Tumor microenvironment types in breast cancer

Abstract

Aspects of the present disclosure relate to methods, systems, and computer-readable storage media useful for characterizing a subject having a particular cancer, e.g., breast cancer. The present disclosure is based, in part, on methods for determining a breast cancer molecular type and / or tumor microenvironment (TME) type of a breast cancer subject, and methods for identifying a prognosis for the subject and / or one or more therapeutic agents for treating the subject based on the determination of the TME type.

Description

[Technical field]

[0001] Related Applications This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application No. 63 / 273,171, filed October 29, 2021, entitled “TUMOR MICROENVIRONMENT TYPES IN BREAST CANCER,” the entire contents of which are incorporated herein by reference. [Background technology]

[0002] Breast cancer is a highly heterogeneous disease. Accurately characterizing the type of cancer a patient or subject is suffering from and potentially selecting one or more effective treatments for that patient can be crucial for the patient's survival and overall well-being. 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 a method for identifying molecular types of breast cancer (BC). In some embodiments, the four breast cancer types are basal-like breast cancer (BLBC), luminal breast cancer, normal-like breast cancer, and HER2-enriched breast cancer (H2EBC). In some embodiments, each of the molecular subtypes can be further subdivided independently into tumor microenvironment (TME) subtypes. In some embodiments, the subject's breast cancer TME type is indicative of one or more characteristics of the subject (or the subject's cancer), for example, the likelihood that the subject will have a good prognosis or respond to a therapeutic agent such as an immunotherapy (also called an IO agent or immuno-oncology agent), an anti-VEGF therapy, or a tyrosine kinase inhibitor (TKI).

[0004] Thus, in some aspects, the present disclosure provides a method for determining a basal-like breast cancer (BLBC) tumor microenvironment (TME) type of a subject having, suspected of having, or at risk of having basal-like breast cancer, the method comprising: performing, using at least one computer hardware processor, the steps of obtaining RNA expression data of the subject indicative of an RNA expression level of at least some genes in each of at least some gene groups of a plurality of gene groups listed in Table 1; generating a BLBC TME signature of the subject using the RNA expression data, the BLBC TME signature including a gene group score for each gene group of at least some gene groups of the plurality of gene groups, the generating step comprising determining the gene group score using the RNA expression levels; and identifying a BLBC TME type of the subject from among a plurality of BLBC TME types using the BLBC TME signature.

[0005] In some embodiments, the method further comprises using the RNA expression levels to generate one or more PROGENy signatures.

[0006] In some embodiments, the one or more PROGENy signatures include a TGFb, NFkB, and / or VEGF signaling PROGENy signature. In some embodiments, identifying the subject's BLBC TME type includes using one or more PROGENy signatures.

[0007] 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.

[0008] In some embodiments, the RNA expression 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.

[0009] In some embodiments, the RNA expression 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.

[0010] In some embodiments, the method further comprises normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the BLBC TME signature.

[0011] In some embodiments, obtaining RNA expression data for the subject comprises sequencing a biological sample obtained from the subject.

[0012] In some embodiments, the biological sample comprises breast tissue of the subject. In some embodiments, the biological sample comprises tumor tissue of the subject.

[0013] In some embodiments, RNA expression levels are measured for the following gene groups: MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Class II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOME S, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell population: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B;Granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1 , MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CD H5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; EMT signature group: CDH2, Z EB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; tertiary lymphoid structure (TLS) population: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; follicular dendritic cell population: FDCSP, SERPINE2, PRNP, PDPN, LTBR, ​​BST1, CLU, C1S, and C4A; follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6;and the granulocyte group: RNA expression levels of at least three genes from each of at least two of CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33;

[0014] In some embodiments, RNA expression levels are measured for the following gene groups: MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Class II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOME S, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell population: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B;Granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1 , MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CD H5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; EMT signature group: CDH2, Z EB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; tertiary lymphoid structure (TLS) population: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; follicular dendritic cell population: FDCSP, SERPINE2, PRNP, PDPN, LTBR, ​​BST1, CLU, C1S, and C4A; follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6;and the granulocyte group: RNA expression levels for each of the genes from each of CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33;

[0015] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining a gene group score for said particular gene group using RNA expression levels of at least three genes in said particular gene group, the gene group score for said particular gene group being determined using the following: MHC I group: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC II group: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; effector cell group: ZAP70, GZMB, GZ MK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KL RF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell group: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TR AT1, TBX21, CD5, TRAC, and CD3D; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, I L12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4;Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and C OL3A1; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE 1;EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2;Tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6;Follicle dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, ​​BST1, CLU, C1S, and C4A;Follicle B helper T cell group: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6;and granulocyte group: determining a gene group score for each of at least two of the genes including CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33;

[0016] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining a gene group score for each particular gene group using RNA expression levels for each of the genes in each gene group, the following: MHC class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC class II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; effector cell group: ZAP70, GZMB, GZ MK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KL RF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell group: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TR AT1, TBX21, CD5, TRAC, and CD3D; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, I L12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4;Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and C OL3A1; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE 1;EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2;Tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6;Follicle dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, ​​BST1, CLU, C1S, and C4A;Follicle B helper T cell group: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6;and granulocyte group: determining a gene group score for each of the genes including CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33;

[0017] In some embodiments, the step of determining the gene group score uses single sample enrichment analysis (ssGSEA) technology to identify the following gene groups: MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOME, and IFNG. S, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell population: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B;Granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1 , MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CD H5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; EMT signature group: CDH2, Z EB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; tertiary lymphoid structure (TLS) population: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; follicular dendritic cell population: FDCSP, SERPINE2, PRNP, PDPN, LTBR, ​​BST1, CLU, C1S, and C4A; follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6;and determining a first score for the first group of genes from RNA expression levels for at least a portion of the genes in one of the following: Granulocyte group: CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33;

[0018] In some embodiments, the step of determining the gene group score uses single sample GSEA (ssGSEA) technology to determine the gene group scores for the following gene groups: MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOME, and IFNG. S, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell population: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B;Granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1 , MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CD H5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; EMT signature group: CDH2, Z EB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; tertiary lymphoid structure (TLS) population: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; follicular dendritic cell population: FDCSP, SERPINE2, PRNP, PDPN, LTBR, ​​BST1, CLU, C1S, and C4A; follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6;and granulocyte group: determining a gene group score from the RNA expression levels for each of the genes in each of CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33;

[0019] In some embodiments, generating a BLBC TME signature further comprises normalizing the gene group scores, where normalizing comprises applying median scaling to the gene group scores.

[0020] In some embodiments, the multiple BLBC TME types are associated with a respective multiple BLBC TME signature clusters, wherein identifying a subject's BLBC TME type from among the multiple BLBC TME types using the BLBC TME signature comprises: associating the subject's BLBC TME signature with a particular one of the multiple BLBC TME signature clusters; and identifying the subject's BLBC TME type as the BLBC TME type corresponding to the particular one of the multiple BLBC TME signature clusters with which the subject's BLBC TME signature is associated.

[0021] In some embodiments, the method further comprises generating a plurality of BLBC TME signature clusters, the generating step comprising: obtaining a plurality of sets of RNA expression data by sequencing biological samples from a plurality of respective subjects, each of the plurality of sets of RNA expression data indicating RNA expression levels of at least some genes in each of at least some of the plurality of gene groups listed in Table 1; generating a plurality of BLBC TME signatures from the plurality of sets of RNA expression data, each of the plurality of BLBC TME signatures comprising a gene group expression score for each gene group in the plurality of gene groups, the generating step comprising, for each particular one of the plurality of BLBC TME signatures, determining the BLBC TME signature by determining the gene group expression score using the RNA expression levels in the particular set of RNA expression data from which the particular one BLBC TME signature was generated; and clustering the plurality of BLBC TME signatures to obtain a plurality of BLBC TME signature clusters.

[0022] In some embodiments, the method further includes updating a plurality of BLBC TME signature clusters using the BLBC TME signature of the subject, where the BLBC TME signature of the subject is one of a threshold number of BLBC TME signatures for a threshold number of subjects, where the BLBC TME signature cluster is updated upon generation of the threshold number of BLBC TME signatures, where the threshold number of BLBC 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 BLBC TME signatures.

[0023] 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.

[0024] In some embodiments, the method further comprises determining a BLBC TME type of the second subject, wherein the BLBC TME type of the second subject is identified using an updated BLBC TME signature cluster, wherein the identifying comprises: determining a BLBC TME signature of the second subject from RNA expression data obtained by sequencing a biological sample obtained from the second subject; associating the BLBC TME signature of the second subject with a particular one of the plurality of updated BLBC TME signature clusters; and identifying the BLBC TME type of the second subject as the BLBC TME type corresponding to the particular one of the plurality of updated BLBC TME signature clusters with which the BLBC TME signature of the second subject is associated.

[0025] In some embodiments, the multiple BLBC TME types include the following: immune-enriched (IE) type, TLS (TLS) type (also called B cell-enriched type), desert (D) type, fibrotic (F) type, and granulocyte-enriched (G) type.

[0026] In some embodiments, the method further comprises using the subject's BLBC TME type to identify at least one therapeutic agent for administration to the subject.

[0027] In some embodiments, the at least one therapeutic agent comprises a cancer immunotherapy (IO) agent. In some embodiments, the IO agent comprises an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor comprises an anti-PD-1 antibody, an anti-PD-L1 antibody, or an anti-CTLA4 antibody.

[0028] In some embodiments, the at least one therapeutic agent comprises an anti-VEGF therapeutic agent. In some embodiments, the anti-VEGF therapeutic agent comprises an anti-VEGF antibody.

[0029] In some embodiments, the at least one therapeutic agent comprises a tyrosine kinase inhibitor (TKI).

[0030] In some embodiments, identifying at least one therapeutic agent based on the subject's BLBC TME type comprises identifying an immune checkpoint inhibitor as the at least one therapeutic agent if the subject is identified as having a BLBC TME of G, IE, or TLS type, In some embodiments, the method further comprises administering the identified immune checkpoint inhibitor to the subject.

[0031] In some embodiments, identifying at least one therapeutic agent based on the subject's BLBC TME type comprises identifying an anti-VEGF therapeutic agent as the at least one therapeutic agent if the subject is identified as having BLBC TME type F. In some embodiments, the method further comprises administering the identified anti-VEGF therapeutic agent to the subject.

[0032] In some embodiments, identifying at least one therapeutic agent based on the subject's BLBC TME type comprises identifying a TKI therapy if the subject is identified as having BLBC TME type D. In some embodiments, the method further comprises administering the identified TKI to the subject.

[0033] In some aspects, the present disclosure provides a method for determining a luminal or normal-like breast cancer (LNLBC) tumor microenvironment (TME) type of a subject having, suspected of having, or at risk of having luminal or normal-like breast cancer, the method comprising: performing, using at least one computer hardware processor, the steps of obtaining RNA expression data of the subject indicative of an RNA expression level of at least some genes in each group of at least some of the gene groups listed in Table 2; generating an LNLBC TME signature of the subject using the RNA expression data, the LNLBC TME signature comprising a gene group score for each gene group of at least some of the gene groups of the plurality of gene groups, the generating step comprising determining the gene group score using the RNA expression levels; and identifying the LNLBC TME type of the subject from among a plurality of LNLBC TME types using the LNLBC TME signature.

[0034] In some embodiments, the method further comprises using RNA expression levels to generate one or more PROGENy signatures. In some embodiments, the one or more PROGENy signatures comprise an estrogen PROGENy signature. In some embodiments, identifying the subject's LNLBC TME type comprises using one or more PROGENy signatures.

[0035] In some embodiments, obtaining RNA expression data for the subject comprises obtaining sequencing data previously obtained by sequencing a biological sample obtained from the subject.

[0036] In some embodiments, the RNA expression 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.

[0037] In some embodiments, the RNA expression 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.

[0038] In some embodiments, the sequencing data comprises microarray data.

[0039] In some embodiments, the method further comprises normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the LNLBC TME signature.

[0040] In some embodiments, obtaining RNA expression data for the subject comprises sequencing a biological sample obtained from the subject. In some embodiments, the biological sample comprises breast tissue of the subject. In some embodiments, the biological sample comprises tumor tissue of the subject.

[0041] In some embodiments, RNA expression levels are measured for the following gene groups: MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Class II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD 3D; T cell trafficking group: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, T NFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibitors. harm group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1;Macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP 5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling Angiogenesis group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; Angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CD H5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLE C14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: comprising RNA expression levels of at least three genes from each of at least two of CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2;

[0042] In some embodiments, RNA expression levels are measured for the following gene groups: MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Class II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD 3D; T cell trafficking group: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, T NFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibitors. harm group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1;Macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, CO L5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: ing group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMR N1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0043] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining a gene group score for said particular gene group using RNA expression levels of at least three genes in said particular gene group, the gene group score for said particular gene group being determined as follows: MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1 , GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking population: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5;MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: ADAMTS 4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenic group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0044] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining a gene group score for each particular gene group using RNA expression levels for each of the genes in each gene group, the following: MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Class II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1 , GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking population: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5;MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A 1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: AD AMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenic group: VEGFC, VEGFA, PDGFC, KDR, CDH5, V EGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14 The method includes determining a respective gene group score for each of the gene groups including: A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0045] In some embodiments, the step of determining the gene group score uses Single Sample Gene Set Enrichment Analysis (ssGSEA) technology to analyze the following gene groups: MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1. , GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking population: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5;MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP , PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: ADAMTS4, ADAM TS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenic group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; increased determining a first score for a first group of genes from RNA expression levels for at least a portion of the genes in one of the following: proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2;

[0046] In some embodiments, the step of determining the gene group score uses single sample GSEA (ssGSEA) technology to identify the following gene groups: MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD 3D; T cell trafficking group: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, T NFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibitors. harm group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1;Macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: ADAMTS 4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenic group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2, determining a gene group score from the RNA expression levels for each of the genes in each of the proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2;

[0047] In some embodiments, generating the LNLBC TME signature further comprises normalizing the gene group scores, where normalizing comprises applying median scaling to the gene group scores.

[0048] In some embodiments, the multiple LNLBC TME types are associated with a respective multiple LNLBC TME signature clusters, wherein identifying a subject's LNLBC TME type from among the multiple LNLBC TME types using the LNLBC TME signature comprises: associating the subject's LNLBC TME signature with a particular one of the multiple LNLBC TME signature clusters; and identifying the subject's LNLBC TME type as the LNLBC TME type corresponding to the particular one of the multiple LNLBC TME signature clusters with which the subject's LNLBC TME signature is associated.

[0049] In some embodiments, the method further comprises generating a plurality of LNLBC TME signature clusters, the generating step comprising: 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 an RNA expression level of at least some genes in each of at least some of the plurality of gene groups listed in Table 2; generating a plurality of LNLBC TME signatures from the plurality of sets of RNA expression data, wherein each of the plurality of LNLBC TME signatures comprises a gene group expression score for each gene group in the plurality of gene groups, the generating step comprising, for each particular one of the plurality of LNLBC TME signatures, determining the LNLBC TME signature by determining the gene group expression score using the RNA expression levels in the particular set of RNA expression data from which the particular one LNLBC TME signature was generated; and clustering the plurality of LNLBC TME signatures to obtain a plurality of LNLBC TME signature clusters.

[0050] In some embodiments, the method further includes updating a plurality of LNLBC TME signature clusters using the subject's LNLBC TME signature, where the subject's LNLBC TME signature is one of a threshold number of LNLBC TME signatures for a threshold number of subjects, where the LNLBC TME signature cluster is updated upon generation of the threshold number of LNLBC TME signatures, where the threshold number of LNLBC 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 LNLBC TME signatures.

[0051] 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.

[0052] In some embodiments, the method further includes determining an LNLBC TME type of the second subject, wherein the LNLBC TME type of the second subject is identified using an updated LNLBC TME signature cluster, wherein the identifying step includes: determining an LNLBC TME signature of the second subject from RNA expression data obtained by sequencing a biological sample obtained from the second subject; associating the LNLBC TME signature of the second subject with a particular one of the plurality of updated LNLBC TME signature clusters; and identifying the LNLBC TME type of the second subject as the LNLBC TME type corresponding to the particular one of the plurality of updated LNLBC TME signature clusters with which the LNLBC TME signature of the second subject is associated.

[0053] In some embodiments, the multiple LNLBC TME types include the following: immune desert (D) type, fibrotic (F) type, immune enriched / non-fibrotic (IE) type, immune enriched / fibrotic (IE / F) type, and angiogenic (E) type.

[0054] In some embodiments, the method further comprises using the subject's LNLBC TME type to identify at least one therapeutic agent for administration to the subject.

[0055] In some embodiments, the at least one therapeutic agent comprises a cancer immunotherapy (IO) agent. In some embodiments, the IO agent comprises an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor comprises an anti-PD-1 antibody, an anti-PD-L1 antibody, or an anti-CTLA4 antibody.

[0056] In some embodiments, the at least one therapeutic agent comprises an anti-VEGF therapeutic agent. In some embodiments, the anti-VEGF therapeutic agent comprises an anti-VEGF antibody.

[0057] In some embodiments, identifying at least one therapeutic agent based on the subject's LNLBC TME type comprises identifying an immune checkpoint inhibitor as the at least one therapeutic agent if the subject is identified as having IE or IE / F LNLBC TME, In some embodiments, the method further comprises administering the identified immune checkpoint inhibitor to the subject.

[0058] In some embodiments, identifying at least one therapeutic agent based on the subject's LNLBC TME type comprises identifying an anti-VEGF therapeutic agent as the at least one therapeutic agent if the subject is identified as having LNLBC TME type F. In some embodiments, the method further comprises administering the identified anti-VEGF therapy to the subject.

[0059] In some aspects, the present disclosure provides a method for determining a HER2 enriched breast cancer (H2EBC) tumor microenvironment (TME) type of a subject having, suspected of having, or at risk of having HER2 enriched breast cancer, the method comprising: using at least one computer hardware processor to perform acquisition of RNA expression data of the subject, the data indicating an RNA expression level of at least some genes in each group of at least some of the gene groups listed in Table 3; using the RNA expression data to generate an H2EBC TME signature of the subject, the H2EBC TME signature including a gene group score for each gene group of at least some of the gene groups of the plurality of gene groups, the generating step including determining the gene group score using the RNA expression levels; and using the H2EBC TME signature to identify the H2EBC TME type of the subject from among a plurality of H2EBC TME types.

[0060] In some embodiments, the method further comprises using the RNA expression levels to generate one or more PROGENy signatures. In some embodiments, the one or more PROGENy signatures comprise estrogen, androgen, and / or EGFR signaling PROGENy signatures. In some embodiments, identifying the subject's H2EBC TME type comprises using one or more PROGENy signatures.

[0061] In some embodiments, obtaining RNA expression data for the subject comprises obtaining sequencing data previously obtained by sequencing a biological sample obtained from the subject.

[0062] In some embodiments, the RNA expression 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.

[0063] In some embodiments, the RNA expression 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.

[0064] In some embodiments, the sequencing data comprises microarray data.

[0065] In some embodiments, the method further comprises normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the H2EBC TME signature.

[0066] In some embodiments, obtaining RNA expression data for the subject comprises sequencing a biological sample obtained from the subject. In some embodiments, the biological sample comprises breast tissue of the subject. In some embodiments, the biological sample comprises tumor tissue of the subject.

[0067] In some embodiments, RNA expression levels are measured for the following gene groups: coactivator group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, K LRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRA T1, TBX21, CD5, TRAC, and CD3D; T cell trafficking group: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell group: CD22, TNF RSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CC L3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; Treg trafficking group: CCR10, CCL28, CCL17, CCR4, CCL22, CCR8, and CCL1;Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; Granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXC L8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; MDSC transport group: CXCL8, CSF3R, CXCL12, CSF1R, CSF2RA, IL6R, CSF1, CCL26, CXCR2, IL6, CXCR4, CCL15, CXCL5, CSF2, and CSF3; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL 10; Cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; Matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3 A1; matrix remodeling group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2;The proliferation rate group includes RNA expression levels of at least three genes from each of at least two of the following: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and the EMT signature group includes RNA expression levels of at least three genes from each of at least two of the following: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0068] In some embodiments, RNA expression levels are measured for the following gene groups: MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Class II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD 3D; T cell trafficking group: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, T NFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibitors. harm group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1;Macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, M FAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix Remodeling group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; Angiogenesis group: VEGFC, VEGFA, PDGFC , KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, M The RNA expression levels for each of the genes from each of the following groups are included: MRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0069] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining a gene group score for said particular gene group using RNA expression levels of at least three genes in said particular gene group, the gene group score for said particular gene group being determined as follows: MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1 , GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking population: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5;MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblasts ( CAF group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; Matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN The method includes determining a gene group score for each of at least two of the following genes: proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0070] In some embodiments, the step of determining the gene group score comprises, for a particular gene group, determining a gene group score for each particular gene group using RNA expression levels for each of the genes in each gene group, the following: MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Class II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1 , GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking population: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5;MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblasts Cell (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3 , LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGAL S7, and COL3A1; matrix remodeling group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, F The method includes determining a gene group score for each of the gene groups, including: LT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0071] In some embodiments, the step of determining the gene group score uses Single Sample Gene Set Enrichment Analysis (ssGSEA) technology to analyze the following gene groups: MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1. , GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking population: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5;MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblasts (CAFs) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; Trichotillomania modeling group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, EN determining a first score for the first group of genes from RNA expression levels for at least a portion of the genes in one of: proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2;

[0072] In some embodiments, the step of determining the gene group score uses single sample GSEA (ssGSEA) technology to identify the following gene groups: MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD 3D; T cell trafficking group: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, T NFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibitors. harm group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1;Macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: AD AMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenic group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, EN determining a gene group score from the RNA expression levels for each of the genes in each of the following groups: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2;

[0073] In some embodiments, generating the H2EBC TME signature further comprises normalizing the gene group scores, where normalizing comprises applying median scaling to the gene group scores.

[0074] In some embodiments, the multiple H2EBC TME types are associated with a respective multiple H2EBC TME signature clusters, wherein identifying the subject's H2EBC TME type from among the multiple H2EBC TME types using the H2EBC TME signature comprises: associating the subject's H2EBC TME signature with a particular one of the multiple H2EBC TME signature clusters; and identifying the subject's H2EBC TME type as the H2EBC TME type corresponding to the particular one of the multiple H2EBC TME signature clusters with which the subject's H2EBC TME signature is associated.

[0075] In some embodiments, the method further comprises generating a plurality of H2EBC TME signature clusters, the generating step comprising: obtaining a plurality of sets of RNA expression data by sequencing biological samples from a plurality of respective subjects, each of the plurality of sets of RNA expression data indicating an RNA expression level of at least some genes in at least some of the plurality of gene groups listed in Table 3; generating a plurality of H2EBC TME signatures from the plurality of sets of RNA expression data, each of the plurality of H2EBC TME signatures comprising a gene group expression score for each gene group in the plurality of gene groups, the generating step comprising, for each particular one of the plurality of H2EBC TME signatures, determining the H2EBC TME signature by determining the gene group expression score using the RNA expression levels in the particular set of RNA expression data from which the particular one H2EBC TME signature was generated; and clustering the plurality of H2EBC TME signatures to obtain a plurality of H2EBC TME signature clusters.

[0076] In some embodiments, the method further includes updating a plurality of H2EBC TME signature clusters using an H2EBC TME signature of the subject, where the H2EBC TME signature of the subject is one of a threshold number of H2EBC TME signatures for a threshold number of subjects, where upon generation of the threshold number of H2EBC TME signatures, the H2EBC TME signature cluster is updated, where the threshold number of H2EBC TME signatures are at least 50, at least 75, at least 100, at least 200, at least 500, at least 1000, or at least 5000 H2EBC TME signatures.

[0077] 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.

[0078] In some embodiments, the method further comprises determining an H2EBC TME type of the second subject, wherein the H2EBC TME type of the second subject is identified using an updated H2EBC TME signature cluster, wherein the identifying step comprises: determining an H2EBC TME signature of the second subject from RNA expression data obtained by sequencing a biological sample obtained from the second subject; associating the H2EBC TME signature of the second subject with a particular one of the plurality of updated H2EBC TME signature clusters; and identifying the H2EBC TME type of the second subject as the H2EBC TME type corresponding to the particular one of the plurality of updated H2EBC TME signature clusters with which the H2EBC TME signature of the second subject is associated.

[0079] In some embodiments, the multiple H2EBC TME types include the following: immune desert (D) type, fibrotic (F) type, immune enriched / non-fibrotic (IE) type, moderate immune enriched (IE-med) type, and endothelial enriched (End-Ar-H) type.

[0080] In some embodiments, the method further comprises using the subject's H2EBC TME type to identify at least one therapeutic agent for administration to the subject.

[0081] In some embodiments, the at least one therapeutic agent comprises a cancer immunotherapy (IO) agent. In some embodiments, the IO agent comprises an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor comprises an anti-PD-1 antibody, an anti-PD-L1 antibody, or an anti-CTLA4 antibody.

[0082] In some embodiments, the at least one therapeutic agent comprises an anti-VEGF therapeutic agent. In some embodiments, the anti-VEGF therapeutic agent comprises an anti-VEGF antibody.

[0083] In some embodiments, identifying at least one therapeutic agent based on the subject's H2EBC TME type comprises identifying an immune checkpoint inhibitor as the at least one therapeutic agent if the subject is identified as having IE or IE-med H2EBC TME, in some embodiments, the method further comprises administering the identified immune checkpoint inhibitor to the subject.

[0084] In some embodiments, identifying at least one therapeutic agent based on the subject's H2EBC TME type comprises identifying an anti-VEGF therapeutic agent as the at least one therapeutic agent if the subject is identified as having H2EBC TME type F. In some embodiments, the method further comprises administering the identified anti-VEGF therapy to the subject.

[0085] In some aspects, the present disclosure provides a system that includes: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method described by the present disclosure.

[0086] In some aspects, the present disclosure provides at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method described by the present disclosure. [Brief description of the drawings]

[0087] [Figure 1] FIG. 1 illustrates a flowchart of an exemplary process 100 for determining a subject's basal-like breast cancer (BLBC) tumor microenvironment (TME) type, according to some embodiments of the technology described herein. [Diagram 2] FIG. 2 illustrates a flowchart of an exemplary process 200 for determining a subject's luminal and normal-like breast cancer (LNLBC) tumor microenvironment (TME) type, according to some embodiments of the technology described herein. [Diagram 3] FIG. 3 illustrates a flowchart of an exemplary process 300 for determining a subject's HER2-enriched breast cancer (H2EBC) tumor microenvironment (TME) type, according to some embodiments of the technology described herein. [Figure 4] FIG. 1 shows a flowchart of an exemplary process for processing sequencing data to obtain RNA expression data, according to some embodiments of the technology described herein. [Diagram 5] FIG. 1 shows an exemplary technique for determining gene group scores according to some embodiments of the techniques described herein. [Figure 6] FIG. 1 shows an exemplary technique for identifying basal-like breast cancer (BLBC) tumor microenvironment (TME) type using a BLBC TME signature, according to some embodiments of the techniques described herein. [Figure 7] Representative heatmap of basal-like breast cancer (BLBC) samples classified into five different TME subtypes (G, IE, TLS, F, D) based on unsupervised dense clustering of BLBC TME signatures. Each column represents one sample. The heatmap shows the z-score of each BG signature. [Figure 8] Representative data showing select gene panel scores compared across BLBC TME types. [Figure 9] Representative data from cell deconvolution analysis across samples are shown. Columns represent single samples and are ordered by BLBC TME type: G, IE, TLS, F, and D. Bars represent cell fractions. Markers in each legend correspond to specific cell types. [Figure 10A-10C] Representative data are shown showing that the BLBC TME type classification is supported by TCGA histological data: Figure 10A shows representative TCGA histological images of TME types; Figure 10B shows representative box plots showing the percentage of stromal tumor infiltrating lymphocytes (sTIL); Figure 10C shows representative data of fibrosis levels between TME types. [Figure 11] Representative data of overall survival (OS) analysis of chemotherapy-treated primary basal-like breast cancer samples across different TME subtypes are shown. Kaplan-Meier (top) and log hazard ratios (bottom) are shown. [Figure 12] Representative heatmaps of single molecule expression across BLBC TME types (G, IE, TLS, F, D). Each column represents one sample. The top panel corresponds to the sample annotation. The heatmap shows the z-scores for each of the signatures. [Figure 13]Representative heatmaps of luminal and normal-like breast cancer (LNLBC) samples classified into five distinct TME types (D, IE, IE / F, E, F) based on unsupervised dense clustering of LNLBC TME signatures are shown. [Figure 14] Representative data showing selected gene cluster scores compared across BLBC TME types. [Figure 15] Representative data from cell deconvolution analysis across samples are shown. Columns represent single samples and are ordered by LNLBC TME type: D, IE, IE-med, E, and F. Bars represent cell fractions. Markers in each legend correspond to specific cell types. [Figure 16] Representative data for an analysis of overall survival (OS) between LNLBC TME types of hormone-treated samples in the Metabric dataset are shown. [Figure 17] Representative heatmaps of single molecule expression across LNLBC TME types (D, IE, IE / F, E, and F) are shown. The top panel corresponds to the annotation of the samples. The heatmap is the median scaled expression of each molecule. [Figure 18] Representative heatmaps of HER2-enriched luminal and normal-like breast cancer (H2EBC) samples classified into five different TME types (D, IE-med, IE, F, and End-Ar-H) based on unsupervised dense clustering of the H2EBC TME signature. Each column represents one sample. The heatmap shows the signal of each of the signatures. [Figure 19] Representative data showing select gene panel scores compared across BLBC TME types. [Figure 20] Representative data from cell deconvolution analysis across samples are shown. Columns represent single samples and are ordered by TME subtype: D, IE, IE-med, E, and F. The top panel corresponds to the annotation of the samples. Markers in each legend correspond to a specific cell type. [Figure 21]Representative data of overall survival (OS) analysis between H2EBC TME types of chemotherapy-treated samples in the SCAN-B dataset. [Figure 22] Representative heatmaps of single molecule expression across H2EBC TME types (D, IE-med, IE, F, and End-Ar-H). The top panel corresponds to the annotation of the samples. The heatmap is the median scaled expression of each molecule. [Figure 23] 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 breast cancer and for determining the tumor microenvironment (TME) type of breast cancer in a subject. The present disclosure is based, in part, on classifying intrinsic subtypes of breast cancer (basal-like breast cancer, luminal breast cancer, normal-like breast cancer, and HER2-enriched breast cancer) into phenotypically distinct TME types within each intrinsic subtype. In some embodiments, the method includes identifying a subject as having a particular breast cancer TME type based on a TME signature calculated for the subject from RNA expression data. The breast cancer TME type identified for a subject may have various prognostic, diagnostic, and / or therapeutic applications. For example, in some embodiments, the methods developed by the present inventors and described herein are useful for identifying a subject's prognosis, such as a treatment response prognosis, based on the TME type identified for the subject.

[0089] Breast cancer is a highly heterogeneous group of solid tumors that arise in breast tissue. Worldwide, breast cancer is the most common invasive cancer in women, with more than 2 million cases diagnosed annually. Classification of a subject's breast cancer type is an important process that can provide insight into tumor biology and the subject's prognosis. Tumor classification can also guide physicians' decisions regarding patient treatment and surgical intervention. Historically, breast cancer has been classified by histopathological methods. However, due to the high heterogeneity of breast cancer tumor phenotypes, histopathological classification lacks the degree of separation to provide informative data at the molecular level.

[0090] Molecular biological characterization of breast cancer has also been described, for example, by Perou et al. (Nature. 2000;406(6796):747-752). Four intrinsic subtypes of breast cancer have been identified: basal-like breast cancer (BLBC), luminal breast cancer (also called luminal A breast cancer), normal-like breast cancer (also called luminal B breast cancer), and HER2-enriched breast cancer (H2EBC). In some embodiments, luminal breast cancer (e.g., luminal A breast cancer and normal-like breast cancer together may be referred to as luminal-normal-like breast cancer (LNLBC). Luminal cancers express estrogen receptor (ER) and contain variable cell proliferation markers. HER2 overexpression is a defining feature of H2EBC tumors. H2EBC tumors also lack expression of ER and progesterone receptor (PR). Basal-like breast cancer (BLBC) lacks expression of ER, PR, and HER2 (and thus may be referred to as "triple negative" breast cancer). BLBC tumors express basal cell markers, such as cytokeratin (CK) 5 / 6 and / or epidermal growth factor receptor (EGFR). The molecular classification of breast cancer into four intrinsic molecular types has been described, for example, by Zhang (Arch Pathol Lab Med. 2022 Sep 2020). As described in (22.doi:10.5858 / arpa.2022-0070-RA), the histopathological classification of breast cancers faces many of the same challenges. In particular, there remains a high level of tumor heterogeneity within each of the intrinsic cancer types, such that existing classifications do not reflect the complexity of the tumor biology underlying each type. Furthermore, high levels of variability have been observed within each intrinsic breast cancer type, with regard to prognosis and diverse treatment responses.

[0091] Aspects of the present disclosure relate to statistical techniques for analyzing expression data (e.g., RNA expression data) obtained from biological samples obtained from subjects who have, are suspected of having, or are at risk for developing breast cancer, for the purpose of generating a gene expression signature of a subject (referred to herein as a "TME signature") and using this signature to identify the particular TME type to which the subject may be afflicted.

[0092] The inventors recognize that certain intrinsic molecular subtypes of breast cancer (e.g., BLBC, LNLBC, and H2EBC) may be further divided into phenotypically distinct TME types within each intrinsic subtype. For example, the inventors recognize that each intrinsic breast cancer molecular subtype may be characterized as having five phenotypically distinct types. In some embodiments, BLBC may be characterized as having five phenotypically distinct types: immune-enriched (IE) type, TLS (TLS) type (also called B-cell enriched type), desert (D) type, fibrotic (F) type, and granulocyte-enriched (G) type. In some embodiments, LNLBC may be characterized as having five phenotypically distinct types: immune-desert (D) type, fibrotic (F) type, immune-enriched / non-fibrotic (IE) type, immune-enriched / fibrotic (IE / F) type, and angiogenic (E) type. In some embodiments, H2EBC can be characterized as having five phenotypically distinct types: immune desert (D), fibrotic (F), immune enriched / non-fibrotic (IE), moderate immune enriched (IE-med), and endothelial enriched (End-Ar-H). As described in more detail in the Examples, each breast cancer TME type was identified using a combination of gene group expression scores and, optionally, one or more PROGENy signatures to generate a breast cancer TME signature that characterizes patients with breast cancer more accurately than previously developed methods. In some embodiments, such TME types are useful for identifying a prognosis and / or likelihood that a subject will respond to a particular therapeutic intervention (e.g., immunotherapeutic agents, anti-VEGF agents, tyrosine kinase inhibitors, etc.).

[0093] The use of the TME signature comprising the combination of gene group scores described by the present disclosure represents an improvement over previously described molecular characterization of breast cancer. Because the specific gene groups used to generate the TME signatures described herein are associated with the underlying biological pathways that control tumor behavior and the molecular tumor microenvironment of breast cancer, the gene groups described above better reflect the molecular tumor microenvironment of breast cancer. Such targeted combinations of gene groups (e.g., gene groups consisting of some or all of the gene group genes listed in Table 1, Table 2, and / or Table 3) are unconventional and different from previously described molecular signatures that do not take into account the high level of genotypic and phenotypic heterogeneity within each broad molecular subtype of breast cancer.

[0094] The TME type classification method described herein has several utilities. For example, by identifying the TME type of a subject using the methods described herein, the subject can be diagnosed as having (or at high risk of developing) an aggressive form of breast cancer (e.g., BLBC TME type D or G) at a time when previously described breast cancer characterization methods cannot. The early detection of aggressive breast cancer types, enabled by the TME signatures described herein, improves patient diagnostic techniques by allowing earlier chemotherapeutic intervention than currently possible for patients who are tested for breast cancer using other methods (e.g., histological analysis).

[0095] As described herein, the inventors have also determined that subjects identified by the methods described herein as having a particular TME type (e.g., BLBC TME type G, IE, or TLS; LNLBC TME type IE or IE / F; H2EBC TME type IE or IE-med) are characterized as having a high likelihood of responding to immunotherapeutic agents, e.g., immune checkpoint inhibitors. Conversely, the inventors have determined that subjects with other TME types (e.g., BLBC, LNLBC, or H2EBC TME type F) are characterized as having a high likelihood of responding to anti-VEGF agents, whereas subjects with BLBC TME type D are characterized as having a high likelihood of responding to TKIs. Thus, the technology developed by the inventors and described herein improves patient treatment and associated outcomes by increasing patient comfort while avoiding the toxic side effects of chemotherapy that is not expected to be effective for the subject.

[0096] Breast cancer Aspects of the present disclosure relate to methods for determining breast cancer TME type in a subject having, suspected of having, or at risk of having breast cancer. As used herein, a subject may be a mammal, such as 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, the subject is a human. The term "individual" or "subject" may be used synonymously with "patient." As used herein, "breast cancer" or "BC" refers to any breast cancer, such as ductal carcinoma in situ, invasive ductal carcinoma, inflammatory breast cancer, and metastatic breast cancer, or any other type of malignant tumor resulting from one or more various genetic mutations in the body that affect cells of the subject's breast and / or surrounding breast tissue (either originally present therein 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 cancer, cancer of the bone marrow or lymphatic system, etc. In some embodiments, the breast cancer is basal-like breast cancer (BLBC), luminal breast cancer (luminal A and luminal B or normal-like breast cancer; LNLBC), or HER2-enriched breast cancer (H2EBC).

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

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

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

[0100] FIG. 1 is a flowchart of an exemplary process 100 for determining a subject's BLBC TME signature, using the determined BLBC TME signature to identify the subject's BLBC TME type, and using the subject's BLBC TME signature to identify whether the subject is likely to respond to immunotherapy.

[0101] Various (e.g., some or all) operations of process 100 may be implemented using any suitable computing device. For example, in some embodiments, one or more operations of exemplary process 100 may be implemented in a clinical setting or a laboratory setting. For example, one or more operations of process 100 may be implemented on a computing device located in a clinical or laboratory environment. In some embodiments, the computing device may obtain the RNA expression data directly from a sequencing device located in a clinical or laboratory environment. For example, a computing device included in a sequencing device may obtain the RNA expression data directly from the sequencing device. In some embodiments, the computing device may obtain the RNA expression data indirectly from a sequencing device located inside or outside of a clinical or laboratory environment. For example, a computing device located in a clinical or laboratory environment may obtain the expression data via a communication network, such as the Internet or any other suitable network, as aspects of the technology described herein are not limited to a particular communication network.

[0102] Additionally or alternatively, one or more operations of the exemplary process 100 may be implemented in a setting remote 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 Figure 1 are implemented using one or more computing devices. For example, operation 120 of administering one or more immunotherapeutic agents to a subject may be implemented manually (e.g., by a clinician).

[0104] Process 100 begins at operation 102 where sequencing data for a subject is obtained. In some embodiments, the sequencing data may be obtained by sequencing a biological sample obtained from the subject (e.g., a breast biopsy and / or tumor tissue) using any suitable sequencing technology. 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."

[0105] 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.

[0106] Process 100 then proceeds to operation 104 where the sequencing data obtained in operation 102 is processed to obtain RNA expression data. This may be done in any suitable manner and may involve normalizing the bulk sequencing data to transcripts per million (TPM) units (or other units) and / or log-transforming the RNA expression levels in TPM units. Conversion and normalization of data to TPM units is described herein with reference to FIG. 4.

[0107] Process 100 then proceeds to operation 106, where a basal-like breast cancer (BLBC) 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. 4).

[0108] As described herein, in some embodiments, the BLBC 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, 28, 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.

[0109] Thus, operation 106 includes the following: operation 108 where a gene group score is determined, operation 110 where a BLBC TME signature is determined using the gene groups determined in operation 108, and operation 114 where a BLBC TME type is determined by using the BLBC TME signature determined in operation 110. In some embodiments, determining the gene group score includes determining a gene group score for each of a plurality (e.g., some or all) of the gene groups listed in Table 1. In some embodiments, determining the gene group score includes 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, or 28 gene groups (e.g., the gene groups listed in Table 1). In some embodiments, determining the gene set score further comprises determining one or more PROGENy signatures, as shown in operation 112. The gene set score for a particular gene set may be determined using RNA expression levels of at least some of the genes in the gene set (e.g., the RNA expression levels obtained in operation 104). The RNA expression levels may be processed using Gene Set Enrichment Analysis (GSEA) techniques to determine the score for the particular gene set.

[0110] For example, in some embodiments, determining the BLBC TME signature comprises determining a gene group score using RNA expression levels of at least three genes from each of at least two of the gene groups, the gene groups being: MHC I group: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC II group: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; effector cell group: ZAP70, GZMB, GZ MK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KL RF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell group: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TR AT1, TBX21, CD5, TRAC, and CD3D; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, I L12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4;Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and C OL3A1; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE 1;EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2;Tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6;Follicle dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, ​​BST1, CLU, C1S, and C4A;Follicle B helper T cell group: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6;and granulocyte group: including CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33.

[0111] An embodiment of determining the gene group score is described herein with reference to FIG. 5, in the section entitled "Gene Expression Signatures."

[0112] As described above, a BLBC TME signature is generated at operation 110. In some embodiments, the BLBC TME signature consists solely of gene group scores for one or more (e.g., all) of the gene groups listed in Table 1. In some embodiments, the BLBC TME signature includes 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, or 28 gene groups listed in Table 1. In some embodiments, each gene group score for a particular gene group is determined using RNA expression levels of some or all (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, etc.) of the genes in each gene group listed in Table 1. In other embodiments, the BLBC TME signature includes one or more other gene group scores in addition to the gene group scores listed in Table 1.

[0113] Process 100 then proceeds to operation 114, where the BLBC TME type of the subject is identified using the BLBC TME signature generated in operation 110. This can be performed in any suitable manner. For example, in some embodiments, each of the possible BLBC TME types is associated with a respective one of a plurality of BLBC TME signature clusters. In such embodiments, the BLBC TME type of the subject can also be identified by associating the subject's BLBC TME signature with a particular one of the plurality of BLBC TME signature clusters; and identifying the subject's BLBC TME type as the BLBC TME type corresponding to the particular one of the plurality of BLBC TME signature clusters with which the subject's BLBC TME signature is associated. Examples of BLBC TME types are described herein. Aspects of identifying the subject's BLBC TME type are described herein, including in the section entitled "Generating BLBC TME Signatures and Identifying TME Type" below.

[0114] In some embodiments, operation 114 further includes one or more PROGENy signatures in addition to the BLBC TME signature to identify the BLBC TME type. In some embodiments, the one or more PROGENy signatures are selected from TGFb, NFkB, and / or VEGF signaling PROGENy signatures.

[0115] As previously discussed, the subject's BLBC TME type is identified in operation 114. In some embodiments, the subject's BLBC TME type is identified as being one of the following BLBC TME types: immune-enriched (IE) type, TLS (TLS) type (also called B cell-enriched), desert (D) type, fibrotic (F) type, and granulocyte-enriched (G) type.

[0116] Process 100 then proceeds to operation 118, where the BLBC TME type identified in operation 114 is used to determine the likelihood of the subject responding to a treatment. In some embodiments, if the subject is identified in operation 114 as having BLBC TME type IE or BLBC TME type TLS, the subject is identified in operation 118 as having a higher likelihood of responding to an immunotherapy (e.g., a PD1 antibody such as pembrolizumab) compared to subjects with other BLBC TME types. In some embodiments, if the subject is identified in operation 114 as having BLBC TME type F, the subject is identified in operation 118 as having a higher likelihood of responding to an anti-VEGF therapy compared to subjects with other BLBC TME types. In some embodiments, if the subject is identified in operation 114 as having BLBC TME type D, the subject is identified in operation 118 as having a higher likelihood of responding to a TKI compared to subjects with other BLBC TME types. Aspects of identifying whether a subject is likely to respond to a treatment are described herein, including in the section below entitled "Indications for Therapeutic Agents."

[0117] In some embodiments, process 100 is complete after completion of act 118. In some such embodiments, the determined BLBC TME signature and / or the identified LNLBC TME type may be stored for later use, provided to one or more recipients (e.g., clinicians, researchers, etc.), and / or used to update a BLBC TME signature cluster (described below).

[0118] However, in some embodiments, one or more other operations are performed after operation 114. For example, in the embodiment shown in FIG. 1, process 100 may include one or more of optional operations 116 and 120, shown in dashed lines in FIG. 1. For example, operation 116 may identify a prognosis for the subject. In another example, if the subject is identified as having BLBC TME type IE or BLBC TME type TLS in operation 114 and / or is identified as likely to respond to an immunotherapeutic agent in operation 118, the subject is administered one or more immunotherapeutic agents in operation 120. Examples of immunotherapeutic agents and other therapies are provided herein.

[0119] In the example of FIG. 1, operations 112, 116, and 120 are shown as optional, but it should be understood that in other embodiments, one or more other operations may be optional (in addition to or instead of operations 112, 116, and 120). For example, in some embodiments, operations 102 and 104 may be optional (e.g., if sequencing data was previously acquired and processed to obtain RNA expression data, process 100 may begin at operation 106 by accessing the previously acquired RNA expression data). In some embodiments, process 100 may include operations 102, 104, 106, 118, and 120 without operations 114 and 116. In some embodiments, process 100 may include operations 102, 104, 106, 116, 114, 118, and 120 without operation 116.

[0120] [Table 1]

[0121] [Table 2]

[0122] FIG. 2 is a flowchart of an exemplary process 200 for determining a subject's LNLBC TME signature, using the determined LNLBC TME signature to identify the subject's LNLBC TME type, and using the subject's LNLBC TME type to identify whether the subject is likely to respond to immunotherapy.

[0123] Various (e.g., some or all) operations of process 200 may be implemented using any suitable computing device. For example, in some embodiments, one or more operations of exemplary process 200 may be implemented in a clinical setting or a laboratory setting. For example, one or more operations of process 200 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.

[0124] Additionally or alternatively, one or more operations of the exemplary process 200 may be implemented in a setting separate from a clinical or laboratory setting. For example, one or more operations of the process 200 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.

[0125] It should be understood that in some embodiments, not all operations of process 200 as shown in Figure 2 are implemented using one or more computing devices. For example, operation 220 of administering one or more therapeutic agents to a subject may be implemented manually (e.g., by a clinician).

[0126] Process 200 begins at operation 202 where sequencing data for a subject is obtained. In some embodiments, the sequencing data may be obtained by sequencing a biological sample obtained from the subject (e.g., a breast 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."

[0127] As an illustrative example, in some embodiments, the sequencing data may include bulk sequencing data. The bulk sequencing data may include 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 includes 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 includes microarray data.

[0128] Process 200 then proceeds to operation 204, where the sequencing data obtained in operation 202 is processed to obtain RNA expression data. This may be done in any suitable manner, and may involve normalizing the bulk sequencing data to transcripts per million (TPM) units (or other units) and / or log-transforming the RNA expression levels in TPM units. Conversion and normalization of data to TPM units is described herein with reference to FIG. 4. However, one of ordinary skill in the art will recognize that the processes described in operations 104 and 204 may be identical.

[0129] Process 200 then proceeds to operation 206, where a basal-like breast cancer (LNLBC) tumor microenvironment (TME) signature is generated for the subject using the RNA expression data generated in operation 204 (e.g., from bulk sequence data, converted to TPM units and then log-normalized as described herein with reference to FIG. 4).

[0130] As described herein, in some embodiments, the LNLBC 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, etc.) gene group scores. 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 2.

[0131] Thus, operation 206 includes operation 208 in which a gene group score is determined, operation 210 in which an LNLBC TME signature is determined using the gene groups determined in operation 208, and operation 214 in which an LNLBC TME type is determined using the LNLBC TME signature determined in operation 210. In some embodiments, determining the gene group score includes determining a respective gene group score for each of a plurality (e.g., some or all) of the gene groups listed in Table 2. In some embodiments, determining the gene group score includes determining a respective gene group score for 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, or 28 gene groups (e.g., the gene groups listed in Table 2). In some embodiments, determining the gene set score further comprises determining one or more PROGENy signatures, as shown in operation 212. The gene set score for a particular gene set may be determined using RNA expression levels of at least some of the genes in the gene set (e.g., the RNA expression levels obtained in operation 204). Gene Set Enrichment Analysis (GSEA) techniques may also be used to determine the score for a particular gene set by processing the RNA expression levels.

[0132] For example, in some embodiments, determining the LNLBC TME signature comprises determining a gene group score using RNA expression levels of at least three genes from each of at least two of the gene groups, the gene groups being as follows: MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Class II: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; Group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1 , GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking population: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell population: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IF NB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5;MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10 Cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; Matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A 1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0133] An embodiment of determining the gene group score is described herein with reference to FIG. 5, in the section entitled "Gene Expression Signatures."

[0134] As described above, an LNLBC TME signature is generated at operation 210. In some embodiments, the LNLBC TME signature consists solely of gene group scores for one or more (e.g., all) gene groups listed in Table 2. In some embodiments, the LNLBC 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, or 24 gene groups listed in Table 2. In some embodiments, each gene group score for a particular gene group is determined using RNA expression levels of some or all (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, etc.) of the genes in each gene group listed in Table 2. In other embodiments, the LNLBC TME signature comprises one or more other gene group scores in addition to the gene group scores listed in Table 2.

[0135] Process 200 then proceeds to operation 214, where the LNLBC TME type of the subject is identified using the LNLBC TME signature generated in operation 210. This can be performed in any suitable manner. For example, in some embodiments, each of the possible LNLBC TME types is associated with a respective plurality of LNLBC TME signature clusters. In such embodiments, the LNLBC TME type of the subject can also be identified by associating the LNLBC TME signature of the subject with a particular one of the plurality of LNLBC TME signature clusters; and identifying the LNLBC TME type of the subject as the LNLBC TME type corresponding to the particular one of the plurality of LNLBC TME signature clusters with which the LNLBC TME signature of the subject is associated. Examples of LNLBC TME types are described herein. Aspects of identifying the LNLBC TME type of the subject are described herein, including in the section entitled "Generating TME Signatures and Identifying TME Type" below.

[0136] In some embodiments, operation 214 further includes identifying the LNLBC TME type using one or more PROGENy signatures in addition to the LNLBC TME signature. In some embodiments, the one or more PROGENy signatures include an estrogen PROGENy signature.

[0137] As previously described, the subject's LNLBC TME type is identified in operation 214. In some embodiments, the subject's LNLBC TME type is identified as being one of the following LNLBC TME types: immune desert (D) type, fibrotic (F) type, immune enriched / non-fibrotic (IE) type, immune enriched / fibrotic (IE / F) type, and angiogenic (E) type.

[0138] Process 200 then proceeds to operation 218, where the LNLBC TME type identified in operation 214 is used to determine the likelihood that the subject will respond to a treatment. In some embodiments, if the subject is identified in operation 214 as having LNLBC TME type IE or LNLBC TME type IE / F, then the subject is identified in operation 218 as having a higher likelihood of responding to an immunotherapy (e.g., a PD1 antibody such as pembrolizumab) compared to subjects with other LNLBC TME types. In some embodiments, if the subject is identified in operation 214 as having LNLBC TME type F, then the subject is identified in operation 218 as having a higher likelihood of responding to an anti-VEGF therapy compared to subjects with other LNLBC TME types. Aspects of identifying whether a subject is likely to respond to a treatment are described herein, including in the section below entitled "Indications for Treatment."

[0139] In some embodiments, process 200 is complete after completion of act 218. In some such embodiments, the determined LNLBC TME signature and / or the identified LNLBC TME type may be stored for later use, provided to one or more recipients (e.g., clinicians, researchers, etc.), and / or used to update an LNLBC TME signature cluster (described below).

[0140] However, in some embodiments, one or more other operations are performed after operation 214. For example, in the illustrated embodiment of FIG. 2, process 200 may include one or more of optional operations 216 and 220, shown in dashed lines in FIG. 2. For example, operation 216 may identify a prognosis for the subject. In another example, if the subject is identified as having LNLBC TME type IE or LNLBC TME type IE / F in operation 214 and / or identified as likely to respond to an immunotherapeutic agent in operation 218, the subject is administered one or more immunotherapeutic agents in operation 220. Examples of immunotherapeutic agents are provided herein.

[0141] In the example of FIG. 2, operations 212, 216, and 220 are shown as optional, but it should be understood that in other embodiments, one or more other operations may be optional (in addition to or instead of operations 212, 216, and 220). For example, in some embodiments, operations 202 and 204 may be optional (e.g., if sequencing data was previously acquired and processed to obtain RNA expression data, process 200 may begin at operation 206 by accessing the previously acquired RNA expression data). In some embodiments, process 200 may include operations 202, 204, 206, 218, and 220 without operations 214 and 216. In some embodiments, process 200 may include operations 202, 204, 206, 216, 214, 218, and 220 without operation 216.

[0142] [Table 3]

[0143] [Table 4]

[0144] FIG. 3 is a flowchart of an exemplary process 300 for determining a subject's H2EBC TME signature, using the determined H2EBC TME signature to identify the subject's H2EBC TME type, and using the subject's H2EBC TME type to identify whether the subject is likely to respond to an immunotherapeutic drug.

[0145] Various (e.g., some or all) operations of process 300 may be implemented using any suitable computing device. For example, in some embodiments, one or more operations of exemplary process 300 may be implemented in a clinical setting or a laboratory setting. For example, one or more operations of process 300 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. Aspects of the technology described herein are not limited to a particular communication network, so 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.

[0146] Additionally or alternatively, one or more operations of the exemplary process 300 may be implemented in a setting separate from a clinical or laboratory setting. For example, one or more operations of the process 300 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.

[0147] It should be understood that in some embodiments, not all operations of process 300 as shown in Figure 3 are implemented using one or more computing devices. For example, operation 320 of administering one or more therapeutic agents to a subject may be implemented manually (e.g., by a clinician).

[0148] Process 300 begins at operation 302, where sequencing data for a subject is obtained. In some embodiments, the sequencing data may be obtained by sequencing a biological sample obtained from the subject (e.g., a breast 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."

[0149] As an illustrative example, in some embodiments, the sequencing data may include bulk sequencing data. The bulk sequencing data may include 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 includes 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 includes microarray data.

[0150] Process 300 then proceeds to operation 304, where the sequencing data obtained in operation 302 is processed to obtain RNA expression data. This may be done in any suitable manner, and may involve normalizing the bulk sequencing data to transcripts per million (TPM) units (or other units) and / or log-transforming the RNA expression levels in TPM units. Conversion and normalization of data to TPM units is described herein with reference to FIG. 4. However, one of ordinary skill in the art will recognize that the processes described in operations 104 and 304 may be identical.

[0151] Process 300 then proceeds to operation 306, where a basal-like breast cancer (H2EBC) tumor microenvironment (TME) signature is generated for the subject using the RNA expression data generated in operation 304 (e.g., from bulk sequence data, converted to TPM units and then log-normalized as described herein with reference to FIG. 4).

[0152] As described herein, in some embodiments, the H2EBC 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, 28, 29, 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 3.

[0153] Thus, operation 306 includes operation 308 in which a gene group score is determined, operation 310 in which an H2EBC TME signature is determined using the gene groups determined in operation 308, and operation 314 in which an H2EBC TME type is determined using the H2EBC TME signature determined in operation 310. In some embodiments, determining the gene group score includes determining a respective gene group score for each of a plurality (e.g., some or all) of the gene groups listed in Table 3. In some embodiments, determining the gene group score includes determining a respective gene group score for 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, or 28 gene groups (e.g., the gene groups listed in Table 3). In some embodiments, determining the gene set score further comprises determining one or more PROGENy signatures, as shown in operation 312. The gene set score for a particular gene set may be determined using RNA expression levels of at least some of the genes in the gene set (e.g., the RNA expression levels obtained in operation 304). Gene Set Enrichment Analysis (GSEA) techniques may also be used to determine the score for a particular gene set by processing the RNA expression levels.

[0154] For example, in some embodiments, determining the H2EBC TME signature comprises determining a gene group score using RNA expression levels of at least three genes from each of at least two of the gene groups, the gene groups being: co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; Group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD 244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking population: CXCL10, CX3CL1, CX3CR1 , CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF , and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; antitumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4;Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; Treg trafficking group: CCR10, CCL28, CCL17, CCR4, CCL22, CCR8, and CCL1; Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; Granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1 , IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; MDSC trafficking group: CXCL8, CSF3R, CXCL12, CSF1R, CSF2RA, IL6R, CSF1, CCL26, CXCR2, IL6, CXCR4, CCL15, CXCL5, CSF2, and CSF3; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10: macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7: Th 2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN , LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5;The endothelial group includes KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; the proliferation rate group includes CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and the EMT signature group includes CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0155] An embodiment of determining the gene group score is described herein with reference to FIG. 5, in the section entitled "Gene Expression Signatures."

[0156] As described above, an H2EBC TME signature is generated at operation 310. In some embodiments, the H2EBC TME signature consists solely of gene group scores for one or more (e.g., all) gene groups listed in Table 3. In some embodiments, the H2EBC TME signature includes 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, or 29 gene groups listed in Table 3. In some embodiments, each gene group score for a particular gene group is determined using RNA expression levels of some or all (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, etc.) of the genes in each gene group listed in Table 3. In other embodiments, the H2EBC TME signature includes one or more other gene group scores in addition to the gene group scores listed in Table 3.

[0157] Process 300 then proceeds to operation 314, where the H2EBC TME type of the subject is identified using the H2EBC TME signature generated in operation 310. This can be performed in any suitable manner. For example, in some embodiments, each of the possible H2EBC TME types is associated with a respective plurality of H2EBC TME signature clusters. In such embodiments, the H2EBC TME type of the subject can also be identified by associating the H2EBC TME signature of the subject with a particular one of the plurality of H2EBC TME signature clusters; and identifying the H2EBC TME type of the subject as the H2EBC TME type corresponding to the particular one of the plurality of H2EBC TME signature clusters with which the H2EBC TME signature of the subject is associated. Examples of H2EBC TME types are described herein. Aspects of identifying the H2EBC TME type of the subject are described herein, including in the section entitled "Generation of TME Signatures and Identification of TME Type" below.

[0158] In some embodiments, operation 314 further includes identifying the H2EBC TME type using one or more PROGENy signatures in addition to the H2EBC TME signature. In some embodiments, the one or more PROGENy signatures include an androgen PROGENy signature, an estrogen PROGENy signature, and / or an EGFR PROGENy signature.

[0159] As previously described, the subject's H2EBC TME type is identified in operation 314. In some embodiments, the subject's H2EBC TME type is identified as being one of the following H2EBC TME types: immune desert (D) type, fibrotic (F) type, immune enriched / non-fibrotic (IE) type, moderate immune enriched (IE-med) type, and endothelial enriched (End-Ar-H) type.

[0160] Process 300 then proceeds to operation 318, where the H2EBC TME type identified in operation 314 is used to determine the likelihood that the subject will respond to a treatment. In some embodiments, if the subject is identified in operation 314 as having H2EBC TME type IE or H2EBC TME type IE-med, the subject is identified in operation 318 as having a higher likelihood of responding to an immunotherapy (e.g., a PD1 antibody such as pembrolizumab) compared to subjects with other H2EBC TME types. In some embodiments, if the subject is identified in operation 314 as having H2EBC TME type F, the subject is identified in operation 318 as having a higher likelihood of responding to an anti-VEGF therapy compared to subjects with other H2EBC TME types. Aspects of identifying whether a subject is likely to respond to a treatment are described herein, including in the section below entitled "Indications for Therapeutic Agents."

[0161] In some embodiments, process 300 is complete after completion of act 318. In some such embodiments, the determined H2EBC TME signature and / or the identified H2EBC TME type may be stored for later use, provided to one or more recipients (e.g., clinicians, researchers, etc.), and / or used to update an H2EBC TME signature cluster (described below).

[0162] However, in some embodiments, one or more other operations are performed after operation 314. For example, in the exemplary embodiment of FIG. 3, process 300 may include one or more of optional operations 316 and 320, shown in dashed lines in FIG. 3. For example, at operation 316, a prognosis for the subject is identified. In another example, if the subject is identified at operation 314 as having H2EBC TME type IE or H2EBC TME type IE-med and / or identified at operation 318 as likely to respond to an immunotherapeutic agent, the subject is administered one or more immunotherapeutic agents at operation 320. Examples of immunotherapeutic agents are provided herein.

[0163] In the example of FIG. 3, operations 312, 316, and 320 are shown as optional, but it should be understood that in other embodiments, one or more other operations may be optional (in addition to or instead of operations 312, 316, and 320). For example, in some embodiments, operations 302 and 304 may be optional (e.g., if sequencing data was previously acquired and processed to obtain RNA expression data, process 300 may begin at operation 306 by accessing the previously acquired RNA expression data). In some embodiments, process 300 may include operations 302, 304, 306, 318, and 320 without operations 314 and 316. In some embodiments, process 300 may include operations 302, 304, 306, 316, 314, 318, and 320 without operation 316.

[0164] [Table 5]

[0165] [Table 6]

[0166] Biological samples Aspects of the present disclosure relate to methods for determining a subject's breast cancer TME type by obtaining sequencing data from a biological sample obtained from the subject.

[0167] The biological sample may be 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, breasts, etc. 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, bone, bone marrow, brain, thymus, spleen, appendix, colon, rectum, anus, liver, biliary tract, pancreas, kidney, ureter, bladder, urethra, uterus, vagina, vulva, ovaries, cervix, scrotum, penis, prostate, testicles, seminal vesicles, and / or any type of tissue (e.g., muscle tissue, epithelial tissue, connective tissue, or nerve tissue).

[0168] The biological sample may be any type of sample, including, for example, a sample of a bodily fluid, one or more cells, one or more tissue or organ slices. In some embodiments, the biological sample comprises a breast tissue sample from a subject. In some embodiments, the breast tissue sample comprises one or more cell types derived from the breast (e.g., endothelial cells, secretory luminal cells, basal / myoepithelial cells, etc.). In some embodiments, the breast tissue sample comprises tumor cells.

[0169] In some embodiments, a tissue sample may be obtained from a subject using a surgical procedure (e.g., laparoscopy, microsurgical surgery, or endoscopy), a bone marrow biopsy, a punch biopsy, an endoscopic biopsy, or a needle biopsy (e.g., fine needle aspiration, core needle biopsy, vacuum assisted biopsy, or image guided biopsy).

[0170] 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.

[0171] 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.

[0172] In some embodiments, a blood sample is taken to obtain cell-free nucleic acid (eg, cell-free DNA) in the blood.

[0173] 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).

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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 citrate dextrose (e.g., for blood samples).

[0178] 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.

[0179] 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.

[0180] Acquiring RNA expression data Aspects of the present disclosure relate to methods of determining a subject's breast cancer TME type using sequencing or RNA expression data obtained from a biological sample from the subject.

[0181] The RNA expression data used in the methods described herein is typically obtained from sequencing data obtained from biological samples.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.doi.org / 10.1093 / nar / gkv007 PMID:25605792, PMCID:PMC4402510, the entire contents of which are incorporated by reference herein.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] In some embodiments, the expression data comprises next generation sequencing (NGS) data. In some embodiments, the expression data comprises microarray data.

[0190] 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 set of non-limiting examples, the expression data may include, for each gene group listed in Table 1, Table 2, or Table 3, expression data for at least 5, at least 10, at least 15, at least 20, or at least 25 genes selected from each gene group.

[0191] 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).

[0192] 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.

[0193] 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.

[0194] 4 illustrates an exemplary process 104 for processing sequencing data to obtain RNA expression data from the sequencing data. Process 104 is similar to processes 204 and 304 illustrated in FIGS. 2 and 3, respectively. 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 device. In other embodiments, process 104 may be performed by one or more computing devices external to the sequencing device.

[0195] Process 104 begins with operation 400 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."

[0196] In some embodiments, the sequencing data obtained in operation 400 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 breast tumor cells.

[0197] Process 104 then proceeds to operation 402, where the sequencing data obtained in operation 400 is normalized to 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

[0198] Process 104 then proceeds to operation 404, where the RNA expression levels in TPM (determined in operation 402) may be log transformed. Process 104 is exemplary and variations exist. For example, in some embodiments, one or both of operations 402 and 404 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.

[0199] The RNA expression data obtained by process 104 may include sequence data generated by a sequencing protocol (e.g., a series of nucleotides in a nucleic acid molecule identified by next generation sequencing, Sanger sequencing, etc.), as well as information contained therein (e.g., information indicative of source, tissue type, etc.), which may also be considered information that can be inferred or determined from the sequence data. In some embodiments, the expression data obtained by process 104 may include information contained in a FASTA file, descriptions and / or quality scores contained in a FASTQ file, alignment positions contained in a BAM file, and / or any other suitable information obtained from any suitable file.

[0200] Gene expression signatures Aspects of the present disclosure relate to processing expression data to determine one or more gene expression signatures (e.g., breast cancer TME signatures). In some embodiments, the 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.

[0201] In some embodiments, the expression data may be processed by one or more software programs running on a computing device.

[0202] In some embodiments, the methods described herein include an act of determining a BLBC TME signature comprising a gene group score for each gene group in the plurality of gene groups. In some embodiments, the BLBC 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, or 28). In some embodiments, the methods described herein include an act of determining a LNLBC TME signature comprising a gene group score for each gene group in the plurality of gene groups. In some embodiments, the LNLBC TME signature comprises a gene group score for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24) of the gene groups listed in Table 2. In some embodiments, the methods described herein comprise an act of determining an H2EBC TME signature comprising a gene group score for each gene group in the plurality of gene groups. In some embodiments, the H2EBC TME signature comprises a gene group score for at least one of the gene groups listed in Table 3 (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, or 29).

[0203] The number of genes in a gene group used to determine the gene group score can vary. In some embodiments, all RNA expression levels for all 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., 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).

[0204] In some embodiments, the TME signature comprises a gene group score for the MHC I class. 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 class defined by its constituent genes: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5.

[0205] In some embodiments, the TME signature comprises a gene group score for the angiogenesis group, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, or at least fourteen) genes in the angiogenesis group defined by its constituent genes: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5.

[0206] In some embodiments, the 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 (e.g., at least three, at least four, or at least five) genes in the anti-tumor cytokine group defined by its constituent genes: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10.

[0207] In some embodiments, the TME signature comprises a gene group score for a B cell population, which in some embodiments may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen) genes in a B cell population defined by its constituent genes: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK.

[0208] In some embodiments, the TME signature comprises a gene group score for the CAFs. In some embodiments, this gene group score may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, or at least eighteen) genes in a CAFs defined by its constituent genes: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1.

[0209] In some embodiments, the 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, at least four, at least five, at least six, at least seven, at least eight, or at least nine) in the checkpoint inhibition group defined by its constituent genes: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4.

[0210] In some embodiments, the TME signature comprises a gene group score for the co-activator molecule group, which in some embodiments may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen) genes in the co-activator molecule group defined by its constituent genes: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86.

[0211] In some embodiments, the TME signature comprises a gene group score for an effector cell population, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or at least eleven) genes in an effector cell population defined by its constituent genes: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B.

[0212] In some embodiments, the TME signature comprises a gene group score for the EMT signature group, which may be calculated using the RNA expression levels of at least three (e.g., at least three, at least four, at least five, or at least six) genes in the EMT signature group defined by its constituent genes: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0213] In some embodiments, the TME signature comprises a gene group score for the endothelial group, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine) genes in the endothelial group defined by its constituent genes: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2.

[0214] In some embodiments, the TME signature comprises a gene group score for the follicular B helper T cell population, which in some embodiments may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine) genes in the follicular B helper T cell population defined by its constituent genes: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6.

[0215] In some embodiments, the TME signature comprises a gene group score for the follicular dendritic cell population, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, or at least eight) genes in the follicular dendritic cell population defined by its constituent genes: FDCSP, SERPINE2, PRNP, PDPN, LTBR, ​​BST1, CLU, C1S, C4A.

[0216] In some embodiments, the TME signature comprises a gene group score for the granulocyte trafficking group, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, or at least eight) genes in the granulocyte trafficking group defined by its constituent genes: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5.

[0217] In some embodiments, the TME signature comprises a gene group score for the granulocyte population, which in some embodiments comprises a gene group score for at least three (e.g., at least three) of the granulocyte population defined by its constituent genes: CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33. In some embodiments, the RNA expression levels of at least one gene may be calculated using the RNA expression levels of at least one, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, at least twenty-four, at least twenty-five, at least twenty-six, at least twenty-seven, at least twenty-eight, at least twenty-nine, at least thirty, or at least thirty-one genes.

[0218] In some embodiments, the 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 (e.g., at least three, at least four, at least five, at least six, at least seven, or at least eight) genes in the M1 signature group defined by its constituent genes: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A.

[0219] In some embodiments, the TME signature comprises a gene group score for the macrophage population, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, or at least seven) genes in the macrophage population defined by its constituent genes: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10.

[0220] In some embodiments, the 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 (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, or at least fourteen) genes in the matrix group defined by its constituent genes: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1.

[0221] In some embodiments, the TME signature comprises a gene group score for the MDSC population, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, or at least six) genes in the MDSC population defined by its constituent genes: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1.

[0222] In some embodiments, the TME signature comprises a gene group score for the MHC II class, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, or at least eight) genes in the MHC II class defined by its constituent genes: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1.

[0223] In some embodiments, the 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 (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine) genes in the M neutrophil signature group defined by its constituent genes: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B.

[0224] In some embodiments, the 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 (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, or at least sixteen) genes in the NK cell population defined by its constituent genes: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160.

[0225] In some embodiments, the TME signature comprises a gene group score for a proliferation rate group, which in some embodiments may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, or at least fourteen) genes in a proliferation rate group defined by its constituent genes: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1.

[0226] In some embodiments, the 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 (e.g., at least three, at least four, at least five, or at least six) genes in the tumor-promoting cytokine group defined by its constituent genes: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10.

[0227] In some embodiments, the TME signature comprises a gene group score for a T cell population, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten) genes in a T cell population defined by its constituent genes: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D.

[0228] In some embodiments, the 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 (e.g., at least three, at least four, at least five, or at least six) genes in the Th1 signature group defined by its constituent genes: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4.

[0229] In some embodiments, the TME signature comprises a gene group score for the Th2 signature group, which in some embodiments may be calculated using RNA expression levels of at least three (e.g., at least three or at least four) genes in the Th2 signature group defined by its constituent genes: IL13, CCR4, IL10, IL4, and IL5.

[0230] In some embodiments, the TME signature comprises a gene group score for the TLS group, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, or at least eight) genes in the TLS group defined by its constituent genes: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6.

[0231] In some embodiments, the TME signature comprises a gene group score for the Treg population, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, or at least six) genes in the Treg population defined by its constituent genes: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4.

[0232] In some embodiments, the 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 (e.g., at least three, at least four, at least five, at least six, or at least seven) genes in the macrophage DC trafficking group defined by its constituent genes: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7.

[0233] In some embodiments, the TME signature comprises a gene group score for the matrix remodeling group, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten) genes in the matrix remodeling group defined by its constituent genes: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2.

[0234] In some embodiments, the TME signature comprises a gene group score for the T cell trafficking group, which in some embodiments may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, at least six, at least seven, or at least eight) genes in the T cell trafficking group defined by its constituent genes: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, CCL4.

[0235] In some embodiments, the TME signature comprises a gene group score for the MDSC trafficking population, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, or at least six) genes in the MDSC population defined by its constituent genes: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1.

[0236] In some embodiments, the TME signature comprises a gene group score for the Treg trafficking group, which may be calculated using RNA expression levels of at least three (e.g., at least three, at least four, at least five, or at least six) genes in the Treg trafficking group defined by its constituent genes: CCR10, CCL28, CCL17, CCR4, CCL22, CCR8, and CCL1.

[0237] In some embodiments, determining the BLBC TME signature comprises, for a particular gene group, determining a respective gene group score for each of at least two of the following gene groups using RNA expression levels of at least three genes within the particular gene group to determine a gene group score for said particular group, said gene groups being: MHC I group: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC II group: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KL RK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell group: TRBC2, C D3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, I L23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; antitumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4;Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; Granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; Th2 signature group: IL 13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2 , E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; follicular dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, ​​BST1, CLU, C1S, and C4A;Follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6; and granulocyte population: CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33.

[0238] In some embodiments, determining the LNLBC TME signature comprises, for a particular gene group, determining a respective gene group score for each of at least two of the following gene groups using RNA expression levels of at least three genes within the particular gene group to determine a gene group score for said particular group, said gene groups being: MHC Class I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC Class II: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; Group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR 2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking population: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CX CL9, and CCL4; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4;Granulocyte trafficking group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; tumor Promotive cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A 1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2; angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANG PT1, and CXCL5; endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0239] In some embodiments, determining the H2EBC TME signature comprises, for a particular gene group, determining a respective gene group score for each of at least two of the following gene groups using RNA expression levels of at least three genes within the particular gene group to determine a gene group score for said particular group, said gene groups being: co-activation molecule group: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; MHC group II: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; Group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1 Effector cell groups: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; NK cells Cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH 2D1B, NCR3, EOMES, and CD160; T cell group: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; T cell trafficking group: CXCL10, CX3CL1, CX3CR1, CXCL11, CXCR3, CCL5, CCL3, CXCL9, and CCL4; B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; anti-tumor cytokine group: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10;Checkpoint inhibition group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; Treg trafficking group: CCR10, CCL28, CCL17, CCR4, CCL22, CCR8, and CCL1; Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3. B; Granulocyte transport group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; MDSC transport group: CXCL 8, CSF3R, CXCL12, CSF1R, CSF2RA, IL6R, CSF1, CCL26, CXCR2, IL6, CXCR4, CCL15, CXCL5, CSF2, and CSF3; macrophage group: MRC1, SIGLEC1, MSR1, CD163, CSF 1R, CD68, IL4I1, and IL10; macrophage DC trafficking group: CCL8, CSF1R, CSF1, CCR2, XCL1, XCR1, CCL2, and CCL7; Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; tumor-promoting cytokine group: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A 2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; matrix remodeling group: ADAMTS4, ADAMTS5, CA9, LOX, MMP1, MMP11, MMP12, MMP2, MMP3, MMP7, MMP9, and PLOD2;Angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; Endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; Proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; and EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2.

[0240] Lists of gene groups are provided in Tables 1, 2, and 3 at the end of this specification.

[0241] As mentioned above, an embodiment of the present disclosure relates to determining a breast cancer TME signature of a subject. The signature may include a gene group score (e.g., a gene group score generated using RNA expression data for a gene group listed in Table 1, Table 2, and / or Table 3). An embodiment of determining a TME signature will now be described with reference to FIG. 5.

[0242] In some embodiments, the TME signature comprises gene group scores generated using gene set enrichment analysis (GSEA) techniques to determine gene group scores 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, or 29) gene groups listed in Table 1, Table 2, and / or Table 3. In some embodiments, the TME signature comprises gene group scores generated using gene set enrichment analysis (GSEA) techniques to determine gene group scores for eight or more (e.g., 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20) gene groups listed in Table 1. In some embodiments, the TME signature comprises gene group scores generated using gene set enrichment analysis (GSEA) techniques to determine gene group scores for eight or more (e.g., 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20) gene groups listed in Table 2. In some embodiments, the TME signature comprises gene group scores generated using gene set enrichment analysis (GSEA) techniques to determine gene group scores for eight or more (e.g., 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20) gene groups listed in Table 3.

[0243] In some embodiments, each gene group score is generated using Gene Set Enrichment Analysis (GSEA) technology using RNA expression levels of at least some of the genes in the gene group. In some embodiments, using GSEA technology comprises using 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

[0244] Figure 5 shows an example of how a gene group score may be determined as part of operation 108 of process 100, operation 208 of process 200, or operation 308 of process 300. As shown in the example of Figure 5, a "TME signature" includes a plurality of gene group scores 520 determined for each of a plurality of gene groups. Each gene group score is calculated for a particular gene group by performing GSEA 510 (e.g., using ssGSEA) on the RNA expression data for 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 the particular gene group 500.

[0245] For example, as shown in FIG. 5, a gene group score (labeled "gene group score 1") for gene group 1 (e.g., Treg group) is calculated from the RNA expression data of one or more genes in the gene group. As another example, a gene group score (labeled "gene group score 2") for gene group 2 (e.g., T cell group) is calculated from the RNA expression data of one or more genes in gene group 2. As another example, a gene group score (labeled "gene group score 3") for gene group 3 (e.g., NK cell group) is calculated from the RNA expression data of one or more genes in gene group 3. As another example, a gene group score (labeled "gene group score 4") for gene group 4 (e.g., B cell group) is calculated from the RNA expression data of one or more genes in gene group 4. As another example, a gene group score (labeled "gene group score 5") for gene group 5 (e.g., MDSC group) is calculated from the RNA expression data of one or more genes in gene group 5. As another example, a gene group score (labeled "gene group score 6") for gene group 6 (e.g., the CAF group) is calculated from RNA expression data of one or more genes in gene group 6. As another example, a gene group score (labeled "gene group score 7") for gene group 7 (e.g., the proliferation rate group) is calculated from RNA expression data of one or more genes in gene group 7. As another example, a gene group score (labeled "gene group score 8") for gene group 8 (e.g., the co-activating molecule group) is calculated from RNA expression data of one or more genes in gene group 8.

[0246] While the example of Figure 5 shows that the gene expression group score includes eight gene group scores for each set of eight gene groups, it should be understood that in other embodiments, the first gene expression signature may include scores for any suitable number of groups (e.g., not just eight; the number of groups may be fewer or more than eight). As indicated by the vertical ellipses in Figure 5, determining the gene group scores for the TME signature may comprise determining gene group scores for 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more gene groups using RNA expression data from one or more respective genes within each gene group, such that aspects of the technology described herein are not limited in this respect. In another example, the TME signature may include scores for only a subset of the gene groups listed in Table 1, Table 2, or Table 3. As another example, the gene expression group score may include one or more scores for one or more gene groups other than those listed in Table 1 (in addition to the scores for the groups in Table 1, or instead of one or more scores for the groups of genes listed in Table 1, e.g., Table 2 or Table 3).

[0247] In some embodiments, the RNA expression levels of a particular set of genes may be embodied in at least one data structure having a field for storing the expression levels. The data structure or data structures may be provided as input to software comprising code implementing GSEA technology (e.g., ssGSEA technology) to process the expression levels in the at least one data structure to calculate a score for the particular set of genes.

[0248] The number of genes in a gene group used to determine the gene group score may vary. In some embodiments, all of the RNA expression levels of all of the genes in a particular gene group may be used to determine the gene group score for a particular gene group. In other embodiments, RNA expression data for fewer than all of the genes may be used (e.g., RNA expression levels for at least 2 genes, at least 3 genes, at least 5 genes, 2-10 genes, 5-15 genes, or any other suitable range within these ranges).

[0249] In some embodiments, the RNA expression levels of a particular gene group may be embodied in at least one data structure having a field for storing the expression level. The data structure or data structures may be provided as input to software comprising code configured to perform appropriate scaling (e.g., median scaling) to generate a score for the particular gene group.

[0250] In some embodiments, ssGSEA is performed on expression data that includes three or more (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20) gene groups listed in Table 1, Table 2, or Table 3. In some embodiments, each of the gene groups includes 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, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, In some embodiments, the TME signature is generated by performing ssGSEA on all of the gene groups in Table 1, where each gene group includes all of the genes listed in Table 1. In some embodiments, the TME signature is generated by performing ssGSEA on all of the gene groups in Table 2, where each gene group includes all of the genes listed in Table 2. In some embodiments, the TME signature is generated by performing ssGSEA on all gene groups in Table 3, where each gene group includes all genes listed in Table 3.

[0251] In some embodiments, one or more (e.g., multiple) enrichment scores are normalized to generate a TME signature of expression data (e.g., expression data of a subject or cohort of subjects). In some embodiments, the enrichment scores are normalized by median scaling. In some embodiments, the enrichment scores are normalized by rank estimation and median scaling. In some embodiments, the median scaling includes 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. In some embodiments, the median scaling generates a TME signature of the subject.

[0252] In some embodiments, the subject's TME signature is processed using a clustering algorithm to identify tumor microenvironment types (e.g., BLBC TME type, LNLBC TME type, H2EBC TME type). In some embodiments, the clustering comprises unsupervised clustering. In some embodiments, the unsupervised clustering comprises a confluent clustering approach. In some embodiments, the unsupervised clustering comprises a hierarchical clustering approach. In some embodiments, the clustering comprises calculating sample-to-sample similarities (e.g., using the Pearson correlation coefficient, which takes values ​​in the range [-1, 1]), converting the distance matrix into a graph where each sample forms a node and two nodes form an edge with a weight equal to the Pearson correlation coefficient, removing edges with weights lower than a specified threshold, and applying the Louvain community detection algorithm to calculate the partitioning of the graph into clusters. In some embodiments, the optimal weight thresholds for the observed clusters were calculated by using the minimum DaviesBouldin, maximum Calinski-Harabasz, and Silhouette techniques. In some embodiments, isolation of low population clusters (<5% of samples) is excluded.

[0253] In some embodiments, the subject's TME signature is compared to existing clusters of TME types and a TME type is assigned based on the comparison.

[0254] 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.

[0255] Generation of TME signatures and identification of TME types As described herein, Figures 1-3 show determining a subject's breast cancer TME signature, identifying the subject's TME type using the TME signature, and identifying whether the subject is likely to respond to a therapeutic agent based on the identified TME type.

[0256] As described herein, in some embodiments, the TME signature determined for a subject using the techniques described herein can also be used to identify one of a plurality of different breast cancer TME types for the subject. In some embodiments, the plurality of BLBC TME types includes immune-enriched (IE) type, TLS (TLS) type (also called B-cell enriched), desert (D) type, fibrotic (F) type, and granulocyte-enriched (G) type, as described further herein and below. In some embodiments, the plurality of LNLBC TME types includes immune-desert (D), fibrotic (F), immune-enriched / non-fibrotic (IE), immune-enriched / fibrotic (IE / F), and angiogenic (E) type, as described further herein and below. In some embodiments, the multiple H2EBC TME types include immune desert (D) type, fibrotic (F) type, immune enriched / non-fibrotic (IE) type, moderate immune enriched / (IE-med) type, and endothelial enriched (End-Ar-H) type, as further described herein and below.

[0257] In some embodiments, each of the multiple TME types is associated with a respective TME signature cluster in the multiple TME signature clusters. The TME type of a subject can be determined by: (1) associating the subject's TME signature with a particular one of the multiple TME signature clusters; and (2) identifying the subject's TME type as the TME type corresponding to the particular one of the multiple TME signature clusters with which the subject's TME signature is associated.

[0258] 6 shows an exemplary BLBC TME signature 600. In some embodiments, the signature 600 is an LNLBC TME signature or an H2EBC TME signature. In some embodiments, the TME signature (e.g., BLBC TME signature, LNLBC TME signature, H2EBC TME signature) includes at least eight gene group scores for the gene groups listed in Table 1, Table 2, or Table 3. However, it should be understood that the TME signature may include fewer scores than the number of scores shown in FIG. 5 (e.g., by omitting scores for one or more gene groups listed in Table 1, Table 2, or Table 3) or more scores than the number of scores shown in FIG. 5 (e.g., by including scores for one or more other gene groups in addition to or in place of the gene groups listed in Table 1, Table 2, or Table 3). In some embodiments, the TME signature may be embodied in at least one data structure that includes a field that stores the gene group score portion of the TME signature.

[0259] In some embodiments, the TME signature clusters may be generated by (1) obtaining TME signatures for a plurality of subjects (using the techniques described herein) and (2) clustering the 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.

[0260] For example, inter-sample similarity may be calculated using Pearson correlation. The distance matrix may be converted into a graph where each sample forms a node and two nodes form an edge with a weight equal to the Pearson correlation coefficient. Edges with weights lower than a specified threshold may be removed. The partition of the graph into clusters may be calculated applying the Louvain community detection algorithm. Minimum DaviesBouldin, maximum Calinski-Harabasz, and Silhouette techniques may be used to mathematically determine optimal weight thresholds for the observed clusters. Separation of low population clusters (less than 5% of samples) may be excluded.

[0261] Thus, in some embodiments, generating a 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 TME signatures from the plurality of sets of RNA expression data, each of the plurality of sets of RNA expression data indicative of RNA expression levels of genes in a first plurality of gene groups (e.g., one or more of the gene groups in Table 1, Table 2, or Table 3), each of the plurality of TME signatures comprising a gene group score for a respective gene group, and generating comprises, for each particular one of the plurality of TME signatures, (i) determining the 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 TME signature was generated; and (ii) clustering the plurality of signatures to obtain a plurality of TME signature clusters.

[0262] The resulting TME signature clusters can each contain any suitable number of 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), although aspects of the technology described herein are not limited in this respect.

[0263] The number of 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 certain intrinsic types of breast cancer (e.g., BLBC, LNLBC, H2EBC) can be characterized into 5 types based on the generation of TME signatures using the methods described herein.

[0264] 6, a subject's BLBC TME signature 600 may be associated with one of five BLBC TME clusters: 602, 604, 606, 608, and 610. Each of the clusters 602, 604, 606, 608, and 610 may be associated with a respective BLBC TME type. In this example, the BLBC TME signature 600 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 BLBC TME signature 600 is associated with the closest signature cluster ("closest" in the sense that a distance-based comparison is performed or a distance metric or measure is used). In this example, the BLBC TME signature 600 is associated with BLBC TME type cluster 5 610 (shown in homogenous shading) because the measured distance D5 between the BLBC TME signature 600 and cluster 610 (e.g., its center of mass or other representative point) is less than the measured distances D1, D2, D3, and D4 between the BLBC TME signature 600 and clusters 602, 604, 606, and 608 (e.g., their center of mass or other representative point), respectively.

[0265] In some embodiments, a subject's TME signature can be associated with one of five breast cancer 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 TME signature to one of the five breast cancer TME signature clusters. The machine learning technique can be trained to assign a TME signature to a meta-cohort represented by the signatures in the cluster.

[0266] In some embodiments, BLBC TME types include immune-enriched (IE), TLS (also called B-cell enriched), desert (D), fibrotic (F), and granulocyte-enriched (G). In some embodiments, LNLBC TME types include immune-desert (D), fibrotic (F), immune-enriched / nonfibrotic (IE), immune-enriched / fibrotic (IE / F), and angiogenic (E). In some embodiments, H2EBC TME types include immune-desert (D), fibrotic (F), immune-enriched / nonfibrotic (IE), moderately immune-enriched (IE-med), and endothelial-enriched (End-Ar-H). The breast cancer 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 a subject with a different type of breast cancer (e.g., a different TME type within the same intrinsic subtype, e.g., BLBC, LNLBC, or H2EBC). In some embodiments, a "low" signal refers to a gene expression signal or score (e.g., enrichment score) that is decreased 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 a subject with a different type of TME (e.g., a different TME type within the same intrinsic subtype, e.g., BLBC, LNLBC, or H2EBC).

[0267] The tumor microenvironment of BC can contain variable numbers of immune cells, stromal cells, blood vessels, and extracellular matrix.

[0268] In some embodiments, BLBC TME types include immune-enriched (IE) type, TLS type (also called B cell-enriched), desert (D) type, fibrotic (F) type, and granulocyte-enriched (G) type.

[0269] In some embodiments, the BLBC granulocyte (G) type TME is characterized by a high percentage of M1 macrophages, granulocytes, and cytokines that control granulocyte trafficking. In some embodiments, the G type TME comprises an upregulated NFkB signaling pathway compared to other BLBC TME types. In some embodiments, the G type BLBC TME comprises a high tumor growth rate signal compared to other BLBC TME types.

[0270] In some embodiments, the BLBC immune-enriched non-fibrotic (IE) TME type is characterized by abundant immune-active infiltrates containing cytotoxic effector cells. In some embodiments, the BLBC IE sample comprises an immune-inflammatory sample. In some embodiments, the BLBC IE type comprises a lower percentage of malignant cells compared to other BLBC TME types. In some embodiments, the IE type BLBC TME type is associated with a favorable prognosis.

[0271] In some embodiments, BLBC fibrotic (F) type is highly fibrotic with dense collagen formation (compared to other BLBC TME types). In some embodiments, F type BLBC TME samples contain minimal leukocyte / lymphocyte infiltration (are non-inflamed) compared to other BLBC TME types. In some embodiments, F type BLBC TME samples contain high levels of angiogenesis compared to other BLBC TME types. In some embodiments, cancer associated fibroblasts (CAFs) are abundant in BLBC F type TME samples. In some embodiments, BLBC TME type F is associated with poor prognosis.

[0272] In some embodiments, BLBC immune desert (D) type TME contains the highest percentage of malignant cells (compared to other BLBC TME types). In some embodiments, type D BLBC TME samples contain minimal or no leukocyte / lymphocyte infiltration. In some embodiments, type D BLBC TME comprises an immune non-inflammed, immune desert phenotype. Type D BLBC TME types are associated with high tumor growth rates and poor prognosis.

[0273] In some embodiments, the BLBC B cell-enriched, tertiary lymphoid structure (TLS)-like (TLS) TME type is characterized by an immune infiltrate, high vascularity, and a significant number of B cells compared to other BLBC TME types. In some embodiments, the BLBC TLS type TME contains a moderate distribution of stromal and fibrous elements compared to other BLBC TME types. In some embodiments, the BLBC TME TLS type is associated with a favorable prognosis.

[0274] In some embodiments, LNLBC TME types include immune desert (D) type, fibrotic (F) type, immune enriched / non-fibrotic (IE) type, immune enriched / fibrotic (IE / F) type, and angiogenic (E) type.

[0275] In some embodiments, the LNLBC immune-enriched / fibrotic (IE / F) TME type is characterized by increased blood distribution and high levels of immune infiltrate compared to other LNLBC TME types. In some embodiments, the LNLBC IE / FTME type comprises an immune non-inflammatory phenotype. In some embodiments, the LNLBC IE / FTME type comprises a lower percentage of malignant cells compared to other LNLBC TME types. In some embodiments, the LNLBC IE / FTME type is associated with lower estrogen expression and lower tumor growth rate compared to other LNLBC TME types.

[0276] In some embodiments, the LNLBC immune-enriched / non-fibrotic (IE) TME type is characterized by abundant immune-active infiltrates containing cytotoxic effector cells compared to other LNLBC TME types. In some embodiments, the LNLBC IE type TME comprises an immune-inflammatory phenotype. In some embodiments, the LNLBC TME IE type is associated with poor prognosis in patients undergoing hormone therapy.

[0277] In some embodiments, the LNLBC fibrotic (F) TME type is highly fibrotic with dense collagen formation (compared to other LNLBC TME types). In some embodiments, the LNLBC type TME is characterized by minimal leukocyte / lymphocyte infiltration compared to other LNLBC TME types. In some embodiments, the LNLBC F type TME comprises a non-inflammatory phenotype. In some embodiments, the LNLBC F type TME sample is enriched in cancer-associated fibroblasts (CAFs). In some embodiments, epithelial-mesenchymal transition (EMT) is present in the F type LNLBC TME sample.

[0278] In some embodiments, the LNLBC immune desert (D) type LNLBC TME contains the highest percentage of malignant cells compared to other LNLBC TME types. In some embodiments, leukocyte / lymphocyte infiltration is minimal or completely absent compared to other LNLBC TME types. In some embodiments, the LNLBC D type TME comprises an immune non-inflammatory, immune desert phenotype. In some embodiments, the LNLBC D type TME samples are characterized by high estrogen expression. In some embodiments, the LNLBC D type TME is associated with a high tumor growth rate compared to other LNLBC TME types.

[0279] In some embodiments, the LNLBC angiogenic (E) TME type is characterized by strong angiogenesis and moderate levels of immune infiltrates compared to other LNLBC TME types. In some embodiments, cancer-associated fibroblasts (CAFs) are enriched in LNLBC type E TME samples. In some embodiments, epithelial-mesenchymal transition (EMT) is present in E type LNLBC samples. In some embodiments, E type TME samples contain high levels of tumor-promoting cytokines compared to other LNLBC TME types. In some embodiments, LNLBC TME type E contains low levels of estrogen expression and low tumor growth rates compared to other LNLBC TME types. In some embodiments, LNLBC E type is associated with a good prognosis for patients undergoing hormone therapy.

[0280] In some embodiments, H2EBC TME types include immune desert (D), fibrotic (F), immune enriched / non-fibrotic (IE), moderately immune enriched (IE-med), and endothelial enriched (End-Ar-H).

[0281] In some embodiments, H2EBC immune desert (D) type TME contains the highest percentage of malignant cells compared to other H2EBC TME types. In some embodiments, leukocyte / lymphocyte infiltration is minimal or completely absent in H2EBC type D TME samples. In some embodiments, H2EBC type D TME comprises an immune non-inflammatory, immune desert phenotype. In some embodiments, type D H2EBC TME is characterized by high estrogen pathway activity compared to other H2EBC TME types.

[0282] In some embodiments, H2EBC moderately immune-enriched (IE-med) type TME is characterized by a moderate number of tumor-infiltrating immune cells (such as B cells, cytotoxic effector cells, and regulatory T cells) compared to other H2EBC TME types. In some embodiments, the level of immune cell abundance in IE-med type TME samples is lower than IE type H2EBC TME. In some embodiments, there is a low level of vascularity in H2EBC IE-med samples compared to other H2EBC TME types. In some embodiments, H2EBC IE-med type TME is associated with a poor prognosis in patients undergoing chemotherapy.

[0283] In some embodiments, H2EBC immune-enriched non-fibrotic (IE) type TME is characterized by abundant immune-active infiltrates including cytotoxic effector cells and regulatory T cells compared to other H2EBC TME types. In some embodiments, IE type H2EBC TME is characterized by an immune-inflammatory phenotype with neovascularization. In some embodiments, the percentage of malignant cells in H2EBC IE type TME is low compared to other H2EBC TME types. In some embodiments, H2EBC IE type TME is associated with a favorable prognosis for patients undergoing chemotherapy.

[0284] In some embodiments, the fibrotic / hypoxic (F) type TME is associated with a high degree of vascularization compared to other H2EBC TME types. In some embodiments, the H2EBC F type TME is characterized by dense collagen formation and epithelial-mesenchymal transition (EMT). In some embodiments, the H2EBC F type TME comprises an immune-mediated non-inflammatory phenotype. Cancer-associated fibroblasts (CAFs) are enriched in this H2EBC TME compared to other H2EBC TME types.

[0285] In some embodiments, the H2EBC endothelial-enriched (End-Ar-H) type TME is characterized by a higher number of endothelial cells compared to other H2EBC TME types. In some embodiments, the End-Ar-H type is associated with angiogenesis and epithelial-mesenchymal transition (EMT). In some embodiments, the End-Ar-H sample comprises an immune non-inflammatory phenotype. In some embodiments, the End-Ar-H sample comprises tumor-promoting cytokines. In some embodiments, the End-Ar-H type is characterized by higher androgen pathway activity and lower tumor growth rate compared to other H2EBC TME types. In some embodiments, the End-Ar-H TME type is characterized by higher angiogenesis compared to other H2EBC TME types.

[0286] Tables 4-6 provide examples of breast cancer TME signatures and gene group scores generated by ssGSEA analysis and normalization (e.g., median scaling) of expression data from one or more breast cancer subjects.

[0287] [Table 7]

[0288] [Table 8]

[0289] [Table 9]

[0290] [Table 10]

[0291] [Table 11]

[0292] [Table 12]

[0293] [Table 13]

[0294] [Table 14]

[0295] [Table 15]

[0296] [Table 16]

[0297] [Table 17]

[0298] In some embodiments, the disclosure provides methods for identifying a subject having, suspected of having, or at risk of having BC as likely to have a good prognosis (e.g., as measured by overall survival (OS) or progression-free survival (PFS)). In some embodiments, the methods comprise determining the subject's BC TME type as described herein.

[0299] In some embodiments, the method comprises identifying the subject as having a lower risk of BC progression compared to other BC TME types. In some embodiments, a "reduced risk of BC progression" may indicate that the subject has a better prognosis for BC or is less likely to have progressive disease. In some embodiments, a "reduced risk of BC progression" may indicate that the subject with BC is expected to be more responsive to a particular treatment. For example, a "reduced risk of BC 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 BC patient or population of BC patients (e.g., patients with BC but not the same BC TME type as the subject).

[0300] In some embodiments, the method further comprises identifying the subject as having a higher risk of BC progression compared to other BC TME types. In some embodiments, "increased risk of BC progression" may indicate that the subject has a less favorable prognosis for BC or is more likely to have progressive disease. In some embodiments, "increased risk of BC progression" may indicate that a subject with BC 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 BC 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 BC patient or population of BC patients (e.g., patients with BC but not the same BC TME type as the subject).

[0301] In some embodiments, the methods described herein comprise the use of at least one computer hardware processor to perform the determination.

[0302] In some embodiments, the disclosure provides methods for providing a prognosis, predicting survival, or stratifying patient risk in a subject suspected of having or at risk of having BC, hi some embodiments, the methods comprise determining the subject's BC TME type as described herein.

[0303] Updating TME clusters based on new data Techniques for generating breast cancer TME clusters are described herein. It should be understood that the TME clusters may be updated when additional TME signatures are calculated for a patient. In some embodiments, the TME signature of a subject is one of a threshold number of TME signatures for a threshold number of subjects. In some embodiments, the TME signature clusters are updated when a threshold number of TME signatures are generated. For example, when a new threshold number of TME signatures (e.g., 1 new signature, 10 new signatures, 100 new signatures, 500 new signatures, any suitable threshold number of signatures in the range of 10-1,000) are obtained, the new signatures may be combined with the TME signatures previously used to generate the TME clusters, and the combined set of old and new TME signatures may be clustered again (e.g., using any of the clustering algorithms described herein or any other suitable clustering algorithm) to obtain an updated set of TME signature clusters.

[0304] In this way, data from future patients may be analyzed in a manner that leverages information learned from patients whose 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 TME type that future patients may have, improving the selection of treatments for these patients.

[0305] Indications for therapeutic drugs Aspects of the present disclosure relate to methods of identifying or selecting a therapeutic agent for a subject based on the determination of the subject's breast cancer TME type. The present disclosure is based, in part, on the recognition that subjects with intrinsic breast cancer types (e.g., BLBC, LNLBC, H2EBC) and certain TME types of those cancers (e.g., BLBC G type, BLBC IE type, BLBC TLS type, LNLBC IE type, LNLBC IE / F type, H2EBC IE type, H2EBC IE-med type) are more likely to respond to certain therapies (e.g., immunotherapy drugs) than subjects with other combinations of intrinsic breast cancer and TME types. The present disclosure is also based, in part, on the recognition that subjects with TME F types (e.g., BLBC F type, LNLBC F type, H2EBC F type) are more likely to respond to anti-VEGF therapy than subjects with other combinations of intrinsic breast cancer and TME types. The present disclosure also relates to the recognition that subjects with BLBC D type may be responsive to tyrosine kinase inhibitors (TKIs).

[0306] In some embodiments, the therapeutic agent is an immuno-oncology (IO) drug. The IO drug can 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 drug comprises a PD1 inhibitor, a PD-L1 inhibitor, or a PD-L2 inhibitor. Examples of IO drugs include, but are not limited to, cemipilimab, nivolumab, pembrolizumab, avelumab, durvalumab, atezolizumab, BMS1166, BMS202, and the like. In some embodiments, the IO drug comprises a combination of atezolizumab and albumin-bound paclitaxel, pembrolizumab and albumin-bound paclitaxel, pembrolizumab and paclitaxel, or pembrolizumab and gemcitabine and carboplatin.

[0307] In some embodiments, the therapeutic agent is a tyrosine kinase inhibitor (TKI). The TKI can 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. In some embodiments, the TKI inhibitor includes neratinib, apatinib, toripalimab, and anlotinib, or anlotinib.

[0308] In some embodiments, the therapeutic agent is an anti-VEGF agent. The anti-VEGF 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 foregoing. Examples of anti-VEGF therapeutic agents include, but are not limited to, bevacizumab (Avastin®), sunitinib, sorafenib, pazopanib, and the like. In some embodiments, the anti-VEGF agent includes liposomal doxorubicin, bevacizumab, and everolimus.

[0309] In some embodiments, the methods described by the present disclosure further include administering one or more therapeutic agents to the subject based on the determination of the subject's TME type. In some embodiments, one or more (e.g., 1, 2, 3, 4, 5, or more) IO agents are administered to the subject. In some embodiments, one or more (e.g., 1, 2, 3, 4, 5, or more) TKIs are administered to the subject. In some embodiments, one or more (e.g., 1, 2, 3, 4, 5, or more) anti-VEGF agents are administered to the subject.

[0310] Aspects of the present disclosure relate to methods of treating a subject having (or suspected of having, or at risk of having) breast cancer based on a determination of the subject's breast cancer 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.

[0311] 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.

[0312] 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.

[0313] 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. One or more aspects of the cancer (e.g., tumor microenvironment, tumor formation, tumor growth, TME type, etc.) may be analyzed to assess the effectiveness of the administered anti-cancer therapeutic.

[0314] 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 TME types described herein. The dosing regimen (including the therapeutic agent used) may vary over time.

[0315] Dosing of immuno-oncology drugs is well known, for example, as described by Louedec et al. Vaccines (Basel). 2020 Dec; 8(4): 632. For example, dosing of pembrolizumab includes administration of 200 mg every 3 weeks or 400 mg every 6 weeks by infusion over 30 minutes.

[0316] Dosage setting 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. Coadministration of TKIs and IO agents is also known, for example, as described by Rassy et al. Ther Adv Med Oncol. 2020; 12: 1758835920907504.

[0317] 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.

[0318] 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.

[0319] 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.

[0320] 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 the cancer, one or more symptoms of breast cancer, or a predisposition to breast cancer.

[0321] Palliating breast cancer includes delaying the onset or progression of the disease or reducing the severity of the disease. Palliating the disease does not necessarily result in a cure. As used herein, "delaying" the onset of a disease (e.g., cancer) means to postpone, prevent, slow, retard, stabilize, and / or postpone the progression of the disease. This delay can be for a variety of times depending on the disease and / or the medical history of the individual being treated. A method of "delaying" or palliating the onset of a disease, or a method of delaying the onset of a disease, is a method that reduces the probability of developing one or more symptoms of the disease within a given time period and / or reduces the severity of the 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.

[0322] "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.

[0323] 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).

[0324] 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.

[0325] 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.

[0326] Examples of surgical therapies include, but are not limited to, curative surgery (eg, tumor removal surgery), preventative surgery, laparoscopic surgery, and laser surgery.

[0327] 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;

[0328] In some embodiments, the present disclosure provides methods for treating breast cancer (BC), the methods comprising administering one or more therapeutic agents (such as one or more anti-cancer agents, e.g., one or more immunotherapeutic agents) to a subject identified as having a particular breast cancer TME type, where the subject's breast cancer TME type has been identified by a method as described by the present disclosure.

[0329] 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.

[0330] 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.

[0331] Computer implementation An exemplary implementation of a computer system 2300 that may be used in connection with any of the embodiments of the technology described herein (e.g., the methods of FIG. 1, FIG. 2, or FIG. 3, etc.) is shown in FIG. 23. The computer system 2300 includes one or more processors 2310 and one or more articles of manufacture that include a non-transitory computer-readable storage medium (e.g., memory 2320 and one or more non-volatile storage media 2330). The processor 2310 may control the writing and reading of data to and from the memory 2320 and the non-volatile storage 2330 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 2310 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., memory 2320), which may function as a non-transitory computer-readable storage medium that stores processor-executable instructions for execution by the processor 2310.

[0332] Computing device 2300 may also include a network input / output (I / O) interface 2340, 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 2350, 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.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] Thus, while several aspects and embodiments of the technology described in this disclosure have been described, it should be understood that various changes, modifications, and improvements will readily occur to those skilled in the art. Such changes, modifications, and improvements are intended to be within the spirit and scope of the technology described herein. For example, those skilled in the art will readily envision a variety of alternative means and / or structures for performing the functions described herein and / or obtaining one or more of the results and / or advantages described herein, and each of such variations and / or modifications is deemed to be within the scope of the embodiments described herein. Those skilled in the art will recognize or be able to ascertain, using no more than routine experimentation, many equivalents to the specific embodiments described herein. Thus, the foregoing embodiments are presented by way of example only, and it should be understood that, within the scope of the appended claims and their equivalents, the embodiments of the invention may be practiced otherwise than as specifically described. Furthermore, where features, systems, articles, materials, kits, and / or methods described herein are not inconsistent, any combination of two or more of such features, systems, articles, materials, kits, and / or methods is within the scope of the present disclosure.

[0338] The above embodiments may be realized in any of numerous ways. One or more aspects and embodiments of the present disclosure involving the performance of a process or method may utilize program instructions executable by a device (e.g., a computer, processor, or other device) to perform or control the performance of the process or method. In this regard, various inventive concepts may be embodied as a computer-readable storage medium (or multiple computer-readable storage media) (e.g., computer memory, one or more floppy disks, compact disks, optical disks, magnetic tapes, flash memories, circuitry in field programmable gate arrays or other semiconductor devices, or other tangible computer storage media) encoded with one or more programs that, when executed by one or more computers or other processors, perform a method of performing one or more of the various embodiments described above. The computer-readable medium or readable media may be portable such that the programs stored thereon can be loaded into one or more different computers or other processors to perform various of the above-mentioned aspects. In some embodiments, the computer-readable medium may be a non-transitory medium.

[0339] The terms "program" or "software" are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be used to program a computer or other processor to implement various aspects as described above. Furthermore, according to one aspect, it should be understood that one or more computer programs that, when executed, implement the methods of the present disclosure need not reside on a single computer or processor, but may be distributed in a modular manner among several different computers or processors to implement various aspects of the present disclosure.

[0340] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0341] Also, the data structures may be stored in a computer-readable medium in any suitable format. For ease of explanation, the data structures may be depicted as having fields that are related through their locations within the data structure. Such relationships may also be achieved by assigning storage for the fields with locations within the computer-readable medium that convey the relationship between the fields. However, relationships between information in fields of the data structures may be established using any suitable mechanism, including the use of pointers, tags, or other mechanisms that establish relationships between data elements.

[0342] If implemented in software, the software code may be executed on any suitable processor or collection of processors, whether provided on a single computer or distributed across multiple computers.

[0343] Further, it should be understood that a computer may be embodied in any of a number of forms, such as, by way of non-limiting examples, a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be incorporated into devices not generally considered to be computers, but which have suitable processing capabilities, including a personal digital assistant (PDA), a smartphone, a tablet, or any other suitable portable or fixed electronic device.

[0344] A computer may also have one or more input / output devices. These devices may be used, among other things, to display a user interface. Examples of output devices that may be used to provide a user interface include a printer or display screen for visually presenting output, and a speaker or other sound generating device for audibly presenting output. Examples of input devices that may be used for a user interface include keyboards and pointing devices such as mice, touchpads, and digitizing tablets. As another example, a computer may receive input information via voice recognition or other audible form.

[0345] Such computers may be interconnected by one or more networks of any suitable type, including local area networks, or wide area networks such as enterprise networks, and intelligent networks (IN) or the Internet. Such networks may be based on any suitable technology and operate according to any suitable protocol, and may include wireless networks, wired networks, or fiber optic networks.

[0346] Also, as described, some aspects may be embodied as one or more methods. Operations performed as part of a method may be ordered in any suitable manner. Thus, while an example embodiment may be shown as sequential operations, embodiments may be constructed in which operations are performed in an order different from that shown, and such embodiments may include performing some operations simultaneously.

[0347] [Table 18]

[0348] [Table 19]

[0349] [Table 20]

[0350] [Table 21]

[0351] [Table 22]

[0352] [Table 23]

[0353] [Table 24]

[0354] [Table 25]

[0355] [Table 26]

[0356] [Table 27]

[0357] [Table 28]

[0358] [Table 29] EXAMPLES

[0359] Example 1: Basal-like breast cancer To describe the TME molecular functional types of basal-like breast cancer (BLBC), a meta-cohort was collected from public datasets. The mega-cohort included RNA expression data from the following datasets: TCGA, Metabric, FUSCCTNBC, GSE103091, GSE106977, GSE21653, GSE25066, GSE41998, GSE47994, GSE81538, GSE96058. Basal-like breast cancer samples were isolated based on the expression profile of 50 genes (PAM50). Gene expression signatures (e.g., using the gene groups and gene group genes described in Table 1, or the gene groups shown in Figure 7), as well as PROGENy signatures for TGFb, NFkB, VEGF (e.g., those described by www.nature.com / articles / s41467-017-02391-6) were used to evaluate different biological processes in each of the samples. Rank estimation and median scaling transformation were used to overcome batch effects from different datasets. Using unsupervised clustering, five stable tumor microenvironment (TME) types (Figure 7) were identified. Four of the molecular types have been reported in other cancers and were found to be present in BLBC: immune-enriched type (IE), B-cell-enriched type or TLS type (TLS), desert type (D), and fibrotic (F) type. In addition, a novel "granulocyte-enriched type" (G type) was also identified in BLBC.

[0360] The detected biological processes were confirmed by comparison of selected biological process signatures between BLBC TME types (Figure 8). It was observed that type G BLBC has the highest granulocyte, M1 macrophage, and NF-kB signals, and type G shares a high angiogenesis signal with type F BLBC samples. TLS type shows the highest B cell signal. TLS type and type F also contain the highest endothelial signature among the five BLBC TME types. The identified BLBC TME types are described below:

[0361] Granulocyte-enriched (G): G type is characterized by a high percentage of M1 macrophages, granulocytes, and cytokines that control granulocyte trafficking. The NFkB signaling pathway is upregulated compared to other BLBC TME types. High tumor proliferation rate signals were observed.

[0362] Immune-enriched / non-fibrotic (IE): The IE type is characterized by an abundant immune-active infiltrate containing cytotoxic effector cells. An immune-inflammatory phenotype is observed. The percentage of malignant cells present in the subject sample is low. This BLBC TME type is associated with a favorable prognosis.

[0363] Fibrotic (F): Type F is highly fibrotic with dense collagen formation. Type F TME samples contain minimal leukocyte / lymphocyte infiltration (non-inflammatory) with intense vascularization. Cancer-associated fibroblasts (CAFs) are abundant. This BLBC TME type is associated with poor prognosis.

[0364] Immune Desert (D): Type D BLBC samples contain the highest percentage of malignant cells, with minimal or complete absence of leukocyte / lymphocyte infiltration. An immune-non-inflammatory, immune desert phenotype was observed. This BLBC TME type is associated with high tumor growth rate and poor prognosis.

[0365] B-cell-enriched, tertiary lymphoid structure (TLS)-like: The TLS-like BLBC TME type is characterized by a high level of immune infiltration, high vascularity, and substantial numbers of B cells. A moderate incidence of interstitial and fibrotic elements was observed. This BLBC TME type is associated with a favorable prognosis.

[0366] For RNA-seq samples, the high B cell content was also probed by the cellular deconvolution algorithm, Kassandra (described, for example, in International PCT Publication WO 2021 / 183917, the entire contents of which are incorporated herein by reference). This algorithm allows reconstructing the cellular composition from bulk RNA-seq data and estimating the percentage of different cell types (fibroblasts, B cells, T cells, macrophages, etc.). E-type samples proved to have the highest percentage of B cells (Figure 9).

[0367] Further evaluation by quantitative histopathological analysis was performed. The data show that the gene expression patterns of the identified BLBC TME types correlate with the histological characteristics of tumors from patients with basal breast cancer. TCGA slide images were used to compare immune infiltration and fibrotic compartments in samples belonging to different TME subtypes (Figures 10A-10C). Samples with IE-type and TLS BLBC TME types had a high percentage of stromal tumor-infiltrating lymphocytes, whereas F-type BLBC TME showed the highest fibroblast composition. Samples with D-type BLBC TME were characterized by extensive cellular fibrosis with a low percentage of stromal tumor-infiltrating lymphocytes (sTILs), and samples with G-type BLBC TME showed low cellular collagenous stroma and an intermediate percentage of sTILs.

[0368] Immune-enriched samples were observed to have better postoperative outcomes and better responses to immunochemotherapy. Furthermore, TLS-positive samples were observed to be associated with better disease-free survival. Primary breast cancers had better postoperative outcomes. Analysis of overall survival (OS) revealed a better prognosis for TLS and IE BLBC TME types compared to stromal-enriched (e.g., D-type) and G-type BLBC TME types (Figure 11).

[0369] Basal-like tumors have been observed to lack expression of estrogen receptors, progesterone receptors, and human epidermal growth factor receptors (EGFR). Thus, treatment options for this type of breast cancer are very limited, which creates the need to use targeted therapies. Samples of G, IE, and TLS BLBC TME types were observed to contain high expression of immune checkpoint genes such as CD274, PDCD1, and CTLA4, indicating that anti-PD1 or anti-CTLA4 therapy can be recommended for patients with these BLBC TME types. Similarly, anti-VEGF therapy is recommended for F-type BLBC, and TKI inhibitors may be recommended for D-type BLBC due to their high expression of VEGFA and FGFR1 (Figure 12).

[0370] Example 2: Luminal and normal-like breast cancer A metacohort was compiled using publicly available data to identify breast cancer TME types in luminal and normal-like breast cancer (LNLBC) (e.g., as defined by PAM50). Datasets used to create the metacohort included GEO datasets (e.g., GSE102484, GSE20181, GSE20685, GSE25066, GSE59515, GSE93204, and GSE96058, which are part of the SCAN-B dataset), Metabric dataset, and TCGA dataset. The metacohort was classified into intrinsic breast cancer types based on the expression of 50 genes (PAM50). Samples classified as luminal A, luminal B, and normal-like were retained for further analysis (5952 samples). For each sample, 24 gene cluster signatures (e.g., using the gene clusters and gene cluster genes listed in Table 2, or the gene clusters shown in FIG. 13) and one PROGENy pathway signature (e.g., estrogen PROGENy signature) were calculated. Rank and median scaling transformations were applied to the samples to overcome batch effects. Unsupervised Leiden clustering identified five LNLBC TME types (FIG. 13): immune desert (D), fibrotic (F), immune enriched / non-fibrotic (IE), immune enriched / fibrotic (IE / F), and angiogenic (E) clusters. Angiogenic (E) type TME is characterized by high vascularity, endothelial, and EMT signatures, and in some embodiments is referred to as highly vascular (HV) TME type.

[0371] The detected biological processes were confirmed by comparing selected biological process signatures between LNLBC TME types (Figure 14). Type E and Type F samples were observed to have the highest angiogenesis and stromal (matrix signature) signals. Type IE and Type IE / F samples were observed to have the highest T cell and B cell signals. The identified LNLBC TME types are shown below.

[0372] Immune-enriched / fibrotic (IE / F). The IE / F LNLBC TME type is characterized by increased vascularity and high levels of immune infiltrate compared to other LNLBC TME types. An immune-inflammatory phenotype was observed. The percentage of malignant cells was low compared to other LNLBC TME types. This LNLBC TME type is associated with low estrogen expression and low tumor growth rate.

[0373] Immune-enriched / nonfibrotic (IE). The IE LNLBC TME type is characterized by abundant immune-active infiltrates containing cytotoxic effector cells and regulatory T cells. An immune-inflammatory phenotype was observed. This LNLBC TME type is associated with poor prognosis in patients receiving hormonal therapy.

[0374] Fibrosis (F). Type F LNLBC TME is highly fibrotic with dense collagen formation. Samples were characterized by minimal leukocyte / lymphocyte infiltration and had a non-inflammatory phenotype. Cancer-associated fibroblasts (CAFs) are abundant. Signs of epithelial-mesenchymal transition (EMT) are present in type F LNLBC TME samples.

[0375] Immune Desert (D). Type D LNLBC TME contains the highest percentage of malignant cells compared to other LNLBC TME types. Leukocyte / lymphocyte infiltration was observed to be minimal or absent altogether. An immune non-inflammation, immune desert phenotype was observed. Type D LNLBC TME samples were characterized by high estrogen expression. This LNLBC TME type is associated with high tumor growth rate.

[0376] Angiogenesis (E): This LNLBC TME type is characterized by intense angiogenesis and moderate levels of immune infiltration compared to other LNLBC TME types. Cancer-associated fibroblasts (CAFs) are abundant. Signs of epithelial-mesenchymal transition (EMT) are present in type E LNLBC samples. High levels of protumor cytokines were observed. LNLBC TME type E was associated with low estrogen expression and low tumor growth rate. This LNLBC TME type is generally associated with a good prognosis for patients undergoing hormonal therapy.

[0377] Additional validation of the identified clusters was performed on TCGA samples by a cell deconvolution algorithm, Kassandra (described, for example, in International PCT Publication WO 2021 / 183917, the entire contents of which are incorporated herein by reference) (Figure 15). Analysis of overall survival (OS) in the Metabric dataset showed that upon hormone therapy, the angiogenic (E) LNLBC TME type had a better prognosis compared to the immune-enriched / non-fibrotic (IE) and immune-desert (D) types (Figure 16). The IE and IE / F subtypes were observed to have the highest expression of immune checkpoint genes, such as CD274, PDCD1, and CTLA4, among the LNLBC TME types, indicating that anti-PD1 or anti-CTLA4 therapy may be recommended for patients with these LNLBC TME types (Figure 17).

[0378] Example 3: HER2-enriched breast cancer To identify the type of TME of HER2-enriched breast cancer, RNA expression data were obtained from luminal and normal-like breast cancer types. Publicly available data were included, including the GEO dataset (GSE102484, GSE20685, GSE96058 (part of the SCAN-B dataset), GSE59515, GSE76360, GSE55348, GSE58984), Metabric dataset, and TCGA dataset. The metacohort was classified into intrinsic breast cancer types based on the expression of 50 genes (PAM50). Samples classified as HER2-enriched were selected (924 samples). For each sample, 29 expression signatures and 3 PROGENy pathway signatures (e.g., using the gene clusters and gene cluster genes listed in Table 3, or the gene clusters shown in Figure 18) were calculated. To overcome batch effects, rank and median scale transformations were applied to the samples. Unsupervised Leiden clustering identified five TME types (Figure 18). Three of these types have also been described in other cancer types: immune desert (D), fibrotic (F), and immune-enriched / non-fibrotic (IE). Furthermore, novel moderately immune-enriched (IE-med) and endothelial-enriched (End-Ar-H) types were identified in HER2-enriched breast cancer (H2EBC). The IE-med type TME is characterized by a moderate level of tumor-infiltrating immune cells, which is lower than the IE type breast cancer TME but higher than the other breast cancer TME types. End-Ar-H was observed to have the highest number of endothelial cells compared to other H2EBC TME types and higher activity of the androgen PROGENy pathway compared to other H2EBC TME types.

[0379] The identified H2EBC TME types were also confirmed by comparing selected biological process signature TME types (Figure 19). The IE type was observed to have the highest T cell and B cell signals compared to other H2EBC TME types. End-Ar-H and F H2EBC types were observed to have the highest stromal (e.g., matrix signature) signals. End-Ar-H TME type also had the highest endothelial signature signal. The identified H2EBC TME types are shown below.

[0380] Immune Desert (D). Type D H2EBC TME contains the highest percentage of malignant cells compared to other H2EBC TME types. Leukocyte / lymphocyte infiltration was observed to be very low or absent altogether. An immune non-inflammation, immune desert phenotype was observed. Type D H2EBC TME is characterized by high estrogen pathway activity compared to other H2EBC TME types.

[0381] Moderate immune enrichment (IE-med). IE-med type H2EBC TME is characterized by intermediate numbers of tumor-infiltrating immune cells, including B cells, cytotoxic effector cells, and regulatory T cells, compared to other H2EBC TME types. The levels of immune cell numbers are lower than in IE type H2EBC TME. Low levels of vascularity were observed. This TME type is associated with a poor prognosis with regard to chemotherapy.

[0382] Immune-enriched / non-fibrotic (IE). IE type H2EBC TME is characterized by abundant immune-active infiltrates including cytotoxic effector cells and regulatory T cells. An immune-inflammatory phenotype is observed, with signs of angiogenesis. The percentage of malignant cells is low compared to other H2EBC TME types. This TME type is associated with a favorable prognosis for chemotherapy.

[0383] Fibrosis / hypoxia (F). Type F H2EBC TME is densely vascularized compared to other H2EBC TMEs. Type F TME samples are characterized by dense collagen formation and epithelial-mesenchymal transition (EMT). An immune-mediated non-inflammatory phenotype was observed. Cancer-associated fibroblasts (CAFs) are abundant in this H2EBC TME type.

[0384] Endothelial-rich (End-Ar-H). End-Ar-H type H2EBC TME is characterized by the highest number of endothelial cells compared to other H2EBC TME types. This type is associated with angiogenesis and epithelial-mesenchymal transition (EMT). An immune-mediated non-inflammatory phenotype was observed with evidence of tumor-promoting cytokines. End-Ar-H TME type is characterized by higher androgen pathway activity and lower tumor proliferation rate compared to other H2EBC TME types. In some embodiments, End-Ar-H H2EBC TME type is characterized by higher vascularity compared to other H2EBC TME types.

[0385] Additional validation of the identified H2EBC TME types was performed on TCGA samples by the cell deconvolution algorithm, Kassandra (described, for example, in International PCT Publication WO 2021 / 183917, the entire contents of which are incorporated herein by reference) (Figure 20). Analysis of overall survival (OS) in the GSE96058 dataset showed that immune-enriched / non-fibrotic (IE) H2EBC TME type showed a better prognosis with respect to chemotherapy compared to other TME types. Moderate immune-enriched (IE-med) TME type was associated with poor prognosis (Figure 21). Expression of immune checkpoint genes was highest in IE and IE-med samples across all TME types. This indicates that anti-PD1 or anti-CTLA4 therapy may be recommended for patients with these TME types. Similarly, anti-VEGF therapy may be recommended for H2EBC F type due to the high expression of VEGFA (Figure 22).

[0386] Equivalent Thus, while several aspects and embodiments of the technology described in this disclosure have been described, it will be understood that various changes, modifications, and improvements will readily occur to those skilled in the art. Such changes, modifications, and improvements are intended to be within the spirit and scope of the technology described herein. For example, those skilled in the art will readily envision a variety of alternative means and / or structures for performing the functions and / or obtaining the results and / or one or more of the advantages described herein, and each of such variations and / or modifications is considered to be within the scope of the embodiments described herein. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation many equivalents to the specific embodiments described herein. Thus, the foregoing embodiments are presented by way of example only, and it should be understood that within the scope of the appended claims and their equivalents, the embodiments of the invention may be practiced otherwise than as specifically described. Furthermore, where features, systems, articles, kits, and / or methods described herein are not inconsistent, any combination of two or more of such features, systems, articles, kits, and / or methods is within the scope of the present disclosure.

[0387] The above embodiments may be implemented in any of numerous ways. One or more aspects and embodiments of the present disclosure involving the performance of a process or method may utilize program instructions executable by a device (e.g., a computer, processor, or other device) to perform or control the performance of the process or method. In this regard, various inventive concepts may be embodied as a computer-readable storage medium (or multiple computer-readable storage media) (e.g., computer memory, one or more floppy disks, compact disks, optical disks, magnetic tapes, flash memories, circuitry of field programmable gate arrays or other semiconductor devices, or other tangible computer storage media) encoded with one or more programs that, when executed on one or more computers or other processors, perform a method for performing one or more of the various embodiments described above. The computer-readable medium or readable media may be portable such that a program stored thereon may be loaded into one or more different computers or other processors to implement various of the above-mentioned aspects. In some embodiments, the computer-readable medium may be a non-transitory medium.

[0388] The terms "program" or "software" are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be used to program a computer or other processor to implement various aspects as described above. Furthermore, according to one aspect, one or more computer programs, when executed, for performing methods of the present disclosure need not reside on a single computer or processor, but rather may be distributed in a modular manner among several different computers or processors to implement various aspects of the present disclosure.

[0389] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0390] Additionally, the data structures may be stored in a computer-readable medium in any suitable format. For ease of explanation, the data structures may be depicted as having fields that are related through their locations within the data structure. Such relationships may similarly be achieved by assigning storage for the fields locations within the computer-readable medium that convey the relationship between the fields. However, relationships between information within fields of the data structures may be established using any suitable mechanism, including the use of pointers, tags, or other mechanisms that establish relationships between data elements.

[0391] If implemented in software, the software code may be executed on any suitable processor or collection of processors, whether provided on a single computer or distributed across multiple computers.

[0392] Further, it should be understood that a computer may be embodied in any of a number of forms, such as, by way of non-limiting examples, a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be incorporated into devices not generally considered to be computers, but which have suitable processing capabilities, including personal digital assistants (PDAs), smartphones, tablets, or other suitable portable or fixed electronic devices.

[0393] A computer may also have one or more input / output devices. These devices may be used, among other things, to display a user interface. Examples of output devices that may be used to provide a user interface include a printer or display screen for visually presenting output, and a speaker or other sound generating device for audibly presenting output. Examples of input devices that may be used for a user interface include keyboards and pointing devices such as mice, touchpads, and digitizing tablets. As another example, a computer may receive input information via voice recognition or other audible form.

[0394] Such computers may be interconnected by one or more networks of any suitable type, including local area networks, or wide area networks such as enterprise networks, and intelligent networks (IN) or the Internet. Such networks may be based on any suitable technology and operate according to any suitable protocol, and may include wireless networks, wired networks, or fiber optic networks.

[0395] Also, as described, some aspects may be embodied as one or more methods. The acts performed as part of a method may be ordered in any suitable manner. Thus, while an example embodiment shows acts as sequential, embodiments may be constructed in which acts are performed in an order different from that shown, which may include performing some acts simultaneously.

[0396] All definitions, as defined and used herein, should be understood to supersede any dictionary definition, definitions in documents incorporated by reference, and / or ordinary meaning of the defined term.

[0397] The indefinite articles "a" and "an," as used in the specification and claims, unless expressly stated to the contrary, should be understood to mean "at least one."

[0398] The term "and / or" as used herein and in the claims should be understood to mean "either or both" of the elements so conjoined, i.e., elements that are conjunctive in some cases and disjunctive in other cases. Multiple elements listed with "and / or" should be interpreted in the same manner, i.e., "one or more" of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the "and / or" clause, whether or not associated with the specifically identified elements. Thus, as a non-limiting example, when used in conjunction with open-ended language such as "comprising," "A and / or B" may refer in one embodiment to only A (optionally including elements other than B); in another embodiment to only B (optionally including elements other than A); in yet another embodiment to both A and B (optionally including other elements), etc.

[0399] As used in this specification and claims, the phrase "at least one" referring to a list of one or more elements is understood to mean at least one element selected from any one or more of the elements in the list of elements, but does not necessarily include at least one of each and every element specifically listed in the list of elements, and does not exclude any combinations of elements in the list of elements. This definition also allows for the optional presence of elements other than those specifically identified in the list of elements to which the phrase "at least one" refers, whether or not they are related to the identified elements. Thus, as a non-limiting example, "at least one of A and B" (or, equivalently, "at least one of A or B" or, equivalently, "at least one of A and / or B") can refer, in one embodiment, to at least one, optionally more than one, A, in which B is absent (and optionally including elements other than B); in another embodiment, to at least one, optionally more than one, B, in which A is absent (and optionally including elements other than A); in yet another embodiment, to at least one, optionally more than one, A, and at least one, optionally more than one, B (and optionally including other elements), etc.

[0400] In the claims, as well as in the above specification, all transitional phrases such as "comprising," "including," "holding," "having," "containing," "involving," "containing," "consisting of," and the like, are understood to be open-ended, i.e., meaning including but not limited to. Only the transitional phrases "consisting of" and "consisting essentially of" must be closed or semi-closed transitional phrases, respectively.

[0401] The terms "approximately," "substantially," and "about" may be used in some embodiments to mean within ±20% of a target value, in some embodiments within ±10% of a target value, in some embodiments within ±5% of a target value, and in some embodiments within ±2% of a target value. The terms "approximately," "substantially," and "about" may include the target value.

Claims

1. 1. A method for determining a basal-like breast cancer (BLBC) tumor microenvironment (TME) type in a subject having, suspected of having, or at risk of having basal-like breast cancer, comprising: Using at least one computer hardware processor: obtaining RNA expression data for the subject indicative of RNA expression levels of at least some genes in at least some of the gene groups listed in Table 1; generating a BLBC TME signature for the subject using the RNA expression data, the BLBC TME signature including a gene group score for each gene group of at least a portion of the plurality of gene groups, the generating step comprising: determining a gene group score using said RNA expression levels; using the BLBC TME signature to identify the subject's BLBC TME type from among a plurality of BLBC TME types; Steps to perform A method comprising:

2. 10. The method of claim 1, further comprising using said RNA expression levels to generate one or more PROGENy signatures.

3. 3. The method of claim 2, wherein the one or more PROGENy signatures comprise a TGFb, NFkB, and / or VEGF signaling PROGENy signature.

4. 3. The method of claim 2, wherein identifying the subject's BLBC TME type comprises using the one or more PROGENy signatures.

5. 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.

6. 6. The method of claim 5, 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.

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

8. The method of claim 5 , wherein the sequencing data comprises microarray data.

9. normalizing the RNA expression data to transcripts per million (TPM) units prior to generating the BLBC TME signature.

10. The method of claim 1, further comprising:

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

11. 10. The method of claim 1, wherein the biological sample comprises breast tissue of the subject, and optionally the biological sample comprises tumor tissue of the subject.

12. The RNA expression levels of the following genes: (a) MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; (b) MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; (c) Co-activating molecules: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; (d) effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; (e) NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; (f) T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; (g) B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; (h) M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; (i) Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; (j) antitumor cytokines: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; (k) Checkpoint inhibitor group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; (l) Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; (m) Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; (n) Granulocyte transport group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; (o) MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; (p) Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; (q) tumor-promoting cytokines: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; (r) Cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; (s) Matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; (t) Angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; (u) Endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; (v) proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; (w) EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; (x) tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; (y) follicular dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, BST1, CLU, C1S, and C4A; (z) Follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6; and 2. The method of claim 1, comprising: (aa) the RNA expression levels of at least three genes from each of at least two of the following populations: granulocytes: CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33.

13. The RNA expression levels of the following genes: (a) MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; (b) MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; (c) Co-activating molecules: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; (d) effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; (e) NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; (f) T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; (g) B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; (h) M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; (i) Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; (j) antitumor cytokines: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; (k) Checkpoint inhibitor group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; (l) Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; (m) Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; (n) Granulocyte transport group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; (o) MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; (p) Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; (q) tumor-promoting cytokines: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; (r) Cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; (s) Matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; (t) Angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; (u) Endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; (v) proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; (w) EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; (x) tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; (y) follicular dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, BST1, CLU, C1S, and C4A; (z) Follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6; and 2. The method of claim 1, comprising the RNA expression level for each of the genes from each of: (aa) granulocyte group: CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33.

14. The step of determining the gene group score comprises: For a particular gene group, the RNA expression levels of at least three genes in the particular gene group are used to determine the gene group score for the particular gene group, as follows: (a) MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; (b) MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; (c) Co-activating molecules: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; (d) effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; (e) NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; (f) T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; (g) B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; (h) M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; (i) Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; (j) antitumor cytokines: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; (k) Checkpoint inhibitor group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; (l) Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; (m) Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; (n) Granulocyte transport group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; (o) MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; (p) Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; (q) tumor-promoting cytokines: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; (r) Cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; (s) Matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; (t) Angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; (u) Endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; (v) proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; (w) EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; (x) tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; (y) follicular dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, BST1, CLU, C1S, and C4A; (z) Follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6; and (aa) Granulocyte group: CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33 2. The method of claim 1, comprising determining a gene group score for each of at least two of the gene groups, including:

15. The step of determining the gene group score comprises: For a particular gene group, the RNA expression level for each of the genes in each gene group is used to determine the gene group score for the particular gene group, as follows: (a) MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; (b) MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; (c) Co-activating molecules: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; (d) effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; (e) NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; (f) T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; (g) B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; (h) M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; (i) Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; (j) antitumor cytokines: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; (k) Checkpoint inhibitor group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; (l) Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; (m) Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; (n) Granulocyte transport group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; (o) MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; (p) Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; (q) tumor-promoting cytokines: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; (r) Cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; (s) Matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; (t) Angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; (u) Endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; (v) proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; (w) EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; (x) tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; (y) follicular dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, BST1, CLU, C1S, and C4A; (z) Follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6; and (aa) Granulocyte group: CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33 2. The method of claim 1, comprising determining a gene group score for each of the gene groups, including:

16. The step of determining the gene group scores uses single sample gene set enrichment analysis (ssGSEA) technology to determine the following gene groups: (a) MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; (b) MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; (c) Co-activating molecules: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; (d) effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; (e) NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; (f) T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; (g) B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; (h) M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; (i) Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; (j) antitumor cytokines: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; (k) Checkpoint inhibitor group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; (l) Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; (m) Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; (n) Granulocyte transport group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; (o) MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; (p) Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; (q) tumor-promoting cytokines: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; (r) Cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; (s) Matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; (t) Angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; (u) Endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; (v) proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; (w) EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; (x) tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; (y) follicular dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, BST1, CLU, C1S, and C4A; (z) Follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6; and (aa) Granulocyte group: CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33 2. The method of claim 1, comprising determining a first score for the first set of genes from RNA expression levels for at least a portion of the genes in one of:

17. The step of determining the gene group scores uses single sample gene set enrichment analysis (ssGSEA) technology to determine the following gene groups: (a) MHC group I: HLA-C, TAPBP, HLA-B, B2M, TAP2, HLA-A, TAP1, and NLRC5; (b) MHC group II: HLA-DQB1, HLA-DMA, HLA-DMB, HLA-DRA, CIITA, HLA-DQA1, HLA-DPB1, HLA-DRB1, and HLA-DPA1; (c) Co-activating molecules: TNFRSF4, CD27, CD80, CD40LG, TNFRSF9, CD40, CD28, ICOSLG, CD83, TNFSF9, CD70, TNFSF4, ICOS, and CD86; (d) effector cell group: ZAP70, GZMB, GZMK, IFNG, FASLG, EOMES, TBX21, GZMA, CD8A, GNLY, PRF1, and CD8B; (e) NK cell group: NKG7, FGFBP2, CD244, KLRK1, KIR2DL4, CD226, KLRF1, GNLY, GZMB, KLRC2, NCR1, GZMH, IFNG, SH2D1B, NCR3, EOMES, and CD160; (f) T cell population: TRBC2, CD3E, CD3G, ITK, CD28, TRBC1, TRAT1, TBX21, CD5, TRAC, and CD3D; (g) B cell group: CD22, TNFRSF13C, STAP1, CD79B, PAX5, CR2, TNFRSF13B, CD79A, TNFRSF17, FCRL5, MS4A1, CD19, and BLK; (h) M1 signature group: CMKLR1, SOCS3, IRF5, NOS2, IL1B, IL12B, IL23A, TNF, and IL12A; (i) Th1 signature group: IL21, TBX21, IL12RB2, CD40LG, IFNG, IL2, and STAT4; (j) antitumor cytokines: CCL3, IL21, IFNB1, IFNA2, TNF, and TNFSF10; (k) Checkpoint inhibitor group: PDCD1, BTLA, HAVCR2, CD274, VSIR, LAG3, TIGIT, PDCD1LG2, and CTLA4; (l) Treg group: IKZF2, TNFRSF18, IL10, FOXP3, CCR8, IKZF4, and CTLA4; (m) Neutrophil signature group: CD177, FFAR2, PGLYRP1, CXCR1, MPO, CXCR2, ELANE, CTSG, PRTN3, and FCGR3B; (n) Granulocyte transport group: CCR3, KITLG, CCL11, CXCL2, CXCR1, CXCR2, CXCL8, CXCL1, and CXCL5; (o) MDSC group: ARG1, IL6, CYBB, IL10, PTGS2, IDO1, and IL4I1; (p) Th2 signature group: IL13, CCR4, IL10, IL4, and IL5; (q) tumor-promoting cytokines: TGFB2, MIF, IL6, TGFB3, TGFB1, IL22, and IL10; (r) Cancer-associated fibroblast (CAF) group: COL6A3, PDGFRB, COL6A1, MFAP5, COL5A1, FAP, PDGFRA, FGF2, ACTA2, COL6A2, FBLN1, CD248, COL1A1, MMP2, COL1A2, MMP3, LUM, CXCL12, and LRP1; (s) Matrix group: LAMC2, TNC, COL11A1, VTN, LAMB3, COL1A1, FN1, LAMA3, LGALS9, COL1A2, COL4A1, COL5A1, ELN, LGALS7, and COL3A1; (t) Angiogenesis group: VEGFC, VEGFA, PDGFC, KDR, CDH5, VEGFB, PGF, TEK, ANGPT2, CXCR2, FLT1, CXCL8, VWF, ANGPT1, and CXCL5; (u) Endothelial group: KDR, CDH5, NOS3, VCAM1, VWF, FLT1, MMRN1, CLEC14A, ENG, and MMRN2; (v) proliferation rate group: CCND1, CCNB1, CETN3, CDK2, E2F1, AURKA, BUB1, AURKB, PLK1, MCM6, ESCO2, MYBL2, MKI67, MCM2, and CCNE1; (w) EMT signature group: CDH2, ZEB1, ZEB2, TWIST1, SNAI1, SNAI2, and TWIST2; (x) tertiary lymphoid structure (TLS) group: CXCL13, CCL19, LAMP3, SELL, CXCR4, CCL21, CCR7, CD86, and BCL6; (y) follicular dendritic cell group: FDCSP, SERPINE2, PRNP, PDPN, LTBR, BST1, CLU, C1S, and C4A; (z) Follicular B helper T cell population: SH2D1A, IL6, MAF, CD40LG, IL21, ICOS, IL4, CD84, CXCR5, and BCL6; and (aa) Granulocyte group: CCR3, FFAR2, CXCL2, CMA1, SIGLEC8, TPSAB1, RNASE3, IL5, PRG3, CXCR1, CXCR2, ELANE, CXCL8, CXCL1, PRTN3, RNASE2, FCGR3B, GATA1, EPX, IL13, MPO, IL5RA, CTSG, CXCL5, PRG2, CD177, CPA3, CCL11, PGLYRP1, MS4A2, IL4, and PRSS33 2. The method of claim 1, comprising determining the gene group score from RNA expression levels for each of the genes in each of:

18. 2. The method of claim 1, wherein generating the BLBC TME signature further comprises normalizing the gene group scores, wherein the normalizing comprises applying median scaling to the gene group scores.

19. the plurality of BLBC TME types are associated with a respective plurality of BLBC TME signature clusters; wherein the step of identifying the subject's BLBC TME type from among a plurality of BLBC TME types using the BLBC TME signature comprises: associating the subject's BLBC TME signature with a particular one of the plurality of BLBC TME signature clusters; identifying the subject's BLBC TME type as the BLBC TME type corresponding to the particular one of the plurality of BLBC TME signature clusters with which the subject's BLBC TME signature is associated; The method of claim 1 , comprising:

20. The method further includes generating the plurality of BLBC 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 the RNA expression level of at least some genes in at least some of the plurality of gene groups listed in Table 1; generating a plurality of BLBC TME signatures from the plurality of sets of RNA expression data, each of the plurality of BLBC TME signatures comprising a gene group expression score for a respective gene group in the plurality of gene groups, and wherein said generating step comprises, for each particular one of the plurality of BLBC TME signatures: determining the BLBC TME signature by determining the gene group expression score using the RNA expression levels in the particular set of RNA expression data from which the particular BLBC TME signature was generated; clustering the plurality of BLBC TME signatures to obtain the plurality of BLBC TME signature clusters; 20. The method of claim 19, comprising:

21. and updating the plurality of BLBC TME signature clusters using a BLBC TME signature of the subject, wherein the BLBC TME signature of the subject is one of a threshold number of BLBC TME signatures for a threshold number of subjects, and the BLBC TME signature clusters are updated once the threshold number of BLBC TME signatures are generated; 19. The method of claim 18, wherein the threshold number of BLBC 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 BLBC TME signatures.

22. 22. The method of claim 21, 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.

23. determining a BLBC TME type of a second subject, wherein the BLBC TME type of the second subject is identified using the updated BLBC TME signature cluster, wherein the identifying step comprises: determining a BLBC TME signature of the second subject from RNA expression data obtained by sequencing a biological sample obtained from the second subject; associating the second subject BLBC TME signature with a particular one of the plurality of updated BLBC TME signature clusters; identifying a BLBC TME type of the second subject as the BLBC TME type corresponding to a particular one of the plurality of updated BLBC TME signature clusters with which the BLBC TME signature of the second subject is associated; 20. The method of claim 18, comprising:

24. 2. The method of claim 1, wherein the plurality of BLBC TME types comprises the following: immune-enriched (IE) type, TLS (TLS) type, desert (D) type, fibrotic (F) type, and granulocyte-enriched (G) type.

25. 10. The method of claim 1, further comprising using the subject's BLBC TME type to identify at least one therapeutic agent for administration to the subject.

26. 26. The method of claim 25, wherein the at least one therapeutic agent comprises an immuno-oncology (IO) agent, and optionally, the IO agent comprises an immune checkpoint inhibitor.

27. 27. The method of claim 26, wherein the immune checkpoint inhibitor comprises an anti-PD-1 antibody, an anti-PD-L1 antibody, or an anti-CTLA4 antibody.

28. 26. The method of claim 25, wherein the at least one therapeutic agent comprises an anti-VEGF therapeutic agent, optionally wherein the anti-VEGF therapeutic agent comprises an anti-VEGF antibody.

29. 26. The method of claim 25, wherein the at least one therapeutic agent comprises a tyrosine kinase inhibitor (TKI).

30. 26. The method of claim 25, wherein identifying the at least one therapeutic agent based on the subject's BLBC TME type comprises identifying an immune checkpoint inhibitor as the at least one therapeutic agent if the subject is identified as having IE or TLS BLBC TME.

31. 26. The method of claim 25, wherein identifying the at least one therapeutic agent based on the subject's BLBC TME type comprises identifying an anti-VEGF therapeutic agent as the at least one therapeutic agent if the subject is identified as having type F BLBC TME.

32. 26. The method of claim 25, wherein identifying the at least one therapeutic agent based on the subject's BLBC TME type comprises identifying a TKI inhibitor therapy if the subject is identified as having type D BLBC TME.

33. at least one computer hardware processor; and At least one non-transitory computer readable medium storing processor-executable instructions that, 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 32. A system including:

34. 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 the method of any one of claims 1 to 32.

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