Methods and compositions for use of tumor specific antigens in adoptive immunotherapy

Abstract

Methods and compositions for use of tumor self-antigens in adoptive immunotherapy are disclosed herein, including search engines and algorithms for identifying tumor-specific immunotherapy targets. An example computer-implemented method can include receiving, by at least one processor, sequence data; identifying, by the at least one processor, one or more tumor-specific events associated with a plurality of molecular event classes from the sequence data; determining, by the at least one processor, a search space based on the one or more tumor-specific events; interrogating, by the at least one processor, a plurality of datasets based on the determined search space; and determining, by the at least one processor, one or more immunotherapy targets based on genetic aberrations from the sequence data.

Description

METHODS AND COMPOSITIONS FOR USE OF TUMOR SPECIFIC ANTIGENS IN ADOPTIVE IMMUNOTHERAPYGovernment Support Statement

[0001] This invention was made with government support under CA290738 and CA301080 awarded by the National Institutes of Health. The government has certain rights in the invention.Cross-Reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 563,765, filed on March 11, 2024, and U.S. Provisional Patent Application No. 63 / 746,907, filed on January 17, 2025, the contents of which are incorporated by reference herein in their entireties.Field of the Disclosure

[0003] The present disclosure relates to methods, products, and uses thereof for immunopeptidomics search engines and / or algorithms for identifying tumor-specific immunotherapy targets.Background

[0004] T-cell-based immunotherapies, for example, chimeric antigen receptor (CAR) T- cell therapies, have improved cancer treatment, especially in blood cancers (See, e.g., Ref. 2). However, their effectiveness varies due to specific antigens and tumor mutational burden. Current state-of-the-art strategies focus on membrane proteins, which lack tumor specificity, causing side effects (See, e.g., Refs. 3, 4). Cell targeting strategies are needed to distinguish malignant from healthy tissues. Human leukocyte antigen (HLA) molecules present a snapshot of the cellular proteome, exposing potential tumor-specific antigens (See, e.g., Ref. 6). Other state-of-the-art approaches, such as peptide-centric CAR (PC-CAR), expand targeting to low-to-medium mutational tumors (See, e.g., Ref. 12). Identifying tumor-specific HLAs from a broad range of genetic aberrations can reveal additional tumor vulnerabilities. Immunopeptidomics can uncover these antigens, but comprehensive identification requires advanced computational workflows and large-scale normal references to avoid toxicity (See, e.g., Refs. 13-16).

[0005] Human HLA-I molecules display tumor-specific antigens to cytotoxic CD8+ T cells, facilitating targeted cancer immunotherapies that eradicate tumors while sparing normal tissues. These antigens arise from various genetic aberrations, but current prediction tools arelimited, underestimating potential targets and impeding therapy development for low- mutation and non-responsive tumors (See, e.g., Ref. 1’).

[0006] Accordingly, there is a need to address and / or at least partially overcome at least some of the deficiencies described herein.Summary

[0007] To expand the current repertoires and depict the complete tumor antigen landscape, according to certain embodiments of the present disclosure, an exhaustive tumor antigen search engine (labeled herein as “ImmunoVerse” can be provided which can comprehensively interrogate, for example, 14 aberration categories from RNA-Seq data, including, but not limited to, (1) gene expression, (2) single nucleotide polymorphisms (SNPs), (3) insertions and deletions (INDELs), (4) splicing junctions, (5) intron retention, (6) gene fusions, (7) Ribonucleic acid (RNA) editing, (8) tumor-resident pathogens, (9) transposable elements, (10) endogenous retroviruses, (11) circular RNAs, (12) cryptic open reading frames (ORFs), (13) proteasomal spliced antigens, and (14) post-translational modifications. The exemplary mechanism can be used for a comprehensive mapping of the entire tumor antigen space in every tumor type. Applying ImmunoVerse to neuroblastoma, medulloblastoma, and osteosarcoma, well-known somatic mutations (i.e., ALK) and gene amplifications (i.e., MYCN) can be recapitulated, while also new tumor-specific antigens from previously unexplored aberrations can be unveiled. Importantly, for example, at least four antigens derived from non-annotated ORFs in neuroblastoma can be identified, distinguished by their high-confidence immunopeptidome spectrum and peptide abundance. A further exemplary utilization through Ribosome Sequencing (Ribo-Seq) profiling confirmed the translation of these cryptic ORFs, highlighting the additional antigens that might have been missed without the comprehensive search strategy. The widespread adoption of the methods and products according to the present disclosure, such as ImmunoVerse, can broaden the actionable targets available for current cancer immunotherapy. These and other objects, features, and advantages of the exemplary embodiments of the present disclosure can become apparent upon reading the following detailed description of the exemplary embodiments of the present disclosure when taken in conjunction with the appended paragraphs.

[0008] In an aspect, a computer-implemented method is disclosed comprising receiving, by at least one processor, sequence data; identifying, by the at least one processor, one or more tumor-specific events associated with a plurality of molecular event classes from thesequence data; determining, by the at least one processor, a search space based on the one or more tumor-specific events; interrogating, by the at least one processor, a plurality of immunopeptidome datasets based on the determined search space; and determining, by the at least one processor, one or more immunotherapy targets based on genetic aberrations from the sequence data.

[0009] In some embodiments, the sequence data includes ribonucleic acid (RNA)- sequence data.

[0010] In some embodiments, the one or more immunotherapy targets are used to direct a treatment for one or more patients.

[0011] In some embodiments, the plurality of immunopeptidome datasets are each associated with one or more aberration categories.

[0012] In some embodiments, the one or more aberration categories include at least one of protein-coding genes, single nucleotide polymorphisms (SNPs), insertions and deletions (INDELs), splicing junctions, intron retention, gene fusions, Ribonucleic acid (RNA) editing, tumor-resident pathogens, transposable elements, endogenous retroviruses, circular RNAs, cryptic open reading frames (ORFs), proteasomal spliced antigens, and post-translational modifications.

[0013] In some embodiments, the one or more immunotherapy targets include canonical and non-canonical peptides for at least one cancer type.

[0014] In some embodiments, the plurality of immunopeptidome datasets comprises histology-matched immunopeptidome datasets.

[0015] In some embodiments, determining the one or more immunotherapy targets comprises identifying one or more peptides, the computer-implemented method further comprises rescoring, by the at least one processor, each identified peptide using a deeplearning model to boost immunopeptidome detection sensitivity; and determining, by the at least one processor, a HLA binding prediction for each identified peptide to eliminate false positives.

[0016] In some embodiments, the computer-implemented method described herein further comprises generating or outputting, by the at least one processor, a report, summary, and / or data object describing the genetic aberrations and / or list of antigens.

[0017] In some embodiments, the generated output is used as an input for rendering an interactive web portal for users to interrogate the identified tumor-specific antigens and make an informed decision.

[0018] In one aspect, a vaccine for treatment of a cancer is disclosed comprising a therapeutically acceptable amount of one or more neoantigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111, SEQ ID NO: 112, SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117, and SEQ ID NO: 118; and a pharmaceutically acceptable carrier.

[0019] In one aspect, disclosed herein is chimeric antigen receptor (CAR) that binds to one or more antigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO:52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103. In one aspect, the CAR is expressed on a T cell, natural killer (NK) cell, or macrophage. In one aspect, disclosed herein are isolated nucleic acids encoding the CAR.

[0020] In one aspect, disclosed herein is T cell receptor (TCR) that binds to one or more antigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO:53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101,and / or SEQ ID NO: 103. In one aspect, disclosed herein are isolated nucleic acids encoding the TCR. In one aspect, disclosed herein is a T cell comprising a nucleic acid encoding any of the TCR disclosed herein.

[0021] Also disclosed herein, a method of treating a cancer in a subject is disclosed comprising administering to the subject the above-discussed vaccine, CAR, TCR, antibody, TIL.

[0022] In one aspect, disclosed herein is a method of treating, inhibiting, decreasing, reducing, ameliorating, and / or preventing a cancer, cancer recurrence, and / or metastasis in a subject comprising administering to the subject an agent that inhibits expression of one or more neoantigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103.

[0023] In some embodiments, the agent includes an antisense oligonucleotide, short hairpin RNA (shRNA), long non-coding RNA (IncRNA), small interfering RNA (siRNA), RNAi, or small molecule.

[0024] In one aspect, disclosed herein is a method of treating, inhibiting, decreasing, reducing, ameliorating, and / or preventing a cancer, cancer recurrence, and / or metastasis in asubject comprising administering to the subject a therapeutically effective amount of a cell therapy; wherein the cell comprises a chimeric antigen receptor (CAR) or T cell receptor that binds to SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103.

[0025] In another aspect, a system is provided. The system can include at least one computing device having a processor and a memory having instructions thereon, wherein the instructions when executed by the processor, cause the processor to: receive sequence data; identify one or more tumor-specific events associated with a plurality of molecular event classes from the sequence data; determine a search space based on the one or more tumorspecific events; interrogate a plurality of immunopeptidome datasets based on the determined search space; and determine one or more immunotherapy targets based on genetic aberrations from the sequence data.

[0026] In some embodiments, the instructions when executed by the processor, cause the processor to further: generate a plurality of workflows for processing the received sequence data, including a first workflow for identifying the one or more tumor-specific events, a second workflow for determining the search space, and a third workflow for interrogating the plurality of immunopeptidome datasets.

[0027] In some embodiments, the instructions when executed by the processor, cause the processor to further: transfer at least a portion of the received sequence data to at least another computing device for processing based on time and memory constraints.

[0028] In some embodiments, determining the one or more immunotherapy targets comprises identifying one or more peptides, and the instructions when executed by the processor, cause the processor to further: rescore each identified peptide using a deeplearning model to boost immunopeptidome detection sensitivity; and determine a HLA binding prediction for each identified peptide to eliminate false positives.

[0029] In another aspect, a non-transitory computer-readable medium is provided. The non-transitory computer-readable medium can have instructions stored thereon, wherein execution of the instructions by a processor, cause the processor to: receive sequence data; identify one or more tumor-specific events associated with a plurality of molecular event classes from the sequence data; determine a search space based on the one or more tumorspecific events; interrogate a plurality of immunopeptidome datasets based on the determined search space; and determine one or more immunotherapy targets based on genetic aberrations from the sequence data.

[0030] In some embodiments, the vaccine or agent is determined using a computer- implemented method of any one of the above-discussed methods.Brief Description of Drawings

[0031] Further objects, features and advantages of the present disclosure will become apparent from the following detailed description taken in conjunction with the accompanying Figures showing illustrative embodiments of the present disclosure, in which:

[0032] Figure 1 A is an exemplary diagram listing the cancer types considered by exemplary embodiments of the present disclosure;

[0033] Figure IB is an exemplary computational pipeline diagram detailing the steps used to identify, refine, and enhance tumor-specific antigens according to an exemplary embodiment of the present disclosure;

[0034] Figure 1C is an exemplary table showing the distribution of tumor-specific antigens identified across all analyzed cancers according to an exemplary embodiment of the present disclosure;

[0035] Figure ID is a flowchart of an example computer-implemented method in accordance with certain implementations of the present disclosure;

[0036] Figure IE is a diagram of an example system in accordance with certain implementations of the present disclosure;

[0037] Figure 2A shows an analysis highlighting 47 previously unrecognized antigens with favorable therapeutic windows, rendering them as candidates for conventional CAR-T therapy.

[0038] Figure 2B is an exemplary visualization of 284 tumor-specific gene that can give rise to tumor-specific antigens, annotated by whether they belong to membrane or intracellular protein, along with the Depmap reported dependency score, according to an exemplary embodiment of the present disclosure;

[0039] Figure 2C is an exemplary chart showing enriched gene ontology and pathway associated with 61 clustered pan-cancer targets according to an exemplary embodiment of the present disclosure;

[0040] Figure 2D is an exemplary bar chart showing patient coverage when evaluated by gene expression alone, and accounting for cancer-specific HLA restrictions according to an exemplary embodiment of the present disclosure;

[0041] Figure 2E is an exemplary chart showing peptide abundance comparison between ccRCC and normal tissue of a recurrent, abundant and tumor-specific peptide from HAVCR1 (DLSRRDVSL (SEQ ID NO: 45)), according to an exemplary embodiment of the present disclosure;

[0042] Figure 2F is an exemplary Alphafol d2 modeling of HAVCR1 peptide (DLSRRDVSL (SEQ ID NO: 45)) and cognate HLA-B*0801 complex, according to an exemplary embodiment of the present disclosure;

[0043] Figure 3 A is an exemplary bar chart showing the proportion of non-canonical antigens arising from gene fusion, somatic mutation, splicing, intron retention and cryptic open reading frames according to an exemplary embodiment of the present disclosure;

[0044] Figure 3B is an exemplary bar chart showing the number of immunopeptidome evidenced neoantigens for each cancer according to an exemplary embodiment of the present disclosure;

[0045] Figure 3C is an exemplary table showing the most frequently detected neoantigens and the number of peptides identified according to an exemplary embodiment of the present 30 disclosure;

[0046] Figure 3D is an exemplary visualization showing recurrently detected cryptic ORF derived tumor-specific antigens according to an exemplary embodiment of the present disclosure. Shown are sequences STIRVLSGY (SEQ ID NO: 1), RVEEAIHPV (SEQ ID NO:2), FLWDKRTGL (SEQ ID NO: 3), RYLHGLQKF (SEQ ID NO: 4), NYYKVNWTF (SEQ ID NO: 5), ATRRESELY (SEQ ID NO: 6), RSFADSYAQWK (SEQ ID NO: 7), ALPEVQKQV (SEQ ID NO: 8), RYLPSSVFL (SEQ ID NO: 9), DAPQTRQV (SEQ ID NO: 10), EAQRPTAEY (SEQ ID NO: 11), EAAGEVRAM (SEQ ID NO: 12), AAVGTTVSL (SEQ ID NO: 13), VYKPVSIYL (SEQ ID NO: 14), AAADAANPL (SEQ ID NO: 15), RSSSEASFLRK (SEQ ID NO: 16), RLSDAEIMGK (SEQ ID NO: 17), RVFGVEINK (SEQ ID NO: 18), RLQAVGSLLEK (SEQ ID NO: 19), AETDERRLL (SEQ ID NO: 20), RVKMWVMGK (SEQ ID NO: 21), SRHLGAEAL (SEQ ID NO: 22), IHQQEDSARFF (SEQ ID NO: 23), APRGDGVQVSA (SEQ ID NO: 24), APVASHGLVL (SEQ ID NO: 25), LYLETRSEF (SEQ ID NO: 26), AYPASLQTL (SEQ ID NO: 27), KLLDFSTRI (SEQ ID NO: 28), RLYSLSTALR (SEQ ID NO: 29), SAFQQMWISK (SEQ ID NO: 30), RTNNILLPR (SEQ ID NO: 31), RPSPVRVAAL (SEQ ID NO: 32), SPILTSSTL (SEQ ID NO: 33), ATHHQPWAQK (SEQ ID NO: 34), RLQDPQAGISK (SEQ ID NO: 35), RVKERDFFLK (SEQ ID NO: 36), RIMPLLIAK (SEQ ID NO: 37), KSLAAELLVLK (SEQ ID NO: 38), GMPWHFMLK (SEQ ID NO: 39), VLMEVTLEGK (SEQ ID NO: 40), and DHDSVDKLVI (SEQ ID NO: 41);

[0047] Figure 3E is an exemplary illustration of exon skipping of exon 5 of PMEL results in a splice variant compared to two canonical antigens according to an exemplary embodiment of the present disclosure. Shown is the exon transition from exons 4 to 5 KTWGQYWQV (SEQ ID NO: 42), the exon transition from exon 5 to 6 ITDQVPFSV (SEQ ID NO: 43), and the PMEL exon skipping moving from exon 4 to 6 KTWDQVPFSV (SEQ ID NO: 44);

[0048] Figure 3F is an exemplary Alphafold2 structural modeling of the splice variant with cognate A0201 allele according to an exemplary embodiment of the present disclosure;

[0049] Figure 3G is an exemplary graph showing normalized peptide abundance amongst the PMEL spliced variant, and two canonical counterparts according to an exemplary embodiment of the present disclosure;

[0050] Figure 4A is an exemplary visual representation of the number of peptides detected in immunopeptidome in each species across cancers according to an exemplary embodiment of the present disclosure;

[0051] Figure 4B is an exemplary Venn diagram comparison between IEDB documented HBV peptides and the ones detected in liver cancer primary tumor immunopeptidome according to an exemplary embodiment of the present disclosure;

[0052] Figure 4C is an exemplary visual representation of 7 previously documented CMV peptides in IEBD and also detected in primary tumor immunopeptidome according to an exemplary embodiment of the present disclosure. Shown are DLLSALQQL (SEQ ID NO: 46), TLLVYLFSL (SEQ ID NO: 47), VLEETSVML (SEQ ID NO: 48), IARLAKIPL (SEQ ID NO: 49), LLDGVTVSL (SEQ ID NO: 50), LPVESLPLL (SEQ ID NO: 51), and YTSRGALYL (SEQ ID NO: 52);

[0053] Figure 4D is an exemplary Alphafold2 modeling of A02:01 -LLDGVTVSL (SEQ ID NO: 50) according to an exemplary embodiment of the present disclosure;

[0054] Figure 4E is an exemplary Venn diagram comparing number of detected N. Circulans derived peptides across ovarian and esophageal cancers and neuroblastoma cell line according to an exemplary embodiment of the present disclosure;

[0055] Figure 5A is an exemplary illustration of diverse approaches including TE chimeric transcript and self-translated TE according to an exemplary embodiment of the present disclosure;

[0056] Figure 5B is an exemplary visualization of tumor-specific HLA antigens detected in the immunopeptidome across all major transposable element (TE) categories in 21 cancer types 30 according to an exemplary embodiment of the present disclosure;

[0057] Figure 5C is an exemplary illustration showing detected peptide fragments for canonical ORF2 in immunopeptidome across cancers according to an exemplary embodiment of the present disclosure. Shown are RLKIKGWRK (SEQ ID NO: 53), KIYQANGKQK (SEQ ID NO: 54), DTLTSQLKEL (SEQ ID NO: 55), KEIETQKTL (SEQ ID NO: 56), TEIQTTIREYY (SEQ ID NO: 57), EAFPLKTGTR (SEQ ID NO: 58), IVSAQNLLK (SEQ ID NO: 59), SGYKINVQK (SEQ ID NO: 60), ILPKVIYRF (SEQ ID NO: 61), RIAKSILSQK (SEQ ID NO: 62), RIYNELKOIYK (SEQ ID NO: 63), REMQIKTTM (SEQ ID NO: 64), and LTPVRMAI (SEQ ID NO: 65);

[0058] Figure 6A is an exemplary visualization showing key genes related in HLA presentation pathway and their gene expression across cancers and normal according to an exemplary embodiment of the present disclosure;

[0059] Figure 6B is an exemplary graph showing the presentation potential between 611 human signal peptides and their stable protein counterpart according to an exemplary embodiment of the present disclosure;

[0060] Figure 6C and Figure 6D are exemplary illustrations showing peptides from Diffuse Large B-Cell lymphoma (DLBC) immunopeptidome and rhabdoid tumor (RT) immunopeptidome by Gibbs clustering, wherein an optimal number of clusters is determinedby maximum Kullback-Leibler divergence (n=2 in DLBC, n=4 in RT) according to an exemplary embodiment of the present disclosure.

[0061] Figure 7A is an exemplary bar chart showing a number of RNA-Seq datasets for each tumor across the Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) according to an exemplary embodiment of the present disclosure;

[0062] Figure 7B is an exemplary bar chart showing a number of immunopeptidome datasets for each tumor collected throughout the public repository according to an exemplary embodiment of the present disclosure;

[0063] Figure 7C is an exemplary chart showing the tumor-specific molecular events in the atlas across 21 tumor types according to an exemplary embodiment of the present disclosure;

[0064] Figure 8A is an exemplary chart showing TPM gene expression for HAVCR1 between clear cell Renal Cell Carcinoma (ccRCC) and 17,384 GTEx normal tissue across 51 histologies according to the exemplary embodiments of the present disclosure;

[0065] Figure 8B is an exemplary graph showing refolding results for B0801 / peptide complex according to the exemplary embodiments of the present disclosure;

[0066] Figure 8C is an exemplary graph showing high confidence MS spectrum for the HAVCR1 antigen according to the exemplary embodiments of the present disclosure. Show in DLSRRDVSL (SEQ ID NO: 45);

[0067] Figure 9A is an exemplary illustration showing workflow of PDX neuroblastoma according to the exemplary embodiments of the present disclosure;

[0068] Figure 9B is an exemplary illustration the number of validated cryptic ORF antigen in NB PDX according to the exemplary embodiments of the present disclosure;

[0069] Figure 9C is an exemplary illustration showing ribosome occupancies on an untranslated region (UTR) of TMEM203 from Ribo-Seq, wherein the STIRVLSGY (SEQ ID NO: 1) peptide is highlighted according to the exemplary embodiments of the present disclosure.

[0070] Figure 9D is an exemplary mirror plot showing synthetic peptide and original spectra to confirm multiple sclerosis (MS) identifications according to the exemplary embodiments of the present disclosure, showing the spectrum for STIRVLSGY (SEQ ID NO: 1).

[0071] Figure 9E is an exemplary illustration showing the size exclusion chromatography for the refolded STIRVLSGY (SEQ ID NO: l)-HLA-A*26:01 complex according to the exemplary embodiments of the present disclosure.

[0072] Figure 9F is a series of exemplary mirror plots showing original spectrum from tumor samples and synthetic peptide spectra to validate the MS identification for POLR2K 5UTR, ZNF749 5UTR, C19orf48_pseudogene (top to bottom).

[0073] Figure 9G is a series of exemplary illustration showing the size exclusion chromatography of the refolded pHLA for LYLETRSEF (SEQ ID NO: 26)-A2402 complex, RYLPSSVFL (SEQ ID NO: 9)-A2402 complex, AYPASLQTL (SEQ ID NO: 27)-A2402 complex (top to bottom).

[0074] Figure 10A is an exemplary visual representation of frequently detected splicing antigen across cancers, annotated by splicing types and whether they are in-frame splice variant or out-of-frame according to an exemplary embodiment of the present disclosure. Shown are HAAASFETL (SEQ ID NO: 66), RLLGTEFQT (SEQ ID NO: 67), FTDSQGNDIK (SEQ ID NO: 68), NLLAEIHGV (SEQ ID NO: 69), LLAEIHGV (SEQ ID NO: 70), LLAEIHGV (SEQ ID NO: 71), WLQQTKSLY (SEQ ID NO: 72), SEWENGSGM (SEQ ID NO: 73), TLFPERREW (SEQ ID NO: 74), DRVGGRRDL (SEQ ID NO: 75), SEQDPENRAW (SEQ ID NO: 76), QAQLEVSVQY (SEQ ID NO: 77), FLYQDPENQAL (SEQ ID NO: 78), YLLENLRSL (SEQ ID NO: 79), VTDGVSLLL (SEQ ID NO: 80), SPSRPPSSL (SEQ ID NO: 81), SLAPLVHLV (SEQ ID NO: 82), VPHTRPVSL (SEQ ID NO: 83), VVSGATGAK (SEQ ID NO: 84), VWMRSPLSTF (SEQ ID NO: 85), and GIKHASIAR (SEQ ID NO: 86);

[0075] Figure 10B is an exemplary graph showing refolding experiment of PMEL spliced variant and A0201 allele according to an exemplary embodiment of the present disclosure;

[0076] Figure 10C is an exemplary visual representation of tumor-specific splicing junctions across cancer types according to an exemplary embodiment of the present disclosure;

[0077] Figure 10D is an exemplary graph showing TCR-T killing assay using specific TCR for canonical ITD antigens toward both canonical ITD and spliced variant according to an exemplary embodiment of the present disclosure;

[0078] Figure 10E is an exemplary graph showing spike-in validation for the PMEL variant peptide according to an exemplary embodiment of the present disclosure;

[0079] Figure 11 A is an exemplary process of identifying neoantigens as an immunotherapy target.

[0080] Figure 1 IB is an exemplary illustration, at a peptide level, showing two peptides, QYNPIRTTF (SEQ ID NO: 88) and FLDETLRSLA (SEQ ID NO: 89), with high confidence.

[0081] Figure 11C is an exemplary illustration showing peptide counts reported in (i) a previous study and (ii) in this study using MaxQuant.

[0082] Figure 1 ID is an exemplary illustration showing a mutated peptide (e.g., FPFEKGSVQY (SEQ ID NO: 92) mutated to FEFEKGSAQY (SEQ ID NO: 87)) resulting from a V140A mutation in a DEK gene.

[0083] Figure 1 IE is an exemplary illustration showing a high-confidence peptide, APAAGALHAA (SEQ ID NO: 91), originating from an in-frame 5’ UTR cryptic open reading frame (ORF) on an FAM20C gene.

[0084] Figure 12 is an exemplary illustration of an exemplary block diagram of an exemplary system in accordance with certain exemplary embodiments of the present disclosure.

[0085] Throughout the drawings, the same reference numerals and characters, unless otherwise stated, are used to denote features, elements, components, or portions of the illustrated embodiments. Moreover, while the present disclosure will now be described in detail with reference to the figures, it is done so in connection with the illustrative embodiments and is not limited by the particular embodiments illustrated in the figures and the appended paragraphs.Detailed DescriptionDefinitions

[0086] An "increase" can refer to any change that results in a greater amount of a symptom, disease, composition, condition or activity. An increase can be any individual, median, or average increase in a condition, symptom, activity, composition in a statistically significant amount. Thus, the increase can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% increase so long as the increase is statistically significant.

[0087] A "decrease" can refer to any change that results in a smaller amount of a symptom, disease, composition, condition, or activity. A substance is also understood to decrease the genetic output of a gene when the genetic output of the gene product with the substance is less relative to the output of the gene product without the substance. Also for example, a decrease can be a change in the symptoms of a disorder such that the symptoms are less than previously observed. A decrease can be any individual, median, or averagedecrease in a condition, symptom, activity, composition in a statistically significant amount. Thus, the decrease can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% decrease so long as the decrease is statistically significant.

[0088] Inhibit," "inhibiting," and "inhibition" mean to decrease an activity, response, condition, disease, or other biological parameter. This can include but is not limited to the complete ablation of the activity, response, condition, or disease. This may also include, for example, a 10% reduction in the activity, response, condition, or disease as compared to the native or control level. Thus, the reduction can be a 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%, or any amount of reduction in between as compared to native or control levels.

[0089] By “reduce” or other forms of the word, such as “reducing” or “reduction,” is meant lowering of an event or characteristic (e.g., tumor growth). It is understood that this is typically in relation to some standard or expected value, in other words it is relative, but that it is not always necessary for the standard or relative value to be referred to. For example, “reduces tumor growth” means reducing the rate of growth of a tumor relative to a standard or a control.

[0090] By “prevent” or other forms of the word, such as “preventing” or “prevention,” is meant to stop a particular event or characteristic, to stabilize or delay the development or progression of a particular event or characteristic, or to minimize the chances that a particular event or characteristic will occur. Prevent does not require comparison to a control as it is typically more absolute than, for example, reduce. As used herein, something could be reduced but not prevented, but something that is reduced could also be prevented. Likewise, something could be prevented but not reduced, but something that is prevented could also be reduced. It is understood that where reduce or prevent are used, unless specifically indicated otherwise, the use of the other word is also expressly disclosed.

[0091] The term “subject” refers to any individual who is the target of administration or treatment. The subject can be a vertebrate, for example, a mammal. In one aspect, the subject can be human, non-human primate, bovine, equine, porcine, canine, or feline. The subject can also be a guinea pig, rat, hamster, rabbit, mouse, or mole. Thus, the subject can be a human or veterinary patient. The term “patient” refers to a subject under the treatment of a clinician, e.g., physician.

[0092] The term “treatment” refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causaltreatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder.

[0093] "Biocompatible" generally refers to a material and any metabolites or degradation products thereof that are generally non-toxic to the recipient and do not cause significant adverse effects to the subject.

[0094] “Effective amount” of an agent refers to a sufficient amount of an agent to provide a desired effect. The amount of agent that is “effective” will vary from subject to subject, depending on many factors such as the age and general condition of the subject, the particular agent or agents, and the like. Thus, it is not always possible to specify a quantified “effective amount.” However, an appropriate “effective amount” in any subject case may be determined by one of ordinary skill in the art using routine experimentation. Also, as used herein, and unless specifically stated otherwise, an “effective amount” of an agent can also refer to an amount covering both therapeutically effective amounts and prophylactically effective amounts. An “effective amount” of an agent necessary to achieve a therapeutic effect may vary according to factors such as the age, sex, and weight of the subject. Dosage regimens can be adjusted to provide the optimum therapeutic response. For example, several divided doses may be administered daily or the dose may be proportionally reduced as indicated by the exigencies of the therapeutic situation.

[0095] A "pharmaceutically acceptable" component can refer to a component that is not biologically or otherwise undesirable, i.e., the component may be incorporated into a pharmaceutical formulation provided by the disclosure and administered to a subject as described herein without causing significant undesirable biological effects or interacting in a deleterious manner with any of the other components of the formulation in which it is contained. When used in reference to administration to a human, the term generally implies the component has met the required standards of toxicological and manufacturing testing or that it is included on the Inactive Ingredient Guide prepared by the U.S. Food and Drug Administration.

[0096] "Pharmaceutically acceptable carrier" (sometimes referred to as a “carrier”) means a carrier or excipient that is useful in preparing a pharmaceutical or therapeutic composition that is generally safe and non-toxic and includes a carrier that is acceptable for veterinary and / or human pharmaceutical or therapeutic use. The terms "carrier" or "pharmaceutically acceptable carrier" can include, but are not limited to, phosphate buffered saline solution, water, emulsions (such as an oil / water or water / oil emulsion) and / or various types of wetting agents. As used herein, the term "carrier" encompasses, but is not limited to, any excipient, diluent, filler, salt, buffer, stabilizer, solubilizer, lipid, stabilizer, or other material well known in the art for use in pharmaceutical formulations and as described further herein.

[0097] “Pharmacologically active” (or simply “active”), as in a “pharmacologically active” derivative or analog, can refer to a derivative or analog (e.g., a salt, ester, amide, conjugate, metabolite, isomer, fragment, etc.) having the same type of pharmacological activity as the parent compound and approximately equivalent in degree.

[0098] “Therapeutic agent” refers to any composition that has a beneficial biological effect. Beneficial biological effects include both therapeutic effects, e.g., treatment of a disorder or other undesirable physiological condition, and prophylactic effects, e.g., prevention of a disorder or other undesirable physiological condition (e.g., a non- immunogenic cancer). The terms also encompass pharmaceutically acceptable, pharmacologically active derivatives of beneficial agents specifically mentioned herein, including, but not limited to, salts, esters, amides, proagents, active metabolites, isomers, fragments, analogs, and the like. When the terms “therapeutic agent” is used, then, or when a particular agent is specifically identified, it is to be understood that the term includes the agent per se as well as pharmaceutically acceptable, pharmacologically active salts, esters, amides, proagents, conjugates, active metabolites, isomers, fragments, analogs, etc.

[0099] The term “therapeutically effective” refers to the amount of the composition used is of sufficient quantity to ameliorate one or more causes or symptoms of a disease or disorder. Such amelioration only requires a reduction or alteration, not necessarily elimination.

[0100] “Therapeutically effective amount” or “therapeutically effective dose” of a composition (e.g. a composition comprising an agent) refers to an amount that is effective to achieve a desired therapeutic result. In some embodiments, a desired therapeutic result is the control of type I diabetes. In some embodiments, a desired therapeutic result is the control of obesity. Therapeutically effective amounts of a given therapeutic agent will typically vary with respect to factors such as the type and severity of the disorder or disease being treatedand the age, gender, and weight of the subject. The term can also refer to an amount of a therapeutic agent, or a rate of delivery of a therapeutic agent (e.g., amount over time), effective to facilitate a desired therapeutic effect, such as pain relief. The precise desired therapeutic effect will vary according to the condition to be treated, the tolerance of the subject, the agent and / or agent formulation to be administered (e.g., the potency of the therapeutic agent, the concentration of agent in the formulation, and the like), and a variety of other factors that are appreciated by those of ordinary skill in the art. In some instances, a desired biological or medical response is achieved following administration of multiple dosages of the composition to the subject over a period of days, weeks, or years.

[0101] A “control” is an alternative subject or sample used in an experiment for comparison purposes. A control can be "positive" or "negative."NEOANTIGENS AND COMPOSITIONS THEREOF

[0102] Disclosed herein, in one aspect are neoantigens identified by the methods disclosed herein. In one aspect, the neoantigen can comprise STIRVLSGY (SEQ ID NO: 1), RVEEAIHPV (SEQ ID NO: 2), FLWDKRTGL (SEQ ID NO: 3), RYLHGLQKF (SEQ ID NO: 4), NYYKVNWTF (SEQ ID NO: 5), ATRRESELY (SEQ ID NO: 6), RSFADSYAQWK (SEQ ID NO: 7), ALPEVQKQV (SEQ ID NO: 8), RYLPSSVFL (SEQ ID NO: 9), DAPQTRQV (SEQ ID NO: 10), EAQRPTAEY (SEQ ID NO: 11), EAAGEVRAM (SEQ ID NO: 12), AAVGTTVSL (SEQ ID NO: 13), VYKPVSIYL (SEQ ID NO: 14), AAADAANPL (SEQ ID NO: 15), RSSSEASFLRK (SEQ ID NO: 16), RLSDAEIMGK (SEQ ID NO: 17), RVFGVEINK (SEQ ID NO: 18), RLQAVGSLLEK (SEQ ID NO: 19), AETDERRLL (SEQ ID NO: 20), RVKMWVMGK (SEQ ID NO: 21), SRHLGAEAL (SEQ ID NO: 22), IHQQEDSARFF (SEQ ID NO: 23), APRGDGVQVSA (SEQ ID NO: 24), APVASHGLVL (SEQ ID NO: 25), LYLETRSEF (SEQ ID NO: 26), AYPASLQTL (SEQ ID NO: 27), KLLDFSTRI (SEQ ID NO: 28), RLYSLSTALR (SEQ ID NO: 29), SAFQQMWISK (SEQ ID NO: 30), RTNNILLPR (SEQ ID NO: 31), RPSPVRVAAL (SEQ ID NO: 32), SPILTSSTL (SEQ ID NO: 33), ATHHQPWAQK (SEQ ID NO: 34), RLQDPQAGISK (SEQ ID NO: 35), RVKERDFFLK (SEQ ID NO: 36), RIMPLLIAK (SEQ ID NO: 37), KSLAAELLVLK (SEQ ID NO: 38), GMPWHFMLK (SEQ ID NO: 39), VLMEVTLEGK (SEQ ID NO: 40), DHDSVDKLVI (SEQ ID NO: 41), KTWDQVPFSV (SEQ ID NO: 44), DLSRRDVSL (SEQ ID NO: 45), DLLSALQQL (SEQ ID NO: 46), TLLVYLFSL (SEQ ID NO: 47), VLEETSVML (SEQ ID NO: 48), IARLAKIPL (SEQ ID NO: 49), LLDGVTVSL (SEQ ID NO: 50), LPVESLPLL (SEQ ID NO: 51),YTSRGALYL (SEQ ID NO: 52), RLKIKGWRK (SEQ ID NO: 53), KIYQANGKQK (SEQ ID NO: 54), DTLTSQLKEL (SEQ ID NO: 55), KEIETQKTL (SEQ ID NO: 56), TEIQTTIREYY (SEQ ID NO: 57), EAFPLKTGTR (SEQ ID NO: 58), IVSAQNLLK (SEQ ID NO: 59), SGYKINVQK (SEQ ID NO: 60), ILPKVIYRF (SEQ ID NO: 61), RIAKSILSQK (SEQ ID NO: 62), RIYNELKOIYK (SEQ ID NO: 63), REMQIKTTM (SEQ ID NO: 64), LTPVRMAI (SEQ ID NO: 65), HAAASFETL (SEQ ID NO: 66), RLLGTEFQT (SEQ ID NO: 67), FTDSQGNDIK (SEQ ID NO: 68), NLLAEIHGV (SEQ ID NO: 69), LLAEIHGV (SEQ ID NO: 70), LLAEIHGV (SEQ ID NO: 71), WLQQTKSLY (SEQ ID NO: 72), SEWENGSGM (SEQ ID NO: 73), TLFPERREW (SEQ ID NO: 74), DRVGGRRDL (SEQ ID NO: 75), SEQDPENRAW (SEQ ID NO: 76), QAQLEVSVQY (SEQ ID NO: 77), FLYQDPENQAL (SEQ ID NO: 78), YLLENLRSL (SEQ ID NO: 79), VTDGVSLLL (SEQ ID NO: 80), SPSRPPSSL (SEQ ID NO: 81), SLAPLVHLV (SEQ ID NO: 82), VPHTRPVSL (SEQ ID NO: 83), VVSGATGAK (SEQ ID NO: 84), VWMRSPLSTF (SEQ ID NO: 85), GIKHASIAR (SEQ ID NO: 86), FEFEKGSAQY (SEQ ID NO: 87), QYNPIRTTF (SEQ ID NO: 88), FLDETLRSLA (SEQ ID NO: 89), KATEYVHSL (SEQ ID NO: 90), APAAGALHAA (SEQ ID NO: 91), RIKNCPPR (SEQ ID NO: 93), WYKYTMEYY (SEQ ID NO: 94), FLPVKKAQL (SEQ ID NO: 95), FLLTRILTI (SEQ ID NO: 96), YMLDLQPET (SEQ ID NO: 97), RVNSSVLKQGR (SEQ ID NO: 98), GEKEALVY (SEQ ID NO: 99), FLGRGRSGK (SEQ ID NO: 100), RSFIVWGLTF (SEQ ID NO: 101), IMDQVPFSV (SEQ ID NO: 103), TIDELQKI (SEQ ID NO: 104), LSDLGSGIYR (SEQ ID NO: 105), IEKEVISKY (SEQ ID NO: 106), LSAKQNLEI (SEQ ID NO: 107), VRGKDIFII (SEQ ID NO: 108), QPKTKLLLL (SEQ ID NO: 109), LDIHTFGLYY (SEQ ID NO: 110), KLKPGILKK (SEQ ID NO: 111), KMAEVIGLSK (SEQ ID NO: 112), LVGPNGVGK (SEQ ID NO: 113), SPWSPSPSL (SEQ ID NO: 114), LPLVGFTSI (SEQ ID NO: 115), DAGSQILTL (SEQ ID NO: 116), QYNRGTNGW (SEQ ID NO: 117), and TVVVRDVTV (SEQ ID NO: 118).

[0103] There are a variety of molecules disclosed herein that are nucleic acid based, including for example the nucleic acids that encode, for example SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO:34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103, or various functional nucleic acids. The disclosed nucleic acids are made up of for example, nucleotides, nucleotide analogs, or nucleotide substitutes. Non-limiting examples of these and other molecules are discussed herein. It is understood that for example, when a vector is expressed in a cell, that the expressed mRNA will typically be made up of A, C, G, and U. Likewise, it is understood that if, for example, an antisense molecule is introduced into a cell or cell environment through for example exogenous delivery, it is advantageous that the antisense molecule be made up of nucleotide analogs that reduce the degradation of the antisense molecule in the cellular environment.Nucleotides and related molecules

[0104] A nucleotide is a molecule that contains a base moiety, a sugar moiety and a phosphate moiety. Nucleotides can be linked together through their phosphate moieties and sugar moieties creating an internucleoside linkage. The base moiety of a nucleotide can be adenin-9-yl (A), cytosin-l-yl (C), guanin-9-yl (G), uracil-l-yl (U), and thymin-l-yl (T). The sugar moiety of a nucleotide is a ribose or a deoxyribose. The phosphate moiety of a nucleotide is pentavalent phosphate. An non-limiting example of a nucleotide would be 3'- AMP (3'-adenosine monophosphate) or 5'-GMP (5'-guanosine monophosphate). There are many varieties of these types of molecules available in the art and available herein.

[0105] A nucleotide analog is a nucleotide which contains some type of modification to either the base, sugar, or phosphate moieties. Modifications to nucleotides are well known in the art and would include for example, 5-methylcytosine (5-me-C), 5 -hydroxymethyl cytosine, xanthine, hypoxanthine, and 2-aminoadenine as well as modifications at the sugaror phosphate moieties. There are many varieties of these types of molecules available in the art and available herein.

[0106] Nucleotide substitutes are molecules having similar functional properties to nucleotides, but which do not contain a phosphate moiety, such as peptide nucleic acid (PNA). Nucleotide substitutes are molecules that will recognize nucleic acids in a Watson- Crick or Hoogsteen manner, but which are linked together through a moiety other than a phosphate moiety. Nucleotide substitutes are able to conform to a double helix type structure when interacting with the appropriate target nucleic acid. There are many varieties of these types of molecules available in the art and available herein.

[0107] It is also possible to link other types of molecules (conjugates) to nucleotides or nucleotide analogs to enhance for example, cellular uptake. Conjugates can be chemically linked to the nucleotide or nucleotide analogs. Such conjugates include but are not limited to lipid moieties such as a cholesterol moiety. (Letsinger et al., Proc. Natl. Acad. Set. USA, 1989, 86, 6553-6556). There are many varieties of these types of molecules available in the art and available herein.

[0108] A Watson-Crick interaction is at least one interaction with the Watson-Crick face of a nucleotide, nucleotide analog, or nucleotide substitute. The Watson-Crick face of a nucleotide, nucleotide analog, or nucleotide substitute includes the C2, Nl, and C6 positions of a purine based nucleotide, nucleotide analog, or nucleotide substitute and the C2, N3, C4 positions of a pyrimidine based nucleotide, nucleotide analog, or nucleotide substitute.

[0109] A Hoogsteen interaction is the interaction that takes place on the Hoogsteen face of a nucleotide or nucleotide analog, which is exposed in the major groove of duplex DNA. The Hoogsteen face includes the N7 position and reactive groups (NH2 or O) at the C6 position of purine nucleotides.Sequences

[0110] There are a variety of sequences related to the neoantigens disclosed herein, for example, SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44,SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103, all of which are encoded by nucleic acids or are nucleic acids. The sequences for the human analogs of these genes, as well as other analogs, and alleles of these genes, and splice variants and other types of variants, are available in a variety of protein and gene databases, including Genbank. Those of skill in the art understand how to resolve sequence discrepancies and differences and to adjust the compositions and methods relating to a particular sequence to other related sequences. Primers and / or probes can be designed for any given sequence given the information disclosed herein and known in the art.Functional Nucleic Acids[OHl] Functional nucleic acids are nucleic acid molecules that have a specific function, such as binding a target molecule or catalyzing a specific reaction. Functional nucleic acid molecules can be divided into the following categories, which are not meant to be limiting. For example, functional nucleic acids include antisense molecules, aptamers, ribozymes, triplex forming molecules, and external guide sequences. The functional nucleic acid molecules can act as affectors, inhibitors, modulators, and stimulators of a specific activity possessed by a target molecule, or the functional nucleic acid molecules can possess a de novo activity independent of any other molecules.

[0112] Functional nucleic acid molecules can interact with any macromolecule, such as DNA, RNA, polypeptides, or carbohydrate chains. Thus, functional nucleic acids can interact with the mRNA of any nucleic acid encoding any of the neoantigens disclosed herein, such as SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22,SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103 or they can interact with the neoantigens directly. Often functional nucleic acids are designed to interact with other nucleic acids based on sequence homology between the target molecule and the functional nucleic acid molecule. In other situations, the specific recognition between the functional nucleic acid molecule and the target molecule is not based on sequence homology between the functional nucleic acid molecule and the target molecule, but rather is based on the formation of tertiary structure that allows specific recognition to take place.

[0113] Antisense molecules are designed to interact with a target nucleic acid molecule through either canonical or non-canonical base pairing. The interaction of the antisense molecule and the target molecule is designed to promote the destruction of the target molecule through, for example, RNAseH mediated RNA-DNA hybrid degradation. Alternatively the antisense molecule is designed to interrupt a processing function that normally would take place on the target molecule, such as transcription or replication. Antisense molecules can be designed based on the sequence of the target molecule. Numerous methods for optimization of antisense efficiency by finding the most accessible regions of the target molecule exist. Exemplary methods would be in vitro selection experiments and DNA modification studies using DMS and DEPC. It is preferred that antisense molecules bind the target molecule with a dissociation constant (kd)less than or equal to 10'6, 10'8, 10'10, or 10'12. A representative sample of methods and techniques which aid in the design and use of antisense molecules can be found in the following non-limitinglist of United States patents: 5,135,917, 5,294,533, 5,627,158, 5,641,754, 5,691,317, 5,780,607, 5,786,138, 5,849,903, 5,856,103, 5,919,772, 5,955,590, 5,990,088, 5,994,320, 5,998,602, 6,005,095, 6,007,995, 6,013,522, 6,017,898, 6,018,042, 6,025,198, 6,033,910, 6,040,296, 6,046,004, 6,046,319, and 6,057,437.

[0114] Aptamers are molecules that interact with a target molecule, preferably in a specific way. Typically aptamers are small nucleic acids ranging from 15-50 bases in length that fold into defined secondary and tertiary structures, such as stem-loops or G-quartets. Aptamers can bind small molecules, such as ATP (United States patent 5,631,146) and theophiline (United States patent 5,580,737), as well as large molecules, such as reverse transcriptase (United States patent 5,786,462) and thrombin (United States patent 5,543,293). Aptamers can bind very tightly with kas from the target molecule of less than 10'12M. It is preferred that the aptamers bind the target molecule with a kd less than 10'6, 10'8, 10'10, or 10’12. Aptamers can bind the target molecule with a very high degree of specificity. For example, aptamers have been isolated that have greater than a 10000 fold difference in binding affinities between the target molecule and another molecule that differ at only a single position on the molecule (United States patent 5,543,293). It is preferred that the aptamer have a kd with the target molecule at least 10, 100, 1000, 10,000, or 100,000 fold lower than the kd with a background binding molecule. It is preferred when doing the comparison for a polypeptide for example, that the background molecule be a different polypeptide.Representative examples of how to make and use aptamers to bind a variety of different target molecules can be found in the following non-limiting list of United States patents: 5,476,766, 5,503,978, 5,631,146, 5,731,424 , 5,780,228, 5,792,613, 5,795,721, 5,846,713, 5,858,660 , 5,861,254, 5,864,026, 5,869,641, 5,958,691, 6,001,988, 6,011,020, 6,013,443, 6,020,130, 6,028,186, 6,030,776, and 6,051,698.

[0115] Ribozymes are nucleic acid molecules that are capable of catalyzing a chemical reaction, either intramolecularly or intermolecularly. Ribozymes are thus catalytic nucleic acid. It is preferred that the ribozymes catalyze intermolecular reactions. There are a number of different types of ribozymes that catalyze nuclease or nucleic acid polymerase type reactions which are based on ribozymes found in natural systems, such as hammerhead ribozymes, (for example, but not limited to the following United States patents: 5,334,711, 5,436,330, 5,616,466, 5,633,133, 5,646,020, 5,652,094, 5,712,384, 5,770,715, 5,856,463, 5,861,288, 5,891,683, 5,891,684, 5,985,621, 5,989,908, 5,998,193, 5,998,203, WO 9858058 by Ludwig and Sproat, WO 9858057 by Ludwig and Sproat, and WO 9718312 by Ludwig and Sproat) hairpin ribozymes (for example, but not limited to the following United Statespatents: 5,631,115, 5,646,031, 5,683,902, 5,712,384, 5,856,188, 5,866,701, 5,869,339, and 6,022,962), and tetrahymena ribozymes (for example, but not limited to the following United States patents: 5,595,873 and 5,652,107). There are also a number of ribozymes that are not found in natural systems, but which have been engineered to catalyze specific reactions de novo (for example, but not limited to the following United States patents: 5,580,967, 5,688,670, 5,807,718, and 5,910,408). Preferred ribozymes cleave RNA or DNA substrates, and more preferably cleave RNA substrates. Ribozymes typically cleave nucleic acid substrates through recognition and binding of the target substrate with subsequent cleavage. This recognition is often based mostly on canonical or non-canonical base pair interactions. This property makes ribozymes particularly good candidates for target specific cleavage of nucleic acids because recognition of the target substrate is based on the target substrates sequence. Representative examples of how to make and use ribozymes to catalyze a variety of different reactions can be found in the following non-limiting list of United States patents: 5,646,042, 5,693,535, 5,731,295, 5,811,300, 5,837,855, 5,869,253, 5,877,021, 5,877,022, 5,972,699, 5,972,704, 5,989,906, and 6,017,756.

[0116] Triplex forming functional nucleic acid molecules are molecules that can interact with either double-stranded or single-stranded nucleic acid. When triplex molecules interact with a target region, a structure called a triplex is formed, in which there are three strands of DNA forming a complex dependent on both Watson-Crick and Hoogsteen base-pairing. Triplex molecules are preferred because they can bind target regions with high affinity and specificity. It is preferred that the triplex forming molecules bind the target molecule with a ka less than 10'6, 10'8, 10'10, or 10'12. Representative examples of how to make and use triplex forming molecules to bind a variety of different target molecules can be found in the following non-limiting list of United States patents: 5,176,996, 5,645,985, 5,650,316, 5,683,874, 5,693,773, 5,834,185, 5,869,246, 5,874,566, and 5,962,426.

[0117] External guide sequences (EGSs) are molecules that bind a target nucleic acid molecule forming a complex, and this complex is recognized by RNase P, which cleaves the target molecule. EGSs can be designed to specifically target a RNA molecule of choice. RNAse P aids in processing transfer RNA (tRNA) within a cell. Bacterial RNAse P can be recruited to cleave virtually any RNA sequence by using an EGS that causes the target RNA:EGS complex to mimic the natural tRNA substrate. (WO 92 / 03566 by Yale, and Forster and Altman, Science 238:407-409 (1990)).

[0118] Similarly, eukaryotic EGS / RNAse P-directed cleavage of RNA can be utilized to cleave desired targets within eukarotic cells. (Yuan et al., Proc. Natl. Acad. Ser USA89:8006-8010 (1992); WO 93 / 22434 by Yale; WO 95 / 24489 by Yale; Yuan and Altman, EMBO J 14: 159-168 (1995), and Carrara et al., Proc. Natl. Acad. Scr (USA) 92:2627-2631 (1995)). Representative examples of how to make and use EGS molecules to facilitate cleavage of a variety of different target molecules be found in the following non-limiting list of United States patents: 5,168,053, 5,624,824, 5,683,873, 5,728,521, 5,869,248, and 5,877,162.

[0119] In one aspect, disclosed herein are engineered cells (for example, T cells) comprising a nucleic acid encoding a receptor that binds to any of the neoantigens disclosed herein, including, but not limited to SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103.

[0120] It is understood and herein contemplated that the methods disclosed herein are specifically designed for the identification and validation of neoantigens that can serve as the target for an anti-cancer therapeutic or be used as a component of an anti-cancer vaccine. In one aspect, disclosed herein are vaccines for the treatment of a cancer comprising a therapeutically acceptable amount of one or more neoantigens selected from the group consisting of STIRVLSGY (SEQ ID NO: 1), RVEEAIHPV (SEQ ID NO: 2), FLWDKRTGL(SEQ ID NO: 3), RYLHGLQKF (SEQ ID NO: 4), NYYKVNWTF (SEQ ID NO: 5), ATRRESELY (SEQ ID NO: 6), RSFADSYAQWK (SEQ ID NO: 7), ALPEVQKQV (SEQ ID NO: 8), RYLPSSVFL (SEQ ID NO: 9), DAPQTRQV (SEQ ID NO: 10), EAQRPTAEY (SEQ ID NO: 11), EAAGEVRAM (SEQ ID NO: 12), AAVGTTVSL (SEQ ID NO: 13), VYKPVSIYL (SEQ ID NO: 14), AAADAANPL (SEQ ID NO: 15), RSSSEASFLRK (SEQ ID NO: 16), RLSDAEIMGK (SEQ ID NO: 17), RVFGVEINK (SEQ ID NO: 18), RLQAVGSLLEK (SEQ ID NO: 19), AETDERRLL (SEQ ID NO: 20), RVKMWVMGK (SEQ ID NO: 21), SRHLGAEAL (SEQ ID NO: 22), IHQQEDSARFF (SEQ ID NO: 23), APRGDGVQVSA (SEQ ID NO: 24), APVASHGLVL (SEQ ID NO: 25), LYLETRSEF (SEQ ID NO: 26), AYPASLQTL (SEQ ID NO: 27), KLLDFSTRI (SEQ ID NO: 28), RLYSLSTALR (SEQ ID NO: 29), SAFQQMWISK (SEQ ID NO: 30), RTNNILLPR (SEQ ID NO: 31), RPSPVRVAAL (SEQ ID NO: 32), SPILTSSTL (SEQ ID NO: 33), ATHHQPWAQK (SEQ ID NO: 34), RLQDPQAGISK (SEQ ID NO: 35), RVKERDFFLK (SEQ ID NO: 36), RIMPLLIAK (SEQ ID NO: 37), KSLAAELLVLK (SEQ ID NO: 38), GMPWHFMLK (SEQ ID NO: 39), VLMEVTLEGK (SEQ ID NO: 40), DHDSVDKLVI (SEQ ID NO: 41), KTWDQVPFSV (SEQ ID NO: 44), DLSRRDVSL (SEQ ID NO: 45), DLLSALQQL (SEQ ID NO: 46), TLLVYLFSL (SEQ ID NO: 47), VLEETSVML (SEQ ID NO: 48), IARLAKIPL (SEQ ID NO: 49), LLDGVTVSL (SEQ ID NO: 50), LPVESLPLL (SEQ ID NO: 51), YTSRGALYL (SEQ ID NO: 52), RLKIKGWRK (SEQ ID NO: 53), KIYQANGKQK (SEQ ID NO: 54), DTLTSQLKEL (SEQ ID NO: 55), KEIETQKTL (SEQ ID NO: 56), TEIQTTIREYY (SEQ ID NO: 57), EAFPLKTGTR (SEQ ID NO: 58), IVSAQNLLK (SEQ ID NO: 59), SGYKINVQK (SEQ ID NO: 60), ILPKVIYRF (SEQ ID NO: 61), RIAKSILSQK (SEQ ID NO: 62), RIYNELKOIYK (SEQ ID NO: 63), REMQIKTTM (SEQ ID NO: 64), LTPVRMAI (SEQ ID NO: 65), HAAASFETL (SEQ ID NO: 66), RLLGTEFQT (SEQ ID NO: 67), FTDSQGNDIK (SEQ ID NO: 68), NLLAEIHGV (SEQ ID NO: 69), LLAEIHGV (SEQ ID NO: 70), LLAEIHGV (SEQ ID NO: 71), WLQQTKSLY (SEQ ID NO: 72), SEWENGSGM (SEQ ID NO: 73), TLFPERREW (SEQ ID NO: 74), DRVGGRRDL (SEQ ID NO: 75), SEQDPENRAW (SEQ ID NO: 76), QAQLEVSVQY (SEQ ID NO: 77), FLYQDPENQAL (SEQ ID NO: 78), YLLENLRSL (SEQ ID NO: 79), VTDGVSLLL (SEQ ID NO: 80), SPSRPPSSL (SEQ ID NO: 81), SLAPLVHLV (SEQ ID NO: 82), VPHTRPVSL (SEQ ID NO: 83), VVSGATGAK (SEQ ID NO: 84), VWMRSPLSTF (SEQ ID NO: 85), GIKHASIAR (SEQ ID NO: 86), FEFEKGSAQY (SEQ ID NO: 87), QYNPIRTTF (SEQ ID NO: 88), FLDETLRSLA (SEQ ID NO: 89), KATEYVHSL (SEQ ID NO: 90), APAAGALHAA (SEQ ID NO: 91),RIKNCPPR (SEQ ID NO: 93), WYKYTMEYY (SEQ ID NO: 94), FLPVKKAQL (SEQ ID NO: 95), FLLTRILTI (SEQ ID NO: 96), YMLDLQPET (SEQ ID NO: 97), RVNSSVLKQGR (SEQ ID NO: 98), GEKEALVY (SEQ ID NO: 99), FLGRGRSGK (SEQ ID NO: 100), RSFIVWGLTF (SEQ ID NO: 101), IMDQVPFSV (SEQ ID NO: 103), TIDELQKI (SEQ ID NO: 104), LSDLGSGIYR (SEQ ID NO: 105), IEKEVISKY (SEQ ID NO: 106), LSAKQNLEI (SEQ ID NO: 107), VRGKDIFII (SEQ ID NO: 108), QPKTKLLLL (SEQ ID NO: 109), LDIHTFGLYY (SEQ ID NO: 110), KLKPGILKK (SEQ ID NO: 111), KMAEVIGLSK (SEQ ID NO: 112), LVGPNGVGK (SEQ ID NO: 113), SPWSPSPSL (SEQ ID NO: 114), LPLVGFTSI (SEQ ID NO: 115), DAGSQILTL (SEQ ID NO: 116), QYNRGTNGW (SEQ ID NO: 117), and TVVVRDVTV (SEQ ID NO: 118); and a pharmaceutically acceptable carrier.

[0121] It is further understood and herein contemplated that the disclosed neoantigens can not only serve as an acitve component of a vaccine, but can be a target for a tumor infiltrating lymphocyte (TIL), T cell receptor (TCR), antibodies, scFv, bispecific T cell engagers (BiTEs), nanobodies, diabodies, and / or chimeric antigen receptor (CAR) including, but not limited to CAR T cells, CAR natural killer (NK) cells (CAR NK cells), CAR NK T cells, CAR macrophage (CARMA), and peptide-centric CARs (PC-CARs).Antibodies

[0122] The term “antibodies” is used herein in a broad sense and includes both polyclonal and monoclonal antibodies. In addition to intact immunoglobulin molecules, also included in the term “antibodies” are fragments or polymers of those immunoglobulin molecules, and human or humanized versions of immunoglobulin molecules or fragments thereof, as long as they are chosen for their ability to bind SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ IDNO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103. The antibodies can be tested for their desired activity using the in vitro assays described herein, or by analogous methods, after which their in vivo therapeutic and / or prophylactic activities are tested according to known clinical testing methods. There are five major classes of human immunoglobulins: IgA, IgD, IgE, IgG and IgM, and several of these may be further divided into subclasses (isotypes), e.g., IgG-1, IgG-2, IgG-3, and IgG-4; IgA- 1 and IgA-2. One skilled in the art would recognize the comparable classes for mouse. The heavy chain constant domains that correspond to the different classes of immunoglobulins are called alpha, delta, epsilon, gamma, and mu, respectively.

[0123] The term “monoclonal antibody” as used herein refers to an antibody obtained from a substantially homogeneous population of antibodies, i.e., the individual antibodies within the population are identical except for possible naturally occurring mutations that may be present in a small subset of the antibody molecules. The monoclonal antibodies herein specifically include "chimeric" antibodies in which a portion of the heavy and / or light chain is identical with or homologous to corresponding sequences in antibodies derived from a particular species or belonging to a particular antibody class or subclass, while the remainder of the chain(s) is identical with or homologous to corresponding sequences in antibodies derived from another species or belonging to another antibody class or subclass, as well as fragments of such antibodies, as long as they exhibit the desired antagonistic activity.

[0124] The disclosed monoclonal antibodies can be made using any procedure which produces mono clonal antibodies. For example, disclosed monoclonal antibodies can be prepared using hybridoma methods, such as those described by Kohler and Milstein, Nature, 256:495 (1975). In a hybridoma method, a mouse or other appropriate host animal is typically immunized with an immunizing agent to elicit lymphocytes that produce or are capable of producing antibodies that will specifically bind to the immunizing agent. Alternatively, the lymphocytes may be immunized in vitro.

[0125] The monoclonal antibodies may also be made by recombinant DNA methods. DNA encoding the disclosed monoclonal antibodies can be readily isolated and sequencedusing conventional procedures (e.g., by using oligonucleotide probes that are capable of binding specifically to genes encoding the heavy and light chains of murine antibodies). Libraries of antibodies or active antibody fragments can also be generated and screened using phage display techniques, e.g., as described in U.S. Patent No. 5,804,440 to Burton et al. and U.S. Patent No. 6,096,441 to Barbas et al.

[0126] In vitro methods are also suitable for preparing monovalent antibodies. Digestion of antibodies to produce fragments thereof, particularly, Fab fragments, can be accomplished using routine techniques known in the art. For instance, digestion can be performed using papain. Examples of papain digestion are described in WO 94 / 29348 published Dec. 22, 1994 and U.S. Pat. No. 4,342,566. Papain digestion of antibodies typically produces two identical antigen binding fragments, called Fab fragments, each with a single antigen binding site, and a residual Fc fragment. Pepsin treatment yields a fragment that has two antigen combining sites and is still capable of cross-linking antigen.

[0127] As used herein, the term “antibody or fragments thereof’ encompasses chimeric antibodies and hybrid antibodies, with dual or multiple antigen or epitope specificities, diabodies, and fragments, such as F(ab’)2, Fab’, Fab, Fv, sFv, scFv, nanobodies, and the like, including hybrid fragments. Thus, fragments of the antibodies that retain the ability to bind their specific antigens are provided. For example, fragments of antibodies which maintain SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103 binding activity are included within the meaning of the term “antibody or fragment thereof.” Such antibodies and fragments can be made by techniques known in the art and can be screened for specificity and activity according to the methods set forth in the Examples and in general methods for producing antibodies and screening antibodies for specificity and activity (See Harlow and Lane. Antibodies, A Laboratory Manual. Cold Spring Harbor Publications, New York, (1988)).

[0128] Also included within the meaning of “antibody or fragments thereof’ are conjugates of antibody fragments and antigen binding proteins (single chain antibodies).

[0129] The fragments, whether attached to other sequences or not, can also include insertions, deletions, substitutions, or other selected modifications of particular regions or specific amino acids residues, provided the activity of the antibody or antibody fragment is not significantly altered or impaired compared to the non-modified antibody or antibody fragment. These modifications can provide for some additional property, such as to remove / add amino acids capable of disulfide bonding, to increase its bio-longevity, to alter its secretory characteristics, etc. In any case, the antibody or antibody fragment must possess a bioactive property, such as specific binding to its cognate antigen. Functional or active regions of the antibody or antibody fragment may be identified by mutagenesis of a specific region of the protein, followed by expression and testing of the expressed polypeptide. Such methods are readily apparent to a skilled practitioner in the art and can include site-specific mutagenesis of the nucleic acid encoding the antibody or antibody fragment. (Zoller, M. J. Curr. Opin. Biotechnol. 3:348-354, 1992).

[0130] As used herein, the term “antibody” or “antibodies” can also refer to a human antibody and / or a humanized antibody. Many non-human antibodies (e.g., those derived from mice, rats, or rabbits) are naturally antigenic in humans, and thus can give rise to undesirable immune responses when administered to humans. Therefore, the use of human or humanized antibodies in the methods serves to lessen the chance that an antibody administered to a human will evoke an undesirable immune response.Human antibodies

[0131] The disclosed human antibodies can be prepared using any technique. The disclosed human antibodies can also be obtained from transgenic animals. For example, transgenic, mutant mice that are capable of producing a full repertoire of human antibodies,in response to immunization, have been described (see, e.g., Jakobovits et al., Proc. Natl. Acad. Sci. USA, 90:2551-255 (1993); Jakobovits et al., Nature, 362:255-258 (1993);Bruggermann et al., Year in Immunol., 7:33 (1993)). Specifically, the homozygous deletion of the antibody heavy chain joining region (1(H)) gene in these chimeric and germ-line mutant mice results in complete inhibition of endogenous antibody production, and the successful transfer of the human germ-line antibody gene array into such germ-line mutant mice results in the production of human antibodies upon antigen challenge. Antibodies having the desired activity are selected using Env-CD4-co-receptor complexes as described herein.Humanized antibodies

[0132] Antibody humanization techniques generally involve the use of recombinant DNA technology to manipulate the DNA sequence encoding one or more polypeptide chains of an antibody molecule. Accordingly, a humanized form of a non-human antibody (or a fragment thereof) is a chimeric antibody or antibody chain (or a fragment thereof, such as an sFv, Fv, Fab, Fab’, F(ab’)2, or other antigen-binding portion of an antibody) which contains a portion of an antigen binding site from a non-human (donor) antibody integrated into the framework of a human (recipient) antibody.

[0133] To generate a humanized antibody, residues from one or more complementarity determining regions (CDRs) of a recipient (human) antibody molecule are replaced by residues from one or more CDRs of a donor (non-human) antibody molecule that is known to have desired antigen binding characteristics (e.g., a certain level of specificity and affinity for the target antigen). In some instances, Fv framework (FR) residues of the human antibody are replaced by corresponding non-human residues. Humanized antibodies may also contain residues which are found neither in the recipient antibody nor in the imported CDR or framework sequences. Generally, a humanized antibody has one or more amino acid residues introduced into it from a source which is non-human. In practice, humanized antibodies are typically human antibodies in which some CDR residues and possibly some FR residues are substituted by residues from analogous sites in rodent antibodies. Humanized antibodies generally contain at least a portion of an antibody constant region (Fc), typically that of a human antibody (Jones et al., Nature, 321 :522-525 (1986), Reichmann et al., Nature, 332:323-327 (1988), and Presta, Curr. Opin. Struct. Biol., 2:593-596 (1992)).

[0134] Methods for humanizing non-human antibodies are well known in the art. For example, humanized antibodies can be generated according to the methods of Winter and co-workers (Jones et al., Nature, 321 :522-525 (1986), Riechmann et al., Nature, 332:323-327 (1988), Verhoeyen et al., Science, 239: 1534-1536 (1988)), by substituting rodent CDRs orCDR sequences for the corresponding sequences of a human antibody. Methods that can be used to produce humanized antibodies are also described in U.S. Patent No. 4,816,567 (Cabilly et al.), U.S. Patent No. 5,565,332 (Hoogenboom et al.), U.S. Patent No. 5,721,367 (Kay et al.), U.S. Patent No. 5,837,243 (Deo et al.), U.S. Patent No. 5, 939,598 (Kucherlapati et al.), U.S. Patent No. 6,130,364 (Jakobovits et al.), and U.S. Patent No. 6,180,377 (Morgan et al.).Administration of antibodies

[0135] Administration of the antibodies can be done as disclosed herein. Nucleic acid approaches for antibody delivery also exist. The broadly neutralizing anti SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103 antibodies and antibody fragments can also be administered to patients or subjects as a nucleic acid preparation (e.g., DNA or RNA) that encodes the antibody or antibody fragment, such that the patient's or subject's own cells take up the nucleic acid and produce and secrete the encoded antibody or antibody fragment. The delivery of the nucleic acid can be by any means, as disclosed herein, for example.T cell receptor

[0136] In one aspect, disclosed herein is T cell receptor (TCR) that binds to one or more antigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103. In one aspect, disclosed herein are isolated nucleic acids encoding the TCR. In one aspect, disclosed herein is a T cell comprising a nucleic acid encoding any of the TCR disclosed herein.

[0137] In embodiments of the disclosure, there are: a composition encoding a and 0 subunits of a TCR; and instructions for use of the composition. The composition may be, for example, a recombinant virus, or a viral vector. In some embodiments, the composition comprises one or more sequences encoding TCR subunits comprising at least one of: a variable region substantially as described herein.

[0138] In some embodiments, vectors can be used to introduce polynucleotide sequences that encode all or part of a functional TCR into a packaging cell line for the preparation of a recombinant virus. In addition to the elements as described herein, the vectors can contain polynucleotide sequences encoding the various components of the recombinant virus and at least one variable region as described herein, as well as any components necessary for theproduction of the virus that are not provided by the packaging cell line. In other embodiments, in addition to the elements as described herein, the vectors can contain polynucleotide sequences encoding the various components of the recombinant virus and at least one variable region as described here, as well as any components necessary for the production of the virus that are not provided by the packaging cell line. Eukaryotic cell expression vectors are well known in the art and are available from a number of commercial sources.

[0139] In some embodiments, one or more multi ci stronic expression vectors are utilized that include two or more of the elements (e.g., the viral genes, at least one of: an ml-a sequence and an m 1-0 sequence, a suicide gene or genes) necessary for production of a desired recombinant virus in packaging cells. The use of multi ci stronic vectors reduces the total number of vectors required and thus avoids the possible difficulties associated with coordinating expression from multiple vectors. In a multi ci stronic vector the various elements to be expressed are operably linked to one or more promoters (and other expression control elements as necessary). In some embodiments a multi ci stronic vector comprising a suicide gene and / or a reporter gene, viral elements and nucleotide sequences encoding all or part of an a or 0 subunit of a TCR, is used, wherein the nucleotide sequences are substantially as described herein.

[0140] Each component to be expressed in a multicistronic expression vector may be separated, for example, by an IRES element or a viral 2A element, to allow for separate expression of the various proteins from the same promoter. IRES elements and 2A elements are known in the art (U.S. Patent 4,937,190; de Felipe et al, 2004. Traffic 5: 616-626, each of which is incorporated herein by reference in its entirety). In one embodiment, oligonucleotides encoding furin cleavage site sequences (RAKR) (Fang et al, 2005. Nat. Biotech 23: 584-590, which is incorporated herein by reference in its entirety) linked with 2A-like sequences from foot-and-mouth diseases virus (FMDV), equine rhinitis A virus (ERAV), and thosea asigna virus (TaV) (Szymczak etal, 2004. Nat. Biotechnol. 22: 589-594, which is incorporated herein by reference in its entirety) are used to separate genetic elements in a multicistronic vector. The efficacy of a particular multicistronic vector for use in synthesizing the desired recombinant virus can readily be tested by detecting expression of each of the genes using standard protocols. Exemplary protocols that are well known in the art include, but are not limited to, antibody- specific immunoassays such as Western blotting.

[0141] Vectors will usually contain a promoter that is recognized by the packaging cell and that is operably linked to the polynucleotide(s) encoding the targeting molecule, viral components, and the like. A promoter is an expression control element formed by a nucleic acid sequence that permits binding of RNA polymerase and transcription to occur. Promoters are untranslated sequences that are located upstream (5') to the start codon of a structural gene (generally within about 100 to 1000 bp) and control the transcription and translation of the antigen-specific polynucleotide sequence to which they are operably linked. Promoters may be inducible or constitutive. The activity of the inducible promoters is induced by the presence or absence of biotic or abiotic factors. Inducible promoters can be a useful tool in genetic engineering because the expression of genes to which they are operably linked can be turned on or off at certain stages of development of an organism or in a particular tissue. Inducible promoters can be grouped as chemically-regulated promoters, and physically- regulated promoters. Typical chemically-regulated promoters include, not are not limited to, alcohol -regulated promoters (e.g., alcohol dehydrogenase I (alcA) gene promoter), tetracycline-regulated promoters (e.g., tetracycline-responsive promoter), steroid-regulated promoter (e.g., rat glucocorticoid receptor (GR)-based promoter, human estrogen receptor (ER)-based promoter, moth ecdysone receptor-based promoter, and the promoters based on the steroid / retinoid / thyroid receptor superfamily), metal-regulated promoters (e.g., metallothionein gene-based promoters), and pathogenesis-related promoters (e.g.,Arabidopsis and maize pathogen-related (PR) protein-based promoters). Typical physically-regulated promoters include, but are not limited to, temperature-regulated promoters (e.g., heat shock promoters), and light-regulated promoters (e.g., soybean SSU promoter). Other exemplary promoters are described elsewhere, for example, in hypertext transfer protocol: world-wide- web at patentlens.net / daisy / promoters / 768 / 271.html.

[0142] One of skill in the art will be able to select an appropriate promoter based on the specific circumstances. Many different promoters are well known in the art, as are methods for operably linking the promoter to the gene to be expressed. Both native promoter sequences and many heterologous promoters may be used to direct expression in the packaging cell and target cell. However, heterologous promoters are contemplated, as they generally permit greater transcription and higher yields of the desired protein as compared to the native promoter.

[0143] The promoter may be obtained, for example, from the genomes of viruses such as polyoma virus, fowlpox virus, adenovirus, bovine papilloma virus, avian sarcoma virus, cytomegalovirus, a retrovirus, hepatitis-B virus and Simian Virus 40 (SV40). The promotermay also be, for example, a heterologous mammalian promoter, e.g., the actin promoter or an immunoglobulin promoter, a heat-shock promoter, or the promoter normally associated with the native sequence, provided such promoters are compatible with the target cell. In one embodiment, the promoter is the naturally occurring viral promoter in a viral expression system.

[0144] Transcription may be increased by inserting an enhancer sequence into the vector(s). Enhancers are typically cis-acting elements of DNA, usually about 10 to 300 bp in length, that act on a promoter to increase its transcription. Many enhancer sequences are now known from mammalian genes (globin, elastase, albumin, a-fetoprotein, and insulin). An enhancer from a eukaryotic cell virus will be used is particularly contemplated. Examples include the SV40 enhancer on the late side of the replication origin (bp 100-270), the cytomegalovirus early promoter enhancer, the polyoma enhancer on the late side of the replication origin, and adenovirus enhancers. The enhancer may be spliced into the vector at a position 5' or 3' to the antigen-specific polynucleotide sequence and may be located at a site 5' from the promoter.

[0145] Other vectors and methods suitable for adaptation to the expression of viral polypeptides, are well known in the art and are readily adapted to the specific circumstances.

[0146] Using the teachings provided herein, one of skill in the art will recognize that the efficacy of a particular expression system can be tested by transforming packaging cells with a vector comprising a gene encoding a reporter protein and measuring the expression using a suitable technique, for example, measuring fluorescence from a green fluorescent protein conjugate. Suitable reporter genes are well known in the art.

[0147] A vector that encodes a core virus is also known as a“viral vector.” There are a large number of available viral vectors that are suitable for use with the invention, including those identified for human gene therapy applications, such as those described by Pfeifer and V erma (2001 , incorporated herein by reference in its entirety). Suitable viral vectors include vectors based on RNA viruses, such as retrovirus-derived vectors, e.g., Moloney murine leukemia virus (MLV)-derived vectors, and include more complex retrovirus-derived vectors, e.g., lentivirus-derived vectors. Human Immunodeficiency virus (HlV-l)-derived vectors belong to this category. Other examples include lentivirus vectors derived from HIV- 2, feline immunodeficiency virus (Hy), equine infectious anemia virus, simian immunodeficiency virus (SIV) and maedi / visna virus.

[0148] The viral vector in particular may comprise one or more genes encoding components of the recombinant virus as well as nucleic acids encoding all or part of afunctional MART-1 TCR. In some embodiments, the viral vector encodes components of the recombinant virus and at least one of: an ml -a variable region, an ml-b variable region and an ih2-b variable region, and optionally, a suicide or reporter gene. In other embodiments, the viral vector encodes components of the recombinant virus and at least one of: an ml -a subunit, an ml-b subunit and an ih2-b subunit, and optionally, a suicide or reporter gene. The viral vector may also comprise genetic elements that facilitate expression of the corresponding a and b polynucleotide sequences in a target cell, such as promoter and enhancer sequences. In order to prevent replication in the target cell, endogenous viral genes required for replication may be removed and provided separately in the packaging cell line.

[0149] In a particular embodiment the viral vector comprises an intact retroviral 5' LTR and a self-inactivating 3' LTR.

[0150] Any method known in the art may be used to produce infectious retroviral and / or lentiviral particles whose genome comprises an RNA copy of the viral vector. To this end, the viral vector (along with other vectors encoding at least one of: an ml -a subunit and an ml- b subunit of a TCR that recognizes a peptide antigen from Table 1, and optionally, a suicide gene) may be introduced into a packaging cell line that packages viral genomic RNA based on the viral vector into viral particles. Table 1 shows metadata for tumor RNA-Seq data used in the conducted study, where each row is an RNA-Seq sample.

[0151] The packaging cell line provides the viral proteins that are required in trans for the packaging of the viral genomic RNA into viral particles. The packaging cell line may be any cell line that is capable of expressing retroviral proteins. Particular packaging cell lines include 293 (ATCC CCL X), Platinum A, HeLa (ATCC CCL 2), D17 (ATCC CCL 183), MDCK (ATCC CCL 34), BHK (ATCC CCL-10) and Cf2Th (ATCC CRL 1430). The packaging cell line may stably express the necessary viral proteins. Such a packaging cell line is described, for example, in U.S. Patent 6,218,181, which is incorporated herein by reference in its entirety. Alternatively, a packaging cell line may be transiently transfected with plasmids comprising nucleic acid that encodes one or more necessary viral proteins, including, but not limited to, gag, pol, rev, and any envelope protein that facilitates transduction of a target cell, along with the viral vectors encoding at least one of an ml -a subunit and an ml-b subunit of a TCR that recognizes a peptide antigen from Table 1.

[0152] Viral particles comprising a polynucleotide containing a gene of interest, which typically includes at least one of: an ml -a variable region nucleotide sequence, an ml- b variable region nucleotide sequence, an ih2-b variable region nucleotide sequence, and optionally, a suicide or reporter gene, are collected and allowed to infect the target cell. Insome embodiments, the gene of interest includes at least one of: an ml -a subunit nucleotide sequence, an ml-b subunit nucleotide sequence and an ih2-b subunit nucleotide sequence. In some embodiments, the virus is pseudotyped to achieve target cell specificity. Methods for pseudotyping are well known in the art and also described herein.

[0153] In one embodiment, the recombinant virus used to deliver the gene of interest is a modified lentivirus and the viral vector is based on a lentivirus. As lentiviruses are able to infect both dividing and non-dividing cells, in this embodiment it is not necessary for target cells to be dividing (or to stimulate the target cells to divide).

[0154] In another embodiment, the recombinant virus used to deliver the gene of interest is a modified gammaretrovirus and the viral vector is based on a gammaretro virus.

[0155] In another embodiment the vector is based on the murine stem cell virus (MSCV; (Hawley, R. G., etal. (1996) Proc. Natl. Acad. Sci. USA 93: 10297-10302; Keller, G, et al. (1998) Blood 92:877-887; Hawley, R. G, et al. (1994) Gene Ther. 1 : 136-138, each of the foregoing which is incorporated herein by reference in its entirety). The MSCV vector provides long-term stable expression in target cells, particularly hematopoietic precursor cells and their differentiated progeny.

[0156] In another embodiment, the vector is based on a modified Moloney virus, for example a Moloney Murine Leukemia Virus. The viral vector can also can be based on a hybrid virus such as that described in Choi, J. K., et al. (2001. Stem Cells 19, No. 3, 236-246, which is incorporated herein by reference in its entirety).

[0157] A DNA viral vector may be used, including, for example adenovirus- based vectors and adeno-associated virus (AAV)-based vectors. Likewise, retroviral- adenoviral vectors also can be used with the methods of the invention.

[0158] Other vectors also can be used for polynucleotide delivery including vectors derived from herpes simplex viruses (HSVs), including amplicon vectors, replicationdefective HSV and attenuated HSV (Krisky et al. 1998. Gene Ther. 5: 1517-30, which is incorporated herein by reference in its entirety).

[0159] Other vectors that have recently been developed for gene therapy uses can also be used with the methods of the invention. Such vectors include those derived from baculoviruses and alpha- viruses. Jolly, D.J. (1999). Emerging viral vectors pp 209-40 in Friedmann T, ed. (1999). The development of human gene therapy. New York: Cold Spring Harbor Lab, which is incorporated herein by reference in its entirety.

[0160] In some particular embodiments, the viral construct comprises sequences from a lentivirus genome, such as the HIV genome or the SIV genome. The viral construct maycomprise sequences from the 5' and 3' LTRs of a lentivirus. More particularly, the viral construct comprises the R and U5 sequences from the 5' LTR of a lentivirus and an inactivated or self-inactivating 3' LTR from a lentivirus. The LTR sequences may be LTR sequences from any lentivirus from any species. For example, they may be LTR sequences from HIV, SIV, FIV or BIV. In particular, the LTR sequences are HIV LTR sequences.

[0161] The viral construct may comprise an inactivated or self-inactivating 3' LTR. The 3' LTR may be made self-inactivating by any method known in the art. In a particular embodiment the U3 element of the 3' LTR contains a deletion of its enhancer sequence, such as the TATA box, Spl and NF-kappa B sites. As a result of the self-inactivating 3' LTR, the provirus that is integrated into the host cell genome will comprise an inactivated 5' LTR.

[0162] Optionally, the U3 sequence from the lentiviral 5' LTR may be replaced with a promoter sequence in the viral construct. This may increase the titer of virus recovered from the packaging cell line. An enhancer sequence may also be included. Any enhancer / promoter combination that increases expression of the viral RNA genome in the packaging cell line may be used. In a particular embodiment the CMV enhancer / promoter sequence is used.

[0163] In some embodiments, the viral construct may comprise an inactivated or selfinactivating 3' LTR. The 3' LTR may be made self-inactivating by any method known in the art. In a particular embodiment, the U3 element of the 3' LTR contains a deletion of its enhancer sequence, such as the TATA box, Spl and NF-kappa B sites. As a result of the self inactivating 3' LTR, the provirus that is integrated into the host cell genome will comprise an inactivated 5' LTR.

[0164] The viral construct generally comprises a gene of interest, which typically includes at least one of: an ml -a variable region nucleotide sequence, an ml-b variable region nucleotide sequence, an ih2-b variable region nucleotide sequence, an ml -a subunit nucleotide sequence, an ml-b subunit nucleotide sequence and an ih2-b subunit nucleotide sequence, and optionally, a suicide or reporter gene that is desirably expressed in one or more target cells. The gene of interest may located between the 5' LTR and 3' LTR sequences. Further, the gene of interest may in particular be in a functional relationship with other genetic elements, for example transcription regulatory sequences such as promoters and / or enhancers, to regulate expression of the gene of interest in a particular manner once the gene is incorporated into the target cell. In certain embodiments, the useful transcriptional regulatory sequences are those that are highly regulated with respect to activity, both temporally and spatially.

[0165] In some embodiments, the gene of interest is in a functional relationship with internal promoter / enhancer regulatory sequences. An “internal” promoter / enhancer is one that is located between the 5' LTR and the 3' LTR sequences in the viral construct and is operably linked to the gene that is desirably expressed.

[0166] The internal promoter / enhancer may be any promoter, enhancer or promoter / enhancer combination known to increase expression of a gene with which it is in a functional relationship. A “functional relationship” and “operably linked” mean, without limitation, that the gene is in the correct location and orientation with respect to the promoter and / or enhancer that expression of the gene will be affected when the promoter and / or enhancer is contacted with the appropriate molecules.

[0167] The internal promoter / enhancer may be selected based on the desired expression pattern of the gene of interest and the specific properties of known promoters / enhancers. Thus, the internal promoter may be a constitutive promoter. Non-limiting examples of constitutive promoters that may be used include the promoter for ubiquitin, CMV (Karasuyama et al., 1989. J. Exp. Med. 169: 13, which is incorporated herein by reference in its entirety), beta-actin (Gunning et al., 1989. Proc. Natl. Acad. Sci. USA 84:4831-4835, which is incorporated herein by reference in its entirety) and pgk (see, for example, Adra et ah, 1987. Gene 60:65-74; Singer-Sam et al, 1984. Gene 32:409-417; and Dobson et al, 1982. Nucleic Acids Res. 10:2635-2637, each of the foregoing which is incorporated herein by reference in its entirety).

[0168] In addition, promoters may be selected to allow for inducible expression of the gene. A number of systems for inducible expression are known in the art, including the tetracycline responsive system and the lac operator-repressor system. It is also contemplated that a combination of promoters may be used to obtain the desired expression of the gene of interest. The skilled artisan will be able to select a promoter based on the desired expression pattern of the gene in the organism and / or the target cell of interest.

[0169] Among the sub-types and subpopulations of T cells (e.g., co4+ and / or cos+ T cells) are naive T (TN) cells, effector T cells (TEFF), memory T cells and sub-types thereof, such as stem cell memory T (TSCM), central memory T (TCM), effector memory T (Tm,l), or terminally differentiated effector memory T cells, tumor-infiltrating lymphocytes (TIL), immature T cells, mature T cells, helper T cells, cytotoxic T cells, mucosa-associated invariant T (MAIT) cells, naturally occurring and adaptive regulatory T (Treg) cells, helper T cells, such as THI cells, TH2 cells, TH3 cells, THI 7 cells, TH9 cells, TH22 cells, follicular helper T cells, alpha / beta T cells, and delta / gamma T cells.Chimeric antigen receptors

[0170] In one aspect, disclosed herein is chimeric antigen receptor (CAR) that binds to one or more antigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103.

[0171] It is understood and herein contemplated that disclosed CAR can be expressed on any cell capable of expressing said CAR including, but not limited to CAR T cells, CAR natural killer (NK) cells (CAR NK cells), CAR NK T cells, CAR macrophage (CARMA). In some aspects the CAR is a peptide-centric CAR (PC-CAR).

[0172] The antigen recognition domain of the disclosed CAR is usually an scFv. There are however many alternatives. An antigen recognition domain from native T-cell receptor (TCR) alpha and beta single chains have been described, as have simple ectodomains (e.g. CD4 ectodomain to recognize HIV infected cells) and more exotic recognition components such as a linked cytokine (which leads to recognition of cells bearing the cytokine receptor). In fact almost anything that binds a given target with high affinity can be used as an antigen recognition region.

[0173] The endodomain is the business end of the CAR that after antigen recognition transmits a signal to the immune effector cell, activating at least one of the normal effector functions of the immune effector cell. Effector function of a T cell, for example, may be cytolytic activity or helper activity including the secretion of cytokines. Therefore, the endodomain may comprise the “intracellular signaling domain” of a T cell receptor (TCR) and optional co-receptors. While usually the entire intracellular signaling domain can be employed, in many cases it is not necessary to use the entire chain. To the extent that a truncated portion of the intracellular signaling domain is used, such truncated portion may be used in place of the intact chain as long as it transduces the effector function signal.

[0174] Cytoplasmic signaling sequences that regulate primary activation of the TCR complex that act in a stimulatory manner may contain signaling motifs which are known as immunoreceptor tyrosine-based activation motifs (IT AMs). Examples of ITAM containing cytoplasmic signaling sequences include those derived from CD8, CD3(^, CD35, CD3y, CD3s, CD32 (Fc gamma Rlla), DAP10, DAP12, CD79a, CD79b, FcyRIy, FcyRIIIy, FcsRip (FCERIB), and FcsRIy (FCERIG).

[0175] In particular embodiments, the intracellular signaling domain is derived from CD3 zeta (CD3Q (TCR zeta, GenBank aceno. BAG36664.1). T-cell surface glycoprotein CD3 zeta (CD3Q chain, also known as T-cell receptor T3 zeta chain or CD247 (Cluster of Differentiation 247), is a protein that in humans is encoded by the CD247 gene.

[0176] First-generation CARs typically had the intracellular domain from the CD3(^ chain, which is the primary transmitter of signals from endogenous TCRs. Second-generation CARs add intracellular signaling domains from various costimulatory protein receptors (e.g., CD28, 4 IBB, ICOS) to the endodomain of the CAR to provide additional signals to the T cell. Preclinical studies have indicated that the second generation of CAR designs improves the antitumor activity of T cells. More recent, third-generation CARs combine multiple signaling domains to further augment potency. T cells grafted with these CARs have demonstrated improved expansion, activation, persistence, and tumor-eradicating efficiency independent of costimulatory receptor / ligand interaction (Imai C, et al. Leukemia 2004 18:676-84; Maher J, et al. Nat Biotechnol 2002 20:70-5).

[0177] For example, the endodomain of the CAR can be designed to comprise the CD3(^ signaling domain by itself or combined with any other desired cytoplasmic domain(s) useful in the context of the CAR of the invention. For example, the cytoplasmic domain of the CAR can comprise a CD3(^ chain portion and a costimulatory signaling region. The costimulatory signaling region refers to a portion of the CAR comprising the intracellular domain of acostimulatory molecule. A costimulatory molecule is a cell surface molecule other than an antigen receptor or their ligands that is required for an efficient response of lymphocytes to an antigen. Examples of such molecules include CD27, CD28, 4-1BB (CD137), 0X40, CD30, CD40, ICOS, lymphocyte function-associated antigen-1 (LFA-1), CD2, CD7, LIGHT, NKG2C, B7-H3, and a ligand that specifically binds with CD83, CD8, CD4, b2c, CD80, CD86, DAP10, DAP12, MyD88, BTNL3, and NKG2D. Thus, while the CAR is exemplified primarily with CD28 as the co-stimulatory signaling element, other costimulatory elements can be used alone or in combination with other co-stimulatory signaling elements. Thus, specifically contemplated herein are CARs comprising any one or combination of two more co-stimulatory signaling elements for the group consisting of CD27, CD28, 4-1BB (CD137), 0X40, CD30, CD40, ICOS, LFA-1, CD2, CD7, LIGHT, NKG2C, B7-H3, and a ligand that specifically binds with CD83, CD8, CD4, b2c, CD80, CD86, DAP10, DAP12, MyD88, BTNL3, and NKG2DCD28 and 4-1BB, CD28 and 0X40, CD28 and LFA-1, CD28 and CD40. Thus, for example, specifically contemplated herein are CARs comprising co- stimulatory signaling elements for CD28 and CD40, CD28 and 4-1BB, CD28 and 0X40, and CD28 and LFA-1.

[0178] In some embodiments, the CAR comprises a hinge sequence. A hinge sequence is a short sequence of amino acids that facilitates antibody flexibility (see, e.g., Woof et al., Nat. Rev. Immunol., 4(2): 89-99 (2004)). The hinge sequence may be positioned between the antigen recognition moiety (e.g., anti-IL13Ra2 scFv) and the transmembrane domain. The hinge sequence can be any suitable sequence derived or obtained from any suitable molecule. In some embodiments, for example, the hinge sequence is derived from a CD8 alpha molecule or a CD28 molecule.

[0179] The transmembrane domain may be derived either from a natural or from a synthetic source. Where the source is natural, the domain may be derived from any membrane-bound or transmembrane protein. For example, the transmembrane region may be derived from (i.e. comprise at least the transmembrane region(s) of) the alpha, beta or zeta chain of the T-cell receptor, CD28, CD3 epsilon, CD45, CD4, CD5, CD8 (e.g., CD8 alpha, CD8 beta), CD9, CD16, CD22, CD33, CD37, CD64, CD80, CD86, CD134, CD137, or CD154, KIRDS2, 0X40, CD2, CD27, LFA-1 (CDl la, CD18) , ICOS (CD278) , 4-1BB (CD 137) , GITR, CD40, BAFFR, HVEM (LIGHTR) , SLAMF7, NKp80 (KLRF1) , CD 160, CD19, IL2R beta, IL2R gamma, IL7R a, ITGA1, VLA1, CD49a, ITGA4, IA4, CD49D, ITGA6, VLA-6, CD49f, ITGAD, CD l id, ITGAE, CD 103, ITGAL, CDl la, LFA-1, IT GAM, CDl lb, ITGAX, CDl lc, ITGB1, CD29, ITGB2, CD18, LFA-1, ITGB7, TNFR2, DNAM1(CD226) , SLAMF4 (CD244, 2B4) , CD84, CD96 (Tactile) , CEACAM1, CRT AM, Ly9 (CD229) , CD160 (BY55) , PSGL1, CD100 (SEMA4D) , SLAMF6 (NTB-A, LylO8) , SLAM (SLAMF1, CD 150, IPO-3) , BLAME (SLAMF8) , SELPLG (CD 162) , LTBR, and PAG / Cbp. Alternatively the transmembrane domain may be synthetic, in which case it will comprise predominantly hydrophobic residues such as leucine and valine. In some cases, a triplet of phenylalanine, tryptophan and valine will be found at each end of a synthetic transmembrane domain. A short oligo- or polypeptide linker, such as between 2 and 10 amino acids in length, may form the linkage between the transmembrane domain and the endoplasmic domain of the CAR. In some embodiments, the linker can be a spacer derived from the same source as the transmembrane domain. For example in some instances, the spacer can and the transmembrane domain are both derived from the CD28 or CD8 alpha, or from any other source for the transmembrane domain listed above including, but not limited to, the alpha, beta or zeta chain of the T-cell receptor, CD3 epsilon, CD45, CD4, CD5, CD8 beta, CD9, CD16, CD22, CD33, CD37, CD64, CD80, CD86, CD134, CD137, or CD154, KIRDS2, 0X40, CD2, CD27, LFA-1 (CDl la, CD18) , ICOS (CD278) , 4-1BB (CD137) , GITR, CD40, BAFFR, HVEM (LIGHTR) , SLAMF7, NKp80 (KLRF1) , CD 160, CD 19, IL2R beta, IL2R gamma, IL7R a, ITGA1, VLA1, CD49a, ITGA4, IA4, CD49D, ITGA6, VLA-6, CD49f, ITGAD, CDl ld, ITGAE, CD103, ITGAL, CDl la, LFA-1, ITGAM, CDl lb, ITGAX, CDl lc, ITGB1, CD29, ITGB2, CD18, LFA-1, ITGB7, TNFR2, DNAM1 (CD226) , SLAMF4 (CD244, 2B4) , CD84, CD96 (Tactile) , CEACAM1, CRTAM, Ly9 (CD229) , CD160 (BY55) , PSGL1, CD100 (SEMA4D) , SLAMF6 (NTB-A, Lyl08) , SLAM (SLAMF1, CD 150, IPO-3) , BLAME (SLAMF8) , SELPLG (CD 162) , LTBR, and PAG / Cbp. In other embodiments, the transmembrane domain and the liner (such as a spacer) can be derived from different sources, for example, a CD28 transmembrane domain and a CD8 alpha spacer or a CD8 alpha transmembrane domain and a CD28 spacer.

[0180] In some embodiments, the CAR has more than one transmembrane domain, which can be a repeat of the same transmembrane domain, or can be different transmembrane domains.

[0181] In some embodiments, the CAR is a multi-chain CAR, as described in WO2015 / 039523, which is incorporated by reference for this teaching. A multi-chain CAR can comprise separate extracellular ligand binding and signaling domains in different transmembrane polypeptides. The signaling domains can be designed to assemble in juxtamembrane position, which forms flexible architecture closer to natural receptors, that confers optimal signal transduction. For example, the multi-chain CAR can comprise a partof an FCERI alpha chain and a part of an FCERI beta chain such that the FCERI chains spontaneously dimerize together to form a CAR.Pharmaceutical carriers / Delivery of pharmaceutical products

[0182] As described above, the compositions can also be administered in vivo in a pharmaceutically acceptable carrier. By "pharmaceutically acceptable" is meant a material that is not biologically or otherwise undesirable, i.e., the material may be administered to a subject, along with the nucleic acid or vector, without causing any undesirable biological effects or interacting in a deleterious manner with any of the other components of the pharmaceutical composition in which it is contained. The carrier would naturally be selected to minimize any degradation of the active ingredient and to minimize any adverse side effects in the subject, as would be well known to one of skill in the art.

[0183] The compositions may be administered orally, parenterally (e.g., intravenously), by intramuscular injection, by intraperitoneal injection, transdermally, extracorporeally, topically or the like, including topical intranasal administration or administration by inhalant. As used herein, "topical intranasal administration" means delivery of the compositions into the nose and nasal passages through one or both of the nares and can comprise delivery by a spraying mechanism or droplet mechanism, or through aerosolization of the nucleic acid or vector. Administration of the compositions by inhalant can be through the nose or mouth via delivery by a spraying or droplet mechanism. Delivery can also be directly to any area of the respiratory system (e.g., lungs) via intubation. The exact amount of the compositions required will vary from subject to subject, depending on the species, age, weight and general condition of the subject, the severity of the allergic disorder being treated, the particular nucleic acid or vector used, its mode of administration and the like. Thus, it is not possible to specify an exact amount for every composition. However, an appropriate amount can be determined by one of ordinary skill in the art using only routine experimentation given the teachings herein.

[0184] Parenteral administration of the composition, if used, is generally characterized by injection. Injectables can be prepared in conventional forms, either as liquid solutions or suspensions, solid forms suitable for solution of suspension in liquid prior to injection, or as emulsions. A more recently revised approach for parenteral administration involves use of a slow release or sustained release system such that a constant dosage is maintained. See, e.g., U.S. Patent No. 3,610,795, which is incorporated by reference herein.

[0185] The materials may be in solution, suspension (for example, incorporated into microparticles, liposomes, or cells). These may be targeted to a particular cell type via antibodies, receptors, or receptor ligands. The following references are examples of the use ofthis technology to target specific proteins to tumor tissue (Senter, et al., Bioconjugate Chem., 2:447-451, (1991); Bagshawe, K.D., Br. J. Cancer, 60:275-281, (1989); Bagshawe, et al., Br. J. Cancer, 58:700-703, (1988); Senter, et al., Bioconjugate Chem., 4:3-9, (1993); Battelli, et al., Cancer Immunol. Immunother ., 35:421-425, (1992); Pietersz and McKenzie, Immunolog. Reviews, 129:57-80, (1992); and Roffler, et al., Biochem. Pharmacol, 42:2062-2065, (1991)). Vehicles such as "stealth" and other antibody conjugated liposomes (including lipid mediated drug targeting to colonic carcinoma), receptor mediated targeting of DNA through cell specific ligands, lymphocyte directed tumor targeting, and highly specific therapeutic retroviral targeting of murine glioma cells in vivo. The following references are examples of the use of this technology to target specific proteins to tumor tissue (Hughes et al., Cancer Research, 49:6214-6220, (1989); and Litzinger and Huang, Biochimica et Biophysica Acta, 1104: 179-187, (1992)). In general, receptors are involved in pathways of endocytosis, either constitutive or ligand induced. These receptors cluster in clathrin-coated pits, enter the cell via clathrin-coated vesicles, pass through an acidified endosome in which the receptors are sorted, and then either recycle to the cell surface, become stored intracellularly, or are degraded in lysosomes. The internalization pathways serve a variety of functions, such as nutrient uptake, removal of activated proteins, clearance of macromolecules, opportunistic entry of viruses and toxins, dissociation and degradation of ligand, and receptor-level regulation. Many receptors follow more than one intracellular pathway, depending on the cell type, receptor concentration, type of ligand, ligand valency, and ligand concentration. Molecular and cellular mechanisms of receptor-mediated endocytosis has been reviewed (Brown and Greene, DNA and Cell Biology 10:6, 399-409 (1991)).Pharmaceutically Acceptable Carriers

[0186] The compositions, including antibodies, can be used therapeutically in combination with a pharmaceutically acceptable carrier.

[0187] Suitable carriers and their formulations are described in Remington: The Science and Practice of Pharmacy (19th ed.) ed. A.R. Gennaro, Mack Publishing Company, Easton, PA 1995. Typically, an appropriate amount of a pharmaceutically-acceptable salt is used in the formulation to render the formulation isotonic. Examples of the pharmaceutically- acceptable carrier include, but are not limited to, saline, Ringer's solution and dextrose solution. The pH of the solution is preferably from about 5 to about 8, and more preferably from about 7 to about 7.5. Further carriers include sustained release preparations such as semipermeable matrices of solid hydrophobic polymers containing the antibody, which matrices are in the form of shaped articles, e.g., films, liposomes or microparticles. It will beapparent to those persons skilled in the art that certain carriers may be more preferable depending upon, for instance, the route of administration and concentration of composition being administered.

[0188] Pharmaceutical carriers are known to those skilled in the art. These most typically would be standard carriers for administration of drugs to humans, including solutions such as sterile water, saline, and buffered solutions at physiological pH. The compositions can be administered intramuscularly or subcutaneously. Other compounds will be administered according to standard procedures used by those skilled in the art.

[0189] Pharmaceutical compositions may include carriers, thickeners, diluents, buffers, preservatives, surface active agents and the like in addition to the molecule of choice. Pharmaceutical compositions may also include one or more active ingredients such as antimicrobial agents, anti-inflammatory agents, anesthetics, and the like.

[0190] The pharmaceutical composition may be administered in a number of ways depending on whether local or systemic treatment is desired, and on the area to be treated. Administration may be topically (including ophthalmically, vaginally, rectally, intranasally), orally, by inhalation, or parenterally, for example by intravenous drip, subcutaneous, intraperitoneal or intramuscular injection. The disclosed antibodies can be administered intravenously, intraperitoneally, intramuscularly, subcutaneously, intracavity, or transdermally.

[0191] Preparations for parenteral administration include sterile aqueous or non-aqueous solutions, suspensions, and emulsions. Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, vegetable oils such as olive oil, and injectable organic esters such as ethyl oleate. Aqueous carriers include water, alcoholic / aqueous solutions, emulsions or suspensions, including saline and buffered media. Parenteral vehicles include sodium chloride solution, Ringer's dextrose, dextrose and sodium chloride, lactated Ringer's, or fixed oils. Intravenous vehicles include fluid and nutrient replenishers, electrolyte replenishers (such as those based on Ringer's dextrose), and the like. Preservatives and other additives may also be present such as, for example, antimicrobials, anti-oxidants, chelating agents, and inert gases and the like.

[0192] Formulations for topical administration may include ointments, lotions, creams, gels, drops, suppositories, sprays, liquids and powders. Conventional pharmaceutical carriers, aqueous, powder or oily bases, thickeners and the like may be necessary or desirable.

[0193] Compositions for oral administration include powders or granules, suspensions or solutions in water or non-aqueous media, capsules, sachets, or tablets. Thickeners, flavorings, diluents, emulsifiers, dispersing aids or binders may be desirable.

[0194] Some of the compositions may potentially be administered as a pharmaceutically acceptable acid- or base- addition salt, formed by reaction with inorganic acids such as hydrochloric acid, hydrobromic acid, perchloric acid, nitric acid, thiocyanic acid, sulfuric acid, and phosphoric acid, and organic acids such as formic acid, acetic acid, propionic acid, glycolic acid, lactic acid, pyruvic acid, oxalic acid, malonic acid, succinic acid, maleic acid, and fumaric acid, or by reaction with an inorganic base such as sodium hydroxide, ammonium hydroxide, potassium hydroxide, and organic bases such as mono-, di-, trialkyl and aryl amines and substituted ethanolamines.Therapeutic Uses

[0195] Effective dosages and schedules for administering the compositions may be determined empirically, and making such determinations is within the skill in the art. The dosage ranges for the administration of the compositions are those large enough to produce the desired effect in which the symptoms of the disorder are effected. The dosage should not be so large as to cause adverse side effects, such as unwanted cross-reactions, anaphylactic reactions, and the like. Generally, the dosage will vary with the age, condition, sex and extent of the disease in the patient, route of administration, or whether other drugs are included in the regimen, and can be determined by one of skill in the art. The dosage can be adjusted by the individual physician in the event of any counterindications. Dosage can vary, and can be administered in one or more dose administrations daily, for one or several days. Guidance can be found in the literature for appropriate dosages for given classes of pharmaceutical products. For example, guidance in selecting appropriate doses for antibodies can be found in the literature on therapeutic uses of antibodies, e.g., Handbook of Monoclonal Antibodies, Ferrone et al., eds., Noges Publications, Park Ridge, N.J., (1985) ch. 22 and pp. 303-357;Smith et al., Antibodies in Human Diagnosis and Therapy, Haber et al., eds., Raven Press, New York (1977) pp. 365-389. A typical daily dosage of the antibody used alone might range from about 1 pg / kg to up to 100 mg / kg of body weight or more per day, depending on the factors mentioned above.Method of treating cancer

[0196] The disclosed compositions can be used to treat any disease where uncontrolled cellular proliferation occurs such as cancers. A representative but non-limiting list of cancers that the disclosed compositions can be used to treat is the following: lymphomas such as Bcell lymphoma and T cell lymphoma; mycosis fungoides; Hodgkin’s Disease; myeloid leukemia (including, but not limited to acute myeloid leukemia (AML) and / or chronic myeloid leukemia (CML)); bladder cancer; brain cancer; nervous system cancer; head and neck cancer; squamous cell carcinoma of head and neck; renal cancer; lung cancers such as small cell lung cancer, non-small cell lung carcinoma (NSCLC), lung squamous cell carcinoma (LUSC), and Lung Adenocarcinomas (LU AD); neuroblastoma / glioblastoma; ovarian cancer; pancreatic cancer; prostate cancer; skin cancer; hepatic cancer; melanoma; squamous cell carcinomas of the mouth, throat, larynx, and lung; cervical cancer; cervical carcinoma; breast cancer including, but not limited to triple negative breast cancer; genitourinary cancer; pulmonary cancer; esophageal carcinoma; head and neck carcinoma; large bowel cancer; hematopoietic cancers; testicular cancer; and colon and rectal cancers.

[0197] In one aspect, the treatment of the cancer can include the administration of one or more neoantigens disclosed herein or the administration of one or more therapeutic agents that inhibit the expression of one or more neoantigens disclosed herein. Accordingly, disclosed herein are methods of treating, decreasing, reducing, inhibiting, ameliorating, and / or preventing a cancer, cancer recurrence, and / or metastasis in a subject comprising administering to the subject any of the vaccines disclosed herein. For example, disclosed herein are methods of treating, decreasing, reducing, inhibiting, ameliorating, and / or preventing a cancer, cancer recurrence, and / or metastasis in a subject comprising administering to the subject a vaccine comprising a therapeutically acceptable amount of one or more neoantigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73,SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ IDNO: 107, SEQ ID NO: 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111, SEQ IDNO: 112, SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ IDNO: 117, and SEQ ID NO: 118; and a pharmaceutically acceptable carrier.

[0198] Also disclosed herein, a method of treating a cancer in a subject is disclosed comprising administering to the subject the above-discussed vaccine, CAR, TCR, antibody, TIL. For example, in one aspect, disclosed herein are methods of treating, decreasing, reducing, inhibiting, ameliorating, and / or preventing a cancer, cancer recurrence, and / or metastasis in a subject comprising administering to the subject a cell therapy comprising a T cell expressing a T cell receptor (TCR) (including, but not limited to engineered T cells and TILs) that binds to one or more antigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103.

[0199] In one aspect, also disclosed herein are methods of treating, decreasing, reducing, inhibiting, ameliorating, and / or preventing a cancer, cancer recurrence, and / or metastasis in a subject comprising administering to the subject a cell therapy comprising a chimeric antigen receptor (CAR)(including, but not limited to administration of CAR expressing T cells (CAR T cells), natural killer (NK) cells (CAR NK cells), CAR NK T cells, CAR macrophage (CARMA), and / or peptide-centric CAR) that binds to one or more antigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, and / or SEQ ID NO: 103.

[0200] Also disclosed herein are methods of treating, decreasing, reducing, inhibiting, ameliorating, and / or preventing a cancer, cancer recurrence, and / or metastasis in a subject comprising administering to the subject an agent (such as, for example, antisense oligonucleotide, short hairpin RNA (shRNA), long non-coding RNA (IncRNA), small interfering RNA (siRNA), RNAi, or small molecule) that inhibits the expression of one or more neoantigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8,SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO:101, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO:107, SEQ ID NO: 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111, SEQ ID NO:112, SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO:117, and SEQ ID NO: 118.

[0201] It is understood and herein contemplated that the disclosed treatment regimens can used alone or in combination with any anti-cancer therapy known in the art including, but not limited to Abemaciclib, Abiraterone Acetate, ABITREXATE® (Methotrexate), ABRAXANE® (Paclitaxel Albumin-stabilized Nanoparticle Formulation), ABVD, ABVE, ABVE-PC, AC, AC-T, ADCETRIS® (Brentuximab Vedotin), ADE, Ado-Trastuzumab Emtansine, ADRIAMYCIN® (Doxorubicin Hydrochloride), Afatinib Dimaleate, AFINITOR® (Everolimus), AKYNZEO® (Netupitant and Palonosetron Hydrochloride), ALDARA® (Imiquimod), Aldesleukin, ALECENSA® (Alectinib), Alectinib, Alemtuzumab, ALIMTA® (Pemetrexed Disodium), ALIQOPA® (Copanlisib Hydrochloride), ALKERAN™ for Injection (Melphalan Hydrochloride), ALKERAN™ Tablets (Melphalan), ALOXI® (Palonosetron Hydrochloride), ALUNBRIG® (Brigatinib), AMBOCHLORIN® (Chlorambucil), AMBOCLORIN® (Chlorambucil), Amifostine, Aminolevulinic Acid, Anastrozole, Aprepitant, AREDIA® (Pamidronate Disodium), ARIMIDEX® (Anastrozole),AROMASIN® (Exemestane),ARRANON® (Nelarabine), Arsenic Trioxide, ARZERRA® (Ofatumumab), Asparaginase Erwinia chrysanthemi, Atezolizumab, AVASTIN® (Bevacizumab), Avelumab, Axitinib, Azacitidine, BAVENCIO® (Avelumab), BEACOPP, BECENUM® (Carmustine), BELEODAQ® (Belinostat), Belinostat, Bendamustine Hydrochloride, BEP, BESPONSA® (Inotuzumab Ozogamicin) , Bevacizumab, Bexarotene, BEXXAR® (Tositumomab and Iodine I 131 Tositumomab), Bicalutamide, BICNU® (Carmustine), Bleomycin, Blinatumomab, BLINCYTO® (Blinatumomab), Bortezomib, BOSULIF® (Bosutinib), Bosutinib, Brentuximab Vedotin, Brigatinib, BuMel, Busulfan, BUSULFEX® (Busulfan), Cabazitaxel, CABOMETYX® (Cabozantinib-S-Malate), Cabozantinib-S-Malate, CAF, CAMPATH® (Alemtuzumab), CAMPTOSAR® (Irinotecan Hydrochloride), Capecitabine, CAPOX, CARAC® (Fluorouracil— Topical), Carboplatin, CARBOPLATIN-TAXOL, Carfilzomib, CARMUBRIS® (Carmustine), Carmustine, Carmustine Implant, CASODEX® (Bicalutamide), CEM, Ceritinib, CERUBIDINE® (Daunorubicin Hydrochloride), CERVARIX® (Recombinant HPV Bivalent Vaccine), Cetuximab, CEV, Chlorambucil, CHLORAMBUCIL-PREDNISONE, CHOP, Cisplatin, Cladribine, CLAFEN® (Cyclophosphamide), Clofarabine, CLOFAREX® (Clofarabine), CLOLAR® (Clofarabine), CMF, Cobimetinib, COMETRIQ® (Cabozantinib-SMalate), Copanlisib Hydrochloride, COPDAC, COPP, COPP-AB V, COSMEGEN® (Dactinomycin), COTELLIC® (Cobimetinib), Crizotinib, CVP, Cyclophosphamide, CYFOS® (Ifosfamide), CYRAMZA® (Ramucirumab), Cytarabine, Cytarabine Liposome, CYTOSAR-U® (Cytarabine), CYTOXAN® (Cyclophosphamide), Dabrafenib, Dacarbazine, DACOGEN® (Decitabine), Dactinomycin, Daratumumab, DARZALEX® (Daratumumab), Dasatinib, Daunorubicin Hydrochloride, Daunorubicin Hydrochloride and Cytarabine Liposome, Decitabine, Defibrotide Sodium, DEFITELIO® (Defibrotide Sodium), Degarelix, Denileukin Diftitox, Denosumab, DEPOCYT® (Cytarabine Liposome), Dexamethasone, Dexrazoxane Hydrochloride, Dinutuximab, Docetaxel, DOXIL® (Doxorubicin Hydrochloride Liposome), Doxorubicin Hydrochloride, Doxorubicin Hydrochloride Liposome, DOX-SL® (Doxorubicin Hydrochloride Liposome), DTIC-DOME® (Dacarbazine), Durvalumab, EFUDEX® (Fluorouracil— Topical), ELITEK® (Rasburicase), ELLENCE® (Epirubicin Hydrochloride), Elotuzumab, ELOXATIN® (Oxaliplatin), Eltrombopag Olamine, EMEND® (Aprepitant), EMPLICITI® (Elotuzumab), Enasidenib Mesylate, Enzalutamide, Epirubicin Hydrochloride , EPOCH, ERBITUX® (Cetuximab), Eribulin Mesylate, ERIVEDGE® (Vismodegib), Erlotinib Hydrochloride, ERWINAZE® (Asparaginase Erwinia chrysanthemi), ETHYOL® (Amifostine), Etopophos ETOPOPHOS® (Etoposide Phosphate), Etoposide, EtoposidePhosphate, EV ACET® (Doxorubicin Hydrochloride Liposome), Everolimus, EVISTA® (Raloxifene Hydrochloride), EVOMELA® (Melphalan Hydrochloride), Exemestane, 5-FU® (Fluorouracil Injection), 5-FU® (Fluorouracil— Topical), FARESTON® (Toremifene), FARYDAK® (Panobinostat), FASLODEX® (Fulvestrant), FEC, FEMARA® (Letrozole), Filgrastim, FLUDARA® (Fludarabine Phosphate), Fludarabine Phosphate, FLUOROPLEX® (Fluorouracil— Topical), Fluorouracil Injection, Fluorouracil— Topical, Flutamide, FOLEX® (Methotrexate), FOLEX PFS® (Methotrexate), FOLFIRI, FOLFIRI-BEVACIZUMAB, FOLFIRI-CETUXIMAB, FOLFIRINOX, FOLFOX, FOLOTYN® (Pralatrexate), FU-LV, Fulvestrant, GARDASIL® (Recombinant HPV Quadrivalent Vaccine), GARDASIL 9® (Recombinant HPV Nonavalent Vaccine), GAZYVA® (Obinutuzumab), Gefitinib, Gemcitabine Hydrochloride, GEMCITABINE-CISPLATIN, GEMCITABINEOXALIPLATIN, Gemtuzumab Ozogamicin, GEMZAR® (Gemcitabine Hydrochloride), GILOTRIF® (Afatinib Dimaleate), GLEEVEC® (Imatinib Mesylate), GLIADEL® (Carmustine Implant), GLIADEL WAFER® (Carmustine Implant), Glucarpidase, Goserelin Acetate, HALAVEN® (Eribulin Mesylate), HEMANGEOL® (Propranolol Hydrochloride), HERCEPTIN® (Trastuzumab), HPV Bivalent Vaccine, Recombinant, HPV Nonavalent Vaccine, Recombinant, HPV Quadrivalent Vaccine, Recombinant, HYCAMTIN® (Topotecan Hydrochloride), HYDREA® (Hydroxyurea), Hydroxyurea, Hyper-CVAD, IBRANCE® (Palbociclib), Ibritumomab Tiuxetan, Ibrutinib, ICE, ICLUSIG® (Ponatinib Hydrochloride), IDAMYCIN® (Idarubicin Hydrochloride), Idarubicin Hydrochloride, Idelalisib, IDHIFA® (Enasidenib Mesylate), IFEX® (Ifosfamide), Ifosfamide, IFOSFAMIDUM® (Ifosfamide), IL-2 (Aldesleukin), Imatinib Mesylate, IMBRUVICA® (Ibrutinib), IMFINZI® (Durvalumab), Imiquimod, IMLYGIC® (Talimogene Laherparepvec), INLYTA® (Axitinib), Inotuzumab Ozogamicin, Interferon Alfa-2b, Recombinant, Interleukin-2 (Aldesleukin), INTRON A® (Recombinant Interferon Alfa-2b), Iodine 1 131 Tositumomab and Tositumomab, Ipilimumab, IRESSA® (Gefitinib), Irinotecan Hydrochloride, Irinotecan Hydrochloride Liposome, ISTODAX® (Romidepsin), Ixabepilone, Ixazomib Citrate, IXEMPRA® (Ixabepilone), JAKAFI® (Ruxolitinib Phosphate), JEB, JEVTANA® (Cabazitaxel), KADCYLA® (Ado-Trastuzumab Emtansine), KEOXIFENE® (Raloxifene Hydrochloride), KEPIVANCE® (Palifermin), KEYTRUDA® (Pembrolizumab), KISQALI® (Ribociclib), KYMRIAH® (Tisagenlecleucel), KYPROLIS® (Carfilzomib), Lanreotide Acetate, Lapatinib Ditosylate, LARTRUVO® (Olaratumab), Lenalidomide, Lenvatinib Mesylate, LENVIMA® (Lenvatinib Mesylate), Letrozole, Leucovorin Calcium, LEUKERAN® (Chlorambucil), Leuprolide Acetate, LEUSTATIN® (Cladribine),LEVULAN® (Aminolevulinic Acid), LINFOLIZIN® (Chlorambucil), LIPODOX® (Doxorubicin Hydrochloride Liposome), Lomustine, LONSURF® (Trifluridine and Tipiracil Hydrochloride), LUPRON® (Leuprolide Acetate), LUPRON DEPOT® (Leuprolide Acetate), LUPRON DEPOT-PED® (Leuprolide Acetate), LYNPARZA® (Olaparib), MARQIBO® (Vincristine Sulfate Liposome), MATULANE® (Procarbazine Hydrochloride), Mechlorethamine Hydrochloride, Megestrol Acetate, MEKINIST® (Trametinib), Melphalan, Melphalan Hydrochloride, Mercaptopurine, Mesna, MESNEX® (Mesna), METHAZOLASTONE® (Temozolomide), Methotrexate, METHOTREXATE LPF® (Methotrexate), Methylnaltrexone Bromide, MEXATE® (Methotrexate), MEXATE-AQ® (Methotrexate), Midostaurin, Mitomycin C, Mitoxantrone Hydrochloride, MITOZYTREX® (Mitomycin C), MOPP, MOZOBIL® (Plerixafor), MUSTARGEN® (Mechlorethamine Hydrochloride) , MUTAMYCIN® (Mitomycin C), MYLERAN® (Busulfan), MYLOSAR® (Azacitidine), MYLOTARG® (Gemtuzumab Ozogamicin), NANOPARTICLE PACLITAXEL® (Paclitaxel Albumin-stabilized Nanoparticle Formulation), NAVELBINE® (Vinorelbine Tartrate), Necitumumab, Nelarabine, NEOSAR® (Cyclophosphamide), Neratinib Maleate, NERLYNX® (Neratinib Maleate), Netupitant and Palonosetron Hydrochloride, NEULASTA® (Pegfilgrastim), NEUPOGEN® (Filgrastim), NEXAVAR® (Sorafenib Tosylate), NILANDRON® (Nilutamide), Nilotinib, Nilutamide, NINLARO® (Ixazomib Citrate), Niraparib Tosylate Monohydrate, Nivolumab, NOLVADEX® (Tamoxifen Citrate), NPLATE® (Romiplostim), Obinutuzumab, ODOMZO® (Sonidegib), OEPA, Ofatumumab, OFF, Olaparib, Olaratumab, Omacetaxine Mepesuccinate, ONCASPAR® (Pegaspargase), Ondansetron Hydrochloride, ONIVYDE® (Irinotecan Hydrochloride Liposome), ONTAK® (Denileukin Diftitox), OPDIVO® (Nivolumab), OPP A, Osimertinib, Oxaliplatin, Paclitaxel, Paclitaxel Albumin-stabilized Nanoparticle Formulation, PAD, Palbociclib, Palifermin, Palonosetron Hydrochloride, Palonosetron Hydrochloride and Netupitant, Pamidronate Disodium, Panitumumab, Panobinostat, PARAPLAT® (Carboplatin), PARAPLATIN® (Carboplatin), Pazopanib Hydrochloride, PCV, PEB, Pegaspargase, Pegfilgrastim, Peginterferon Alfa-2b, PEG-INTRON® (Peginterferon Alfa-2b), Pembrolizumab, Pemetrexed Disodium, PERJETA® (Pertuzumab), Pertuzumab, PLATINOL® (Cisplatin), PLATINOL-AQ® (Cisplatin), Plerixafor, Pomalidomide, POMALYST® (Pomalidomide), Ponatinib Hydrochloride, PORTRAZZA® (Necitumumab), Pralatrexate, Prednisone, Procarbazine Hydrochloride, PROLEUKIN® (Aldesleukin), PROLIA® (Denosumab), PROMACTA® (Eltrombopag Olamine), Propranolol Hydrochloride, PROVENGE® (Sipuleucel-T), PURINETHOL®(Mercaptopurine), PURIXAN® (Mercaptopurine), Radium 223 Dichloride, Raloxifene Hydrochloride, Ramucirumab, Rasburicase, R-CHOP, R-CVP, Recombinant Human Papillomavirus (HPV) Bivalent Vaccine, Recombinant Human Papillomavirus (HPV) Nonavalent Vaccine, Recombinant Human Papillomavirus (HPV) Quadrivalent Vaccine, Recombinant Interferon Alfa-2b, Regorafenib, RELISTOR® (Methylnaltrexone Bromide), R-EPOCH, REVLIMID® (Lenalidomide), RHEUMATREX® (Methotrexate), Ribociclib, R-ICE, RITUXAN® (Rituximab), RITUXAN HYCELA® (Rituximab and Hyaluronidase Human), Rituximab, Rituximab and , Hyaluronidase Human, ,Rolapitant Hydrochloride, Romidepsin, Romiplostim, RUBIDOMYCIN® (Daunorubicin Hydrochloride), RUBRACA® (Rucaparib Camsylate), Rucaparib Camsylate, Ruxolitinib Phosphate, RYDAPT® (Midostaurin), Sclerosol Intrapleural Aerosol (Talc), Siltuximab, Sipuleucel-T, SOMATULINE DEPOT® (Lanreotide Acetate), Sonidegib, Sorafenib Tosylate, SPRYCEL® (Dasatinib), STANFORD V, Sterile Talc Powder (Talc), STERITALC® (Talc), STIVARGA® (Regorafenib), Sunitinib Malate, SUTENT® (Sunitinib Malate), SYLATRON® (Peginterferon Alfa-2b), SYLVANT® (Siltuximab), Synribo SYNRIBO® (Omacetaxine Mepesuccinate), TABLOID® (Thioguanine), TAC, TAFINLAR® (Dabrafenib), TAGRISSO® (Osimertinib), Talc, Talimogene Laherparepvec, Tamoxifen Citrate, TARABINE PFS® (Cytarabine), TARCEVA® (Erlotinib Hydrochloride), TARGRETIN® (Bexarotene), TASIGNA® (Nilotinib), TAXOL® (Paclitaxel), TAXOTERE® (Docetaxel), TECENTRIQ® (Atezolizumab), TEMODAR® (Temozolomide), Temozolomide, Temsirolimus, Thalidomide, THALOMID® (Thalidomide), Thioguanine, Thiotepa, Tisagenlecleucel, TOLAK® (Fluorouracil— Topical), Topotecan Hydrochloride, Toremifene, TORISEL® (Temsirolimus), Tositumomab and Iodine 1 131 Tositumomab, TOTECT® (Dexrazoxane Hydrochloride), TPF, Trabectedin, Trametinib, Trastuzumab, TREANDA® (Bendamustine Hydrochloride), Trifluridine and Tipiracil Hydrochloride, TRISENOX® (Arsenic Trioxide), TYKERB® (Lapatinib Ditosylate) , UNITUXIN® (Dinutuximab), Uridine Triacetate, VAC, Vandetanib, VAMP, VARUBI® (Rolapitant Hydrochloride), VECTIBIX® (Panitumumab), VelP, VELBAN® (Vinblastine Sulfate), VELCADE® (Bortezomib), VELSAR® (Vinblastine Sulfate), Vemurafenib, VENCLEXTA® (Venetoclax), Venetoclax, VERZENIO® (Abemaciclib), VIADUR® (Leuprolide Acetate), VID AZA® (Azacitidine), Vinblastine Sulfate, VINCASAR PFS® (Vincristine Sulfate), Vincristine Sulfate, Vincristine Sulfate Liposome, Vinorelbine Tartrate, VIP, Vismodegib, VISTOGARD® (Uridine Triacetate), VORAXAZE® (Glucarpidase), Vorinostat, VOTRIENT® (Pazopanib Hydrochloride),VYXEOS® (Daunorubicin Hydrochloride and Cytarabine Liposome), WELLCOVORIN® (Leucovorin Calcium), XALKORI® (Crizotinib), XELODA® (Capecitabine), XELIRI, XELOX, XGEVA® (Denosumab), XOFIGO® (Radium 223 Dichloride), XTANDI® (Enzalutamide), YERVOY® (Ipilimumab), YONDELIS® (Trabectedin), ZALTRAP® (Ziv- Aflibercept), ZARXIO® (Filgrastim), ZEJULA® (Niraparib Tosylate Monohydrate), ZELBORAF® (Vemurafenib), ZEVALIN® (Ibritumomab Tiuxetan), ZINECARD® (Dexrazoxane Hydrochloride), Ziv-Aflibercept, ZOFRAN® (Ondansetron Hydrochloride), ZOLADEX® (Goserelin Acetate), Zoledronic Acid, ZOLINZA® (Vorinostat), ZOMETA® (Zoledronic Acid), ZYDELIG® (Idelalisib), ZYKADIA® (Ceritinib), and / or ZYTIGA® (Abiraterone Acetate). The treatment methods can include or further include checkpoint inhibitors including, but are not limited to antibodies that block PD-1 (such as, for example, Nivolumab (BMS-936558 or MDX1106), pembrolizumab, cemiplimab , CT-011, MK-3475), PD-L1 (such as, for example, atezolizumab, avelumab, durvaluniab, MDX-1105 (BMS- 936559), MPDL3280A, or MSB0010718C), PD-L2 (such as, for example, rHIgM12B7), CTLA-4 (such as, for example, Ipilimumab (MDX-010), Tremelimumab (CP-675,206)), IDO, B7-H3 (such as, for example, MGA271, MGD009, omburtamab), B7-H4, B7-H3, T cell immunoreceptor with Ig and ITIM domains (TIGIT)(such as, for example BMS-986207, OMP-313M32, MK-7684, AB-154, ASP-8374, MTIG7192A, or PVSRIPO), CD96, B- and T-lymphocyte attenuator (BTLA), V-domain Ig suppressor of T cell activation (VISTA)(such as, for example, JNJ-61610588, CA-170), TIM3 (such as, for example, TSR-022, MBG453, Sym023, INCAGN2390, LY3321367, BMS-986258, SHR-1702, RO7121661), LAG-3 (such as, for example, BMS-986016, LAG525, MK-4280, REGN3767, TSR-033, BI754111, Sym022, FS118, MGD013, and Immutep).

[0202] The following description of exemplary embodiments provides non-limiting representative examples referencing numerals to particularly describe features and teachings of different exemplary aspects and exemplary embodiments of the present disclosure. The exemplary embodiments described should be recognized as capable of implementation separately, or in combination, with other exemplary embodiments from the description of the exemplary embodiments. A person of ordinary skill in the art reviewing the description of the exemplary embodiments should be able to learn and understand the different described aspects of the present disclosure. The description of the exemplary embodiments should facilitate understanding of the exemplary embodiments of the present disclosure to such an extent that other implementations, not specifically covered but within the knowledge of aperson of skill in the art having read the description of embodiments, would be understood to be consistent with an application of the exemplary embodiments of the present disclosure.

[0203] To overcome the challenges detailed above, the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure provide an exhaustive search engine to comprehensively profile (e.g., 11) classes of genetic aberrations from RNA-Seq, encompassing all known tumor-specific events. The exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can generate a cancer-specific molecular catalogue of (e.g., 11) classes of molecular alterations across (e.g., 21) histologies, and then can utilize this catalogue as a search space to interrogate (e.g., 1,564) immunopeptidomics datasets, revealing a multitude of actionable immunotherapy targets across cancers.

[0204] Human leukocyte antigen (HLA) molecules present a snapshot of the human proteome on the cell surface, rendering intracellular antigens accessible for various cancer immunotherapies. To characterize the actionable antigen repertoire, the exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the present disclosure can conduct a comprehensive pan-cancer analysis, and in one example, integrating data from, e.g., 7,473 RNA-Seq datasets, e.g., 1,564 immunopeptidome experiments, and compared with, e.g., 17,384 normal samples covering, e.g., 51 tissues. In one example, the exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the present disclosure uncovered, e.g., 15,079 tumor-specific HLA antigens, deriving from, e.g., 11 distinct molecular mechanisms, across, e.g., 21 tumor types, providing a comprehensive resource for immunotherapy development. The exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the present disclosure can highlight a large number (e.g., 128) of appropriate new tumor targets and validated several targets across five antigen classes. Among the antigens uncovered, the exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the present disclosure can highlight a PMEL splicing peptide that can be a superior antigen to the clinical target, self-antigens, peptides derived from ORFs previously unknown to encode proteins, and tumor-specific microbial targets.

[0205] T-cell-based immunotherapies have revolutionized cancer treatment, offering cures to subsets of patients who have historically had limited effective therapeutic options. Treatments such as CAR T cell therapies have shown remarkable efficacy, particularly in hematologic malignancies such as acute lymphoblastic leukemia and non-Hodgkinlymphoma. While targeting lineage specific proteins shared between malignant and healthy B cells has proven clinically manageable in blood cancers as patients can tolerate the loss of healthy B cells, such on-target off-tumor effects are unacceptable in solid tumors, where off- tumor reactivity to essential healthy tissues could be catastrophic. This limitation necessitates the identification of antigens with substantial therapeutic windows. Currently, targeting strategies predominantly focus on membrane proteins, which, while promising, often lack sufficient tumor specificity, contributing to on-target, off-tumor side effects. Indeed, a recent report suggests we may be approaching the saturation point for identifying optimal membrane targets for CAR-T therapy, indicating a critical need for innovative targeting strategies that can distinguish between malignant and healthy tissues more effectively.

[0206] Human Leukocyte Antigen (HLA) molecules present a snapshot of the cellular proteome on the membrane of tumor cells, exposing potential tumor-specific antigens on the cell surface. Multiple therapeutic strategies that exert their therapeutic effects through peptides presented on HLA have delivered curative responses in the clinic, including immune checkpoint inhibitors (ICIs), adaptive transfer of Tumor Infiltrating Lymphocytes (TILs), TCR therapies as well as recent complete response from neoantigen vaccines.Immunotherapies such as ICIs and TILs often rely on high mutational burden, therefore the majority of curative responses to these therapies are applicable only to a limited number of highly mutated tumors. Our group and others have recently demonstrated new approaches, such as peptide-centric chimeric antigen receptors (PC-CARs), that can enable the targeting of any peptide on HLA, expanding the targeting of intracellular antigens to low-to-medium mutational tumors. Central to the efficacy of such immunotherapies is the identification of tumor-specific HLA-presented peptides (pHLAs), which can be derived from a broad range of genetic aberrations including both tumor dependency genes that underpin the biology of various cancers or accompanying passenger mutations. Elucidating the landscape of pHLAs derived from these various cancer processes could reveal additional tumor vulnerabilities.

[0207] In the past decade, increasing evidence has revealed actionable HLA-presented epitopes arising from a wide spectrum of tumor-specific genetic aberrations, including canonical protein-coding genes and mutations, alternative splicing, ectopically expressed transposable elements, cryptic ORFs, and post-translational modifications (PTMs). Many of these findings have been corroborated by immunopeptidomics, the empirical characterization of HLA-presented peptides (pHLAs) by LC-MS / MS proteomics. It has been reported that over 50% of spectra remain unannotated when mapped to the database of potential peptides from the canonical proteome, representing the “dark matter” of the immunopeptidome. Wehypothesize that the molecular instability of cancer will result in a multitude of aberrations that can result in potential immunotherapy targets but are not captured using conventional immunopeptidomics. Hence, uncovering the “dark matter” of the immunopeptidome from other non-canonical molecular sources will reveal a rich set of additional tumor antigens. While a large number of immunopeptidomic datasets are available, most do not have matched RNA-Seq data, restricting the peptides recovered from these searches to the canonical proteome. Comprehensively identifying all categories of molecular events requires harmonized transcriptomic and immunopeptidomic datasets, multimodal computational workflows along with well-curated largescale normal reference to exclude non-tumor- specific molecular events, a challenge that hasn’t been tackled until now. To overcome these challenges, an exhaustive search engine was developed to comprehensively profile 11 classes of genetic aberrations from RNA-Seq, encompassing all known tumor-specific molecular events. We first generated a cancer-specific molecular catalogue of 11 classes of cancerspecific molecular alterations across 21 histologies. We then utilized this catalogue as a search space to interrogate 1,564 immunopeptidomics datasets, revealing a multitude of actionable immunotherapy targets across cancers.Exemplary Results

[0208] Exemplary multimodal approach to establish the comprehensive antigen landscape

[0209] To comprehensively identify HLA antigens derived from diverse molecular events, the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can establish a multimodal computational pipeline to process tumor transcriptome data to profile (e.g., 11) classes of molecular aberrations (see Exemplary Methods below). The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can apply stringent filters for tumor specificity, utilizing, e.g., 17,384 GTEx samples to exclude molecular events detected in normal tissue. The retained tumor-specific events can be translated into putative HLA peptides as search space and used in searching against histology-matched immunopeptidome datasets. Subsequently, the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can rescore each peptide using a deep-learning model to refine these results (See, e.g., Ref. 17), enhancing sensitivity and incorporating HLA binding predictions to eliminate false positives (See, e.g., Ref. 18). This strategy aims to increase the detection sensitivity of immunopeptidome search, while also controlling for commonly occurring false discoveries (See Figures 1 A and IB).

[0210] The exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can source RNA-Seq tumor data from, e.g., The Cancer Genome Atlas Program (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and immunopeptidome data from public repositories like, e.g., Proteomics Identification Database (PRIDE) and Mass Spectrometry Interactive Virtual Environment (Massive). A dataset utilized by the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can encompass e.g., 7,473 RNA-Seq and 1,564 immunopeptidome datasets across 21 tumor types (see Figure 1 A). Notably, breast cancer and melanoma possess the highest number of RNA-Seq (n=l,l 18) and immunopeptidome samples (n=267), respectively (see Figures 7A and 7B, and Tables 1 and 2). Figure 7C shows an exemplary chart of tumorspecific molecular events across tumor types.

[0211] The tumor immunopeptidome cohort can span a broad spectrum of e.g., 140 HLA allotypes, covering 100% of common HLA- A and HLA-C alleles and 92% of HLA-B alleles in the U.S. population (frequency > 1%) (see Table 3). Additionally, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can utilize GTEx data to compile corresponding normal controls across at least 31 histologies, filtering out non-tumor-specific events. Overall, the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can provide a pipeline that identifies antigens from (e.g., 11) molecular sources known to yield bona fide HLA antigens, including canonical protein-coding genes, alternative splicing, gene fusion, TE-chimeric transcript, self-translating TE, mutation, intron retention, tumor-resident pathogen, IncRNA, pseudogene and cryptic ORFs. While this pipeline is capable of profiling Ribonucleic acid (RNA) editing and circular RNAs, detecting circular RNAs necessitates specific preparation techniques, including depleting ribosomal RNA, enriching for poly(A) negative RNA, or removing linear RNA. Consequently, the poly(A) enriched Messenger RNA (mRNA) protocol predominantly used in TCGA studies is suboptimal for profiling circular RNAs. Additionally, although RNA editing has the potential to generate tumor antigens, it cannot be reliably distinguished from deoxyribonucleic acid (DNA) mutations without matched WGS and RNA-Seq data.

[0212] There is currently no system or method that can identify actional tumor-specific antigens by taking only tumor RNA-Seq data, profiling 11 classes of molecular events, and automatically searching against immunopeptidome to identify actional tumor-specific antigens. Each of the 11 molecular event classes can give rise to tumor-specific antigens;accordingly not considering them jointly will overlook bona-fide therapeutic targets. Identifying even one tumor-specific antigen can directly enable the development of new immunotherapies, offering hope to patients in need of better treatment options.

[0213] The proposed approach provides an algorithm that addresses complex computational challenges, and the integration of all components creates a tool with broad applicability in cancer therapy development. The proposed method and system enables the identification of complex molecular events, such as intron retention, frameshift-derived antigens, and cryptic open reading frames, which current computational approaches fall short of. Strategies were implemented to detect these events and systematically generate potential antigens from them. Additionally, 11 classes of molecular events were consolidated into a unified search space, making comprehensive analysis feasible at an unprecedented scale.

[0214] Existing tools, such as NeoDisc, lack full functionality and require tedious installations with substantial computational demands, rendering them inaccessible to cancer biologists without advanced programming expertise. In contrast, the proposed solution is designed for seamless use without requiring users to write a single line of code. Additionally, the proposed tool uniquely enables the generation of an interactive web portal, allowing users to directly explore their antigen predictions and make an informed decision. This disclosure contemplates that an informed decision can be a clinician’s decision to pursue or not pursue a given target based on multiple criteria, including, but not limited to, tumor specificity, peptide abundance, peptide recurrency, HLA frequency, and / or the like. In some embodiments, the web portal automatically generates all the information to allow interactive query and visualization, facilitating the decision-making process for the clinician. In some examples, the web portal can output a recommendation to pursue or not pursues the given target based on criteria provided by the clinician through the user interface.

[0215] Referring now to Figure ID, a flowchart of an example computer-implemented method 100 for determining / identifying one or more immunotherapy targets based on genetic aberrations from sequence data is provided. In some implementations, the method 100 can be performed by a processing circuitry (for example, but not limited to, an application-specific integrated circuit (ASIC), or a central processing unit (CPU)). In some examples, the processing circuitry may be electrically coupled to and / or in electronic communication with other circuitries of an example computing device, such as, but not limited to, the example computing device 1100 described below in connection with Figure 12. In some examples, embodiments may take the form of a computer program product on a non-transitory computer-readable storage medium storing computer-readable program instruction (e.g.,computer software). Any suitable computer-readable storage medium may be utilized, including non-transitory hard disks, CD-ROMs, flash memory, optical storage devices, or magnetic storage devices.

[0216] At step / operation 102, the method 100 includes receiving sequence data for one or more subjects. In some embodiments, the sequence data can comprise RNA-sequence data from a single patient or a plurality of patients (e.g., thousands of patients).

[0217] At step / operation 104, the method 100 includes identifying one or more tumorspecific events associated with a plurality of molecular event classes from the sequence data. It should be understood that step / operation 104 can comprise filtering or excluding molecular events detected in normal tissue as described in more detail herein.

[0218] At step / operation 106, the method 100 includes determining (e.g., programmatically defining) a search space based on the one or more tumor-specific events. In other words, retained tumor-specific events can be translated into, for example, putative HLA peptides as search space and used in searching against histology-matched immunopeptidome datasets.

[0219] At step / operation 108, the method 100 includes interrogating, by the at least one processor, a plurality of datasets based on the determined search space. The plurality of datasets can be or comprise histology-matched immunopeptidome datasets and / or may each be associated with one or more aberration categories. In some implementations, the one or more aberration categories include at least one of gene expression, SNPs, INDELs, splicing junctions, intron retention, gene fusions, RNA editing, tumor-resident pathogens, transposable elements, endogenous retroviruses, circular RNAs, cryptic ORFs, proteasomal spliced antigens, and post-translational modifications.

[0220] Optionally, at step / operation 110, the method 100 includes rescoring identified peptide(s) using a machine learning model, such as a deep learning model as described in more detail below. Accordingly, the proposed method 100 can be used to refine results to enhance sensitivity and accuracy of subsequent searches.

[0221] Additionally, at step / operation 112, the method 100 optionally further includes determining HLA binding prediction(s) for the identified peptide(s) to eliminate false positives. This strategy can increase the detection sensitivity of the immunopeptidome search, while controlling for false discoveries.

[0222] At step / operation 114, the method 100 includes determining one or more immunotherapy targets based on genetic aberrations from the sequence data. The immunotherapy targets can include canonical and non-canonical peptides for at least onecancer type. In some implementations, the method 100 outputs one or more identified antigens with a respective optimal form of immunotherapy (e.g., cellular immunotherapy), bispecific T-cell engagers, in vivo delivered CARs (e.g., viral, RNA, LNP, and the like), and peptide vaccines (mRNA and / or synthetic peptides). In various implementations, the one or more immunotherapy targets can be used to direct a treatment for one or more patients, for example, to generate therapeutic entities for treating specific cancers. Treatment approaches can vary depending on the specific subtype, stage, and patient factors but may include, but are not limited to, chemotherapy, radiation therapy, immunotherapy, targeted therapies, and stem cell transplantation. This disclosure contemplates that the operations related to providing diagnosis, prognosis, and / or treatment options can be performed using one or more computing devices / sy stems (e.g., at least the configuration illustrated in Figure 12). Optionally, in some implementations, the method further includes administering the recommended treatment to the subject.

[0223] Optionally, at step / operation 116, the method 100 includes generating a report, summary, and / or data object describing at least a portion of the analysis. For example, step / operation 116 can include outputting a report describing the one or more immunotherapy targets, identified genetic aberrations, and / or a list of antigens. An example report can provide a cohort-level summary reflecting the recurrence of each genetic aberration. In some implementations, the generated output is used as in input to a search engine to identify tumorspecific antigens. For example, such data can serve as the input for modern Mass Spectrometry search engines (e.g., MaxQuant, Byonic, PEAKS DB, MSFragger, etc.), providing exhaustive searches for all types of tumor-specific antigens. In some implementations, the output of the method 100 is provided via an interactive web portal, allowing end users to directly explore antigen predictions.

[0224] Alternatively or additionally, the method 100 optionally further includes generating display data for the report. Alternatively or additionally, the method 100 optionally further includes transmitting the report over a network. This disclosure contemplates that operations related to generation of the report can be performed using one or more computing devices / sy stems (e.g., at least the configuration illustrated in Figure 12).

[0225] Various embodiments of the present disclosure address technical shortcomings of conventional data processing systems and database management systems, particularly in healthcare applications. For example, various implementations of the present disclosure introduce innovative data management systems that can efficiently process data frommultiple sources in a manner that conserves computational resources while returning more sensitive results than conventional systems.

[0226] Figure IE is a diagram of an example system 200 configured to determine one or more immunotherapy targets based on genetic aberrations from sequence data 202. The sequence data 202 may be sequence data from one or more subjects (e.g., thousands of patients). The system 200 can be configured to integrate data from multiple sources, for example a first data entity 220a, a second data entity 220b, and a third data entity 220c. The plurality of data entities 220a, 220b, 220c can be or comprise databases or datasets associated with research institutions, data management providers and / or the like. The data management system 201 can analyze the sequence data 202 to determine immunotherapy targets and / or additional data 211 (e.g., user interface data) as described above in connection with Figure ID.

[0227] As depicted in Figure IE, the data management system 201 comprises one or more machine learning model(s) 205, a search space engine 214, filtering component 216, visualization engine 217, analyzing component 218, and a parallelization component 219. In some implementations, as illustrated, the machine learning model(s) 205 can include a rescoring component 213 configured to rescore identified peptides (e.g., using a deeplearning model) and an HL A binding component 215 configured to generate HLA binding predictions for identified peptides to eliminate false positives. The search space engine 214 can be configured to determine a search space based on one or more tumor-specific events and the filtering component 216 can be configured to filter or remove data corresponding with normal tissue expression to reduce the amount of data required for analysis. The visualization engine 217 can be configured to generate user interface data for display to an end user (e.g., a report, summary, and / or data object(s) describing genetic aberrations and / or a list of antigens). In some implementations, output data (e.g., immunotherapy targets or additional data 111) can be used as an input to a search engine 225 to identify tumor-specific antigens. The analyzing component 218 can process, pre-process and / or transform sequence data 202 for downstream operations. Additionally, the data management system 201 includes a parallelizing component 219 that employs a parallelized approach to generating workflows to optimize system operations. For example, the parallelizing component 219 can split data into multiple distinct workflows (e.g., a first workflow to obtain HLA alleles and quantify gene-level expression, a second workflow to detect mutations, insertions, deletions, splicing events, intron retentions, pathogens, long non-coding RNAs (IncRNA), and RNA editing, and a third workflow to identify gene fusions). In some implementations, at least a portion of thereceived / generated data can be transferred to other computing entities or devices as needed to increase processing speed or efficiently handle large amounts of data. Workflow allocation can be optimized based on time and memory constraints while considering internal dependencies.Table 1: Metadata for Tumor RNA-Seq data, each row is an RNA-Seq sampleTable 2: Metadata for tumor immunopeptidome data used in the study, each row is an immunopeptidome runTable 3: HLA allotype coverage in each cancer, first row represents allele frequency inU.S. population, starting from 3rd row, each row represents one cancer

[0228] Analysis of tumor-specific events at the RNA level can reveal high numbers of somatic mutations in melanoma and lung squamous cell carcinoma, followed by colon and stomach cancers often characterized by microsatellite instability. Immunologically cold tumors, including mesothelioma and neuroblastoma, exhibit the fewest mutations. Acute Myeloid Leukemia (AML) shows a high prevalence of tumor-specific transposable elements (n=l 1,264) and intron retention events (n=3,954). AML is characterized by widespread intron retention events (See, e.g., Ref. 19), and endogenous retrovirus can serve as the enhancer in AML tumorigenesis See, e.g., Ref. 20), thus it possibly reflects an interaction between endogenous retroviruses, epigenetic changes and splicing landscape changes. Further investigation into HLA antigens revealed that while melanoma and AML have the highest mutation-derived and TE-derived antigens, discrepancies arise in other cancers like neuroblastoma, which, despite high splicing events, yields fewer splicing antigens (see Figure 1C), suggesting the potential post-transcriptional regulation mechanisms. Notably, the low antigen identification in cancers like stomach cancer can be attributed to minimal HLA coverage and limited sample availability in the immunopeptidome data, underscoring theneed for additional immunopeptidomic studies for such cancer types. Taken together, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can provide a landscape for exploring tumor antigens, spanning both a variety of tumors and molecular events.

[0229] Membrane proteins provide limited new therapeutic opportunities

[0230] The landscape of surface proteins suitable for conventional CAR-T therapy can be evaluated, applying the identical filtering stringency used for peptide-human leukocyte antigen (pHLA) targets to all annotated human cell membrane proteins with exposed extracellular domains. This unbiased analysis identified (e.g., 88) membrane protein candidates across tumors with clear therapeutic windows by comparing their gene expression in tumors to all normal human tissues. This list can include TNFRSF17 / BCMA and CD 19, the two FDA-approved CAR-T targets to date, along with eleven additional targets being investigated in clinical trials (e.g., CLEC12A, FOLR1 / PSMA, IL1RAP, CD70, CEACAM5, MSLN, ULBP2, MUC16, GPC2, CDH17, CLDN6). Previous studies show that 20 additional targets have established CARs and have been tested in preclinical models.

[0231] Figure 2A shows an analysis highlighting 47 previously unrecognized antigens with favorable therapeutic windows, rendering them as candidates for conventional CAR-T therapy (also shown in Table 4). Notably, several clinical and preclinical CAR targets, including ERBB2 / HER2, EPC AM, and MUC1, cannot pass the filtering, consistent with reported preclinical and clinical toxicities or a lack of a therapeutic window for effective targeting (e.g., 22-24). CAR targets such as GD2, a glycosphingolipid, are not identifiable through this analysis, and several resulting targets are due to RNA from infiltrating immune cells in tumors. This analysis reveals new membrane proteins that can serve as potential CAR targets and underscores the need to identify additional tumor antigens.Table 4: Viable membrane CAR-T targets from pan-cancer analysis, each row represents one target, values represent median gene Transcript Per Million (TPM)

[0232] Self-antigens represent a source of PC-C AR targets

[0233] Our related recent work established the use of PC-CARs to target low-to-medium mutational tumors by enabling the robust and specific targeting of peptides abundantly presented on HLA molecules. (See, e.g., Ref. 12). Initial efforts focused on neuroblastoma, targeting the developmental transcription factor PHOX2B. This factor is silenced in healthy differentiated tissues but is co-opted by tumor cells to maintain tumorigenicity. Encouraged by these results, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can expand the strategy to a broader range of cancer patients.

[0234] To address this, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can analyze tumor-specific canonical protein genes in the immunopeptidome dataset. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can identify, for example, 284 tumor-specific genes with clear therapeutic windows by comparing tumor expression to comprehensive normal tissue controls. Expression profiling can reveal distinct clusters of genes exclusive to specific cancers (see Table 4). Notably, cancers such as melanoma, liver cancer, and ovarian cancer possess uniquely expressed genes, including well-known targets like PRAME and PMEL in melanoma (see Figure 2B). This pattern suggests tumors expose vulnerabilities by upregulating specific genes unique to each cancer type. Additionally, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can identify a cluster of potential pan-cancer targets. These genes, involved in cell cycle and mitosis regulation like CDC6 and CDC45, are broadly expressed across multiple tumors (see Figure 2C). Given the role of these genes in abnormal cell replication, targeting them could exploit a common vulnerability in cancer cells, providing a strategic advantage.

[0235] To further expand the pool of actionable targets for T-cell based therapies, we applied our analysis to pHLAs by comprehensively characterizing tumor-specific selfantigens that are well-suited for PC-CAR targeting, scoring canonical proteins in our immunopeptidomics datasets by (1) abundance, (2) recurrence, (3) absence in normal tissue RNA and immunopeptidomes, (4) HLA binding affinity, (5) importance in maintaining tumor progression and (6) homogeneity as determined by single cell coverage. We identified 3,635tumor-specific antigens from 284 genes that are highly-enriched in tumors without appreciable level of expression in normal tissues (Table 4). In contrast to the mere 88 viable membrane proteins that meet the same criteria, pHLAs can drastically expand the current antigen repertoire. Expression profiling revealed distinct clusters of genes exclusive to specific cancers (Table 4). Notably, cancers such as melanoma, colon cancer, and ovarian cancer possess uniquely expressed genes, including well-characterized immunotherapy targets like PRAME, MUC16 and CDH17. (Figure 3 A). This pattern suggests that tumors expose vulnerabilities by upregulating specific genes, unique to each cancer type. Additionally, we identified a cluster of peptides derived from genes involved in cell cycle and mitosis regulation like CDC6 and CDC45, broadly expressed across multiple tumors (Figure 3B). Although cell therapies targeting cell cycle genes alone may affect normal cycling cells, given the role of these genes and broad upregulation in cancers, incorporating them into AND-gated strategies with other antigens could represent a viable therapeutic strategy, exploiting a common vulnerability in cancer cells while sparing the proliferative cells residing in normal tissues.

[0236] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can show how many cancer patients can benefit from HLA antigen-centric therapies, including PC-CARs and engineered TCR- Ts. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, make clear that most cancers are considered to have at least one tumor-specific gene capable of generating tumor antigens. When considering the HLA restrictions, the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, provide evidence that 12 out of 21 cancer types achieved more than 99% coverage, demonstrating the broad potential of these therapies. However, some cancers, like mesothelioma and rhabdoid tumors, can show lower coverage (see Figure 2D). The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, indicate that a wide population can benefit from PC- CAR therapies, albeit with consideration of HLA restrictions to maximize the therapeutic application of each antigen.

[0237] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can work under the assumption that an ideal target should meet several criteria, including (1) recurrent detection, (2) high abundance in tumors, (3) absence in normal RNA and immunopeptidomes, (4) HLA affinity, (5)importance in maintaining tumor progression, and (6) homogeneity as determined by single cell coverage. An example is clear cell renal cell carcinoma, which represents a portion of kidney cancers and is linked to severe clinical outcomes. (See, e.g., Ref. 27). The tumorspecific gene HAVCR1 presents the peptide DLSRRDVSL to HLA-B*08:01, covering over 15% of the US population. This peptide is absent in normal immunopeptidome data, and both RNA and peptide levels suggest favorable therapeutic windows (see Figure 2E and 8A). The peptide's MS spectrum is of high confidence with 10 fragment ions and consecutive y ions to support the evidence on the peptide level (see Figure 8C). AlphaFold2 modeling indicates the binding to the cognate HLA, with key biophysical features that enhance targetability due to exposed polar residues (see Figure 2F). Peptide / HLA binding can be validated by refolding the HAVCR1 peptide with HLA-B*08:01 and performing size exclusion chromatography, demonstrating the formation of the HAVCR1 / HLA-B*08:01 / p2M complex (see Figure 8B). The comprehensive pan-cancer analysis and focused investigations of the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can underscore the potential of targeting self-antigens (e.g., HLA- presented) across a wide range of cancers (e.g., using PC-CARs and TCRs).

[0238] Mutation-Derived Neoantigens from Immunopeptidome

[0239] While neoantigens arising from somatic mutations and gene fusions have been tested in clinical trials due to their tumor specificity, most antigens are selected based on computational predictions incorporating gene expression and HLA binding affinity. Only 2.4% of predicted strong pMHC binders are detected within the immunopeptidome of mesothelioma See, e.g., Ref. 28). Only 1.6% of mutated antigens demonstrate immunogenicity in gastrointestinal cancer patients(29). Therefore, the vaccine cocktail typically given in cancer vaccines contains a high proportion of peptides that tumors may not present. Moreover, neoantigens may undergo immunoediting at early stages of immune infiltration and can be present only in low amounts (See, e.g., Ref. 30). For example, although the KRAS G12D mutation can be detected when overexpressed in engineered cell lines (See, e.g., Ref. 31). or using high-sensitivity acquisition methods (See, e.g., Ref. 32)., it is not identifiable in primary tumors with conventional immunopeptidome acquisition techniques (See, e.g., Ref. 33). and has evaded therapeutic targeting on HLA despite decades of efforts, aside from a case report describing the targeting of this mutation through an infrequent HLA- C allele (See, e.g., Ref. 34).. Furthermore, most mutations and fusions are unique to individual patients, making the development of off-the-shelf therapies challenging.

[0240] Consistent with their rare detection in the immunopeptidome, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, reveal that SNVs constitute only 0.009% of non- canonical pHLAs on average across tumors (see Figure 3 A). Specifically, lung squamous cell carcinoma (LUSC), skin cutaneous melanoma (SKCM), and colon cancer exhibited the highest numbers of mutation-derived neoantigens, with counts of 223, 92, and 38, respectively, consistent with reported mutational frequencies at the genetic level (See, e.g., Ref. 35) (see Figure 3B). None of these neoantigens have been documented in the Immune Epitope Database (IEDB). While the IEDB does not catalog all known neoantigens, and some may have been previously reported elsewhere, this highlights the power of using large-scale genomics and transcriptomics data to discover rare antigens in the immunopeptidome and warrants further investigation of these newly uncovered neoantigens. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, confirm a shared AML neoantigens (e.g., AVEEVSLRK) deriving from the NPM1 frameshift mutation. Further, the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, show ALK, MUC16, and BRCA2 harbor the highest number of neoantigens evidenced from the immunopeptidome data and that 21% of evidenced neoantigens are derived from known cancer driver genes (see Figure 3C, Table 5). Furthermore, although gene fusions represent a smaller fraction of neoantigens and are exclusive to individual patients, promising candidates such as UBXN1-BSCL2 and NCOA7-TPD52L1 in breast cancer and SNX6-CD8B in glioblastoma were identified, (see Table 6). The pan-cancer molecular catalogues in the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can be used to identify previously unreported neoantigens and suggest that the empirical detection of these antigens in the immunopeptidome bolsters the rationale for their inclusion in cancer vaccine cocktails or expansion of TILs.Table 5: Parental genes with number of neoantigens identified from immunopeptidomeTable 6: Tumor-specific splicing junctions, values represent the median of splicing junction counts

[0241] Splicing and Cryptic ORFs yield more targets than Neoantigens

[0242] Splicing alterations and non-coding open reading frames (ORFs) can generate actionable targets across various cancers, offering more shared targets. (See, e.g., Refs. 13, 15). To address this, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can consider the contribution of detected antigens from mutations, fusions, alternative splicing, intron retention, and cryptic ORFs. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can show that cryptic ORFs and alternative splicing dominate the antigenic landscape, accounting for approximately 91.1% and 7.0% of the repertoire on average, respectively. In particular, cryptic ORFs provide a rich source of tumor-specific antigens (see Figure 3D). To validate the translational activity of these cryptic ORFs, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can perform ribosome profiling (Ribo-seq) on matched neuroblastoma patient- derived xenograft (PDX) models (e.g., eight) (see Figure 9A). This profiling process can show the presence of ribosomes on, e.g., 37% of these ORF-derived HLA antigens, further supporting their active translation and the ability to generate peptides (see Figure 9B). One example is the peptide STIPVLSGY from the downstream untranslated region (UTR) of the TMEM203 gene, which can be confirmed by synthesizing the spike-in and refolding experiments (see Figures 9C - 9F). This tumor-specific antigen has also been found in cervical cancer, lung cancer, glioblastoma, and melanoma, representing its potential as a pancancer target. Figure 9G is a series of graphs showing the size exclusion chromatography of the refolded pHLA for LYLETRSEF (SEQ ID NO: 26)-A2402 complex, RYLPSSVFL (SEQID NO: 9)-A2402 complex, AYPASLQTL (SEQ ID NO: 27)-A2402 complex (top to bottom).

[0243] In examining splicing-derived antigens, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can identify a rich source of tumor-specific splicing junctions that are distinct from those in normal tissues and clustered into cancer-specific events (see Figures 10A and 10C, and Table 6). Further, the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can show a recurrent detection of a splicing variant from PMEL exon 5 skipping, which can result in a distinct antigen while retaining both HLA-A2 anchor sites shared with two different known PMEL epitopes derived from the canonical proteins (see Figure 3E). Validity can be confirmed through a spike-in validation and refolding experiment to verify that it is an HLA- A*02:01 binder (see Figures 10B and 10E). Both structural modeling and killing assays suggest it as a unique TCR epitope compared to the canonical sequence (see Figures 3F and 10D). Further, despite lower binding affinity, this splicing variant can produce a more abundant peptide, possibly due to enhanced degradation of short-lived proteins (see Figure 3G). This non-canonical PMEL antigen of the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, may have superior therapeutic properties than the canonical PMEL antigen currently targeted in the clinic based on its increased abundance and tumor specificity (see Figure 3E).Additionally, as one subset of the aberrant splicing variant, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can reveal intron retention-derived antigens in AML, including retained HERC1 intron 19, MAP3K5 intron 10, and TXNDC15 intron 5 (see Table 7). The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can show that tumor-specific antigens derived from alternative splicing events and cryptic open reading frames can constitute the majority of actionable targets across various cancers, with potential in expanding immunotherapies.Table 7: Peptides deriving from tumor-specific intron retention events

[0244] Tumor-resident pathogens provide additional therapeutic vaccine targets

[0245] Viral infections, including Hepatitis B Virus (HBV) and Human Papillomavirus (HPV), are oncogenic viruses linked to liver and cervical cancers. For HBV and HPV, the integration of viral DNA into the host genome disrupts cellular regulatory networks, inducing oncogenesis by activating oncogenes and inhibiting tumor suppressor genes. (See, e.g., Ref. 36). Prophylactic vaccinations against these viruses have proven effective in reducing cancer incidence, underscoring the capacity of the immune system to clear tumors through pathogen- derived antigens. See, e.g., Refs. 37, 38). The exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can show that tumor-resident viral pathogens not only contribute to oncogenesis but may also alter tumor immunogenicity by presenting antigens, which can be exploited therapeutically. To identify such known vaccine antigen candidates, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can focus on the immunopeptidome of liver and cervical cancers known to harbor HBV and HPV infections, respectively (see Figure 4A). Notably, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can identify known viral peptides also residing in primary tumor tissues, such as FLLTRILTI (SEQ ID NO: 96) from the HBV large surface antigen and YMLDLQPET (SEQ ID NO: 97) from the HPV protein E7. A comparative analysis with the Immune Epitope Database (IEDB) reveals that 45% of the HBV peptides detected in primary liver tumors were previously documented as immunogenic, highlighting their potential utility in therapeutic vaccine development (see Figure 4B). Building on these observations, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can further consider other common oncogenic viruses linked to cancers. While cytomegalovirus (CMV) infection has been implicated in multiple cancers, and its DNA and protein being detected in cancers such as neuroblastoma, colon cancer, and glioblastoma, it is unclear whether CMV infection can generate additional HLA antigens suitable for therapeutic development. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can identify, e.g., 158 CMV-derived antigens across, e.g., 6 different tumor types (see Table 8). Cross-referencing the CMV peptides identified in primary tumors with theIEDB database reveals seven peptides that have been previously documented, (see Figure 4C). Particularly, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can show LLDGVTVSL, which can be recurrently detected in five cancers (e.g., ovarian cancer, breast cancer, neuroblastoma, glioblastoma, cervical cancer) and presented by the common HLA-A*02:01 allele, providing a target for various immunotherapies (see Figure 4D).Table 8: Peptides deriving from CMV with the median peptide intensity in immunopeptidome per cancer

[0246] In addition to oncogenic viruses, the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can show the presence of additional microbiomes, including bacteria and fungi, that can result in antigens. Although pathogens can be predicted from bulk transcriptome data, the existence of a tumor-specific microbiome remains contentious due to the complexity of distinguishing genuine microbial sequences from host-derived or environmental contaminants. (See, e.g., Ref. 39). This uncertainty is exemplified by the fact that many unmapped reads can be reclassified into the latest human genome assembly or maybe artifacts of contamination, leading to potential false discoveries. (See, e.g., Ref. 40). To address these challenges, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can establish a stringent framework to classify unmapped reads into various species, including fungi and bacteria. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, may assign a read to a specific species if, e.g., 90% of its kmers can bemapped to that species. Additionally, the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can exclude species detected in normal tissue to ensure tumor specificity and corroborate outputs with previously published studies. (See, e.g., Ref. 41). The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can identify several recurrent strains at the RNA level that also generate peptides in the primary tumor immunopeptidome, including e.g., F. nucleatum, a pathogen implicated in colon cancer and melanoma (See, e.g., Refs. 43, 44), and H. pylori, associated with gastric cancer (See, e.g., Ref. 42). Besides these well-known strains, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can detect N. circulans, which has been implicated in sepsis among immunocompromised patients. (See, e.g., Ref. 45), in over 90% of ovarian RNA-Seq samples (see Figure 4A). Notably, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can reveal 652 N. circulans peptides in ovarian cancer derived from 594 pathogen genes, with 18 consistently detected in esophageal cancer. Since immunopeptidome search is high with false positives, the exemplary systems, methods, and computer accessible medium, according to the exemplary embodiments of the present disclosure, can search N. circulans proteome against 8 neuroblastoma cell lines as a negative control, where none of the 18 peptides can be shown, suggesting that N.circulans can be identified at both the RNA and immunopeptidomic level (see Figure 4E). The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can validate these peptides by performing LC / MS-MS on the synthetic peptides, confirming the presence of these bacteria peptides in tumors.. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can explore the landscape of pathogen-derived antigens in primary tumors and validate antigens originating from oncogenic viruses or bacteria, highlighting the therapeutic potential of these pathogens when presented as tumor HLA peptides.

[0247] Long interspersed nuclear element- 1 (LINE1) ORF2-dervied peptides are detected in immunopeptidome

[0248] Transposable elements (TEs) have been recognized in numerous studies as a class of HLA antigens. (See, e.g., Refs. 46, 47). Due to their strict epigenetic regulation in normal tissues and aberrant activation in tumors, TEs provide a source of tumor-specific antigens. While previous studies have focused on the exonization of TEs through TE-chimerictranscripts arising from TE-promoters (See, e.g., Ref. 49), donors, or acceptors (See, e.g., Refs. 47, 50), certain TEs can encode proteins themselves, including Long interspersed Nuclear Element (LINE), Long Terminal Repeat (LTR) elements (See, e.g, Ref. 51), and sporadic reports of Alu, DNA transposons, and SINE-VNTR-Alus (SVA) retrotransposons (See, e.g, Ref. 52) (see Figure 5A). The exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can establish how various TE categories can contribute to tumor-specific antigens and characterize their prevalence across multiple cancer types. pHLAs derived from TE chimeras can be detected in melanoma and lung squamous cell carcinoma, while autonomous-TE- derived pHLAs, which correlate with the highest number of tumor-specific TE elements, are shown by exemplary embodiments of the present disclosure to be prevalent in acute myeloid leukemia (AML) (see Figure 5B and Table 9).Table 9: Peptides deriving from transposable element in immunopeptidome

[0249] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can detect canonical ORF2 peptides within Long Interspersed Nuclear Element- 1 (LINE-1) across nine types of cancer with colon, head and neck, and lung cancers showing the highest frequency of detection (see Figure 5C). ORF2 is critical in tumorigenesis by encoding an endonuclease and reverse transcriptase that facilitates the retrotransposition of the LI and Alu elements in an ORF1 oncogene. Meanwhile, the detection of ORF2 protein has been evaded through conventional methods like western blotting and whole-cell proteomics. (See, e.g., Ref. 54). The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can confirm its presence by synthesizing and validating three peptides recurrently identified from the immunopeptidome. This evidence suggests that ORF2 may be a short-lived protein rapidly degraded by the proteasome, a pathway shared with HLA presentation. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can show the potential of transposable elements to generate tumor-specific HLA antigens through various mechanisms and can highlight a previously elusive ORF2 peptide, suggesting that the immunopeptidome represents an alternative approach for identifying difficult-to-detect peptides.

[0250] HLA presentation pathway exhibits distinct expression patterns across cancers

[0251] The HLA presentation pathway is can involve multiple components. Proteins and peptides are ubiquitinated and subjected to proteasomal degradation, producing shorter peptides with specific C-terminal cleavage sites. Once degraded, the peptides are transported into the endoplasmic reticulum (ER), further trimmed at the N-terminus by ERAP, and loaded onto the corresponding HLA / p2-microglobulin (P2M) with the help of the chaperone proteins. The assembled pHLA complexes are subsequently transported to the cell surface for immune surveillance (See, e.g., Ref 55).

[0252] Given the variations in antigen landscape across various cancers, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can explore potential intrinsic differences within the HLA presentation pathway. The pathway initiates with proteasomal degradation, where the specialized immunoproteasome, characterized by its unique beta subunits, alters proteolytic preferences to produce peptides that bind efficiently to HLA molecules. (See, e.g., Ref. 56). Notably, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, show that DLBC can exhibit the highest expression of immunoproteasome signature genes PSMB8 / 9 / 10, which may be attributed to the predominant role of B cells as antigen-presenting cells (see Figure 6A). The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can find 1.2% of eluted peptides in DLBC eluted peptides end with acidic residues (aspartic acid and glutamic acid), compared to 8.1% of acidic C-terminus peptides in Rhabdoid Tumor (RT), in which both has similar number of eluted total peptides, suggesting reduced propensity for acidic residues at the C-terminus in DLBC (p=0.0006, chi-square independent test). The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can show a lack of acidic C-terminus motifs in DLBC compared to RT, supporting the role of immunoproteasome in altering the DLBC antigen landscapes (see Figures 6C and 6D).

[0253] Further investigation into the TAP transporter genes (e.g., TAPI, TAP2, and TAPBP) reveals that TAPI is upregulated in AML, HNSC, and CESC, indicating an enhanced immune response for presenting antigens on the cell surface, which can be corroborated by pro-inflammatory genes (e.g., CXCL8, CXCL10, NFKB1) and IFN-y signaling pathway (e.g., IFNGR). TAP2 expression remains unchanged across cancers, while TAPBP exhibits high expression (see Figure 6A). This can suggest a strategic adaptation by tumors to present a repertoire of less immunogenic antigens, thereby evading immune detection. Additionally, the peptidases ERAP1 and ERAP2 responsible for trimming the N- terminus of peptides do not show high expression in tumors, aligning with previous studies (See, e.g., Ref. 57) (see Figure 6A and Table 10), suggesting that cancer cells maintain standard trimming activity. However, the elevated expression of both ERAP1 and ERAP2 in AML may indicate that the downstream effects are contingent on the ERAP haplotypes, which can exhibit hyper-functional, normal, or hypofunctional trimming activity (See, e.g., Ref. 58). Amongst the genes encoding components of the peptide loading complex, including the chaperones CALR, CANX, and PDIA3, the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can show higher expression levels in cancers than in normal tissues, showing the increased demand for HLA presentation coupled with a response to activation and inflammatory signaling in the tumor microenvironment.

[0254] The only normal tissue exhibiting elevated expression of these chaperones is the thyroid, which correlates with its high demand for secreting thyroid hormones that depend on ER functionality. Lastly, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can show elevated expression of HLA- A and P2M in DLBC and KIRC, contrasted with low expression of HLA- B and HLA-C across cancers. This pattern suggests a nuanced modulation of HLA expression, potentially influencing the immune system's ability to recognize and respond to malignant cells. While HLA expressions are upregulated upon IFN-y stimulation, the discrepancies for HLA-B and HLA-C may indicate allele-specific regulations and responses (See, e.g., Ref. 59).Table 10: Gene expression (TPM) of key HLA presentation pathway genes across cancers

[0255] Signal peptides are preferentially presented on HLA

[0256] The defective ribosomal product (DRiPs) hypothesis suggests that the HLA presentation pathway preferentially selects peptides arising from errors in protein biosynthesis, such as those produced during defective ribosomal assembly or unsuccessful protein folding. (See, e.g., Ref. 46). While conventional immunopeptidome analyses focus on the canonical, normally synthesized proteome as the predominant source of HLA antigens, there is growing evidence that peptides from short-lived proteins may be selectively presented on the cell surface. See, e.g., Refs. 47, 48). In light of the considerable number of non-canonical peptides identified in the pan-cancer analysis, the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can aim to determine whether these non-stable peptides exhibit a higher likelihood of presentation compared to their stable counterparts. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can examine signal peptides, a prime model of rapidly degraded peptides that are absent in the mature protein and have a comparable stable counterpart. Utilizing the comprehensive pan-cancer database, the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can analyze all human protein signal peptides to assess whether these peptides, likely degraded and short-lived, are preferentially presented compared to their non-signal peptide counterparts. For example, among 1,067 human membrane proteins with high-confidence predictions of signal peptides, 611 exhibited detectable HLA antigens in either normal or cancer-derived immunopeptidome datasets (see Table 9). Notably, when the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure focus on 85 proteins whose signal peptides could generate at least one HLA antigens, they can show that 97.6% of these proteins exhibited higher presentation potential than peptides derived from the mature protein (see Figure 6B). Furthermore, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can identify, e.g., 22 cases where only the signal peptide regions can generate detectable HLA antigens, which is surprising given the drastically shorter length of the signal peptide. This underscores a general trend where signal peptides can be more likely to be presented by HLA molecules, highlighting theimportance of accounting for non-canonical and non-stable protein products in immunopeptidome search.Discussion

[0257] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can provide a comprehensive atlas of the “dark matter” of the immunopeptidome, revealing a rich source of immunotherapy target candidates. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can create and utilize a multimodal approach to generate molecular catalogs of non-canonical alterations in tumors, leveraging large-scale histology-matched cancer RNA-Seq data and immunopeptidomics to establish an exhaustive search space for potential HLA antigens. The exemplary systems, methods, and computer-medium, according to the exemplary embodiments of the present disclosure, can reveal a significantly expanded antigen landscape across various classes of molecular aberrations. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can identify dozens of targets and catalog thousands of promising tumor antigens, each offering potential for investigation in immunotherapeutic development. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can include a search space that can facilitate the integration of users’ immunopeptidome data, particularly when matched RNA-Seq data is unavailable or lacks statistical power due to small sample sizes, allowing for the uncovering of numerous rare aberrations. The computational pipeline of the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can be applied to any in-house RNA-Seq or immunopeptidome data to expand its usage beyond the tumor types considered in the study. Moreover, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, also reveal insights into the preferential presentation of defective and short-lived peptides, such as splicing products and signal peptides. These insights and the identification of numerous non-canonical antigens are consistent with the belief that DRiPs contribute to the HLA peptide repertoire. This contribution appears to occur alongside peptides derived from the canonical proteome, highlighting antigen presentation's diverse and complex nature and its implications for immune surveillance and therapy. Second, the exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the present disclosure can uncover new tumor-resident pathogens-derived antigens spanning both oncogenic viruses andbacteria and can detect previously elusive ORF2 peptides from LINE1 element. The exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the present disclosure can validate targets (e.g., 20) by synthesizing the peptides and performing LC / MS-MS (see Table 10), demonstrating complete concordance in 100% of the peptides considered, which suggest the high confidence of the peptides identified through the exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the present disclosure. These resources are poised to reshape the understanding of immunopeptidome analysis going forward. Data from Tables 1-10 can be found at https: / / www.biorxiv.org / content / 10.1101 / 2025.01.22.634237vl. supplementary- material, the content of which is hereby incorporated by reference herein in its entirety.

[0258] Furthermore, the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can establish a schema to prioritize high-confidence HLA antigens based on criteria such as tumor specificity, high abundance, gene dependency, and homogeneity, the latter of which requires further exploration through single-cell RNA-Seq data in the future to further disqualify targets merely covering a subset of tumor clones. While the HLA-I peptides are considered the main source for tumor intrinsically present antigens, existing literature also indicates that HLA-II can also be expressed on tumor cells, especially those derived from epithelial and hematopoietic origins (See, e.g., Ref. 49). The exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the present disclosure, although focused on HLA-I, is compatible with HLA-II search and the exact same functionalities. Additionally, antigens arising from BCR hypermutation, particularly pertinent to blood cancers like B cell lymphoma, may represent a promising avenue given advancements in high-accuracy BCR sequencing and its superior tumor specificity.

[0259] Tumor mutation burden is an inconsistent predictor of immunotherapy responses such as immune checkpoint inhibitors (See, e.g., Ref. 65). Given the number of non-canonical antigens beyond SNV-derived neoantigens, the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can show that a more comprehensive tumor antigen burden may provide a more effective predictor for the prognosis and correlative analysis. Another future direction can involve the dual presentation of peptides by tumor cells and antigen-presenting cells (APCs). If a peptide can be presented concurrently by both tumor cells and APCs, it can amplify the therapeutic benefits, suggesting a new dimension to exploring immunotherapeutic strategies. Overall, the exemplary systems, methods, and computer-accessible medium, according to the exemplaryembodiments of the present disclosure, can address the bottleneck in immunotherapy development: the identification of tumor-specific targets. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, show that at least one tumor-specific antigen is present in 86% of the tumors considered, an increase in the tumors that benefit from immunotherapies compared to the high mutational tumors that represent fewer than a third of tumors. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, represent the most comprehensive atlas of tumor antigens reported to date. Exemplary Method

[0260] Collection of public bulk RNA-Seq

[0261] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can obtain the bulk RNA-Seq data from e.g., Cancer Genome Cloud (CGC) upon approved dbGaP access of TCGA (phs000178.vl 1) and TARGET (phs000218.v26). CGC platform hosts the uniformly deposited BAM file from TCGA and TARGET pre-aligned to the hg38 human genome using STAR 2-step passing.

[0262] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can select 21 adult and pediatric tumor types based on the availability of matched immunopeptidome public datasets, including Breast Invasive Carcinoma (BRCA), Kidney renal clear cell carcinoma (KIRC), Colon adenocarcinoma (COAD), Stomach adenocarcinoma (STAD), Mesothelioma (MESO), Liver hepatocellular carcinoma (LIHC), Esophageal carcinoma (ESCA), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Bladder Urothelial Carcinoma (BLCA), Rhabdoid Tumor (RT), Acute Myeloid Leukemia (AML), Diffuse Large B-Cell lymphoma (DLBC), Glioblastoma multiforme (GBM), Neuroblastoma (NBL), Pancreatic adenocarcinoma (PAAD), Head and Neck squamous cell carcinoma (HNSC), Ovarian serous cystadenocarcinoma (OV), Lung squamous cell carcinoma (LUSC), Lung adenocarcinoma (LU AD), Cholangiocarcinoma (CHOL), and Skin Cutaneous Melanoma (SKCM). The normal tissue bulk RNA-Seq raw data is downloaded from NCBI SRA upon dbGap approval (phs000424.v6.pl). The normal tissue gene expression data (GTEx_Analysis_2017-06- 05_v8_RNASeQCvl. l.9_gene_tpm.gct.gz) is retrieved from the GTEx portal as precalculated Transcripts Per Millions (TPMs) values.

[0263] Collection of public immunopeptidome data

[0264] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can obtain available immunopeptidome data sets from the PRIDE and Massive platforms. The biological context associated with each immunopeptidome experiment is retrieved from the original publications and the reported HLA alleles. Normal tissue immunopeptidome raw data, sample annotations, and HLA alleles are downloaded from the HLA ligand atlas with the accession number PXDO 19643.

[0265] Collection of public tumor single-cell RNA-Seq data

[0266] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can obtain available tumor single-cell RNA-Seq for 14 cancer types (e.g., thyroid carcinoma, colon cancer, gastric cancer, bladder cancer, head and neck cancer, pancreatic adenocarcinoma, ovarian cancer, lung squamous cell carcinoma, lung adenocarcinoma, glioblastoma, sarcoma, rhabdoid tumor, Uterine corpus endometrial carcinoma, breast cancer) from Cancer Single-Cell Expression map (CancerSCEM vl.O) (See, e.g., Ref. 66). Uniformly processed h5ad files are downloaded and the malignant cells are selected based on marker genes expression (e.g., EPCAM, FOLH1, KLK3, KRT8, KRT18, KRT19) as documented by CancerSCEM official tutorial. The average gene expression across malignant cells is calculated and ranked (in ascending order), and the percentile of each gene across all detected genes is reported as the single-cell coverage (homogeneity) in the analysis.

[0267] Computational Pipeline to identify diverse tumor-specific events

[0268] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can develop an efficient computational pipeline to profile classes of tumor-specific events (e.g., 11).

[0269] (1) Self-gene: For any user-supplied data, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can subject the raw RNA-Seq fastq files to Kallisto (version 0.44.0) pipeline (See, e.g., Ref. 67) using transcriptome index generated from the human Gencode V36. Transcripts Per Millions (TPMs) for each gene can be calculated by summing up all transcript-level TPMs associated with each gene. Each gene's normal tissue safety profiles are retrieved from the BayesTS database based on multiple evidence, including normal tissue gene expression, protein staining, and essential tissue distributions (See, e.g., Ref. 3). The median TPM in each normal tissue is calculated, and the maximum of them serves as a proxy for normal tissue expression for filtering purposes. Next, A gene is defined as tumor-specific only if metfollowing criteria: (a) median TPM in a tumor is greater than 20, (b) median TPM in a tumor should be greater than the normal expression calculated above, (c) BayesTS score is less than 0.3 to ensure safety of targeting it therapeutically, (d) upregulated in the tumor versus histology-matched normal tissue when the differential gene analysis data is available. For the TCGA and TARGET data, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can retrieve the gene expression TPMs from Xena Browser for each cancer, retrieve the differential gene expression between tumor versus histology-matched controls from GEPIA (See, e.g., Ref. 68) API, and define the genes with adjusted p-value less than 0.05 and log fold change (LFC) greater than 0.58 as upregulated genes in each tumor. The protein sequence of canonical selfgenes and isoforms can be downloaded from Ensembl and Uniprot, respectively. There are differences between the Ensembl-annotated main transcript and the Uniprot-annotated main transcript for a subset of genes; thus, utilizing the references from both sources can cover all the reviewed protein-coding isoforms. Using the tumor gene expression, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can filter out proteins and isoforms not expressed in the tumor sample, and assemble the expressed proteins and isoforms as either sample or tumor-specific search space in fasta format.

[0270] (2) Alternative Splicing: The exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the present disclosure can utilize pysam package to quantify the read counts supporting each splicing junctions by find introns function from the BAM file retrieved from TCGA and TARGET (See, e.g., Ref. 69). All the splicing junctions can be reported and filtered using a comprehensive normal tissue splicing junction database compiled from SNAF database (e.g., n sample = 2,948, n tissue = 51) (See, e.g., Ref. 13). To only consider tumor-specific splicing junctions, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can retain the splicing junctions with an average read count > 10 in tumor data, have less than 1 read count on average in GTEx tissue, log fold change > 4 as tumor-specific splicing events. To further remove alignment error, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can further remove splicing junctions that both ends are unannotated splicing sites. Lastly, since the immunopeptidome data is only matched by broad tumor type with the public RNA-Seq data, the exemplary systems, methods, and computer accessible medium according to the exemplary embodiments of the presentdisclosure can particularly look for recurrently detected splicing junctions in over 20% of samples. When matched immunopeptidome and RNA-Seq data are available, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, may not apply any recurrency filters at this step. The tumor splicing junctions can be subject to in-silico translation, where the junction's flanking regions (33nt) can be retained and translated to a peptide in 3 different reading frames. When the local tumor splicing junction happens to be part of the documented tumor-specific isoforms, the whole document isoform protein retrieved from Ensembl can be used instead of the local junction peptides.

[0271] (3) Intron Retention: The exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can utilize Stringtie2 in both guided (e.g., by known transcripts reference) and denovo mode (assemble transcript) from the retrieved BAM files (See, e.g., Ref. 70). The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can then collapse all human isoforms to identify all possible intron regions in the human genome. For each possible intron region, the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can identify as an intron retention event when the intron is not overlapping with another expressed exons (from guided stringtie result) and are within part of the transcript from stringtie denovo output. The corresponding exon expression from stringtie can serve as a proxy for the expression of this intron retention event. To further predict the probable intron-retention rederived peptide, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can utilize the NCBI ORFfinder algorithm on the transcript sequence and take the longest open reading frames. When the open reading frames overlap with the intron region of interest, these open reading frames can be translated and serve as a possible intron retention-derived peptide. To ensure tumor specificity, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can utilize two normal controls: first, the normal intron retention database from SNAF, and second, since SNAF utilizes a different strategy to identify intron retention, to further make sure the tumorspecific intron retention does not arise from pipeline difference, the exemplary systems, methods, and computer-accessible medium according to the exemplary embodiments of the present disclosure can subsequently compile representative normal cohorts (e.g., each tissue has 5 samples) spanning e.g., 31 histological groups from GTEx and re-run the exact samepipeline. The recurrent intron retention events can be defined as intron retention present in more than 15% of tumor samples. Given the high background for intron retention regions, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can require that tumor-specific intron retention should not be detected in normal tissue. The corresponding tumor-specific intron peptides can be used for immunopeptidome search.

[0272] (4) Tumor-resident microbiome: the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can use samtools (See, e.g., Ref. 71) to retrieve the reads that remain unmapped to the human genome from the BAM file and revert the unmapped reads into fastq format using bedtools (See, e.g., Ref. 72). Next, the fastq file can be classified using the kraken2 program (See, e.g., Ref. 73) with an index file containing both eukaryotes, virus, bacteria and fungi. The human genome can be further included in this database due to the caveats that even unmapped reads can still be from humans (See, e.g., Ref. 32). To increase the accuracy, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, may only report species-level reads that pass the confidence threshold (e.g., confidence>0.9) indicating 90% of the segments can be assigned to the species-specific genome. Similar to intron retention, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can compile a representative normal cohort and apply the same pipeline to remove pathogens detected in normal tissue as well. The retained tumor-specific pathogens, if present in more than, e.g., 20% of tumor samples, can be further cross-referenced with a previously published TCGA pathogen atlas TCMbio (See, e.g., Ref. 41), which utilizes a Bayesian inference step to redistribute the parental taxonomy reads and with a stringent decontamination step. Given the high false positive rate of tumor resident pathogen solely from transcriptome data, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can remove pathogen species not present in TCMbio. Lastly, to further ensure the absence of selected pathogen species in normal tissue, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can consider genus-level reads and can remove ones with substantial genus-level reads such that the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, cannot confidently rule out the possibility the species are not present. In the case of previously reported tumor resident viruses, such as HB V inliver cancer, HPV in cervical cancer, the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can also include them in the search space.

[0273] (5) Transposable element (TE) chimeric transcript: Since TE-chimeric transcripts require fusing into a splicing transcript, the exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can further annotate the tumor-specific junction if one of the splicing sites coincides with the transposable element regions, regardless of strands. When this happens, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can instead use the annotated TE regions as the template to translate the splicing junction, resulting in a longer stretch of peptides. Splicing junction cutoffs can provide tumor specificity.

[0274] (6) Autonomous transposable element (TE): Aside from the TE chimera transcript, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can also consider the transposable elements themselves serve as the transcripts to give rise to peptides. For this category, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can first utilize the TElocus pipeline to quantify the expression of each individual TE region, leveraging both uniquely mapped and multi-mapped reads. The raw read counts can be normalized by count per million (CPM) following previous results to account for sequencing depth difference (See, e.g., Ref. 76). Similarly, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can utilize the representative normal controls and can be subject to the exact same pipeline to remove TE regions with expression in normal tissues. Since not all categories can generate antigens, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can focus on Long Terminal Repeats (LTRs), including human endogenous retrovirus (HERVs), long interspersed nuclear element (LINE), short interspersed nuclear element (SINE), DNA transposon and SVA element from retroposon category. Both sense and antisense can be considered for translation due to the presence of opposite promoters for transposable elements. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can focus on TE with log fold change > 5 between tumor and normal tissues, detected in over 15% of patients.

[0275] (7) Gene fusion: When the fusion data is available, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can retrieve the computed fusion data from ChimerDB database, which utilized multiple fusion detecting programs (See, e.g., Ref. 78) to detect gene fusion events in TCGA and matched normal tissues. The exemplary systems, methods, and computer- accessible medium, according to the exemplary embodiments of the present disclosure, can retain the gene fusion events that are only in tumor tissues and absent in normal tissues and can retrieve the junction sequence for translating into the fusion peptides. When the fusion data is unavailable, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can utilize StarFusion (See, e.g., Ref. 77) to detect gene fusion events and remove the ones labelled as possible normal tissue presence in the output (e.g., ConjoinG, GTEx_recurrent_StarF2019, BodyMap, DGD PARALOGS, HGNC GENEFAM, Greger Normal, Babiceanu Normal).

[0276] (8) Variants and somatic mutation: For user-supplied RNA-Seq raw data, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can identify variants from RNA-Seq reads using an RNA variant detection protocol (e.g., Opossum) that achieves superior performance in the benchmark (See, e.g., Ref. 79). Specifically, Opossum can be applied to the raw BAM files to conduct a series of quality controls to render the resultant BAM files suitable for variant caller, including (a) removing low quality and improperly-aligned reads, (b) deduplication, (c) collapsing overlapping reads, and (d) separating intron-spanning reads into multiple segments. All the parameters are left as default except the MinFlankStart and MinFlankEnd are set as 10 to remove spurious bases at the start or end of aligning reads, a common artifact in calling variants from genomics and transcriptomics data (See, e.g., Ref. 80). Subsequently, the modified BAM files can be analyzed by Platypus (See, e.g., Ref. 81), a haplotype-based variant caller to report high-confidence variants in the tumor samples. Variant Effect Predictor (VEP) can be applied to characterize the impact of each variant on protein coding regions (See, e.g., Ref. 82). The missense variant, inframe deletion and insertion, along with frameshift variants are retained. While calling somatic mutations from tumor samples without the sample-matched normal control is impossible, certain predictors can achieve prediction power to distinguish somatic mutation from germline polymorphisms (See, e.g., Ref. 83). A variant can be predicted as somatic mutation if it meets all following criteria: (a) absence or extremely low population frequency (<0.0001) in dbSNP, (b) absence in normal tissue RNA editing database REDIportal (See, e.g., Ref. 84), and (c) maximum variant allele frequenciesin tumor samples less than 0.95. When a variant is confirmed as somatic mutation from the external COSMIC database, it can also be retained (See, e.g., Ref. 85). While these variants are annotated as somatic mutations, all the variants can be utilized to construct the search space as germline polymorphism also affects the protein sequence and should be considered. Additionally, all the detected A to G variants can be annotated as RNA editing events in the final results. For TCGA data, somatic mutations called from Whole Exome / Genome Sequencing with matched normal samples can be downloaded from Xena Browser. For the TARGET data, the uniformly processed somatic mutation vcf files can be downloaded from CGC platforms. Due to lack of mutation data in TARGET for rhabdoid tumor, a previous rhabdoid tumor WGS study (See, e.g., Ref. 86) can be used instead to impute the somatic mutation burden for this tumor type.

[0277] (9) IncRNA (See, e.g., Ref. 10), Pseudogene (See, e.g., Ref. 11), and cryptic openreading frames (ORFs): The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can retrieve the Ribo-Seq backed cryptic open reading frames, which can include both long non-coding RNA, pseudogene, and ORFs residing in downstream / upstream or out-of-frame regions of proteincoding genes. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can download the coordinates mapped to hgl9 from nuORF (See, e.g., Ref. 15) and can validate the resultant ORF length. Since the cryptic ORFs are not detected on RNA level due to detection bias, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can incorporate this database into the search space to find additional post-transcriptional antigens.

[0278] Immunopeptidome Data Analysis

[0279] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can generate an exhaustive tumorspecific search space by incorporating all tumor-specific molecular aberrations to search the histology-matched large-scale immunopeptidome datasets using MaxQuant (See, e.g., Ref. 87) (version 2.4.9.0) in nonspecific enzyme mode with peptide length ranging from 8-11 mer. Match between run and de novo sequencing is enabled for each search by default. Particularly, no peptide FDR is enforced in the initial run to obtain the full list of peptide- spectrum matches (PSMs). The full PSM list is then subject to MS2Rescore program (See, e.g., Ref. 18), which shows an increase of the immunopeptidome identification rate by 46% using additional features and deep-learning predicted spectral libraries to rescore the originalp-value and re-rank the PSM lists. Subsequently, a 5% FDR, typically used in immunopeptidome search and has been shown to rescue bona fide antigens that can otherwise be missed at 1% FDR (See, e.g., Refs. 60 and 90) can be applied to both MaxQuant reported Posterior Error Probability (PEP) and MS2Rescore reported q-value to get the FDR- controlled peptide lists. The union of both are summarized for each tumor immunopeptidome dataset as the initial list of evidenced peptides.

[0280] Next, the exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can employ netMHCpan 4.1 See, e.g., Ref. 91) to predict the binding affinity between each identified peptide and the cognate HLA alleles from the sample, both strong (rank < 0.5%) and weak binders (rank < 2%) can be considered, and only the peptides predicted to bind with cognate HLAs can be retained to further remove possible false discovery. The MaxQuant-reported precursor intensity associated with each PSM can estimate the peptide abundance in each immunopeptidome experiment, when multiple scans mapped to the same peptide, maximum precursor intensity is selected as the peptide abundance estimate. When comparing peptide abundance across different peptides, quantile normalization is conducted on the raw intensity values. Briefly, all the eluted peptides in each immunopeptidome experiment can be ranked in descending order based on raw intensity values, the upper quantile (75%) value as selected as the base, and the raw intensity of each peptide was divided by the base value and then been logarithmized with the base 2. The absence of Cryptic-ORF-derived peptides can be cross- referenced to lEAtlas database (See, e.g., Ref. 92).

[0281] Alphafold2 modeling

[0282] Alphafold2 (e.g., version 2.3.0) (See, e.g., Ref. 93) can be installed based on the non-docker instructions on the HPC cluster. pHLC 3D structures can be modeled by multimer mode without relaxation. SignalP (e.g., version 5) can be used for removing the HLA allele signal peptide, the HLA non-signal peptide region, and P2M protein, and the peptide can serve as fasta input to the Alphafold2 program with the -t argument set e.g., us 2023-04-29, the highest ranked PDB files can be used for visualization in Pymol software.

[0283] Ribo-Seq Analysis

[0284] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can conduct Ribo-seq for 8 neuroblastoma PDX models. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can analyze the Ribo-Seq data using the RibORF (e.g., version 2.0) pipeline (See, e.g., Ref. 95). Briefly, theadapter sequence (TGGAATTCTCGGGTGCCAAGG)(SEQ ID NO: 102) can be removed from the pair-end Ribo-Seq fastq files. Bowtie2 (e.g., version 2.3.1) (See, e.g., Ref. 96) can first build the ribosome RNA index and align the raw reads to the rRNA reference to remove the unwanted reads. Further, the cleaned fastq files can be aligned to human hgl9 genome by TopHat See, e.g., Ref. 97) to get the SAM file. RibORF pipeline can first generate a series of diagnostic plots for users to specify the correspondence between read length and offset length, usually arising from the ribosome length. The specified offset can convert each aligned read to its P site to obtain the corrected SAM file. The corrected and original SAM files can be viewed on IGV to evaluate the ribosomal occupancy of selected high-confidence cryptic ORF regions. Further, RibORF can output a predicted transcript level probability to indicate the likelihood of a translatable cryptic ORF. Visual inspection and quantitative prediction can be used to decide whether a region is translated into neuroblastoma PDX models.

[0285] Peptide Spike-in Validation

[0286] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can synthesize twenty peptide candidates across different molecular classes (e.g., GenScript, Piscataway, NJ) at a minimum of 95% purity with an average yield of 5-9 mg. Peptides can be reconstituted with 100% water, 5% DMSO / 95% water, or 5% HCOOH / 95% water to a final stock concentration of lOpmol / uL. Peptides can be pooled and subject to MS run using a similar setting as the peptides can be identified in the original paper. Synthetic peptide raw files can be searched against the 20 peptides, and the matched spectra can be visualized using spectrum utils (See, e.g., Ref. 98) and pyteomics packages and assessed by MS experts to confirm their identity.

[0287] Peptide MHC (pMHC) refolding

[0288] HLA expression. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can express and purify the HLA heavy chain using the bacterial expression vector pET28. BL21 / pBirA bacterial strain can be transformed with the pET28 vector containing the HLA-inserted DNA and incubated overnight at 37 °C, 170 rpm in 5 mL LB medium supplemented with chloramphenicol (Cm) at 15 pg / mL and Kanamycin (Kan) at 30 pg / mL. 100 mL culture of 2xYT medium supplemented with Cm and Kan can be inoculated with 2 mL of preculture. The culture can be grown at 37°C with shaking at 170 rpm until an optical density at 600 nm (O.D.600) of approximately 0.5. At this point, biotin can be added to the culture at a final concentration of 50 pM, and incubation can be continued for 10 minutes. HLA expressioncan be induced by adding 1 mM IPTG, followed by incubation for 4 hours at 37°C with shaking at 170 rpm. After induction, the culture can be centrifuged to pellet the cells. The pellet can be lysed by sonication, washed, and resuspended in 8 M Urea buffer to extract inclusion bodies. The Urea-soluble fraction can be subjected to affinity chromatography using Econo-Pac Chromatography Columns (e.g., Bio-Rad, cat. no. 7321010) packed with Ni Sepharose 6 Fast Flow histidine-tagged protein purification resin (e.g., Cytiva, cat. no. 17531801). HLA bound to the resin can be eluted with 10 mL of urea buffer containing 0.5 M imidazole. Protein-containing fractions can be pooled and concentrated using an Amicon Ultra Centrifugal Filter (e.g., 10 kDa MWCO, Millipore, cat. no. UFC9010). A diafiltration step with Urea buffer can be performed to remove the Imidazole, and the protein can be further concentrated to a final concentration of >100 pM.

[0289] fl2m expression. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can inoculate the BL21 (DE3) pHFT2_p2M bacterial stock into 5 mL of LB medium supplemented with kanamycin (30 pg / mL) and incubate the composition overnight at 37°C with shaking. 100 mL culture of 2xYT medium with Kanamycin can be inoculated with 2 mL of the overnight preculture and grown at 37°C, shaking at 170 rpm, until the O.D.600 reaches approximately 0.5. Expression of p2-microglobulin (P2m) can be induced by adding 1 mM IPTG, and the culture can be incubated overnight under the same conditions. The following day, the culture can be centrifuged to pellet the cells. P2m purification can be performed using a protocol similar to HLA, with a key modification to include an on-column refolding step. This can be achieved by passing a gradient of Urea buffer (e.g., 8 M urea, 0.1 M Tris-HCl, pH 8) and Native buffer (50 mM Tris-HCl, 250 mM NaCl, pH 8) through the column. The gradient can reduce the urea concentration until only a native buffer is used. P2m can be eluted with 10 mL of Native buffer containing 0.5 M Imidazole, and protein-containing fractions can be pooled and concentrated. To remove Imidazole and separate persistent aggregates from the monomeric fraction, the concentrated P2m solution can be loaded onto the AKTA Pure system and subjected to size exclusion chromatography (SEC) using a Superdex 75 Increase 10 / 300 GL column (e.g., Cytiva, cat. no. 29148721). Monomeric P2m fractions can be collected for further processing. Since P2m is a small protein, the His-tag used during purification can interfere with subsequent MHC complex refolding and alignment. To address this, the His-tag can be cleaved using TEV protease (e.g., Sigma, cat. no. T4455), as the P2m sequence includes a TEV protease cleavage site. After cleavage, the protein mixture can beagain run through the AKTA system to isolate the cleaved P2m monomer. The purified monomeric P2m, free of His-tag, can then be used for peptide-MHC refolding.

[0290] pMHC refolding. The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can dissolve synthetic peptides to a final concentration of 2 mM in the appropriate buffer, as determined by the solubility test. Refolding of the peptide-MHC (pMHC) complex can be performed in the PBS buffer using 3 pM cleaved P2m, 30 pM peptide, and 3 pM HLA, added sequentially in a total volume of 2 mL PBS. The solution can be incubated overnight at 4°C with gentle rotation to ensure proper mixing and refolding. The refolding mixture can be centrifuged the following day to remove any insoluble material. The clarified supernatant containing the pMHC complex can be loaded onto the AKTA Pure system for purification. The fractions containing the correctly refolded pMHC complex can be collected and further analyzed.

[0291] Cross-reactivity TCR experiment

[0292] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can culture mouse 58- / - T cell hybridoma cells (See, e.g., Ref. 99), which express mouse CD3 but not TCRab and Chinese hamster ovary (CHO) cells expressing HLA-A2, in RPMI 1640 medium and DMEM, respectively, supplemented with 10% FBS, sodium pyruvate, non-essential amino acids, glutaMAX-1, penicillin-streptomycin and P-mercaptoethanol. Human variable-mouse constant chimeric gpl00(2M)-specific TCR constructs- 19LF6 or 16LD6(100) and human CD8 can be retrovirally transduced into the hybridoma cells See, e.g, Ref. 101). Transduced cells can be sorted, expanded for 6 days, quantified for TCR and CD3e expression, and prepared for the coculture cytokine assay. 10,000 CHO cells expressing HLA-A2 can be loaded with different concentrations of gpl00(2M) peptide (IMDQVPFSV)(SEQ ID NO: 103), in which anchor position 2 can be mutated from original peptide T to M to increase binding affinity and PMEL spliced variant peptide (KTWDQVPFSV)(SEQ ID NO: 44) and incubated with 10,000 T cell hybridoma clones expressing human CD8 and 19LF6 or 16LD6 TCR in triplicates for 16 h at 37 °C, 5% CO2. A standard ELISA sandwich can be used to quantify cytokine mouse IL-2 production (See, e.g, Ref. 102).

[0293] Clustering analysis of eluted peptide motifs

[0294] The exemplary systems, methods, and computer-accessible medium, according to the exemplary embodiments of the present disclosure, can conduct clustering analysis using GibbsCluster (See, e.g., Ref. 103) version 2.0 through the web server. All eluted HLA- presented antigens from DLBC and Rhabdoid tumor (RT) can be included as input andsubject to Gibbs clustering using the default setting. The optimal number of clusters can be determined by the maximum Kullback- Leibler divergence.

[0295] GibbsClustering is a clustering algorithm for finding meaningful short peptide motifs, especially relevant in HLA-presented peptides. The algorithm aligns and clusters a set of peptides with diverse lengths to achieve the optimal number of clusters. The algorithm starts with randomly assigned g clusters and gradually minimizes the intra-cluster distance while maximizing inter-cluster distance, thus reaching the maximum separation of different groups. The resultant group of peptides can be represented as a position weight matrix (motif), indicating the frequency / preference of each amino acid in each position.Additional Discussion

[0296] Targetable tumor-specific antigens present an avenue for advancing cancer immunotherapy to a wider spectrum of cancer patients, encompassing both off-the-shelf therapy and personalized treatment. According to certain embodiments of the present disclosure, the algorithms can offer a one-stop solution to explore all categories of tumorspecific antigens. Leveraging RNA-Seq data, which has become a standard in most cancer research labs, can extend its impact to the entire scientific community and industry partners.

[0297] Unlike previous antigen discovery tools that often focus on specific types, the workflows, according to certain embodiments of the present disclosure, can stand out by addressing all antigen variations. This can be a crucial distinction, as other tools may erroneously report the absence of tumor-specific antigens in a particular cancer type due to their prevalence in other aberrations (low mutation burden tumor may not have mutation- derived antigen). Additionally, the lack of consideration for all tumor antigen types hinders the creation of a comprehensive antigen atlas. While previous studies attempt integrating different classes of antigens, they often require complex input types (e.g., DNA and RNA, tumor, and control) and only consider a subset of tumor antigens. In light of these challenges, the methods, products, workflows, algorithms, etc., according to certain embodiments of the present disclosure, may mark a leap toward understanding tumor-specific antigen types, thereby enhancing the repertoire of actionable targets for cancer therapy development.

[0298] Exemplary systems, methods, and computer-accessible medium, according to exemplary embodiments of the present disclosure, can provide an algorithm to identify tumor-specific immunotherapy targets from tumor RNA-Seq data. Tumor-specific antigens, composed of short peptides presented by HLA molecules, are pivotal in initiating T cell responses and are crucial components of various cancer immunotherapy treatment strategies. Current state-of-the-art pipelines only explore limited categories of tumor-specific antigensand underestimate the targetable antigen space. An algorithm according to exemplary embodiments is unique in discerning tumor-specific antigens originating from diverse genetic aberrations, including, but not limited to, (1) tumor-specific genes, (2) mutation, (3) insertion, (4) deletion, (5) gene fusion, (6) splicing isoform, (7) RNA editing, (8) endogenous retrovirus, (9) transposable element, (10) pathogen, (11) IncRNA, and (12) circular RNA. By taking patient RNA-Seq data as input, the algorithm, according to exemplary embodiments, can detect all 12 types of genetic aberrations, providing a comprehensive snapshot of the transcriptomic landscape associated with cancer patients.

[0299] Exemplary systems, methods, and computer-accessible medium, according to exemplary embodiments of the present disclosure, can generate a human-readable summary to characterize the genetic profile of individual patients and, when applied to multiple patients, can produce a cohort-level summary reflecting the recurrence of each genetic aberration. It can also facilitate in-silico translation of the above-mentioned aberrations, yielding a sample-specific HLA-presented antigens list. The final output, a collection of fasta files, can serve as the input for modern Mass Spectrometry search engines (e.g., MaxQuant, Byonic, PEAKS DB, MSFragger, etc.), providing exhaustive searches for all types of tumorspecific antigens. Since current immunopeptidome searches rely on user-supplied databases, the algorithm of exemplary systems, methods, and computer-accessible medium, according to exemplary embodiments of the present disclosure, can fill the gap and emerge as an essential tool for expanding the repertoire of tumor-specific antigens identifiable for cancer immunotherapy (see Figure 11 A).

[0300] According to exemplary systems, methods, and computer-accessible medium, a process / workflow may start with single or multiple (up to thousands) cancer patients' RNA- Seq data (in raw fastq format), either generated in-house or data that are publicly available for dozens of cancer types. From the raw RNA fastq file, the pipeline employs a parallelized approach by splitting into distinct workflows on a multi-core, high-performance computing system. Workflow 1 encompasses, for example, computational HLA typing to obtain 4-digit HLA alleles, followed by quantifying gene-level expression in each tumor sample and identifying circular RNA and tumor-specific transposable elements, including human endogenous retroviruses (HERV). Workflow 2 is dedicated to detecting, for example, mutations, insertions, deletions, splicing events, intron retentions, pathogens, long noncoding RNAs (IncRNA), and RNA editing. Meanwhile, Workflow 3 focuses on identifying, for example, gene fusions. The allocation of workflows is determined by, for example, optimizing the usage of time and memory and considering internal dependencies to achieveoptimal performance. Processing a standard RNA-Seq dataset (pair-end, >50M reads) can be completed on any modern Linux HPC node within a day. The described processes employ a combination of open-source tools and custom Python scripts to identify various types of genetic aberrations. For enhanced alignment accuracy, Telomere-to-Telomere (T2T) human reference is prioritized wherever possible. In instances where only hg38 or hgl9 annotations are available, one may revert to, for example, hg38 / 19 to maintain consistency with the available annotations.

[0301] All identified genetic aberrations may undergo assembly into tabular data, facilitating user inspection of recurrent aberrations. Additionally, a large-scale set of normal controls has been curated through customized scripts or by sourcing from public databases. These controls may play a pivotal role in filtering out genetic aberrations that are not tumorspecific, as only such aberrations have the potential to generate ideal tumor-specific antigens. In the subsequent in-silico translation step, only tumor-specific aberrations meeting specific criteria (user-tunable) may be utilized. These criteria may be exposed to the user to provide tailored applications for various use cases. To reduce the number of false positively identified antigens, for example, the most probable open reading frame (ORF) is focused on wherever possible by harnessing the prior information and facilitating cryptic ORFs to be included for increased sensitivity. For example, peptides derived from intron retention may be inferred from computationally reconstructed full-length isoforms (e.g., StringTie v2), determining the phase for translation. Leveraging this prior knowledge reduces the number of peptides for subsequent search compared to a 3-way translation. This reduction of the database is preferred for lowering the false discovery rate during the mass spectrometry analysis step. Since the majority of the MHC-I presented antigens typically range from 8-11 mers, a maximum of 10 residues flanking the aberration site, for example, is included, ensuring the inclusion of the aberrations in the final peptide list for the subsequent search.

[0302] For every cancer patient, the workflow, according to exemplary systems, methods, and computer-accessible medium, can generate a list of fasta files with clear, interpretable headers and accompanying sequences. These files can be integrated into all modern mass spectrometry software for immunopeptidome searches. The identified peptides can be traced back to their source using the header information, and the results can be visualized using popular tools such as the UCSC Genome Browser or Integrated Genome Viewer (IGV). The reported quantification information associated with each antigen provides additional evidence, aiding in prioritizing high-confidence hits for subsequent experiments. To harness the parallelization features embedded in exemplary embodiments, running the algorithm ofexemplary embodiments on a Linux machine with a multicore structure may be advisable. Exemplary systems, methods, and computer-accessible medium, according to exemplary embodiments of the present disclosure, can be developed in Python, utilizing additional open- source software written in C and Fortran, providing robust and efficient functionality.

[0303] Exemplary systems, methods, and computer-accessible medium, according to exemplary embodiments of the present disclosure, suggest the need to consider non-canonical space and PTM epitope, among other factors.

[0304] Human HLA-I molecules display potential tumor-specific antigens to cytotoxic CD8+ T cells, facilitating the development of targeted cancer immunotherapies to eradicate tumors while sparing normal tissues. While tumor-specific antigens are known to arise from a broad spectrum of genetic aberrations such as oncogene upregulation and somatic mutations, prevalent prediction tools are limited to a narrow range, which leads to underestimation of actionable target space and impedes the development of therapies for low-mutation and non- responsive tumor types. To expand the current repertoires and depict the complete tumor antigen landscape, according to exemplary systems, methods, and computer-accessible medium, ImmunoVerse can be provided, which is an exhaustive tumor antigen search engine designed to interrogate categories, including but not limited to, 14 aberration categories from RNA-Seq data, encompassing (1) gene expression, (2) single nucleotide polymorphisms (SNPs), (3) insertions and deletions (INDELs), (4) splicing junctions, (5) intron retention, (6) gene fusions, (7) RNA editing, (8) tumor-resident pathogens, (9) transposable elements, (10) endogenous retroviruses, (11) circular RNAs, (12) cryptic open reading frames (ORFs), (13) proteasomal spliced antigens, and (14) post-translational modifications. The scope provides a mapping of the entire tumor antigen space in every tumor type. ImmunoVerse can be applied to, for example, neuroblastoma, medulloblastoma, and osteosarcoma, well-known somatic mutations (e.g., ALK) and gene amplifications (such as MYCN) can be recapitulated while unveiling new tumor-specific antigens from previously unexplored aberrations. At least four antigens derived from non-annotated ORFs in neuroblastoma can be identified, distinguished by their high-confidence immunopeptidome spectrum and peptide abundance. Further validation through Ribosome Sequencing (Ribo-Seq) profiling confirmed the translation of these cryptic ORFs, highlighting the additional antigens that might have been missed without the search strategy. The widespread adoption of exemplary systems, methods, and computer- accessible medium, according to exemplary embodiments of the present disclosure, such as ImmunoVerse, may broaden the actionable targets available for current cancer immunotherapy.

[0305] Exemplary systems, methods, and computer accessible medium, according to exemplary embodiments of the present disclosure, can be applied to different cancer types, including and not limited to, osteosarcoma, neuroblastoma, chordoma, and medulloblastoma, with data such as transcriptome data and optional immunopeptidome data. For example, exemplary workflow on neuroblastoma has been validated to evaluate its accuracy and ability to define new tumor-specific targets.

[0306] Utilizing published neuroblastoma dataset, exemplary systems, methods, and computer-accessible medium can validate each component of the pipeline. Starting with RNA-based HLA typing, which, at a 4-digit resolution, can demonstrate 100% accuracy when compared to previously validated HLA types derived from DNA samples, underscoring the robustness of exemplary systems, methods, and computer-accessible medium, according to exemplary embodiments of the present disclosure. Furthermore, the use of the kallisto- based gene expression analysis identifies six neuroblastoma-specific genes (e.g., PHOX2B, CHRNA3, GFRA2, HMX1, IGFBPL1, TH) with expression patterns consistent with those reported against GTEx tissues (Figure 2A). When applied to published breast cancer patient data, the circular RNA pipeline, according to exemplary embodiments, can detect the previously reported circular RNA, 20 circFAM53B, incorporating exon 2 circle (chrlO: 124681607-124682379). In the Neuroblastoma cohort, evidence was found for confirmed somatic mutations such as ALK Fl 174L and others from TP53, and well-known copy number alteration (amplification) is reflected as high gene expression for genes, e.g., MYCN, CDK4, and MDM2. The lack of a subset of somatic mutation on the RNA level may explain the absence of mutational antigens from the original immunopeptidome, highlighting the importance of RNA-Seq. At the peptide level, the pipeline recapitulates two previously validated peptides, QYNPIRTTF (SEQ ID NO: 88) from PHOX2B and FLDETLRSLA (SEQ ID NO: 89) from GFRA2, with high confidence (see Figure 1 IB), along with KATEYVHSL KATEYVHSL (SEQ ID NO: 90) from MYCN with a rare C16:01 allele as previously reported. A reported peptide abundance, according to exemplary embodiments, can have a high correlation with previously reported AUC from PD. Among the 5,912 high-confidence peptides reported in the original study, the pipeline adopting a new version of MaxQuant recovers 90% of these peptides (see Figure 11C). Such results not only validate the efficacy and reliability of the pipeline but also demonstrate its potential to contribute to the identification of tumor-specific antigens and the development of targeted therapies.

[0307] The workflow according to exemplary systems, methods, and computer-accessible medium of exemplary embodiments of the present disclosure can demonstrate capability inidentifying additional non-canonical peptides. In the case of neuroblastoma, mutated peptides can be discovered with high confidence, one such example being a mutated peptide (e.g., FPFEKGSVQY (SEQ ID NO: 92) mutated to FEFEKGSAQY (SEQ ID NO: 87)) resulting from a V140A mutation in the DEK gene (see Figure 1 ID). The same genetic locus can be found to have a somatic mutation, V140D, in colorectal cancer, indicating its somatic nature pending further validation. Additionally, exemplary embodiments can identify a high- confidence peptide, APAAGALHAA (SEQ ID NO: 91), originating from an in-frame 5’ UTR cryptic open reading frame (ORF) on the FAM20C gene (see Figure 1 IE). This peptide has been confirmed in multiple cancer types, including neuroblastoma, melanoma, and ovarian cancer, and is notably absent across hundreds of normal samples spanning 30 tissue types. Beyond these findings, the pipeline of exemplary embodiments also unveiled peptides derived from intron retention and endogenous retroviruses (ERVs), among others. Combined, algorithms according to exemplary systems, methods, and computer-accessible medium can identify known antigens and uncover new targets based on their search space.

[0308] According to certain embodiments of the present disclosure, 5-10 high-confidence non-canonical targets in neuroblastoma may be selected, based on safety screening results conducted by re-analyzing the raw MS spectrum from a large-scale normal immunopeptidome, including the HLA Ligand Atlas, IEDB, and TOFIMS Atlas. Their existence may be validated using synthetic peptides followed by mass spectrometry, which can confirm the identity of the peptide and its presence in the PDX model. Then using a model such as a PDX model, experiments such as a refolding experiment and phage panning may be conducted to select possible receptors that can target these antigens. The T-cell clones that may respond to those antigens may be detected and TCR therapy may be explored. The identification of either scFv or TCR may lay the foundation for further studies, such as the killing assay and persistent experiment.

[0309] The methods, pipelines, workflows, products, etc. according to the present disclosure may be applied to a large TCGA cohort and may create a genetic search space for every cancer type along with each tumor subtypes. This can facilitate the researchers to use and search against any cancer immunopeptidome data, increasing the identification for both canonical and non-canonical peptides for various cancer types.Artificial Intelligence and Machine Learning

[0310] Embodiments of the present disclosure utilize machine learning models and artificial intelligence for various data processing operations to, for example, refine results to enhance sensitivity and to enhance the accuracy of subsequent searches.

[0311] The term “artificial intelligence” is defined herein to include any technique that enables one or more computing devices or comping systems (i.e., a machine) to mimic human intelligence. Artificial intelligence (Al) includes, but is not limited to, knowledge bases, machine learning, representation learning, and deep learning. The term “machine learning” is defined herein to be a subset of Al that enables a machine to acquire knowledge by extracting patterns from raw data. Machine learning techniques include, but are not limited to, logistic regression, support vector machines (SVMs), decision trees, Naive Bayes classifiers, and artificial neural networks. The term “representation learning” is defined herein to be a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, or classification from raw data. Representation learning techniques include, but are not limited to, autoencoders. The term “deep learning” is defined herein to be a subset of machine learning that that enables a machine to automatically discover representations needed for feature detection, prediction, classification, etc. using layers of processing. Deep learning techniques include, but are not limited to, artificial neural network or multilayer perceptron (MLP).

[0312] Machine learning models include supervised, semi-supervised, and unsupervised learning models. In a supervised learning model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or targets) during training with a labeled data set (or dataset). In an unsupervised learning model, the model learns patterns (e.g., structure, distribution, etc.) within an unlabeled data set. In a semisupervised model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or target) during training with both labeled and unlabeled data.

[0313] An artificial neural network (ANN) is a computing system including a plurality of interconnected neurons (e.g., also referred to as “nodes”). This disclosure contemplates that the nodes can be implemented using a computing device (e.g., a processing unit and memory as described herein). The nodes can be arranged in a plurality of layers such as input layer, output layer, and optionally one or more hidden layers. An ANN having hidden layers can be referred to as deep neural network or multilayer perceptron (MLP). Each node is connected to one or more other nodes in the ANN. For example, each layer is made of a plurality of nodes, where each node is connected to all nodes in the previous layer. The nodes in a given layer are not interconnected with one another, i.e., the nodes in a given layer function independently of one another. As used herein, nodes in the input layer receive data from outside of the ANN, nodes in the hidden layer(s) modify the data between the input andoutput layers, and nodes in the output layer provide the results. Each node is configured to receive an input, implement an activation function (e.g., binary step, linear, sigmoid, tanH, or rectified linear unit (ReLU) function), and provide an output in accordance with the activation function. Additionally, each node is associated with a respective weight. ANNs are trained with a dataset to maximize or minimize an objective function. In some implementations, the objective function is a cost function, which is a measure of the ANN’S performance (e.g., error such as LI or L2 loss) during training, and the training algorithm tunes the node weights and / or bias to minimize the cost function. This disclosure contemplates that any algorithm that finds the maximum or minimum of the objective function can be used for training the ANN. Training algorithms for ANNs include, but are not limited to, backpropagation. It should be understood that an artificial neural network is provided only as an example machine learning model. This disclosure contemplates that the machine learning model can be any supervised learning model, semi-supervised learning model, or unsupervised learning model. Optionally, the machine learning model is a deep learning model. Machine learning models are known in the art and are therefore not described in further detail herein.

[0314] A convolutional neural network (CNN) is a type of deep neural network that has been applied, for example, to image analysis applications. Unlike a traditional neural networks, each layer in a CNN has a plurality of nodes arranged in three dimensions (width, height, depth). CNNs can include different types of layers, e.g., convolutional, pooling, and fully-connected (also referred to herein as “dense”) layers. A convolutional layer includes a set of filters and performs the bulk of the computations. A pooling layer is optionally inserted between convolutional layers to reduce the computational power and / or control overfitting (e.g., by downsampling). A fully-connected layer includes neurons, where each neuron is connected to all of the neurons in the previous layer. The layers are stacked similar to traditional neural networks.Example System / Computing Device

[0315] Figure 12 shows a block diagram of an exemplary embodiment of a system according to the present disclosure. For example, exemplary procedures in accordance with the present disclosure described herein can be performed by a processing arrangement and / or a computing arrangement (e.g., computer hardware arrangement) 1105. Such processing / computing arrangement 1105 can be, for example, entirely or a part of, or include, but not limited to, a computer / processor 1110 that can include, for example, one or moremicroprocessors, and use instructions stored on a computer-accessible medium (e.g., RAM, ROM, hard drive, or other storage device).

[0316] As shown in Figure 12, for example, a computer-accessible medium 1115 (e.g., as described herein above, a storage device such as a hard disk, floppy disk, memory stick, CDROM, RAM, ROM, etc., or a collection thereof) can be provided (e.g., in communication with the processing arrangement 1105). The computer-accessible medium 1115 can contain executable instructions 1120 thereon. In addition, or alternatively, a storage arrangement 1125 can be provided separately from the computer-accessible medium 1115, which can provide the instructions to the processing arrangement 1105 so as to configure the processing arrangement to execute certain exemplary procedures, processes, and methods, as described herein above, for example. Further, the exemplary processing arrangement 1105 can be provided with or include an input / output port 1135, which can include, for example, a wired network, a wireless network, the internet, an intranet, a data collection probe, a sensor, etc. As shown in Figure 12, the exemplary processing arrangement 1105 can be in communication with an exemplary display arrangement 1130, which, according to certain exemplary embodiments of the present disclosure, can be a touch-screen configured for inputting information to the processing arrangement in addition to outputting information from the processing arrangement, for example. Further, the exemplary display arrangement 1130 and / or a storage arrangement 1125 can be used to display and / or store data in a user- accessible format and / or user-readable format.

[0317] According to the exemplary embodiments of the present disclosure, numerous specific details have been set forth. It is to be understood, however, that implementations of the disclosed technology can be practiced without these specific details. In other instances, wellknown methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. References to “some examples,” “other examples,” “one example,” “an example,” “various examples,” “one embodiment,” “an embodiment,” “some embodiments,” “example embodiment,” “various embodiments,” “one implementation,” “an implementation,” “example implementation,” “various implementations,” “some implementations,” etc., indicate that the implementation(s) of the disclosed technology so described may include a particular feature, structure, or characteristic, but not every implementation necessarily includes the particular feature, structure, or characteristic. Further, repeated use of the phrases “in one example,” “in one exemplary embodiment,” or “in one implementation” does not necessarily refer to the same example, exemplary embodiment, or implementation, although it may.

[0318] As used herein, unless otherwise specified the use of the ordinal adjectives “first,” “second,” “third,” etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

[0319] While certain implementations of the disclosed technology have been described in connection with what is presently considered to be the most practical and various implementations, it is to be understood that the disclosed technology is not to be limited to the disclosed implementations, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended paragraphs. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

[0320] The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures which, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. Various different exemplary embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art. In addition, certain terms used in the present disclosure, including the specification and drawings, can be used synonymously in certain instances, including, but not limited to, for example, data and information. It should be understood that, while these words, and / or other words that can be synonymous to one another, can be used synonymously herein, that there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly incorporated herein in its entirety. All publications referenced are incorporated herein by reference in their entireties.

[0321] Throughout the disclosure, the following terms take at least the meanings explicitly associated herein, unless the context clearly dictates otherwise. The term “or” is intended to mean an inclusive “or.” Further, the terms “a,” “an,” and “the” are intended to mean one or more unless specified otherwise or clear from the context to be directed to a singular form.

[0322] This written description uses examples to disclose certain implementations of the disclosed technology, including the best mode, and also to enable any person skilled in the art to practice certain implementations of the disclosed technology, including making and using any devices or systems and performing any incorporated methods. The patentable scope of certain implementations of the disclosed technology is defined in the appended paragraphs, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the appended paragraphs if they have structural elements that do not differ from the literal language of the appended paragraphs, or if they include equivalent structural elements with insubstantial differences from the literal language of the appended paragraphs.Exemplary References

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[0065] Hsiehchen D, Elliott A, Xiu J, Seebe...

Claims

What is claimed:

1. A computer-implemented method comprising: receiving, by at least one processor, sequence data; identifying, by the at least one processor, one or more tumor-specific events associated with a plurality of molecular event classes from the sequence data; determining, by the at least one processor, a search space based on the one or more tumor-specific events; interrogating, by the at least one processor, a plurality of immunopeptidome datasets based on the determined search space; and determining, by the at least one processor, one or more immunotherapy targets based on genetic aberrations from the sequence data.

2. The computer-implemented method of claim 1, wherein the sequence data comprises Ribonucleic acid (RNA)-sequence data.

3. The computer-implemented method of claim 1 or 2, wherein the one or more immunotherapy targets are used to direct a treatment for one or more patients.

4. The computer-implemented method of any one of claims 1-3, wherein the plurality of immunopeptidome datasets are each associated with one or more aberration categories.

5. The computer-implemented method of claim 4, wherein the one or more aberration categories include at least one of: protein-coding genes, single nucleotide polymorphisms (SNPs), insertions and deletions (INDELs), splicing junctions, intron retention, gene fusions, Ribonucleic acid (RNA) editing, tumor-resident pathogens, transposable elements, endogenous retroviruses, circular RNAs, cryptic open reading frames (ORFs), proteasomal spliced antigens, and post-translational modifications.

6. The computer-implemented method of any one of claims 1-5, wherein the one or more immunotherapy targets include canonical and non-canonical peptides for at least one cancer type.

7. The computer-implemented method of any one of claims 1-6, wherein the plurality of immunopeptidome datasets comprises histology-matched immunopeptidome datasets.

8. The computer-implemented method of any one of claims 1-7, wherein determining the one or more immunotherapy targets comprises identifying one or more peptides, the computer-implemented method further comprising: rescoring, by the at least one processor, each identified peptide using a deep-learning model to boost immunopeptidome detection sensitivity; and determining, by the at least one processor, a human leukocyte antigen (HLA) binding prediction for each identified peptide to eliminate false positives.

9. The computer-implemented method of any one of claims 1-8, further comprising: generating or outputting, by the at least one processor, a report, summary, and / or data object describing the genetic aberrations and / or list of antigens.

10. The computer-implemented method of any one of claims 1-9, wherein the generated output is used as an input for rendering an interactive web portal for users to interrogate the identified tumor-specific antigens and make an informed decision.

11. A vaccine for treatment of a cancer comprising a therapeutically acceptable amount of one or more neoantigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO:

41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89,SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ IDNO: 101, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ IDNO: 107, SEQ ID NO: 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111, SEQ IDNO: 112, SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ IDNO: 117, and SEQ ID NO: 118; and a pharmaceutically acceptable carrier.

12. A chimeric antigen receptor (CAR), wherein the CAR binds to one or more neoantigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO:

41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO: 107, SEQID NO: 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111, SEQ ID NO: 112, SEQID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117, andSEQ ID NO: 118.

13. The CAR of claim 12, wherein the CAR is expressed on a T cell, natural killer (NK) cell, NK T cell, or macrophage.

14. A T cell receptor (TCR), wherein the TCR binds to one or more neoantigens selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO:

41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111, SEQ ID NO: 112, SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117, and SEQ ID NO: 118.

15. A nucleic acid encoding the TCR of claim 14.

16. An engineered T cell comprising the nucleic acid of claim 15.

17. A method of treating a cancer in a subject comprising administering to the subject the vaccine of claim 11, the CAR of claims 12 or 13, the TCR of claim 13, the nucleic acid of claim 15, or the engineered T cell of claim 16.

18. A method of treating a cancer in a subject comprising administering to the subject an agent that inhibits expression of one or more neoantigens selected from the group consistingof SEQ ID NO: 1, SEQ ID NO: 2, SEQ I D NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ I D NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ I D NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ I D NO: 18, SEQ ID NO: 19, SEQ ID NO: 20, SEQ ID NO: 21, SEQ ID NO: 22, SEQ I D NO: 23, SEQ ID NO: 24, SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ I D NO: 28, SEQ ID NO: 29, SEQ ID NO: 30, SEQ ID NO: 31, SEQ ID NO: 32, SEQ I D NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, SEQ ID NO: 36, SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO:

41. SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, SEQ I D NO: 48, SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ I D NO: 53, SEQ ID NO: 54, SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ I D NO: 58, SEQ ID NO: 59, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 62, SEQ I D NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, SEQ ID NO: 66, SEQ ID NO: 67, SEQ I D NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, SEQ ID NO: 72, SEQ I D NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, SEQ I D NO: 78, SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQID NO: 82, SEQ ID NO: 83, SEQ ID NO: 84, SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, SEQ ID NO: 90, SEQ ID NO: 91, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, SEQ ID NO: 96, SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 103, SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 106, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 111, SEQ ID NO: 112, SEQ ID NO: 113, SEQ ID NO: 114, SEQ ID NO: 115, SEQ ID NO: 116, SEQ ID NO: 117, and SEQ ID NO: 118.

19. The method of claim 18, wherein the agent comprises an antisense oligonucleotide, short hairpin RNA (shRNA), long non-coding RNA (IncRNA), small interfering RNA (siRNA), RNAi, or small molecule.

20. The vaccine of claim 11, the CAR of claims 12 or 13, the TCR of claim 13, the nucleic acid of claim 15, the engineered T cell of claim 16, or the method of any one of claims 17-19, wherein the vaccine or agent is determined using a computer-implemented method of any one of claims 1-10.

21. A system comprising: at least one computing device having a processor and a memory having instructions thereon, wherein the instructions when executed by the processor, cause the processor to:receive sequence data; identify one or more tumor-specific events associated with a plurality of molecular event classes from the sequence data; determine a search space based on the one or more tumor-specific events; interrogate a plurality of immunopeptidome datasets based on the determined search space; and determine one or more immunotherapy targets based on genetic aberrations from the sequence data.

22. The system of claim 21, wherein the instructions when executed by the processor, cause the processor to further: generate a plurality of workflows for processing the received sequence data, including a first workflow for identifying the one or more tumor-specific events, a second workflow for determining the search space, and a third workflow for interrogating the plurality of immunopeptidome datasets.

23. The system of claim 21 or 22, wherein the instructions when executed by the processor, cause the processor to further: transfer at least a portion of the received sequence data to at least another computing device for processing based on time and memory constraints.

24. The system of any one of claims 21-23, wherein determining the one or more immunotherapy targets comprises identifying one or more peptides, and wherein the instructions when executed by the processor, cause the processor to further: rescore each identified peptide using a deep-learning model to boost immunopeptidome detection sensitivity; and determine a HLA binding prediction for each identified peptide to eliminate false positives.

25. A non-transitory computer-readable medium having instructions stored thereon, wherein execution of the instructions by a processor, cause the processor to: receive sequence data; identify one or more tumor-specific events associated with a plurality of molecular event classes from the sequence data;determine a search space based on the one or more tumor-specific events; interrogate a plurality of immunopeptidome datasets based on the determined search space; and determine one or more immunotherapy targets based on genetic aberrations from the sequence data.