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US-12618115-B2 - Determination of cytotoxic gene signature and associated systems and methods for response prediction and treatment

US12618115B2US 12618115 B2US12618115 B2US 12618115B2US-12618115-B2

Abstract

Disclosed herein are systems, methods, and compositions for treating a subject diagnosed with, or suffering from cancer. In some embodiments, the method comprises determining whether a tumor sample from the subject includes a cytotoxic gene signature, and treating the subject based on the determination. In some embodiments, the subject has or is suspected of having a loss of heterozygosity in human leukocyte antigen (HLA) class I genes. In some embodiments, the therapy comprises one or more checkpoint inhibitors. In some embodiments, the cancer is colorectal, uterine, stomach, lung, skin, head or neck, or non-small cell lung carcinoma.

Inventors

  • Denise Lau
  • Aly A. Khan
  • Yinjie Gao
  • Michelle Marie Stein
  • Ameen Salahudeen
  • Timothy Rand
  • Sonal Khare

Assignees

  • TEMPUS AI, INC.

Dates

Publication Date
20260505
Application Date
20211119

Claims (14)

  1. 1 . A method comprising: (A) performing RNA sequencing on a sample of a cancer from a subject to provide RNA sequence data; at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors: (B) processing the RNA sequence data to provide expression level data for at least 10 signature genes selected from the group consisting of CCL5, GZMA, NKG7, CCL4, GZMH, CST7, GZMB, GZMK, GNLY, PRF1, CCL4L2, CD52, IL32, CD74, CRIP1, CCL3, ITM2C, S100A10, TUBA4A, FGFBP2, S100A4, EOMES, HOPX, DUSP4, PLEK, S100A11, LGALS1, CTSC, ZNF683, SRRT, CLEC2B, LDHA, ENC1, OASL, ZEB2, HSPE1, GIMAP4, PTMS, GIMAP7, TNFSF9, ISG20, CCL3L1, PKM, CXCR3, SLA, XCL2, RNF213, SLAMF7, FABP5, FCRL6, ITM2A, SYTL3, TXNIP, TYROBP, RPS27L, H2AFZ, ITGA1, XCL1, RGCC, CACYBP, LYST, GGA2, ID2, PTPN7, MT2A, TGFB1, HAVCR2, ISG15, GBP5, KRT86, MAP3K8, SYNE1, SLC7A5, ARHGAP30, HSPH1, CORO1B, KIAA1551, PARP8, THEMIS, MYOIF, FKBP4, CTSW, IFNG, LAG3, ABI3, KLRD1, SIPR5, RGS1, LGALS3, KLRG1, TRAT1, SAMSN1, CRTAM, DUSP2, CXCR6, DNAJA1, PDCD1, TBX21, FASLG, and CD70, wherein the at least 10 signature genes comprises CCL5, granzyme A, NKG7, CCL4, granzyme B, granzyme H, granulysin, CCL4L2, and perforin 1; (C) inputting the expression levels of the at least 10 signature genes to one or more models that are collectively trained to identify subjects as not likely to experience a progression event while being treated with an immune checkpoint inhibitor (ICI) therapy; and (D) identifying the subject as not likely to experience a progression event while being treated with the ICI therapy; and (E) administering the ICI therapy to the subject identified as not likely to experience a progression event while being treated with the ICI therapy.
  2. 2 . The method of claim 1 , wherein the one or more models are collectively trained to provide a cytotoxic (CT) score.
  3. 3 . The method of claim 1 , wherein the one or more models are collectively trained to provide a tumor mutation burden (TMB) for the subject's cancer wherein the TMB is determined based on an analysis of a mutation status for the one or more genes in the subject's cancer, and wherein the one or more genes is selected from ABCB1, ABCC3, ABL1, ABL2, FAM175A, ACTA2, ACVR1, ACVR1B, AGO1, AJUBA, AKT1, AKT2, AKT3, ALK, AMER1, APC, APLNR, APOB, AR, ARAF, ARHGAP26, ARHGAP35, ARIDIA, ARIDIB, ARID2, ARID5B, ASNS, ASPSCR1, ASXL1, ATIC, ATM, ATP7B, ATR, ATRX, AURKA, AURKB, AXIN1, AXIN2, AXL, B2M, BAP1, BARD1, BCL10, BCL11B, BCL2, BCL2L1, BCL2L11, BCL6, BCL7A, BCLAF1, BCOR, BCORL1, BCR, BIRC3, BLM, BMPR1A, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTK, BUB1B, C11orf65, C3orf70, C8orf34, CALR, CARD11, CARM1, CASP8, CASR, CBFB, CBL, CBLB, CBLC, CBR3, CCDC6, CCND1, CCND2, CCND3, CCNE1, CD19, CD22, CD274, CD40, CD70, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN1C, CDKN2A, CDKN2B, CDKN2C, CEBPA, CEP57, CFTR, CHD2, CHD4, CHD7, CHEK1, CHEK2, CIC, CIITA, CKS1B, CREBBP, CRKL, CRLF2, CSF1R, CSF3R, CTC1, CTCF, CTLA4, CTNNA1, CTNNB1, CTRC, CUL1, CUL3, CUL4A, CUL4B, CUX1, CXCR4, CYLD, CYP1B1, CYP2D6, CYP3A5, CYSLTR2, DAXX, DDB2, DDR2, DDX3X, DICER1, DIRC2, DIS3, DIS3L2, DKC1, DNM2, DNMT3A, DOT1L, DPYD, DYNC2H1, EBF1, ECT2L, EGF, EGFR, EGLN1, EIF1AX, ELF3, TCEB1, C11orf30, ENG, EP300, EPCAM, EPHA2, EPHA7, EPHB1, EPHB2, EPOR, ERBB2, ERBB3, ERBB4, ERCC1, ERCC2, ERCC3, ERCC4, ERCC5, ERCC6, ERG, ERRFI1, ESR1, ETS1, ETS2, ETV1, ETV4, ETV5, ETV6, EWSR1, EZH2, FAM46C, FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM, FAS, FAT1, FBXO11, FBXW7, FCGR2A, FCGR3A, FDPS, FGF1, FGF10, FGF14, FGF2, FGF23, FGF3, FGF4, FGF5, FGF6, FGF7, FGF8, FGF9, FGFR1, FGFR2, FGFR3, FGFR4, FH, FHIT, FLCN, FLT1, FLT3, FLT4, FNTB, FOXA1, FOXL2, FOXO1, FOXO3, FOXP1, FOXQ1, FRS2, FUBP1, FUS, G6PD, GABRA6, GALNT12, GATA1, GATA2, GATA3, GATA4, GATA6, GEN1, GLI1, GLI2, GNA11, GNA13, GNAQ, GNAS, GPC3, GPS2, GREM1, GRIN2A, GRM3, GSTP1, H19, H3F3A, HAS3, HAVCR2, HDAC1, HDAC2, HDAC4, HGF, HIF1A, HISTIHIE, HIST1H3B, HIST1H4E, HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DPB2, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DRB6, HLA-E, HLA-F, HLA-G, HNF1A, HNF1B, HOXA11, HOXB13, HRAS, HSD11B2, HSD3B1, HSD3B2, HSP90AA1, HSPH1, IDH1, IDH2, IDO1, IFIT1, IFIT2, IFIT3, IFNAR1, IFNAR2, IFNGR1, IFNGR2, IFNL3, IKBKE, IKZF1, IL1ORA, IL15, IL2RA, IL6R, IL7R, ING1, INPP4B, IRF1, IRF2, IRF4, IRS2, ITPKB, JAK1, JAK2, JAK3, JUN, KAT6A, KDM5A, KDM5C, KDM5D, KDM6A, KDR, KEAP1, KEL, KIF1B, KIT, KLF4, KLHL6, KLLN, KMT2A, KMT2B, KMT2C, KMT2D, KRAS, L2HGDH, LAG3, LATS1, LCK, LDLR, LEF1, LMNA, LMO1, LRP1B, LYN, LZTR1, MAD2L2, MAF, MAFB, MAGI2, MALT1, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K7, MAPK1, MAX, MCIR, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MET, MGMT, MIB1, MITF, MKI67, MLH1, MLH3, MLLT3, MN1, MPL, MRE11A, MS4A1, MSH2, MSH3, MSH6, MTAP, MTHFD2, MTHFR, MTOR, MTRR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, MYH11, NBN, NCOR1, NCOR2, NF1, NF2, NFE2L2, NFKBIA, NHP2, NKX2-1, NOP10, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NPM1, NQO1, NRAS, NRG1, NSD1, WHSC1, NT5C2, NTHL1, NTRK1, NTRK2, NTRK3, NUDT15, NUP98, OLIG2, P2RY8, PAK1, PALB2, PALLD, PAX3, PAX5, PAX7, PAX8, PBRM1, PCBP1, PDCD1, PDCDILG2, PDGFRA, PDGFRB, PDK1, PHF6, PHGDH, PHLPP1, PHLPP2, PHOX2B, PIAS4, PIK3C2B, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIM1, PLCG1, PLCG2, PML, PMS1, PMS2, POLD1, POLE, POLH, POLQ, POT1, POU2F2, PPARA, PPARD, PPARG, PPM1D, PPP1R15A, PPP2RIA, PPP2R2A, PPP6C, PRCC, PRDM1, PREX2, PRKAR1A, PRKDC, PARK2, PRSS1, PTCH1, PTCH2, PTEN, PTPN11, PTPN13, PTPN22, PTPRD, PTPRT, QKI, RAC1, RAD21, RAD50, RAD51, RAD51B, RAD51C, RAD51D, RAD54L, RAF1, RANBP2, RARA, RASA1, RB1, RBM10, RECQL4, RET, RHEB, RHOA, RICTOR, RINT1, RIT1, RNF139, RNF43, ROS1, RPL5, RPS15, RPS6KB1, RPTOR, RRM1, RSF1, RUNX1, RUNXIT1, RXRA, SCG5, SDHA, SDHAF2, SDHB, SDHC, SDHD, SEC23B, SEMA3C, SETBP1, SETD2, SF3B1, SGK1, SH2B3, SHH, SLC26A3, SLC47A2, SLC9A3R1, SLIT2, SLX4, SMAD2, SMAD3, SMAD4, SMARCA1, SMARCA4, SMARCB1, SMARCE1, SMCIA, SMC3, SMO, SOCS1, SOD2, SOX10, SOX2, SOX9, SPEN, SPINK1, SPOP, SPRED1, SRC, SRSF2, STAG2, STAT3, STAT4, STAT5A, STAT5B, STAT6, STK11, SUFU, SUZ12, SYK, SYNE1, TAF1, TANC1, TAP1, TAP2, TARBP2, TBC1D12, TBL1XR1, TBX3, TCF3, TCF7L2, TCL1A, TERT, TET2, TFE3, TFEB, TFEC, TGFBR1, TGFBR2, TIGIT, TMEM127, TMEM173, TMPRSS2, TNF, TNFAIP3, TNFRSF14, TNFRSF17, TNFRSF9, TOP1, TOP2A, TP53, TP63, TPM1, TPMT, TRAF3, TRAF7, TSC1, TSC2, TSHR, TUSC3, TYMS, U2AF1, UBE2T, UGT1A1, UGT1A9, UMPS, VEGFA, VEGFB, VHL, C10orf54, WEE1, WNK1, WNK2, WRN, WT1, XPA, XPC, XPO1, XRCC1, XRCC2, XRCC3, YEATS4, ZFHX3, ZMYM3, ZNF217, ZNF471, ZNF620, ZNF750, ZNRF3, and ZRSR2.
  4. 4 . The method of claim 1 , wherein at least one of the one or more models generate an IO Progression Risk Score.
  5. 5 . The method of claim 4 , wherein the IO Progression Risk Score reflects the probability of a progression event occurring in 3 months.
  6. 6 . The method of claim 4 , wherein the IO Progression Risk Score reflects the probability of a progression event occurring in 6 months.
  7. 7 . The method of claim 1 , wherein the subject's cancer is stage IV.
  8. 8 . The method of claim 1 , wherein the subject's cancer is non-small cell lung carcinoma (NSCLC).
  9. 9 . The method of claim 1 , wherein the subject's cancer is stage IV NSCLC, or non-stage IV NSCLC with a metastasis event.
  10. 10 . The method of claim 1 , wherein the subject's cancer is stage IV, or is earlier than stage IV with a metastasis event and no prior treatment with immune-oncology (IO) therapy.
  11. 11 . The method of claim 1 , wherein at least one of the one or more models calculates a CT score, at least one of the one or more models calculates a TMB, and at least one of the one or more models calculates an IO Progression risk score.
  12. 12 . The method of claim 11 , wherein the IO Progression Risk score is calculated based on the CT score and the TMB.
  13. 13 . The method of claim 1 , wherein the subject's cancer has a tumor mutational burden (TMB) below a TMB threshold.
  14. 14 . The method of claim 1 , wherein the at least 10 signature genes comprise NKG7, CCL5, GZMA, CCL4, CST7, GZMH, GZMB, GZMK, PRF1, GNLY, CCL4L2, CD74, IL32, CD52, CCL3, LAG3, CTSW, CTSC, CXCR6, ABI3, S100A4.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 63/115,760, filed Nov. 19, 2020, U.S. Provisional Application No. 63/165,004, filed Mar. 23, 2021, U.S. Provisional Application No. 63/176,100, filed Apr. 16, 2021, and U.S. Provisional Application No. 63/260,259, filed Aug. 13, 2021. The content of each of the above-listed applications is incorporated herein by reference in its entirety. BACKGROUND CD8+ T-cells kill tumor cells upon recognizing antigens presented on Human Leukocyte Antigen (HLA) class I molecules (HLA-I). HLA-I proteins are expressed on the surface of all nucleated cells and are vital for immune surveillance. When tumor-specific proteins (neoantigens) are presented on HLA molecules, CD8+ T cell recognition can drive immune responses against the tumor and lead to tumor destruction. CD8+ T-cells express T-cell receptors (TCRs) that can recognize a specific antigen. An antigen is a molecule capable of stimulating an immune response. For example, the immune system can recognize antigens produced by pathogens and cancer cells. Antigens inside a tumor cell are bound to HLA-I, and brought to the surface of the cell, where they can be recognized by the CD8+ T-cell. If the TCR is specific for that antigen, it binds to both the antigen and to the complex of the class I MHC molecule, and the T cell destroys the tumor cell. Mechanisms used by CD8+ cells to destroy tumor cells are known in the art. For example, CD8+ cells express molecules such as perforin, which perforates a target tumor cell, and granzymes, a family of serine protease that induce programmed cell death in the tumor cell. Over time, CD8+ T-cells can become exhausted due to prolonged antigen stimulation and upregulate inhibitory checkpoint molecules. Checkpoint inhibitor therapies can rejuvenate an exhausted CD8+ T-cell population by blocking these inhibitory molecules so that the CD8+ T cells can continue to target tumor cells. As described above, HLA-I expression is required for effective CD8+ T cell recognition. One common mechanism by which tumor cells evade the immune system is loss of heterozygosity in HLA genes (HLA-LOH) (4, see paragraph [0370]). A retrospective study has shown that patients with partial or complete loss of the HLA-I locus have worse overall survival when treated with immune checkpoint blockade regimens (5). However, some patients with defective class I antigen presentation still have durable responses to immune checkpoint blockade (ICB), suggesting that a non-class I restricted mechanism of anti-tumor immunity also exists. CD4+ T cells interact with HLA class II molecules (HLA-II) rather than with HLA-I. While HLA-II is normally expressed only on professional antigen-presenting cells such as dendritic cells, mononuclear phagocytes, and B cells, expression on tumor cells has also been documented (12). Tumor expression of HLA-II allows CD4+ T cells to recognize and potentially kill tumor cells. Concordantly, a handful of studies have shown that CD4+ T cells can mediate direct killing of tumor cells (1,2,3), including one early study that showed that CD4+ T cells alone are sufficient to reject a tumor in a mouse model of melanoma (1). Checkpoint inhibitor use has now become standard of care in several indications, including metastatic NSCLC. Currently, there are only two biomarkers being used in the clinic to prescribe immuno-oncology (IO) therapies (including checkpoint inhibitors): PD-L1 protein level (often measured by expensive, time-consuming immunohistochemical staining methods) and tumor mutational burden (TMB). However, each of these biomarkers has disadvantages. For example, PD-L1 level is not always predictive of patient response to IO, and TMB is only currently approved for prescribing IO therapy to patients on the last line of therapy. Thus, there is an unmet need for diagnostics, biomarkers, and/or tools that complement these methods and aid in clinical decision making, for example, to inform physician management of IO therapy courses (see Haslam and Prasad, JAMA Net Open 2018). SUMMARY Disclosed herein are systems, methods, and compositions for treating a subject diagnosed with, or suffering from cancer. In one aspect of the current disclosure, methods for predicting response to checkpoint inhibitor in a subject suffering from cancer are provided. In some embodiments, the method comprises: at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors: (A) obtaining, in electronic format, a plurality of sequence reads, wherein the plurality of sequence reads is obtained for a plurality of nucleic acid molecules from a sample of the cancer obtained from the subject; (B) determining, from the plurality of sequence reads, a plurality of data elements for the subject's cancer comprising: (i) a first set of nucleic acid sequence reads co