Bioinformatics

Abstract

The present invention relates to devices and methods for identifying tumor antigens; tumor antigens identified after use of said devices and / or methods; pharmaceutical compositions comprising said tumor antigens; methods of treating cancer using said devices and / or methods; methods of stratifying patients for cancer treatment using said devices and / or methods; treatment regimens comprising stratifying patients for cancer treatment using said devices and / or methods followed by administration of a cancer therapeutic; and tumor antigens identified using said devices and / or methods for use as cancer vaccines or immunogenic agents or cancer treatments.

Description

[Technical Field]

[0001] The present invention relates to devices and methods for identifying tumor antigens; tumor antigens identified after use of said devices and / or methods; pharmaceutical compositions comprising said tumor antigens; methods of treating cancer using said devices and / or methods; methods of stratifying patients for cancer treatment using said devices and / or methods; treatment regimens comprising stratifying patients for cancer treatment using said devices and / or methods followed by administration of a cancer therapeutic; and tumor antigens identified using said devices and / or methods for use as cancer vaccines or immunogenic agents or cancer treatments. [Background technology]

[0002] CD8 + T cells play an important role in the detection and elimination of cells that display abnormal peptides on their surface as a result of pathogenic infections, such as viral infections, or malignant transformation. Cross-reactivity of T cell receptors (TCRs) means that T cells can recognize a wide variety of different target peptides. This phenomenon allows a relatively small number of T cells to recognize multiple pMHC (peptide:major histocompatibility complex) molecules that represent abnormal cells and thus potentially pose a threat to health or life.

[0003] However, an undesirable side effect of this mechanism is that the immune response directed against the pathogen can exceed the tolerance threshold to highly homologous self-antigens, causing harmful off-target effects mediated by T cell cross-reactivity, a process known as molecular mimicry.The potentially harmful consequences of such homology between self and pathogenic peptides are well known in the field of autoimmunity, but have not been explored in cancer.

[0004] Indeed, the best prognostic markers for successful outcomes in cancer immunotherapy treatment have been considered to be high tumor antigen mutation rates and abundant T cell infiltration, based on the fact that tumors with numerous mutations have a higher chance of being recognized and eliminated by infiltrating T cells.

[0005] Nevertheless, some studies have shown that the qualitative characteristics of tumor antigens may be even more important than the quantity of tumor antigens. Furthermore, antiviral T cells have been observed to assemble in the tumor microenvironment

[28] , but it remains unclear whether their role is active or merely bystander.

[0006] Tumor immunology and immunotherapy have completely changed the way cancer is treated over the past decade, especially when using checkpoint inhibitors (ICIs), which have shown remarkable clinical results, but unfortunately only in a small number of patients. It is becoming clear that immunotherapy using ICIs only works well when targeting specific tumor antigens. However, at present, there is no easy and rapid method for identifying these tumor antigens.

[0007] Herein, we hypothesize that tumors may present pathogen-derived peptides, particularly peptides that share a high degree of homology or affinity scoring with viral peptides, which may enable pathogenically generated cross-reactive T cells to recognize and kill tumor cells.

[0008] To this end, we developed a bioinformatics device called HEX (Homology Evaluation of Xenopeptides) to automatically and easily identify tumor antigens highly similar to viral peptides. Using this device, we observed that antiviral T cell immunity mediated by peptides with high homology or affinity scores to cancer antigens can also actively control tumor growth in both preventive and therapeutic settings. This observation demonstrates the cross-reactivity of activated T cells to both homologous virus- and tumor-derived peptides.

[0009] Subsequently, we also found that humoral responses to cytomegalovirus (CMV) can stratify melanoma patients' responses to checkpoint inhibitor therapy (anti-PD1). Indeed, peptides homologous to CMV and melanoma identified through the use of the HEX device were found to promote Inf-g release in peripheral blood mononuclear cells (PBMCs) from CMV-seropositive melanoma patients. Summary of the Invention [Means for solving the problem]

[0010] According to a first aspect of the present invention, there is provided a device for tumor antigen identification, comprising: A device is provided that includes at least one flow-through channel containing a plurality of supports having attached thereto at least one molecule or at least one complex to which at least one anti-MHC (major histocompatibility complex) antibody or at least one anti-pan-human leukocyte antigen (HLA) antibody is bound, wherein the at least one antibody can be used to extract pMHC (peptide:major histocompatibility complex) from a sample flowing through the channel.

[0011] Reference herein to anti-pan-HLA antibodies is to antibodies that recognize or have specificity for any of the different HLA present in mammals, particularly humans.

[0012] Even more ideally, the antibody recognizes or has specificity for a particular HLA type, such as MHC-I, selected from at least one of the following groups: MHC classes IA, B and C.

[0013] Additionally or alternatively, the antibody recognizes or has specificity for a particular HLA type, such as MHC-II, selected from at least one of the following groups: MHC class IIDP, DM, DO, DQ and DR.

[0014] Even more preferably, said antibody is anti-human.

[0015] In a preferred embodiment of the invention, the molecule or complex is a complex of thiol and alkyl ("ene") functional groups, ideally comprising a stoichiometric ratio of 1.5 to 1.0 within the range of 0.15:0.1 to 1500:1000 (tetrathiol:triallyl).

[0016] Even more ideally, fabrication of the device is based on a UV-initiated photoreaction between thiol and alkyl ("ene") functional groups. In a further preferred embodiment of the invention, the device is fabricated by UV photopolymerization using biotin-PEG4-alkyne (Sigma, 764213). This is followed by reaction with avidin:streptavidin. However, it is within the scope of the invention to attach avidin to the biological agent before attaching the biological agent to the pillars, and vice versa.

[0017] In yet a further preferred embodiment, the device is functionalized by reacting the antibody with the streptavidin. Ideally, more than one antibody is reacted with the streptavidin, and each streptavidin-functionalized complex carries multiple, e.g., two or three, of the antibodies, e.g., multiple anti-pan-HLA antibodies.

[0018] In a further preferred embodiment of the present invention, the micropillar array was pre-trimmed with a protein such as bovine serum albumin (BSA) (ideally 100 μg / mL in 15 mM PBS, 10 min incubation) after streptavidin functionalization and before antibody binding.

[0019] In a more preferred aspect of the invention, the support comprises a plurality of micropillars, such as those present in a microfluidic device, preferably arranged in an array within the device, and ideally the micropillars are made of a composition that is the same as or similar to a composition used in conventional microfluidic devices, for example to perform immobilized enzymatic reactions.

[0020] According to a further aspect of the present invention there is provided a method for tumor antigen identification comprising the steps of: i) dissolving or suspending a tumor sample in a fluid; ii) passing said fluid through a device for tumor antigen identification comprising at least one flow-through channel containing a plurality of supports to which is attached at least one molecule or at least one complex to which at least one anti-MHC (major histocompatibility complex) antibody or at least one anti-pan-human leukocyte antigen (HLA) antibody is bound, said at least one antibody being used to extract pMHC (peptide:major histocompatibility complex) from a sample flowing through said channel; iii) allowing at least one pMHC (peptide:major histocompatibility complex) in said sample to bind to said at least one antibody; iv) optionally removing the at least one bound pMHC (peptide:major histocompatibility complex) of part iii) from the device; v) comparing said peptide of said bound pMHC (peptide:major histocompatibility complex) to a library of pathogen-derived antigens to determine whether said peptide exhibits homology or affinity (molecular mimicry) with at least one pathogen-derived antigen or portion thereof, and if there is greater than 60% homology / affinity; vi) identifying said peptide as a tumor antigen for use in cancer therapy; A method is provided, comprising:

[0021] In preferred embodiments of any aspect of the invention, the homology / affinity may be any one of the following percentages: 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 and 100%.

[0022] References herein to homology / affinity include references to comparison of sequence structures in part v) above using one of: homology / affinity as defined herein; or identity as determined by the number of identical residues over a given length in a given alignment; or similarity as determined by the number of identical residues or conservative substitutions with similar physicochemical properties over a given length in a given alignment.

[0023] In a preferred method of the invention, the library is of antigens derived from human pathogens, and ideally the library comprises a curated library of antigens derived from known pathogens derived from pathogenic viruses, or more suitably from their proteomes, wherein the viruses infect mammals, ideally humans, such as any one or more or any combination of the following viruses:

[0024] Family: Abyssoviridae; Family: Ackermannviridae; Family: Actantavirinae; Family: Adenoviridae; Family: Agantavirinae; Family: Aglimvirinae; Family: Alloherpesviridae; Family: Alphaflexiviridae; Family: Alphaherpesvirinae; Family: Alphahairidovirinae; Family: Alphasatellitidae; Family: Alphatetraviridae; Family: Alvernaviridae; Family: Amalgaviridae; Family: Amnoonviridae; Family: Ampullaviridae; Family: Anelloviridae; Family: Arenaviridae e); Arquatrovirinae; Arteriviridae; Artoviridae; Ascoviridae; Asfarviridae; Aspiviridae; Astroviridae; Autographivirinae; Avsunviroidae; Avulavirinae inae); Family Bacilladnaviridae; Family Baculoviridae; Family Barnaviridae; Subfamily Bastilevirinae; Subfamily Bclasvirinae; Family Belpaoviridae; Family Benyviridae; Family Betaflexiviridae; Subfamily Betaherpesvirinae;Betairidovirinae (subfamily); Bicaudaviridae (family); Bidnaviridae (family); Birnaviridae (family); Bornaviridae (family); Botourmiaviridae (family); Brockvirinae (subfamily); Bromoviridae (family); Bullavirinae (subfamily); Caliciviridae (family); Subfamily Calvusvirinae; Family Carmotetraviridae; Family Caulimoviridae; Subfamily Ceronivirinae; Subfamily Chebruvirinae; Subfamily Chordopoxvirinae; Family Chrysoviridae; Family Chuviridae; Family Circoviridae; Family Clav aviridae; Closteroviridae; Comovirinae; Coronaviridae; Corticoviridae; Crocarterivirinae; Cruliviridae; Crustonivirinae; Cvivirinae; Cystoviridae; Dclasvirinae; Deltaflexiviridae; Densovirinae; Dicistroviridae; Endornaviridae; Entomopoxvirinae; Equarterivirinae; Eucampyvirinae; Euroniviridae Family Filoviridae; Family Fimoviridae; Subfamily Firstpapillomavirinae; Family Flaviviridae; Family Fuselloviridae; Family Gammaflexiviridae; Subfamily Gammaherpesvirinae; Subfamily Geminialphasatellitinae; Family Geminiviridae; Family Genomoviridae; Family Globuloviridae; Subfamily Gokushovirinae; Subfamily Guernseyvirinae; Family Guttaviridae; Family Hantaviridae; Family Hepadnaviridae; Family Hepeviridae; Family Herelleviridae; Family Heroarteri Subfamily: Heroarterivirinae; Family: Herpesviridae; Family: Hexponivirinae; Family: Hypoviridae; Family: Hytrosaviridae; Family: Iflaviridae; Family: Inoviridae; Family: Iridoviridae; Family: Jasinkavirinae; Family: Kitaviridae; Family: Rabidaviridae Lavidaviridae; Leishbuviridae; Letovirinae; Leviviridae; Lipothrixviridae; Lispiviridae; Luteoviridae; Malacoherpesviridae; Mammantavirinae; Marnaviridae;Family Marseilleviridae; Family Matonaviridae; Subfamily Mccleskeyvirinae; Mclasvirinae (subfamily); Medioniviridae (family); Medionivirinae (subfamily); Megabirnaviridae (family); Mesoniviridae (family); Metaparamyxovirinae (subfamily); Metaviridae (family); Microviridae (family); Mimiviridae (family); Mononiviridae (family); Mononivirinae (subfamily); Mymonaviridae (family); Myoviridae (family); Mypoviridae (family); Nairoviridae (family); Nanoarf Subfamily: Nanoalphasatellitinae; Family: Nanoviridae; Family: Narnaviridae; Subfamily: Nclasvirinae; Family: Nimaviridae; Family: Nodaviridae; Family: Nudiviridae; Family: Nyamiviridae; Subfamily: Nymbaxtervirinae; Subfamily: Okanivirinae; Subfamily: Orthocoronavirinae; Family: Orthomyxoviridae; Subfamily: Orthoparamyxovirinae; Subfamily: Orthoretrovirinae Subfamily Ounavirinae; Family Ovaliviridae; Family Papillomaviridae; Family Paramyxoviridae; Family Partitiviridae; Family Parvoviridae; Subfamily Parvovirinae; Subfamily Pclasvirinae; Subfamily Peduovirinae; Family Peribunyaviridae ridae); Permutotetraviridae; Phasmaviridae; Phenuiviridae; Phycodnaviridae; Picobirnaviridae; Picornaviridae; Picovirinae; Piscanivirinae; Plasmaviridae; Plasmaviridae Pleolipoviridae; Pneumoviridae; Podoviridae; Polycipiviridae; Polydnaviridae; Polyomaviridae; Portogloboviridae; Pospiviroidae; Potyviridae; Poxviridae ae); Procedovirinae; Pseudoviridae; Qinviridae; Quadriviridae; Quinvirinae; Regressovirinae; Remotovirinae; Reoviridae; Repantavirinae; Retroviridae;Family: Rhabdoviridae; Family: Roniviridae; Subfamily: Rubulavirinae; Family: Rudiviridae; Family: Sarthroviridae; Subfamily: Secondpapillomavirinae; Family: Secoviridae; Subfamily: Sedoreovirinae; Subfamily: Sepvirinae; Subfamily: Serpentovirinae virinae);Simarterivirinae;Siphoviridae;Smacoviridae;Solemoviridae;Solinviviridae;Sphaerolipoviridae;Spinareovirinae;Spiraviridae;Spounavirinae;Spounavirinae Spumaretrovirinae; Sunviridae; Tectiviridae; Tevenvirinae; Tiamatvirinae; Tobaniviridae; Togaviridae; Tolecusatellitidae; Tombusviridae; Torovirinae; Family: Tospoviridae; Family: Totiviridae; Family: Tristromaviridae; Subfamily: Trivirinae; Subfamily: Tunavirinae; Subfamily: Tunicanivirinae; Family: Turriviridae; Subfamily: Twortvirinae; Family: Tymoviridae; Subfamily: VariarterivirinaeThe subfamily Vequintavirinae, Virgaviridae, Wupedeviridae, Xinmoviridae, Yueviridae, and Zealarterivirinae.

[0025] More preferably, the pathogenic virus is cytomegalovirus (CMV) or Epstein-Barr virus (EBV), or preferably a herpesvirus, poxvirus, hepadnavirus, influenza virus, coronavirus, hepatitis virus, HIV or bunyaviridae.

[0026] Most preferably, the virus is non-oncolytic, ie, its replication is not specifically restricted to cancer cells.

[0027] In an alternative embodiment, the virus is oncolytic, ie, capable of infecting and killing cancer cells by selective replication in tumor cells compared to normal cells.

[0028] In a preferred method, the comparison of the peptide of the bound pMHC (peptide:major histocompatibility complex) with a library of pathogen-derived antigens comprises multiple scoring steps (peptide affinity scoring, alignment scoring, similarity scoring and MHC binding affinity scoring) to determine the homology / affinity or identity or similarity. Most preferably, the scoring step is homology / affinity scoring as described below.

[0029] In one embodiment, a matrix is ​​generated with rows (or columns) representing amino acid positions in tumor peptides and columns (or rows) representing each of the 20 standard amino acids, and amino acid positions in tumor peptides are assigned the same high score, while other positions are assigned the same low score as pathogen-derived antigens / peptides (in order of preference): a) overall sequence structure (obviously, the highest score is given to a tumor peptide with 100% identity to a pathogen-derived peptide); b) the identity of key amino acids in hotspots or important binding sites; and c) the highest number of important amino acids in hot spots or important binding sites; In terms of the above, the tumor peptide with the highest degree of homology / affinity with the pathogen-derived antigen / peptide is given the highest score.

[0030] Alignments are calculated pairwise between peptides in the query (pathogen-derived) set against the tumor set, or vice versa. For a given pair of peptides, their alignment is calculated by summing the distance scores between pairs of amino acids at the same position. Scoring is weighted to favor similarities between more centrally located amino acids in the peptides.

[0031] Additionally, MHC class I binding affinity predictions are performed using methods known in the art, such as using NetMHC (NetMHC4.0 or NetMHCpan4.1.) via the IEDB application programming interface (http: / / tools.iedb.org / main / tools-api / ), which are then analyzed and collated within the tool.

[0032] In an alternative embodiment of the present invention, the comparison involves inputting a list of tumor peptides, 8-12 amino acids in length, into software. First, the tool BLAST is used to find hits (similar sequences) in a library of pathogen-derived antigens. The PAM30 tool is commonly used for this task, but both BLOSUM and PAM substitution matrices across several evolutionary distances are supported. Next, pairwise refinement alignments are performed using at least BLOSUM62, ideally the substitution matrix developed by Kim et al. (BMC Bioinformatics. 2009;10:394. Published 2009 Nov 30. doi:10.1186 / 1471-2105-10-394 Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. Kim Y, Sidney J, Pinilla C, Sette A, Peters B.) to refine the alignment using positional weighting. Peptide pairs with high similarity in the TCR-interacting region (the central portion of the peptide) are given higher similarity scores. Finally, MHC binding affinity predictions are generated for both tumor and viral cognate peptides using NetMHC4.0 or NetMHCpan4.1 as stand-alone command-line tools. The results are then automatically analyzed and collated within the tool to produce a final score summarizing the above analysis.

[0033] According to yet a further aspect of the present invention, there is provided a cancer therapeutic or immunogenic agent or cancer vaccine comprising a tumor antigen identified using one or both of the aforementioned devices and / or methods.

[0034] According to yet a further aspect of the present invention there is provided a tumor antigen for use in cancer therapy identified using one or both of the aforementioned devices and / or methods.

[0035] According to yet a further aspect of the present invention there is provided a tumor antigen for use in the manufacture of a medicament for treating cancer, said antigen being identified using one or both of the devices and / or methods described above.

[0036] Most preferably, the cancer therapeutic agent or immunogenic agent or cancer vaccine or tumor antigen is any one or more of those listed in Tables 1-6.

[0037] According to yet a further aspect of the present invention there is provided a method for treating a cancer patient, comprising: using the devices and / or methods of the invention to identify a tumor antigen and then administering the tumor antigen to an individual, or using the tumor antigen to expand a population of T cells active against the tumor antigen and then administering the T cells to a patient; A method is provided, comprising:

[0038] In a preferred embodiment, said T cells are ex vivo and / or cultured T cells, either allogeneic or autologous T cells.

[0039] According to yet a further aspect of the present invention there is provided a pharmaceutical composition or immunogenic agent or vaccine comprising the tumor antigen of the present invention and a pharmaceutically acceptable carrier, adjuvant, diluent or excipient.

[0040] Suitable pharmaceutical excipients are well known to those skilled in the art. The pharmaceutical composition can be formulated for administration by any suitable route, for example, intratumoral, intramuscular, intraarterial, intravenous, intrapleural, intravesicular, intracavity or intraperitoneal injection, buccal, nasal or bronchial (inhalation), transdermal or parenteral, and can be prepared by any method well known in the art of pharmacy.

[0041] The composition can be prepared by mixing the tumor antigen with a carrier. Generally, the formulation is prepared by uniformly and intimately mixing the tumor antigen with a liquid carrier, or a finely divided solid carrier, or both, and then, if necessary, shaping the product. The present invention extends to a method for preparing a pharmaceutical composition, which comprises combining or mixing the tumor antigen defined herein with a pharmaceutically or veterinarily acceptable carrier or vehicle.

[0042] According to a further aspect of the present invention, there is provided a combination therapy for the treatment of cancer comprising a tumor antigen identified using the device and / or method of the present invention and at least one additional cancer therapeutic agent.

[0043] Preferably, the additional cancer therapeutic agent downregulates T regulatory cells, and therefore most suitably comprises cyclophosphamide, however, as will be appreciated by those skilled in the art, the additional therapeutic agent may be any anti-cancer agent known in the art.

[0044] Preferably, the additional cancer therapeutic comprises a checkpoint inhibitor (ICI).

[0045] The best-characterized pathways for checkpoint inhibition are the cytotoxic T-lymphocyte protein 4 (CTLA-4) pathway and the programmed cell death protein 1 pathway (PD-1 / PD-L1). Thus, the present invention can be used in combination with at least one checkpoint modulator, such as an anti-CTLA-4, anti-PD1, or anti-PD-L1 molecule, to counteract the immunosuppressive tumor environment and elicit a potent anti-immune response.

[0046] According to yet a further aspect of the present invention there is provided a method of stratifying patients for checkpoint inhibitor cancer treatment comprising the steps of: i) obtaining a tumor sample from a patient; ii) dissolving or suspending the sample in a fluid; iii) passing the fluid through a device for tumor antigen identification comprising at least one flow-through channel containing a plurality of supports to which is attached at least one molecule or at least one complex to which at least one anti-MHC (major histocompatibility complex) antibody or at least one anti-pan-human leukocyte antigen (HLA) antibody is bound, wherein the at least one antibody can be used to extract pMHC (peptide:major histocompatibility complex) from a sample flowing through the channel; iv) allowing at least one pMHC (peptide:major histocompatibility complex) in said sample to bind to said at least one antibody; v) optionally removing the at least one bound pMHC (peptide:major histocompatibility complex) of part iv) from the device; vi) comparing said peptide of said bound pMHC (peptide:major histocompatibility complex) to a library of antigens derived from a human pathogen to determine whether said peptide exhibits sequence homology / affinity with at least one antigen derived from a human pathogen or a portion thereof, and if there is greater than 60% homology / affinity; vii) identifying said peptide as a tumor antigen; viii) if said peptide is found, administering to said patient an effective amount of at least one checkpoint inhibitor (ICI); A method is provided, comprising:

[0047] According to yet a further aspect or embodiment of the present invention there is provided a method of stratifying patients for checkpoint inhibitor cancer treatment comprising: determining whether the patient is CMV seropositive, and if CMV seropositive, selecting said patient for treatment with an effective amount of at least one checkpoint inhibitor (ICI); A method is provided which includes:

[0048] According to yet a further aspect of the present invention there is provided a method of treating cancer, comprising: i) obtaining a tumor sample from a patient; ii) dissolving or suspending the sample in a fluid; iii) passing the fluid through a device for tumor antigen identification comprising at least one flow-through channel containing a plurality of supports to which is attached at least one molecule or at least one complex to which at least one anti-MHC (major histocompatibility complex) antibody or at least one anti-pan-human leukocyte antigen (HLA) antibody is bound, wherein the at least one antibody can be used to extract pMHC (peptide:major histocompatibility complex) from a sample flowing through the channel; iv) allowing at least one pMHC (peptide:major histocompatibility complex) in said sample to bind to said at least one antibody; v) optionally removing the at least one bound pMHC (peptide:major histocompatibility complex) of part iv) from the device; vi) comparing said peptide of said bound pMHC (peptide:major histocompatibility complex) to a library of antigens derived from a human pathogen to determine whether said peptide exhibits sequence homology / affinity with at least one antigen derived from a human pathogen or a portion thereof, and if there is greater than 60% homology / affinity; vii) identifying the peptide as a tumor antigen; and administering an effective amount of the peptide to the patient to stimulate or activate T cells against the tumor antigen and thus against the cancer from which the sample was obtained; or using the tumor antigen to expand a population of T cells active against the tumor antigen and then administering the T cells to the patient; A method is provided, comprising:

[0049] According to yet a further aspect or embodiment of the present invention there is provided a method of treating cancer comprising: determining whether the patient is CMV seropositive, and if CMV seropositive, administering to said patient an effective amount of at least one checkpoint inhibitor (ICI); A method is provided which includes:

[0050] According to yet a further aspect of the present invention there is provided a method of stratifying patients for adenoviral cancer treatment, comprising the steps of: i) obtaining a tumor sample from a patient; ii) dissolving or suspending the sample in a fluid; iii) passing the fluid through a device for tumor antigen identification comprising at least one flow-through channel containing a plurality of supports to which is attached at least one molecule or at least one complex to which at least one anti-MHC (major histocompatibility complex) antibody or at least one anti-pan-human leukocyte antigen (HLA) antibody is bound, wherein the at least one antibody can be used to extract pMHC (peptide:major histocompatibility complex) from a sample flowing through the channel; iv) allowing at least one pMHC (peptide:major histocompatibility complex) in said sample to bind to said at least one antibody; v) optionally removing the at least one bound pMHC (peptide:major histocompatibility complex) of part iv) from the device; vi) comparing said peptide of said bound pMHC (peptide:major histocompatibility complex) to a library of antigens derived from a human pathogen to determine whether said peptide exhibits homology / affinity with at least one antigen derived from a human pathogen or a portion thereof, and if there is greater than 60% homology / affinity; vii) identifying said peptide as a tumor antigen; viii) if said peptide is found, attaching said peptide to the capsid of an adenoviral vector and administering an effective amount of said adenoviral vector to said patient; A method is provided, comprising:

[0051] In a preferred embodiment, the peptide is attached to the adenoviral vector using known techniques such as those described in WO 2015 / 177098 and / or contained herein, see PeptiCRAd preparation.

[0052] Reference herein to an "effective amount" is an amount sufficient to achieve a desired biological effect, such as cancer cell death.

[0053] It is understood that the effective dosage will depend on the age, sex, health, and weight of the recipient, type of concurrent treatment, if any, frequency of treatment, and the nature of the desired effect. Typically, the effective amount will be determined by the person administering the treatment.

[0054] Most preferably, the cancers referred to herein include the following cancers: nasopharyngeal carcinoma, synovial carcinoma, hepatocellular carcinoma, renal carcinoma, cancer of connective tissue, melanoma, lung cancer, intestinal cancer, colon cancer, rectal cancer, colorectal cancer, brain cancer, throat cancer, oral cancer, liver cancer, bone cancer, pancreatic cancer, choriocarcinoma, gastrinoma, pheochromocytoma, prolactinoma, T-cell leukemia / lymphoma, neuroma, von Hippel-Lindau disease, Zollinger-Ellison syndrome, adrenal gland cancer, anal cancer, bile duct cancer, bladder cancer, ureteral cancer, oligodendroglioma, neuroblastoma, meningioma, spinal cord tumor, osteochondroma, chondrosarcoma, Ewing's sarcoma, carcinoma of unknown primary site, carcinoid, carcinoid of the digestive tract, fibrosarcoma, breast cancer, Paget's disease, cervical cancer, esophageal cancer. , gallbladder cancer, head cancer, eye cancer, neck cancer, kidney cancer, Wilms' tumor, liver cancer, Kaposi's sarcoma, prostate cancer, testicular cancer, Hodgkin's disease, non-Hodgkin's lymphoma, skin cancer, mesothelioma, multiple myeloma, ovarian cancer, endocrine pancreatic cancer, glucagonoma, parathyroid cancer, penile cancer, pituitary cancer, soft tissue sarcoma, retinoblastoma, small intestine cancer, stomach cancer, thymus cancer, thyroid cancer, choriocarcinoma, hydatidiform mole, uterine cancer, endometrial cancer, vaginal cancer, vulvar cancer, acoustic neuroma, mycosis fungoides, insulinoma, carcinoid syndrome, somatostatinoma, gum cancer, heart cancer, lip cancer, meningeal cancer, mouth cancer, nerve cancer, palate cancer, parotid gland cancer, peritoneal cancer, pharyngeal cancer, pleural cancer, salivary gland cancer, tongue cancer, and tonsil cancer.

[0055] In certain preferred embodiments of the invention, the sample is taken from a melanoma and the tumor antigen is homologous to a CMV antigen or peptide.

[0056] In the following claims and the preceding description of the invention, unless the context otherwise requires, either by express language or necessary implication, the word "comprise" or variations such as "comprises" or "comprising" are used in their inclusive sense, i.e., to specify the presence of stated features but not to exclude the presence or addition of further features in various aspects of the invention.

[0057] All references cited herein, including any patents or patent applications, are hereby incorporated by reference. No admission is made that any reference constitutes prior art. Further, no admission is made that any of the prior art constitutes part of the common general knowledge in the art.

[0058] Preferred features of each aspect of the invention may be as described in relation to any of the other aspects.

[0059] Other features of the present invention will become apparent from the following examples. Generally speaking, the present invention extends to any novel one or any novel combination of features disclosed in this specification (including the accompanying claims and drawings). Accordingly, any feature, integer, property, compound, or chemical moiety described in connection with a particular aspect, embodiment, or example of the present invention should be understood to be applicable to any other aspect, embodiment, or example described herein, except to the extent inconsistent therewith.

[0060] Moreover, unless stated otherwise, any feature disclosed in this specification may be replaced by an alternative feature serving the same or a similar purpose.

[0061] Throughout the description and claims of this specification, the singular encompasses the plural unless the context requires otherwise. In particular, where the indefinite article is used, the specification should be understood to contemplate the plural as well as the singular, unless the context requires otherwise.

[0062] Aspects of the present invention will now be described, by way of example only, with reference to the following: [Brief explanation of the drawings]

[0063] [Figure 1A] [A] The interior of the PeptiCHIP. The interior of the PeptiCHIP consists of thousands of pillars coated with linkers to which biotin is attached. The biotin is then reacted with streptavidin, which can then react with three molecules of HLA-specific capture antibodies. [B] Microchip technology as a novel immunopurification platform for rapid antigen discovery. A schematic diagram illustrating the new microchip methodology developed is shown. A thiol-ene microchip incorporating free surface thiols is derivatized with biotin-PEG4-alkynethiolene (step 1) and functionalized with a layer of streptavidin (step 2). Subsequently, biotinylated pan-HLA antibodies are immobilized on the micropillar surface (step 3), and cell lysates are loaded into the microchip (step 4). After sufficient incubation and washing steps, the HLA molecules are eluted by adding 7% acetic acid (step 5). [Figure 1B][A] The interior of the PeptiCHIP. The interior of the PeptiCHIP consists of thousands of pillars coated with linkers to which biotin is attached. The biotin is then reacted with streptavidin, which can then react with three molecules of HLA-specific capture antibodies. [B] Microchip technology as a novel immunopurification platform for rapid antigen discovery. A schematic diagram illustrating the new microchip methodology developed is shown. A thiol-ene microchip incorporating free surface thiols is derivatized with biotin-PEG4-alkynethiolene (step 1) and functionalized with a layer of streptavidin (step 2). Subsequently, biotinylated pan-HLA antibodies are immobilized on the micropillar surface (step 3), and cell lysates are loaded into the microchip (step 4). After sufficient incubation and washing steps, the HLA molecules are eluted by adding 7% acetic acid (step 5). [Figure 2] Characterization of the selectivity of microchip functionalization with biotinylated pan-HLA antibodies. A) Binding efficiency of Alexa Fluor 488-streptavidin on thiol-ene micropillars pre-coated with biotin-PEG4-alkyne at two different streptavidin incubation times (15 min and 1 h). B) Effect of streptavidin (non-fluorescent) concentration on the amount of immobilized biotinylated pan-HLA antibodies quantified via an Alexa Fluor 488-labeled secondary antibody. C) Effect of BSA incubation on the amount of immobilized biotinylated pan-HLA antibodies quantified via an Alexa Fluor 488-labeled secondary antibody. The efficiency of BSA in blocking nonspecific binding sites was assessed by pre-conditioning the micropillar array with BSA either before (BSA-pan-HLA) or after (pan-HLA-BSA) immobilization of biotinylated pan-HLA antibodies. D) Total amount of biotinylated pan-HLA antibody bound on a single chip as a function of loading cycle. For each cycle, a new batch of the same (constant) pan-HLA antibody concentration was used. Significance was assessed by two-tailed unpaired Student's t-test, *p<0.05. [Figure 3A-B]Characterization of HLA-I peptidome datasets obtained from the JY cell line. A) Number of unique peptides eluted from 50 x 106, 10 x 106, and 1 x 106 JY cells. B) Overall peptide length distribution of HLA peptides in the three datasets derived from the JY cell line. C-E) The length distribution of HLA peptides is illustrated as the number of unique peptides (left y-axis) and percentage occurrence (right y-axis) for 50 x 106 (C), 10 x 106 (D), and 1 x 106 (E). [Figure 3C-E] Characterization of HLA-I peptidome datasets obtained from the JY cell line. A) Number of unique peptides eluted from 50 x 106, 10 x 106, and 1 x 106 JY cells. B) Overall peptide length distribution of HLA peptides in the three datasets derived from the JY cell line. C-E) The length distribution of HLA peptides is illustrated as the number of unique peptides (left y-axis) and percentage occurrence (right y-axis) for 50 x 106 (C), 10 x 106 (D), and 1 x 106 (E). [Figure 4A] Accurate analysis of HLA ligands isolated from the JY cell line. A) Eluted 9-mers were analyzed for their binding affinity to HLA-A*02:01 and HLA-B*07:02. Binders (green dots) and nonbinders (black dots) were identified in the NetMHCpan 4.0 Server (applied rank 2%). B) HLA-I consensus binding motif. Gibbs clustering analysis was performed to define a consensus binding motif among the eluted 9-mer peptides. The reference motif is depicted in the upper right corner. Clusters with the best fit (higher KLD values, orange stars) are shown, and for each cluster, the sequence logo is depicted along with the number of HLA-Is. [Figure 4B-1]Accurate analysis of HLA ligands isolated from the JY cell line. A) Eluted 9-mers were analyzed for their binding affinity to HLA-A*02:01 and HLA-B*07:02. Binders (green dots) and nonbinders (black dots) were identified in the NetMHCpan 4.0 Server (applied rank 2%). B) HLA-I consensus binding motif. Gibbs clustering analysis was performed to define a consensus binding motif among the eluted 9-mer peptides. The reference motif is depicted in the upper right corner. Clusters with the best fit (higher KLD values, orange stars) are shown, and for each cluster, the sequence logo is depicted along with the number of HLA-Is. [Figure 4B-2] Accurate analysis of HLA ligands isolated from the JY cell line. A) Eluted 9-mers were analyzed for their binding affinity to HLA-A*02:01 and HLA-B*07:02. Binders (green dots) and nonbinders (black dots) were identified in the NetMHCpan 4.0 Server (applied rank 2%). B) HLA-I consensus binding motif. Gibbs clustering analysis was performed to define a consensus binding motif among the eluted 9-mer peptides. The reference motif is depicted in the upper right corner. Clusters with the best fit (higher KLD values, orange stars) are shown, and for each cluster, the sequence logo is depicted along with the number of HLA-Is. [Figure 5A] Flowchart of the HEX algorithm. A matrix is ​​generated based on the amino acid composition of tumor peptides (reference peptides). This matrix is ​​then used to scan a viral database and rank the resulting viral peptides in order of log-likelihood of recognition. Each viral peptide is assigned an alignment score and a score for MHC-I binding prediction. Based on the following criteria: MHC-I binding prediction score > alignment score > B score, candidate viral peptides are ranked, and the highest-scoring peptides are experimentally analyzed. [Figure 5B]Flowchart for using existing software. A list of tumor peptides, 8–12 amino acids long, can be used as input for the software. First, the tool BLAST is used to find hits (similar sequences) in a library of pathogen-derived antigens. For this task, PAM30 is commonly used, but both BLOSUM and PAM substitution matrices across several evolutionary distances are supported. Next, pairwise refinement alignments are performed using at least BLOSUM62, ideally the substitution matrix developed by Kim et al. (BMC Bioinformatics. 2009;10:394. Published 2009 Nov 30. doi:10.1186 / 1471-2105-10-394 Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. Kim Y, Sidney J, Pinilla C, Sette A, Peters B.) to refine the alignment by weighting it by position. Peptide pairs with high similarity in the TCR-interacting region (the central portion of the peptide) are given higher similarity scores. Finally, MHC binding affinity predictions are generated for both tumor and viral cognate peptides using NetMHC4.0 or NetMHCpan4.1 as stand-alone command-line tools. The results are then automatically analyzed and collated within the tool to produce a final score summarizing the above analysis. [Figure 6]Immunization with viral peptides homologous to tumor antigens slows tumor growth. A: Schematic of animal experiments. To evaluate whether viral peptides similar to tumor peptides can affect tumor growth, four groups of C57BL6 mice were formed. A group of naive mice was used as a mock immunization, and the other three groups were immunized with different pools of viral peptides. Mice were immunized at two time points, 14 days and 7 days, before tumor engraftment. B: Two weeks after the first immunization, mice were subcutaneously injected with 3 x 105 murine melanoma B16-OVA cells. After engraftment, tumor growth was measured every two days for 19 days using a digital caliper. P values ​​were calculated using a two-way ANOVA with Tukey's correction for multiple comparisons. C: At the end point, mice were euthanized. Splenocytes from each group were collected and pooled for ELISpot assays. To assess the response to treatment, each pool was then pulsed with the respective viral peptide (viral peptide homologous to TYR1, viral peptide homologous to TRP2, viral peptide homologous to GP100). The dotted line indicates the background generated by the negative control. [Figure 7]Viral peptides with higher affinity for MHC are more immunogenic and can induce stronger cross-reactive responses. A: To assess responses to each original tumor epitope, splenocytes from each immunization group were pooled together and pulsed with the corresponding tumor peptide. B: Comparison between responses induced by pulsed application of pooled viral peptides and the corresponding original tumor peptide to splenocytes. C: Predicted affinity of the original tumor and each analogous peptide viral pool for mouse MHC class I. Following IEDB guidelines, 50 nM was considered the threshold for defining peptides with "high" and "medium / low" affinity. D: Correlation between data from IFN-γ responses and predicted affinity. E: Peptide stratification based on peptide affinity and ability to stimulate IFN-γ production. High-affinity peptides (IC50<50 nM) promote significantly higher IFN-γ production compared to medium / low-affinity peptides (IC50>50 nM). P values ​​were calculated using a t-test with Mann-Whitney correction. The range of p-values ​​was marked with an asterisk according to the following criteria: >0.05 (not significant), ≦0.05 (*), ≦0.01 (**), ≦0.001 (***), ≦0.0001 (****). [Figure 8A-B]Viral peptides homologous to tumor antigens can reduce tumor growth in established tumors. A: Schematic of the animal experiment: Four groups of C57BL mice were formed for each tumor cell line to be tested. On day 0, the mice were subcutaneously injected with either B16-OVA or B16F10 cells. Once tumors became palpable, the mice were treated with saline solution (mock group), uncoated adenovirus (uncoated virus group), adenovirus coated with a pool of viral peptides homologous to TRP2 (Viral PeptiCRAd, VPC), or adenovirus coated with a TRP2 peptide (TRP2 PeptiCRAd, TPC). B: Individual growth of B16-OVA tumors. The threshold for defining successful treatment is determined by the median volume of all tumors on the final day. C: B16-OVA tumor volume at the end point. The median tumor volume, shown as a dotted line, defines the threshold for successful treatment. D: B16 OVA split plot shows the number of responders per treatment group. E: Individual growth of B16 F10 tumors. The threshold for defining treatment success is identified by the median of all tumor volumes on the final day. F: B16 F10 tumor volume at endpoint. The median tumor volume, shown as a dotted line, defines the threshold for treatment success. G: B16 OVA split plot shows the number of responders per treatment group. (C-F) P values ​​were calculated using one-way ANOVA with Tukey's correction. P value ranges are marked with asterisks according to the following criteria: >0.05 (not significant), ≤0.05 (*), ≤0.01 (**), ≤0.001 (***), and ≤0.0001 (****). (D-G) P values ​​were calculated using chi-square (and Fisher's exact) tests of odds ratios. The range of p-values ​​was marked with an asterisk according to the following criteria: >0.05 (not significant), ≦0.05 (*), ≦0.01 (**), ≦0.001 (***), ≦0.0001 (****). [Figure 8C-G]Viral peptides homologous to tumor antigens can reduce tumor growth in established tumors. A: Schematic of the animal experiment: Four groups of C57BL mice were formed for each tumor cell line to be tested. On day 0, the mice were subcutaneously injected with either B16-OVA or B16F10 cells. Once tumors became palpable, the mice were treated with saline solution (mock group), uncoated adenovirus (uncoated virus group), adenovirus coated with a pool of viral peptides homologous to TRP2 (Viral PeptiCRAd, VPC), or adenovirus coated with a TRP2 peptide (TRP2 PeptiCRAd, TPC). B: Individual growth of B16-OVA tumors. The threshold for defining successful treatment is determined by the median volume of all tumors on the final day. C: B16-OVA tumor volume at the end point. The median tumor volume, shown as a dotted line, defines the threshold for successful treatment. D: B16 OVA split plot shows the number of responders per treatment group. E: Individual growth of B16 F10 tumors. The threshold for defining treatment success is identified by the median of all tumor volumes on the final day. F: B16 F10 tumor volume at endpoint. The median tumor volume, shown as a dotted line, defines the threshold for treatment success. G: B16 OVA split plot shows the number of responders per treatment group. (C-F) P values ​​were calculated using one-way ANOVA with Tukey's correction. P value ranges are marked with asterisks according to the following criteria: >0.05 (not significant), ≤0.05 (*), ≤0.01 (**), ≤0.001 (***), and ≤0.0001 (****). (D-G) P values ​​were calculated using chi-square (and Fisher's exact) tests of odds ratios. The range of p-values ​​was marked with an asterisk according to the following criteria: >0.05 (not significant), ≦0.05 (*), ≦0.01 (**), ≦0.001 (***), ≦0.0001 (****). [Figure 9]T cell cross-reactivity between viral and tumor antigens sharing a high degree of homology / affinity. The peptides listed in Table 2 were pulsed onto PBMCs from a patient with HLA-A*02:01 and high serum levels of anti-CMV Abs. The level of IFN-γ secreted by activated CD8+ T cells was detected by ELISpot assay. The dotted line indicates the noise level resulting from nonspecific activation of CTLs in the negative control (DMSO) (A). PBMCs from a patient with HLA-A*02:01 and high serum levels of anti-CMV Abs, as well as a healthy donor (HS) who tested positive for CMV responses, were tested for anti-CMV responses by ELISpot assay using the CMV-specific HLA-A*02:01-restricted peptide NLVPMVATV. P values ​​were calculated using a t-test with Mann-Whitney correction (B). [Figure 10A] A microchip-based platform reveals immunopeptidomic profiles in scarce tumor biopsies. A) Pre-processing sample weight, total number, and enrichment of unique peptides and 7-13mer specimens are summarized here. B) Peptide length distribution for absolute number and percentage of peptides is shown as a bar graph. [Figure 10B] A microchip-based platform reveals immunopeptidomic profiles in scarce tumor biopsies. A) Pre-processing sample weight, total number, and enrichment of unique peptides and 7-13mer specimens are summarized here. B) Peptide length distribution for absolute number and percentage of peptides is shown as a bar graph. [Figure 11A] Immunopeptidomic analysis of ccRCC and bladder tumor patient-derived organoids (PDO). A) Number of unique peptides detected in ccRCC and bladder PDO. B) Peptide length distribution is shown as the total number of unique peptides (left y-axis) and percentage occurrence (right y-axis) for each PDO (ccRCC upper panel, bladder lower panel). [Figure 11B]Immunopeptidomic analysis of ccRCC and bladder tumor patient-derived organoids (PDO). A) Number of unique peptides detected in ccRCC and bladder PDO. B) Peptide length distribution is shown as the total number of unique peptides (left y-axis) and percentage occurrence (right y-axis) for each PDO (ccRCC upper panel, bladder lower panel). [Figure 12A] Peptide testing. Balb / c mice were subcutaneously injected with the syngeneic tumor model CT26 in the left and right flanks (day 0, Figure 12A). Once tumors were established (day 7, Figure 12A), Valo-mD901 was coated with each of the polylysine-modified peptide pairs in our list (PeptiCRAd1, PeptiCRAd2, PeptiCRAd3, Table) and injected intratumorally into the right tumor only. PeptiCRAd4 consisted of Valo-mD901 coated with gp70423-431 (AH1-5), a known immunodominant antigen of CT26 derived from a genomic-encoded autoantigen. Mock and Valo-mD901 groups were also used as controls. PeptiCRAd1 and PeptiCRAd2 improved not only tumor growth control but also Valo-mD901 in injected lesions, as shown by the single tumor growth per mouse per treatment group (Figure 12B, right side of the graph panel). Specifically, only PeptiCRAd1 significantly improved antitumor activity in untreated tumors, whereas Valo-mD901 did not elicit an effect (Figure 12B, left side of the graph panel). The peptide in PeptiCRAd1 was obtained from HEX analysis. PeptiCRAd4 consisted of Valo-mD901 coated with gp70 423-431 (AH1-5). [Figure 12B]Peptide testing. Balb / c mice were subcutaneously injected with the syngeneic tumor model CT26 in the left and right flanks (day 0, Figure 12A). Once tumors were established (day 7, Figure 12A), Valo-mD901 was coated with each of the polylysine-modified peptide pairs in our list (PeptiCRAd1, PeptiCRAd2, PeptiCRAd3, Table) and injected intratumorally into the right tumor only. PeptiCRAd4 consisted of Valo-mD901 coated with gp70423-431 (AH1-5), a known immunodominant antigen of CT26 derived from a genomic-encoded autoantigen. Mock and Valo-mD901 groups were also used as controls. PeptiCRAd1 and PeptiCRAd2 improved not only tumor growth control but also Valo-mD901 in injected lesions, as shown by the single tumor growth per mouse per treatment group (Figure 12B, right side of the graph panel). Specifically, only PeptiCRAd1 significantly improved antitumor activity in untreated tumors, whereas Valo-mD901 did not elicit an effect (Figure 12B, left side of the graph panel). The peptide in PeptiCRAd1 was obtained from HEX analysis. PeptiCRAd4 consisted of Valo-mD901 coated with gp70 423-431 (AH1-5). [Figure 13-1]A schematic diagram of the present invention is shown. The device of the present invention is shown in diagram form and is pre-functionalized with multiple anti-MHC (major histocompatibility complex) or anti-pan-human leukocyte antigen (HLA) antibodies capable of capturing pMHC (peptide:major histocompatibility complex). Tumor lysates are passed through the device, and the antibodies extract the pMHC. The pMHC is eluted, and peptides from the pMHC complexes are analyzed, for example, by liquid chromatography with mass spectrometry (LC-MS / MS), resulting in a list of tumor peptides. Each tumor peptide is compared to a library of pathogen-derived proteins (including antigens) to determine the level of homology / affinity between each tumor peptide identified using the device and the pathogen-derived proteins (including pathogen antigens / peptides) in the library. This generates a list of candidates, ideally prioritized in terms of homology / affinity, for use in cancer therapy. [Figure 13-2] A schematic diagram of the present invention is shown. The device of the present invention is shown in diagram form and is pre-functionalized with multiple anti-MHC (major histocompatibility complex) or anti-pan-human leukocyte antigen (HLA) antibodies capable of capturing pMHC (peptide:major histocompatibility complex). Tumor lysates are passed through the device, and the antibodies extract the pMHC. The pMHC is eluted, and peptides from the pMHC complexes are analyzed, for example, by liquid chromatography with mass spectrometry (LC-MS / MS), resulting in a list of tumor peptides. Each tumor peptide is compared to a library of pathogen-derived proteins (including antigens) to determine the level of homology / affinity between each tumor peptide identified using the device and the pathogen-derived proteins (including pathogen antigens / peptides) in the library. This generates a list of candidates, ideally prioritized in terms of homology / affinity, for use in cancer therapy. DETAILED DESCRIPTION OF THE INVENTION

[0064] Table 1. Peptides used in animal experiments. Known melanoma tumor peptides (TRP2180-188, hGP10025-33, and TYR208-216) were analyzed by HEX. The best viral candidate peptides proposed by the software were selected, and pools consisting of the best four heterologous peptides per each original tumor epitope were tested in vivo.

[0065] Table 2. Peptides used for ELISpot on patient PBMCs. Known melanoma-associated antigens were analyzed by HEX. The best human CMV-derived candidate peptides for each antigen were selected and tested in vitro.

[0066] Table 3. Comparative analysis between microchip-based immunoprecipitation technique and standard procedure. The table reports the total amount of antibody coated during the microchip-based IP technique and the standard procedure.

[0067] Table 4. Thirteen tumor MHC-restricted peptides identified by the HEX output are shown along with their corresponding pathogen-derived peptides.

[0068] Table 5. Table of peptides tested in the ELISPOT assay.

[0069] Table 6. Table of selected peptides for the in vivo PeptiCRAd assay shown in Figure 12.

[0070] Methods and Materials Device fabrication The device of the present invention (known as PeptiCHIP) is a flow-through structure containing thousands of pillars coated with linkers to which biotin is attached, which are then connected to HLA-specific capture antibodies, specifically streptavidin bound to three molecules of HLA-specific capture antibodies.

[0071] Using conventional microfluidic fabrication techniques, the fabrication of PeptiCHIP is based on a UV-initiated photoreaction between thiol and allyl ("ene") functional groups according to the following non-stoichiometric ratio of 150:100 (tetrathiol:triallyl).

[0072] Immediately after the above fabrication, the PeptiCHIP was derivatized with biotin-PEG4-alkyne (Sigma, 764213) by UV photopolymerization and then reacted with avidin, as follows:

[0073] Step 1: We prepared a 1 mM biotin-PEG4-alkyne solution (stock solution of biotin-PEG4-alkyne at 10 mM in ethylene glycol). We took a small aliquot and added 1% (m / v) photoinitiator (Igracure® TPO-L, BASF) using a 10% photoinitiator stock solution in methanol, so the final solution is 1 mM biotin + 1% Lucirin in EG-MeOH 9:1*.

[0074] We filled the chip with 1 volume of a solution of biotin-PEG4-alkyne and 1% Lucirin in EG:MeOH 9:1 (v / v) and exposed it to UV light for 1 min (LED UV lamp λ = 365 nm, λ = 15 mW / cm). 2 (Use

[0075] We thoroughly rinsed first with methanol and then with Milli-Q water, flowing 1–2 mL of each solvent through the channel, and then dried and stored (if necessary).

[0076] Step 2: After derivatization with biotin, the chip was functionalized with streptavidin. We prepared a stock solution of 0.01 mg / ml streptavidin in PBS and added it to the chip for 15 minutes in the dark at room temperature. We washed three times with 200 μl of PBS.

[0077] We added 100ug / ml of 15mM BSA in PBS for 10 minutes at room temperature.

[0078] Step 3: Reaction with anti-pan-HLA antibody. We added 25 μl of biotin anti-human HLA-A, B, C 1.6 μg / μl (Biolegend Cat. No. 311434) for 15 minutes at room temperature. We then washed three times with 200 μl of PBS, and the CHIPs were now ready for immunoprecipitation of MHC complexes.

[0079] Tumor sample preparation: Cells were detached with EDTA 4mM and washed once with PBS. Add 25ul of Igepal 1% in PBS + protease inhibitors. Centrifuge at 500xg for 10 minutes + 4°C. Centrifuge at 20000xg for 10 minutes + 4°C.

[0080] Optimized microfluidic pillar arrays The immunopurification step was performed within a single microfluidic chip by adding biotinylated pan-HLA antibodies to a solid support structure (i.e., a micropillar array) pre-functionalized with streptavidin, followed by immobilization of HLA on the solid surface coated with the pan-HLA antibodies.

[0081] In summary, nonstoichiometric thiol-ene (OSTE) polymer-based micropillar arrays were fabricated and biotinylated using UV-replica molding technology. The biotinylated micropillars were then functionalized with streptavidin, and a biotinylated pan-HLA antibody was added. Cell lysates were then directly loaded into the microfluidic chip to selectively capture HLA-I complexes. After extensive washing, the captured HLA-I complexes were eluted at room temperature by applying 7% acetic acid (Figure 1B). The protocol then followed a standard immunopeptidomics workflow, including purification of the eluted HLA peptides with SepPak®-C18 in acetonitrile and evaporating them to dryness using vacuum centrifugation.

[0082] We investigated the efficiency of streptavidin functionalization on micropillar arrays using fluorescent AlexaFluor 488-streptavidin for two different incubation times (15 min and 1 h). We found that the shorter incubation time was long enough to build up the first streptavidin layer (Figure 2A). Furthermore, to determine the effect of streptavidin concentration on the final amount of immobilized biotinylated pan-HLA antibody, we tested several concentrations of non-fluorescent streptavidin in the presence of a fixed amount of biotinylated pan-HLA antibody. In this case, the biotinylated pan-HLA antibody was incubated for 15 min, followed by three washing steps with PBS (200 μl each). To quantify the amount of immobilized biotinylated pan-HLA antibody at each streptavidin concentration, we titrated the immobilized biotinylated pan-HLA antibody using a fluorescently labeled AlexaFluor 488 secondary antibody. Interestingly, even a 10-fold increase in streptavidin concentration did not significantly affect the amount of immobilized biotinylated pan-HLA antibody (Figure 2B), likely due to steric hindrance limiting the number of available streptavidin binding sites. Based on this finding, we did not investigate further concentrations of streptavidin. However, to ensure maximum binding of biotinylated pan-HLA antibody, we used the highest streptavidin concentration tested (0.1 mg / mL) in all subsequent experiments. However, to further investigate the selectivity of antibody binding onto the streptavidin-functionalized micropillar surface, we investigated the effect of an additional coating step with bovine serum albumin (BSA) on the amount of immobilized biotinylated pan-HLA antibody, with the aim of eliminating nonspecific interactions. To this end, we pre-conditioned the micropillar arrays with BSA (100 µg / mL in 15 mM PBS, 10 min incubation) after streptavidin functionalization, and again determined the efficiency of subsequent binding of biotinylated pan-HLA antibodies using fluorescently labeled secondary antibodies. This procedure significantly reduced the amount of immobilized pan-HLA antibodies compared to non-pre-conditioned surfaces (Figure 2C), suggesting that a simple BSA pre-incubation step is sufficient to block nonspecific binding sites.Therefore, the BSA incubation step was adapted for all further experiments.

[0083] Finally, we attempted to characterize the maximum amount of immobilized biotinylated pan-HLA antibody that could be bound on a single chip using the optimized protocol. This was assessed by using multiple loading cycles of a new antibody batch at the same concentration (0.5 mg / mL) per single microfluidic chip. In this case, the amount of immobilized pan-HLA antibody was determined by comparing the amount of pan-HLA antibody in the feed solution with the amount of pan-HLA antibody in the output solution via an ELISA assay. We observed that the amount of immobilized antibody increased almost linearly with the number of loading cycles (Figure 2D), allowing for precise adjustment of the total amount of immobilized biotinylated pan-HLA antibody based on the number of loading cycles. After 7 cycles, the amount of immobilized antibody reached approximately 45 μg, and after 10 cycles, the amount of immobilized antibody reached approximately 45 μg. 9 For the investigation of 100 cells, 10 mg of pan-HLA (3.88 x 10) was prepared by the prior art method. 16 This is, at least theoretically, sufficient for immunopeptidome surveys of scarce biological material, since only specific antibodies (of the molecule) are required [22A] (Table 3).

[0084] The microchip device measures 1.74 x 10 14 The molecular weight of the antibody that can be immobilized is 4.5 x 10. 6 We were able to investigate the cells of

[0085] Microchip-based antigen enrichment implemented in an immunopeptidomics workflow allows identification of naturally presented HLA-I peptides To evaluate whether the developed thiol-ene microchip could be utilized as a platform for antigen discovery, we immunopurified HLA peptides from the human B-cell lymphoblastoid cell line JY. The JY cell line has high expression of class I HLAs, is homozygous for three common alleles in the human population (HLA-A*02:01, HLA-B*07:02, and HLA-C*07:02), and is widely adopted for ligandome analysis. Consequently, the JY cell line was considered to be an appropriate model for benchmarking and evaluating microchip-based antigen enrichment immunoprecipitation techniques.

[0086] Therefore, we immunoaffinity purified HLA-I complexes using thiol-ene microchips functionalized with pan-HLA antibody amounts as described above. Furthermore, to determine the sensitivity of our approach, we used 50 × 10 6 , 10×10 6 and 1 × 10 6 The protocol was validated by using a low total cell number of 50 × 10. Lysates were loaded into microchips and washed extensively with PBS. After that, peptides were eluted with 7% acetic acid and analyzed by tandem mass spectrometry. The entire workflow, from streptavidin functionalization to tumor peptide elution, took less than 24 hours on average. To generate data with high confidence, a stringent false discovery rate threshold of 1% was applied for peptide and protein identification. We performed a 50 × 10 6 , 10×10 6 and 1 × 10 6 We were able to identify 5589, 2100, and 1804 unique peptides from the cells, respectively (Fig. 3A).

[0087] We sought to carefully analyze the microchip technology's ability to enrich for natural HLA-I binders and avoid potential coelution contaminants. Therefore, we extensively characterized the eluted peptides. First, eluted peptides from the JY cell line represented a length distribution typical of a ligandome dataset, with 9-mers being the most enriched peptide species (Figure 3B-E). Next, we determined predicted binding affinities for two HLA-I alleles (HLA-A*0201 and HLA-B*0702) expressed in JY cells. Although JY cells also harbor low levels of the allele HLA-C*0702, this binding motif overlaps with that of HLA-A*0201 and HLA-B*0702, so we considered only these alleles in subsequent analyses.

[0088] Of the unique 9-mers, 78%, 83%, and 67% were 50 × 10 6 , 10×10 6 and 1 × 10 6 For cells, the peptides were predicted to be binders (listed as binders in NetMHCpan4.0, applying a rank of 2% [24A-26A]) to either the HLA-A*0201 or HLA-B*0702 allele (Figure 4A). Furthermore, Gibbs analysis was performed to identify consensus binding motifs for each HLA-I allele from the eluted 9-mer peptides. These clustered into two distinct groups, favoring reduced amino acid complexity for residues at positions P2 and Ω, and closely matched those known for HLA-A*0201 and HLA-B*0702 (Figure 4B).

[0089] Next, to determine the role of the identified peptides, we performed Gene Ontology (GO) term enrichment analysis on our list of proteins that supplied the 9-mer binders. We observed enrichment for nuclear and intracellular proteins, primarily proteins that interact with DNA, RNA, or are involved in catabolic activity. Finally, we set up an in vitro killing assay to further demonstrate the capability of the microchip technology in isolating peptides in complex with HLA-I. To this end, we selected a set of three peptides from our JY dataset to stimulate HLA-matched PBMCs, purified CD8+ T cells from the PBMCs, and employed them as effector cells in coculture with JY cells. Unstimulated PBMCs were used as a control to account for nonspecific cytotoxicity by the effector cells themselves. Real-time cell lysis was then monitored. Interestingly, CD8+ T cells pulsed with peptides QLVDIIEKV (SEQ ID NO:75; gene name PSME3) and KVLEYVIKV (SEQ ID NO:76; gene name MAGEA1) showed approximately 10% specific cytolysis, whereas CD8+ T cells pulsed with peptide ILDKKVEKV (SEQ ID NO:77; gene name HSP90AB3P) induced 15% specific cytolysis, demonstrating specific lysis in the presence of the defined peptides.

[0090] To assess the validity of our HLA-I peptide list identified by microchip technology, we consulted SysteMHC, a repository of immunopepidomics datasets generated by mass spectrometry. Of the unique 9-mer binders identified in our data, 69%, 77%, and 81% were within 50 × 10 6 , 10×10 6 and 1 × 10 6This was also found in a previously published ligandome dataset derived from the JY cell line (pride ID PXD000394) [3A] (Figure 6A). Furthermore, a positive correlation was confirmed between source protein abundance and HLA presentation, with the most abundant proteins being the primary source of HLA peptides.

[0091] Thus, these results demonstrated that the chip-based protocol can be utilized as a reliable immunoprecipitation platform within an immunopeptidomics workflow.

[0092] Using the device Application of sample to chip and subsequent elution of fractions for analysis We applied the sample in multiple cycles and incubated for 5 minutes (working at 4°C). We washed three times with 200 μl of PBS and aspirated the final wash to empty the chip. We prepared a solution of 50% MeOH and 50% MilliQ. Using this solution, we prepared a 7% solution of acetic acid. We applied this solution to the chip and collected fractions. The elution time was 5 minutes. On the same day, we purified the collected fractions. We prepared and labeled SepPak cartridges for each tissue sample and HLA-I peptide sample. Using a syringe and dedicated adapter, we first washed the cartridge once with 1 ml of 80% ACN in 0.1% TFA, then twice with 1 ml of 0.1% TFA. We loaded each of the biological samples into the SepPak cartridge. We slowly passed the biological sample through (at a rate of approximately 1 ml in 20 seconds). We washed the cartridge twice with 1 ml of 0.1% TFA. We eluted the HLA-binding peptides into a collection tube with 300 μl of 30% ACN in 0.1% TFA.

[0093] HEX (homology evaluation of heterologous peptides) Heterologous peptide homology / affinity evaluation (HEX) is a novel in silico platform that compares the similarity between tumor peptides (reference peptides) and pathogen-derived peptides (query peptides), such as viral peptides. HEX utilizes several metrics to expedite candidate peptide selection. It does this by incorporating both novel methods (peptide scoring and alignment scoring algorithms) and integrated existing methods (MHC-I binding prediction). HEX includes a large precompiled database of known proteins, including those from viral pathogens and the human proteome (33).

[0094] The associated scoring matrix was generated ad hoc based on the amino acid composition of the reference peptide, rather than experimentally. In particular, for rows of the matrix representing amino acid positions in the peptide and columns representing each of the 20 standard amino acids, the same high score was assigned to amino acid positions in the reference peptide, and the same low score was assigned to other positions.

[0095] Alignments are calculated pairwise between peptides in the query set against a reference set. For a given pair of peptides, their alignment is calculated by summing the distance scores between pairs of amino acids at the same position. Scoring is weighted to favor similarity between more centrally located amino acids in the peptides. HEX supports both BLOSUM and PAM substitution matrices across several evolutionary distances. Specifically, BLOSUM62 was the matrix of choice for this study.

[0096] MHC class I binding affinity predictions are performed using NetMHC

[27] via the IEDB application programming interface (http: / / tools.iedb.org / main / tools-api / ), and then parsed and collated within the tool. Users can specify a desired scoring method or several recommended results can be returned. Predictions for a large number of human and mouse MHC-I alleles are supported.

[0097] Users can select peptides based on their own criteria or have them selected by a random forest model. The random forest was trained on experimental results of peptides selected by the authors. Feature importance was determined by the out-of-bag (OOB) increase in mean squared error (MSE) and cross-validated against unknown samples of peptides. HEX was developed as a web application using the R package Shiny and is accessible without user registration at https: / / picpl.arcca.cf.ac.uk / hex / app / . The source code is available at https: / / github.com / whalleyt / hex.

[0098] In an alternative embodiment of the present invention, HEX supports both BLOSUM and PAM substitution matrices across several evolutionary distances. Specifically, BLOSUM62 was the matrix used in this study. HEX first performs a BLAST search using PAM30 to find hits (similar sequences) in the reference library. Then, HEX performs a pairwise refinement alignment using both BLOSUM62 and the substitution matrix developed by Kim et al.

[0099] Reference: Kim Y, Sidney J, Pinilla C, Sette A, Peters B. Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior.BMC Bioinformatics.2009;10:394.Published 2009 Nov 30.doi:10.1186 / 1471-2105-10-394.

[0100] MHC class I binding affinity predictions are performed using NetMHC4.0 (or NetMHCpan4.1) as a stand-alone command-line tool, and then parsed and collated within the tool. Users can specify the desired scoring method or return several suggested results. Predictions for multiple human and mouse MHC-I alleles are supported.

[0101] Patients and samples A total of 16 patients with stage IV metastatic melanoma were treated with anti-PD1 monoclonal antibodies at the Helsinki University Central Hospital (HUCH) Comprehensive Cancer Center. Patients were randomly selected to receive either nivolumab (n=7) infusions every 2 weeks or pembrolizumab (n=9) infusions every 3 weeks. The study was approved by the Helsinki University Central Hospital (HUCH) Ethics Committee (Dnro115 / 13 / 03 / 02 / 15). Written informed consent was received from all patients, and the study was conducted in accordance with the Declaration of Helsinki.

[0102] Peripheral blood samples (3 ml EDTA blood, 50 ml heparinized blood) were collected at three time points: before the start of treatment, 1 month after treatment, and 3 months after treatment. Plasma was separated from these samples by centrifugation and then stored at -70°C. CMV and EBV IgG levels were measured from thawed EDTA plasma samples using the VIDAS CMV IgG kit (BioMérieux, Marcy-l'Etoile, France) and the Siemens Enzygnost Anti-EBV / IgG kit (Siemens Healthcare Diagnostics, Marburg, Germany). Immunoglobulins (IgA, IgM, IgG) from thawed heparinized plasma were measured in a central laboratory (HUSLAB) at Helsinki University Central Hospital.

[0103] Cell lines and human samples The mouse melanoma cell line B16-F10 was purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA). Cells were cultured in RPMI (Gibco, Thermo Fisher Scientific, US) containing 10% fetal bovine serum (FBS) (Life Technologies), 1% Glutamax (Gibco, Thermo Fisher Scientific, US), and 1% penicillin and streptomycin (Gibco, Thermo Fisher Scientific, US) at 37°C / 5% CO.

[0104] The cell line B16-OVA, a mouse melanoma cell line engineered to constitutively express chicken ovalbumin (OVA), was kindly provided by Professor Richard Vile (Mayo Clinic, Rochester, MN, USA). These cells were cultured at 37°C / 5% CO2 in RPMI low glucose (Gibco, Thermo Fisher Scientific, US) containing 10% FBS (Gibco, Thermo Fisher Scientific, US), 1% Glutamax, 1% penicillin and streptomycin (Gibco, Thermo Fisher Scientific, US), and 1% Geneticin (Gibco, Thermo Fisher Scientific, US).

[0105] All cells were tested for mycoplasma contamination using a commercially available detection kit (Lonza-Basel, Switzerland). Isolated human PBMCs were frozen in FBS supplemented with 10% DMSO and then kept in liquid nitrogen until use. Cryopreserved PBMCs were thawed and incubated overnight at 37°C / 5% CO2 in complete RPMI medium supplemented with 10% FBS, 1% Glutamax, and 1% penicillin-streptomycin. PBMCs were then seeded for ELISPOT.

[0106] peptide All peptides used in this study were purchased from Zhejiang Ontores Biotechnologies Co. (Zhejiang, China) with a purity of 5 mg >90%. The sequences of all peptides used in this study are shown in Tables 1 and 2.

[0107] Preparation of PeptiCRAd All PeptiCRAd complexes described in this study were prepared by mixing adenovirus and polyK-tailed peptides according to the following protocol: 1 × 10 9The vp (viral particles) were mixed with 20 μg of poly-K-tailed peptide (resuspended in water). After vortexing, the mixture was incubated at room temperature for 15 minutes. After incubation, PBS was added sequentially up to the injection volume (50 μL / mouse), and the solution was vortexed again and used for assays or animal injections. For TRP2-PeptiCRAd, 1x10 9 6K-TRP2 with 20ug of vp 180~188 The Viral-PeptiCRAd was mixed with the peptide, while the Viral-PeptiCRAd was mixed with the TRP2 180~188 1x10 mixed with 5ug of each viral 6K peptide homologous to 9 vp was used.

[0108] Fresh PeptiCRAds were prepared before each experiment using fresh reagents. All dilutions of virus and peptides required prior to incubation for PeptiCRAd preparation were performed in sterile PBS or water. PeptiCRAds were then diluted in the buffer required by the assay.

[0109] Viruses were generated, propagated, and characterized as described elsewhere

[20] .

[0110] Animal experiments and ethical approval All animal experiments were reviewed and approved by the Laboratory Animal Committee of the University of Helsinki and the Provincial Government of Southern Finland.

[0111] All experiments were performed using C57BL / 6JOlaHsd mice obtained from Scanbur (Karlslunde, Denmark).

[0112] For the immunization experiments, 8-9 week-old immunocompetent female C57BL / 6J mice were divided into four groups. N = 3 mice were used as the mock group, and n = 7 mice were used to form each of the three different treatment groups. Each treatment group was vaccinated with a different heterologous peptide. Mice were vaccinated twice, injected at the base of the tail with 40 μg of peptide and 40 μg of adjuvant (VacciGrade poly(I:C)-Invivogen) in a final injectable volume of 100 μl, at a weekly interval (days 0 and 7). Naive mice (injected with PBS) were used as the mock group. On day 14, mice were injected with 3 × 10 vaccinated mice in the right flank. 5 B16-OVA cells were injected and tumor growth was followed until the endpoint was reached.

[0113] For the treatment of established tumors, two different tumor cell lines were tested: B16-OVA cells and the more aggressive B16F10 cells. 5 1 x 10 B16-OVA cells and 1 x 10 5 B16-F10 was injected subcutaneously into the right flank of 8-9 week-old immunocompetent female C57BL / 6J mice. These mice were then randomly divided into four groups of 7-8 mice for each tumor cell line. The mock group was treated with PBS, the second group was treated with uncoated adenovirus, and the third group was treated with TRP2. 180~188 The first group was treated with adenovirus coated with a TRP2-homologous viral peptide (Viral-PeptiCRAd), and the last group was treated with adenovirus coated with a TRP2-homologous viral peptide (Viral-PeptiCRAd).

[0114] Mice were treated intratumorally twice, with injections administered 2 days apart (days 10 and 12 after tumor engraftment), and tumor growth was followed until the endpoint was reached. The median tumor volume measurement on the final day identifies the treatment success threshold, shown as a dotted line. Mice with tumor volumes below the threshold at the endpoint were considered responders, while mice exceeding the threshold were considered non-responders.

[0115] Tumor growth was tracked with digital calipers measuring the two dimensions of the tumor, and volume was then mathematically calculated according to the following formula: ((long side) x (short side) 2 ) / 2 In all experiments, tumors were measured every 2 or 3 days until tumor size reached the maximum allowed, then mice were sacrificed and spleens were harvested.

[0116] ELISpot assay To assess the amount of active antigen-specific T cells, interferon-γ (IFN-γ) secretion was measured by ELISPOT assay from IMMUNOSPOT (CTL, Ohio USA) for mouse IFN-γ and by MABTECH (Mabtech AB, Nacka Strand, Sweden) for human IFN-γ.

[0117] Fresh mouse splenocytes collected at the end of the experiment were used. The procedure was performed according to the manufacturer's instructions. Briefly, for mouse IFN-γ, 3 × 10 5 Spleen cells / well were seeded on day 0. Cells were stimulated with 2 μg / well of peptide. After 3 days of incubation at 37°C / 5% CO2, plates were developed according to the kit protocol.

[0118] For human IFN-γ ELISPOT, human PBMCs were thawed and incubated overnight at 37°C / 5% CO in complete medium. The next day, 3 × 10 5 PBMCs / well were seeded and stimulated with 2 μg / well of peptide. After 48 hours of incubation at 37°C / 5% CO2, plates were developed according to the manufacturer's protocol. Plates were sent to CTL-Europe GmbH for analysis.

[0119] Cell lines and reagents The EBV-transformed human lymphoblastoid B-cell line JY (ECACC HLA-type collection, Sigma Aldrich) was cultured in RPMI 1640 (GIBCO, Invitrogen, Carlsbad, CA, USA) supplemented with 1% GlutaMAX (GIBCO, Invitrogen, Carlsbad, CA, USA) and 10% heat-inactivated fetal bovine serum (HI-FBS, GIBCO, Invitrogen, Carlsbad, CA, USA).

[0120] Streptavidin (Streptomyces avidinii, affinity purified and lyophilized from 10 m m potassium phosphate, ≥ 13 U / mg protein) was purchased from Sigma-Aldrich (Saint Louis, Missouri, USA).

[0121] Biotin-conjugated anti-human HLA-A, B, C clone w 6 / 32 was purchased from Biolegend (San Diego, CA, USA) for analysis.

[0122] The following peptides were purchased from Ontores Biotechnologies Co., Ltd. and used throughout this study: KVLEYVIKV (SEQ ID NO: 75; gene name MAGE A1), ILDKKVEKV (SEQ ID NO: 76; gene name HSP90), and QLVDIIEKV (SEQ ID NO: 77; gene name PSME3).

[0123] Additionally, the following peptides were purchased from Chempeptide (Shangai, China): VIMDALKSSY (SEQ ID NO: 78; gene name NNMT), FLAEGGGVR (SEQ ID NO: 79; gene name FGA) and EVAQPGPSNR (SEQ ID NO: 80; gene name HSPG2).

[0124] Ovarian tumor biopsy and ethical considerations Ovarian tumor biopsies were collected from patients with ovarian metastatic tumors (high-grade serous) who signed informed consent under a study approved by the Research Ethics Committee of the Northern Savo Hospital District under approval number 350 / 2020. Samples were minced into small pieces and treated with a digestion buffer containing 1 mg / ml collagenase type D (Roche), 100 μg / ml hyaluronidase (Sigma-Aldrich), and 1 mg / ml DNase I (Roche) at 37°C for 1 hour. The cell suspension was then passed sequentially through 500 μm and 300 μm cell strainers (pluriSelect) to obtain single cells.

[0125] Renal cell carcinoma and bladder tumor samples and ethical considerations Patient tissue samples for organoid culture were obtained from the DEDUCER study (Development of Diagnostics and Treatment of Urological Cancers) at Helsinki University Central Hospital under approval number HUS / 71 / 2017, 26.04.2017, ethics committee approval number Dnro154 / 13 / 03 / 02 / 2016, and with patient consent. Kidney samples were obtained from a nephrectomy of an adult male with clear cell renal cell carcinoma (ccRCC, pTNM stage pT3aG2). Benign kidney tissue samples were used for the experiment. Urothelial carcinoma (bladder cancer, high-grade, grade III, 1 x 1 cm) was obtained from an adult female, and cancerous tissue samples were used for organoid culture.

[0126] Culture of clear cell renal carcinoma and bladder tumor organoids Cells were isolated from the original tissue immediately after surgery by dissociating the tissue into small pieces and treating it with collagenase (40 units / ml) for 2–4 hours. Renal benign and cancerous cells from patients with clear cell renal cell carcinoma were grown as organoids in F medium [3:1 (v / v) F-12 nutrient mixture (Ham)-10% Matrigel (Corning)] supplemented with DMEM (Invitrogen), 5% FBS, 8.4 ng / mL cholera toxin (Sigma), 0.4 μg / mL hydrocortisone (Sigma), 10 ng / mL epidermal growth factor (Corning), 24 μg / mL adenine (Sigma), 5 μg / mL insulin (Sigma), 10 μM ROCK inhibitor (Y-27632, Enzo Life Sciences, Lausen, Switzerland), and 1% penicillin-streptomycin. Bladder tumor-derived organoids were grown in hepatocyte calcium medium (Corning) [15A] supplemented with 5% CSFBS (Thermo Fisher Scientific), 10 μM Y-27632 RHO inhibitor (Sigma), 10 ng / mL epidermal growth factor (Corning), 1% GlutaMAX (Gibco), 1% penicillin-streptomycin, and 10% Matrigel (Corning). 6 × 10 6 Cells were collected by centrifugation, washed in PBS to remove Matrigel, and flash frozen before analysis.

[0127] HLA typing Clinical HLA typing of tumor samples (ccRCC and bladder) was performed by the European Federation for Immunogenetics (EFI)-accredited HLA laboratory at the Finnish Red Cross Blood Service. Allele determination of the three classical HLA-I genes, HLA-A, -B, and -C, was performed by targeted PCR-based next-generation sequencing (NGS) technology (NGSgo® Workflow, GenDx, Utrecht, The Netherlands) according to the protocol provided by the manufacturer.

[0128] Allele assignment at the 4-division level was performed by NGSengine version: 2.11.0.11444 (GenDx, Utrecht, Netherlands) using the IPD IMGT / HLA database release 3.33.0, https: / / www.ebi.ac.uk / ipd / imgt / hla / .

[0129] Flow cytometry analysis To analyze cell surface expression of HLA-A2 and HLA-A, B, and C, the following antibodies were used: PE-conjugated anti-human HLA-A2 (clone BB7.2, BioLegend 343306, San Diego, CA, USA), PE-conjugated anti-human HLA-A, B, and C (clone W6 / 32, BioLegend 311406, San Diego, CA, USA), and Human TruStain FcX block (BioLegend B247182, San Diego, CA, USA).

[0130] Data were acquired using a BDLSR FORTESSA Flow Cytometer. Flow cytometry analysis of renal cell carcinoma and bladder tumor-derived organoids was performed using a BD Accuri 6 plus (BD Biosciences) and analyzed with FlowJo software (Tree Star, Ashland, OR, USA).

[0131] result Development of the Homology Evaluated Heterologous Peptides (HEX) tool to identify virus- and tumor-derived peptides with high molecular mimicry To study whether molecular mimicry between viruses and tumors can affect tumor growth, we needed to identify peptides that share a high degree of homology / affinity. However, to this extent, tools to facilitate the identification of suitable targets are lacking. Therefore, we developed HEX (Homology Evaluation of Xenopeptides), a device that compares input sequences with a database of pathogen-derived antigen / peptide or protein sequences and selects highly homologous candidate pairs of peptides based on at least two of the following three criteria: 1) a B score, which corresponds to the likelihood that the peptide will be recognized by a given TCR; 2) a position-weighted alignment score, which prioritizes similarities in the region of interaction with the TCR; and 3) predicted MHC class I binding affinity (Figure 5A). Alternatively, traditional software can be used, as shown in Figure 5B and described above.

[0132] We investigated the efficacy of three melanoma-associated antigens that have been successfully applied in many vaccination studies: TRP2 180~188 (tyrosinase-related protein 2), GP100 25~33 (also known as PMEL; premelanosome protein) and TYR1 208~216 We started with TYR1 (tyrosinase 1). 208~216 was not predicted to be a binder of mouse MHC and was therefore considered an "inappropriate target." Using HEX, we identified viral peptides that shared a high degree of homology / affinity with the input tumor epitopes. Pools of four virus-derived peptides per each original tumor epitope (Table 1) were selected for further evaluation in vivo.

[0133] Antiviral immunity controls tumor growth through molecular mimicry of tumor antigens To assess whether viral peptides influence tumor growth, we decided to mimic viral infection by immunizing C57BL6 mice with selected viral peptide pools, followed by tumor engraftment (Figure 6A). All immunized mice showed reduced tumor growth compared to mock. Furthermore, significant differences were observed between treatment groups. Tumor growth was most reduced in mice immunized with viral pools of peptides homologous to TRP2 or gp100 (Figure 6B).

[0134] To further investigate the contribution of selected viral peptides to tumor growth reduction, we collected mouse splenocytes for ELISpot assays at the endpoint (Fig. 6C). Splenocytes from mice immunized with viral peptide pools homologous to TRP2 or gp100 promoted higher IFN-γ responses compared with splenocytes from mice immunized with a control TYR1-homologous epitope, correlating with tumor growth outcomes.

[0135] We further examined the reactivity of these splenocytes obtained from virus-preimmunized mice to their cognate tumor antigens (Figure 7A). In a side-by-side comparison, only viral peptides homologous to TRP2 exhibited higher IFN-γ secretion compared with their cognate tumor peptides (Figure 7B). Furthermore, we retrospectively evaluated the affinity of each tumor antigen and their homologous viral pools for C57BL / 6J MHC class I molecules (Figure 7C) and observed a significant correlation with their respective abilities to stimulate IFN-γ secretion (Figure 7D). Following the guidelines of the Immune Epitope Database (IEDB), we used a threshold of 50 nM to separate peptides into high and low affinity (http: / / tools.iedb.org / mhci / help / ) and observed that peptides with high MHC-I affinity promoted greater IFN-γ production compared with peptides with low MHC-I affinity (Figure 7E). Collectively, these results validate the predictive efficiency of the HEX tool for identifying homologous peptides and highlight that molecular mimicry between viruses and tumors plays a role in antitumor immune responses via cross-reactive T cells.

[0136] Viral epitopes sharing high similarity with tumor epitopes reduce the growth of established melanoma in vivo We demonstrated that molecular mimicry between viral and tumor antigens can affect tumor growth in pre-immunized mice. Next, we wanted to evaluate whether responses induced by molecular mimicry could affect already established tumors in naive mice. To this end, we implanted B16OVA tumors or the more aggressive and immunosuppressive B16F10 tumors into mice and subsequently treated them with PeptiCRAd (20), a vaccine platform previously developed to mimic viral infection (Figure 8A). Briefly, PeptiCRAd is a vaccine technology consisting of an adenovirus coated with an MHC-I-restricted peptide bearing a positively charged poly-amino acid tail. In this case, we proceeded with the most effective pool of peptides obtained from the initial in vivo experiments. The original TRP2 180~188 We prepared vaccines by coating adenovirus with the epitope (TRP2-PeptiCRAd) or the corresponding pool of virus-derived peptides mimicking TRP2 (Viral PeptiCRAd). Intratumoral injection of PeptiCRAd significantly reduced tumor progression compared with treatment with saline buffer or uncoated virus for both B16OVA (Figure 8B-C) and B16F10 tumors (Figure 8E-F). Mice treated with the original tumor antigen or viral homologues showed significantly higher numbers of responders in both tumor models (Figure 8D, 8G). In both tumor models, we observed a significant reduction in tumor growth when mice were treated with viral peptides mimicking the tumor peptides, again suggesting that molecular mimicry may play a fundamental role in antitumor immunity by driving cross-reactive T cells.

[0137] Testing whether molecular mimicry between CMV and tumor antigens could explain better prognosis We selected a pool of melanoma-associated proteins that were compared with the CMV proteome using HEX, which generates a list of tumor peptides with high similarity to CMV

[21] (Table 2). Using tumor peptides and their CMV counterparts in ELISPOT assays, we demonstrated that in responder patients, PBMCs consistently responded to both the virus and their corresponding tumor antigens, suggesting that CMV infection expands viral T cell clones capable of attacking and killing tumor cells, and that seropositive patients are more likely to respond to melanoma-specific epitopes similar to CMV (Figures 9A and 9B). Frustratingly, this observation had the limitation that it did not directly indicate whether viral and tumor peptides expanded the same T cell clones.

[0138] A novel microfluidic chip-based platform identifies immunopeptidome profiles in scarce tumor biopsy tissue We validated this platform for the investigation of scarce tumor biopsies. To this end, we collected an ovarian metastatic tumor (high-grade serous) from a patient. Four sections were obtained from the tumor margin (S1, S2, S3, and S4), and a central section of the tumor was also collected (S5). The samples were then weighed and averaged to determine sizes ranging from 0.01 g to 0.06 g, as summarized in Figure 10A. After sample digestion, the resulting single-cell suspension was lysed and processed through a microchip. Applying a stringent false discovery rate threshold of 1% for peptide and protein identification, we identified 916, 695, 172, 1128, and 256 unique peptides in S1, S2, S3, S4, and S5, respectively (Figure 10A). Consistent with a typical ligandome profile, a general enrichment (>70%) was observed in 7- to 13-mer specimens (Figure 10A). The amino acid length distribution, in terms of absolute number and percentage, showed that 9-mer analytes were most represented (Figure 10B), confirming our and others' previous immunopeptidome analyses. Next, we applied gene ontology (GO) enrichment analysis to further investigate the source proteins found in our data. Consistent with the typical ligandome profile, metabolic processes were enriched in all examined samples. Furthermore, this analysis revealed an increase in skin development pathway proteins, consistent with the epithelial nature of the ovarian serous tumors analyzed here. Overall, these results highlight the feasibility of utilizing the developed microfluidic chip platform to analyze scarce tumor biopsies and obtain personalized antigenic peptides for tumor treatment.

[0139] Microchip-based protocol reveals immunopeptidome landscape in patient-derived organoids Only 6 x 10 patient-derived organoids (PDO) 6We validated the microchip technology with 100 cells. We selected two patients from an ongoing personalized medicine study for urological cancers: a nephrectomy sample containing both benign and cancerous tissue from a patient with clear cell renal cell carcinoma (ccRCC) and a 1 x 1 cm sample from a patient with bladder cancer, and further processed them as 3D primary organoid cultures.

[0140] Applying the developed microchip technology and a stringent 1% false discovery rate threshold for peptide and protein identification, we were able to identify a total of 576 and 2,089 unique peptides in ccRCC and bladder PDO, respectively (Figure 11A). The number of recovered peptides differed between the two samples, with bladder samples yielding more peptides than ccRCC samples. It is well known that HLA expression influences the amount of isolated HLA-I peptides [22A]. Consistent with this, flow cytometry analysis revealed higher surface levels of HLA-A, HLA-B, and HLA-C in our bladder samples than in our ccRCC samples, explaining the different yields of peptides recovered from our samples. Peptide analysis showed a preference for 9-mer to 12-mer peptides (56.4% in ccRCC and 47.9% in bladder tumors), and enrichment of the 9-mer population, consistent with the length distribution typical of ligandome analysis

[33] (Figure 11B). To identify HLA-I binders and contaminants, we performed Gibbs clustering and NetMHC4.0 analysis. First, 9-mer analysis showed that 55% and 67% of the bladder and ccRCC PDOs, respectively, matched at least one of the patient's HLA alleles. Next, we applied NetMHC4.0 to all 9-mers identified in our datasets. Of these, 46% and 69% were predicted to be binders of the patient's specific HLA. Next, we investigated the source proteins present in both our datasets. To this end, we performed gene ontology (GO) enrichment analysis. Consistent with our previous observations and published data, both samples showed enrichment for intracellular and nuclear proteins that interact with RNA and are involved in catabolic / metabolic processes.

[0141] To demonstrate that this technology can be utilized for the rapid development of therapeutic cancer vaccines, we performed a killing assay. We focused our analysis on ccRCC samples. We used the transcriptome level to select tumor antigen candidates using PBMCs and healthy kidney tissue as reference sets. We then pulsed selected peptides into PBMCs from healthy volunteers, and CD8+ T cells isolated from these cells were used in the assay. T cells pulsed with the peptide EVAQPGPSNR (gene name HSPG2) showed approximately 10% specific cell lysis.

[0142] Finally, we investigated recall T cell responses in ccRCC patients. To this end, unfractionated PBMCs from patients were stimulated in vitro with the peptide EVAQPGPSNR, while unstimulated PBMCs served as a control. The induced CD8+ T cells were then added to ccRCC PDOs and showed an approximately 7% increase in killing activity compared to the control group.

[0143] Peptides derived from patient organoids have been shown to be therapeutically effective in in vivo studies The list of peptides was analyzed with HEX. First, the software prioritized peptides that were simultaneous strong binders (cutoff IC50 range 50 nM-500 nM according to NetMHC4.0) and showed higher weighted alignment scores (normalized weighted alignment score cutoff 0.8-1). This latter focuses on peptide similarity in the region of interaction most likely to engage the TCR of CD8+ T cells to induce a mediated immune response. The resulting peptides were then analyzed by their overall percentage identity to pathogen-derived antigens / peptides and IC 50 Further classification was performed based on the binding affinity score. The final output consisted of 13 peptides along with their corresponding pathogenic peptides (Table 4).

[0144] To determine peptide immunogenicity, mice were pre-immunized with each single peptide subcutaneously in the presence of the adjuvant Poly(I:C), and groups of mice were injected with either Poly(I:C) alone or saline as a control. Splenocytes from the mice were harvested and tested for IFNγ production upon specific stimulation in an ELISpot assay (Table 5).

[0145] We next sought to verify whether candidate peptides could be used as cancer vaccines for the treatment of established tumors. To this end, we employed our previously developed cancer vaccine platform, PeptiCRAd, which combines an oncolytic adenovirus with a capsid attached to a polylysine-modified peptide (via a polylysine linker). The adenovirus used here was VALO-mD901, genetically engineered to express murine OX40L and CD40L, and previously shown to control tumor growth and induce a systemic antitumor response in a mouse melanoma model. Therefore, Balb / c mice were subcutaneously injected with the syngeneic tumor model CT26 in the left and right flanks (day 0, Figure 12A). Once tumors were established (day 7, Figure 12A), Valo-mD901 was coated with each polylysine-modified peptide pair in our list (PeptiCRAd1, PeptiCRAd2, PeptiCRAd3, Table 6) and injected intratumorally into the right tumor only. PeptiCRAd4 consisted of Valo-mD901 coated with gp70423-431 (AH1-5). Mock and Valo-mD901 groups were also used as controls. PeptiCRAd1 and PeptiCRAd2 improved not only tumor growth control but also Valo-mD901 in injected lesions (Figure 12B, right panel). Advantageously, PeptiCRAd1 improved antitumor growth control in untreated tumors, in contrast to Valo-mD901, which did not elicit an effect.

[0146] summary HEX is a bioinformatics tool that analyzes peptide sequences and compares them to curated databases of pathogen-derived viral proteomes looking for sequences with high similarity.

[0147] Because presented peptides are primarily involved in interactions with anchor residues of the MHC

[25] , our software returns positionally weighted alignment scores to prioritize similarities in the central part of the peptide, i.e., the position most involved in interactions with the TCR

[26] . Furthermore, to increase the likelihood that our target is a presented epitope, in one example, we ranked the resulting peptides according to their binding affinity to the selected MHC using the IEDB NetMHC prediction tool API

[27] or the standalone tools NetMHC4.0 or NetMHCpan4.1b.

[0148] We used HEX to identify viral peptides homologous to three extensively characterized tumor-associated antigens.

[0149] We found that pre-immunization with our selected pool of viral peptides simulated an antiviral immune state before tumor establishment and efficiently slowed the growth of subcutaneously injected melanoma tumor cells in mice, indicating that prior exposure to virus-derived peptides can affect tumor growth.

[0150] Next, we observed that tumor-homologous virus-derived peptides had similar effects on already established tumors when administered during tumor progression, indicating that viral infections occurring during tumor progression can still affect tumor growth.

[0151] Taken together, our results suggest that molecular mimicry between virus- and tumor-derived antigens can exert control over tumor growth independent of pre-exposure or pre-acquired immunity.

[0152] Novel ICPIs have significantly improved patient survival in several solid tumors, particularly metastatic melanoma, compared with other commonly used treatments such as radiation and chemotherapy. However, despite improved survival and effective response rates, the reasons why some patients do not benefit from ICPI therapy remain unknown. Our results showed that patients with high CMV-specific IgG titers had significantly longer progression-free survival.

[0153] We observed that PBMCs from patients with high anti-CMV IgG titers reacted with melanoma antigens similar to CMV peptides. This data indicates that CMV seropositivity contributes to melanoma-specific T cell immunity and thus provides a clinical benefit for these patients receiving ICPI therapy.

[0154] Here, we demonstrate that viral infection can affect tumor growth and elimination. Furthermore, we demonstrate cross-reactivity of cytotoxic T cells against virus-derived antigens selected using HEX and homologous tumor-derived antigens. Based on our results, we conclude that molecular mimicry could be exploited in the future to develop novel therapeutic approaches or, when combined with immunotherapy, potentially enhance the antitumor immune effects achieved by these therapies.

[0155] Furthermore, reliable identification of tumor peptides that bind to HLA-I is necessary to design effective tumor rejection and protection strategies. Direct identification of peptides from HLA-I complexes remains the best method. Nevertheless, the immunopeptidomics workflow is relatively complex and therefore represents a major bottleneck in the antigen discovery process. Currently, the inability to analyze the immunopeptidome from small amounts of biological material (e.g., tissue needle biopsies), sample throughput, cost, and the affinity matrix employed in conventional platforms (which are labor-intensive and expensive to manufacture) are presented as major technical challenges that need to be addressed.

[0156] In this study, we addressed several technical issues that hinder ligandome studies, focusing primarily on the limited availability of materials to analyze, the cost of consumables and lengthy protocols.

[0157] By utilizing the well-characterized biotin-streptavidin interaction to immobilize biotinylated pan-HLA antibodies on streptavidin-functionalized surfaces, the inventors were able to replace conventional techniques based on affinity matrices prepared via cross-linking reactions using a microchip platform. The constraints imposed by material scarcity (e.g., needle biopsies) inspired research into implementing microfluidic protocols. In this study, the inventors used a custom-tailored microchip protocol containing a thiol-ene polymer-based micropillar array as a solid support for further biofunctionalization, allowing the entire IP procedure to be performed on a single microfluidic chip.

[0158] Because JY cells were killed in a specific CD8+ T cell-dependent manner, validation of the identified peptides in an in vitro killing assay confirmed that the peptides identified in the present invention were indeed presented on the JY cell surface. The peptide ILDKKVEKV (SEQ ID NO:3) found in our dataset elicited a higher percentage of specific cell lysis.

[0159] Thus, our approach provides an underexplored tool for immunoaffinity purification.

[0160] References 20.C.Capasso,M.Hirvinen,M.Garofalo,D.Romaniuk,L.Kuryk,T.Sarvela,A.Vitale,M.Antopolsky,A.Magarkar,T.Viitala,T.Suutari,A.Bunker,M.Yliperttula,A.Urtti,V.Cerullo,Oncolytic adenoviruses coated with MHC-I tumor epitopes increase the antitumor immunity and efficacy against melanoma,Oncoimmunology 5,e1105429(2016)。

[0161] 21.D.Weinstein,J.Leininger,C.Hamby,B.Safai,Weinstein 2014 Diagnostic and Prognostic biomarkers in melanoma,J.Clin.Aesthet.Dermatol.7,13-24(2014)。

[0162] 25.J.Sidney,E.Assarsson,C.Moore,S.Ngo,C.Pinilla,A.Sette,B.Peters,Quantitative peptide binding motifs for 19 human and mouse MHC class i molecules derived using positional scanning combinatorial peptide libraries,Immunome Res.4,2(2008)。

[0163] 26.A.K.Sharma,J.J.Kuhns,S.Yan,R.H.Friedline,B.Long,R.Tisch,E.J.Collins,Class I Major Histocompatibility Complex Anchor Substitutions Alter the Conformation of T Cell Receptor Contacts,J.Biol.Chem.276,21443-21449(2001)。

[0164] 27.M.Andreatta,M.Nielsen,Gapped sequence alignment using artificial neural networks:Application to the MHC class i system,Bioinformatics 32,511-517(2016)。

[0165] 28.P.C.Rosato,S.Wijeyesinghe,J.M.Stolley,C.E.Nelson,R.L.Davis,L.S.Manlove,C.A.Pennell,B.R.Blazar,C.C.Chen,M.A.Geller,V.Vezys,D.Masopust,Virus-specific memory T cells populate tumors and can be repurposed for tumor immunotherapy,Nat.Commun.10,567(2019)。

[0166] 33.S.Giguere,A.Drouin,A.Lacoste,M.Marchand,J.Corbeil,F.Laviolette,MHC-NP:Predicting peptides naturally processed by the MHC,J.Immunol.Methods 400-401,30-36(2013)。

[0167] 3A.Bassani-Sternberg M,Pletscher-Frankild S,Jensen LJ,Mann M.Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation.Mol Cell Proteomics.2015;14(3):658-73。

[0168] 15A.Lee SH,Hu W,Matulay JT,Silva MV,Owczarek TB,Kim K,et al.Tumor Evolution and Drug Response in Patient-Derived Organoid Models of Bladder Cancer.Cell.2018;173(2):515-28 e17。

[0169] 22A.Purcell AW,Ramarathinam SH,Ternette N.Mass spectrometry-based identification of MHC-bound peptides for immunopeptidomics.Nat Protoc.2019;14(6):1687-707。

[0170] 24A.Jurtz V,Paul S,Andreatta M,Marcatili P,Peters B,Nielsen M.NetMHCpan-4.0:Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.J Immunol.2017;199(9):3360-8。

[0171] 25A. Nielsen M, Andreatta M. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med. 2016;8(1):33.

[0172] 26A. Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, Lund O, et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics. 2009;61(1):1 - 13. [Table 1] [Table 2] [Table 3] [Table 4] [Table 5] [Table 6]

Claims

1. 1. A method for tumor-specific antigen identification, comprising: i) dissolving or suspending a tumor sample in a fluid; ii) passing the fluid through a microfluidic device for tumor-specific antigen identification, the device comprising at least one flow-through channel containing a plurality of micropillars arranged in an array having attached thereto at least one molecule or at least one complex to which at least one anti-major histocompatibility complex antibody or at least one anti-pan-human leukocyte antigen antibody is bound, wherein the at least one antibody can be used to extract peptide:major histocompatibility complex (pMHC) molecules from the sample flowing through the channel; iii) allowing at least one pMHC in said sample to bind to said at least one antibody; iv) removing the at least one bound pMHC of part iii) from the device; v) comparing said peptide of said bound pMHC to a library of pathogen-derived antigens to determine whether said peptide exhibits homology / affinity with at least one pathogen antigen or portion thereof, and if there is greater than 90% homology / affinity; vi) identifying said peptide as a tumor-specific antigen for use in cancer therapy; A method comprising:

2. 10. The method of claim 1, wherein the library of pathogen-derived antigens comprises a curated library of known pathogen antigens.

3. The method of claim 1 or 2, wherein the pathogenic antigen is a human pathogenic antigen.

4. The method according to any one of claims 1 to 3, wherein the pathogenic antigen is a virus.

5. The pathogenic antigens include any combination of the following: Abyssoviridae; Ackermannviridae; Actantavirinae; Adenoviridae; Agantavirinae; Aglimvirinae; Alloherpesviridae; Alphaflexiviridae; Subfamily Alphaherpesvirinae; Alphairidovirinae; Alphasatellitidae; Alphatetraviridae; Alvernaviridae; Amalgaviridae; Amnoonviridae; Ampullaviridae; Anelloviridae oviridae; Arenaviridae; Arquatrovirinae; Arteriviridae; Altoviridae; Ascoviridae; Asfarviridae; Aspiviridae; Astroviridae; Autographivirinae; Absinthe viroidae sunviroidae); Avulavirinae; Bacilladnaviridae; Baculoviridae; Barnaviridae; Bastillevirinae; Bclasvirinae; Belpaoviridae; Benyviridae; Betaflexiviridae;Subfamily: Betaherpesvirinae; Betairidovirinae; Family: Bicaudaviridae; Family: Bidnaviridae; Family: Birnaviridae; Family: Bornaviridae; Family: Botourmiaviridae; Subfamily: Brockvirinae; Family: Bromoviridae; Subfamily: Bullaviridae virinae); Caliciviridae; Calvusvirinae; Carmotetraviridae; Caulimoviridae; Ceronivirinae; Chebruvirinae; Chordopoxvirinae; Chrysoviridae; Chuviridae; Circoviruses Family (Circoviridae); Family Clavaviridae; Family Closteroviridae; Subfamily Comovirinae; Family Coronaviridae; Family Corticoviridae; Subfamily Crocarterivirinae; Family Cruliviridae; Subfamily Crustonivirinae; Subfamily Cvivirinae; Cyst Family Cystoviridae; Subfamily Dclasvirinae; Family Deltaflexiviridae; Subfamily Densovirinae; Family Dicistroviridae; Family Endornaviridae; Subfamily Entomopoxvirinae; Subfamily Equarterivirinae; Subfamily Eucampyvirinae;Family: Euroniviridae; Family: Filoviridae; Family: Fimoviridae; Subfamily: Firstpapillomavirinae; Family: Flaviviridae; Family: Fuselloviridae; Family: Gammaflexiviridae; Subfamily: Gammaherpesvirinae; Subfamily: Geminialp hasatellitinae; Geminiviridae; Genomoviridae; Globuloviridae; Gokushovirinae; Guernseyvirinae; Guttaviridae; Hantaviridae; Hepadnaviridae; Hepeviridae; Herrellvirus Family (Herelleviridae); Subfamily Heroarterivirinae; Family Herpesviridae; Subfamily Hexponivirinae; Family Hypoviridae; Family Hytrosaviridae; Family Iflaviridae; Family Inoviridae; Family Iridoviridae; Subfamily Jasinkavirinae; Kitavirus Family: Kitaviridae; Family: Lavidaviridae; Family: Leishbuviridae; Subfamily: Letovirinae; Family: Leviviridae; Family: Lipothrixviridae; Family: Lispiviridae; Family: Luteoviridae; Family: Malacoherpesviridae; Subfamily: Mantavirinae;Family Marnaviridae; Family Marseilleviridae; Family Matonaviridae; Subfamily Mccleskeyvirinae; Subfamily Mclasvirinae; Family Medioniviridae; Family Medionivirinae; Family Megabirnaviridae; Family Mesoniviridae; Family Metaparamiviridae Subfamily Metaparamyxovirinae; Family Metaviridae; Family Microviridae; Family Mimiviridae; Family Mononiviridae; Subfamily Mononivirinae; Family Mymonaviridae; Family Myoviridae; Family Mypoviridae; Family Nairoviridae; Subfamily Nanoalphasatellinae (N anoalphasatellitinae; Nanoviridae; Narnaviridae; Nclasvirinae; Nimaviridae; Nodaviridae; Nudiviridae; Niamiviridae; Nymbaxtervirinae; Okanivirinae; Orthocoronavirus ( Orthocoronavirinae; Family Orthomyxoviridae; Subfamily Orthoparamyxovirinae; Subfamily Orthoretrovirinae; Subfamily Ounavirinae; Family Ovaliviridae; Family Papillomaviridae; Family Paramyxoviridae; Family Partitiviridae;Family Parvoviridae; Subfamily Parvovirinae; Subfamily Pclasvirinae; Subfamily Peduovirinae; Family Peribunyaviridae; Family Permutotetraviridae; Family Phasmaviridae; Family Phenuiviridae; Family Phycodnaviridae; Family Picovirnaui Family: Picoviridae; Family: Picornaviridae; Family: Picovirinae; Family: Piscanivirinae; Family: Plasmaviridae; Family: Pleolipoviridae; Family: Pneumoviridae; Family: Podoviridae; Family: Polycipiviridae; Family: Polydnaviridae e); Polyomaviridae; Portogloboviridae; Pospiviroidae; Potyviridae; Poxviridae; Procedovirinae; Pseudoviridae; Qinviridae; Quadriviridae; Quinvirinae nae); Regressovirinae; Remotovirinae; Reoviridae; Repantavirinae; Retroviridae; Rhabdoviridae; Roniviridae; Rubulavirinae; Rudiviridae; Sarusroviridae;Subfamily Secondpapillomavirinae; Family Secoviridae; Subfamily Sedoreovirinae; Subfamily Sepvirinae; Subfamily Serpentovirinae; Subfamily Simarterivirinae; ; Siphoviridae; Smacoviridae; Solemoviridae; Solinviviridae; Sphaerolipoviridae; Spinareovirinae; Spiraviridae; Spounavirinae; Spumaretrovirinae Family: Etrovirinae; Family: Sunviridae; Family: Tectiviridae; Family: Tevenvirinae; Family: Tiamatvirinae; Family: Tobaniviridae; Family: Togaviridae; Family: Tolecusatellitidae; Family: Tombusviridae; Family: Torovirinae e); Tospoviridae; Totiviridae; Tristromaviridae; Trivirinae; Tunavirinae; Tunicanivirinae; Turriviridae; Twortvirinae; Tymoviridae; Varial theriae 5. The method of any one of claims 1 to 4, wherein the virus is derived from at least one virus selected from the group comprising: subfamily Variarterivirinae; subfamily Vequintavirinae; family Virgaviridae; family Wupedeviridae; family Xinmoviridae; family Yueviridae; and subfamily Zealarterivirinae.

6. The method according to any one of claims 1 to 5, wherein the pathogenic antigen is a cytomegalovirus antigen, or an Epstein-Barr virus antigen, or a herpesvirus antigen, or a poxvirus antigen, or a hepadnavirus antigen, or an influenza virus antigen, or a coronavirus antigen, or a hepatitis virus antigen, or an HIV antigen, or a bunyavirus antigen.

7. 7. The method of any one of claims 1 to 6, wherein comparing the peptides of the bound pMHC to a library of pathogen antigens comprises peptide scoring and / or alignment scoring to determine the homology / affinity.

8. comparing said peptides of said bound pMHC to a library of pathogen antigens; a) the similarity or identity of the overall sequence structure of said peptide and said pathogenic antigen; and / or b) similarity or identity of key amino acids in key binding sites of said peptide and said pathogenic antigen; and / or c) the greatest number of similar or identical key amino acids in the key binding sites of said peptide and said pathogenic antigen; The method of any one of claims 1 to 7, comprising determining:

9. A device for tumor-specific antigen identification, comprising: a microfluidic device comprising at least one flow-through channel containing a plurality of micropillars arranged in an array having attached thereto at least one molecule or at least one complex to which at least one anti-major histocompatibility complex antibody or at least one anti-pan-human leukocyte antigen (HLA) antibody is bound, wherein the at least one antibody can be used to extract peptide:major histocompatibility complex (pMHC) from a sample flowing through the channel; a processor adapted to identify the peptides bound to the major histocompatibility complex and compare the peptides of the bound pMHC to a library of pathogen-derived peptide antigens to determine whether the peptides bound to or bound to the major histocompatibility complex exhibit homology / affinity with at least one pathogen antigen or portion thereof, and if there is greater than 90% homology / affinity; and designate the peptides bound to or bound to the major histocompatibility complex as tumor-specific antigens for use in cancer therapy; A device comprising:

10. The device of claim 9, wherein the anti-pan-HLA antibody is selected from the group comprising MHC-I class A, B and C.

11. The device of claim 9, wherein the anti-pan-HLA antibody is selected from the group comprising MHC-II classes DP, DM, DO, DQ and DR.

12. The device of any one of claims 9 to 11, wherein the antibody is anti-human.

13. The device of any one of claims 9 to 12, wherein the molecule or complex is a complex of a thiol and an alkene functional group.

14. The device of any one of claims 9 to 13, wherein the molecule or complex comprises biotin and streptavidin.

15. The device of any one of claims 9 to 14, wherein the molecule or complex comprises more than one antibody bound to streptavidin.

16. The device of any one of claims 9 to 15, wherein the molecule or complex is prepared by coating with a protein prior to binding to the antibody.

17. A device according to any one of claims 9 to 16, wherein said device is adapted to carry out a method according to any one of claims 1 to 8.

18. i) a tumor-specific antigen identified by the method according to any one of claims 1 to 8, and ii) an effective amount of at least one checkpoint inhibitor 2. A cancer treatment drug comprising:

19. i) A tumor-specific antigen identified by the method according to any one of claims 1 to 8, wherein the tumor-specific antigen is attached to the capsid of an adenovirus vector. A cancer treatment drug comprising

20. 1. A method of generating an isolated T cell population for treating cancer, comprising: i) dissolving or suspending a tumor sample taken from a patient in a fluid; ii) passing the fluid through a microfluidic device for tumor-specific antigen identification, the device comprising at least one flow-through channel containing a plurality of micropillars arranged in an array having attached thereto at least one molecule or at least one complex to which at least one anti-major histocompatibility complex antibody or at least one anti-pan-human leukocyte antigen antibody is bound, wherein the at least one antibody can be used to extract peptide:major histocompatibility complex (pMHC) molecules from the sample flowing through the channel; iii) allowing at least one pMHC in said sample to bind to said at least one antibody; iv) removing the at least one bound pMHC of part iv) from the device; v) comparing said peptides of said bound pMHC to a library of antigens derived from human pathogens to determine whether said peptides exhibit homology / affinity with at least one human pathogen antigen or portion thereof, and if there is greater than 90% homology / affinity; vi) identifying said peptide as a tumor-specific antigen; vii) administering an effective amount of the peptide to an isolated population of T cells from the patient to stimulate or activate the T cells against the tumor-specific antigen and thus against the cancer from which the sample was taken; or using the tumor-specific antigen to expand the isolated population of T cells active against the tumor-specific antigen; A method comprising:

21. The cancer may be nasopharyngeal cancer, synovial cancer, hepatocellular carcinoma, renal cancer, cancer of connective tissue, melanoma, lung cancer, intestinal cancer, colon cancer, rectal cancer, colorectal cancer, brain cancer, throat cancer, oral cancer, liver cancer, bone cancer, pancreatic cancer, choriocarcinoma, gastrinoma, pheochromocytoma, prolactinoma, T-cell leukemia / lymphoma, neuroma, von Hippel-Lindau disease, Zollinger-Ellison syndrome, adrenal gland cancer, anal cancer, bile duct cancer, bladder cancer, ureter cancer, oligodendroglioma, neuroblastoma, meningioma, spinal cord tumor, osteochondroma, chondrosarcoma, Ewing's sarcoma, cancer of unknown primary site, carcinoid, carcinoid of the digestive tract, fibrosarcoma, breast cancer, Paget's disease, cervical cancer, esophageal cancer, gallbladder cancer, head cancer, eye cancer, neck cancer, 21. The method of claim 20, wherein the cancer is selected from the group comprising kidney cancer, Wilms' tumor, liver cancer, Kaposi's sarcoma, prostate cancer, testicular cancer, Hodgkin's disease, non-Hodgkin's lymphoma, skin cancer, mesothelioma, multiple myeloma, ovarian cancer, endocrine pancreatic cancer, glucagonoma, parathyroid cancer, penile cancer, pituitary cancer, soft tissue sarcoma, retinoblastoma, small intestine cancer, gastric cancer, thymus cancer, thyroid cancer, choriocarcinoma, hydatidiform mole, uterine cancer, endometrial cancer, vaginal cancer, vulvar cancer, acoustic neuroma, mycosis fungoides, insulinoma, carcinoid syndrome, somatostatinoma, gum cancer, heart cancer, lip cancer, meningeal cancer, mouth cancer, nerve cancer, palate cancer, parotid gland cancer, peritoneal cancer, pharyngeal cancer, pleural cancer, salivary gland cancer, tongue cancer, and tonsil cancer.

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