Methods of detecting neoantigen-specific t cells and t cell receptor sequences

Abstract

Disclosed are methods of detecting neoantigen-specific T cells and T cell receptor (TCR) sequences, polynucleotides comprising the neoantigen-specific TCRs, methods of making recombinant T cells, recombinant T cells comprising the disclosed polynucleotides, pharmaceutical compositions comprising the recombinant T cells, and methods of using the pharmaceutical compositions and recombinant T cells for the treatment of cancer.

Description

METHODS OF DETECTING NEOANTIGEN-SPECIFIC T CELLS AND T CELL RECEPTOR SEQUENCESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to U.S. Provisional Patent Application No. 63 / 558,060 that was filed February 26, 2024, the entire contents of which are hereby incorporated by reference.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] Not applicable.REFERENCE TO A SEQUENCE LISTING

[0003] A Sequence Listing accompanies this application and is submitted as an xml file of the sequence listing named “650053_01144.xml” which is 111,561 bytes in size and was created on February 25, 2025. The sequence listing is electronically submitted via Patent Center and is incorporated by reference herein in its entirety.BACKGROUND

[0004] Squamous cell carcinoma (SCC) is an aggressive malignancy with poor prognosis. Despite intensive treatments including surgery' and radiotherapy, 5-year survival remains below 50% for advanced disease. Immunotherapy with PD-1 blockade has demonstrated recent promise but achieves only rare durable control. For effective immunotherapy, cancer-specific T cells must traffic to the tumor in sufficient numbers, access suitable nutrients, and overcome mechanisms of immunosuppression. The foundation of this cascade is T cell tumor-specificity. Without T cell receptors (TCRs) that can recognize neo-antigen and induce a cytolytic response, T cell-based immunotherapies will be ineffective. Therefore, to improve immunotherapy for SCC we must identify and target tumor-specific T cells. Unfortunately, only a small subset of tumor-infiltrating lymphocytes (TILs) can recognize tumor neo-antigen and efficient methods to identify these key TfLs have not been established. Therefore, novel approaches to identify neoantigen-specific TILs and harness anti -tumor immunity are needed for the treatment of cancer, e.g., SCC.SUMMARY

[0005] In an aspect of the current disclosure, methods of detecting neoantigen-specific T cell receptor (TCR) sequences are provided. In some embodiments, the methods comprise obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR- seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality7of the T cells; andpartitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH. IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CD8, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB. GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67, STMN 1 , CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the partitioned TCR sequencing data comprises neoantigen-specific T cell receptor (TCR) sequences. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67. STMN1, CENPF, TOP2A, TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises CXCL13, MKI67, STMN1, CENPF, TOP2A. PDCD1, TGIT, LAG3, HAVCR2, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA. GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the method further comprises generating a polynucleotide comprising one of the plurality of detected neoantigen-specific TCR sequences.

[0006] In some embodiments, the methods comprise: performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; and partitioning the TCR sequencing data based on positive expression of a set of markers in the RNAsequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT. LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CD8, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67. STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the partitioned TCR sequencing data comprises neoantigen-specific T cell receptor (TCR) sequences. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A. TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises CXCL13, MKI67, STMN1, CENPF, TOP2A, PDCD1, TGIT, LAG3. HAVCR2, and ENTPD1. In some embodiments, the set of markers comprises MKI67. STMN1, CENPF. TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1. CENPF. TOP2A, TYMS. CXCL13, GZMA, GZMB. GZMH, IFNG, ITGAE. PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the method further comprises generating a polynucleotide comprising one of the plurality of detected neoantigen-specific TCR sequences.

[0007] In an aspect of the current disclosure, polynucleotides are provided. In some embodiments, the polynucleotides comprise a neoantigen-specific TCR sequence generated by obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; and partitioning the TCR sequencing data based on positive expression of a set ofmarkers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF. TOP2A. TYMS, CXCL13, GZMA, GZMB. GZMH, IFNG, ITGAE, PDCD1. HAVCR2. TIGIT, LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences. In some embodiments, the polynucleotides comprise a neoantigen-specific TCR sequence generated by performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; and partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1. CENPF, TOP2A, TYMS, CXCL13, GZMA. GZMB, GZMH. IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences. In some embodiments, the polynucleotide comprises a regulatory' element operably linked to the isolated neoantigen-specific TCR sequence. In some embodiments, the regulatory element comprises a promoter.

[0008] In an aspect of the current disclosure, recombinant neoantigen-specific T cells are provided. In some embodiments, the T cells comprise a polynucleotide. In some embodiments, the polynucleotides comprise a neoantigen-specific TCR sequence generated by obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; and partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences. In some embodiments, the polynucleotides comprise a neoantigen-specific TCR sequence generated by performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; and partitioning the TCR sequencing databased on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA, GZMB. GZMH, IFNG. ITGAE, PDCD1, HAVCR2. TIGIT, LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences. In some embodiments, the polynucleotide comprises a regulatory element operably linked to the isolated neoantigen-specific TCR sequence. In some embodiments, the regulatory element comprises a promoter.

[0009] In an aspect of the current disclosure, methods of generating a recombinant neoantigen- specific T cell are provided. In some embodiments, the methods comprise: introducing a polynucleotide into to a T cell. In some embodiments, the polynucleotides comprise a neoantigen- specific TCR sequence generated by obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; and partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences. In some embodiments, the polynucleotides comprise a neoantigen-specific TCR sequence generated by performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; and partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, w herein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE. PDCD1, HAVCR2, TIGIT. LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences. In some embodiments, the polynucleotide comprises a regulatory element operably linked to the isolated neoantigen-specific TCR sequence. In some embodiments, the regulatory element comprises a promoter.

[0010] In an aspect of the current disclosure, methods of generating a recombinant neoantigenspecific T cell are provided. In some embodiments, the methods comprise: obtaining datacomprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA. GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; and introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen- specific T cell. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CDS. MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA. GZMB, GZMH. IFNG. ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13. GZMA, GZMB, GZMH, IFNG. ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the partitioned TCR sequencing data comprises neoantigen- specific T cell receptor (TCR) sequences. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB. GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises CXCL13. MKI67, STMN 1. CENPF. TOP2A. PDCD1, TGIT, LAG3, HAVCR2, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67. STMN1. CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA. GZMB, GZMH, IFNG, ITGAE. PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the polynucleotide comprises a regulatory7element operably linked to the isolated neoantigen-specific TCR sequence. In some embodiments, the regulatory7element comprises a promoter. In someembodiments, introducing the polynucleotide comprises transfection or transduction of the polynucleotide. In some embodiments, introducing the polynucleotide comprises introducing the polynucleotide by transfection. In some embodiments, the method further comprises introducing to the T cell one or more additional polynucleotides that collectively encode (1) a Cas nuclease, and (2) one or more guide RNAs. In some embodiments, the one or more guide RNAs target a 3’ region of the endogenous T cell receptor sequence and wherein introduction comprises inducing a double stranded break in the endogenous T cell receptor sequence and homologous recombination of the polynucleotide into the genome of the T cell.

[0011] In some embodiments, the methods of generating a recombinant neoantigen-specific T cell, the method comprise: performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality7of neoantigen-specific TCR sequences; and introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigenspecific TCR sequences to generate the recombinant neoantigen-specific T cell. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CD8. MKJ67. STMN 1. CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13. GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1. HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the partitioned TCR sequencing data comprises neoantigen-specific T cell receptor (TCR) sequences. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1. CENPF. TOP2A. TYMS, CXCL13, GZMA, GZMB,GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA. GZMB, GZMH. IFNG, ITGAE, PDCD1. HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the polynucleotide comprises a regulatory element operably linked to the isolated neoantigen-specific TCR sequence. In some embodiments, the regulatory element comprises a promoter. In some embodiments, introducing the polynucleotide comprises transfection or transduction of the polynucleotide. In some embodiments, introducing the polynucleotide comprises introducing the polynucleotide by transfection. In some embodiments, the method further comprises introducing to the T cell one or more additional polynucleotides that collectively encode (1) a Cas nuclease, and (2) one or more guide RNAs. In some embodiments, the one or more guide RNAs target a 3’ region of the endogenous T cell receptor sequence and wherein introduction comprises inducing a double stranded break in the endogenous T cell receptor sequence and homologous recombination of the polynucleotide into the genome of the T cell.

[0012] In an aspect of the current disclosure, populations of recombinant neoantigen-specific T cells are provided. In some embodiments, the populations of recombinant T cells are generated by: obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality’ of neoantigen-specific TCR sequences; and introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen-specific T cell. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CD8, MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13,GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA. GZMB, GZMH. IFNG, ITGAE, PDCD1. HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the partitioned TCR sequencing data comprises neoantigen-specific T cell receptor (TCR) sequences. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF. TOP2A, TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, GMZH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA. GZMB, GZMH. IFNG, ITGAE. PDCD1. HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13. GZMA, GZMB, GZMH, IFNG. ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1. CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the polynucleotide comprises a regulator}' element operably linked to the isolated neoantigen-specific TCR sequence. In some embodiments, the regulatory element comprises a promoter. In some embodiments, introducing the polynucleotide comprises transfection or transduction of the polynucleotide. In some embodiments, introducing the polynucleotide comprises introducing the polynucleotide by transfection. In some embodiments, the method further comprises introducing to the T cell one or more additional polynucleotides that collectively encode (1) a Cas nuclease, and (2) one or more guide RNAs. In some embodiments, the one or more guide RNAs target a 3’ region of the endogenous T cell receptor sequence and wherein introduction comprises inducing a double stranded break in the endogenous T cell receptor sequence and homologous recombination of the polynucleotide into the genome of the T cell.

[0013] In some embodiments, the populations of recombinant T cells are generated by: performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA- seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the samplefrom the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; and introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen-specific T cell. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CD8, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13. GZMA, GZMB, GZMH, IFNG. ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the partitioned TCR sequencing data comprises neoantigen-specific T cell receptor (TCR) sequences. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKJ67, STMN1, CENPF, TOP2A, TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA. GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3. and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13. GZMA, GZMB, GZMH, IFNG. ITGAE, PDCD1. HAVCR2. TIGIT. LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the polynucleotide comprises a regulatory element operably linked to the isolated neoantigen-specific TCR sequence. In some embodiments, the regulatory element comprises a promoter. In some embodiments, introducing the polynucleotide comprises transfection or transduction of thepolynucleotide. In some embodiments, introducing the polynucleotide comprises introducing the polynucleotide by transfection. In some embodiments, the method further comprises introducing to the T cell one or more additional polynucleotides that collectively encode (1) a Cas nuclease, and (2) one or more guide RNAs. In some embodiments, the one or more guide RNAs target a 3’ region of the endogenous T cell receptor sequence and wherein introduction comprises inducing a double stranded break in the endogenous T cell receptor sequence and homologous recombination of the polynucleotide into the genome of the T cell.

[0014] In an aspect of the current disclosure, pharmaceutical compositions are provided. In some embodiments, the pharmaceutical compositions comprise a population of recombinant neoantigen-specific T cells generated by: obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67. STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; and introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen- specific TCR sequences to generate the recombinant neoantigen-specific T cell. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CD8, MKJ67. STMN 1. CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13. GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1. HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the partitioned TCR sequencing data comprises neoantigen-specific T cell receptor (TCR) sequences. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1. CENPF. TOP2A. TYMS, CXCL13, GZMA, GZMB,GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA. GZMB, GZMH. IFNG, ITGAE, PDCD1. HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the polynucleotide comprises a regulatory element operably linked to the isolated neoantigen-specific TCR sequence. In some embodiments, the regulatory element comprises a promoter. In some embodiments, introducing the polynucleotide comprises transfection or transduction of the polynucleotide. In some embodiments, introducing the polynucleotide comprises introducing the polynucleotide by transfection. In some embodiments, the method further comprises introducing to the T cell one or more additional polynucleotides that collectively encode (1) a Cas nuclease, and (2) one or more guide RNAs. In some embodiments, the one or more guide RNAs target a 3’ region of the endogenous T cell receptor sequence and wherein introduction comprises inducing a double stranded break in the endogenous T cell receptor sequence and homologous recombination of the polynucleotide into the genome of the T cell.

[0015] In some embodiments, the pharmaceutical compositions comprise a population of recombinant neoantigen-specific T cells generated by: performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA. GZMB, GZMH, IFNG, ITGAE. PDCD1, HAVCR2, TIGIT, LAG3. and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; and introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen- specific T cell. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CD8, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH. IFNG,ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13. GZMA, GZMB, GZMH, IFNG. ITGAE. PDCD1. HAVCR2. TIGIT, LAG3, and ENTPD1. In some embodiments, the partitioned TCR sequencing data comprises neoanti genspecific T cell receptor (TCR) sequences. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises CXCL13, MKI67, STMN1, CENPF, TOP2A, PDCD1, TGIT, LAG3, HAVCR2, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF. TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA. GZMB, GZMH. IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the polynucleotide comprises a regulatory element operably linked to the isolated neoantigen-specific TCR sequence. In some embodiments, the regulatory element comprises a promoter. In some embodiments, introducing the polynucleotide comprises transfection or transduction of the polynucleotide. In some embodiments, introducing the polynucleotide comprises introducing the polynucleotide by transfection. In some embodiments, the method further comprises introducing to the T cell one or more additional polynucleotides that collectively encode (1) a Cas nuclease, and (2) one or more guide RNAs. In some embodiments, the one or more guide RNAs target a 3’ region of the endogenous T cell receptor sequence and wherein introduction comprises inducing a double stranded break in the endogenous T cell receptor sequence and homologous recombination of the polynucleotide into the genome of the T cell.

[0016] In some embodiments, methods of treating a cancer in a subject in need thereof are provided and comprise administering a therapeutically effective amount of a pharmaceutical composition comprising a population of recombinant neoantigen-specific T cells generated by: obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptorsequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; and introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen-specific T cell, to the subject to treat the cancer. In some embodiments, the pharmaceutical compositions comprise: a population of recombinant neoantigen-specific T cells generated by: performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; and introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen-specific T cell, a population of recombinant neoantigen-specific T cells generated by: performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; and introducing a polynucleotide into a T cell, thepolynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen-specific T cell

[0017] In an aspect of the current disclosure, methods of treating a cancer in a subject in need thereof are provided and comprise: obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. to generate a plurality of neoantigen-specific TCR sequences; introducing a polynucleotide into a population of T cells, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate a population of recombinant neoantigen-specific T cells; administering a therapeutically effective amount of the recombinant neoantigen-specific T cells to the subject to treat the cancer. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CD8, MKI67. STMN 1 , CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises CXCL13. MKI67, STMN1, CENPF, TOP2A. PDCD1, TGIT, LAG3, HAVCR2. and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA. GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consistingof squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the methods further comprise administering an additional treatment to the subject selected from the group consisting of a chemotherapy, a radiation therapy, a surgical therapy, a hormonal therapy, and an immunotherapy. In some embodiments, the additional treatment is an immunotherapy. In some embodiments, the immunotherapy is an immune checkpoint blockade (ICB) therapy.

[0018] In some embodiments, the methods of treating a cancer in a subject in need thereof comprise: performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1, to generate a plurality of neoantigen-specific TCR sequences; introducing a polynucleotide into a population of T cells, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate a population of recombinant neoantigen-specific T cells; administering a therapeutically effective amount of the recombinant neoantigen-specific T cells to the subject to treat the cancer. In some embodiments, the T cells are CD8 T cells and the set of markers comprises CD8, MKI67. STMN1, CENPF, TOP2A. TYMS. CXCL13, GZMA. GZMB. GZMH, IFNG. ITGAE, PDCD1, HAVCR2. TIGIT, LAG3, and ENTPD1. In some embodiments, the T cells are CD4 T cells and the set of markers comprises CD4, MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises CXCL13, MKI67, STMN1, CENPF, TOP2A. PDCD1, TGIT, LAG3. HAVCR2, and ENTPD1. In some embodiments, the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13. GZMA, GZMB, GZMH,IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the set of markers consists of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1. HAVCR2, TIGIT, LAG3, and ENTPD1. In some embodiments, the subject has been diagnosed with a cancer. In some embodiments, the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC). In some embodiments, the methods further comprise administering an additional treatment to the subject selected from the group consisting of a chemotherapy, a radiation therapy, a surgical therapy, a hormonal therapy, and an immunotherapy. In some embodiments, the additional treatment is an immunotherapy. In some embodiments, the immunotherapy is an immune checkpoint blockade (ICB) therapy.

[0019] In an aspect of the current disclosure, kits, systems, and platforms are provided. In some embodiments, the kits, systems, or platforms comprise the disclosed polynucleotides, the disclosed populations of neoantigen-specific T cells, and / or the disclosed pharmaceutical compositions and, optionally, further comprise instructions for using the kit, system, or platform.BRIEF DESCRIPTION OF THE FIGURES

[0020] FIGS. 1A-1H show multi-omic analyses across treatment timepoints reveals immediate and profound TIL depletion after radiotherapy in HNSCC. (FIG. 1A) Schematic of trial design including timepoints and translational correlative analyses. (FIG. IB) Combined integrated single-cell datasets including 11 separate HyPR-HN biopsies from four patients over three timepoints. Analysis across timepoints reveals immediate T cell depletion on the last day of radiation both (FIG. 1C) quantitatively as a percent of the single-cell transcriptome and (FIG. ID) visually across UMAP profiles. (FIG. IE) mFC confirms immediate TIL depletion in response to radiation, despite (FIG. IF) preserved overall immune infiltrate and increased intra-tumoral myeloid cells. (FIG. 1G) Representative mFC plots of CD8 and CD4 T cell infiltrate and (FIG. 1H) mIF with corresponding clinical MR (pre-treatment and last day of radiation) and CT (6 weeks post-radiation) imaging are shown for HyPR-HN Patient 01 serially across treatment timepoints. Antibody fluorescent conjugates include CD8: light blue, CD4: green, CD20: red, Foxp3: yellow, CD68: orange, Pan-CK: magenta, DAPI: dark blue. Scale bar = 50pM.

[0021] FIGS. 2A-2D show immunophenotyping by flow cytometry across treatment timepoints reveals immediate and profound TIL depletion after radiotherapy in HNSCC. 8-color immunophenotyping by multiplex flow cytometry of each biopsy at each timepoint including gating strategy for (FIG. 2A) HyPR-HN Patient 01 and (FIG. 2B) Patient 02. Gate frequencies are provided as percentage of live cells. 8-color immunophenotyping by multiplex flow cytometryof each biopsy at each timepoint including gating strategy for (FIG. 2C) HyPR-HN Patient 03 and (FIG. 2D) Patient 04. Gate frequencies are provided as percentage of live cells.

[0022] FIGS. 3A-3E show single-cell RNA sequencing demonstrates global transcriptional conservation of TILs and phenotypic sub-classifications across treatment timepoints. (FIG. 3A) Integrated single-cell datasets of 11 separate HyPR-HN biopsies from four patients with T cells extracted and re-clustered reveals immunophenotypic T cell subsets. (FIG. 3B) Violin plot demonstrating expression of canonical T cell markers in each sub-cluster. (FIG. 3C) Heatmap of the top genes identified in unsupervised clustering analysis from each T cell sub-cluster. Pre-ex: Pre-exhausted T cells; Reg: Regulatory T cells; TRM: Tissue-resident memory T cells; Prolif: Proliferative T cells; ExCD8: Exhausted CD8 T cells; ExCD4: Exhausted CD4 T cells; IFN: Type I interferon-responsive T cells. (FIG. 3D) Heatmaps demonstrate global conservation of sub-cluster transcriptional profiling across treatment timepoints. (FIG. 3E) Violin plots demonstrate global conservation of sub-cluster canonical marker expression across treatment timepoints.

[0023] FIGS. 4A-4G show multi-omic analyses across treatment timepoints reveals immediate and persistent loss of ICRh,g11TILs after radiotherapy in HNSCC. (FIG. 4A) Integrated single-cell datasets of 11 separate HyPR-HN biopsies with T cells extracted and re-clustered from four patients over three timepoints with UMAP profiling demonstrating global loss of TILs postradiation and revealing persistent loss of exhausted CD8 T cells. (FIG. 4B) Analysis across timepoints confirms immediate T cell depletion on the last day of radiation quantitatively as a percent of the T cell transcriptome. (FIG. 4C) Characterization of T cells co-expressing multiple ICRs (PDCD1. HAVCR2, T1GIT, and LAGS) demonstrates immediate and persistent loss of ICR expression in response to radiotherapy. (FIG. 4D) mFC confirms immediate loss of ICRhlghTILs as a percentage of viable cells in response to radiation, despite (FIG. 4E) preserved expression as a percentage of CD8 T cells. Representative mFC plots of CD8 T cell infiltration and ICR expression are shown for HyPR-HN Patient 02 (FIG. 4F) pre-treatment and (FIG. 4G) on the last day of radiation. Gating is provided as a percentage of total live cells. Pre-ex: Pre-exhausted T cells; Reg: Regulatory T cells; TRM: Tissue-resident memory T cells; Prolif: Proliferative T cells; ExCD8: Exhausted CD8 T cells; ExCD4: Exhausted CD4 T cells; IFN: Type I interferon-responsive T cells.

[0024] FIGS. 5A-5D show immunophenotyping by flow cytometry across treatment timepoints reveals immediate and profound ICRhlghTIL depletion after radiotherapy in HNSCC. CD8 T cell exhaustion panel immunophenotyping by multiplex flow cytometry of each biopsy at each timepoint including gating strategy for (FIG. 5A) HyPR-HN Patient 01 and (FIG. 5B) Patient02. Gate frequencies are provided as percentage of live cells. CD8 T cell exhaustion panel immunophenotyping by multiplex flow cytometry of each biopsy at each timepoint including gating strategy for (FIG. 5C) HyPR-HN Patient 03 and (FIG. 5D) Patient 04. Gate frequencies are provided as percentage of live cells.

[0025] FIGS. 6A-6C show spatial transcriptomics demonstrates post- radiotherapy loss of exhausted CD8 T cells throughout the tumor microenvironment. (FIG. 6A) Spatial transcriptomics demonstrates loss of overall CD8 and ICR expression as a percentage of transcriptional spots analyzed spatially. Representative mIF and spatial transcriptomic images from (FIG. 6B) HyPR-HN Patient 02 pre-treatment and (FIG. 6C) 6 weeks post-radiation demonstrate uniform spatial loss of ICRhlghCD8 T cell expression despite preservation of myeloid marker expression. ICRhlghTILs defined as expressing CD3E, CD8A, and an ICR (PDCD1, HAVCR2, LAG3. or TIGIT) with log transformed normalized expression >1. Antibody fluorescent conjugates include CD8: light blue, CD4: green, CD20: red, Foxp3: yellow, CD68: orange, Pan-CK: magenta, DAPI: dark blue. Scale bar = 50pM.

[0026] FIGS. 7A-7C show single-cell TCR sequencing analyses demonstrate conserved clonal and phenotypic architecture of post-radiation repopulating TILs except for the persistent loss of ICRhigh clonot pes. which demonstrate extensive clonal overlap with proliferative TILs suggesting tumor-reactivity. (FIG. 7A) Clonal frequency projections onto T cell UMAP profiles from all 11 HyPR-HN datasets demonstrate a diverse T cell landscape of both singletons and hyper-expanded clones. Repopulating T cells largely recapitulate the pre-treatment clonal architecture, particularly among less differentiated states including naive- like and pre-exhausted populations. (FIG. 7B) While exhausted CD8 T cell clonotypes have overlap with several subclusters, they demonstrate the greatest clonal overlap with proliferative TILs, which are subsequently lost post- radiotherapy and do not return to the tumor microenvironment. (FIG. 7C) Overlapping exhausted and proliferative TIL clonotypes contain a conserved and unique gene transcriptional profile suggesting tumor antigen-reactivity. Pre-ex: Pre-exhausted T cells; Reg: Regulatory T cells; TRM: Tissue-resident memory T cells; Prolif: Proliferative T cells; ExCD8: Exhausted CD8 T cells; ExCD4: Exhausted CD4 T cells: IFN: Type I interferon- responsive T cells.

[0027] FIGS. 8A-8D show single-cell TCR sequencing analysis demonstrates a wide clonal network associated with exhausted T cells, but the most extensive overlap with the proliferative sub-cluster. (FIG. 8A) Clonal network analysis of clonotypes in the ExCD8 sub-cluster from all 11 integrated single-cell sequencing datasets obtained serially over treatment timepoints reveals wide overlap with numerous other immunophenotype classifications. However, the mostextensive overlap is seen with proliferating T cells for (FIG. 8B) HyPR-HN Patient 02, (FIG. 8C) Patient 03, and (FIG. 8D) Patient 04. Pre-ex: Pre-exhausted T cells; Reg: Regulatory' T cells; TRM: Tissue-resident memory T cells; Prolif: Proliferative T cells; ExCD8: Exhausted CD8 T cells; EXCD4: Exhausted CD4 T cells; IFN: Type I interferon-responsive T cells.

[0028] FIGS. 9A-9C show integrated analysis of multiple single-cell sequencing datasets reveals conserved expression of the TProhf-Toxgene signature across HNSCCs without expression in normal tissues. (FIG. 9A) Integrated analyses of multiple internal and external datasets with T cells subsetted demonstrates (FIG. 9B) conserved clustering and T cell functional groups. (FIG. 9C) Despite globally conserved transcriptional T cell sub- classifications, the TProllf Toxgene signature is tumor-specific without any expression in normal oral mucosa. Pre-ex: Pre-exhausted T cells; Reg: Regulatory T cells; Prolif: Proliferative T cells; Ex: Exhausted T cells.

[0029] FIGS. 10A-10F show the TProllf Toxgene signature is found across both primary tumor and metastatic nodal HNSCC with clonotype overlap, suggesting shared tumor antigen targets. (FIG. 10A) Schematic of biospecimens and tissue analyses for patient P120. (FIG. 10B) Exomic and transcriptional mutational analyses demonstrates conserved coding mutations across tumor, involved node, and patient-derived malignant line. (FIG. 10C) Co-culture between a patient- derived malignant line and autologous extracted TILs demonstrates concentration-depended tumor cell killing, while cultures of autologous cancer-associated fibroblasts (CAF) enriched for patient-matched non-malignant cells are largely preserved. Ratios represent target-to-TIL concentration. Single-cell sequencing of tumor, involved regional node, and circulating T cells from a HNSCC patient (P120) reveals (FIG. 10D) similar clustering and clonal architecture to HyPR-HN samples, and (FIG. 10E) the conserved presence of TProllf Toxsignature expression in both tumor and node, without expression in blood. (FIG. 10F) Several TProlif Toxclonotypes demonstrate identical overlap across primary tumor and involved node but are undetectable in blood. SEQ ID NOs: 4, 5, 2, 3. 6, and 7. Pre-ex: Pre-exhausted T cells; Reg: Regulatory T cells; Prolif: Proliferative T cells; ExCD8: Exhausted CD8 T cells; ExCD4: Exhausted CD4 T cells; IFN: Type I interferon responsive T cells; HSP: Heat-shock protein-enriched T cells; SCM: stem-cell memory' T cells; EM: effector memory T cells; CM: central memory' T cells.

[0030] FIGS. 11A-11B show integrated single-cell sequencing analysis of patient P120 primary tumor and regional metastatic node with T cell sub-setting and re-clustering. Single-cell sequencing from a HNSCC patient (P120) reveals similar clustering between primary' tumor and regional in both (A) overall tumor microenvironment and (B) T cell sub-clusters. pDCs: plasmacytoid dendritic cells; Pre-ex: Pre-exhausted T cells: Reg: Regulatory’ T cells; Prolif:Proliferative T cells; ExCD8: Exhausted CD8 T cells; ExCD4: Exhausted CD4 T cells; IFN: Type I interferon-responsive T cells; EISP: Heat-shock protein-enriched T cells.

[0031] FIGS. 12A-12E show the TProllf Toxgene signature and clonotypes are conserved across primary tumor and metastatic sites in breast cancer. (FIG. 12A) Schematic of breast cancer biospecimens and secondary bioinformatic analyses for single-cell sequencing data of a matched primary tumor and metastatic regional node. (FIG. 12B) Integrated analysis of single-T cell sequencing reveals similar (FIG. 12B) clustering and (FIG. 12C) clonal architecture to HNSCC. (FIG. 12D) Clonotypes expressing the TProlif Tox gene signature are conserved across both proliferative and exhausted T cell sub-clusters, and (FIG. 12E) are found in both the primary tumor and involved regional node. Pre-ex: Pre-exhausted T cells; Reg: Regulatory T cells; Prolif: Proliferative T cells; ExCD8: Exhausted CD8 T cells; IFN: Type I interferon-responsive T cells; MT: T cells with predominant expression of mitochondrial genes. FIG. 12E shows SEQ ID NOs: 20-61.

[0032] FIGS. 13A-13E show the TProllf Toxgene signature and clonotypes are conserved spatially across the tumor landscape in renal cell carcinoma and demonstrate hyper-expansion in ICI-treated disease. (FIG. 13 A) Schematic of renal cell cancer biospecimens and secondary bioinformatic analyses for single-cell sequencing data of multi-spatial biopsies sampled across the tumor. (FIG. 13B) Integrated analysis of single-T cell sequencing demonstrates clonal hyperexpansion across proliferative, acutely activated, and exhausted clusters. (FIG. 13C) The TProlif loxgene signature is identified across these sub-clusters and is conserved multi-spatially across the tumor without expression in circulating T cells. (FIG. 13D) Hyper-expanded 'f1>I'’lll T'lxclonotypes are identical multi-spatially across the tumor but are absent in circulating T cells. FIG. 13E shows SEQ ID NOs: 62-71. (FIG. 13E) Integrated clonal frequency plots of tumor and circulating T cells reveals a single dominant TProlif-Toxc|oricqypeacross each multi-spatial tumor biopsy with complete absence in the circulating T cell pool. Pre-ex: Pre-exhausted T cells; Reg: Regulatory T cells; Prolif: Proliferative T cells; Ex: Exhausted T cells; IFN: Type I interferon-responsive T cells; Act: acutely activated T cells; EM: effectory memory T cells; CM: central memory T cells; Eff: effector T cells.

[0033] FIGS. 14A-14D show the TProllf Toxgene signature is present in pancreatic adenocarcinoma and TProllf Toxclonotypes demonstrate overlap with experimentally validated tumor-reactive TCRs. (FIG. 14A) Schematic of pancreatic cancer biospecimens and secondary^ bioinformatic analyses for single-cell sequencing data and experimentally -verified tumor-reactive T cell clonotypes. Integrated analysis of T cells from a pancreatic adenocarcinoma demonstrates(FIG. 14B) the presence of the TProlif Tox gene signature and (FIG. 14C) TProlif Tox clonotypes across exhausted and proliferative sub-clusters. FIG. 14C shows SEQ ID NOs: 72-97. (FIG. 14D) TProlif Tox clonotypes demonstrate extensive overlap with experimentally validated tumor-reactive TCRs (overlapping clonotypes are denoted by *). FIG. 14D shows SEQ ID NOs: 74, 75, 82, 83, 88, 89, 98, 99, and 90-93. Pre-ex: Pre-exhausted T cells; Reg: Regulatory T cells; Prolif: Proliferative T cells; ExCD8: Exhausted CD8 T cells; HSP: Heat-shock protein-enriched T cells; CM: central memory T cells.

[0034] FIGS. 15A-15D show structural modeling demonstrates that pre-treatment TProlif-Toxclonotypes are predicted to bind patient-matched HLA-specific tumor neo-antigen but the ■ Prohf Toxsjgnature ancl the clones that express it are lost after radiotherapy. (FIG. 15A) Whole exome and RNA sequencing mutational analyses were performed pre-treatment and at 6 weeks post-radiation for the primary tumor of HyPR Patient 02, demonstrating substantial overlap in transcribed coding mutations. (FIG. 15B) Predicted binding between TCRs and neo-antigen-HLA complexes for HyPR-HN Patient 02. Binding scores were zero-shifted and normalized. Binding prediction for each neo-peptide-HLA complex was compared betweenrrPro,,f-ToxTCRs and patient-matched irrelevant naive TCRs. For each neo-peptide-HLA complex, a TProlif Tox clonotype demonstrates the greatest predicted binding affinity. FIG. 15B shows SEQ ID NOs: 8- 19. Among all HyPR-HN patients, (FIG. 15C) the TProlif-Toxexpression profile and (FIG. 15D) the associated clonotypes which express it are lost after radiation and do not return to the tumor.

[0035] FIGS. 16A-16B show structural modeling demonstrates that pre-treatment Tl>rol'l loxclonotypes are predicted to bind patient-matched HLA-specific tumor neo-antigen and these clonotypes are found with consistent transcriptional signatures across tumor and metastatic sites. (FIG. 16A) Predicted binding between TCRs and neo-peptide-HLA complexes for HNSCC patient P 120. Binding scores were zero-shifted and normalized. Binding prediction for each neo- peptide-HLA complex was compared between TProlif Tox TCRs and patient-matched irrelevant naive TCRs. For each neo-peptide-HLA complex, a TProlif Tox clonotype demonstrates the greatest predicted binding affinity. FIG. 16A shows SEQ ID NOs: 4, 5, 2, 3, 100, and 101. (FIG. 16B) The TProlif Tox clonotypes with high predicted binding affinity are found with a consistent gene expression profile in both tumor and metastatic node, but are absent from the circulating T cell pool. FIG. 16B shows SEQ ID NOs: 4, 5, 100, 101, 2, and 3.

[0036] FIGS. 17A-17F show the TProllf Toxgene signature and associated clonotypes are lost after radiotherapy across every' HyPR-HN patient. Expression and clonotype tracking of integrated single-cell sequencing analysis serially over treatment timepoints reveals immediateand persistent loss the TProlif Tox gene expression profile and clonotypes which express this gene signature in response to radiotherapy for (FIG. 17A-17B) HyPR-HN Patient 02, (FIG. 17C- 17D) Patient 03, and (FIG. 17E-17F) Patient 04. FIG. 17F shows SEQ ID NOs: 102, 103, 8, 9. 104, 105, 12, 13, 106, and 107-127 Pre-ex: Pre-exhausted T cells; Reg: Regulatory T cells; TRM: Tissue-resident memory T cells; Prolif: Proliferative T cells; ExCD8: Exhausted CD8 T cells; ExCD4: Exhausted CD4 T cells; IFN: Type I interferon-responsive T cells.

[0037] FIGS. 18A-18E show T cell repopulation following radiation-induced TIL depletion is driven by returning high- frequency clonotypes and newly infiltrating singletons, globally independent of TCR sequencing patterns. (FIG. 18A) Radiation-induced TIL depletion is associated with a major reduction in overall TCR clonotype diversity7, which begins to return by 6 weeks post-radiation in all patients. (FIG. 18B) Clonotype frequency mapping reveals broad immediate TIL depletion with return of high-frequency and intermediate-frequency clonotypes by 6 weeks post-radiation. (FIG. 18C) Despite alterations in overall number of clonotypes at each timepoint, diversity7is conserved on the amino acid-level at the CDR3 region. (FIG. 18D) Among all repopulating clonotypes approximately 30% are returning clones while the remaining are newly infiltrating. (FIG. 18E) Clonal tracking of the 30 most frequent clonotypes within pretreatment tumors reveals that returning clonotypes are predominantly composed of pre- treatment expanded populations.

[0038] FIGS. 19A-19F show post-radiation TIL repopulation is driven primarily by returning high-frequency non-tumor- specific circulating clonotypes and newly infiltrating regulatory and naive clones. Integrated single-cell sequencing across treatment timepoints demonstrates extensive clonal overlap between high-frequency circulating clonoty pes and the pre-exhausted sub-cluster within tumors, showing that T cell re- infiltration after radiation-induced TIL depletion is driven in part by common circulating clonoty pes in both (FIGS. 19A-19B) HyPR- HN Patient 02 and (FIGS. 19C-19D) HyPR-HN Patient 04 . Clonal mapping of post-radiation regulatory and naive clonotypes further demonstrates that these sub-clusters are composed largely of new clonotypes not seen in the pre-treatment tumor in both (FIG. 19E) HyPR-HN Patient 02 and (FIG. 19F) HyPR-HN Patient 04 . Pre-ex: Pre-exhausted T cells; Reg: Regulatory7T cells; TRM: Tissue-resident memory T cells; Prolif: Proliferative T cells; ExCD8: Exhausted CD8 T cells; ExCD4: Exhausted CD4 T cells; IFN: Type I interferon-responsive T cells.

[0039] FIGS. 20A-20G show radiorecurrent HNSCCs after conventionally' fractionated radiation are relatively CD8 TIL depleted including ICRhlghTILs. (FIG. 20A) HNSCC patients included in a prospective tumor registry underwent multiplex immunofluorescence (mIF)demonstrating lower CD8 and CD4 T cell populations in radiation-recurrent compared to previously untreated disease. (FIG. 20B) This effect was consistent across both stromal and cancer compartments. (FIG. 20C) Analysis of patient- matched biopsies obtained pre-treatment and at the time of in-field radiation recurrence confirms relative CD8 TIL depletion of radiation recurrent tumors compared to matched pre-treatment levels, (FIG. 20D) an effect not seen other immune cell subsets. Representative PET / CT, mIF, and mFC images are shown for (FIG. 20E) a patient- matched previously untreated and (FIG. 20F) radiorecurrent HNSCC. Gate frequencies are provided as percentage of live cells. Antibody fluorescent conjugates include CD8: light blue, CD4: green, CD20: red, Foxp3: yellow, CD68: orange, Pan-CK: magenta, DAPI: dark blue. Scale bar = 50pM. (FIG. 20G) Exhausted T cell populations were particularly diminished in radiorecurrent disease.

[0040] FIGS. 21A-21F show pre-treatment and post-radiation recurrent HNSCCs after conventionally fractionated radiotherapy are relatively CD8 T cell depleted including ICRhigh TILs. In addition to the mIF shown in Figure 9, a subset set of HNSCC patients included in our prospective tumor registry underwent mFC which confirmed decreased T cell infiltrate in radiation recurrent cancers as both (FIG. 21 A) a percentage of overall viable cells and (FIG. 2 IB) as a percentage of overall immune infiltrate. Representative mIF and mFC images are shown for unmatched (FIG. 21C) previously untreated and (FIG. 21D) radiorecurrent HNSCCs. Additional representative mIF and mFC images are shown for a patient-matched (FIG. 21E) previously untreated and (FIG. 21F) radiorecurrent HNSCC biopsied from the same anatomic location within the primary tumor pre-treatment and at the time of radiation recurrence. Gate frequencies are provided as percentage of live cells, unless otherwise indicated. Antibody fluorescent conjugates include CD8: light blue, CD4: green, CD20: red, Foxp3: yellow, CD68: orange, Pan-CK: magenta, DAPI: dark blue. Scale bar = 50gM.

[0041] FIG. 22 suggests ways in which understanding the TPlolif-Toxcluster could be leveraged for novel therapeutics including T cell engineering and adoptive cell transfer after radiotherapy.

[0042] FIG. 23 delineates why traditional methods to identify tumor antigen-specific T cell receptors are not tenable for clinical application and therefore why leveraging the TProlif-Toxcluster is a major innovation.

[0043] FIG. 24 demonstrates an additional method to confirm tumor antigen-specificity of the TProlif _Toxc]uster using single tumor cell-single T cell co-culture.

[0044] FIG. 25 demonstrates a method to offload tumor-killing T cells from a single-cell coculture assay for single-cell T cell receptor sequencing to identify tumor antigen-specific T cell receptors.

[0045] FIG. 26 shows a real-time image of offloading tumor-killing T cells from a single-cell co-culture assay.

[0046] FIG. 27 demonstrates the full process to confirm tumor antigen-specificity of the - Prohf Toxc|us[er using single tumor cell-single T cell co-culture paired with single-cell RNA and TCR sequencing.

[0047] FIG. 28 demonstrates the potential clinical application of using the TProllf Toxcluster as a marker of tumor antigen-specificity, enabling rapid T cell receptor engineering.2

[0048] FIG. 29 demonstrates important additional considerations when applying T cell engineering to the clinical setting.

[0049] FIG. 30 demonstrates a schematic for T cell receptor engineering, to insert a tumor antigen-specific T cell receptor into the TRAC locus.

[0050] FIG. 31 demonstrates a method to identify tumor-killing T cells using single tumor cell-single T cell co-culture.

[0051] FIG. 32 indicates the challenge of directly applying single tumor cell-single T cell coculture to clinical therapeutic applications.

[0052] FIG. 33 demonstrates the importance of identifying the T cell receptor used to recognized and kill tumor cells.

[0053] FIG. 34 demonstrates the long clinical timeline that would be necessary by applying single tumor cell-single T cell co-culture with off-chip T cell receptor sequencing to clinical therapeutic applications.

[0054] FIG. 35 introduces the potential utility of using RNA expression profiling to rapidly identify tumor antigen-specific T cells.

[0055] FIG. 36 illustrates the basic premise behind single-cells sequencing.

[0056] FIG. 37 delineates the hypothesis that thecluster harbors tumor antigenspecific T cells based on expression of immune checkpoint receptors, differentiation markers, proliferative genes, and cytotoxicity features and shows expression of CENPF and TOP2A in theTProlif ToxciusterDETAILED DESCRIPTION

[0057] The inventors identified a novel rapidly proliferating sub-cluster of tumor infiltrating lymphocytes (TILs) with conserved gene expression across samples from different oral squamouscell carcinoma (OSCC) patients. Based on expression of markers involved in cell cycling (e.g., MKI67, STMN1, CENPF. TOP2A, TYMS), late differentiation (e.g., CXCL13), cytotoxicity (e.g., GZMA, GZMB, GZMH. IFNG), and immune checkpoint receptor (ICR) expression (e.g.. PDCD1, HAVCR2, TIGIT, LAG3, ENTPD1), the inventors discovered a population of neoantigen-specific TILs termed T^'^-TOX jnThe inventors subsequently confirmed the existence of this sub-cluster in a syngeneic murine model of OSCC, demonstrating that TProllf-Toxis both clinically and evolutionarily conserved.

[0058] Accordingly, disclosed herein are methods of partitioning neoantigen-specific T cell receptor (TCR) sequences, polynucleotides comprising the partitioned neoantigen-specific TCR sequences, recombinant neoantigen-specific T cells comprising the polynucleotides, methods of generating recombinant neoantigen-specific T cells, pharmaceutical compositions comprising the neoantigen-specific T cells, and methods of treating cancer using the recombinant neoantigen- specific T cells.Method of detecting neoantigen-specific T cell receptor sequences

[0059] In an aspect of the current disclosure, methods are provided. In some embodiments, the methods comprise: a. obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. The markers may consist of one or more of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB. GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

[0060] In some embodiments, the methods comprise: a. performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS,CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1

[0061] In an aspect of the current disclosure, methods of detecting neoantigen-specific T cell receptor (TCR) sequences are provided. In some embodiments, the methods comprise: a. obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67. STMN1, CENPF. TOP2A. TYMS, CXCL13, GZMA, GZMB. GZMH, IFNG, ITGAE. PDCD1. HAVCR2. TIGIT. LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences.

[0062] In some embodiments, the methods comprise: a. performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13. GZMA. GZMB, GZMH. IFNG. ITGAE. PDCD1. HAVCR2. TIGIT, LAG3, and ENTPD1 to detect a plurality' of neoantigen-specific TCR sequences.

[0063] As used herein “positive expression" refers to detection of expression of the set of markers in the cells, rather than the lack of expression in the cells. Positive expression refers to a level of expression of the set of markers of greater than 0. The cells may have expression of each of the markers in the set. For example, if the set of markers comprises MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1, positive expression of the set of markers may be expression of MKI67, STMN1, CENPF, TOP2A. TYMS, CXCL13, GZMA. GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 each greater than 0 in the cell.

[0064] In some embodiments, the cells do not express FOXP3, which is a marker of T regulatory cells (Tregs).

[0065] As used herein “partitioning the T cell receptor sequencing data” refers to separating the TCR sequencing data to isolate or enrich for TCR sequences of interest, i.e., TCR sequences belonging to cells which have positive expression for the disclosed set of markers. The inventors have demonstrated that a subset of T cells that express the disclosed set of markers includes neoantigen-specific T cells. The neoantigen-specific T cells may be cells of interest and, accordingly, sequences of interest to be partitioned may comprise the TCR sequences of the neoantigen-specific T cells.

[0066] The set of markers may comprise one or more markers selected from MKI67, STMN 1 , CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. The set of markers may further comprise CD8a, also referred to as “CD8,” CD8b, or CD4. The set of markers may comprise two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, or all sixteen of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCE13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. The set of markers may consist of one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB. GZMH, IFNG, ITGAE. PDCD1. HAVCR2. TIGIT. LAG3, and ENTPD1. The set of markers may consist of two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, etc. or all sixteen of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. The set of markers may comprise additional markers or may be limited to aselection of only MKI67, STMN1, CENPF. TOP2A. TYMS. CXCL13. GZMA, GZMB. GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. The set of markers may consist of MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 or any subgenus of the former markers.

[0067] The inventors discovered that a characteristic of the novel subset of T cells, also referred to as “rpProllCTox” jsthe expression of cell cycle and proliferation genes. Therefore, the set of markers may comprise one or more cell cycle / proliferation marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, TYMS, and one or more marker selected from the group consisting of CXCL13, GZMA, GZMB, GZMH, IFNG. PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. The set of markers may compnse MKI67, STMN1, CENPF, TOP2A, TYMS and one or more marker, two or more markers, three or more markers, four or more markers, five or more markers, or six or more markers or more selected from the group consisting of CXCL13, GZMA, GZMB, GZMH, IFNG, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

[0068] Expression of the markers is detected based on RNA expression or protein expression. The expression of the markers may be detected based on single cell RNA sequencing (scRNA- seq) or by combined scRNA-seq and T cell receptor sequencing (TCR-seq). The TCR-seq may be single cell a / p TCR sequencing. Methods for performing the sequencing procedures are known in the art, e.g., Sievers, C, et al. “Phenotypic plasticity and reduced tissue retention of exhausted tumor-infiltrating T cells following neoadjuvant immunotherapy in head and neck cancer” Cancer Cell. 2023 May 8;41(5):887-902.e5 and Abdulrahman Z. et al. “Tumor-specific T cells support chemokine-driven spatial organization of intratumoral immune microaggregates needed for long survival” J. Immunother. Cancer. 2022 Feb;10(2):e004346, each of which are incorporated by reference herein in their entireties.

[0069] RNA sequencing data and TCR sequencing data may comprise aligned and processed data. Such that the person of ordinary skill in the art may interrogate the expression levels of the set of markers and determine the TCR sequences for cells of interest. The analysis of the scRNA / TCR seq may be performed with the publicly available tool Seurat.

[0070] In some embodiments, the subject has been diagnosed with a cancer, e.g., head and neck cancer, e.g., squamous cell carcinoma or oral squamous cell carcinoma.

[0071] The sample may be, e.g., a tumor biopsy or a blood sample or other suitable sample believed to contain neoantigen-specific T cells, e.g., a sample from a lymph node near the site of a tumor.

[0072] The neoantigen-specific T cell receptors detected by the foregoing methods may be used to generate polynucleotides for expression of the neoantigen-specific TCRs, e.g., for the generation of recombinant T cells. Therefore, the methods may further comprise generating a polynucleotide comprising one of the plurality of detected neoantigen-specific TCR sequences.Polynucleotides or set of polynucleotides

[0073] In an aspect of the current disclosure, polynucleotides are provided. In some embodiments, the polynucleotides comprise a neoantigen-specific TCR sequence generated by the disclosed methods. The neoantigen-specific TCR sequence may be a cDNA sequence determined by RNA sequencing that encodes the neoantigen specific TCR sequence.

[0074] In some embodiments, the neoantigen-specific TCR sequences comprise or consist of one of more of the disclosed TCR sequences, e g., one or more of SEQ ID NOs: 2-127.

[0075] The polynucleotides may comprise a regulatory element, e.g., a promoter or enhancer, examples of which are known in the art.

[0076] The polynucleotides or set of polynucleotides may collectively further comprise (1) a Cas nuclease and (2) one or more guide RNAs (gRNAs). The Cas nuclease and gRNAs may be encoded on a single polynucleotide or on separate polynucleotides. The one or more gRNAs may be targeted to a locus or loci in the genome of a T cell and may be used to induce a doublestranded break in the genome to facilitate the incorporation of the neoantigen-specific TCR sequence. See, for example, FIG. 30 for an exemplary strategy. The Cas nuclease may be Cas9, e.g., Streptococcus pyogenes Cas9 (spCas9). Sequences for gRNAs, e.g.. sgRNAs are known in the art. An exemplary sgRNA sequence compnsing a protospacer specific for TRAC is SEQ ID NO: 1. In some embodiments, the sgRNA sequence comprises modified nucleotides, e.g., methylation, e.g., 2-O-methylation, phosphorothioate modification, etc. In some embodiments, SEQ ID NO: 1 may comprise methylation of the first three and the final three residues.

[0077] The one or more guide RNAs may target a 3’ region of the endogenous T cell receptor sequence and wherein introduction comprises inducing a double stranded break in the endogenous T cell receptor sequence and homologous recombination of the polynucleotide into the genome of the T cell. The region may be, e.g., within the endogenous TRAC gene.Methods of generating recombinant neoantigen-specific T cells

[0078] The inventors have discovered that the TPlollf Toxsubset of T cells comprises neoantigen-specific T cells which may be used to generate therapeutically effective T cells for autologous administration. Therefore, in an aspect of the current disclosure, methods of generating recombinant neoantigen-specific T cells are provided. In some embodiments, the methods comprise: a. obtaining data compnsing single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1. HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; and c. introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen-specific T cell.

[0079] In some embodiments, the methods comprise: a. performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing(TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB, GZMH, IFNG, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality’ of neoantigen-specific TCR sequences; and c. introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen-specific T cell.

[0080] The polynucleotides comprise one of the plurality of neoantigen-specific TCR sequences detected by the disclosed methods.

[0081] “Introduction” of a polynucleotide may comprise transfection, transduction, electroporation, or any other suitable modality to allow the polynucleotide access through the cellular membrane to be expressed in the cell or to be incorporated into the genome of the cell.

[0082] The methods of generating recombinant neoantigen-specific T cells may be used to generate a population of recombinant neoantigen-specific T cells. The populations of neoantigen- specific recombinant T cells may comprise one clone or clonotype of TCR or a plurality of clones or clonotypes. The population may be generated by first generating a single recombinant T cell bearing a neoantigen-specific TCR, or a many recombinant T cells bearing neoantigen-specific TCRs, and further expanding the recombinant T cell or cells in culture. Methods of expanding recombinant T cells in culture are known in the art and include culturing the T cells with certain reagents including, but not limited to, CD3 agonists, CD28 agonists, IL-2, IL-7, IL- 15, or IL-21.

[0083] The specificity of the recombinant T cells may be tested by, e.g., culturing the T cells with target cells loaded with neoantigen, if a neoantigen is known, or by culturing the T cells with cancer cells from the subject. See, e.g., FIG. 26.Pharmaceutical compositions

[0084] In an aspect of the current disclosure, pharmaceutical compositions are provided. In some embodiments, the pharmaceutical compositions comprise the disclosed recombinant neoantigen-specific T cells or the populations of recombinant neoantigen-specific T cells.

[0085] As used herein, “therapeutically effective amount” or “effective amount” refer to the amount of the pharmaceutical composition necessary7to improve one sign or symptom of a subject's cancer, e.g., reduction in tumor burden. A therapeutically effective amount maycomprise about IxlO6recombinant neoantigen-specific T cells to about IxlO14neoantigenspecific T cells. A therapeutically effective amount may comprise about lOxlO11cells or more to treat a solid tumor.

[0086] The pharmaceutical compositions may be administered by any appropriate route, e g., parenterally, e.g., intravenously or intratumorally. The particular dose and administration route can be determined by a physician.Methods of treatment

[0087] In an aspect of the cunent disclosure, methods of treatment are provided. In some embodiments, the methods are methods of treating a cancer in a subject in need thereof and comprise: a. obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) from a sample from a subject, wherein the sample from the subject comprises T cells, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS. CXCL13, GZMA, GZMB, GZMH, ITGAE, PDCD1. HAVCR2, TIGIT, LAG3, and ENTPD1, to generate a plurality of neoantigen-specific TCR sequences; c. introducing a polynucleotide into a population of T cells, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate a population of recombinant neoantigen-specific T cells; and d. administering a therapeutically effective amount of the recombinant neoantigen- specific T cells to the subject to treat the cancer.

[0088] In some embodiments, the methods comprise: performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subject, wherein the sample from the subject comprises T cells, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, TYMS, CXCL13, GZMA, GZMB. GZMH, ITGAE, PDCD1, HAVCR2, TIGIT, LAG3. and ENTPD1, to generate a plurality of neoantigen-specific TCR sequences; c. introducing a polynucleotide into a population of T cells, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate a population of recombinant neoantigen-specific T cells; and d. administering atherapeutically effective amount of the recombinant neoantigen-specific T cells to the subject to treat the cancer.

[0089] The cancer may be, e.g., head and neck cancer, e.g., squamous cell carcinoma or oral squamous cell carcinoma.

[0090] The methods may further comprise administering an additional treatment to the subject, the additional treatment selected from a chemotherapy, a radiation therapy, a surgical therapy, a hormonal therapy, and an immunotherapy. The immunotherapy may comprise an immune checkpoint blockade (ICB) therapy, e.g., anti-PD-1 antibody, anti-PD-Ll antibody, anti-CTLA4 antibody etc.Kits, systems, and platforms

[0091] In an aspect of the current disclosure, kits, systems, and platforms are provided. In some embodiments, the kits, systems, or platforms comprise the polynucleotides of the instant disclosure, the recombinant neoantigen-specific T cells or population thereof of the instant disclosure, or the pharmaceutical compositions of the instant disclosure. The kits, systems, or platforms may further comprise instructions for using the kits, systems, or platforms.Further definitions

[0092] The present invention is described herein using several definitions, as set forth below and throughout the application.

[0093] The disclosed subject matter may be further described using definitions and terminology’ as follows. The definitions and terminology used herein are for the purpose of describing particular embodiments only and are not intended to be limiting.

[0094] As used in this specification and the claims, the singular forms “a,” "an." and “the” include plural forms unless the context clearly dictates otherwise. For example, the term “a substituent” should be interpreted to mean “one or more substituents,” unless the context clearly dictates otherwise.

[0095] As used herein, “about”, “approximately,” “substantially,” and “significantly” will be understood by persons of ordinary skill in the art and will vary to some extent on the context in which they are used. If there are uses of the term which are not clear to persons of ordinary' skill in the art given the context in which it is used, “about” and “approximately” will mean up to plus or minus 10% of the particular term and “substantially” and “significantly” will mean more than plus or minus 10% of the particular term, up to plus or minus 20% of the particular term.

[0096] As used herein, the terms “include” and “including” have the same meaning as the terms “comprise” and “comprising.” The terms “comprise” and “comprising” should beinterpreted as being “open” transitional terms that permit the inclusion of additional components further to those components recited in the claims. The terms “consist” and “consisting of’ should be interpreted as being “closed” transitional terms that do not permit the inclusion of additional components other than the components recited in the claims. The term “consisting essentially of’ should be interpreted to be partially closed and allowing the inclusion only of additional components that do not fundamentally alter the nature of the claimed subject matter.

[0097] The phrase “such as” should be interpreted as “for example, including.” Moreover, the use of any and all exemplary language, including but not limited to “such as”, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.

[0098] Furthermore, in those instances where a convention analogous to “at least one of A, B and C, etc.” is used, in general such a construction is intended in the sense of one having ordinary skill in the art would understand the convention (e.g., “a system having at least one of A, B and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together.). It will be further understood by those within the art that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the description or figures, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or ‘B or “A and B.”

[0099] All language such as “up to,” “at least.” “greater than,” “less than.” and the like, include the number recited and refer to ranges which can subsequently be broken down into ranges and subranges. A range includes each individual member. Thus, for example, a group having 1-3 members refers to groups having 1, 2, or 3 members. Similarly, a group having 6 members refers to groups having 1. 2, 3, 4. or 6 members, and so forth.

[0100] The modal verb “may” refers to the preferred use or selection of one or more options or choices among the several described embodiments or features contained within the same. Where no options or choices are disclosed regarding a particular embodiment or feature contained in the same, the modal verb “may” refers to an affirmative act regarding how to make or use and aspect of a described embodiment or feature contained in the same, or a definitive decision to use a specific skill regarding a described embodiment or feature contained in the same. In this latter context, the modal verb “may” has the same meaning and connotation as the auxiliary verb “can.”EXAMPLES

[0101] The following Examples are illustrative and should not be interpreted to limit the scope of the claimed subject matter.Example 1 - Computational Identification of Neo-antigen-specific T cells (COIN-T) in solid tumors through single-cell transcriptomics combined with marker gene panel expression

[0102] The inventors performed single-cell RNA and TCR sequencing of clinical SCCs to develop a pre-treatment gene signature identifying neo-antigen-specific tumor infiltrating lymphocytes (TILs). The inventors identified a rapidly proliferating sub-cluster of TILs with conserved gene expression across samples from different oral squamous cell carcinoma (OSCC) patients. Based on expression of markers involved in cell cycling (MKI67, STMN1, CENPF, TOP2A), late differentiation (CXCL13), cytotoxicity (GZMB), and immune checkpoint receptor (ICR) expression, e.g., PDCD1, HAVCR2. TIGIT. LAG3. or ENTPD1, the inventors hypothesize that cells with this expression pattern within this T cell sub-cluster, which the inventors have termed TProllf Tox, represents a neo-antigen-specific TIL population in SCC. The inventors subsequently confirmed the existence of this sub-cluster in a syngeneic murine model of OSCC, demonstrating that TProlif-Toxis both clinically and evolutionarily conserved.

[0103] The inventors hypothesize that this proliferative, differentiated, and cytotoxic phenotype results from tumor neo-antigen experience. Recent studies in other solid malignancies have suggested that the terminally exhausted TIL population (TEx) with heavy expression of ICRs may harbor neo-antigen-specific TCRs. ICR expression helps to explain barriers to neo-antigen- specific TIL function and rationalize the efficacy of immune checkpoint blockade. However, ICR upregulation can be induced by inflammatory signals in the absence of TCR stimulation. Therefore, ICR expression alone is likely insufficient to predict neo-antigen specificity in OSCC. Instead, we anticipate that the TProlif Tox cluster will provide a more accurate marker of neoantigen specific TILs. TProlif Tox demonstrates not only ICR upregulation but an extensive set of T cell activation markers, suggesting recent TCR stimulation (IL12RB1, TNFRSF9, NR4A1, ICOS). Several of these are unique to the TProlif_Tox cluster including cycling genes (MKI67, STMN1) and evidence of TCR-specific signaling (NR4A1). We have found the TProlif Tox TIL cluster across OSCC patients. It can be reliably identified through principal component analysis of single-cell sequencing data in even' case the inventors have analyzed. Further, the inventors have compared their OSCC data to available single-cell sequencing data from normal oral mucosa. In healthy oral tissues the TProlif-Toxcluster is absent. This suggests T|,rolll l oxplays a tumor-specific role and further supports the inventors’ hypothesis that it represents a neo-antigen-specific TILpopulation. Finally, the inventors have found that the TProlil Toxcluster is highly clonal. This confirms the replicative capacity of the TProllf_Tox pop^ponanc| jsconsistent with evidence suggesting that only a few clonotypes can react against neo-antigen in solid tumors. Together, these data support the potential neo-anti gen-specificity of T cell clonotypes within the TProllf-Toxcluster.

[0104] An exemplary process to identify neo-antigen-specific T cells from the T^O'^-TOXC|US LERwe have termed Computational Identification of Neo-antigen-specific T cells (COIN-T). The step-by-step process is shown in the schema below:

[0105] 1. Single-cell RNA and TCR sequencing of cancer, e.g., squamous cell cancer, biopsies

[0106] 2. Process single-cell transcriptomics with, e.g., the publicly available program Seurat

[0107] 3. Use differential gene expression analysis to identify all T cell clusters

[0108] 4. Subset out cells within these T cell clusters by filtering for these clusters and limiting included cells to a T cell specific marker, e.g., CD3E expression >2

[0109] 5. Re-cluster T cells and perform differential gene expression analysis to identify theTProlif_Toxcluster

[0110] 6. Run a marker gene panel (shown in the diagram below) to identify neo-antigen- specific CD8 T cells from the TProllf-Toxc|us^er

[0111] 7. Use scRepetoire to pair TCR sequence with transcriptomics cell-by-cell

[0112] 8. Extract the TCR alpha and beta chain sequence from the identified neo-antigen- specific T cellsExample 2 - Treatment of cancer with the disclosed methods using recombinant neoantigen-specific T cells.

[0113] In one example, a sample is taken from a subject suffering from cancer, e.g., a blood sample or a biopsy is taken of cancerous tissue. Cells are isolated from the biopsy tissue and, optionally, enriched for T cells, e.g.. by negative magnetic bead selection, and the cells undergo single cell RNA and TCR sequencing. The disclosed methods are used to determine a plurality of TCR sequences belonging to the T^^-TOXsu^se^eg , yce||sexpressing one or more of MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1. The TCR sequences belonging to a T cell from the TProllf Toxsubset are used to generate a polynucleotide comprising the TCR sequence, e.g., a cDNA that encodes the TCR. The polynucleotide is then introduced into a T cell or population of T cells from the subject, i.e., autologous T cells, where the T cell receptor is expressed, thereby generating recombinant autologous T cells expressing a neoantigen-specific T cell receptor. The T cells are then expandedand administered as an infusion intravenously in a therapeutically effective amount. Signs and sy mptoms of the cancer may be reduced or eliminated by the administration of the recombinant T cells. Treatment may be administered daily, every other day. even’ third day, once per week, once per month, once per year or on a schedule as determined by the patient's progress, pursuant to a physician's decision. It is anticipated that the subject may experience an increase in quality of life associated with reduction in signs or symptoms of the cancer as compared to an untreated subject. Methods of measuring reductions in signs and symptoms of cancer are known in the art, e.g., reduction in tumor burden by physical measurement or by radiological measurement.Example 3 - Radiation-induced lymphodepletion results in preferential loss of tumor antigen-specific T cells in head and neck cancer

[0114] Human Papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) remains a challenging malignancy, with radiotherapy often failing to achieve durable disease control and showing limited success when combined with immune checkpoint inhibitors. To investigate the mechanisms underlying immunologic radioresistance, we conducted longitudinal multi-omic analyses of pre-treatment and post-radiation biopsies from a Phase I clinical trial testing a pre-operative hypofractionated radiation regimen. Across patients, radiation rapidly depleted tumor-infiltrating lymphocytes (TILs), specifically ablating a subpopulation characterized by a proliferative, cytotoxic, and tissue-resident gene signature Tlollf Tox. indicative of tumor antigen-specificity. Post-radiation, these TProllf-Toxclonotypes did not reappear, with repopulation driven by regulatory, naive, and non-specific high-frequency circulating T cell clones. We provide multi-dimensional evidence for tumor antigen-specificity of TProlll Toxclonotypes, demonstrating their absence in non-malignant tissues and presence across metastatic sites. Finally, we correlate TIL depletion with radiorecurrent disease in patients treated with conventionally fractionated radiation, emphasizing the potential impact of radiation- induced TIL loss regardless of fractionation. These findings suggest that tumor-specific TIL depletion contributes to immunologic radioresistance and highlight the potential to improve outcomes by restoring tumor antigen-specific T cell clonotypes. This study provides key insights into radiotherapy-induced alterations in the immune microenvironment and proposes strategies to improve radioimmunotherapy in HNSCC.

[0115] Introduction

[0116] Human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC), which includes tumors of the oral cavity, pharynx, and larynx, exceed 65,000 cases per year resulting in over 14,000 deaths annually in the United States (1). It also remains a globalhealth problem and is among the top cancers in south Asia (2). Unlike patients with HPV- positive disease (3, 4), the HPV-negative HNSCC population carries a particularly poor prognosis, and current therapies often lead to profound detriments in quality of life (5, 6).

[0117] Surgery and radiation are the mainstays of HPV-negative HNSCC management but 5- year survival remains under 50% for locoregionally advanced disease using current treatments (7). The genomic heterogeneity of HNSCC and absence of uniform oncogenic drivers has stymied efforts at targeted therapies such as EGFR inhibition (3, 8. 9). Immunotherapeutic approaches, including adoptive T cell therapies and immune checkpoint inhibition (ICI), have demonstrated long-term durable disease control in a select group of HNSCC patients (10-12). While these results emphasize the potential power of immuno-oncology for HNSCC, greater than 80% of patients treated with available immunotherapeutic agents do not derive long-term benefit, experiencing eventual disease progression (13).

[0118] To improve HNSCC outcomes, recent clinical trials have combined radiation-based therapies with ICI. These efforts were largely based on pre-clinical evidence which had suggested that radiation efficacy depended on immunologic mechanisms, including upregulation of antigen presentation and increased effector T cell function (14-19). In light of these data, there was hope that such an approach would expand the number of HNSCC patients who benefit from immunotherapy. However, three recent large randomized trials combining radiation with ICI in HNSCC have had disappointing results (5, 20, 21). These data imply that immunologic mechanisms beyond immune checkpoint receptors (ICRs) may contribute to disease relapse and immunologic radioresistance in HNSCC.

[0119] To investigate the mechanisms that underly this surprising lack of synergy between combined radiotherapy and immune checkpoint blockade, we performed a multi-omic analysis in HPV-negative HNSCCs. both pre- and post-radiation, including single-cell RNA and T cell receptor (TCR) sequencing. In this study, we leverage serial biopsies prospectively collected across treatment timepoints during a Phase I clinical trial investigating the safety and efficacy of Hypofractionated Pre-operative Radiotherapy in HNSCC (HyPR-HN, NCT05538533). While conventionally fractionated radiation (2Gy per fraction, delivered in 30-35 fractions over 6-7 weeks) has long been considered immunosuppressive, shorter-course high-dose-per-fraction regimens (hypofractionated) have shown immunogenic potential in pre-clinical studies, including improvements in CD8 T cell infiltration, effector function, and tumor antigen presentation (14, 22-25). Pre-operative hypofractionated radiation has several other logistical benefits including shortening total treatment package time, avoiding post-operative delays to adjuvant radiotherapy,and preventing treatment breaks during radiation, all of which are associated with improved cancer control outcomes (26-28). For these reasons, we initiated the HyPR-HN trial evaluating both safety and immunological effects of pre-operative hypofractionated radiation in HNSCC. including three progressively hypofractionated regimens (46Gy in 10 fractions, 40Gy in 7 fractions, or 35Gy in 5 fractions). The design of all dose levels maintains an EQD2 to late- responding tissues identical to conventionally fractionated radiation (70Gy) while providing an adequate dose to clear microscopic disease (EQD2 a / 0=lO > 50 Gy) (29-31).

[0120] Here, we report on patients treated on the first dose-level (4.6Gy per fraction, delivered in 10 fractions over 2 weeks) which has completed accrual, including all sample acquisition and translational correlative analyses. Specifically, we obtained samples of the primary tumor pretreatment, on the last day of radiation, and 6 weeks later at the time of definitive surgery.

[0121] Through this novel approach, we demonstrate that clinical radiotherapy induces immediate and profound depletion of tumor-infiltrating lymphocytes (TILs), including ablation of TIL sub- populations demonstrating consistent expression of proliferative, exhausted, tissueresident, and cytotoxic markers (which we have termed TProllf_Tox)^ indicative of a capacity for tumor-reactivity. Importantly, Tn'ollf loxclonotypes are almost entirely depleted by clinical radiation and fail to reappear in the tumor microenvironment post-therapy. In their absence, T cell reintegration into HNSCCs is governed by non-specific infiltration from the peripheral circulation, dominated by regulatory', naive, and common effector clonotypes. Finally, we analyze an institutional tumor registry’ of patients treated with conventionally fractionated radiotherapy, including biospecimen analysis pre-treatment and at the time of recurrence in matched patients. We find that TIL reduction is strongly associated yvith long-term in-field cancer recurrence, emphasizing the potential impact of radiation-induced TIL depletion irrespective of fractionation. Relapsed disease is further characterized by a persistent loss of the ICRhigh TIL populations critical for effective anti-tumor immunity. Collectively, these data offer novel insights into radiation-induced dynamic alterations in the T cell landscape and highlight the need for strategies to preserve or restore tumor antigen-specific TILs during treatment.

[0122] Results

[0123] Pre-operative hypofractionated radiation demonstrates safety, feasibility, and efficacy in delivering direct cytotoxic effects against tumors in HNSCC

[0124] The Phase I trial HyPR-HN (NCT05538533, MPIs JZ / MA / HH) tests the safety and efficacy of pre-operative hypofractionated radiation in HPV-negative HNSCC. In this study, biopsies of the primary tumor are taken pre-treatment, on the last day of radiation (immediatelyafter the last fraction), and then 6 weeks later at the time of definitive surgical resection of remaining disease. Here, we report clinical and translational data from four patients enrolled on the first radiation dose level which included 10 fractions of 4.6Gy to the primary tumor and involved nodes (PTVhigh, EQD2=56Gy). Elective nodal radiation was also given simultaneously to a dose of 35Gy in 10 fractions. All enrolled patients had locoregionally advanced oral squamous cell carcinomas. Radiotherapy was well-tolerated in all patients with no dose-limiting toxicities (DLTs) during or after treatment. Radiographic partial response (PR) to radiation was seen in all patients, as measured by RECIST vl. l, with substantial pathological treatment effect but no complete pathological responses. Median follow up was 13.5 months in living patients (range 9-18 months). No patients developed locoregional failure. One patient succumbed to lung metastases during follow up and a second patient expired due to unexpected pulmonary thromboembolism unrelated to therapy (Table 1). Two patients remain alive and disease-free.Table 1 : Demographic, tumor, and treatment characteristics of HyPR-HN patients. PNI: perineural invasion; LVI: lymphovascular invasion.

[0125] The efficacy of hypofractionated radiation in HNSCC may be constrained by immediate and profound T cell depletion

[0126] To dissect the immune alterations occurring in response to hypofractionated radiation and identify mechanisms of immunologic radioresistance, we performed multi-omic analyses on serial tumor biopsy specimens. To maintain consistency, all biopsies were taken from the same anatomical location in the primary tumor for each patient at each timepoint by a single study investigator (JZ, FIG. 1A). Biopsies were completed for all timepoints apart from the final specimen for the third enrolled patient due to a medical complication unrelated to protocol treatment (n=l l total samples). For each patient, biopsy specimens were divided into pieces forparallel analytic pathways including paired single-cell RNA and TCR sequencing, multiplex immunofluorescence (mlF), multiplex flow cytometry (mFC), whole exome sequencing (WES), bulk RNA sequencing, and spatial transcriptomics. Dissociative single-cell sequencing was prioritized and performed immediately on freshly digested biopsy tissues. Additional analytical methods were performed when a sufficient quantity7of biopsy material was available which was determined on a specimen-by-specimen basis (Table 2).Table 2: Biospecimen characteristics of HNSCCs which underwent next-generation sequencing for this study.

[0127] Initial processing of single-cell sequencing datasets was performed using unsupervised clustering analyses with cell-type annotation based on differential gene expression and canonical markers (FIG. IB). Comparative analysis of transcriptomic profiles across timepoints revealed significant and selective lymphocyte depletion on the last day of radiation as a percentage of single cells sequenced, with a relative increase in myeloid cell subsets (FIGS. 1C-1D). To validate this finding on the protein level, we simultaneously performed mFC (allHyPR-HN patients, FIGS. 1E-1G, and FIGS. 2A-3D) and mlF (HyPR-HN Patient 01, FIG. 1H) which revealed immediate and profound TIL depletion after radiotherapy despite a preserved overall immune infiltrate. Despite this significant T cell depletion, however, tumors exhibited substantial radiographic regression, indicating a radiation-induced direct cytotoxic effect independent of T cell activity7(FIG. 1H). Although intra-tumoral T cell populations began to recover by 6 weeks post-treatment, they did not reach pre-treatment levels, most notably in CD8 sub-populations.

[0128] Multi-omic evaluation identifies persistent loss of ICRhigh CD8 TILs postradiotherapy

[0129] Given the absence of synergy seen between radiotherapy and ICI for HNSCC in multiple recent Phase II and III clinical trials (5, 20, 21), we evaluated the immediate effects of radiation on TIL ICR expression using several orthogonal approaches. We first performed integrated single-cell RNA sequencing analyses of TILs across treatment timepoints. T cells identified by both transcriptional clustering and expression of CD3 complex genes were subsetted and re- clustered again (FIG. 3A) through unsupervised analysis with immunophcnotypic subcluster annotation based on canonical markers (FIG. 3B) and differentially expressed genes (FIG. 3C) which were globally conserved across treatment timepoints (FIGS. 3D-3E). These analyses confirmed TIL depletion across sub-clusters immediately upon completion of radiation.

[0130] Surprisingly, the phenotypic pattern of TIL repopulation was selective, with early infiltration regulatory, naive, and pre-exhausted TILs and persistent near-complete loss of the terminally exhausted ICRhigh CD8 sub-cluster (ExCD8) (FIGS. 4A-4C).

[0131] To confirm these findings at the protein level, we simultaneously performed mFC on the same biopsy samples which revealed a significant numerical loss in viable ICRhigh (PD-1, TIM3, LAG3, and TIGIT) CD8 TILs immediately post-radiotherapy (FIGS. 4D-4G, FIGS. 5A- 5D). We further confirmed these findings using spatial analysis from one enrolled patient for whom we had pre-treatment and 6-week post-radiotherapy samples of sufficient quality for spatial transcriptomics (HyPR-HN Patient 02). This analysis demonstrated spatial depletion of postradiotherapy TILs throughout the tumor microenvironment with minimal ICR expression despite preserved myeloid infiltrate in both pre- and post-radiation samples (FIGS. 6A-6C).

[0132] These findings raised critical questions about the immunological impact of radiation on TIL functionality. The loss of the highly expanded ICRhigh ExCD8 population, a key target of ICIs, may have detrimental consequences if this sub-cluster contains tumor antigen-specific TILs in HNSCC. Furthermore, it remained uncertain whether the re-infiltrating non-exhausted T cell landscape supports or impairs anti-tumor immunity. To address these issues, we investigated whether radiation preferentially depletes tumor antigen-specific T cells and whether repopulating TILs retain a tumor-reactive phenotype or are instead dominated by non-specific TCRs.

[0133] Post-radiation T cell clonal repopulation patterns suggest conserved mechanisms of T cell infiltration in HNSCC

[0134] The serial biopsy design of the HyPR-HN trial, combined with paired single-cell RNA and TCR sequencing, enabled precise tracking of T cell clonotypes across treatment timepoints. First, using cell-specific barcode pairing, clonotype frequency data was projected onto the matching TIL transcriptomic states. These projections demonstrated consistency with TIL subcluster immunophenotypic classifications, with naive-like and regulatory clusters largely composed of singleton and low-frequency clonotypes while proliferative, pre-exhausted, and exhausted clusters showed extensive clonal expansion (FIG. 7A). T cells repopulating the tumor post-radiation largely mirrored the clonal architecture of pre-treatment TILs. The clonal and transcriptional overlap observed among TIL subsets associated with less differentiated states, such as naive-like and pre-exhausted clusters, suggested that the same conserved chemotactic forces driving TIL infiltration pre-treatment remained active post-radiation.

[0135] However, the notable absence of the expanded ICRhigh ExCD8 sub-cluster postradiation raised the possibility of selective tumor-reactive clonotype depletion or functional alteration, which may have significant implications for anti -tumor immunity.

[0136] T cell clonal tracking demonstrates clonal and transcriptional overlap between pretreatment ICRhigh and proliferative TILs suggestive of anti-tumor immunogenicity

[0137] To further investigate the immuno-oncologic relevance of ExCD8 T cell loss, we tracked pre-treatment ExCD8 T cell clonotypes within tumors across timepoints for each patient to determine if these clones were fully lost or instead reprogrammed into a new phenotypic subcluster. Pre-treatment ExCD8 clonotypes overlapped with several sub-clusters, including regulatory T cells, suggesting that an exhaustion gene signature alone is insufficient to reliably identify tumor antigen-specific and reactive TILs (FIG. 8A). Surprisingly, the most prominent clonotype overlap was observed between ExCD8 and proliferative T cell clusters in all pretreatment samples (FIG. 7B, FIGS. 8B-8D). This suggested that a subset of ExCD8 TILs is not terminally exhausted but rather exists in dynamic equilibrium between over-stimulated anergic and activated proliferating states. Through differential gene expression analyses, we found that this overlapping TIL sub-population within pre-treatment proliferative and ExCD8 not only shares TCR clonotypes but has a remarkably conserved gene expression profile unique among pre-treatment TILs and distinct from post-radiation proliferating TILs (FIG. 7C). This conserved gene expression profile was ennched for CD8 markers (CD8A, CD8B), proliferative genes (MKI67, STMN1, TYMS), immune checkpoint molecules (PDCD1, HAVCR2, LAG3, TIGIT, ENTPD1), indicators of cytotoxicity (GZMA, GZMB, GZMH, IFNG), a marker of tissueresidence (ITGAE), and CXCL13, which has been previously associated with tumor antigen load,development of tertiary lymphoid structures, and anti-tumor immune response (32, 33). This constellation of gene expression programs, to include proliferative, ICRhigh, cy totoxic, tissueresidence, and CXCL13+ (hereafter referred to as the TProllf-Toxexpression signature), combined with TCR clonotype overlap between exhausted and proliferating TILs, suggested the possibility of tumor reactivity and, therefore, tumor antigen- specificity within this sub-population. Although these TProlil l oxclonotypes were conserved within each patient across exhausted and proliferative clusters, we found no overlap in TCR sequences between patients, suggesting each patient carried unique antigenic targets.

[0138] The TProlif-Toxgene signature is present across HNSCCs but is absent in normal oral mucosa

[0139] To further investigate the potential of the TProlif-Toxgene expression profile to predict tumor antigen-specificity, we next asked whether this signature was unique to malignant tissues and whether it was present across other HNSCCs, external to the HyPR-HN dataset. To address this, we integrated our pre-treatment single-cell RNA sequencing data with publicly available single-cell datasets including other HPV -negative HNSCCs and single-cell sequencing of normal oral mucosal biopsies (34, 35). As above, unsupervised clustering was performed and T cells were subsetted, re-clustered, annotated, and compared across studies and tissue types (FIGS. 9A-9B). T cell gene expression profiling revealed that the TProllf-Toxgene signature was present across HNSCCs but fully absent from normal tissue single-cell sequencing profiles (FIG. 9C), demonstrating the tumor-specificity TProllf Tox

[0140] The TProllf-Toxgene signature and clonotypes are conserved across metastatic sites in HNSCC We next evaluated whether the TProlif-Toxgene signature and associated clonotypes are conserved across metastatic sites. If TProlif ToxTCRs have tumor antigen-specificity and similar tumor antigens are shared across metastatic sites, the samerrProlif-Toxclonotypes should also be present in both the primary tumor and metastatic deposits. To investigate this, we performed single-cell RNA and TCR sequencing of a previously untreated HNSCC patient (P120, not enrolled on HyPR-HN) including samples of the primary tumor, a metastatic regional lymph node, and circulating T cells from peripheral blood (FIG. 10A). We first confirmed that TILs from P120 harbored a subset of T cells which could objectively recognize tumor antigen and kill patient- matched tumor cells. To test this, we derived in vitro TIL cultures and a malignant cell line from separate biopsy pieces of P120’s primary tumor, with isolated cancer-associated fibroblasts (CAFs) as a patient-matched control line. To ensure translational validity of this system, we performed whole exome sequencing (WES) and bulk RNA sequencing of P120’s primary tumor,involved regional node, and the tumor-derived malignant cell line. Through tumor mutational analyses and a neo-antigen prediction pipeline, we identified substantial neo- antigenic overlap between all three samples (FIG. 10B). This finding supports the existence of shared neo-antigens across both primary tumor and metastatic sites, which are also present in the tumor-derived malignant line. Through co-culture cytotoxicity assays, we found concentration-dependent tumorspecific TIL cytotoxicity (>80% kill at 10 TIL:! tumor cell), suggesting that a subset of TILs can recognize tumor antigen and induce a relevant immunologic response (FIG. 10C). Next, we analyzed single-cell sequencing data from additional pieces of Pl 20 biopsy samples which were processed at the time of surgical resection. We performed integration, clustering, T cell subsetting, and clonal frequency projections (FIGS. 11A-11B, FIG. 10D). Gene expression profiling identified the TProllf_Toxgenesignature in both tumor and regional node (FIG. 10E). We additionally found that several identical TProllf-Toxclonotypes were present in both the primary tumor and metastatic node despite being undetectable in blood (FIG. 10F). Taken together, these results suggest that a subset of TILs in HNSCC have tumor-killing capability and that several of these clonotypes may target similar antigens across primary tumor and metastatic disease.

[0141] The TProllf Toxgene signature is conserved across other malignant histologies and can predict tumor antigen-specific TCRs

[0142] We next analyzed publicly available single-cell datasets to evaluate whether the ■ Prohf Tox gene sjgnature may represent a tumor antigen-specific population, not only in HNSCC but also across other solid tumors. We first examined a breast cancer dataset covering both primary tumor and a matched metastatic regional lymph node (FIG. 12A) (36). We again identified a similar clustering (FIG. 12B) and clonal architecture (FIG. 12C) to HNSCC including expanded Prolif and ExCD8 sub-clusters. We identified the T1'"’1'1 Toxgene signature in both tumor and regional node (FIG. 12D) with shared TProllf_Toxclonotypes across primary and metastatic sites (FIG. 12E). Next, we evaluated the effects of ICI therapy on TPr°llf_Toxclonotypes through a secondary analysis of an ICI-treated renal cell carcinoma (RCC) single-cell dataset (FIG. 13 A) (37). IfrpPro,iCToxharbors tumor antigen-specific TCRs, release from immune checkpoint blockade by ICI therapy may lead to TProllf-Toxclonal expansion. Consistent with this hypothesis, in ICI- treated RCC we identified the TProllf-Toxgene signature among clonally expanded sub- clusters (FIGS. 13B-13C). Hyper-expanded TProlif-Toxclonotypes were shared multi-spatially across tumor biopsies, but were absent in peripheral blood (FIGS. 13D-13E). Finally, we performed a secondary analysis of a single-cell pancreatic adenocarcinoma dataset for which tumor antigenspecific TCRs had been externally validated through in vitro studies (FIG. 14A) (38). We againidentified the Tl>rollf Toxgene signature and clonotypes across proliferative and exhausted subclusters (FIGS. 14B-C). We found extensive overlap between in vitro validated tumor antigenspecific TCRs and TProllf Toxclonotypes predicting tumor-reactivity (FIG. 14D).

[0143] In silico predicted binding of TCRs to neo-antigen-HLA complexes supports tumor antigen- specificityof TProllf Toxclonotypes in HNSCC

[0144] To more directly evaluate the potential of TProllf-Toxclonotypes to bind tumor neoantigen in HNSCC, we used a custom prediction pipeline to evaluate the binding affinity of patient- matched neo-antigen peptide-HLA pairs with TProlif-Toxclonotypes. Based on availability of samples, we performed WES and bulk RNA sequencing of HyPR-HN Patient 02’s tumor biopsy pre-treatment and at 6 weeks post-radiation. These analyses confirmed that there is substantial overlap in identical coding mutations between pre-treatment and post-radiotherapy samples (FIG. 15 A), suggesting the existence of similar neo-antigens across treatment timepoints. To evaluate the in silico potential of TI',OIII TOXclonotypes to bind patient-specific neo-peptide- loaded HLA (pHLA), we employed TCRdock (39). This package uses structural modeling and TCR:pHLA docking algorithms to simulate the binding interface between a given TCR and its corresponding pHLA. To develop inputs for this model, we first predicted neo-antigens from HyPR-HN Patient 02 using a custom script creating 8-l lmer neo-peptides sliding across all conserved coding mutations identified from WES and validated in RNA sequencing. These were filtered for patient-specific HLA-binding through netMHCpan 4.1 and prioritized based on normalized expression in bulk RNA sequencing (40). From this filtered set of neo-peptides. we then identified the pHLA complexes with the closest predicting docking geometry to TProlll-ToxTCRs, indicating high binding affinity. These pHLAs demonstrated significantly greater predicted binding to select TPr<l111 ToxTCRs when compared to a set of patient-matched control TCRs extracted from naive T cells (FIG. 15B). This analysis also found two distinct clonotypes with high predicted binding affinity to the same tumor neo-antigen. supporting shared antigen specificityof T1>rolll ToxTCRs, which aligns with the expected redundancy of the T cell repertoire in recognizing common targets. We then repeated the TCRdock pipeline using predicted neoantigens from the WES and bulk RNA sequencing data from patient P120's tumor, described above (FIGS. 10A-10B). Binding prediction through TCRdock again demonstrated greater predicted pHLA complex binding affinity to TProllf Toxclonotypes when compared to control patient-matched naive T cells (FIG. 16A), which were found with the TProllt-Toxphenotype in both tumor and node but were absent from peripheral blood (FIG. 16B). In particular, one clonotype (CAVIILQSQGNLIF CAWASTGELFF, SEQ ID NOs: 2 and 3) was predicted to strongly bindseveral neo-antigens, consistent with the known degeneracy of TCR specificity (41). An ability to target multiple neo-antigens may further explain the observed expansion of this clonotype in Pl 20 across both tumor and metastatic node and its high expression of the T cell activation marker TNFRSF9

[0145] TProiif Toxc|onotypes are lost after radiotherapy and do not return to the tumor microenvironment

[0146] With these data supporting the potential tumor antigen-specificity and immunological relevance of the TPlollf Toxsub-population, we next evaluated the effects of radiation on TProlif-Toxclonotypes across treatment timepoints. Remarkably, across all enrolled patients, the TProllf Toxgene expression profile (FIG. 15C) and the clonotypes (FIG. 15D) which express these markers were both lost post-radiotherapy and did not return to the tumor in any patient studied (FIGS. 17A-17F). Together, these data suggested that pre-treatment tumor antigen- specific TILs are ablated by radiotherapy and do not re-infiltrate the tumor, at least within 6 weeks of treatment. T cell repopulation of TIL-depleted tumors is driven by non-specific re-infiltration including high- frequency circulating clonotypes and low -frequency regulatory and naive T cells

[0147] In light of the loss of TProlif-Toxclonotypes in response to radiation, we sought to understand the immuno-oncologic relevance of TIL repopulation thorough evaluation of clonotype dynamics. Unsurprisingly, the number of unique clonoty pes was profoundly reduced immediately after radiation-induced TIL depletion but began to recover by the 6 week timepoint (FIGS. 18A-18B). Despite extensive TIL ablation and subsequent repopulation, however, amino acid-level entropy at each position along the CDR3 region remained unchanged across all timepoints, indicating that neither overall clonotype depletion nor repopulation w as specific to TCR sequencing pattern (FIG. 18C). This aligns with evidence that tumor antigen-specific TILs constitute only a small fraction of the total repertoire, such that their loss or recruitment would have minimal impact on overall TCR diversity dynamics. Clonotype alluvial plotting revealed that approximately 30% of clones return to the tumor microenvironment post-radiation (FIG. 18D), with the majority' of these representing the most frequent TCR sequences within each pretreatment tumor (FIG. 18E).

[0148] To further investigate the functional relevance and potential antigenic targets of these repopulating TILs, we identified the most common circulating T cell clones through single-cell RNA and TCR sequencing of blood samples which were available from tw o enrolled patients prior to radiation (HyPR-HN Patient 02 and 04) and one patient at 6 weeks post-radiotherapy (HyPR-HN Patient 02). Since very high-frequency circulating clonotypes carry predominantlyviral-epitope-specific TCRs, this approach allowed us to track clonotype dynamics among a population of T cells irrelevant to the tumor antigen response (42-45). As expected, this analysis revealed that the frequency of these abundant circulating clones remained unchanged in the blood pool pre- and post-radiation (FIG. 19A). Importantly, we observed that many of these clones were also present in the pre-exhausted niche of the pre-treatment tumor and reappeared post-radiation with nearly identical transcriptional and clonal distributions (FIGS. 19B-19D).

[0149] Conversely, no T,ol,l l oxclonotypes from HyPR-HN Patients 02 or 04 were found in their respective tested circulating T cell pools, consistent with TProllf-Toxtumor-specificity. Additionally, we found that the remaining repopulating TILs were largely composed of newly infiltrating regulator ' and naive clonotypes that were absent in the pre-treatment tumor (FIGS. 19E-19F). Together, these findings suggest that the migration of diverse repopulating TILs is driven by non-specific and generalized mechanisms, such as chemotactic gradients, rather than selective pressures from tumor antigens.

[0150] Recurrent HNSCCs after conventionally fractionated radiotherapy are relatively CD8 T cell depleted

[0151] To determine if the post-radiation TIL depletion observed in hypofractionated radiotherapy may also play a role in immunologic radioresistance and HNSCC recurrence after conventionally fractionated standard of care radiation, we compared immune infiltration in both stromal and cancer compartments between previously untreated (n=51 patients) and radiation recurrent (n=16 patients) HNSCCs enrolled on a prospective tumor registry. All patients had HPV-negative HNSCC involving a mucosal subsite of the upper aerodigestive tract (Table 3). Through mIF analysis, we observed that previously untreated HNSCCs demonstrated a wide range of T cell infiltration from heavily infiltrated to immune-cold and overall pre-treatment TIL infiltrate had no clear association with disease control. However, in-field radiation recurrent tumors were comparatively T cell depleted (FIG. 20A) with the most significant effects on CD8 TIL populations, including both stromal and cancer compartments (FIG. 20B). For a subset of these patients (n=20 previously untreated, n=l 1 radiation recurrent) we also performed concurrent immunophenotyping of fresh preparations from the same biopsy specimens using mFC. These orthogonal analyses confirmed T cell depletion in recurrent HNSCCs with greatest effect on CD8 T cells, both as a percentage of total viable cells and of the intra-tumoral immune fraction (FIGS. 21A-21D).Table 3: Demographic, tumor, and treatment characteristics of HNSCC tumor registry patients. *67 total samples from 61 patients. Six patients are represented twice due to analysis of matched tumor samples obtained both pre-treatment and at the time of radiation recurrence from the same patients in the same anatomic location **One patient presented with an isolated regional recurrence after radiation with a controlled primary site (TO).

[0152] We next asked whether the relatively low CD8 T cell infiltrate seen in recurrent tumors was a pre-treatment feature of resistant HNSCCs or, instead, whether dynamic changes in T cell populations during therapy, as seen in hypofractionated radiation, may contribute to cancer recurrence. To investigate this, we analyzed a subset of patients for whom we were able to obtain HNSCC biopsies pre-treatment and then at the time of local recurrence after conventionally fractionated radiotherapy, from the same anatomical location in the same patients (n=6 patients). T cell infiltration was assessed through both mIF and mFC of freshly digested HNSCC surgicalbiopsies. In each case, we found reduced CD8 T cell infiltration in radiation recurrent HNSCCs, despite high pre-treatment CD8 TIL populations in several cases (FIGS. 20C-20F).

[0153] ICRhigh CD8 TILs in radiation recurrent HNSCC are markedly reduced

[0154] Finally, to evaluate whether limited repopulation of tumor-reactive TILs may contribute to cancer recurrence, we tested whether the relative T cell depletion observed in radiation recurrent HNSCCs also affected ICRhigh CD8 TILs. To address this, we measured PD- 1 and TIM3 expression among HNSCC TILs by mFC from freshly digested surgical biopsies. We observed that viable ICRhigh CD8 TILs were profoundly depleted in recurrent HNSCCs. We confirmed this effect in both unmatched cohorts comparing previously untreated and radiorecurrent disease (FIG. 20G) as well as in matched biopsies obtained pre-treatment and at the time of in-field radiation recurrence from the same HNSCC patient in the same anatomical location of the primary tumor (FIGS. 21E-21F). Together, these data suggest that radiation- induced TIL depletion and loss of pre-treatment tumor antigen-specific clonotypes with limited repopulation by new tumor-reactive TILs may be a barrier to cancer control for both hypofractionated and conventionally fractiationed radiation.

[0155] Discussion

[0156] Through paired single-cell RNA and TCR sequencing analyses on serial biopsies at multiple timepoints across the treatment period for individual patients, this study provides a unique window into the real-time effect of radiation on the T cell landscape within HNSCCs. Remarkably, we find that radiotherapy leads to profound T cell depletion in every patient studied immediately upon completion of treatment. Importantly, T cell clonotypes with transcriptional features highly suggestive of tumor-reactivity and neo-antigen specificity are persistently lost from the tumor microenvironment and do not return post-radiation. Instead, T cell re-population of the tumor is primarily driven by regulatory', naive, and high-frequency circulating effectormemory clones, which are unlikely to exert beneficial immuno-oncologic effects.

[0157] These findings help to clarify the transcriptional and functional states of TILs in HNSCC with direct implications for treatment. It is clear that TILs in HNSCC are a heterogenous group and overall TIL infiltrate has proven an inconsistent predictor of prognosis (46-51). Similarly, while we show that radiorecurrent disease is associated with low TIL levels, pretreatment TILs range widely in HNSCC and have no clear association with disease control. Yet despite the limited predictive power of overall TIL infiltrate, the objective clinical success of ICI therapy in metastatic HNSCC has established that immunogenic and oncologically relevant TILs exist and can be leveraged for therapy (11). Further, recent data has demonstrated and visualizedobjective killing of patient-derived tumor cells by individual patient-matched TILs in HNSCC (52). It appears, however, that such TILs are rare within clinical samples, and we show that the majority of intra-tumoral T cells are either naive, regulatory, or high-frequency non-tumor- specific clones prevalent in the circulation. Taken together, this evidence suggests that a small subset of HNSCC TILs are tumor antigen-specific, carry ing T cell receptors which can recognize cancer as a foreign entity and induce a relevant immunologic response. Although directly and efficiently characterizing these tumor antigen-specific TILs has remained elusive, such efforts could facilitate development of targeted therapies including TCR engineenng.

[0158] Our present study provides deeper insight into this tumor antigen-specific TIL population in HNSCC and we develop a rational gene expression signature to identify these TILs across patients and metastatic sites. We find that a subset of TILs exist across HNSCCs as expanded clonotypes found in both proliferating and exhausted sub-clusters. The existence of such a TIL population, which has also been identified in other malignacies (53, 54), suggests that a subset of ICRhigh TILs is not terminally exhausted but instead dynamically fluctuating between activation-induced proliferation and temporary' anergy. We then use overlapping transcriptional programs of these clonotypes shared between proliferative and ICRhigh subsets to develop a predictive transcriptional signature associated with tumor-reactivity, including multi-dimensional supporting evidence of tumor antigen-specificity'. The TProllf-Toxsignature is found in proliferative and ICRhigh sub-clusters in every' pre-treatment HNSCC patient we have analyzed including internal and external datasets but is completely absent from normal oral mucosa. It is found across both primary and metastatic sites including identical clonotype overlap suggesting shared antigen specificity at both primary' tumor and regional nodes. The TI>rollf l oxsignature can be found in other malignancies and is consistent with in vitro-confirmed tumor antigen-specific TCRs. Finally, we further validate tumor antigen-specificity of 'pProllf_Toxclonotypes in HNSCC through patient tumor- and HLA-specific in silico binding prediction.

[0159] Our TProllf_Toxapproach is distinct from prior aligorithms predicting tumor antigen specificity as it relies largely on a concerted expression of transcriptional programs, rather than requiring full expression of an exact geneset (38, 53-65). While our signature has overlap with prior prediction models including expression of CXCL13 (38. 53-56, 58. 60-63, 65), PDCD1 (53,56, 58, 64, 65), IFNG(53, 57, 58, 61), MKI67 (65), and ITGAE (53, 65), approaches requinng full expression of precise genesets are limited in applicability across different tumors and disease sites given the low transcriptomic coverage, technical dropout, temporal -dependent transcription, and stochasticity inherent to single-cell sequencing data. For example, while TNFRSF9 iscommonly used as an indicator of T cell activation (53, 65), and we find it highly correlated to the TProllf-Toxgene signature, its expression is temporally restricted and may only be captured in a short window after TCR ligation. Further, many algorithms developed to predict tumor antigenspecific TILs are based only on TCRs reacting to synthesized neo-antigen in vitro. While valuable, such studies of in vitro peptide presentation may include false positives or negatives due to differential peptide processing or HLA-loading between tumor and cell culture. Further, in vitro studies may miss key neo-antigens undergoing post-translational modification. RNA editing, or non-canonical expression (66-68). Instead, we leverage clonotype overlap in clustering, transcriptional program expression biologically associated with tumor-reactivity, and changes in TIL states at multiple timepoints across the treatment period.

[0160] Importantly, we find that radiotherapy persistently ablates TProllf-ToxTILs which may help to explain the failure of combination immunoradiotherapy to improve disease control outcomes in multiple recent large clinical trials in HNSCC (5, 20, 21). These data are unique given our abi 1 i ty to perform serial single-cell RNA and TCR sequencing of tumor samples across timepoints including both the last day of radiation and 6 weeks post-therapy, enabling us to track individual clonotypes across time within the same patients. While response to ICI therapy is multi-factorial (69), without a sufficient presence of tumor antigen-specific TILs, agents designed to activate or potentiate T cell function will be ineffective. This finding is surpising given the wealth of pre-clinical data emphasizing the immunogenicity7of radiotherapy and supporting its concurrent use with ICI (14-17). Such discordance between pre-clinical models and clinical findings demonstrates the importance of multi-omic investigation of on-trial longitudinal clinical biopsies.

[0161] Despite profound radiation-induced TIL depletion and loss of TProlif Toxclonotypes, T cells do reinfiltrate the tumor microenvironment. However, this is driven by new regulatory and naive TILs as well as common circulating clonotypes which appear at similar frequencies in both pre- treatment and post-therapy blood samples. Since the most common circulating clonotypes are likely viral-epitope specific from prior infectious agent exposures, these are unlikely to induce an anti -tumor immune response as they reinfiltrate post-radiation. Similarly, regulatory T cells will suppress post-radiation anti-tumor immunity, while new naive T cells, in the absence of tumor antigen specificity and intra-tumoral priming, are unlikely to mount an effective anti-tumor response. However, these findings do demonstrate that a TIL-depleted tumor in the immediate post-radiation setting, temporarily devoid of regulatory T cells, could be receptive to prevalent circulating clonotypes. If a tumor antigen-specific T cell therapy product can be delivered at thistimepoint, or if new tumor antigen-specific T cells can be primed and released systemically, existing chemotactic gradients are likely to lead to T cell infiltration from the circulation. Recent data in other maligancies supports this possibility, suggesting that tumor antigen-specificity may be key to the efficacy and infiltration of adoptive cell therapies (70). Further, given the mutational and neo-antigenic overlap identified between pre-treatment and post-radiation tumors, a TCR- engineered T cell product designed based on pre-treatment TProllf-Toxclonotypes may be efficacious in the post-radiation setting.

[0162] While these data provide a valuable window into the real-time effects of radiation on the tumor microenvironment with implications for current and future therapeutics, there are several limitations. Although we provide extensive supporting data that TProlll l oxis tumor antigen- specific, this will require further confirmation. The fraction of tumor cells which may be targeted by each TProllf Toxclonotype must also be determined if these are to be leveraged for TCR engineering in future work. Additionally, while the clinical radiation dosage delivered in this study is iso-equivalent to standard of care, it was delivered using a hypofractionated regimen.

[0163] Whether intra-tumoral T cell responses are fractionation-dependent remains uncertain and will being investigated in future cohorts from the HyPR-HN trial. Further, the radiation prescription included elective doses to regional nodes to treat micrometastatic disease. How this might affect TIL repopulation of tumors is unclear. Although there is increased interest in neoadjuvant radiotherapy approaches that spare draining lymphatics hypothesizing that radiotherapywill be immunogenic in this setting (71), given the predicted neo-antigen overlap between pre- and post-radiation samples, it is unclear if such approaches will lead to a large number of new immunologically relevant tumor antigen-specific TILs. HNSCCs take months to develop, including cycles of necrosis and regrowth, draining antigen and often metastatic cells to regional nodes. Despite this, we find Tl>loIll l oxclonal overlap between primary tumor and metastatic sites, suggesting that the limited number of T cells capable of recognizing and reacting to tumor neoantigen may have already been activated. However, based on limitiations in sample size and available tissue for biopsy, these conclusions will require validation in larger datasets. Finally, while tumor antigen-specific TILs must be at the foundation of any T cell-based immuno- oncologic therapy, there are numerous other factors which influence clinical cancer outcomes including immune editing, immune exclusion, nutrient deprivation, and other soluble and cellular mechanisms of intra-tumoral immunosuppression which must be addressed. In particular, we demonstrate that recurrent HNSCCs are commonly CD8 TIL-depleted. Whether delivery oractivation of tumor antigen-specific TILs in the post-radiation setting can improve TIL infiltration, expansion, and anti-tumor functionality is not yet established.

[0164] Methods

[0165] Sex as a biological variable

[0166] Our study examined male and female participants, and similar findings are reported for both sexes.

[0167] HNSCC specimen acquisition: Prospective Institutional Tumor Registry

[0168] Patients were eligible for inclusion who had HPV-negative HNSCC undergoing standard of care treatment of their tumor at the Medical College of Wisconsin (MCW). Patients were consented for additional research-specific biopsies which were performed in the clinic setting or during a standard of care surgical procedure. Study size was planned as a fixed available sample of HNSCCs obtained over approximately a one-year period. Patients were consented consecutively. Biopsy samples were triaged for translational studies depending on specimen size. Samples were first preserved for subsequent multiplex immunofluorescence (mlF). In select cases with sufficient tissue, additional pieces were freshly digested for single-cell sequencing, immunophenotyping by multiplex flow cytometry (mFC), and tissue preservation for whole exome and bulk RNA sequencing.

[0169] HNSCC specimen acquisition: HyPR-HN Study

[0170] The clinical trial Hypofractionated Pre-operative Radiation for HNSCC (HyPR-HN, NCT05538533) is a single-institution Phase I study evaluating the safety and efficacy of preoperative neo-adjuvant hypofractionated radiation in HPV-negative HNSCC. Eligible patients have advanced stage disease (T3-4 and / or clinical node-positive) without radiographic extracapsular extension. Enrolled patients undergo biopsies pre-treatment and immediately after the last fraction of radiotherapy in the clinic setting. Approximately 6 w eeks after completion of radiation, patients are taken for definitive resection of residual disease with an additional researchspecific biopsy at the time of surgery. All enrolled patients included in this report were treated with 46Gy in 10 fractions to PTVhigh regions (to encompass gross disease) and 35Gy in 10 fractions to PTVlow regions (to encompass microscopic spread including nodal regions at risk).

[0171] As above, biopsy samples were triaged for translational studies. However, for the HyPR- HN study samples were prioritized first for single-cell sequencing. After single-cell suspensions were processed for sequencing, the remaining cells from the sample underw ent mFC. In cases with additional sufficient tissue samples, separate pieces w ere preserved for mlF, bulk RNA sequencing, whole exome sequencing, and spatial transcriptomics.

[0172] Multiplex Immunofluorescence (mIF)

[0173] Biopsy pieces planned for mIF were first fixed in 10% neutral-buffered formalin for 24 hours. The tissues were subsequently dehydrated, cleared in xylene, and embedded in paraffin wax. The paraffin-embedded tissue blocks were then sectioned at 5pm and mounted on glass slides. Tumor-immune spatial heterogeneity was subsequently quantified through histologic analysis as previously described (52). Briefly, multiplex immunostaining was performed using an Opal Polaris 7-Color Automation IHC Kit (Akoya Biosciences), as recommended by the manufacturer. All multiplex immunofluorescence slides were scanned on an Akoya Vectra Polaris (RRID:SCR_025508) at 20X using MOTiF™ protocol. Whole slide images were then loaded into InForm image analysis software for automated cell type density' quantification.

[0174] Biopsy processing for single-cell analyses.

[0175] To facilitate analysis by single-cell sequencing and flow cytometry, the biopsy specimen was freshly digested according to the Miltenyi Tumor dissociation kit protocol in a Miltenyi gentleMACS C tube using the Miltenyi gentleMACS tissue dissociator (RRID:SCR_020267).

[0176] Cellular debris and dead cells were removed from the dissociated cell suspension when necessary, using Debris Removal Solution and the Dead Cell Removal Kit, respectively (Miltenyi).

[0177] Multiplex Flow Cytometry' (mFC)

[0178] One million cells per sample were used for immunostaining with conjugated monoclonal antibodies. 7-AAD (420404. BioLegend) was used to exclude dead cells. Single color tubes were employed to set up a compensation matrix, and a fluorescence minus one control tube was included to ensure specific staining. Antibody panels are listed in Table 4. Cells were washed and suspended in 0.2 mL staining buffer (PBS with 2% FBS). Staining was done at 4°C for 30 minutes. The samples were washed again and then acquired on a MACSQuant 10 Analyzer Flow Cytometer (Miltenyi RRID:SCR_020268). Data was analyzed using FlowJo software version 10.10 (BD Life Sciences). To verify gating and purify, all populations were routinely backgated. Cell-type specific immunophenotyping was performed on live single-cell populations and gated using canonical surface marker expression.Table 4: Antibody panels used for multi-plex flow cytometry.

[0179] Whole exome sequencing (WES)

[0180] DNA was extracted and exome libraries prepared. All tumor WES was complimented by patient-matched germline WES obtained from peripheral blood mononuclear cells (PBMCs) to validate mutational calls. Library preparation and sequencing was completed using the Illumina DNA prep with enrichment that utilizes on-bead transposase activity and panel capture by hybridization. Processing followed the Illumina prep protocol and WES was completed on the NovaSeq6000 (RRID:SCR_016387) obtaining 150bp paired end reads and aiming for >100x depth of coverage across all samples (Novogene) (72). DNA reads were cleaned using TrimGalore. Illumina short reads were mapped to the current reference genome, GRCh38, using BWA-mem (73). Somatic mutations including single nucleotide variants and indels were evaluated by Mutect2, SomaticSniper, and Varscan2 with comparison to patient-matched germline WES obtained from isolated peripheral blood mononuclear cells (74-76).

[0181] Bulk RNA sequencing

[0182] RNA was extracted, quantified, and integrity assessed using RIN values from Agilent

[0183] Fragment Analyzer 5200 (RRID:SCR_019417). RNA libraries were prepared according to manufacturer’s protocols utilizing Illumina’s TruSeq stranded mRNA library kit before sequencing on the Illumina NovaSeq6000 (RRID:SCR_016387) with paired end 100 base pair reads targeting >100 million reads per sample (Novogene).

[0184] Single-cell RNA and TCR sequencing

[0185] Biopsy specimens designated for immediate single-cell sequencing underwent tumor digestion, as above. After dissociation, single cells were resuspended, counted, and viability assessed. Library preparation was then completed at the MCW Mellowes Center (RRID:SCR_022926) with lOx Genomics Chromium Next GEM Single Cell 5’ Reagent Kits (lOx Genomics, sample dual indexes with cellular and molecular barcodes with VDJ amplification). Target capture of 10,000 cells per sample was used. Prior to sequencing, libraries were quantified and pooled by qPCR (Kapa Library Quantification Kit, Kapa Biosystems). Sequencing was completed on the NovaSeq6000 (RRID:SCR_016387) targeting 5,000 reads for VDJ libraries and 50,000 transcript reads per cell per condition for expression. For all patients included in this study apart from HyPR-HN Patient 01, single-cell RNA and TCR sequencing were obtained simultaneously from the same samples to enable T cell clonotype tracking across transcriptomic profiles. Samples from HyPR-HN Patient 01 underwent single-cell RNA sequencing only.

[0186] Single-cell data pre-processing

[0187] CellRanger (v8.0. 1) was used to demultiplex raw reads, align to GRCh38. and quantify unique molecular identifiers (UMI). Sequencing quality was assessed based on the number of genes detected per cell and the proportion of mitochondrial genes expressed. Cells with less than 200 features or greater than 5000 features were filtered out to exclude low-quality cells or doublets, respectively. Cells were also filtered out when the proportion of mitochondrial genes exceeded 15% to exclude dead or dying cells. Finally, T cell receptor variable genes (TRAV, TRBV. TRDV. TRGV) were excluded from the analysis to avoid clonotype bias in clustering. We did not perform regression of cell cycle or type I interferon genes, as has been previously described (61), to avoid loss of relevant biological information in the context of radiation response.

[0188] Single-cell data integration and clustering

[0189] Seurat (5.1.0) was used to normalize the raw count data, identify highly variable features, scale features, and integrate samples (77). Canonical Correlation Analysis (CCA) was used to mitigate batch effects between samples. The filtered log-transformed UMI matrix was used to perform truncated singular value decomposition with k = 50. Principal component analysis was performed based on the 4.000 most variable features identified using the vst method implemented in Seurat. Cell types were then annotated based on expression of known marker genes visualized on the Uniform Manifold Approximation and Projection (UMAP) plot and by performing unbiased gene marker analysis. For the latter, a Wilcoxon Rank-Sum Test was used to perform differential gene expression (DEG) by comparing cells in each cluster to the rest ofthe cell profiles. Genes with FDR < 0.01 and log-fold change > 1 were selected as candidate celltype markers. Sub-cluster DEGs and canonical cell-type annotation markers were used to define cell subsets as described previously (34. 78. 79). To evaluate changes in cell- type composition within single-cell sequencing profiles across treatment, the number of cells within each immunophenotypic subset were extracted. Cell subset numbers were normalized to total number of cells sequenced within each sample to enable proportional comparison across samples and timepoints.

[0190] To further analyze T cell subsets, T cell-containing clusters identified through DEGs and known marker analyses were extracted and cells within this set expressing at least one CD3 complex subunit (CD3D | CD3E | CD3G | CD247) were retained. These cells were re-clustered and T cell subset immunopheno typing was performed through canonical marker expression and unbiased gene marker analysis, as above (80, 81).

[0191] Single-T cell clonotype tracking

[0192] scRepertoire (v2.0.3) was used to link T cell clonotype with single-cell gene expression profiling (82). Barcode-linked TCR data was extracted from the filtered contig annotation output derived from the CellRanger pipeline. Individual TCRa and TCR[3 chains were paired into full clonotypes through barcode matching. T cell clonotypes were then projected onto the T cell UMAP profiles again through barcode linkage. Within this framework, T cell clonoty pe tracking was then performed within individual patients between T cell sub-clusters, across primary and metastatic tumor sites, and across treatment timepoints.

[0193] Differential expression tests for development of the 'I ’1"1'1 Tosignature

[0194] To identity' transcriptional programs enriched in T cells that demonstrate clonotype overlap between classical terminally exhausted and proliferating T cell sub-clusters, DEG analyses were performed on pre-treatment samples from the HyPR-HN trial. Cells present in exhausted or proliferative T cell sub-clusters with TCRs overlapping across both sub-clusters were identified. Transcriptional profiles of this cell subset were compared to all other T cells within the pre-treatment HyPR-HN samples and to post-treatment proliferative T cells using DEG analysis through a Wilcoxon Rank-Sum Test. DEG results were confirmed orthogonally with Model-based Analysis of Single-cell Transcriptomics (MAST) (83). Of the top differentially expressed genes we selected transcriptional programs rationally associated with T cell- mediated tumor-specific reactivity . These included non-regulatory (FOXP3-negative), CD8 markers (CD8A, CD8B), proliferative genes (MKI67, STMN1, TYMS), immune checkpoint molecules (PDCD1, HAVCR2, LAG3, TIGIT, ENTPD1), indicators of cytotoxicity (GZMA, GZMB,GZMH, IFNG), a marker of tissue-residence (ITGAE), and CXCL13. Given the limitations in sequencing depth of single-cell approaches resulting in stochastic technical dropout on a cell- bycell basis, we required only that a single gene from each program demonstrated scaled expression >1 within any given cell.

[0195] Predicted TCR binding to tumor neo-antigens

[0196] To evaluate whether ^clonotypes within each patient may target tumor neoantigens, we evaluated in silico prediction of patient-specific TCR:neo-peptide-HLA (TCR:pHLA) binding pairs involving mutated neo-peptides. To accomplish this, we first performed neo- antigen prediction based on mutations identified through WES data, as described above. Bam files obtained from WES from tumor and matched germline, bam files obtained from bulk RNA sequencing of tumor, and variant call files (VCF) files obtained from all three variant callers were imported into custom R software with use of the following libraries: VariantAnnotationl.48. 1, stringrl.5.1, AnnotationHub3. 12.0, ensembldb2.26.0, Rsamtools2.20.0, GenomicAlignments 1.40.0. Coding sequence (CDS) transcripts annotated in Ensemble Genome Browser 111 that overlap regions of somatic mutations called by any of the three variant callers were extracted from tumor RNA bam files. Germline RNA transcripts (including up to a 36-nucleotide flank) overlapping the somatic mutation locus were extracted from the tumor bulk RNA sequencing (which come from normal cells within the tumor and / or unmutated tumor haplotypes), with the two most common transcripts expressing the germline sequence at the locus representing the expressed germline alleles. Tumor RNA transcripts containing the known mutation and up to 36 nucleotides on each end of the somatic mutation were extracted, to determine up to 11 adjacent amino acids to each end of the mutation. The most common RNA transcript in the tumor that identically matched one of the germline alleles except at the site of the mutation was used as the ground truth tumor mutation. Any mutation identified in WES that did not exist in tumor bulk RNA sequencing was discarded. Using the known CDS site from EnsemblDB to obtain the reference coding frame, both germline and tumor transcripts were translated using VariantAnnotation. Custom R software was then used to determine 8, 9, 10 and 11 -mer peptides around all mutated amino acids from these translated sequences to select all neo-peptides derived from the translated tumor transcript that were not present in the translated germline transcript. Neo-peptides were designed to slide across the mutation to form all possible combinations. From this set of neo-peptides, we then normalized mutated transcript expression to total reads to adjust for depth of sequencing. This scaled expression value was then used to prioritize neo-peptides for import into netMHCpan4. 1 to assess for HLA binding to any of the 6major type I HLA types for that patient (40). Neo-peptides predicted to be strong binders to any HLA (predicted binding affinity IC50 < 50 nM) were carried forward for testing against patient- matched TCRs for in silico TCR:pHLA binding prediction using TCRdock (39).

[0197] For each confirmed TCR and pHLA complex, a pdb file was generated for AlphaFold using TCRdock and then imported into the TCRdock-specific version of AlphaFold. Docking geometries were determined with AlphaFold TCR pipeline simulations using sequence homology, as described previously (39). Fifty naive TCRs from patient-matched blood identified through single-cell RNA sequencing data were used to control for variations attributable to diverse pHLA complexes. We then calculated the residue-residue TCR:pHLA predicted aligned error (PAE) for ■ Prohf Tox TCRS. Low residue-residue PAE scores indicate close TCR:pHLA geometric approximation suggesting favorable molecular interactions and high binding affinity. We corrected for TCR-specific variability by subtracting the mean binding of naive TCRs to each pHLA complex from the PAE of each TP1'ollf-ToxTCR:pHLA complex for every tested pHLA. We then zero-shifted and normalized these TCR-corrected binding scores for a given TCR by subtracting the mean binding score of each pHLA complex to a specific TCR and dividing by the standard deviation (SD) of each TCR. pHLA complexes with predicted binding to TProlif-T°xTCRs demonstrating extreme low residue-residue PAE scores (>3 SD below mean) were selected for further interrogation. For each patient, binding of these pHLA complexes was tested against both Proirf Tox CRS and an additional set of 25 separate patient-matched naive TCRs.

[0198] Spatial Transcriptomics.

[0199] Slides were processed through the lOx Genomics gene expression workflow with presequencing H&E imaging completed on the Keyence Microscope. Next, transcripts were decrosslinked, hybridized to a probe panel, ligation completed, and probes released for capture on the gridded, barcoded surface of the Visium slide (CytAssist, lOx Genomics, RRID: SCR 024570). After barcoding, the samples underwent library preparation, amplification, and clean-up with quality assessment ensuring that fragments were ~240bp. Libraries were sequenced at the Mellowes Center Facility’ on the Illumina NovaSeq6000 with paired end reads per the lOx Genomics protocol and generating 25 million read pairs per spot covered by tissue. The quality control of raw reads was processed by FastQC. Raw reads were mapped to the human GRCh38 reference genome with Visium Probe_Set_v2.0 by Space Ranger. Fiducial detection, barcode / UMI counting, zero-count spot filtering, and normalization were also performed by Space Ranger. Visualization was performed with lOx Loupe Browser. Co- expression of canonical markers (CD3E, CD8A) was used to identify transcriptomic spots containing CD8 Tcells with subsequent evaluation for immune checkpoint receptor (ICR) co- expression (PDCD1, HAVCR2, LAG3, TIGIT). The spatial distribution of these co-expression networks was evaluated before and after radiation treatment for HyPR-HN Patient 02.

[0200] Generation of patient-derived malignant HNSCC lines.

[0201] Cell lines were directly derived from fresh patient HNSCC biopsies. Tumor specimens were minced and cultured such that a broad surface of tumor was directly adherent to the culture plate. Culture media included DMEM / F 12 with 1.2g / L sodium bicarbonate, 2.5mML- glutamine, 15mMHEPES, and 0.5mM sodium pyruvate supplemented with 400ng / mL hydrocortisone with 10% FBS. 2% antibiotic-antimycotic (Gibco) was used to limit contamination. Bulk tumor pieces were removed from the plate once adherent cells were established. All cell lines were cultured in an incubator at 37°C with 5% CO2. Malignant populations were separated from cancer-associated fibroblasts (CAFs) through serial differential trypsinization given the absence of fully reliable surface markers for malignant cells in HNSCC. Mixed cell lines were incubated for approximately 2 minutes in the presence of TiypLE (Gibco) which preferentially harvests the less adherent fibroblastic population. Since malignant HNSCC cells can lose epithelial surface markers and express fibroblastic and mesenchymal signatures (84, 85), serial differential trypsinization remains the most reliable method to enrich for malignant lines while maximally preserving tumor clonal heterogeneity.

[0202] TIL isolation

[0203] TILs were extracted from separate pieces of the same biopsy specimens used to generated malignant lines. For TIL generation, fresh HNSCC specimens were finely minced (1mm sections) and cultured. TIL culture media included ImmunoCult XF Expansion media (StemCell Technologies), 20ng / mL IL7 and 20ng / mL IL15 (Peprotech), and 2% antibiotic- antimycotic (Gibco). Cultures were inoculated on Day 0 with soluble CD3 / CD28 activator (StemCell Technologies). Once expanding T cell colonies were established, T cells were enriched with CD3 magnetic column selection (Miltenyi). Once fully expanded, TILs were then cryopreserved (Cryostor) until patient-matched malignant lines had matured.

[0204] Circulating T cell isolation

[0205] For HNSCC patients who underwent peripheral blood sampling concurrent with biopsy, approximately 45mL of blood was taken. Ficoll-gradient separation was used to isolate the buffy coat which was aspirated. For flow cytometry, circulating T cells were immediately processed fresh. The remaining T cells were then cryopreserved (Cryostor) for later analyses.

[0206] xCELLigence

[0207] Autologous adherent tumor cells derived from a HNSCC biopsy were plated in a 96- well xCELLigence E-Plate at 10.000 cells / well. The plate was incubated at room temperature for 30 minutes to facilitate uniform immobilization of the target cells on plate bottom and then placed in the xCELLigence instrument. Data acquisition was initiated and the plate was incubated at 37

[0208] °C. After 24 hours, TILs were plated in triplicate at several effector to target ratios. Appropriate negative (TIL or tumor cells only) and positive (addition of Agilent cytolysis reagent) controls were performed. After addition of the effector cells, the plate was incubated at room temperature for 30 minutes to facilitate uniform distribution of the effectors. The plate was placed back onto the xCELLigence instrument and incubated for 24 hours at 37 °C with continuous data acquisition. Data were analyzed using RTCA Software Pro v2.3.2 and Graphpad Prism vl0.0.3.

[0209] Statistics

[0210] Statistical analysis was performed using GraphPad Prism version 10.0.3. For analysis of flow cytometry and immunofluorescence data multiple T-tests with Holm-Sidak’s multiple comparison correction were used to analyze differences between groups. Differential gene expression analyses between single-cell clusters were performed in Seurat using the Wilcoxon Rank-Sum Test and MAST, as described.

[0211] External single-cell sequencing data sets.

[0212] The following datasets were re-analyzed for this study.Table 5: external single-cell sequencing dataset.

[0213] Study Approval

[0214] Prospective tumor registry7activities were approved by the MCW Institutional Review Board (approval PR000040992) and the Institutional Biosafety Committee (approval IBC20210026). The HyPR-HN clinical trial (NCT05538533), including all tissue biopsies and handling, were approved by the MCW Institutional Review Board (approval PR000044863).Specifically, the approved protocol allows for publication of translational data from initial dose levels, prior to full study completion and endpoint reporting.

[0215] In the foregoing description, it will be readily apparent to one skilled in the art that varying substitutions and modifications may be made to the invention disclosed herein without departing from the scope and spirit of the invention. The invention illustratively described herein suitably may be practiced in the absence of any element or elements, limitation or limitations which is not specifically disclosed herein. The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention that in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention. Thus, it should be understood that although the present invention has been illustrated by specific embodiments and optional features, modification and / or variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention.

[0216] Citations to a number of patent and non-patent references may be made herein. The cited references are incorporated by reference herein in their entireties. In the event that there is an inconsistency between a definition of a term in the specification as compared to a definition of the term in a cited reference, the term should be interpreted based on the definition in the specification.

Claims

CLAIMS1. A method of detecting neoantigen-specific T cell receptor (TCR) sequences, the method comprising: a. obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to detect a plurality of neoantigen-specific TCR sequences.

2. A method of detecting neoantigen-specific T cell receptor (TCR) sequences, the method comprising: a. performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subj ect, wherein the sample from the subj ect comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on positive expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1. CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to detect a plurality of neoanti gen-specific TCR sequences.

3. The method of claim 1 or 2, wherein the T cells are CD8 T cells and the set of markers comprises CD8, MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

4. The method of claim 1 or 2, wherein the T cells are CD4 T cells and the set of markers comprises CD4. MKI67, STMN1, CENPF, TOP2A. CXCL13, GZMB. PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

5. The method of claim 1 or claim 2, wherein the partitioned TCR sequencing data comprises neoantigen-specific T cell receptor (TCR) sequences.

6. The method of claim 1 or claim 2, wherein the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A and one or more marker selected from the group consisting of CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

7. The method of claim 1 or claim 2, wherein the set of markers comprises CXCL13, MKI67, STMN1, CENPF, TOP2A, PDCD1. TGIT, LAG3, HAVCR2, and ENTPD1.

8. The method of claim 1 or claim 2, wherein the set of markers comprises MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

9. The method of claim 1 or claim 2, wherein the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

10. The method of claim 1 or claim 2, wherein the set of markers consists of MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

11. The method of claim 1 or claim 2, wherein the subject has been diagnosed with a cancer.

12. The method of claim 11, wherein the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC).

13. The method of claim 1 or claim 2, wherein the method further comprises generating a polynucleotide comprising one of the plurality of detected neoantigen-specific TCR sequences.

14. A polynucleotide comprising a neoantigen-specific TCR sequence generated by the method of claim 1 or claim 2.

15. The polynucleotide of claim 14, wherein the polynucleotide comprises a regulatory7element operably linked to the isolated neoantigen-specific TCR sequence.

16. The polynucleotide of claim 15, wherein the regulatory element comprises a promoter.

17. A recombinant neoantigen-specific T cell comprising the polynucleotide of claim 14.

18. A method of generating a recombinant neoantigen-specific T cell, the method comprising: introducing the polynucleotide of claim 14 to a T cell.

19. A method of generating a recombinant neoantigen-specific T cell, the method comprising: a. obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells; b. partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; c. introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen-specific T cell.

20. A method of generating a recombinant neoantigen-specific T cell, the method comprising: a. performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subj ect, wherein the sample from the subj ect comprises T cells, wherein the subject has been diagnosed with a cancer, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality of the T cells;b. partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67. STMN1. CENPF. TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1 to generate a plurality of neoantigen-specific TCR sequences; c. introducing a polynucleotide into a T cell, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate the recombinant neoantigen-specific T cell.

21. The method of claim 19 or 20. wherein the T cells are CD8 T cells and the set of markers compnses CD8, MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

22. The method of claim 19 or 20, wherein the T cells are CD4 T cells and the set of markers compnses CD4, MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

23. The method of claim 19 or 20, wherein the partitioned TCR sequencing data comprises neoantigen-specific T cell receptor (TCR) sequences.

24. The method of claim 19 or claim 20, wherein the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF. TOP2A and one or more marker selected from the group consisting of CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

25. The method of claim 19 or claim 20, wherein the set of markers comprises CXCL13, MKI67, STMN1, CENPF, TOP2A, PDCD1, TGIT, LAG3, HAVCR2, and ENTPD1.

26. The method of claim 19 or claim 20, wherein the set of markers comprises MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

27. The method of claim 19 or claim 20, wherein the set of markers comprises one or more markers selected from the group consisting of MKI67, STMN1, CENPF. TOP2A. CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

28. The method of claim 19 or claim 20, wherein the set of markers consists of MKJ67. STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

29. The method of claim 19 or claim 20, wherein the subject has been diagnosed with a cancer.

30. The method of claim 29, wherein the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC).

31. The method of claim 19 or claim 20, wherein the polynucleotide comprises a regulatory element operably linked to the isolated neoantigen-specific TCR sequence.

32. The method of claim 19 or claim 20, wherein the regulatory element comprises a promoter.

33. The method of claim 19 or claim 20. wherein introducing the polynucleotide comprises transfection or transduction of the polynucleotide.

34. The method of claim 19 or claim 20. wherein introducing the polynucleotide comprises introducing the polynucleotide by transfection.

35. The method of claim 34, wherein the method further comprises introducing to the T cell one or more additional polynucleotides that collectively encode (1) a Cas nuclease, and (2) one or more guide RNAs.

36. The method of claim 35, wherein the one or more guide RNAs target a 3’ region of the endogenous T cell receptor sequence and wherein introduction comprises inducing a double stranded break in the endogenous T cell receptor sequence and homologous recombination of the polynucleotide into the genome of the T cell.

37. A population of recombinant neoantigen-specific T cells generated by the method of claim 19 or claim 20.

38. A pharmaceutical composition comprising the population of recombinant neoantigen-specific T cells of claim 37.

39. A method of treating a cancer in a subject in need thereof, the method comprising administering a therapeutically effective amount of the pharmaceutical composition of claim 38 to the subject to treat the cancer.

40. A method of treating a cancer in a subject in need thereof, the method comprising: a. obtaining data comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) data from a sample from a subject, wherein the sample from the subject comprises T cells, and wherein thesequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality' of the T cells; b. partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A, CXCL13. GZMB. PDCD1, HAVCR2, TIGIT, LAG3. and ENTPD1, to generate a plurality of neoantigen-specific TCR sequences; c. introducing a polynucleotide into a population of T cells, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate a population of recombinant neoantigen-specific T cells; d. administering a therapeutically effective amount of the recombinant neoantigen-specific T cells to the subject to treat the cancer.

41. A method of treating a cancer in a subject in need thereof, the method comprising: a. performing one or more sequencing procedures comprising single cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) on a sample from a subj ect, wherein the sample from the subj ect comprises T cells, and wherein the sequencing procedures provide RNA sequencing data and TCR sequencing data for a plurality- of the T cells; b. partitioning the TCR sequencing data based on expression of a set of markers in the RNA sequencing data for each T cell, wherein the set of markers comprises one or more markers selected from MKI67, STMN1, CENPF, TOP2A. CXCL13. GZMB. PDCD1, HAVCR2. TIGIT, LAG3. and ENTPD1, to generate a plurality of neoantigen-specific TCR sequences; c. introducing a polynucleotide into a population of T cells, the polynucleotide comprising one of the plurality of neoantigen-specific TCR sequences to generate a population of recombinant neoantigen-specific T cells; d. administering a therapeutically effective amount of the recombinant neoantigen-specific T cells to the subject to treat the cancer.

42. The method of claim 40 or 41, yvherein the T cells are CD8 T cells and the set of markers comprises CD8, MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

43. The method of claim 40 or 41, wherein the T cells are CD4 T cells and the set of markers comprises CD4. MKI67, STMN1, CENPF, TOP2A. CXCL13, GZMB. PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

44. The method of claim 40 or claim 41, wherein the set of markers comprises one or more cell cycle marker selected from the group consisting of MKI67, STMN1, CENPF, TOP2A and one or more marker selected from the group consisting of CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

45. The method of claim 40 or claim 41, wherein the set of markers comprises CXCL13, MKI67, STMN1, CENPF, TOP2A, PDCD1, TGIT, LAG3, HAVCR2, and ENTPD1.

46. The method of claim 40 or claim 41, wherein the set of markers comprises MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

47. The method of claim 40 or claim 41, wherein the set of markers comprises one or more markers selected from the group consisting of MK167, STMN1, CENPF. TOP2A. CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

48. The method of claim 40 or claim 41, wherein the set of markers consists of MKI67, STMN1, CENPF, TOP2A, CXCL13, GZMB, PDCD1, HAVCR2, TIGIT, LAG3, and ENTPD1.

49. The method of claim 40 or claim 41, wherein the subject has been diagnosed with a cancer.

50. The method of claim 49, wherein the cancer is selected from the group consisting of squamous cell carcinoma (SCC) and oral squamous cell carcinoma (OSCC).

51. The method of claim 40 or claim 41 , further comprising administering an additional treatment to the subject selected from the group consisting of a chemotherapy, a radiation therapy, a surgical therapy, a hormonal therapy, and an immunotherapy.

52. The method of claim 51, wherein the additional treatment is an immunotherapy.

53. The method of claim 52, wherein the immunotherapy is an immune checkpoint blockade (ICB) therapy.

54. A kit system or platform comprising the polynucleotide of any one of claims 14-16, the population ofneoantigen-specific T cells of claim 37, or the pharmaceutical composition of claim 38. optionally, further comprising instructions for using the kit, system, or platform.

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