WO2025213105A1 - Procédés d'identification de séquences de récepteur des lymphocytes t (tcr) - Google Patents

Procédés d'identification de séquences de récepteur des lymphocytes t (tcr)

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Publication number
WO2025213105A1
WO2025213105A1 PCT/US2025/023272 US2025023272W WO2025213105A1 WO 2025213105 A1 WO2025213105 A1 WO 2025213105A1 US 2025023272 W US2025023272 W US 2025023272W WO 2025213105 A1 WO2025213105 A1 WO 2025213105A1
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Prior art keywords
tcr
subjects
sub
group
chain sequence
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PCT/US2025/023272
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WO2025213105A9 (fr
Inventor
Michael Steven ROONEY
Emily Jackson
Victor MIKHAYLOV
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Biontech US Inc
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Biontech US Inc
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Publication of WO2025213105A1 publication Critical patent/WO2025213105A1/fr
Publication of WO2025213105A9 publication Critical patent/WO2025213105A9/fr
Pending legal-status Critical Current
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    • C—CHEMISTRY; METALLURGY
    • C40—COMBINATORIAL TECHNOLOGY
    • C40B—COMBINATORIAL CHEMISTRY; LIBRARIES, e.g. CHEMICAL LIBRARIES
    • C40B40/00—Libraries per se, e.g. arrays, mixtures
    • C40B40/04—Libraries containing only organic compounds
    • C40B40/06—Libraries containing nucleotides or polynucleotides, or derivatives thereof
    • C40B40/08—Libraries containing RNA or DNA which encodes proteins, e.g. gene libraries
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K40/00—Cellular immunotherapy
    • A61K40/10—Cellular immunotherapy characterised by the cell type used
    • A61K40/11—T-cells, e.g. tumour infiltrating lymphocytes [TIL] or regulatory T [Treg] cells; Lymphokine-activated killer [LAK] cells
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K40/00—Cellular immunotherapy
    • A61K40/30—Cellular immunotherapy characterised by the recombinant expression of specific molecules in the cells of the immune system
    • A61K40/32—T-cell receptors [TCR]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K40/00—Cellular immunotherapy
    • A61K40/40—Cellular immunotherapy characterised by antigens that are targeted or presented by cells of the immune system
    • A61K40/46—Viral antigens
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/10—Processes for the isolation, preparation or purification of DNA or RNA
    • C12N15/1034—Isolating an individual clone by screening libraries
    • C12N15/1089—Design, preparation, screening or analysis of libraries using computer algorithms
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6881—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B35/00—ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
    • G16B35/20—Screening of libraries
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • C—CHEMISTRY; METALLURGY
    • C40—COMBINATORIAL TECHNOLOGY
    • C40B—COMBINATORIAL CHEMISTRY; LIBRARIES, e.g. CHEMICAL LIBRARIES
    • C40B20/00—Methods specially adapted for identifying library members
    • C40B20/04—Identifying library members by means of a tag, label, or other readable or detectable entity associated with the library members, e.g. decoding processes
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation

Definitions

  • providing in (a) comprises: providing the plurality of libraries of TCR chain sequences from a population of subjects; identifying the first sub-group of at least 10 subjects as WSGR Docket No.: 50401-784.601 (i) having the same disease or condition, (ii) having the same antigen profile, (iii) expressing the MHC encoded by the same HLA allele, or (iv) the same combination thereof, and the second sub- group of subjects as (i) not having the same disease or condition as the first sub-group, (ii) not having the same antigen profile as the first sub-group, (iii) not expressing an MHC encoded by the same HLA allele as the first sub-group, or (iv) not having the same combination thereof as the first sub-group.
  • the statistically significant difference in the frequency is a p-value of at most 0.05, at most 0.001, or at most 0.0001. In some embodiments, the p-value is an adjusted p-value.
  • the selected TCR chain sequence is a TCR chain sequence that is present in at least 10 subjects from the first sub-group of at least 10 subjects. In some embodiments, the first sub-group of at least 10 subjects have the same disease or condition, and the second sub-group of at least 10 subjects do not have the same disease or condition as the first sub-group.
  • the method further comprises repeating (a)-(d) using the plurality of libraries of TCR chain sequences from a first sub-group of at least 10 subjects that express an MHC encoded by a same HLA allele, and a second sub-group of at least 10 subjects that do not express an MHC encoded by the same HLA allele as the first sub-group.
  • the first sub-group of at least 10 subjects have the same antigen profile
  • the second sub-group of at least 10 subjects do not have the same antigen profile as the first sub- group.
  • the method further comprises repeating (a)-(d) using the plurality of libraries of TCR chain sequences from a first sub-group of at least 10 subjects that express an MHC encoded by a same HLA allele, and a second sub-group of at least 10 subjects that do not express an MHC encoded by the same HLA allele as the first sub-group.
  • the first sub-group of at least 10 subjects have the same disease or condition and the same antigen profile
  • the second sub-group of at least 10 subjects do not have the same disease or condition as the first sub-group and does not have the same antigen profile as the first sub-group.
  • the method further comprises repeating (a)-(d) using the plurality of libraries of TCR chain sequences from a first sub-group of at least 10 subjects that express an MHC encoded by a same HLA allele, and a second sub-group of at least 10 subjects that do not express an MHC encoded by the same HLA allele as the first sub-group.
  • the first sub-group of at least 10 subjects express an MHC encoded by a same HLA allele
  • a second sub-group of at least 10 subjects do not express an MHC encoded by the same HLA allele as the first sub-group.
  • the method further comprises repeating (a)-(d) using the plurality of libraries of TCR chain sequences from a first sub-group of at least 10 subjects that have the same disease or condition, and the second sub-group of at least 10 subjects that do not have the same disease or condition as the first sub-group.
  • the method further comprises repeating (a)-(d) using the plurality of libraries WSGR Docket No.: 50401-784.601 of TCR chain sequences from a first sub-group of at least 10 subjects that have the same antigen profile, and the second sub-group of at least 10 subjects that do not have the same antigen profile as the first sub-group.
  • the method further comprises (i) providing single-cell TCR sequencing data from a subject having the same disease or condition, having the same antigen profile, expressing an MHC encoded by the same HLA allele, or having the same combination thereof, (ii) determining if a TCR alpha chain sequence is present in the single-cell TCR sequencing data that is the same as or has at most 1, 2, 3, 4 or 5 amino acid differences with the selected TCR alpha chain sequence, and (iii) identifying a TCR beta chain sequence associated with or cognately paired to the selected TCR alpha chain sequence in the single-cell TCR sequencing data.
  • the selected TCR chain sequence is a TCR beta chain sequence.
  • the method further comprises selecting a TCR alpha chain sequence that is present at a higher frequency in the first sub-group of at least 10 subjects relative to the frequency of the same TCR alpha chain sequence in the second sub-group of at least 10 subjects, and is present in at least 2 subjects (e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 500, at least 1,000 or more) from the first sub-group of at least 10 subjects.
  • at least 2 subjects e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 500, at least 1,000 or more
  • the method further comprises identifying a TCR alpha chain sequence associated with or cognately paired to the selected TCR beta chain.
  • the method further comprises (i) providing a single-cell TCR sequencing data from a subject having the same disease or condition, having the same antigen profile, expressing an MHC encoded by the same HLA allele, or having the same combination thereof, (ii) determining if a TCR beta chain sequence is present in the single-cell TCR sequencing data that is the same as or has at most 1, 2, 3, 4 or 5 amino acid differences with the selected TCR beta chain sequence, WSGR Docket No.: 50401-784.601 and (iii) identifying a TCR alpha chain sequence associated with or cognately paired to the selected TCR beta chain sequence in the single-cell TCR sequencing data.
  • the method further comprises pairing the TCR alpha chain sequence and the TCR beta chain sequence to form a paired TCR chain sequences.
  • the selected TCR chain sequence is a TCR gamma chain sequence.
  • the method further comprises selecting a TCR delta chain sequence that is present at a higher frequency in the first sub-group of at least 10 subjects relative to the frequency of the same TCR delta chain sequence in the second sub-group of at least 10 subjects, and is present in at least 5 subjects from the first sub-group of at least 10 subjects.
  • the method further comprises identifying a TCR delta chain sequence associated with or cognately paired to the selected TCR gamma chain.
  • the method further comprises (i) providing a single-cell TCR sequencing data from a subject having the same disease or condition, having the same antigen profile, expressing an MHC encoded by the same HLA allele, or having the same combination thereof, (ii) determining if a TCR gamma chain sequence is present in the single-cell TCR sequencing data that is the same as or has at most 1, 2, 3, 4 or 5 amino acid differences with the selected TCR gamma chain sequence, and (iii) identifying a TCR delta chain sequence associated with the selected TCR gamma chain sequence in the single-cell TCR sequencing data.
  • the selected TCR chain sequence is a TCR delta chain sequence.
  • the method further comprises selecting a TCR gamma chain sequence that is present at a higher frequency in the first sub-group of at least 10 subjects relative to the frequency of the same TCR gamma chain sequence in the second sub-group of at least 10 subjects, and is present in at least 5 subjects from the first sub-group of at least 10 subjects. [0010] In some embodiments, the method further comprises identifying a TCR gamma chain sequence associated with or cognately paired to the selected TCR delta chain.
  • the method further comprises (i) providing a single-cell TCR sequencing data from a subject having the same disease or condition, having the same antigen profile, expressing an MHC encoded by the same HLA allele, or having the same combination thereof, (ii) determining if a TCR delta chain sequence is present in the single-cell TCR sequencing data that is the same as or has at most 1, 2, 3, 4 or 5 amino acid differences with the selected TCR delta chain sequence, and (iii) identifying a TCR gamma chain sequence associated with the selected TCR delta chain sequence in the single-cell TCR sequencing data.
  • the method further comprises pairing the TCR gamma chain sequence and the TCR delta chain sequence to form a paired TCR chain sequences.
  • the method further comprises, prior to determining the statistical significance, grouping two or more TCR chain sequences of the one or more TCR chain sequences based on sequence identity into a grouped TCR chain sequences, wherein the grouped TCR chain sequences share at least 70% sequence identity.
  • variable regions of the grouped TCR WSGR Docket No.: 50401-784.601 chain sequences share at least 70% sequence identity.
  • CDR3 sequences of the grouped TCR chain sequences share at least 70% sequence identity.
  • the one or more TCR chain sequences comprise two or more TCR chain sequences that have been grouped based on sequence identity, and wherein the grouped TCR chain sequences share at least 70% sequence identity.
  • variable regions of the grouped TCR chain sequences share at least 70% sequence identity.
  • CDR3 sequences of the grouped TCR chain sequences share at least 70% sequence identity.
  • selecting the TCR chain sequences comprises selecting a plurality of TCR chain sequences, and wherein the method further comprises aligning the plurality of TCR chain sequences to obtain conserved residues or sequence motifs. [0012] In some embodiments, the method further comprises generating a paring score for a TCR having the paired TCR chain sequences, wherein the paring score predicts likelihood of the TCR to be an antigen-specific TCR to be validated experimentally.
  • the pairing score is calculated based on enrichment p-value, single-cell hit analysis, de novo match analysis, and/or enrichment match analysis, or combinations thereof.
  • the enrichment p-value is at most 1e-10.
  • the single-cell hit comprises identification of the TCR in a single-cell sample.
  • the de novo match or enrichment match comprises identification of the paired TCR chain sequences at a radius of a centroid of at least 5 of another identified antigen-specific TCR.
  • the method further comprises generating an enrichment score for the TCR chain sequence or the plurality of TCR chain sequences selected in (d), wherein the enrichment score predicts likelihood of the TCR chain sequences or the plurality of TCR chain sequences to be an antigen-specific TCR when paired with a corresponding TCR chain to be validated experimentally.
  • the enrichment score is calculated based on performing a singleton condition-enrichment analysis, and/or a cluster-based condition- enrichment analysis.
  • the first sub-group of at least 10 subjects have the same antigen profile as the second sub-group of at least 10 subjects, and wherein the first sub-group of at least 10 subjects express a protein with a same antigen or RNA encoding the protein with a same antigen at a higher level than the expression of the protein with a same antigen or RNA encoding the protein with a same antigen in the second sub-group of at least 10 subjects.
  • the same antigen comprises a mutation.
  • WSGR Docket No.: 50401-784.601 In some embodiments, the first sub-group of at least 10 subjects have the same disease or condition and the second sub-group of at least 10 subjects do not have the same disease or condition.
  • TCR T-cell receptor
  • the reference TCR chain sequence comprises a reference TCR alpha chain and a reference TCR beta chain
  • analyzing one or more TCR chain sequences WSGR Docket No.: 50401-784.601 comprises analyzing each TCR alpha chain sequence and each TCR beta chain sequence separately.
  • analyzing one or more TCR chain sequences in (b) comprises analyzing each TCR alpha chain sequence of the libraries of TCR sequences to determine sequence similarity between each TCR alpha chain sequence and a reference TCR alpha chain sequence.
  • a TCR comprising the identified TCR chain sequence is specific for the epitope from KRAS-G12C in complex with HL-A*11.
  • a TCR comprising the reference TCR chain WSGR Docket No.: 50401-784.601 sequence is specific for an epitope from KRAS-G12D.
  • a TCR comprising the reference TCR chain sequence is specific for the epitope from KRAS-G12D in complex with HLA-A*03, HLA-A*11, or HLA-C*08.
  • a TCR comprising the identified TCR chain sequence is specific for the epitope from KRAS-G12D in complex with HLA-A*03, HLA-A*11, or HLA-C*08.
  • a TCR comprising the reference TCR chain sequence is specific for an epitope from KRAS-G12V.
  • a TCR comprising the reference TCR chain sequence is specific for the epitope from KRAS-G12V in complex with HLA-A*03 or HLA-A*11.
  • a TCR comprising the identified TCR chain sequence is specific for the epitope from KRAS-G12V in complex with HLA-A*03 or HLA- A*11.
  • the method further comprises analyzing the likelihood for the identified TCR chain to be generated by thymic selection.
  • the reference TCR chain sequence is a TCR chain sequence selected by the methods described above. [0018] Further provided herein is a method of making a TCR comprising preparing a TCR comprising a TCR chain sequence selected according to the methods described above. [0019] Further provided herein is a method of making a TCR comprising preparing a TCR comprising a TCR chain sequence identified according to the methods described above and the reference TCR chain sequence.
  • a pharmaceutical composition comprising a TCR comprising a TCR chain sequence selected according to the methods described above or a cell comprising the TCR comprising the TCR chain sequence selected according to the methods described above, and a pharmaceutically acceptable carrier.
  • a pharmaceutical composition comprising a TCR comprising a TCR chain sequence selected according to the methods described above and the reference TCR chain sequence or a cell comprising the TCR comprising the TCR chain sequence selected according to the methods described above and the reference TCR chain sequence, and a pharmaceutically acceptable carrier.
  • a method of treating a subject in need thereof comprising administering the pharmaceutical composition described above into the subject.
  • TCR comprising a TCR chain sequence selected according to the methods described above
  • a cell comprising the TCR comprising the TCR chain sequence selected according to the methods described above
  • a TCR comprising a TCR chain sequence selected according to the methods described above and the reference TCR chain sequence a cell comprising the TCR comprising the TCR chain sequence selected according to the methods described above and the reference TCR chain sequence
  • a pharmaceutical composition described above in the manufacture of a medicament for treating a disease or a pharmaceutical composition described above in the manufacture of a medicament for treating a disease.
  • FIG.1A depicts a schematic example of the de novo method of identifying T-cell receptors (TCRs) from a plurality of libraries of TCR chain sequences.
  • FIG. 1B depicts a schematic example of the bait method of identifying TCR chains from a plurality of libraries of TCR chain sequences.
  • FIG. 2 depicts the results of the de novo method for identifying statistically associated TCR chain sequences for a particular condition.
  • FIG.1A depicts a schematic example of the de novo method of identifying T-cell receptors (TCRs) from a plurality of libraries of TCR chain sequences.
  • FIG. 1B depicts a schematic example of the bait method of identifying TCR chains from a plurality of libraries of TCR chain sequences.
  • FIG. 2 depicts the results of the de novo method for identifying statistically associated TCR chain sequences for a particular condition.
  • FIG. 3A depicts the results of the bait method for identifying statistically associated TCR chain sequences for a particular condition.
  • FIG. 3B depicts sequence logo plots for identified neighbors of each bait (e.g., the known reference TCR chain sequence(s)) to determine if particular amino acids are enriched in any CDR3 position relative to the bait sequence.
  • FIG. 4 depicts an association study using bulk TCR-Seq to identify candidate antigen- specific TCR chains.
  • FIG. 5A depicts sample counts for HPV-relevant cancers.
  • FIG. 5B depicts sample counts for KRAS-relevant cancers.
  • FIG. 6 depicts a method of identifying alpha and beta chains enriched in patient populations of interest. [0035] FIG.
  • the region with 100% identity to a native sequence generally has a length of: less than or equal to 600 amino acid residues, less than or equal to 500 amino acid residues, less than or equal to 400 amino acid residues, less than or equal to 250 amino acid residues, less than or equal to 100 amino acid residues, less than or equal to 85 amino acid residues, less than or equal to 75 amino acid residues, less than or equal to 65 amino acid residues, and less than or equal to 50 amino acid residues.
  • the TCR chain sequence can be present at a higher frequency in the first sub-group of at least 10 subjects relative to the frequency of the same TCR chain sequence in the second sub-group of subjects.
  • the TCR chain sequence can be present in at least 5 subjects from the first sub-group of at least 10 subjects.
  • the method of identifying therapeutically relevant T-cell receptor (TCR) chain sequences can comprise (a) providing a plurality of libraries of TCR chain sequences having a first plurality of libraries of TCR chain sequences from a first sub-group of at least 10 subjects and a second plurality of libraries of TCR chain sequences from a second sub-group of at least 10 subjects, wherein each library of TCR chain sequences from a subject of the first sub-group of at least 10 subjects is from a single subject and each library of TCR chain sequences from a subject of the second sub-group of at least 10 subjects is from a single subject; wherein: each of the subjects of the first sub-group of at least 10 subjects has the same disease or condition, and each of the subjects of the second sub-group of at least 10 subjects does not have the same disease or condition as the first sub-group of at least 10 subjects; each of the subjects of the first sub-group of at least 10 subjects has the same antigen profile, and each of the subjects of the second sub- group of
  • the TCR chain sequence can be present in at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 240, at least 280, at least 320, at least 360, at least 400, at least 480, at least 560, at least 640, at least 720, at least 800, at least 920, at least 1040, at least 1160, at least 1280, at least 1400, at least 1600, at least 1800, at least 2000, at least 2200, at least 2400, at least 2800, at least 3200, at least 3600, at least 4000, at least 4400, at least 5000 or more subjects from the first subgroup of at least 10 subjects.
  • the number of subjects of the first sub-group of at least 10 subjects with the TCR chain sequence selected over the total number of unique TCR chain sequences of the first sub-group of at least 10 subjects can have a statistically significant difference compared to the number of subjects of the second sub-group of subjects with the TCR chain sequence selected over the total number of unique TCR chain sequences of the second sub-group of subjects.
  • whether the subjects have the same disease or condition, whether the subjects have the same antigen profile, and/or whether the subjects express the same MHC encoded by the same HLA allele may not be known.
  • the statistically significant difference in the frequency can be a p-value of at most 0.05, at most 0.001, or at most 0.0001.
  • the p value can be at most at most 0.01, at most 0.001, at most 0.0001, at most 0.00001, at most 0.000001, at most 0.0000001, at most 0.00000001, at most 0.000000001, at most 0.0000000001, at most 0.00000000001, at most 0.000000000001, at most 0.0000000000001, at most 0.00000000000001, at most 0.000000000000000001, at most 0.000000000000001, at most 0.0000000000000001, at most 0.00000000000000001 or less.
  • the p-value can be an adjusted p- value.
  • the selected TCR chain sequence can be a TCR chain sequence that is present in at least 10 subjects from the first sub-group of at least 10 subjects.
  • the selected TCR chain sequence can be a TCR chain sequence that is present in at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 240, at least 280, at least 320, at least 360, at least 400, at least 480, at least 560, at least 640, at least 720, at least 800, at least 920, at least 1040, at least 1160, at least 1280, at least 1400, at least 1600, at least 1800, at least 2000, at least 2200, at least 2400, at least 2800, at least 3200, at
  • FIG. 4 depicts an example association study using bulk TCR-Seq to identify candidate antigen-specific TCR chains.
  • Bulk TCR-seq data was segregated by TCR chain sequences statistically associated with and without HPV and with and without HLA-A*02:01 to produce 4 sub-groups. Dark gray figures represent patients with the query TCR chain.
  • Light gray figures represent patients without query TCR chains.
  • Candidate antigen-specific TCR chains can be enriched in the HPV+ HLA- A*02:01+ subgroup.
  • the first sub-group of at least 10 subjects can have the same disease or condition, and the second sub-group of at least 10 subjects may not have the same disease or condition as the first sub-group.
  • the method can further comprise repeating the method (a)-(d) described above using WSGR Docket No.: 50401-784.601 the plurality of libraries of TCR chain sequences from a first sub-group of at least 10 subjects that express an MHC encoded by a same HLA allele, and a second sub-group of at least 10 subjects that do not express an MHC encoded by the same HLA allele as the first sub-group.
  • the first sub-group of at least 10 subjects can have the same antigen profile.
  • the second sub-group of at least 10 subjects may not have the same antigen profile as the first sub-group.
  • the method can further comprise repeating the method (a)-(d) as described above using the plurality of libraries of TCR chain sequences from a first sub-group of at least 10 subjects that express an MHC encoded by a same HLA allele, and a second sub-group of at least 10 subjects that do not express an MHC encoded by the same HLA allele as the first sub-group.
  • the first sub-group of at least 10 subjects can have the same disease or condition and the same antigen profile, and the second sub-group of at least 10 subjects may not have the same disease or condition as the first sub-group and may not have the same antigen profile as the first sub-group.
  • the method can further comprise repeating the method (a)-(d) as described above using the plurality of libraries of TCR chain sequences from a first sub-group of at least 10 subjects that express an MHC encoded by a same HLA allele, and a second sub-group of at least 10 subjects that do not express an MHC encoded by the same HLA allele as the first sub-group.
  • the first sub-group of at least 10 subjects may express an MHC encoded by a same HLA allele
  • a second sub-group of at least 10 subjects may not express an MHC encoded by the same HLA allele as the first sub-group.
  • the method can further comprise repeating the method (a)-(d) as described above using the plurality of libraries of TCR chain sequences from a first sub- group of at least 10 subjects that have the same disease or condition, and the second sub-group of at least 10 subjects that do not have the same disease or condition as the first sub-group.
  • the method can further comprise repeating the method (a)-(d) as described above using the plurality of libraries of TCR chain sequences from a first sub-group of at least 10 subjects that have the same antigen profile, and the second sub-group of at least 10 subjects that do not have the same antigen profile as the first sub-group.
  • the first sub-group of at least 10 subjects can comprise at least 100, at least 1,000, or more subjects.
  • the first sub-group of at least 10 subjects can comprise at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 240, at least 280, at least 320, at least 360, at least 400, at least 480, at least 560, at least 640, at least 720, at least 800, at least 920, at least 960, at least 1000, at least 1040, at least 1160, at least 1280, at least 1400, at least 1600, at least 1800, at least 2000, at least 2200, at least 2400, at least 2800, at least 3200, at least 3600, at least 4000, at least 4400, at least 5000 or more subjects.
  • the second sub-group of at least 10 subjects can comprise at least 100, at least 1,000, or more subjects.
  • the second sub-group of at least 10 subjects can comprise at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 240, at least 280, at least 320, at least 360, at least 400, at least 480, at least 560, at least 640, at least 720, at least 800, at least 920, at least 960, at least 1000, at least 1040, at least 1160, at least 1280, at least 1400, at least 1600, at least 1800, at least 2000, at least 2200, at least 2400, at least 2800, at least 3200, at least 3600, at least 4000, at least 4400, at least 5000
  • the selected TCR chain sequence can be a TCR alpha chain sequence.
  • the method can further comprise selecting a TCR beta chain sequence that is present at a higher frequency in the first sub-group of at least 10 subjects relative to the frequency of the same TCR beta chain sequence in the second sub-group of at least 10 subjects, and is present in at least 2 subjects (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 240, at least 280, at least 320, at least 360, at least 400, at least 480, at least 560, at least 640, at least 720, at least 800, at least 920, at least 1040, at least 1160, at least 1280, at least
  • the method can further comprise identifying a TCR beta chain sequence associated with or cognately paired to the selected TCR alpha chain.
  • the method can further comprise (i) providing single-cell TCR sequencing data from a subject having the same disease or condition, having the same antigen profile, expressing an MHC encoded by the same HLA allele, or having the same combination thereof, (ii) determining if a TCR alpha chain sequence is present in the single-cell TCR sequencing data that is the same as or has at most 1, 2, 3, 4 or 5 amino acid differences with the selected TCR alpha chain sequence, and (iii) identifying a TCR beta chain sequence associated with or cognately paired to the selected TCR alpha chain sequence in the single-cell TCR sequencing data.
  • the selected TCR chain sequence can be a TCR beta chain sequence.
  • the method can further comprise selecting a TCR alpha chain sequence that is present at a higher frequency in the first sub-group of at least 10 subjects relative to the frequency of the same TCR alpha chain sequence in the second sub-group of at least 10 subjects, and is present in at least 2 subjects (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 120, at least 140, at least 160, at least WSGR Docket No.: 50401-784.601 180, at least 200, at least 240, at least 280, at least 320, at least 360, at least 400, at least 480, at least 560, at least 640, at least 720, at least 800, at least 920, at least
  • the method can further comprise identifying a TCR alpha chain sequence associated with or cognately paired to the selected TCR beta chain.
  • the method can further comprise (i) providing a single-cell TCR sequencing data from a subject having the same disease or condition, having the same antigen profile, expressing an MHC encoded by the same HLA allele, or having the same combination thereof, (ii) determining if a TCR beta chain sequence is present in the single-cell TCR sequencing data that is the same as or has at most 1, 2, 3, 4 or 5 amino acid differences with the selected TCR beta chain sequence, and (iii) identifying a TCR alpha chain sequence associated with or cognately paired to the selected TCR beta chain sequence in the single-cell TCR sequencing data.
  • the method can further comprise pairing the TCR alpha chain sequence and the TCR beta chain sequence to form a paired TCR chain sequences.
  • the selected TCR chain sequence can be a TCR gamma chain sequence.
  • the method can further comprise selecting a TCR delta chain sequence that is present at a higher frequency in the first sub-group of at least 10 subjects relative to the frequency of the same TCR delta chain sequence in the second sub-group of at least 10 subjects, and is present in at least 2 subjects (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 240, at least 280, at least 320, at least 360, at least 400, at least 480, at least 5
  • the method can further comprise identifying a TCR delta chain sequence associated with or cognately paired to the selected TCR gamma chain.
  • the method can further comprise (i) providing a single-cell TCR sequencing data from a subject having the same disease or condition, having the same antigen profile, expressing an MHC encoded by the same HLA allele, or having the same combination thereof, (ii) determining if a TCR gamma chain sequence is present in the single-cell TCR sequencing data that is the same as or has at most 1, 2, 3, 4 or 5 amino acid differences with the selected TCR gamma chain sequence, and (iii) identifying a TCR delta chain sequence associated with the selected TCR gamma chain sequence in the single-cell TCR sequencing data.
  • the selected TCR chain sequence can be a TCR delta chain sequence.
  • the method can further comprise selecting a TCR gamma chain sequence that is present at a higher frequency in the first sub-group of at least 10 subjects relative to the frequency of the same TCR gamma chain sequence in the second sub-group of at least 10 subjects, and is present in at least 2 subjects (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 120, at least 140, at least 160, at least 180, at least 200, at least 240, at least 280, at least 320, at least 360, at least 400, at least 480, at least 560, at least 640, at least 720, at least 800, at least
  • the first sub-group of at least 10 subjects can comprise a same HLA allele.
  • the same condition can comprise HPV infection.
  • the same condition can comprise a cancer.
  • the cancer can be associated with an antigen recognized by a TCR having the paired TCR chain sequences.
  • the cancer cannot be associated with an antigen recognized by a TCR having the paired TCR chain sequences.
  • the same condition can comprise an autoimmune disorder.
  • the same condition can comprise an infectious disease.
  • the same antigen profile can comprise having a same cancer mutation, a same neoantigen, a same tumor-associated antigen, or any combination WSGR Docket No.: 50401-784.601 thereof.
  • the same cancer mutation can comprise KRAS-G12C, KRAS-G12D, or KRAS-G12V mutation.
  • the same HLA allele can comprise an allele selected from the group consisting of HLA- A:01:01, HLA-A:02:01, HLA-A:03:01, HLA-A:11:01, HLA-B:07:02, HLA-B:08:01, HLA- B:44:02, HLA-B:44:03, HLA-C:04:01, HLA-C:05:01, HLA-C:06:02, HLA-C:07:02, and HLA- C:08:02.
  • Each library of TCR chain sequences can be a sequencing dataset obtained by bulk sequencing of TCR chain sequences from a subject.
  • the subject can be a cancer patient.
  • the subject may also include a healthy subject.
  • An epitope or an HLA allele that a TCR comprising the paired TCR chain sequences recognizes can be unknown.
  • the method can further comprise assaying a TCR comprising the paired TCR chain sequences for a binding affinity against an epitope associated with the same disease or condition or associated with the same antigen profile in complex with the same HLA allele.
  • a pharmaceutical composition comprising an active agent such as an immune cell described herein, in combination with one or more adjuvants can be formulated in conventional manner using one or more physiologically acceptable carriers, comprising excipients, diluents, and/or auxiliaries, e.g., which facilitate processing of the active agents into preparations that can be administered. Proper formulation can depend at least in part upon the route of administration chosen.
  • the agent(s) described herein can be delivered to a patient using a number of routes or modes of administration, including oral, buccal, topical, rectal, transdermal, transmucosal, subcutaneous, intravenous, and intramuscular applications, as well as by inhalation.
  • the composition comprises at least one agent that helps preserve cell viability through at least one cycle of freeze-thaw. In some embodiments, the composition comprises at least one agent that helps preserve cell viability through at least more than one cycle of freeze-thaw.
  • the vehicle can be chosen from those known in art to be suitable, including aqueous solutions or oil suspensions, or emulsions, with sesame oil, corn oil, cottonseed oil, or peanut oil, as well as elixirs, mannitol, dextrose, or a sterile aqueous solution, and similar pharmaceutical vehicles.
  • the formulation can also comprise polymer compositions which are biocompatible, biodegradable, such as poly(lactic-co-glycolic) acid. These materials can be made into micro or nanospheres, loaded with drug and further coated or derivatized to provide superior sustained release performance.
  • Vehicles suitable for periocular or intraocular injection include, for example, suspensions of therapeutic agent in injection grade water, WSGR Docket No.: 50401-784.601 liposomes and vehicles suitable for lipophilic substances. Other vehicles for periocular or intraocular injection are well known in the art.
  • pharmaceutical composition is formulated in accordance with routine procedures as a pharmaceutical composition adapted for intravenous administration to human beings.
  • the preservative or stabilizer is selected from a cytokine, a growth factor or an adjuvant or a chemical substance.
  • the at least one antigen specific T cell is administered to a subject within 28 days from collecting a PBMC sample from the subject.
  • the active agents can also be formulated as a depot preparation. Such long-acting formulations can be administered by implantation or transcutaneous delivery (for example subcutaneously or intramuscularly), intramuscular injection or use of a transdermal patch.
  • the method of treating a disease in a subject can comprise identifying an immunogenic neoantigen peptide in a subject according to the methods described herein; and synthesizing the polynucleotide, such as an mRNA, that encodes the immunogenic neoantigen peptide or a precursor thereof, and manufacturing T cells specific for identified neoantigens; and administering the neoantigen specific T cells to the subject.
  • the agents and compositions provided herein may be used alone or in combination with conventional therapeutic regimens such as surgery, irradiation, chemotherapy and/or bone marrow transplantation (autologous, syngeneic, allogeneic or unrelated).
  • Certain specific antigens may be specifically expressed in cancer cells of certain cancer types, WSGR Docket No.: 50401-784.601 and not in other cancer types.
  • Various cancer are contemplated herein that may not be restricted to a specific cell type, tissue type or organ, or even a certain stage of cancer.
  • the TCRs of the present invention are directed to cancer cells that express a cancer antigen, that may be patient specific, which can be found during sequencing of a subject’s genome from biological sample obtained from a cancer cell, cancer site or cancer tissue and compared to a corresponding non- cancer sample from the same subject; wherein the patient-specific antigen may be expressed in the cancer cell, and not on the non-cancer cell of the subject.
  • cancer antigens may be cancer specific, where the antigen is reportedly present in the type of cancer observed in multiple patients in the human population, who have been diagnosed of the specific cancer.
  • certain types are cancers are associated with an antigen, a protein (e.g., a viral protein) a gene mutation. All forms of cancer are contemplated herein.
  • the cancer is a solid cancer.
  • the cancer is a liquid / blood cancer.
  • the cancer can express or be diagnosed as expressing a tumor antigen.
  • the tumor antigen can be a tumor-associated antigen or a tumor-specific antigen.
  • the cancer expresses a tumor-associated antigen (TAA).
  • TAA tumor-associated antigen
  • the cancer expresses a tumor-specific antigen (TSA).
  • TAA tumor-specific antigen
  • the cancer is a cancer expressing or diagnosed as expressing a TAA.
  • the cancer is a cancer expressing or diagnosed as expressing a TSA.
  • the current classification of TAA can include the following group: a) Cancer testis (CT) antigen: Since testis cells do not express HLA class I and class II molecules, these antigens may not be recognized by T cells in normal tissues and may therefore be immunologically considered tumor specific.
  • CT antigens include members of the MAGE family and NY-ESO-1;
  • Differentiation antigen both tumor and normal tissue (from which the tumor originates) may contain TAAs.
  • Differentiation antigens may be found, for example, in melanoma and normal melanocytes. Many of these melanocyte lineage-associated proteins may be involved in melanin biosynthesis and therefore these proteins may not tumor-specific, but may still widely be used for immunotherapy of cancer. Examples include, but are not limited to, tyrosinase for melanoma and PSA for Melan-A/MART-1 or prostate cancer; c) Overexpressed TAA: gene-encoded widely expressed TAAs may be detected in histologically diverse tumors and in many normal tissues, with generally low expression levels.
  • TAAs include Her-2/neu, survivin, telomerase or WT1; d) tumor specific antigen can include unique TAAs resulted from mutations in normal genes (e.g., beta-catenin, CDK4). Some of these molecular changes can be associated with neoplastic transformation and/or progression. Tumor-specific antigens can generally induce strong immune responses without risking from the autoimmune response to normal tissue strips.
  • TAAs may only be associated with the exact tumor on which they are confirmed, and may not commonly shared among many individual tumors.
  • peptide tumor specificity or relatedness
  • TAA resulting from aberrant post-translational modification such TAAs may result from proteins in the tumor that are neither specific nor overexpressed, but which still have tumor relevance (this relevance is due to posttranslational processing that is primarily active on tumors).
  • TAAs may result from an altered glycosylation pattern, resulting in a tumor producing a novel epitope for MUC1 or in an event such as protein splicing during degradation, which may or may not be tumor specific; and f) Tumor virus protein: these TTAs are viral proteins that may play a key role in the oncogenic process and, because they are foreign proteins (non-human proteins), may be able to trigger T cell responses.
  • Non-limiting examples of such proteins include human papilloma type 16 viral proteins, E6 and E7, which are expressed in cervical cancer.
  • tumor antigens include, but not limited to new antigens expressed during tumorigenesis, products of oncogenes and tumor suppressor genes, overexpressed or abnormally expressed intracellular proteins (e.g., HER2, MUC1, PSA, MUC1), carcinoembryonic antigen (CEA), tumor viruses (e.g., EBC, HPV, HBV, HCB, HTLV), cancer testis antigens (CTA) (e.g., MAGE family, NY-ESO), oncofetal antigens, altered surface glycolipids and glycoproteins, cell type-specific differentiation antigens (e.g., MART-1), or a derivative thereof.
  • intracellular proteins e.g., HER2, MUC1, PSA, MUC1
  • CEA carcinoembryonic antigen
  • tumor viruses e.g., EBC, HPV, HBV, HCB, HTLV
  • CTA cancer testis antigens
  • oncofetal antigens e.g., MAGE
  • the tumor antigens can be selected from the group consisting of NY-ESO-1, Her2/neu, SSX-2, MAGE-C2, MAGE-A1, M-2433-233, MAGE-A10254-262, KK-LC-1, p53, PRAME, Alpha fetoprotein, HPV6-E6, HPV16-E7, EBV-LMP1, RAS: G12D, RAS: G12C, RAS: G12A, RAS: G12S, RAS: G12R, RAS: G12R, RAS: G12R, RAS: G122 V, RAS: Q61H, RAS: Q61L, RAS: Q61R, RAS: G13D, TP53: V157G, TP53: V157F, TP53: R248Q, TP53: R248W, TP53: G245S, TP53: Y163C, TP53: G249S, TP53: Y240C, TP53: R1
  • the RAS can be KRAS, HRAS, or NRAS.
  • tumor-associated antigen or tumor-specific antigen includes antigens from Human Papilloma Virus, Epstein-Barr Virus, Merkel cell polyomavirus, Human Immunodeficiency Virus, Human T-cell Leukemia Virus, Human Herpes Virus 8, Hepatitis B virus, Hepatitis C virus, HCV, HBC, Cytomegalovirus, or from the group of single- point mutated antigens derived from the group consisting of the antigens of ctnnbl gene, casp8 gene, HER2 gene, p53 gene, KRAS gene, NRAS gene, or particular tumor antigens issued or derived from the group consisting of RAS oncogene, BCR-ABL tumor antigens, ETV6-AML1 tumor antigens, melanoma-antigen encoding genes (MAGE), BAGE antigens, GAGE antigens, ssx antigens, ny-eso
  • the cancer cells express the tumor antigens, including and not limited to, NY-ESO-1, Her2/neu, SSX-2, MAGE-C2, MAGE-A1, M-2433-233, MAGE-A10254-262, KK- LC-1, p53, PRAME, Alpha fetoprotein, HPV6-E6, HPV16-E7, EBV-LMP1, RAS: G12D, RAS: G12C, RAS: G12A, RAS: G12S, RAS: G12R, RAS: G12R, RAS: G12R, RAS: G122 V, RAS: Q61H, RAS: Q61L, RAS: Q61R, RAS: G13D, TP53: V157G, TP53: V157F, TP53: R248Q, TP53: R248W, TP53: G245S, TP53: G245S, TP53: Y163C, TP53: G249S, TP53
  • the methods of the disclosure can be used to treat any type of cancer known in the art.
  • Non-limiting examples of cancers to be treated by the methods of the present disclosure can include melanoma (e.g., metastatic malignant melanoma), renal cancer (e.g., clear cell carcinoma), prostate cancer (e.g., hormone refractory prostate adenocarcinoma), pancreatic adenocarcinoma, breast cancer, colon cancer, lung cancer (e.g., non-small cell lung cancer), esophageal cancer, squamous cell carcinoma of the head and neck, liver cancer, ovarian cancer, cervical cancer, thyroid cancer, glioblastoma, glioma, leukemia, lymphoma, and other neoplastic malignancies.
  • melanoma e.g., metastatic malignant melanoma
  • renal cancer e.g., clear cell carcinoma
  • prostate cancer e.g., hormone refractory prostate adenocarcinoma
  • pancreatic adenocarcinoma breast cancer
  • a cancer to be treated by the methods of treatment of the present disclosure is selected from the group consisting of carcinoma, squamous carcinoma, adenocarcinoma, sarcomata, endometrial cancer, breast cancer, ovarian cancer, cervical cancer, fallopian tube cancer, primary peritoneal cancer, colon cancer, colorectal cancer, squamous cell carcinoma of the anogenital region, melanoma, renal cell carcinoma, lung cancer, non-small cell lung cancer, squamous cell carcinoma of the lung, stomach cancer, bladder cancer, gall bladder cancer, liver cancer, thyroid cancer, laryngeal cancer, salivary gland cancer, esophageal cancer, head and neck cancer, glioblastoma, glioma, squamous
  • the articles of manufacture provided herein contain packaging materials.
  • packaging materials include, but are not limited to, blister packs, bottles, tubes, bags, containers, bottles, and any packaging material suitable for a selected formulation and intended mode of administration and treatment.
  • a kit typically includes labels listing contents and/or instructions for use, and package inserts with instructions for use.
  • a set of instructions can also be included.
  • Example 1 A “de novo” method of identifying TCR chains from a plurality of patient libraries of TCR chain sequences [0229] This example shows a method of identifying TCR chains from a plurality of patient libraries of TCR chain sequences using “de novo” method. The general procedure is summarized in FIG. 1A.
  • FIG. 6 depicts the results of a Fisher exact test for one TCR chain by condition and by allele. The Fisher exact tests show association using patient counts.
  • TCR chain detection events e.g., one TCR chain detected in one patient
  • TCR chain enrichment by allele was plotted against TCR chain enrichment by condition to identify chains of interest (FIG. 7B).
  • FIG. 8A summarizes the number of highest tier candidate chains identified by condition.
  • specific TCR chains of interest two approaches were taken. First, paired TCR chains enriched in the same patient were searched for. For example, if a number of patients (e.g., 5 patients) had an patients (FIG. 10A). Second, publicly available scTCR datasets from these patient populations (e.g.
  • HPV+ cancers for HPV and KRAS-mutation-enriched cancers for KRAS were used to search for T cells contains the chains of interest plus paired chains (FIG. 10B).
  • the number of paired chains by condition identified using enrichment analysis versus both enrichment analysis and scTCR shows that both methods can be paired to maximize paired chains uncovered (FIG. 13A).
  • Example 2 A “Bait” method of identifying TCR chains from a plurality of patient libraries of TCR chain sequences [0238] This example shows a method of identifying TCR chains from a plurality of patient libraries of TCR chain sequences using a known antigen specific TCR chain. The general procedure is summarized in FIG. 1B.
  • HPV cohorts included subjects with head and neck squamous cell carcinomas (HNSCC) and cervical squamous cell carcinoma (CESC), and KRAS cohorts included subjects with lung adenocarcinoma (LUAD), colon adenocarcinoma (COAD), and pancreatic adenocarcinoma (PAAD).
  • HNSCC head and neck squamous cell carcinomas
  • CESC cervical squamous cell carcinoma
  • KRAS cohorts included subjects with lung adenocarcinoma (LUAD), colon adenocarcinoma (COAD), and pancreatic adenocarcinoma (PAAD).
  • LAD lung adenocarcinoma
  • COAD colon adenocarcinoma
  • PAAD pancreatic adenocarcinoma
  • ROS9a, ROS9b, ROS9d share the same alpha chain (CDR3a: CLVGDMDQAGTALIF (SEQ ID NO: 16)) while ROS9c TRA differs in one amino acid (CLVGDRDQAGTALIF (SEQ ID NO: 17)) (FIG. 9C).
  • HPV-specific TCRs identified from the “de novo” and “bait” methods HPV-specific TCRs identified from the “de novo” method
  • HPV HPV-specific TCRs identified from the “de novo” method
  • HPV-specific TCRs identified from the “bait” method (I) This example provides data analysis and summary of the HPV-specific TCR chains identified by the “bait” method as described in Example 2. [0256] For HPV, neighbors were found with medium to strong evidence of specificity for three out of 31 baits. Probably the strongest evidence is for the beta chain of the NCI TCR (or CRL4 TCR), for which two neighbors were found that appear in four and two HPV+ HLA-A*02+ samples respectively and do not appear in any HPV- or HLA-A*02- samples.
  • HPV-specific TCRs identified from the “bait” method (II) HPV-specific TCRs identified from the “bait” method (II)
  • the “bait approach” was used to find close neighbors of a published TCR that recognized an epitope of HPV E7, YMLDLQPET (SEQ ID NO: 44), presented by HLA-A*02:01, referred to below as the NCI TCR chain (Jin et al. 2018. Engineered T cells targeting E7 mediate regression of human papillomavirus cancers in a murine model. JCI Insight 3(8):e99488; Nagarsheth et al. 2021.
  • TCR-engineered T cells targeting E7 for patients with metastatic HPV-associated epithelial chain were significantly enriched in HPV+ patients compared to HPV- patients, as well as in patients with the A*02:01 allele compared to all other patients (FIG. 15A; see triangle indicated with an arrow).
  • the table below shows the CDR3 sequences and TRBV/TRBJ genes for the original NCI TCR chain and two close neighbors discovered from the bait analysis (FIG. 15B). Positions that differ between the original CDR3b and the newly discovered CDR3b sequences are highlighted in red.
  • Example 5 Experimental approaches for candidate TCR validation [ 0262] This example provides two approaches to experimentally validating candidate TCR chains.
  • the first approach is to use Surface plasmon resonance (SPR) (FIG. 14A).
  • SPR Surface plasmon resonance
  • pMHCs and soluble TCRs are produced, and affinity is measured using SPR.
  • the second approach is to use cell lines (FIG. 14B) To validate using cell lines candidate TCR chains are expressed in Jurkat cells and then recognition against continuously infected HPV+, HLA-A:02:01 cell lines is tested. If recognition is seen, assays to reveal epitope and avidity are used.
  • HPV-specific TCRs validated from the “bait” method (II) [ 0263] Binding of the two variant TCRs plus the NCI TCR to HPV E7-YMLDLQPET (SEQ ID NO: 44)/ HLA-A*02:01 was measured using surface plasmon resonance (SPR). The single- mutant hit (Hit 2) showed an improved Kd compared to the original NCI TCR, driven by an improved K off value three times lower than that of the original (FIG. 15C). [ 0264] Taken together, these results indicate that both variant TCRs identified using the bait approach recognize the same target as the original NCI TCR.

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Abstract

La présente divulgation concerne des procédés d'identification de récepteurs de lymphocytes T (TCR) à partir de données de séquençage. Les procédés peuvent comprendre l'identification d'une chaîne alpha du TCR, d'une chaîne bêta du TCR, d'une chaîne gamma du TCR ou d'une chaîne delta du TCR à partir des données de séquençage, puis l'identification de la chaîne appariée correspondante. Le procédé « de novo » peut comprendre l'identification de séquences de chaînes TCR statistiquement associées par évaluation de l'état antigénique des patients et/ou des allèles HLA. Dans le procédé « appât », une séquence de chaîne TCR d'un TCR connu pour avoir un certain degré de réactivité antigénique peut être utilisée pour rechercher des séquences associées dans la grande base de données. Les séquences résultantes peuvent présenter ou non des enrichissements statistiques qui peuvent être détectés à l'aide de l'approche « de novo ». La divulgation concerne divers procédés d'appariement. Des chaînes du TCR appariées identifiées peuvent être utilisées pour des thérapies à base de cellules.
PCT/US2025/023272 2024-04-04 2025-04-04 Procédés d'identification de séquences de récepteur des lymphocytes t (tcr) Pending WO2025213105A1 (fr)

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