Predicting response to treatment in patients with clear cell renal cell carcinoma

Abstract

Aspects of the present disclosure relate to methods, systems, computer readable storage media, and graphical user interfaces (GUIs) useful for characterizing subjects with certain cancers, such as, for example, clear cell renal cancer (ccRCC), such as renal cell carcinoma. The present disclosure is based in part on methods for determining a renal cancer (RC) tumor microenvironment (TME) type (RC TME type) of a renal cancer subject, and determining a subject's prognosis and / or the likelihood that the subject will respond to a particular therapy (e.g., immunotherapy or tyrosine kinase inhibitor) based on the determination of the renal cancer type.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 63 / 158,825, filed March 9, 2021, entitled “PREDICTING RESPONSE TO TREATMENTS IN PATIENTS WITH CLEAR CELL RENAL CARCINOMA,” 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 certain renal (kidney) cancers, such as clear cell renal cancer (ccRCC). The present disclosure is based in part on a method for identifying the tumor microenvironment (TME) of a subject with renal cancer (e.g., ccRCC) by generating a renal cancer (RC) tumor microenvironment (TME) signature (referred to as a RC TME signature) using gene expression data obtained from the subject, which when processed by the methods disclosed herein allows for the assignment of a RC TME type to the subject. In some embodiments, the subject's RC TME type indicates one or more characteristics of the subject (or the subject's cancer), such as, for example, the likelihood that the subject will have a good prognosis or will respond to a therapeutic agent, such as an immunotherapy (also referred to as an IO agent) or a tyrosine kinase inhibitor (TKI). Aspects of the present disclosure also relate to machine learning techniques for determining whether (e.g., the likelihood of) a subject having a good prognosis or responding to an IO agent or a TKI.

[0004] Thus, in some aspects, the disclosure provides a method for determining a renal cancer (RC) tumor microenvironment (TME) type of a subject having, suspected of having, or at risk of having renal cancer, the method comprising performing, using at least one computer hardware processor, the steps of obtaining RNA expression data for the subject; generating an RC TME signature for the subject using the RNA expression data; and identifying the RC TME type of the subject from among a plurality of RC TME types using the RC 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 RC 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] In some aspects, the disclosure provides a method for determining a renal cancer (RC) myogenesis signature of a subject having, suspected of having, or at risk of having renal cancer, the method comprising performing, using at least one computer hardware processor, the steps of obtaining RNA expression data for the subject; and generating a myogenesis signature for the subject using the RNA expression data, wherein the RNA expression data indicates RNA expression levels of at least some genes in the gene groups listed in Table 2, and the myogenesis signature consists of gene group scores for the gene groups listed in Table 2, and the gene group scores are determined using the RNA expression levels.

[0006] In some aspects, the disclosure provides a method for predicting the likelihood of a subject having, suspected of having, or at risk of having renal cancer to respond to an immuno-oncology (IO) agent, the method comprising: performing, using at least one computer hardware processor, the steps of: generating a set of input features using RNA expression data obtained from the subject; providing the set of input features as input to a machine learning model and obtaining a corresponding output indicative of a responder score; and identifying the subject as likely to respond to the IO agent if the responder score exceeds a specified threshold, wherein the set of input features comprises at least two of the following features: the subject's RC TME type; one or more RNA expression levels of genes PD1, PD-L1, and PD-L2; the subject's ECM-associated signature; the subject's angiogenesis signature; the subject's proliferation rate signature; and a similarity score indicative of a similarity between the subject's RC TME signature and a RC TME signature associated with a RC TME type B and / or a RC TME type C sample, wherein the responder score is indicative of the likelihood of the subject to respond to the immuno-oncology (IO) agent.

[0007] In some aspects, the disclosure provides a method for predicting the likelihood of a subject having, suspected of having, or at risk of having renal cancer to respond to a tyrosine kinase inhibitor (TKI), the method comprising: performing, using at least one computer hardware processor, the steps of: generating a set of input features using RNA expression data obtained from the subject; providing the set of input features as input to a machine learning model and obtaining a corresponding output indicative of a responder score; and identifying the subject as likely to respond to the TKI if the responder score exceeds a specified threshold, wherein the set of input features comprises at least two of the following features: a macrophage signature of the subject; an angiogenesis signature of the subject; a proliferation rate signature of the subject; and a similarity score indicative of a similarity between the RC TME signature of the subject and a RC TME signature associated with a RC TME type B sample, wherein the responder score is indicative of the likelihood of the subject to respond to the TKI.

[0008] In some aspects, the disclosure provides a method of identifying one or more therapeutic agents for administration to a subject having renal cancer, the method comprising the steps of generating an International Metastatic RCC Database Consortium (IMDC) risk score for the subject; if the subject is identified as having a poor IMDC risk score, identifying a combination of an immuno-oncology (IO) agent and a TKI as the one or more therapeutic agents for administration to the subject; if the subject is identified as having a good or intermediate IMDC risk score, generating an IO responder score according to the methods described herein; a TKI responder score according to the methods described herein; and using the IO responder score and the TKI responder score to identify one or more therapeutic agents for the subject.

[0009] In some aspects, the present disclosure provides a system comprising at least one computer hardware processor; and at least one non-transitory computer readable 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 determining a renal cancer (RC) tumor microenvironment (TME) type of a subject, as described herein.

[0010] In some aspects, the present disclosure provides at least one non-transitory computer-readable 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 determining a renal cancer (RC) tumor microenvironment (TME) type of a subject, as described herein.

[0011] In some aspects, the present disclosure provides a system comprising at least one computer hardware processor; and at least one non-transitory computer readable 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 determining a renal cancer (RC) myogenesis signature of a subject, as described herein.

[0012] In some aspects, the present disclosure provides at least one non-transitory computer-readable 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 determining a renal cancer (RC) myogenesis signature of a subject, as described herein.

[0013] In some aspects, the present disclosure provides a system comprising at least one computer hardware processor; and at least one non-transitory computer readable 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 of predicting the likelihood that a subject will respond to an immuno-oncology (IO) agent, as described herein.

[0014] In some aspects, the present disclosure provides at least one non-transitory computer-readable 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 of predicting the likelihood that a subject will respond to an immuno-oncology (IO) agent, as described herein.

[0015] In some aspects, the present disclosure provides a system comprising at least one computer hardware processor; and at least one non-transitory computer readable 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 of predicting the likelihood that a subject will respond to a tyrosine kinase inhibitor (TKI), as described herein.

[0016] In some aspects, the present disclosure provides at least one non-transitory computer-readable 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 of predicting the likelihood that a subject will respond to a tyrosine kinase inhibitor (TKI), as described herein.

[0017] In some aspects, the present disclosure provides a system comprising at least one computer hardware processor; and at least one non-transitory computer-readable 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 one or more therapeutic agents for administration to a subject having renal cancer, as described herein.

[0018] In some aspects, the present disclosure provides at least one non-transitory computer-readable 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 one or more therapeutic agents for administration to a subject having renal cancer, as described herein.

[0019] In some embodiments, obtaining RNA expression data for the subject comprises obtaining previously obtained sequencing data by sequencing a biological sample obtained from the subject.

[0020] 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. 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.

[0021] In some embodiments, the method further comprises normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the RC TME signature.

[0022] 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 kidney tissue of the subject. In some embodiments, the biological sample comprises tumor tissue of the subject.

[0023] 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: effector cell population: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; NK cell population: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; Anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; Checkpoint inhibitor group: CTL A4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (i) Granulocyte trafficking group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXC R1;MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1;Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R;Cancer-associated fibroblast (CAF) group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2;Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN , COL1A2; Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, M CM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI 1, ZEB1, TWIST2, CDH2; citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, I DH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, F AHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT;Glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; and fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.;

[0024] 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: MHC I group: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; MHC II group: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; co-activation molecule group: CD80, TNFRSF4, CD27, CD83, TNF SF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; Effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8 A, GZMA, GNLY; T cell transport group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, N CR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD 5; B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8;T reg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; macrophage DC transport group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; Th2 signature group: IL13, CCR4, IL10, IL5, IL4; tumor-promoting cytokine group: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR;Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; Cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY 2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; glycolysis and gluconeogenesis group: SLC2A 9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5 A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, H K2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2;Citric acid circuit group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and, fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.;

[0025] In some embodiments, the RNA expression levels further comprise RNA expression levels of at least three genes from each of at least two of the following gene groups: ECM-related group: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA2, SCUBE1, SEMA4B, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; TLS-kidney group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; NRF2 signature group: TRIM1 6L, UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F11, CYP 4F3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1, C1orf131 , CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, IDH1, MAFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11;and tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, P MEL, BIRC7, PLK5, ADARB2, CFAP61, TUBB4A, PLIN4, ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF 4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17. ;

[0026] In some embodiments, the RNA expression levels comprise the RNA expression levels of each gene from each of the following gene groups: effector cell population: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; NK cell population: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; T cell population: TRAC, TRBC2, T BX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; Anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; Checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; Granulocyte trafficking group: CXCL8 , CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL 4I1, CD68, IL10, CSF1R; Cancer-associated fibroblast (CAF) group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2;Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; Citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16 A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, A CSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GO T2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT;Glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; and fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.;

[0027] In some embodiments, the RNA expression levels of genes in a plurality of gene groups comprise the RNA expression levels of each gene from each of the following gene groups: MHC I group: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; MHC II group: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; co-activation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; Effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, G ZMA, GNLY; T cell trafficking group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1 , GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226;T cell population: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5;B Cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; M1 signature group: IL1B, IL12B, NOS2, SOCS3, IR F5, IL23A, TNF, IL12A, CMKLR1; Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; T reg trafficking group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1;Good neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; Macrophage DC transport group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; Th2 signature group: IL13, CCR4, IL10, IL5, IL4; Tumor-promoting cytokine group: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; Matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3;EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BP GM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11 , PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALD OB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC 2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2;Citric acid circuit group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and, fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.;

[0028] In some embodiments, the RNA expression levels of genes in the plurality of gene groups further comprise the RNA expression levels of each gene from each of the following gene groups: ECM-related group: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA2, SCUBE1, SEMA4B, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; TLS-kidney group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; NRF2 signature group: TRIM16L. , UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F11, CYP4F 3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1, C1orf131, CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, IDH1, M AFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11;and tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, P MEL, BIRC7, PLK5, ADARB2, CFAP61, TUBB4A, PLIN4, ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF 4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17. ;

[0029] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining the gene group score for a particular group using RNA expression levels of at least three genes in the particular gene group, Effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FG FBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; Anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; Checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; Granulocyte trafficking group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXC L5, CXCR1; MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; cancer-associated fibroblast (CAF) group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2;Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN , COL1A2; Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, M CM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI 1, ZEB1, TWIST2, CDH2; citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, I DH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, F AHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT;Glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PF KP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC 16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, AL and fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.

[0030] 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, the MHCII group: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; MHCII group: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; co-activation molecule group: CD80, TN FRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; Effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK , EOMES, FASLG, CD8A, GZMA, GNLY; T cell transport group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28 , TRAT1, CD5; B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8;Treg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; Macrophage DC transport group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; Th2 signature group: IL13, CCR4, IL10, IL5, IL4; Tumor-promoting cytokine group: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; Matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR;Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; Cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY 2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; glycolysis and gluconeogenesis group: SLC2A 9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5 A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, H K2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2;Citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADS L, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.

[0031] In some embodiments, 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, ECM-related group: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA2, SCUBE1, SEMA4B, SEMA7A, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; TLS-renal group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; NRF2 Signature group: TRIM16L, UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F11, CYP4F3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1, C1 orf131, CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, ID H1, MAFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11;and tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, PMEL, BIRC7, PLK5, ADARB2, CFAP61, T The method further comprises determining a respective gene group score for each of at least two of the gene groups including UBB4A, PLIN4, ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17;

[0032] 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 the gene group score for each particular group, Effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1 B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; Anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; Checkpoint Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; Granulocyte trafficking group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1;MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1;Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R;Cancer-associated fibroblast (CAF) group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2;Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN , COL1A2; Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, M CM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI 1, ZEB1, TWIST2, CDH2; citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, I DH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, F AHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT;Glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK 2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A1 2, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PF determining the respective gene group scores for each gene group including KFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; and fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12;

[0033] 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, MHC I group: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; MHC II group: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; co-activation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; T cell transport group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; NK cell group :GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; T cell group :TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; Th1 signature group: Gneta group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; Checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8;Treg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; Macrophage DC transport group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; Th2 signature group: IL13, CCR4, IL10, IL5, IL4; Tumor-promoting cytokine group: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; Matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR;Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; Cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY 2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; glycolysis and gluconeogenesis group: SLC2A 9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5 A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, H K2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2;Citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, O GDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, and determining the respective gene group scores for each gene group including ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.

[0034] 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 the gene group score for each particular group, ECM related group: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA2, SCUBE1, SEMA4B, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; TLS renal group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; NRF2 signature group: TRIM16L, UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F11 , CYP4F3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1, C1orf 131, CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, IDH1 , MAFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11;and tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, PMEL, BIRC7, PLK5, ADARB2, C The method further comprises determining a respective gene group score for each gene group, including FAP61, TUBB4A, PLIN4, ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17;

[0035] In some embodiments, determining the gene group score comprises using single sample GSEA (ssGSEA) technology to determine a first score for a first gene group from RNA expression levels of at least some of the genes in one of the following gene groups: MHC group I: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; MHC group II: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; co-activation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; effector cell group: PRF1, GZMB, TBX2. 1, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; T cell trafficking group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, I FNG, NCR3, KLRC2, CD226; T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; M1 signature group: IL1B, IL12B, NOS2, S OCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1;Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; T reg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; Macrophage DC transport group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; Th2 signature group: IL13, CCR4, IL10, IL5, IL4; Tumor-promoting cytokine group: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; Matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR;Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; Cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY 2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; glycolysis and gluconeogenesis group: SLC2A 9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5 A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, H K2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2;Citric acid circuit group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and, fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.;

[0036] 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: MHC group I: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; MHC group II: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; co-activation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; T cell transport group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; NK cell group :GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; T cell group :TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; Th1 signature group: Gneta group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; Checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8;T reg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; macrophage-DC transport group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; Th2 signature group: IL13, CCR4, IL10, IL5, IL4; tumor-promoting cytokine group: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR;Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; Cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY 2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; glycolysis and gluconeogenesis group: SLC2A 9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5 A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, H K2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2;Citric acid circuit group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and, fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.;

[0037] In some embodiments, determining the gene group score is performed using single sample GSEA (ssGSEA) technology using RNA expression levels of each gene in each of the following gene groups: ECM-related group: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA2, SCUBE1, SEMA4B, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; TLS-renal group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; NRF2 signature: Controller group: TRIM16L, UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F 11, CYP4F3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1, C1or f131, CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, IDH 1, MAFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11;and tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, P MEL, BIRC7, PLK5, ADARB2, CFAP61, TUBB4A, PLIN4, ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF 4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17. ;

[0038] In some embodiments, generating the RC TME signature further comprises normalizing the gene group scores. In some embodiments, the normalizing comprises applying median scaling to the gene group scores.

[0039] In some embodiments, the multiple RC TME types are associated with a respective multiple RC TME signature clusters, wherein identifying the target RC TME type from among the multiple RC TME types using the RC TME signature comprises: associating the target RC TME signature with a particular one of the multiple RC TME signature clusters; and identifying the target RC TME type as the RC TME type corresponding to the particular one of the multiple RC TME signature clusters with which the target RC TME signature is associated.

[0040] In some embodiments, the methods described herein further comprise the steps of: generating a plurality of RC TME signature clusters; generating a plurality of RC TME signatures from a plurality of sets of RNA expression data; and clustering the plurality of RC signatures to obtain a plurality of RC TME signature clusters, wherein the generating step comprises obtaining a plurality of sets of RNA expression data by sequencing biological samples from a plurality of respective subjects, wherein each of the plurality of sets of RNA expression data indicates RNA expression levels of at least some genes of a plurality of gene groups listed in Table 1, and each of the plurality of RC TME signatures comprises a gene group expression score for each gene group in the plurality of gene groups, and the generating step comprises determining, for each particular one of the plurality of RC TME signatures, the RC TME signature by determining the gene group expression score using the RNA expression levels in the particular set of RNA expression data for which the particular one RC TME signature was generated.

[0041] In some embodiments, the clustering comprises agglomerative clustering, spectral clustering, k-means clustering, hierarchical clustering, and / or agglomerative clustering.

[0042] In some embodiments, the method further comprises updating a plurality of RC TME signature clusters using the RC TME signature of the subject, where the RC TME signature of the subject is one of a threshold number of RC TME signatures for a threshold number of subjects, and where the RC TME signature cluster is updated upon generation of the threshold number of RC TME signatures, where the threshold number of RC 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 RC TME signatures.

[0043] In some embodiments, the updating step is performed using a clustering algorithm selected from the group consisting of a dense clustering algorithm, a spectral clustering algorithm, a k-means clustering algorithm, a hierarchical clustering algorithm, and an agglomerative clustering algorithm.

[0044] In some embodiments, the method further comprises the steps of: determining an RC TME type of the second subject; associating the RC TME signature of the second subject with a particular one of the plurality of updated RC TME signature clusters; and identifying the RC TME type of the second subject as the RC TME type corresponding to the particular one of the plurality of updated RC TME signature clusters with which the RC TME signature of the second subject is associated, wherein the RC TME type of the second subject is identified using the updated RC TME signature cluster, wherein the identifying step comprises determining the RC TME signature of the second subject from RNA expression data obtained by sequencing a biological sample obtained from the second subject.

[0045] In some embodiments, the multiple RC TME types comprise RC TME type A, RC TME type B, RC TME type C, RC TME type D, and RC TME type E.

[0046] In some embodiments, the method further comprises using the subject's RC TME type to identify at least one therapeutic agent for administration to the subject. In some embodiments, the at least one therapeutic agent comprises an immuno-oncology (IO) agent. In some embodiments, the at least one therapeutic agent comprises a tyrosine kinase inhibitor (TKI).

[0047] In some embodiments, identifying at least one therapeutic agent based on the subject's RC TME type comprises identifying a TKI as the at least one therapeutic agent if the subject is identified as having RC TME type E.

[0048] In some embodiments, identifying at least one therapeutic agent based on the subject's RC TME type comprises identifying a combination of a TKI and an IO agent as the at least one therapeutic agent if the subject is identified as having RC TME type A or RC TME type B.

[0049] In some embodiments, the method further comprises administering at least one identified therapeutic agent to the subject.

[0050] In some embodiments, the method comprises normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the RC myogenesis signature.

[0051] In some embodiments, the RNA expression levels comprise RNA expression levels of at least three of the following genes: CASQ1, TNNI1, MB, MYLPF, MYH7, CKM, MYL2, MYL1, CSRP3, ACTA1, MYOZ1, TNNT3, TNNC2, and TNNC1.

[0052] In some embodiments, the RNA expression levels comprise the RNA expression levels of each of the following genes: CASQ1, TNNI1, MB, MYLPF, MYH7, CKM, MYL2, MYL1, CSRP3, ACTA1, MYOZ1, TNNT3, TNNC2, and TNNC1.

[0053] In some embodiments, the RC myogenesis signature is determined from the RNA expression levels of each of the following genes: CASQ1, TNNI1, MB, MYLPF, MYH7, CKM, MYL2, MYL1, CSRP3, ACTA1, MYOZ1, TNNT3, TNNC2, and TNNC1 using single sample GSEA (ssGSEA) technology.

[0054] In some embodiments, the method described by the present disclosure further comprises determining whether the value of the RC myogenesis signature is greater than a specified threshold. In some embodiments, the specified threshold is 4.

[0055] In some embodiments, if the value of the RC myogenesis signature exceeds a specified value, the method further comprises identifying the subject as a non-responder to the immuno-oncology (IO) agent.

[0056] In some embodiments, the method further comprises identifying one or more non-IO agents for the subject. In some embodiments, the one or more non-immunotherapeutic agents comprise a TKI. In some embodiments, the method further comprises administering the identified one or more non-IO agents to the subject.

[0057] In some embodiments, generating the set of input features comprises determining the subject's RC TME type using RNA expression data as described herein.

[0058] In some embodiments, generating the set of input features comprises determining the RNA expression level of one or more of the PD1, PD-L1, and PD-L2 genes.

[0059] In some embodiments, generating a set of input features comprises determining an ECM-associated signature of the subject using the RNA expression data by performing ssGSEA on the RNA expression data of at least three of the "ECM-associated signature" genes listed in Table 1. In some embodiments, determining the ECM-associated signature further comprises performing ssGSEA on the RNA expression data of at least four, five, six, seven, eight, nine, or ten of the "ECM-associated signature" genes listed in Table 1. In some embodiments, determining the ECM-associated signature further comprises performing ssGSEA on the RNA expression data of each of the "ECM-associated signature" genes listed in Table 1.

[0060] In some embodiments, generating a set of input features comprises determining an angiogenesis signature of the subject using the RNA expression data by performing ssGSEA on the RNA expression data of at least three of the "angiogenesis" genes listed in Table 1. In some embodiments, determining the angiogenesis signature further comprises performing ssGSEA on the RNA expression data of at least four, five, six, seven, eight, nine, or ten of the "angiogenesis" genes listed in Table 1. In some embodiments, determining the angiogenesis signature further comprises performing ssGSEA on the RNA expression data of each of the "angiogenesis" genes listed in Table 1.

[0061] In some embodiments, generating a set of input features comprises determining a growth rate signature of the subject using the RNA expression data by performing ssGSEA on the RNA expression data of at least three of the "growth rate" genes listed in Table 1. In some embodiments, determining the growth rate signature further comprises performing ssGSEA on the RNA expression data of at least four, five, six, seven, eight, nine, or ten of the "growth rate" genes listed in Table 1. In some embodiments, determining the growth rate signature further comprises performing ssGSEA on the RNA expression data of each of the "growth rate" genes listed in Table 1.

[0062] In some embodiments, generating a set of input features comprises determining a similarity score by comparing the gene group score of the subject's RC TME signature to an average of the gene group scores of multiple RC TME signatures from RC TME type B samples and / or an average of the gene group scores of multiple RC TME signatures from RC TME type C samples.

[0063] In some embodiments, determining the similarity score comprises calculating a Spearman correlation coefficient between the gene group scores for each of the multiple gene groups of the subject's RC TME signature and the averaged gene group scores for the multiple gene groups of other RC type B and / or RC type C samples.

[0064] In some embodiments, the method further comprises identifying the subject as "IO low" if the responder score is less than or equal to 0.05, "IO intermediate" if the responder score is greater than or equal to 0.05 and less than 0.5, or (iii) "IO high" if the subject score is greater than or equal to 0.5.

[0065] In some embodiments, the specified threshold is 0.5.

[0066] In some embodiments, the methods described herein further comprise identifying an IO agent for administration to the subject if the subject's responder score exceeds a specified threshold or if the subject is identified as "IO high."

[0067] In some embodiments, the method further comprises administering to the subject an IO agent if the subject's responder score exceeds a specified threshold or if the subject is identified as "IO high." In some embodiments, the IO agent comprises a PD1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, or a CTLA-4 inhibitor.

[0068] In some embodiments, the RNA expression data comprises average scaled expression levels of PD1 and PDL1.

[0069] In some embodiments, the methods described by the present disclosure further comprise determining whether the subject comprises one or more of the following biomarkers: ploidy>4; a value of the subject's RC myogenesis signature greater than 4; one or more mTOR activating mutations; and / or one or more mutations in genes associated with antigen presentation. In some embodiments, the determining step is performed before the input features are input into the machine learning model.

[0070] In some embodiments, the methods described by the present disclosure further comprise identifying the subject as having a responder score of 0 if the subject comprises one or more biomarkers.

[0071] In some embodiments, generating the set of input features comprises determining the subject's RC TME type using RNA expression data as described herein.

[0072] In some embodiments, generating a set of input features comprises determining a macrophage signature of the subject using the RNA expression data by performing ssGSEA on the RNA expression data of at least three of the "macrophage" genes listed in Table 1. In some embodiments, determining the macrophage signature further comprises performing ssGSEA on the RNA expression data of at least four, five, six, seven, eight, nine, or ten of the "macrophage" genes listed in Table 1. In some embodiments, determining the macrophage signature further comprises performing ssGSEA on the RNA expression data of each of the "macrophage" genes listed in Table 1.

[0073] In some embodiments, generating the input features comprises determining an angiogenesis signature of the subject using the RNA expression data by performing ssGSEA on the RNA expression data of at least three of the "angiogenesis" genes listed in Table 1. In some embodiments, determining the angiogenesis signature further comprises performing ssGSEA on the RNA expression data of at least four, five, six, seven, eight, nine, or ten of the "angiogenesis" genes listed in Table 1. In some embodiments, determining the angiogenesis signature further comprises performing ssGSEA on the RNA expression data of each of the "angiogenesis" genes listed in Table 1.

[0074] In some embodiments, generating a set of input features comprises determining a growth rate signature of the subject using the RNA expression data by performing ssGSEA on the RNA expression data of at least three of the "growth rate" genes listed in Table 1. In some embodiments, determining the growth rate signature further comprises performing ssGSEA on the RNA expression data of at least four, five, six, seven, eight, nine, or ten of the "growth rate" genes listed in Table 1. In some embodiments, determining the growth rate signature further comprises performing ssGSEA on the RNA expression data of each of the "growth rate" genes listed in Table 1.

[0075] In some embodiments, generating the set of input features comprises determining a similarity score by comparing the gene group score of the subject's RC TME signature to an average of the gene group scores of the multiple RC TME signatures from the RC TME type B samples. In some embodiments, determining the similarity score comprises calculating a Spearman correlation coefficient between the gene group score for each of the multiple gene groups of the subject's RC TME signature and the averaged gene group scores for the multiple gene groups of the other RC type B and / or RC type C samples.

[0076] In some embodiments, the methods described by the present disclosure further comprise identifying the subject as "TKI low" if the responder score is less than or equal to 0.75, "TKI intermediate" if the responder score is greater than or equal to 0.75 and less than 0.95, or "TKI high" if the responder score is greater than or equal to 0.95.

[0077] In some embodiments, the specified threshold is 0.95.

[0078] In some embodiments, the methods described by the present disclosure further comprise a step of identifying a TKI for administration to the subject if the subject's responder score exceeds a specified threshold or if the subject is identified as "TKI-intermediate" or "TKI-high."

[0079] In some embodiments, the methods described by the present disclosure further comprise administering a TKI to the subject if the subject's responder score exceeds a specified threshold or if the subject is identified as "TKI-intermediate" or "TKI-high."

[0080] In some embodiments, the TKI comprises a small molecule or an antibody. In some embodiments, the antibody is a monoclonal antibody.

[0081] In some embodiments, the renal cancer is clear cell renal cancer (ccRCC).

[0082] In some embodiments, if the subject is identified as "TKI low" using the TKI responder score, the methods described herein further comprise identifying the one or more therapeutic agents as a TKI if the subject is identified as "IO low" using the IO responder score; a combination of a TKI and an IO agent if the subject is identified as "IO low" using the IO responder score; or a combination of a TKI and an IO agent if the subject is identified as "IO intermediate" or "IO high" using the IO responder score.

[0083] In some embodiments, if the subject is identified as "TKI-medium" using the TKI responder score, the methods described by the present disclosure further comprise identifying the one or more therapeutic agents as a combination of a TKI and an IO agent if the subject is identified as "IO-high" using the IO responder score.

[0084] In some embodiments, if the subject is identified as "TKI high" using the TKI responder score, the methods described by the present disclosure further comprise identifying the one or more therapeutic agents as a TKI if the subject is identified as "IO low" or "IO intermediate" using the IO responder score; or a combination of a TKI and an IO agent if the subject is identified as "IO high" using the IO responder score.

[0085] In some embodiments, the methods described by the present disclosure further comprise the step of administering the identified therapeutic agent or agents to the subject.

[0086] In some embodiments, the methods described by the present disclosure further comprise the step of providing a recommendation to administer the identified one or more therapeutic agents to the subject. [Brief description of the drawings]

[0087] [Figure 1] 1 is a diagram showing a flow chart of an exemplary process for determining a renal cancer (RC) tumor microenvironment (TME) type for a subject having, suspected of having, or at risk of having renal cancer, 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 RC TME type using a RC TME signature, according to some embodiments of the techniques described herein. [Diagram 5]1 provides an exemplary heatmap of clear cell renal carcinoma (ccRCC) samples classified into five different RC TME types (A, B, C, D, E) using a RC TME signature comprising 33 gene group scores according to some embodiments of the technology described herein. [Figure 6] Representative data are provided showing the association of RCT IE / F (also referred to as RC TME type A) and IE (RC TME type B) with excellent clinical response (response rate >50%) in IO+TKI (atezolizumab+bevacizumab) cohorts according to some embodiments of the technology described herein. RC TME type C, characterized by fibrotic gene enrichment, responded poorly to IO-containing regimens (45-67%); whereas RC TME type D, a desert subtype, showed intermediate response to all four regimens. Conversely, subjects with RC TME type E, characterized by enhanced angiogenesis and lack of immune cell infiltration, showed significantly better response to single-agent TKI (sunitinib response rate ~80%). [Figure 7] 1 is a schematic diagram illustrating a flow chart of an exemplary process of a machine learning model for assessing the likelihood of a subject's response to IO treatment, according to some embodiments of the technology described herein. [Figure 8] 1 provides an example of training and validation of a machine learning model for assessing a subject's likelihood of response to IO treatment, according to some embodiments of the technology described herein. [Figure 9] 1 is a schematic diagram depicting a flow chart of an exemplary process of a machine learning model for assessing the likelihood of a subject's response to TKI treatment, according to some embodiments of the technology described herein. [Figure 10] 1 provides an example of training and validation of a machine learning model for assessing a subject's likelihood of response to TKI treatment, according to some embodiments of the technology described herein. [Figure 11A]11A provides representative data for a machine learning model for assessing the likelihood of a subject's response to IO or TKI therapy according to some embodiments of the technology described herein. Figure 11A shows data suggesting that IO responder scores are consistent across various datasets, whereas other biomarkers and signatures are not. [Figure 11B] 11A-11C provide representative data for a machine learning model for assessing the likelihood of a subject's response to IO or TKI therapy according to some embodiments of the technology described herein. Figure 11B shows data suggesting that TKI responder scores are consistent across various datasets, whereas other biomarkers and signatures are not. [Figure 11C] Figure 11C provides representative data of a machine learning model for assessing the likelihood of a subject's response to IO or TKI therapy according to some embodiments of the technology described herein. Figure 11C shows representative data suggesting that TKI responder scores show a strong correlation across the entire data set. [Figure 11D] Figure 11D provides representative data of a machine learning model for assessing the likelihood of a subject's response to IO or TKI therapy according to some embodiments of the technology described herein. Figure 11D shows representative data suggesting that the combined IO / TKI responder score shows a strong association across the entire data set. [Figure 11E] Figure 11E provides representative data of a machine learning model for assessing the likelihood of a subject's response to IO or TKI therapy according to some embodiments of the technology described herein. Figure 11E shows representative data suggesting that IO and TKI responder scores improve prediction of median progression-free survival (mPFS) and overall response rate (oRR). [Figure 12]

[0023] Figure 1 provides representative data for a myogenic signature according to some embodiments of the technology described herein. The figure provides a representative heatmap showing the generation of a myogenic signature for clear cell renal cell carcinoma (ccRCC) samples based on ssGSEA analysis and median scaling of 14 genes (left) and data showing that the 14 genes of the signature are expressed primarily in myoblasts or muscle cells. [Figure 13A] Representative data for samples with high myogenic signature are provided. Figure 13A shows representative data showing RECIST characterization (complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD)) plotted against myogenic signature. [Figure 13B] 13A provides representative data of samples with high myogenic signature. Figure 13B shows representative data that samples with high myogenic signature are obtained from bone metastasis (MBO) patients according to some embodiments of the technology described herein. [Figure 14] 1 is a schematic diagram showing a flow chart of an exemplary process for selecting one or more therapeutic agents for administration to a subject with renal cancer. First, an International Metastatic RCC Database Consortium (IMDC) risk score is generated for the subject. If the subject is determined to have a poor IMDC risk score, it is determined that the subject should be administered a combination of an immuno-oncology agent and a TKI. If the subject's IMDC risk score is identified as good or moderate, an IO responder score and a TKI responder score are generated. After the responder score is generated, the IO and TKI responder scores are used to select one or more therapeutic agents for the subject. [Figure 15] 1 illustrates an exemplary implementation of a computer system that may be used in connection with some embodiments of the technology described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0088] Aspects of the present disclosure relate to methods for characterizing subjects with certain renal (kidney) cancers (RC), such as clear cell renal carcinoma (ccRCC). Clear cell renal cell carcinoma (ccRCC) is one of the most common kidney cancers and is known to have significant genetic intratumor heterogeneity (ITH). ITH has been found to underlie tumor progression, metastasis, and clinical response to various therapies. Research has focused on the use of whole exome sequencing to reveal patterns of progression, mutational profiles underlying ITH, clonal architecture, and tumor differences between patients. Multiregion DNA sequencing revealed that del(3p) and ampl(5q) are two common abnormalities observed in 43% of ccRCC and are the initial genetic events of renal cell malignant transformation. Further DNA sequence analysis suggests that RC tumors can be classified into three prognostic subtypes. Currently, immunotherapy has dramatically improved clinical outcomes for RC patients, and the combination of ipilimumab and nivolumab has received FDA approval for frontline metastatic ccRCC. However, significant differences in patient responses to immunotherapy have been observed, which cannot be explained solely by genetic heterogeneity. Therefore, the inventors recognized the need to develop methods for molecular characterization of RC types, based specifically on the underlying biological properties of both the tumor microenvironment and malignant cells, rather than more broadly defined cancer biomarkers and / or ITH.

[0089] Aspects of the present disclosure relate to statistical methods for analyzing expression data (e.g., RNA expression data) obtained from biological samples obtained from subjects having, suspected of having, or at risk of developing renal cancer (RC) to generate a gene expression signature for the subject (referred to herein as an "RC TME signature" or "RC TME signature") and use this signature to identify a particular RC TME type that the subject may have. In some embodiments, the RC is ccRCC.

[0090] 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) may be combined to generate a RC TME signature that characterizes patients with RC more accurately than previously developed methods. A RC TME signature comprising a combination of gene group scores associated with tumor microenvironment and gene group scores associated with malignant cells may then be used to identify a subject as having a particular renal cancer (RC) tumor microenvironment (TME) type. In some embodiments, such RC TME types are useful for identifying prognosis and / or the likelihood that a subject will respond to a particular therapeutic intervention (e.g., immuno-oncology (IO) agents, tyrosine kinase inhibitors (TKIs), a combination of IO and TKIs, etc.).

[0091] The inventors also recognize that data on specific gene expression signatures (e.g., RC TME signature, myogenesis signature, etc.) of subjects with RC may be used to train machine learning-based models to generate responder scores that reflect the likelihood that a subject will respond to treatment with a particular therapeutic agent (e.g., IO agent, TKI, combination of IO agent and TKI, etc.). Such responder scores are useful in some embodiments to guide the selection of therapeutic agents for treating RC patients by helping to select therapeutic agents to which the patient is more likely to respond. This method also helps to steer the selection away from therapeutic agents to which the patient is less likely to respond, as in the case of "apparent IO non-responders" as further described in the Examples.

[0092] The use of a RC TME signature comprising a combination of gene group scores described by the present disclosure represents an improvement over previously described RC molecular biomarker or tumor microenvironment analyses, since the specific gene groups used to generate the RC TME signature described herein better reflect the molecular tumor microenvironment of RC, as these gene groups are related to 1) immune and stromal tumor biology, and 2) renal cancer metabolic pathway activity. These gene group combinations of interest (e.g., gene groups consisting of some or all of the genes listed in Table 1, or gene groups consisting of some or all of the genes in the myogenesis signature listed in 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 attempt to consider only a specific subset of genes involved in cancer (e.g., analyses limited to only immune cells).

[0093] The RC TME typing method described herein has several uses.For example, by using the method described herein to identify the RC TME type of a subject, it may be possible to diagnose that the subject has (or is at high risk of developing) a highly aggressive form of RC at a time when the RC characterization method described previously cannot.The early detection of aggressive RC type, which is enabled by the RC TME signature described herein, improves the diagnostic technique of the patient by enabling earlier chemotherapy intervention for the patient than is currently possible for the patient who is tested for RC using other methods.

[0094] As described herein, the inventors have also determined that subjects identified by the methods described herein as having RX TME type A or RC TME type B are characterized as having a favorable prognosis and / or being more likely to respond to a particular therapy, such as a combination of an IO agent and a TKI.

[0095] Conversely, the inventors have determined, using the methods described herein, that once a subject is identified as having RC TME type E, they are unlikely to respond to IO agents, but may respond to TKIs. Furthermore, the inventors have determined that subjects identified as having high myogenic signatures and / or certain other biomarkers (e.g., hyperploidy, mutations in genes associated with mTOR activation or antigen presentation, etc.) are likely to be "apparent IO non-responders" and therefore should not be administered immunotherapeutic agents as first-line therapy for RC (e.g., ccRCC). Thus, the technology developed by the inventors and described herein improves patient treatment and associated outcomes by increasing patient comfort and avoiding adverse side effects of therapies that are not expected to be effective for the subject.

[0096] Clear cell renal cell carcinoma (ccRCC) Aspects of the present disclosure relate to a method for determining renal cancer (RC) tumor microenvironment (TME) type (of a subject having, suspected of having, or at risk of having RC). 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 terms "individual" or "subject" may be used synonymously with "patient." As used herein, "renal cancer" refers to any renal cancer or renal adenocarcinoma, or any other type of malignant tumor caused by one or more various genetic mutations in the body that affect the cells of the kidney of a subject (either originally present in the kidney or metastasized thereto). 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 renal cancer include, but are not limited to, renal adenocarcinomas derived from the proximal nephron and / or tubular epithelium of the kidney, such as clear cell renal cell carcinoma (ccRCC), and malignant epithelial cells with compact alveolar (nested) or alveolar growth patterns with clear cytoplasm and interspersed with a complex branching vasculature.

[0097] A subject with RC may exhibit one or more signs or symptoms of RC, such as, for example, the presence of cancerous cells (e.g., tumor cells), fever, swelling, bleeding (e.g., hematuria), nausea and vomiting, persistent back pain, and weight loss. In some embodiments, a subject with RC does not exhibit one or more signs or symptoms of RC. In some embodiments, a subject with RC has been diagnosed by a medical professional (e.g., a licensed physician) as having RC based on one or more assays (e.g., clinical assays, molecular diagnostics, etc.) that suggest that the subject has ccRCC, even in the absence of one or more signs or symptoms.

[0098] A subject suspected of having RC typically exhibits one or more signs or symptoms of RC, such as, for example, ccRCC. In some embodiments, a subject suspected of having RC exhibits one or more signs or symptoms of RC, but has not been diagnosed by a medical professional (e.g., a licensed physician) and / or has not received test results (e.g., clinical tests, molecular diagnostics, etc.) that suggest that the subject has RC.

[0099] A subject at risk of having RC may or may not show one or more signs or symptoms of RC. In some embodiments, a subject at risk of having RC comprises one or more risk factors that increase the likelihood that the subject will develop RC. Examples of risk factors include the presence of precancerous cells in clinical samples, the subject having one or more genetic mutations that predispose the subject to cancer (e.g., RC, such as ccRCC), taking one or more drugs that increase the likelihood that the subject will develop cancer (e.g., RC, such as ccRCC), family history of RC, etc.

[0100] FIG. 1 is a flow chart of an exemplary process 100 for determining a subject's RC TME signature and using the determined RC TME signature to identify the subject's RC TME type.

[0101] Various operations of process 100 may be implemented using any suitable computing device. For example, in some embodiments, one or more operations of the exemplary process 100 may be implemented in a clinical or 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.

[0102] 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.

[0103] It should be understood that in some embodiments, not all operations of process 100 as shown in FIG. 1 are implemented using one or more computing devices. For example, operation 114 of identifying a prognosis of a subject may be implemented manually (e.g., by a clinician), automatically (e.g., by software that identifies an RC TME type associated with a particular prognosis), or partially manually and partially automatically (e.g., a clinician may identify an RC TME type associated with a particular prognosis, in part, using information generated by software, e.g., using techniques described herein). Process 100 begins with operation 102, in which sequencing data of a subject is obtained. In some embodiments, the sequencing data may be obtained by sequencing a biological sample obtained from the subject (e.g., kidney 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."

[0104] 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. Process 100 then proceeds to operation 104, where the sequencing data obtained in operation 102 is processed to obtain gene 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 into TPM units is described herein with reference to FIG.

[0105] Process 100 then proceeds to operation 106, where a renal cancer (RC) 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).

[0106] As described herein, in some embodiments, the RC 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.

[0107] Thus, operation 106 comprises operation 108 where a gene group score is determined, operation 110 where a RC TME signature is determined, and operation 112 where a RC TME type is determined by using the RC TME signature. In some embodiments, determining the gene group score comprises determining a gene 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, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, or more gene groups (e.g., the gene groups listed in Table 1). 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 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.

[0108] In some embodiments, determining the RC TME gene signature comprises determining a gene group score using RNA expression levels of at least three genes from each of at least two gene groups, including: (a) MHC I group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; (b) MHC II group: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; and (c) MHC II group: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1. Coactivation molecule group: CD28, CD40, TNFRSF4, ICOS, TNFRSF9, CD27, CD80, CD86, CD40LG, CD83, TNFSF4, ICOSLG, TNFSF9, CD70; (d) Effector cell group: IFNG, GZMA, GZMB, PR (f) NK cell group: G ZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (g) T cell group: TRAC, TRBC2, TBX2 1, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (h) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (i) M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; (j) Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; (k) anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (l) checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1;(m) Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; (n) Treg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; (o) Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (p) Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; (q) MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; (r) MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; (s) Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; (t) Macrophage DC transport group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; (u) Th2 signature group: IL13, CCR4, IL10, IL5, IL4; (v) Tumor-promoting cytokine group: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; (w) Cancer-associated fibroblast (CAF) group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (x) Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; (y) Matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; (z) Angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5;(aa) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (bb) Proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; (cc) EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; (dd) Cyclic nucleotide metabolism group: ADCY4, PDE11A, PD E6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; (ee) glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2 , SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA , SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2;(xx) Citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1 , ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SL C13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and (ff) fatty acid metabolic group: MLYCD, ALDH3A2, S LC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12. ;

[0109] An embodiment of determining gene group enrichment scores is described herein with reference to FIG. 3, in the section entitled "Gene Expression Signatures."

[0110] As described above, at operation 110, an RC TME signature is generated. In some embodiments, the RC TME signature consists solely of gene group scores for one or more (e.g., all) gene groups listed in Table 1. In some embodiments, the RC 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, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, or 33 gene groups listed in Table 1. In some embodiments, each gene group score is determined using some or all of the RNA expression levels (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).

[0111] Process 100 then proceeds to operation 112, where the RC TME signature generated in operation 110 is used to identify the RC TME type of the subject. This may be done in any suitable manner. For example, in some embodiments, each of the possible RC TME types is associated with a respective plurality of RC TME signature clusters. In such embodiments, the RC TME type of the subject may be identified by associating the RC TME signature of the subject with a particular one of the plurality of RC TME signature clusters; and identifying the RC TME type of the subject as the RC TME type corresponding to the particular one of the plurality of RC TME signature clusters with which the RC TME signature of the subject is associated. Examples of RC TME types are described herein. Aspects of identifying the RC TME type of the subject are described herein, including in the section entitled "Generating RC TME Signatures and Identifying TME Type" below. In some embodiments, process 100 is complete after operation 112 is completed. In some such embodiments, the determined RC TME signature and / or the identified RC TME type may be stored for subsequent use, provided to one or more recipients (e.g., clinicians, researchers, etc.), and / or used to update RC TME signature clusters (described below).

[0112] However, in some embodiments, one or more other operations are performed after operation 112. For example, in the illustrated embodiment, a prognosis for the subject may be identified based on the RC TME type determined for the subject. For example, in some embodiments, if the subject is identified as having RC TME type D or RC TME type E, the subject is identified (at operation 114) as having a good prognosis. Subsequently, or as an alternative to operation 114, process 100 may proceed to operation 116, where the subject's RC TME type identified in operation 112 is used to identify (or recommend) a therapeutic agent for administration to the subject. For example, if the subject is identified as having RC type E, the subject may be identified as likely to respond to a TKI.

[0113] In some embodiments, process 100 is complete after operations 114 or 116 are completed. In some such embodiments, the determined RC TME signature and / or the identified RC TME type may be stored for subsequent use, provided to one or more recipients (e.g., clinicians, researchers, etc.), and / or used to update RC TME signature clusters (described below).

[0114] Biological samples Aspects of the present disclosure relate to methods for determining a subject's RC TME type by obtaining sequencing data from a biological sample obtained from the subject.

[0115] 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, and kidneys. Other sources within the subject's body may be saliva, tears, synovial fluid, cerebrospinal fluid, pleural fluid, pericardial fluid, peritoneal 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, stomach, small intestine, bone, bone marrow, brain, thymus, spleen, appendix, colon, rectum, anus, liver, biliary tract, pancreas, 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).

[0116] 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 kidney tissue sample from a subject. Examples of kidney tissue samples include, but are not limited to, glomerular parietal cells, glomerular podocytes, proximal tubule brush border cells, Henle's loop thin segment cells, thick ascending limb cells, distal tubule cells, collecting duct principal cells, collecting duct interstitial cells, interstitial renal cells, and kidney tumor cells.

[0117] In some embodiments, a kidney tissue sample may be obtained from a subject using a surgical procedure (e.g., laparoscopy, microsurgical surgery, or endoscopy), 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).

[0118] 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.

[0119] 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.

[0120] In some embodiments, a blood sample is taken to obtain cell-free nucleic acid (eg, cell-free DNA) in the blood.

[0121] 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).

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] Acquiring RNA expression data Aspects of the present disclosure relate to methods of determining a subject's RC TME type using sequencing or RNA expression data obtained from a biological sample from the subject.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] In some embodiments, the expression data comprises next generation sequencing (NGS) data. In some embodiments, the expression data comprises microarray data.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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, as 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 platform. In other embodiments, process 104 may be performed by one or more computing devices external to the sequencing platform.

[0142] 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."

[0143] In some embodiments, the bulk 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 RC tumor cells.

[0144] 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

[0145] 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.

[0146] The 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.

[0147] Gene expression signatures Aspects of the present disclosure relate to processing expression data to determine one or more gene expression signatures (e.g., RC 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.

[0148] In some embodiments, the expression data may be processed by one or more software programs running on a computing device.

[0149] In some embodiments, the methods described herein comprise an act of determining an RC TME signature comprising a gene group score for each gene group in a plurality of gene groups. In some embodiments, the RC TME signature comprises a gene group score for at least one of the gene groups listed in Table 1 (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, or 33).

[0150] 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).

[0151] In some embodiments, the RC 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.

[0152] In some embodiments, the RC 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-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1.

[0153] In some embodiments, the RC TME signature comprises a gene group score for a co-activator molecule 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, 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.

[0154] In some embodiments, the RC 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.

[0155] In some embodiments, the RC TME signature comprises a gene group score for the T cell trafficking 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, 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, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1.

[0156] In some embodiments, the RC 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: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226.

[0157] In some embodiments, the RC TME signature comprises a gene group score for a T 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, or at least ten genes) in a T cell population defined by its constituent genes: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5.

[0158] In some embodiments, the RC TME signature gene group comprises a score for a B cell population. 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, at least nine genes, at least ten genes, or more than ten genes) in a B cell population defined by its constituent genes: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5.

[0159] In some embodiments, the RC 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: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1.

[0160] In some embodiments, the RC TME signature comprises a gene group score for the Th1 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, or at least five genes) in the Th1 signature group defined by its constituent genes: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG.

[0161] In some embodiments, the RC 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: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1.

[0162] In some embodiments, the RC 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: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1.

[0163] In some embodiments, the RC TME signature comprises a gene group score for the Treg 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 the Treg group defined by its constituent genes: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8.

[0164] In some embodiments, the RC TME signature comprises a gene group score for the T reg trafficking 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 the T reg trafficking group defined by its constituent genes: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1.

[0165] In some embodiments, the RC 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 signature group defined by its constituent genes: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1.

[0166] In some embodiments, the RC TME signature comprises a gene group score for the granulocyte trafficking 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 the granulocyte trafficking group defined by its constituent genes: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1.

[0167] In some embodiments, the RC 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.

[0168] In some embodiments, the RC TME signature comprises a gene group score for the MDSC trafficking 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 MDSC trafficking group defined by its constituent genes: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R.

[0169] In some embodiments, the RC TME signature comprises a gene group score for the macrophage 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, or at least seven genes) in the macrophage population defined by its constituent genes: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R.

[0170] In some embodiments, the RC TME signature comprises a gene group score for the macrophage DC trafficking 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, at least six genes, or at least seven genes) in the macrophage DC trafficking group defined by its constituent genes: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R.

[0171] In some embodiments, the RC TME signature comprises a gene group score for the Th2 signature group, which in some embodiments may be calculated using 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, or at least seven genes) in the Th2 signature group defined by its constituent genes: IL13, CCR4, IL10, IL5, IL4.

[0172] In some embodiments, the RC TME signature comprises a gene group score for a tumor-promoting 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, at least five genes, at least six genes, or at least seven genes) of the tumor-promoting cytokine group defined by its constituent genes: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22.

[0173] In some embodiments, the RC TME signature comprises a gene group score for the cancer-associated fibroblast (CAF) population. In some embodiments, this gene group score may be calculated using the 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 cancer-associated fibroblast (CAF) population defined by its constituent genes: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2.

[0174] In some embodiments, the RC TME signature comprises a gene group score for the matrix 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 matrix group defined by its constituent genes: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2.

[0175] In some embodiments, the RC TME signature comprises a gene group score for the matrix remodeling 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 matrix remodeling group defined by its constituent genes: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9.

[0176] In some embodiments, the RC 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.

[0177] In some embodiments, the RC TME signature comprises a gene group score for the endothelial 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 three 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 endothelial group defined by its constituent genes: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR.

[0178] In some embodiments, the RC 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.

[0179] In some embodiments, the RC TME signature comprises a gene group score for the EMT 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, at least nine genes, at least ten genes, or more than ten genes) in the EMT signature group defined by its constituent genes: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2.

[0180] In some embodiments, the RC TME signature comprises a gene cluster score for the cyclic nucleotide metabolism group. In some embodiments, the gene cluster score is determined by the following genes: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, It may be calculated using 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 cyclic nucleotide metabolism group defined by NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3.

[0181] In some embodiments, the RC TME signature comprises gene group scores for glycolysis and gluconeogenesis.In some embodiments, the gene cluster score is determined by the following gene sequence: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PG Glycolysis and gluconeogenesis groups defined by AM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; Acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3 , ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC 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 13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT may be calculated.

[0182] In some embodiments, the RC TME signature comprises a gene group score for the fatty acid metabolism group. In some embodiments, the 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, at least nine genes, at least ten genes, or more than ten genes) in the fatty acid metabolism group defined by its constituent genes: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12.

[0183] In some embodiments, determining the RC TME signature comprises determining a gene group score for a particular gene group using RNA expression levels of at least three genes in the particular gene group (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, all genes in the gene group, or any number in between) for the particular gene group: MHC I group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, NLRC5, TAPBP; MHC II group: HLA-DQ A1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; Co-activating 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, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; B cell group: C R2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; anti-tumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1;Checkpoint inhibition group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; Treg trafficking group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR 1; Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; MDSC group: IDO1, ARG1, IL10, CYBB, PTGS2, IL4I1, IL6; MDSC transport group: CCL1 5, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; Macrophage group: MRC1, CD163, MSR1, SIGLEC1, I L4I1, CD68, IL10, CSF1R; macrophage DC trafficking group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; Th2 signature group: IL13, CCR4, IL10, IL5, IL4; tumor-promoting cytokine group: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; cancer-associated fibroblast (CAF) group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MM P2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2;Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2;Matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9;Angiogenic group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PGF, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5; Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; Proliferative rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, CCNB1, MCM2, MCM6; EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; Circulating nuclease group: Creotide metabolizing group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1 A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3;Glycolysis and Gluconeogenesis Group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; Citric Acid Cycle Group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT;and fatty acid metabolism group: determining respective gene group scores for at least two of each of the gene groups including MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12;

[0184] The list of genes is shown in Table 1 below.

[0185] [Table 1]

[0186] [Table 2]

[0187] [Table 3]

[0188] [Table 4]

[0189] As explained above, aspects of the present disclosure relate to determining a subject's RC TME signature, which may include a gene group score (e.g., a gene group score generated using RNA expression data for some or all of the gene groups listed in Table 1). Aspects of determining these signatures are now described with reference to FIG. 3.

[0190] In some embodiments, the RC 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, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, or 37) gene groups listed in Table 1. In some embodiments, each gene group score is generated using RNA levels of at least some of the genes in each 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

[0191] 3 illustrates an exemplary process 108 for determining gene enrichment scores according to some embodiments of the technology described herein. As shown in the example of FIG. 3, a "RC 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.

[0192] For example, as shown in FIG. 3 , a gene group score (labeled “gene group score 1”) for gene group 1 (e.g., Treg cell group) is calculated from 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., Th1 group) is calculated from 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., MHC II group) is calculated from 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., effector cell group) is calculated from RNA expression data of one or more genes in gene group 4. As another example, the gene group score (labeled "gene group score 5") for gene group 5 (e.g., the anti-tumor cytokine group) is calculated from the RNA expression data of one or more genes in gene group 5. As another example, the gene group score (labeled "gene group score 6") for gene group 6 (e.g., the M1 group) is calculated from the RNA expression data of one or more genes in gene group 6. As another example, the gene group score (labeled "gene group score 7") for gene group 7 (e.g., the neutrophil signature group) is calculated from the RNA expression data of one or more genes in gene group 7. As another example, the gene group score (labeled "gene group score 8") for gene group 8 (e.g., the checkpoint inhibition group) is calculated from the RNA expression data of one or more genes in gene group 8.

[0193] While the example of Figure 3 shows that the gene expression group expression 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 fewer or more than eight). As indicated by the vertical ellipses in Figure 3, determining the gene group scores for the RC TME signature may comprise determining gene group scores for 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, or more gene groups using RNA expression data from one or more respective genes in each gene group, such that aspects of the technology described herein are not limited in this respect. In another example, a RC TME signature may include scores of only a subset of the genes listed in Table 1 above.

[0194] The number of genes in a gene group used to determine the gene group expression 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 genes may be used (e.g., at least 2 genes, at least 3 genes, at least 5 genes, 2-10 genes, 5-15 genes, or other suitable ranges within these ranges).

[0195] In some embodiments, the RNA expression levels of a particular group 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 and calculate a score for the particular group of genes. 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, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, or 37) gene groups listed in Table 1. In some embodiments, each of the gene groups separately 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, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 or more) genes listed in Table 1. In some embodiments, the RC TME signature is generated by performing ssGSEA on all 33 gene groups in Table 1, each gene group comprising genes listed in Table 1. In some embodiments, the RC TME signature is generated by performing ssGSEA on all 37 gene groups in Table 1, each gene group comprising genes listed in Table 1.

[0196] In some embodiments, one or more (e.g., multiple) enrichment scores are normalized to generate an RC 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 median scaling generates an RC TME signature of the subject. In some embodiments, the median scaling comprises clipping the 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.

[0197] In some embodiments, the subject's RC TME signature is processed using a clustering algorithm to identify a RC tumor microenvironment type (e.g., a RC TME type). In some embodiments, the clustering comprises unsupervised clustering. In some embodiments, the unsupervised clustering comprises a dense clustering approach. In some embodiments, the subject's RC TME signature is compared to existing clusters of RC TME types, and a RC TME type is assigned based on the comparison. In some embodiments, the clustering comprises generating a graph with correlations between samples at the nodes and ssGSEA scores at the edges. In some embodiments, each node has 75 neighbors. In some embodiments, the clustering further comprises applying the Leiden algorithm to the resulting graph.

[0198] In some embodiments, the subject's RC TME signature is compared to existing clusters of RC TME types and the RC 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] Updating the RC TME cluster based on new data Techniques for generating RC TME clusters are described herein. It should be understood that the RC TME clusters may be updated when additional RC TME signatures are calculated for a patient. In some embodiments, the RC TME signature of a subject is one of a threshold number of RC TME signatures for a threshold number of subjects. In some embodiments, the RC TME signature clusters are updated when a threshold number of RC TME signatures are generated. For example, once a new threshold number of RC 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 RC TME signatures previously used to generate the RC TME clusters, and the combined set of old and new RC 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 RC TME signature clusters.

[0201] In this way, data from future patients may be analyzed in a manner that leverages information learned from patients whose RC 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 RC TME type that future patients may have, improving treatment options for these patients.

[0202] Myogenesis signature Aspects of the present disclosure relate to a method for generating a myogenic signature of a subject. The present disclosure is based in part on the recognition that the myogenic signature calculated as described herein can be used to identify subjects who are likely to be non-responsive to treatment with an immuno-oncology (IO) agent in some embodiments. In some embodiments, the myogenic signature is generated using RNA expression data of at least some (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14) of the genes listed in Table 2.

[0203] In some embodiments, the myogenic signature comprises a myogenic gene group score. In some embodiments, the myogenic gene group score is generated using RNA expression levels of at least some (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14) of the genes listed in Table 2. In some embodiments, generating the myogenic gene group score comprises performing GSEA (e.g., ssGSEA) using RNA expression data of two or more genes listed in Table 2. In some embodiments, median scaling is performed on the gene expression (e.g., gene enrichment) scores obtained from GSEA. The myogenic signature may be represented by a score ranging from -3 to 20 (e.g., -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20). In some embodiments, a subject with a myogenic signature (score) higher than 4 is considered to have a "high myogenic score". In some embodiments, a subject with a myogenic signature (score) higher than 8 is considered to have a "high myogenic score". In some embodiments, a subject with a myogenic signature (score) higher than 10 is considered to have a "high myogenic score". In some embodiments, a subject with a myogenic signature (score) higher than 15 is considered to have a "high myogenic score". In some embodiments, a subject with a "high" myogenic signature (score) is considered to be a "non-responder to IO therapy". A "non-responder to IO therapy" is a subject with RC (e.g., ccRCC) who is significantly less likely to respond to treatment with an immunotherapeutic (IO) agent compared to a subject without a "high" myogenic signature (score) (e.g., a RC subject).

[0204] The number of genes in the gene group used to determine the myogenic signature may vary. In some embodiments, the total RNA expression level of all genes in the myogenic signature gene group may be used to determine the myogenic gene group score. In other embodiments, RNA expression data of fewer than all genes may be used (e.g., RNA expression levels of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13 genes). A list of genes in the myogenic signature gene group is provided in Table 2 below.

[0205] [Table 5]

[0206] Generation of RC TME signatures and identification of TME types As described herein, FIG. 1 illustrates the determination of a subject's RC TME signature, and, optionally, identifying a subject's prognosis using the identified RC TME signature.

[0207] As described herein, in some embodiments, the RC TME signature determined for a subject using the techniques described herein may be used to identify one of a number of different RC TME types for the subject. In some embodiments, the RC TME types include RC TME type A, RC TME type B, RC TME type C, RC TME type D, and RC TME type E.

[0208] In some embodiments, each of the multiple RC TME types is associated with a respective RC TME signature cluster in the multiple RC TME signature clusters. The RC TME type of the subject may be determined by: (1) associating the RC TME signature of the subject with a particular one of the multiple RC TME signature clusters; and (2) identifying the RC TME type of the subject as the RC TME type corresponding to the particular one of the multiple RC TME signature clusters with which the RC TME signature of the subject is associated.

[0209] 4 shows an exemplary RC TME signature 400. In some embodiments, the RC TME signature comprises at least three gene group scores for the gene groups listed in Table 1. However, it should be understood that the RC TME signature may include more or less scores than the number of scores shown in FIG. 4 (e.g., by omitting one or more scores of the gene groups listed in Table 1, or by including scores of one or more other gene groups in addition to or instead of the gene groups listed in Table 1). In some embodiments, the RC TME signature may be embodied in at least one data structure comprising a field that stores the gene group score portion of the RC TME signature.

[0210] In some embodiments, the RC TME signature clusters may be generated by (1) obtaining RC TME signatures for a plurality of subjects (using techniques described herein) and (2) clustering the RC 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.

[0211] 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 Davies Bouldin, 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.

[0212] Thus, in some embodiments, generating a RC 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 RC 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 RC TME signatures comprising a gene group score for a respective gene group, and generating comprises, for each particular one of the plurality of RC TME signatures, (i) determining the RC 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 RC TME signature was generated; and (ii) clustering the plurality of RC signatures to obtain a plurality of RC TME signature clusters.

[0213] The resulting RC TM signature clusters may each contain any suitable number of RC 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.

[0214] The number of RC 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 RCs may be characterized into five types based on the generation of RC TME signatures using the methods described herein.

[0215] 4, a subject's RC TME signature 400 may be associated with one of five RC TME clusters 402, 404, 406, 408, and 410. Each of the clusters 402, 404, 406, 408, and 410 may be associated with a respective RC TME type. In this example, the RC 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 RC TME signature 400 is associated with the closest RC 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, RC TME signature 400 is associated with RC TME type clusters 5, 410 (as shown by consistent shading) because the measured distance D5 between RC 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 RC TME signature 400 (e.g., of the centers of mass of clusters 402, 404, 406, and 408, respectively).

[0216] In some embodiments, a subject's RC TME signature may be associated with one of the five RC 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 RC TME signature to one of the five RC TME signature clusters. The machine learning technique may be trained to assign RC TME signatures to meta-cohorts represented by the signatures in the cluster.

[0217] In some embodiments, RC TME types include RC TME type A, RC TME type B, RC TME type C, RC TME type D, and RC TME type E. The RC 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 RC. 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 RC.

[0218] In some embodiments, the subject is identified as having "immune-enriched, fibrotic (IE / F)", also referred to as "RC TME type A" RC. In some embodiments, RC TME type A is characterized by a high prevalence of immune cells and a high proportion of cancer-associated fibroblasts (CAFs) compared to other RC TME types. In some embodiments, RC TME type A comprises abundant tumor-promoting, immunosuppressive infiltrates, including significant numbers of regulatory T cells. In some embodiments, the proportion of malignant cells in RC TME type A is low compared to other RC TME types. In some embodiments, mutations in the tumor suppressor BAP1 are frequent in RC TME type A. In some embodiments, subjects with RC TME type A are responsive to immune checkpoint inhibitors alone or in combination with tyrosine kinase inhibitors. In some embodiments, RC TME type A is characterized by a high tumor growth rate compared to other RC TME types. In some embodiments, subjects with RC TME type A have a poorer prognosis compared to subjects with other RC TME types.

[0219] In some embodiments, the subject is characterized as having "immune-enriched, non-fibrotic (IE)", also referred to as "RC TME type B". In some embodiments, RC TME type B is characterized by an abundant immune-active infiltrate, including cytotoxic effector cells, and a lower distribution of stromal and fibrotic elements compared to other RC TME types. In some embodiments, RC TME type B is characterized by immune-induced inflammation. In some embodiments, the subject with RC TME type B comprises a mutation in the tumor suppressor BAP1. In some embodiments, the subject with RC TME type B is responsive to immune checkpoint inhibitors alone or in combination with a tyrosine kinase inhibitor.

[0220] In some embodiments, the subject is characterized as having "fibrosis (F)", also referred to as "RC TME type C". In some embodiments, RC TME type C is highly fibrotic (compared to other RC TME types) with dense collagen formation. In some embodiments, RC TME type C is characterized as being less inflammatory than certain other RC TME types E. In some embodiments, RC TME type C is characterized by minimal leukocyte / lymphocyte infiltration compared to other RC TME types. In some embodiments, cancer-associated fibroblasts (CAFs) are abundant in type C RC. In some embodiments, signs of epithelial-mesenchymal transition (EMT) are present in subjects with RC TME type C. In some embodiments, RC TME type C is associated with a poorer prognosis compared to other RC TME types.

[0221] In some embodiments, subjects are characterized as having "immune desert (D) with metabolic content," also referred to as "RC TME type D." In some embodiments, the RC TME type contains the highest percentage of malignant cells compared to other RC TME types and is characterized by minimal or complete absence of leukocyte / lymphocyte infiltration. In some embodiments, immune-mediated inflammation is absent. In some embodiments, signs of metabolic activation are present in subjects with RC TME type D. In some embodiments, RC TME type D is associated with a favorable prognosis compared to other RC TME types.

[0222] In some embodiments, subjects are characterized as having "angiogenic, non-inflammatory," also referred to as "RC TME type E." In some embodiments, RC TME type E is characterized by robust angiogenesis and low levels of immune infiltration compared to other RC TME types. In some embodiments, signs of epithelial-mesenchymal transition (EMT) are present in subjects with RC TME type E. In some embodiments, RC TME type E is associated with low cancer stage and usually does not require treatment. In some embodiments, subjects with RC TME type E are often responsive to tyrosine kinase inhibitors (TKIs). In some embodiments, RC TME type E is associated with a better prognosis compared to other RC TME types.

[0223] In some embodiments, the present disclosure provides methods for identifying the prognosis of a RC subject using a RC TME signature generated using the methods described herein.

[0224] In some embodiments, the method comprises identifying a subject as having a reduced risk of RC progression when the subject is assigned to RC TME type E or RC TMED, compared to other RC TME types. In some embodiments, a "reduced risk of RC progression" may indicate that the subject has a good prognosis for RC or is less likely to have progressive disease. In some embodiments, a "reduced risk of RC progression" may indicate that a subject with RC is expected to be more responsive to a particular treatment. For example, a "reduced risk of RC 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 RC patient or population of RC patients (e.g., patients with RC but not the same RC TME type as the subject).

[0225] In some embodiments, the method further comprises identifying the subject as having an increased risk of RC progression when the subject is assigned to a RC TME type other than RC TME type E, such as, for example, RC TME type A, compared to other RC TME types. In some embodiments, "increased risk of RC progression" may indicate that the subject has a less favorable prognosis for RC or is more likely to have progressive disease. In some embodiments, "increased risk of RC progression" may indicate that a subject with RC 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 RC 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 RC patient or population of RC patients (e.g., patients with RC but not the same RC TME type as the subject).

[0226] Responder Score Aspects of the present disclosure relate to methods for determining whether a subject with RC (e.g., ccRCC) is likely to respond to a particular therapeutic agent, such as an immunotherapeutic (IO) agent or a TKI.

[0227] In some embodiments, the therapeutic agent is an immuno-oncology (IO) agent. The IO agent may be a small molecule, a peptide, a protein (e.g., an antibody such as a monoclonal antibody), an interfering nucleic acid, or a combination of any of the above. In some embodiments, the IO agent comprises a PD1 inhibitor, a PD-L1 inhibitor, or a PD-L2 inhibitor. Examples of IO agents include, but are not limited to, cemiplimab, nivolumab, pembrolizumab, avelumab, durvalumab, atezolizumab, BMS1166, BMS202, ipilimumab, and the like.

[0228] In some embodiments, the therapeutic agent is a tyrosine kinase inhibitor (TKI). The TKI may be a small molecule, a peptide, a protein (e.g., an antibody, such as a monoclonal antibody), an interfering nucleic acid, or a combination of any of the above. Examples of TKIs include, but are not limited to, imatinib mesylate (Gleevec®), dasatinib (Sprycel®), nilotinib (Tasigna®), bosutinib (Bosulif®), sunitinib (Sutent®), and the like.

[0229] Aspects of the present disclosure relate to a method for determining the likelihood that a subject with RC (e.g., ccRCC) will respond to an IO agent. The present disclosure is based in part on the identification of a specific subgroup of RC patients that comprises biomarkers that indicate response to an IO agent. In some embodiments, if a subject is determined to comprise (e.g., using the methods described herein, the subject is identified to have) one or more of the following biomarkers: high ploidy (e.g., calculated by RTumor bioinformatics analysis), high myogenic signature (e.g., described throughout the specification in the section entitled Myogenic Signature), RC TME type E (determined by the methods described throughout the specification), the presence of mTOR activating mutations, or the presence of mutations in antigen presentation machinery, the subject is unlikely to respond to IO therapy. Examples of mTOR activating mutations include, but are not limited to, mutations in MTOR, TSC1 / 2, PTEN, MET, and the mutations described in Cancer Discov. 2014 May;4(5):554-63. Doi:10.1158 / 2159-8290. CD-13-0929. Epub 2014 Mar14. Examples of mutations in antigen presentation include, but are not limited to, mutations in PSMB5, ​​PSMB6, PSMB7, PSMB8, PSMB9, PSMB10, TAP1, TAP2, ERAP1, ERAP2, CANX, CALR, PDIA3, TAPBP, B2M, HLA-A, HLA-B, and HLA-C. In some embodiments, subjects with one or more of the aforementioned biomarkers are referred to as "IO non-responders."

[0230] In some aspects, the present disclosure provides a method for predicting the likelihood of a subject responding to an immuno-oncology (IO) agent by identifying the subject's RC TME type using the subject's gene expression data, and then using a machine learning model to obtain a responder score indicative of the likelihood of the subject responding to IO. In some embodiments, the machine learning model comprises a gradient boosting model. In some embodiments, the machine learning model comprises a CatBoost classifier. In some embodiments, the machine learning model is trained using the following inputs from a plurality of samples (e.g., samples from a cohort of patients): RC TME type; expression levels of PD1, PD-L1, and / or PD-L2 obtained from gene expression data; an ECM-related signature (e.g., a gene group score generated using two or more genes, such as, for example, 2, 3, 4, 5, 6, or more, from the ECM-related genes of Table 1); an angiogenesis signature (e.g., a gene group score generated using two or more genes, such as, for example, 2, 3, 4, 5, 6, or more, from the angiogenesis genes of Table 1); a proliferation rate signature (e.g., a gene group score generated using two or more genes, such as, for example, 2, 3, 4, 5, 6, or more, from the proliferation rate genes of Table 1); and a similarity score generated by comparing the RC TME type identified for the subject with RC TME type B gene group scores and / or RC TME type C gene group scores from other subjects. In some embodiments, the similarity score is generated by comparing the gene group score of the subject's RC TME signature with the average of the gene group scores of multiple RC TME signatures from RC TME type B samples and / or the average of the gene group scores of multiple RC TME signatures from RC TME type C samples. In some embodiments, subjects identified as "IO non-responders" are excluded as input from the machine learning algorithm. In some embodiments, the ECM-associated signature comprises gene enrichment scores for 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 genes listed in Table 1.In some embodiments, the angiogenesis signature comprises a gene enrichment score for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 genes listed in Table 1. In some embodiments, the proliferation rate signature comprises a gene enrichment score for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 genes listed in Table 1.

[0231] The generated responder score (e.g., IO responder score) for the subject is then compared to a specified threshold to determine whether the subject is likely to respond to the immunotherapeutic agent. In some embodiments, the specified threshold is used to determine (e.g., classify) the subject as "IO low," "IO intermediate," or "IO high." The value of the specified threshold may range from 0.2 to about 0.8 units (e.g., 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or any unit value therebetween). In some embodiments, the specified threshold ranges from about 0 to about 1 unit. In some embodiments, the specified threshold is 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or 1.0. In some embodiments, the responder score is used to identify a subject as "IO low" if the responder score is 0.05 or less, "IO intermediate" if the responder score is 0.05 or more and less than 0.5, or "IO high" if the responder score is 0.5 or more. In some embodiments, a subject identified as having a responder score above a specified threshold is identified as likely to respond to treatment with an IO agent. In some embodiments, a subject having a responder score below a specified threshold (e.g., below 0.5) is unlikely to respond positively to IO therapy (e.g., an IO agent is unlikely to be therapeutically effective in the subject). In some embodiments, a responder score of 0.5 or more indicates that the subject is likely to respond positively to IO therapy (e.g., an IO agent is unlikely to be therapeutically effective in the subject).

[0232] Referring to the drawings, FIG. 7 provides an illustration of one example of a process (700) for using a computer hardware processor to predict the likelihood that a subject will respond to an immuno-oncology (IO) agent. First, in operation 702, sequencing data of a subject is obtained. Methods for obtaining sequencing data are described throughout this specification, including in the section entitled Sequencing Data and Gene Expression Data. The sequencing data may be processed to obtain gene expression data. Then, in operation 704, the gene expression data is used to identify the RC TME type of the subject. In some embodiments, the gene expression data is RNA expression data. In some embodiments, the RC TME type of the subject is identified by using the RNA expression data to generate a RC TME signature of the subject (e.g., using the RNA levels of the expression data to generate a gene group score for one or more of the gene groups listed in Table 1), and then using the subject's RC TME signature to identify the RC TME type of the subject.

[0233] After the subject's RC TME type has been identified, in process 706, the machine learning model is used to obtain an output indicative of a responder score (a responder score indicative of the likelihood that the subject will respond to the IO agent). In some embodiments, the obtaining step comprises generating a set of input features using RNA expression data obtained from the subject, the set of input features comprising at least two (e.g., 2, 3, 4, 5, or 6) of the following features: the subject's RC TME type; RNA expression levels of one or more of the PD1, PD-L1, and PD-L2 genes; the subject's ECM-associated signature; the subject's angiogenesis signature; the subject's proliferation rate signature; and in operation 708, a similarity score indicative of the similarity of the subject's RC TME signature to RC TME signatures associated with RC TME type B and / or RC TME type C samples.

[0234] In some embodiments, generating the set of input features comprises determining RNA expression levels of one or more of the PD1, PD-L1, and PD-L2 genes. In some embodiments, generating the set of input features comprises using the RNA expression data to determine an ECM-associated signature of the subject by performing ssGSEA on the RNA expression data of at least three (e.g., three, four, five, six, or all) of the "ECM-associated signature" genes listed in Table 1 to generate an ECM-associated gene family score. In some embodiments, generating the set of input features comprises using the RNA expression data to determine an angiogenic signature of the subject by performing ssGSEA on the RNA expression data of at least three (e.g., three, four, five, six, or all) of the "angiogenic signature" genes listed in Table 1 to generate an angiogenic gene family score. In some embodiments, generating a set of input features comprises determining a growth rate signature of the subject using the RNA expression data by performing ssGSEA on the RNA expression data for at least three (e.g., three, four, five, six, or all) of the "growth rate signature" genes listed in Table 1 to generate a growth rate gene group score. In some embodiments, generating a set of input features comprises determining a similarity score by comparing the gene group score of the RC TME signature of the subject to an average of the gene group scores of a plurality of RC TME signatures from RC TME type B samples and / or an average of the gene group scores of a plurality of RC TME signatures from RC TME type C samples.

[0235] The set of input features generated at operation 708 is then used as an input for a machine learning model comprising a gradient boosting model, which is used to obtain a corresponding output indicative of a responder score at operation 710. In some embodiments, the gradient boosting model comprises a CatBoost classifier.

[0236] The machine learning model used to generate the immuno-oncology (IO) responder score may be of any suitable type. For example, in some embodiments, the machine learning model may be a gradient boosted machine learning model. Non-limiting examples of gradient boosted machine learning models include an XGBoost model, a LightGBM model, a CatBoost model, an Adaboost model, or a random forest model. However, the machine learning model may be of any other suitable type, including, for example, a non-linear regression model (e.g., a logistic regression model), a neural network model, a support vector machine, a Gaussian mixture model, a random forest model, a decision tree model, or any other suitable type of machine learning model, although aspects of the technology described herein are not limited in this respect.

[0237] In some embodiments, the machine learning model may comprise between 10 and 100 parameters, between 100 and 1000 parameters, between 1000 and 10,000 parameters, between 10,000 and 100,000 parameters, or more than 100,000 parameters. Processing the input data with the machine learning model comprises performing calculations using values ​​of the parameters of the machine learning model and values ​​of the inputs to the machine learning model to obtain corresponding outputs. In some embodiments, such calculations may involve hundreds, thousands, tens of thousands, hundreds of thousands, or more calculations.

[0238] In some embodiments, the machine learning model may include a number of parameters whose values ​​can be estimated using training data. The process of estimating parameter values ​​using training data is referred to as "training." In some embodiments, the machine learning model may include one or more hyperparameters in addition to the number of parameters. The values ​​of the hyperparameters may be estimated during training.

[0239] After the responder score is output from the machine learning model, the subject may optionally be identified as "IO low," "IO medium," or "IO high" based on the responder score at operation 712. The subject's responder score is then compared to a specified threshold to determine whether the subject is likely to respond to the immunotherapeutic agent at operation 714. The specified threshold value may vary. In some embodiments, the specified threshold value ranges from about 0.2 to about 0.8 units. In some embodiments, the specified threshold is 0.5. If the subject is identified as having a responder score above the specified threshold, the subject is identified as likely to respond to the IO agent. In some embodiments, if the subject has a responder score of 0.05 or less, the subject is identified as "IO medium" if the responder score is 0.05 or more and less than 0.5. In some embodiments, if the responder score is 0.5 or more, the subject is identified as "IO high." However, it should be understood that depending on the value of the specified threshold, a subject having a responder score of less than 0.5 may be identified as "IO-high" (e.g., if the threshold is 0.4, then a subject identified as having a responder score of less than 0.4 is identified as "IO-high"). In some embodiments, the method further comprises administering IO therapy to the subject at operation 716.

[0240] In some aspects, the present disclosure provides a method for predicting the likelihood that a subject will respond to a tyrosine kinase inhibitor (TKI). In some embodiments, the method comprises using RNA expression data obtained from a subject to generate a set of input features, the set of input features comprising at least two (e.g., two, three, or four) of the following features: a macrophage signature of the subject; an angiogenesis signature of the subject; a proliferation rate signature of the subject; and a similarity score indicating the similarity between the RC TME signature of the subject and an RC TME signature associated with an RC TME type B sample. In some embodiments, a machine learning model is trained using the set of input features to obtain a corresponding output indicating a responder score indicating the likelihood that the subject will respond to the TKI. In some embodiments, the machine learning model comprises a logistic regression model.

[0241] In some embodiments, the macrophage signature comprises a macrophage gene group score generated using RNA levels of 1, 2, 3, 4, 5, 6, 7, or 8 of the macrophage group genes listed in Table 1. In some embodiments, the angiogenesis signature comprises angiogenesis gene group scores of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 angiogenesis genes listed in Table 1. In some embodiments, the proliferation rate signature comprises gene enrichment scores for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 genes listed in Table 1.

[0242] The responder score (e.g., TKI responder score) generated for the subject is then compared to a specified threshold to determine whether the subject is likely to respond to the TKI. In some embodiments, the specified threshold is used to determine (e.g., classify) the subject as "TKI-low", "TKI-intermediate", or "TKI-high". The value of the specified threshold may range between 0.1 and about 1.5 units (e.g., 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, or 1.5, or any unit value therebetween). In some embodiments, the specified threshold ranges from about 0 to about 1 unit. In some embodiments, the specified threshold is 0.25, 0.5, 0.75, 0.85, 0.95, or 1.0. In some embodiments, subjects identified as having a responder score above the specified threshold are identified as likely to respond to treatment with a TKI agent. In some embodiments, a subject with a responder score of less than 0.6 is unlikely to respond positively to the TKI (e.g., the TKI is unlikely to be therapeutically effective in the subject). In some embodiments, a responder score of 0.6 or greater indicates that the subject is likely to respond positively to the TKI (e.g., the TKI is unlikely to be therapeutically effective in the subject). In some embodiments, the responder score is used to identify a subject as "TKI-low" if the responder score is 0.75 or less, "TKI-intermediate" if the responder score is 0.75 or greater but less than 0.95, or "TKI-high" if the responder score is 0.95 or greater.

[0243] FIG. 9 provides an example of a process for performing a method 900 for predicting the likelihood that a subject will respond to a tyrosine kinase inhibitor (TKI) using a computer hardware processor, according to some embodiments of the technology described herein. First, a subject's RNA expression data is obtained, 902. Methods for obtaining sequencing data are described throughout the specification, including in the section entitled Sequencing Data and Gene Expression Data. The sequencing data is processed to obtain RNA expression data. Optionally, the subject's RC TME type is identified using the RNA expression data in operation 904. In some embodiments, the gene expression data is RNA expression data. In some embodiments, the subject's RC TME type is identified by using the RNA expression data to generate a subject's RC TME signature (e.g., using the RNA levels of the expression data to generate a gene group score for one or more of the gene groups listed in Table 1), and then using the subject's RC TME signature to identify the subject's RC TME type.

[0244] After the subject's RC TME type has been identified, in process 906, a machine learning model is used to obtain an output indicative of a responder score (responder score indicative of the likelihood that the subject will respond to the TKI) from the set of input features. In some embodiments, the obtaining step comprises using RNA expression data obtained from the subject to generate a set of input features, the set of input features comprising at least two (e.g., 2, 3, or 4) of the following features: a macrophage-associated signature of the subject; an angiogenesis signature of the subject; a proliferation rate signature of the subject; and, in operation 908, a similarity score indicative of the similarity between the RC TME signature of the subject and an RC TME signature associated with an RC TME type B sample.

[0245] In some embodiments, generating a set of input features comprises using the RNA expression data to determine a macrophage signature of the subject by performing ssGSEA on the RNA expression data of at least three (e.g., three, four, five, six, or all) of the "macrophage signature" genes listed in Table 1 to generate a macrophage gene group score. In some embodiments, generating a set of input features comprises using the RNA expression data to determine a proliferation rate signature of the subject by performing ssGSEA on the RNA expression data of at least three (e.g., three, four, five, six, or all) of the "angiogenesis signature" genes listed in Table 1 to generate an angiogenesis gene group score. In some embodiments, generating a set of input features comprises using the RNA expression data to determine a proliferation rate signature of the subject by performing ssGSEA on the RNA expression data of at least three (e.g., three, four, five, six, or all) of the "growth rate signature" genes listed in Table 1 to generate a proliferation rate gene group score. In some embodiments, generating a set of input features comprises determining a similarity score by comparing a gene group score of the subject's RC TME signature with an average of gene group scores of multiple RC TME signatures from RC TME type B samples.

[0246] The set of input features generated in operation 908 is then used as an input for a machine learning model comprising a logistic regression model, which is used to obtain a corresponding output indicative of a responder score in operation 910.

[0247] The machine learning model used to generate the TKI responder score may be of any suitable type. For example, in some embodiments, the machine learning model may be a non-linear regression model (e.g., a logistic regression model). However, the machine learning model may be of any other suitable type, such as a gradient boosting model (e.g., XGBoost, CatBoost, LightGBM, etc.), a neural network model, a support vector machine, a Gaussian mixture model, a random forest model, a decision tree model, or any other suitable type of machine learning model, although aspects of the technology described herein are not limited in this respect.

[0248] In some embodiments, the machine learning model may comprise between 10 and 100 parameters, between 100 and 1000 parameters, between 1000 and 10,000 parameters, between 10,000 and 100,000 parameters, or more than 100,000 parameters. Processing the input data with the machine learning model comprises performing calculations using values ​​of the parameters of the machine learning model and values ​​of the inputs to the machine learning model to obtain corresponding outputs. In some embodiments, such calculations may involve hundreds, thousands, tens of thousands, hundreds of thousands, or more calculations.

[0249] In some embodiments, the machine learning model may include a number of parameters whose values ​​can be estimated using training data. The process of estimating parameter values ​​using training data is referred to as "training." In some embodiments, the machine learning model may include one or more hyperparameters in addition to the number of parameters. The values ​​of the hyperparameters may be estimated during training.

[0250] After the responder score is output from the machine learning model, the subject may optionally be identified as "TKI low," "TKI intermediate," or "TKI high" based on the responder score, at operation 912.

[0251] The subject's responder score is then compared to a specified threshold in operation 914 to determine whether the subject is likely to respond to the immunotherapeutic agent. The specified threshold value may vary. In some embodiments, the specified threshold value ranges from about 0.1 to about 1.5 units. In some embodiments, the specified threshold is 0.6. If a subject is identified as having a responder score above the specified threshold, the subject is identified as likely to respond to the TKI. In some embodiments, if the subject has a responder score of 0.75 or less, the subject is identified as "TKI low". In some embodiments, if the responder score is 0.75 or more and less than 0.95, the subject is identified as "TKI medium". In some embodiments, if the responder score is 0.95 or more, the subject is identified as "TKI high". However, it should be understood that depending on the specified threshold value, a subject with a responder score of less than 0.95 may be identified as "TKI high" (e.g., if the threshold is 0.6, a subject identified as having a responder score of less than 0.6 is identified as "TKI high"). In some embodiments, the method further comprises administering a TKI to the subject at operation 916.

[0252] Immuno-oncology (IO) / TKI decision tree Aspects of the present disclosure relate to methods for selecting one or more therapeutic agents for a subject with renal cancer (e.g., ccRCC). The present disclosure is based in part on a method using RNA sequencing data obtained from a subject to identify the likelihood of the patient's response to either an immuno-oncology (IO) agent and / or a tyrosine kinase inhibitor (TKI) and generate one or more responder scores for the subject (e.g., IO responder score, TKI responder score, etc.). Without wishing to be bound by any particular theory, the therapeutic agent selection methods described herein provide physicians with increased confidence in identifying classes of therapeutic agents or combinations of therapeutic agents to which patients are more likely to respond (and conversely, physicians can avoid prescribing therapeutic agents to which patients are less likely to respond), thereby improving patient treatment techniques. A schematic diagram illustrating one example of the method described in this section is shown in FIG. 14.

[0253] In some aspects, the disclosure provides a method of identifying one or more therapeutic agents for administration to a subject having renal cancer, the method comprising the steps of generating an International Metastatic RCC Database Consortium (IMDC) risk score for the subject; if the subject is identified as having a poor IMDC risk score, identifying a combination of an immuno-oncology (IO) agent and a TKI as the one or more therapeutic agents for administration to the subject; if the subject is identified as having a good or intermediate IMDC risk score, generating an IO responder score according to the methods described herein; a TKI responder score according to the methods described herein; and using the IO responder score and the TKI responder score to identify one or more therapeutic agents for the subject.

[0254] The IMDC risk score may be calculated using any suitable method, for example, as described by Guida et al. Oncotarget. 2020;11:4582-4592. Typically, the IMDC risk score classifies patients into one of the "good" (also referred to as "favorable"), "intermediate", or "poor" categories based on six clinical prognostic factors: activity index (e.g., Karnofsky Performance Status [KPS] score less than 80), hemoglobin level less than the lower limit of normal [LNL]), time from diagnosis to start of systemic treatment [DTT] (less than 1 year), corrected serum calcium level (above the upper limit of normal [UNL]), neutrophil count (above the UNL), and platelet count (above the UNL). Patients lacking these poor factors are identified as having a "good" (or "favorable") prognosis; patients exhibiting one or two of the factors have a "moderate" risk of death; and for patients with three or more factors, the expected risk prognosis is "poor." In some embodiments of the methods described by the present disclosure, if a sample from a subject is identified as having a "poor" IMDC risk score, a combination of TKI and IO agent is selected for the subject without further analysis of the IO responder score or TKI responder score.

[0255] In some embodiments, the method comprises generating a TKI responder score for a subject having an IMDC risk score of "moderate" or "good". Methods for generating an IO responder score are described elsewhere herein, for example in FIG. 9 and in the section entitled "Responder Score". In some embodiments, a subject is identified as having a TKI responder score between 0 and 1 (e.g., any value between 0 and 1, inclusive). In some embodiments, a subject is identified as "TKI low" if the subject has a responder score of 0.75 or less. In some embodiments, a subject is identified as "TKI intermediate" if the responder score is 0.75 or more and less than 0.95. In some embodiments, a subject is identified as "TKI high" if the responder score is 0.95 or more.

[0256] In some embodiments, the method comprises generating an IO responder score for a subject with an IMDC risk score of "moderate" or "good". In some embodiments, prior to generating the IO responder score, it is determined whether the subject is an "IO non-responder". Methods for identifying an "IO non-responder" are described elsewhere in this disclosure, for example in the section entitled "Responder Score". In some embodiments, identifying a subject as an "IO non-responder" comprises identifying the subject (e.g., a biological sample obtained from the subject) as having one or more of the following biomarkers: high ploidy (e.g., calculated by R Tumor bioinformatics analysis), high myogenic signature (e.g., described throughout this specification in the section entitled Myogenic Signature), RC TME type E (determined by methods described throughout this specification), presence of mTOR activating mutations, or presence of mutations in antigen presentation machinery. In some embodiments, the method comprises selecting one or more TKIs for a subject identified as an "IO non-responder".

[0257] If the subject is not identified as an "IO non-responder," an IO responder score is generated for the subject. Methods for generating an IO responder score are described elsewhere herein, for example, in FIG. 7 and the section entitled "Responder Score." In some embodiments, the subject is identified as having an IO responder score between 0 and 1 (e.g., any value between 0 and 1, inclusive). In some embodiments, if the subject has a responder score of 0.05 or less, the subject is identified as "IO low." In some embodiments, if the responder score is 0.05 or greater but less than 0.5, the subject is identified as "IO medium." In some embodiments, if the responder score is 0.5 or greater, the subject is identified as "IO high."

[0258] In some embodiments, the method comprises using the TKI and IO responder scores to select (or provide a recommendation for selecting) one or more therapeutic agents for the subject (e.g., generate a report recommending the selection of one or more therapeutic agents for the subject). In some embodiments, if the subject is identified as "TKI low" and "IO low", a TKI agent is selected for the subject. In some embodiments, if the subject is identified as "TKI low" and "IO low", a combination of a TKI agent and an IO agent is selected for the subject. In some embodiments, if the subject is identified as "TKI medium" and "IO low" or "IO medium", a combination of a TKI agent and an IO agent is selected for the subject. In some embodiments, if the subject is identified as "TKI medium" and "IO low" or "IO medium", a TKI agent is selected for the subject. In some embodiments, if the subject is identified as "TKI medium" and "IO high", a combination of a TKI agent and an IO agent is selected for the subject. In some embodiments, if the subject is identified as "TKI high" and "IO low" or "IO medium", a TKI agent is selected for the subject. In some embodiments, if the subject is identified as "TKI-high" and "IO-high," a combination of a TKI agent and an IO agent is selected for the subject. In some aspects, the method further comprises administering the identified one or more therapeutic agents (e.g., a TKI agent, or a combination of a TKI agent and an IO agent) to the subject. Methods of administering a TKI agent or a combination of a TKI agent and an IO agent to a subject are described in further detail herein, e.g., in the section entitled "Therapeutic Indications."

[0259] 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 RC TME type and / or responder score (e.g., IO responder score or TKI responder score).The present disclosure is based in part on the recognition that subjects with RC TME type E are less likely to respond to a particular therapy (e.g., IO agent) compared to subjects with other RC TME types, but may still respond to other therapies, such as, for example, TKIs.The present disclosure is based in part on the recognition that subjects with RC TME type A or RC TME type B are more likely to respond to a particular therapy (e.g., IO agent) compared to subjects with other RC TME types.

[0260] In some embodiments, the therapeutic agent is an immuno-oncology (IO) agent. The IO agent may be a small molecule, a peptide, a protein (e.g., an antibody such as a monoclonal antibody), an interfering nucleic acid, or a combination of any of the above. In some embodiments, the IO agent comprises a PD1 inhibitor, a PD-L1 inhibitor, or a PD-L2 inhibitor. Examples of IO agents include, but are not limited to, cemiplimab, nivolumab, pembrolizumab, avelumab, durvalumab, atezolizumab, BMS1166, BMS202, and the like.

[0261] In some embodiments, the therapeutic agent is a tyrosine kinase inhibitor (TKI). The TKI may be a small molecule, a peptide, a protein (e.g., an antibody, such as a monoclonal antibody), an interfering nucleic acid, or a combination of any of the above. Examples of TKIs include, but are not limited to, axitinib (Inlyta®), cabozantinib (Cabometyx®), imatinib mesylate (Gleevec®), dasatinib (Sprycel®), nilotinib (Tasigna®), bosutinib (Bosulif®), sunitinib (Sutent®), and the like.

[0262] In some embodiments, the method described by the present disclosure further comprises administering one or more therapeutic agents to the subject based on the determination of the subject's RC TME type and / or responder score. In some embodiments, the subject is administered one or more (e.g., 1, 2, 3, 4, 5, or more) IO agents. In some embodiments, the subject is administered one or more (e.g., 1, 2, 3, 4, 5, or more) TKIs. In some embodiments, the subject is administered a combination of one or more IO agents and one or more TKIs.

[0263] Aspects of the present disclosure relate to methods of treating a subject having (or suspected of having, or at risk of having) RC based on the determination of the subject's RC 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.

[0264] 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.

[0265] 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.

[0266] 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 RC TME type, etc.) may be analyzed.

[0267] 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 RC TME types described herein. The dosing regimen (including the therapeutic agent used) may vary over time.

[0268] 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.

[0269] 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.

[0270] 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.

[0271] 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, RC, or a predisposition to RC.

[0272] Alleviating RC includes delaying the onset or progression of disease or reducing disease severity. Alleviating disease does not necessarily result in a cure. As used herein, "delaying" the onset of disease (e.g., cancer) means to postpone, prevent, slow, retard, stabilize, and / or postpone the progression of 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 disease, or delaying the onset of 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.

[0273] "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.

[0274] 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).

[0275] 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.

[0276] 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.

[0277] Examples of surgical therapies include, but are not limited to, curative surgery (eg, tumor removal surgery), preventative surgery, laparoscopic surgery, and laser surgery.

[0278] 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;

[0279] In some aspects, the disclosure provides methods for treating renal cancer (RC), 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 RC TME type, where the subject's RC TME type has been identified by the methods described by the present disclosure.

[0280] In some embodiments, the subject is identified as having RC TME type A, RC TME type B, RC TME type C, RC TME type D, or RC TME type E. In some embodiments, the subject is identified as having RC TME type B or RC TME type A.

[0281] The present disclosure is based in part on the inventors' recognition that subjects with certain RC TME types may respond well to certain immunotherapies (e.g., immune checkpoint inhibitors such as pembrolizumab or nivolumab, or TKIs). Administration of immune oncology agents is well known, for example, as described by Louedec et al. Vaccines (Basel). 2020 Dec; 8(4): 632. For example, doses of pembrolizumab include, for example, 200 mg every 3 weeks or 400 mg every 6 weeks, administered by infusion over 30 minutes. Administration of TKIs is also well known, for example, as described by Gerritse et al. Cancer Treat Rev. 2021 Jun; 97: 102171. doi: 10.1016 / j.ctrv.2021.102171. Combined administration of a TKI and an IO agent is also known, as described, for example, by Rassy et al. Ther Adv Med Oncol. 2020;12:1758835920907504.

[0282] Aspects of the present disclosure are based on the inventors' recognition that subjects with certain RC TME types are less likely to respond well to certain therapeutic agents, such as immunotherapeutic agents or TKIs. Thus, in some embodiments, if a subject is identified as having RC TME type E, or if a subject is identified as an "obvious IO non-responder," the therapeutic agent comprises a therapeutic agent other than an immunotherapy. In some embodiments, the other therapeutic agent is a TKI.

[0283] 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 may identify the type of RC 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.

[0284] 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 RC TME environment (e.g., belonging to one of the types described herein), recommendations for alternative treatments, 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.

[0285] Computer implementation An exemplary implementation of a computer system 1500 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. 15. The computer system 1500 includes one or more processors 1510 and one or more articles of manufacture that include a non-transitory computer-readable storage medium (e.g., memory 1520 and one or more non-volatile storage media 1530). The processor 1510 may control the writing and reading of data to and from the memory 1520 and the non-volatile storage 1530 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 1510 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., memory 1520), which may function as a non-transitory computer-readable storage medium that stores processor-executable instructions for execution by the processor 1510.

[0286] Computing device 1500 may also include a network input / output (I / O) interface 1540, 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 1550, 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.

[0287] 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 using microcode or software.

[0288] 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.

[0289] 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.

[0290] 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

[0291] Example 1 Kidney cancer is among the top 10 most frequently diagnosed cancers worldwide, with clear cell renal cell carcinoma (ccRCC) accounting for approximately 75% of all cases. The advent of combination strategies of immuno-oncology (IO) agents and anti-angiogenic tyrosine kinase inhibitors (TKIs) has significantly improved clinical outcomes in this patient population, but there are currently no reliable biomarkers to guide treatment decisions.

[0292] This example describes a novel approach that integrates whole-exome and transcriptome sequencing data from ccRCC samples (n=1,527) to classify the RCC tumor microenvironment (TME) into five major types (referred to as RCC types or RCTs) with distinct immunological compositions: immune-enriched, fibrotic (IE / F), immune-enriched (IE), fibrotic (F), immune desert with metabolic content (D), and desert with high endothelial cell content (D / E). In addition, multiple genomic and transcriptomic-based biomarkers that correlate with lack of IO response were identified. Furthermore, a machine learning-based algorithm was used to generate a multifaceted IO and TKI responder score that integrated factors including RCCT, angiogenesis, proliferation, macrophage signature, and expression of PD-1, PD-L1, and PD-L2 genes.

[0293] Data show an association between RCT "IE / F" and superior clinical response in the IO+TKI cohort (41% response rate). Conversely, RCT "D / E", characterized by enhanced angiogenesis and lack of immune cell infiltration, responded significantly better to single-agent TKI (50% response rate). Among genomic and transcriptomic biomarkers, activating mutations in genes within the mTOR signaling pathway, mutations in antigen presentation machinery, high ploidy (>4), and high myogenic signature were significantly enriched in IO-resistant patients.

[0294] A multi-omics responder score was then retrospectively constructed and validated in multiple public cohorts, including patients treated with sunitinib (Beuselinck, n=53), pazopanib or sunitinib (COMPARZ, n=341), avelumab and axitinib or sunitinib (JAVELIN Renal 101, n=726), atezolizumab or atezolizumab and bevacizumab (IMmotion 150, n=160), nivolumab (CheckMate 009 / 010 / 025, n=172), and a mixed cohort of patients from the University of Washington (WUSMRCC, n=75). In all cohorts, implementation of the scoring system described herein resulted in improvements in progression-free survival and overall survival and appeared to be superior to all currently available approaches.

[0295] In conclusion, a machine learning-based multifaceted approach based on genomic and transcriptomic analysis of ccRCC tumors and TME composition was used to predict response to IOs and TKIs. Retrospective analyses of multiple different cohorts supported the clinical utility of these novel biomarkers.

[0296] Example 2 This example describes a novel approach that integrates whole-exome and transcriptome sequencing data from ccRCC samples (n=approximately 1,500) to classify the tumor microenvironment (TME) into five major RC types (also referred to as RC TME types) with distinct immunological composition: immune-enriched, fibrotic (IE / F or "Type A"), immune-enriched (IE or "Type B"), fibrotic (F or "Type C"), desert with metabolic content (Type "D"), and desert with high endothelial cell content ("Type E"). RC TME types were identified using gene signatures shown in Table 1 that reflect the immune and stromal portions of the tumor, and the activity of metabolic pathways. ccRCC is considered a cancer driven by metabolic alterations due to the high frequency of mutations in genes that control aspects of metabolism, such as VHL mutations in the hypoxia pathway, and mutations in the PI3K-AKT-mTOR pathway (MTOR, TSC1 / 2, PTEN, and MET) that dysregulate the control of growth in response to nutrient levels. Metabolic shifts in glycolysis, oxidative phosphorylation, the TCA cycle, fatty acid metabolism, and other processes have been observed in ccRCC cells and subjects.

[0297] For input to the analysis, gene expression data were obtained using standard bioinformatics analysis packages. For example, RNAseq gene expression data were described in transcripts per million (TPM). In some cases, FPKM / RPKM values ​​were used. For all cohorts, only ccRCC samples were selected (other histological types and normal samples were excluded). There were nine datasets (n = approx. 1500) collected from various platforms. In the analysis, 33 gene signatures were used to identify five RC TME types. The activity of each signature in each sample was measured using the ssGSEA algorithm.

[0298] The ssGSEA scores for each signature were moderately scaled within each cohort. We then ran a graph-based clustering algorithm to generate a graph containing the correlation of samples at nodes and ssGSEA scores at edges. Each node had 120 neighbors. We then applied the Leiden algorithm to the resulting graph, which identified five RC TME types. A representative heatmap showing clear cell kidney cancer samples classified into five distinct RC TME types (A, B, C, D, E) based on unsupervised dense clustering of the 33 gene expression signatures is shown in Figure 5.

[0299] The data shown in Figure 6 indicate that in the IO+TKI (atezolizumab+bevacizumab) cohort, RC TME type A (also referred to as "IE / F") and RC TME type B (also referred to as "IE") are associated with clinical response (>50% response rate). Conversely, RC TME type E, characterized by enhanced angiogenesis and lack of immune cell infiltration, showed a significantly better response to single-agent TKI (sunitinib response rate of approximately 80%).

[0300] Example 3 To prescribe the right treatment for a tumor, typically the patient undergoes a biopsy or a part of the tumor is taken after surgical resection. It is then sequenced (targeted exome normal, targeted exome tumor, WES normal, WES tumor, RNAseq tumor) and annotated with all the necessary molecular functional features. From these features, the resulting model predicts the probability of response to a therapeutic agent. The physician then decides which drug to use (if there are multiple alternative treatment options) based on the model's prediction, depending on the patient's previous treatment, current disease state, and other clinical factors.

[0301] There are over a dozen different approved drugs for the treatment of metastatic and progressive clear cell renal cancer (ccRCC). When selecting a treatment, physicians are usually guided by the patient's disease state and mono-biomarkers such as PDL1 IHC, TMB, and MSI, which may not fully consider the complex composition of the whole tumor. This example describes a model that can assess the likelihood of response to the two most common treatment options for ccRCC: immuno-oncology (IO) agents and tyrosine kinase inhibitors (TKIs).

[0302] A machine learning-based algorithm was created that generates a multifaceted IO and TKI responder score. The score integrates factors including RC TME type, angiogenic signature, proliferation signature, macrophage signature, and expression of PD-1, PD-L1, and PD-L2 genes. Among the genomic and transcriptomic biomarkers significantly enriched in IO-resistant patients were 1) activating mutations in genes within the mTOR signaling pathway, 2) mutations in the antigen presentation machinery, 3) high ploidy (>4), and 4) high myogenic signature (described throughout this specification, beginning with Example 4).

[0303] To create the model, we collected all currently available public datasets (genomic + transcriptomic) acquired on different platforms for patients with the same diagnosis. Patients in these datasets were treated with the same drugs and have known response or survival rates. All previously published biomarkers for treatment response and other important traits were also collected and used as features for the model. We collected 10 public datasets on ccRCC diagnosis, response to IO and TKI treatment, transcriptomic and genomic data. For each type of treatment, we used machine learning methods to select the most important biomarker features of all known features that can be obtained from tumor DNA and RNA using NGS and microarrays. These features provide the best way to determine the likelihood of drug response. Thus, the most important features for IO were found to be the expression of genes PD1, PDL1, and PDL2, as well as signatures characterizing ECM-related genes, angiogenesis, and proliferation rate. For TKIs, the most important parameters were found to be signatures of angiogenesis, macrophages, and proliferation rate.

[0304] Since all features are integrated, they can be compared between datasets. Of all features for the final model, only those with the greatest predictive power and that do not introduce noise are selected. Some of the datasets are used for training and some for validation. The output is a model that provides a single "responder score" that predicts the likelihood of response to treatment (e.g., IO or TKI).

[0305] In all cohorts, implementation of the scores from the models resulted in improved progression-free survival (PFS) and overall survival (OS) and appeared to be superior to all currently available approaches. By performing NGS cancer biopsy tests and applying these models to a set of calculated biomarkers, physicians can assess the likelihood of response to a particular treatment and make more informed decisions.

[0306] IO Model An example of training and validation of a machine learning model to assess the likelihood of response to IO is shown in Figures 7 and 8. Prior to model training, a group of biomarkers that were strongly associated with non-response to IO regimens were identified. Patients with these biomarkers were termed "IO non-responders." Samples were classified as "IO non-responders" if they met at least one of the following criteria: ●Ploidy>4 High myogenic signature ●RC TME Type E - Presence of mTOR activating mutations, Presence of mutations in the antigen presentation mechanism If a sample was classified as an “IO non-responder,” it was automatically assigned a responder score of 0 and the sample was not used for model training and validation.

[0307] A CatBoost classifier model was used with the parameter auto_class_weights set to "Balanced". The model was trained based on eight transcriptomic features, expression of PD1, PDL1, PDL2; endothelial signature, angiogenesis signature, and proliferation rate signature. The similarity of the samples to RC TME type "B" and RC TME type "C" was also used for training. The similarity of the samples to a particular RC TME type was calculated as the Spearman correlation coefficient between the sample's ssGSEA score of the 33 gene signature (e.g., based on the gene group shown in Table 1) and the mean ssGSEA score of the particular RC TME type of the 33 gene signature (e.g., based on the gene group shown in Table 1). The resulting model score (e.g., responder score) ranged from 0 to 1.

[0308] The IO model was trained on samples treated with IO-containing therapies from three cohorts: WUSMRCC (31 IPI+NIVO patients, 10 CABO+NIVO / AXI+PEM patients), Immotion150 (77 ATEZO patients, 83 ATEZO+BEV patients), and CheckMate25 (172 NIVO patients). For model training, patients were selected from these three cohorts with RECIST (Response Evaluation Criteria in Solid Tumors) of CR (25 patients) and PD (93 patients). These patients were used as the two arms of the predictive model. The model was validated in the JAVELIN cohort (354 AVE+AXI patients).

[0309] TKI Model Examples of training and validation of machine learning models to assess the likelihood of response to TKIs are shown in Figures 9 and 10. A logistic regression model from the scikit-learn package was used with default parameters. The model was trained based on four transcriptome features, a macrophage signature, an angiogenesis signature, and a proliferation rate signature. Sample similarity to RC TME type B was also used. The similarity of a sample to a particular RC TME type was calculated as the Spearman correlation coefficient between the sample's ssGSEA score of the 33-gene signature (e.g., based on the gene group shown in Table 1) and the average ssGSEA score of the particular RC TME type of the 33-gene signature (e.g., based on the gene group shown in Table 1). The resulting model scores (e.g., responder scores) ranged from 0 to 1.

[0310] The TKI model was trained on samples treated with TKI therapy from two cohorts: MUSMRCC (37 PAZ / SUN / CABO / AXI patients) and Beuselinck (53 SUN patients). For model training, patients were selected from these two cohorts with RECIS of CR+PR (45 patients) and PD (13 patients). These patients were used as the two groups for the predictive model. The model was validated in the JAVELIN cohort (372 SUN patients) and the COMPARZ cohort (341 PAZ / SUN patients).

[0311] Figures 11A-11E provide representative data showing that IO and TKI responder scores using the machine learning algorithm described by these examples are more consistent across datasets than previously used classification methods.

[0312] Example 4 A small group of "IO non-responder" patients was observed to show extremely high activity of the myogenic signature, consisting of 14 genes (listed in Table 2) that are primarily expressed in myoblasts and skeletal muscle cells. The myogenic signature was calculated using the gene cluster signature pipeline described above: ssGSEA scores were calculated and the scores within the cohort were median scaled. Figure 12 provides a representative heatmap showing the generation of a myogenic signature for RC (e.g., clear cell renal carcinoma) samples based on ssGSEA analysis and median scaling of the 14 genes. This myogenic signature was not used to identify RC TME types, but was used to identify IO "non-responders" in the IO model described in Example 3. Figure 13A provides representative data showing RECIST characterization (complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD)) plotted against the myogenic signature. It was observed that samples with high myogenesis scores were derived from patients with bone metastasis (MBO) (Figure 13B).

[0313] [Table 6]

[0314]

Table 7

[0315]

Table 8

[0316]

Table 9

[0317]

Table 10

[0318]

Table 11

[0319]

Table 12

[0320]

Table 13

[0321]

Table 14

[0322]

Table 15

[0323]

Table 16

[0324]

Table 17

[0325]

Table 18

[0326]

Table 19

[0327]

Table 20

[0328]

Table 21

[0329]

Table 22

[0330]

Table 23

[0331]

Table 24

[0332]

Table 25

[0333]

Table 26

[0334]

Table 27

[0335]

Table 28

[0336]

Table 29

Claims

1. Using at least one computer hardware processor, (a) obtaining RNA expression data of a subject; (b) using the RNA expression data to generate a RC TME signature for the subject; (c) using the RC TME signature to identify the subject's RC TME type among a plurality of RC TME types; 20. A method for determining a renal cancer (RC) tumor microenvironment (TME) type in a subject having, suspected of having, or at risk of having renal cancer, comprising the steps of: 【Table 1】 【Table 2】 【Table 3】 【Table 4】 wherein said RC TME signature comprises a gene group score for each gene group in at least some of said plurality of gene groups, and said generating comprises determining said gene group scores using said RNA expression levels.

2. 2. The method of claim 1, wherein obtaining RNA expression data for the subject comprises obtaining sequencing data previously obtained by sequencing a biological sample obtained from the subject.

3. 3. The method of claim 2, 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.

4. 4. The method of claim 2 or 3, 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.

5. The method of claim 2 or 3, wherein the sequencing data comprises microarray data.

6. 6. The method of any one of claims 1 to 5, further comprising normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the RC TME signature.

7. The method of any one of claims 1 to 6, wherein obtaining RNA expression data of the subject comprises sequencing a biological sample obtained from the subject.

8. 8. The method of claim 7, wherein the biological sample comprises kidney tissue of the subject, and optionally the biological sample comprises tumor tissue of the subject.

9. The RNA expression level (a) Effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (b) NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (c) T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (d) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (e) Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (f) checkpoint inhibitor group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (g) Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; (h) Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (i) Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; (j) MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; (k) macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; (l) Cancer-associated fibroblast (CAF) group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (m) matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; (n) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (o) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (p) Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; (q) EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; (r) Citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MD H2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC 25A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1 , IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; (s) Glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PG AM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; and (t) Fatty acid metabolic group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25 A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 9. The method of claim 1, comprising RNA expression levels of at least three genes from each of at least two of the gene groups.

10. The RNA expression level (a) MHC group I: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; (c) Coactivation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; (d) エフェクターcell group: PRF1, GZMB, TBX21, CD8B, ZAP7 0. IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (e) T cell delivery group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; (f) NK cell population: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (g) T cell populations: TRAC, TRBC2, TBX21, CD3E, CD3D, ITC, TRBC1, CD3G, CD28, TRAT1, CD5; (h) B cell population: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRF17, TNFRF13B, CD19, BLK, CD79B, TNFRF13C, CD22, PAX5; (i) M1シグネチャ group: IL1B, IL12B, NOS2, SOC S3, IRF5, IL23A, TNF, IL12A, CMKLR1; (j) Th1シグネチャ group: IL12RB2, IL2, TB X21, IFNG, STAT4, IL21, CD40LG; (k) Anti-swelling drug group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (l) チェックポイント blocking group: CTLA4, HAVCR2, CD274, L AG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (m)Treg group: TNFRSF18, IKZF2, IL 10. IKZF4, CTLA4, FOXP3, CCR8; (n) Treg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; (o) Good hitting team: FCGR3B, CD177, CTSG, PGLYR P1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (p) Granular ball conveying group: CXCL8, CCR3, CXCR2, CXCL2, CC11, KITLG, CXCL1, CXCL5, CXCR1; (q)MDSC group: ARG1、IL4I1、IL10、CYBB、IL6、PTGS2、IDO1; (r) MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; (s) Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; (t) macrophage DC trafficking group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; (u) Th2 signature group: IL13, CCR4, IL10, IL5, IL4; (v) Tumor-promoting cytokines: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; (w) CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, CO L5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (x) matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; (y) matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; (z) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (aa) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (bb) Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; (cc) EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; (dd) cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; (ee) Glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, P GAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5 A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; (ff) Citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC2 5A1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and (gg) Fatty acid metabolic group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25 A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 10. The method of claim 1, comprising RNA expression levels of at least three genes from each of at least two of the gene groups.

11. The RNA expression level (a) ECM related groups: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA 2, SCUBE1, SEMA4B, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; (b) TLS kidney group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; (c) NRF2 signature group: TRIM16L, UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F11, CYP4F3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1 , C1orf131, CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, ID H1, MAFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11; and (d) tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB 13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, P MEL, BIRC7, PLK5, ADARB2, CFAP61, TUBB4A, PLIN4, ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF 4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17 11. The method of claim 9 or 10, further comprising the RNA expression levels of at least three genes from each of at least two of the gene groups.

12. The RNA expression level (a) Effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (b) NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (c) T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (d) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (e) Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (f) checkpoint inhibitor group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (g) Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; (h) Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (i) Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; (j) MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; (k) macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; (l) Cancer-associated fibroblast (CAF) group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (m) matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; (n) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (o) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (p) Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; (q) EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; (r) Citric acid cycle group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, M DH2, ACO1, IDH1, SLC5A6, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A 1, ACSS2, SDHC, ACSS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; (s) Glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PG AM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; and (t) Fatty acid metabolic group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25 A20, AADAC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 12. The method of claim 1, comprising the RNA expression level of each gene from each of the gene groups.

13. The RNA expression levels of genes in the plurality of gene groups are (a) MHC group I: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; (c) Coactivation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; (d) effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (e) T cell transport group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; (f) NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (g) T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (h) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (i) M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; (j) Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; (k) Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (l) Checkpoint inhibitor group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (m) Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; (n) T reg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; (o) Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (p) Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; (q) MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; (r) MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; (s) Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; (t) macrophage DC trafficking group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; (u) Th2 signature group: IL13, CCR4, IL10, IL5, IL4; (v) Tumor-promoting cytokines: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; (w) CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, CO L5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (x) matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; (y) マトリックス re-formed group: MMP1, PLOD2, MMP2, MMP12, ADAM TS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; (z) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (aa) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (bb) Growth rate group: AURRKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURRKB, BUB1, MKI67, PLK1, CETN3; (cc) EMTシグネチャ group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; (dd) Cyclic ヌクレオチド metabolite group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A、GUCY2C、GUCY1A3、ADCY9、ADCY2、PDE 6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GU CY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUC Y1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, P DE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; (ee) Glycolysis and Gluconeogenesis Group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFK-M, LDHA-L6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHA-L6A, SLC2A1, PGAM2; (ff) Citric Acid Cycle Group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACS-S2, SDHC, ACS-S1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and (gg) Fatty Acid Metabolism Group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 The method of any one of claims 1 to 12, comprising the RNA expression level of each gene from each gene group.

14. The RNA expression levels of genes in the plurality of gene groups are (a) ECM related groups: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA 2, SCUBE1, SEMA4B, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; (b) TLS kidney group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; (c) NRF2 signature group: TRIM16L, UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F11, CYP4F3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1 , C1orf131, CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, ID H1, MAFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11; and (d) tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB 13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, P MEL, BIRC7, PLK5, ADARB2, CFAP61, TUBB4A, PLIN4, ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF 4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17 14. The method of claim 12 or 13, further comprising the RNA expression level of each gene from each gene group.

15. The step of determining the gene group score comprises: for a particular gene group, determining a gene group score for said particular group using the RNA expression levels of at least three genes in said particular gene group; (a) Effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (b) NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (c) T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (d) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (e) Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (f) checkpoint inhibitor group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (g)Treg group: TNFRSF18, IKZF2, IL 10. IKZF4, CTLA4, FOXP3, CCR8; (h) Good hits: FCGR3B, CD177, CTSG, PGLYR P1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (i) Granular ball conveying group: CXCL8, CCR3, CXCR2, CXCL2, CC11, KITLG, CXCL1, CXCL5, CXCR1; (j) MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; (k) Makoto Group: MRC1, CD163, MSR1, S IGLEC1, IL4I1, CD68, IL10, CSF1R; (1) CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (m) Mateo Group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGAL S9、LGALS7、LAMC2、TNC、LAMA3 , COL3A1, COL5A1, VTN, COL1A2; (n) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (o) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (p) Growth rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BuB1, MKI67, PLK1, CETN3; (q) EMTシグネチャ group: SNAI2, TWIST1, Z EB2, SNAI1, ZEB1, TWIST2, CDH2; (r) Citric acid circuit group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACS2, SDHC, ACS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; (s) Glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKFBM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; and (t) Fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 15. The method of any one of claims 1 to 14, comprising determining a respective gene group score for each of at least two of the gene groups, including:

16. The step of determining the gene group score comprises: for a particular gene group, determining a gene group score for said particular group using the RNA expression levels of at least three genes in said particular gene group; (a) MHC group I: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; (c) Coactivation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; (d) effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (e) T cell transport group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; (f) NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (g) T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (h) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (i) M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; (j) Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; (k) Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (l) Checkpoint inhibitor group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (m) Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; (n) T reg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; (o) Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (p) Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; (q) MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; (r) MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; (s) Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; (t) macrophage DC trafficking group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; (u) Th2 signature group: IL13, CCR4, IL10, IL5, IL4; (v) Tumor-promoting cytokines: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; (w) CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, CO L5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (x) matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; (y) matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; (z) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (aa) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (bb) Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; (cc) EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; (dd) cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; (ee) Glycolysis and Gluconeogenesis Group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFK-M, LDHA-L6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHA-L6A, SLC2A1, PGAM2; (ff) Citric Acid Cycle Group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACS2, SDHC, ACS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and (gg) Fatty Acid Metabolism Group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 16. The method of any one of claims 1 to 15, comprising determining a respective gene group score for each of at least two of the gene groups, including:

17. The step of determining the gene group score comprises: for a particular gene group, determining a gene group score for said particular group using the RNA expression levels of at least three genes in said particular gene group; (a) ECM related groups: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA 2, SCUBE1, SEMA4B, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; (b) TLS kidney group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; (c) NRF2 signature group: TRIM16L, UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F11, CYP4F3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1 , C1orf131, CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, ID H1, MAFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11; and (d) tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, PMEL, BIRC7, PLK5, ADARB2, CFAP61, TUBB4A, PLIN4 , ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17.

18. The step of determining the gene group score comprises: For each gene group, the RNA expression level of each gene in each gene group is used to determine a gene group score for each particular group; (a) Effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (b) NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (c) T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (d) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (e) Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (f) checkpoint inhibitor group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (g)Treg group: TNFRSF18, IKZF2, IL 10. IKZF4, CTLA4, FOXP3, CCR8; (h) Good hits: FCGR3B, CD177, CTSG, PGLYR P1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (i) Granular ball conveying group: CXCL8, CCR3, CXCR2, CXCL2, CC11, KITLG, CXCL1, CXCL5, CXCR1; (j) MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; (k) Makoto Group: MRC1, CD163, MSR1, S IGLEC1, IL4I1, CD68, IL10, CSF1R; (1) CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (m) Mateo Group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGAL S9、LGALS7、LAMC2、TNC、LAMA3 , COL3A1, COL5A1, VTN, COL1A2; (n) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (o) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (p) Growth rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BuB1, MKI67, PLK1, CETN3; (q) EMTシグネチャ group: SNAI2, TWIST1, Z EB2, SNAI1, ZEB1, TWIST2, CDH2; (r) Citric acid circuit group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACS2, SDHC, ACS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; (s) Glycolysis and gluconeogenesis group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFKM, LDHAL6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHAL6A, SLC2A1, PGM2; and (t) Fatty acid metabolism group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 The method of any one of claims 1 to 17, comprising determining a respective gene group score for each gene group, including

19. The step of determining the gene group score comprises: For each gene group, the RNA expression level of each gene in each gene group is used to determine a gene group score for each particular group; (a) MHC group I: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; (c) Coactivation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; (d) effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (e) T cell transport group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; (f) NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (g) T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (h) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (i) M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; (j) Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; (k) Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (l) Checkpoint inhibitor group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (m) Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; (n) T reg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; (o) Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (p) Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; (q) MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; (r) MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; (s) Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; (t) macrophage DC trafficking group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; (u) Th2 signature group: IL13, CCR4, IL10, IL5, IL4; (v) Tumor-promoting cytokines: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; (w) CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, CO L5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (x) matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; (y) matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; (z) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (aa) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (bb) Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; (cc) EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; (dd) cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; (ee) Glycolysis and Gluconeogenesis Group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFK-M, LDHA-L6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHA-L6A, SLC2A1, PGAM2; (ff) Citric Acid Cycle Group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACS2, SDHC, ACS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and (gg) Fatty Acid Metabolism Group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 The method of any one of claims 1 to 18, comprising determining a respective gene group score for each gene group, including

20. The step of determining the gene group score comprises: For each gene group, the RNA expression level of each gene in each gene group is used to determine a gene group score for each particular group; (a) ECM related groups: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA 2, SCUBE1, SEMA4B, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; (b) TLS kidney group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; (c) NRF2 signature group: TRIM16L, UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F11, CYP4F3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1 , C1orf131, CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, ID H1, MAFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11; and (d) tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB 13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, P MEL, BIRC7, PLK5, ADARB2, CFAP61, TUBB4A, PLIN4, ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF 4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17 The method of any one of claims 1 to 19, further comprising determining a respective gene group score for each gene group, including:

21. The step of determining the gene group score comprises: Using the single sample GSEA (ssGSEA) technique, (a) MHC group I: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; (c) Coactivation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; (d) effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (e) T cell transport group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; (f) NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (g) T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (h) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (i) M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; (j) Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; (k) Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (l) Checkpoint inhibitor group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (m) Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; (n) T reg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; (o) Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (p) Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; (q) MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; (r) MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; (s) Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; (t) macrophage DC trafficking group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; (u) Th2 signature group: IL13, CCR4, IL10, IL5, IL4; (v) Tumor-promoting cytokines: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; (w) CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, COL5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (x) Matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; (y) Matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; (z) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (aa) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (bb) Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MK167, PLK1, CETN3; (cc) EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; (dd) Cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; (ee) Glycolysis and Gluconeogenesis Group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFK-M, LDHA-L6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHA-L6A, SLC2A1, PGAM2; (ff) Citric Acid Cycle Group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACS2, SDHC, ACS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and (gg) Fatty Acid Metabolism Group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 21. The method of claim 1, comprising determining a first score for a first set of genes from the RNA expression levels of at least some of the genes in one of the sets of genes.

22. The step of determining the gene group score comprises: Using the single sample GSEA (ssGSEA) technique, (a) MHC group I: HLA-C, B2M, HLA-B, HLA-A, TAP1, TAP2, NLRC5, TAPBP; (b) MHC group II: HLA-DQA1, HLA-DMA, HLA-DRB1, HLA-DMB, CIITA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DQB1; (c) Coactivation molecule group: CD80, TNFRSF4, CD27, CD83, TNFSF9, CD40LG, CD70, ICOS, CD86, CD40, TNFSF4, ICOSLG, TNFRSF9, CD28; (d) effector cell group: PRF1, GZMB, TBX21, CD8B, ZAP70, IFNG, GZMK, EOMES, FASLG, CD8A, GZMA, GNLY; (e) T cell transport group: CXCL9, CCL3, CXCR3, CXCL10, CXCL11, CCL5, CCL4, CX3CL1, CX3CR1; (f) NK cell group: GZMB, NKG7, CD160, GZMH, CD244, EOMES, KLRK1, NCR1, GNLY, KLRF1, FGFBP2, SH2D1B, KIR2DL4, IFNG, NCR3, KLRC2, CD226; (g) T cell group: TRAC, TRBC2, TBX21, CD3E, CD3D, ITK, TRBC1, CD3G, CD28, TRAT1, CD5; (h) B cell group: CR2, MS4A1, CD79A, FCRL5, STAP1, TNFRSF17, TNFRSF13B, CD19, BLK, CD79B, TNFRSF13C, CD22, PAX5; (i) M1 signature group: IL1B, IL12B, NOS2, SOCS3, IRF5, IL23A, TNF, IL12A, CMKLR1; (j) Th1 signature group: IL12RB2, IL2, TBX21, IFNG, STAT4, IL21, CD40LG; (k) Antitumor cytokine group: IFNA2, CCL3, TNF, TNFSF10, IL21, IFNB1; (l) Checkpoint inhibitor group: CTLA4, HAVCR2, CD274, LAG3, BTLA, VSIR, PDCD1LG2, TIGIT, PDCD1; (m) Treg group: TNFRSF18, IKZF2, IL10, IKZF4, CTLA4, FOXP3, CCR8; (n) T reg transport group: CCL28, CCR10, CCR4, CCR8, CCL17, CCL22, CCL1; (o) Neutrophil signature group: FCGR3B, CD177, CTSG, PGLYRP1, FFAR2, CXCR2, PRTN3, ELANE, MPO, CXCR1; (p) Granulocyte transport group: CXCL8, CCR3, CXCR2, CXCL2, CCL11, KITLG, CXCL1, CXCL5, CXCR1; (q) MDSC group: ARG1, IL4I1, IL10, CYBB, IL6, PTGS2, IDO1; (r) MDSC transport group: CCL15, IL6R, CSF2RA, CSF2, CXCL8, CXCL12, IL6, CSF3, CCL26, CXCR4, CXCR2, CSF3R, CSF1, CXCL5, CSF1R; (s) Macrophage group: MRC1, CD163, MSR1, SIGLEC1, IL4I1, CD68, IL10, CSF1R; (t) macrophage DC trafficking group: CCL7, CCL2, XCR1, XCL1, CSF1, CCR2, CCL8, CSF1R; (u) Th2 signature group: IL13, CCR4, IL10, IL5, IL4; (v) Tumor-promoting cytokines: MIF, TGFB1, IL10, TGFB3, IL6, TGFB2, IL22; (w) CAF group: PDGFRB, COL6A3, FBLN1, CXCL12, COL6A2, COL6A1, LUM, CD248, CO L5A1, MMP2, COL1A1, MFAP5, PDGFRA, LRP1, FGF2, MMP3, FAP, COL1A2, ACTA2; (x) matrix group: COL11A1, LAMB3, FN1, COL1A1, COL4A1, ELN, LGALS9, LGALS7, LAMC2, TNC, LAMA3, COL3A1, COL5A1, VTN, COL1A2; (y) matrix remodeling group: MMP1, PLOD2, MMP2, MMP12, ADAMTS5, ADAMTS4, LOX, MMP9, MMP11, MMP3, MMP7, CA9; (z) Angiogenesis group: PGF, CXCL8, FLT1, ANGPT1, ANGPT2, VEGFC, VEGFB, CXCR2, VEGFA, VWF, CDH5, CXCL5, PDGFC, KDR, TEK; (aa) Endothelial group: NOS3, MMRN1, FLT1, CLEC14A, MMRN2, VCAM1, ENG, VWF, CDH5, KDR; (bb) Proliferation rate group: AURKA, MCM2, CCNB1, MYBL2, MCM6, CDK2, E2F1, CCNE1, ESCO2, CCND1, AURKB, BUB1, MKI67, PLK1, CETN3; (cc) EMT signature group: SNAI2, TWIST1, ZEB2, SNAI1, ZEB1, TWIST2, CDH2; (dd) cyclic nucleotide metabolism group: ADCY4, PDE11A, PDE6A, PDE9A, PDE6C, ADCY7, PDE4A, PDE8A, PDE1B, PDE1A, GUCY2C, GUCY1A3, ADCY9, ADCY2, PDE6B, ADCY8, PDE8B, GUCY2F, PDE4C, PDE3A, GUCY1A2, PDE6G, PDE1C, GUCY2D, ADCY10, GUCY1B3, GUCY1B2, PDE7B, PDE5A, PDE6D, NPR2, ADCY5, NPR1, ADCY6, PDE7A, PDE2A, PDE4B, PDE10A, PDE6H, PDE4D, ADCY1, PDE3B, ADCY3; (ee) Glycolysis and Gluconeogenesis Group: SLC2A9, PFKL, GCK, PFKFB4, SLC16A7, PCK1, PGAM2, GAPDH, BPGM, G6PC2, FBP2, LDHD, SLC2A3, GPI, ENO1, SLC25A11, PFKFB3, PFK-M, LDHA-L6B, SLC2A2, G6PC3, SLC2A6, GAPDHS, SLC2A11, PCK2, PFKP, PGK1, ALDOC, SLC2A10, ACYP2, SLC2A4, PKLR, HKDC1, PGK2, SLC2A8, PGAM1, SLC5A1, SLC5A12, SLC16A1, ALDOB, HK3, HK1, SLC5A9, GPD2, PFKFB1, SLC2A7, SLC5A11, SLC5A3, ACYP1, SLC16A8, PFKFB2, ALDOA, SLC5A2, HK2, ENO3, SLC2A12, FBP1, LDHA, LDHB, LDHC, G6PC, SLC2A14, SLC5A8, TPI1, SLC16A3, PKM2, ENO2, PGM1, UEVLD, LDHA-L6A, SLC2A1, PGAM2; (ff) Citric Acid Cycle Group: ACLY, FAH, PC, MDH1B, SLC16A7, IREB2, PCK1, MDH1, SLC33A1, ALDH1B1, IDH3B, DLST, PDHB, MDH2, ACO1, IDH1, SLC5A6, HICDH, SLC16A8, GOT1, ME3, ME1, CS, OGDH, SDHA, ALDH5A1, CLYBL, SDHD, IDH3A, SLC25A1, ACS2, SDHC, ACS1, SUCLA2, SLC13A5, PDHX, SDHB, ALDH4A1, PCK2, DLD, ACO2, PDHA1, SLC13A2, FAHD1, IDH2, GOT2, ME2, ADSL, SUCLG2, SLC13A3, SUCLG1, SLC25A10, FH, IDH3G, SLC16A1, SLC25A11, PDHA2, DLAT; and (gg) Fatty Acid Metabolism Group: MLYCD, ALDH3A2, SLC27A5, SLC27A3, LIPC, SLC27A2, ACSL4, ACSL1, PCCB, SLC25A20, AADC, SLC22A4, SLC22A5, ECH1, PCCA, SLC27A1, SLC27A4, CROT, ACSL5, ACSL3, CYP4F12 The method of any one of claims 1 to 21, comprising determining a gene group score from the RNA expression level of each gene in each gene group.

23. The step of determining the gene group score comprises: Using the single sample GSEA (ssGSEA) technique, (a) ECM related groups: ADAM8, ADAMTS4, C1QL3, CST7, CTSW, CXCL8, FASLG, LTB, MUC1, OSM, P4HA 2, SCUBE1, SEMA4B, SEMA7A, SERPINE1, TCHH, TGFA, TGM2, TNFSF11, TNFSF9, WNT10B; (b) TLS kidney group: ZNF683, POU2AF1, LAX1, CD79A, CXCL9, XCL2, JCHAIN, SLAMF7, CD38, SLAMF1, TNFRSF17, IRF4, HSH2D, PLA2G2D, MZB1; (c) NRF2 signature group: TRIM16L, UGDH, KIAA1549, PANX2, FECH, LRP8, AKR1C2, FTH1, AKR1C3, CBR1, PFN2, CBX2, TXN, CYP4F11, CYP4F3, AKR1C1, AKR1B15, G6PD, PRDX1, TALDO1, EPT1, SRXN1, JAKMIP3, FTHL3, UCHL1, TXNRD1 , C1orf131, CASKIN1, PGD, GPX2, OSGIN1, KIAA0319, CABYR, AIFM2, TRIM16, AKR1B10, GCLC, ABCC2, ETFB, ID H1, MAFG, NECAB2, ME1, PTGR1, PIR, GSR, RIT1, GCLM, ALDH3A1, NQO1, PKD1L2, NRG4, ABHD4, HRG, SLC7A11; and (d) tRCC signature group: FST, TRIM63, SLC10A2, ANTXRL, ERVV-2, SNX22, INHBE, SV2B, FAM124A, EPHA5, LUZP2, CPEB1, HOXB 13, ALLC, KCNF1, NDRG4, GREB1, ASTN1, JSRP1, UBE2U, KCNQ4, MYO7B, BRINP2, C1QL2, CCDC136, SLC51B, CATSPERG, P MEL, BIRC7, PLK5, ADARB2, CFAP61, TUBB4A, PLIN4, ABCB5, SYT3, HCN4, CTSK, SPACA1, TRIM67, NMRK2, LGI3, ARHGEF 4, NTSR2, KEL, SNCB, PLD5, ADGRB1, CYP17A1, IGFBPL1, TRIM71, SLC45A2, TP73, IP6K3, HABP2, RGS20, IGFN1, CDH17 The method of claim 22, wherein the method is performed using the RNA expression level of each gene in each gene group.

24. 24. The method of any one of claims 1 to 23, wherein generating the RC TME signature further comprises normalizing the gene group scores, wherein the normalizing comprises applying median scaling to the gene group scores.

25. the plurality of RC TME types are associated with a respective plurality of RC TME signature clusters; identifying a RC TME type of the subject from among a plurality of RC TME types using the RC TME signature, associating the RC TME signature of the subject with a particular one of the plurality of RC TME signature clusters; identifying the subject's RC TME type as a RC TME type corresponding to a particular one of the plurality of RC TME signature clusters with which the subject's RC TME signature is associated; The method according to any one of claims 1 to 24, comprising:

26. generating the plurality of RC TME signature clusters; generating a plurality of RC TME signatures from the plurality of sets of RNA expression data; and clustering the plurality of RC signatures to obtain the plurality of RC TME signature clusters. said generating step comprising obtaining a plurality of sets of RNA expression data by sequencing biological samples from a plurality of respective subjects; each of said plurality of sets of RNA expression data indicative of RNA expression levels of at least some genes in each of at least some of the plurality of gene groups listed in said table; each of the plurality of RC TME signatures comprises a gene group expression score for a respective gene group in the plurality of gene groups; 26. The method of claim 25, wherein the generating step comprises determining, for each particular one of the plurality of RC TME signatures, the RC TME signature by determining the gene group expression score using RNA expression levels in the particular set of RNA expression data for which the particular one RC TME signature was generated.

27. 27. The method of claim 26, wherein the clustering comprises agglomerative clustering, spectral clustering, k-means clustering, hierarchical clustering, and / or agglomerative clustering.

28. 28. The method of claim 25, further comprising updating the plurality of RC TME signature clusters using the RC TME signature of the target, comprising: the RC TME signature of the subject is one of a threshold number of RC TME signatures for a threshold number of subjects; updating the RC TME signature cluster once the threshold number of RC TME signatures have been generated; The method, wherein the threshold number of RC 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 RC TME signatures.

29. 30. The method of claim 28, wherein the updating step is performed using a clustering algorithm selected from the group consisting of a dense clustering algorithm, a spectral clustering algorithm, a k-means clustering algorithm, a hierarchical clustering algorithm, and an agglomerative clustering algorithm.

30. determining the RC TME type of the second subject; associating the second subject RC TME signature with a particular one of the plurality of updated RC TME signature clusters; and identifying the RC TME type of the second subject as a RC TME type corresponding to a particular one of the plurality of updated RC TME signature clusters with which the RC TME signature of the second subject is associated, comprising:

30. The method of any one of claims 25 to 29, wherein the RC TME type of the second subject is identified using the updated RC TME signature cluster, said identifying step comprising determining an RC TME signature of the second subject from RNA expression data obtained by sequencing a biological sample obtained from the second subject.

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

32. 32. The method of any one of claims 1 to 31, further comprising using the subject's RC TME type to identify at least one therapeutic agent for administration to the subject.

33. 33. The method of claim 32, wherein the at least one therapeutic agent comprises an immuno-oncology (IO) agent.

34. 34. The method of claim 32 or 33, wherein the at least one therapeutic agent comprises a tyrosine kinase inhibitor (TKI).

35. 35. The method of any one of claims 32-34, wherein identifying the at least one therapeutic agent based on the subject's RC TME type comprises identifying a TKI as the at least one therapeutic agent if the subject is identified as having RC TME type E.

36. 35. The method of any one of claims 32-34, wherein identifying the at least one therapeutic agent based on the subject's RC TME type comprises identifying a combination of a TKI and an IO agent as the at least one therapeutic agent if the subject is identified as having RC TME type A or RC TME type B.

37. 37. The method of any one of claims 32 to 36, further comprising the step of preparing the at least one identified therapeutic agent for administration to the subject.

38. At least one computer hardware processor; and At least one non-transitory computer readable medium storing processor executable instructions which, when executed by said at least one computer hardware processor, cause said at least one computer hardware processor to perform the method of any one of claims 1 to 36. A system comprising:

39. At least one non-transitory computer readable medium storing processor executable instructions which, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform the method of any one of claims 1 to 36.

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