publication. Briefly, adapter sequences were trimmed from the raw data and mapped to the reference hg19-HERV transcriptome using cutadapt1.1 with parameters (--quality-base=33-O12-m20-q5).

[0188] statistical analysis All analyses were performed using R statistical software version 3.6.0. Differential HERV expression analysis was performed using DESEQ2 v1.24.0 (MI Love et al., Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)), and log fold changes were reduced by the apeglm package (A. Zhu et al., Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. 35, 2084-2092 (2019)).

[0189] Biological samples Blood from healthy donors was obtained from the Etablissement Francais du Sang (Lyon). Fresh TNBC (n=11) were obtained from the tissue bank of Centre Leon Berard (CLB) (BB-0033-00050, CRB-CLB, Lyon, France; French agreement number: AC-2013-1871) after approval from the Institutional Review Board and Ethics Committee (L-06-36 and L-11-26) and after written informed consent from the patients, in accordance with the Declaration of Helsinki.

[0190] Peptide synthesis Peptides were synthesized at JPT peptide Technologies (GE, EU) to certain specifications and purity >90%. Lyophilized powder was resuspended in 1% DMSO in distilled water.

[0191] cell line MDA-MB-231 basal breast cancer epithelial cells were obtained from American Type Culture Collection (ATCC catalog name: HTB-26) and cultured in 10% FBS DMEM (Gibco, FR, EU) 1% penicillin / streptomycin 1% L-glutamine. HMEC primary cells were obtained from Promocell (GE, EU) and cultured in Mammary Epithelial Growth Medium (PromoCell, GE, EU).

[0192] In vitro priming assay PBMCs were obtained by Ficoll density gradient centrifugation (Eurobio, FR, EU). They were rapidly thawed at 37°C, washed extensively, and left overnight at room temperature or 37°C before their viability was assessed. 0.15x106 PBMCs / well were cultured on day 0 in AIM V medium (Gibco, FR, EU) enriched with 5 μg / mL R-848 (Resquimod), 10 μg / mL HMW poly IC (both Invivogen, FR, EU), 20 IU / mL IL-2 (PROLEUKIN aldesleukin, Novartis Pharma, CH, EU), and 10 μg / mL of the peptide of interest. After 3, 6, and 10 days, 100 μL of medium was replaced with fresh enriched medium (IL-2 and peptide only on day 6, IL-2 only on day 10) and split as needed. On day 12, cells were harvested and counted for analysis.

[0193] Feeding Protocol Single cell sorted CD8 by dextramer + T cells were expanded on feeders consisting of 35 Gy irradiated allogeneic PBMCs and B lymphoblast cell lines at a ratio of 10:1. Feeder cells were cultured at 0.10x10 in RPMI 5% human serum containing 1.5 μg / mL PHA-L (MerckKgAa, GE, EU) and 150 IU / mL IL-2 (NovartisPharma, CH, EU). 6 Plate in 96-well round-bottom plates at a concentration of cells / well up to 5x10 3 Sorted cells were added per well. Cells were cultured for 14 days and the medium was replaced with fresh IL-2 enriched RPMI 5% human serum as needed. This process was repeated as needed.

[0194] TCR immunosequencing DNA from specific CD8+ T cells and corresponding bulk PBMCs was extracted using QIAGEN QIAmp® DNA Blood Micro kits (QIAGEN, GE, EU) and sent to Adaptive Biotechnologies (WA, US) for TCR investigation and detailed analysis.

[0195] 3D model generation and refinement The complete TCR sequences of both alpha and beta chains of each T cell clone were reconstructed from the results of immunosequencing as previously published (A. Gros et al., Recognition of human gastrointestinal cancer neoantigens by circulating PD-1+ lymphocytes. J Clin Invest. 129, 4992-5004 (2019)). For the variable domains, the TRA and TRB CDR3 nucleotide sequences were obtained from immunosequencing (Figure 5A), and the 5' and 3' ends of the TRAV and TRBV regions were obtained from the International Immunogenetics Information System (IMGT) online database. The human constant domains of TRA and TRB were added 3' of the variable domains to reconstruct the full-length TCR. These full-length TCR sequences, together with the MHC and peptide sequences, were submitted to the CBS TCRpMHCmodels-1.0 web server (KK Jensen et al., TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes. Sci Rep. 9, 14530 (2019)), which was specifically developed for automated structural modelling of TCR-pMHC complexes using template-based modelling. TCR residues were renumbered using a standardised procedure (KRA Bhinandan, ACR Martin, Analysis and improvements to Kabat and structurally correct numbering of antibody variable domains. Mol Immunol. 45, 3832-3839 (2008); MS Klausen et al., LYRA, a webserver for lymphocyte receptor structural modelling. Nucleic Acids Res. 43, W349-355 (2015)).The initial model generated by the web server was further refined in four rounds using a protocol adapted from Bobisse et al. (S. Bobisse et al., Sensitive and frequent identification of high avidity neo-epitope specific CD8+ T cells in immunotherapy-naive ovarian cancer. Nat Commun. 9, 1-10 (2018)). Briefly, the CDR loops were refined pairwise using Modeller software version 9.25 (A. Fiser, A. Sali, Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol. 374, 461-491 (2003); A. Sali, T. L. Blundell, Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol. 234, 779-815 (1993)): CDR loops α1 / α2 in round 1, α1 / α3 in round 2, β1 / β2 in round 3, and β1 / β3 in round 4. Only residues in the coil structure in the initial model were refined. For each round, 500 models were generated and the best model was selected based on Modeller's internal DOPE score and used as input for the next round.

[0196] At the end of the four rounds, a representative model was selected based on the unweighted contact consensus as follows: Contacts between residues at the TCR-pMHC interface (defined by distances of less than 5 angstroms between heavy atoms) were counted in the set of 4*25 models with the highest DOPE scores of each refinement round. Then, among the 25 models with the highest DOPE scores in the final round, the model with the highest number of frequent contacts, called the non-normalized CONSRANK score (G. Launay et al., Evaluation of CONSRANK-Like Scoring Functions for Rescoring Ensembles of Protein-Protein Docking Poses. Front Mol Biosci. 7, 559005 (2020); R. Oliva et al., Ranking multiple docking solutions based on the conservation of inter-residue contacts. Proteins. 81, 1571-1584 (2013)), was selected as the representative model.

[0197] A quantitative view of the potential stabilizing interactions of the TCR and pMHC is obtained by the frequency of inter-residue contacts observed in the set of 4*25 models with the highest DOPE scores for each refinement round. The CDR1 and CDR3 loops of both the TCR α and TCR β chains interact with both MHC and peptide, while the CDR2 loop primarily interacts with the MHC molecule.

[0198] 3D structural analysis The structural similarity between the representative models of the different complexes was evaluated by the RMSD between the backbone CDR loops computed by UCSF Chimera (EF Petterssen et al., UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem. 25, 1605–1612 (2004)). As expected, the structural variability was highest within the CDR β3 loop. UCSF Chimera was also used for detection of hydrogen bonds and hydrophobic contacts as well as visualization of the structures.

[0199] Prediction of binding affinity Binding affinities were predicted using the prodigy method (A. Vangone, A. M. Bonvin, Contacts-based prediction of binding affinity in protein-protein complexes. Elife. 4, e07454 (2015)), which uses a linear model based on the number and type of contacts at the interface. For each complex, instead of performing one prediction for a representative model, we averaged the predictions obtained for the 25 models with the highest DOPE scores obtained in the fourth round of the refinement protocol.

[0200] Fluorospot PBMC or CD8 + After co-culture of T cells with or without peptide-pulsed T2 cells at 37°C and 9% CO2 for 24 hours, double-color Fluorospot (CTLGmbH, CA, US) using IFN-γAF488 and Grz-bCTL-red was performed according to the manufacturer's instructions. Revelation plates were read on an ImmunoSpot® S6 ULTIMATE UV image analyzer and analyzed by ImmunoSpot® analysis software.

[0201] functional connectivity Specific CD8 isolated by dextramer for selected peptides (P1, P2, P6) + T cells were used in a functional binding IFN-γ production test by enzyme-linked immunosorbent assay (IFN-γ ELISA, Thermo Fisher Scientific, FR, EU). Two CMVT cell clones specific for the immunodominant epitope N9V (NLVPMVATV) were used. N9V-1 was a CD8 T cell clone selected by pp65 dextramers. + T cell-responsive, N9V-2 was cloned from CD8 + T cell clone. Specific CD8 + The functional avidity of T cell responses was measured using 10 -4 ~10 -9 M (log) was assessed by performing limiting peptide dilutions. After washing, peptide-pulsed T2 cells were co-cultured with specific CD8+ T cells at a 1:1 ratio in AIM-V medium (Gibco, FR, EU) supplemented with 5% human serum. After 18 hours, supernatants were collected and ELISA was performed. Half-maximal cytokine responses (EC 50 The peptide concentration required to achieve a 50% EC (EC 50 ) was used for the determination of R > 0.98).

[0202] Epitope validation and quantification by MS Epitope validation and quantification by MS was performed by Complete Omics Inc. (Maryland, USA) according to previously described methods (J. Douglass et al. Bispecific antibodies targeting mutant RAS neoantigens. Sci Immunol. 6, eabd5515 (2021)) with further modifications. Briefly, a total of 300 million cells were lysed and peptide-HLA complexes were immunoprecipitated using self-packed Valid-NEO neoantigen enrichment columns preloaded with anti-human HLA-A, B, C antibody clone W6 / 32 (Bio-X-Cell). After elution, dissociation, filtration, and purification, peptides were lyophilized before further analysis. Transition parameters for each epitope peptide were inspected and curated by the Valid-NEO method builder bioinformatics pipeline to exclude ions with excess noise due to co-elution with impurities and to increase the detectability of important ions by recursive optimization. The absolute copy numbers of peptides present on the cell surface were calculated based on quantification using heavy isotope-labeled peptides. MS data are archived by ProteomeXchange and can be accessed by identifier PASS01698.

[0203] Live imaging Cells were plated in DMEM (Gibco, FR, EU) medium containing 10% SVF, 1% penicillin / streptomycin. For IncuCyte analysis, medium was removed from 96-well plates after overnight cell attachment. Blocking HLA-A2 antibody (GeneTex, clone BB7.2, GTX75806, CA, US) was added in AIM-V medium (Gibco, FR, EU) for 1 h according to conditions. T cells were then added at an effector to target (E:T) ratio of 2:1 in the presence of IncuCyte Cytotox dye (Essen Bioscience, UK, EU) for quantification of cell death. Live imaging for 48 h was performed at 37°C, 5% CO2 with an IncucyteZoom. Cell death was calculated as the total number of stained cells counted corrected for the number of stained cells counted at baseline. Maximal killing was established using DMSO. Specific lysis was calculated according to the following formula: % specific lysis = ((HERV-specific T cell-induced target cell death - spontaneous target cell death) - (non-specific dextramer-negative T cell-induced target cell death - spontaneous target cell death)) / (DMSO-induced target cell death - spontaneous target cell death)) x 100

[0204] For Nanolive imaging, T cells were added in E:T 10:1 and phase imaging was performed every minute using the Nanolive microscope 3D cell explorer.

[0205] Breaking down tumors: Organoid and TIL expansion Tumor tissues were cut into approximately 1 mm3 fragments and disrupted with collagenase IV and DNAse for 45 min in 20% SVF-enriched RPMI. Tumor lysates were centrifuged at 1500 rpm for 5 min and resuspended in 5% human serum-enriched RPMI. Cells were counted and plated at 5x10 in flat-bottom 96-well plates with anti-CD3-anti-CD28 Dynabeads (Dynabeads, Gibco, EU) and IL-2 at 100 IU / mL at a bead-to-cell ratio of 1:4. 4 Cells were plated at a density of 1000 cells / well.

[0206] For organoids, a portion of the tumor lysate (3–10 million cells) was resuspended in 10 mL of Advanced DMEM / F12 medium. The cells were centrifuged at 500 rcf for 10 seconds and then resuspended in the full medium. This protocol was repeated 3–5 times, depending on the starting cell number, to enrich the cell suspension for epithelial cells. These cells were then cultured according to the protocol previously described by Drehuis et al. (E. Driehuis et al., Establishment of patient-derived cancer organoids for drug-screening applications. Nature Protocols. 15, 3380-3409 (2020)).

[0207] Multiparametric Flow Cytometry T cells were counted and co-cultured with T2 cells loaded or unloaded with cognate peptide at a ratio of 5:1. After 1 hour, CD107a antibody (BD, clone H4A3) was added to each well along with Golgi plug (1 / 1000) (10 μg / mL, BD, FR, EU). After 5 hours, live cell staining, surface staining and intracellular staining were performed. CD8 + To evaluate cytokine expression in T cells, intracellular staining with FoxP3 fixation and permeabilization kit (Thermo Fisher scientific, Life Technologies, CA, US) was used according to the manufacturer's instructions.

[0208] Dextramers staining was performed on PBMCs after 12 days of culture (priming protocol) or on TILs grown for 14 days after tumor dissection. Cells were washed with 2 mL of washing buffer (PBS+2% FBS+2mM EDTA (Sigma Alderich, MI, US)) and stained with dextramer (Immudex ApS, DK, EU) for 10 min at room temperature before viability and surface marker staining. Washing was performed twice to avoid non-specific dextramer staining. CMV pp65 NLVPMVATV was used as a positive control. For TIL analysis, dextramer complexed with a non-natural irrelevant peptide (ALIAPVHAV) was used as a negative control.

[0209] All samples were analyzed on an LSR-Fortessa (BD Biosciences, FR, EU) with settings stored throughout the study. Data were analyzed using FlowJo software (Tree Star v10.4, NJ, USA).

[0210] result Machine learning-based approach enables identification of HERVs associated with CTL responses To optimize epitope detection, we developed a novel pipeline to annotate HERVs. For this, we surveyed multiple HERV databases and selected the most up-to-date and complete references of 3,173 HERVs, most of which consist of complete proviral sequences and therefore have a higher probability of containing translated peptides. We used HervQuant to evaluate HERV expression in 8,893 primary tumor samples from 29 different cancer types from The Cancer Genome Atlas (TCGA) pan-cancer RNAseq database. We selected cancers (n=14) with at least 10 matched peritumoral samples to filter HERVs that are highly expressed in tumors and not in normal tissues (cancer-associated HERVs or CAHs). Differential HERV expression analysis revealed 1,134 CAH candidates (Figure 1A). To reduce the number of candidates to be examined, we then applied a second filter to retain only HERVs associated with CTL responses among CAHs (cyt-HERVs). The annotation of cyt-HERVs was based on two inclusion criteria, i.e., the association of each HERV with at least one phenotypic (A) and functional (B) signature of CD4 or CD8 T cells, and one exclusion criterion, i.e., its expression by purified T or NK cells (A and B but not C) (Figure 1B). To reduce the risk of false-positive associations and to control for high collinearity with HERV expression, these associations were calculated by L1-penalized regression (R. Tibshirani, Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. 58, 267-288 (1996)) to retain only those HERVs that were highly associated with CTL responses, while controlling for cancer subtype. Using a machine learning-based approach, the associations were examined separately for each cancer type (see Methods for details), leading to the final identification of 192 cyt-HERVs (Figure 1C).Subcancer analysis revealed that colon adenocarcinoma (COAD), lung squamous cell carcinoma (LUSC), head and neck squamous cell carcinoma (HNSC), bladder urothelial carcinoma (BLCA), and lung adenocarcinoma (LUAD) were the top five cancers with the highest total number of cyt-HERVs ( Figure 1 D).

[0211] Overall, cyt-HERVs accounted for approximately 15% of all CAH cases, greatly reducing the number of potential candidates. Among the most shared cyt-HERVs, 11 were overexpressed in more than 10 cancer types, including three HERVs (herv_2256, herv_6069, herv_4700) previously reported to induce CD8+ T cell responses. Analysis of the average beta values ​​of the 10 nearest neighboring probes from the TCGA Illumina 450k methylation data revealed more cyt-HERVs that significantly correlated with local demethylation (n=37) than with methylation (n=15), suggesting partial epigenetic regulation of these HERVs.

[0212] Selection of conserved Gag and Pol HERV-K / HML-2 motifs among cyt-HERVs leads to identification of common CD8+ T cell epitopes We next focused on HLA-A2, the most common HLA class I allele, and assessed the presence of common T cell epitopes among these cyt-HERVs. We translated the 192 cyt-HERV sequences into six possible frames, retaining predicted open reading frames (ORFs) of at least 10 amino acids. To reduce the number of false positives (non-translated sequences), we aligned these ORFs against known HERV-K / HML-2 Gag and Pol proteins referenced in UniProt, and only kept ORFs with 90% homology to known existing HML-2 proteins. This conservative approach identified 57 HML-2 HLA-A*0201 epitope candidates from 27 distinct ORFs (Figure 2A), including herv_2410 and herv_6069, which show the highest number of conserved HML-2-derived ORFs. To better understand the distribution of these epitopes, we realigned each peptide among all CAHs. The top 25 most shared epitopes are shown in Figure 2B. Thirteen unique epitopes were present in at least 10 different HERVs (Figure 2B). Six most shared epitope candidates were selected for further biological validation and immunological assays; three from Gag (P1, P2, and P4) and three from Pol (P3, P5, and P6) (Figure 2C). Analysis of mass spectrometry (MS) data from TCGA and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) showed evidence of translation of the P1, P2, P3, P5, and P6 peptides. P4 was also selected because it was described in HLA-I eluted peptides from tumors (WO 2019 / 162110(A1)). Importantly, alignment to the human proteome revealed that the sequences of these epitope candidates did not match any self-protein sequences. Notably, these HLA-A2 epitopes were not predicted as strong binders for the other most common HLA-A and B alleles.

[0213] Triple-negative breast cancer (TNBC) is characterized by numerous cyt-HERVs that contain common HERV epitopes. Owing to the well-characterized expression of HERVs in TNBC and the availability of an RNA sequencing (RNAseq) database containing normal samples, we focused on breast cancer. Differential HERV expression analysis revealed a total of 497 CAHs expressed across different breast cancer subtypes, of which we annotated 91 as cyt-HERVs. Fifty-four of these 91 cyt-HERVs were expressed in the basal subtype (Figure 3A). The average expression of these 54 cyt-HERVs was significantly higher in TNBC and ER+ samples compared to peritumoral or normal breast tissue from an independent dataset (Figure 3B). We confirmed the high expression of these 54 cyt-HERVs in breast cancer cell lines sequenced in the Varley et al. study and in cell lines from the Broad Institute Cancer Cell Line Encyclopedia. The top 25 most shared epitope candidates among all CAHs expressed in the basal subtype included six previously identified peptides P1-P6.

[0214] We then selected HERVs containing the sequences of the six previously identified candidate epitopes P1-P6 among those expressed in the basal subtype of CAH, and found that 18 different CAH ORFs contained at least one of the six peptides in their ORFs (Table 1). [Table 1]

[0215] Genomic mapping of the corresponding loci indicated the diffuse location of these 18 HERVs on chromosomes. To quantify the differential expression of epitope-containing HERVs in TNBC versus normal tissues (here represented by each available peritumoral sample), we used a π-value score that considers both statistical significance, indicated by p-value, and biological significance, represented by fold change. A cumulative expression score was then calculated for each epitope by summing the π-values ​​of all HERVs containing that sequence. This score was in most cases between 10 and 200, which confirmed the marked overexpression of epitope-containing HERVs in TNBC versus each normal sample evaluated. Finally, analysis of ribosome profiling (riboseq) data from a previously published study revealed evidence of translation of the 18 peptide-containing CAHs in four different breast cancer cell lines, including two basal subtypes and two luminal A subtypes (Figure 3C).

[0216] Overall, our bioinformatics approach allowed us to select a limited number of HERV-derived T cell epitopes that are specifically overexpressed by tumor cells and that are likely to be the most immunogenic among a large number of HERV candidates.

[0217] HERV-derived epitopes induce potent and polyfunctional T cell responses Next, we evaluated the ability of the selected epitope candidates to induce efficient T cell responses. The HLA-A2 affinity of the six selected peptides was first confirmed using an in vitro binding assay against purified HLA-A*02:01 molecules. To evaluate the immunogenicity of these six peptides, an optimized in vitro priming assay was developed for peripheral blood mononuclear cells (PBMCs) of HLA-A2 positive donors (see Figure 4A and Methods for details). Dextramers-based quantification of peptide-specific CD8+ T cells revealed the presence of specific T cells against all peptides, with variability between donors (Figure 4B). P1 appeared to be the most immunogenic peptide with significant T cell responses in 9 out of 11 donors, followed by P4 (7 / 10), P6 (4 / 9) and P2 (3 / 11) (Figure 4B). The immunogenicity of these peptides was further confirmed by conventional assays using monocyte-derived dendritic cells (MoDCs) prepared from five HLA-A2-positive healthy donors. Flow cytometry analysis showed CD8+ T cell IFN-γ production when peptide-stimulated PBMCs were co-cultured with T2 cells pulsed with the cognate epitope. Notably, P1 also induced the highest IFN-γ response compared to other peptides. Consistent with bioinformatics predictions, specific T cell induction was not observed when using PBMCs from HLA-A2-negative donors (n=5).

[0218] Based on these results, P1, P2, P4, and P6 were selected for further experiments. Polyfunctional IFN-γ+TNF-α+specific CD8+T cell responses were observed upon co-culture of stimulated PBMCs with peptide-pulsed T2 cells, associated with the presence of the degranulation marker CD107a (Figure 4C). Fluorospot assays in the same co-culture conditions confirmed the secretion of IFN-γ and granzyme B in the presence of double-positive cells.

[0219] Epitope-specific CD8+ T cell clones are characterized by high predicted affinity T cell receptors (TCRs). P1, P2, P4, and P6-specific CD8+ T cells were sorted by flow cytometry using dextramer staining and expanded on feeder cells (see Methods). More than 90% (90-99%) of CD8+ T cells were dextramer positive after one (P1) or two (P2, P4, and P6) steps of selection-expansion. TCRβ immunosequencing confirmed the presence of dominant clones with unique Vβ rearrangements in 90.8%, 90.7%, 99.6%, and 76% of expanded T cells for P1, P2, P4, and P6, respectively (Figure 5A). Of note, the V / D / J recombination sequences of TCRβ that characterize these clones were not present in the T cell bulk before peptide stimulation (threshold sensitivity: 3x10-6). The TCRα chain was also sequenced, confirming the presence of unique dominant clones for P1, P4, and P6, allowing TCR pairing and modeling. Two major Vα rearrangements were obtained for P2, so the major rearrangement occurring with a frequency of 60% was used for TCR modeling.

[0220] The affinity of T cell clones specific for peptides P1, P2, P4, and P6 was then characterized by considering a 3D model of the TCR-peptide-MHC (pMHC) complex (see Methods). The stability of the macromolecular complex is due to the formation of favorable interactions at the interface, such as hydrogen bonds, salt bridges, and hydrophobic interactions. These interactions require specific side chains of the peptide to be exposed at the TCR-pMHC interface. In the TCR P1 complex, the peptide side chains Phe1, Phe4, and Trp8 form multiple hydrophobic interactions, and the backbone atoms of Phe4 and Ile9 participate in hydrogen bonds. In the TCR P2 complex, several hydrophobic interactions are mediated by peptide residues Pro4, Tyr5, and Trp7. In TCRP4, peptide residues Ile5, Ile7, and Leu8 form several hydrophobic interactions, while the Tyr1 and Lys6 side chains, and the Phe4 and Leu8 backbone atoms participate in hydrogen bonds. In TCRP6, Tyr1, Ser4, Asn5, Leu6, and Phe7 form multiple hydrophobic interactions, while the Ser4 / Leu6 / Ser8 backbone and Tyr1 / Ser4 / Asn5 / Ser8 side chains form eight hydrogen bonds.

[0221] Overall, this analysis of the predicted 3D models suggests that the TCR-pMHC complex is stabilized by several favorable non-covalent interactions, confirming that the TCRs identified after clonal expansion of HERV-specific T cells form stable complexes with peptides presented by HLA-A2 molecules. To gain further insight, the 3D models were submitted to binding affinity prediction (Figure 5B). Compared to reference TCR-pMHC complexes available in the Protein Structure Data Bank and derived from crystal structure data, the predicted affinities of the identified TCRs match those of clinically relevant TCRs, such as those targeting MAGE-A3, NY-ESO-1, MART-1, HTLV, or CMV (Figure 5C). Thus, the identified HERV-specific TCRs are predicted to stably interact with their respective pMHC complexes, implying high affinity TCRs.

[0222] High-affinity HERV-specific T cell clones recognize and kill tumor cells The functionality of the sorted and expanded epitope-specific CD8+ T cells was confirmed by Fluorospot using peptide-pulsed T2 cells. Functional avidity was then assessed by adding decreasing concentrations of the cognate peptide (ranging from 10-4 to 10-9 M) to T2 cells and measuring the lowest peptide concentration required to elicit an IFN-γ response in 50% of the cells (defined as half the maximal effective concentration, i.e., EC50). For P1, P2, and P6-specific T cells, 6.6x10 -7 M, 1.9x10 -6 M, 6.8x10 -6 The EC values, estimated at M, were 1.2 x 10 for neoepitope-specific T cell clones (28) and CMV-specific T cells (N9V-1 and N9V-2, respectively). -6 and 1.9x10 -6 ) was the same order of magnitude as the α-terminal tail (Fig. 6A).

[0223] We next evaluated the ability of these HERV epitope-specific CD8+ T cells to recognize and kill tumor cells. We selected the HLA-A2-positive MDA-MB-231 basal BRCA tumor cell line, previously shown to express HERVs containing the epitope sequence, as a target candidate (Figure 3C). To provide evidence that the epitopes are indeed present on the cell surface, we used an MS-based method to analyze peptides eluted from HLA molecules. The P1 and P6 epitopes were clearly detected by MS. Based on comparison with heavy isotope-labeled controls, we estimated that 1.8 copies of the P1-HLA complex were present on the MDA-MB-231 cell surface.

[0224] Tumor cells were co-cultured with epitope-specific T cells or with a dextramer-negative CD8+ T cell fraction (negative control) that had been sorted and expanded under the same conditions. Flow cytometry analysis highlighted the production of IFN-γ by approximately 25% of epitope-specific T cells in contact with MDA-MB-231, with a marked increase (>6-fold) compared to the background observed with non-specific T cells. This production of IFN-γ was inhibited by an HLA-A2 blocking monoclonal antibody, demonstrating that the T cell clone specifically recognized tumor cells in an HLA-A2 restricted manner.

[0225] To monitor tumor cell death in real time, immune cell killing assays were performed using IncuCyte technology. T cell clones induced a significant, HLA-A2-restricted killing of MDA-MB-231 cells, as shown by a time-dependent increase in the amount of Cytotox fluorescent reagent on target cells. In contrast, the dextramer-negative fraction of T cells did not induce significant cell death of MDA-MB-231 cells (pulsed or not pulsed with peptide) (Figure 6B). Specific lysis (at an effector-to-target ratio E:T = 2:1) was calculated based on the quantification of target cell death after 48 h after subtracting the alloreactive background (assessed by target cell death induced by the corresponding dextramer-negative T cell fraction) (see Methods). Particularly high specific lysis of tumor cells was achieved with P1- and P2-specific T cells (35% and 44%, respectively), and even moderate lysis (15%) was achieved with P6-specific T cells. Specific lysis was further increased when target tumor cells were pulsed with the cognate epitope, reaching 55%, 80%, and even 95% for P1, P2, and P6-specific T cells, respectively. Of note, epitope-specific T cell clones did not kill HLA-A2-positive human mammary epithelial cells (HMEC), used here as negative normal cell controls (Figure 6C). These results were further validated using 3D microscopy Nanolive technology, showing morphological signs of specific T cell activation associated with the killing of the majority of tumor cells after 4.5 hours (E:T = 10:1). Again, cell death was not observed when specific T cells were co-cultured with HMEC and when MDA-MB-231 cells were co-cultured with non-specific T cells. Similar results were obtained using the TNBC HLA-A2-positive cell line HCC1599 as target. Overall, these data indicate that selected epitopes induce high avidity CD8+ T cell clones that selectively recognize and kill HERV-expressing tumor cells.

[0226] HERV-specific T cells are present among tumor-infiltrating T cells To test the hypothesis that an adaptive immune response against HERVs may exist in cancer patients, the presence of HERV epitope-specific T cells in polyclonally expanded tumor-infiltrating lymphocytes (TILs) from TNBC HLA-A2 patients (without any peptide-specific stimulation) was evaluated by dextramer staining. HERV-specific TILs were observed against at least one epitope in 7 of 11 tumor samples analyzed, differing in epitope specificity and frequency from patient to patient. P1, P4, and P6 were the most frequently recognized peptides, identified on a dextramer basis in 4 / 11, 4 / 11, and 5 / 11 cases, respectively.

[0227] These results led us to investigate the potential association between the outcome of TNBC patients and the expression of 18 CAHs containing these HLA-A2 epitopes. We established a score based on the average expression of these 18 HERVs in HLA-A2 patients with basal breast cancer from the TCGA cohort. Interestingly, HLA-A2-positive patients with high or moderate 18-HERV scores had significantly better overall survival than those with low scores (P=0.0066) (Figure 7A). This prognostic impact was not observed in the overall population.

[0228] Finally, we evaluated the antitumor activity of HERV-specific T cells against primary tumor cells by using organoids derived from the tumor of patient 8 (see Methods). RNAseq analysis confirmed the expression of 18 epitope-containing CAH at early and late passages. Tumor organoids were co-cultured with P1, P6, or CMV-specific CD8+ T cell clones in 3D microscopy Nanolive experiments (E:T=10:1). No T cell activation was observed in CMV-specific T cells, whereas P1 and P6-specific T cells exhibited signs of vigorous proliferation associated with organoid lysis (Figure 7B).

[0229] Overall, these last results suggest that HERV-specific T cells may be induced during tumor development and participate in the antitumor immune response.

Claims

1. 1. A method for identifying a human endogenous retrovirus (HERV) derived T cell epitope associated with at least one cancer, comprising the steps of: (a) identifying a HERV associated with at least one cancer; and (b) selecting T cell epitopes among the HERV identified in the previous step; and Next steps: (i) selecting a HERV associated with a cytotoxic T cell response among the cancer-associated HERVs identified in step (a), the selecting step being between steps (a) and (b); and / or (ii) assessing the expression at protein or peptide level of the HERV-derived T-cell epitopes identified in step (b) in a tumor sample, said assessing step being subsequent to step (b). and further comprising at least one, preferably two, of Preferably, said steps (a), (b), (i) and (ii) are in silico steps, or said steps (a), (b) and (i) are in silico steps and said step (ii) is an in vitro step. method.

2. 2. The method of claim 1, wherein step (a) comprises comparing HERV expression in a tumor sample and in a normal sample.

3. 3. The method of claim 1 or 2, wherein step (b) comprises aligning the sequence of the HERV identified in the previous step with a HERV protein.

4. 4. The method of claim 3, wherein step (b) further comprises predicting binding of a sequence that shares at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or more identity with a HERV protein to an MHC class I molecule.

5. 5. The method according to any one of claims 1 to 4, wherein in step (i) the association of said cancer-associated HERV with cytotoxic T cell response is assessed by association of each HERV with at least one CD4 or CD8 T cell signature, association of each HERV with a functional signature, which is either an interferon (IFN)-γ signature or cytolytic activity, and absence of expression of each HERV in normal purified T cells or NK cells, preferably said association is assessed by a machine learning based approach.

6. The method of any one of claims 1 to 5, further comprising the step of selecting an epitope among the most shared epitopes in the cancer-associated HERV identified in step (a) after step (b) or before step (ii).

7. 7. The method of any one of claims 1 to 6, further comprising the step of aligning the HERV-derived T-cell epitopes with the human proteome after step (b) or after step (ii).

8. A peptide comprising or consisting of an epitope identified by the method of any one of claims 1 to 7.

9. A peptide comprising or consisting of an epitope having a sequence selected from the group consisting of RMLTDLRAV (SEQ ID NO: 3), LMAQAITGV (SEQ ID NO: 11), VLQDFDQPI (SEQ ID NO: 13), MLLAALMIV (SEQ ID NO: 15) and YIDDILCAA (SEQ ID NO: 16).

10. 10. An expression vector directing the expression of one or more peptide(s) according to claim 8 or claim 9.

11. Cytotoxic T lymphocytes of a subject treated with one or more peptide(s) of claim 8 or claim 9, or one or more expression vector(s) of claim 10.

12. A modified T cell expressing a T cell receptor that recognizes the peptide of claim 8 or claim 9.

13. One or more peptide(s) according to claim 8 or claim 9, one or more expression vector(s) according to claim 10, one or more cytotoxic T lymphocyte(s) according to claim 11, or one or more modified T cell(s) according to claim 12 for use as a vaccine or medicament.

14. One or more peptide(s) according to claim 8 or claim 9, one or more expression vector(s) according to claim 10, one or more cytotoxic T lymphocyte(s) according to claim 11, or one or more modified T cell(s) according to claim 12 for use in preventing or treating cancer in a subject in need thereof.

15. 15. The one or more peptide(s), the one or more expression vector(s), the one or more cytotoxic T lymphocyte(s) or the one or more modified T cell(s) for use according to claim 14, wherein the cancer comprises or is selected from the group consisting of breast cancer including triple negative breast cancer, ovarian cancer, melanoma, sarcoma, teratocarcinoma, bladder cancer, lung cancer including non-small cell lung cancer and small cell lung cancer, head and neck cancer, colorectal cancer, glioblastoma, leukemia, lymphoma, and other solid tumors and hematological malignancies.

Images