Neojunction-derived cancer antigen epitopes, specific t cell receptors (TCRS) for immunotherapy, and discovery platform for the same

Abstract

Provided herein are peptides derived from neoantigens generated by an aberrant splicing. In addition to its use as a cancer vaccine, the peptide can be used to prime immune cells. T cell receptors, cells containing the same, methods for inducing an immune response and methods of treatment are also provided. A discovery platform for identifying other cancer neoantigens is also described.

Description

[0001] NEOJUNCTION-DERIVED CANCER ANTIGEN EPITOPES, SPECIFIC T CELL RECEPTORS (TCRS) FOR IMMUNOTHERAPY, AND DISCOVERY PLATFORM FOR THE SAME

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Provisional Application No. 63 / 541,617, filed September 29, 2023, which application is incorporated herein by reference in its entirety.

[0004] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0005] This invention was made with government support under grant nos. R01 CA222965, R35 NS 105068 and R01CA244838 awarded by The National Institutes of Health. The government has certain rights in the invention.

[0006] INCORPORATION BY REFERENCE OF SEQUENCE LISTING PROVIDED AS A SEQUENCE LISTING XML FILE

[0007] A Sequence Listing is provided herewith as a Sequence Listing XML, “UCSF- 743WO_SEQLIST”, created on September 26, 2024 and having a size of 21,701 bytes. The contents of the Sequence Listing XML are incorporated herein by reference in their entirety.

[0008] BACKGROUND

[0009] Immunotherapy has emerged as a promising strategy for cancer treatment, showing remarkable clinical efficacy in various disease types. However, many cases demonstrate that tumors manage to evade eradication due to intratumoral heterogeneity (ITH). ITH refers to the diversification of a tumor’s cellular and genetic landscape over space and time, which enables neoplastic subsets to acquire immunotherapy resistance and immune evasion. Despite the success of immunotherapy in tumors with high immune infiltration and mutational load, cancer types with extensive ITH or lower mutational burdens are much more resistant.

[0010] Current immunotherapies targeting tumor- specific antigens (TSAs) have focused on peptides that arise from somatic mutations in canonical coding regions of the genome. Due to its reliance on TSAs, this approach struggles in tumors with low mutational burdens. To ameliorate this problem, recent studies have explored aberrant alternative splicing events across multiple cancers as an additional source of TSAs. These cancer-specific splicing events (neojunctions) were shown to be prevalent in tumors and to be capable of generating novel TSAs that potentiate CD8+ T-cell-mediated expansion and responses in select cancer types. Nevertheless, no study has yet examined the persistence of neojunctions throughout the tumor landscape, and therefore, those identified neojunction-derived targets face similar challenges due to ITH.

[0011] SUMMARY

[0012] Provided herein are tumor-wide neoantigens generated by an aberrant splicing event.

[0013] Peptides derived from the antigen (e.g., any of SEQ ID NOS: 1-3) may be used on a variety of immunotherapeutic applications, including to stimulate the immune system (e.g., as a cancer vaccine). In some embodiments, the peptide may be presented by a class 1 MHC on an antigen- presenting cell. This cell can be used to activate immune cells (e.g., T cells) in vitro or in vivo. TCRs that bind to a MHC- peptide complex are also provided. CD8+ T cells expressing these TCRs can recognize and kill cells that express the neoantigen.

[0014] Because this neoantigen is shared between patients and also tumor- wide (i.e., intratumorally-expressed in a heterogenous tumors such as glioblastoma (GBM) and low-grade glioma (LGG)), the present peptides are believed to represent a new class of "off-the-shelf" cancer immunotherapy, offering promising avenues for improving cancer treatment strategies, particularly for heterogeneic cancers that may have low somatic mutational burden such as glioma, such as glioblastoma (GBM) and low-grade glioma (LGG).

[0015] A discovery platform for identifying such antigens is also provided.

[0016] BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The skilled artisan will understand that the drawings, described below, are for illustration purposes only. The drawings are not intended to limit the scope of the present teachings in any way.

[0018] Fig. 1: Novel neoantigen discovery pipeline for discovering public and tumor- ide targets for cellular immunotherapy across various cancers. TCGA RNA sequencing data across multiple cancers were analyzed for non- annotated, protein-coding, and cancer-specific splicing junctions (GTEx positive sample rate < 1%; neojunctions). Interpatiently conserved (TCGA positive sample rate > 10%; public neojunctions) were retained for downstream analysis of intratumoral heterogeneity (ITH). Tumors with sequencing data extracted from multiple intratumoral regions were used to evaluate each public neojunction’s ITH. Independent prediction algorithms were used to assess proteasomal processing and MHC-I binding of peptide sequences translated from public, intratumorally conserved neojunctions. The expression of these neojunctions and their peptide derivatives were validated by RNA sequencing and mass spectrometry analysis of patient-derived tumor samples and cell lines. T- cell receptors (TCRs) were cloned and characterized for top predicted candidates through in vitro sensitization of PBMC-dcrivcd CD8+ T-cells against the corresponding neoantigens and subsequent lOx V(D)J single-cell sequencing. Transduction of these neoantigen-reactive TCR sequences in TCR-null Iurkat76 cells and PBMC-derived CD8+ T-cells allowed the demonstration of neoantigen-specific immunogenicity and tumor- specific killing.

[0019] Figs. 2A-2I: Public putative neojunctions are characterized across multiple cancer types. A. Pipeline for identifying putative, spatially conserved, cancer- specific alternative splicing events (neojunctions) from RNA- sequencing data collected from The Cancer Genome Atlas (TCGA). B. TCGA tumor sets with corresponding multi-biopsy RNA sequencing data available for analysis. C. Tumor purity of TCGA tumor samples. Samples with confirmed tumor purity of 60% and above (solid fraction) were retained for downstream analysis. D. Interpatient frequency (positive sample rate; PSR) of putative neojunctions identified in each tumor type. Public neojunctions are defined as those with a PSR > 10% (red line). E. Total public neojunctions detected per sample across tumor types. F. Iog2(read frequency) of public neojunctions across tumor types. G-H. Distribution of public neojunctions based on splice types (G) and frame-shift status (H). I. Expression of all public neojunctions (log2(CPM)) across all studied TCGA tumor types.

[0020] Figs 3A-3G: Subsets of neojunctions are expressed tumor-wide. A. Overview of the tumor- wide characterization of neojunctions by investigating RNA sequencing of multiple intratumoral regions in various cancers. B. Heatmaps representing log2(CPM) of neojunctions (rows) across five samples within the same tumor (columns) in colon cancer, kidney cancer, liver cancer, and stomach cancer. Neojunctions found across all five intratumoral samples are annotated in yellow. C. Heatmap illustrating intratumoral heterogeneity of neojunctions (rows) within multiple samples of each tumor (columns) in liver cancer and prostate cancer. The intensity of each cell indicates the percentage of regions within the same tumor that has putative expression of each neojunction. D. 3-D model of a human brain and glioma (green). Approximately 10 spatially mapped and maximally distanced biopsies (red) were taken within each tumor. Whole-exome sequencing, RNA sequencing, and further analyses were conducted on each of these regions. E. Heatmap representing the intratumoral heterogeneity of neojunctions (rows) across various glioma subtypes (columns), including glioblastomas (blue), astrocytomas (yellow), and oligodendrogliomas (red). The intensity of each cell indicates the percentage of regions within the same tumor that has putative expression of each ncojunction. F. Distribution of glioma- specific neojunctions (n=789) based on the number of intratumoral samples detected across all spatially mapped tumors. G. Distribution of glioma- specific neojunctions based on the number of tumors in which they are detected tumor-wide (blue).

[0021] Figs 4A-4K: Neojunction-derived neoepitopes are predicted to be processed and presented by MHC-L A-B. RNA-sequencing from patient-derived LGG (A) and GBM (B) cell lines validates detectable levels of neojunction expression (colored) compared to the expression of canonical splicing (gray). C. Mass spectrometry analysis of CPTAC-derived GBM samples (n=99) indicates detectable levels of neojunction-derived peptides (purple) that are expressed in comparable intensity as other peptides (gray). D. Schematic (D) in identifying high-confidence neojunctions for downstream analysis. Forty-four neojunctions were selected as they were found to be expressed in all three sequencing platforms. E. Diagram illustrating the mechanism of neoantigen production by the introduction of a neojunction. Multimer partitioning was employed subsequently to generate a peptide bank for prediction analysis. (WT sequence is SEQ ID NO:20; NJ-derived sequences are SEQ ID NOs: 21-23, respectively). F-G. Histogram of presentation scores of the top scoring 10-percentile of n-mers categorized by HLA-allele (F) or n-mer length (G). H-I. Bai- graphs illustrating the overall score of top-scoring candidates binding across HLA-alleles based on frame-shift status (H) or alternative splicing category (I). J. schematic (J) describing the Immunogenicity of the final candidate list based on top-scoring neojunction-derived n-mers derived from neojunctions detectable in patient-derived mass spectrometry and RNA sequencing data. K. Heatmap illustrating the intratumoral heterogeneity of final HLA-A*02:01 -presented neojunctions across all spatially-mapped glioma samples. Figs. 5A-5J: T-cell receptors (TCRs) specifically react to neojunction-derived neoantigens. A. Pipeline overview for identifying neojunction-derived neoantigen-reactive TCRs through in vitro sensitization (IVS) of healthy -donor PBMC-derived CD8+ T-cells followed by subsequent lOx V(D)J single-cell RNA sequencing (scRNA-seq). B. IFNy ELISA of reactive CD8+ T-cell populations following IVS with neoantigen. C. lOx V(D)J IFNG signatures of highly proliferated TCR clonotypes cultured against T2 cells pulsed with the neoantigen (colored), a decoy antigen (dark-gray), or no antigen (dark- grey). Reactive TCR clonotypes were identified in Donor 3 CD8+ T-cclls IVS-trcatcd against NJRPL22 (left), Donor 4 CD8 + T-cells IVS-treated against NJRPL22 (center), and Donor 4 CD8+ T-cells IVS-treated against NJGNAS (right). D. Clonotype frequency of all TCR clones identified in Donor 3 CD8+ T-cells IVS-treated against NJRPL22 (left), Donor 4 CD8 + T-cells IVS-treated against (center), and Donor 4 CD8+ T-cells IVS-treated against NJGNAS (right). E. Pipeline for validating TCR clonotype specificity against neojunction-derived neoantigen candidates utilizing a TCR-transduced triple-reporter Jurkat76 system followed by flow cytometry analysis. F-G. NjRPL22-derived (top) and NJGNAS-derived (bottom) neoantigen-specific TCR-transduced triple-reporter Jurkat76 cells (F) and PBMC-derived CD8+ T-cells (G) were activated against neoantigen-pulsed T2 cells in a dose-dependent manner. TCR-transduced cells were co-cultured with decoy peptide-pulsed T2 cells at the highest dose concentration (right). TCR activation of TCR-transduced triple-reporter Jurkat76 is measured by flow cytometry analysis of AP-1- mCherry. PBMC-derived CD8+ T-cells were stained with CD107a and CD 137 antibodies and surface expression of the TCR co-activation markers were analyzed by flow cytometry. H. IFNy ELISA of NJRPL22-derived (top) and NJGNAS-derived (bottom) neoantigen- specific TCR- transduced CD8+ T-cells co-cultured with dose-dependent neoantigen-pulsed (left) and decoy peptide-pulsed T2 cells (right). I. 2? -derived (top) and NJGNAS-derived (bottom) neoantigen-specific TCR-transduced triple-reporter Jurkat76 cells are co-cultured with nonpulsed T2 cells (left), 0.1 pM neoantigen-pulsed T2 cells (center), or 0.1 pM neoantigen-pulsed T2 cells treated with HLA-A2 blocking antibody (right). Cells were stained with CD3 antibody, and TCR activation was evaluated by AP-l-mCherry activity. J. Alanine scanning mutagenesis of NJRPL22 -derived (top) and NJGNAS-derived (bottom) neoantigen- specific TCR-transduced triple-reporter Jurkat76 cells co-cultured with alanine-substituted neoantigen-pulsed T2 cells, neoantigen-pulsed T2 cells, or non-pulsed T2 cells. Flow analysis was performed to evaluate TCR activity through NFAT-GFP (left), NFKB-CFP (center), and AP-l-mCherry (right) activity

[0022] Figs. 6A-6G: Neojunction-derived neoantigens are endogenously processed and presented by MHC-I to elicit neoantigen-specific TCR activation and tumor-specific killing. A. Pipeline overview for validating endogenous proteolytic cleavage of neoantigen candidates and the subsequent binding and presentation by MHC-I by two methods. HLA-null antigen-presenting cells (APCs; COS7 or K562) arc electroporated with mRNAs encoding the full-length gene with the neojunction mutation and HLA-A*02:01. Validation by TCR- activation begins with APCs co-cultured with neojunction-derived neoantigen-specific TCR- transduced triple-reporter Jurkat76 cells prior to flow cytometry analysis for TCR activation readouts. MHC-I-bound peptides are validated with IP-MS / MS of transfected APCs to sequence HLA-A*0201 -bound peptides. B-C. NJoNAS-derived (B) and NJRPL22-derived (C) neoantigenspecific TCR-transduced triple-reporter Jurkat76 cells were co-cultured against transfected COS7. Cultured cells were either non-transfected (left), transfected with mRNA encoding the neoantigen n-mer sequence and HLA-A*02:01 (center), or transfected with mRNA encoding the full-length (FL) mutant peptide and HLA-A*02:01 (right). TCR activation of TCR-transduced triple-reporter Jurkat76 is measured by flow cytometry analysis of NFAT-GFP. D. Mass spectra of NJoNAS-derived (bottom) and NJRPL22-derived (top) neoantigen n-mers detected through IP- MS / MS following HLA-A*02:01 pulldown. E. NJoNAS-derived (top) and NJRPL22-derived (bottom) neoantigen- specific TCR-transduced triple-reporter Jurkat76 cells were cultured against glioma cell lines, and TCR activation is evaluated by NFAT-GFP expression by flow cytometry analysis. F. NjRpr.22-derived (left; colored), NJoNAS-derived (right; colored) neoantigen-specific TCR-transduced, or non-transduced (gray) CD8+ T-cells were cultured against GBM115 tumor cells. Cell index indicates tumor cell adherence to the xCELLigence plate platform, with decreased cell index indicated tumor death. The assay was performed at an E:T ratio of 1 : 1 (top) or 2: 1 (bottom). G. GBM115 tumor cells were blocked with HLA-A2 antibody (yellow) or isotype control (purple) or pulsed with 1 nM of corresponding neoantigen peptide (blue). NJRPL22-derived (top) and NJoNAS-derived (bottom) neoantigen-specific TCR- transduced CD8+ T-cells are cultured against these GBM115 tumor cells to show whether tumor- specific killing is mediated by HLA-A2 presentation. Figs 7A-7L: Glioma-specific disease subtypes demonstrate differential levels of neojunction expression. A-B. Density and box-and-whisker plots depict the total number of putative neojunctions expressed in IDHlmut cases (orange) and IDHlwt cases (green) in TCGA GBM / LGG samples (A) and the in-house spatially-mapped GBM / LGG dataset (B). C-D. Histogram and box-and-whisker plot depict the number total number of putative neojunctions expressed in IDHlwt (blue), astrocytoma (yellow), and oligodendroglioma cases (red) in TCGA GBM / LGG samples (C) and the in-house spatially mapped GBM / LGG dataset (D). E-F. Volcano plots illustrating significantly uprcgulatcd (blue) and downrcgulatcd (red) gene sets comparing IDHlmutO cases vs. IDHlwt cases (left), IDHlmutA cases vs. IDHlwt cases (center), and IDHlmutO cases vs. IDHlmutA cases (right). Gene sets categorized under the Gene Ontology Biological Processes (GOBP, E) and Gene Ontology Cellular Component (GOCC, F) were investigated in the analyses. Splicing-related gene sets were denoted by points labeled with text. G. Box-and-whisker plot depicting splicing-related genes detected from GOBP above and GOCC gene sets with a significant (p < 0.05) log2fold increase in expression of 1.5 between IDHlmutA (yellow) and IDHlmutO (blue) cases when compared to IDHlwt cases (red). H. Box-and-whisker plot depicting chromosome Ip or chromosome 19q splicing- related genes detected from GOBP above and GOCC gene sets with a significant ( / ? < 0.05) log2fold decrease in expression of 1.5 between IDHlmutO (blue) when compared to IDHlmutA (yellow) and IDHlwt cases (red). I. Pearson correlation analyses of glioma- specific neojunctions against the expression of CELF2 (left) and SNRPD2 (right) in IDHlmutO (z-axis), IDHlmutA (y-axis), and IDHlwt (x-axis) cases. Neojunctions with a Pearson correlation greater than or equal to 0.10 with the corresponding gene are denoted with purple dots and those with a Pearson correlation less than or equal to -0.10 with the corresponding gene are denoted with yellow dots. J. Jitter plots denoting the splice junction CPM of the neojunctions with the most positive average correlation scores against CELF2 (left) or the most negative average correlation score against SNRPD2 (right). K. Expression of NJACAP2 in LGG cell lines, SF10417 (left) and SF10602 (right), treated with control siRNA or siCELF2. L. Expression of NJACAPI in GBM115 treated with control siRNA or siSNRPD2.

[0023] Fig. 8 shows the sequences of the neoantigen peptides identified in the present studies (i.e., the GNAS neoantigen, the RPL22 neoantigen (9-mer) and the RPL22 neoantigen (10-mer)) as well as the sequences of TCRs that bind to those antigens (i.e., the GNAS TCR and the RPL22 TCR) and their CDRs (as defined by the IM GT system).

[0024] Fig 9A: T-cell receptors (TCRs) specifically react to neojunction-derived neoantigens. A. NeoAoNAS-dextramer staining of bulk CD8+T-cells derived from an HLA- A*02:01 healthy donor (left) and glioma patients (right) following 2 cycles of NJGNAS TVS.

[0025] Figs. 10A-10B: Neojunction-derived neoantigens are endogenously processed and presented by HLA to elicit neoantigen-specific T cells in patients and TCR-dependent tumor-specific killing. A. xCELLigence live-cytotoxicity assay of CD8+T-cells co-cultured with GBM39 cells (left) or HLA-A*02;01-transduced GBM39 cells (right) co-cultured with non-transduced or NJoNAS-TCR-transduced CD8+T-cells. B. Bar plot of the surface expression of CD107a and CD137 on NJoNAS-TCR-transduced (purple) or non-transduced (gray) CD8+T- cells when cultured with tumor cell lines.

[0026] DEFINITIONS As used herein, the terms "treatment," "treating," “treat” and the like, refer to obtaining a desired pharmacologic and / or physiologic effect and / or a response related to the treatment. The effect can be prophylactic in terms of completely or partially preventing a disease or symptom thereof and / or can be therapeutic in terms of a partial or complete cure for a disease and / or adverse effect attributable to the disease. "Treatment," as used herein, covers any treatment of a disease in a mammal, particularly in a human, and includes: (a) preventing the disease from occurring in a subject which can be predisposed to the disease but has not yet been diagnosed as having it; (b) inhibiting the disease, i.e., arresting its development; and (c) relieving the disease, i.e., causing regression of the disease.

[0027] A “therapeutically effective amount” or “efficacious amount” refers to the amount of an agent (including biologic agents, such as cells), or combined amounts of two agents, that, when administered to a mammal or other subject for treating a disease, is sufficient to effect such treatment for the disease. The “therapeutically effective amount” will vary depending on the agent(s), the disease and its severity and the age, weight, etc., of the subject to be treated. The terms “individual,” “subject,” “host,” and “patient,” used interchangeably herein, refer to a mammal, including, but not limited to, murines (e.g., rats, mice), non-human primates, humans, canines, felines, ungulates (e.g., equines, bovines, ovines, porcines, caprines), lagomorphs, etc. In some cases, the individual is a human. In some cases, the individual is a non-human primate. In some cases, the individual is a rodent, e.g., a rat or a mouse. In some cases, the individual is a lagomorph, e.g., a rabbit.

[0028] The term “refractory”, used herein, refers to a disease or condition that does not respond to treatment. With regard to cancer, “refractory cancer”, as used herein, refers to cancer that does not respond to treatment. A refractory cancer may be resistant at the beginning of treatment or it may become resistant during treatment. Refractory cancer may also called resistant cancer.

[0029] The terms “T cell receptor” and “TCR” are used interchangeably and will generally refer to a molecule found on the surface of T cells, or T lymphocytes, that is responsible for recognizing fragments of antigen as peptides bound to major histocompatibility complex (MHC) molecules. The TCR complex is a disulfide-linked membrane- anchored heterodimeric protein normally consisting of the highly variable alpha (a) and beta (0) chains expressed as part of a complex with CD3 chain molecules. Many native TCRs exist in heterodimeric 0.0 or y8 forms. The complete endogenous TCR complex in heterodimeric a0 form includes eight chains, namely an alpha chain (referred to herein as TCRa or TCR alpha), beta chain (referred to herein as TCR0 or TCR beta), delta chain, gamma chain, two epsilon chains and two zeta chains. In some instance, a TCR is generally referred to by reference to only the TCRa and TCR0 chains, however, as the assembled TCR complex may associate with endogenous delta, gamma, epsilon and / or zeta chains an ordinary skilled artisan will readily understand that reference to a TCR as present in a cell membrane may include reference to the fully or partially assembled TCR complex as appropriate.

[0030] Recombinant or engineered individual TCR chains and TCR complexes have been developed. References to the use of a TCR in a therapeutic context may refer to individual recombinant TCR chains. As such, engineered TCRs may include individual modified TCRa or modified TCR0 chains as well as single chain TCRs that include modified and / or unmodified TCRa and TCR0 chains that are joined into a single polypeptide by way of a linking polypeptide.

[0031] The terms MHC and HLA are synonymous. The term “binding” refers to a direct association between two molecules, due to, for example, covalent, electrostatic, hydrophobic, and ionic and / or hydrogen-bond interactions, including interactions such as salt bridges and water bridges. Non-specific binding would refer to binding with an affinity of less than about 10'7M, e.g., binding with an affinity of 10'6M, 10’5M, IO’4M, etc.

[0032] The terms “domain” and “motif’, used interchangeably herein, refer to both structured domains having one or more particular functions and unstructured segments of a polypeptide that, although unstructured, retain one or more particular functions. For example, a structured domain may encompass but is not limited to a continuous or discontinuous plurality of amino acids, or portions thereof, in a folded polypeptide that comprise a three-dimensional structure which contributes to a particular function of the polypeptide. In other instances, a domain may include an unstructured segment of a polypeptide comprising a plurality of two or more amino acids, or portions thereof, that maintains a particular function of the polypeptide unfolded or disordered. Also encompassed within this definition are domains that may be disordered or unstructured but become structured or ordered upon association with a target or binding partner. Non-limiting examples of intrinsically unstructured domains and domains of intrinsically unstructured proteins are described, e.g., in Dyson & Wright. Nature Reviews Molecular Cell Biology 6:197-208.

[0033] The terms “synthetic”, “chimeric” and “engineered” as used herein generally refer to artificially derived polypeptides or polypeptide encoding nucleic acids that are not naturally occurring. Synthetic polypeptides and / or nucleic acids may be assembled de novo from basic subunits including, e.g., single amino acids, single nucleotides, etc., or may be derived from preexisting polypeptides or polynucleotides, whether naturally or artificially derived, e.g., as through recombinant methods. Chimeric and engineered polypeptides or polypeptide encoding nucleic acids will generally be constructed by the combination, joining or fusing of two or more different polypeptides or polypeptide encoding nucleic acids or polypeptide domains or polypeptide domain encoding nucleic acids. Chimeric and engineered polypeptides or polypeptide encoding nucleic acids include where two or more polypeptide or nucleic acid “parts” that are joined are derived from different proteins (or nucleic acids that encode different proteins) as well as where the joined parts include different regions of the same protein (or nucleic acid encoding a protein) but the parts are joined in a way that does not occur naturally. The term "recombinant1', as used herein describes a nucleic acid molecule, e.g., a polynucleotide of genomic, cDNA, viral, semisynthetic, and / or synthetic origin, which, by virtue of its origin or manipulation, is not associated with all or a portion of the polynucleotide sequences with which it is associated in nature. The term recombinant as used with respect to a protein or polypeptide means a polypeptide produced by expression from a recombinant polynucleotide. The term recombinant as used with respect to a host cell or a virus means a host cell or virus into which a recombinant polynucleotide has been introduced. Recombinant is also used herein to refer to, with reference to material (e.g., a cell, a nucleic acid, a protein, or a vector) that the material has been modified by the introduction of a heterologous material (e.g., a cell, a nucleic acid, a protein, or a vector).

[0034] The term “operably linked” refers to a juxtaposition wherein the components so described are in a relationship permitting them to function in their intended manner. For instance, a promoter is operably linked to a coding sequence if the promoter affects its transcription or expression. Operably linked nucleic acid sequences may but need not necessarily be adjacent. For example, in some instances a coding sequence operably linked to a promoter may be adjacent to the promoter. In some instances, a coding sequence operably linked to a promoter may be separated by one or more intervening sequences, including coding and non-coding sequences. Also, in some instances, more than two sequences may be operably linked including but not limited to e.g., where two or more coding sequences are operably linked to a single promoter.

[0035] The terms “polynucleotide” and “nucleic acid,” used interchangeably herein, refer to a polymeric form of nucleotides of any length, either ribonucleotides or deoxyribonucleotides. Thus, this term includes, but is not limited to, single-, double-, or multi- stranded DNA or RNA, genomic DNA, cDNA, DNA-RNA hybrids, or a polymer comprising purine and pyrimidine bases or other natural, chemically or biochemically modified, non-natural, or derivatized nucleotide bases.

[0036] The terms “polypeptide,” “peptide,” and “protein”, used interchangeably herein, refer to a polymeric form of amino acids of any length, which can include genetically coded and non- genetically coded amino acids, chemically or biochemically modified or derivatized amino acids, and polypeptides having modified peptide backbones. The term includes fusion proteins, including, but not limited to, fusion proteins with a heterologous amino acid sequence, fusions with heterologous and homologous leader sequences, with or without N-terminal methionine residues; immunologically tagged proteins; and the like.

[0037] A "vector" or "expression vector" is a replicon, such as plasmid, phage, virus, or cosmid, to which another DNA segment, i.e. an "insert", may be attached so as to bring about the replication of the attached segment in a cell.

[0038] The term “heterologous”, as used herein, means a nucleotide or polypeptide sequence that is not found in the native (e.g., naturally-occurring) nucleic acid or protein, respectively. Heterologous nucleic acids or polypeptide may be derived from a different species as the organism or cell within which the nucleic acid or polypeptide is present or is expressed. Accordingly, a heterologous nucleic acids or polypeptide is generally of unlike evolutionary origin as compared to the cell or organism in which it resides.

[0039] As used herein, a "therapeutically effective amount" refers to that amount of the therapeutic agent, e.g. an infusion of primed T cells, a peptide or polynucleotide vaccine, etc, sufficient to treat or manage a disease or disorder. A therapeutically effective amount may refer to the amount of therapeutic agent sufficient to delay or minimize the onset of disease, e.g., to delay or minimize the spread of cancer, or the amount effective to decrease or increase signaling from a receptor of interest. A therapeutically effective amount may also refer to the amount of the therapeutic agent that provides a therapeutic benefit in the treatment or management of a disease. Further, a therapeutically effective amount with respect to a therapeutic agent of the invention means the amount of therapeutic agent alone, or in combination with other therapies, that provides a therapeutic benefit in the treatment or management of a disease.

[0040] As used herein, the term “dosing regimen” refers to a set of unit doses (typically more than one) that are administered individually to a subject, typically separated by periods of time. In some embodiments, a given therapeutic agent has a recommended dosing regimen, which may involve one or more doses. In some embodiments, a dosing regimen comprises a plurality of doses each of which are separated from one another by a time period of the same length; in some embodiments, a dosing regimen comprises a plurality of doses and at least two different time periods separating individual doses. In some embodiments, all doses within a dosing regimen are of the same unit dose amount. In some embodiments, different doses within a dosing regimen are of different amounts. In some embodiments, a dosing regimen comprises a first dose in a first dose amount, followed by one or more additional doses in a second dose amount different from the first dose amount. In some embodiments, a dosing regimen comprises a first dose in a first dose amount, followed by one or more additional doses in a second dose amount same as the first dose amount. In some embodiments, a dosing regimen is correlated with a desired or beneficial outcome when administered across a relevant population (i.e., is a therapeutic dosing regimen).

[0041] As used herein, the terms "cancer" (or "cancerous"), or "tumor" are used to refer to cells having the capacity for autonomous growth (e.g., an abnormal state or condition characterized by rapidly proliferating cell growth). Hypcrprolifcrativc and neoplastic disease states may be categorized as pathologic (e.g., characterizing or constituting a disease state), or they may be categorized as non-pathologic (e.g., as a deviation from normal but not associated with a disease state). The terms are meant to include all types of cancerous growths or oncogenic processes, metastatic tissues or malignantly transformed cells, tissues, or organs, irrespective of histopathologic type or stage of invasiveness. Pathologic hyperproliferative cells occur in disease states characterized by malignant tumor growth. Examples of non-pathologic hyperproliferative cells include proliferation of cells associated with wound repair. The terms "cancer" or "tumor" are also used to refer to malignancies of the various organ systems, including those affecting the lung, breast, thyroid, lymph glands and lymphoid tissue, gastrointestinal organs, and the genitourinary tract, as well as to adenocarcinomas which are generally considered to include malignancies such as most colon cancers, renal-cell carcinoma, prostate cancer and / or testicular tumors, non-small cell carcinoma of the lung, cancer of the small intestine and cancer of the esophagus.

[0042] The term "carcinoma" is art-recognized and refers to malignancies of epithelial or endocrine tissues including respiratory system carcinomas, gastrointestinal system carcinomas, genitourinary system carcinomas, testicular carcinomas, breast carcinomas, prostatic carcinomas, endocrine system carcinomas, and melanomas. An "adenocarcinoma" refers to a carcinoma derived from glandular tissue or in which the tumor cells form recognizable glandular structures.

[0043] Exemplary cancer types include but are not limited to AML, ALL, CML, adrenal cortical cancer, anal cancer, aplastic anemia, bile duct cancer, bladder cancer, bone cancer, bone metastasis, brain cancers, central nervous system (CNS) cancers, peripheral nervous system (PNS) cancers, breast cancer, cervical cancer, childhood Non-Hodgkin's lymphoma, colon and rectal cancer, endometrial cancer, esophagus cancer, Ewing's family of tumors (e.g., Ewing's sarcoma), eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors, gestational trophoblastic disease, Hodgkin's lymphoma, Kaposi's sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, liver cancer, lung cancer, lung carcinoid tumors, Non-Hodgkin's lymphoma, male breast cancer, malignant mesothelioma, multiple myeloma, myelodysplastic syndrome, myeloproliferative disorders, nasal cavity and paranasal cancer, nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumor, prostate cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcomas, melanoma skin cancer, non-melanoma skin cancers, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer (e.g. uterine sarcoma), transitional cell carcinoma, vaginal cancer, vulvar cancer, mesothelioma, squamous cell or epidermoid carcinoma, bronchial adenoma, choriocarinoma, head and neck cancers, teratocarcinoma, or Waldenstrom's macroglobulinemia.

[0044] Before the present invention is further described, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0045] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0046] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, the preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited.

[0047] It must be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a cell” includes a plurality of such cells and reference to “the cell” includes reference to one or more cells and equivalents thereof known to those skilled in the art, and so forth. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely,” “only” and the like in connection with the recitation of claim elements, or use of a “negative” limitation.

[0048] It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination. All combinations of the embodiments pertaining to the invention are specifically embraced by the present invention and are disclosed herein just as if each and every combination was individually and explicitly disclosed. In addition, all sub-combinations of the various embodiments and elements thereof are also specifically embraced by the present invention and are disclosed herein just as if each and every such sub-combination was individually and explicitly disclosed herein.

[0049] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

[0050] DETAILED DESCRIPTION

[0051] An isolated peptide comprising an amino acid sequence of SLLLPSFHL (SEQ ID NO:1, which may be referred to as the “GNAS” neoantigen herein) or IMDAANFFL (SEQ ID NO: 2, which may be referred to as the “RPL22” neoantigen herein) is provided. The length of the peptide may vary depending on how the peptide is going to be used. In any embodiment, the peptide may be 9 to 100 amino acids in length (e.g., 9 to 40 amino acids). In any embodiment, the peptide may be relatively short (e.g., 9, 10, or 11 amino acids in length). In any embodiments, the peptides may be relatively long, e.g., 15-50 or 18 to 40 amino acids in length. In some embodiments, the peptide may consist of SEQ ID NO:1, 2 or 3.

[0052] In any embodiment, a peptide disclosed herein can be flanked by additional amino acid residues as long as the peptide retains its TCR inducibility. Such peptides can be less than about 40 amino acids, for example, less than about 20 amino acids, for example, less than about 15 amino acids. The amino acid sequence flanking the peptides consisting of the amino acid sequence of SEQ ID NOS: 1 and 2 is not limited and can be composed of any kind of amino acids so long as it does not inhibit the TCR recognition. The amino acid sequence may be modified by substituting wherein one or more amino acids. One of skill in the art will recognize that individual additions or substitutions to an amino acid sequence which alters a single amino acid or a small percentage of amino acids results in the conservation of the properties of the original amino acid side-chain; it is thus is referred to as "conservative substitution" or "conservative modification", wherein the alteration of a protein results in a protein with similar functions.

[0053] In addition to the above-mentioned sequence modification of the peptides, the peptides can be further linked to other substances, so long as they retain the TCR binding activity. Usable substances include: peptides, lipids, sugar and sugar chains, acetyl groups, natural and synthetic polymers, etc. The peptides can contain modifications such as glycosylation, side chain oxidation, or phosphorylation; so long as the modifications do not destroy the biological activity of the peptides as described herein. These kinds of modifications can be performed to confer additional functions (e.g., targeting function, and delivery function) or to stabilize the polypeptide.

[0054] For example, to increase the in vivo stability of a polypeptide, it is known in the ail to introduce particularly useful various D-amino acids, amino acid mimetics or unnatural amino acids; this concept can also be adopted for the present polypeptides. The stability of a polypeptide can be assayed in a number of ways. For instance, peptidases and various biological media, such as human plasma and serum, have been used to test stability (see, e.g., Verhoef et al., Eur J Drug Metab Pharmacokin 11: 291-302, 1986). The peptides disclosed herein can be prepared using well known techniques. For example, the peptides can be prepared synthetically, by recombinant DNA technology or chemical synthesis. Peptides disclosed herein can be synthesized individually or as longer polypeptides comprising two or more peptides (e.g., two or more peptides or a peptide and a non- peptide). The peptides can be isolated i.e., purified to be substantially free of other naturally occurring host cell proteins and fragments thereof, e.g., at least about 70%, 80% or 90% purified.

[0055] Also provided is a composition comprising a peptide and a pharmaceutically acceptable excipient where, in some embodiments, the composition may additionally contain an adjuvant that has an immuno stimulatory effect.

[0056] In some embodiments, the peptide may be complexed with a major histocompatibility complex (MHC) (i.e., may be in an MHC-peptide complex). As such, in some embodiments, MHC-peptide complex may comprising a class I MHC and the peptide. In other embodiments, the composition may comprise a class I MHC and the peptide. If the MHC is soluble (which may be produced by expressing an MHC without its transmembrane domain) then the complex may be in solution. In other embodiments, the MHC complex may be on the surface of a cell. A cell expressing this major histocompatibility complex (MHC) complex on its surface is therefore provided. In some embodiments, the cell may be antigen presenting cell, e.g., a professional antigen presenting cell such as a dendritic cell. As would be apparent, in some embodiments the MHC and the peptide may be recombinantly expressed. In other embodiments, the MHC may be recombinantly expressed and the peptide may be exogenously added. In other embodiments, the peptide may be exogenously added to cells that naturally express an MHC. In any embodiment, the cell may be in vitro or in vivo. In embodiments in which the peptide is exogenously added, the peptide may be relatively short (e.g., 9, 10, or 11 amino acids in length. In these embodiments, the peptide may directly bind to the MHC. In other embodiments, a longer peptide may be added (e.g., a peptide in the range of e.g., 15-50 amino acids). In these embodiments, the peptide may be processed by the cell before it is bound by the MHC. In any embodiment, the MHC may be a class I MHC that has an HLA-A*02:01 binding domain (which is the most prevalent domain). However, other MHC binding domains should bind the peptide so this is not required. Also provided is a T cell receptor. In some embodiments, the T cell receptor may be a first (or “GNAS”) T cell receptor that recognizes a complex containing a class I MHC (particularly a class I MHC having an HLA-A*02:01 binding domain) bound to the GNAS neoantigen. In these embodiments, the T cell receptor may comprise an alpha chain having the CDR sequences of SEQ ID NOS 5, 6 and 7 and a beta chain having the CDR sequences of SEQ ID NOS 9, 10 and 11. In any embodiment, the first T cell receptor may have (a) an alpha chain variable domain having an amino acid sequence that is at least 90% identical to (e.g., at least 95% or at least 98% identical to) SEQ ID NO. 4 and a beta chain variable domain having an amino acid sequence that is at least 90% (e.g., at least 95% or at least 98% identical to) SEQ ID NO. 8. In other embodiments, the T cell receptor may be a second (or “RPL22”) T cell receptor that recognizes a complex containing a class I MHC (particularly a class I MHC having an HLA-A*02:01 binding domain) bound to the RPL22 neoantigen. In these embodiments, the T cell receptor may comprise an alpha chain having the CDR sequences of SEQ ID NOS 13, 14 and 14 and a beta chain having the CDR sequences of SEQ ID NOS 17, 18 and 19. In any embodiment, the second T cell receptor may have (a) an alpha chain variable domain having an amino acid sequence that is at least 90% identical to (e.g., at least 95% or at least 98% identical to) SEQ ID NO. 12 and a beta chain variable domain having an amino acid sequence that is at least 90% (e.g., at least 95% or at least 98% identical to) SEQ ID NO. 16. It is noted that binding between a TCR and the MHC complex is mainly mediated by the CDR3 domains of the alpha and beta chains. These sequences are illustrated in Fig. 8.

[0057] An immune cell engineered to express the first or second T cell receptor is also provided. In some cases, the immune cell is a mammalian cell, particularly a human, mouse cell or non-human primate cell. Suitable mammalian immune cells include primary cells and immortalized cell lines. Suitable mammalian cell lines include human cell lines, non-human primate cell lines, rodent (e.g., mouse, rat) cell lines, and the like. In some instances, the cell is not an immortalized cell line, but is instead a cell (e.g., a primary cell) obtained from an individual. For example, in some cases, the cell is an immune cell, immune cell progenitor or immune stem cell obtained from an individual. As an example, the cell is a lymphoid cell, e.g., a lymphocyte, or progenitor thereof, obtained from an individual. As another example, the cell is a cytotoxic cell, or progenitor thereof, obtained from an individual. Such cells include, e.g., lymphoid cells, i.e., lymphocytes (T cells, B cells, natural killer (NK) cells), and myeloid-derived cells (neutrophil, eosinophil, basophil, monocyte, macrophage, dendritic cells). “T cell” includes all types of immune cells expressing CD3 including T-helper cells (CD4+ cells) and cytotoxic T-cells (CD8+ cells). A “cytotoxic cell” includes CD8+ T cells, natural-killer (NK) cells, and neutrophils, which cells are capable of mediating cytotoxicity responses.

[0058] These cells may be generated by any convenient method. Nucleic acids encoding one or more components of a subject circuit may be stably or transiently introduced into the subject immune cell, including where the subject nucleic acids are present only temporarily, maintained extrachromosomally, or integrated into the host genome. Introduction of the subject nucleic acids and / or genetic modification of the subject immune cell can be carried out in vivo, in vitro, or ex vivo.

[0059] An RNA or DNA encoding the peptide is also provided.

[0060] In some cases, the introduction of the subject nucleic acids and / or genetic modification is carried out ex vivo. For example, a primary T lymphocyte, a stem cell, or an NK cell is obtained from an individual; and the cell obtained from the individual is modified to express the present components. In some embodiments, allogeneic immune cells may be used.

[0061] Given that the genetic code is known, a sequence that encodes any of the present proteins can be readily determined. In some embodiments, the coding sequence may be codon optimized for expression in mammalian (e.g., human or mouse) cells, strategies for which are well known (see, e.g., Mauro et al., Trends Mol. Med. 201420: 604-613 and Bell et al Human Gene Therapy Methods 27: 6). As would be understood, the coding sequence may be operably linked to a promoter, which may be inducible, tissue-specific, or constitutive. In some embodiments, the promoter may be activated by an engineered transcription factor that is heterologous to the cell, e.g., a Gal4-, LexA-, Tet-, Lac-, dCas9-, zinc-finger- and TALE-based transcription factors. In some embodiments, the peptides may be made synthetically, e.g., by a peptide synthesizer.

[0062] A promoter can be a constitutively active promoter (i.e., a promoter that is constitutively in an active / ”ON” state), it may be an inducible promoter (i.e., a promoter whose state, active / ”ON” or inactive / “OFF”, is controlled by an external stimulus, e.g., the presence of a particular temperature, compound, or protein.), it may be a spatially restricted promoter (i.e., transcriptional control element, enhancer, etc.)(e.g., tissue specific promoter, cell type specific promoter, etc.), and it may be a temporally restricted promoter (i.e., the promoter is in the “ON” state or “OFF” state during specific stages of embryonic development or during specific stages of a biological process, e.g., hair follicle cycle in mice).

[0063] For expression in a eukaryotic cell, suitable promoters include, but are not limited to, light and / or heavy chain immunoglobulin gene promoter and enhancer elements; cytomegalovirus immediate early promoter; herpes simplex virus thymidine kinase promoter; early and late SV40 promoters; promoter present in long terminal repeats from a retrovirus; mouse metallothionein-I promoter; and various art-known tissue specific promoters.

[0064] Suitable reversible promoters, including reversible inducible promoters are known in the art. Such reversible promoters may be isolated and derived from many organisms, e.g., eukaryotes and prokaryotes. Modification of reversible promoters derived from a first organism for use in a second organism, e.g., a first prokaryote and a second a eukaryote, a first eukaryote and a second a prokaryote, etc., is well known in the art. Such reversible promoters, and systems based on such reversible promoters but also comprising additional control proteins, include, but are not limited to, alcohol regulated promoters (e.g., alcohol dehydrogenase I (alcA) gene promoter, promoters responsive to alcohol transactivator proteins (AlcR), etc.), tetracycline regulated promoters, (e.g., promoter systems including TetActivators, TetON, TetOFF, etc.), steroid regulated promoters (e.g., rat glucocorticoid receptor promoter systems, human estrogen receptor promoter systems, retinoid promoter systems, thyroid promoter systems, ecdysone promoter systems, mifepristone promoter systems, etc.), metal regulated promoters (e.g., metallothionein promoter systems, etc.), pathogenesis-related regulated promoters (e.g., salicylic acid regulated promoters, ethylene regulated promoters, benzothiadiazole regulated promoters, etc.), temperature regulated promoters (e.g., heat shock inducible promoters (e.g., HSP-70, HSP-90, soybean heat shock promoter, etc.), light regulated promoters, synthetic inducible promoters, and the like.

[0065] Inducible promoters suitable for use include any inducible promoter described herein or known to one of ordinary skill in the art. Examples of inducible promoters include, without limitation, chemically / biochemically-regulated and physically-regulated promoters such as alcohol-regulated promoters, tetracycline-regulated promoters (e.g., anhydrotetracycline (aTc)- responsive promoters and other tetracycline-responsive promoter systems, which include a tetracycline repressor protein (tetR), a tetracycline operator sequence (tetO) and a tetracycline transactivator fusion protein (tTA)), steroid-regulated promoters (e.g., promoters based on the rat glucocorticoid receptor, human estrogen receptor, moth ecdysone receptors, and promoters from the steroid / retinoid / thyroid receptor superfamily), metal-regulated promoters (e.g., promoters derived from metallothionein (proteins that bind and sequester metal ions) genes from yeast, mouse and human), pathogenesis-regulated promoters (e.g., induced by salicylic acid, ethylene or benzothiadiazole (BTH)), temperature / heat-inducible promoters (e.g., heat shock promoters), and light-regulated promoters (e.g., light responsive promoters from plant cells).

[0066] In some cases, the promoter is a CD8 cell-specific promoter, a CD4 cell-specific promoter, a neutrophil- specific promoter, or an NK-specific promoter. For example, a CD4 gene promoter can be used; see, e.g., Salmon et al. (1993) Proc. Natl. Acad. Sci. USA 90: 7739; and Marodon et al. (2003) Blood 101:3416. As another example, a CD8 gene promoter can be used. NK cell-specific expression can be achieved by use of an Ncrl (p46) promoter; see, e.g., Eckelhart et al. (2011) Blood 117:1565.

[0067] In some cases, the promoter is a cardiomyocyte- specific promoter. In some cases, the promoter is a smooth muscle cell-specific promoter. In some cases, the promoter is a neuronspecific promoter. In some cases, the promoter is an adipocyte- specific promoter. Other cell type-specific promoters are known in the art and are suitable for use herein.

[0068] Suitable expression vectors include, but are not limited to, viral vectors (e.g. viral vectors based on vaccinia virus; poliovirus; adenovirus (see, e.g., Li et al., Invest Opthalmol Vis Sci 35:2543 2549, 1994; Borras et al., Gene Ther 6:515 524, 1999; Li and Davidson, PNAS 92:77007704, 1995; Sakamoto et al., Hum Gene Ther 5:1088 1097, 1999; WO 94 / 12649, WO 93 / 03769; WO 93 / 19191; WO 94 / 28938; WO 95 / 11984 and WO 95 / 00655); adeno-associated virus (see, e.g., Ali et al., Hum Gene Ther 9:81 86, 1998, Flannery et al., PNAS 94:6916 6921, 1997; Bennett et al., Invest Opthalmol Vis Sci 38:2857 2863, 1997; Jomary et al., Gene Ther 4:683 690, 1997, Rolling et al., Hum Gene Ther 10:641 648, 1999; Ali et al., Hum Mol Genet 5:591 594, 1996; Srivastava in WO 93 / 09239, Samulski et al., J. Vir. (1989) 63:3822-3828; Mendelson et al., Virol. (1988) 166:154-165; and Flotte et al., PNAS (1993) 90:10613-10617); SV40; herpes simplex virus; human immunodeficiency virus (see, e.g., Miyoshi et al., PNAS 94:10319 23, 1997; Takahashi et al., J Virol 73:78127816, 1999); a retroviral vector (e.g., Murine Leukemia Virus, spleen necrosis virus, and vectors derived from retroviruses such as Rous Sarcoma Virus, Harvey Sarcoma Virus, avian leukosis virus, a lentivirus, human immunodeficiency virus, myeloproliferative sarcoma virus, and mammary tumor vims); and the like. In some cases, the vector is a lentivirus vector. Also suitable are transposon-mediated vectors, such as piggyback and sleeping beauty vectors.

[0069] The peptides and T cell receptors described may be used prime T cells in vitro and in vivo and in various in vaccinations / method of treatments and the like. Specifically, if the peptides arc combined with antigen presenting cells in vitro, then the cells may be used to select for and expand immune cells that recognize the MHC complex, in vivo or in vitro. This method may comprise contacting an antigen presenting cells such as a dendritic cell that has an MHC- complex comprising the peptide on its surface with a population of T cells and incubating the T cells with the cell under conditions by which T cells that recognize the MHC -peptide complex become activated and / or expand.

[0070] In some embodiments, the method may induce an immune response. This method may comprises administering to an individual an effective dose of a peptide or a nucleic acid (DNA or RNA) encoding the same, composition or cell described above. These embodiments may comprise administering a peptide to the individual. Again, in these embodiments, the peptide may be relatively short (e.g., 9, 10, or 11 amino acids in length. In these embodiments, the peptide may directly bind to the MHC in, e.g., an antigen presenting cell. In other embodiments, a longer peptide may be added (e.g., a peptide in the range of e.g., 15-50 amino acids), in which case it may be processed before being presented by the MHC. In these embodiments, the peptide, APCs that present the peptide, and T cells that recognize the MHC-peptide complex that comprises the peptide may be used as an off the shelf cancer vaccine because the neoantigen is found in so many cancers. In alternative embodiments, an RNA could be administered.

[0071] In some embodiments, the method may be a method of treatment. In these embodiments, the method may comprise administering a peptide, composition, cell described above to an individual in need thereof. In these embodiments, the individual may have glioblastoma, low- grade glioma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, liver hepatocellular carcinoma, stomach adenocarcinoma, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, kidney chromophobe, colon adenocarcinoma or prostate adenocarcinoma.

[0072] In some embodiments, the method is for treating cancer (e.g., reducing tumor cell growth, promoting tumor cell death) by administering to an individual a peptide or a polynucleotide encoding a peptide disclosed herein, or a modified T cell as described above. In a related aspect, isolated primed T cells that have been primed with a peptide disclosed herein can be used. In another aspect, an antigen-presenting cell is provided, which comprises a complex formed between an HLA antigen and a peptide disclosed herein. In some embodiments, the antigen presenting cell is isolated.

[0073] If needed, the pharmaceutical agents, composed of either a peptide or a polynucleotide encoding a peptide, can optionally include other therapeutic substances as an active ingredient, so long as the substance does not inhibit the TCR stimulating effect of the peptide of interest. For example, formulations can include anti-inflammatory agents, pain killers, chemotherapeutic s, and the like. In addition to including other therapeutic substances in the medicament itself, the medicaments can also be administered sequentially or concurrently with the one or more other pharmacologic agents. The amounts of medicament and pharmacologic agent depend, for example, on what type of pharmacologic agent(s) is / are used, the disease being treated, and the scheduling and routes of administration.

[0074] The compositions can be administered directly as a pharmaceutical agent, if necessary, that has been formulated by conventional formulation methods. In such cases, in addition to the peptides, carriers, excipients, and such that are ordinarily used for drugs can be included as appropriate without particular limitations. Examples of such carriers are sterilized water, physiological saline, phosphate buffer, culture fluid and such. Furthermore, the pharmaceutical agents can contain as necessary, stabilizers, suspensions, preservatives, surfactants and such. The pharmaceutical agents can be used for treating and / or preventing cancer.

[0075] The peptides can be prepared in a combination. The peptides can be in a cocktail or can be conjugated to each other using standard techniques.

[0076] The compositions can optionally comprise an adjuvant so that cellular immunity will be established effectively, or they can be administered with other active ingredients, and they can be administered by formulation into granules. An adjuvant refers to a compound that enhances the immune response against the protein when administered together (or successively) with the protein having immunological activity. An adjuvant that can be applied includes those described in the literature. Exemplary adjuvants include aluminum phosphate, aluminum hydroxide, alum, cholera toxin, salmonella toxin, and such, but are not limited thereto.

[0077] Furthermore, liposome formulations, granular formulations in which the peptide is bound to few-mcm diameter beads, and formulations in which a lipid is bound to the peptide can be conveniently used. Alternatively, intracellular vesicles called exosomes are provided, which present complexes formed between the peptides and HLA antigens on their surface. The exosomes can be inoculated as vaccines, similarly to the peptides.

[0078] In some embodiments a present composition may comprise a component that primes T lymphocytes. Lipids have been identified as agents capable of priming CTL in vivo against viral antigens. For example, palmitic acid residues can be attached to the epsilon-and alpha-amino groups of a lysine residue and then linked to a peptide disclosed herein. The lipidated peptide can then be administered either directly in a micelle or particle, incorporated into a liposome, or emulsified in an adjuvant. As another example of lipid priming of CTL responses, E. coli lipoproteins, such as tripalmitoyl-S-glycerylcysteinlyseryl- serine (P3CSS) can be used to prime CTL when covalently attached to an appropriate peptide (see, e.g., Deres et al., Nature 342: 561, 1989).

[0079] The method of administration can be oral, intradermal, subcutaneous, intravenous injection, or such, and systemic administration or local administration to the vicinity of the targeted sites finds use. The administration can be performed by single administration or boosted by multiple administrations. The dose of the peptides can be adjusted appropriately according to the disease to be treated, age of the patient, weight, method of administration, and such, and is ordinarily 0.001 mg to 1000 mg, for example, 0.001 mg to 1000 mg, for example, 0.1 mg to 10 mg, and can be administered once every a few days to once every few months. One skilled in the art can appropriately select the suitable dose.

[0080] The pharmaceutical agents disclosed herein can also comprise nucleic acids encoding the peptides disclosed herein in an expressible form. Herein, the phrase "in an expressible form" means that the polynucleotide, when introduced into a cell, will be expressed in vivo as a polypeptide that has stimulates anti-tumor immunity. In one embodiment, the nucleic acid sequence of the polynucleotide of interest includes regulatory elements necessary for expression of the polynucleotide in a target cell. The polynucleotide(s) can be equipped to stably insert into the genome of the target cell (see, e.g., Thomas KR & Capecchi MR, Cell 51: 503-12, 1987 for a description of homologous recombination cassette vectors). See, e.g., Wolff et al., Science 247: 1465-8, 1990; U.S. Patent Nos. 5,580,859; 5,589,466; 5,804,566; 5,739,118; 5,736,524; 5,679,647; and WO 98 / 04720. Examples of DNA-based delivery technologies include "naked DNA", facilitated (bupivicaine, polymers, peptide-mediated) delivery, cationic lipid complexes, and particle-mediated ("gene gun") or pressure-mediated delivery (see, e.g., U.S. Patent No. 5,922,687).

[0081] Also provided arc antigen-presenting cells (APCs) that present complexes formed between an MHC and a peptide on its surface. APCs are obtained by contacting cells with the peptides, or the nucleotides encoding the peptides, and can be prepared from subjects who are the targets of treatment and / or prevention, and can be administered as vaccines by themselves or in combination with other drugs including the peptides, exosomes, or cytotoxic T cells. The APCs are not limited to any kind of cells and includes dendritic cells (DCs), Langerhans cells, macrophages, B cells, and activated T cells, all of which are known to present proteinaceous antigens on their cell surface so as to be recognized by lymphocytes. Since DC is a representative APC having the strongest CTL inducing action among APCs, DCs find particular use as the APCs.

[0082] For example, an APC can be obtained by inducing dendritic cells from the peripheral blood monocytes and then contacting (stimulating) them with the peptides in vitro, ex vivo or in vivo. When the peptides are administered to the subjects, APCs that have the peptides immobilized to them are stimulated in the body of the subject, "inducing APC" includes contacting (stimulating) a cell with the peptides, or nucleotides encoding the peptides to present complexes formed between HLA antigens and the peptides on cell's surface. Alternatively, after immobilizing the peptides to the APCs, the APCs can be administered to the subject as a vaccine. For example, the ex vivo administration can comprise steps of: a: collecting APCs from subject:, and b: contacting with the APCs of step a, with the peptide. The APCs obtained by step b can be administered to the subject as a vaccine.

[0083] Such APCs can be prepared by a method which comprises the step of transferring genes comprising polynucleotides that encode the peptides to APCs in vitro. The introduced genes can be in the form of DNAs or RNAs. For the method of introduction, without particular limitations, various methods conventionally performed in this field, such as lipofection, electroporation, and calcium phosphate method can be used.

[0084] A T cell stimulated by any of the peptides disclosed herein can be used as a vaccine similar to the peptides. Thus, the present invention provides isolated T cells that are stimulated by any of the present peptides. Such T cells can be obtained by (1) administering to a subject or (2) contacting (stimulating) subject-derived APCs, and CD8-positive cells, or peripheral blood mononuclear leukocytes in vitro with the peptide. T cells, which have been stimulated by stimulation from APCs that present the peptides, can be derived from subjects who arc targets of treatment and / or prevention, and can be administered by themselves or in combination with other drugs including the peptides or exosomes for the purpose of regulating effects. The obtained T cells act specifically against target cells presenting the peptides, for example, the same peptides used for priming. The target cells can be cells that express endogenously, or cells that are transfected with genes, and cells that present the peptides on the cell surface due to stimulation by these peptides can also become targets of attack. In some embodiments, T cells are contacted with a peptide in vitro, i.e. where the T cells are then transferred to a recipient.

[0085] Effector cells, for the purposes of the disclosure, can include autologous or allogeneic immune cells having cytolytic activity against a target cell, including without limitation tumor cells. The effector cells can be obtained by engineering peripheral blood lymphocytes (PBL) in vitro, then culturing with a cytokine and / or antigen combination that increases activation. The cells are optionally separated from non-desired cells prior to culture, prior to administration, or both. Cell-mediated cytolysis of target cells by immunological effector cells is believed to be mediated by the local directed exocytosis of cytoplasmic granules that penetrate the cell membrane of the bound target cell.

[0086] Cytotoxic T lymphocytes (CTL) reactive to tumor cells are specific effector cells for adoptive immunotherapy and are of interest for engineering by priming with peptides disclosed herein, or engineering to express a TCR disclosed herein. Induction and expansion of CTL is antigen- specific and MHC restricted.

[0087] T cells collected from a subject may be separated from a mixture of cells by techniques that enrich for desired cells, or may be engineered and cultured without separation. An appropriate solution may be used for dispersion or suspension. Such solution will generally be a balanced salt solution, e.g. normal saline, PBS, Hank’s balanced salt solution, etc., conveniently supplemented with fetal calf serum or other naturally occurring factors, in conjunction with an acceptable buffer at low concentration, generally from 5-25 mM. Convenient buffers include HEPES, phosphate buffers, lactate buffers, etc.

[0088] Techniques for affinity separation may include magnetic separation, using antibody- coated magnetic beads, affinity chromatography, cytotoxic agents joined to a monoclonal antibody or used in conjunction with a monoclonal antibody, e.g., complement and cytotoxins, and "panning" with antibody attached to a solid matrix, e.g., a plate, or other convenient technique. Techniques providing accurate separation include fluorescence activated cell sorters, which can have varying degrees of sophistication, such as multiple color channels, low angle and obtuse light scattering detecting channels, impedance channels, etc. The cells may be selected against dead cells by employing dyes associated with dead cells (e.g., propidium iodide). Any technique may be employed which is not unduly detrimental to the viability of the selected cells. The affinity reagents may be specific receptors or ligands for the cell surface molecules indicated above. In addition to antibody reagents, peptide-MHC antigen and T cell receptor pairs may be used; peptide ligands and receptor; effector and receptor molecules, and the like.

[0089] The collected and optionally enriched cell population may be used immediately for genetic modification, or may be frozen at liquid nitrogen temperatures and stored, being thawed and capable of being reused. The cells will usually be stored in 10% DMSO, 50% FCS, 40% RPMI 1640 medium.

[0090] The engineered cells may be infused to the subject in any physiologically acceptable medium by any convenient route of administration, normally intravascularly, although they may also be introduced by other routes, where the cells may find an appropriate site for growth. Usually, at least 1x106 cells / kg will be administered, at least 1x107 cells / kg, at least 1x108 cells / kg, at least 1x109 cells / kg, at least 1x1010 cells / kg, or more, usually being limited by the number of T cells that are obtained during collection.

[0091] Screening methods

[0092] Methods for screening for HLA-binding peptides comprising a ncojunction arc provided. As described herein, is a cancer- specific aberrant splicing event. Peptides comprising a neojunction may be understood to originate from translation of an aberrantly spliced transcript. The screening methods comprise obtaining transcriptome sequence data from a tumor tissue sample and a normal tissue sample. In some embodiments, transcriptome data may be obtained from a plurality of tumor tissue samples and a plurality of normal tissue samples. In some cases, the plurality of tumor tissue samples may be derived from a single tumor, e.g., from a sectioned tumor. In some cases, transcriptomic sequence data may be obtained from a plurality of tumor tissue samples of the same type (e.g., glioblastoma) and from a plurality of normal tissue samples of the same type (e.g., brain tissue). In some cases, transcriptomic data may be obtained from a plurality of tumor tissue samples of different types and from a plurality of normal tissue types. Obtaining transcriptomic sequence data from a plurality of tumor and normal tissue samples may be advantageous when, for example, screening for peptides comprising neojunctions that are conserved in a tumor-type across individuals or conserved across tumor types. Methods, reagents, and kits for sequencing transcriptomes are well known in the art and readily available. Alternatively, transcriptome sequence data repositories of both tumor tissues samples and normal tissue samples are known in the art and readily available. For example, transcriptomic sequence data of tumor tissue samples may be obtained from The Cancer Genome Atlas (TCGA) or the Mayo Clinic Brain Tumor Patient Derived Xenograft National Resource. As another example, transcriptomic sequence data for normal tissue samples may be obtained from the Genotype Tissue Expression (GTEx) repository. The methods for screening for HLA-binding peptides comprising a neojunction may further comprise identifying one or more transcripts in a tumor tissue sample comprising a neojunction. In some cases, identifying the one or more transcripts in a tumor tissue sample comprising a neojunction comprises quantifying detected splicing junctions (i.e., quantifying sequencing reads comprising a detected splicing junction) in protein-coding transcripts expressed in the tumor tissue sample, or in a plurality of tumor tissue samples, and quantifying detected splicing junctions in protein-coding transcripts expressed in the normal tissue sample, or a plurality of normal tissue samples. Methods for detecting splice junction events are well known in the art and implemented in readily available algorithms (e.g., STAR aligner, IRFinder). The identifying the one or more transcripts in a tumor tissue sample comprising a neojunction may further comprise identifying un-annotated splicing junctions as the set of detected splicing junctions not present in an annotated set of canonical splicing junctions. Annotated datasets of canonical splicing junctions are well known in the ail and readily available for a reference genome of choice. For example, canonical splicing junctions annotations for GRCh37 human reference genome are available from the GENCODE database. The identifying the one or more transcripts in a tumor tissue sample comprising a neojunction may further comprise defining a splicing frequency for the unannotated splicing junctions. In some cases, the splicing frequency of un-annotated splicing junctions may be defined as: splicing junction reads splicing frequency — - — - - - - - - - —

[0093] (splicing junction reads -I- canonical splicing junction reads)

[0094] In some embodiments, neojunctions may be identified as the un-annotated splicing junctions with a splicing frequency of at least 0.1 in the tumor tissue sample, or in the plurality of tumor tissue samples, and not detected in the normal tissue sample, or in less than 1% of the plurality of normal tissue samples. The methods for screening for HLA-binding peptides comprising a neojunction may further comprise inputting the sequences of the of the one or more transcripts comprising a neojunction into a computer readable medium and generating an in silica library of possible multimer peptide sequences from the sequences of the one or more transcripts comprising a neojunction. In some embodiments, the generating an in silica library of possible multimer peptide sequences comprises translating the transcripts comprising neojunctions in silica to their corresponding amino acid sequences. In some embodiments, the generating an in silico library of possible multimer peptide sequences further comprises, for each amino acid sequence, partitioning the amino acid sequence into all possible multimer peptide sequences of a specified length. In some embodiments, the length of the possible multimer peptide sequences may be in the range of 8-40 amino acids, e.g., in some cases the length of the multimer peptide sequences may be in the range from 8-9 amino acids, from 8-10 amino acids, from 8-11 amino acids, from 8-12 amino acids, from 8-15 amino acids, from 8-20 amino acids, from 8-25 amino acids, from 8-30 amino acids, from 8-40 amino acids. In some embodiments, the generating an in silico library of possible multimer peptide sequences further comprises discarding each multimer peptide sequence that is detectable in a reference proteome dataset of normal tissue samples. Reference proteome datasets are known in the art and readily available. Suitable reference proteomes for use in the present methods may include, without limitation, UniProt Proteome ID #UP000005640. The methods for screening for HLA-binding peptides comprising a neojunction may further comprise generating, for each peptide of the in silico multimer peptide library, a first MHC I presentation score with an antigen-processing favorability algorithm, and a second MHC I presentation score with a pan-HLA-A binding affinity algorithm. In some cases, HLA-A binding affinity may be assessed against HLA-A*01:01, HLA-A*02:01, HLA-A*03:01, HLA-A*l l:01, and HLA-A*24:02 alleles. In some embodiments, the antigen-processing favorability algorithm may be implemented with the MHCFlurry 2.0 software. In some embodiments, the pan-HLA-A binding affinity algorithm may be implemented with the HLAthena software. The methods for screening for HLA-binding peptides comprising a ncojunction may further comprise identifying one or more peptides that bind to HLA as determined by the first and second MHC-I presentation scores. The identifying the one or more peptides that bind to HLA may comprise ranking the multimer peptides in the library by the first and second MHC-I presentation scores. In some embodiments, the identifying the one or more peptides that bind to HLA may further comprise identifying the one or more HLA-binding peptides as the multimer peptides in the top 10 percentile of both the first and second MHC-I presentation scores. In some cases, the one or more HLA-binding peptides may be identified as the multimer peptides in the top 1 percentile of both the first and second MHC-I presentation scores. An exemplary embodiment of the methods for screening for HLA- binding peptides comprising a neojunction is depicted schematically in FIG. 1.

[0095] Methods for screening for TCRs that recognize and bind to a neojunction epitope are provided. In some embodiments, the methods for screening for TCRs comprise introducing an HLA-binding peptide comprising a neojunction to a population of antigen presenting cells (APCs) to produce a population of APCs loaded with the HLA-binding peptide (i.e., presenting the HLA-binding peptide on the surface of the cell in complex with MHC-I). In some embodiments, a population of APCs may be transformed or transduced to recombinantly express a polypeptide comprising a peptide comprising a neojunction, which is further processed by the APC and presented on the surface of the cell in complex with MHC-I. In some embodiments, an APC may be a professional APC (e.g., a dendritic cell). In some cases, the APC may be defective for endogenous antigen processing (e.g., a T2 cell). The methods for screening for TCRs may further comprise contacting a population of T cells with the population of APCs loaded with an HLA-binding peptide. The T cells may be any suitable T cells. In some embodiments, the T cells are naive CD8+ T cells. In some cases, the T cells may be subsequently harvested and the step of contacting the population of T cells with a population of APCs loaded with an HLA-binding peptide may be iterated one or more times. In some embodiments, the methods for screening for TCRs may further comprise selecting for T cells for increased cytokine production. In some cases the cytokines may comprise IFNy and TNFa. The methods for screening for TCRs may further comprise performing paired single-cell RNA and V(D)J sequencing (i.e., TCR sequencing) on the T cells. In some cases, the T cells may be T cells selected for increased cytokine production. In some embodiments, the sequencing method comprises stimulating the T cells by contacting the T cells with a population of APCs loaded with the HLA-binding peptide prior to harvesting the T cells for sequencing. Methods, reagents, and kits for single-cell RNA and V(D)J sequencing are known in the art and readily available and described, for example in Hwang, Byungjin, Ji Hyun Lee, and Duhee Bang. "Single-cell RNA sequencing technologies and bioinformatics pipelines." Experimental & molecular medicine 50.8 (2018): 1-14 and Pai, Joy A., and Ansuman T. Satpathy. "High-throughput and single-cell T cell receptor sequencing technologies." Nature methods 18.8 (2021): 881-892, the disclosures of which are incorporated herein by reference. The methods for screening for TCRs may further comprise identifying the TCR sequences of reactive T cells as determined by single-cell RNA sequencing. In some embodiments, the reactive T cells may be determined by increased cytokine expression in the T cell relative to a control. In some embodiments, reactive T cells may be determined by increased expression of IFNG, TNF, and / or GZMB in the T cell relative to a control. In some embodiments the control may comprise a T cell contacted with an APC loaded with a decoy peptide (i.e., non-antigenic peptide) prior to sequencing. In some embodiments the control may comprise a T cell contacted with an APC that loaded with no peptide prior to sequencing. An exemplary embodiment of the methods for screening for TCRs that recognize and bind to a neojunction epitope is depicted schematically in FIG. 1.

[0096] EXAMPLES

[0097] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperature, etc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Celsius, and pressure is at or near atmospheric. Standard abbreviations may be used, e.g., bp, base pair(s); kb, kilobase(s); pl, picoliter(s); s or sec, second(s); min, minute(s); h or hr, hour(s); aa, amino acid(s); kb, kilobase(s); bp, base pair(s); nt, nucleotide(s); i.m., intramuscular(ly); i.p., intraperitoneally ); s.c., subcutaneous(ly); and the like.

[0098] Example 1

[0099] While promising, T-cell-mediated immunotherapy is somewhat limited by the extent to which cancer-specific targets (neoantigens) are expressed throughout the entire tumor landscape. Splicing aberrations in cancer (neojunctions) can represent a potential source of the public (shared between patients) and tumor-wide (expressed across all intratumoral samples) neoantigens, and thus, a novel pipeline for identifying these targets across multiple cancers was developed. The analyses characterized multiple public, tumor-wide neojunctions and identified CD8+ T-cell clones specific for neoantigens derived from splicing mutations in GNAS and RPL22, the former of which was observed tumor-wide. TCR-engineered CD8+ T-cells targeting these mutations demonstrated a neoantigen-specific response and tumor-specific killing. Upregulation in neojunction levels associated with dysregulated splicing-related gene expression was observed in IDH1 -mutant glioma subtypes compared with its wild-type variant. Notably, knock-down of chromosome lp / 19q codeletion-associated splicing-related genes led to a robust increase in neojunction expression. Overall, a subset of neojunctions were revealed that are both interpatiently and intratumorally conserved, indicating the potential development of novel T-cell-based immunotherapies addressing intratumoral heterogeneity.

[0100] MATERIALS AND METHODS

[0101] Data download: Bulk RNA-sequencing data for glioblastoma (GBM; n=167), low-grade glioma (LGG; n=516), lung adenocarcinoma (LU AD, n=517), lung squamous cell carcinoma (LUSC, n=501), mesothelioma (MESO, n=516), liver hepatocellular’ carcinoma (LIHC, n=371), stomach adenocarcinoma (STAD, n=415), kidney renal clear cell carcinoma (KIRC; n=533), kidney renal papillary cell carcinoma (KIRP; n=290), kidney chromophobe (KICH, n=66), colon adenocarcinoma (COAD; n=458), and prostate adenocarcinoma (PRAD; n=497) samples were downloaded from TCGA in FASTQ format. Download of intratumoral multi-region sampling sequencing data is detailed in the previous section. Similarly, bulk RNA-sequencing data for 9,166 normal tissue samples in FASTQ format were downloaded from the Genotype- Tissue Expression (GTEx) repository. Bulk-RNA sequencing data for 66 patient-derived GBM cell lines were received from the Mayo Clinic Brain Tumor Patient-Derived Xenograft National Resource. Proteomics data for 100 GBM samples were downloaded from the Clinical Proteomic Tumor Analysis Consortium (CPTAC).

[0102] RNA sequencing alignment: All downloaded RNA-scqucncing data sets were individually aligned using a STAR aligner-based processing pipeline. Using the STAR software (version 2.7.7a), a genome index was constructed containing non-annotated junctions through the initial alignment pass of the input data. The complete set of command line parameters: — runThreadN 1 \ — outFilterMultimapScoreRange 1 \ — outFilterMultimapNmax 20 \ — outFilterMismatchNmax 10 \ — alignlntronMax 500000 \ — alignMatesGapMax 1000000 \ — sjdbScore 2 \ — alignSJDBoverhangMin 1 \ — genomeLoad NoSharedMemory \ — limitBAMsortRAM 80000000000 \ -readFilesCommand gunzip -c \ — outFilterMatchNminOverLread 0.33 \ — outFilterScoreMinOverLread 0.33 \ — sjdbOverhang 100 \ — outSAMstrandField intronMotif \ -outSAMattributes NH HI NM MD AS XS \ — limitSjdblnsertNsj 2000000 \ — outSAMunmapped None \ — outSAMtype BAM SortedByCoordinate \ — outSAMheaderHD @HD VN1.4 \ — twopassMode Basic \ — outSAMmultNmax 1 \ and aligned using the GRCH37 STAR index file.

[0103] TCGA sample selection and gene expression quantification: TCGA tumor samples with an absolute tumor purity greater than 0.60 were retained downstream in silico analysis. (Aran et al. 2015, Ceccarelli et al. 2016) Non-mitochondrial, protein-coding transcripts defined by the Ensembl Homo Sapiens GRCH37.87 gene annotation gene transfer format (GTE) file were selected and this curated set was utilized to select and retain protein-coding transcript isoforms within the TCGA RNA-sequencing data. Transcript-level expression data (log2[RSEM-TPM+0.001]) for all TCGA samples were downloaded from the UCSC Xena Toilpipeline and transformed into standard TPM values. Protein-coding transcript isoforms with a median TPM > 10 were retained for downstream analysis. In the case of glioma TCGA cases, subsequent expression data in TPM was subset into 6 disease type categories: all cases (n=429), GBM cases (n=l 15), LGG cases (zz=314), IDH / -WV cases (n=166), / D / 7 / -MUT astrocytoma cases (72=140), IDH1-MUT oligodendroglioma (n=123). Protein-coding transcript isoforms with a median TPM > 10 in at least one of the 6 disease types were retained for further analysis.

[0104] Characterization of public neojunctions: For public cancer- specific splicing event counting, a custom R script was designed that detected and quantified non- annotated, cancerspecific splicing events found across each corresponding patient cohort. From the output files derived from STAR aligner in the previous step, alternative splicing events were quantified in detected junction counts within the corresponding sj. out. tab file. Splicing events detected in the GRCh37.87 GTF sj.out.tab file were removed to define non-annotated splicing junctions. Nonannotated splicing junctions that overlap non-mitochondrial, protein-coding genes identified in the previous step were retained for continued analytical processing. All splicing junctions with less than 10 of its target spliced reads (count) or less than 20 total spliced reads (depth) over the whole cohort were removed. Similarly to previous studies, spliced frequency was computed as the sum of the total number of target spliced reads divided by the collective sum of spliced reads from the target and canonical junctions. Public splicing junctions were defined as ones that were putatively expressed with the aforementioned criteria across at least 10% of the studied patient cohort and retained those for further analysis. To characterize cancer-specific splicing events, otherwise known as neojunctions, all junctions that putatively expressed with the same parameters in more than 1% of GTEx normal samples were removed.

[0105] Detection of cancer-specific intron retention events

[0106] Intronic splicing events were detected and characterized using IRFinder vl.2.3. RNA sequencing data from TCGA (GBM / LGG) and GTEx (CNS) aligned to GRCh37 (hg 19) were imported into the software for the detection of intron retention events. General linear model (GLM)-based analysis was used for differential intron retention assessment. The intron retention ratio is calculated as (intronic reads / sum(intronic reads, normal spliced reads). Significant intron retention changes are defined as (1) no less than 10% in both directions (2) adjusted p-values less than 0.05. An intron retention event’s PSR within TCGA or GTEx is defined as the number of cases that fulfill these criteria divided by the total number of cases within the cohort. Putative cancer-specific intron retention neojunctions are characterized as intron retention events with a TCGA PSR > 0.10 and a GTEx PSR < 0.01.

[0107] Transcriptomic validation of expressed neojunctions Detection of expressed neojunctions in patient-derived GBM / LGG cell lines: RNA sequencing data derived from GBM PDX cell lines were downloaded from the Mayo Clinic Brain Tumor Patient-Derived Xenograft National Resource. Patient-derived LGG cell lines were generated from surgically-resected specimens in University of California, San Francisco’s Neurological Surgery Brain Tumor Center. RNA sequencing data from GBM and LGG cell lines were aligned and processed as described above. Public neojunctions with splice junction counts per million (CPM) > 0 are considered detectable in cell line-derived RNA sequencing data. Detection of expressed ncojunctions in multi-region cases: In the cohort of spatially- mapped glioma cases, approximately ten or more maximally-distanced anatomical biopsies were collected from each patient, allowing for intratumoral assessment of genetic heterogeneity via RNA- sequencing, whole-exome sequences, etc. RNA-sequencing data collected from each multi-region sample was processed and aligned as described above. Putative neojunctions previously characterized from TCGA were searched within each multi-region sampling dataset. Public neojunctions with CPM > 0 were considered detectable. Public neojunctions with putative expression (> 10 spliced reads) in two or more mapped samples within the same case are considered spatial-conserved neojunctions. Neojunctions detected in all multi-region samples within the same tumor are considered tumor- wide neojunctions.

[0108] Proteomic validation of expressed neojunction-derived peptides

[0109] From the putative neojunctions detected in the above pipeline, a database of all plausible polypeptides derived from all neojunctions was generated. To detect neojunction-derived polypeptides within GBM cases, .RAW files of GBM and LGG MS data housed in the Clinical Proteomics Tumor Analysis Consortium (CPTAC, zz=99), Bader et al. ( / z=99), Lam et al. (zi=92), and Yanovich- Arad et al. (n=84) were analyzed. MaxQuant (vl.6.17.0) was used to identify tryptic sequences from the corresponding CPTAC MS data set. Predicted neojunction- derived peptides, decoy sequences, and a human reference proteome (UniProt Proteome ID #UP000005640) were inputted as a FASTA file into MaxQuant, and tryptic sequences derived from the input file were matched against the CPTAC database. Cancer- specific peptides spanning neojunction-derived protein sequences were considered CPTAC-confirmed. The relative detection levels of the neojunction-derived peptides and normal tissue-derived peptides were evaluated by their log2(peak intensities). Aside from the default settings, the following commands and parameters were modified and used for MS analysis in MaxQuant: Digestion mode = Trypsin / P; Max missed = 3; Minimum peptide length = 5; Minimum peptide length for unspecific search = 5.

[0110] Peptide processing and MHC-I binding and presentation predictions

[0111] Cancer-specific transcripts with associated neojunctions were translated in silica into their corresponding amino acid sequences. A library of all possible multi-mers of 8 to 11 amino acids in length was then generated, and cancer-specific sequences were selected by removing those detectable in normal tissue peptide isoforms in a reference human proteome dataset (UniProt Proteome ID #UP000005640). All canccr-spccific multi-mcrs with their upstream and downstream flanking sequences (maximum flanking length of 30 amino acids) were independently analyzed and ranked by MHCFlurry 2.0 and HLAthena MSiC. HLAMHC-I binding affinity was assessed against HLA-A*01:01, HLA-A*02:01, HLA-A*03:01, HLA- A* 11:01, and HLA-A*24:02 in both cases. In the HLAthena evaluation of antigen binding and presentation to the corresponding HLA haplotypes, peptides were assigned to alleles by rank with a threshold of 0.1. Context of up to 30 flanking amino acids on both N and C terminus were utilized with aggregation by peptide and no log-transformed expression. Baseline MHCFlurry 2.0 models with both peptide:MHC-I binding affinity (BA) predictor and antigen processing (AP) predictor was used. Overall, multi-mer:HLA presentation scores were characterized by mhcflurry_presentation_score and MSiC_HLA scores in MHCFlurry 2.0 and HLAthena, respectively. To select for high-binders, lists of multi-mer:HLA complexes within the top 10 percentile of scores from both prediction algorithms were curated.

[0112] Cell culture

[0113] GBM PDX cell culture: Patient-derived xenograft (PDX) glioblastoma cell lines, GBM34, GBM43, GBM108, GBM115, GBM118, GBM102, GBM137, GBM148, GBM164, and GBM195, were obtained from the Mayo Clinic Brain Tumor PDX national resource. Xenograft lines were cultured as by recommended conditions in previous literature and passaged a maximum of 20 times before restoration to earlier passages. Cells were cultured in Dulbecco’s Modified Eagle’s medium (DMEM):F12 supplemented with 10% fetal bovine serum and 1% penicillin and streptomycin (P / S). Cell culture plates were treated overnight at 4 °C with DPBS (with calcium and magnesium) and 10% laminin (Gibco1MCat. #23017015) prior to use. Primary patient-derived GBM / LGG cell culture: Primary patient-derived wildtype IDH1 GBM (SF7996), mutant IDH1 astrocytoma (SF10602), and mutant IDH1 oligodendroglioma (SF10417) cell lines were previously internally generated from dissociated glioma biopsies and cultured as previously described. Cells were cultured in serum-free, glioma neural stem (GNS) cell medium, which comprises of Neurocult NS-A (STEMCELL Technologies Cat. #05751) supplemented with N-2 supplement (Invitrogen Cat. #17502048), B- 27 supplement minus vitamin A (Invitrogen Cat. #12587010), 1% P / S, 1% glutamine, and 1% sodium pyruvate. Prior to immediate use in culture, GNS media is supplemented with 20 ng / mL EGF (Peprotech Cat. #AF-100-15), bFGF (Peprotech Cat. #AF-100-18B), and PDGF-AA (Pcprotcch Cat. #AF-100-13A). Similarly with the GBM PDX cell lines, cell culture plates were incubated overnight at 4 °C with DPBS (with calcium and magnesium) and 10% laminin (Gibco™ Cat. #23017015) prior to use. Jurkat76 cell culture: Jurkat76 cells were used as the TCR a- and P-negative human T-cell derivative that allowed for noncompeting introduction of exogenous TCRs. CD8+ Jurkat76 cells were cultured in RPMI supplemented with 10% fetal bovine serum and 1% P / S. T2 cell culture: T2 cells were used in the study to monitor immune cell response to the exogenous antigen of interest in a non-competitive environment. T2 cells are deficient in a peptide transporter involved in antigen processing (TAP), and as such, induction of these cells with exogenously administered peptides allows for their association and presentation by MHC class I molecules, HLA-A*0102 in particular. T2 cells were cultured in IMDM medium supplemented with 20% FBS. COS7 and K562 cell culture: COS7 (ATCC Cat. #CRL-1651) and K562 (ATCC Cat. #CCL-243) cell lines were used as respective primate and human artificial antigen presentation cell (aAPC) models. These cell lines do not express HLA molecules, which allows for the introduction of the HLA allele of interest. COS7 cells were cultured in DMEM medium supplemented with 10% FBS and 1% P / S. K562 cells were cultured in IMDM medium supplemented with 10% FBS and 1% P / S. THP-1 cell culture: THP-1 cells (ATCC Cat. #TIB-202) were used to investigate immune reactivity against neoantigen presentation by dendritic cells. THP-1 cells were cultured in RPMI- 1640 supplemented with 10% FBS.

[0114] In-vitro sensitization of healthy donor PBMCs

[0115] HLA-A*02:01:01 -positive PBMCs were purchased from StemExpress in either fresh or cryopreserved format. Fresh PBMCs (StemExpress Cat. #LE001F) of approximately IxlO9cells were immediately proportioned into aliquots of 3 x 108cells and cryopreserved in liquid nitrogen, with one aliquot actively used for downstream IVS. Cryopreserved PBMCs (StemExpress Cat. #PBMNC300C) totaling approximately 3xl08cells per cryovial were used in one vial per IVS procedure. PBMCs were thawed with 1:1000 Benzonase:RPMI (Sigma Aldrich Cat. #E8263). The CD 14+ population was isolated from the PBMCs using CD14+. Miltenyi microbeads (Miltenyi Biotec Cat. #130-050-201) per manufacturer’s instructions. The CD14- flowthrough was cryopreserved for 6 days prior to naive CD8+ T-cell isolation. Isolated CD 14+ cells were cultured in CellGenix GMP DC medium (CellGenix Cat #20801-0500) supplemented with 1% human serum (Sigma Aldrich Cat #H6914) and 1% P / S in non-treated 24- well plates at a seeding density of 5xl05cells per well for 3 days. On Day 3, the cultured CD14+ population is stimulated with 1000 U / mL recombinant human IL-4 (Peprotech Cat. #200-04) and GM-CSF (Peprotech Cat. #300-03). On Day 5, the monocytes within the CD14+ population were stimulated with 1000 U / mL recombinant human IL-4 and GM-CSE and matured into dendritic cells (DCs) with the introduction of 250 ng / mL LPS (Sigma Aldrich Cat. #L6529). Naive CD8+ T-cells were isolated from the thawed CD 14- population on Day 6 using the Easy Sep Human Naive CD8+ T Cell Isolation Kit (STEMCELL Technologies Cat. #19258) as per manufacturer’s instructions. Isolated naive CD8+ T-cells were cultured in X-Vivo 15 medium (Lonza Cat. #04-418Q) supplemented with 5% human serum, 1% P / S, and 10 ng / mL of recombinant human IL-7 (Peprotech Cat. #200-07) in 48-well plates at a seeding density of 5xl05cells per well. On Day 8, adherent matured DCs were harvested from the plate using cold PBS. The collected DCs (IxlO6cells / mL) were exogenously pulsed with either 1 pM of the neoantigen peptide, influenza peptide, or no peptide for 1 hour at 37 °C. The peptide-pulsed or non-pulsed DCs were then co-cultured with naive CD8+ T-cells at an optimal DC:T-cell ratio of 1:4 in 48-well plates. The co-culture was maintained with X-Vivo 15-medium supplemented with 10 ng / mL of recombinant human IL-7, 10 ng / mL recombinant human IL- 15 (Peprotech Cat. #200-15), and 60 ng / mL of recombinant human IL-21 (Peprotech Cat. #200-21) for 10 days with IL-7 and IL- 15 restimulation every 2 days. Cells were reseeded into subsequent 24-well, 12-well, and 6-well plates based on confluency. This concludes the first cycle of IVS of the neoantigens and influenza peptides. On Days 19 and 29, sensitized-CD8+ T-cells are reintroduced to a second and third round of stimulation with newly-pulsed DCs, and the coculture is maintained for 10 additional days until the end of the second and third cycle of IVS. Immunogenic cytokine assays were performed at the end of the second and third cycles of IVS to determine whether a peptide-reactive T-cell population has expanded. Mutation-specific ELISA screen

[0116] Aliquots containing CD8+ T-cells from individual parent IVS wells were harvested and split equally into 96-well plate daughter wells containing IxlO5cells per well. Daughter wells in triplicate were stimulated with T2 cells pulsed with either the neoantigen peptide of interest, decoy peptide, no peptide, or no T2 cells at all for 16 hours at an effector-to-target (E:T) ratio of 1 : 1. T2 cells are either pulsed with 1 pM to as much as 1 pM of the neoantigen peptide of interest, decoy peptide, or no peptides for 1 hour at 37 °C. Influenza-reactive T-cells are co- culturcd against influenza pcptidc-pulscd T2 cells as a positive control. Co-culturc supernatant was collected and diluted for use in IFNy (BD Biosciences Cat. #555142) and TNFa (BD Biosciences Cat. #555212) ELISAs as per manufacturer’s instructions. ELISA readouts were performed on the Epoch Microplate Spectrophotometer (BioTek Instruments) using the BioTek Gen5 Data Analysis software (version 1.11). Wells with significantly increased expression levels of IFNy and TNFa were selected for downstream single-cell immune profiling using single-cell RNA and V(D)J sequencing.

[0117] Single-cell immune profiling

[0118] Once an expanded neoantigen-reactive CD8+ T-cell population from IVS is identified, single-cell RNA and V(D)J sequencing were performed using the lOx Genomics platform. Prior to sequencing, CD8+ T-cells from the expanded neoantigen-reactive (ELISA screen-positive) wells were harvested and co-cultured with T2 cells pulsed with 1 pM of the neoantigen peptide of interest, a decoy peptide, or no peptides at an E:T ratio of 1:1. One co-culture replicate was performed for 3 hours for single-cell RNA sequencing analysis, and another was performed for 16 hours for IFNy and TNFa ELISA confirmation. The final cell concentration was adjusted to approximately IxlO4cells / pL with an initial cell viability of at least 90% to maximize the likelihood of achieving the desired cell recovery target. Independent CD8+ T-cell and nonpulsed T2 single cultures were sequenced alongside the co-culture conditions for differentiating cell types in the downstream single-cell sequencing analysis. The Chromium Next GEM Single Cell 5’ Reagent Kit v2 (Dual Index) (lOxGenomics, Cat. #CG000331) was used for preparation for single-cell sequencing analysis. Gel bead in emulsions (GEMs) were generated by combining the single cell 5’ gel beads, partitioning oil, and the master mix containing the cells onto the Chromium Next GEM Chip K. Cell lysis and barcoded reverse transcription of RNAs in all single cells were finished inside their corresponding GEM. Barcoded cDNA product was recovered through post-GEM-RT cleanup and PCR amplification. cDNA quality control and quantification were performed on the Fragment Analyzer System (Agilent Technologies). 50 ng of cDNA was used for the construction of the 5’ gene expression library, and each sample was indexed by a Chromium i7 Sample Index Kit. This process was performed on an Illumina NovaSeq 6000 sequencer at the UCSF Institute of Human Genetics (IHG) with a minimum of 20,000 read pairs per cell for the 5’ Gene Expression library. The enriched product was measured by the Fragment Analyzer System. 50 ng of enrichment TCR product was used for library construction. Single-cell V(D)J enriched libraries were subsequently sequenced on the Illumina NovaSeq 6000 with a minimum of 5,000 read pairs per cell for the V(D)J library. Cell Ranger 7.0.0 (lOx Genomics Cloud Analysis) was used to pre-process raw single-cell RNA sequencing and identifying V(D)J clonotypes. The annotation files ‘vdj_GRCh38_alts_ensembl- 3.1.0-3.1.0’ and ‘GRCh38-3.0.0’ were used for demultiplexing cellular barcodes, performing read alignments, and generating feature-barcode matrices. Only cells for which clonotype information was available were retained for downstream analysis. Single-cell gene expression and corresponding V(D)J sequences of candidate T-cell clonotypes were analyzed on the Loupe V(D)J browser. T-cells with detectable CD8A expression were specifically isolated as the CD8+ cell population and subsequently grouped according to their clonotypes. To identify T-cell clonotypes associated with a neoantigen-specific response, expanded TCR clonotypes with significantly increased levels of IFNG, TNF, and GZMB expressions in the T-cell:neoantigen- pulsed T2 condition compared to the T-cell:decoy-pulsed T2 and T-cell:non-pulsed T2 conditions were selected.

[0119] HLA typing

[0120] OptiType 1.3.1 was used for genotyping HLA alleles from available WES data available for glioma cell lines with default parameters.

[0121] Plasmids and peptides

[0122] HLA-A*02:01 and neojunction-derived gene sequences were all synthesized and cloned into the pTwist Lend SFFV Puro WPRE vector (Twist Biosciences). Constructs encoding full- length and truncated multi-mer versions of the wildtype and mutant GNAS and RPL22 sequences were generated. TCR a / p was synthesized and cloned into the pTwist Lenti SFFV vector (Twist Biosciences). HPLC grade neojunction-derived neoantigen peptide multi-mers (>95%) were manufactured by TC Laboratories. Lentiviral transduction

[0123] HEK293T cells were plated in 6-well culture plates at a density of IxlO6cells per well with 2 mL DMEM medium supplemented with 10% FBS without antibiotics. After approximately 18 to 24 hours or at 90% confluency, HEK293T cells were transfected with the expression construct, see above, and lentiviral packaging plasmids, pMD2.G (Addgene, #12259) and psPAX2 (Addgene, #12260). TCR a / B transduction: 1.0 pg TCR a / p transfer plasmid, 0.75 pg psPAX2, and 0.25 pg pMD2.G were combined with 200 pL Opti-MEM (Thermo Fischer Scientific Cat. #31985062). 6 pL of Xtrcmcgcnc HP was added to this mixture and complex formation was allowed to occur for 15 minutes at room temperature at which point this reaction mixture was added to corresponding HEK293T cells. Transfection medium was replaced with fresh DMEM media after 24 hours. Viral supernatant was collected after 48 hours, and a functional virus titer was performed on 6-well plates seeded with Jurkat76 cells or CD8+ T-cells at 60-70% confluency. Viral transduction was performed with 3-fold serial dilutions of the virus stock supplemented with polybrene at a final concentration of 4 pg / mL. Media was changed 24 hours following viral transduction. Cells were assessed for transduction efficiency after 3-4 days by measuring surface expression of TCR a / p and CD3 by fluorescence-activated cell sorting (FACS) analysis. Cells demonstrating high double-positive expression of TCR ct / p and CD3 were flow-sorted and maintained for downstream co-culture and immunogenicity assays. HLA and neoantigen transduction: Constructs expressing HLA-A*02:01 were linearized and restricted with BamHI and Xhol (New England Biolabs) and purified using the Zymoclean Gel DNA Recovery Kit (Zymo Research Cat. #D4007). The HLA-A*0201 sequence was then ligated into a lentiviral construct downstream of an EFlA-core promoter and upstream of an IRES followed by a Blasticidin resistance gene. 1.0 pg of either HLA-A*02:01 or neoantigen transfer plasmid, 0.75 pg psPAX2, and 0.25 pg pMD2.G were combined with 200 pL Opti- MEM (Thermo Fischer Scientific Cat. #31985062). 6 pL of Xtremegene HP was added to this mixture, and complex formation was allowed to occur for 15 minutes at room temperature at which point this reaction mixture was added to corresponding HEK293T cells. As stated above, neoantigen constructs encode either the full-length or truncated multi-mer version of the neojunction-derived peptide. The transfection medium was replaced with fresh DMEM media after 24 hours. HLA-A*02:01 lentiviral transduction and screening was performed first prior to neoantigen lentiviral transduction and screening for streamlined drug selection. Viral supernatant was collected after a subsequent 48 hours, and a functional virus titer was performed on 6-well plates seeded with COS7 or K562 cells at 60-70% confluency. Viral transduction was performed with 3-fold serial dilutions of the virus stock supplemented with 4 pg / mL polybrene. Media was changed 24 hours following viral transduction and replaced with complete media supplemented with blasticidin. Cells were assessed for transduction efficiency after 3-4 days by drug screening. HLA-A*02:01 -transduced APCs were cultured in medium treated with 10 pg / mL Blasticidin for approximately 7 days before assessing for cell viability across titers. Ncoantigcn-lcntiviral transduction is subsequently performed, and APCs transduced with both HLA-A*02:01 and neoantigen-expressing constructs arc then cultured in medium treated with 3 pg / mL puromycin for approximately 7 days. Cell viability was assessed afterwards across all titer conditions. Cells were assessed for transduction efficiency after 3-4 days by measuring surface expression of HLA-A2 fluorescence-activated cell sorting (FACS) analysis.

[0124] Dose-dependent assessment of TCR reactivity against neoantigen

[0125] Specificity of neoantigen-reactive CD8+ T-cells and TCR-transduced T-cells was assessed by human IFNy (BD Biosciences Cat. #555142), IL-2 (BD Biosciences Cat. #555190), and TNFa ELISA (BD Biosciences Cat. #555212). Assessment of TCR recognition against exogenously introduced neoantigen peptides presented by MHC-I molecules was conducted by co-culturing T-cells with peptide-pulsed T2 cell conditions. T2 cells are either pulsed with neoantigen peptide of interest at a concentration between 1 pM through 1 pM, decoy peptide, or no peptides for 1 hour at 37 °C. Influenza- reactive T-cells are co-cultured against influenza peptide-pulsed T2 cells as a positive control. T-cells and T2 cells were co-cultured in a 96-well round-bottom plate at a concentration of 1 x 105of each cell type in 200 pL of medium for 16 hours. Supernatant was collected and diluted for cytokine detection assays. Supernatant was collected and diluted for cytokine release assays per manufacturer’s instructions. ELISA assay readouts were performed on Epoch Microplate Spectrophotometer using the BioTek Gen5 Data Analysis software. To characterize the dose-dependent activation of the TCRs in transduced triple-reporter Jurkat76 cells, flow analysis was performed to assess the level of expression of NFAT-GFP, NFKB-CFP, AP-l-mCherry following 16 hours of co-culture. Similarly, reactivity of TCR-transduced CD8+ T-cells was evaluated by flow analysis following anti-CD107a (BioLegend, Cat #328620) and anti-CD137 antibody (4-1BB; Biolegend Cat #309804) staining. In vitro transcription (IVT) synthesis of mRN A

[0126] All constructs were subcloned into pcDNA3.1 (Invitrogen, 2520855) and linearized by Xhol restriction enzyme with the plasmid DNA template transcribed downstream from the bacteriophage T7 promoter sequence. For long (> 0.5 kb) and short (< 0.5 kB) transcripts, 1 pg and 0.5 pg of template were used, respectively. Reactions were assembled at room temperature using the mMESSAGE mMACHINE T7 Transcription Kit as per manufacturer’s instructions (Invitrogen, 2582905) and incubated at 37°C for 1 hour for long transcripts and 16 hours for short transcripts. Following DNase treatment, a Poly(A) tailing reaction was performed for 1 hour according to the HiScribe T7 ARCA manual (NEB, E2060S). Subsequently, the synthesized mRNA was purified by LiCl precipitation using 70% DEPC-based ethanol. Synthesized mRNA was heat-shocked (70°C, 5mins) with the formaldehyde loading dye to verify quality via gel electrophoresis. mRNA transfection of HLA-A*02:01, truncated neoantigen, and full-length neojunction-encoding mRNA

[0127] Transfection of IVT- synthesized mRNA into COS7 and K562 cells was performed with electroporation using the Neon Transfection System 100 pL Kit (Invitrogen, MPK10096) per manufacturer’s instructions. 1 x 106COS7 and K562 cells were washed and resuspended with 100 pL of Neon™ Resuspension Buffer. 5 pg of HLA-A2 and 5 pg of candidate (either the truncated neoantigen sequence or the full-length neojunction sequence) mRNA were added into the cell solution. Electroporation was performed on the Neon NxT Electroporation System (Invitrogen, NEON1). Electroporation of COS7 cells was performed with the following optimized conditions: pulse voltage of 1200 V, width of 30 ms, and 2 pulses. Electroporation of K562 was performed with the following optimized conditions: pulse voltage of 1450 V, width of 10 ms, and 3 pulses. Transfected cells were immediately transferred into warm RPMI with no antibiotics. Aliquots of transfected cells were retained for validation of HLA-A2 expression by staining with HLA-A2 monoclonal antibody (BB7.2, Thermo Scientific, 17-9876-42) and subsequent flow cytometry analysis.

[0128] Evaluation of TCR specificity against endogenously processed and MHC-I presented neoantigen

[0129] Characterization of neoantigens that are endogenously processed and presented by surface MHC-I is conducted by co-culturing HLA-A*02:01 / neoantigen-transfected COS7 or K562 cells with TCR-transduced T-cells. Similarly, T-cells and COS7 / K562 cells were cocultured in a 96- well flat-bottom plate at a concentration of 1 x 105of each cell type in 200 pL of medium for 16 hours. Supernatant was collected and diluted for cytokine release assays per manufacturer’s instructions, and cytokine release levels were assessed with the Epoch Microplate Spectrophotometer and BioTek Gen5 Data Analysis software. In all cytokine release assay experiments, maximum cellular cytokine release per well was determined by the addition of 0.2 pL Cell Activation Cocktail (without Brefeldin A) (BioLegend Cat. #423302) per 100 pL cell solution. Evaluation of endogenously processed and presented neoantigens in glioma cell lines was performed by co-culturing TCR-transduced triple-reporter Jurkat76 cells with glioma cells at a 1:1 E:T ratio (1 x 105per well in a 96-well plate). Flow analysis was performed to assess the level of expression of NFAT-GFP, NFKB-CFP, AP-l-mCherry following 16 hours of co-culture.

[0130] HLA-IP and LC-MS / MS

[0131] COS-7 cells were co-electroporated with 10 pg of each mRNA encoding HLA-A*02:01 allele and the full-length coding sequence of the mutated GNAS or RPL22 using the Neon Transfection system (100-pl tip, setting: 1,050 V / 10 ms / 2 pulses). 20 x 106 cells were electroporated per condition and plated in six-well non-TC plates overnight. Cells were harvested by incubating with 1 mM EDTA (Millipore Sigma) for 10 minutes at 37 °C. For the immunoprecipitation experiments, cells were lysed in 8 ml of 1% CHAPS (Millipore Sigma) for 1 hour at 4°C, lysates were then spun down for 1 hour at 20,000g and 4°C, and supernatant collected. For the affinity-column based immunopurification of HLA-I ligands, 40 mg of cyanogen bromide-activated-Sepharose 4B (MilliporeSigma) was activated with 1 mM hydrochloric acid (MilliporeSigma) for 30 minutes. Subsequently, 1 mg of W6 / 32 antibody (Bio X Cell) were coupled to Sepharose in the presence of binding buffer (150 mM sodium chloride, 50 mM sodium bicarbonate, pH 8.3; sodium chloride) for 2 hours at room temperature. Sepharose was blocked for 1 hour with glycine and washed 3 times with PBS. Supernatants of cell lysates were run over the affinity of column through peristaltic pumps at 6 mL / min flow rate overnight at 4°C. HLA complexes and binding peptides were eluted from the column five times using 1% TFA. Peptides and HLA-I complexes were separated using C18 columns (Sep- Pak C18 1 cc Vac Cartridge, 50 mg of sorbent per cartridge, 37-55-pm particle size, Waters). C18 columns were pre-conditioned with 80% ACN (Millipore Sigma) in 0.1% TFA and equilibrated with two washes of 0.1% TFA. Samples were loaded, washed twice with 0.1% TFA and eluted in 300 pl of 30%, 40% and 50% acetonitrile in 0.1% TFA. All three fractions were pooled, dried down using vacuum centrifugation and stored at -80 °C until further processing. HLA-I ligands were isolated by solid-phase extractions using in-house C 18 mini-columns. Samples were analyzed by high-resolution / high-accuracy LC MS / MS (Lumos Fusion, Thermo Fisher Scientific). MS and MS / MS were operated at resolutions of 60,000 and 30,000, respectively. Only charge states 1, 2 and 3 were allowed. The isolation window was chosen as 1.6 Thomson, and collision energy was set at 30%. For MS / MS, maximum injection time was 100 ms with an automatic gain control of 50,000. MS data were processed using FragPipe. Protein FDR was set at 0.01. Oxidization of methionine, phosphorylation of serine, threonine and tyrosine, as well as N-terminal acetylation were set as variable modifications for all samples. Samples were searched against a database comprising UniProt Cercopithecus aethiops reviewed proteins supplemented with human HLA-A*02:01 allele sequence, mutRPL22 and mutGNAS, as well as common contaminants.

[0132] Characterization of CD8+ T-cell-mediated anti-tumor reactivity

[0133] To determine whether TCR-transduced T-cells were capable of mounting an anti-tumor response, TCR-transduced J76 or CD8+ T-cells were co-cultured with patient-derived GBM or LGG cell lines. CD8+ T-cells were isolated from healthy donor-derived PBMCs using the EasySep™ Human CD8+ T Cell Isolation Kit (STEMCELL Technologies, Cat. # 17953). CD8+ T-cells were then activated with Dynabeads™ Human T-Activator CD3 / CD28 for T Cell Expansion and Activation (Thermo Scientific, Cat. #11161D) at a concentration of 25 pL / 1 x 106cells. CD8+ T-cells were cultured for 7 days with IL-7 (30 pL / 1 x 106cells) supplemented every 2 days. CD8+ T-cells were then LV transduced with neoantigen- specific TCRs with a hybridized murine TCR constant region using the above transduction procedure. This additional step removes the likelihood of TCR a-chain and -chain mispairing and allows us to evaluate TCR-transduction efficiency by staining with anti-murine TCR constant region antibody (Clone H57-597; BioLegend Cat. #109208). Flow sorting was performed to isolate highly-transduced CD8+ T-cells by selecting for a cells stained strongly with anti-CD3 and anti-murine TCR constant region antibody. Sorted transduced CD8+ T-cells were expanded for 7 days before use in co-culture assays. Killing assays were performed using an xCELLigence RTCA S16 Real- Time Cell Analyzer. Tumor cells were cultured in media pre-treated with 100 ng / mL IFNy (Peprotech, Cat. #300-02) for 48 hours and washed twice with PBS prior to seeding. 1 x 104tumor cells were plated per well in a 96- well E-plate (Agilent), and impedance is read for 16 hours during incubation. TCR-transduced CD8+ T-cells were introduced to each well at E;T ratio of either 1:1 or 2:1, and tumor- specific killing is measured by changes in cell index over 24-48 hours.

[0134] Identification of MHC-I restricted CD8+ T-cell mediated reactivity against neoantigens

[0135] Evaluation of MHC-I restricted reactivity was performed by perturbing TCR and HLA:pcptidc interaction with the introduction of anti-MHC-I antibodies. In dosc-dcpcndcnt immunogenicity assays, T2 cells at a concentration of 1 x 105tumor cells per well of a 96-well plate were washed twice with PBS and incubated for 30 min with blocking anti-MHC-I antibody (50 pg / well; clone W6 / 32, Bio X Cell, Cat. #BE0079) or isotype control (50 pg / well; Bio X Cell, Cat. #BEOO85) at a total volume of 100 pL. Without any additional washes, T-cells were added in to achieve a final volume of 200 pL. In tumor-killing assays, tumor cells were added to each well of a 96-well E-plate in a total volume of 50 pL for initial seeding. Anti- MHC-I antibody or isotype control (50 pg / well) is added to each well 30 minutes prior to the addition of T-cells to reach a total volume of 100 pL. T-cells were added in each well to achieve a final volume of 200 pL, and impedance was measured for the following 24-48 hours.

[0136] FACS analysis and antibodies

[0137] TCR-transduced cell lines were stained with anti-human TCR a / (Clone IP26, BioLegend Cat. #306717) and anti-human CD3 antibody (Clone HIT3a, BioLegend Cat. #300307) to assess the surface-level expression of the transduced TCR. CD8+ T-cells were stained with anti-CD107a (BioLegend, Cat #328620) and anti-CD137 antibody (4- IBB; Biolegend Cat #309804) to assess CD8+ T-cell degranulation and TCR activation, respectively. Viability of cells were assessed with the Zombie Green™ Fixable Viability Kit (BioLegend, Cat. #423111) APCs and patient-derived glioma cell lines were stained with HLA-A2 monoclonal antibody (Clone BB7.2, Thermo Fisher Scientific Cat. #17-9876-42). Approximately IxlO6cells per 100 pL FACS buffer (PBS supplemented with 1% BSA (Sigma Aldrich Cat. #L6529)) is incubated with one test volume of antibody for 20 minutes as indicated by the manufacturer. Stained cells were washed once with FACS buffer before resuspension to a concentration of 4 l05cells per 100 pL FACS buffer. Cells were then analyzed with the Attune NxT flow cytometer (Thermo Fischer Scientific). Gene set enrichment analysis

[0138] Differential gene expression of TCGA, GTEx, and UCSF GBM / LGG RNA-sequencing was performed and quantified using DESeq2. Only genes with an absolute fold change > 2 and a Benjamini- Hochberg adjusted P value <0.05 called by DESeq2 were considered to be differentially expressed. Pre-ranked gene set enrichment analysis (GSEA) was carried out by ranking genes with the product of their fold-change sign and the -loglO(adjusted P value). Disease subtype-specific differential gene analysis: GSEA comparison was performed between IDH1 mutation subtypes (wildtypc IDH1 and mutant IDH1 ) as well as glioma disease subtypes (wildtype IDH1 glioblastoma, mutant IDH1 astrocytoma, and mutant IDH1 oligodendroglioma). Splicing-related gene sets were selected based on keyword search, and gene sets with an adjusted P value <0.05 when comparing two groups are considered differentially enriched. Unbiased hierarchical clustering of differentially enriched gene sets allows the characterization of subgroup-specific upregulated genes. Selection of mutant IDH1 upregulated genes was determined by splicing-related genes expressed with a significant (p < 0.05) log2fold increase of 1.5 in mutant IDH1 cases when compared to their wild-type counterpart. Selection of splicinggenes affected by oligodendroglioma- specific loss of chromosomes lp / 19q was determined by chromosome lp / 19q splicing-related genes expressed with a significant (p < 0.05) log2fold decrease of 1.5 in IDH-0 cases compared to both IDH-A and IDH-wt cases. To determine correlation factors between each of the identified public neojunctions with each splicing-gene of interest, a Pearson correlation analysis was performed against each neojunction and splicing- related gene pair. Neojunction load-specific differential gene analysis: TCGA LGG and GBM samples were ranked according to the total putative neojunctions expressed per sample. High (NJHI) and low neojunction load (NJi o) samples within each disease subtype were characterized as the upper and lower 0.10 percentile of ranked samples, respectively. GSEA is carried out between the NJHI and NJLO samples of each disease subgroup. Gene sets with a unidirectional fold-change and adjusted P value <0.05 were considered to be enriched gene sets associated with neojunction load. Splicing -related gene sets were selected based on keyword searches. Leading edge genes shared across all disease subgroups within the same gene set are defined as enriched genes associated with neojunction load.

[0139] 1. Vaubcl, R.A., Tian, S., Rcmondc, D., Schroeder, M.A., Mladck, A.C., Kitangc, G.J., Caron, A., Kollmeyer, T.M., Grove, R., Peng, S., et al. (2020). Genomic and Phenotypic Characterization of a Broad Panel of Patient-Derived Xenografts Reflects the Diversity of Glioblastoma. Clin. Cancer Res. 26, 1094-1104. Edwards, N.J., Oberti, M., Thangudu, R.R., Cai, S., McGarvey, P.B., Jacob, S., Madhavan, S., and Ketchum, K.A. (2015). The CPTAC Data Portal: A Resource for Cancer Proteomics Research. J. Proteome Res. 14, 2707-2713. Kahles, A., Lehmann, K.-V., Toussaint, N.C., Hiiser, M., Stark, S.G., Sachsenberg, T., Stegle, O., Kohlbacher, O., Sander, C., Cancer Genome Atlas Research Network, et al. (2018). Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients. Cancer Cell 34, 21 l-224.e6. Jones, L.E., Hilz, S., Grimmer, M.R., Mazor, T., Najac, C., Mukherjee, J., McKinney, A., Chow, T., Pieper, R.O., Ronen, S.M., et al. (2020). Patient-derived cells from recurrent tumors that model the evolution of IDH-mutant glioma. Neurooncol Adv 2, vdaaO88. Stathias, V., Jermakowicz, A.M., Maloof, M.E., Forlin, M., Walters, W., Suter, R.K., Durante, M.A., Williams, S.L., Harbour, J.W., Volmar, C.-H., et al. (2018). Drug and disease signature integration identifies synergistic combinations in glioblastoma. Nat. Commun. 9, 5315. Jermakowicz, A.M., Rybin, M.J., Suter, R.K., Sarkaria, J.N., Zeier, Z., Feng, Y., and Ayad, N.G. (2021). The novel BET inhibitor UM-002 reduces glioblastoma cell proliferation and invasion. Sci. Rep. 77, 23370. Zhang, Y., Liu, Y., Moxley, K.M., Golden-Mason, L., Hughes, M.G., Liu, T., Heemskerk, M.H.M., Rosen, H.R., and Nishimura, M.I. (2010). Transduction of human T cells with a novel T-cell receptor confers anti-HCV reactivity. PLoS Pathog. 6, el001018. Bossi, G., Gerry, A.B., Paston, S.J., Sutton, D.H., Hassan, N.J., and Jakobsen, B.K. (2013). Examining the presentation of tumor-associated antigens on peptide -pulsed T2 cells. Oncoimmunology 2, e26840. Hosken, N.A., and Bevan, M.J. (1990). Defective presentation of endogenous antigen by a cell line expressing class 1 molecules. Science 248, 367-370. Chandran, S.S., Ma, J., Klatt, M.G., Diindar, F., Bandlamudi, C., Razavi, P., Wen, H.Y., Weigelt, B., Zumbo, P., Fu, S.N., et al. (2022). Immunogenicity and therapeutic targeting of a public neoantigen derived from mutated PIK3CA. Nat. Med. 28, 946-957. Britten, C.M., Meyer, R.G., Kreer, T., Drexler, I., Wolfel, T., and Herr, W. (2002). The use of HLA-A*0201 -transfected K562 as standard antigen-presenting cells for CD8(+) T lymphocytes in IFN-gamma ELISPOT assays. J. Immunol. Methods 259, 95-110. Suhoski, M.M., Golovina, T.N., Aqui, N.A., Tai, V.C., Varela-Rohena, A., Milone, M.C., Carroll, R.G., Riley, J.L., and June, C.H. (2007). Engineering artificial antigen-presenting cells to express a diverse array of co-stimulatory molecules. Mol. Ther. 75, 981-988. 13. Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 75, 550.

[0140] 14. Matsushima, S., Ajiro, M., lida, K., Chamoto, K., Honjo, T., and Hagiwara, M. (2022). Chemical induction of splice-neoantigens attenuates tumor growth in a preclinical model of colorectal cancer. Sci. Transl. Med. 14, eabn6056.

[0141] 15. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102, 15545-15550.

[0142] 16. Blum, A., Wang, P., and Zenklusen, J.C. (2018). SnapShot: TCGA-Analyzed Tumors. Cell 173, 530.

[0143] RESULTS

[0144] Characterization of public, intratumorally -conserved neojunctions

[0145] To investigate the expression of neojunctions, RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) was first utilized to identify nonannotated splicing reads across various cancer types (Figure 2A). The analysis was applied to primary tumor samples with corresponding spatially mapped tumor RNA-seq data (Figures 2B). These included sequencing data sets from glioblastoma (GBM; n=167), low-grade glioma (LGG; n=516), lung adenocarcinoma (LU AD, n=517), lung squamous cell carcinoma (LUSC, n=501), mesothelioma (MESO, n=516), liver hepatocellular carcinoma (LIHC, n=371), stomach adenocarcinoma (STAD, n=415), kidney renal clear cell carcinoma (KIRC; n=533), kidney renal papillary cell carcinoma (KIRP; n=290), kidney chromophobe (KTCH, n=66), colon adenocarcinoma (COAD; n=458), and prostate adenocarcinoma (PRAD; n=497). To minimize healthy tissue cell contamination, only samples with measured tumor purities of 0.60 or higher were utilized within the pipeline (Figure 2C). Protein-coding, nonannotated junctions were identified by selecting for splicing events not characterized in GENCODE (data not shown). A junction’s positive sample rate (PSR) represents the percentage of samples within a cohort expressing the splicing event with a junction read frequency of at least 10% compared to the canonical splicing junction. By focusing on robustly expressed neojunctions that are shared between patients, a filter for public neojunctions that demonstrate elevated interpatient PSRs within their tumor cohort (PSRTCGA > 10%) was added (Figure 2D). Following previous neojunction nomenclature, splicing events expressed with a PSR lower than 1% in normal tissue RNA- sequencing data derived from the Genotype-Tissue Expression (GTEx) project (n=9166; PSRGTEX < 1%) were selected for (data not shown). As a result, an average of 373 neojunctions and 202 public neojunctions were identified (Figure 2E) across TCGA tumor types, all of which were expressed at similar prevalence. (Figure 2F). Further characterization of public neojunctions across tumor types demonstrated a predominance of alternative 3’ and 5’ splice site changes (Figure 2G), and the proportion of frame shift-generating ncoj unctions remains consistent across tumors (Figure 2H). While the expression of putative public neojunctions is largely disease- specific (not shown), hierarchical clustering reveals subgroups of shared neojunctions expressed consistently across multiple cancer types (Figure 21). These results indicate a significant conservation of neojunction expression between patients and highlight a subset of interpatient-conserved neojunctions expressed across multiple cancer types.

[0146] Intratumorally-conserved neojunctions are identified through multi-site analysis

[0147] Immunotherapy approaches targeting a single TSA may not be adequate in eradicating tumors, resulting in the repopulation of the tumor by clones lacking the targeted neoantigen. In the case of targeted T-cell therapies, this underscores the importance of focusing on multiple neoantigens shared by the entire malignant compartment to prevent immune evasion by antigen loss. Neojunctions have previously been shown to generate immunogenic antigens, and to that end, the ITH of public neojunctions was explored by mapping for neojunction reads across multiple samples within the same tumor (Figure 3A). RNA- sequencing data derived from multiple intratumoral neoplastic samples in prostate, liver, colon, stomach, kidney, and lung were analyzed to investigate whether public neojunctions persist across the tumor landscape in various cancer types (data not shown). Analysis of intratumor multi-site datasets revealed subsets of public neojunctions expressed in multiple samples across the tumor landscape, some of which were found in all biopsies and were considered tumor-wide (Figure 3B). The identification of public neojunctions expressed across multiple samples within the same tumor suggests the persistence of splice event subsets across the tumor landscape. In datasets containing multiple cases with multi-site sampling, public spatially conserved neojunctions were expressed readily across all cases (Figure 3C). Targeting these neojunctions, which were intratumorally conserved across a multitude of patients, would offer a promising strategy for overcoming ITH in various cancer types.

[0148] The notoriously predominant heterogenous nature of CNS tumors is further evidenced by studies with single-cell RN A- sequencing data from IDHl-wild type and IDH1 -mutant gliomas revealing the presence of numerous subclones with branched evolutionary trajectories. While publicly-available multi-site RNA sequencing provided data from a handful of intratumoral biopsies, it is difficult to conclude whether a few samples are sufficient in fully recapitulating a tumor’s genomic landscape, especially in highly heterogeneous tumors, such as gliomas. As such, an in-house dataset was established that greatly expanded the number of intratumoral biopsies analyzed across all glioma subtypes. Building upon the previous dataset, approximately 10 maximally distanced, spatially-mapped biopsies across 56 LGG and GBM tumors for whole-exome sequencing and RNA sequencing were evaluated (Figure 3D). Similar to multi-region studies in other tumor types, the expression of neojunctions was detected intratumorally across multiple patients. Hierarchal clustering revealed neojunction subsets associated with mutant IDH1 subtypes, wild-type IDH1 type subtypes, or abundantly expressed across both subtypes (Figure 3E). When investigating putative expression (junction reads > 10 reads) of the 789 total neojunctions initially identified across both LGG and GBM TCGA cases, 774 (98.10%) and 547 (69.33%) neojunctions were demonstrated to be detectable in more than one region within at least one tumor and 10% of a glioma subtype cohort, respectively (Figure 3F). This indicates that the majority of the public neojunctions identified from TCGA LGG / GBM analysis are consistently expressed in multiple tumor regions across the comprehensive patient database. Neojunctions present in multiple but not all regions of the tumor were defined as intratumorally conserved neojunctions (icNJs). Neojunctions detectable in all biopsies within a single tumor were termed tumor- wide neojunctions (twNJs): remarkably, 280 (34.49%) of the characterized neojunctions were robustly expressed tumor-wide within at least one case, half of which (n=141, 17.87% of the total) were found tumor-wide in two or more cases (Figure 3G). These findings highlight a novel class of neojunctions that appear throughout the tumor landscape in multiple patients, establishing a promising new repertoire of targets for cancer therapy. Public neojunctions are detectable on an RNA and peptide level in patient-derived tumor samples

[0149] I was next sought to validate whether the expression of public neojunctions and their protein derivatives can be detected in patient-derived transcriptomic and proteomic data. The analysis and the remaining portions of the pipeline were focused on investigating gliomaspecific public neojunctions. RNA-seq data of patient-derived GBM 1 / 7=66; obtained from the Mayo Clinical Brain Tumor PDX national resource) and LGG (n=2) cell lines were investigated. RNA-seq for primary patient-derived astrocytoma and oligodendroglioma LGG cell lines were generated in a previous study. In total, 510 and 767 of the previously characterized public neojunctions were detected in GBM and LGG disease types, respectively (Figures 4A, 4B). However, it can be argued that the fragmented reads produced by short-read RNA sequencing lead to difficulties in accurately identifying the complete mRNA structure bearing the spliced aberration. Therefore, primers spanning select neojunctions and their flanking exons were designed and amplicon sequencing on the PCR-amplified products was performed to detect continuous reads harboring select neojunctions.

[0150] In order to evaluate the extent to which neojunctions are translated into proteins, mass spectrometry (MS) data derived from glioma patients (n=447) in publically available MS data sets was analyzed. The analysis confirmed the expression of 49 unique neojunction- specific peptide sequences that map back to 75 (9.5%) unique public neojunctions (Figure 4C). It was confirmed that these detected peptide sequences span the aberrantly spliced regions with sequence specific searches within the MS data and subsequent analysis of the resulting MS spectra (data not shown). When considering both RNA-sequencing and MS confirmation of glioma- specific neojunctions, the presence of 44 (5.6%) public neojunctions expressed across all patient-derived samples was validated(Figure 4D). Overall, these findings demonstrate the recurrence of public neojunctions across multiple transcriptomic and proteomic platforms.

[0151] Tumor-wide neojunctions encode neoantigens predicted to be processed and presented by MHC-I

[0152] Reasoning that a fraction of neojunctions are translated and presented as targetable neoantigens, it was investigated whether the characterized public neojunctions can generate peptides that are loaded onto MHC-I following proteasomal processing. Neojunction-derived sequences from TCGA were translated in silico to generate a neojunction-derived protein database. Iterating through all possible n-mers of 8 to 11 amino acids (Figure 4E), tumorspecific n-mers were defined as those absent in normal tissue peptide sequences in a reference human proteome dataset (n= 67,364; UniProt Proteome ID #UP000005640). Past studies typically focus on MHC-binding affinity as the primary determinant for peptide-MHC presentation and immune response. Despite this, presentation prediction based solely on binding affinity data often overlooks factors beyond HLA-binding affinity, such as endogenous peptide processing steps.

[0153] To address these oversights and adhere to the step-wise biological processes of proteasomal processing followed by binding to MHC, two independent prediction algorithms were incorporated into the pipeline to identify neoepitope sequences that were likely to be processed and presented: MHCflurry 2.0 and HLAthena. (data not shown). Each prediction algorithm displayed robust capabilities in predicting either peptide processing motifs or pan- MHC binding affinities. MHCflurry 2.0’s antigen processing predictor models alleleindependent sequence properties and scored neoantigens favorably if their sequences (combined flanking amino acids and n-mer) are consistent with established motifs for proteasomal cleavage. Meanwhile, HLAthena’ s prediction algorithm is modeled using a large peptidome dataset comprised of 95 cell lines expressing single HLA class I alleles, allowing for independent HLA binding motif characterization. To identify putative public neoantigen candidates detectable across the diverse patient profile the binding potential of the n-mer candidates were examined against the known most commonly expressed human leukocyte antigen-A (HLA- A) haplotypes across a wide range of demographics. As such, this analytical pipeline investigates the presentation likelihood of neoantigen candidates against HLA- A*01:01, HLA-A*02:01, HLA-A*03:01, HLA-A*ll:01, and HLA-A*24:02. To select high- binding targets, n-mer candidates that scored in the top 10% of both HLAthena and MHCElurry 2.0 algorithms were focused on (Figures 4F-4G). Candidates that yielded these scores (rz=832) were expected to be readily processed and presented in glioma and were subsequently retained for downstream analysis (data not shown). Mapping these top candidates to their originating neojunctions, 315 neoantigen-encoding junctions (NEIs; 39.92% of the originally characterized public neojunctions) were determined to produce cancer-specific peptide sequences containing top n-mer candidates. While a greater number of top-scoring n-mer candidates are generated from frameshifts and alternative 3’ splice site loss (data not shown), presentation scores remained relatively consistent across n-mer candidates generated from NEJs with or without frameshift mutations or any particular alternative splice type (Figures 4H-4I). To further narrow the NEJ candidate list, the 315 NEJs were cross-referenced with the 44 neojunctions that were previously characterized as shared across transcriptomic and proteomic platforms (Figure 4D). Of the 44 neojunctions, 20 were characterized as NEJs through this analysis (data not shown), and were focused on the downstream analyses on NEJs that generate candidates that bind strongly to HLA-A*02:01 (n=8) due to the HLA allele’s high prevalence across populations and its extensively characterized nature in neoantigen studies (Figure 4J). When characterizing the ITH of these eight neojunctions across the spatially mapped dataset, high intratumoral conservation can be seen in most of these NEJs, particularly the neojunction located within GNAS (NJGJV ') (Figure 4K). These findings demonstrate that public spatially conserved neojunctions can potentially generate MHC-I-presented isoforms that can be targeted by immunotherapeutic approaches.

[0154] NEJ-specific T-cell receptors can be isolated from neoantigen-reactive CD8+ T-cells

[0155] To determine whether NEJ-derived neoantigens can be recognized by T-cells, in vitro sensitization (IVS) was performed to stimulate and identify a neoantigen-reactive CD8+ T-cell population from healthy donor-derived peripheral mononuclear cells (PBMCs) by neoantigen- stimulated increases in IFNy (Figure 5A). The initial analysis was focused on 4 out of the 8 top NEJ candidates (NJGAAS, NJWOOA6, NJT?PL22, NJ TCF12) that were determined to generate multimers with high binding affinity to HLA-A*02:01 (Figure 4J-4K). Due to the vast diversity of the TCR repertoire, it is theoretically expected that any individual can harbor a naive T-cell repertoire specific against any MHC:peptide complex. Following that logic, IVS of naive CD8+ T-cells was performed against neoantigen-pulsed autologous monocyte-derived dendritic cells (moDCs) collected from HLA-A*02:01+ healthy donors (n=5) to ultimately retrieve TCR gene sequences that confer specificity against these neoantigens. T-cells were sensitized against mature dendritic cells pulsed with either NEJ-derived neoantigen multimers, influenza multimer positive control, or no peptide over the course of three 10-day cycles. To determine whether a neoantigen-reactive CD8+ T-cell subset has expanded at the final timepoint, the sensitized CD8+ T-cells were cultured against T2 cells loaded with exogenously administered neoantigen or influenza peptides. T2 cells are deficient in a peptide transporter involved in antigen processing (TAP), and as such, they are often used as a model for probe recognition of T-cells to exogenous antigens in a noncompetitive manner. Subsequent IFNy and TNFa ELISA screens on the corresponding T2:CD8+ conditions revealed neoantigen- specific immunogenicity in 2 out of the 4 public NEJ-derived neoantigens: NCOARPL22 and NBOAGNAS (Figure 5B). These results indicate that NEJ-specific CD8+ T-cell subsets exist within the T-cell repertoire of healthy donors and can be amplified in vitro.

[0156] To characterize and identify the TCR sequences reactive to the NEJ-derived multimcrs, the peptide-pulsed-T2:CD8+ T-cell co-culture assay was repeated on RPL22A3LIF - and GNASA3LFS-reactive CD8+ T-cell populations and performed combined single-cell V(D)J and RNA sequencing on all co-cultured conditions. IFNy and TNFa ELISA assays were additionally performed with the supernatant of the same co-culture conditions to confirm each neoantigen’s immunogenicity against the CD8+ T-cell populations (data not shown). Neoantigen-specific TCR clonotypes were associated with significantly elevated IFNG, TNFA, GZMB signatures when cultured with the corresponding neo antigen-pulsed APCs but not decoy peptide-pulsed or non-pulsed APCs. Through this method, seven NBOARPL22 -specific TCRs were identified, two from Donor 3 (TCRR3.7 and TCRR3.9) and five from Donor 4 (TCR R4.5, TCR R4.6, TCR R4.7, TCR R4.9, and TCR R4.H), and one NeoAGNAS-specific TCR from Donor 4 (TCRG4.I) (Figure 5C). While only one NeoAGNAS-specific TCR clonotype was characterized, this same clonotype was the most proliferated TCR clone, expanding to over 4% of the TCR repertoire in the CD8+ T- cell population (Figure 5D). These findings demonstrate that neoantigen- specific CD8+ subsets from donor-derived PBMCs expand upon recognition of MHC-I-presented multimers, allowing for the accurate determination of neoantigen- specific TCR sequences.

[0157] NEJ-specific TCRs exclusively recognize NEJ-derived neoantigens in an MHC-I-restricted manner

[0158] To further establish the specificity of the identified TCRR3.9 and TCRG4.I -reactive T-cell clones, transgenic CD8 -expressing, TCR-null triple-reporter (TR) Jurkat76 cells and PBMC- derived CD8+ T-cells were transduced with lentiviral (LV) vectors encoding the TCR a- and P- chains. Jurkat76 cells are TCR a- and P-negative and CD4-null human T-cell derivatives that allow for the noncompetitive introduction of exogenous TCRs. Jurkat76 cells used in this study express transgenic CD8The TR Jurkat76 cells have previously been transduced with transcriptional reporter constructs such that the response elements of NFAT, NF-KB, and AP-1 drive the respective expression of eGFP, CFP, and mCherry. Therefore, TCR activation can be evaluated by simultaneously quantifying fluorophore expression levels (Figure 5E). TCR- transduced TR Jurkat76 cells cultured with T2 cells pulsed with varying concentrations of neoantigen demonstrated a dose-dependent reactivity (Figures 5F). Both TCR demonstrated neoantigen-recognition against even low levels of peptide presentation (1 nM), illustrating high immunogenicity of the neoantigen target and high avidity of the corresponding TCR. The antigen- specificity of these reactions was supported by negligible TCR activation in the presence of maximal levels of decoy peptide presentation (1 pM). TCR-transduced PBMC- derived CD8+ T-cells demonstrated similar dose-dependent neoantigen- specific behavior (Figure 5G, 5H). TCR-transduced CD8+ T-cells were stained for surface expression of CD137 and CD107a to evaluate T-cell stimulation and proliferation by flow analysis. T-cell activation was observed at neoantigen-peptide pulsing concentrations as low as 1 pM (Figure 5G). Similarly, IFNy and TNFa expression levels measured by ELISA suggested strong specificity by both TCRs as indicated by their EC50 between 0.01 to 0.1 nM (Figure 5H). To verify that T- cell activation is mediated by MHC-I-restricted presentation of the neoantigen, neoantigen- pulsed T2 cells were treated with an HLA -blocking antibody prior to co-culture with the TCR- transduced TR Jurkat76 cells. As expected, activation was completely blocked with the anti- HLA-I antibodies (Figure 51). Finally, alanine scanning mutagenesis was performed on both TCRs to determine whether there are other known human proteins that share the peptide motif recognized by these TCRs. Single alanine mutations were introduced at every position within the NCOARPT.22 and NCOAGNAS n-mer epitopes. TCR-transduced triple-reporter Jurkat76 cells were cultured against residue-substituted neoantigen isoforms, and key residues were characterized by the diminished TCR activation (Figure 5J). Alterations in stability of peptide binding to HLA-A*02:01 indicates that the substituted residue was critical for HLA-A*02:01 binding or TCR recognition. Referencing the key residue sequence to a normal human proteome library (UniProt Proteome ID #UP000005640) demonstrated that there are no known human proteins that share the same set of key amino acid residues for TCR recognition, indicating that the TCRs specifically recognize the cancer-specific region of the neoantigen sequence. These results reveal TCRs that exclusively recognize public NEJ-derived neoantigens with robust sensitivity and highlight a potential immunotherapeutic approach utilizing TCR-engineered T- cells in targeting this novel neoantigen repertoire. EJ-de rived neoantigens are endogenously translated, processed, and MHC-I-presented

[0159] It was next validated that neojunction-derived transcripts are effectively translated, processed by the proteasome, and presented at the cell surface by MHC-I. Presentation of NEJ- derived neoantigens was evaluated by two separate approaches: functional TCR-recognition and HLA-immunoprccipitation (HLA-IP) followed by subsequent liquid-chromatography-MS / MS (LC-MS / MS) (Figure 6A). COS7 or K562 cells were transfected with mRNAs encoding HLA- A*02:01 and the full-length mutated transcript. COS7 and K562 cells lack HLA expression, allowing for the noncompetitive transgenic introduction of any HLA of interest.

[0160] To demonstrate that expression of the neojunction at the transcript level can ultimately lead to a neo antigen-mediated immune response, COS7 cells transfected with the same HLA and full-length mutated transcript were co-cultured with TCR-transduced TR Jurkat76 cells. COS7 cells were incubated for 24 hours following mRNA electroporation to allow for optimal expression before the introduction of TCR-transduced TR Jurkats76. Both transfected COS7 cell lines led to neoantigen-specific activation of TCRR3. and TCRG4.I -transduced TR Jurkat76 cells, demonstrating that the identified public NEJs undergo all major biological steps of proteolytic cleavage and MHC-I binding prior to MHC-I-restricted presentation and recognition (Figure 6B-C). Affinity-column-based immunopurification of HLA-I ligands was performed on HLA and mutant transcript-transfected COS7 cells. MS analysis showed that the same NCOAGXAS multimer candidate was identified as a high-confidence and high-abundance HLA- A2-bound peptide in HLA-A*02:01 and full-length NJev / is-transfected COS7 cells. Likewise, both NCOARPL22 multimers were detected with high confidence in HLA-A*02:01, and NJPP 22- transduced COS7 cells, with the higher scoring NCOARPL22 9-mer identified with higher abundance (Figure 6D). These results align with the earlier in silico predictions for proteasomal processing and HLA-binding (data not shown).

[0161] TCR-transduced CD8+ T-cells mediate cytotoxic effects against glioma cells expressing NEJ- derived neoantigens

[0162] Based on the impressive sensitivity of neoantigen- specific TCR (Figures 5F, 5G), it was anticipated that intrinsic levels of public NEJ expression in tumor cells would be sufficient to elicit a neoantigen- specific immune response. To test this hypothesis, the TCRR3.9- and TCRG4.I- transduced triple reporter Jurkat76 cells were co-cultured with IFNy-prctrcatcd HLA-A2+ LGG and GBM cell lines (data not shown) that were previously determined to express either NJft / .22 or NJGV in RNA sequencing data. As expected, flow analysis following co-culture showed tumor- specific immune reactivity mounted by both TCR-transduced CD8+ T-cell lines against the studied GBM cell lines (Figure 6E). To evaluate whether this same neoantigen recognition is sufficient in causing tumor- specific killing, hybridized formats of TCRR3.9 and TCRG4.I were transduced into PBMC-dcrivcd CD8+ T-cclls. Constant regions of TCRs specific to NCOARPL22 or NCOAGNAS were replaced with a murine constant region to allow for accurate pairing of transgene-derived alpha and beta chains, a highly transduced population was selected for by flow sorting for CD8+ T-cells stained positive with murine constant region Ab. Tumor- specific cytotoxicity against endogenously-expressed NJ / w7.22 and NJGTVAS was assessed by co-culturing TCR-transduced CD8+ T-cells with a glioma cell line, GBM115, which demonstrated consistent proliferation and seeding profiles. As a positive cytotoxicity control, neoantigen peptide-loaded GBM 115 cell lines were used to define maximum cell killing. At a 1:1 effectortarget ratio, TCRR3. and TCRG4.I -transduced CD8+ T-cells efficiently demonstrated cytotoxic killing of 37.18% and 61.52% of glioma cells, as detected using an impedance-based cell growth cytotoxicity assay (xCELLigence) (Figure 6F). Increasing the effectontarget ratio to 2:1 increases the potency of both clones, with 42.97% and 82.74% of tumor cells killed by TCRR3.9 and TCRG4.i-transduced CD8+ T-cells, respectively (Figure 6F). To confirm that tumor- specific killing is initiated by TCR recognition of the MHC:peptide complex, HLA -I-blocking Ab was introduced into the co-culture conditions. The presence of anti-HLA-I antibodies blocked killing compared to isotype controls (Figure 6G). It was conclude that NEJs are endogenously processed and presented at sufficient levels in tumor cells for initiating tumor- specific cytotoxicity by neoantigen-specific CD8+ T-cells

[0163] Disease subtype-specific factors drive differences in neojunction expression

[0164] The next interest was investigating the differences in putative neojunction expression across all glioma subtypes. Across both TCGA and the in-house spatially-mapped RNA-seq platforms, it was revealed that the average expression of putative neojunctions is significantly greater in mutant IDH1 gliomas compared to their wild-type variant (Figures 7A, 7B). What was further noteworthy was the dichotomy of neojunction expression when comparing wildtype 1DH1 (IDHlwt), mutant 1DH1 astrocytoma (ZD7 7mut-A), and mutant 1DH1 oligodendroglioma ( / DH7mut-0) glioma subtypes. While / £> / / / mut- A gliomas demonstrated significantly higher average levels of neojunction expression compared to IDHlwt gliomas, IDH1 mut-0 neojunction expression far exceeds that of the other disease subtypes (Figure 7C, 7D). Pairwise Pearson correlation analyses that explored the prevalence of neojunction expression in conjunction with somatic mutations in commonly mutated splice factors revealed mutations in several splice factors that co-occurrcd with IDH1 mutations (data not shown). FUBP1 mutations have previously been reported as prevalent in IDHlmut-0 glioma subtypes. However, hierarchal clustering of neojunctions revealed no significant trend of neojunction expression with the mutation status of FUBP1, SF3A1, or NIPBL (data not shown). Prior studies have reported that dysregulation of individual splicing factors can result in aberrant splicing, and to that end, aberrations in the expression levels of splicing-related genes in relation to the generation of neojunctions was explored. To investigate possible drivers for the observed glioma-subtype differences in neojunction expression, differential gene expression analysis (DESeq2) was performed on RNA sequencing data from all three disease subtypes from TCGA. Gene set enrichment analysis (GSEA) was subsequently performed on the DESeq2 results and differentially expressed gene sets were investigated (data not shown). GSEA highlighted significantly upregulated splicing-related gene sets in mutant IDH1 cases compared to their wild-type counterpart across Gene Ontology Biological Processes (GOBP) (Figure 7E) and Gene Ontology Cellular Component (GOCC) databases (Figure 7F). When ordered based on increasing neojunction expression, splicing-related genes highly expressed in either mutant IDH1 disease types largely clustered together (data not shown).

[0165] To further investigate specific splicing-related genes that may lead to increased neojunction expression in mutant IDH1 gliomas (Figures 7G-7H), GOBP splicing-related genes (ra=24) with a statistically significant (p < 0.05) 1.5-fold increase in expression in mutant IDH1 cases compared to wild-type were selected for(Figure 7G). Notably, CELF2 and ELAVL4 have previously been reported to generate splice aberrations when overexpressed. To this end, correlation analyses of the expression of CELF2 against the expression of all 789 public neojunctions identified through the pipeline was employed. It was identified that a greater percentage of neojunctions whose expression generally increased (average Pearson correlation coefficient > 0.10) with the expression of both splicing-related genes across all glioma subtypes (Figure 71). Of the 789 neojunctions, 359 (45.5%) neojunctions increased in expression with CELF2 expression as opposed to the 81 (10.3%) neojunctions that tended to decrease in expression with increasing levels of CELF2. Selecting the highest correlating neojunction associated with both splicing-related genes (data not shown), it was confirmed that the expression of these neojunctions was significantly amplified in mutant IDH1 cases (Figure 7J). siRNA-mediated knockdown of CELF2 in mutant IDH1 cell lines, SF10417 and SF10602 was then pcrformcd(data not shown), and it was observed that decreased expression of the associated neojunction across both cases (Figure 7K). These findings suggest that neojunction prevalence is associated with disease subtype- specific dysregulated expression of splice-regulated genes and that modulation of these genes can effectively lead to changes in neojunction levels.

[0166] When revisiting GOBP splicing-related gene sets (data not shown), subclusters of genes were significantly downregulated in 7 H7mut-O cases. Most of the genes found within these clusters reside on either chromosome Ip or 19q. A distinctive diagnostic feature of IDH1 mut-0 gliomas is the co-deletion of chromosomes Ip and 19q. As such, it was corroboratory to witness the consistently decreased expression of chromosome Ip-based splicing-related genes. To evaluate whether the downregulation of these genes could lead to the characteristic increase in putative neojunction expression seen in the IDH1 mut-0 subtype, GOBP splicing-related genes (n=26) with a statistically significant (p < 0.05) 1.5-fold decrease in expression in IDH1 mut-0 cases compared to both 7DH7mut-A and IDHlw cases were selected for (Figure 7H). Of these splicing genes, disruption of proper SNRPD2 expression was previously reported to lead to aberrations in splicing. Correlation analysis of SNRPD2 expression against the expression of the 789 neojunctions across all glioma subtypes supports the hypothesis that decreased SNRPD2 expression leads to greater neojunction expression. Of the 789 neojunctions, 385 (48.8%) neojunctions increased in expression with decreased SNRPD2 expression compared with the 93 (11.8%) neojunctions that tended to increase in expression with increasing levels of CELF2. Notably, siRNA knockdown of SNRPD2 in GBM115 cell lines (data not shown) led to a significant increase in the expression levels of the associated neojunction across three replicates (Figure 7L). This illustrates that the loss of splicing-related gene expression due to the codeletion of chromosomes Ip and 19q can lead to the upregulation of certain neojunctions. These findings illustrate that certain components of splicing machinery can be targeted to potentially increase levels of neojunction expression and ultimately neoantigen expression.

[0167] DISCUSSION

[0168] While a plethora of neoantigen-discovery pipelines have confirmed immune cell reactivity against tumor- specific antigens, few studies consider the ITH of their targets, and no study to date has investigated the ITH of neoantigens derived from RNA splicing aberrations. These results pioneered the discovery of public intratumorally conserved neojunctions that generate tumor-specific peptides for presentation on HLA-I molecules, and it was demonstrated that recognition of these neoantigens can lead to tumor- specific killing. In validating the immunogenicity of NIGNAS and the specificity of TCRG4.I, it was successfully identified that a public tumor- wide NEJ that is robustly expressed intratumorally across the patients (Figure 4K). When characterizing neoantigens, their persistence throughout the tumor landscape is often overlooked, and in this study, a novel subset of NEJs is highlighted that can be found tumorwide through multiple biopsied sites. Incorporating intratumoral conservation of neoantigen expression in the computational pipeline offers the advantage of addressing the challenges of ITH across multiple cancer types. The endogenous processing and presentation of NCOARPL22 and NCOAGNAS further prove that the novel in silico pipeline accurately predicts tumor-specific peptide candidates that are properly processed by the proteasome and strongly bind to MHC-I.

[0169] While only the immunogenicity of 4 out of the 8 top HLA-A*02:01 -presented neoantigens was investigated, the NEJs with candidates that successfully demonstrated immunogenicity against specific TCR clones, NJRPL22 and NJGNAS, were identified in other cancers in the pan-cancer study as neojunctions, as well. Notably, certain pan-cancer studies with large cohorts of multi-site samples indicate the expression of NJRPL22 across multiple samples within the same tumor and the tumor-wide expression of NJGNAS in multiple tumors (Figure 3C). The higher expression level of the canonical GNAS allele than RPL22 seems to largely contribute to the prevalence of NJGNAS detected across all studies (data not shown). This supports the observation of greater immunogenicity and tumor- specific killing by TCRG4.I (Figures 7F-H) as there is a greater frequency in the generation and presentation NCOAGNAS. These findings indicate that the public NEJs identified in the study, notably NJGNAS, can be targeted in cancer types beyond gliomas. This study studied the dysregulated expression of splicing -related genes associated with the IDH1 mutation in gliomas. Mutations in IDH1 and IDH2 are prevalent in various types of cancer beyond gliomas, including acute myeloid leukemia (AML), cholangiocarcinoma, chondrosarcoma, and sinonasal undifferentiated carcinoma, and angioimmunoblastic T cell lymphoma. Further analysis can determine whether similar splice-related genes are dysregulated in these cancers and whether their aberrant expression leads to a similar increase in neojunction levels. Similarly, this question pertains to mutations in splicing factors frequently found in tumors. While the analysis of mutant FUBP1 indicated moderate changes in ncojunction expression (data not shown), it was anticipated that splicing factor mutations in various cancers would lead to the generation of splicing anomalies.

[0170] Overall, this study highlights RNA splicing aberrations as an unanticipated robust source of public and intratumorally-conserved TSAs that the immune system can recognize. The ability to target these tumor-wide neoantigens with engineered T-cells enables a powerful therapeutic approach that tackles the challenges of ITH that many neoantigen-based immunotherapies struggle with. Ultimately, the results from this study could allow us to design effective vaccine panels comprising identified tumor-wide neoantigen targets and engineer T-cell-based modalities that target intratumorally-conserved splice-derived antigens across a wide range of cancer types

[0171] Example 2

[0172] This example includes some information from the above examples, but also includes additional information.

[0173] RESULTS

[0174] Characterization of public, pan-cancer neojunctions

[0175] To investigate neojunction expression, RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) was first utilized to identify nonannotated junction reads across cancer types (data not shown). To assess ITH, 12 tumor types with multiple spatially mapped tumor samples were included (data not shown). Only samples with inferred tumor purities of > 60% were included (data not shown) when identifying protein-coding, nonannotated junctions (data not shown). A junction’s positive sample rate (PSR) represents the percentage of samples within a cohort expressing the neojunction with a junction read frequency >1% compared to the canonical splicing junction. This allows us to define public neojunctions as those demonstrating elevated PSRs within each TCGA tumor cohort (PSRTCGA > 10%) (data not shown). Following neojunction nomenclature, cancer-specific splicing events were selected as those with a PSR <1% in normal tissue from the Genotype-Tissue Expression (GTEx) project (n=9166; PSRGTEX < 1%) (data not shown). An average of 94 public ncojunctions per TCGA tumor type were identified (data not shown), which were expressed at similar frequencies (data not shown). Further characterization of public neojunctions across tumor types demonstrated varying distributions of splice type (data not shown) and consistent proportions of frame shift-generating splicing events (data not shown). Subsets of these neojunctions are detectable in recent splicing studies (data not shown). Unbiased hierarchical clustering revealed that cases from the same tumor type tend to cluster together, suggesting similar neojunction expression profiles across patients with the same cancer type. A subset of neojunctions were expressed in multiple tumor types (data not shown). Thus, neojunction expression can be conserved among patients across multiple cancer types, suggesting potential pan-cancer immunotherapy targets produced by aberrant splicing.

[0176] Neojunctions exhibit intratumoral heterogeneity

[0177] Immunotherapy approaches targeting a single TSA might be inadequate to eradicate tumors due to the outgrowth of cancer cells without the TSA. For targeted T-cell therapies, this underscores the importance of focusing on multiple neoantigens shared by the entire tumor to prevent immune evasion by antigenic evolution. Neojunctions can generate immunogenic antigens. To that end, the ITH of public neojunctions was explored by filtering for neojunction reads across multiple samples from the same tumor (data not shown). RNA-seq data from multiple intratumor samples from prostate, liver, colon, stomach, kidney, and lung cancers were analyzed to investigate whether public neojunctions are present throughout the tumor landscape (data not shown). This analysis revealed public neojunctions expressed in multiple intratumoral samples (data not shown), and public spatially-conserved neojunctions were identified across all cases in datasets with multi-site sampling, further revealing the ideal neojunction targets (data not shown). Of the cancer types analyzed in this study, gliomas have a notoriously greater degree of ITH that further complicates immunotherapy. It is important to determine the number of samples required to fully represent a tumor’ s transcriptomic landscape. Therefore, the number of intratumoral biopsies across the three main glioma subtypes was increased. Approximately 10 maximally distanced, spatially-mapped samples from each of 51 tumors with exome and RNA-seq was evaluated (data not shown), and detected expression of neojunctions intratumorally across multiple patients. Iterating from one to ten samples, the number of ubiquitously expressed ncojunctions inversely correlated with the number of samples (data not shown). These findings highlight the critical need for sampling multiple biopsies per tumor to more confidently characterize neojunctions as tumor-wide.

[0178] Hierarchal clustering of the large intratumoral dataset revealed neojunction subsets that tend to associate with either mutant isocitrate dehydrogenase (IDHmu ) or wild-type (IDHwt) subtypes (data not shown). Interestingly, IDHmut gliomas expressed a significantly greater number of tumor-wide neojunctions than IDHwt gliomas. While the proportion of tumor-wide neojunctions is significantly less than other sub-clonally-expressed neojunctions across patients (data not shown), at least one tumor-wide neojunction in 45 (88.2%) of the patients was detected (data not shown), with 13 (25.5%) patients expressing over 50 tumor-wide neojunctions (data not shown). While 774 (98.1%) of the TCGA-characterized LGG and GBM neojunctions were detectable in more than one region of at least one tumor in the cohort, 37 (4.7%) neojunctions were found across all samples in more than 10% of the study cohort, (data not shown). Therefore, the majority of the public neojunctions identified from the TCGA LGG / GBM analysis are expressed in multiple tumor regions, but not tumor-wide, in the multi-sampled tumor dataset. This finding suggests combining neojunctions could be a reasonable approach to cover an entire tumor landscape.

[0179] Spatially and temporally-conserved neojunctions were characterized at metastasis and recurrence, respectively. Analysis of public cutaneous melanoma (SKCM) RNA-seq data revealed 13 (9.6%) neojunctions that were detected across metastatic sites in at least one patient (data not shown). When examining matched primary / metastasis pairs in TCGA, it was observed that 43.8% to 72.6% of neojunctions identified in primary tumors persist in metastases across COAD, PRAD, and SKCM cancers (data not shown). Similarly, an average of 36.4% of neojunctions were conserved at recurrence when investigating matched primary / recurrence pairs across TCGA COAD, GBM, LGG, LIHC, and LU AD cancers (data not shown). In the glioma dataset, 79.2% and 82.3% of neojunctions were conserved in gliomas at recurrence following temozolomide treatment (data not shown). Altogether, these findings demonstrate that neojunctions can persist across space and time.

[0180] Disease subtype-specific factors drive differences in neojunction expression

[0181] Noting the subtype- specific expression of neojunctions across glioma subtypes in both TCGA and the spatially-mapped data set (data not shown), dysregulation in splicing machinery that may contribute to these patterns was investigated. While prior investigations report a potential role of the IDH mutation in splicing aberrations, this study revealed much more complexity. Both TCGA and the spatially mapped data sets showed significantly greater total numbers of public neojunctions per case in IDH mutant than IDH wild-type gliomas (data not shown). While ZDHmut-A gliomas demonstrated significantly higher average levels of neojunction expression than IDHvA gliomas, / DHmut-0 neojunction expression was even greater (data not shown). Pairwise Pearson correlation analyses was performed to explore whether neojunction expression is associated with somatic mutations in commonly mutated RNA splicing factors (data not shown). A high correlation was found between FUBP1, SF3A1, or NIPBL mutations with the IDH mutation. FUBP1 mutations are prevalent in 7DHmut-0 gliomas. However, hierarchal clustering of neojunctions revealed no significant trend of neojunction expression with FUBP1, SF3A1, or NIPBL mutation status (data not shown). Dysregulation of individual splicing factors can result in aberrant splicing. Based on these findings, it was investigated whether aberrations in the expression levels of splicing-related genes correlate with neojunction production. To investigate possible drivers for the glioma-subtype differences in neojunction expression, differentially expressed gene sets on the three glioma subtypes from TCGA were evaluated (data not shown). Gene set enrichment analysis highlighted significantly upregulated splicing-related gene sets in mutant IDH cases compared to their wild-type counterpail across Gene Ontology Biological Processes (GOBP) (data not shown) and Gene Ontology Cellular Component (GOCC) databases (data not shown). When ordered based on neojunction expression, splicing -related genes highly expressed in both mutant IDH tumor subtypes largely clustered together (data not shown).

[0182] To further investigate specific splicing-related genes that may lead to increased neojunction expression in IDHmut gliomas (data not shown), GOBP splicing-related genes (n=24) with a statistically significant (p < 0.05) 1.5-fold increase in expression in IDHmut cases compared to wild-type were selected (data not shown). Notably, CELF2 has previously been reported to generate splice aberrations when overexpressed. The correlation analyses of the expression of CELF2 against the expression of all 789 public neojunctions identified a greater percentage of neojunctions whose expression generally increased (average Pearson correlation coefficient > 0.10) with the increasing CELF2 expression across all glioma subtypes (data not shown). Of the 789 neojunctions, 359 (45.5%) neojunctions increased in expression with CEEF2 expression as opposed to the 81 (10.3%) ncojunctions that tended to decrease in expression with increasing levels of CELF2. Selecting the highest correlating neojunction associated with this splicing- related gene, siRNA-mediated knockdown of CELF2 was performed in two patient-derived IDH mutant cell lines, SF10417 and SF10602 (data not shown), and observed a trend of decreased expression of the associated neojunction across both lines (data not shown). 244 neojunctions that are significantly upregulated in IDH mutant compared with wild-type glioma cases (log2 fold change > 1.5, p-value < 0.05) were characterized, a subset of which were detected in other TCGA IDHmut cancer types (data not shown). RNA-sequencing analysis demonstrated an associated decrease in the expression of 19 (8.6%) and 28 (12.7%) ZDHmut-associated neojunctions, respectively, in SF10417 and SF10602 following CELF2 knockdown compared to non-treated controls (data not shown) and a correlative increase in a candidate IDHmut neojunction expression with increased expression of ZDHmut-associated splicing-related genes (data not shown). These findings suggest that neojunction prevalence is regulated by altered expression of RNA-binding proteins in tumor subtypes and that modulation of these genes can lead to changes in neojunction levels.

[0183] When re-examining GOBP splicing-related gene sets (data not shown), Subclusters of genes significantly downregulated in IDHmut-0 cases were discovered. Most of the genes found within these clusters reside on either chromosome Ip or 19q, co-deletion of which is a distinctive diagnostic feature of IDHmut-0 gliomas. To evaluate whether the downregulation of these genes could lead to the characteristic increase in putative neojunction expression seen in the IDHmut-0 subtype, GOBP splicing-related genes (77=26) with a statistically significant (p < 0.05) 1.5-fold decrease in expression in IDHmut-0 cases compared to both IDHmut-A and IDHwt cases were selected (data not shown). Of these splicing genes, disruption of normal SNRPD2 and SF3A3 expression was previously reported to lead to splicing aberrations. Correlation analysis of SNRPD2 and SF3A3 expression against the expression of the 789 neojunctions across all glioma subtypes supported the hypothesis that decreased SNRPD2 and SF3A3 expression might contribute to greater neojunction expression (data not shown). Of the 789 neojunctions, 385 (48.8%) increased in expression with decreased SNRPD2 expression compared with the 93 (11.8%) neojunctions that tended to increase in expression with increasing levels of CELF2. Similarly, with increasing levels of SF3A3 expression, 178 (22.6%) neojunctions tended to increase in expression, and 127 (16.1%) neojunctions tended to decrease in expression. Notably, siRNA knockdown of either SNRPD2 or SF3A3 in the GBM115 cell line (data not shown) led to a significant increase in the expression levels of their associated neojunctions (data not shown). 52 / DHmut-O-associated neojunctions were also characterized as those were significantly upregulated in mutant IDH-Q glioma cases compared to their mutant IDH-A and wild-type counterparts (log2 fold change > 1.5, p-value < 0.05). Increased expression of 7 (13.5%) and 4 (7.7%) / DHmut-O-associated neojunctions were found in GBM115 cells treated with SF3A3 or SNRPD2 siRNA, respectively (data not shown). While previous studies linked splicing factor mutations to neojunctions in cancers, these results shed light on a previously undescribed mechanism showcasing that decreased wild-type splicing factor expression consistently generates neojunctions. These findings suggest that commonly altered components of the RNA splicing machinery in gliomas are mechanistically linked to increased neojunction expression.

[0184] Finally, this analysis was expanded across the remaining TCGA cancer types used in this study to identify tumor subtypes with significantly dysregulated neojunction expression. While neojunction expression remained relatively consistent across SKCM, KIRP, KICH, and PRAD cancers (data not shown), the iCluster 3 and iCluster6 subtypes within TCGA LIHC and LU AD, respectively, demonstrated significantly differentiated neojunction expression compared with other iCluster subtypes (data not shown). Gene set enrichment analysis of the six LU AD iCluster subtypes revealed decreased expression of splicing-related gene pathways. Notably, 23 of these splicing-related gene sets were consistently downregulated in LU AD iCluster 6 compared with all five other iCluster subtypes. Together, these results indicate that in addition to splicing factor mutations, dysregulated expression of canonical splicing-related genes can lead to the generation of disease-specific neojunctions. Public neojunctions are detectable on an RNA and peptide level in patient-derived tumor samples

[0185] It was next sought to validate whether expression of public neojunctions and their protein products can be detected in cell line transcriptomic data and tumor tissue proteomic data. For this analysis, public neojunctions expressed by gliomas were focused on as these tumors display a high degree of ITH and poor clinical outcomes. GBM patient-derived xenografts (PDX) (n=66) and LGG (n=2) cell lines were first investigated. In total, the expression of 767 (97.2%) and 510 (64.6%) of the characterized public ncojunctions were measured in GBM and LGG, respectively (data not shown). Bulk RNA-seq has limited reads spanning each splicing aberration. We, therefore, designed primers spanning a subset of neojunctions and their flanking exons, performed deep amplicon sequencing, and confirmed mRNA expression of neojunctionspanning reads expressed within glioma cell lines (data not shown).

[0186] To test whether neojunctions are translated into proteins, mass spectrometry (MS) data from glioma patients (n=447) was analyzed using publicly available MS data sets. This analysis detected expression of neopeptides that map back to 302 (38.3%) unique public neojunctions (data not shown). It was confirmed that these peptide sequences span the aberrantly spliced regions with sequence-specific searches within the MS data and subsequent analysis of the resulting MS spectra (data not shown). Interestingly, 41.7% of the detected peptides mapped back to neojunctions that result in frame shifts (data not shown), indicating that splicing aberrations inducing a frame shift can lead to detectable levels of translated peptides. The peptidome analysis determined that neojunction-encoding transcripts are actively translated into detectable levels of protein products. When considering both RNA-seq and MS confirmation of glioma-specific neojunctions, the presence of 192 (24.3%) public neojunctions expressed across all patient-derived samples was validated (data not shown). These findings demonstrate the existence of recurrent public neojunctions that generate tumor- specific polypeptides.

[0187] Tumor-wide neojunctions encode neoantigens predicted to be processed and presented by HLA

[0188] Reasoning that a subset of translated neojunctions could be presented as targetable neoantigens, it was investigated whether the 789 characterized public neojunctions can generate peptides that are loaded onto human leukocyte antigen (HLA) class I following proteasomal processing. All properly translated neojunction-derived sequences from TCGA were translated in silica to generate a neojunction-derived protein data set. Iterating through all possible n-mers of 8 to 11 amino acids (data not shown), tumor- specific n-mers were defined as those absent from a UniProt reference normal human tissue proteome dataset.

[0189] Prediction of HLA class I-presented peptides needs to integrate key aspects of antigenpresentation machinery, including peptide processing and HLA binding. To this end, two independent prediction algorithms were incorporated to identify neoepitope sequences that were likely to be processed and presented: MHCflurry 2.0 and HLAthena (data not shown). To rank candidate public ncopcptidcs, the binding potential of n-mcr candidates against the most prevalent HLA-A types expressed across a wide range of demographics was assessed. Amongst the 36 demographic ally predominant HLA-A alleles (data not shown), these analyses investigated the presentation likelihood of neoantigen candidates by HLA-A*01:01, HLA- A*02:01, HLA-A*03:01, HLA-A* 11:01, and HLA-A*24:02. Together, these alleles are expressed by the majority of the global population. To select high-binding targets, n-mer candidates that scored in the top 1% with both algorithms were focused on (data not shown). Candidate n-mers that yielded these scores (n=832) were predicted to be processed and presented in glioma, and were retained for downstream analysis (data not shown). Mapping these top candidates to their originating neojunctions, it was determined that 315 neopeptide- encoding neojunctions (NEJs; 39.9% of the originally characterized public neojunctions) produced cancer- specific peptide sequences containing top n-mer candidates. While a greater number of top-scoring n-mer candidates are generated from frameshifts and alternative exonic 3’ splice sites (data not shown), presentation scores remained relatively consistent across n-mer candidates generated from NEJs with or without frameshift mutations or any specific splice type (data not shown). To further narrow the NEJ candidate list, the 315 NEJs were cross-referenced with the 192 neojunctions that were previously characterized as being shared across transcriptomic and proteomic platforms (data not shown). Of the 192 neojunctions, 81 were characterized as NEJs through this analysis (data not shown), with many NEJs encoding multiple strongly predicted candidates. The downstream analyses was focused on 32 candidate NEJs that were predicted to bind strongly to HLA-A*02:01 due to this allele’s high prevalence across North American and European populations and the ability to benchmark to other neoantigen studies (data not shown). When ITH of these 32 neojunctions was investigated in the data set from spatially mapped samples, high intratumoral conservation was observed for most of these NEJs, particularly the two nucleotide A3 loss-encoding neojunction located within GNAS (NJGA'AS) (data not shown). These findings demonstrate that intratumorally conserved public neojunctions may generate HLA-presented neopeptides.

[0190] NEJ-reactive T-cell receptors can be isolated from donor CD8+T-cells

[0191] It was next sought to determine whether NEJ-derived neopeptides can drive T-cell immunogenicity. In vitro sensitization (IVS) was performed to identify neoantigen-reactive CD8+T-cell populations from healthy donor (HD)-derived peripheral mononuclear cells (PBMCs) (data not shown). The initial analysis was focused on a subset (n=4) of the 32 top NEJ candidates predicted to generate high-affinity binders to HLA-A*02:01 (data not shown). IVS of naive CD8+T-cells against neopeptide-pulsed autologous monocyte-derived dendritic cells (moDCs) collected from HLA-A*02:01+HDs (n=5) was therefore performed with the goal of retrieving TCR gene sequences that confer specificity against these neoantigens. Subsequent IFNy ELISA assays on the corresponding APC:CD8+conditions revealed neoantigen-reactive immunogenicity in two out of four of the public NEJ-derived neoantigens (NeoAs): NCOARPL22 and NCOAGNAS (data not shown). NCOAGNAS results in an A3 loss of 2 nucleotides that generates a frame-shift and premature stop codon. NCOARPL22 encodes for an in-frame A3 loss of 6 nucleotides, resulting in a loss of two amino acids in an alpha helix (data not shown). These results additionally indicate that NEJ-reactive CD8+T-cells can exist within the naturally occurring human T-cell repertoire.

[0192] To retrieve TCR gene sequences that confer reactivity to these neoantigens, the peptide-pulsed- APC:CD8+T-cell co-culture on NCOARPL2 - and NeoAcNAS-reactive CD8+T-cell populations was repeated and combined single-cell V(D)J and RNA-seq was performed. Neoantigen- reactive TCR clonotypes were associated with significantly elevated IFNG. TNFA, GZMB transcripts in neoantigen peptide-specific manners. Using this method, seven NCOARPL22- reactive TCRs were identified, two from Donor 3 (TCRR3.7 and TCRR3.9) and five from Donor 4 (TCR R4.5, TCR R4.6, TCR R4.7, TCR R4.9, and TCR R4.H), and one NeoAcNAS-reactive TCR from Donor 4 (TCRG4.I) (data not shown). While only one NeoAcNAS-reactive TCR clonotype was characterized, this same clonotype was the most proliferated TCR clone, expanding to over 4% of the TCR repertoire in the CD8+T-cell population (data not shown). The expansion of neoantigen-reactive CD8+T-cell clones suggests a strong immunogenic proponent of these two neoantigens. NEJ-reactive TCRs recognize NEJ-derived neoantigens in an HLA-restricted manner

[0193] To further test the specificity of the identified TCRR3.9 and TCRG4.I -reactive T-cell clones, TCR- null triple-reporter (TR) Jurkat76 cells which express the CD8a / b heterodimer (Jurkat76 / CD8) or PBMC-derived CD8+T-cells were transduced with lentiviral vectors encoding the retrieved TCR a- and fl-chains. The TR Jurkat76 / CD8 cells have response elements for NF AT, NF-KB, and AP-1 which drive expression of eGFP, CFP, and mCherry, respectively (data not shown). TCR-transduced TR Jurkat76 cells cultured with T2 cells pulsed with varying concentrations of ncoantigcn peptide demonstrated dose-dependent reactivity (data not shown). Both TCRs demonstrated nM-level neoantigen-recognition, illustrating a relatively high functional avidity of the corresponding TCRs. The antigen- specificity of these receptors was supported by negligible TCR activation in the presence of supraphysiologic levels of the control peptide ( 1 p.M). TCR-transduced PBMC-derived CD8+T-cells displayed similar dose-dependent neoantigen-specific behavior (data not shown). TCR-transduced CD8+T-cells were stained for surface expression of the T-cell activation and degranulation markers, CD137 and CD107a, to quantify markers of T-cell activation and effector function, respectively. T-cell activation was observed at neoantigen-peptide concentrations as low as 1 pM (data not shown). Similarly, IF y and TNFa expression levels measured by ELISA suggested strong potency of both TCRs as indicated by their half-maximal effective peptide concentrations (ECsos) between 0.01 to 0.1 nM (data not shown). Treatment of neoantigen-pulsed T2 cells with an HLA-blocking antibody prior to co-culture with the TCR-transduced TR Jurkat76 cells validated that neopeptide T-cell activation is HLA-dependent (data not shown).

[0194] Next, alanine scanning mutagenesis was performed to determine whether either NEJ-reactive TCR can recognize polypeptides derived from off-target normal human proteins. TCR- transduced triple-reporter Jurkat76 / CD8 cells were cultured against residue-substituted neoantigen isoforms, and key residues were defined by those that result in diminished TCR activation (data not shown). Alterations in the recognition of a variant peptide indicate that the substituted residue was critical for TCR recognition. Referencing each TCR’s peptide recognition motif to a normal human proteome library (UniProt Proteome ID #UP000005640) demonstrated that no known human proteins share the key residues required for TCR recognition. Together, these results reveal TCRs that exclusively recognize NEJ-derived public neoantigens with robust sensitivity and highlight a potential immunotherapeutic approach utilizing TCR-engineered T-cells to target this novel class of shared neoantigens.

[0195] Finally, it was determined whether circulating NEJ-reactive CD8+T-cell populations could be detected in glioma patients by immune monitoring studies on archived PBMC samples obtained from three HLA-A*02:01 glioma patients with known expression of NEJGNAS (data not shown). NEJGNAS IVS of these PBMC samples led to the detection of an immunogenic response in 1 of 3 glioma patients with no immunogenicity against an irrelevant HLA-A*02-restricted 9-mer ncoantigcn dcxtramcr control (Figure 9A). These findings further support the immunogenicity and potential clinical application of targeting NEJ-derived neoantigens.

[0196] NEJ-derived public neoantigens are endogenously processed and presented by HLA Next, it was tested whether neojunction-derived transcripts generate peptides that are functionally presented by HLA and recognized by reactive TCRs. The presentation of NEJ- derived neoantigens was evaluated using two approaches: functional TCR-recognition and HLA-immunoprecipitation followed by liquid-chromatography-MS / MS (data not shown). To determine whether the neojunction transcript expression leads to immune recognition, COS7 cells transfected with the HLA-A2 and full-length mutated transcript were co-cultured together with either TCR-transduced TR Jurkat76 or CD8+T-cells. TCRR3.9 and TCRG4.i-transduced TR Jurkat76 and CD8+T-cells reacted against COS7 cells transfected with their respective neoantigen, demonstrating endogenous processing and presentation of the public NEJs (data not shown). Affinity-column-based immunopurification of HLA-I ligands was then performed on COS7 cells co-transfected with HLA / mutant transcript. The MS analysis identified the same NCOAGXAS peptide as the highly abundant HLA-A2-bound peptide with high-confidence. Likewise, both NCOARPI.22 neopeptides were detected with high confidence on COS7 cells cotransfected with HLA-A*02:01 and NJ / p / .22 with the higher scoring NCOARPL22 9-mer identified with higher relative abundance (data not shown). Furthermore, the HLA-A*02:01 -restricted NCOAGNAS peptide could be detected in an unmodified GBM cell line (GBM115) (data not shown). This finding demonstrates that physiological levels of neojunction expression in tumor cells are sufficient to generate a NEJ-derived neoantigen. Together, these experimental observations confirm the in silica predictions for proteasomal processing and HLA-binding (data not shown). TCR-transduced CD8+T-cells mediate cytotoxicity against glioma cells expressing NEJ- derived public neoantigens

[0197] Based on the sensitivity of neoantigen-specific TCR (data not shown) and the endogenous presentation of NEJ-derived neoantigens by tumor cells (data not shown), it was anticipated that physiological levels of public NEJ expression in tumor cells would elicit a neoantigen- specific cytotoxic T-cell response. The cytotoxicity of TCR-transduced CD8+T-cells against HLA- A*02:01+tumor cells endogenously expresssing NJRPL22 and NJ was evaluated. As a positive control, ncoantigcn pcptidc-pulscd tumor cells were used to define maximum cell killing. At a 1:1 effector:target ratio, TCRR3.9 and TCRG4.I -transduced CD8+T-cells mediated TCR- dependent cytotoxicity against GBM115 cells (data not shown). TCRG4.I -transduced CD8+T- cells mediated comparable levels of tumor killing against a second glioblastoma cell line, GBM102 and two melanoma cell lines, RPMI-7951 and WM-266-4 (data not shown). Adding an HLA-I blocking antibody partially blocked killing compared to an isotype control, verifying tumor cell killing is initiated by TCR recognition of the HLA:peptide complex (data not shown). Co-culture of TCRG4 i-transduced CD8+T-cell with an HLA-A2-negative, NJGAMS expressing GBM cell line (Mayo PDX GBM39) revealed HLA-A2 dependent cytotoxicity (Figure 10A). These results illustrate that the recognition and killing of NEJ-expressing tumor cells is mediated by HLA-dependent neoantigen presentation. Relative to non-transduced CD8+T-cells, increased surface expression of CD107a and CD137 on TCR-transduced CD8+T-cells cocultured with tumor cells further confirmed neoantigen-specific T-cell activation (Figure 10B). Together, these data indicate that NEJs are endogenously processed and presented at sufficient levels to enable tumor- specific cytotoxicity by neo antigen- specific CD8+T-cells.

[0198] DISCUSSION

[0199] Our analysis of cohorts of multi-site samples indicated the expression of NJRPL22 across multiple samples within the same tumor. Most notably, NJGNAS was expressed tumor-wide in diverse tumor types, including glioma, mesothelioma, prostate cancer, and hepatocellular carcinoma (data not shown). The discovery of a targetable tumor-wide neoantigen in GBM provides a novel potential therapeutic approach for this devastating disease. The higher expression level of the canonical GNAS allele over RPL22 may contribute to the prevalence of NJGNAS detected across all analyses. This supports the observation of greater immunogenicity and tumor- specific killing by TCRG4.I (data not shown) as there is a greater frequency in generating and presenting NCOAGXAS. Indeed, a NJcNAS-specific CD8+T-cell population could be detected in the circulation of an HLA-A*02:01+glioma patient whose tumor exhibited NJGNAS expression (data not shown)

[0200] The dysregulated expression of splicing-related genes associated with the increased expression of neojunctions in / D / 7- mutated gliomas was also investigated. Mutations in IDH and IDH2 are prevalent in other cancers, including acute myeloid leukemia (AML), cholangiocarcinoma, chondrosarcoma, sinonasal undifferentiated carcinoma, and angioimmunoblastic T cell lymphoma. This study demonstrated dysregulation in splicing factor expression in different disease types and that these aberrations lead to significant changes in neojunction production. In the case of IDH mutant oligodendrogliomas, SNRPD2 expression is decreased due to the characteristic co-deletion of chromosomes Ip and 19q, and targeted knockdown increased neojunction expression. This suggests that components of the RNA splicing machinery are mechanistically linked to the generation of neojunctions. Inhibiting these components in future studies can potentially bolster targetable NEJ-derived neoantigen expression.

[0201] METHODS

[0202] Data and code availability

[0203] Spatially-mapped biopsy RNA sequencing data will be deposited. HLA-IP and LC-MS / MS data will be deposited and made publicly available at the date of publication. Additionally, singlecell V(D)J sequencing data of identified TCRs will be deposited and made publicly available as of the date of publication. Accession numbers are listed in the key resources table. All original code for the identification of tumor-wide public neojunctions have been deposited at GitHub (https: / / github(.)com / dakwok / SSNIP) and are publicly available as of the date of publication. Any additional information required to reanalyze the data reported in this paper is available from the lead contacts upon request.

[0204] Human clinical datasets

[0205] The intratumoral multi-region sampling cohort for various cancer types utilizes RNA sequencing data from the following studies:

[0206] 1. This paper, for multi-region sampling of glioblastoma and low-grade glioma . Yang et al. (Genome Medicine, 2022), for multi-region sampling of hepatocellular carcinoma.

[0207] 3. Joung et al. (PLoS One, 2016), for multi-region sampling of hepatocellular carcinoma, stomach adenocarcinoma, renal cell carcinoma, and colon adenocarcinoma.

[0208] 4. Ku et al. (Briefings in Bioinformatics, 2021), for multi-region sampling of prostate cancer.

[0209] 5. Meiller et al. (Genome Medicine, 2021), for multi-region sampling of mesothelioma.

[0210] 6. Bakir ct al. (Nature, 2023), for multi-region sampling of non-small cell lung cancer

[0211] Analysis of neojunction expression within multi-region samples were conducted immediately with the neojunction prediction pipeline if the FASTQ file is available. If RNA- sequencing data is only available in BAM format, the sequencing file is converted into FASTQ format utilizing the Picard software (version 2.7.7a). Neojunction prediction is detailed in the Method Details section.

[0212] Data download

[0213] Bulk RNA- sequencing data for glioblastoma (GBM; n=167), low-grade glioma (LGG; n=516), lung adenocarcinoma (LU AD, n=517), lung squamous cell carcinoma (LUSC, n=501), mesothelioma (MESO, n=516), liver hepatocellular carcinoma (LIHC, n=371), stomach adenocarcinoma (STAD, n=415), kidney renal clear cell carcinoma (KIRC; n=533), kidney renal papillary cell carcinoma (KIRP; n=290), kidney chromophobe (KICH, n=66), colon adenocarcinoma (COAD; n=458), and prostate adenocarcinoma (PRAD; n=497) samples were downloaded from TCGA in FASTQ format. Download of intratumoral multi-region sampling sequencing data is detailed in the previous section. Similarly, bulk RNA- sequencing data for 9,166 normal tissue samples in FASTQ format were downloaded from the Genotype-Tissue Expression (GTEx) repository. Bulk-RNA sequencing data for 66 patient-derived GBM cell lines were received from the Mayo Clinic Brain Tumor Patient-Derived Xenograft National Resource. Proteomics data for 100 GBM samples were downloaded from the Clinical Proteomic Tumor Analysis Consortium (CPTAC).

[0214] RNA sequencing alignment

[0215] All downloaded RNA- sequencing data sets were individually aligned using a STAR alignerbased processing pipeline. Using the STAR software (version 2.7.7a), a genome index containing non-annotated junctions through the initial alignment pass of the input data was constructed. The complete set of command line parameters: — runThreadN 1 \ — outFilterMultimapScoreRange 1 \ — outFilterMultimapNmax 20 \ — outFilterMismatchNmax 10 \ — alignlntronMax 500000 \ — alignMatesGapMax 1000000 \ — sjdbScore 2 \ — alignSJDBoverhangMin 1 \ — genomeLoad NoSharedMemory \ — limitBAMsortRAM 80000000000 \ -readFilesCommand gunzip -c \ — outFilterMatchNminOverLread 0.33 \ -- outFilterScoreMinOverLread 0.33 \ — sjdbOverhang 100 \ — outSAMstrandField intronMotif \ — outSAMattributcs NH HI NM MD AS XS \ — limitSjdblnscrtNsj 2000000 \ — outSAMunmappcd None \ — outSAMtype BAM SortedByCoordinate \ — outSAMheaderHD @HD VN1.4 \ — twopassMode Basic \ — outSAMmultNmax 1 \ and aligned using the GRCH37 STAR index file. TCGA sample selection and gene expression quantification

[0216] TCGA tumor samples with an absolute tumor purity greater than 0.60 were retained downstream in silico analysis. (Aran et al. 2015, Ceccarelli et al. 2016) Non-mitochondrial, protein-coding transcripts defined by the Ensembl Homo Sapiens GRCH37.87 gene annotation gene transfer format (GTF) file were selected and this curated set was utilized to select and retain protein-coding transcript isoforms within the TCGA RNA-sequencing data. Transcriptlevel expression data (log2[RSEM-TPM+0.001]) for all TCGA samples were downloaded from the UCSC Xena Toil-pipeline and transformed into standard TPM values. Protein-coding transcript isoforms with a median TPM > 10 were retained for downstream analysis. In the case of glioma TCGA cases, subsequent expression data in TPM was subset into 6 disease type categories: all cases (n=429), GBM cases (n=l 15), LGG cases (n=314), IDHl-WT cases (n=166), IDH1-M JT astrocytoma cases (n=140), / DH / -MUT oligodendroglioma (zz=123). Protein-coding transcript isoforms with a median TPM > 10 in at least one of the 6 disease types were retained for further analysis.

[0217] Characterization of public neojunctions

[0218] For public cancer-specific splicing event counting, a custom R script was designed that detected and quantified non-annotated, cancer-specific splicing events found across each corresponding patient cohort. From the output files derived from STAR aligner in the previous step, alternative splicing events were quantified in detected junction counts within the corresponding sj. out. tab file. Splicing events detected in the GRCh37.87 GTF sj.out.tab (GENCODE v33) file were removed to define non-annotated splicing junctions. Non-annotated splicing junctions that overlap non-mitochondrial, protein-coding genes identified in the previous step were retained for continued analytical processing. All splicing junctions with less than 10 of its target spliced reads (count) or less than 20 total spliced reads (depth) over the whole cohort were removed. Similarly to previous studies, spliced frequency was computed as the sum of the total number of target spliced reads divided by the collective sum of spliced reads from the target and canonical junctions. Splicing junctions with a read frequency greater than 1% were retained for downstream analyses. Public splicing junctions were defined as ones that were putatively expressed with the aforementioned criteria of total read count, read depth, and read frequency across at least 10% of the studied patient cohort and retained those for further analysis. To characterize cancer-specific splicing events, otherwise known as neojunctions, all junctions that putatively expressed with the same parameters in more than 1% of GTEx normal samples were removed.

[0219] Detection of cancer-specific intron retention events

[0220] Intronic splicing events were detected and characterized using IRFinder vl.2.3. RNA sequencing data from TCGA (GBM / LGG) and GTEx (CNS) aligned to GRCh37 (hg 19) were imported into the software for the detection of intron retention events. General linear model (GLM)-based analysis was used for differential intron retention assessment. The intron retention ratio is calculated as (intronic reads / sum(intronic reads, normal spliced reads). Significant intron retention changes are defined as (1) no less than 10% in both directions (2) adjusted p-values less than 0.05. An intron retention event’s PSR within TCGA or GTEx is defined as the number of cases that fulfill these criteria divided by the total number of cases within the cohort. Putative cancer-specific intron retention neojunctions are characterized as intron retention events with a TCGA PSR > 0.10 and a GTEx PSR < 0.01.

[0221] Transcriptomic validation of expressed neojunctions

[0222] Detection of expressed neojunctions in patient-derived GBM / LGG cell lines: RNA sequencing data derived from GBM PDX cell lines were downloaded from the Mayo Clinic Brain Tumor Patient-Derived Xenograft National Resource. Patient-derived LGG cell lines were generated from surgically-resected specimens in University of California, San Francisco’s Neurological Surgery Brain Tumor Center. RNA sequencing data from GBM and LGG cell lines were aligned and processed as described above. Public neojunctions with splice junction counts per million (CPM) > 0 are considered detectable in cell line-derived RNA sequencing data. Detection of expressed neojunctions in multi-region cases: In the cohort of spatially-mapped glioma cases, approximately ten or more maximally-distanced anatomical biopsies were collected from each patient, allowing for intratumoral assessment of genetic heterogeneity via bulk RNA-seq and whole-exome sequencing. Multi-region sequencing data of various other cancer types vary in the number of sampled regions per tumor and are detailed in the corresponding references (data not shown). RNA-sequencing data collected from each multiregion sample was processed and aligned as described above. Putative neojunctions previously characterized from TCGA within each multi-region sampling dataset were searched for. Public neojunctions with CPM > 0 were considered detectable. Public neojunctions with putative expression (> 10 spliced reads) in two or more mapped samples within the same case are considered spatial-conserved neojunctions. Neojunctions detected in all multi-region samples within the same tumor are considered tumor- wide neojunctions.

[0223] Proteomic validation of expressed neojunction-derived peptides

[0224] From the putative neojunctions detected in the above pipeline, a database of all plausible polypeptides derived from all neojunctions was generated. Neojunction-encoding transcripts were generated by mapping the junction coordinates to an hgl9 human genome assembly within the Ensembl annotation database (AH13964, EnsDb.Hsapiens.v75). Prediction of neojunction- derived amino acid sequences were subsequently performed, and appropriately translated sequences (methionine starting residue, removal of sequences following first stop codon) were retained for downstream n-mer iteration. To detect neojunction-derived polypeptides within GBM cases, .RAW files of GBM and LGG MS dta housed in the Clinical Proteomics Tumor Analysis Consortium (CPTAC, n=99), Bader et al. (n=99), Lam et al. (n=92), and Yanovich- Arad et al. (n=84) were analyzed. MaxQuant (v 1.6.17.0) was used to identify tryptic sequences from the corresponding MS data sets. Predicted neojunction-derived peptides, decoy sequences, and a human reference proteome (UniProt Proteome ID #UP000005640) were inputted as a FASTA file into MaxQuant, and tryptic sequences derived from the input file were matched against the publicly available MS databases. Cancer- specific peptides spanning neojunction- derived protein sequences were considered MS-confirmed. The relative detection levels of the neojunction-derived peptides and normal tissue-derived peptides were evaluated by their log2(peak intensities). Aside from the default settings, the following commands and parameters were modified and used for MS analysis in MaxQuant: Digestion mode = Trypsin / P; Max missed = 3; Minimum peptide length = 5; Minimum peptide length for unspecific search = 5. Peptide processing and HLA binding and presentation predictions

[0225] Cancer- specific transcripts with associated neojunctions were translated in silica into their corresponding amino acid sequences. A library of all possible peptides of 8 to 11 amino acids in length was then generated, and cancer- specific sequences were selected by removing those detectable in normal tissue peptide isoforms in a reference human proteome dataset (UniProt Protcomc ID #UP000005640). All cancer- specific peptides with their upstream and downstream flanking sequences (maximum flanking length of 30 amino acids) were independently analyzed and ranked by MHCFlurry 2.0 and HLAthena MSiC. HLA-I binding affinity was assessed against HLA-A*01:01, HLA-A*02:01, HLA-A*03:01, HLA-A*ll:01, and HLA-A*24:02 in both cases. In the HLAthena evaluation of antigen binding and presentation to the corresponding HLA haplotypes, peptides were assigned to alleles by rank with a threshold of 0.1. Context of up to 30 flanking amino acids on both N and C terminus were utilized with aggregation by peptide and no log-transformed expression. Baseline MHCFlurry 2.0 models with both peptide:HLA binding affinity (BA) predictor and antigen processing (AP) predictor was used. Overall, peptide:HLA presentation scores were characterized by mhcflurry_presentation_score and MSiC_HLA scores in MHCFlurry 2.0 and HLAthena, respectively. To select for high-binders, lists of peptide:HLA complexes within the top 10 percentile of scores from both prediction algorithms were curated.

[0226] Cell culture

[0227] GBM PDX cell culture: Patient-derived xenograft (PDX) glioblastoma cell lines, GBM34, GBM43, GBM108, GBM115, GBM118, GBM102, GBM137, GBM148, GBM164, and GBM 195, were obtained from the Mayo Clinic Brain Tumor PDX national resource. Xenograft lines were cultured as by recommended conditions in previous literature and passaged a maximum of 20 times before restoration to earlier passages. Cells were cultured in Dulbecco’s Modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and 1% penicillin and streptomycin (P / S). Cell culture plates were treated overnight at 4 °C with DPBS (with calcium and magnesium) and 10% laminin (Gibco1MCat. #23017015) prior to use. Primary patient-derived GBM / LGG cell culture: Primary patient-derived wildtype IDH1 GBM (SF7996), mutant IDH1 astrocytoma (SF10602), and mutant 1DH1 oligodendroglioma (SF10417) cell lines were previously internally generated from dissociated glioma biopsies and cultured as previously described. Cells were cultured in serum-free, glioma neural stem (GNS) cell medium, which comprises of Neurocult NS-A (STEMCELL Technologies Cat. #05751) supplemented with N-2 supplement (Invitrogen Cat. #17502048), B-27 supplement minus vitamin A (Invitrogen Cat. #12587010), 1% P / S, 1% glutamine, and 1% sodium pyruvate. Prior to immediate use in culture, GNS media is supplemented with 20 ng / mL EGF (Peprotech Cat. #AF-100-15), bFGF (Peprotech Cat. #AF-100-18B), and PDGF-AA (Peprotech Cat. #AF-100- 13A). Similarly with the GBM PDX cell lines, cell culture plates were incubated overnight at 4 °C with DPBS (with calcium and magnesium) and 10% laminin (Gibco™ Cat. #23017015) prior to use. Jurkat76 cell culture: Jurkat76 cells were used as the TCR a- and P-negative human T-cell derivative that allowed for noncompeting introduction of exogenous TCRs. CD8+ Jurkat76 cells were cultured in RPMI supplemented with 10% fetal bovine serum and 1% P / S. T2 cell culture: T2 cells were used in the study to monitor immune cell response to the exogenous antigen of interest in a non-competitive environment. T2 cells are deficient in a peptide transporter involved in antigen processing (TAP), and as such, induction of these cells with exogenously administered peptides allows for their association and presentation by HLA molecules, HLA-A*0102 in particular. T2 cells were cultured in IMDM medium supplemented with 20% FBS. COS7 and K562 cell culture: It was opted to use COS7 (ATCC Cat. #CRL- 1651) and K562 (ATCC Cat. #CCL-243) cell lines as the respective primate and human artificial antigen presentation cell (aAPC) models. These cell lines do not express HLA molecules, which allows for the introduction of the HLA allele of interest. COS7 cells were cultured in DMEM medium supplemented with 10% FBS and 1% P / S. K562 cells were cultured in IMDM medium supplemented with 10% FBS and 1% P / S. THP-1 cell culture: THP-1 cells (ATCC Cat. #TIB-202) were used to investigate immune reactivity against neoantigen presentation by dendritic cells. THP-1 cells were cultured in RPMI- 1640 supplemented with 10% FBS. siRNA-mediated knockdowns of splicing related genes

[0228] Cells were seeded in 2 mL of antibiotic free media in a 6- well plate at the following densities: GBM115 - 45,000 cells / well, SF10417 - 100,000 cells / well, and SF10602 - 100,000 cells / well. Twenty-four hours post seeding cells were transfected by adding 400 uL reaction containing serum free media, 2.0 uL DharmaFECT 1 reagent (Horizon, #T-2001-02), and their respective siRNA pools (4 siRNA equimolar mix) at a final concentration of 30 nM. Twenty-four hours post transfection, media was changed to complete media. At seventy-two hours post transfection, RNAs were isolated and purified via the Zymo Quick-RNA microprep kit (Zymo Research, #R1058).

[0229] Reverse Transcription Quantitative PCR (RT-qPCR)

[0230] 1000 ng of DNAse-treated RNA was converted to cDNA using the iScript cDNA synthesis kit (BioRad, #1708891). This cDNA was then diluted 1:3 using ultrapure, nuclease-free water, and 2 uL was used per qPCR reaction. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed using the Applied Biosystems POWER SYBR Green Master Mix (Applied Biosystems, #4367659). All samples were run in biological triplicates, with technical triplicates for each biological triplicate using the Quantstudio 5 (Thermo Scientific) and all gene expression data were normalized to the housekeeping gene GUSB. The cycling protocol is as follows: 2 minutes at 50 °C, 10 minutes at 95 °C, followed by 40 cycles of 95 °C for 15 s, and 60 °C for 60 s. Dissociation curves were performed to confirm specific product amplification. Primer sequences corresponding to each gene for the mRNA expression analysis were designed using NCBI Primer.

[0231] Amplicon-sequencing for validation of neojunction expression

[0232] RNAs from respective cell lines were isolated and purified via the Zymo Quick-RNA microprep kit (Zymo Research, #R1058). 1000 ng of DNAse-treated RNA was converted to cDNA using the iScript cDNA synthesis kit (BioRad, #1708891). This cDNA was then diluted 1:3 using ultrapure, nuclease-free water, and 2 uL was used per PCR reaction. 16 reactions were carried out per amplicon per cell line using Q5 High-Fidelity 2x master mix (NEB, #M0492L) with primers containing partial Illumina adapters. Reaction mixtures were set up according to manufacturer guidelines. These products were then purified by separation on a 1.0% agarose gel at 100 volts constant for 1 hour and were then purified via the Monarch DNA gel extraction kit (NEB, #T1020L). Purified products were quantified with qubit high sensitivity dsDNA kit (Invitrogen, #Q32851) and prepared and submitted according to Azenta life sciences (Genewiz) guidelines for amplicon- sequencing.

[0233] In-vitro sensitization of healthy donor PBMCs

[0234] HLA-A*02:01:01 -positive PBMCs were purchased from StemExpress in either fresh or cryopreserved format. Fresh PBMCs (StemExpress Cat. #LE001F) of approximately IxlO9cells were immediately proportioned into aliquots of 3 x 108cells and cryopreserved in liquid nitrogen, with one aliquot actively used for downstream IVS. Cryopreserved PBMCs (StemExpress Cat. #PBMNC300C) totaling approximately 3xl08cells per cryovial were used in one vial per IVS procedure. PBMCs were thawed with 1:1000 Benzonase:RPMI (Sigma Aldrich Cat. #E8263). The CD 14+ population was isolated from the PBMCs using CD14+. Miltenyi microbeads (Miltenyi Biotec Cat. #130-050-201) per manufacturer’s instructions. The CD14- flowthrough was cryopreserved for 6 days prior to naive CD8+ T-cell isolation. Isolated CD14+ cells were cultured in CcllGcnix GMP DC medium (CcllGcnix Cat #20801-0500) supplemented with 1% human serum (Sigma Aldrich Cat #H6914), 1% P / S, 1000 U / mL recombinant human IL-4 (Peprotech Cat. #200-04), and GM-CSF (Peprotech Cat. #300-03) in non-treated 24-well plates at a seeding density of 5xl03cells per well. On Day 3, recombinant human IL-4 and GM- CSF (1000 U / mL each) is added to the DC culture. On Day 5, DC culture is matured with 250 ng / mL LPS (Sigma Aldrich Cat. #L6529) in addition to supplementation of recombinant human IL-4 and GM-CSF (1000 U / mL each). Naive CD8+ T-cells were isolated from the thawed CD14- population on Day 6 using the EasySep Human Naive CD8+ T Cell Isolation Kit (STEMCELL Technologies Cat. #19258) as per manufacturer’s instructions. Isolated naive CD8+ T-cells were cultured in X-Vivo 15 medium (Lonza Cat. #04-418Q) supplemented with 5% human serum, 1% P / S, and 10 ng / mL of recombinant human IL-7 (Peprotech Cat. #200-07) in 48-well plates at a seeding density of 5xl05cells per well. On Day 8, adherent matured DCs were harvested from the plate using cold PBS. The collected DCs (IxlO6cells / mL) were exogenously pulsed with either 1 pM of the neoantigen peptide, influenza peptide, or no peptide for 1 hour at 37 °C. The peptide-pulsed or non-pulsed DCs were then co-cultured with naive CD8+ T-cells at an optimal DC:T-cell ratio of 1:4 in 48-well plates. The co-culture was maintained with X-Vivo 15-medium supplemented with 10 ng / mL of recombinant human IL-7, 10 ng / mL recombinant human IL- 15 (Peprotech Cat. #200-15), and 60 ng / mL of recombinant human IL-21 (Peprotech Cat. #200-21) for 10 days with IL-7 and IL-15 restimulation every 2 days. Cells were reseeded into subsequent 24-well, 12-well, and 6-well plates based on confluency. This concludes the first cycle of IVS of the neoantigens and influenza peptides. On Days 19 and 29, sensitized-CD8+ T-cells are reintroduced to a second and third round of stimulation with newly-pulsed DCs, and the co-culture is maintained for 10 additional days until the end of the second and third cycle of IVS. Immunogenic cytokine assays were performed at the end of the second and third cycles of IVS to determine whether a peptide-reactive T-cell population has expanded.

[0235] Mutation-specific ELISA screen

[0236] Aliquots containing CD8+T-cells from individual parent IVS wells were harvested and split equally into 96-well plate daughter wells containing IxlO5cells per well. Daughter wells in triplicate were stimulated with T2 cells pulsed with either the neoantigen peptide of interest, control peptide, no peptide, or no T2 cells at all for 16 hours at an effector-to-target (E:T) ratio of 1 : 1. T2 cells were pulsed with 1 pM to 1 pM of the neoantigen peptide of interest, control peptide, or no peptides for 1 hour at 37 °C. Influenza-reactive T-cells were co-cultured against influenza peptide-pulsed T2 cells as a positive control. Co-culture supernatant was collected and diluted for use in IFNy (BD Biosciences Cat. #555142) and TNFa (BD Biosciences Cat. #555212) ELISAs as per manufacturer’s instructions. ELISA readouts were performed on the Epoch Microplate Spectrophotometer (BioTek Instruments) using the BioTek Gen5 Data Analysis software (version 1.11). Wells with significantly increased expression levels of IFNy and TNFa were selected for downstream single-cell immune profiling using single-cell RNA and V(D)J sequencing.

[0237] Single-cell immune profiling

[0238] Once an expanded neoantigen-reactive CD8+T-cell population from IVS is identified, singlecell RNA and V(D)J sequencing were performed using the lOx Genomics platform. Prior to sequencing, CD8+T-cells from the expanded neoantigen-reactive (ELISA screen-positive) wells were harvested and co-cultured with T2 cells pulsed with 1 pM of the neoantigen peptide of interest, a control peptide, or no peptides at an E:T ratio of 1:1. One co-culture replicate was performed for 3 hours for single-cell RNA sequencing analysis, and another was performed for 16 hours for IFNy and TNFa ELISA confirmation. The final cell concentration was adjusted to approximately I lO4cells / pL with an initial cell viability of at least 90% to maximize the likelihood of achieving the desired cell recovery target. Independent CD8+T-cell and nonpulsed T2 single cultures were sequenced alongside the co-culture conditions for differentiating cell types in the downstream single-cell sequencing analysis. The Chromium Next GEM Single Cell 5’ Reagent Kit v2 (Dual Index) (lOxGenomics, Cat. #CG000331) was used for preparation for single-cell sequencing analysis. Gel bead in emulsions (GEMs) were generated by combining the single cell 5’ gel beads, partitioning oil, and the master mix containing the cells onto the Chromium Next GEM Chip K. Cell lysis and barcoded reverse transcription of RNAs in all single cells were finished inside their corresponding GEM. Barcoded cDNA product was recovered through post-GEM-RT cleanup and PCR amplification. cDNA quality control and quantification were performed on the Fragment Analyzer System (Agilent Technologies). 50 ng of cDNA was used for the construction of the 5’ gene expression library, and each sample was indexed by a Chromium i7 Sample Index Kit. This process was performed on an Illumina NovaSeq 6000 sequencer at the UCSF Institute of Human Genetics (IHG) with a minimum of 20,000 read pairs per cell for the 5’ Gene Expression library. The enriched product was measured by the Fragment Analyzer System. 50 ng of enrichment TCR product was used for library construction. Single-cell V(D)J enriched libraries were subsequently sequenced on the Illumina NovaSeq 6000 with a minimum of 5,000 read pairs per cell for the V(D)J library. Cell Ranger 7.0.0 (lOx Genomics Cloud Analysis) was used to pre-process raw single-cell RNA sequencing and identifying V(D)J clonotypes. The annotation files ‘vdj_GRCh38_alts_ensembl- 3.1.0-3.1.0’ and ‘GRCh38-3.0.0’ were used for demultiplexing cellular barcodes, performing read alignments, and generating feature-barcode matrices. Only cells for which clonotype information was available were retained for downstream analysis. Single-cell gene expression and corresponding V(D)J sequences of candidate T-cell clonotypes were analyzed on the Loupe V(D)I browser. Single cells with detectable CD8A expression were specifically isolated and characterized as the CD8+T-cell population and subsequently grouped according to their TCR clonotypes. To identify T-cell clonotypes associated with a neoantigen-specific response, expanded TCR clonotypes with significantly increased levels of IFNG, TNF, and GZMB expressions in the T-cell:neoantigen-pulsed T2 condition compared to the T-cell:control-pulsed T2 and T-cell:non-pulsed T2 conditions were selected.

[0239] HLA typing

[0240] OptiType 1.3.1 was used for genotyping HLA alleles from available WES data available for glioma cell lines with default parameters.

[0241] Plasmids and peptides

[0242] HLA-A*02:01 and neojunction-derived gene sequences were all synthesized and cloned into the pTwist Lenti SFFV Puro WPRE vector (Twist Biosciences). Constructs encoding full-length and truncated multi-mer versions of the wildtype and mutant GNAS and RPL22 sequences were generated. TCR a / p was synthesized and cloned into the pTwist Lenti SFFV vector (Twist Biosciences). HPLC grade neojunction-derived neoantigen peptide multi-mers (>95%) were manufactured by TC Laboratories.

[0243] Lentiviral transduction

[0244] HEK293T cells were plated in 6-well culture plates at a density of IxlO6cells per well with 2 mL DMEM medium supplemented with 10% FBS without antibiotics. After approximately 18 to 24 hours or at 90% confluency, HEK293T cells were transfected with the expression construct, see above, and lentiviral packaging plasmids, pMD2.G (Addgene, #12259) and psPAX2 (Addgcnc, #12260). TCR a / B transduction: 1.0 pg TCR a / p transfer plasmid, 0.75 pg psPAX2, and 0.25 pg pMD2.G were combined with 200 pL Opti-MEM (Thermo Fischer Scientific Cat. #31985062). 6 pL of Xtremegene HP was added to this mixture and complex formation was allowed to occur for 15 minutes at room temperature at which point this reaction mixture was added to corresponding HEK293T cells. Transfection medium was replaced with fresh DMEM media after 24 hours. Viral supernatant was collected after 48 hours, and a functional vims titer was performed on 6-well plates seeded with Jurkat76 / CD8 cells or PBMC- derived CD8+T-cells at 60-70% confluency. Viral transduction was performed with 3-fold serial dilutions of the vims stock supplemented with polybrene at a final concentration of 4 pg / mL. Media was changed 24 hours following viral transduction. Cells were assessed for transduction efficiency after 3-4 days by measuring surface expression of TCR a / p and CD3 by fluorescence-activated cell sorting (FACS) analysis. Cells demonstrating high double-positive expression of TCR a / p and CD3 were flow-sorted and maintained for downstream co-culture and immunogenicity assays. HLA and neoantigen transduction: Constructs expressing HLA- A*02:01 were linearized and restricted with BamHI and Xhol (New England Biolabs) and purified using the Zymoclean Gel DNA Recovery Kit (Zymo Research Cat. #D4007). The HLA-A*0201 sequence was then ligated into a lentiviral construct downstream of an EFlA-core promoter and upstream of an IRES followed by a Blasticidin resistance gene. 1.0 pg of either HLA-A*02:01 or neoantigen transfer plasmid, 0.75 pg psPAX2, and 0.25 pg pMD2.G were combined with 200 pL Opti-MEM (Thermo Fischer Scientific Cat. #31985062). 6 pL of Xtremegene HP was added to this mixture, and complex formation was allowed to occur for 15 minutes at room temperature at which point this reaction mixture was added to corresponding HEK293T cells. As stated above, neoantigen constructs encode either the full-length or tmneated version of the neojunction-derived peptide. The transfection medium was replaced with fresh DMEM media after 24 hours. HLA-A*02:01 lentiviral transduction and screening was performed first prior to neoantigen lentiviral transduction and screening for streamlined drug selection. Viral supernatant was collected after a subsequent 48 hours, and a functional virus titer was performed on 6-well plates seeded with COS7 or K562 cells at 60-70% confluency. Viral transduction was performed with 3-fold serial dilutions of the virus stock supplemented with 4 pg / mL polybrene. Media was changed 24 hours following viral transduction and replaced with complete media supplemented with blasticidin. Cells were assessed for transduction efficiency after 3-4 days by drug screening. HLA-A*02:01 -transduced APCs were cultured in medium treated with 10 pg / mL Blasticidin for approximately 7 days before assessing for cell viability across titers. Neoantigen-lentiviral transduction is subsequently performed, and APCs transduced with both HLA-A*02:01 and neoantigenexpressing constructs are then cultured in medium treated with 3 pg / mL puromycin for approximately 7 days. Cell viability was assessed afterwards across all titer conditions. Cells were assessed for transduction efficiency after 3-4 days by measuring surface expression of HLA-A2 fluorescence-activated cell sorting (FACS) analysis.

[0245] Dose-dependent assessment of TCR reactivity against neoantigen

[0246] Specificity of neoantigen-reactive CD8+T-cells and TCR-transduced T-cells was assessed by human IFNy (BD Biosciences Cat. #555142), IL-2 (BD Biosciences Cat. #555190), and TNFa ELISA (BD Biosciences Cat. #555212). Assessment of TCR recognition against exogenously introduced neoantigen peptides presented by HLA molecules was conducted by co-culturing T- cells with pep tide-pulsed T2 cell conditions. T2 cells are either pulsed with neoantigen peptide of interest at a concentration between 1 pM through 1 pM, decoy peptide, or no peptides for 1 hour at 37°C. Influenza-reactive T-cells are co-cultured against influenza peptide-pulsed T2 cells as a positive control. T-cells and T2 cells were co-cultured in a 96-well round-bottom plate at a concentration of 1 x 105of each cell type in 200 pL of medium for 16 hours. Supernatant was collected and diluted for cytokine release assays per manufacturer’s instructions. ELISA assay readouts were performed on Epoch Microplate Spectrophotometer (input wavelength 450 nm and output wavelength 570 nm) using the BioTek Gen5 Data Analysis software. To characterize the dose-dependent activation of the TCRs in transduced triple-reporter Jurkat76 / CD8 cells, flow analysis was performed to assess the level of expression of NFAT- GFP, NFKB-CFP, AP-l-mCherry following 16 hours of co-culture. Similarly, reactivity of TCR-transduced PBMC-derived CD8+T-cells was evaluated by flow analysis following anti- CD107a (BioLegend, Cat #328620) and anti-CD137 antibody (4-1BB; Biolegend Cat #309804) staining.

[0247] In vitro transcription (IVT) synthesis of mRN A

[0248] All constructs were subcloned into pcDNA3.1 (Invitrogen, 2520855) and linearized by Xhol restriction enzyme with the plasmid DNA template transcribed downstream from the bacteriophage T7 promoter sequence. For long (> 0.5 kb) and short (< 0.5 kB) transcripts, 1 pg and 0.5 pg of template were used, respectively. Reactions were assembled at room temperature using the mMESSAGE mMACHINE T7 Transcription Kit as per manufacturer’s instructions (Invitrogen, 2582905) and incubated at 37°C for 1 hour for long transcripts and 16 hours for short transcripts. Following DNase treatment, a Poly(A) tailing reaction was performed for 1 hour according to the HiScribe T7 ARCA manual (NEB, E2060S). Subsequently, the synthesized mRNA was purified by LiCl precipitation using 70% DEPC-based ethanol. Synthesized mRNA was heat-shocked (70°C, 5mins) with the formaldehyde loading dye to verify quality via gel electrophoresis. mRNA transfection of HLA-A*02:01, truncated neoantigen, and full-length neojunctionencoding mRNA

[0249] Transfection of IVT- synthesized mRNA into COS7 and K562 cells was performed with electroporation using the Neon Transfection System 100 pL Kit (Invitrogen, MPK10096) per manufacturer’s instructions. 1 x 106COS7 and K562 cells were washed and resuspended with 100 pL of Neon™ Resuspension Buffer. 5 pg of HLA-A2 and 5 pg of candidate (either the truncated neoantigen sequence or the full-length neojunction sequence) mRNA were added into the cell solution. Electroporation was performed on the Neon NxT Electroporation System (Invitrogen, NEON1). Electroporation of COS7 cells was performed with the following optimized conditions: pulse voltage of 1200 V, width of 30 ms, and 2 pulses. Electroporation of K562 was performed with the following optimized conditions: pulse voltage of 1450 V, width of 10 ms, and 3 pulses. Transfected cells were immediately transferred into warm RPMI with no antibiotics. Aliquots of transfected cells were retained for validation of HLA-A2 expression by staining with HLA-A2 monoclonal antibody (BB7.2, Thermo Scientific, 17-9876-42) and subsequent flow cytometry analysis. Evaluation of CR specificity against endogenously processed and HLA-presented neoantigen

[0250] Characterization of neoantigens that are endogenously processed and presented by surface HLA is conducted by co-culturing HLA-A*02:01 / neoantigen-transfected COS7 or K562 cells with TCR-transduced T-cells. Similarly, T-cells and COS7 / K562 cells were co-cultured in a 96-well flat-bottom plate at a concentration of 1 x 105of each cell type in 200 pL of medium for 16 hours. Supernatant was collected and diluted for cytokine release assays per manufacturer’s instructions, and cytokine release levels were assessed with the Epoch Microplatc Spectrophotometer and BioTek Gen5 Data Analysis software. In all cytokine release assay experiments, maximum cellular cytokine release per well was determined by the addition of 0.2 pL Cell Activation Cocktail (without Brefeldin A) (BioLegend Cat. #423302) per 100 pL cell solution. Evaluation of endogenously processed and presented neoantigens in glioma cell lines was performed by co-culturing TCR-transduced triple-reporter Jurkat76 cells with glioma cells at a 1:1 E:T ratio (1 x 105per well in a 96-well plate). Flow analysis was performed to assess the level of expression of NFAT-GFP, NFKB-CFP, AP-l-mCherry following 16 hours of coculture.

[0251] HLA-IP and LC-MS / MS

[0252] COS-7 cells were co-electroporated with 10 pg of each mRNA encoding HLA-A*02:01 allele and the full-length coding sequence of the mutated GNAS or RPL22 using the Neon Transfection system (100-pl tip, setting: 1,050 V / 10 ms / 2 pulses). 20 x 106cells were electroporated per condition and plated in six-well non-TC plates overnight. For the GMB 115 cell line sample, approximately lOOxlO6cells were used. Cells were harvested by incubating with 1 mM EDTA (Millipore Sigma) for 10 minutes at 37 °C. For the immunoprecipitation experiments, cells were lysed in 8 ml of 1% CHAPS (Millipore Sigma) for 1 hour at 4°C, lysates were then spun down for 1 hour at 20,000g and 4°C, and supernatant collected. For the affinitycolumn based immunopurification of HLA-I ligands, 40 mg of cyanogen bromide-activated- Sepharose 4B (MilliporeSigma) was activated with 1 mM hydrochloric acid (MilliporeSigma) for 30 minutes. Subsequently, 1 mg of W6 / 32 antibody (Bio X Cell) were coupled to Sepharose in the presence of binding buffer (150 mM sodium chloride, 50 mM sodium bicarbonate, pH 8.3; sodium chloride) for 2 hours at room temperature. Sepharose was blocked for 1 hour with glycine and washed 3 times with PBS. Supernatants of cell lysates were run over the affinity of column through peristaltic pumps at 6 mL / min flow rate overnight at 4°C. HLA complexes and binding peptides were eluted from the column five times using 1% TFA. Peptides and HLA-I complexes were separated using C18 columns (Sep-Pak C18 1 cc Vac Cartridge, 50 mg of sorbent per cartridge, 37-55-pm particle size, Waters). C18 columns were pre-conditioned with 80% ACN (Millipore Sigma) in 0.1% TFA and equilibrated with two washes of 0.1% TFA. Samples were loaded, washed twice with 0.1% TFA and eluted in 300 pl of 30%, 40% and 50% acetonitrile in 0.1% TFA. All three fractions were pooled, dried down using vacuum centrifugation and stored at -80 °C until further processing. HLA-I ligands were isolated by solid-phase extractions using in-house C18 mini-columns. Samples were analyzed by high- resolution / high-accuracy LC-MS / MS (Lumos Fusion, Thermo Fisher Scientific). COS-7 samples were ran at DDA mode while GMB115 samples at DIA. MS and MS / MS were operated at resolutions of 60,000 and 30,000, respectively. Only charge states 1, 2 and 3 were allowed. The isolation window was chosen as 1.6 Thomson, and collision energy was set at 30%. For MS / MS, maximum injection time was 100 ms with an automatic gain control of 50,000. MS data were processed using FragPipe. Protein FDR was set at 1%. Oxidization of methionine, phosphorylation of serine, threonine and tyrosine, as well as N-terminal acetylation were set as variable modifications for all samples. Samples were searched against a database comprising UniProt Cercopithecus aethiops or Uniprot Human reviewed proteins supplemented with human HLA-A*02:01 allele sequence, mutRPL22 and mutGNAS, as well as common contaminants.

[0253] Characterization of CD8+T-cell-mediated anti-tumor reactivity

[0254] To determine whether TCR-transduced T-cells were capable of mounting an anti-tumor response, TCR-transduced Jurkat76 / CD8 or PBMC-derived CD8+T-cells were co-cultured with patient-derived GBM or LGG cell lines. CD8+ T-cells were isolated from healthy donor-derived PBMCs using the EasySep™ Human CD8+T Cell Isolation Kit (STEMCELL Technologies, Cat. # 17953). CD8+T-cells were then activated with Dynabeads™ Human T-Activator CD3 / CD28 for T Cell Expansion and Activation (Thermo Scientific, Cat. #11161D) at a concentration of 25 pL / 1 x 106cells. CD8+T-cells were cultured for 7 days with IL-7 (30 pL / 1 x 106cells) supplemented every 2 days. CD8+T-cells were then LV transduced with neoantigenspecific TCRs with a hybridized murine TCR constant region using the above transduction procedure. This additional step removes the likelihood of TCR a-chain and -chain mispairing and allows us to evaluate TCR-transduction efficiency by staining with anti-murine TCR constant region antibody (Clone H57-597; BioLegend Cat. #109208). Flow sorting was performed to isolate highly-transduced CD8+T-cells by selecting for cells stained strongly with anti-CD3 and anti-murine TCR constant region antibody. Sorted transduced CD8+T-cells were expanded for 7 days before use in co-culture assays. Killing assays were performed using an xCELLigence RTCA S16 Real-Time Cell Analyzer. Tumor cells were cultured in media pretreated with 100 ng / mL IFNy (Peprotech, Cat. #300-02) for 48 hours and washed twice with PBS prior to seeding. 1 x 104tumor cells were plated per well in a 96- well E-platc (Agilent), and impedance is read for 16 hours during incubation. TCR-transduced CD8+T-cells were introduced to each well at E:T ratio of either 1:1 or 2:1, and tumor- specific killing is measured by changes in cell index over 24-48 hours.

[0255] Identification of HLA restricted CD8+T-cell mediated reactivity against neoantigens Evaluation of HLA restricted reactivity was performed by perturbing TCR and HLA:peptide interaction with the introduction of anti-HLA antibodies. In dose-dependent immunogenicity assays, T2 cells at a concentration of 1 x 105tumor cells per well of a 96-well plate were washed twice with PBS and incubated for 30 min with blocking anti-HLA antibody (50 pg / well; clone W6 / 32, Bio X Cell, Cat. #BE0079) or isotype control (50 pg / well; Bio X Cell, Cat. #BEOO85) at a total volume of 100 pL. Without any additional washes, T-cells were added in to achieve a final volume of 200 pL. In tumor-killing assays, tumor cells were added to each well of a 96-well E-plate in a total volume of 50 pL for initial seeding. Anti-HLA antibody or isotype control (50 pg / well) is added to each well 30 minutes prior to the addition of T-cells to reach a total volume of 100 pL. T-cells were added in each well to achieve a final volume of 200 pL, and impedance was measured for the following 24-48 hours.

[0256] Immune-monitoring of cancer patients expressing mutGNAS-NJ

[0257] The presence of mutGNAS specific CD8+ T cells in the circulation of GNAS NJ expressing HLA-A*02 cancer patients was tested by FACS using dual-color HLA-A*02 dextramers loaded with the MS-identified mutGNAS peptide. Patient CD8+ cells were stimulated in vitro with NJ expressing HLA-A*02 matched moDCs for 2 weeks prior the FACS staining. To generate moDCs, HLA-A*02 healthy donor PBMCs were plated in tissue culture flasks at 1 x 106 cells per cm2 in complete media without cytokines for 2 hours at 37 °C to separate the adherent (monocyte-containing) and non-adherent (T-cell-containing) fractions. The adherent fraction was washed with PBS, and fresh human A / B serum-containing media supplemented with recombinant human IL-4 and GM-CSF (400 IU ml-1) was provided every 3 days. On day 6, moDCs were matured with LPS (Invitrogen) and IFN-y (Miltenyi Biotec) for 24 hours before transfection. moDCs were electroporated with 100 pg ml-1 of mRNA encoding full-length mutGNAS using the Neon Transfection system (10-pl tip, setting: 1,325 V / 10 ms / 3 pulses). Patient CD8+ cells were enriched from PBMCs by negative selection (STEMCELL Technologies) and co-cultured with mutGNAS-NJ expressing HLA-A*02 matched moDCs at a 2:1 ratio in non-tis sue- treated 24- well plates (FALCON) in the presence of 300 IU ml-1 of IL-2 and 50ng ml-1 of IL-7, IL-15, and IL-21. Cytokines were replenished every 3 days. As a control, similarly isolated and co-cultured HLA-A*02:01 matched CD8+ cells from a healthy donor were used. For dextramer labeling, HLA-A*02 multimers bound to mutGNAS and conjugated to PE or APC were purchased from Immudex. As a specificity control, HLA-A*02 multimers bound to the 9-mer peptide from P53 R175H (HMTEVVRHC) were used. Cells were labeled with dual fluorophore-conjugated dextramers for 15 minutes at room temperature, followed by surface antibodies against CD3-BV785, CD4-BV421, CD8-BV65O (Biolegend) for an additional 15 minutes at 4 °C. Cells were washed twice, stained with the viability dye 7-AAD (biolegend) and acquired on a BD Fortessa X20 flow cytometer.

[0258] FACS analysis and antibodies

[0259] TCR-transduced cell lines were stained with anti-human TCR a / p (Clone IP26, BioLegend Cat. #306717) and anti-human CD3 antibody (Clone HIT3a, BioLegend Cat. #300307) to assess the surface-level expression of the transduced TCR. CD8+T-cells were stained with anti-CD107a (BioLegend, Cat #328620) and anti-CD137 antibody (4-1BB; Biolegend Cat #309804) to assess CD8+T-cell degranulation and TCR activation, respectively. Viability of cells were assessed with the Zombie Green™ Fixable Viability Kit (BioLegend, Cat. #423111) APCs and patient- derived glioma cell lines were stained with HLA-A2 monoclonal antibody (Clone BB7.2, Thermo Fisher Scientific Cat. #17-9876-42). Approximately IxlO6cells per 100 pL FACS buffer (PBS supplemented with 1% BSA (Sigma Aldrich Cat. #L6529)) is incubated with one test volume of antibody for 20 minutes as indicated by the manufacturer. Stained cells were washed once with FACS buffer before resuspension to a concentration of 4xl05cells per 100 pL FACS buffer. Cells were then analyzed with the Attune NxT flow cytometer (Thermo Fischer Scientific). Please have Inaki add patient immune-monitoring methods here. Gene set enrichment analysis

[0260] Differential gene expression of TCGA, GTEx, and UCSF GBM / LGG RNA-sequencing was performed and quantified using DESeq2. Only genes with an absolute fold change > 1.5 and a Benjamini- Hochberg adjusted P value <0.05 called by DESeq2 were considered to be differentially expressed. Pre-ranked gene set enrichment analysis (GSEA) was carried out by ranking genes with the product of their fold-change sign and the -loglO(adjusted P value). Disease subtype-specific differential gene analysis: GSEA comparison was performed between IDH1 mutation subtypes (wildtypc IDH1 and mutant IDH1 ) as well as glioma disease subtypes (wildtype IDH1 glioblastoma, mutant IDH1 astrocytoma, and mutant IDH1 oligodendroglioma). Splicing-related gene sets were selected based on keyword search, and gene sets with an adjusted P value <0.05 when comparing two groups are considered differentially enriched. Unbiased hierarchical clustering of differentially enriched gene sets allows the characterization of subgroup-specific upregulated genes. Neojunction load-specific differential gene analysis: TCGA LGG and GBM samples were ranked according to the total putative neojunctions expressed per sample. High (NJHI) and low neojunction load (NJLO) samples within each disease subtype were characterized as the upper and lower 0.10 percentile of ranked samples, respectively. GSEA is carried out between the NJHI and NJLO samples of each disease subgroup. Gene sets with a unidirectional fold-change and adjusted P value <0.05 were considered to be enriched gene sets associated with neojunction load. Splicing-related gene sets were selected based on keyword searches. Leading edge genes shared across all disease subgroups within the same gene set are defined as enriched genes associated with neojunction load.

[0261] Neojunction and splicing-related gene correlation analysis

[0262] Selection of mutant IDH1 upregulated genes was determined by splicing-related genes expressed with a significant (p < 0.05) log2fold increase of 1.5 in mutant IDH1 cases when compared to their wild-type counterpail. Selection of splicing-genes affected by oligodendroglioma- specific loss of chromosomes lp / 19q was determined by chromosome lp / 19q splicing-related genes expressed with a significant (p < 0.05) log2fold decrease of 1.5 in IDH-0 cases compared to both IDH-A and IDH-wt cases. Splicing-related genes that were selected for in vitro validation was chosen based on previously reported confirmation of aberrant splicing due to their dysregulated expression. To determine correlation factors between each of the identified public neojunctions with each splicing-gene of interest, a Pearson correlation analysis was performed against each neojunction and splicing-related gene pair. Neojunctions with the highest positive correlation score against the select mutant IDH1 upregulated genes (CELF2. ELAVL4) averaged across all three glioma subtypes were tested in downstream qPCR assays. Similarly, neojunctions with the most negative correlation score against select chromosome Ip or 19q splicing-related genes downregulated in ID Ilmut-0 cases (SNRPD2, SF3A3) averaged across all three glioma subtypes were also tested in downstream qPCR assays.

[0263] AlphaFold2 structure predictions

[0264] AlphaFold v2.3.2 and its reference databases were installed. AlphaFold was run in multimer mode with default options and the highest rank resulting pdb file was visualized using Pymol. Image was exported with settings “ray 5000,5000” and “png image, dpi=2400”.

[0265] QUANTIFICATION AND STATISTICAL ANALYSIS

[0266] All statistical analysis was performed in base R.

[0267] METHODS-SPECIFIC REFERENCES

[0268] 1. Vaubel, R.A., Tian, S., Remonde, D., Schroeder, M.A., Mladek, A.C., Kitange, G.J., Caron, A., Kollmeyer, T.M., Grove, R., Peng, S., et al. (2020). Genomic and Phenotypic Characterization of a Broad Panel of Patient-Derived Xenografts Reflects the Diversity of Glioblastoma. Clin. Cancer Res. 26, 1094-1104.

[0269] 2. Edwards, N.J., Oberti, M., Thangudu, R.R., Cai, S., McGarvey, P.B., Jacob, S., Madhavan, S., and Ketchum, K.A. (2015). The CPTAC Data Portal: A Resource for Cancer Proteomics Research. J. Proteome Res. 14, 2707-2713.

[0270] 3. Kahles, A., Lehmann, K.-V., Toussaint, N.C., Hiiser, M., Stark, S.G., Sachsenberg, T., Stegle, O., Kohlbacher, O., Sander, C., Cancer Genome Atlas Research Network, et al. (2018). Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients. Cancer Cell 34, 211-224. e6.

[0271] 4. Jones, L.E., Hilz, S., Grimmer, M.R., Mazor, T., Najac, C., Mukherjee, J., McKinney, A., Chow, T., Pieper, R.O., Ronen, S.M., et al. (2020). Patient-derived cells from recurrent tumors that model the evolution of IDH-mutant glioma. Neurooncol Adv 2, vdaaO88. 5. Stathias, V., Jermakowicz, A.M., Maloof, M.E., Forlin, M., Walters, W., Suter, R.K., Durante, M.A., Williams, S.L., Harbour, J.W., Volmar, C.-H., et al. (2018). Drug and disease signature integration identifies synergistic combinations in glioblastoma. Nat. Commun. 9, 5315.

[0272] 6. Jermakowicz, A.M., Rybin, M.J., Suter, R.K., Sarkaria, J.N., Zeier, Z., Feng, Y., and Ayad, N.G. (2021). The novel BET inhibitor UM-002 reduces glioblastoma cell proliferation and invasion. Sci. Rep. 11, 23370.

[0273] 7. Zhang, Y., Liu, Y., Moxley, K.M., Golden-Mason, L., Hughes, M.G., Liu, T., Heemskerk, M.H.M., Rosen, H.R., and Nishimura, M.I. (2010). Transduction of human T cells with a novel T-cell receptor confers anti-HCV reactivity. PLoS Pathog. 6, el001018.

[0274] 8. Bossi, G., Gerry, A.B., Paston, S.J., Sutton, D.H., Hassan, N.J., and Jakobsen, B.K. (2013). Examining the presentation of tumor-associated antigens on pep tide-pulsed T2 cells. Oncoimmunology 2, e26840.

[0275] 9. Hosken, N.A., and Bevan, M.J. (1990). Defective presentation of endogenous antigen by a cell line expressing class I molecules. Science 248, 367-370.

[0276] 10. Chandran, S.S., Ma, J., Klatt, M.G., Diindar, F., Bandlamudi, C., Razavi, P., Wen, H.Y., Weigelt, B., Zumbo, P., Fu, S.N., et al. (2022). Immunogenicity and therapeutic targeting of a public neoantigen derived from mutated PIK3CA. Nat. Med. 28, 946-957.

[0277] 11. Britten, C.M., Meyer, R.G., Kreer, T., Drexler, L, Wolfel, T., and Herr, W. (2002). The use of HLA-A*0201-transfected K562 as standard antigen-presenting cells for CD8(+) T lymphocytes in IFN-gamma ELISPOT assays. J. Immunol. Methods 259, 95-110.

[0278] 12. Suhoski, M.M., Golovina, T.N., Aqui, N.A., Tai, V.C., Varela-Rohena, A., Milone, M.C., Carroll, R.G., Riley, J.L., and June, C.H. (2007). Engineering artificial antigen-presenting cells to express a diverse array of co- stimulatory molecules. Mol. Ther. 75, 981-988.

[0279] 13. Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 75, 550.

[0280] 14. Matsushima, S., Ajiro, M., lida, K., Chamoto, K., Honjo, T., and Hagiwara, M. (2022). Chemical induction of splice-neoantigens attenuates tumor growth in a preclinical model of colorectal cancer. Sci. Transl. Med. 14, eabn6056.

[0281] 15. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102, 15545-15550.

[0282] 16. Pique, L., Martinez de Paz, A., Pineyro, D., Martmez-Cardus, A., Castro de Moura, M., Llinas-Arias, P., Setien, F., Gomez-Miragaya, J., Gonzalez-Suarez, E., Sigurdsson, S., et al. (2019). Epigenetic inactivation of the splicing RNA-binding protein CELF2 in human breast cancer. Oncogene 38, 7106-7112.

[0283] 17. Yanaizu, M., Washizu, C., Nukina, N., Satoh, J. -I., and Kino, Y. (2020). CELF2 regulates the species- specific alternative splicing of TREM2. Sci. Rep. 10, 17995.

[0284] 18. Bowles, K.R., Silva, M.C., Whitney, K., Bertucci, T., Berlind, J.E., Lai, J.D., Garza, J.C., Boles, N.C., Mahali, S., Strang, K.H., et al. (2021). ELAVL4, splicing, and glutamatergic dysfunction precede neuron loss in MAPT mutation cerebral organoids. Cell 184, 4547- 4563.el7.

[0285] 19. Ciesla, M., Ngoc, P.C.T., Cordero, E., Martinez, A.S., Morsing, M., Muthukumar, S., Beneventi, G., Madej, M., Munita, R., Jonsson, T., et al. (2021). Oncogenic translation directs spliceosome dynamics revealing an integral role for SF3A3 in breast cancer. Mol. Cell 81, 1453-1468. el2.

[0286] 20. Wyatt, C.D.R., Pernaute, B., Gohr, A., Miret-Cuesta, M., Goyeneche, L., Rovira, Q., Salzer, M.C., Boke, E., Bogdanovic, O., Bonnal, S., et al. (2022). A developmentally programmed splicing failure contributes to DNA damage response attenuation during mammalian zygotic genome activation. Sci Adv 8, eabn4935.

[0287] REFERENCES

[0288] 1. Weber, E.W., Maus, M.V., and Mackall, C.L. (2020). The Emerging Landscape of Immune Cell Therapies. Cell 181, 46-62.

[0289] 2. Touat, M., Li, Y.Y., Boynton, A.N., Spurr, L.F., lorgulescu, J.B., Bohrson, C.L., Cortes- Ciriano, I., Birzu, C., Geduldig, J.E., Pelton, K., et al. (2020). Mechanisms and therapeutic implications of hypermutation in gliomas. Nature 580, 517-523.

[0290] 3. Russo, M., Crisafulli, G., Sogari, A., Reilly, N.M., Arena, S., Lamba, S., Bartolini, A., Amodio, V., Magri, A., Novara, L., et al. (2019). Adaptive mutability of colorectal cancers in response to targeted therapies. Science 366, 1473-1480. 4. Huang, Y., Jiang, X., Liang, X., and Jiang, G. (2018). Molecular and cellular mechanisms of castration resistant prostate cancer. Oncol. Lett. 15, 6063-6076.

[0291] 5. Kim, C., Gao, R., Sei, E., Brandt, R., Hartman, J., Hatschek, T., Crosetto, N., Foukakis, T., and Navin, N.E. (2018). Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing. Cell 173, 879-893. el3.

[0292] 6. Topalian, S.L., Hodi, F.S., Brahmer, J.R., Gettinger, S.N., Smith, D.C., McDermott, D.F., Powderly, J.D., Carvajal, R.D., Sosman, J. A., Atkins, M.B., et al. (2012). Safety, Activity, and Immune Correlates of Anti-PD-1 Antibody in Cancer. N. Engl. J. Med. 366, 2443-2454.

[0293] 7. Motzer, R.J., Tannir, N.M., McDermott, D.F., Aren Frontera, O., Melichar, B., Choueiri, T.K., Plimack, E.R., Barthelemy, P., Porta, C., George, S., et al. (2018). Nivolumab plus Ipilimumab versus Sunitinib in Advanced Renal-Cell Carcinoma. N. Engl. J. Med. 378, 1277- 1290.

[0294] 8. Le, D.T., Uram, J.N., Wang, H., Bartlett, B.R., Kemberling, H., Eyring, A.D., Skora, A.D., Luber, B.S., Azad, N.S., Laheru, D., et al. (2015). PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N. Engl. J. Med. 372, 2509-2520.

[0295] 9. Scholler, J., Brady, T.L., Binder-Scholl, G., Hwang, W. T., Plesa, G., Hege, K.M., Vogel, A.N., Kalos, M., Riley, J.L., Decks, S.G., et al. (2012). Decade-long safety and function of retroviral-modified chimeric antigen receptor T cells. Sci. Transl. Med. 4, 132ra53.

[0296] 10. Kantoff, P.W., Higano, C.S., Shore, N.D., Berger, E.R., Small, E.J., Penson, D.F., Redfern, C.H., Ferrari, A.C., Dreicer, R., Sims, R.B., et al. (2010). Sipuleucel-T Immunotherapy for Castration-Resistant Prostate Cancer. N. Engl. J. Med. 363, 411-422.

[0297] 11. Ott, P.A., Hu, Z., Keskin, D.B., Shukla, S.A., Sun, J., Bozym, D.J., Zhang, W., Luoma, A., Giobbie-Hurder, A., Peter, L., et al. (2017). An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217-221.

[0298] 12. Tirosh, I., Venteicher, A.S., Hebert, C., Escalante, L.E., Patel, A.P., Yizhak, K., Fisher, J.M., Rodman, C., Mount, C., Filbin, M.G., et al. (2016). Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309-313.

[0299] 13. Neftel, C., Laffy, J., Filbin, M.G., Hara, T., Shore, M.E., Rahme, G.J., Richman, A.R., Silverbush, D., Shaw, M.L., Hebert, C.M., et al. (2019). An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. Cell 178, 835-849. e21. 14. Nicholson, J.G., and Fine, H.A. (2021). Diffuse Glioma Heterogeneity and Its Therapeutic Implications. Cancer Discov. 11, 575-590.

[0300] 15. Teixeira, V.H., Pipinikas, C.P., Pennycuick, A., Lee-Six, H., Chandrasekharan, D., Beane, J., Morris, T.J., Karpathakis, A., Feber, A., Breeze, C.E., et al. (2019). Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions. Nat. Med. 25, 517-525.

[0301] 16. van Galen, P., Hovestadt, V., Wadsworth, M.H., li, Hughes, T.K., Griffin, G.K., Battaglia, S., Verga, J.A., Stcphansky, J., Pastika, T.J., Lombardi Story, J., ct al. (2019). SingleCell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity. Cell 176, 1265- 128 Le24.

[0302] 17. Grosselin, K., Durand, A., Marsolier, L, Poitou, A., Marangoni, E., Nemati, F., Dahmani, A., Lameiras, S., Reyal, E, Frenoy, O., et al. (2019). High-throughput single-cell ChlP-seq identifies heterogeneity of chromatin states in breast cancer. Nat. Genet. 51, 1060- 1066.

[0303] 18. Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012). Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing. N. Engl. J. Med. 366, 883-892.

[0304] 19. Yachida, S., Jones, S., Bozic, I., Antal, T., Leary, R., Fu, B., Kamiyama, M., Hruban, R.H., Eshleman, J.R., Nowak, M.A., et al. (2010). Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114-1117.

[0305] 20. Sotillo, E., Barrett, D.M., Black, K.L., Bagashev, A., Oldridge, D., Wu, G., Sussman, R., Lanauze, C., Ruella, M., Gazzara, M.R., et al. (2015). Convergence of Acquired Mutations and Alternative Splicing of CD19 Enables Resistance to CART-19 Immunotherapy. Cancer Discov. 5, 1282-1295.

[0306] 21. Le, D.T., Durham, J.N., Smith, K.N., Wang, H„ Bartlett, B.R., Aulakh, L.K., Lu, S., Kemberling, H., Wilt, C., Luber, B.S., et al. (2017). Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409-413.

[0307] 22. Hellmann, M.D., Nathanson, T., Rizvi, H., Creelan, B.C., Sanchez-Vega, F., Ahuja, A., Ni, A., Novik, J.B., Mangarin, L.M.B., Abu-Akeel, M., et al. (2018). Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer. Cancer Cell 33, 843-852.e4. 23. Samstein, R.M., Lee, C.-H., Shoushtari, A.N., Hellmann, M.D., Shen, R., Janjigian, Y.Y., Barron, D.A., Zehir, A., Jordan, E.J., Omuro, A., et al. (2019). Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202-206.

[0308] 24. Strpnen, E., Toebes, M., Kelderman, S., van Buuren, M.M., Yang, W., van Rooij, N., Donia, M., Bdschen, M.-L., Lund-Johansen, F., Olweus, J., et al. (2016). Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352, 1337-1341.

[0309] 25. Lawrence, M.S., Stojanov, P., Polak, P., Kryukov, G.V., Cibulskis, K., Sivachenko, A., Carter, S.L., Stewart, C., Mcrmcl, C.H., Roberts, S.A., et al. (2013). Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214-218.

[0310] 26. Klebanoff, C.A., Rosenberg, S.A., and Restifo, N.P. (2016). Prospects for gene- engineered T cell immunotherapy for solid cancers. Nat. Med. 22, 26-36.

[0311] 27. Kahles, A., Lehmann, K.-V., Toussaint, N.C., Hiiser, M., Stark, S.G., Sachsenberg, T., Stegle, O., Kohlbacher, O., Sander, C., Cancer Genome Atlas Research Network, et al. (2018). Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients. Cancer Cell 34, 211-224. e6.

[0312] 28. Tretter, C., de Andrade Kratzig, N., Pecoraro, M., Lange, S., Seifert, P., von Frankenberg, C., Untch, J., Zuleger, G., Wilhelm, M., Zolg, D.P., et al. (2023). Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification. Nat. Commun. 14, 4632.

[0313] 29. Dvinge, H., and Bradley, R.K. (2015). Widespread intron retention diversifies most cancer transcriptomes. Genome Med. 7, 45.

[0314] 30. Smart, A.C., Margolis, C.A., Pimentel, H., He, M.X., Miao, D., Adeegbe, D., Fugmann, T., Wong, K.-K., and Van Allen, E.M. (2018). Intron retention is a source of neoepitopes in cancer. Nat. Biotechnol. 36, 1056-1058.

[0315] 31. Bigot, J., Lalanne, A. I., Lucibello, F., Gueguen, P., Houy, A., Dayot, S., Ganier, O., Gilet, J., Tosello, J., Nemati, F., et al. (2021). Splicing Patterns in SF3B1 -Mutated Uveal Melanoma Generate Shared Immunogenic Tumor- Specific Neoepitopes. Cancer Discov. 11, 1938-1951.

[0316] 32. Merlotti, A., Sadacca, B., Arribas, Y.A., Ngoma, M., Burbage, M., Goudot, C., Houy, A., Rocanm-Arjd, A., Lalanne, A., Seguin-Givelet, A., et al. (2023). Noncanonical splicing junctions between exons and transposable elements represent a source of immunogenic recurrent neo-antigens in patients with lung cancer. Sci Immunol 8, eabm6359.

[0317] 33. Burbage, M., Rocanm-Arjd, A., Baudon, B., Arribas, Y.A., Merlotti, A., Rookhuizen, D.C., Heurtebise-Chretien, S., Ye, M., Houy, A., Burgdorf, N., et al. (2023). Epigenetically controlled tumor antigens derived from splice junctions between exons and transposable elements. Sci Immunol 8, eabm6360.

[0318] 34. Pan, Y., Phillips, J.W., Zhang, B.D., Noguchi, M., Kutschera, E., McLaughlin, J., Nesterenko, P.A., Mao, Z., Bangayan, N.J., Wang, R., ct al. (2023). IRIS: Discovery of cancer immunotherapy targets arising from pre-mRNA alternative splicing. Proc. Natl. Acad. Sci. U. S. A. 120, e2221116120.

[0319] 35. Kim, G.B., Fritsche, J., Bunk, S., Mahr, A., Unverdorben, F., Tosh, K., Kong, H., Maldini, C.R., Lau, C., Srivatsa, S., et al. (2022). Quantitative immunopeptidomics reveals a tumor stroma- specific target for T cell therapy. Sci. Transl. Med. 14, eabo6135.

[0320] 36. Aran, D., Sirota, M., and Butte, A.J. (2015). Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971.

[0321] 37. Ceccarelli, M., Barthel, F.P., Malta, T.M., Sabedot, T.S., Salama, S.R., Murray, B.A., Morozova, O., Newton, Y., Radenbaugh, A., Pagnotta, S.M., et al. (2016). Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell 164, 550-563.

[0322] 38. Nejo, T., Wang, L., Leung, K.K., Wang, A., Lakshmanachetty, S., Gallus, M., Kwok, D.W., Hong, C., Chen, L.H., Carrera, D.A., et al. (2024). Challenges in the discovery of tumorspecific alternative splicing-derived cell-surface antigens in glioma. Sci. Rep. 14, 6362.

[0323] 39. Quesnel-Vallieres, M., Jewell, S., Lynch, K.W., Thomas -Tikhonenko, A., and Barash, Y. (2024). MAJIQlopedia: an encyclopedia of RNA splicing variations in human tissues and cancer. Nucleic Acids Res. 52, D213-D221.

[0324] 40. Yamamoto, T.N., Kishton, R.J., and Restifo, N.P. (2019). Developing neoantigen- targeted T cell-based treatments for solid tumors. Nat. Med. 25, 1488-1499.

[0325] 41. Ku, A.T., Wilkinson, S., and Sowalsky, A.G. (2021). Comparison of approaches to transcriptomic analysis in multi-sampled tumors. Brief. Bioinform. 22, bbab337. 42. Joung, J.-G., Bae, J.S., Kim, S.C., Jung, H., Park, W.-Y., and Song, S.-Y. (2016). Genomic Characterization and Comparison of Multi-Regional and Pooled Tumor Biopsy Specimens. PLoS One 11, e0152574.

[0326] 43. Yang, C., Zhang, S., Cheng, Z., Liu, Z., Zhang, L., Jiang, K., Geng, H., Qian, R., Wang, J., Huang, X., et al. (2022). Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer. Genome Med. 14, 142.

[0327] 44. Jeon, A. -J., Teo, Y.-Y., Sekar, K., Chong, S.L., Wu, L., Chew, S.-C., Chen, J., Kcndarsari, R.I., Lai, H., Ling, W.H., ct al. (2023). Multi-region sampling with paired sample sequencing analyses reveals sub-groups of patients with novel patient- specific dysregulation in Hepatocellular Carcinoma. BMC Cancer 23, 118.

[0328] 45. Zhai, W., Lai, H., Kaya, N.A., Chen, J., Yang, H., Lu, B., Lim, J.Q., Ma, S., Chew, S.C., Chua, K.P., et al. (2021). Dynamic phenotypic heterogeneity and the evolution of multiple RNA subtypes in hepatocellular carcinoma: the PLANET study. Natl Sci Rev 9, nwabl92.

[0329] 46. Amadottir, S.S., Jeppesen, M., Lamy, P., Bramsen, J.B., Nordentoft, I., Knudsen, M., Vang, S., Madsen, M.R., Thastrup, O., Thastrup, J., et al. (2018). Characterization of genetic intratumor heterogeneity in colorectal cancer and matching patient-derived spheroid cultures. Mol. Oncol. 12, 132-147.

[0330] 47. Biswas, D., Birkbak, N.J., Rosenthal, R., Hiley, C.T., Lim, E.L., Papp, K., Boeing, S., Krzystanek, M., Djureinovic, D., La Fleur, L., et al. (2019). A clonal expression biomarker associates with lung cancer mortality. Nat. Med. 25, 1540-1548.

[0331] 48. Meiller, C., Montagne, F., Hirsch, T.Z., Caruso, S., de Wolf, J., Bayard, Q., Assie, J.-B., Meunier, L., Blum, Y., Quetel, L., et al. (2021). Multi-site tumor sampling highlights molecular intra-tumor heterogeneity in malignant pleural mesothelioma. Genome Med. 13, 113.

[0332] 49. Bhaduri, A., Di Lullo, E., Jung, D., Muller, S., Crouch, E.E., Espinosa, C.S., Ozawa, T., Alvarado, B., Spatazza, J., Cadwell, C.R., et al. (2020). Outer Radial Glia-like Cancer Stem Cells Contribute to Heterogeneity of Glioblastoma. Cell Stem Cell 26, 48-63.e6.

[0333] 50. Venteicher, A.S., Tirosh, I., Hebert, C., Yizhak, K., Neftel, C., Filbin, M.G., Hovestadt, V., Escalante, L.E., Shaw, M.L., Rodman, C., et al. (2017). Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355.

[0334] 10.1126 / science.aai8478. 51. Brennan, C.W., Verhaak, R.G.W., McKenna, A., Campos, B., Noushmehr, H., Salama, S.R., Zheng, S., Chakravarty, D., Sanborn, J.Z., Berman, S.H., et al. (2013). The somatic genomic landscape of glioblastoma. Cell 155, 462-477.

[0335] 52. McKinney, A.M., Mathur, R., Stevers, N.O., Molinaro, A.M., Chang, S.M., Phillips, J. J., and Costello, J.F. (2022). GABP couples oncogene signaling to telomere regulation in TERT promoter mutant cancer. Cell Rep. 40, 111344.

[0336] 53. Appin, C.L., Hong, C., Suwala, A.K., Hilz, S., Mathur, R., Solomon, D.A., Smirnov, I.V., Stevers, N.O., Shai, A., Wang, A., ct al. (2024). Whole tumor analysis reveals early origin of the TERT promoter mutation and intercellular heterogeneity in TERT expression. Neuro. Oncol. 26, 640-652.

[0337] 54. Mathur, R., Wang, Q., Schupp, P.G., Nikolic, A., Hilz, S., Hong, C., Grishanina, N.R., Kwok, D., Stevers, N.O., Jin, Q., et al. (2024). Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective. Cell 187, 446-463. el6.

[0338] 55. Spain, L., Coulton, A., Lobon, I., Rowan, A., Schnidrig, D., Shepherd, S.T.C., Shum, B., Byrne, F., Goicoechea, M., Piperni, E., et al. (2023). Late-Stage Metastatic Melanoma Emerges through a Diversity of Evolutionary Pathways. Cancer Discov. 13, 1364-1385.

[0339] 56. Van Nostrand, E.L., Freese, P., Pratt, G.A., Wang, X., Wei, X., Xiao, R., Blue, S.M., Chen, J.-Y., Cody, N.A.L., Dominguez, D., et al. (2020). A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711-719.

[0340] 57. Sveen, A., Kilpinen, S., Ruusulehto, A., Lothe, R.A., and Skotheim, R.I. (2016). Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes. Oncogene 35, 2413-2427.

[0341] 58. Seiler, M., Peng, S., Agrawal, A. A., Palacino, J., Teng, T., Zhu, P., Smith, P.G., Cancer Genome Atlas Research Network, Buonamici, S., and Yu, L. (2018). Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types. Cell Rep. 23, 282-296.e4.

[0342] 59. PCAWG Transcriptome Core Group, Calabrese, C., Davidson, N.R., Demircioglu, D., Fonseca, N.A., He, Y., Kahles, A., Lehmann, K.-V., Liu, F., Shiraishi, Y., et al. (2020). Genomic basis for RNA alterations in cancer. Nature 578, 129-136. 60. Bettegowda, C., Agrawal, N., Jiao, Y., Sausen, M., Wood, L.D., Hruban, R.H., Rodriguez, F.J., Cahill, D.P., McLendon, R., Riggins, G., et al. (2011). Mutations in CIC and FUBP1 contribute to human oligodendroglioma. Science 333, 1453-1455.

[0343] 61. Chan, A.K.-Y., Pang, J.C.-S., Chung, N.Y.-F., Li, K.K.-W., Poon, W.S., Chan, D.T.-M., Shi, Z., Chen, L., Zhou, L., and Ng, H.-K. (2014). Loss of CIC and FUBP1 expressions are potential markers of shorter time to recurrence in oligodendroglial tumors. Mod. Pathol. 27, 332-342.

[0344] 62. Ajiro, M., Awaya, T., Kim, Y.J., lida, K., Dcnawa, M., Tanaka, N., Kurosawa, R., Matsushima, S., Shibata, S., Sakamoto, T., et al. (2021). Therapeutic manipulation of IKBKAP mis-splicing with a small molecule to cure familial dysautonomia. Nat. Commun. 12, 4507.

[0345] 63. Pique, L., Martinez de Paz, A., Pineyro, D., Martmez-Cardus, A., Castro de Moura, M., Llinas-Arias, P., Setien, F., Gomez-Miragaya, J., Gonzalez-Suarez, E., Sigurdsson, S., et al. (2019). Epigenetic inactivation of the splicing RNA-binding protein CELF2 in human breast cancer. Oncogene 38, 7106-7112.

[0346] 64. Yanaizu, M., Washizu, C., Nukina, N., Satoh, J. -I., and Kino, Y. (2020). CELF2 regulates the species- specific alternative splicing of TREM2. Sci. Rep. 10, 17995.

[0347] 65. Jones, L.E., Hilz, S., Grimmer, M.R., Mazor, T., Najac, C., Mukherjee, J., McKinney, A., Chow, T., Pieper, R.O., Ronen, S.M., et al. (2020). Patient-derived cells from recurrent tumors that model the evolution of IDH-mutant glioma. Neurooncol Adv 2, vdaaO88.

[0348] 66. Wyatt, C.D.R., Pernaute, B., Gohr, A., Miret-Cuesta, M., Goyeneche, L., Rovira, Q., Salzer, M.C., Boke, E., Bogdanovic, O., Bonnal, S., et al. (2022). A developmentally programmed splicing failure contributes to DNA damage response attenuation during mammalian zygotic genome activation. Sci Adv 8, eabn4935.

[0349] 67. Furney, S.J., Pedersen, M., Gentien, D., Dumont, A.G., Rapinat, A., Desjardins, L., Turajlic, S., Piperno-Neumann, S., de la Grange, P., Roman-Roman, S., et al. (2013). SF3B1 mutations are associated with alternative splicing in uveal melanoma. Cancer Discov. 3, 1122- 1129.

[0350] 68. Vaubel, R.A., Tian, S., Remonde, D., Schroeder, M.A., Mladek, A.C., Kitange, G.J., Caron, A., Kollmeyer, T.M., Grove, R., Peng, S., et al. (2020). Genomic and Phenotypic Characterization of a Broad Panel of Patient-Derived Xenografts Reflects the Diversity of Glioblastoma. Clin. Cancer Res. 26, 1094-1104. 69. Edwards, N.J., Oberti, M., Thangudu, R.R., Cai, S., McGarvey, P.B., Jacob, S., Madhavan, S., and Ketchum, K.A. (2015). The CPTAC Data Portal: A Resource for Cancer Proteomics Research. J. Proteome Res. 14, 2707-2713.

[0351] 70. Bader, J.M., Deigendesch, N., Misch, M., Mann, M., Koch, A., and Meissner, F. (2023). Proteomics separates adult-type diffuse high-grade gliomas in metabolic subgroups independent of lp / 19q codeletion and across IDH mutational status. Cell Rep Med 4, 100877.

[0352] 71. Wong, D., Lee, T.H., Lum, A., Tao, V.L., and Yip, S. (2022). Integrated proteomic analysis of low-grade gliomas reveals contributions of lp-19q co-dclction to oligodendroglioma. Acta Neuropathol Commun 10, 70.

[0353] 72. Blass, E., and Ott, P.A. (2021). Advances in the development of personalized neoantigen-based therapeutic cancer vaccines. Nat. Rev. Clin. Oncol. 18, 215-229.

[0354] 73. O’Donnell, T.J., Rubinsteyn, A., and Laserson, U. (2020). MHCflurry 2.0: Improved Pan- Allele Prediction of MHC Class LPresented Peptides by Incorporating Antigen Processing. Cell Systems 11, 42-48.e7.

[0355] 74. Sarkizova, S., Klaeger, S., Le, P.M., Li, L.W., Oliveira, G., Keshishian, H., Hartigan, C.R., Zhang, W., Braun, D.A., Ligon, K.L., et al. (2020). A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat. Biotechnol. 38, 199- 209.

[0356] 75. Gonzalez-Galarza, F.F., McCabe, A., Santos, E.J.M.D., Jones, J., Takeshita, L., Ortega- Rivera, N.D., Cid-Pavon, G.M.D., Ramsbottom, K., Ghattaoraya, G., Alfirevic, A., et al. (2020). Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools. Nucleic Acids Res. 48, D783-D788.

[0357] 76. Arrieta-Bolanos, E., Hernandez-Zaragoza, D.I., and Barquera, R. (2023). An HLA map of the world: A comparison of HLA frequencies in 200 worldwide populations reveals diverse patterns for class I and class II. Front. Genet. 14, 866407.

[0358] 77. Olivier, T., Haslam, A., Tuia, J., and Prasad, V. (2023). Eligibility for Human Leukocyte Antigen-Based Therapeutics by Race and Ethnicity. JAMA Netw Open 6, e2338612.

[0359] 78. Lo, W., Parkhurst, M., Robbins, P.F., Tran, E., Lu, Y.-C., Jia, L., Gartner, J.J., Pasetto, A., Deniger, D., Malekzadeh, P., et al. (2019). Immunologic Recognition of a Shared p53 Mutated Neoantigen in a Patient with Metastatic Colorectal Cancer. Cancer Immunol Res 7, 534-543. 79. Hsiue, E.H.-C., Wright, K.M., Douglass, J., Hwang, M.S., Mog, B.J., Pearlman, A.H., Paul, S., DiNapoli, S.R., Konig, M.F., Wang, Q., et al. (2021). Targeting a neoantigen derived from a common TP53 mutation. Science 371. 10.1126 / science.abc8697.

[0360] 80. Carreno, B.M., Magrini, V., Becker-Hapak, M., Kaabinejadian, S., Hundal, J., Petti, A.A., Ly, A., Lie, W.-R., Hildebrand, W.H., Mardis, E.R., et al. (2015). Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen- specific T cells. Science 348, 803-808.

[0361] 81. Chen, F., Zou, Z., Du, J., Su, S., Shao, J., Meng, F., Yang, J., Xu, Q., Ding, N., Yang, Y., et al. (2019). Neoantigen identification strategies enable personalized immunotherapy in refractory solid tumors. J. Clin. Invest. 729, 2056-2070.

[0362] 82. Ali, M., Foldvari, Z., Giannakopoulou, E., Bdschen, M.-L., Strpnen, E., Yang, W., Toebes, M., Schubert, B., Kohlbacher, O., Schumacher, T.N., et al. (2019). Induction of neoantigen-reactive T cells from healthy donors. Nat. Protoc. 14, 1926-1943.

[0363] 83. Chandran, S.S., Ma, J., Klatt, M.G., Diindar, F., Bandlamudi, C., Razavi, P., Wen, H.Y., Weigelt, B., Zumbo, P., Fu, S.N., et al. (2022). Immunogenicity and therapeutic targeting of a public neoantigen derived from mutated PIK3CA. Nat. Med. 28, 946-957.

[0364] 84. Bossi, G., Gerry, A.B., Paston, S.J., Sutton, D.H., Hassan, N.J., and Jakobsen, B.K. (2013). Examining the presentation of tumor-associated antigens on pep tide-pulsed T2 cells. Oncoimmunology 2, e26840.

[0365] 85. Jutz, S., Leitner, J., Schmetterer, K., Doel-Perez, I., Majdic, O., Grabmeier- Pfistershammer, K., Paster, W., Huppa, J.B., and Steinberger, P. (2016). Assessment of costimulation and coinhibition in a triple parameter T cell reporter line: Simultaneous measurement of NF-KB, NFAT and AP-1. J. Immunol. Methods 430, 10-20.

[0366] 86. Rosskopf, S., Leitner, J., Paster, W., Morton, L.T., Hagedoorn, R.S., Steinberger, P., and Heemskerk, M.H.M. (2018). A Jurkat 76 based triple parameter reporter system to evaluate TCR functions and adoptive T cell strategies. Oncotarget 9, 17608-17619.

[0367] 87. Weiss, G.A., Watanabe, C.K., Zhong, A., Goddard, A., and Sidhu, S.S. (2000). Rapid mapping of protein functional epitopes by combinatorial alanine scanning. Proc. Natl. Acad. Sci. U. S. A. 97, 8950-8954.

[0368] 88. Sanderson, J.P., Crowley, D.L, Wiedermann, G.E., Quinn, L.L., Crossland, K.L., Tunbridge, H.M., Comforth, T.V., Barnes, C.S., Ahmed, T., Howe, K., et al. (2020). Preclinical evaluation of an affinity-enhanced M AGE- A4- specific T-cell receptor for adoptive T-cell therapy. Oncoimmunology 9, 1682381.

[0369] 89. Mardis, E.R., Ding, L., Dooling, D.J., Larson, D.E., McLellan, M.D., Chen, K., Koboldt, D.C., Fulton, R.S., Delehaunty, K.D., McGrath, S.D., et al. (2009). Recurring mutations found by sequencing an acute myeloid leukemia genome. N. Engl. J. Med. 361, 1058-1066.

[0370] 90. Cancer Genome Atlas Research Network, Ley, T.J., Miller, C., Ding, L., Raphael, B.J., Mungall, A. J., Robertson, A.G., Hoadley, K., Triche, T.J., Jr, Laird, P.W., et al. (2013).

[0371] Genomic and cpigcnomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059-2074.

[0372] 91. Marcucci, G., Maharry, K., Wu, Y.-Z., Radmacher, M.D., Mrozek, K., Margeson, D., Holland, K.B., Whitman, S.P., Becker, H., Schwind, S., et al. (2010). IDH1 and IDH2 gene mutations identify novel molecular subsets within de novo cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study. J. Clin. Oncol. 28, 2348-2355.

[0373] 92. Borger, D.R., Tanabe, K.K., Fan, K.C., Lopez, H.U., Eantin, V.R., Straley, K.S., Schenkein, D.P., Hezel, A.F., Ancukiewicz, M., Liebman, H.M., et al. (2012). Frequent mutation of isocitrate dehydrogenase (IDH)l and IDH2 in cholangiocarcinoma identified through broad-based tumor genotyping. Oncologist 17, 72-79.

[0374] 93. Boscoe, A.N., Rolland, C., and Kelley, R.K. (2019). Frequency and prognostic significance of isocitrate dehydrogenase 1 mutations in cholangiocarcinoma: a systematic literature review. J. Gastrointest. Oncol. 10, 751-765.

[0375] 94. Amary, M.F., Bacsi, K., Maggiani, F., Damato, S., Halai, D., Berisha, F., Pollock, R., O’Donnell, P., Grigoriadis, A., Diss, T., et al. (2011). IDH1 and IDH2 mutations are frequent events in central chondrosarcoma and central and periosteal chondromas but not in other mesenchymal tumours. J. Pathol. 224, 334-343.

[0376] 95. Gloss, S., Jurmeister, P., Thieme, A., Schmid, S., Cai, W.Y., Serrette, R.N., Pemer, S., Ribbat-Idel, J., Pagenstecher, A., Blaker, H., et al. (2021). IDH2 R172 Mutations Across Poorly Differentiated Sinonasal Tract Malignancies: Forty Molecularly Homogenous and Histologically Variable Cases With Favorable Outcome. Am. J. Surg. Pathol. 45, 1190-1204.

[0377] 96. Guilmette, J., and Sadow, P.M. (2019). High-Grade Sinonasal Carcinoma: Classification Through Molecular Profiling. Arch. Pathol. Lab. Med. 143, 1416-1419. 97. Mito, J.K., Bishop, J. A., Sadow, P.M., Stelow, E.B., Faquin, W.C., Mills, S.E., Krane, J.F., French, C.A., Fletcher, C.D.M., Hornick, J.L., et al. (2018). Immunohistochemical Detection and Molecular Characterization of IDH-mutant Sinonasal Undifferentiated Carcinomas. Am. J. Surg. Pathol. 42, 1067-1075.

[0378] 98. Cairns, R.A., Iqbal, J., Lemonnier, F., Kucuk, C., de Leval, L., lais, J.-P., Parrens, M., Martin, A., Xerri, L., Brousset, P., et al. (2012). IDH2 mutations are frequent in angioimmunoblastic T-cell lymphoma. Blood 119, 1901-1903.

[0379] 99. Wang, C., McKcithan, T.W., Gong, Q., Zhang, W., Bouska, A., Roscnwald, A., Gascoyne, R.D., Wu, X., Wang, I., Muhammad, Z., et al. (2015). IDH2R172 mutations define a unique subgroup of patients with angioimmunoblastic T-cell lymphoma. Blood 726, 1741-1752.

[0380] 100. Schumacher, T., Bunse, L., Pusch, S., Sahm, F., Wiestler, B., Quandt, J., Menn, O., Osswald, M., Oezen, I., Ott, M., et al. (2014). A vaccine targeting mutant IDH1 induces antitumour immunity. Nature 572, 324-327.

[0381] 101. Grassl, N., Poschke, I., Lindner, K., Bunse, L., Mildenberger, I., Boschert, T., Jahne, K., Green, E.W., Hiilsmeyer, I., Jiinger, S., et al. (2023). A H3K27M-targeted vaccine in adults with diffuse midline glioma. Nat. Med. 10.1038 / s41591-023-02555-6.

[0382] 102. Tran, E., Turcotte, S., Gros, A., Robbins, P.F., Lu, Y.-C., Dudley, M.E., Wunderlich, I.R., Somerville, R.P., Hogan, K., Hinrichs, C.S., et al. (2014). Cancer immunotherapy based on mutation- specific CD4+ T cells in a patient with epithelial cancer. Science 344, 641-645.

[0383] 103. Linnemann, C., van Buuren, M.M., Bies, L., Verdegaal, E.M.E., Schotte, R., Calis, 1.1. A., Behjati, S., Velds, A., Hilkmann, H., Atmioui, D.E., et al. (2015). High-throughput epitope discovery reveals frequent recognition of neo-antigens by CD4+ T cells in human melanoma. Nat. Med. 27, 81-85.

[0384] 104. Stathias, V., Jermakowicz, A.M., Maloof, M.E., Forlin, M., Walters, W., Suter, R.K., Durante, M.A., Williams, S.L., Harbour, J.W., Volmar, C.-H., et al. (2018). Drug and disease signature integration identifies synergistic combinations in glioblastoma. Nat. Commun. 9, 5315.

[0385] 105. Jermakowicz, A.M., Rybin, M.J., Suter, R.K., Sarkaria, J.N., Zeier, Z., Feng, Y., and Ayad, N.G. (2021). The novel BET inhibitor UM-002 reduces glioblastoma cell proliferation and invasion. Sci. Rep. 77, 23370. 106. Zhang, Y., Liu, Y., Moxley, K.M., Golden-Mason, L., Hughes, M.G., Liu, T., Heemskerk, M.H.M., Rosen, H.R., and Nishimura, M.I. (2010). Transduction of human T cells with a novel T-cell receptor confers anti-HCV reactivity. PLoS Pathog. 6, M001018.

[0386] 107. Hosken, N.A., and Bevan, M.J. (1990). Defective presentation of endogenous antigen by a cell line expressing class I molecules. Science 248, 367-370.

[0387] 108. Britten, C.M., Meyer, R.G., Kreer, T., Drexler, L, Wolfel, T., and Herr, W. (2002). The use of HLA-A*0201-transfected K562 as standard antigen-presenting cells for CD8(+) T lymphocytes in IFN-gamma ELISPOT assays. J. Immunol. Methods 259, 95-110.

[0388] 109. Suhoski, M.M., Golovina, T.N., Aqui, N.A., Tai, V.C., Varela-Rohena, A., Milone, M.C., Carroll, R.G., Riley, J.L., and June, C.H. (2007). Engineering artificial antigen-presenting cells to express a diverse array of co- stimulatory molecules. Mol. Ther. 75, 981-988.

[0389] 110. Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 75, 550.

[0390] 111. Matsushima, S., Ajiro, M., lida, K., Chamoto, K., Honjo, T., and Hagiwara, M. (2022). Chemical induction of splice-neoantigens attenuates tumor growth in a preclinical model of colorectal cancer. Sci. Transl. Med. 14, eabn6056.

[0391] 112. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102, 15545-15550.

[0392] 113. Bowles, K.R., Silva, M.C., Whitney, K., Bertucci, T., Berlind, J.E., Lai, J.D., Garza, J.C., Boles, N.C., Mahali, S., Strang, K.H., et al. (2021). ELAVL4, splicing, and glutamatergic dysfunction precede neuron loss in MAPT mutation cerebral organoids. Cell 184, 4547- 4563.el7.

[0393] 114. Ciesla, M., Ngoc, P.C.T., Cordero, E., Martinez, A.S., Morsing, M., Muthukumar, S., Beneventi, G., Madej, M., Munita, R., Jonsson, T., et al. (2021). Oncogenic translation directs spliceosome dynamics revealing an integral role for SF3A3 in breast cancer. Mol. Cell 81, 1453-1468.el2.

[0394] While the present invention has been described with reference to the specific embodiments thereof, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the true spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation, material, composition of matter, process, process step or steps, to the objective, spirit and scope of the present invention. All such modifications are intended to be within the scope of the claims appended hereto.

Claims

CLAIMSWhat is claimed is:I . An isolated peptide comprising an amino acid sequence of SEQ ID NO: 1 or 2.2 The peptide of claim 1, wherein the peptide is 9 to 40 amino acids in length.

3. The isolated peptide of claim 1, wherein the peptide consists of SEQ ID NO:1, 2 or 3.

4. A composition comprising: a peptide of any of claims 1-3 or an RNA or DNA encoding the same; and a pharmaceutically acceptable excipient.

5. A composition comprising a major histocompatibility complex (MHC)-peptide complex comprising an MHC and a peptide of any of claims 1-3.

6. A cell expressing the composition of claim 5 on its surface.

7. The cell of claim 6, wherein the cell is an antigen presenting cell.

8. The cell of claim 7, wherein the cell is a dendritic cell.

9. A method of priming a T cell, comprising: contacting a cell of any of claims 7 or 8 with a population of T cells; and incubating the T cells with the cell under conditions by which T cells that recognize the MHC-peptide complex become activated and / or expand.

10. The method of claim 9, wherein the cells are contacted in vivo.I I. The method of claim 9, wherein the cells are contacted ex vivo or in vitro.

12. A method of inducing an immune response to a cancer cell antigen, the method comprising: administering to an individual an effective dose of a peptide, composition or cell of any of claims 1-8 or an RNA or DNA encoding the peptide.

13. A T cell receptor comprising: an alpha chain having the CDR sequences of SEQ ID NOs: 5, 6 and 7; and a beta chain having the CDR sequences of SEQ ID NOs: 9, 10 and 11; or an alpha chain having the CDR sequences of SEQ ID NOs: 13, 14 and 15; and a beta chain having the CDR sequences of SEQ ID NOs: 17, 18 and 28.

14. The T cell receptor of claim 13, comprising: an alpha chain having an amino acid sequence that is at least 90% identical to SEQ ID NO: 4; and a beta chain having an amino acid sequence that is at least 90% identical to SEQ ID NO: 8; or an alpha chain having an amino acid sequence that is at least 90% identical to SEQ ID NO: 12; and a beta chain having an amino acid sequence that is at least 90% identical to SEQ ID NO: 16.

15. An immune cell engineered to express a T cell receptor of claim 13 or 14.

16. The immune cell of claim 15, wherein the cell is a T cell.

17. A method of treatment, comprising administering the peptide, composition or cell of any of claims 1-8 or 15-16 to the individual in need thereof.

18. The method of claim 17, wherein the individual has glioblastoma or low-grade glioma.

19. A method for screening for HLA-binding peptides, wherein the HLA-binding peptides comprise a neojunction, comprising: a) obtaining transcriptome sequence data from a tumor tissue sample and a normal tissue sample and identifying one or more transcripts in the tumor tissue sample comprising a neojunction; b) inputting the sequences of the one or more transcripts comprising a neojunction into a computer readable medium and generating an in silica library of possible multimer peptide sequences from the sequences of the one or more transcripts comprising a ncojunction; c) generating, for each peptide of the in silica multimer peptide library: i) a first MHC I presentation score with an antigen-processing favorability algorithm, ii) a second MHC I presentation score with a pan-HLA-A binding affinity algorithm; and d) identifying one or more peptides that bind to HLA as determined by the first and second MHC-I presentation scores.

20. The method of claim 19, wherein the identifying the one or more transcripts comprising a neojunction comprises the steps of: i) quantifying detected splicing junctions in protein-coding transcripts expressed in the tumor tissue sample and quantifying detected splicing junctions in protein-coding transcripts expressed in the normal tissue sample; ii) identifying un-annotated splicing junctions as the set of detected splicing junctions not present in an annotated set of canonical splicing junctions; iii) defining a splicing frequency for the un-annotated splicing junctions as:iv) identifying neojunctions as the un-annotated splicing junctions with a splicing frequency of at least 0.1 in the tumor tissue samples and detected in less than 1% of the plurality of normal tissue samples.I l l21. The method of any one of claims 19-20, wherein the generating an in silica library of possible multimer peptide sequences comprises the steps of: i) translating the transcripts comprising neojunctions in silica to their corresponding amino acid sequences; ii) for each amino acid sequence, partitioning the amino acid sequence into all possible multimer peptide sequences of 8 to 11 amino acids in length; iii) discarding each multimer peptide sequence that is detectable in a reference proteome dataset of normal tissue samples.

22. The method of any one of claims 19-21, wherein the identifying the one or more peptides that bind to HLA comprises the steps of: i) ranking the multimer peptides in the library by the first and second MHC I presentation scores; and ii) identifying one or more HLA-binding peptides as the multimer peptides in the top 10 percentile of both the first and second MHC I presentation scores.

23. A method for screening for TCRs that recognize and bind to a neojunction epitope, the method comprising: a) introducing an HLA-binding peptide comprising a neojunction to a population of antigen presenting cells (APCs) to produce a population of APCs loaded with the HLA-binding peptide; b) contacting a population of T cells with the population of APCs loaded with an HLA- binding peptide; c) performing paired single-cell RNA and V(D)J sequencing on the T cells; and d) identifying the TCR sequences of reactive T cells as determined by single-cell RNA sequencing.

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