Personalized cancer therapy targeting non-normally expressed sequences

Abstract

1. A method for identifying an immunogenic amino acid sequence in a malignant tumor tissue sample from a patient, comprising: A) determining the amino acid sequence of a protein expression product from a malignant tumor tissue; B) analyzing said amino acid sequences to identify protein expression products of the selected genomic sequences in the patient's species; C) identifying an amino acid sequence in the protein expression product that binds to an MHC molecule of the patient; wherein the selected genomic sequences constitute a subset of all sequences of the genome of the species, the subset being made up of sequences encoding proteinaceous expression products in at most 5% of samples from all healthy tissues, the healthy tissues being tissues of a type found in the patient, and the healthy tissues optionally not including testis tissue and / or brain tissue. Also provided are related cancer treatment methods and computer systems. Additionally provided is a method for stratifying cancer patients into groups suitable or unsuitable for immunotherapy based on endogenous retroviral expression load.

Description

[Technical Field]

[0001] The present invention relates to the field of cancer immunotherapy. In particular, the present invention relates to improved means and methods for designing and producing personalized anti-cancer vaccines that target expression products of genomic sequences that are not expressed or are expressed to a very limited extent in normal tissues but are found in cancer tissues of individual patients. The present invention also relates to methods and computer systems for treating cancer. Furthermore, the present invention relates to methods for stratifying cancer patients. [Background technology]

[0002] Treatment of malignant neoplasms in patients has traditionally focused on eradicating / removing the malignant tumor tissue by surgery, radiation therapy, and / or chemotherapy using cytotoxic agents in doses aimed at preferentially killing malignant cells over non-malignant cells.

[0003] In addition to the use of cytotoxic drugs, more recent approaches have focused on targeting specific biological markers within cancer cells in order to reduce the systemic side effects of classical chemotherapy. Monoclonal antibody therapy targeting cancer-associated antigens has proven highly effective in extending prognosis and survival in many malignancies. Although successful, monoclonal antibodies targeting one or more cancer-associated antigens can, by their nature, only be developed to target expression products that are known and present in multiple patients, meaning that the majority of cancer-specific antigens cannot be addressed by this type of therapy (see below).

[0004] The immune surveillance theory was proposed as early as the late 1950s, suggesting that lymphocytes recognize and eliminate self-cells (including cancer cells) that display altered antigenic determinants, and it is now generally accepted that the immune system highly inhibits carcinogenesis. However, immune surveillance is not 100% effective, and developing cancer therapies that improve / stimulate the immune system's ability to eradicate cancer cells remains a continuing challenge.

[0005] One approach is to induce immunity against cancer-associated antigens, but while this approach has potential, it suffers from the same drawbacks as antibody therapy: it can only target a limited number of antigens.

[0006] Many, but not all, tumors express mutations. These mutations may produce new target antigens (neoantigens), which may be beneficial for specific T cell immunotherapy if neoantigens and their antigenic determinants (neoepitopes) can be identified within a clinically relevant timeframe. Current technology allows for the complete sequencing of a cell's genome and the analysis of the presence of altered or novel expression products, enabling the design of personalized vaccines based on neoantigens and their neoepitopes.

[0007] Therefore, multiple bioinformatic pipelines exist for predicting / identifying neoepitopes from patient-derived sequencing data (see Hundal, J. et al. 2016; Bjerregaard, AM et al. 2017; Bais, P. et al. 2017; Rubinsteyn, A. et al. 2017; Schenck, RO et al. 2019). Each pipeline takes into account a different set of features when selecting or ranking neoepitopes, highlighting that the neoepitope selection problem is still unsolved.

[0008] International Patent Application PCT / EP2021 / 071380 discloses a method for selecting epitopes for inclusion in personalized cancer vaccines. The method focuses on identifying and utilizing neoepitopes encoded by somatic variants of genes expressed in cancer cells. Therefore, the method disclosed in PCT / EP2021 / 071380 relies on identifying short peptides present in expression products that differ from the patient's normal expression products, and therefore, the method of PCT / EP2021 / 071380 always requires individual evaluation of the potential utility of such short peptides. Summary of the Invention

[0009] Endogenous retroelements and endogenous viral elements Endogenous retroelements (EREs) occupy a significant proportion of host genomes, accounting for up to 43% of the human genome and 37% of the mouse genome. The majority (70–80% of endogenous retroelements) contain elements without long repeats (LTRs). This group includes short and long retrotransposable elements (SINEs and LINEs), collectively referred to as non-LTR elements. The remaining endogenous retroelements contain LTR-binding elements and include two major groups that occupy similar proportions of the genome: endogenous retroviruses (ERVs) and mammalian apparent LTR retrotransposons (MaLRs) (Kassiotis & Stoye, 2016). Within the large group of endogenous retroelements, there is a subset of genes called endogenous viral elements (EVEs), which are the result of an in silico filtering process based on the presence of viral motifs within genes. As such, this group is mostly composed of ERVs, but may also include members of other distinct subcategories.

[0010] The retroviral life cycle is characterized by reverse transcription of the retroviral RNA genome and subsequent integration of the cDNA into host nuclear DNA. Retroviral infection of early embryonic and germ cells can be transmitted to subsequent generations, and remnants of such ancient proviruses are found in the genome, including what are known today as ERVs. Since the discovery of the first human endogenous retrovirus (hERV) in 1981, over 400,000 hERV fragments have been found in the human genome, contributing to approximately 5% of human DNA.

[0011] ERVs are recognized by their similar genome structure to all retroviruses and generally consist of Gag, Pro, Pol, and Env genes flanked by long terminal repeats (LTRs), although more complex endogenous retroviruses also contain accessory ORFs. The Gag ORF encodes various structural components of the virion capsid, while the Pro and Pol ORFs encode enzymatic activities involved in protein and nucleic acid processing, respectively. Entry of endogenous retroviruses into target cells is achieved by the binding of the endogenous retroviral Env glycoprotein, encoded by the Env ORF, to a cellular receptor. Endogenous retroviral virions encapsidate two copies of viral single-stranded RNA (ssRNA), which is reverse-transcribed by the encapsidated RT enzyme, typically in the cytoplasm of the target cell (Kassiotis & Stoye, 2016). The newly synthesized cDNA, still part of the preintegration complex, is then transported to the nucleus and integrated into host DNA. Thus, endogenous retroviruses are the only endogenous retroelements that have the potential for cell-to-cell infection. However, integrated ERV sequences can degenerate over time through the accumulation of mutations or recombination events, losing the ability to produce infectious virus, but still propagate efficiently in the germline by retrotransposition rather than reinfection (Magiorkinis et al., 2012).

[0012] The classification of hERVs most commonly used in the literature is based on the binding site of the tRNA primer. For example, retroviruses that use ricin (K) tRNA molecules to drive reverse transcription can generate the HERVK gene after genome insertion. While this classification was sufficient in the early days of hERV research, the discovery of many other hERV families in the human genome made this system obsolete.

[0013] Based on their similarity to exogenous retroviruses, the hERV family is currently divided into three classes. hERVs with homology to mammalian type C retroviruses (gammaretroviruses), such as murine leukemia virus (MLV), are classified as class I. Class I is a highly heterogeneous group of hERVs with many different families, and many class I elements are chimeras composed of segments derived from unrelated retroviruses. Class II consists of hERVs related to mammalian type B and type D retroviruses (betaretroviruses), represented by mouse mammary tumor virus (MMTV), and contains most of the genes previously associated with the HERVK group. This group is also characterized by the inclusion of the most recent retrovirus (HERVK10) discovered in the human genome, many of which are human-specific. Class III is characterized by its similarity to human foamy viruses (Spumaviridae), and with a few exceptions, all members lack env-like ORFs. This group also includes a number of mammalian apparent LTR retrotransposons (MaLRs) and non-autonomous elements known as THE1. No endogenous counterparts of exogenous lentiviruses, such as HIV, are known in the human genome (Pavlicek & Jurka, 2006).

[0014] ERV expression Although most, if not all, ERVs have accumulated replication-inactivating mutations, many contain intact ORFs that can produce retroviral proteins. These ERV-derived proteins serve as antigen sources for the immune system (e.g., including mouse MMTV and human HERV-K18), and some ERVs even have biological functions within the host. This evidence suggests that immune tolerance to ERV-derived proteins is not complete (Kassiotis, 2014).

[0015] There are also reports of cases in which the expression of endogenous retroviruses confers benefits to the host. For example, the mouse Fv1 (Friend virus susceptibility 1) and Fv4 loci encode retroviral restriction factors derived from endogenous retroviruses, protecting the host from further infection with exogenous retroviruses. Furthermore, well-known members of the syncytin family, encoded by genes derived from the Env ORF of endogenous retroviruses, are essential for placental development and likely regulate feto-maternal resistance. This evidence highlights the potential interdependence of ERV-derived genes in host species and their reproductive success (Dupressoir et al., 2012).

[0016] ERV activity in cells is neither ubiquitous nor constitutive, as it is regulated by both cell-intrinsic factors and external signals. Epigenetic silencing appears to be the primary mechanism by which cells block transcription of repetitive elements (including ERVs), particularly in the germ plasm and early embryo (Kassiotis, 2014). Nevertheless, a significant proportion of endogenous retroelements may be transcribed in adult cells and tissues. This expression follows tissue-specific patterns and is often the result of co-regulation with tissue-defining host genes (Young et al., 2014).

[0017] Somatic expression of endogenous retroelements is also regulated by various environmental cues, which affect DNA and histone methylation during early development or the balance of transcription factor networks in specific somatic cell types. External cues can involve activation of the xenotic response element (XRE) pathway, immune cell activation, and dietary restriction (Kassiotis & Stoye, 2016).

[0018] In humans, various families of ERVs have been reported to be overexpressed in both tumor samples and cancer cell lines. In a cohort of 89 patients with ovarian cancer and benign ovarian disease, cancer patients showed increased mRNA levels of HERV-K, HERV-R, and HERV-E compared with healthy controls, and their expression may be associated with poor prognosis (Iramaneerat, et al., 2011; Wang-Johanning et al., 2007). Other cancer types, such as renal carcinoma, lymphoma, leukemia, melanoma, sarcoma, endometrial cancer, and lung cancer, have also been associated with overexpression of HERV-K. Furthermore, even though HERV-K is the most widely studied of the human ERV group, other ERVs, particularly HERV-H and HERV-W, may also be involved in carcinogenesis (Vergara Bermejo et al., 2020).

[0019] ERV immunomodulation Env ORFs from several endogenous retroviruses with intact coding capacity are retained in the mouse and human genomes, suggesting an additional physiological role in preventing the decay of these endogenous retroelements even over time. Furthermore, host genomes harbor a group of recently integrated endogenous retroviruses for which evolution has not yet caused irreversible damage, but replication of such proviruses is not known to occur in humans. Finally, epigenetic regulation of endogenous retroviruses is primarily important in protecting germline cells from excessive reinfection or translocation, whereas somatic cells are susceptible to endogenous retroviral reactivation. These properties of endogenous retroviruses, combined with the need for the host immune system to continuously defend against exogenous infection by retroviruses and other pathogens, completely prevent innate and adaptive immune tolerance to endogenous retroviruses. Indeed, mounting evidence indicates that immune responses against endogenous retroviruses are induced, often resulting in pathological consequences, particularly those associated with autoimmune diseases. Various mechanisms have been reported linking hERVs to several chronic diseases, including unwanted immune responses to self-nucleic acids based on reverse-transcribed cDNA intermediates of endogenous retroelements ( Balada et al., 2009 ; Volkman & Stetson, 2014 ).

[0020] Endogenous retroelements can play a dual role in immune regulation, both by influencing immune reactivity against themselves and by acting as immunosuppressants. The latter is evident based on the advantageous characteristics of increased ERV expression during cancer development. Interfering RNA assays demonstrated that silencing the Env gene in B16 mouse melanoma resulted in tumor rejection under conditions in which control melanoma cells grew into lethal tumors, demonstrating the role of the Env gene in tumor T cell-mediated immune escape in vivo (Mangeney et al., 2005). Similar evidence was observed in human breast, pancreatic, and prostate cancers, where Env proteins were also shown to be essential for tumorigenesis and metastasis in cancer cell lines. Knockdown of HERV-K Env blocked the expression of tumor-related genes, including Ras, p-RSK, p-ERK, and p-AKT, resulting in tumor regression (Li et al., 2017; Zhou et al., 2016). Similar to what is now established for HIV, studies have revealed that the immunosuppressive effect of the Env protein is associated with a transmembrane domain called p15E in the murine leukemia virus (MuLV) gene (Scheeren et al., 1992). This domain, also known as the immunosuppressive domain (ISD), has been shown to suppress PBMC activation and regulate cytokine release and gene expression. This immunoregulatory effect has been demonstrated not only for HERV-K but also for HERV-W, HERV-H, and HERV-FDR, whose expression has been detected in the placenta and is involved in tolerance to hemimolar disease through suppression of dendritic cell-mediated T cell activation (Hummel et al., 2015; Lokossou et al., 2020; Morozov et al., 2013).

[0021] Alternatively, induction of IFN responses or cellular activation by replicative intermediates of endogenous retroelement nucleic acids inevitably enhances immune reactivity to unrelated antigens. By mimicking viral infection, endogenous retroelements may provide the "endogenous adjuvant" necessary for immune responses against poorly immunogenic targets. For example, induced ERV expression after treatment with azacytidine, an inhibitor of DNA methylation, induces IFN responses and increases tumor cell immunogenicity (Chiappinelli et al., 2015; Roulois et al., 2015). This viral mimicry by ERVs becomes even more complex when considering the presence of multiple copies of any given group of endogenous retroelements. The inevitable hijacking or incidental sharing of protein products between different groups of endogenous retroelements highlights the potential for different endogenous retroelement loci to interact with each other. Consequently, the potential of an individual endogenous retroelement to induce an immune response depends on its expression, but can be further influenced by the combination of interacting retroelements expressed in a given cell type.

[0022] The role of ERVs in cancer The fact that infectious retroviruses can cause cancer by insertional mutagenesis initially highlighted the idea that ERVs are the causative agents of many cancer types. However, this idea has not withstood experimental scrutiny. Recent recognition of the biological complexity of ERVs and the identification of specialized host mechanisms that regulate ERV activity have revealed new interactions between ERVs and the host that can cause or contribute to disease.

[0023] There are numerous examples of ERV protein expression and virion production in mouse and human cancer cells. However, both ERV proteins and virions are also found in certain healthy tissues, such as the placenta, and in other non-neoplastic diseases, such as infections. Therefore, elevated ERV activity, even if restricted to cancer cells, should not be considered a causal link. Rather, most of this activity likely represents a lack of ERV regulation under these conditions (Kassiotis, 2014).

[0024] Global DNA hypomethylation, leading to epigenetic derepression, is a hallmark of cellular transformation and contributes significantly to oncogene activation. Alterations in the epigenetic landscape of transformed cells are expected to result in the derepression of ERVs (Szpakowski et al., 2009). Distinct patterns of ERV expression are often observed in various tumors, providing unique approaches for immunotherapy. Tumor-restricted expression of several ERVs has been proposed, including HERV-K (HML6) in melanoma, HERV-K (HML2) in germ cell carcinoma, HERV-E in renal cell carcinoma, and Syncytin-1 in various cancers. HERV expression in some of these cases has also been shown to result in immune reactivity against hERV-derived epitopes (Kassiotis, 2014). For example, a recent study by Saini et al. constructed a library of 1,169 peptides derived from 66 previously identified hERV loci that potentially retain translational activity and observed that hERV-directed T cells were enriched in patients with myeloid malignancies compared to healthy donors ( Saini et al., 2020 ).

[0025] Cancer-specific transcription of ERVs not only activates oncogenic pathways but also leads to the production of exosomes, which resemble VLPs capable of expressing Gag and Env. The presence of ERV proteins in these particles elicits innate and adaptive immune responses, generating specific CD8+ T cells, among other immune responses. Similarly, viral proteins processed by cancer cells can be presented by MHC-I or MHC-II, depending on whether they are processed through the proteasome or endosome. However, such cancer-induced immune responses fail to control tumor development, likely due in part to the immunosuppressive state caused by the isotopic deletion of the p15E Env protein. Further activation of the immune system appears necessary to combat tumors (Vergara Bermejo et al. 2020).

[0026] ERV immunogenicity ERVs have been detected in several cancers but remain largely silent in healthy tissues. Their low immunogenicity and immunosuppressive potential help cancers escape immune surveillance. Which specific ERV types, if any, may be cancer-specific enough to be targeted has been a subject of debate (Vergara Bermejo et al., 2020). HERVs have been tested as cancer targets for decades using various delivery methods (dendritic cell pulsing and CAR-T cells targeting specific hERVs) (Vergara Bermejo et al., 2020). Generally, these studies have simultaneously targeted only a single hERV or only a few hERVs. These hERVs have demonstrated cancer specificity in the sense that they have been identified as common across patients grouped based on a single cancer type.

[0027] ERVs account for approximately 4.7% of the mouse genome, and unlike those known from humans, the mouse genome contains replication-competent ERVs capable of producing functional infectious virions, namely the MuLV and MMTV genes. By the early 2000s, the expression of these genes had already been reported in several mouse cell lines, and their presence as MHC class I-restricted T cell antigens had also been reported, but their potential for tumor treatment in in vivo models had not been demonstrated.

[0028] One of the earliest evidences of effective tumor immunization using ERVs was provided by the Takeda group, who reported that a DNA vaccine encoding a B-gal / gp70 fusion protein (but not gp70 alone) was able to induce protective immunity against CT26 challenge (Takeda et al., 2000). Jason Rice et al. also reported that administration of a DNA vaccine encoding gp70 alone was poor at inducing CTLs, but administration of a fusion protein containing a minimal gp70 sequence (AH1 peptide, sequence SPSYVYHQF) and a minimal domain of the tetanus toxin-derived pathogen sequence (fragment C (FrC) minimal domain (DOM)) significantly improved the efficacy and resulted in rapid CTL induction (Rice et al., 2002).

[0029] Further studies using murine ERV (mERV) DNA vaccination of H-2b and H-2d haplotype mice demonstrated robust immune responses only when a CD40 agonist was administered. However, the timing of administration of this agonist appears to be crucial, potentially even potent during immune priming, and it proved most effective during tumor rejection (Bronte et al., 2003). While few studies have used purified ERV proteins as a therapeutic strategy, the study by Casares et al., although different in approach, is consistent with previously published results. In this study, an IFA emulsion containing a combination of purified AH1 peptide and a Th1-stimulating peptide (OVA(323-337)) protected 83% of BALB / c mice from CT26 challenge, whereas an emulsion containing AH1 alone failed to provide protection (Casares et al., 2001).

[0030] Another strategy using ERV-mediated immunization is chimeric antigen receptor (CAR) T-cell therapy. In a study conducted by Krishnamurthy et al., immunohistochemical (IHC) analysis confirmed the expression of HERV-K protein in 220 melanoma samples (various stages) but not in 139 normal organ donor tissues, implicating the HERV-K gene as a TAA-like target. Observations regarding in vivo adoptive transfer of CAR+ T cells generated using the Sleeping Beauty (SB) system (a non-viral approach utilizing transposons and transposases) demonstrated tumor cell lysis in vitro and antigen-specific antitumor effects in vivo (Krishnamurthy et al., 2015).

[0031] One of the most commonly used strategies for ERV immunization in the literature to date is the virus-like particle (VLP) platform, due to its size, repetitive surface pattern, ability to elicit both innate and adaptive immune responses, and safety and economic viability (Qian et al. 2020). Kershaw et al. provided the first evidence of the efficacy of recombinant vaccinia immunization encoding the gp70 minimal determinant (AH1 peptide), which significantly protected mice from subsequent CT26 tumor challenge but proved ineffective against established tumors (Kershaw et al., 2001). The latter was only achieved by pulsing gp70 or p15E peptides to in vitro-generated bone marrow-derived DCs. Similar results have been shown in lung cancer, where injection of modified vaccinia Ankara (MVA) encoding HERV-K Env and Gag prevented lung tumor progression and metastasis in mice ( Kraus et al., 2013 , 2014 ).

[0032] Over the past few years, increasing evidence has demonstrated the efficacy of ERV-based VLP therapy. A vaccination strategy designed to induce both humoral and cellular immune responses against MuLV virus-like particles was implemented by administering adenovirus type 5 encoding the melanoma-associated retrovirus (MelARV) proteins Gag and Env. Despite the lack of antibody induction, a single vaccination induced a strong enough T cell response to block colon CT26 tumor growth and progression in 57% of BALB / c mice, both before and after tumor challenge. When combined with checkpoint inhibitor treatment, the protective effect was further enhanced, leading to complete tumor regression (Neukirch et al., 2019). This protection appears to be long-term, as none of the mice surviving the initial CT26 tumor challenge developed tumors after rechallenge with 4T1 tumor cells.

[0033] Peltonen et al. also used ERVs as therapeutic targets through a similar delivery platform called peptide-coated conditionally replicating adenovirus (PeptiCRAd) and achieved good results in an aggressive triple-negative breast cancer model (4T1) using different mERV targets. This vaccine platform was conjugated to the mERV targets FYLPTIRAV and TYVAGDTQV (Q811J2 Uniprot accession) identified through immunopeptidomics. These peptides map to numerous putative mERV ORFs but not to well-described MuLV genes. Treatment with ERVs demonstrated statistically significant protection compared to virus alone, although the results were not as clear as those described above. With this approach, combination therapy with PD-1 did not increase the level of protection (Peltonen et al., 2021).

[0034] WO 2021 / 005339 discloses cancer-specific LTR element-spanning RNA transcripts associated with small cell lung cancer and / or melanoma, and also discloses the use of expression products from these transcripts for active specific cancer immunotherapy.

[0035] Object of the invention It is an object of aspects of the present invention to provide means and methods for identifying and utilizing nucleic acid sequences and their expression products that are not expressed in normal cells or are expressed at very low frequencies in random normal tissue samples.

[0036] Summary of the Invention Based on the baseline expression (RNA-seq) data of over 6,000 human healthy tissue samples obtained from TCGA, we identified a substantial number of EVE sequences that are not expressed or expressed at very low levels across all normal tissues and may simultaneously be ideal targets for personalized cancer immunotherapy. Because the majority of the identified EVEs can be defined as ERVs, we refer to them generically as ERVs below, even though they may not strictly be ERVs.

[0037] A survey of over 400 melanoma biopsies identified several patient-specific overexpressed ERV sequences.

[0038] By applying a series of HLA ligand prediction tools, HLA-binding ligands with high potential to bind to patient-specific HLA molecules were identified from full-length ERV sequences, and these ERV sequences were shown to have at most low expression in non-malignant tissues.

[0039] Thus, the present invention generally relates to methods, including bioinformatics approaches, for identifying patient-specific ERVs and the HLA-specific ligands contained therein for individual patients, and for identifying broad baseline expression profiles that can be administered to patients as personalized therapy.

[0040] An attractive feature of this approach is that the sequences of the ERVs are known in advance, meaning that the patient's cancer transcriptome only needs to be queried for the presence of RNA transcripts of selected ERV sequences that have previously been screened for off-target activity.

[0041] Rather than applying a traditional "warehouse" approach, as in WO2021 / 005339, in which the same ERVs are used across multiple patients based on past experience and whose expression is correlated with specific cancer types, the inventors apply a personalized approach to ERVs, thereby predicting ligands from patient-specific (overexpressed) ERVs for each patient in the clinic.

[0042] Thus, in a first aspect, the present invention provides a method for identifying immunogenic amino acid sequences in a malignant tumor tissue sample from a patient (preferably a human), comprising: A) determining the amino acid sequence of the protein expression product from malignant tumor tissue; B) analyzing the amino acid sequence to identify the proteinaceous expression product of the selected genomic sequence in the patient species; C) identifying an amino acid sequence in said protein expression product that binds to a patient's MHC (HLA in humans) molecule; wherein said selected genomic sequences constitute a subset of all sequences of the genome of said species, said subset being made up of sequences that encode proteinaceous expression products in at most 5% of samples from any healthy tissue, said healthy tissue being a tissue type found in a patient, and said healthy tissue optionally excluding testis tissue and / or brain tissue.

[0043] In a second aspect, the present invention relates to a method of treating a malignant neoplasm in a patient, preferably a human patient, comprising: A) determining the amino acid sequence of the protein expression product from malignant tumor tissue; B) analyzing the amino acid sequence to identify the proteinaceous expression product of the selected genomic sequence in the patient species; C) identifying an amino acid sequence in said protein expression product that binds to a patient's MHC (HLA in humans) molecule; wherein said selected genomic sequences constitute a subset of all sequences of said species' genome, said subset being made up of sequences that encode proteinaceous expression products in at most 5% of samples from any healthy tissue, said healthy tissue being a tissue type found in a patient, and said healthy tissue optionally excluding testis tissue and / or brain tissue. D) administering to the patient one or more peptides identified in step C, or one or more polypeptides comprising two or more peptides identified in step C, or one or more expression vectors encoding and capable of expressing said one or more peptides identified in step C, or one or more expression vectors expressing one or more polypeptides comprising two or more peptides identified in step C, so as to induce a specific adaptive immune response against said one or more peptides; wherein the selected genomic sequences constitute a subset of all sequences of the genome of the patient's species, said subset being composed of sequences that encode proteinaceous expression products in at most 5% of samples from healthy tissue, said healthy tissue being a tissue type found in the patient, and said healthy tissue optionally excluding testis tissue and / or brain tissue.

[0044] In a closely related aspect, the invention relates to a peptide identified in step C for use in a method of treatment, particularly a method of treatment according to the second aspect of the invention.

[0045] In a third aspect, the present invention relates to a computer or computer system for identifying immunogenic amino acid sequences in a sample of malignant tumor tissue from a patient, said computer or computer system comprising: 1) an input component for inputting amino acid sequence or mRNA data; 2) Optionally, executable code for determining amino acid sequence from mRNA data; 3) a database containing the amino acid sequences of the expression products of genomic sequences; 4) executable code for identifying the presence of sequences in an input amino acid sequence or an amino acid sequence encoded by an input mRNA that are present in amino acid sequences in the database; 5) executable code for identifying and, optionally, ranking the amino acid sequences identified by the executable code of 4 according to their predicted ability to bind to a selected MHC molecule; and 6) a component for outputting or storing the identified and / or ranked amino acid sequences; wherein the genomic sequences in 3 constitute a subset of the total sequences of the genome of a species, said subset being composed of sequences that encode proteinaceous expression products in at most 5% of samples from any healthy tissue type found in that species (wherein said healthy tissue type optionally does not include testis tissue and / or brain tissue). Includes.

[0046] In a fourth aspect, the present invention relates to a method for determining whether a cancer patient is likely to benefit from anti-cancer immunotherapy, the method comprising determining the number of EVEs, such as ERVs, expressed in said patient, and classifying the cancer patient as likely to benefit from anti-cancer immunotherapy if the number of said expressed EVEs or ERVs exceeds a predefined threshold. [Brief explanation of the drawings]

[0047] [Figure 1] FIG. 1 is a graph depicting the response rates of patients treated with high and low tumor mutation burden (TMB). [Figure 2] Figure 2 shows a gray-tone heat map of the expression of nine tumor-specific antigens (TSAs) across various tissue samples. Black indicates expression in 0% of the tissue samples, and white indicates expression in 50% of the tissue samples. See gray tones on the right. [Figure 3] Figure 3 shows a gray-tone heat map of expression of 12,202 selected (i.e., included) hERV expression products across various tissue samples. Black indicates expression in 0% of the tissue samples, and white indicates expression in 50% of the tissue samples. See gray tones shown on the right. [Figure 4] Figure 4 shows a gray-tone heat map of expression of 21,764 unselected (i.e., excluded from the set of potential target sequences) hERV expression products across various tissue samples. Black indicates expression in 0% of the tissue samples, and white indicates expression in 50% of the tissue samples. See gray tones shown on the right. [Figure 5] FIG. 5 is a bar graph showing the expression of TSA (brain and testis excluded), selected / included ERVs and non-selected / non-included ERVs (corresponding to FIG. 3) in each fraction of healthy tissue samples. [Figure 6]Figure 6 is a bar graph showing the expression of TSA (excluding brain and testis), selected / included ERVs, and non-selected / non-included ERVs (corresponding to Figure 3, but showing expression in brain and testis) in each fraction of healthy tissue samples. [Figure 7] Figure 7 is a bar graph showing the number of patients with high (≥50 ERVs) and low (<50 ERVs) ERV burdens in the high and low TMB patient groups, respectively, from three published scientific studies. [Figure 8] Figure 8 shows a line graph of observed patient survival over time in groups with high or low ERV burden from three different published scientific studies. [Figure 9] Figure 9 shows a line graph of observed patient survival over time in patients with high or low ERV burden from three different published scientific studies. [Figure 10] FIG. 10 shows a line graph of observed patient survival over time in patient groups with high or low ERV burden from three different published scientific studies. [Figure 11] FIG. 11 shows a line graph of observed patient survival over time for patients with high and low ERV loads in the TMB high and low patient groups, respectively. [Figure 12]Figure 12 shows graphs of immunization against ERV-encoded expression products and their in vivo tumor protection effects. A: Schematic of the immunization scheme. B: Tumor growth in three vaccinated groups, plotted as volume versus time. C: Tumor growth in three vaccinated groups, plotted as volume versus time. D: Tumor growth rate in three vaccinated groups, plotted as area under the curve (AUC). To investigate whether ERVs could be targeted for immunotherapy, groups of mice were intramuscularly (im) administered PR-ERV, MS-ERV, or mock pDNA at weekly intervals using a vaccination scheme that included two EP-based primary immunizations followed by three poloxamer-based immunizations (Figure 12A). Immunization began 2 weeks before subcutaneous (sc) challenge with a tumorigenic dose of CT26 cancer cells. In contrast to mock-pDNA-treated mice, which developed tumors of significant final volume (Fig. 12B), mice inoculated with PR-ERV and MS-ERV potently prevented the formation of CT26 tumors (Figs. 12B and 12C). [Figure 13] Figure 13 shows two graphs of hazard ratio versus hERV number thresholds: the top graph shows the relationship for the full hERV database, and the bottom graph shows the relationship for the list of hERVs shown in the table in Example 4. [Figure 14] Figure 14 is a bar graph showing the number of samples from 13 cancer types: SKCM: cutaneous melanoma, LUAD: lung adenocarcinoma, LUSC: lung squamous cell carcinoma, BLCA: bladder urothelial carcinoma, COAD: colorectal adenocarcinoma, STAD: gastric adenocarcinoma, THCA: thyroid cancer, BRCA: breast invasive carcinoma, GBM: glioblastoma multiforme, KIRC: renal clear cell carcinoma, LIHC: hepatocellular carcinoma, KIRP: renal papillary cell carcinoma, ESCA: esophageal cancer. [Figure 15] FIG. 15 is a graph relating TMB to EVE burden for the cancers shown in FIG. DETAILED DESCRIPTION OF THE INVENTION

[0048] Detailed Disclosure of the Invention definition Endogenous retroelements (EREs) are genetic elements. EREs account for nearly 50% of the human genome. These elements are present in almost all organisms and are thought to be remnants of transposable elements integrated into germ cells millions of years ago. Most ERE sequences have mutated or truncated open reading frames, losing their ability to transpose within the genome. EREs include short and long interspersed retrotransposable elements (SINEs and LINEs), collectively referred to as non-LTR elements. The remaining endogenous retroelements contain LTR-binding elements, which include two major groups that occupy similar proportions of the genome: endogenous retroviruses (ERVs) and mammalian apparent LTR retrotransposons (MaLRs) (Kassiotis & Stoye, 2016).

[0049] "Endogenous viral elements" ("EVEs") are EREs that are members of a subset of genes that are the result of an in silico filtering process based on the presence of viral motifs within the gene. As such, this group is mostly composed of ERVs, but may also include members of other different subcategories.

[0050] “Novel or unannotated open reading frames” (abbreviated as “nuORs”) are genomic sequences that have not traditionally been a source of translation products, but for which immunopeptide analysis has revealed the presence of MHC-binding peptides derived from malignant tumor tissue (Ouspenskaia T et al., 2021, Nature Biotechnology, doi.org / 10.1038 / s41587-021-01021-3).

[0051] A "malignant neoplasm" (also called cancer or malignant tumor) refers to a group of cells in a multicellular organism that exhibits uncontrolled proliferation, invasive growth, and usually the ability to metastasize.

[0052] A "cancer-specific" antigen is an antigen that is not expressed as an expression product in an individual's non-malignant somatic cells, but is expressed as an expression product in an individual's cancer cells. This contrasts with a "cancer-associated" antigen, which is present in low amounts in normal somatic cells, but is present at high levels in at least some malignant tumor cells. In general, peptides identified in accordance with the present invention are considered to be cancer-specific.

[0053] The term "adjuvant" has its usual meaning in the field of vaccine technology, i.e., a substance or composition that 1) is not itself capable of generating a specific immune response to a vaccine immunogen, but 2) can nevertheless enhance the immune response to the immunogen. In other words, vaccination with an adjuvant alone will not elicit an immune response to the immunogen, vaccination with an immunogen may or may not elicit an immune response to the immunogen, but vaccination with a combination of immunogen and adjuvant will induce an immune response to the immunogen that is stronger than the immune response induced by the immunogen alone.

[0054] MHC molecules (major histocompatibility molecules) are tissue antigens expressed in nucleated cells of vertebrates, which bind to peptide antigens and display ("present") antigens to T cells bearing T cell receptors. MHC class I is expressed in all nucleated cells and primarily presents protein fragments resulting from proteolysis of proteins present within the cell. MHC class II is expressed by specialized antigen-presenting cells, which ingest extracellular proteins, degrade them with lysosomal proteases, and present the protein fragments on their surface. In humans, MHC molecules are known as human leukocyte antigens (HLA), and in the present invention, binding to these MHC molecules is preferably evaluated.

[0055] A "T cell epitope" is an MHC-binding peptide that is recognized as foreign (non-self) by vertebrate T cells upon specific binding between the T cell receptor and cells bearing the MHC-peptide complex on their surface. Thus, a peptide that constitutes a T cell epitope in one individual may not necessarily be a T cell epitope in another individual of the same species. First, two individuals with different MHC molecules that bind different sets of peptides will not necessarily present the same peptides complexed to MHC. Furthermore, if a peptide is autologous to one individual, it may not be able to bind to any T cell receptors.

[0056] A "neoepitope" is an antigenic determinant (typically an MHC class I or II-restricted epitope) that is absent as an expression product in normal somatic cells within an individual due to the lack of a gene encoding the neoepitope, but is present as an expression product in mutant cells (e.g., cancer cells) within the same individual. As a result, neoepitopes, despite their autologous origin, are truly non-self from an immunological standpoint and can therefore be characterized as tumor-specific antigens in the individual in which they are expressed. Because neoepitopes are non-self, they can elicit a specific adaptive immune response in the individual, and the elicited immune response is specific to the antigen and cells bearing the neoepitope. Meanwhile, neoepitopes are specific to that individual because the likelihood of the same neoepitope being expressed in another individual is minimal. Thus, neoepitopes have several characteristics that distinguish them from, for example, epitopes of tumor-specific antigens: they are typically found in multiple cancers of the same type (because they may be the expression products of activated oncogenes) and / or are present, albeit in small amounts, in non-malignant cells due to overexpression of related genes in cancer cells.

[0057] A "neopeptide" is a peptide (i.e., a polyamino acid sequence of up to about 50 amino acid residues) that includes within its sequence a neoepitope, as defined herein. Neopeptides are generally "native," i.e., the entire amino acid sequence of the neopeptide constitutes a fragment of an expression product isolated from an individual; however, neopeptides may also be "artificial," i.e., comprised of a neoepitope and one or two additional amino acid sequences, at least one of which is not naturally associated with the neoepitope. In the latter case, the additional amino acid sequence may simply function as a carrier for the neoepitope or may improve the immunogenicity of the neoepitope (e.g., by facilitating processing of the neopeptide by antigen-presenting cells, improving the biological half-life of the neopeptide, or modifying its solubility).

[0058] "Amino acid sequence" refers to the order in which amino acid residues linked by peptide bonds are arranged in a chain in peptides and proteins. Sequences are conventionally written in NC-terminal order.

[0059] An "immunogenic carrier" is a molecule or moiety to which an immunogen or hapten can be attached to enhance or enable the induction of an immune response to the immunogen / hapten. Classically, immunogenic carriers are relatively large molecules (e.g., tetanus toxoid, KLH, diphtheria toxoid) that can be fused or conjugated to immunogens / haptens that are not sufficiently immunogenic by themselves. Immunogenic carriers generally can elicit strong T-helper lymphocyte responses against the combined entity formed by the immunogen and immunogenic carrier, resulting in improved responses to the immunogen by B-lymphocytes and cytotoxic lymphocytes. More recently, large carrier molecules have been replaced to some extent by so-called promiscuous T-helper epitopes, i.e., short peptides that are recognized by a large proportion of HLA haplotypes in a population and elicit T-helper lymphocyte responses.

[0060] A "T-helper lymphocyte response" is an immune response elicited based on a peptide that can bind to an MHC class II molecule (e.g., an HLA class II molecule) in an antigen-presenting cell and stimulates T-helper lymphocytes of an animal species as a result of the complex of the peptide and the MHC class II molecule presenting the peptide being recognized by a T cell receptor.

[0061] An "immunogen" is a substance that can induce an adaptive immune response in a host whose immune system encounters it. An antigen is a substance that is specifically recognized by the immune system (e.g., when bound by an antibody, or when a fragment of an antigen bound to an MHC molecule is recognized by a T cell receptor), but is not necessarily capable of inducing immunity. However, an antigen is always capable of inducing immunity; that is, a host that has established memory immunity to an antigen will mount a specific immune response to the antigen.

[0062] An "adaptive immune response" is an immune response that responds to an antigen or immunogen, where the immune response is specific for an antigenic determinant of the antigen / immunogen. Examples of adaptive immune responses include the induction of antigen-specific antibody production or the antigen-specific induction / activation of T helper lymphocytes or cytotoxic lymphocytes.

[0063] A "protective adaptive immune response" is an antigen-specific immune response induced in a subject in response to immunization (artificial or natural) with an antigen, which immune response can protect the subject from subsequent exposure to the antigen or a pathologically related substance containing the antigen. Typically, prophylactic vaccination aims to establish a protective adaptive immune response against one or several pathogens. Currently, the immune responses induced by identified peptides are as follows:

[0064] "Stimulating the immune system" means that a substance or composition exhibits a general, nonspecific immunostimulatory effect. Many adjuvants and adjuvant candidates (such as certain cytokines) share the ability to stimulate the immune system. The use of an immunostimulant increases the "wake-up" of the immune system, meaning that simultaneous or subsequent immunization with an immunogen will induce a more effective immune response compared to the immunogen alone.

[0065] The term "polypeptide" refers to both short peptides of 2 to 50 amino acid residues, oligopeptides of 50 to 100 amino acid residues, and polypeptides of 100 or more amino acid residues. Furthermore, the term is intended to include proteins, i.e., functional biomolecules comprising at least one polypeptide. When comprising at least two polypeptides, these may be complexed, covalently linked, or non-covalently linked. The polypeptide(s) in a protein may be glycosylated and / or lipidated and / or contain protease groups.

[0066] Embodiments of the first aspect of the invention A first aspect is a method for identifying immunogenic amino acid sequences in a sample of malignant tumor tissue from a patient, the method comprising: A) determining the amino acid sequence of the protein expression product from malignant tumor tissue; B) analyzing the amino acid sequence to identify the proteinaceous expression product of the selected genomic sequence in the patient's species; and C) identifying from the protein expression products amino acid sequences that bind to MHC molecules of the patient, wherein said selected genomic sequences constitute a subset of all sequences of the genome of said species, said subset being constituted by sequences that encode proteinaceous expression products in at most 5% of samples from all healthy tissues, said healthy tissues being tissues of a type found in the patient, and wherein said healthy tissues optionally do not include testis tissue and / or brain tissue.

[0067] By focusing on the expression of genomic sequences in the patient's healthy tissue, it is possible to avoid or minimize the side effects caused by immune induction in healthy tissue.It can be understood that the target healthy tissue is usually the control tissue in which samples from multiple sources are investigated for the expression of genomic sequences.Therefore, in principle, the expression profile in the patient's own normal tissue is not determined, but by excluding the expression of genomic sequences from multiple tissue samples from other sources that occur in more than 5%, the risk of inducing adverse events is greatly reduced.

[0068] For example, if the patient is female, it does not matter if the amino acid sequence identified in the malignant tumor tissue is expressed in more than 5% of the male samples, and vice versa.

[0069] Furthermore, in this method, the identified amino acid sequences can include sequences derived from testis and / or brain. Both tissues are immune privileged, meaning that the immune response induced against testis-specific or brain-specific antigens is unlikely to be harmful to patients (in the case of the brain, due to the blood-brain barrier). Therefore, in some embodiments, healthy tissues do not include testis tissue, brain tissue, or testis and brain tissue. On the other hand, in the safest embodiment, healthy tissues include testis and brain tissue. This results in fewer identified amino acid sequences compared to the situation where expression in these two tissues is ignored in the selection and identification process.

[0070] Preferably, the genomic sequence is selected from endogenous retroelement (EVE) sequences, such as ERV sequences, nuOFR sequences, and genomic sequences transcribed as alternative splicing sequences, although essentially any genomic sequence can be preselected as long as it is likely to be expressed under the particular circumstances and contains the necessary MHC-binding amino acid stretches, although preferred genomic sequences are ERV sequences.

[0071] In a particularly preferred embodiment, the amino acid sequence of the peptide that binds to a patient's MHC molecules is an amino acid sequence that binds to both MHC class I and MHC class II molecules of the patient (in the sense that after antigen processing, there is binding to both MHC class I and II). This results in an amino acid sequence that maximizes the ability to elicit both humoral and cellular immune responses. However, the amino acid sequence can also bind to a patient's MHC class I molecules but not to MHC class II molecules, or to a patient's MHC class II molecules but not to MHC class I molecules.

[0072] To identify MHC class I binding amino acid sequences, the following algorithm can be used: netMHCpan-4.1: pubmed.ncbi.nlm.nih.gov / 32406916 / MHCflurry: pubmed.ncbi.nlm.nih.gov / 32711842 / mixMHCpred: pubmed.ncbi.nlm.nih.gov / 28832583 /

[0073] The following algorithm can be used to identify MHC class II binding amino acid sequences. netMHCIIpan-4.0: pubmed.ncbi.nlm.nih.gov / 32406916 / mixMHC2pred: pubmed.ncbi.nlm.nih.gov / 31611696 / BERTMHC: pubmed.ncbi.nlm.nih.gov / 34096999 /

[0074] It should be noted that the list of selected genome sequences that ultimately emerges from this method is influenced by the initial selection of MHC molecules.For example, there are ethnic variations in the frequency of HLA class I molecules and HLA class II molecules at the global level (John M et al., 2010, J. Immunol. 184: 4368-4377 and Pidala J et al., 2012, Bone Marrow Transplant 48(3):246-350), and the prediction of MHC (HLA) binding can be optimized in relation to the ethnic composition of the population in the relevant region.For example, if focusing on a different set of HLA molecules, the list provided in Example 4 may have a different composition.

[0075] Step A typically involves determining the amino acid sequence from mRNA of the patient's malignant tissue, ie, mRNA is extracted from the malignant tissue and analyzed for the presence of selected genomic sequences.

[0076] As shown in the examples, selected genomic sequences are identified by determining the expression profile of the genomic sequences across multiple samples from multiple tissues, and selecting genomic sequences that are expressed in less than 5% of the multiple samples. The 5% threshold is arbitrary but is considered a safe threshold that excludes adverse events in the majority of patients. However, for example, in the case of highly malignant cancers, the 5% threshold is unnecessary, and target sequences that are expressed in a higher percentage of normal tissue samples from various tissues can be identified. On the other hand, if it is desired to minimize the number of potential adverse events, the threshold can be lowered to 4%, 3%, or 2%, and the events can be reduced to 1% or less. The effect is that setting a lower threshold reduces the number of selected genomic sequences. Therefore, the list of selected genomic hERV sequences provided in Example 4 will be shorter if a lower threshold is selected. Conversely, setting a higher threshold will result in a longer list.

[0077] However, as above, the multiple tissue samples, in some embodiments, do not include samples from testis and brain tissue, providing a very safe immunization strategy, while in an even safer approach, the multiple tissue samples may include samples from testis and brain tissue.

[0078] Embodiments of the second aspect of the present invention This aspect relates to a method of treating a malignant neoplasm in a patient, preferably a human patient, comprising carrying out steps A to C of the first aspect of the invention and any embodiment discussed herein, and then administering to the patient one or more peptides identified in step C, or one or more polypeptides comprising two or more peptides identified in step C, or one or more expression vectors encoding and capable of expressing said one or more peptides identified in step C, or one or more expression vectors capable of expressing one or more polypeptides comprising two or more peptides identified in step C, so as to induce a specific adaptive immune response against said one or more peptides; wherein said selected genomic sequences constitute a subset of all sequences of the genome of the patient species, said subset being composed of sequences that encode proteinaceous expression products in at most 5% of samples from any healthy tissue, said healthy tissue being a tissue of a type found in the patient, and wherein said healthy tissue optionally does not include testis tissue and / or brain tissue. Regarding the method.

[0079] In a preferred embodiment, the peptide identified in step C, upon which the administration of the peptide / polypeptide / expression vector is based, has been identified in multiple cancer patients. Such "shared" expression products found in samples taken from multiple patients are particularly important, as they may be found in previous samples taken from the patient, and include such shared expression products that are associated with a successful immunotherapy outcome.

[0080] The number of peptides identified in step C and upon which the administration step is based will of course vary from patient to patient. However, when polypeptides or expression vectors encoding such polypeptides are administered, the peptides will generally comprise between 3 and 50 amino acid sequences, typically at least 4, 5, 6, 7, 8, 9 or 10 amino acid sequences of peptides identified in step C, and typically at most 45, 40, 35 or 30 amino acid sequences.

[0081] In a preferred embodiment, this method is provided as part of a combination treatment for a malignant neoplasm, wherein the patient also receives a treatment selected from the group consisting of other therapeutic cancer vaccinations, chemotherapy, radiation therapy, adoptive T cell therapy (such as CAR-T cell therapy), targeted antibody therapy, cytokine therapy, and immune checkpoint inhibitor therapy. Generally, other therapeutic cancer vaccinations will be vaccinations that induce an immune response against neoepitopes or neoantigens, but may also involve targeting cancer-associated antigens.

[0082] Chemotherapy can be any treatment with cytostimulatory or cytotoxic compounds, such as alkylating agents, antimetabolites, anti-microtubule agents, topoisomerase inhibitors, and cytotoxic antibiotics.

[0083] Alkylating agents include nitrogen mustards, nitrosoureas, tetrazines, aziridines, cisplatin, and derivatives. Nitrogen mustards include mechlorethamine, cyclophosphamide, melphalan, chlorambucil, ifosfamide, and busulfan. Nitrosoureas include N-nitroso-N-methylurea (MNU), carmustine (BCNU), lomustine (CCNU), semustine (MeCCNU), fotemustine, and streptozotocin. Tetrazines include dacarbazine, mitozolomide, and temozolomide. Aziridines include thiotepa, mitomycin, and diaziquone (AZQ). Cisplatin and its derivatives include cisplatin, carboplatin, and oxaliplatin. Additionally, alkylating agents include procarbazine and hexamethylmelamine.

[0084] Antimetabolites include antifolates, fluoropyrimidines, deoxynucleoside analogs, and thiopurines. Antifolates include methotrexate and pemetrexed. Fluoropyrimidines include fluorouracil and capecitabine. Deoxynucleoside analogs include cytarabine, gemcitabine, decitabine, azacitidine, fludarabine, nelarabine, cladribine, clofarabine, and pentostatin. Thiopurines include thioguanine and mercaptopurine.

[0085] Antimicrotubule agents include vinca alkaloids and taxanes. Vinca alkaloids include vincristine, vinblastine, vinorelbine, vindesine, and vinflunine. Taxanes include paclitaxel and docetaxel.

[0086] Podophyllotoxin is also an anti-microtubule agent, and exhibits similar effects to the vinca alkaloids.

[0087] Topoisomerase inhibitors include irinotecan and topotecan, etoposide, doxorubicin, mitoxantrone, teniposide, novobiocin, mervalone, and aclarubicin.

[0088] Cytotoxic antibiotics include anthracyclines, bleomycin, mitomycin C, and actinomycin. Important anthracyclines include doxorubicin, daunorubicin, epirubicin, idarubicin, pirarubicin, aclarubicin, and mitoxantrone. Immune checkpoint inhibitors target CTLA4, PD-1, or PD-L1, including ipilimumab (targeting CTLA-4), nivolumab (targeting PD-1), pembrolizumab (targeting PD-1), atezolizumab (targeting PDL-1), avelumab (targeting PDL-1), durvalumab (targeting PDL-1), and semipilimumab (targeting PD-1). Additionally, inhibitors that exhibit ubiquitin ligase activity, such as CISH (cytokine-inducible SH2-containing protein) and CBLB, are also included.

[0089] Cytokines useful in cancer therapy, and therefore in combination with the methods disclosed herein of the second aspect of the invention, include treatment with interleukin 2 (IL-2), interleukin 12 (IL-12), interleukin 15 (IL-15), interleukin 21 (IL-21), granulocyte-macrophage stimulating factor (GM-CSF), interferon-α (IFN-α), tumor necrosis factor (TNF-α), TGF-β, and CSF-1.

[0090] Targeted antibody therapies (only those not including checkpoint inhibitor treatment, as, as noted above, these constitute a separate group of anti-cancer therapies) include antibodies, antibody-drug conjugates, and other antibody-derived therapies that target various cancer-associated antigens. Targeted antibody therapies include, for example, HER-2 (the target of pertuzumab, trastuzumab, and trastuzumab emtansine), VEGF (the target of bevacizumab), EGFR (the target of cetuximab, necitumumab, and panitumumab), CD38, disialoganglioside GD2 antigen (the target of dinutuximab), SLAMF7 (the target of elotuzumab), CD38 (the target of isatuximab), CCR4 (the target of mogamulizumab), CD20 (the target of obinutuzumab, ofatumumab, rituximab, ibritumomab tiuxetan, and I). 131 These include those targeting tositumomab), VEGFR2 (ramucirumab), CD33 (gemtuzumab ozogamicin), CD30 (brentuximab vedotin), CD22 (inotuzumab ozogamicin and moxetumomab pasudotox), CD79B (polatuzumab vedotin), nectin-4 (enformab vedotin), TROP2 (sacituzumab govitecan), and CD19 / CD3 (blinatumomab).

[0091] Formulation and Administration of (Poly)peptides and Vectors Generally, immunization with peptides identified by the methods described herein will follow methods generally known in the art, with respect to routes of administration, formulation techniques, and the like.

[0092] Thus, compositions comprising a peptide identified according to the present invention (or optionally a vector encoding such a peptide) will generally include an immunological adjuvant, which is an aluminum-based adjuvant or one of the other adjuvants described below:

[0093] Adjuvants that enhance the effectiveness of immunogenic compositions include: (1) aluminum salts (alum), such as aluminum hydroxide, aluminum phosphate, and aluminum sulfate; (2) oil-in-water emulsion formulations (with or without the addition of other specific immunostimulants, such as muramyl peptides (see below) or bacterial cell wall components), such as (a) 5% squalene, 0.5% Tween 80, and 0.5% Span 85 (optionally, but not necessarily, containing various amounts of MTP-PE) formulated into submicron particles using a microfluidizer, such as the Model 110Y Microfluidizer (Microfluidics, Newton, MA); (b) 10% squalane, 0.4% Tween 80, 5% Pluronic block polymer L121, and thr-MDP, either microfluidized to a submicron emulsion or vortexed to a larger particle size emulsion; and (c) 2% squalene, 0.2% Tween 85. 80, and one or more bacterial cell wall components from the group consisting of monophosphoryl lipid A (MPL), trehalose dimycolate (TDM), and cell wall skeleton (CWS), preferably MPL + CWS (Detox™), Ribi Adjuvant System (RAS) (Ribi Immunochem, Hamilton, MT); (3) Stimulon (商標) Saponin adjuvants such as (Cambridge Bioscience, Worcester, MA), or particles produced therefrom, such as ISCOMs (immunostimulating complexes), can be used; (4) complete Freund's adjuvant (CFA) and incomplete Freund's adjuvant (IFA); (5) cytokines, such as interleukins (e.g., IL-1, IL-2, IL-4, IL-5, IL-6, IL-7, IL-12, etc.), interferons (e.g., gamma interferon), macrophage colony-stimulating factor (M-CSF), tumor necrosis factor (TNF), etc.; and (6) other substances that act as immunostimulants to enhance the effectiveness of the composition.

[0094] Examples of muramyl peptides include, but are not limited to, N-acetyl-muramyl-L-threonyl-D-isoglutamine (thr-MDP), N-acetyl-normuramyl-L-alanyl-D-isoglutamine (nor-MDP), and N-acetylmuramyl-L-alanyl-D-isoglutaminyl-L-alanine-2"-2'-dipalmitoyl-sn-glycero-3-hydroxyphosphoryloxy)-ethylamine (MTP-PE).

[0095] Immunogenic compositions (e.g., immunizing antigen or immunogen or polypeptide or protein or nucleic acid, pharmaceutically acceptable carrier (and / or diluent and / or vehicle), and adjuvant) will usually include a diluent such as water, saline, glycerol, ethanol, etc. Additionally, auxiliary substances, such as wetting or emulsifying agents, pH buffering substances, and the like, may be present in such vehicles.

[0096] Therefore, the pharmaceutical composition may contain a pharmaceutically acceptable carrier. "Pharmaceutically acceptable carrier" refers to a carrier for administering therapeutic agents such as antibodies, polypeptides, genes, and other therapeutic agents. This term refers to any pharmaceutical carrier that does not itself induce the production of antibodies harmful to the individual receiving the composition and that can be administered without undue toxicity. Suitable carriers may be large, slowly metabolized macromolecules such as proteins, polysaccharides, polylactic acids, polyglycolic acids, polymeric amino acids, amino acid copolymers, and inactive virus particles. Such carriers are well known to those skilled in the art.

[0097] Pharmaceutically acceptable salts that can be used include, for example, mineral acid salts such as hydrochloride, hydrobromide, phosphate, sulfate, etc.; organic acid salts such as acetate, propionate, malonate, benzoate, etc. A comprehensive discussion of pharmaceutically acceptable excipients is provided in Remington's Pharmaceutical Sciences (Mack Pub. Co., NJ 1991).

[0098] Generally, immunogenic compositions are prepared as injections in the form of liquid solution or suspension.Solid forms suitable for dissolving or suspending in liquid vehicles before injection can also be prepared.Preparation can also be emulsified or capsidized in liposomes to enhance adjuvant effect, as described above with pharmaceutically acceptable carriers.

[0099] Immunogenic compositions used as vaccines contain an immunologically effective amount of the relevant immunogen, and optionally any other components described above. By "immunologically effective amount," we mean that administration of that amount to an individual, whether as a single dose or as part of a series, is effective for treatment or prevention. This amount will vary depending on the health and physical condition of the individual being treated, the taxonomic group of the individual being treated (non-primate, primate, etc.), and will also take into account the capacity of the individual's immune system to synthesize antibodies and generally mount an immune response, the degree of protection desired, the vaccine formulation, the attending physician's evaluation of the medical situation, and other relevant factors. It is expected that the amount of immunogen will fall within a relatively wide range that can be determined by routine testing. However, for purposes of protein vaccination, single doses will typically range from 0.5 μg to 500 mg (but often not exceed 5,000 μg), and most often from 10 μg to 200 μg.

[0100] Immunogenic compositions are conventionally administered parenterally, for example, by injection, subcutaneously, intramuscularly, or transdermally (see, for example, WO 98 / 20734). Formulations suitable for other administration methods include oral, pulmonary, nasal, suppository, and transdermal. In the case of nucleic acid vaccination and antibody treatment, intravenous or intraarterial routes can also be applied.

[0101] Dosage treatment may be a single dose schedule or a multiple dose schedule, for example a prime-boost dose regime (primary vaccination followed by one or more booster doses) or a burst regime, i.e., a series of "primary" doses. The vaccine may also be used in conjunction with other immunomodulatory agents as may be convenient or desired.

[0102] Once the exact composition and format of the vaccine has been determined as described above, the invention generally depends on methods familiar to physicians for immune induction and patient follow-up, which also involve administration of the vaccine (typically 0.5 μg to 500 μg per dose for protein / peptide-based vaccines), generally as at least a priming dose, followed by one or more booster doses.

[0103] Malignant neoplasms that can be targeted by the present invention can be selected from the group consisting of epithelial tumors, non-epithelial tumors, and mixed tumors. Epithelial tumors can be both carcinomas and adenocarcinomas, and non-epithelial tumors or mixed tumors are generally liposarcoma, fibrosarcoma, chondrosarcoma, osteosarcoma, leiomyosarcoma, rhabdomyosarcoma, glioma, neuroblastoma, medulloblastoma, malignant melanoma, malignant meningioma, neurofibrosarcoma, leukemia, bone marrow transplant disorder, lymphoma, angiosarcoma, Kaposi's sarcoma, malignant teratoma, dysblastoma, seminoma, or choriosarcoma.

[0104] Additionally, the anatomical location of the malignant neoplasm may be anywhere in the body; it may be in the eye, nose, mouth, tongue, pharynx, esophagus, stomach, colon, rectum, bladder, ureters, urethra, kidney, liver, pancreas, thyroid, adrenal glands, breast, skin, central nervous system, peripheral nervous system, meninges, vasculature, testes, ovaries, uterus, cervix, spleen, bone, or cartilage.

[0105] When the methods of the second aspect are used in combination with other anti-cancer therapies, these will generally be selected from various types of immunological cancer treatments, including immunotherapy with immune checkpoint inhibitors (ICIs) - e.g., those based on PD-1 / PDL-1, CTLA-4 mechanisms, etc. - radiation therapy, surgery, chemotherapy, antibody therapy, or other types of active specific immunotherapy, adoptive cell-based immunotherapy (e.g., CAR-T cells, TCR-T cells, TIL, DC cells, etc.) and other approaches used in immuno-oncology.

[0106] In general, details regarding the formulation and administration of the immunogenic compositions enabled by the first aspect can, in preferred embodiments, follow the same general principles as those disclosed for immunization with neopeptides and neopeptide-encoding vectors disclosed in WO2020 / 141207, WO2020 / 182901, WO2021 / 123232, WO2021 / 204911, PCT / EP2021 / 071380, and PCT / EP2021 / 069582, all of which are incorporated herein by reference in their entirety. Moreover, each and every cancer treatment technique from these disclosures can be combined with the inventions described herein. That is, for example, neoepitope / neopeptide-based immunization approaches can be combined with the immunization strategies disclosed herein.

[0107] Embodiments of the third aspect of the present invention A third aspect relates to a computer or computer system for identifying immunogenic amino acid sequences in a sample of malignant tumor tissue from a patient, said computer or computer system comprising: 1) one or more input components for inputting amino acid sequence or mRNA data; 2) optionally, executable code for determining amino acid sequence from mRNA data; 3) a database containing the amino acid sequences of the expression products of genomic sequences; 4) executable code for identifying the presence of sequences in an input amino acid sequence or an amino acid sequence encoded by an input mRNA that are present in amino acid sequences in the database; 5) executable code for identifying and, optionally, ranking the amino acid sequences identified by the executable code of 4 according to their predicted ability to bind to a selected MHC molecule; and 6) A component for outputting or saving the identified and / or ranked amino acid sequences. wherein the genomic sequences in 3 constitute a subset of the total sequence of the genome of a species, said subset being composed of sequences that encode proteinaceous expression products in at most 5% of samples from any healthy tissue type found in that species, wherein said healthy tissue types optionally exclude testis tissue and / or brain tissue. Thus, the computer or computer system of the present invention is designed to carry out the method of the first aspect of the present invention.

[0108] The input component is typically any device for entering data into a computer's memory or storage medium. In principle, a simple keyboard connected to a computer could serve this purpose, but data is usually read from an external data carrier or data source by means of a connected disk drive or other data carrier (memory stick, memory card, network-related storage), or via a network or Internet connection and an appropriate protocol for file transfer (FTP, FTPS, SFTP, CSP, HTTP or HTTPS, AS2, AS3, AS4, or PeSIT). Similarly, the storage of sequences (persistent or temporary) can be achieved using any convenient data carrier or storage medium (hard drive, solid-state hard drive, memory stick), but can also be stored directly in the computer's or computer system's memory (RAM). Storage formats can be any convenient format, such as relational databases (both row-oriented and column-oriented), the record format of object databases, or the entry format of text files (e.g., as comma-separated values ​​or in a suitable XML format, or as a simple file system or other similar root-and-tree structure).

[0109] The output component is similarly any suitable output device, optionally coupled to a storage medium as described above. In addition to such storage media, the output can also be displayed on paper via a printer or monitor. The output sequence data can then be input to a peptide synthesizer or, if the desired immunogen is a nucleic acid-based vaccine, to a nucleic acid synthesizer.

[0110] The executable code(s) within the computer or computer system can access linked input devices and storage media, as well as the working memory of the computer, to perform the necessary operations, such as encoding amino acid sequences, sorting and comparing amino acid sequences, etc.

[0111] An executable code for determining amino acid sequence from mRNA is simple to encode and is based on the genetic code, in which triplets of nucleotides are translated into amino acid residues.

[0112] Embodiments of the fourth aspect of the present invention A fourth aspect relates to a method for determining whether a cancer patient is likely to benefit from anti-cancer immunotherapy, the method comprising determining the number of EVEs (generally ERVs) expressed in said patient, and classifying the cancer patient as likely to benefit from anti-cancer immunotherapy if the number of expressed EVEs / ERVs exceeds a predefined threshold.

[0113] As shown in Example 5, a high burden of expressed ERVs in cancer patients was found to be strongly correlated with the survival rate when these patients received cancer immunotherapy. Notably, this survival benefit was independent of the exact immunotherapy method and could be observed in patients receiving immune checkpoint inhibitor therapy or adoptive T cell therapy, i.e., in patients receiving passive or active cancer immunotherapy.

[0114] Thus, the predefined threshold of expressed ERVs will generally be at least 1.1 times the number of expressed ERVs in the average patient suffering from any cancer or a particular type of cancer. This number may be higher (at least 1.2 times, at least 1.3 times, at least 1.4 times, at least 1.5 times, at least 1.6 times, at least 1.7 times, at least 1.8 times, at least 1.9 times, at least 2.0 times, at least 2.1 times, at least 2.2 times, at least 2.3 times, at least 2.4 times, at least 2.5 times, at least 2.6 times, at least 2.7 times, at least 2.8 times, at least 2.9 times, at least 3 times, at least 3.5 times, at least 4 times, at least 4.5 times, at least 5 times, at least 7.5 times, and at least 10 times). However, depending on the exact type of cancer, the exact number may vary. In general, the method utilized in Example 5 of the present invention can be used to determine a threshold of ERV expression that will result in a predictive effect in a given population with a certain cancer. However, one useful threshold is that cancer patients with at least 50 ERVs transcribed at a ratio of 1 RNA transcript per million RNA transcripts or more from their malignant tumor tissue may be considered candidates for immunotherapy, although the number 50 is not required.

[0115] Generally, this threshold should be set to provide a hazard ratio of at least 0.4 when applied to the combined transcript datasets obtained from Hugo et al., 2016, and Riaz et al., 2017 (see Example 5) and calculated using the Lifelines package (lifelines.readthedocs.io / en / stable / Citing%20lifelines.html) with the CoxPHFitter().fit command at default settings. See Davidson-Pilon C (2019), DOI: 10.21105 / joss.01317. Thus, if EVE burden is expressed as EVE / ERV transcripts per million (TPM) or as the number of EVE / ERVs with TPM > 1, the specified number exceeds the threshold if it provides a hazard ratio of at least 0.4 when applied to the Hugo et al., 2016, and Riaz et al., 2017 datasets.

[0116] As mentioned above, cancer immunotherapy is not limited to a particular immunotherapeutic approach, and can be selected from therapeutic cancer vaccination, adoptive T cell therapy (such as CAR-T cell therapy), targeted antibody therapy, immune checkpoint inhibitor therapy, and cytokine therapy, etc. Any of the immunotherapeutic approaches discussed as co-treatments under the second aspect of the present invention above can be referred to as cancer immunotherapy.

[0117] In a particularly preferred embodiment of the fourth aspect of the present invention, the therapeutic cancer vaccination induces an immune response against cancer-associated antigens and / or cancer-specific antigens, particularly against neoantigens or antigens expressed from genomic sequences, as specifically discussed in the embodiments of the first aspect of the present invention. Thus, the expressed genomic sequence is preferably selected from endogenous retroelement (EVE) sequences, such as ERV sequences, nuOFR sequences, and alternatively spliced ​​sequences, although essentially any genomic sequence can be preselected if it is deemed likely to be expressed under certain circumstances and contains the required MHC-binding amino acid stretch. However, the preferred expressed genomic sequence to target is an expressed ERV sequence.

[0118] Consequently, the cancer therapy is in a preferred aspect the method disclosed in the second aspect of the invention and any embodiment thereof disclosed herein.

[0119] In a preferred embodiment of the fourth aspect of the invention and the embodiments thereof described above, the cancer patient has a low tumor mutational burden; i.e., the cancer patient has a tumor mutational burden that does not in itself correlate with clinical benefit of cancer immunotherapy.

[0120] As is clear from the above, the finding that the higher the ERV load, the higher the survival rate of patients treated with anti-cancer immunotherapy, even in patients with a low tumor mutation burden, makes it practical to treat patients with a high ERV load with anti-cancer immunotherapy. In other words, when carrying out the method of the fourth aspect, if a cancer patient is classified as likely to benefit from anti-cancer immunotherapy, it is preferable to then subject the patient to the cancer immunotherapy detailed above. [Example]

[0121] Example 1 Identification of hERV-containing EVEs in human cancer samples A FASTA file containing 33,966 human EVE (hERV) cDNA sequences was downloaded from the gEVE database (Nakagawa S and Takahashi MU. (2016)). The hERV FASTA file was appended to a FASTA file containing transcript cDNA sequences from annotated human genes. The human transcript cDNA file, along with the human reference genome, was downloaded from Ensembl (Yates AD et al. (2020)). The combined cDNA fasta file containing annotated human genes and hERV sequences was indexed using Kallisto (Bray NL et al. (2020)).

[0122] RNA-seq data from 400 melanoma patients were downloaded from The Cancer Genome Atlas (TCGA) database (Cancer Genome Atlas Network (2015)). ERV expression was quantified using Kallisto with the hERV aware index constructed as described above. Using a threshold of 1 TPM, 21-901 hERVs were identified in each of the 400 melanoma samples. Furthermore, using a threshold of 1 TPM for hERV expression, we observed that the majority of ERVs were expressed in only a small fraction of tumor samples. This highlights the potential for a personalized approach to select the most appropriate targets for tumors. Importantly, a subset of ERVs was not expressed to a significant extent in healthy tissues.

[0123] Example 2 Selection of ERV-derived epitopes from the CT26 mouse tumor cell line A GTF file containing a list of 61,184 mouse EVEs (referred to as mERVs in this example) and their genomic locations was downloaded from the gEVE database (Nakagawa S and Takahashi MU. (2016)). The mERV GTF file was appended to a gene (annotated) GTF file containing annotation information for the mouse transcripts. The mouse reference transcript GTF file, along with the mouse reference genome, was downloaded from Ensembl (Yates AD et al. (2020)). The reference genome and reference transcript + mERV GTF files were used to index STAR (Dobin A. et al. (2013)) and RSEM (Li B. et al. (2011)).

[0124] To detect mERVs, we used previously generated RNA-seq data from the CT26 cell line (sequenced on an Illumina sequencing instrument using a standard polyA-selected library preparation protocol). Reads were mapped using STAR and RSEM to generate an mERV-aware index. Using this pipeline, 3,484 expressed ERVs were identified. Table 1 lists the 10 most abundant mERVs.

[0125] [Table 1] MHC class I and class II ligands were identified from all expressed mERVs using the MHC ligand prediction tools netMHCpan-4.1 (pubmed.ncbi.nlm.nih.gov / 32406916 / ) for MHC class I and netMHCIIpan-4.0 (pubmed.ncbi.nlm.nih.gov / 32406916 / ) for MHC class II. The MHC ligand prediction tool utilizes amino acid sequence information and transcript expression levels to generate an integrated MHC ligand probability score. Ligand predictions were made only for mouse MHCs present in the CT26 cell line (H2-Dd, H2-Kd, H2-Ld, and H2-IAd).

[0126] The optimal epitope-encoding sequence was then identified from each mERV. From each ERV, overlapping 27-mers (mers) were generated, spaced one amino acid apart. For each 27-mer, an overall MHC ligand score was calculated using MHC ligand prediction. The best-scoring MHC-I and MHC-II ligands (defined as the most probable ligands) were identified, and a final composite MHC ligand probability score was calculated using the following formula: [ka] -In formula, P MHC , P MHCI , and P MHCII are the probability scores for overall MHC binding, MHC class I binding, and MHC class II binding, respectively.

[0127] All overlapping 27-mers were ranked by MHC ligand probability, and the best 27-mer from each mERV was selected as the best epitope hotspot for that ERV. The top 10 epitope hotspots are listed in Table 2. Table 2. Top 10 mERV epitope hotspots in CT26 (In the table, SEQ ID NO: is the sequence number.) [Table 2]

[0128] Example 3 Validation of mERV-derived ligands using MS immunopeptidomics FASTA files containing the amino acid sequences of 61,184 mERVs were downloaded from the gEVE database (Nakagawa S and Takahashi M.U. (2016)). These FASTA files were linked to Uniprot (SwissProt and Trembl) for Mouse Proteome [August 1, 2021] to create a searchable database of mass spectrometry (MS) raw files obtained from LC-MS / MS analysis of CT26 cell lines (IFNy+ and IFNy-) and tumor samples subjected to the immunoaffinity purification protocol described above (Purcell A et al., (2019)). Briefly, cell pellets or tissues from the cancer of interest were homogenized and lysed to release pMHC complexes, which were then captured on an immunoaffinity column using MHC-specific antibodies. MHC-bound peptides were separated from the MHC molecules, eluted, and analyzed by LC-MS / MS. In this way, MHC-binding peptides are separated by mass-to-charge ratio (m / z) and their identities can be determined from the fragmentation spectrum.

[0129] To analyze the spectra, we used the software tool PEAKS (X-Pro), which allows de novo assisted database searching, using the mouse-specific database described above.

[0130] [Table 3-1] [Table 3-2] [Table 3-3]

Table 3-4

Table 3-5

Table 3-6

Table 3-7

Table 3-8

Table 3-9

Table 3-10

Table 3-11

Table 3-12

Table 3-13

Table 3-14

Table 3-15

[0131] Example 4 Define a list of exemplary hERVs for immunotherapy applications RNA-seq data from 6,125 healthy tissue samples from 51 different body sites were retrieved from the GTEx database (www.nature.com / articles / ng.2653) and analyzed for expression of hERV and the MAGE family of tumor-specific antigens (i.e., MAGEA1, MAGEA2, MAGEA3, MAGEA4, MAGEA6, MAGEA9, MAGEA10, MAGEA12, MAGEC1, and MAGEC2). Expression levels were quantified using Kallisto as described in Example 1. Transcripts were considered expressed in a tissue sample if they were ≥1 TPM. For each site and each transcript, the percentage of samples supporting expression was calculated. The results are shown as a heatmap in Figure 2.

[0132] A matrix of the percentage of tissue samples supporting expression of each hERV target is shown as a heat map in Figure 3. Similarly, the percentage of tissue samples supporting expression of unselected hERV is shown in Figure 4.

[0133] Importantly, selected hERV targets are rarely expressed in healthy tissues (as indicated by the "dark" colored heatmap), whereas unselected hERVs are more frequently expressed in at least one tissue (as indicated by the lighter colored cells in the heatmap). This can be compared to Figure 2, which shows the tissue expression of the TAAs mentioned above, and shows tissue expression patterns between Figures 3 and 4.

[0134] The complete set of selected hERVs consists of 15,252 hERVs, including 12,202 hERVs whose heatmaps are shown in Figure 3 and whose heatmaps in Figure 4 show that expression was observed in up to 5% of samples from tissues other than brain and testis. These hERVs were selected because of the immune-privileged nature of brain and testis tissues.

[0135] Figures 5 and 6 present the data in different formats, with the Y-axis showing the highest healthy tissue percentage (values ​​between 0 and 1) for three antigen groups: TAA, selected hERVs, and excluded hERVs. Figure 5 shows the percentage of expressed hERVs when the selected hERVs match those in Figure 3 (i.e., brain and testis expression must also meet a maximum threshold of 5%), while Figure 6 shows the percentage when the selected hERVs allow tissue expression in testis and / or brain. For TSAs, both figures show expression in testis and brain.

[0136] The 27,521 hERVs found to be useful and safe for immunotherapy in this example are shown in the table below.

[0137] In the names of the hERV sequences below, periods separate the following information: Hsap38.X1.X2.X3.X4, where X1 is the human chromosome number, X2 is the start position, X3 is the end position, and X4 is the position on the + or - strand.

[0138] Thus, for example, "Hsap38.chr1.100599636.100600481.-" is located on human chromosome 1 ("chr1"), begins at position 100599636, ends at position 100600481, and is located on the minus strand.

[0139] Tissue expression is indicated by - / -, + / -, - / +, and + / +, indicating "no tissue expression," "expression in brain," "expression in testis," and "expression in brain and testis," respectively. Columns are: 1: Name, 2: Tissue Expression.

[0140] [Table 4] TIFF2025500926000024.tif253163 TIFF2025500926000025.tif253163 TIFF2025500926000026.tif253163 TIFF2025500926000027.tif253163 TIFF2025500926000028.tif253163 TIFF2025500926000029.tif253163 TIFF2025500926000030.tif253163

[0141] TIFF2025500926000031.tif242156 TIFF2025500926000032.tif253163 TIFF2025500926000033.tif253163 TIFF2025500926000034.tif253163 TIFF2025500926000035.tif253163 TIFF2025500926000036.tif253163 TIFF2025500926000037.tif253163 TIFF2025500926000038.tif253163

[0142] TIFF2025500926000039.tif243156 TIFF2025500926000040.tif253163 TIFF2025500926000041.tif253163 TIFF2025500926000042.tif253163 TIFF2025500926000043.tif253163 TIFF2025500926000044.tif253163 TIFF2025500926000045.tif253163 TIFF2025500926000046.tif253163

[0143] TIFF2025500926000047.tif246158 TIFF2025500926000048.tif253163 TIFF2025500926000049.tif253163 TIFF2025500926000050.tif253163 TIFF2025500926000051.tif253163 TIFF2025500926000052.tif253163 TIFF2025500926000053.tif253163 TIFF2025500926000054.tif253163

[0144] TIFF2025500926000055.tif246159 TIFF2025500926000056.tif253163 TIFF2025500926000057.tif253163 TIFF2025500926000058.tif253163 TIFF2025500926000059.tif253163 TIFF2025500926000060.tif253163 TIFF2025500926000061.tif253163 TIFF2025500926000062.tif253163

[0145] TIFF2025500926000063.tif244157 TIFF2025500926000064.tif253163 TIFF2025500926000065.tif253163 TIFF2025500926000066.tif253163 TIFF2025500926000067.tif253163 TIFF2025500926000068.tif253163 TIFF2025500926000069.tif253163 TIFF2025500926000070.tif253163

[0146] TIFF2025500926000071.tif242156 TIFF2025500926000072.tif253163 TIFF2025500926000073.tif253163 TIFF2025500926000074.tif253163 TIFF2025500926000075.tif253163 TIFF2025500926000076.tif253163 TIFF2025500926000077.tif253163 TIFF2025500926000078.tif253163

[0147] TIFF2025500926000079.tif243156 TIFF2025500926000080.tif253163 TIFF2025500926000081.tif253163 TIFF2025500926000082.tif253163 TIFF2025500926000083.tif253163 TIFF2025500926000084.tif253163 TIFF2025500926000085.tif253163 TIFF2025500926000086.tif253163

[0148] TIFF2025500926000087.tif244157 TIFF2025500926000088.tif253163 TIFF2025500926000089.tif253163 TIFF2025500926000090.tif253163 TIFF2025500926000091.tif253163 TIFF2025500926000092.tif253163 TIFF2025500926000093.tif253163 TIFF2025500926000094.tif253163

[0149] TIFF2025500926000095.tif245158 TIFF2025500926000096.tif253163 TIFF2025500926000097.tif253163 TIFF2025500926000098.tif253163 TIFF2025500926000099.tif253163 TIFF2025500926000100.tif253163 TIFF2025500926000101.tif253163 TIFF2025500926000102.tif253163

[0150] TIFF2025500926000103.tif246159 TIFF2025500926000104.tif253163 TIFF2025500926000105.tif253163 TIFF2025500926000106.tif253163 TIFF2025500926000107.tif253163 TIFF2025500926000108.tif253163 TIFF2025500926000109.tif253163 TIFF2025500926000110.tif253163

[0151] TIFF2025500926000111.tif245158 TIFF2025500926000112.tif253163 TIFF2025500926000113.tif253163 TIFF2025500926000114.tif253163 TIFF2025500926000115.tif253163 TIFF2025500926000116.tif253163 TIFF2025500926000117.tif253163 TIFF2025500926000118.tif253163

[0152] TIFF2025500926000119.tif245158 TIFF2025500926000120.tif253163 TIFF2025500926000121.tif253163 TIFF2025500926000122.tif253163 TIFF2025500926000123.tif253163 TIFF2025500926000124.tif253163 TIFF2025500926000125.tif253163 TIFF2025500926000126.tif253163

[0153] TIFF2025500926000127.tif247159 TIFF2025500926000128.tif253163 TIFF2025500926000129.tif253163 TIFF2025500926000130.tif253163 TIFF2025500926000131.tif253163 TIFF2025500926000132.tif253163 TIFF2025500926000133.tif253163 TIFF2025500926000134.tif253163

[0154] TIFF2025500926000135.tif245158 TIFF2025500926000136.tif253163 TIFF2025500926000137.tif253163 TIFF2025500926000138.tif253163 TIFF2025500926000139.tif253163 TIFF2025500926000140.tif253163 TIFF2025500926000141.tif253163 TIFF2025500926000142.tif253163

[0155] TIFF2025500926000143.tif247159 TIFF2025500926000144.tif253163 TIFF2025500926000145.tif253163 TIFF2025500926000146.tif253163 TIFF2025500926000147.tif253163 TIFF2025500926000148.tif253163 TIFF2025500926000149.tif253163 TIFF2025500926000150.tif253163

[0156] TIFF2025500926000151.tif245158 TIFF2025500926000152.tif253163 TIFF2025500926000153.tif253163 TIFF2025500926000154.tif253163 TIFF2025500926000155.tif253163 TIFF2025500926000156.tif253163 TIFF2025500926000157.tif253163 TIFF2025500926000158.tif253163

[0157] TIFF2025500926000159.tif247159 TIFF2025500926000160.tif253163 TIFF2025500926000161.tif253163 TIFF2025500926000162.tif253163 TIFF2025500926000163.tif253163 TIFF2025500926000164.tif253163 TIFF2025500926000165.tif253163 TIFF2025500926000166.tif253163

[0158] TIFF2025500926000167.tif245158 TIFF2025500926000168.tif253163 TIFF2025500926000169.tif253163 TIFF2025500926000170.tif253163 TIFF2025500926000171.tif253163 TIFF2025500926000172.tif253163 TIFF2025500926000173.tif253163 TIFF2025500926000174.tif253163

[0159] TIFF2025500926000175.tif243157 TIFF2025500926000176.tif253163 TIFF2025500926000177.tif253163 TIFF2025500926000178.tif253163 TIFF2025500926000179.tif253163 TIFF2025500926000180.tif253163 TIFF2025500926000181.tif253163 TIFF2025500926000182.tif253163

[0160] TIFF2025500926000183.tif245158 TIFF2025500926000184.tif253163 TIFF2025500926000185.tif253163 TIFF2025500926000186.tif253163 TIFF2025500926000187.tif253163 TIFF2025500926000188.tif253163 TIFF2025500926000189.tif253163 TIFF2025500926000190.tif253163

[0161] TIFF2025500926000191.tif246159 TIFF2025500926000192.tif253163 TIFF2025500926000193.tif253163 TIFF2025500926000194.tif253163 TIFF2025500926000195.tif253163 TIFF2025500926000196.tif253163 TIFF2025500926000197.tif253163 TIFF2025500926000198.tif253163

[0162] TIFF2025500926000199.tif246159 TIFF2025500926000200.tif253163 TIFF2025500926000201.tif253163 TIFF2025500926000202.tif253163 TIFF2025500926000203.tif253163 TIFF2025500926000204.tif253163 TIFF2025500926000205.tif253163 TIFF2025500926000206.tif253163

[0163] TIFF2025500926000207.tif246159 TIFF2025500926000208.tif253163 TIFF2025500926000209.tif253163 TIFF2025500926000210.tif253163 TIFF2025500926000211.tif253163 TIFF2025500926000212.tif253163 TIFF2025500926000213.tif253163 TIFF2025500926000214.tif253163

[0164] TIFF2025500926000215.tif245158 TIFF2025500926000216.tif253163 TIFF2025500926000217.tif253163 TIFF2025500926000218.tif253163 TIFF2025500926000219.tif253163 TIFF2025500926000220.tif253163 TIFF2025500926000221.tif253163 TIFF2025500926000222.tif253163

[0165] TIFF2025500926000223.tif247159 TIFF2025500926000224.tif253163 TIFF2025500926000225.tif253163 TIFF2025500926000226.tif253163 TIFF2025500926000227.tif253163 TIFF2025500926000228.tif253163 TIFF2025500926000229.tif253163 TIFF2025500926000230.tif253163

[0166] TIFF2025500926000231.tif244157 TIFF2025500926000232.tif253163 TIFF2025500926000233.tif253163 TIFF2025500926000234.tif253163 TIFF2025500926000235.tif253163 TIFF2025500926000236.tif253163 TIFF2025500926000237.tif253163 TIFF2025500926000238.tif253163

[0167] TIFF2025500926000239.tif244157 TIFF2025500926000240.tif253163 TIFF2025500926000241.tif253163 TIFF2025500926000242.tif253163 TIFF2025500926000243.tif253163 TIFF2025500926000244.tif253163 TIFF2025500926000245.tif253163 TIFF2025500926000246.tif253163

[0168] TIFF2025500926000247.tif245158 TIFF2025500926000248.tif253163 TIFF2025500926000249.tif253163 TIFF2025500926000250.tif253163 TIFF2025500926000251.tif253163 TIFF2025500926000252.tif253163 TIFF2025500926000253.tif253163 TIFF2025500926000254.tif253163

[0169] TIFF2025500926000255.tif245158 TIFF2025500926000256.tif253163 TIFF2025500926000257.tif253163 TIFF2025500926000258.tif253163 TIFF2025500926000259.tif253163 TIFF2025500926000260.tif246158

[0170] Example 5 Patient stratification and ERV burden The abundance of somatic mutations (also known as “tumor mutation burden,” abbreviated TMB) predicts the prognosis of cancer patients receiving immunotherapy (see Hugo et al., 2016 , Lauss et al., 2017 , and Riaz et al., 2017 ).

[0171] Against this background, we investigated whether the number of expressed ERVs, or more generally EVEs as a potential alternative source of neoantigens, also stratifies patients undergoing immunotherapy into groups with different outcomes. For this reason, the number of expressed ERVs / EVEs is hereafter referred to as "ERV burden" and "EVE burden," respectively.

[0172] Three published studies were investigated in which baseline biopsies from metastatic melanoma patients (stage III / IV) were characterized by RNA sequencing and whole-exome sequencing: Hugo et al., 2016 , Lauss et al., 2017 , and Riaz et al., 2017 .

[0173] ERV / EVE load Endogenous retroviral expression (using gEVE annotation) was calculated by mapping RNA-seq reads using STAR (Dobin et al. 2017) and quantified by RSEM (Li B. and Dewey CN 2011). A threshold of one transcript per million (TPM) was used to define whether an ERV was considered expressed, and ERV burden was determined as the number of ERVs / EVEs with an expression level of 1 TPM or greater. For practical reasons, high ERV / EVE burden was defined in this case as the presence of 50 or more ERVs or EVEs in a tumor biopsy, while fewer than 50 ERVs / EVEs were considered low ERV / EVE burden. For a general definition of high or low ERV / EVE burden, see below.

[0174] Tumor mutation burden In the studies by Hugo et al. (2016) and Lauss et al. (2017), missense somatic mutations were identified by mapping whole-exome sequencing data from tumor biopsies and matched healthy samples to the human reference genome (GRCh38) using bwa mem (Li H (2017)). Somatic mutations were called using mutect2 (Benjamin D et al. 2019) and filtered using the GATK suite (Mckenna A et al. 2010). Somatic mutations were annotated using Variant Effect Predictor (McLaren W et al. 2016). Tumor mutation burden (TMB) was defined as the number of missense somatic mutations. A TMB of >1000 was considered high mutation burden, while missense somatic mutations with a TMB of <1000 were considered low mutation burden.

[0175] In the Riaz et al. (2017) study, due to data access limitations on whole-exome sequencing data, high / low TMB group assignments were retrieved from the supplementary information.

[0176] The data are presented in Figure 1 and show a general pattern of higher EVE burden in responders than in non-responders in the low TMB group.

[0177] Patient stratification Figure 7 shows the number of samples found in each patient group. It was observed that considering ERV burden alone allowed stratification of patients based on overall survival (see Figure 8).

[0178] Furthermore, when we performed grouping based on TMB, we found that ERV burden did not predict patient survival in the TMB-high group (see Figure 9), whereas an enhanced stratification signal based on ERV burden was observed in the TMB-low group (see Figure 10), which corresponds to the data shown in Figure 1.

[0179] This suggests that the outcome of immunotherapy depends on the number of targetable tumor antigens and that ERVs serve as an alternative source of targetable tumor antigens.

[0180] Considering the three studies together, a strong patient stratification signal based on ERV load was observed in patients with low TMB across the three immunotherapies, but no stratification signal by ERV load was observed in patients with high TMB (Figure 11).

[0181] In addition to ERVs serving as novel prognostic biomarkers, our analysis also supports their use as complementary targets to tumor antigens in personalized immunotherapy, thereby providing a source of tumor antigens that may enable the design of personalized immunotherapies for patients whose tumors are found to have a low mutational burden and for cancer indications generally characterized by a paucity of somatic mutations.

[0182] To provide a robust measure of this patient stratification, Hugo et al. (2016) and Riaz et al. (2017) subsetted the data to available public data and defined a universal measure that could determine whether ERV / EVE burden was considered above or below a desired threshold.

[0183] Analysis of EVE burden thresholds for stratification was performed on the entire dataset and on the list of hERVs found to be rarely expressed in healthy tissues of low TMB patients (the list of ERVs shown in the table in Example 4). See Figure 13. Choosing a threshold of hazard ratio = 0.4, the EVE burden threshold for the entire list ranges from 40 to 87. The reduced safe list has an EVE burden threshold of 3 to 6. Therefore, low ERV patients can be considered as those stratified by a hazard ratio of less than 0.4 in the specific combined datasets of Hugo et al. (2016) and Riaz et al. (2017). Stratification was calculated using the CoxPHFitter().fit command with default settings, as described in Davidson-Pilon C (2019).

[0184] We analyzed the vast number of cancer samples from TCGA to provide an overview of which cancer types are useful for targeting. The number and types of samples analyzed are shown in Figure 14, and a scatterplot showing the median number of expressed EVEs (median EVE burden) per project versus the median number of missense somatic variants is shown in Figure 15.

[0185] Example 6 Personalized immunotherapy with ERVs induces potent antitumor and T cell responses in the CT26 colon cancer model We used the preclinical cancer model CT26 to investigate whether ERVs could be a potential therapeutic target for personalized immunotherapy. ERV expression levels in in vivo-grown CT26 tumors were quantified by RNA sequencing, and BALB / c mice were subjected to in silico immunotherapy against CT26, designed based on ERV expression levels and BALB / c MHC type. The in silico design consisted of 13 top-ranked ERV peptides (PR-ERVs) encoded in pTVG4 plasmid DNA (pDNA). For comparison, we constructed pDNA constructs encoding 13 ERV-derived peptides containing MHC-I ligands validated by immunopeptidomics (MS-ERV).

[0186] The two pDNA constructs were administered in vivo by electroporation (EP) and formulated with non-ionic block copolymers (hereafter referred to as "poloxamers") to extend the longevity of the pDNA after injection and increase antigen expression and exposure.

[0187] To investigate whether ERVs could be targeted for immunotherapy, groups of mice were inoculated intramuscularly (i.m.) with PR-ERV, MS-ERV, or mock pDNA at weekly intervals using a vaccination scheme involving two EP-based priming immunizations and three poloxamer-based immunizations (Figure 12A). Immunizations began 2 weeks before subcutaneous (sc) challenge with a tumorigenic dose of CT26 cancer cells. In contrast to mock mice administered pDNA, which developed tumors of significant terminal volume (Figure 12B), mice inoculated with PR-ERV and MS-ERV potently prevented the formation of CT26 tumors (Figures 12B and 12C).

[0188] Methods and Materials Mouse cell lines The BALB / c syncytial colon carcinoma cell line CT26 (#CRL2638) was purchased from ATCC and cultured in R10 medium prepared from RPMI (Gibco #72400-021) supplemented with 10% heat-inactivated fetal calf serum (FCS, Gibco #10500-064) according to the supplier's instructions at 37°C and 5% CO. Cells were grown to 60-70% confluence, trypsinized, washed twice with serum-free RPMI, and prepared for inoculation into mice.

[0189] Animal experiments Animals were maintained in the animal facility at Evaxion Biotech (Horsholm, Denmark). All experiments were performed in accordance with the Danish Animal Experiments Act (BEK nr. 12 of 2016 / 7 / 01), which complies with the European Directive (2010 / 63 / EU), under license 2017-15-0201-01209 from the Danish Animal Experiments Inspectorate.

[0190] Six- to eight-week-old female BALB / cJRj SPF mice were obtained from Janvier Labs (France). Mice were allowed to acclimate for one week before the start of the experiment. Mice from different experimental groups (13 mice per group) were distributed in separate cages to avoid potential cage effects. Mice were immunized weekly with 100 μg of study DNA in the left and right tibialis anterior (im) muscles for a total of five doses. Vaccinations began 2 weeks before the subcutaneous inoculation of CT26 cells (defined as study day 0). For the first two immunizations, 2 × 50 μl of vaccine containing DNA formulated in PBS was administered via electroporation (EP). For the final three immunizations, DNA was administered in 2 × 75 μl of vaccine solution prepared in PBS with the block copolymer poloxamer 188 (gift from BASF, Germany) at a final concentration of 3%. On the day of the primary CT26 cell inoculation, mice were inoculated twice in the flank. In tumor rechallenge experiments, age-matched naive mice that rejected the primary cancer cell challenge were inoculated sc with the same tumorigenic dose of CT26 cells in the contralateral (left) flank.

[0191] Once tumors were formed, tumor volumes were measured three times a week using digital calipers, and tumor volume V was calculated using the following formula: [ka] where d1 and d2 are the orthogonal diameters of the tumor. Mice were euthanized by cervical dislocation when the majority of tumors in the control group reached the maximum allowable size of 15 mm in diameter or when the human end point was reached.

[0192] Graphs were created, statistically analyzed, and tools were used in GraphPad Prism 9 for Mac OS X. Data were checked for normality using the Kolmogorov-Smirnov test (α = 0.05). Parametric data were analyzed using ordinary ANOVA with Sidak's multiple comparison correction. Nonparametric data were analyzed using the Mann-Whitney test (for two comparisons) or the Kruskal-Wallis test with Dunn's multiple comparison correction (for more than two comparisons). For all results, the following statistical significance levels were applied: *p ​​< 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

[0193] Reference list 1) Balada, E. et al. (2009). Reviews in Medical Virology, 19(5), 273-286. doi.org / 10.1002 / rmv.622 2) Benjamin, D. et al. (2019). bioRxiv doi.org / 10.1101 / 861054 3) Bronte, V. et al. (2003). Journal of Immunology (Baltimore, Md.: 1950), 171(12), 6396-6405. doi.org / 10.4049 / jimmunol.171.12.6396 4) Casares, N. et al. (2001). European Journal of Immunology, 31(6), 1780-1789. doi.org / 10.1002 / 1521-4141(200106)31:6<1780::aid-immu1780>3.0.co;2-i 5) Chiappinelli, KB et al. (2015). Cell, 162(5), 974-986. doi.org / 10.1016 / j.cell.2015.07.011 6) Davidson-Pilon C (2019), Journal of Open Source Software,4(40): 1-3 DOI: 10.21105 / joss.01317 7) Dobin, A. et al. (2013). Bioinformatics, 29(1), 15-21. doi.org / 10.1093 / bioinformatics / bts635 8) Dupressoir, A. et al. (2012). Placenta, 33(9), 663-671. doi.org / 10.1016 / j.placenta.2012.05.005 9) Hugo, W. et al. (2016). Cell, 165, 35-44. dx.doi.org / 10.1016 / j.cell.2016.02.065 10) Hummel, J. et al. (2015). European Journal of Immunology, 45(6), 1748-1759. doi.org / 10.1002 / eji.201445366 11) Iramaneerat, K. et al. (2011). International Journal of Gynecological Cancer: Official Journal of the International Gynecological Cancer Society, 21(1), 51-57. doi.org / 10.1097 / IGC.0b013e3182021c1a 12) Kassiotis, G. (2014). Journal of Immunology (Baltimore, Md.: 1950), 192(4), 1343-1349. doi.org / 10.4049 / jimmunol.1302972 13) Kassiotis, G., and Stoye, J. P. (2016). Nature Reviews Immunology, 16(4), 207-219. doi.org / 10.1038 / nri.2016.27 14) Kershaw, M. H. et al. (2001). Cancer Research, 61(21), 7920-7924. 15) Kraus, B., et al. (2013). PloS One, 8(8), e72756. doi.org / 10.1371 / journal.pone.0072756 16) Kraus, B. et al. (2014). Virology Journal, 11, 58. doi.org / 10.1186 / 1743-422X-11-58 17) Krishnamurthy, J. et al. (2015). Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 21(14), 3241-3251. doi.org / 10.1158 / 1078-0432.CCR-14-3197 18) Lauss, M et al. (2017). Nature Communications, 8(1):1738 doi: 10.1038 / s41467-017-01460-0 19) Li , B. and Dewey C. N. (2011). BMC Bioinformatics, 12;323 www.biomedcentral.com / 1471-2105 / 12 / 323 20) Li, H. (2013). arXiv:1303.3997v2 DOI:10.6084 / M9.FIGSHARE.963153.V1 21) Li, M., et al. (2017). Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 23(19), 5892-5911. doi.org / 10.1158 / 1078-0432.CCR-17-0001 22) Lokossou, A. G. et al. (2020). Biology of Reproduction, 102(1), 185-198. doi.org / 10.1093 / biolre / ioz124 23) Magiorkinis, G. et al. (2012). Proceedings of the National Academy of Sciences of the United States of America, 109(19), 7385-7390. doi.org / 10.1073 / pnas.1200913109 24) Mangeney, M. (2005) et al. Cancer Research, 65(7), 2588-2591. doi.org / 10.1158 / 0008-5472.CAN-04-4231 25) McKenna, A. et al. (2010). Genome Res, 20(9), 1297-1303 doi: 10.1101 / gr.107524.110 26) McLaren, W. et al. (2016). Genome Biology, 17:122 DOI: 10.1186 / s13059-016-0974-4 27) Morozov, V. A. et al. (2013). PloS One, 8(8), e70399. doi.org / 10.1371 / journal.pone.0070399 28) Neukirch, L. et al. (2019). Oncotarget, 10(14), 1458-1472. doi.org / 10.18632 / oncotarget.26680 29) Pavlicek, A. and Jurka, J. (2006). In: Genomic Disorders: The Genomic Basis of Disease (pp. 57-72). Humana Press. doi.org / 10.1007 / 978-1-59745-039-3_4 30) Peltonen, K. et al. (2021). In: Cancers 13(14), 3408. doi.org / 10.3390 / cancers13143408 31) Qian, C. et al. (2020). Vaccines, 8(1). doi.org / 10.3390 / vaccines8010139 32) Riaz, N. et al. (2017). Cell 171(4): 934-949. doi: 10.1016 / j.cell.2017.09.028 33) Rice, J., et al. (2002). Journal of Immunology (Baltimore, Md.: 1950), 169(7), 3908-3913. doi.org / 10.4049 / jimmunol.169.7.3908 34) Roulois, D. et al. (2015). Cell, 162(5), 961-973. doi.org / 10.1016 / j.cell.2015.07.056 35) Saini, S. K. et al. (2020). Nature Communications, 11(1), 5660. doi.org / 10.1038 / s41467-020-19464-8 36) Scheeren, R. A. et al. (1992). Clinical and Experimental Immunology, 89(1), 94-99. doi.org / 10.1111 / j.1365-2249.1992.tb06884.x 37) Szpakowski, S., et al. (2009). Gene, 448(2), 151-167. doi.org / 10.1016 / j.gene.2009.08.006 38) Takeda, J. et al. (2000). Cellular Immunology, 204(1), 11-18. doi.org / 10.1006 / cimm.2000.1691 39) Vergara Bermejo, A. et al. (2020). International Journal of Molecular Sciences, 21(14), 4843. doi.org / 10.3390 / ijms21144843 40) Volkman, H. E. and Stetson, D. B. (2014). Nature Immunology, 15(5), 415-422. doi.org / 10.1038 / ni.2872 41) Wang-Johanning, F. et al. (2007). International Journal of Cancer, 120(1), 81-90. doi.org / 10.1002 / ijc.22256 42) Young, G. R. et al. (2014). Retrovirology, 11(1), 59. doi.org / 10.1186 / 1742-4690-11-59 43) Zhou, F. et al. (2016). Oncotarget, 7(51), 84093-84117. doi.org / 10.18632 / oncotarget.11455 44) Nakagawa S and Takahashi MU. (2016). Database: the journal of biological databases and curation. 2016:baw087. Available from: academic.oup.com / database / article-lookup / doi / 10.1093 / database / baw087 45) Yates A. D. et al. (2020). Nucleic acids research, 48(D1):D682-8. Available from: academic.oup.com / nar / advance-article / doi / 10.1093 / nar / gkz966 / 5613682 46) Bray N. L. et al. (2020). Nature biotechnology 34(5):525-7. Available from: www.ncbi.nlm.nih.gov / pubmed / 27043002 47) Cancer Genome Atlas Network (2015). Genomic Classification of Cutaneous Melanoma. Cell. 18;161(7):1681-96. Available from: linkinghub.elsevier.com / retrieve / pii / S0092867415006340 48) Dobin A. et al. (2013). Bioinformatics. 29(1):15-21. 49) Li B. et al. (2011). BMC bioinformatics. 12(1):323. Available from: bmcbioinformatics.biomedcentral.com / articles / 10.1186 / 1471-2105-12-323 50) Purcell et al. (2019). Nature Protocols. 14: 1687-1707.

Claims

1. 1. A method for identifying an immunogenic amino acid sequence in a malignant tumor tissue sample from a patient, comprising: A) determining the amino acid sequence of a protein expression product from malignant tumor tissue; B) analyzing the amino acid sequence to identify the protein expression product of the selected genomic sequence in the patient species; C) identifying amino acid sequences in the protein expression product that bind to the patient's MHC molecules; wherein said selected genomic sequences constitute a subset of all sequences of the genome of said species, said subset being made up of sequences that encode protein expression products in at most 5% of samples from all healthy tissues, said healthy tissues being tissues of a type found in a patient, and wherein said healthy tissues optionally exclude testicular tissue and / or brain tissue.

2. The method of claim 1 , wherein the healthy tissue does not include testicular tissue, brain tissue, or testicular and brain tissue.

3. The method of claim 1 , wherein the healthy tissue comprises testis and brain tissue.

4. 4. The method of any one of claims 1 to 3, wherein the genomic sequence is selected from an endogenous viral element (EVE), such as an endogenous retrovirus (ERV) sequence; a novel or unannotated open reading frame (nuOFR) sequence; and a genomic sequence that is transcribed as an alternative splicing sequence.

5. The method according to claim 4, wherein the genomic sequence is an ERV sequence, in particular a human ERV sequence.

6. The amino acid sequence of the peptide that binds to the patient's MHC molecule is - binds to both MHC class I and MHC class II molecules of the patient; - binds to MHC class II molecules of the patient but does not bind to MHC class II molecules; or - Binds to the patient's MHC class II molecules but not to MHC class I molecules 6. The method of claim 1 , wherein the sequence is an amino acid sequence.

7. 7. The method of claim 1, wherein step A comprises determining an amino acid sequence from mRNA of the patient's malignant tumor tissue.

8. 8. The method of any one of claims 1 to 7, wherein the selected genomic sequence is identified by determining an expression profile of the genomic sequence across multiple samples from multiple tissues and selecting genomic sequences that are expressed in less than 5% of the multiple samples.

9. 9. The method of claim 8, wherein the multiple samples of multiple tissues do not include testicular and brain tissue samples.

10. The method, wherein the multiple samples of multiple tissues include samples of testicular and brain tissue.

11. A method for treating a malignant neoplasm in a patient, preferably a human patient, comprising carrying out steps A to C according to any one of claims 1 to 10, and then administering to the patient one or more expression vectors encoding and capable of expressing one or more peptides identified in step C, or one or more polypeptides comprising two or more peptides identified in step C, or one or more polypeptides comprising two or more peptides identified in step C, so as to induce a specific adaptive immune response against one or more peptides, wherein the selected genomic sequences constitute a subset of the total sequences of the genome of the patient's species, said subset being constituted by sequences encoding protein expression products in at most 5% of samples from any healthy tissue, said healthy tissue being a type of tissue found in the patient, said healthy tissue optionally excluding testicular tissue and / or brain tissue.

12. 12. The method of claim 11 provided as part of a combination treatment for a malignant neoplasm, wherein the patient also receives a treatment selected from the group consisting of other therapeutic cancer vaccinations, chemotherapy, radiation therapy, cytokine therapy, adoptive T cell therapy such as CAR-T therapy, targeted antibody therapy, and immune checkpoint inhibitor therapy.

13. 13. The method of claim 11 or 12, wherein when one or more peptides are administered, they are formulated with an immunological adjuvant.

14. 14. The method of any one of claims 11 to 13, wherein the patient receives a priming immunization and one or more booster immunizations.

15. The method according to any one of claims 11 to 14, wherein the other therapeutic cancer vaccination is a vaccination that induces an immune response against a neo-epitope or neo-antigen.

16. 16. The method of claim 11, wherein the peptides identified in step C are further identified in a plurality of cancer patients.

17. 1. A computer or computer system for identifying immunogenic amino acid sequences in a sample of malignant tumor tissue from a patient, comprising: 1) an input component for inputting amino acid sequence or mRNA data; 2) Optionally, executable code for determining amino acid sequence from mRNA data; 3) a database containing amino acid sequences of expression products of genomic sequences; 4) executable code for identifying the presence of sequences in an input amino acid sequence or an amino acid sequence encoded by an input mRNA that are present in the amino acid sequences in the database; 5) executable code for identifying and, optionally, ranking the amino acid sequences identified by the executable code of 4) above according to their predicted ability to bind to a selected MHC molecule; and 6) a component for outputting or storing the identified and / or ranked amino acid sequences; wherein the genomic sequence in 3) above constitutes a subset of the total sequence of the genome of a species, the subset being constituted by sequences encoding protein expression products in at most 5% of samples from any healthy tissue type found in that species, the healthy tissue type optionally excluding testis tissue and / or brain tissue. A computer or computer system.

18. 18. A computer or computer system according to claim 17, adapted to carry out a method according to any one of claims 1 to 10.

19. 1. A method for determining whether a cancer patient is likely to benefit from anti-cancer immunotherapy, comprising determining the number of EVEs, e.g. ERVs, expressed in the patient, and classifying the cancer patient as likely to benefit from the anti-cancer immunotherapy if the number of expressed EVEs or ERVs exceeds a predefined threshold.

20. 20. The method of claim 19, wherein the predefined threshold is at least 1.2 times the number of expressed EVEs or ERVs in a patient suffering from cancer.

21. 20. The method of claim 19, wherein the predefined threshold is at least 1.2 times the number of expressed EVEs or ERVs in a patient suffering from the same cancer as the cancer patient.

22. 22. The method of any one of claims 19 to 21, wherein the predefined threshold is about 50 EVEs or ERVs, each transcribed at a rate of 1 RNA transcript per 1 million RNA transcripts or more from malignant tumor tissue of a cancer patient.

23. 23. The method of any one of claims 19 to 22, ensuring that a predefined threshold provides a hazard ratio of at least 0.4 when applied to a combined dataset of all EVE / ERV transcripts derivable from Hugo et al. 2016 and Riaz et al. 2017.

24. 24. The method according to any one of claims 19 to 23, wherein the cancer immunotherapy is selected from therapeutic cancer vaccination, adoptive T cell therapy such as CAR-T cell therapy, targeted antibody therapy, immune checkpoint inhibitor therapy, cytokine therapy.

25. The method according to claim 24, wherein the therapeutic cancer vaccination induces an immune response against cancer-associated and / or cancer-specific antigens, in particular neoantigens or antigens expressed from a genomic sequence as defined in claim 4.

26. 26. The method according to any one of claims 19 to 25, wherein the cancer treatment is a method according to any one of claims 11 to 16.

27. 27. The method of any one of claims 19 to 26, wherein the cancer patient has a low tumor mutation burden.

28. 28. The method of any one of claims 19 to 27, wherein if the cancer patient is classified as likely to benefit from an anti-cancer immunotherapy, then the cancer patient is provided with said cancer immunotherapy.

Images