Gastric cancer tumor microenvironment

Abstract

A technique for identifying whether a subject is likely to respond to immunotherapy based at least in part on a gastric cancer (GC) tumor microenvironment (TME) type of a subject having, suspected of having, or at risk of having gastric cancer, the technique includes obtaining RNA expression data for the subject; generating a GC TME signature for the subject using the RNA expression data; identifying a GC TME type for the subject from among a plurality of GC TME types using the GC TME signature; and identifying whether the subject is likely to respond to immunotherapy using the GC TME type, the GC TME signature comprising a gene group score for each gene group of a plurality of gene groups, the generating step comprising determining the gene group score using the RNA expression data.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit under 35 U.S.C. §119(e) of the filing date of U.S. Provisional Application No. 63 / 158,818, filed March 9, 2021, entitled “B CELL-ENRICHED TUMOR MICROENVIRONMENTS,” the entire contents of which are incorporated herein by reference. [Background technology]

[0002] Accurately characterizing the type of cancer a patient or subject has, and potentially selecting one or more effective treatments for that patient, can be critical to the patient's survival and overall health. Advances are needed in characterizing cancer, predicting prognosis, identifying effective treatments, and supporting personalized medicine for patients with cancer. Summary of the Invention [Means for solving the problem]

[0003] Aspects of the present disclosure relate to techniques for characterizing subjects with gastric cancer. The present disclosure is based in part on a method for identifying the tumor microenvironment (TME) of a subject with gastric cancer by creating a gastric cancer (GC) tumor microenvironment (TME) signature using RNA expression data obtained from the subject with gastric cancer, which allows the assignment of a GC TME type to the subject. In some embodiments, the subject's GC TME type indicates one or more characteristics of the subject (or the subject's cancer), such as, for example, that the subject is likely to have a good prognosis and / or that the subject is likely to respond to treatment with a particular therapeutic agent, such as an immunotherapeutic agent (e.g., an immune checkpoint inhibitor).

[0004] Thus, in some aspects, the disclosure provides a method for identifying whether a subject is likely to respond to immunotherapy based at least in part on a gastric cancer (GC) tumor microenvironment (TME) type of a subject having, suspected of having, or at risk of having gastric cancer, comprising performing, using at least one computer hardware processor, the steps of: (a) obtaining RNA expression data for the subject; (b) using the RNA expression data to generate a GC TME signature for the subject; (c) using the GC TME signature to identify a GC TME type for the subject from among a plurality of GC TME types; and (d) using the GC TME type to identify whether the subject is likely to respond to immunotherapy, wherein the RNA expression data indicates RNA expression levels of at least some genes in each group of at least some of the gene groups listed in Table 1, and the GC TME signature comprises a gene group score for each gene group in at least some of the plurality of gene groups, and the generating step comprises determining the gene group score using the RNA expression levels.

[0005] Aspects of the present disclosure include a method for predicting a subject's disease control rate (DCR) to immunotherapy, comprising performing, using at least one computer hardware processor, the steps of: (i) identifying a subject's GC TME using a method according to any one of the preceding claims; and (ii) if the subject is assigned to GC TME type B or GC TME type E, identifying the subject as likely to have increased DCR compared to subjects having GC TME type A, GC TME type C, or GC TME type D.

[0006] Aspects of the disclosure include a system comprising at least one computer hardware processor; and at least one computer readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying whether a subject is likely to respond to immunotherapy based at least in part on a gastric cancer (GC) tumor microenvironment (TME) type of a subject having, suspected of having, or at risk of having gastric cancer, the method comprising: (a) obtaining RNA expression data of the subject; (b) using the RNA expression data to generate a GC TME signature of the subject; (c) using the GC TME signature to identify a GC TME type of the subject from among a plurality of GC TME types; and (d) using the GC TME type to identify whether the subject is likely to respond to immunotherapy, wherein the RNA expression data is indicative of RNA expression levels of at least some genes in each group of at least some of the gene groups listed in Table 1, The TME signature comprises a gene group score for each gene group in at least some of the plurality of gene groups, and the generating step comprises determining the gene group score using the RNA expression levels.

[0007] Aspects of the disclosure include at least one computer readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying whether a subject is likely to respond to immunotherapy based at least in part on a gastric cancer (GC) tumor microenvironment (TME) type of a subject having, suspected of having, or at risk of having gastric cancer, the method comprising: (a) obtaining RNA expression data for the subject; (b) using the RNA expression data to generate a GC TME signature for the subject; (c) using the GC TME signature to identify a GC TME type for the subject from among a plurality of GC TME types; and (d) using the GC TME type to identify whether the subject is likely to respond to immunotherapy, wherein the RNA expression data is indicative of RNA expression levels of at least some genes in each group of at least some of the plurality of gene groups listed in Table 1, The TME signature comprises a gene group score for each gene group in at least some of the plurality of gene groups, and the generating step comprises determining the gene group score using the RNA expression levels.

[0008] Aspects of the present disclosure include a system comprising at least one computer hardware processor; and at least one non-transitory computer readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for predicting a disease control rate (DCR) to immunotherapy in a subject, the method comprising: (i) identifying a GC TME in a subject using a method according to any one of the preceding claims; and (ii) if the subject is assigned to GC TME type B or GC TME type E, identifying the subject as likely to have an increased DCR compared to subjects having GC TME type A, GC TME type C, or GC TME type D.

[0009] Aspects of the present disclosure include at least one non-transitory computer readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for predicting a disease control rate (DCR) to immunotherapy in a subject, comprising: (i) identifying a subject's GC TME using a method according to any one of the preceding claims; and (ii) if the subject is assigned to GC TME type B or GC TME type E, identifying the subject as likely to have increased DCR compared to subjects having GC TME type A, GC TME type C, or GC TME type D.

[0010] In some embodiments, the RNA expression data further indicates RNA expression levels of at least some genes in each of at least some of the second plurality of gene groups listed in Table 2, and wherein the GC TME signature further comprises a second gene group score for at least some respective gene groups of the second plurality of gene groups, and wherein the generating step further comprises determining the second gene group score using the second RNA expression levels.

[0011] In some embodiments, the RNA expression data further indicates RNA expression levels of at least some genes in each of at least some of the second plurality of gene groups listed in Table 2, and the method further comprises (e) generating a second GC TME signature using the RNA expression data, the second GC TME signature comprising a second gene group score for each gene group in at least some of the second plurality of gene groups, and the generating step comprises determining the second gene group score using the second RNA expression levels.

[0012] In some embodiments, obtaining RNA expression data for the subject comprises obtaining previously obtained sequencing RNA data by sequencing a biological sample obtained from the subject.

[0013] In some embodiments, the sequencing data comprises at least 1 million reads, at least 5 million reads, at least 10 million reads, at least 20 million reads, at least 50 million reads, or at least 100 million reads.

[0014] In some embodiments, the sequencing data comprises whole exome sequencing (WES) data, bulk RNA sequencing (RNA-seq) data, single cell RNA sequencing (scRNA-seq) data, or next generation sequencing (NGS) data. In some embodiments, the sequencing data comprises microarray data.

[0015] In some embodiments, the methods described herein further comprise normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the GC TME signature.

[0016] In some embodiments, obtaining RNA expression data for the subject comprises sequencing a biological sample obtained from the subject.

[0017] In some embodiments, the biological sample comprises gastrointestinal tissue of the subject. In some embodiments, the biological sample comprises tumor tissue of the subject.

[0018] In some embodiments, the RNA expression levels comprise RNA expression levels of at least three genes from each of at least two of the following gene groups: (i) NK cell population: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell population: TBX21, ITK, CD3D, CD3 E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7 , LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMT S4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93.;

[0019] In some embodiments, the RNA expression levels comprise the RNA expression levels of each gene from each of the following gene groups: (i) NK cell population: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell population: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC. 1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCR L5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS 9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93.;

[0020] In some embodiments, the RNA expression levels comprise RNA expression levels of at least three genes from each of at least two of the following gene groups: (a) MHC I group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC II group: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) co-activation molecule group: CD28, CD40, TNFR. SF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) Effector cell group: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNLY , FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell trafficking group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD 244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, CD3D, CD3E, CD3G , TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, S TAP1; (i) M1 signature group: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) anti-tumor cytokine group: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2;(m) Neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (s) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93.;

[0021] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, using RNA expression levels of at least three genes in the particular gene group to determine a gene group score for the particular group: (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: Group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFR SF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: ID O1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, AD AMTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4 , FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PI determining a respective gene group score for at least two of the genes, including TPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0022] In some embodiments, the step of determining the gene group score comprises, for each gene group, using the RNA expression levels of each gene in each gene group to determine a gene group score for each particular group: (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, IT K, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD2 2, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1 , IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGA LS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAM TS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EP HA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, determining a respective gene group score for each gene group including NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0023] In some embodiments, determining the gene group score comprises determining a first score for a first gene group using single sample GSEA (ssGSEA) technology from RNA expression levels of at least some of the genes in one of the following gene groups: (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, S H2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR 2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2 (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A 1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2 , ADAMTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93.;

[0024] In some embodiments, determining the gene group score comprises determining the gene group score using single sample GSEA (ssGSEA) technology from the RNA expression levels of each gene in each of the following gene groups: (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17 , TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VT N, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, A DAMTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93.;

[0025] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining a gene group score for a particular group using RNA expression levels of at least three genes in the particular gene group, comprising: (a) MHC I group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC II group: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) co-activating Sexualizing molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) Effector cell group: IFNG, GZMA, GZMB, PRF 1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell trafficking group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, CD3D , CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (i) M1 signature group: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) anti-tumor cytokine group: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2;(m) Good neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (s) Lgr5ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FA M64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITP determining a respective gene group score for at least two of each of a set of genes including NC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0026] In some embodiments, determining the gene group score comprises using single sample GSEA (ssGSEA) technology to determine the gene group score from RNA expression levels of at least some genes in each one of the following gene groups: (a) MHC I group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC II group: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA- DQA1, CIITA; (c) Co-activating molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) Effector cell group: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell trafficking group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR 6; (f) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, (i) M1 signature group: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) anti-tumor cytokine group: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2;(m) Neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (s) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93.;

[0027] In some embodiments, determining the gene group score comprises determining the gene group score using single sample GSEA (ssGSEA) technology from the RNA expression levels of each gene in each of the following gene groups: (a) MHC I group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC II group: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA, HLA-DQA1, HLA-DQB2, HLA-DQB3, HLA-DQB4, HLA-DQB5, HLA-DQB6, HLA-DQB7, HLA-DQB8, HLA-DQB9, HLA-DQB10, HLA-DQB11, HLA-DQB12, HLA-DQB13, HLA-DQB14, HLA-DQB15, HLA-DQB16, HLA-DQB17, HLA-DQB18, HLA-DQB19, HLA-DQB20, HLA-DQB21 ... ;(c) Co-activating molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70;(d) Effector cell group: IFNG, GZMA, GZM B, PRF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell trafficking group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; (f) NK Cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, C D3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL (i) M1 signature group: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) anti-tumor cytokine group: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2;(m) Neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (s) Lgr5ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, D YNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, L OC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93. ;

[0028] In some embodiments, generating a GC TME signature further comprises normalizing the gene group scores, where normalizing comprises applying a rank estimate and / or median scaling to the gene group scores.

[0029] In some embodiments, the multiple GC TME types are associated with a respective multiple GC TME signature clusters, wherein identifying a target GC TME type from among the multiple GC TME types using the GC TME signature comprises: associating the target GC TME signature with a particular one of the multiple GC TME signature clusters; and identifying the target GC TME type as the GC TME type corresponding to the particular one of the multiple GC TME signature clusters with which the target GC TME signature is associated.

[0030] In some embodiments, the methods described herein further comprise generating a plurality of GC TME signature clusters, comprising: obtaining a plurality of sets of RNA expression data by sequencing biological samples from a plurality of respective subjects; generating a plurality of GC TME signatures from the plurality of sets of RNA expression data, each of the plurality of sets of RNA expression data indicating RNA expression levels of at least some genes in each of at least some of the plurality of gene groups listed in Table 1; each of the plurality of GC TME signatures comprising a gene group score for each gene group in the plurality of gene groups; for each particular one of the plurality of GC TME signatures, determining a GC TME signature by determining a gene group score using the RNA expression levels in the particular set of RNA expression data for which the particular one GC TME signature was generated; and clustering the plurality of GC signatures to obtain a plurality of GC TME signature clusters.

[0031] In some embodiments, the clustering comprises agglomerative clustering, spectral clustering, k-means clustering, hierarchical clustering, and / or agglomerative clustering, In some embodiments, the hierarchical clustering is performed using the Louvain Community Detection algorithm.

[0032] In some embodiments, the methods described herein further comprise updating a plurality of GC TME signature clusters using a GC TME signature of the subject, the GC TME signature of the subject being one of a threshold number of GC TME signatures for a threshold number of subjects, and the GC TME signature clusters are updated once the threshold number of GC TME signatures have been generated.

[0033] In some embodiments, the threshold number of GC TME signatures is at least 50, at least 75, at least 100, at least 200, at least 500, at least 1000, or at least 5000 GC TME signatures.

[0034] In some embodiments, the updating comprises applying agglomerative clustering, spectral clustering, K-means clustering, hierarchical clustering, and / or agglomerative clustering, In some embodiments, hierarchical clustering is performed using the Louvain Community Detection algorithm.

[0035] In some embodiments, the methods described herein further comprise determining a GC TME type of the second subject, wherein the GC TME type of the second subject is identified using an updated GC TME signature cluster, wherein the identifying comprises: determining a GC TME signature of the second subject from RNA expression data obtained by sequencing a biological sample obtained from the second subject; associating the GC TME signature of the second subject with a particular one of the plurality of updated GC TME signature clusters; and identifying the GC TME type of the second subject as the GC TME type corresponding to the particular one of the plurality of updated GC TME signature clusters with which the GC TME signature of the second subject is associated.

[0036] In some embodiments, the multiple GC TME types comprise GC TME type A, GC TME type B, GC TME type C, GC TME type D, and GC TME type E.

[0037] In some embodiments, the methods described herein further comprise the step of identifying the subject as having tertiary lymphoid structures (TLS) if the subject is identified as having GC TME type E.

[0038] In some embodiments, the methods described herein further comprise identifying the subject as likely to have a good prognosis. In some embodiments, the high likelihood of having a good prognosis is measured by overall survival (OS) or progression-free survival (PFS) when the subject is identified as having GC TME type E.

[0039] In some embodiments, the RNA expression levels of genes in the second plurality of gene groups comprise RNA expression levels of at least three genes from each of at least two of the following gene groups: (i) Th17 signature group: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, CCR6; (ii) gamma delta (γΔ) T cell group: TRGC1, TRDC, TRDV2, TRGC2, TRDV1; iii) Tumor suppressor group: LATS1, LATS2, FBXW7, BAP1, KLF4, SASH1, CASP3, MLKL, SOCS1; (iv) Oncogene group: KRAS, AKT1, EGFR, PIK3CA, PI K3CB, MYC, VAV3, MET, CCND1, INPPL1, CHD1L, RACGAP1; (v)Myc target group: AIMP2, BYSL, CBX3, CDK4, DCTPP1, DDX18, DUSP2, EXOS C5, FARSA, GNL3, GRWD1, HK2, HSPD1, HSPE1, IMP4, IPO4, LAS1L, MAP3K6, MCM4, MCM5, MPHOSPH10, MRTO4, MYBBP1A, MYC, N DUFAF4, NIP7, NOC4L, NOLC1, NOP16, NOP2, NOP56, NPM1, PA2G4, PES1, PHB, PLK1, PLK4, PPAN, PPRC1, PRMT3, PUS1, RABEPK , RCL1, RRP12, RRP9, SLC19A1, SLC29A2, SORD, SRM, SUPV3L1, TBRG4, TCOF1, TFB2M, TMEM97, UNG, UTP20, WDR43, WDR74; (vi) Th2 signature group: IL4, IL5, IL13, IL10, GATA3, CCR4; and (vii) Th1 signature group: IFNG, IL2, CD40LG, IL21, TBX21, STAT4, IL12RB2.

[0040] In some embodiments, the RNA expression levels of genes in the second plurality of genes comprises an RNA expression level of each gene from each of the following gene groups: (i) Th17 signature group: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, CCR6; (ii) gamma delta (γΔ) T cell group: TRGC1, TRDC, TRDV2, TRGC2, TRDV1; (iii) tumor suppressor group: LAT S1, LATS2, FBXW7, BAP1, KLF4, SASH1, CASP3, MLKL, SOCS1; (iv) Oncogenes: KRAS, AKT1, EGFR, PIK3CA, PIK3CB, MYC, VAV3, MET, CCND1, INPPL1, CHD1L, RACGAP1; (v) Myc targets: AIMP2, BYSL, CBX3, CDK4, DCTPP1, DDX18, DUSP2, EXOSC5, FARSA, G NL3, GRWD1, HK2, HSPD1, HSPE1, IMP4, IPO4, LAS1L, MAP3K6, MCM4, MCM5, MPHOSPH10, MRTO4, MYBBP1A, MYC, NDUFAF4, N IP7, NOC4L, NOLC1, NOP16, NOP2, NOP56, NPM1, PA2G4, PES1, PHB, PLK1, PLK4, PPAN, PPRC1, PRMT3, PUS1, RABEPK, RCL1 , RRP12, RRP9, SLC19A1, SLC29A2, SORD, SRM, SUPV3L1, TBRG4, TCOF1, TFB2M, TMEM97, UNG, UTP20, WDR43, WDR74; (vi) Th2 signature group: IL4, IL5, IL13, IL10, GATA3, CCR4; and (vii) Th1 signature group: IFNG, IL2, CD40LG, IL21, TBX21, STAT4, IL12RB2.

[0041] In some embodiments, determining the gene group score comprises determining a first score for a first gene group using single sample GSEA (ssGSEA) technology from second RNA expression levels of at least some of the genes in one of the following gene groups: (i) Th17 signature group: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, CCR6; (ii) gamma delta (γΔ) T cell group: TRG C1, TRDC, TRDV2, TRGC2, TRDV1; (iii) Tumor suppressor group: LATS1, LATS2, FBXW7, BAP1, KLF4, SASH1, CASP3, MLKL, SOCS1; (iv) Oncogene group: K RAS, AKT1, EGFR, PIK3CA, PIK3CB, MYC, VAV3, MET, CCND1, INPPL1, CHD1L, RACGAP1; (v) Myc target group: AIMP2, BYSL, CBX3, CDK4, DCTPP 1, DDX18, DUSP2, EXOSC5, FARSA, GNL3, GRWD1, HK2, HSPD1, HSPE1, IMP4, IPO4, LAS1L, MAP3K6, MCM4, MCM5, MPHOSPH10, MRTO4, MYBBP1A, MYC, NDUFAF4, NIP7, NOC4L, NOLC1, NOP16, NOP2, NOP56, NPM1, PA2G4, PES1, PHB, PLK1, PLK4, PPAN, PPRC1, PRMT3, PU S1, RABEPK, RCL1, RRP12, RRP9, SLC19A1, SLC29A2, SORD, SRM, SUPV3L1, TBRG4, TCOF1, TFB2M, TMEM97, UNG, UTP20, WDR43, WDR74; (vi) Th2 signature group: IL4, IL5, IL13, IL10, GATA3, CCR4; and (vii) Th1 signature group: IFNG, IL2, CD40LG, IL21, TBX21, STAT4, IL12RB2.

[0042] In some embodiments, determining the gene group score comprises using single sample GSEA (ssGSEA) technology to determine the gene group score from the RNA expression levels of each gene in each one of the following gene groups: (i) Th17 signature group: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, CCR6; (ii) gamma delta (γΔ) T cell group: TRGC1, TRDC, TRD V2, TRGC2, TRDV1; (iii) tumor suppressor group: LATS1, LATS2, FBXW7, BAP1, KLF4, SASH1, CASP3, MLKL, SOCS1; (iv) oncogene group: KRAS, AKT1, EGFR, PIK3CA, PIK3CB, MYC, VAV3, MET, CCND1, INPPL1, CHD1L, RACGAP1; (v)Myc target group: AIMP2, BYSL, CBX3, CDK4, DCTPP1, DDX18 , DUSP2, EXOSC5, FARSA, GNL3, GRWD1, HK2, HSPD1, HSPE1, IMP4, IPO4, LAS1L, MAP3K6, MCM4, MCM5, MPHOSPH10, MRTO4, MYBBP 1A, MYC, NDUFAF4, NIP7, NOC4L, NOLC1, NOP16, NOP2, NOP56, NPM1, PA2G4, PES1, PHB, PLK1, PLK4, PPAN, PPRC1, PRMT3, PUS1, R ABEPK, RCL1, RRP12, RRP9, SLC19A1, SLC29A2, SORD, SRM, SUPV3L1, TBRG4, TCOF1, TFB2M, TMEM97, UNG, UTP20, WDR43, WDR74; (vi) Th2 signature group: IL4, IL5, IL13, IL10, GATA3, CCR4; and (vii) Th1 signature group: IFNG, IL2, CD40LG, IL21, TBX21, STAT4, IL12RB2.

[0043] In some embodiments, the identifying step in (c) of the methods described herein is performed using a second GC TME signature. In some embodiments, the identifying step in (d) of the methods described herein is performed using a second GC TME signature.

[0044] In some embodiments, the methods described herein further comprise identifying the subject as likely to respond to immunotherapy if the subject is assigned to GC TME type B. In some embodiments, the methods described herein further comprise identifying the subject as likely to respond to immunotherapy if the subject is assigned to GC TME type E.

[0045] In some embodiments, the subject is microsatellite stable (MSS). In some embodiments, the subject is Epstein-Barr Virus (EBV) low or EBV negative.

[0046] In some embodiments, the methods described herein further comprise administering to the subject an immunotherapy. In some embodiments, the immunotherapy comprises a PD1 inhibitor. In some embodiments, the PD1 inhibitor comprises pembrolizumab.

[0047] In some embodiments, the DCR of subjects with GC TME type B is greater than 50%. In some embodiments, the DCR of subjects with GC TME type E is greater than 50%.

[0048] Aspects of the present disclosure relate to methods, systems, and computer-readable storage media that can be used to identify a subject's gastric cancer (GC) tumor microenvironment (TME) type. In some aspects, the present disclosure provides a method for identifying a subject's gastric cancer (GC) tumor microenvironment (TME) type, comprising using at least one computer hardware processor to perform: (a) obtaining RNA expression data of the subject; (b) using the RNA expression data to generate a GC TME signature of the subject; and (c) using the GC TME signature to identify the subject's GC TME type among a plurality of GC TME types, wherein the RNA expression data indicates the RNA expression levels of at least some genes in each group of at least some gene groups of a plurality of gene groups listed in Table 1, and the GC TME signature comprises a gene group score for each gene group in at least some of the plurality of gene groups, and the generating step comprises using the RNA expression levels to determine the gene group score.

[0049] Aspects of the present disclosure include a system comprising at least one computer hardware processor; and at least one computer readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying a gastric cancer (GC) tumor microenvironment (TME) type of a subject, the method comprising: obtaining RNA expression data for the subject; generating a GC TME signature for the subject using the RNA expression data; and identifying the subject's GC TME type from among a plurality of GC TME types using the GC TME signature, wherein the RNA expression data indicates RNA expression levels of at least some genes in each group of at least some of the plurality of gene groups listed in Table 1, and the GC TME signature comprises a gene group score for each gene group in at least some of the plurality of gene groups, and the generating step comprises determining the gene group score using the RNA expression levels.

[0050] An embodiment of the present disclosure includes at least one computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying a gastric cancer (GC) tumor microenvironment (TME) type of a subject, the method comprising: obtaining RNA expression data for the subject indicative of RNA expression levels of at least some genes in each of at least some of the gene groups listed in Table 1; generating a GC TME signature for the subject comprising a gene group score for each of at least some of the gene groups of the plurality of gene groups using the RNA expression data, the gene group score comprising determining a gene group score using the RNA expression levels; and identifying the GC TME type of the subject from among the plurality of GC TME types using the GC TME signature.

[0051] In some embodiments, the subject has, is suspected of having, or is at risk of having gastric cancer.

[0052] In some embodiments, the methods described herein further comprise the steps of identifying a subject as having GC TME type E; and administering an immunotherapy to the subject if the subject is identified as having GC TME type E.

[0053] In some embodiments, obtaining RNA expression data for the subject comprises obtaining previously obtained sequencing RNA data by sequencing a biological sample obtained from the subject.

[0054] In some embodiments, the sequencing data comprises at least 1 million reads, at least 5 million reads, at least 10 million reads, at least 20 million reads, at least 50 million reads, or at least 100 million reads.

[0055] In some embodiments, the sequencing data comprises whole exome sequencing (WES) data, bulk RNA sequencing (RNA-seq) data, single cell RNA sequencing (scRNA-seq) data, or next generation sequencing (NGS) data. In some embodiments, the sequencing data comprises microarray data.

[0056] In some embodiments, the methods described herein further comprise normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the GC TME signature.

[0057] In some embodiments, obtaining RNA expression data of the subject comprises sequencing a biological sample obtained from the subject. In some embodiments, the biological sample comprises gastrointestinal tissue of the subject. In some embodiments, the biological sample comprises tumor tissue of the subject.

[0058] In some embodiments, RNA expression levels are determined by: (i) NK cell population: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell population: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1 ;(iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv)T reg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3 , LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAM TS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A , FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PIT The RNA expression levels of at least three genes from each of at least two of the following gene groups: PNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0059] In some embodiments, RNA expression levels are determined by: (i) NK cell population: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell population: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1 ;(iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv)T reg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3 , LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAM TS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FA M64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, N The RNA expression level of each gene from each of the following gene groups: PNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0060] In some embodiments, RNA expression levels are determined for: (a) MHC Class I: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC Class II: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) co-activation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) effector cell group: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNL Y, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell trafficking group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK , CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (i) M1 signature group: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) anti-tumor cytokine group: TNF, IFN B1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (m) neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1;(n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and(s)Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A , FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PIT The RNA expression levels of at least three genes from each of at least two of the following gene groups: PNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0061] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining a gene group score for a particular group using RNA expression levels of at least three genes within the particular gene group, comprising: (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: T BX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF1 3B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1 , ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, L GALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADA MTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPN determining a gene group score for each of at least two of the genes including: C1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0062] In some embodiments, the step of determining the gene group score comprises, for each gene group, using the respective RNA expression levels of the genes in each gene group to determine a gene group score for each particular group: (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TN FRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN , LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, AD AMTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64 A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, P determining a gene group score for each of the genes including ITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0063] In some embodiments, the step of determining the gene group scores uses single sample GSEA (ssGSEA) technology to determine: (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, T RBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, F CRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS 2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGAL S9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGF R4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1 , RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0064] In some embodiments, the step of determining the gene group scores uses single sample GSEA (ssGSEA) technology to determine: (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, T RBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, F CRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS 2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGAL S9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, F GFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, determining a gene group score from the RNA expression levels for each gene in each of the gene groups: PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0065] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining a gene group score for a particular group using RNA expression levels of at least three genes within the particular gene group, and comprising: (a) MHC group I: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) co-activation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) effector cell group: IFNG, GZMA, GZMB, P RF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell trafficking group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1 , CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP 2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4 A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (i) M1 signature group: NOS2, TNF, IL1B, SO CS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) anti-tumor cytokine group: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2;(m) Neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and(s)Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPN determining a gene group score for each of at least two of the genes including: C1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0066] In some embodiments, the step of determining the gene group score uses single sample GSEA (ssGSEA) technology to determine: (a) MHC group I: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) co-activation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) effector cell group: IFNG, GZMA, GZMB, P RF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell trafficking group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1 , CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP 2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4 A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (i) M1 signature group: NOS2, TNF, IL1B, SO CS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) anti-tumor cytokine group: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2;(m) Neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and(s)Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGF R4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1 determining a gene group score from RNA expression levels for at least a portion of the genes in each one of the gene groups: RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0067] In some embodiments, the step of determining the gene group score uses single sample GSEA (ssGSEA) technology to determine: (a) MHC group I: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) co-activation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) effector cell group: IFNG, GZMA, GZMB, P RF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell trafficking group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1 , CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP 2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4 A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (i) M1 signature group: NOS2, TNF, IL1B, SO CS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) anti-tumor cytokine group: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2;(m) Neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and(s)Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, F GFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, determining a gene group score from the RNA expression levels for each gene in each of the gene groups: PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0068] In some embodiments, generating a GC TME signature further comprises normalizing the gene group scores. In some embodiments, the normalizing comprises applying a rank estimate and / or median scaling to the gene group scores.

[0069] In some embodiments, the multiple GC TME types are associated with a respective multiple GC TME signature clusters, wherein identifying a target GC TME type from among the multiple GC TME types using the GC TME signature comprises: associating the target GC TME signature with a particular one of the multiple GC TME signature clusters; and identifying the target GC TME type as the GC TME type corresponding to the particular one of the multiple GC TME signature clusters with which the target GC TME signature is associated.

[0070] In some embodiments, the methods described herein comprise the steps of: obtaining a plurality of sets of RNA expression data by sequencing biological samples from a plurality of respective subjects; generating a plurality of GC TME signatures from the plurality of sets of RNA expression data; and clustering the plurality of GC signatures to obtain a plurality of GC TME signature clusters, wherein each of the plurality of sets of RNA expression data indicates an RNA expression level of at least some genes in each of at least some gene groups of the plurality of gene groups listed in Table 1, and for a particular one of each of the plurality of GC TME signatures, each of the plurality of GC TME signatures comprises a gene group score for each gene group in the plurality of gene groups, and the generating step further comprises determining the GC TME signature by determining the gene group score using the RNA expression levels in the particular set of RNA expression data for which the particular one GC TME signature was generated.

[0071] In some embodiments, the clustering comprises agglomerative clustering, spectral clustering, k-means clustering, hierarchical clustering, and / or agglomerative clustering, In some embodiments, the hierarchical clustering is performed using the Louvain Community Detection algorithm.

[0072] In some embodiments, the methods described herein further comprise updating a plurality of GC TME signature clusters using a GC TME signature of the subject, where the GC TME signature of the subject is one of a threshold number of GC TME signatures for a threshold number of subjects, where the GC TME signature cluster is updated once the threshold number of GC TME signatures are generated.

[0073] In some embodiments, the threshold number of GC TME signatures is at least 50, at least 75, at least 100, at least 200, at least 500, at least 1000, or at least 5000 GC TME signatures.

[0074] In some embodiments, the updating comprises applying agglomerative clustering, spectral clustering, K-means clustering, hierarchical clustering, and / or agglomerative clustering, In some embodiments, hierarchical clustering is performed using the Louvain Community Detection algorithm.

[0075] In some embodiments, the methods described herein further comprise determining a GC TME type of the second subject, wherein the GC TME type of the second subject is identified using an updated GC TME signature cluster, wherein the identifying comprises: determining a GC TME signature of the second subject from RNA expression data obtained by sequencing a biological sample obtained from the second subject; associating the GC TME signature of the second subject with a particular one of the plurality of updated GC TME signature clusters; and identifying the GC TME type of the second subject as the GC TME type corresponding to the particular one of the plurality of updated GC TME signature clusters with which the GC TME signature of the second subject is associated.

[0076] In some embodiments, the multiple GC TME types comprise GC TME type A, GC TME type B, GC TME type C, GC TME type D, and GC TME type E.

[0077] In some embodiments, the methods described herein further comprise the step of identifying the subject as having tertiary lymphoid structures (TLS) if the subject is identified as having GC TME type E.

[0078] In some embodiments, the methods described herein further comprise identifying the subject as likely to have a good prognosis. In some embodiments, the increased likelihood of having a good prognosis is measured by overall survival (OS) or progression-free survival (PFS) when the subject is identified as having GC TME type E.

[0079] In some embodiments, the methods described herein further comprise administering immunotherapy to the subject. If the subject is identified as having GC TME type E, immunotherapy may be administered. If the subject is identified as having TLS, immunotherapy may be administered. In some embodiments, the immunotherapy comprises a PD1 inhibitor. In some embodiments, the PD1 inhibitor comprises pembrolizumab. [Brief description of the drawings]

[0080] [Figure 1] 1 is a diagram showing a flowchart of an exemplary process for determining a gastric cancer (GC) tumor microenvironment (TME) type for a subject having, suspected of having, or at risk of having GC, according to some embodiments of the technology described herein. [Diagram 2] 1 is a diagram showing a flow chart of an exemplary process for processing sequencing data to obtain RNA expression data, according to some embodiments of the technology described herein. [Diagram 3] 1 is a diagram showing an exemplary technique for determining gene group scores according to some embodiments of the techniques described herein. [Figure 4] 1 is a diagram showing an exemplary technique for identifying a subject's GC TME type using a GC TME signature, according to some embodiments of the techniques described herein. [Figure 5A]1 provides an exemplary heatmap of GC samples classified into five different GC TME types (A, B, C, D, E) using an example of a GC TME signature comprising 20 gene group scores according to some embodiments of the technology described herein. Gene groups are listed vertically on the left side of the heatmap. Each row represents one sample. The top panel corresponds to the annotation of the sample. MFP: gastric cancer type; Cohort: ID of the dataset; Lauren: histological subtype of gastric cancer; Stage: tumor stage. [Figure 5B] 1 provides a heatmap of pairwise gene group score correlations for the gene groups listed in Table 1. [Figure 6] 1 provides an example of gene group score comparison across GC TME types according to some embodiments of the technology described herein. [Figure 7-1] 1 provides an exemplary heatmap of GC TME type gene group scores and cell deconvolution across samples according to some embodiments of the technology described herein. Columns represent one sample and are ordered by GC TME type: B, E, D, A, and C. The top panel corresponds to the annotation of the sample. MFP: GC TME type; Lauren: gastric cancer histological subtype; Cohort: dataset ID. The bar graph below represents the cell composition fraction. Each color corresponds to one cell type (according to the legend on the right). The box plots on the right represent a comparison of cell composition across GC TME types. [Figure 7-2] 1 provides an exemplary heatmap of GC TME type gene group scores and cell deconvolution across samples according to some embodiments of the technology described herein. Columns represent one sample and are ordered by GC TME type: B, E, D, A, and C. The top panel corresponds to the annotation of the sample. MFP: GC TME type; Lauren: gastric cancer histological subtype; Cohort: dataset ID. The bar graph below represents the cell composition fraction. Each color corresponds to one cell type (according to the legend on the right). The box plots on the right represent a comparison of cell composition across GC TME types. [Figure 8A] 8A-8F provide representative data showing that histology data from The Cancer Genome Atlas (TCGA) supports the GC TME types identified by the methods described herein, according to some embodiments of the technology described herein. GC TME type A (mesenchymal, EMT) and GC TME type C (fibrotic) histology slides (left) show high levels of fibrous tissue and fibroblasts and low purity (e.g., malignant cell content); GC TME type B (immune-enriched, non-fibrotic) histology slides (second from left) show lymphocytic infiltration and an inflammatory immune phenotype; GC TME type D (depleted) histology slides (second from right) show high purity, immune cell desert, and low fibroblast counts; and GC TME type E (B cell-enriched) histology slides (right) show lymphocytic infiltration (inflammatory immune phenotype) and the presence of germinative centers (e.g., high levels of B cells, as indicated by arrows). [Figure 8B] The relative cell type content of GC TME type A is displayed. [Figure 8C] The relative cell type content of GC TME type B is displayed. [Figure 8D] The relative cell type content of GC TME type C is displayed. [Figure 8E] The relative cell type content of GC TME type D is displayed. [Figure 8F] The relative cell type content of GC TME type E is displayed. [Figure 9-1] 1 provides representative data showing survival and progression analysis across different GC TME types according to some embodiments of the technology described herein. [Figure 9-2] 1 provides representative data showing survival and progression analysis across different GC TME types according to some embodiments of the technology described herein. [Figure 10]In accordance with some embodiments of the technology described herein, representative data is provided showing that most microsatellite unstable / instability (MSI) samples were classified as having GC TME type B, while most microsatellite stable (MSS) samples were classified as having GC TME type A. [Figure 11A] 11A-11B provide representative data of mutational burden across GC TME types, showing that GC TME type B (i.e., immune-enriched type) has the highest mutational burden (ML). [Figure 11B] Based on ACGR cohort data, the highest purity (eg, amount of cellular or malignant cells) (left) and highest ploidy (right) were in GC TME type D. [Figure 12] Representative data are provided showing a strong relationship between EBV positive status and GC TME types B and E (immune enrichment and B cell enrichment). 1.0=EBV positive; 00.0=EBV negative. [Figure 13] 1 illustrates an exemplary implementation of a computer system that may be used in connection with some embodiments of the technology described herein. [Figure 14] 1 provides representative data showing a comparison of Th17 T cell and gamma delta γδ T cell signatures across gastric cancer TME types, according to some embodiments of the technology described herein. [Figure 15] 1 provides a representative heatmap of gastric cancer samples classified into five different GC TME types (B, E, D, A, C) based on unsupervised dense clustering of GC TME signatures comprising 27 gene group scores according to some embodiments of the technology described herein. Gene groups are listed vertically on the left side of the heatmap. Each row represents one sample. The top panel corresponds to the annotation of the samples. MFP: GCT; Cohort: ID of the dataset; Lauren: Histological subtype of gastric cancer; Stage: Tumor stage. [Figure 16-1]Representative data is provided regarding the landscape of TCGA cohorts across GC TME types and the percentage of EBV positive / MSI samples according to some embodiments of the technology described herein. Each row represents one sample. The top panel corresponds to the GC TME type: presence or absence of EBV (black is EBV positive, white is negative); MSI or MSS status; number of somatic mutations; process of the GC TME type. On the right are the percentage of samples in each GC TME type with EBV and MSI / MSS features. [Figure 16-2] Representative data is provided regarding the landscape of TCGA cohorts across GC TME types and the percentage of EBV positive / MSI samples according to some embodiments of the technology described herein. Each row represents one sample. The top panel corresponds to the GC TME type: presence or absence of EBV (black is EBV positive, white is negative); MSI or MSS status; number of somatic mutations; process of the GC TME type. On the right are the percentage of samples in each GC TME type with EBV and MSI / MSS features. [Figure 17] 1 provides representative data of the number of responders classified as having each GC TME type according to some embodiments of the technology described herein. PD = progressive disease, SD = stable disease, PR = partial response, CR = complete response. [Figure 18-1] 1 provides representative heatmaps of GC TME types, process signatures, and cellular deconvolution across samples of the Cabazi cohort according to some embodiments of the technology described herein. Columns represent one sample and are ordered as B, E, D, A, and C for each GC TME type. The top panel corresponds to the annotations of the samples. MFP: GC TME type; Recist to Cabazitaxel treatment; Lauren: histological subtype of gastric cancer; Biopsy site: location of biopsy; Specific site: additional biopsy site of the sample. The bottom bar graph represents the cell composition fraction. Each color corresponds to one cell type (according to the legend on the right). [Figure 18-2]1 provides representative heatmaps of GC TME types, process signatures, and cellular deconvolution across samples of the Cabazi cohort according to some embodiments of the technology described herein. Columns represent one sample and are ordered as B, E, D, A, and C for each GC TME type. The top panel corresponds to the annotations of the samples. MFP: GC TME type; Recist to Cabazitaxel treatment; Lauren: histological subtype of gastric cancer; Biopsy site: location of biopsy; Specific site: additional biopsy site of the sample. The bottom bar graph represents the cell composition fraction. Each color corresponds to one cell type (according to the legend on the right). [Figure 19-1] Representative data is provided to compare GC TME types in pre- and post-treatment samples. Left panel shows pre- and post-treatment samples, similar to Figure 17. Right panel: Dynamic changes in GC TME types in response to immunotherapy treatment were identified using principal component analysis. [Figure 19-2] Representative data is provided to compare GC TME types in pre- and post-treatment samples. Left panel shows pre- and post-treatment samples, similar to Figure 17. Right panel: Dynamic changes in GC TME types in response to immunotherapy treatment were identified using principal component analysis. [Figure 19-3] Representative data is provided to compare GC TME types in pre- and post-treatment samples. Left panel shows pre- and post-treatment samples, similar to Figure 17. Right panel: Dynamic changes in GC TME types in response to immunotherapy treatment were identified using principal component analysis. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0081] Aspects of the present disclosure relate to methods for characterizing a subject having a particular cancer, such as gastric cancer. The present disclosure is based in part on a method for determining a tumor microenvironment (TME) type of a gastric cancer in a subject. In some embodiments, the method comprises identifying the subject as having a particular gastric cancer (GC) TME type based on a GC TME signature calculated for the subject from RNA expression data. The GC TME signature may include gene group scores for gene groups associated with malignant cells (e.g., cancer cells, tumor cells, etc.) and particular immune cells (e.g., T cells, B cells, Treg cells, etc.). The GC TME type identified for a subject may have various prognostic, diagnostic, and / or therapeutic applications. For example, in some embodiments, the methods developed by the inventors and described herein are useful for identifying a prognosis for a subject based on the GC TME type identified for the subject. In some embodiments, the identification of a subject's GC TME type is useful for determining whether the subject is likely to respond to treatment with a particular therapeutic agent (e.g., immunotherapy).

[0082] Gastric cancer is the fifth most common cancer worldwide. The 5-year overall survival rate for patients with advanced gastric cancer is approximately 20%. Gastric cancer tumours are highly heterogeneous, which poses significant therapeutic challenges.

[0083] Methods for classifying gastric cancer generally include histological and / or molecular analysis. Histological analysis involves obtaining a gastric tumor biopsy sample, analyzing the tissue to confirm the presence of specific cell morphologies, and characterizing the tumor based on the analysis. One example of histological analysis of gastric cancer is the Lauren classification, as described, for example, by Ma et al. Oncol Lett. 2016 May;11(5):2959-2964. Histological analysis is generally considered to be costly, laborious, and time-consuming. Furthermore, histological classification typically relies on subjective judgment by a lab technician, so there is a possibility of inaccuracy or human error.

[0084] To address the limitations of histological analysis, efforts have also been made to classify gastric cancer using molecular techniques. However, as described, for example, by Hennequin et al. 2016 OncoImmunology, 5:2, DOI:10.1080 / 2162402X.2015.1054598, many studies performing molecular analysis of gastric cancer tissues are limited to gene expression studies of the tumor microenvironment, especially tumor-infiltrating immune lymphocytes (TIILs). Thus, we recognized the need to develop methods for molecular characterization of GC types based specifically on the underlying biological properties of both the tumor microenvironment and malignant cells, rather than more broadly defined cancer biomarkers.

[0085] Aspects of the present disclosure relate to statistical techniques for analyzing expression data (e.g., RNA expression data) obtained from biological samples obtained from subjects having, suspected of having, or at risk for developing gastric cancer (GC) to generate a GC tumor microenvironment (TME) signature for the subject (referred to herein as a "GC TME signature"), and using this GC TME signature to identify the particular GC TME type the subject may have.

[0086] The inventors have recognized that certain gene group scores (e.g., gene group scores for at least some of the gene groups listed in Table 1 and / or Table 2) determined using a subject's RNA expression data may be combined to form a GC TME signature that characterizes a patient with GC more accurately than previously developed methods. A GC TME signature comprising a combination of gene group scores from gene groups associated with the tumor microenvironment and gene group scores from gene groups associated with malignant cells may then be used to identify a subject as having a particular gastric cancer (GC) tumor microenvironment (TME) type. In some embodiments, such GC TME types are useful for identifying prognosis and / or the likelihood that a subject will respond to a particular therapeutic intervention (e.g., an immunotherapeutic agent).

[0087] The use of a GC TME signature comprised of a combination of gene group scores described in this disclosure represents an improvement over previously described GC molecular biomarker or tumor microenvironment analyses, as the specific gene groups used to generate the GC TME signature described herein are associated with 1) gastric cancer tumor cells, and 2) the gastric cancer tumor microenvironment, and therefore better reflect the molecular tumor microenvironment of GC. These focused combinations of gene groups (e.g., gene groups consisting of some or all of the genes in the gene groups listed in Table 1 and / or Table 2) are unconventional and distinct from previously described molecular signatures that attempt to incorporate expression data from a very large number of genes or that only consider TIILs.

[0088] The GC TME typing method described herein has several uses.For example, by using the method described herein to identify the GC TME type of a subject, it may be possible to diagnose the subject as having (or at high risk of developing) a highly aggressive form of GC at a time when the GC characterization method described previously cannot.The early detection of aggressive GC type, enabled by the GC TME signature described herein, improves patient diagnostics by enabling earlier chemotherapy intervention for patients than is currently possible for patients who are tested for GC using other methods (e.g., histological analysis).

[0089] As described herein, the inventors have also determined that subjects identified as having GC TME type E by the methods described herein are more likely to have tertiary lymphoid structures (TLS) and therefore are characterized as having a good prognosis and / or a high likelihood of responding to therapeutic treatment (e.g., immunotherapy). As further described in the Examples, subjects identified as having GC TME type B were also observed to respond to immunotherapy treatment, regardless of MSS / MSI or EBV status. The realization that GC TME type E and GC TME type B patients have a good prognosis or may respond to immunotherapy, regardless of MSS / MSI or EBV status, is surprising because it allows for the selection of certain patients (e.g., patients with low MSS or EBV) who are more likely to respond to immunotherapy but who would typically be characterized as potential non-responders if other GC characterization methods (e.g., MSS / MSI or EBV status) were used.

[0090] Conversely, the inventors have determined, using the methods described herein, that subjects identified as having GC TME type A or GC TME type C are less likely to have TLS and / or more likely to have aggressive or treatment-resistant forms of GC. Thus, the technology developed by the inventors and described herein improves patient treatment and associated outcomes by increasing patient comfort and avoiding the deleterious side effects of chemotherapy that is not expected to be effective for the subject.

[0091] In some aspects, the disclosure relates to generating one or more additional GC TME signatures (e.g., in addition to a GC TME signature comprising gene group scores from some or all of the gene groups listed in Table 1) for a gastric cancer subject. For example, the disclosure provides a method of determining a second GC TME signature comprising one or more gene group scores for some or all of the gene groups listed in Table 2. In some embodiments, the second GC signature comprises a Th17 signature gene group score and / or a gamma delta (γΔ) T cell gene group score. In some embodiments, the Th17 signature gene group score is generated using RNA expression levels of one or more (e.g., 1, 2, 3, 4, 5, 6, 7, or 8) genes in a TH17 gene group comprising the following genes: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, and CCR6. In some embodiments, the γΔT cell gene group score is generated using RNA expression levels of one or more (e.g., 1, 2, 3, 4, or 5) genes in a γΔT cell gene group comprising the following genes: TRGC1, TRDC, TRDV2, TRGC2, and TRDV1. Without wishing to be bound by any particular theory, calculation of the Th17 signature gene group score and / or the γΔT cell gene group score, either alone or in combination with the GC TME type, may help to identify dynamic changes in GC TME type that correlate with response to a particular therapeutic agent.

[0092] Stomach cancer Aspects of the present disclosure relate to a method for determining a gastric cancer (GC) TME type of a subject having, having, suspected of having, or at risk of having gastric cancer (GC). As used herein, a subject may be a mammal, such as, for example, a human, a non-human primate, a rodent (e.g., rat, mouse, guinea pig, etc.), a dog, a cat, a horse, etc. In some embodiments, a subject is a human. The term "individual" or "subject" may be used synonymously with "patient." As used herein, "gastric cancer" or "GC" or "stomach cancer" refers to any gastric or gastrointestinal cancer, e.g., gastric or gastrointestinal adenocarcinoma, or any other type of malignant tumor, caused by one or more various genetic mutations in the body that affect (either originally present therein or metastasized to) the stomach and / or intestinal cells of a subject. As used herein, "cancer" refers to any malignant and / or invasive growth or tumor caused by abnormal cell proliferation in a subject, including solid tumors, blood cancers, cancers of the bone marrow or lymphatic system, etc. Examples of gastric cancer include, but are not limited to, esophageal cancer, gastric cancer, liver cancer, pancreatic cancer, colorectal cancer, anal cancer, gastric adenocarcinoma, granulomatous (polypoid) gastric cancer, ulcerative gastric cancer, superficial metastatic gastric cancer, diffuse metastatic gastric cancer, malignant lymphoma of the stomach, liposarcoma, fibrosarcoma, carcinosarcoma, and gastrointestinal stromal tumor (GST).

[0093] A subject with GC may exhibit one or more signs or symptoms of GC, such as, for example, the presence of cancerous cells (e.g., tumor cells), fever, swelling, bleeding, nausea and vomiting, heartburn, and weight loss. In some embodiments, a subject with GC does not exhibit one or more signs or symptoms of GC. In some embodiments, a subject with GC has been diagnosed by a medical professional (e.g., a qualified physician) as having GC based on one or more assays (e.g., clinical assays, molecular diagnostics, etc.) that suggest that the subject has GC, even in the absence of one or more signs or symptoms.

[0094] A subject suspected of having GC typically exhibits one or more signs or symptoms of GC. In some embodiments, a subject suspected of having GC exhibits one or more signs or symptoms of GC, but has not been diagnosed by a medical professional (e.g., a licensed physician) and / or has not received a test result (e.g., a clinical test, a molecular diagnosis, etc.) that suggests the subject has GC.

[0095] A subject at risk of having GC may or may not show one or more signs or symptoms of GC. In some embodiments, a subject at risk of having GC comprises one or more risk factors that increase the likelihood that the subject will develop GC. Examples of risk factors include the presence of precancerous cells in a clinical sample, having one or more genetic mutations that predispose the subject to cancer (e.g., GC), taking one or more drugs that increase the likelihood that the subject will develop cancer (e.g., GC), a family history of GC, etc.

[0096] FIG. 1 is a flow chart of an exemplary process 100 for determining a subject's GC TME signature, using the determined GC TME signature to identify the subject's GC TME type, and using the subject's GC TME type to identify whether the subject is likely to respond to immunotherapy.

[0097] Various (e.g., some or all) operations of process 100 may be implemented using any suitable computing device. For example, in some embodiments, one or more operations of exemplary process 100 may be implemented in a clinical setting or a laboratory setting. For example, one or more operations of process 100 may be implemented on a computing device located in a clinical or laboratory environment. In some embodiments, the computing device may obtain the RNA expression data directly from a sequencing device located in a clinical or laboratory environment. For example, a computing device included in a sequencing device may obtain the RNA expression data directly from the sequencing device. In some embodiments, the computing device may obtain the RNA expression data indirectly from a sequencing device located inside or outside of a clinical or laboratory environment. For example, a computing device located in a clinical or laboratory environment may obtain the expression data via a communication network, such as the Internet or any other suitable network, as aspects of the technology described herein are not limited to a particular communication network.

[0098] Additionally or alternatively, one or more operations of the exemplary process 100 may be implemented in a setting away from a clinical or laboratory setting. For example, one or more operations of the process 100 may be implemented on a computing device located outside of a clinical or laboratory setting. In this case, the computing device may indirectly obtain RNA expression data generated using a sequencing device located inside or outside of the clinical or laboratory environment. For example, the expression data may be provided to the computing device via a communication network, such as the Internet or any other suitable network.

[0099] It should be understood that in some embodiments, not all operations of process 100 as shown in Figure 1 are implemented using one or more computing devices. For example, operation 120 of administering one or more immunotherapies to a subject may be performed manually (e.g., by a clinician).

[0100] Process 100 begins at operation 102 where sequencing data for a subject is obtained. In some embodiments, the sequencing data may be obtained by sequencing a biological sample obtained from the subject (e.g., a gastric biopsy and / or tumor tissue) using any suitable sequencing technique. The sequencing data may include any suitable type of sequencing data from any suitable source and may be in any suitable format. Examples of sequencing data, sources of sequencing data, and formats of sequencing data are described herein, including in the section entitled "Obtaining RNA Expression Data."

[0101] As an illustrative example, in some embodiments, the sequencing data may comprise bulk sequencing data. The bulk sequencing data may comprise at least 1 million reads, at least 5 million reads, at least 10 million reads, at least 20 million reads, at least 50 million reads, or at least 100 million reads. In some embodiments, the sequencing data comprises bulk RNA sequencing (RNA-seq) data, single-cell RNA sequencing (scRNA-seq) data, or next-generation sequencing (NGS) data. In some embodiments, the sequencing data comprises microarray data.

[0102] Process 100 then proceeds to operation 104, where the sequencing data obtained in operation 102 is processed to obtain RNA expression data. This may be done in any suitable manner, and may involve normalizing the bulk sequencing data to transcripts per million (TPM) units (or other units) and / or log-transforming the RNA expression levels in TPM units. Conversion and normalization of data to TPM units is described herein with reference to FIG. 2.

[0103] Process 100 then proceeds to operation 106, where a gastric cancer (GC) tumor microenvironment (TME) signature is generated for the subject using the RNA expression data generated in operation 104 (e.g., from bulk sequence data, converted to TPM units and then log-normalized, as described herein with reference to FIG. 2).

[0104] As described herein, in some embodiments, a GC TME signature comprises two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, etc.) gene groups. In some embodiments, the two or more gene group scores comprise gene group scores (sometimes referred to as gene group enrichment scores or gene group expression scores) for some or all of the gene groups set forth in Table 1.

[0105] Thus, operation 106 comprises operation 108 where a gene group score is determined, operation 110 where a GC TME signature is determined, and operation 112 where a GC TME type is determined by using the GC TME signature. In some embodiments, determining the gene group score comprises determining a gene group score for each of a plurality (e.g., some or all) of the gene groups listed in Table 1. In some embodiments, determining the gene group score comprises determining a gene group score for each of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 gene groups (e.g., the gene groups listed in Table 1). In some embodiments, determining the gene group score further comprises determining a respective gene group score for 1, 2, 3, 4, 5, 6, or 7 gene groups listed in Table 2. A gene set score for a particular gene set may be determined using the RNA expression levels of at least some of the genes in the gene set (e.g., the RNA expression levels obtained in operation 104). The RNA expression levels may be processed using Gene Set Enrichment Analysis (GSEA) techniques to determine a score for the particular gene set.

[0106] For example, in some embodiments, determining the GC TME signature includes determining the NK cell population: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; T cell population: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; B Cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; Cancer-associated fibroblasts (C AF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN 1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LA MB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADA MTS5, LOX; proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASS determining a gene group score using RNA expression levels of at least three genes from each of at least two gene groups, including F4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0107] In some embodiments, determining the GC TME gene signature comprises determining the GC TME gene signature from: (a) MHC class I: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC class II: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; and (c) co-activation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) effector cell group: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GN LY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell trafficking group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; (f) NK cell group: NKG7 , CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, IT K, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (i) M1 signature group: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) anti-tumor cytokine group: TNF, IFN B1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (m) neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1;(n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and(s)Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASS determining a gene group score using RNA expression levels of at least three genes from each of at least two gene groups, including F4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0108] An embodiment of determining the gene group score is described herein with reference to FIG. 3, in the section entitled "Gene Expression Signature."

[0109] As described above, a GC TME signature is generated at operation 110. In some embodiments, the GC TME signature consists solely of gene group scores for one or more (e.g., all) of the gene groups listed in Table 1. In some embodiments, the GC TME signature comprises gene group scores for at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 gene groups listed in Table 1. In some embodiments, each gene group score for a particular gene group is determined using RNA expression levels of some or all (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, etc.) of the genes in each gene group listed in Table 1. In other embodiments, the GC TME signature comprises one or more other gene group scores in addition to the gene group scores listed in Table 1. In some embodiments, the GC TME signature further comprises gene group scores from at least 2, 3, 4, 5, 6, or 7 gene groups listed in Table 2. In some embodiments, each gene group score for a particular gene group is determined using RNA expression levels of some or all (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, etc.) of the genes in each gene group listed in Table 2.

[0110] In some embodiments, operation 110 comprises generating a GC TME signature using only gene group scores from some or all of the gene groups listed in Table 1. In some embodiments, operation 110 further comprises generating a second GC TME signature comprising gene group scores for some or all of the gene groups listed in Table 2. In some embodiments, the second GC TME signature comprises a Th17 signature gene group score (e.g., a gene group score generated using RNA expression levels of some or all of the genes in the Th17 signature gene group listed in Table 2) and / or a γΔT cell gene group score (e.g., a gene group score generated using RNA expression levels of some or all of the genes in the γΔT cell gene group listed in Table 2). In some embodiments, the second GC TME signature is not used in operation 112 to identify the GC TME type of the subject. In some embodiments, the second GC TME signature is indicative of a change in the GC TME type of the subject following treatment with immunotherapy.

[0111] Process 100 then proceeds to operation 112, where the GC TME signature generated in operation 110 is used to identify the GC TME type of the subject. This may be done in any suitable manner. For example, in some embodiments, each of the possible GC TME types is associated with a respective one of a plurality of GC TME signature clusters. In such embodiments, the GC TME type of the subject may be identified by: associating the GC TME signature of the subject with a particular one of the plurality of GC TME signature clusters; and identifying the GC TME type of the subject as the GC TME type corresponding to the particular one of the plurality of GC TME signature clusters with which the GC TME signature of the subject is associated. Examples of GC TME types are described herein. Aspects of identifying the GC TME type of the subject are described herein, including in the section entitled "Generating a GC TME Signature and Identifying a TME Type" below.

[0112] As described above, the subject's GC TME type is identified in operation 112. In some embodiments, the subject's GC TME type is identified as being one of the following GC TME types: GC TME type A, GC TME type B, GC TME type C, GC TME type D, or GC TME type E.

[0113] Process 100 then proceeds to operation 118, where the GC TME type identified in operation 112 is used to identify the likelihood that the subject will respond to immunotherapy. In some embodiments, if the subject is identified in operation 112 as having GC TME type E or GC TME type B, the subject is identified in operation 118 as more likely to respond to immunotherapy (e.g., a PD1 antibody such as pembrolizumab) compared to subjects with other GC TME types. Aspects of identifying whether a subject is likely to respond to immunotherapy are described herein, including in the section entitled "Therapeutic Indications" below.

[0114] In some embodiments, process 100 is complete after operation 118 is completed. In some such embodiments, the determined GC TME signature, and / or the identified GC TME type, and / or the identified likelihood that the subject will respond to immunotherapy are stored for subsequent use, provided to one or more recipients (e.g., clinicians, researchers, etc.), and / or used to update the GC TME signature clusters (described below herein).

[0115] However, in some embodiments, one or more other operations are performed after operation 112. For example, in the illustrated embodiment of Figure 1, process 100 may include one or more of optional operations 114, 116, and 120, which are shown in dashed lines in Figure 1.

[0116] For example, in operation 114, the subject may be identified as having tertiary lymphoid structures (TLS) based on the GC TME type determined for the subject. For example, if the subject is identified as having GC TME type E, the subject may be identified (in operation 114) as having TLS.

[0117] In another example, a prognosis for the subject may be identified in act 116. In some embodiments, if a subject is identified as having GC TME type E in act 112 and / or the subject is identified as having TLS in act 114, then the subject is identified as likely to have a good prognosis in act 116 (e.g., as measured by overall survival (OS) or progression free survival (PFS).

[0118] In another example, if the subject is identified in act 112 as having GC TME type B or GC TME type E and / or is identified in act 118 as likely to respond to immunotherapy, the subject is administered one or more immunotherapies in act 120. Examples of immunotherapies are provided herein.

[0119] 1, operations 114, 116, and 120 are shown as optional, it should be understood that in other embodiments, one or more other operations may be optional (in addition to or instead of operations 114, 116, and 120). For example, in some embodiments, operations 102 and 104 may be optional (e.g., if sequencing data is previously acquired and processed to obtain RNA expression data, process 100 may begin at operation 106 by accessing the previously acquired RNA expression data). In some embodiments, process 100 may comprise operations 102, 104, 106, 118, and 120 without operations 114 and 116. In some embodiments, process 100 may comprise operations 102, 104, 106, 116, 114, 118, and 120 without operation 116.

[0120] Biological samples Aspects of the present disclosure relate to methods for determining a subject's GC TME type by obtaining sequencing data from a biological sample obtained from the subject.

[0121] The biological sample may be derived from any source within the subject's body, including, but not limited to, any fluid such as blood (e.g., whole blood, serum, or plasma), lymph nodes, stomach, small intestine. Other sources within the subject's body may be saliva, tears, synovial fluid, cerebrospinal fluid, pleural fluid, pericardial fluid, ascites fluid, and / or urine, hair, skin (including parts of the epidermis, dermis, and / or subcutaneous tissue), oropharynx, laryngopharynx, esophagus, bronchi, salivary glands, tongue, oral cavity, nasal cavity, vaginal cavity, anal cavity, bone, bone marrow, brain, thymus, spleen, appendix, colon, rectum, anus, liver, biliary tract, pancreas, kidney, ureter, bladder, urethra, uterus, vagina, vulva, ovaries, cervix, scrotum, penis, prostate, testicles, seminal vesicles, and / or any type of tissue (e.g., muscle tissue, epithelial tissue, connective tissue, or nerve tissue).

[0122] The biological sample may be any type of sample, including, for example, a sample of a body fluid, one or more cells, one or more tissue or organ slices. In some embodiments, the biological sample comprises a gastrointestinal tissue sample of the subject. Examples of gastrointestinal tissue samples include, but are not limited to, mucosal tissue, submucosa tissue, muscularis tissue, and serosal layer tissue (also referred to as serosal tissue). In some embodiments, the gastrointestinal tissue sample comprises one or more cell types derived from the stomach (e.g., mucous cells, parietal cells, chief cells, endocrine cells, etc.). In some embodiments, the gastrointestinal tissue sample comprises one or more cell types derived from gastrointestinal tissue, such as, for example, enterocytes, Paneth cells, goblet cells, neuroendocrine cells, etc.

[0123] In some embodiments, a gastrointestinal tissue sample may be obtained from a subject using a surgical procedure (e.g., laparoscopy, microsurgical surgery, or endoscopy), a bone marrow biopsy, a punch biopsy, an endoscopic biopsy, or a needle biopsy (e.g., fine needle aspiration, core needle biopsy, vacuum assisted biopsy, or image-guided biopsy).

[0124] A lymph node or blood sample, in some embodiments, refers to a sample comprising cells, e.g., cells from a blood sample or a lymph node sample. In some embodiments, the sample comprises non-cancerous cells. In some embodiments, the sample comprises pre-cancerous cells. In some embodiments, the sample comprises cancerous cells. In some embodiments, the sample comprises blood cells. In some embodiments, the sample comprises lymph node cells. In some embodiments, the sample comprises lymph node cells and blood cells.

[0125] The blood sample may be a whole blood sample or a fractionated blood sample. In some embodiments, the blood sample comprises whole blood. In some embodiments, the blood sample comprises fractionated blood. In some embodiments, the blood sample comprises buffy coat. In some embodiments, the blood sample comprises serum. In some embodiments, the blood sample comprises plasma. In some embodiments, the blood sample comprises a thrombus.

[0126] In some embodiments, a blood sample is taken to obtain cell-free nucleic acid (eg, cell-free DNA) in the blood.

[0127] In some embodiments, the sample may be from a cancerous tissue or organ, or a tissue or organ suspected of having one or more cancerous cells. In some embodiments, the sample may be from a healthy (e.g., non-cancerous) tissue or organ. In some embodiments, a sample from a subject (e.g., a biopsy from a subject) may include both healthy and cancerous cells and / or tissues. In certain embodiments, one sample is taken from a subject for analysis. In some embodiments, two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) samples may be taken from a subject for analysis. In some embodiments, one sample from a subject is analyzed. In certain embodiments, two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) samples may be analyzed. When more than one sample from a subject is analyzed, the samples may be obtained at the same time (e.g., two or more samples may be taken during the same procedure) or the samples may be collected at different time points (e.g., during different treatments, including 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 days; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 weeks; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 months, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years, or 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 years of treatment after the first treatment). The second or subsequent samples may be taken or obtained from the same area (e.g., from the same tumor or tissue area) or from a different area (e.g., including a different tumor). The second or subsequent samples may be taken or obtained from the subject after one or more treatments and may be taken from the same area or a different area. As non-limiting examples, a second or subsequent sample may be useful to determine whether the cancer in each sample has different characteristics (e.g., in the case of samples taken from two physically separate tumors from a patient) or whether the cancer has responded to one or more treatments (e.g., in the case of two or more samples from the same tumor before and after treatment).

[0128] Any of the biological samples described herein may be obtained from a subject using any known technique. See, for example, the following publications on biological sample collection, processing, and storage, each of which is incorporated herein by reference in its entirety: Biospecimens and biorepositories: from afterthought to science by Vaught et al. (Cancer Epidemiol Biomarkers Prev. 2012 Feb; 21(2): 253-5), and Biological sample collection, processing, storage and information management by Vaught and Henderson (IARC Sci Publ. 2011; (163): 23-42).

[0129] Any biological sample from a subject described herein may be stored using any method that maintains the stability of the biological sample. In some embodiments, maintaining the stability of the biological sample means that the components of the biological sample (e.g., DNA, RNA, protein, or tissue structure or morphology) are prevented from decomposing until they are measured, so that the measurements represent the state of the sample at the time the sample is obtained from the subject. In some embodiments, the biological sample is stored in a composition that can permeate the biological sample and protect the components of the biological sample (e.g., DNA, RNA, protein, or tissue structure or morphology) from decomposition. As used herein, degradation is when a component changes from one form to another, and the first form is no longer detected at the same level as it was before degradation.

[0130] In some embodiments, the biological sample is preserved using cryopreservation. Non-limiting examples of cryopreservation include, but are not limited to, step-down freezing, blast freezing, direct plunge freezing, snap freezing, slow freezing using a programmable freezer, and vitrification. In some embodiments, the biological sample is preserved using freeze-drying. In some embodiments, the biological sample is placed in a container that already contains a preservative (e.g., RNALater for preserving RNA) after the biological sample is collected from the subject, and then frozen (e.g., by snap freezing). In some embodiments, such storage in a frozen state is performed immediately after the collection of the biological body sample. In some embodiments, the biological sample may be stored in a preservative or in a buffer without a preservative at either room temperature or 4° C. for a period of time (e.g., up to 1 hour, up to 8 hours, or up to 1 day, or several days) before being frozen.

[0131] Non-limiting examples of preservatives include formalin solution, formaldehyde solution, RNALater or other equivalent solution, TriZol or other equivalent solution, DNA / RNA Shield or equivalent solution, EDTA (e.g., Buffer AE (10 mM Tris·Cl; 0.5 mM EDTA, pH 9.0)) and other coagulants, and Dextrose Citrate (Dextronse) (e.g., for blood samples).

[0132] In some embodiments, special containers may be used to collect and / or store biological samples. For example, a vacutainer may be used to store blood. In some embodiments, the vacutainer may comprise a preservative (e.g., a coagulant or anticoagulant). In some embodiments, the container in which the biological sample is stored may be housed within a secondary container for better preservation or to avoid contamination.

[0133] Any of the biological samples from a subject described herein may be stored under any conditions that maintain the stability of the biological sample. In some embodiments, the biological sample is stored at a temperature that maintains the stability of the biological sample. In some embodiments, the sample is stored at room temperature (e.g., 25° C.). In some embodiments, the sample is stored under refrigeration (e.g., 4° C.). In some embodiments, the sample is stored under freezing (e.g., −20° C.). In some embodiments, the sample is stored under ultra-low temperature conditions (e.g., −50° C. to −800° C.). In some embodiments, the sample is stored under liquid nitrogen (e.g., −1700° C.). In some embodiments, the biological sample is stored at -60°C to -8°C (e.g., -70°C) for up to 5 years (e.g., up to 1 month, up to 2 months, up to 3 months, up to 4 months, up to 5 months, up to 6 months, up to 7 months, up to 8 months, up to 9 months, up to 10 months, up to 11 months, up to 1 year, up to 2 years, up to 3 years, up to 4 years, or up to 5 years). In some embodiments, the biological sample is stored for up to 20 years (e.g., up to 5 years, up to 10 years, up to 15 years, or up to 20 years) as described by any of the methods described herein.

[0134] Acquiring RNA expression data Aspects of the present disclosure relate to methods of determining a subject's GC TME type using sequencing or RNA expression data obtained from a biological sample from the subject.

[0135] The RNA expression data used in the methods described herein is typically derived from sequencing data obtained from biological samples.

[0136] Sequencing data may be obtained from the biological sample using any suitable sequencing technique and / or device. In some embodiments, the sequencing device used to sequence the biological sample may be selected from any suitable sequencing device known in the art, including, but not limited to, Illumina™, SOLid™, Ion Torrent™, PacBio™, nanopore-based sequencers, Sanger sequencers, or 454™ sequencers. In some embodiments, the sequencing device used to sequence the biological sample is an Illumina sequencing (e.g., NovaSeq™, NextSeq™, HiSeq™, MiSeq™, or MiniSeq™) device.

[0137] After the sequencing data is obtained, it is processed to obtain RNA expression data. The RNA expression data may be obtained using any method known in the art, including, but not limited to, whole transcriptome sequencing, whole exome sequencing, whole RNA sequencing, mRNA sequencing, targeted RNA sequencing, RNA exome capture sequencing, next generation sequencing, and / or deep RNA sequencing. In some embodiments, the RNA expression data may be obtained using a microarray assay.

[0138] In some embodiments, the sequencing data is processed to generate RNA expression data. In some embodiments, the RNA sequence data is processed by one or more bioinformatics methods or software tools, such as, for example, an RNA sequence quantification tool (e.g., Kallisto) and a genome annotation tool (e.g., Gencode v23) to generate expression data. The Kallisto software is described in Nicolas L Bray, Harold Pimentel, Pall Melsted and Lior Pachter, Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, 525-527 (2016), doi:10.1038 / nbt.3519, which is incorporated herein by reference in its entirety.

[0139] In some embodiments, the microarray expression data is processed using a bioinformatics R package such as "affy" or "limma" to generate expression data. The "affy" software is described in Bioinformatics. 2004 Feb 12;20(3):307-15. doi:10.1093 / bioinformatics / btg405. "affy--analysis of Affymetrix GeneChip data at the probe level" by Laurent Gautier 1, Leslie Cope, Benjamin M Bolstad, Rafael A Irizarry PMID:14960456 DOI:10.1093 / bioinformatics / btg405, the contents of which are incorporated herein by reference in their entirety. The "limma" software is described in Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK "limma powers differential expression analyses for RNA-sequencing and microarray studies." Nucleic Acids Res. 2015 Apr 20;43(7):e47.20. https: / / doi.org / 10.1093 / nar / gkv007 PMID:25605792, PMCID:PMC4402510, the entire contents of which are incorporated herein by reference.

[0140] In some embodiments, the sequencing data and / or expression data comprises a length of more than 5 kilobases (kb). In some embodiments, the size of the acquired RNA data is at least 10 kb. In some embodiments, the size of the acquired RNA data is at least 100 kb. In some embodiments, the size of the acquired RNA data is at least 500 kb. In some embodiments, the size of the acquired RNA data is at least 1 megabase (Mb). In some embodiments, the size of the acquired RNA data is at least 10 Mb. In some embodiments, the size of the acquired RNA data is at least 100 Mb. In some embodiments, the size of the acquired RNA data is at least 500 Mb. In some embodiments, the size of the acquired RNA data is at least 1 gigabase (Gb). In some embodiments, the size of the acquired RNA data is at least 10 Gb. In some embodiments, the size of the acquired RNA data is at least 100 Gb. In some embodiments, the size of the acquired RNA data is at least 500 Gb.

[0141] In some embodiments, the expression data is obtained through bulk RNA sequencing. Bulk RNA sequencing may include obtaining the expression level of each gene across the entire RNA extracted from a large input cell population (e.g., a mixture of different cell types). In some embodiments, the expression data is obtained through single-cell sequencing (e.g., scRNA-seq). Single-cell sequencing may include sequencing of individual cells.

[0142] In some embodiments, the bulk sequencing data comprises at least 1 million reads, at least 5 million reads, at least 10 million reads, at least 20 million reads, at least 50 million reads, or at least 100 million reads. In some embodiments, the bulk sequencing data comprises 1 million reads to 5 million reads, 3 million reads to 10 million reads, 5 million reads to 20 million reads, 10 million reads to 50 million reads, 30 million reads to 100 million reads, or 1 million reads to 100 million reads (or any number of reads in between).

[0143] In some embodiments, the expression data comprises next generation sequencing (NGS) data. In some embodiments, the expression data comprises microarray data.

[0144] Expression data (e.g., indicating expression levels) of a plurality of genes may be used in any of the methods or compositions described herein. The number of genes that can be tested may include up to all genes of a subject. In some embodiments, expression levels may be determined for all genes of a subject. As a non-limiting example, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 or more, 30 or more, 35 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 125 or more, 150 or more, 175 or more, 200 or more, 225 or more, 250 or more, 275 or more, or 300 or more genes may be used for any evaluation described herein. As another non-limiting set of examples, the expression data may include, for each gene group listed in Table 1, expression data for at least 5, at least 10, at least 15, at least 20, at least 25, at least 35, at least 50, at least 75, at least 100 genes selected from each gene group.

[0145] In some embodiments, the RNA expression data is obtained by accessing the RNA expression data from at least one computer storage medium on which the RNA expression data is stored. Additionally or alternatively, in some embodiments, the RNA expression data may be received from one or more sources via any suitable type of communication network. For example, in some embodiments, the RNA expression data may be received from a server (e.g., an SFTP server, or Illumina BaseSpace).

[0146] The acquired RNA expression data may be in any suitable format, as aspects of the technology described herein are not limited in this respect. For example, in some embodiments, the RNA expression data may be acquired in a text-based file (e.g., FASTQ, FASTA, BAM, or SAM format). In some embodiments, the file in which the sequencing data is stored may include a quality score of the sequencing data. In some embodiments, the file in which the sequencing data is stored may include sequence identifier information.

[0147] The expression data, in some embodiments, includes gene expression levels. Gene expression levels may be detected by detecting products of gene expression, such as mRNA and / or protein. In some embodiments, gene expression levels are determined by detecting levels of mRNA in a sample. As used herein, the terms "determining" or "detecting" may include assessing the presence, absence, quantity and / or amount (which may be an effective amount) of a substance in a sample, including deriving a qualitative or quantitative concentration level of such substance, or otherwise assessing the value and / or classification of such substance in a sample from a subject.

[0148] 2 illustrates an exemplary process 104 for processing sequencing data to obtain RNA expression data from the sequencing data. Process 104 may be performed by any suitable computing device or devices, such that aspects of the technology described herein are not limited in this respect. For example, process 104 may be performed by a computing device portion of a sequencing device. In other embodiments, process 104 may be performed by one or more computing devices external to the sequencing device.

[0149] Process 104 begins with operation 200 where sequencing data is obtained from a biological sample obtained from a subject. The sequencing data is obtained using any suitable method, such as, for example, any of the methods described herein, including in the section entitled "Biological Samples."

[0150] In some embodiments, the sequencing data obtained in operation 104 comprises RNA-seq data. In some embodiments, the biological sample comprises blood or tissue. In some embodiments, the biological sample comprises one or more tumor cells, such as, for example, one or more GC tumor cells.

[0151] Process 104 then proceeds to operation 202, where the sequencing data obtained in operation 200 is normalized to units of transcripts per million kilobases (TPM). Normalization may be performed in any suitable manner using any suitable software. For example, in some embodiments, TPM normalization may be performed according to the techniques described in Wagner et al. (Theory Biosci. (2012) 131:281-285), the contents of which are incorporated herein by reference in their entirety. In some embodiments, TPM normalization may be performed using a software package, such as the gcrma package. Aspects of the gcrma package are described in Wu J, Gentry RIwcfJMJ (2021). "gcrma: Background Adjustment Using Sequence Information. R package version 2.66.0.", the contents of which are incorporated herein by reference in their entirety. In some embodiments, the RNA expression level in TPM of a particular gene may be calculated according to the following formula:

number

[0152] Process 104 then proceeds to operation 204, where the RNA expression levels in TPM (determined in operation 202) may be log transformed. Process 104 is exemplary and variations exist. For example, in some embodiments, one or both of operations 202 and 204 may be omitted. Thus, in some embodiments, the RNA expression levels are not normalized to transcripts per million units, but instead may be converted to another type of unit (e.g., reads per million kilobases (RPKM), fragments per million kilobases (FPKM), or other suitable units). Additionally or alternatively, in some embodiments, the log transformation may be omitted. Alternatively, in some embodiments, no transformation may be applied, or one or more other transformations may be applied instead of the log transformation.

[0153] The RNA expression data obtained by process 104 may include sequence data generated by a sequencing protocol (e.g., a series of nucleotides in a nucleic acid molecule identified by next generation sequencing, Sanger sequencing, etc.), as well as information contained therein (e.g., information indicative of source, tissue type, etc.), which may also be considered information that can be inferred or determined from the sequence data. In some embodiments, the expression data obtained by process 104 may include information contained in a FASTA file, descriptions and / or quality scores contained in a FASTQ file, alignment positions contained in a BAM file, and / or any other suitable information obtained from any suitable file.

[0154] Gene expression signatures Aspects of the present disclosure relate to processing expression data to determine one or more gene expression signatures (e.g., GC TME signatures). In some embodiments, expression data (e.g., RNA expression data) is processed using a computing device to determine one or more gene expression signatures. In some embodiments, the computing device may be operated by a user, such as a physician, clinician, researcher, patient, or other individual. For example, a user may provide expression data as input to the computing device (e.g., by uploading a file) and / or may provide user input that specifies processing or other methods to be performed using the expression data.

[0155] In some embodiments, the expression data may be processed by one or more software programs running on a computing device.

[0156] In some embodiments, the methods described herein comprise an act of determining a GC TME signature comprising a gene group score for each gene group in a plurality of gene groups. In some embodiments, the GC TME signature comprises a gene group score for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20) of the gene groups listed in Table 1. In some embodiments, the GC TME signature comprises a gene group score for at least one (e.g., 1, 2, 3, 4, 5, 6, or 7) of the gene groups listed in Table 2. In some embodiments, a GC TME signature comprises a gene group score for at least one gene group (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20) listed in Table 1 and a gene group score for at least one gene group (e.g., 1, 2, 3, 4, 5, 6, or 7) listed in Table 2.

[0157] The number of genes in a gene group used to determine the gene group score may vary. In some embodiments, all of the RNA expression levels of all of the genes in a particular gene group may be used to determine the gene group score for a particular gene group. In other embodiments, RNA expression data for fewer than all of the genes may be used (e.g., RNA expression levels for at least 2 genes, at least 3 genes, at least 5 genes, 2-10 genes, 5-15 genes, 3-30 genes, or any other suitable range within these ranges).

[0158] In some embodiments, the GC TME signature comprises a gene group score for MHC I. In some embodiments, this gene group score may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, or at least seven genes) in the MHC I gene group defined by its constituent genes: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP.

[0159] In some embodiments, the GC TME signature comprises a gene group score for MHC II. In some embodiments, this gene group score may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes) in the MHC II gene group defined by its constituent genes: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA.

[0160] In some embodiments, the GC TME signature comprises a gene group score for a co-activator molecule group, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, or at least ten genes) in the co-activator molecule group defined by its constituent genes: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70.

[0161] In some embodiments, the GC TME signature comprises a gene group score for an effector cell population, which may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes) in an effector cell population defined by its constituent genes: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B.

[0162] In some embodiments, the GC TME signature comprises a gene group score for the T cell trafficking group, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, or at least eight genes) in the T cell trafficking group defined by its constituent genes: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6.

[0163] In some embodiments, the GC TME signature comprises a gene group score for the NK cell population, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes) in the NK cell population defined by its constituent genes: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3.

[0164] In some embodiments, the GC TME signature comprises a gene group score for a T cell population, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, or at least ten genes) in a T cell population defined by its constituent genes: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6.

[0165] In some embodiments, the GC TME signature comprises a gene group score for a B cell population, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes) in a B cell population defined by its constituent genes: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1.

[0166] In some embodiments, the GC TME signature comprises a gene group score for the M1 signature group, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, or at least eight genes) in the M1 signature group defined by its constituent genes: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A.

[0167] In some embodiments, the GC TME signature comprises a gene group score for the anti-tumor cytokine group, which may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, or at least five genes) in the anti-tumor cytokine group defined by its constituent genes: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21.

[0168] In some embodiments, the GC TME signature comprises a gene group score for the checkpoint inhibition group, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes) in the checkpoint inhibition group defined by its constituent genes: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR.

[0169] In some embodiments, the GC TME signature comprises a gene group score for the Treg group, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, or at least six genes) in the Treg group defined by its constituent genes: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2.

[0170] In some embodiments, the GC TME signature comprises a gene group score for the neutrophil signature group, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, or at least nine genes) in the neutrophil group defined by its constituent genes: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1.

[0171] In some embodiments, the GC TME signature comprises a gene group score for the MDSC population, which may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, or at least six genes) in the MDSC population defined by its constituent genes: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6.

[0172] In some embodiments, the GC TME signature comprises a gene group score for the M2 signature group, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, or at least seven genes) in the M2 group defined by its constituent genes: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68.

[0173] In some embodiments, the GC TME signature comprises a gene group score for the cancer-associated fibroblast (CAF) group. In some embodiments, the gene group score is comprised of the following genes: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LG The RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes) in a cancer-associated fibroblast (CAF) population defined by ALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, and LOX may be used.

[0174] In some embodiments, the GC TME signature comprises a gene group score for the angiogenesis group, which in some embodiments may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes) in the angiogenesis group defined by its constituent genes: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5.

[0175] In some embodiments, the GC TME signature comprises a gene group score for a proliferation rate group, which may be calculated using RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, or at least six genes) in a proliferation rate group defined by its constituent genes: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6.

[0176] In some embodiments, the GC TME signature comprises a gene cluster score for the Lgr5 ISC cluster. In some embodiments, the gene cluster score is comprised of the following genes: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR , LOC285141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93, or the RNA expression levels of at least three genes (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes) in the Lgr5 ISC group defined by.

[0177] In some embodiments, determining the GC TME signature comprises, for a particular gene group, determining a gene group score for the particular group using RNA expression levels of at least three genes within the particular gene group, MHC I group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; MHC II group: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; co-activation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG , TNFSF9, CD70; Effector cell group: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; T cell trafficking group: CXCL9, CXCL 10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, I FNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; M1 signature group: NO S2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; anti-tumor cytokine group: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2;Good neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6;and Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC285141, MDFIC, MPP3, NPNT, PITPN determining a gene group score for each of at least two of the genes including: C1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, SORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273, ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93;

[0178] The list of genes is shown in Table 1 below.

[0179] [Table 1]

[0180] In some embodiments, after the GC TME type is assigned, the expression data from the subject is further analyzed. The present disclosure is based in part on a secondary GC TME signature that can be used to identify a shift in the subject's TME type and / or a shift in the subject's response to immunotherapy. In some embodiments, the second GC TME signature is generated using the RNA expression levels of genes in a group of genes listed in Table 2.

[0181] In some embodiments, the second GC TME signature comprises RNA expression levels of at least three (e.g., at least 2, 3, 4, 5, between 2 and 10, and any integer therebetween, or more than 10) genes from each of at least two of the following gene groups: (i) Th17 signature group; (ii) gamma delta (γΔ) T cell group; (iii) tumor suppressor group; (iv) oncogene group; (v) Myc target group; (vi) Th2 signature group; and (vii) Th1 signature group.

[0182] In some embodiments, the second GC TME signature comprises the RNA expression levels of each of the following gene groups: (i) Th17 signature group; (ii) gamma delta (γΔ) T cell group; (iii) tumor suppressor group; (iv) oncogene group; (v) Myc target group; (vi) Th2 signature group; and (vii) Th1 signature group.

[0183] The gene group scores of the second GC TME signature may be determined using gene set enrichment (GSEA) (e.g., using single sample GSEA techniques). For example, in some embodiments, ssGSEA is performed on the RNA expression data using one or more (e.g., 1, 2, 3, 4, 5, 6, or 7) gene groups listed in Table 2. In some embodiments, each group of genes in Table 2 comprises one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or more) genes listed in Table 2. In some embodiments, further analysis comprises performing ssGSEA on the Th17 gene panel and the gamma delta T cell (γΔT cell) gene panel to generate a Th17 signature and a gamma delta (γΔ) T cell signature. Aspects of GSEA and ssGSEA are described herein.

[0184] In some embodiments, the further analysis comprises performing cellular deconvolution analysis and / or histological analysis of the expression data (e.g., by assessing Laurent subtypes).

[0185] In some embodiments, determining one or more additional signatures (e.g., a second GC TME signature) comprises, for a particular gene group, determining a gene group score for the particular group using RNA expression levels of at least two genes in the particular gene group; (i) Th17 signature group: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, CCR6; (ii) gamma delta (γΔ) T cell population: TRGC1, TRDC, TRDV2, TRGC2, TRDV1; (iii) tumor suppressor group: LATS1, LATS2, FBXW7, BAP1, KLF4, SASH1, CASP3, MLKL, SOCS1; (iv) Oncogenes: KRAS, AKT1, EGFR, PIK3CA, PIK3CB, MYC, VAV3, MET, CCND1, INPPL1, CHD1L, RACGAP1; (v)Myc target group: AIMP2, BYSL, CBX3, CDK4, DCTPP1, DDX18, DUSP2, EXOSC5, FARSA, GNL3, GRWD1, HK2, HSPD1, HSP E1, IMP4, IPO4, LAS1L, MAP3K6, MCM4, MCM5, MPHOSPH10, MRTO4, MYBBP1A, MYC, NDUFAF4, NIP7, NOC4L, NOLC 1, NOP16, NOP2, NOP56, NPM1, PA2G4, PES1, PHB, PLK1, PLK4, PPAN, PPRC1, PRMT3, PUS1, RABEPK, RCL1, RRP1 2, RRP9, SLC19A1, SLC29A2, SORD, SRM, SUPV3L1, TBRG4, TCOF1, TFB2M, TMEM97, UNG, UTP20, WDR43, WDR74; (vi) Th2 signature group: IL4, IL5, IL13, IL10, GATA3, CCR4; and (vii) Th1 signature group: IFNG, IL2, CD40LG, IL21, TBX21, STAT4, IL12RB2 determining a respective gene group score for each of at least one of the gene groups, including

[0186] [Table 2]

[0187] As described above, embodiments of the present disclosure relate to determining a subject's GC TME signature, which may include a gene group score (e.g., a gene group score generated using RNA expression data for the gene groups listed in Table 1 and / or Table 2). An embodiment of determining a GC TME signature will now be described with reference to FIG. 3.

[0188] In some embodiments, the GC TME signature comprises a gene group score generated using gene set enrichment analysis (GSEA) technology to determine a gene group score for one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20) gene groups listed in Table 1. In some embodiments, the GC TME signature comprises a gene group score generated using gene set enrichment analysis (GSEA) technology to determine a gene group score for eight or more (e.g., 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20) gene groups listed in Table 1. In some embodiments, each gene group score is generated using gene set enrichment analysis (GSEA) technology using RNA expression levels of at least some of the genes in the gene group. In some embodiments, using GSEA technology comprises using a single sample GSEA. Aspects of single sample GSEA (ssGSEA) are described in Barbie et al. Nature. 2009 Nov 5; 462(7269):108-112, the entire contents of which are incorporated herein by reference. In some embodiments, ssGSEA is performed according to the following formula:

number

[0189] Figure 3 shows an example of how a gene group score may be determined as part of operation 108 of process 100. As shown in the example of Figure 3, a "GC TME signature" comprises a plurality of gene group scores 320 determined for each of a plurality of gene groups. Each gene group score is calculated for a particular gene group by performing GSEA 310 (e.g., using ssGSEA) on the RNA expression data of one or more (e.g., at least two, at least three, at least four, at least five, at least six, etc., or all) genes in a particular gene group 300.

[0190] For example, as shown in FIG. 3, a gene group score (labeled "gene group score 1") for gene group 1 (e.g., Treg group) is calculated from the RNA expression data of one or more genes in the gene group. As another example, a gene group score (labeled "gene group score 2") for gene group 2 (e.g., T cell group) is calculated from the RNA expression data of one or more genes in gene group 2. As another example, a gene group score (labeled "gene group score 3") for gene group 3 (e.g., NK cell group) is calculated from the RNA expression data of one or more genes in gene group 3. As another example, a gene group score (labeled "gene group score 4") for gene group 4 (e.g., B cell group) is calculated from the RNA expression data of one or more genes in gene group 4. As another example, a gene group score (labeled "gene group score 5") for gene group 5 (e.g., MDSC group) is calculated from the RNA expression data of one or more genes in gene group 5. As another example, a gene group score (labeled "gene group score 6") for gene group 6 (e.g., the CAF group) is calculated from RNA expression data of one or more genes in gene group 6. As another example, a gene group score (labeled "gene group score 7") for gene group 7 (e.g., the proliferation rate group) is calculated from RNA expression data of one or more genes in gene group 7. As another example, a gene group score (labeled "gene group score 8") for gene group 8 (e.g., the eLgr5 ISC group) is calculated from RNA expression data of one or more genes in gene group 8.

[0191] While the example of Figure 3 shows that the gene expression group score includes eight gene group scores for each set of eight gene groups, it should be understood that in other embodiments, the first gene expression signature may include scores for any suitable number of groups (e.g., not just eight; the number of groups may be less than or more than eight). As indicated by the vertical ellipses in Figure 3, determining the gene group scores for the GC TME signature may comprise determining gene group scores for 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more gene groups using RNA expression data from one or more respective genes within each gene group, such that aspects of the technology described herein are not limited in this respect. In another example, the GC TME signature may include scores for only a subset of the gene groups listed in Table 1 above. As another example, a gene expression group score may include one or more scores for two or more gene groups other than those listed in Table 1 (in addition to or instead of one or more scores for the groups in Table 1, e.g., the genes listed in the table).

[0192] In some embodiments, the RNA expression levels of a particular set of genes may be embodied in at least one data structure having a field for storing the expression levels. The data structure or data structures may be provided as input to software comprising code implementing GSEA technology (e.g., ssGSEA technology) to process the expression levels in the at least one data structure to calculate a score for the particular set of genes.

[0193] The number of genes in a gene group used to determine the gene group score may vary. In some embodiments, all of the RNA expression levels of all of the genes in a particular gene group may be used to determine the gene group score for a particular gene group. In other embodiments, RNA expression data for fewer than all of the genes may be used (e.g., RNA expression levels for at least 2 genes, at least 3 genes, at least 5 genes, 2-10 genes, 5-15 genes, or any other suitable range within these ranges).

[0194] In some embodiments, the RNA expression levels of a particular gene group may be embodied in at least one data structure having a field for storing the expression level. The data structure or data structures may be provided as input to software comprising code configured to perform appropriate scaling (e.g., median scaling) to generate a score for the particular gene group.

[0195] In some embodiments, ssGSEA is performed on expression data comprising three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20) gene groups set forth in Table 1. In some embodiments, each of the gene groups is one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, In some embodiments, the GC TME signature comprises 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, or more genes separately. In some embodiments, the GC TME signature is generated by performing ssGSEA on all 20 gene groups in Table 1, where each gene group comprises all genes listed in Table 1.

[0196] In some embodiments, one or more (e.g., multiple) enrichment scores are normalized to generate a GC TME signature of expression data (e.g., expression data of a subject or cohort of subjects). In some embodiments, the enrichment scores are normalized by median scaling. In some embodiments, the enrichment scores are normalized by rank estimation and median scaling. In some embodiments, the median scaling comprises clipping a range of the enrichment scores, e.g., clipping to about -1.0 to about +1.0, -2.0 to about +3.0, -3.0 to about +3.0, -4.0 to +4.0, -5.0 to about +5.0. In some embodiments, the median scaling generates a GC TME signature of the subject.

[0197] In some embodiments, the subject's GC TME signature is processed using a clustering algorithm to identify GC tumor microenvironment types (e.g., GC TME types). In some embodiments, the clustering comprises unsupervised clustering. In some embodiments, the unsupervised clustering comprises a dense clustering approach. In some embodiments, the unsupervised clustering comprises a hierarchical clustering approach. In some embodiments, the clustering comprises calculating inter-sample similarities (e.g., using the Pearson correlation coefficient, which takes values ​​in the range [-1, 1]), converting the distance matrix into a graph in which each sample forms a node and two nodes form an edge with a weight equal to the Pearson correlation coefficient, removing edges with weights lower than a specified threshold, and applying the Louvain community detection algorithm to calculate the partitioning of the graph into clusters. In some embodiments, the optimal weight thresholds of the observed clusters were calculated by using the minimum DaviesBouldin, maximum Calinski-Harabasz, and Silhouette techniques. In some embodiments, the separation of low population clusters (less than 5% of the samples) is excluded.

[0198] In some embodiments, the subject's GC TME signature is compared to existing clusters of GC TME types and a GC TME type is assigned based on the comparison.

[0199] Some aspects of determining gene group scores for gene groups are also described in U.S. Patent Application Publication No. 2020-0273543, entitled "SYSTEMS AND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULAR FUNCTIONAL PROFILES," the entire contents of which are incorporated herein by reference.

[0200] Generation of GC TME signatures and identification of TME types As described herein, FIG. 1 illustrates determining a subject's GC TME signature, identifying a subject's GC TME type using the GC TME signature, and identifying whether a subject is likely to respond to immunotherapy based on the identified GC TME type.

[0201] As described herein, in some embodiments, the GC TME signature determined for a subject using the techniques described herein may be used to identify one of a plurality of different GC TME types for the subject. In some embodiments, the GC TME types include GC TME type A, GC TME type B, GC TME type C, GC TME type D, and GC TME type E, as described herein and further below. In some embodiments, each of the plurality of GC TME types is associated with a respective GC TME signature cluster in the plurality of GC TME signature clusters. The GC TME type of the subject may be determined by (1) associating the GC TME signature of the subject with a particular one of the plurality of GC TME signature clusters; and (2) identifying the GC TME type of the subject as the GC TME type corresponding to the particular one of the plurality of GC TME signature clusters with which the GC TME signature of the subject is associated.

[0202] FIG. 4 shows an exemplary GC TME signature 400. In some embodiments, the GC TME signature comprises at least eight gene group scores for the following gene groups: NK cell group, T cell group, B cell group, Treg cell group, MDSC group, CAF group, proliferation rate group, and Lgr5 ISC group. However, it should be understood that the GC TME signature may include fewer scores than the number of scores shown in FIG. 4 (e.g., by omitting scores for one or more gene groups listed in Table 1) or more scores than the number of scores shown in FIG. 4 (e.g., by including scores for one or more other gene groups in addition to or instead of the gene groups listed in Table 1). In some embodiments, the GC TME signature may be embodied in at least one data structure comprising a field that stores the gene group score portion of the GC TME signature.

[0203] In some embodiments, GC TME signature clusters may be generated by (1) obtaining GC TME signatures for a plurality of subjects (using techniques described herein) and (2) clustering the GC TME signatures so obtained into a plurality of clusters. Any suitable clustering technique may be used for this purpose, including, but not limited to, agglomerative clustering algorithms, spectral clustering algorithms, k-means clustering algorithms, hierarchical clustering algorithms, and / or agglomerative clustering algorithms.

[0204] For example, inter-sample similarity may be calculated using Pearson correlation. The distance matrix may be converted into a graph where each sample forms a node and two nodes form an edge with a weight equal to the Pearson correlation coefficient. Edges with weights lower than a specified threshold may be removed. The partition of the graph into clusters may be calculated applying the Louvain community detection algorithm. Minimum DaviesBouldin, maximum Calinski-Harabasz, and Silhouette techniques may be used to mathematically determine optimal weight thresholds for the observed clusters. Separation of low population clusters (less than 5% of samples) may be excluded.

[0205] Thus, in some embodiments, generating a GC TME signature cluster involves: (A) obtaining a plurality of sets of RNA expression data obtained by sequencing biological samples from a plurality of respective subjects; and (B) generating a plurality of GC TME signatures from the plurality of sets of RNA expression data, each of the plurality of sets of RNA expression data indicating RNA expression levels of genes in a first plurality of gene groups (e.g., one or more of the gene groups in Table 1), each of the plurality of GC TME signatures comprising a gene group score for a respective gene group, and generating comprises, for each particular one of the plurality of TME signatures, (i) determining the GC TME signature by determining a gene group score using the RNA expression levels in the particular set of RNA expression data for which the particular one GC TME signature was generated; and (ii) clustering the plurality of GC signatures to obtain a plurality of GC TME signature clusters.

[0206] Each of the resulting GC TM signature clusters may contain any suitable number of GC TME signatures (e.g., at least 10, at least 100, at least 500, at least 1000, at least 5000, between 100 and 10,000, between 500 and 20,000, or any other suitable range within these ranges), as aspects of the technology described herein are not limited in this respect.

[0207] The number of GC TME signature clusters in this example is 5. It should also be understood that in some embodiments the number of clusters may vary, but an important aspect of the present disclosure is the inventors' discovery that GCs may be characterized into five types based on the generation of GC TME signatures using the methods described herein.

[0208] 4, a GC TME signature 400 of a subject may be associated with one of five GC TME clusters 402, 404, 406, 408, and 410. Each of the clusters 402, 404, 406, 408, and 410 may be associated with a respective GC TME type. In this example, the GC TME signature 400 is compared to each cluster (e.g., using a distance-based comparison or any other suitable metric), and based on the results of the comparison, the GC TME signature 400 is associated with the closest GC signature cluster ("closest" in the case where a distance-based comparison is performed, or in any sense in which a distance metric or measure is used). In this example, GC TME signature 400 is associated with GC TME type clusters 5, 410 (as shown by consistent shading) because the measured distance D5 between GC TME signature 400 (e.g., of the center of mass of cluster 410 or other point representative thereof) is less than the measured distances D1, D2, D3, and D4 between GC TME signature 400 (e.g., of the centers of mass of clusters 402, 404, 406, and 408, respectively).

[0209] In some embodiments, a subject's GC TME signature may be associated with one of the five GC TME signature clusters by using a machine learning technique (such as, for example, k-nearest neighbors (KNN) or any other suitable classifier) ​​to assign the GC TME signature to one of the four GC TME signature clusters. The machine learning technique may be trained to assign GC TME signatures to meta-cohorts represented by the signatures in the cluster.

[0210] In some embodiments, GC TME types include GC TME type A, GC TME type B, GC TME type C, GC TME type D, and GC TME type E. The GC TME types described herein may be described by qualitative characteristics, such as, for example, a high signal for a particular gene expression signature or score, or a low signal for a particular other gene expression signature or score. In some embodiments, a "high" signal refers to a gene expression signal or score (e.g., enrichment score) that is increased by at least 1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 20-fold, 50-fold, 100-fold, 1000-fold, or more, compared to the score for the same gene or group of genes in subjects with a different type of GC. In some embodiments, a "low" signal refers to a gene expression signal or score (e.g., an enrichment score) that is reduced by at least 1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 20-fold, 50-fold, 100-fold, 1000-fold, or more, compared to the score for the same gene or group of genes in subjects with a different type of GC.

[0211] Without wishing to be bound by any theory, the tumor microenvironment of GC may contain varying numbers of immune cells, stromal cells, blood vessels, and extracellular matrix. In some embodiments, "GC TME type A" is characterized by a "mesenchymal, EMT" type. In some embodiments, GC TME type A (mesenchymal) type is characterized by having the highest LGR5+ stem cell signature compared to other GC TME types. In some embodiments, GC TME type A GC subjects also have high stromal compartment gene group signatures, e.g., cancer-associated fibroblasts (CAFs) and angiogenesis, compared to other GC TME types. In some embodiments, GC TME type A subjects are characterized by high levels of tumor-promoting cytokines and a moderate immune compartment signature. In some embodiments, GC TME type A subjects have a low tumor growth rate (compared to other GC TME types). In some embodiments, type A GC tumors are diffuse (e.g., as assessed by Lauren classification). GC TME type A has been observed, in some embodiments, to be the most aggressive GC TME type, and subjects with GC TME type A have been observed, in some embodiments, to have a poorer prognosis (e.g., overall survival (OS)) compared to other GC TME types.

[0212] In some embodiments, "GC TME type B" is also referred to as "immune-enriched, non-fibrotic (IE)" type. In some embodiments, GC TME type B (IE) is characterized by a high level of immune-active infiltration, including significant numbers of effector cells and NK cells, compared to other GC TME types. In some embodiments, GC TME type B subjects have the highest tumor mutation burden (TMB). In some embodiments, subjects with GC TME type B have been observed to have a better prognosis (e.g., overall survival (OS)) compared to other GC TME types.

[0213] In some embodiments, "GC TME type C" is also referred to as "fibrotic" or "immune non-inflammatory". In some embodiments, GC TME type C is characterized by a highly fibrotic tumor microenvironment with dense collagen formation. In some embodiments, GC TME type C is characterized by minimal lymphocytic infiltration with enhanced angiogenesis compared to other GC TME types. In some embodiments, the cancer-associated fibroblast (CAF) signature is enriched in GC TME type C. In some embodiments, GC TME type C is characterized by high levels of both tumor-promoting and anti-tumor cytokines. In some embodiments, subjects with GC TME type C have a poorer prognosis (e.g., as measured by OS) than subjects with other GC TME types.

[0214] In some embodiments, GC TME type D is also referred to as an "immune desert" type. In some embodiments, GC TME type D is characterized by the highest percentage of malignant cells compared to other GC TME types. In some embodiments, GC TME type D is characterized by minimal or complete absence of leukocyte / lymphocyte infiltration (e.g., compared to other GC TME types). In some embodiments, GC TME type D is characterized by a high level of tumor growth rate compared to other GC TME types.

[0215] In some embodiments, GC TME type E is also referred to as a "B cell enriched" type. In some embodiments, GC TME type E is characterized by a high level of immune infiltration with a significant number of B cells compared to other GC TME types. In some embodiments, GC TME type D is characterized by having a low proliferation rate compared to other GC TME types. In some embodiments, subjects with GC TME type E have been observed to have a better prognosis (e.g., overall survival (OS)) compared to other GC TME types.

[0216] Tables 3, 4, 5, 6, and 7 below list examples of GC TME signatures and gene group scores generated by ssGSEA analysis and normalization (eg, median scaling) of expression data from one or more GC subjects.

[0217] [Table 3]

[0218] [Table 4]

[0219] [Table 5]

[0220] [Table 6]

[0221] [Table 7]

[0222] In some embodiments, the disclosure provides methods for identifying a subject having, suspected of having, or at risk of having GC as likely to have a good prognosis (e.g., as measured by overall survival (OS) or progression-free survival (PFS)). In some embodiments, the methods comprise determining the subject's GC TME type as described herein.

[0223] In some embodiments, the method comprises identifying a subject as having a lower risk of GC progression if the subject is assigned to GC TME type E, compared to other GC TME types. In some embodiments, a "reduced risk of GC progression" may indicate that the subject has a better GC prognosis or is less likely to have progressive disease. In some embodiments, a "reduced risk of GC progression" may indicate that a subject with GC is expected to be more responsive to a particular treatment. For example, a "reduced risk of GC progression" may indicate that the subject is at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% more likely to experience a progression-free survival event (e.g., recurrence, retreatment, or death) compared to another GC patient or population of GC patients (e.g., patients with GC but not the same GC TME type as the subject).

[0224] In some embodiments, the method further comprises identifying the subject as having a higher risk of GC progression when the subject is assigned to a GC TME type other than GC TME type E, compared to other GC TME types. In some embodiments, "increased risk of GC progression" may indicate that the subject has a less favorable prognosis for GC or is more likely to have progressive disease. In some embodiments, "increased risk of GC progression" may indicate that a subject with GC is expected to be less responsive or non-responsive to a particular treatment and to show less or no improvement in disease symptoms. For example, "increased risk of GC progression" indicates that the subject is at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% more likely to experience a progression-free survival event (e.g., recurrence, retreatment, or death) compared to another GC patient or population of GC patients (e.g., patients with GC but not the same GC TME type as the subject).

[0225] The present disclosure is based, in part, on the recognition that subjects with certain GC TME types (e.g., GC TME type E) are characterized as having (or being more likely to have) tertiary lymphoid structures (TLS) compared to subjects with other types of GC TME. Tertiary lymphoid structures are ectopic lymphoid organs that arise in non-lymphoid tissues at sites of chronic inflammation, including tumors, as described, for example, by Sautes-Fridman et al. Nat Rev Cancer 19, 307-325 (2019). In the context of certain cancers (e.g., gastric cancer), the presence of TLS is associated with improved subject prognosis. Thus, in some embodiments, subjects identified as having GC TME type E have an increased likelihood of having PFS or increased overall survival (OS) compared to subjects with other GC TME types.

[0226] In some embodiments, the methods described herein comprise the use of at least one computer hardware processor to perform the determination.

[0227] In some embodiments, the disclosure provides methods for providing a prognosis, predicting survival, or stratifying patient risk in a subject suspected of having or at risk of having GC, in some embodiments, the method comprises determining the subject's GC TME type as described herein.

[0228] Update of GC TME cluster based on new data Techniques for generating GC TME clusters are described herein. It should be understood that the GC TME clusters may be updated when additional GC TME signatures are calculated for a patient. In some embodiments, the subject's GC TME signature is one of a threshold number of GC TME signatures for a threshold number of subjects. In some embodiments, the GC TME signature clusters are updated when a threshold number of GC TME signatures are generated. For example, once a new threshold number of GC TME signatures are obtained (e.g., 1 new signature, 10 new signatures, 100 new signatures, 500 new signatures, any suitable threshold number of signatures in the range of 10 to 1,000), the new signatures may be combined with the GC TME signatures previously used to generate the GC TME clusters, and the combined set of old and new GC TME signatures may be re-clustered (e.g., using any of the clustering algorithms described herein or any other suitable clustering algorithm) to obtain an updated set of GC TME signature clusters.

[0229] In this way, data from future patients may be analyzed in a manner that leverages information learned from patients whose GC TME signatures were calculated prior to the future patient. In this sense, the machine learning techniques described herein (e.g., unsupervised clustering machine learning techniques) are adaptive and learn as new patient data accumulates. This may facilitate improved characterization of the GC TME types future patients may have, improving treatment options for these patients.

[0230] Treatment indications Aspects of the present disclosure relate to a method for identifying or selecting a therapeutic agent for a subject based on the determination of the subject's GC TME type. The present disclosure is based in part on the recognition that subjects with GC TME type B or GC TME type E are more likely to respond to a particular treatment (e.g., immunotherapy) compared to subjects with other GC TME types. The inventors have surprisingly discovered that the determination of GC TME type E or GC TME type B patients may respond to immunotherapy regardless of MSS / MSI or EBV status.

[0231] In some aspects, the disclosure provides a method for predicting a subject's disease control rate (DCR) to immunotherapy, the method comprising determining the subject's GC TME type and, if the subject is assigned to GC TME type B or GC TME type E, identifying the subject as likely to have increased DCR compared to subjects having GC TME type A, GC TME type C, or GC TME type D.

[0232] As used herein, "disease control rate" or "DCR" refers to the proportion of patients with advanced or metastatic cancer who achieve complete response, partial response, and stable disease to a therapeutic intervention. In some embodiments, subjects determined to have GC TME type B or GC TME type E have a DCR of more than 50% (e.g., at least 50%, 60%, 70%, 80%, 90%, 95%, or 99%) to immunotherapy. In some embodiments, the immunotherapy comprises a PD1 inhibitor. Examples of PD1 inhibitors include, but are not limited to, pembrolizumab, nivolumab, semilimab, JTX-4014, camrelizumab, sintilimab, tisleizumab, toripalimab, dostarlimab, AMP-224, and AMP-514. In some embodiments, the immunotherapy comprises pembrolizumab.

[0233] The present disclosure is based in part on the recognition that certain subjects with GC TME type B or GC TME type E may have a higher likelihood of responding to immunotherapy (e.g., pembrolizumab) even if the subject has previously been characterized as Epstein-Barr Virus (EBV) low and / or microsatellite stable (MSS). This is surprising because such patients have previously been observed to be poor responders to immunotherapy.

[0234] Aspects of the present disclosure relate to methods of treating a subject having (or suspected of having, or at risk of having) GC based on a determination of the subject's GC TME type. In some embodiments, the method comprises administering one or more (e.g., 1, 2, 3, 4, 5, or more) therapeutic agents to the subject. In some embodiments, the one or more therapeutic agents administered to the subject are selected from small molecules, peptides, nucleic acids, radioisotopes, cells (e.g., CAR T cells, etc.), and combinations thereof. Examples of therapeutic agents include chemotherapy (e.g., cytotoxic agents, etc.), immunotherapy (e.g., immune checkpoint inhibitors such as PD-1 inhibitors, PD-L1 inhibitors, etc.), antibodies (e.g., anti-HER2 antibodies), cell therapy (e.g., CAR T cell therapy), gene silencing therapy (e.g., interfering RNA, CRISPR, etc.), antibody drug conjugates (ADC), and combinations thereof.

[0235] In some embodiments, an effective amount of the therapeutic agent is administered to the subject. "Effective amount," as used herein, refers to the amount of each active agent required to provide a therapeutic effect to the subject, alone or in combination with one or more other active agents. Effective amounts, as recognized by those skilled in the art, vary depending on the particular condition being treated, the severity of the condition, individual patient parameters including age, physical condition, size, sex, and weight, duration of treatment, the nature of concomitant therapy (if any), the particular route of administration, and similar factors within the knowledge and expertise of the health care practitioner. These factors are well known to those skilled in the art and can be addressed with no more than routine experimentation. In general, it is preferred to use the maximum dose of the individual components or combinations thereof, i.e., the highest safe dose according to sound medical judgment. However, those skilled in the art will understand that a patient may insist on a lower dose or tolerated dose for medical reasons, psychological reasons, or virtually any other reason.

[0236] Empirical considerations such as the half-life of the therapeutic compound generally contribute to the determination of the dosage. For example, an antibody compatible with the human immune system, such as a humanized antibody or a fully human antibody, may be used to extend the half-life of the antibody and prevent the antibody from being attacked by the host's immune system. The frequency of administration may be determined and adjusted over the course of treatment, and is generally (but not necessarily) based on the treatment and / or suppression and / or suppression and / or delay of cancer. Alternatively, sustained release formulations of anti-cancer therapeutics may be appropriate. Various formulations and devices for achieving sustained release are known in the art.

[0237] In some embodiments, the dose of the anti-cancer therapeutic described herein may be empirically determined in individuals administered one or more doses of the anti-cancer therapeutic. Individuals may be administered increasing doses of the anti-cancer therapeutic. To assess the effectiveness of the administered anti-cancer therapeutic, one or more aspects of the cancer (e.g., tumor microenvironment, tumor formation, tumor growth, or GC TME type, etc.) may be analyzed.

[0238] In general, for administration of any of the anti-cancer antibodies described herein, the initial candidate dose may be about 2 mg / kg. For purposes of this disclosure, a typical daily dosage may range anywhere from 0.1 μg / kg to 3 μg / kg to 30 μg / kg to 300 μg / kg to 3 mg / kg, 30 mg / kg to 100 mg / kg or more, depending on the factors mentioned above. For repeated administration over a period of several days or more, depending on the condition, treatment is sustained until a desired suppression or amelioration of symptoms occurs, or until a therapeutic level sufficient to alleviate the cancer or one or more symptoms thereof is achieved. An exemplary dosing regimen comprises administering an initial dose of about 2 mg / kg of the antibody, followed by a weekly maintenance dose of about 1 mg / kg of the antibody, or a biweekly maintenance dose of about 1 mg / kg. However, other dosing regimens may be useful depending on the pattern of pharmacokinetic decay the practitioner (e.g., physician) wishes to achieve. For example, administration 1 to 4 times per week is envisioned. In some embodiments, a dosage ranging from about 3 μg / mg to about 2 mg / kg (such as about 3 μg / mg, about 10 μg / mg, about 30 μg / mg, about 100 μg / mg, about 300 μg / mg, about 1 mg / kg, and about 2 mg / kg) may be used. In some embodiments, the frequency of administration is once per week, once per 2 weeks, once per 4 weeks, once per 5 weeks, once per 6 weeks, once per 7 weeks, once per 8 weeks, once per 9 weeks, or once per 10 weeks; or once per month, once per 2 months, or once per 3 months, or more. The progress of this treatment may be monitored by conventional techniques and assays and / or by monitoring the GC TME types described herein. The dosing regimen (including the therapeutic agent used) may vary over time.

[0239] If the anti-cancer therapeutic is not an antibody, it may be administered at a rate of about 0.1-300 mg / kg of patient body weight in 1-3 divided doses, or as disclosed herein. In some embodiments, for a normal weight adult patient, a dose ranging from about 0.3-5.00 mg / kg may be administered. The particular dosing regimen, e.g., dosage, timing, and / or repetition, will depend on the particular subject and their individual medical history, as well as the characteristics of the individual agent, such as the half-life of the agent, and other considerations well known in the art.

[0240] For purposes of this disclosure, the appropriate dosage of an anti-cancer therapeutic will depend on the particular anti-cancer therapeutic (or composition thereof) employed, the type and severity of the cancer, whether the anti-cancer therapeutic is administered for prophylactic or therapeutic purposes, previous treatments, the patient's clinical history and response to the anti-cancer therapeutic, and the discretion of the attending physician. Typically, a clinician will administer an anti-cancer therapeutic, such as an antibody, until a dosage is reached that achieves the desired result.

[0241] Administration of the anti-cancer therapeutic agent may be continuous or intermittent, depending, for example, on the physiological condition of the recipient, whether the purpose of administration is therapeutic or prophylactic, and other factors known to those of skill in the art. Administration of the anti-cancer therapeutic agent (e.g., an anti-cancer antibody) may be essentially continuous over a preselected period of time, or may be in a series of spaced doses, for example, either before, during, or after the onset of cancer.

[0242] As used herein, the term "treating" refers to the application or administration of a composition containing one or more active agents to a subject having cancer, a symptom of cancer, or a predisposition to cancer, for the purpose of curing, ameliorating, alleviating, altering, relieving, improving, enhancing, or affecting one or more symptoms of cancer, GC, or a predisposition to GC.

[0243] Alleviating GC includes delaying the onset or progression of the disease or reducing disease severity. Alleviating the disease does not necessarily result in a cure. As used herein, "delaying" the onset of a disease (e.g., cancer) means to postpone, prevent, slow, retard, stabilize, and / or postpone the progression of the disease. This delay can be for a variety of times depending on the disease being treated and / or the medical history of the individual. A method of "delaying" or alleviating the onset of a disease, or a method of delaying the onset of a disease, is a method that reduces the probability of developing one or more symptoms of the disease within a given time period and / or reduces the severity of symptoms within a given time period compared to not using the method. Such comparisons are typically based on clinical studies using a sufficient number of subjects to obtain statistically significant results.

[0244] "Onset" or "progression" of a disease refers to initial symptoms and / or subsequent progression of the disease. Onset of a disease may be detected and assessed using clinical techniques known in the art. Alternatively, or in addition to clinical techniques known in the art, onset of a disease may be detectable and assessed based on other criteria. However, onset also refers to progression that may not be detected. For purposes of this disclosure, onset or progression refers to the biological course of a condition. "Onset" includes occurrence, recurrence, and onset. As used herein, "onset" or "recurrence" of cancer includes initial onset and / or recurrence.

[0245] Examples of antibody anticancer drugs include, but are not limited to, alemtuzumab (Campath), trastuzumab (Herceptin), ibritumomab tiuxetan (Zevalin), brentuximab vedotin (Adcetris), Ado-trastuzumab emtansine (Kadcyla), blinatumomab (Blincyto), bevacizumab (Avastin), cetuximab (Erbitux), ipilimumab (Yervoy), nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (Tecentriq), avelumab (Bavencio), durvalumab (Imfinzi), and panitumab (Vectibix).

[0246] Examples of immunotherapies include, but are not limited to, PD-1 or PD-L1 inhibitors, CTLA-4 inhibitors, adoptive cell transfer, therapeutic cancer vaccines, oncolytic virotherapy, T cell therapy, and immune checkpoint inhibitors.

[0247] Examples of radiation therapy include, but are not limited to, ionizing radiation, gamma radiation, neutron beam radiotherapy, electron beam radiotherapy, proton therapy, brachytherapy, systemic radioisotopes, and radiosensitizers.

[0248] Examples of surgical therapies include, but are not limited to, curative surgery (eg, tumor removal surgery), preventative surgery, laparoscopic surgery, and laser surgery.

[0249] Examples of chemotherapeutic agents include, but are not limited to, R-CHOP, carboplatin or cisplatin, docetaxel, gemcitabine, Nab-paclitaxel, paclitaxel, pemetrexed, and vinorelbine. Additional examples of chemotherapy include platinum agents such as carboplatin, oxaliplatin, cisplatin, nedaplatin, satraplatin, lobaplatin, triplatin, tetranitrate, picoplatin, prolindac, aroplatin, and other derivatives; topoisomerase I inhibitors such as camptothecin, topotecan, irinotecan / SN38, rubitecan, belotecan, and other derivatives; etoposide (VP-16), daunorubicin, doxorubicin agents (e.g., doxorubicin in liposomes); Topoisomerase II inhibitors such as rubicin, doxorubicin hydrochloride, doxorubicin analogs, or doxorubicin, and their salts or analogs, mitoxantrone, aclarubicin, epirubicin, idarubicin, amrubicin, amsacrine, pirarubicin, valrubicin, zorubicin, teniposide, and other derivatives; antimetabolites such as the folic acid family (methotrexate, pemetrexed, raltitrexed, aminopterin, and their analogs or derivatives); purine antagonists drugs (thioguanine, fludarabine, cladribine, 6-mercaptopurine, pentostatin, clofarabine, and their analogs or derivatives) and pyrimidine antagonists (cytarabine, floxuridine, azacitidine, tegafur, carmofur, capacitabine, gemcitabine, hydroxyurea, 5-fluorouracil (5FU), and their analogs or derivatives); nitrogen mustards (e.g., cyclophosphamide, melphalan, chlorambucil, Alkylating agents such as cyclosporine, mechlorethamine, ifosfamide, mechlorethamine, trofosfamide, prednimustine, bendamustine, uramustine, estramustine, and their analogs or derivatives; nitrosoureas (e.g., carmustine, lomustine, semustine, fotemustine, nimustine, ranimustine, streptozocin, and their analogs or derivatives); triazenes (e.g., dacarbazine, altretamine, temozolomide, and their analogs or derivatives);These include, but are not limited to, alkylsulfonates (e.g., busulfan, mannosulfan, treosulfan, and their analogs or derivatives); procarbazine; mitobronitol, and aziridines (e.g., carboquone, triaziquone, thiotepa, triethylenemalamine, and their analogs or derivatives); antibiotics such as hydroxyurea, anthracyclines (e.g., doxorubicin, daunorubicin, epirubicin, and their analogs or derivatives); anthracenediones (e.g., mitoxantrone and their analogs or derivatives); antibiotics of the Streptomyces family (e.g., bleomycin, mitomycin C, actinomycin, and plicamycin); and ultraviolet light;

[0250] In some aspects, the disclosure provides methods for treating gastric cancer comprising administering one or more therapeutic agents (e.g., one or more anti-cancer agents, such as one or more chemotherapeutic agents) to a subject identified as having a GC TME type, where the subject's GC TME type has been identified by a method described by the present disclosure.

[0251] In some embodiments, the subject is identified as having GC TME type A, GC TME type B, GC TME type C, GC TME type D, or GC TME type E. In some embodiments, the subject is identified as having GC TME type B or GC TME type E.

[0252] The present disclosure is based in part on the inventors' recognition that subjects with certain GC TME types may respond well to certain immunotherapies (e.g., immune checkpoint inhibitors such as pembrolizumab). In some embodiments, if the subject is identified as having GC TME type E or GC TME type B, the therapeutic agent comprises pembrolizumab. Dosages for pembrolizumab are well known, for example, 200 mg every 3 weeks or 400 mg every 6 weeks infused over 30 minutes.

[0253] Aspects of the present disclosure are based on the inventors' recognition that subjects with certain GC TME types are less likely to respond well to certain GC therapies, such as immunotherapeutic agents. Thus, in some embodiments, when a subject is identified as having GC TME type C or GC TME type A, the therapeutic agent comprises a therapeutic agent other than an immunotherapy. Examples of other gastric cancer treatments include, but are not limited to, Cyramza (ramucirumab), docetaxel, doxorubicin hydrochloride, Enhertu (Fam-trastuzumab deruxtecan-nxki), 5-FU (fluorouracil injection), Fam-trastuzumab deruxtecan-nxki, fluorouracil injection, Herceptin (trastuzumab), Keytruda (pembrolizumab), Lonsurf (trifluridine and tipiracil hydrochloride), mitomycin, ramucirumab, Taxotere (docetaxel), trastuzumab, and trifluridine and tipiracil hydrochloride.

[0254] Reports In some embodiments, the methods disclosed herein comprise generating a report to aid in the preparation of prognostic and / or treatment recommendations. The generated report provides a summary of information so that a clinician can identify the type of GC TME or appropriate treatment. The reports described herein may be paper reports, electronic records, or any format of report deemed appropriate in the art. The reports may be displayed and / or stored on a computing device known in the art (e.g., a handheld device, a desktop computer, a smart device, a website, etc.). The reports may be displayed and / or stored on an appropriate device, as would be understood by one of ordinary skill in the art.

[0255] In some embodiments, the methods disclosed herein may be used for commercial diagnostic purposes. For example, the generated report may include, but is limited to: information regarding the expression level of one or more genes from any of the gene groups described herein, clinical and pathological factors, patient prognostic analysis, predicted response to treatment, classification of the GC TME environment (e.g., belonging to one of the types described herein), alternative treatment recommendations, and / or other information. In some embodiments, the methods and reports may include database management for maintaining the generated report. For example, the methods disclosed herein may create a record of the subject (e.g., subject 1, subject 2, etc.) in a database and enter the subject's data in a particular record. In some embodiments, the generated report may be provided to the subject and / or clinician. In some embodiments, a network connection may be established to a server computer that includes the data and report for receiving or outputting. In some embodiments, receipt and output of the date or report may be requested from the server computer.

[0256] Computer implementation An exemplary implementation of a computer system 1300 that may be used in connection with any of the embodiments of the technology described herein (e.g., the method of FIG. 1, etc.) is shown in FIG. 13. The computer system 1300 includes one or more processors 1310 and one or more articles of manufacture that include non-transitory computer-readable storage media (e.g., memory 1320 and one or more non-volatile storage media 1330). The processor 1310 may control the writing and reading of data to and from the memory 1320 and non-volatile storage 1330 in any suitable manner, as aspects of the technology described herein are not limited to a particular technology for writing or reading data. To perform any of the functions described herein, the processor 1310 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., memory 1320), which may function as a non-transitory computer-readable storage medium that stores processor-executable instructions for execution by the processor 1310.

[0257] Computing device 1300 may also include a network input / output (I / O) interface 1340, through which the computing device may communicate with other computing devices (e.g., over a network), and may also include one or more user I / O interfaces 1350, through which the computing device may provide output to a user and receive input from a user. The user I / O interfaces may include devices such as a keyboard, a mouse, a microphone, a display device (e.g., a monitor or touch screen), a speaker, a camera, and / or various other I / O devices.

[0258] The above embodiments may be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software, or a combination thereof. If implemented in software, the software code may be executed on any suitable processor (e.g., a microprocessor) or collection of processors, whether provided in a single computing device or distributed across multiple computing devices. It should be understood that any component or collection of components performing the functions described above may be generally thought of as one or more controllers that control the functions described above. The one or more controllers may be implemented in numerous ways, such as dedicated hardware or general-purpose hardware (e.g., one or more processors) programmed with microcode or software.

[0259] In this regard, it should be understood that one implementation of the embodiments described herein comprises at least one computer-readable storage medium (e.g., RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, other tangible non-transitory computer-readable storage medium) encoded with a computer program (i.e., executable instructions) that, when executed on one or more processors, performs the above-described functions of one or more embodiments. The computer-readable medium may be portable such that the program stored therein may be loaded into any computing device to implement aspects of the technology discussed herein. Furthermore, it should be understood that reference to a computer program that, when executed, performs any of the above-described functions is not limited to an application program running on a host computer. Rather, the terms computer program and software are used in a general sense herein to refer to any type of computer code (e.g., application software, firmware, microcode, or any other form of computer instructions) that may be employed to program one or more processors and implement aspects of the technology discussed herein.

[0260] The foregoing description of implementations provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise forms disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the implementations. In other implementations, the methods depicted in these figures may include fewer operations, different operations, operations in a different order, and / or additional operations. Additionally, non-dependent blocks may be performed in parallel.

[0261] As described above, it will be apparent that the exemplary aspects may be implemented in many different forms of software, firmware, and hardware in the implementations shown in the figures. Furthermore, certain portions of the implementation may be implemented as a "module" that performs one or more functions. The module may include hardware, such as a processor, an application specific integrated circuit (ASIC), or a field programmable gate array (FPGA), or a combination of hardware and software. EXAMPLES

[0262] Example 1: Identification of the gastric cancer tumor microenvironment (TME) This example describes an exemplary technique for generating a subject's GC TME signature from the subject's RNA expression data, according to some embodiments of the techniques described herein. The generated GC TME signature reflects and / or indicates the abundance of both malignant and microenvironment (TME) cell subpopulations, and the activity of tumor-promoting and tumor-suppressing processes occurring within the tumor, constituting a personalized tumor map.

[0263] The generated GC TME signature of the subject is used to identify the subject's GC TME type from among five GC TME types: GC TME type A, GC TME type B, GC TME type C, GC TME type D, and GC TME type E.

[0264] Some aspects of the process steps described in this example are described in further detail herein with reference to Figures 1-4 above.

[0265] RNA expression data (including both RNA-seq and microarray expression data) were obtained from multiple public databases. Data were subject to basic quality control (QC) measures. For example, outlier samples and samples with signs of RNA degradation were excluded. Preprocessing of expression data included normalization and log transformation. Microarray normalization was performed automatically using the gcrma package. RNA-seq data were subsequently normalized to TPM (transcripts per million). TPM normalization techniques are described in Wagner et al. (Theory Biosci. (2012) 131:281-285), the contents of which are incorporated herein by reference in their entirety. TPM normalization may be performed using software packages such as, for example, the gcrma package. Aspects of the gcrma package are described in Wu J, Gentry RIwcfJMJ (2021). "gcrma: Background Adjustment Using Sequence Information. R package version 2.66.0," which is incorporated by reference in its entirety. In some embodiments, the RNA expression level in TPM of a particular gene may be calculated according to the following:

number

[0266] The GC TME type determined for a subject is determined using a GC TME signature. The GC TME signature includes a gene group score (e.g., one or more of the gene groups listed in Table 1) obtained using ssGSEA. The gene group score of the GC TME signature was calculated from the log-transformed RNA expression value (valued). ssGSEA was performed according to the following formula:

number

[0267] After calculation, the scores were scaled using rank estimation and median scaling, which was important to remove undesirable batch effects and to be able to combine all the datasets. Median scaling estimates the median and MAD (median absolute deviation) of each signature in each dataset, using the formula x i -Constructed by applying median(x) / MAD(x).

[0268] After calculating GC TME signatures for multiple patients as described above, unsupervised clustering was performed to generate GC TME clusters. To classify new samples, they are grouped together with the dataset used to obtain GC TME types. A score is calculated for the sample and scaled with the selected cohort. Then, a machine learning model (e.g., k-nearest neighbors, "knn") trained on the scaled meta-cohort can be applied to predict sample type.

[0269] In this example, the similarity between samples was calculated using Pearson correlation. The distance matrix was converted into a graph where each sample formed a node and two nodes formed an edge with a weight equal to the Pearson correlation coefficient. Edges with a weight lower than a specified threshold were removed. The Louvain community detection algorithm was applied to calculate the partitioning of the graph into clusters. The minimum DaviesBouldin, maximum Calinski-Harabasz, and Silhouette techniques were used to mathematically determine the optimal weight threshold for the observed clusters. The separation of low population clusters (less than 5% of samples) was excluded. This analysis resulted in the generation of five GC TME signature clusters corresponding to the five GC TME types.

[0270] Example of characterization of GC TME type Using the aforementioned approach on several publicly available cancer datasets, five distinct types of GCs were observed ( Figure 5A ).

[0271] To identify GC TME types, a meta-cohort of gene expression data was collected from public datasets. A GC TME signature, consisting of 20 gene group scores, was used to infer the different biological processes of each sample. The genes included in each gene group are listed in Table 1. To overcome batch effects from different datasets, rank estimation and median scaling transformation were used before clustering, which identified five GC TME types. An example of the process for identifying GC TME types is described in Figure 1. Using unsupervised clustering, five stable GC TME types were identified (Figure 5A): immune-enriched / non-fibrotic (type B), exhausted (type D), mesenchymal / EMT (type A), fibrotic (type C), and "B-cell enriched" (type E).

[0272] Figure 5B provides a heat map of pairwise signature correlations of gene groups listed in Table 1. The data shows that gene group scores for NK cell group, T cell group, B cell group, Treg group, MDSC group, CAF group, proliferation rate group, and Lgr5 ISC group can facilitate typing of GC TME of a subject. Thus, in some embodiments, GC TME signature can include gene group scores for these gene groups. However, in other embodiments, one or more gene scores for one or more other gene groups can be used in addition to or instead of these gene group scores.

[0273] GC TME type E is characterized by: low or moderate stromal / angiogenic components (e.g., lower than GC TME types A and C), moderate or high immune components (e.g., similar to GC TME type B), highest B cell signaling (e.g., among all GC TME types), low or moderate neoplastic features (e.g., low proliferation rate and expression levels of oncogenic gene signatures, higher levels of tumor suppressor signatures compared to other GC TME types).

[0274] In some embodiments, GC TME type is prognostic of patient outcome. Analysis between GC TME types was performed and compared with TCGA histological data. Figure 6 provides a comparison of selected process signatures across GC TME types. The data shows that GC TME types A and C have the highest angiogenesis and stromal (CAF = cancer associated fibroblast) signals, while GC TME type E shows the highest B cell signal, as well as T cell signals. Figure 7 provides an exemplary heat map of gastric cancer TME type gene group signatures and cell deconvolution across samples.

[0275] In RNA-seq samples, the high B cell content was also supported by a cellular deconvolution algorithm-based analysis. This algorithm allows to reconstruct the cellular composition from bulk RNA-seq data and estimate the percentage of different cell types (fibroblasts, B cells, T cells, macrophages, etc.). GC TME type E samples demonstrated the highest percentage of B cells (Figure 5A). The median and cumulative distribution function (CDF) of each signature in each GC TME type are provided in Tables 8 and 9.

[0276] [Table 8]

[0277] [Table 9]

[0278] [Table 10]

[0279] Using TCGA histology data, we compared immune infiltration and stromal compartments in samples belonging to different GC TME types (Figure 8A). GC TME type A and C samples had high stromal content. GC TME type D samples had desert immunity, low fibroblast composition, and high tumor cellularity. Conversely, GC TME type B and E samples showed high immune infiltration. Of note, GC TME type E samples also showed the presence of germinative centers / tertiary lymphoid structures (TLS), i.e., ectopic lymphoid organs occurring in non-lymphoid tissues (indicated by arrows in Figure 8A).

[0280] 8B-8F graphically illustrate the relative cell type content of different GC TME types.

[0281] FIG. 8B shows the relative cell type content of GC TME type A, characterized by WNT activation, and the distribution rate of LGR5+ stem cells; type A was also associated with a low tumor growth rate. Cancer-associated fibroblasts were frequently observed. There was evidence of epithelial-mesenchymal transition (EMT). Subjects with GC TME type A were observed to have a poor prognosis.

[0282] Figure 8C displays the relative cell type content of GC TME type B, characterized by high levels of tumor-infiltrating immune cells, including cytotoxic effector cells. This type has the highest tumor mutation burden (TMB). High PD-L1 expression is commonly observed. This GC TME type is often responsive to immunotherapy and is associated with a good prognosis.

[0283] FIG. 8D displays the relative cell type content of GC TME type C, which is associated with high fibrosis and dense collagen formation. This type is also characterized by minimal lymphocytic infiltration (non-inflammatory) with intense angiogenesis. Cancer-associated fibroblasts (CAFs) are abundant. High levels of both tumor-promoting and anti-tumor cytokines are observed. This GC TME type is associated with poor prognosis.

[0284] Figure 8E displays the relative cell type content of GC TME type D, characterized by minimal or complete absence of leukocyte / lymphocyte infiltration but the highest percentage of malignant cells. High tumor growth rates are observed. This GC TME type is associated with a good prognosis.

[0285] Figure 8F displays the relative cell type content of GC TME type E, characterized by the presence of tertiary lymphoid structures (TLS) and high levels of immune infiltration with significant numbers of B cells. A low tumor growth rate is observed. This GC TME type is associated with a good prognosis.

[0286] Specific GC TME types were observed to be associated with malignant properties: mesenchymal A type constituted the majority of metastatic samples studied and was characterized by the highest signal of the LGR5+ stem cell signature.

[0287] TLS have been observed in various cancers, such as colorectal cancer, lung cancer, renal clear cell carcinoma, sarcoma, and urothelial bladder cancer. The presence of TLS may provide additional opportunities in treatment decision-making. For example, it has been observed that TLS-positive samples in primary gastrointestinal stromal tumors showed better postoperative outcomes. Analysis of overall survival (OS) and progression-free survival (PFS) revealed a better prognosis for GC TME type E compared with stromal-enriched and WNT-activated GC TME types A and C (Figure 9). Samples with GC TME types B, E, and D had the highest overall survival rates.

[0288] Example 2: Additional Embodiments Association of TME type with EBV, MSI, and TMB status GC is classified histologically into intestinal, diffuse, and mixed types, and into four molecular types based on genetic profiling (i.e., microsatellite unstable (MSI), EBV positive, chromosomally unstable, and genomic stable). Here, samples with various statuses of EBV, MSI, and tumor mutation burden (TMB) across GC TME types were examined. Figure 10 shows a comparison of MSS and MSI samples in different GC TME types. As shown in Figure 10, most MSI samples were located in GC TME type B (i.e., immune enriched), indicating that subjects with GC TME type B may have a higher chance of responding to treatments such as immunotherapy.

[0289] Figures 11A-11B show the mutational burden across GC TME types in the TCGA cohort. As shown in Figure 11A, the immune-enriched type has the highest mutational burden (ML) compared to all other GC TME types. However, in the ACGR cohort, the highest malignant cell content (i.e., purity or cellularity) and ploidy were observed in GC TME type D, an immune-depleted type (Figure 11B, left). Additional studies were performed to examine the EBV status of the TCGA cohort across all GC TME types. Figure 12 shows a strong relationship between EBV-positive status (i.e., "1.0") and GC TME types B and E (immune-enriched and B-cell enriched, respectively). The overall results confirmed that subjects with GC TME types B and E were determined to be more likely to have a good prognosis and / or progression-free survival (PFS).

[0290] Example 3: Th17 and γδ T cell signatures for GC typing After identifying the five GC TME types, additional gene set scores were evaluated, including gene set scores for Th17 (T helper 17) and γδ T cells, as well as gene set scores for tumor suppressors, oncogenes, MYC targets, Th2, and Th1. Additional gene sets used to generate additional GC TME signatures are listed in Table 2.

[0291] Comparative analysis revealed that γδ T cell signals were high in immune-enriched GC TME types (B and E) and low in immune desert subtypes. Th17 was high in all three types, while mesenchymal, WNT-activated subtype A had the lowest signal (Figure 14). The combination of novel Th17 and γδ T cell signatures with GC TME types of the TCGA cohort is shown in Figure 15. The dynamic changes of GC TME types may correlate with the response to chemotherapy.

[0292] Example 4: Immunotherapy Immunotherapy for gastric cancer has shown promising results, with Epstein-Barr virus (EBV) positive or microsatellite unstable / instability (MSI) samples (or samples with high mutational burden) showing better responses than other cancer types. GC typing (especially types E and B) may identify responders to immunotherapy among a large population of patients with traditional biomarkers associated with adverse reactions to immunotherapy (e.g., microsatellite stable (MSS) and EBV negativity).

[0293] Analysis of the TCGA cohort revealed that the majority of EBV-positive samples belonged to types E and B; most of the MSI samples were also located in type B (Figure 16). Indeed, 54 samples from a total of 69 type B samples in the TCGA cohort were EBV-positive or MSI with high mutational load. 15 samples were EBV-negative and microsatellite stable (MSS), yet these samples may be better responders to immunotherapy. In type E, more than half of the samples were EBV-negative and MSS (Figure 16).

[0294] Using public datasets, the response to immunotherapy (2nd and 3rd line pembrolizumab) in metastatic gastric cancer was investigated across different GC TME types, taking into account EBV and MSS status. It was observed that patients of types B and E had higher disease control rates (DCR), 9 / 9 for type B and 3 / 4 for type E, while other subtypes had lower DCR, 3 / 6 for type A, 4 / 11 for type C, and 1 / 4 for type D. Next, all patients with known markers for immunotherapy treatment, MSI samples and EBV positive samples were excluded. Interestingly, types B and E still contained a higher percentage of responders (Figure 17). All metrics were calculated for the various groups and are presented in Table 10.

[0295] [Table 11]

[0296] According to the data in Figures 16 and 17 and Table 10, GC TME typing is a predictive parameter independent of MSI / MSS and EBV status. Although MSI and EBV high patients are consistent responders to immunotherapy (Table 10), a significant proportion of MSS and EBV low patients may still show better response rates if they belong to E and B subtypes (Figure 17). As the majority of type E patients from the TCGA cohort were MSS and EBV negative (Figure 16), GC typing may allow coverage of this large group of patients that previously escaped the responder category. Type E and type B metastatic gastric cancer patients had higher disease control rates (DCR) to immunotherapy (second- or third-line pembrolizumab) regardless of MSI and EBV status.

[0297] All five described subtypes were found in the Cabazi cohort (Figure 18). In all subtypes, the distribution of dissemination from stomach, GEJ and esophagus is similar. This cohort comprises samples from various biopsy sites, including metastatic samples. Interestingly, the most metastatic samples (5 / 9) were identified as mesenchymal, WNT-activated (type A). Moreover, these five type A metastatic samples had the highest signal for the LGR5+ stem cell signature, thus indicating the highest tumor stemness. Type E and type D metastatic samples were also observed.

[0298] For nine samples from the Cabazi cohort, both pre- and post-treatment biopsies were sequenced to identify GC TME type and analyze the impact of treatment on type switching. Two of these samples retained their initial GC TME type after treatment (both samples were immune-desert D type). The other seven samples showed various GC TME type switching. Interestingly, only one partial responder to cabazitaxel in this group changed fibrotic type C to immune-enriched type B (Figure 19).

[0299]

Table 12

[0300]

Table 13

[0301]

Table 14

[0302]

Table 15

[0303]

Table 16

[0304]

Table 17

[0305]

Table 18

[0306]

Table 19

[0307]

Table 20

[0308]

Table 21

[0309] [Table 22]

[0310] [Table 23]

[0311] [Table 24]

[0312] Equivalent Thus, while several aspects and embodiments of the technology described in this disclosure have been described, it will be understood that various changes, modifications, and improvements will readily occur to those skilled in the art. Such changes, modifications, and improvements are intended to be within the spirit and scope of the technology described herein. For example, those skilled in the art will readily envision a variety of alternative means and / or structures for performing the functions and / or obtaining the results and / or one or more of the advantages described herein, and each of such variations and / or modifications is considered to be within the scope of the embodiments described herein. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation many equivalents to the specific embodiments described herein. Thus, the foregoing embodiments are presented by way of example only, and it should be understood that within the scope of the appended claims and their equivalents, the embodiments of the invention may be practiced otherwise than as specifically described. Furthermore, where features, systems, articles, kits, and / or methods described herein are not inconsistent, any combination of two or more of such features, systems, articles, kits, and / or methods is within the scope of the present disclosure.

[0313] The above embodiments may be implemented in any of numerous ways. One or more aspects and embodiments of the present disclosure involving the performance of a process or method may utilize program instructions executable by a device (e.g., a computer, processor, or other device) to perform or control the performance of the process or method. In this regard, various inventive concepts may be embodied as a computer-readable storage medium (or multiple computer-readable storage media) (e.g., computer memory, one or more floppy disks, compact disks, optical disks, magnetic tapes, flash memories, circuitry of field programmable gate arrays or other semiconductor devices, or other tangible computer storage media) encoded with one or more programs that, when executed on one or more computers or other processors, perform a method for performing one or more of the various embodiments described above. The computer-readable medium or readable media may be portable such that a program stored thereon may be loaded into one or more different computers or other processors to implement various of the above-mentioned aspects. In some embodiments, the computer-readable medium may be a non-transitory medium.

[0314] The terms "program" or "software" are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be used to program a computer or other processor to implement various aspects as described above. Furthermore, according to one aspect, one or more computer programs, when executed, for performing methods of the present disclosure need not reside on a single computer or processor, but rather may be distributed in a modular manner among several different computers or processors to implement various aspects of the present disclosure.

[0315] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0316] Additionally, the data structures may be stored in a computer-readable medium in any suitable format. For ease of explanation, the data structures may be depicted as having fields that are related through their locations within the data structure. Such relationships may similarly be achieved by assigning storage for the fields locations within the computer-readable medium that convey the relationship between the fields. However, relationships between information within fields of the data structures may be established using any suitable mechanism, including the use of pointers, tags, or other mechanisms that establish relationships between data elements.

[0317] If implemented in software, the software code may be executed on any suitable processor or collection of processors, whether provided on a single computer or distributed across multiple computers.

[0318] Further, it should be understood that a computer may be embodied in any of a number of forms, such as, by way of non-limiting examples, a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be incorporated into devices not generally considered to be computers, but which have suitable processing capabilities, including personal digital assistants (PDAs), smartphones, tablets, or other suitable portable or fixed electronic devices.

[0319] A computer may also have one or more input / output devices. These devices may be used, among other things, to display a user interface. Examples of output devices that may be used to provide a user interface include a printer or display screen for visually presenting output, and a speaker or other sound generating device for audibly presenting output. Examples of input devices that may be used for a user interface include keyboards and pointing devices such as mice, touchpads, and digitizing tablets. As another example, a computer may receive input information via voice recognition or other audible form.

[0320] Such computers may be interconnected by one or more networks of any suitable type, including local area networks, or wide area networks such as enterprise networks, and intelligent networks (IN) or the Internet. Such networks may be based on any suitable technology and operate according to any suitable protocol, and may include wireless networks, wired networks, or fiber optic networks.

[0321] Also, as described, some aspects may be embodied as one or more methods. The acts performed as part of a method may be ordered in any suitable manner. Thus, while an example embodiment shows acts as sequential, embodiments may be constructed in which acts are performed in an order different from that shown, which may include performing some acts simultaneously.

[0322] All definitions, as defined and used herein, should be understood to supersede any dictionary definition, definitions in documents incorporated by reference, and / or ordinary meaning of the defined term.

[0323] The indefinite articles "a" and "an," as used in the specification and claims, unless expressly stated to the contrary, should be understood to mean "at least one."

[0324] The term "and / or" as used herein and in the claims should be understood to mean "either or both" of the elements so conjoined, i.e., elements that are conjunctive in some cases and disjunctive in other cases. Multiple elements listed with "and / or" should be interpreted in the same manner, i.e., "one or more" of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the "and / or" clause, whether or not associated with the specifically identified elements. Thus, as a non-limiting example, when used in conjunction with open-ended language such as "comprising," "A and / or B" may refer in one embodiment to only A (optionally including elements other than B); in another embodiment to only B (optionally including elements other than A); in yet another embodiment to both A and B (optionally including other elements), etc.

[0325] As used in this specification and claims, the phrase "at least one" referring to a list of one or more elements is understood to mean at least one element selected from any one or more of the elements in the list of elements, but does not necessarily include at least one of each and every element specifically listed in the list of elements, and does not exclude any combinations of elements in the list of elements. This definition also allows for the optional presence of elements other than those specifically identified in the list of elements to which the phrase "at least one" refers, whether or not they are related to the identified elements. Thus, as a non-limiting example, "at least one of A and B" (or, equivalently, "at least one of A or B" or, equivalently, "at least one of A and / or B") can refer, in one embodiment, to at least one, optionally more than one, A, in which B is absent (and optionally including elements other than B); in another embodiment, to at least one, optionally more than one, B, in which A is absent (and optionally including elements other than A); in yet another embodiment, to at least one, optionally more than one, A, and at least one, optionally more than one, B (and optionally including other elements), etc.

[0326] In the claims, as well as in the above specification, all transitional phrases such as "comprising," "including," "holding," "having," "containing," "involving," "containing," "consisting of," and the like, are understood to be open-ended, i.e., meaning including but not limited to. Only the transitional phrases "consisting of" and "consisting essentially of" must be closed or semi-closed transitional phrases, respectively.

[0327] The terms "approximately," "substantially," and "about" may be used in some embodiments to mean within ±20% of a target value, in some embodiments within ±10% of a target value, in some embodiments within ±5% of a target value, and in some embodiments within ±2% of a target value. The terms "approximately," "substantially," and "about" may include the target value.

Claims

1. Using at least one computer hardware processor, (a) obtaining RNA expression data of a subject; (b) generating a GC TME signature for the subject using the RNA expression data; (c) using the GC TME signature to identify a GC TME type of the subject from among a plurality of GC TME types; (d) using the subject's GC TME type to identify whether the subject is likely to respond to immunotherapy; 1. A method for identifying whether a subject having, suspected of having, or at risk of having gastric cancer is likely to respond to said immunotherapy based at least in part on a gastric cancer (GC) tumor microenvironment (TME) type of the subject, comprising: The RNA expression data is shown in Table 1 below. Table 1: 【Table 1】 and wherein the RNA expression levels of at least some genes in each group of at least some of the gene groups listed in the GC TME signature comprises a gene group score for each gene group in at least some of the plurality of gene groups; A computer-implemented method, wherein said generating step comprises determining said gene group score using said RNA expression levels.

2. The RNA expression data are shown in Table 2 below. Table 2: 【Table 2】 and further indicating a second RNA expression level of at least some genes in each of at least some of the second plurality of gene groups listed in the GC TME signature further comprises a second gene group score for each gene group of at least some of the second plurality of gene groups; 2. The method of claim 1, wherein said generating step further comprises determining said second gene group score using said second RNA expression level.

3. The RNA expression data are shown in Table 2 below. Table 2 【Table 3】 2. The method of claim 1, further comprising: (e) generating a second GC TME signature for the subject using the RNA expression data.

2. The method of claim 1, further comprising: wherein the second GC TME signature comprises a second gene group score for each gene group in at least some of the second plurality of gene groups; and wherein the generating step comprises determining the second gene group score using the second RNA expression level.

4. 4. The method of any one of claims 1 to 3, wherein obtaining RNA expression data of the subject comprises obtaining sequencing data previously obtained by sequencing a biological sample obtained from the subject.

5. 5. The method of claim 4, wherein the sequencing data comprises at least 1 million reads, at least 5 million reads, at least 10 million reads, at least 20 million reads, at least 50 million reads, or at least 100 million reads.

6. 6. The method of claim 4 or 5, wherein the sequencing data comprises whole exome sequencing (WES) data, bulk RNA sequencing (RNA-seq) data, single cell RNA sequencing (scRNA-seq) data, or next generation sequencing (NGS) data.

7. The method of claim 4 or 5, wherein the sequencing data comprises microarray data.

8. Normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the GC TME signature. The method according to any one of claims 1 to 7, further comprising:

9. The method of any one of claims 1 to 8, wherein obtaining RNA expression data of the subject comprises sequencing a biological sample obtained from the subject.

10. 10. The method of claim 9, wherein the biological sample comprises gastrointestinal tissue of the subject, optionally wherein the biological sample comprises tumor tissue of the subject.

11. The RNA expression level (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRα, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MK167, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 11. The method of claim 1, comprising the RNA expression levels of at least three genes from each of at least two of said gene groups.

12. The RNA expression level (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1 A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11 A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (vii) proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 12. The method of claim 1, comprising the RNA expression level of each gene from each of the gene groups.

13. The RNA expression level (a) MHC group I: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b)MHC Group II: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, H LA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) Co-activated molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFRSF4, ICOSLG, TNFRSF9, CD70; (d) エフェクターcell group: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell transport group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD244, N CR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZM B, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell population: TBX21, ITC, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell population: CD19, MS4A1, TNFRF13C, CR2, TNFRF17, TNFRF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, and STAPL1; (i) M1シグネチャgroup: NOS2, TNF, IL1B, SOCS3 , CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) Anti-swelling drug group: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k)チェックポイント blocking group: PDCD1, CD274, CTLA4, LA G3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l)Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (m) Good hitting team: MPO, ELLANE, PRTN3, CTSG, CX CR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2シグネチャgroup: IL10, MRC1, MSR1, CD 163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1 A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11 A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (s)Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 13. The method of claim 1, comprising the RNA expression levels of at least three genes from each of at least two of the gene groups.

14. the determining the gene group score step comprises, for a particular gene group, determining a gene group score for the particular gene group using RNA expression levels of at least three genes within the particular gene group; (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1 A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11 A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (vii) proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 14. The method of any one of claims 1 to 13, comprising determining a respective gene group score for each of at least two of the gene groups, including:

15. the step of determining a gene group score comprises, for each gene group, determining a gene group score for each particular group using each RNA expression level of the genes in each gene group; (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1 A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11 A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (vii) proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 The method of any one of claims 1 to 14, comprising determining a respective gene group score for each of the gene groups, including

16. The step of determining the gene group score comprises using a single sample GSEA (ssGSEA) technique, (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRc2, KLRK1, CD226, GZMH, GNLY, IFNγ, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRa, FN1, COL1A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (vii) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 16. The method of claim 15, comprising determining a first score for a first set of genes from RNA expression levels for at least a portion of the genes in one of the sets of genes.

17. The step of determining the gene group score comprises using a single sample GSEA (ssGSEA) technique, (i) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (ii) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (iii) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (iv) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (v) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (vi) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1 A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11 A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (vii) proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (viii) Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 17. The method of claim 1, comprising determining a gene group score from the RNA expression level for each gene in each of the gene groups.

18. the determining the gene group score step comprises, for a particular gene group, determining a gene group score for the particular gene group using RNA expression levels of at least three genes within the particular gene group; (a) MHC group I: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) Coactivation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) effector cell group: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell transport group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (i) M1 signature group: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) antitumor cytokines: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibitor group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (m) Good hitting team: MPO, ELLANE, PRTN3, CTSG, CX CR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2シグネチャgroup: IL10, MRC1, MSR1, CD 163, CSF1R, IL4I1, SIGLEC1, CD68; (p) がんAssociated fibroblast cell (CAF) group: LGALS1, COL1A1, COL1A 2. COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, C OL6A2、COL6A3、COL11A1、CXCL12、FBLN1、LUM、 MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1 A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGA LS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11 A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP1 2. MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) angiogenesis group: VEGFA、VEGFB、VEGFC、PDGFC、CXCL8、CXCR2、FLT1、PGF、KDR、ANGPT1、ANGPT2、TEK、VWF、CDH5; (r) Growth rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, A URKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (s)Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 18. The method of any one of claims 1 to 17, comprising determining a respective gene group score for each of at least two of the gene groups, including:

19. The step of determining the gene group score comprises using a single sample GSEA (ssGSEA) technique, (a) MHC group I: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) Coactivation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) effector cell group: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell transport group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (i) M1 signature group: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) antitumor cytokines: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibitor group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (m) Neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1 A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11 A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (s)Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 20. The method of claim 18, comprising determining a gene group score from the RNA expression levels for at least a portion of the genes in each one of the gene groups.

20. The step of determining the gene group score comprises using a single sample GSEA (ssGSEA) technique, (a) MHC group I: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DRA, HLA-DRB1, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, CIITA; (c) Coactivation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) effector cell group: IFNG, GZMA, GZMB, PRF1, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B; (e) T cell transport group: CXCL9, CXCL10, CXCL11, CX3CL1, CCL3, CCL4, CX3CR1, CXCL16, CXCR6; (f) NK cell group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, EOMES, GZMB, FGFBP2, KLRF1, SH2D1B, NCR3; (g) T cell group: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, CD28, CD5, TRAT1; (h) B cell group: CD19, MS4A1, TNFRSF13C, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, STAP1; (i) M1 signature group: NOS2, TNF, IL1B, SOCS3, CMKLR1, IRF5, IL12A, IL12B, IL23A; (j) antitumor cytokines: TNF, IFNB1, IFNA2, CCL3, TNFSF10, IL21; (k) checkpoint inhibitor group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, TIGIT, VSIR; (l) Treg group: FOXP3, CTLA4, IL10, TNFRSF18, CCR8, IKZF4, IKZF2; (m) Neutrophil signature group: MPO, ELANE, PRTN3, CTSG, CXCR1, CXCR2, FCGR3B, CD177, FFAR2, PGLYRP1; (n) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (o) M2 signature group: IL10, MRC1, MSR1, CD163, CSF1R, IL4I1, SIGLEC1, CD68; (p) Cancer-associated fibroblast (CAF) group: LGALS1, COL1A1, COL1A2, COL5A1, ACTA2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, COL11A1, CXCL12, FBLN1, LUM, MFAP5, MMP3, MMP2, PDGFRB, PDGFRA, FN1, COL1 A1, COL1A2, COL4A1, COL3A1, VTN, LGALS7, LGALS9, LAMA3, LAMB3, LAMC2, TNC, COL5A1, COL11 A1, LGALS3, CA9, MMP9, MMP2, MMP1, MMP3, MMP12, MMP7, MMP11, PLOD2, ADAMTS4, ADAMTS5, LOX; (q) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; (r) proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; and (s)Lgr5 ISC group: ABTB2, AFAP1L1, APCDD1, ARHGEF4, ARNT2, AXIN2, BCL2, BEX1, BEX2, CAP2, CCDC46, CYP2E1, DGKG, DLGAP1, DTL, DYNC2H1, EPHA4, FAM64A, FGFR4, FMNL2, FSTL1, GRAMD1A, GRK4, IGF1R, IGFBP4, IL17RD, KIF12, KIF26B, KLHL13, LDHB, LGR5, LIFR, LOC28 5141, MDFIC, MPP3, NPNT, PITPNC1, PLP1, RASSF4, RNF157, SCN2B, SEPT6, SERTAD4, SLC1A2, SLC38A4, SLCO3A1, SLIT2, SOAT1, S ORBS2, SOX4, TACC1, TMEM182, TNFRSF19, UTRN, ZNF141, ZNF273 , ZNF493, ZNF626, ZNF678, ZNF680, ZNF714, ZNF85, ZNF92, ZNF93 20. The method of claim 1, comprising determining a gene group score from the RNA expression level for each gene in each of the gene groups.

21. 21. The method of any one of claims 1 to 20, wherein generating the GC TME signature further comprises normalizing the gene group scores, wherein the normalizing comprises applying rank estimation and / or median scaling to the gene group scores.

22. the plurality of GC TME types are associated with a respective plurality of GC TME signature clusters; identifying a GC TME type of the subject from among a plurality of GC TME types using the GC TME signature, associating the GC TME signature of the subject with a particular one of the plurality of GC TME signature clusters; identifying the GC TME type of the subject as a GC TME type corresponding to a particular one of the plurality of GC TME signature clusters with which the GC TME signature of the subject is associated; The method according to any one of claims 1 to 21, comprising:

23. obtaining a plurality of sets of RNA expression data by sequencing biological samples from a plurality of respective subjects; generating a plurality of GC TME signatures from the plurality of sets of RNA expression data; clustering the plurality of GC TME signatures to obtain the plurality of GC TME signature clusters; wherein each of the plurality of sets of RNA expression data indicates an RNA expression level of at least some genes in each of at least some gene groups of a plurality of gene groups listed in Table 1, and for a particular one of each of the plurality of GC TME signatures, each of the plurality of GC TME signatures comprises a gene group score for each gene group in the plurality of gene groups; the generating step comprises determining the GC TME signature by determining the gene group score using the RNA expression levels in the particular set of RNA expression data for which the particular GC TME signature was generated; The method of claim 22 , further comprising generating the plurality of GC TME signature clusters.

24. 24. The method of claim 23, wherein the clustering comprises agglomerative clustering, spectral clustering, k-means clustering, hierarchical clustering, and / or agglomerative clustering.

25. 25. The method of claim 24, wherein the hierarchical clustering is performed using the Louvain community detection algorithm.

26. 26. The method of claim 23, further comprising updating the plurality of GC TME signature clusters using a GC TME signature of the object, the GC TME signature of the object being one of a threshold number of GC TME signatures for a threshold number of objects, and the GC TME signature clusters being updated once the threshold number of GC TME signatures have been generated.

27. 27. The method of claim 26, wherein the threshold number of GC TME signatures is at least 50, at least 75, at least 100, at least 200, at least 500, at least 1000, or at least 5000 GC TME signatures.

28. 28. The method of claim 27, wherein the updating step comprises applying agglomerative clustering, spectral clustering, k-means clustering, hierarchical clustering, and / or agglomerative clustering.

29. 29. The method of claim 28, wherein the hierarchical clustering is performed using the Louvain community detection algorithm.

30. determining a GC TME type of a second subject, wherein the GC TME type of the second subject is identified using the updated GC TME signature cluster, the identifying step comprising: determining a GC TME signature of the second subject from RNA expression data obtained by sequencing a biological sample obtained from the second subject; associating the second target GC TME signature with a particular one of the updated GC TME signature clusters; identifying the second subject GC TME type as a GC TME type corresponding to a particular one of the plurality of updated GC TME signature clusters with which the second subject GC TME signature is associated; The method of any one of claims 23 to 29, further comprising:

31. 31. The method of any one of claims 1 to 30, wherein the plurality of GC TME types comprises GC TME type A, GC TME type B, GC TME type C, GC TME type D, and GC TME type E.

32. 32. The method of any one of claims 1 to 31, further comprising the step of: if the subject is identified as having GC TME type E, identifying the subject as having tertiary lymphoid structures (TLS).

33. 33. The method of any one of claims 1 to 32, further comprising the step of identifying said subject as likely to have a good prognosis, if said subject is identified as having GC TME type E, optionally as measured by overall survival (OS) or progression free survival (PFS).

34. the RNA expression levels of genes in the second plurality of genes are (i) Th17 signature group: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, CCR6; (ii) gamma delta (γΔ) T cell population: TRGC1, TRDC, TRDV2, TRGC2, TRDV1; (iii) tumor suppressor group: LATS1, LATS2, FBXW7, BAP1, KLF4, SASH1, CASP3, MLKL, SOCS1; (iv) Oncogenic genes: KRAS, AKT1, EGFR, PIK3CA, PIK3CB, MYC, VAV3, MET, CCND1, INPPL1, CHD1L, RACGAP1; (v) Myc target group: AIMP2, BYSL, CBX3, CDK4, DCTPP1, DDX18, DUSP2, EXOSC5, FARSA, GNL3, GRWD1, HK2, HSPD1, HSP E1, IMP4, IPO4, LAS1L, MAP3K6, MCM4, MCM5, MPHOSPH10, MRTO4, MYBBP1A, MYC, NDUFAF4, NIP7, NOC4L, NOLC 1, NOP16, NOP2, NOP56, NPM1, PA2G4, PES1, PHB, PLK1, PLK4, PPAN, PPRC1, PRMT3, PUS1, RABEPK, RCL1, RRP1 2, RRP9, SLC19A1, SLC29A2, SORD, SRM, SUPV3L1, TBRG4, TCOF1, TFB2M, TMEM97, UNG, UTP20, WDR43, WDR74; (vi) Th2 signature group: IL4, IL5, IL13, IL10, GATA3, CCR4; and (vii) Th1 signature group: IFNG, IL2, CD40LG, IL21, TBX21, STAT4, IL12RB2 34. The method of claim 3, comprising the RNA expression levels of at least three genes from each of at least two of the gene groups.

35. the RNA expression levels of genes in the second plurality of genes are (i) Th17 signature group: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, CCR6; (ii) gamma delta (γΔ) T cell population: TRGC1, TRDC, TRDV2, TRGC2, TRDV1; (iii) tumor suppressor group: LATS1, LATS2, FBXW7, BAP1, KLF4, SASH1, CASP3, MLKL, SOCS1; (iv) Oncogenic genes: KRAS, AKT1, EGFR, PIK3CA, PIK3CB, MYC, VAV3, MET, CCND1, INPPL1, CHD1L, RACGAP1; (v) Myc target group: AIMP2, BYSL, CBX3, CDK4, DCTPP1, DDX18, DUSP2, EXOSC5, FARSA, GNL3, GRWD1, HK2, HSPD1, HSP E1, IMP4, IPO4, LAS1L, MAP3K6, MCM4, MCM5, MPHOSPH10, MRTO4, MYBBP1A, MYC, NDUFAF4, NIP7, NOC4L, NOLC 1, NOP16, NOP2, NOP56, NPM1, PA2G4, PES1, PHB, PLK1, PLK4, PPAN, PPRC1, PRMT3, PUS1, RABEPK, RCL1, RRP1 2, RRP9, SLC19A1, SLC29A2, SORD, SRM, SUPV3L1, TBRG4, TCOF1, TFB2M, TMEM97, UNG, UTP20, WDR43, WDR74; (vi) Th2 signature group: IL4, IL5, IL13, IL10, GATA3, CCR4; and (vii) The method of claim 34, comprising the RNA expression levels of each gene from the following gene groups: Th1 signature group: IFNG, IL2, CD40LG, IL21, TBX21, STAT4, IL12RB2.

36. The step of determining the gene group score comprises using a single sample GSEA (ssGSEA) technique, (i) Th17 signature group: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, CCR6; (ii) gamma delta (γΔ) T cell population: TRGC1, TRDC, TRDV2, TRGC2, TRDV1; (iii) tumor suppressor group: LATS1, LATS2, FBXW7, BAP1, KLF4, SASH1, CASP3, MLKL, SOCS1; (iv) Oncogenic genes: KRAS, AKT1, EGFR, PIK3CA, PIK3CB, MYC, VAV3, MET, CCND1, INPPL1, CHD1L, RACGAP1; (v) Myc target group: AIMP2, BYSL, CBX3, CDK4, DCTPP1, DDX18, DUSP2, EXOSC5, FARSA, GNL3, GRWD1, HK2, HSPD1, HSP E1, IMP4, IPO4, LAS1L, MAP3K6, MCM4, MCM5, MPHOSPH10, MRTO4, MYBBP1A, MYC, NDUFAF4, NIP7, NOC4L, NOLC 1, NOP16, NOP2, NOP56, NPM1, PA2G4, PES1, PHB, PLK1, PLK4, PPAN, PPRC1, PRMT3, PUS1, RABEPK, RCL1, RRP1 2, RRP9, SLC19A1, SLC29A2, SORD, SRM, SUPV3L1, TBRG4, TCOF1, TFB2M, TMEM97, UNG, UTP20, WDR43, WDR74; (vi) Th2 signature group: IL4, IL5, IL13, IL10, GATA3, CCR4; and (vii) determining a first score for a first group of genes from a second RNA expression level of at least some of the genes in one of the following gene groups: Th1 signature group: IFNG, IL2, CD40LG, IL21, TBX21, STAT4, IL12RB2.

37. The step of determining the gene group score comprises using a single sample GSEA (ssGSEA) technique, (i) Th17 signature group: IL17F, IL17A, IL26, IL22, RORC, BATF, IL23R, CCR6; (ii) gamma delta (γΔ) T cell population: TRGC1, TRDC, TRDV2, TRGC2, TRDV1; (iii) tumor suppressor group: LATS1, LATS2, FBXW7, BAP1, KLF4, SASH1, CASP3, MLKL, SOCS1; (iv) Oncogenic genes: KRAS, AKT1, EGFR, PIK3CA, PIK3CB, MYC, VAV3, MET, CCND1, INPPL1, CHD1L, RACGAP1; (v) Myc target group: AIMP2, BYSL, CBX3, CDK4, DCTPP1, DDX18, DUSP2, EXOSC5, FARSA, GNL3, GRWD1, HK2, HSPD1, HSP E1, IMP4, IPO4, LAS1L, MAP3K6, MCM4, MCM5, MPHOSPH10, MRTO4, MYBBP1A, MYC, NDUFAF4, NIP7, NOC4L, NOLC 1, NOP16, NOP2, NOP56, NPM1, PA2G4, PES1, PHB, PLK1, PLK4, PPAN, PPRC1, PRMT3, PUS1, RABEPK, RCL1, RRP1 2, RRP9, SLC19A1, SLC29A2, SORD, SRM, SUPV3L1, TBRG4, TCOF1, TFB2M, TMEM97, UNG, UTP20, WDR43, WDR74; (vi) Th2 signature group: IL4, IL5, IL13, IL10, GATA3, CCR4; and (vii) Th1 signature group: IFNG, IL2, CD40LG, IL21, TBX21, STAT4, IL12RB2 37. The method of claim 34, comprising determining said gene group score from the RNA expression level of each gene in each one of said gene groups.

38. The method of any one of claims 34 to 37, wherein the identifying step of (c) is performed using a second GC TME signature.

39. The method of any one of claims 34 to 38, wherein the identifying step of (d) is performed using a second GC TME signature.

40. 40. The method of any one of claims 1 to 39, further comprising the step of identifying said subject as likely to respond to immunotherapy if said subject is assigned to GC TME type B.

41. 40. The method of any one of claims 1 to 39, further comprising the step of identifying said subject as likely to respond to immunotherapy if said subject is assigned to GC TME type E.

42. 42. The method of claim 40 or 41, wherein the subject is microsatellite stable (MSS).

43. The method of any one of claims 40 to 42, wherein the subject is Epstein-Barr Virus (EBV) low titer or EBV negative.

44. 44. The method of any one of claims 1 to 43, wherein the immunotherapy comprises a PD1 inhibitor, optionally pembrolizumab.

45. 1. A method for predicting a disease control rate (DCR) for an immunotherapy in a subject, the method comprising using at least one computer hardware processor: (i) identifying the GC TME of the subject using a method according to any one of claims 1 to 44; (ii) if the subject is assigned to GC TME type B or GC TME type E, identifying the subject as likely to have increased DCR compared to subjects with GC TME type A, GC TME type C, or GC TME type D; A computer-implemented method comprising the steps of:

46. 46. ​​The method of claim 45, wherein the immunotherapy comprises a PD1 inhibitor, optionally pembrolizumab.

47. 47. The method of claim 45 or 46, wherein the DCR in subjects with GC TME type B is greater than 50%.

48. 47. The method of claim 45 or 46, wherein the DCR in subjects with GC TME type E is greater than 50%.

49. The method of any one of claims 45 to 48, wherein the subject is microsatellite stable (MSS).

50. 50. The method of any one of claims 45 to 49, wherein the subject is Epstein-Barr Virus (EBV) low titer or EBV negative.

51. 51. The method of any one of claims 45 to 50, further comprising the step of preparing the subject for administration of immunotherapy.

52. At least one computer hardware processor; and At least one computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying whether a subject having, suspected of having, or at risk of having gastric cancer is likely to respond to immunotherapy based at least in part on a gastric cancer (GC) tumor microenvironment (TME) type of the subject. A system comprising: The method further comprising: (a) obtaining RNA expression data for said subject; (b) generating a GC TME signature for the subject using the RNA expression data; (c) using the GC TME signature to identify a GC TME type of the subject from among a plurality of GC TME types; (d) using the subject's GC TME type to identify whether the subject is likely to respond to the immunotherapy; comprising The RNA expression data is shown in Table 1 below. Table 1: 【Table 4】 and wherein the RNA expression levels of at least some genes in each group of at least some of the gene groups listed in the GC TME signature comprises a gene group score for each gene group in at least some of the plurality of gene groups; The generating step comprises determining the gene group score using the RNA expression levels.

53. 1. At least one computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying whether a subject having, suspected of having, or at risk of having gastric cancer is likely to respond to immunotherapy based at least in part on a gastric cancer (GC) tumor microenvironment (TME) type of the subject, the method comprising: (a) obtaining RNA expression data for said subject; (b) generating a GC TME signature for the subject using the RNA expression data; (c) using the GC TME signature to identify a GC TME type of the subject from among a plurality of GC TME types; (d) using the subject's GC TME type to identify whether the subject is likely to respond to immunotherapy; comprising The RNA expression data is shown in Table 1 below. Table 1: 【Table 5】 and wherein the RNA expression levels of at least some genes in each group of at least some of the gene groups listed in the GC TME signature comprises a gene group score for each gene group in at least some of the plurality of gene groups; The computer-readable storage medium, wherein said generating step comprises determining said gene group score using said RNA expression levels.

54. At least one computer hardware processor; and 1. A system comprising: at least one non-transitory computer readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for predicting a disease control rate (DCR) for an immunotherapy in a subject, the method comprising: The method further comprising: (i) identifying the GC TME of the subject using a method according to any one of claims 1 to 53; (ii) if the subject is assigned to GC TME type B or GC TME type E, identifying the subject as likely to have increased DCR compared to subjects with GC TME type A, GC TME type C, or GC TME type D; A system comprising:

55. At least one non-transitory computer readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for predicting a disease control rate (DCR) to an immunotherapy in a subject, the method comprising: (i) identifying the GC TME of the subject using a method according to any one of claims 1 to 54; (ii) if the subject is assigned to GC TME type B or GC TME type E, identifying the subject as likely to have increased DCR compared to subjects with GC TME type A, GC TME type C, or GC TME type D; A non-transitory computer-readable storage medium comprising:

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