Methods for selecting triple-negative breast cancer patients for targeted therapy using tumor-specific total mRNA expression

Abstract

The present disclosure provides methods for predicting whether a patient diagnosed with triple negative breast cancer will benefit from treatment with an immune checkpoint inhibitor or a tumor extracellular matrix targeting therapy (e.g., anti-VEGF therapy) based on tumor-specific total mRNA (TmS) expression levels.

Description

Atty. Dkt. No.: 642631-0061 METHODS FOR SELECTING TRIPLE-NEGATIVE BREAST CANCER PATIENTS FOR TARGETED THERAPY USING TUMOR-SPECIFIC TOTAL MRNA EXPRESSION CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Appl. No. 63 / 554,048 filed February 15, 2024, which is incorporated herein by reference in its entirety for any and all purposes. TECHNICAL FIELD

[0002] The present technology relates generally to methods for predicting whether a patient diagnosed with triple negative breast cancer will benefit from treatment with an immune checkpoint inhibitor or a tumor extracellular matrix targeting therapy (e.g., anti- VEGF therapy) based on tumor-specific total mRNA (TmS) expression levels. STATEMENT OF GOVERNMENT SUPPORT

[0003] This invention was made with government support under CA268380 awarded by National Institutes of Health. The government has certain rights in the invention. BACKGROUND

[0004] The following description of the background of the present technology is provided simply as an aid in understanding the present technology and is not admitted to describe or constitute prior art to the present technology.

[0005] Breast cancer is the most common cancer among women, claiming over 600,000 lives annually worldwide, with more than 90% of mortalities attributed to metastasis. Triple-negative breast cancer (TNBC) is considered more aggressive and at a higher risk of metastasis compared to other forms of breast cancer. According to SEER, the 5-year relative survival rate for HR- / HER2- (which includes TNBC) is significantly lower than for other subtypes, at 77.6%, compared to 94.8% for HR+ / HER2-. This lower survival rate for TNBC suggests a higher risk of metastasis and disease progression. TNBC is characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. As such, TNBC patients are not amenable to endocrine or HER-2 targeted therapy. Despite being treated as a single disease, TNBC exhibits significant clinical, histological, and molecular heterogeneity. The lack of targetable receptors, heterogeneity, and absence of reliable biomarkers all contribute to theAtty. Dkt. No.: 642631-0061 difficulty in treating TNBC. Chemotherapy remains the primary treatment for both early and advanced stages of TNBC. However, less than 30% of TNBC patients can achieve a pathological complete response (pCR) post-neoadjuvant systemic chemotherapy, a metric that serves as a surrogate marker for survival. In addition, over 50% of these patients, experience a relapse within the initial 3-5 years, typically due to the emergence of chemo- resistance. Many of these chemo-resistant patients still undergo multiple adjuvant chemotherapy cycles, exposing them to increased toxicity without noticeable survival advantages. For example, VEGF inhibitors have not been approved for treating TNBC by the FDA, which cites therapeutic inefficacy. SUMMARY OF THE PRESENT TECHNOLOGY

[0006] In one aspect, the present disclosure provides a method for selecting a triple negative breast cancer patient for treatment with a tumor extracellular matrix (ECM) targeting therapy comprising: (a) detecting levels of tumor-specific total mRNA (TmS) expression below a predetermined threshold in a biological sample obtained from the cancer patient; and (b) administering to the cancer patient an effective amount of the tumor ECM targeting therapy. In some embodiments, the tumor ECM targeting therapy comprises an anti-vascular endothelial growth factor (anti-VEGF) therapeutic agent. Examples of anti- VEGF therapeutic agents include, but are not limited to, bevacizumab, aflibercept, ranibizumab, sorafenib, dasatinib, sunitinib malate, nilotinib, pazopanib, or any combination thereof. The tumor-specific total mRNA expression may be detected via next- generation sequencing, single nucleotide polymorphism (SNP) array, microarray, or a combination of two or more thereof. Additionally or alternatively, in some embodiments, the predetermined threshold is determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC cancer patients that are responsive to chemotherapy. The recursive partitioning survival tree model may be trained based on event-free survival outcome within a cohort of TNBC cancer patients who have received chemotherapy and a TmS cutoff may be established to reflect whether, within a follow-up period, the patients experience any clinical events (e.g., relapse or progression).

[0007] In another aspect, the present disclosure provides a method for prolonging survival of a triple negative breast cancer (TNBC) patient comprising: administering to the cancer patient an effective amount of an anti-VEGF therapeutic agent, wherein tumor-specific total mRNA expression levels in a biological sample obtained from the cancer patient are belowAtty. Dkt. No.: 642631-0061 a predetermined threshold determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC patients that are responsive to chemotherapy. In certain embodiments, the cancer patient exhibits stage I or stage II TNBC, and the predetermined threshold is determined based on a cohort of stage I or stage II TNBC patients that are responsive to chemotherapy. In other embodiments, the cancer patient exhibits stage III or stage IV TNBC, and the predetermined threshold is determined based on a cohort of stage III or stage IV TNBC patients that are responsive to chemotherapy. The tumor-specific total mRNA expression may be detected via next-generation sequencing, single nucleotide polymorphism (SNP) array, microarray, or a combination of two or more thereof. Examples of anti-VEGF therapeutic agents include, but are not limited to, bevacizumab, aflibercept, ranibizumab, sorafenib, dasatinib, sunitinib malate, nilotinib, pazopanib, or any combination thereof.

[0008] In any of the preceding embodiments of the methods disclosed herein, the cancer patient has not received a prior anti-cancer therapy, optionally wherein the anti-cancer therapy comprises one or more of chemotherapy, targeted therapy (e.g., VEGF / VEGFR inhibitors, EGF / EGFR inhibitors, PARP inhibitors etc.), immunotherapy, radiation therapy, or surgery. Additionally or alternatively, in some embodiments, the cancer patient is non- responsive or resistant to chemotherapy.

[0009] The chemotherapy may comprise one or more of alkylating agents, topoisomerase inhibitors, endoplasmic reticulum stress inducing agents, antimetabolites, mitotic inhibitors, nitrogen mustards, nitrosoureas, alkyl sulfonates, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, cytostatic alkaloids, cytotoxic antibiotics, antimetabolites, endocrine / hormonal agents, and bisphosphonate therapy agents.

[0010] In any of the preceding embodiments, the methods of the present technology further comprise sequentially, simultaneously, or separately administering to the cancer patient an effective amount of a TGF-β inhibitor. Examples of TGF-β inhibitor include, but are not limited to, vactosertib (TEW-7197), saracatinib (AZD0530), AVID200, fresolimumab (GC1008), and galunisertib (LY2157299).

[0011] In one aspect, the present disclosure provides a method for selecting a triple negative breast cancer patient for treatment with an immune checkpoint inhibitor comprising: (a) detecting levels of tumor-specific total mRNA (TmS) expression at orAtty. Dkt. No.: 642631-0061 above a predetermined threshold in a biological sample obtained from the cancer patient; and (b) administering to the cancer patient an effective amount of the immune checkpoint inhibitor. The predetermined threshold may be determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC cancer patients that are responsive to chemotherapy. In some embodiments, the immune checkpoint inhibitor comprises one or more of an anti-PD- 1 antibody, an anti-PD-L1 antibody, an anti-PD-L2 antibody, an anti-CTLA-4 antibody, an anti-TIM3 antibody, an anti-4-1BB antibody, an anti-CD73 antibody, an anti-GITR antibody, an anti-LAG-3 antibody, an anti-OX40 antibody, an anti-TIGIT antibody, an anti- B7-H3 antibody, an anti-B7-H4 antibody, or an anti-BTLA antibody. Additionally or alternatively, in some embodiments, the immune checkpoint inhibitor comprises, or consists essentially of, or yet further consists of an antibody or an equivalent thereof recognizing and binding to an immune checkpoint protein, such as an antibody or an equivalent thereof recognizing and binding to CTLA4 (for example, Yervoy (ipilimumab), CP-675,206 (tremelimumab), AK104 (cadonilimab), or AGEN1884 (zalifrelimab)), or an antibody or an equivalent thereof recognizing and binding to PD-1 (for example, Keytruda (pembrolizumab), Opdivo (nivolumab), Libtayo (cemiplimab), Tyvyt (sintilimab), BGB- A317 (tislelizumab), JS001 (toripalimab), SHR1210 (camrelizumab), GB226 (geptanolimab), JS001 (toripalimab), AB122 (zimberelimab), AK105 (penpulimab), HLX10 (serplulimab), BCD-100 (prolgolimab), AGEN2034 (balstilimab), MGA012 (retifanlimab), AK104 (cadonilimab), HX008 (pucotenlimab), PF-06801591 (sasanlimab), JNJ-63723283 (cetrelimab), MGD013 (tebotelimab), CT-011 (pidilizumab), or Jemperli (dostarlimab)), or an antibody or an equivalent thereof recognizing and binding to PD-L1 (for example, Tecentriq (atezolizumab), Imfinzi (durvalumab), Bavencio (avelumab), CS1001 (sugemalimab), or KN035 (envafolimab)).

[0012] In another aspect, the present disclosure provides a method for prolonging survival of a triple negative breast cancer (TNBC) patient comprising: administering to the cancer patient an effective amount of an immune checkpoint inhibitor, wherein tumor-specific total mRNA expression levels in a biological sample obtained from the cancer patient are at or above a predetermined threshold determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC patients that are responsive to chemotherapy. In certain embodiments, the cancer patient exhibits stage I or stage II TNBC, and the predetermined threshold isAtty. Dkt. No.: 642631-0061 determined based on a cohort of stage I or stage II TNBC patients that are responsive to chemotherapy. In other embodiments, the cancer patient exhibits stage III or stage IV TNBC, and the predetermined threshold is determined based on a cohort of stage III or stage IV TNBC patients that are responsive to chemotherapy. The tumor-specific total mRNA expression may be detected via next-generation sequencing, single nucleotide polymorphism (SNP) array, microarray, or a combination of two or more thereof.

[0013] Additionally or alternatively, in some embodiments, the cancer patient has not received a prior anti-cancer therapy, optionally wherein the anti-cancer therapy comprises one or more of chemotherapy, immunotherapy, targeted therapy (e.g., VEGF / VEGFR inhibitors, EGF / EGFR inhibitors, PARP inhibitors etc.), radiation therapy, or surgery. Additionally or alternatively, in some embodiments, the methods of the present technology further comprise sequentially, simultaneously, or separately administering to the cancer patient an effective amount of a chemotherapeutic agent. The chemotherapeutic agent may comprise one or more of alkylating agents, topoisomerase inhibitors, endoplasmic reticulum stress inducing agents, antimetabolites, mitotic inhibitors, nitrogen mustards, nitrosoureas, alkyl sulfonates, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, cytostatic alkaloids, cytotoxic antibiotics, antimetabolites, endocrine / hormonal agents, and bisphosphonate therapy agents.

[0014] Specific chemotherapeutic agents include, but are not limited to, cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, edatrexate (10- ethyl-10-deaza-aminopterin), thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein- bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, gemcitabine, irinotecan, ixabepilone, temozolmide, topotecan, vincristine, vinblastine, eribulin, mutamycin, capecitabine, anastrozole, exemestane, letrozole, leuprolide, abarelix, buserlin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, denosumab, zoledronate, trastuzumab, tykerb, anthracyclines (e.g., daunorubicin and doxorubicin), cladribine, midostaurin, bevacizumab, oxaliplatin, melphalan, etoposide, mechlorethamine, bleomycin, microtubule poisons, annonaceous acetogenins, chlorambucil, ifosfamide, streptozocin, carmustine, lomustine, busulfan, dacarbazine, temozolomide, altretamine, 6-mercaptopurine (6-MP), cytarabine, floxuridine, fludarabine, hydroxyurea, pemetrexed, epirubicin, idarubicin, SN-38, ARC, NPC, campothecin, 9-nitrocamptothecin, 9-aminocamptothecin, rubifen, gimatecan, diflomotecan, BN80927, DX-8951f, MAG-CPT, amsacnne, etoposide phosphate, teniposide, azacitidine (Vidaza), decitabine, accatin III, 10-Atty. Dkt. No.: 642631-0061 deacetyltaxol, 7-xylosyl-10-deacetyltaxol, cephalomannine, 10-deacetyl-7-epitaxol, 7- epitaxol, 10-deacetylbaccatin III, 10-deacetyl cephalomannine, streptozotocin, nimustine, ranimustine, bendamustine, uramustine, estramustine, mannosulfan, camptothecin, exatecan, lurtotecan, lamellarin D9-aminocamptothecin, amsacrine, ellipticines, aurintricarboxylic acid, HU-331, or combinations thereof.

[0015] Examples of antimetabolites include 5-fluorouracil (5-FU), 6-mercaptopurine (6- MP), capecitabine, cytarabine, floxuridine, fludarabine, gemcitabine, hydroxyurea, methotrexate, pemetrexed, and mixtures thereof.

[0016] Examples of taxanes include accatin III, 10-deacetyltaxol, 7-xylosyl-10- deacetyltaxol, cephalomannine, 10-deacetyl-7-epitaxol, 7-epitaxol, 10-deacetylbaccatin III, 10-deacetyl cephalomannine, and mixtures thereof.

[0017] Examples of DNA alkylating agents include cyclophosphamide, chlorambucil, melphalan, bendamustine, uramustine, estramustine, carmustine, lomustine, nimustine, ranimustine, streptozotocin; busulfan, mannosulfan, and mixtures thereof.

[0018] Examples of topoisomerase I inhibitor include SN-38, ARC, NPC, camptothecin, topotecan, 9-nitrocamptothecin, exatecan, lurtotecan, lamellarin D9-aminocamptothecin, rubifen, gimatecan, diflomotecan, BN80927, DX-8951f, MAG-CPT, and mixtures thereof. Examples of topoisomerase II inhibitors include amsacrine, etoposide, etoposide phosphate, teniposide, daunorubicin, mitoxantrone, amsacrine, ellipticines, aurintricarboxylic acid, doxorubicin, and HU-331 and combinations thereof.

[0019] Additionally or alternatively, in some embodiments, the immune checkpoint inhibitor comprises a programmed cell death protein 1 (PD-1) inhibitor, a programmed death-ligand 1 (PD-L1) inhibitor, a cytotoxic T-lymphocyte associated protein 4 (CTLA-4) inhibitor, or any combination thereof. Additionally or alternatively, in some embodiments, the immune checkpoint inhibitor comprises one or more of ipilimumab, tremelimumab, cadonilimab, zalifrelimab, pembrolizumab, nivolumab, cemiplimab, sintilimab, tislelizumab, toripalimab), camrelizumab, geptanolimab, toripalimab, zimberelimab, penpulimab, serplulimab, prolgolimab, balstilimab, retifanlimab, cadonilimab, pucotenlimab, sasanlimab, cetrelimab, tebotelimab, pidilizumab, dostarlimab, atezolizumab, durvalumab, avelumab, sugemalimab, or envafolimab.

[0020] In any of the preceding embodiments of the methods disclosed herein, the methods further comprise sequentially, simultaneously, or separately administering to the cancerAtty. Dkt. No.: 642631-0061 patient an additional anti-cancer therapy, optionally wherein the anti-cancer therapy comprises one or more of chemotherapy, targeted therapy, immunotherapy, radiation therapy, or surgery, optionally wherein the targeted therapy comprises a VEGF / VEGFR inhibitor, EGF / EGFR inhibitor, PARP inhibitor, or a combination thereof or optionally wherein the immunotherapy comprises an immune checkpoint inhibitor therapy.

[0021] In any and all embodiments of the methods disclosed herein, the biological sample comprises plasma, blood, serum, or biopsied tissue. In any of the foregoing embodiments of the methods disclosed herein, the tumor-specific total mRNA (TmS) comprises circulating tumor RNA (ctRNA). BRIEF DESCRIPTION OF THE DRAWINGS

[0022] FIGs.1A-1B: Overall study design and landscape of TmS in TNBC tumors. FIG.1A is a diagram illustrating the calculation and validation of TmS as a biomarker to distinguish TNBC patients. FIG.1B is a violin plot of the distribution of TmS in 582 TNBC patients across four study cohorts (TCGA, METABRIC, SCAN-B and FUSCC). The x-axis indicates the cohort, and the y-axis represents TmS values. The number of tumor samples for each cohort is indicated above each violin plot.

[0023] FIGs.2A-2K: TmS is associated with known TNBC subtypes and stratify patients’ prognostic outcomes with chemotherapy treatment. FIGS.2A-2C are graphs of the distributions of TmS across TNBC type-4 in TCGA (FIG.2A), METABRIC (FIG. 2B) and SCAN-B (FIG.2C) chemo-treated TNBC patients. FIG.2D is a graph of the distribution of TmS across FUSCC TNBC subtypes in FUSCC chemotherapy treated TNBC patients. FIG.2D is a graph of the distribution of tumor infiltrating lymphocytes percentage across TmS category in SCAN-B chemo-treated TNBC patients. FIG.2E shows a distribution of tumor infiltrating lymphocytes percentage across TmS category in SCAN-B chemo-treated TNBC patients. FIGS.2F-2I are Kaplan-Meier curves stratified by TmS subcategories and TNBC subtypes across TCGA (FIG.2F), METABRIC (FIG. 2G), SCAN-B (FIG.2H) and FUSCC (FIG.2I) TNBC patients treated with chemotherapy, along with alluvial diagrams of the proportion of patients within each cohort across TmS subcategories and TNBC subtypes. FIGS.2J-2K are Forest plots of hazard ratios (HRs) (center points) and 95% confidence intervals (CIs) (error bars) of multivariate Cox proportional hazard models within each TNBC cohort. Age (≥50 versus <50), lymph node status (positive versus negative) and TmS (high versus low) (FIG.2I) or continuous TmSAtty. Dkt. No.: 642631-0061 value (FIG.2J) as predictors for TCGA, SCAN-B, and FUSCC cohorts. METABRIC cohort only includes age and TmS (high versus low) (FIG.2I) or continuous TmS value (FIG.2J) as predictors. For FIG.2I and FIG.2J, P values of two-sided Wald tests for the covariates are indicated by asterisks. For all P values, significance levels are denoted as follows: *P < 0.05, **P < 0.01 and ***P < 0.001.

[0024] FIGs.3A-3H: TmS unveils tumor microenvironment dynamics across multi- ethnic TNBCs. FIG.3A is a heatmap of normalized enrichment scores (NES) of top cancer hallmark pathways across four TNBC cohorts with BH adjusted p-value < 0.05. FIG.3B shows heatmap of normalized enrichment scores (NES) of KEGG pathway analysis across four TNBC cohorts with BH adjusted p-value < 0.05. FIG.3C is a heatmap of z-score scaled Pearson correlation between TmS and the average gene expression level of breast cancer gene modules within each cohort. FIG.3D is a heatmap of Pearson correlation between TmS and the CIBERSORTx deconvolved cell type proportion within each cohort. FIGS.3E-3H are graphs of distributions of T cell exclusion scores for patients with high or low TmS in TCGA (FIG.3E), METABRIC (FIG.3F), SCAN-B (FIG.3G), FUSCC (FIG.3H). For FIGS.3E-3H, the BH-adjusted P values for two-sided Wilcoxon rank-sum tests comparing T cell exclusion scores between high and low TmS groups are indicated by asterisks (* P < 0.05, ** < 0.01, *** < 0.001).

[0025] FIGs.4A-4B: Boxplots of stroma ratio in high and low TmS TCGA TNBC patient groups. FIG.4A is a boxplot of total stroma component percentage in high TmS (black box) and low TmS (gray box). FIG.4B is a boxplot of reactive stroma to tumor ratio in high TmS (black box) and low TmS (gray box).

[0026] FIGs.5A-5D: Integrative differential expression across Western TNBC cohorts identifies alternative treatment targets. FIGS.5A and 5B are volcano plots of overlapping differential expressed genes (DEGs) in high versus low TmS comparison in TCGA (FIG.5A) and SCAN-B (FIG.5B) chemotherapy-treated patients. FIGS.5C and 5D are enrichment maps showing pathways enriched in consensus low TmS DEGs (FIG. 5C) and high TmS DEGs (FIG.5D). Nodes in the network represent pathways and edge width represents the number of genes that overlap between enriched pathways. Nodes are colored by the hypergeometric P-value of the pathway enrichment test.

[0027] FIGs.6A-6I: Segmentation masks annotated by TME-seg of nine TCGA TNBC patients’ whole H&E-stained slides. FIGs.6A-6F: Segmentation annotated H&EAtty. Dkt. No.: 642631-0061 slides of six low TmS samples. FIGs.6G-6I: Segmentation annotated H&E slides of three high TmS samples. Color annotation: tumor cells (dark red), necrosis cells (magenta), inflammatory cells (light red), reactive stromal cells (yellow), inactive stromal cells (blue).

[0028] FIGs.7A-7C: TmS unveils tumor microenvironment dynamics across multi- ethnic TNBCs. FIG.7A provides density plots of TmS distribution group by driver mutation status or driver copy number status. FIGs.7B and 7C provide distributions of T cell exclusion score and dysfunctional score for patients with high or low TmS in TCGA, METABRIC, SCAN-B, FUSCC. For boxplots in e and f, the BH-adjusted P values for two- sided Wilcoxon rank-sum tests comparing T cell exclusion scores between high and low TmS groups are indicated by asterisks (* P < 0.05, ** < 0.01, *** < 0.001). DETAILED DESCRIPTION

[0029] It is to be appreciated that certain aspects, modes, embodiments, variations and features of the present methods are described below in various levels of detail in order to provide a substantial understanding of the present technology. It is to be understood that the present disclosure is not limited to particular uses, methods, reagents, compounds, compositions, or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0030] In practicing the present methods, many conventional techniques in molecular biology, protein biochemistry, cell biology, immunology, microbiology, and recombinant DNA are used. See, e.g., Sambrook and Russell eds. (2001) Molecular Cloning: A Laboratory Manual, 3rd edition; the series Ausubel et al. eds. (2007) Current Protocols in Molecular Biology; the series Methods in Enzymology (Academic Press, Inc., N.Y.); MacPherson et al. (1991) PCR 1: A Practical Approach (IRL Press at Oxford University Press); MacPherson et al. (1995) PCR 2: A Practical Approach; Harlow and Lane eds. (1999) Antibodies, A Laboratory Manual; Freshney (2005) Culture of Animal Cells: A Manual of Basic Technique, 5th edition; Gait ed. (1984) Oligonucleotide Synthesis; U.S. Patent No.4,683,195; Hames and Higgins eds. (1984) Nucleic Acid Hybridization; Anderson (1999) Nucleic Acid Hybridization; Hames and Higgins eds. (1984) Transcription and Translation; Immobilized Cells and Enzymes (IRL Press (1986)); Perbal (1984) A Practical Guide to Molecular Cloning; Miller and Calos eds. (1987) Gene Transfer Vectors for Mammalian Cells (Cold Spring Harbor Laboratory); Makrides ed. (2003) Gene TransferAtty. Dkt. No.: 642631-0061 and Expression in Mammalian Cells; Mayer and Walker eds. (1987) Immunochemical Methods in Cell and Molecular Biology (Academic Press, London); and Herzenberg et al. eds (1996) Weir’s Handbook of Experimental Immunology. Methods to detect and measure levels of polypeptide gene expression products (i.e., gene translation level) are well-known in the art and include the use of polypeptide detection methods such as antibody detection and quantification techniques. (See also, Strachan & Read, Human Molecular Genetics, Second Edition. (John Wiley and Sons, Inc., NY, 1999)). Definitions

[0031] Unless defined otherwise, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which this technology belongs. As used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural referents unless the content clearly dictates otherwise. For example, reference to “a cell” includes a combination of two or more cells, and the like. Generally, the nomenclature used herein and the laboratory procedures in cell culture, molecular genetics, organic chemistry, analytical chemistry and nucleic acid chemistry and hybridization described below are those well-known and commonly employed in the art.

[0032] As used herein, the term “about” in reference to a number is generally taken to include numbers that fall within a range of 1%, 5%, or 10% in either direction (greater than or less than) of the number unless otherwise stated or otherwise evident from the context (except where such number would be less than 0% or exceed 100% of a possible value).

[0033] As used herein, the “administration” of an agent or drug to a subject includes any route of introducing or delivering to a subject a compound to perform its intended function. Administration can be carried out by any suitable route, including but not limited to, orally, intranasally, intrathecally, parenterally (intravenously, intramuscularly, intraperitoneally, or subcutaneously), rectally, intrathecally, intraocularly, intradermally, transmucosally, iontophoretically, or topically. Administration includes self-administration and the administration by another.

[0034] As used herein, an “alteration” of a gene or gene product (e.g., a marker gene or gene product) refers to the presence of a mutation or mutations within the gene or gene product, e.g., a mutation, which affects the quantity or activity of the gene or gene product, as compared to the normal or wild-type gene. The genetic alteration can result in changesAtty. Dkt. No.: 642631-0061 in the quantity, structure, and / or activity of the gene or gene product in a cancer tissue or cancer cell, as compared to its quantity, structure, and / or activity, in a normal or healthy tissue or cell (e.g., a control). For example, an alteration which is associated with cancer, or predictive of responsiveness to intraoperative analgesics, can have an altered nucleotide sequence (e.g., a mutation), amino acid sequence, chromosomal translocation, intra- chromosomal inversion, copy number, expression level, protein level, protein activity, in a cancer tissue or cancer cell, as compared to a normal, healthy tissue or cell. Exemplary mutations include, but are not limited to, point mutations (e.g., silent, missense, or nonsense), deletions, insertions, inversions, linking mutations, duplications, translocations, inter- and intra-chromosomal rearrangements. Mutations can be present in the coding or non-coding region of the gene.

[0035] As used herein, the terms “amplify” or “amplification” with respect to nucleic acid sequences, refer to methods that increase the representation of a population of nucleic acid sequences in a sample. Nucleic acid amplification methods are well known to the skilled artisan and include ligase chain reaction (LCR), ligase detection reaction (LDR), ligation followed by Q-replicase amplification, PCR, primer extension, strand displacement amplification (SDA), hyperbranched strand displacement amplification, multiple displacement amplification (MDA), nucleic acid strand-based amplification (NASBA), two- step multiplexed amplifications, rolling circle amplification (RCA), recombinase- polymerase amplification (RPA) (TwistDx, Cambridge, UK), transcription mediated amplification, signal mediated amplification of RNA technology, loop-mediated isothermal amplification of DNA, helicase-dependent amplification, single primer isothermal amplification, and self- sustained sequence replication (3SR), including multiplex versions or combinations thereof. Copies of a particular nucleic acid sequence generated in vitro in an amplification reaction are called “amplicons” or “amplification products.”

[0036] As used herein, the terms “cancer” or “tumor” are used interchangeably and refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell. As used herein, the term “cancer cells” includes precancerous (e.g., benign), malignant, pre-metastatic, metastatic, and non-metastatic cells. Cancers of virtually every tissue are known to those of skill in the art, including solid tumors such as carcinomas,Atty. Dkt. No.: 642631-0061 sarcomas, glioblastomas, melanomas, etc., and circulating cancers such as leukemias. Examples of cancer include, but are not limited to, ovarian cancer, breast cancer, colon cancer, lung cancer, prostate cancer, gastric cancer, pancreatic cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, head and neck cancer, and brain cancer. The phrase “cancer burden” or “tumor burden” refers to the quantity of cancer cells or tumor volume in a subject. Reducing cancer burden accordingly may refer to reducing the number of cancer cells, or the tumor volume in a subject. The term “cancer cell” refers to a cell that exhibits cancer-like properties, e.g., uncontrollable reproduction, resistance to anti- growth signals, ability to metastasize, and loss of ability to undergo programmed cell death (e.g., apoptosis) or a cell that is derived from a cancer cell, e.g., clone of a cancer cell.

[0037] A “composition” is intended to mean a combination of active agent and another compound or composition, inert (for example, a nanoparticle, detectable agent, or label) or active, such as an adjuvant, diluent, binder, stabilizer, buffers, salts, lipophilic solvents, preservative, adjuvant or the like and include carriers, such as pharmaceutically acceptable carriers. In some embodiments, the carrier (such as the pharmaceutically acceptable carrier) comprises, or consists essentially of, or yet further consists of a nanoparticle, such as a polymeric nanoparticle carrier or a lipid nanoparticle that can be used alone or in combination with another carrier, such as an adjuvant or solvent. Carriers also include pharmaceutical excipients and additives proteins, peptides, amino acids, lipids, and carbohydrates (e.g., sugars, including monosaccharides, di-, tri, tetra-oligosaccharides, and oligosaccharides; derivatized sugars such as alditols, aldonic acids, esterified sugars and the like; and polysaccharides or sugar polymers), which can be present singly or in combination, comprising alone or in combination 1-99.99% by weight or volume. Exemplary protein excipients include serum albumin such as human serum albumin (HSA), recombinant human albumin (rHA), gelatin, casein, and the like. Representative amino acid components, which can also function in a buffering capacity, include alanine, arginine, glycine, arginine, betaine, histidine, glutamic acid, aspartic acid, cysteine, lysine, leucine, isoleucine, valine, methionine, phenylalanine, aspartame, and the like. Carbohydrate excipients are also intended within the scope of this technology, examples of which include but are not limited to monosaccharides such as fructose, maltose, galactose, glucose, D- mannose, sorbose, and the like; disaccharides, such as lactose, sucrose, trehalose, cellobiose, and the like; polysaccharides, such as raffinose, melezitose, maltodextrins,Atty. Dkt. No.: 642631-0061 dextrans, starches, and the like; and alditols, such as mannitol, xylitol, maltitol, lactitol, xylitol sorbitol (glucitol) and myoinositol. A composition as disclosed herein can be a pharmaceutical composition. A “pharmaceutical composition” is intended to include the combination of an active agent with a carrier, inert or active, making the composition suitable for diagnostic or therapeutic use in vitro, in vivo or ex vivo.

[0038] As used herein, a "control" is an alternative sample used in an experiment for comparison purpose. A control can be "positive" or "negative." For example, where the purpose of the experiment is to determine a correlation of the efficacy of a therapeutic agent for the treatment for a particular type of disease, a positive control (a compound or composition known to exhibit the desired therapeutic effect) and a negative control (a subject or a sample that does not receive the therapy or receives a placebo) are typically employed.

[0039] As used herein, the phrase “derived” means isolated, purified, mutated, or engineered, or any combination thereof. For example, a cell derived from a subject refers to the cell isolated from a biological sample obtained from the subject, and is optionally engineered.

[0040] “Detecting” as used herein refers to determining the presence of a mutation or alteration in a nucleic acid of interest in a sample. Detection does not require the method to provide 100% sensitivity. Analysis of nucleic acid markers can be performed using techniques known in the art including, but not limited to, sequence analysis, and electrophoretic analysis. Non-limiting examples of sequence analysis include Maxam- Gilbert sequencing, Sanger sequencing, capillary array DNA sequencing, thermal cycle sequencing (Sears et al., Biotechniques, 13:626-633 (1992)), solid-phase sequencing (Zimmerman et al., Methods Mol. Cell Biol, 3:39-42 (1992)), sequencing with mass spectrometry such as matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF / MS; Fu et al., Nat. Biotechnol, 16:381-384 (1998)), and sequencing by hybridization. Chee et al., Science, 274:610-614 (1996); Drmanac et al., Science, 260:1649-1652 (1993); Drmanac et al., Nat. Biotechnol, 16:54-58 (1998). Non- limiting examples of electrophoretic analysis include slab gel electrophoresis such as agarose or polyacrylamide gel electrophoresis, capillary electrophoresis, and denaturing gradient gel electrophoresis. Additionally, next generation sequencing methods can be performed using commercially available kits and instruments from companies such as theAtty. Dkt. No.: 642631-0061 Life Technologies / Ion Torrent PGM or Proton, the Illumina HiSEQ or MiSEQ, and the Roche / 454 next generation sequencing system.

[0041] “Detectable label” as used herein refers to a molecule or a compound or a group of molecules or a group of compounds used to identify a nucleic acid or protein of interest. In some embodiments, the detectable label may be detected directly. In other embodiments, the detectable label may be a part of a binding pair, which can then be subsequently detected. Signals from the detectable label may be detected by various means and will depend on the nature of the detectable label. Detectable labels may be isotopes, fluorescent moieties, colored substances, and the like. Examples of means to detect detectable labels include but are not limited to spectroscopic, photochemical, biochemical, immunochemical, electromagnetic, radiochemical, or chemical means, such as fluorescence, chemifluorescence, or chemiluminescence, or any other appropriate means.

[0042] As used herein, the term “effective amount” refers to a quantity sufficient to achieve a desired therapeutic and / or prophylactic effect, e.g., an amount which results in the prevention of, or a decrease in a disease or condition described herein or one or more signs or symptoms associated with a disease or condition described herein. In the context of therapeutic or prophylactic applications, the amount of a composition administered to the subject will vary depending on the composition, the degree, type, and severity of the disease and on the characteristics of the individual, such as general health, age, sex, body weight and tolerance to drugs. The skilled artisan will be able to determine appropriate dosages depending on these and other factors. The compositions can also be administered in combination with one or more additional therapeutic compounds. In the methods described herein, the therapeutic compositions may be administered to a subject having one or more signs or symptoms of a disease or condition described herein. As used herein, a "therapeutically effective amount" of a composition refers to composition levels in which the physiological effects of a disease or condition are ameliorated or eliminated. A therapeutically effective amount can be given in one or more administrations.

[0043] As used herein, the term “excipient” refers to a natural or synthetic substance formulated alongside the active ingredient of a medication, included for the purpose of long-term stabilization, bulking up solid formulations, or to confer a therapeutic enhancement on the active ingredient in the final dosage form, such as facilitating drug absorption, reducing viscosity, or enhancing solubility.Atty. Dkt. No.: 642631-0061

[0044] As used herein, the term “expression” refers to the process by which polynucleotides are transcribed into mRNA and / or the process by which the transcribed mRNA is subsequently being translated into peptides, polypeptides, or proteins. If the polynucleotide is derived from genomic DNA, expression can include splicing of the mRNA in a eukaryotic cell. The expression level of a gene can be determined by measuring the amount of mRNA or protein in a cell or tissue sample. In one aspect, the expression level of a gene from one sample can be directly compared to the expression level of that gene from a control or reference sample. In another aspect, the expression level of a gene from one sample can be directly compared to the expression level of that gene from the same sample following administration of the compositions disclosed herein. The term “expression” also refers to one or more of the following events: (1) production of an RNA template from a DNA sequence (e.g., by transcription) within a cell; (2) processing of an RNA transcript (e.g., by splicing, editing, 5’ cap formation, and / or 3’ end formation) within a cell; (3) translation of an RNA sequence into a polypeptide or protein within a cell; (4) post-translational modification of a polypeptide or protein within a cell; (5) presentation of a polypeptide or protein on the cell surface; and (6) secretion or presentation or release of a polypeptide or protein from a cell. The level of expression of a polypeptide can be assessed using any method known in art, including, for example, methods of determining the amount of the polypeptide produced from the host cell. Such methods can include, but are not limited to, quantitation of the polypeptide in the cell lysate by ELISA, Coomassie blue staining following gel electrophoresis, Lowry protein assay and Bradford protein assay.

[0045] “Next-generation sequencing or NGS” as used herein, refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput parallel fashion (e.g., greater than 103, 104, 105or more molecules are sequenced simultaneously). In one embodiment, the relative abundance of the nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences in the data generated by the sequencing experiment. Next generation sequencing methods are known in the art, and are described, e.g., in Metzker, M. Nature Biotechnology Reviews 11:31-46 (2010).

[0046] The terms “polynucleotide”, “nucleic acid” and “oligonucleotide” are used interchangeably and refer to a polymeric form of nucleotides of any length, either deoxyribonucleotides or ribonucleotides or analogs thereof, in modified or unmodifiedAtty. Dkt. No.: 642631-0061 form. Polynucleotides can have any three-dimensional structure and may perform any function, known or unknown. The following are non-limiting examples of polynucleotides: a gene or gene fragment (for example, a probe, primer, EST, or SAGE tag), exons, introns, messenger RNA (mRNA), transfer RNA, ribosomal RNA, ribozymes, cDNA, recombinant polynucleotides, branched polynucleotides, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes and primers. A polynucleotide can comprise modified nucleotides, such as methylated nucleotides and nucleotide analogs. If present, modifications to the nucleotide structure can be imparted before or after assembly of the polynucleotide. The sequence of nucleotides can be interrupted by non-nucleotide components. A polynucleotide can be further modified after polymerization, such as by conjugation with a labeling component. Unless otherwise specified or required, any embodiment of this disclosure that is a polynucleotide encompasses both the double-stranded form and each of two complementary single stranded forms known or predicted to make up the double-stranded form. A polynucleotide is composed of a specific sequence of four nucleotide bases: adenine (A); cytosine (C); guanine (G); thymine (T); and uracil (U) for thymine when the polynucleotide is RNA. Thus, the term “polynucleotide sequence” is the alphabetical representation of a polynucleotide molecule. This alphabetical representation can be input into databases in a computer having a central processing unit and used for bioinformatics applications such as functional genomics and homology searching. Polynucleotides include, without limitation, single- and double-stranded DNA, DNA that is a mixture of single- and double-stranded regions, single- and double-stranded RNA, RNA that is mixture of single- and double- stranded regions, and hybrid molecules comprising DNA and RNA that may be single- stranded or, more typically, double-stranded or a mixture of single- and double-stranded regions. In addition, polynucleotide refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The term polynucleotide also includes DNAs or RNAs containing one or more modified bases and DNAs or RNAs with backbones modified for stability or for other reasons.

[0047] As used herein, the term “overall survival” or “OS” means the observed length of life from the start of treatment to death or the date of last contact.

[0048] As used herein, the term “primer” refers to an oligonucleotide, which is capable of acting as a point of initiation of nucleic acid sequence synthesis when placed under conditions in which synthesis of a primer extension product which is complementary to aAtty. Dkt. No.: 642631-0061 target nucleic acid strand is induced, i.e., in the presence of different nucleotide triphosphates and a polymerase in an appropriate buffer (“buffer” includes pH, ionic strength, cofactors etc.) and at a suitable temperature. One or more of the nucleotides of the primer can be modified for instance by addition of a methyl group, a biotin or digoxigenin moiety, a fluorescent tag or by using radioactive nucleotides. A primer sequence need not reflect the exact sequence of the template. For example, a non-complementary nucleotide fragment may be attached to the 5′ end of the primer, with the remainder of the primer sequence being substantially complementary to the strand. The term primer as used herein includes all forms of primers that may be synthesized including peptide nucleic acid primers, locked nucleic acid primers, phosphorothioate modified primers, labeled primers, and the like. The term “forward primer” as used herein means a primer that anneals to the anti-sense strand of dsDNA. A “reverse primer” anneals to the sense-strand of dsDNA.

[0049] As used herein, “primer pair” refers to a forward and reverse primer pair (i.e., a left and right primer pair) that can be used together to amplify a given region of a nucleic acid of interest.

[0050] “Probe” as used herein refers to nucleic acid that interacts with a target nucleic acid via hybridization. A probe may be fully complementary to a target nucleic acid sequence or partially complementary. The level of complementarity will depend on many factors based, in general, on the function of the probe. A probe or probes can be used, for example to detect the presence or absence of a mutation in a nucleic acid sequence by virtue of the sequence characteristics of the target. Probes can be labeled or unlabeled, or modified in any of a number of ways well known in the art. A probe may specifically hybridize to a target nucleic acid. Probes may be DNA, RNA, or an RNA / DNA hybrid. Probes may be oligonucleotides, artificial chromosomes, fragmented artificial chromosome, genomic nucleic acid, fragmented genomic nucleic acid, RNA, recombinant nucleic acid, fragmented recombinant nucleic acid, peptide nucleic acid (PNA), locked nucleic acid, oligomer of cyclic heterocycles, or conjugates of nucleic acid. Probes may comprise modified nucleobases, modified sugar moieties, and modified internucleotide linkages. A probe may be used to detect the presence or absence of a target nucleic acid. Probes are typically at least about 10, 15, 20, 25, 30, 35, 40, 50, 60, 75, 100 nucleotides or more in length.

[0051] As used herein, the terms “polypeptide,” “peptide,” and “protein” are used interchangeably herein to mean a polymer comprising two or more amino acids joined toAtty. Dkt. No.: 642631-0061 each other by peptide bonds or modified peptide bonds, i.e., peptide isosteres. Polypeptide refers to both short chains, commonly referred to as peptides, glycopeptides, or oligomers, and to longer chains, generally referred to as proteins. Polypeptides may contain amino acids other than the 20 gene-encoded amino acids. Polypeptides include amino acid sequences modified either by natural processes, such as post-translational processing, or by chemical modification techniques that are well known in the art.

[0052] As used herein, a “sample” or “biological sample” refers to a body fluid or a tissue sample isolated from a subject. In some cases, a biological sample may consist of or comprise whole blood, platelets, red blood cells, white blood cells, plasma, sera, urine, feces, epidermal sample, vaginal sample, skin sample, cheek swab, sperm, amniotic fluid, cultured cells, bone marrow sample, tumor biopsies, aspirate and / or chorionic villi, cultured cells, endothelial cells, synovial fluid, lymphatic fluid, ascites fluid, interstitial or extracellular fluid and the like. The term "sample" may also encompass the fluid in spaces between cells, including gingival crevicular fluid, bone marrow, cerebrospinal fluid (CSF), saliva, mucus, sputum, semen, sweat, urine, or any other bodily fluids. Samples can be obtained from a subject by any means including, but not limited to, venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage, scraping, surgical incision, or intervention or other means known in the art. A blood sample can be whole blood or any fraction thereof, including blood cells (red blood cells, white blood cells or leukocytes, and platelets), serum and plasma.

[0053] As used herein, the term “separate” therapeutic use refers to an administration of at least two active ingredients at the same time or at substantially the same time by different routes.

[0054] As used herein, the term “sequential” therapeutic use refers to administration of at least two active ingredients at different times. More particularly, sequential use refers to the whole administration of one of the active ingredients before administration of the other or others commences. It is thus possible to administer one of the active ingredients over several minutes, hours, or days before administering the other active ingredient or ingredients. There is no simultaneous treatment in this case.

[0055] As used herein, the term “simultaneous” therapeutic use refers to the administration of at least two active ingredients by the same route and at the same time or at substantially the same time.Atty. Dkt. No.: 642631-0061

[0056] As used herein, the terms “subject”, “patient”, or “individual” can be an individual organism, a vertebrate, a mammal, or a human. In some embodiments, the subject, patient, or individual is a human.

[0057] As used herein, a “sample” refers to a substance that is being assayed for the presence of a mutation in a nucleic acid of interest. Processing methods to release or otherwise make available a nucleic acid for detection are well known in the art and may include steps of nucleic acid manipulation. A biological sample may be a body fluid or a tissue sample. In some cases, a biological sample may consist of or comprise blood, plasma, sera, urine, feces, epidermal sample, vaginal sample, skin sample, cheek swab, sperm, amniotic fluid, cultured cells, bone marrow sample, tumor biopsies, aspirate and / or chorionic villi, cultured cells, and the like. Fresh, fixed, or frozen tissues may also be used. In one embodiment, the sample is preserved as a frozen sample or as formaldehyde- or paraformaldehyde-fixed paraffin-embedded (FFPE) tissue preparation. For example, the sample can be embedded in a matrix, e.g., an FFPE block or a frozen sample. Whole blood samples of about 0.5 to 5 ml collected with EDTA, ACD or heparin as anti-coagulant are suitable.

[0058] As used herein, the terms “subject,” “individual,” or “patient” are used interchangeably and refer to an individual organism, a vertebrate, or a mammal and may include humans, non-human primates, rodents, and the like (e.g., which is to be the recipient of a particular treatment, or from whom cells are harvested). In certain embodiments, the individual, patient, or subject is a human.

[0059] “Substantially” or “essentially” means nearly totally or completely, for instance, 95% or greater of some given quantity. In some embodiments, “substantially” or “essentially” means 95%, 96%, 97%, 98%, 99%, 99.5%, or 99.9%.

[0060] As used herein, the terms “tumor-specific total mRNA expression” or “TmS” are used interchangeably and refer to the consensus tumor-specific total mRNA expression per cell per ploidy according to the following formula:where π is the tumor specific mRNA proportion estimated from RNA sequencing deconvolution, ρ is the tumor purity estimated from DNA sequencing deconvolution, ψTis the tumor ploidy estimated from DNA sequencing deconvolution, and ψNis the assumedAtty. Dkt. No.: 642631-0061 non-tumor ploidy which is typically diploid (ploidy of 2) in normal human cells. TmS values may be categorized into two groups – low TmS and high TmS values using a recursive partitioning survival tree model. The cutoff between low and high TmS can be determined using survival outcome data. In some embodiments, the splitting criteria are Gini index, and the decision tree depth is set to 2. The model can be applied to data from all cancer types to determine a cutoff value across all cancer types, or may be applied to data from individual cancer types to determine a more specific cutoff value for a specific cancer type. The model can also be applied to data split into early stage and late stage. The cutoff value may be about 0.1 to about 10, about 0.5 to about 5, about 0.8 to about 3, about 1 to about 2, or any value or sub-range therebetween.

[0061] As used herein, the term “therapeutic agent” is intended to mean a compound that, when present in an effective amount, produces a desired therapeutic effect on a subject in need thereof.

[0062] “Treating” or “treatment” as used herein covers the treatment of a disease or disorder described herein, in a subject, such as a human, and includes: (i) inhibiting a disease or disorder, i.e., arresting its development; (ii) relieving a disease or disorder, i.e., causing regression of the disorder; (iii) slowing progression of the disorder; and / or (iv) inhibiting, relieving, or slowing progression of one or more symptoms of the disease or disorder. Therapeutic effects of treatment include, without limitation, inhibiting recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastases, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis. By “treating a cancer” is meant that the symptoms associated with the cancer are, e.g., alleviated, reduced, cured, or placed in a state of remission.

[0063] It is also to be appreciated that the various modes of treatment of disorders as described herein are intended to mean “substantial,” which includes total but also less than total treatment, and wherein some biologically or medically relevant result is achieved. The treatment may be a continuous prolonged treatment for a chronic disease or a single, or few time administrations for the treatment of an acute condition.

[0064] Pharmaceutically acceptable salts of compounds described herein are within the scope of the present technology and include acid or base addition salts which retain the desired pharmacological activity and is not biologically undesirable (e.g., the salt is notAtty. Dkt. No.: 642631-0061 unduly toxic, allergenic, or irritating, and is bioavailable). When the compound of the present technology has a basic group, such as, for example, an amino group, pharmaceutically acceptable salts can be formed with inorganic acids (such as hydrochloric acid, hydroboric acid, nitric acid, sulfuric acid, and phosphoric acid), organic acids (e.g., alginate, formic acid, acetic acid, benzoic acid, gluconic acid, fumaric acid, oxalic acid, tartaric acid, lactic acid, maleic acid, citric acid, succinic acid, malic acid, methanesulfonic acid, benzenesulfonic acid, naphthalene sulfonic acid, and p-toluenesulfonic acid) or acidic amino acids (such as aspartic acid and glutamic acid). When the compound of the present technology has an acidic group, such as for example, a carboxylic acid group, it can form salts with metals, such as alkali and earth alkali metals (e.g., Na+, Li+, K+, Ca2+, Mg2+, Zn2+), ammonia or organic amines (e.g., dicyclohexylamine, trimethylamine, triethylamine, pyridine, picoline, ethanolamine, diethanolamine, triethanolamine) or basic amino acids (e.g., arginine, lysine and ornithine). Such salts can be prepared in situ during isolation and purification of the compounds or by separately reacting the purified compound in its free base or free acid form with a suitable acid or base, respectively, and isolating the salt thus formed.

[0065] Those of skill in the art will appreciate that compounds of the present technology may exhibit the phenomena of tautomerism, conformational isomerism, geometric isomerism, and / or stereoisomerism. As the formula drawings within the specification and claims can represent only one of the possible tautomeric, conformational isomeric, stereochemical or geometric isomeric forms, it should be understood that the present technology encompasses any tautomeric, conformational isomeric, stereochemical and / or geometric isomeric forms of the compounds having one or more of the utilities described herein, as well as mixtures of these various different forms. Methods for Detecting Tumor Cell Total mRNA in Triple Negative Breast Cancer Patients

[0066] Currently, no universally approved biomarker is available to predict chemotherapy responses or to gauge long-term survival prospects of TNBC patients. Therefore, an accurate stratification of TNBC patients for recurrence risks at diagnosis is crucial, facilitating personalized therapeutic interventions.

[0067] The main obstacles to stratifying TNBC patients come from its profound heterogeneity, emphasizing the need for a deeper understanding of its underlyingAtty. Dkt. No.: 642631-0061 mechanisms. Throughout the past two decades, attempts have been made to categorize TNBC tumors into molecularly distinct subtypes such as Lehmann’s TNBC type-6, TNBC type-4, and Burstein’s four stable TNBC groups. Yet, none of these subtyping methods can stratify patients’ prognostic outcomes. Emerging evidence suggests that the heterogeneity of TNBC is largely influenced by the tumor microenvironment (TME). The TME comprises a dynamic interplay of cancer, stromal, and immune cells, complemented by the extracellular matrix, secreted factors, and vasculature, all collaboratively determining tumor progression and therapeutic outcomes. The integrative deconvolution metric tumor-specific total mRNA expression (TmS), measures the ratio of total mRNA expression per haploid genome in tumor cells versus surrounding non-tumor cells, potentially capturing the dynamics of the TME. Considering the role of the TME in TNBC heterogeneity, herein the prognostic relevance of TmS within the TNBC context is assessed. Taking into consideration genetic and epigenetic variations across ethnicities, which may significantly modulate disease phenotypes and outcomes, the prognostic utility of TmS in multi-ethnic TNBC cohorts was validated.

[0068] The diagnostic methods of the present technology involve determining TmS expression levels in a biological sample obtained from the subject. In certain embodiments, TmS expression levels are determined via Next Generation Sequencing techniques or massively parallel sequencing. In some embodiments, high throughput, massively parallel sequencing employs sequencing-by-synthesis with reversible dye terminators. In other embodiments, sequencing is performed via sequencing-by-ligation. In yet other embodiments, sequencing is single molecule sequencing. Examples of Next Generation Sequencing techniques include, but are not limited to pyrosequencing, Reversible dye- terminator sequencing, SOLiD sequencing, Ion semiconductor sequencing, Helioscope single molecule sequencing etc.

[0069] The Ion TorrentTM(Life Technologies, Carlsbad, CA) amplicon sequencing system employs a flow-based approach that detects pH changes caused by the release of hydrogen ions during incorporation of unmodified nucleotides in DNA replication. For use with this system, a sequencing library is initially produced by generating DNA fragments flanked by sequencing adapters. In some embodiments, these fragments can be clonally amplified on particles by emulsion PCR. The particles with the amplified template are then placed in a silicon semiconductor sequencing chip. During replication, the chip is flooded with one nucleotide after another, and if a nucleotide complements the DNA molecule in aAtty. Dkt. No.: 642631-0061 particular microwell of the chip, then it will be incorporated. A proton is naturally released when a nucleotide is incorporated by the polymerase in the DNA molecule, resulting in a detectable local change of pH. The pH of the solution then changes in that well and is detected by the ion sensor. If homopolymer repeats are present in the template sequence, multiple nucleotides will be incorporated in a single cycle. This leads to a corresponding number of released hydrogens and a proportionally higher electronic signal.

[0070] The 454TM GS FLXTMsequencing system (Roche, Germany), employs a light- based detection methodology in a large-scale parallel pyrosequencing system. Pyrosequencing uses DNA polymerization, adding one nucleotide species at a time and detecting and quantifying the number of nucleotides added to a given location through the light emitted by the release of attached pyrophosphates. For use with the 454TMsystem, adapter-ligated DNA fragments are fixed to small DNA-capture beads in a water-in-oil emulsion and amplified by PCR (emulsion PCR). Each DNA-bound bead is placed into a well on a picotiter plate and sequencing reagents are delivered across the wells of the plate. The four DNA nucleotides are added sequentially in a fixed order across the picotiter plate device during a sequencing run. During the nucleotide flow, millions of copies of DNA bound to each of the beads are sequenced in parallel. When a nucleotide complementary to the template strand is added to a well, the nucleotide is incorporated onto the existing DNA strand, generating a light signal that is recorded by a CCD camera in the instrument.

[0071] Sequencing technology based on reversible dye-terminators: DNA molecules are first attached to primers on a slide and amplified so that local clonal colonies are formed. Four types of reversible terminator bases (RT-bases) are added, and non-incorporated nucleotides are washed away. Unlike pyrosequencing, the DNA can only be extended one nucleotide at a time. A camera takes images of the fluorescently labeled nucleotides, then the dye along with the terminal 3' blocker is chemically removed from the DNA, allowing the next cycle.

[0072] Helicos's single-molecule sequencing uses DNA fragments with added polyA tail adapters, which are attached to the flow cell surface. At each cycle, DNA polymerase and a single species of fluorescently labeled nucleotide are added, resulting in template- dependent extension of the surface-immobilized primer-template duplexes. The reads are performed by the Helioscope sequencer. After acquisition of images tiling the full array, chemical cleavage and release of the fluorescent label permits the subsequent cycle of extension and imaging.Atty. Dkt. No.: 642631-0061

[0073] Sequencing by synthesis (SBS), like the "old style" dye-termination electrophoretic sequencing, relies on incorporation of nucleotides by a DNA polymerase to determine the base sequence. A DNA library with affixed adapters is denatured into single strands and grafted to a flow cell, followed by bridge amplification to form a high-density array of spots onto a glass chip. Reversible terminator methods use reversible versions of dye-terminators, adding one nucleotide at a time, detecting fluorescence at each position by repeated removal of the blocking group to allow polymerization of another nucleotide. The signal of nucleotide incorporation can vary with fluorescently labeled nucleotides, phosphate-driven light reactions and hydrogen ion sensing having all been used. Examples of SBS platforms include Illumina GA and HiSeq 2000. The MiSeq® personal sequencing system (Illumina, Inc.) also employs sequencing by synthesis with reversible terminator chemistry.

[0074] In contrast to the sequencing by synthesis method, the sequencing by ligation method uses a DNA ligase to determine the target sequence. This sequencing method relies on enzymatic ligation of oligonucleotides that are adjacent through local complementarity on a template DNA strand. This technology employs a partition of all possible oligonucleotides of a fixed length, labeled according to the sequenced position. Oligonucleotides are annealed and ligated and the preferential ligation by DNA ligase for matching sequences results in a dinucleotide encoded color space signal at that position (through the release of a fluorescently labeled probe that corresponds to a known nucleotide at a known position along the oligo). This method is primarily used by Life Technologies’ SOLiDTMsequencers. Before sequencing, the DNA is amplified by emulsion PCR. The resulting beads, each containing only copies of the same DNA molecule, are deposited on a solid planar substrate.

[0075] SMRT^ sequencing is based on the sequencing by synthesis approach. The DNA is synthesized in zero-mode wave-guides (ZMWs)-small well-like containers with the capturing tools located at the bottom of the well. The sequencing is performed with use of unmodified polymerase (attached to the ZMW bottom) and fluorescently labeled nucleotides flowing freely in the solution. The wells are constructed in a way that only the fluorescence occurring at the bottom of the well is detected. The fluorescent label is detached from the nucleotide at its incorporation into the DNA strand, leaving an unmodified DNA strand.Atty. Dkt. No.: 642631-0061 Methods for Selecting Treatments in Triple Negative Breast Cancer Patients Based on Tumor Cell Total mRNA

[0076] The present disclosure provides a method for selecting a triple negative breast cancer patient for treatment with one or more therapies, as disclosed herein. In one aspect, the present disclosure provides a method for selecting a triple negative breast cancer patient for treatment with a tumor extracellular matrix (ECM) targeting therapy comprising: (a) detecting levels of tumor-specific total mRNA (TmS) expression below a predetermined threshold in a biological sample obtained from the cancer patient; and (b) administering to the cancer patient an effective amount of the tumor ECM targeting therapy. In some embodiments, the tumor ECM targeting therapy comprises an anti-vascular endothelial growth factor (anti-VEGF) therapeutic agent. Examples of anti-VEGF therapeutic agents include, but are not limited to, bevacizumab, aflibercept, ranibizumab, sorafenib, dasatinib, sunitinib malate, nilotinib, pazopanib, or any combination thereof. The tumor-specific total mRNA expression may be detected via next-generation sequencing, single nucleotide polymorphism (SNP) array, microarray, or a combination of two or more thereof. Additionally or alternatively, in some embodiments, the predetermined threshold is determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC cancer patients that are responsive to chemotherapy.

[0077] In another aspect, the present disclosure provides a method for prolonging survival of a triple negative breast cancer (TNBC) patient comprising: administering to the cancer patient an effective amount of an anti-VEGF therapeutic agent, wherein tumor-specific total mRNA expression levels in a biological sample obtained from the cancer patient are below a predetermined threshold determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC patients that are responsive to chemotherapy. In certain embodiments, the cancer patient exhibits stage I or stage II TNBC, and the predetermined threshold is determined based on a cohort of stage I or stage II TNBC patients that are responsive to chemotherapy. In other embodiments, the cancer patient exhibits stage III or stage IV TNBC, and the predetermined threshold is determined based on a cohort of stage III or stage IV TNBC patients that are responsive to chemotherapy. The tumor-specific total mRNA expression may be detected via next-generation sequencing, single nucleotide polymorphism (SNP) array, microarray, or a combination of two or more thereof. Examples of anti-VEGFAtty. Dkt. No.: 642631-0061 therapeutic agents include, but are not limited to, bevacizumab, aflibercept, ranibizumab, sorafenib, dasatinib, sunitinib malate, nilotinib, pazopanib, or any combination thereof.

[0078] In any of the preceding embodiments of the methods disclosed herein, the cancer patient has not received a prior anti-cancer therapy, optionally wherein the anti-cancer therapy comprises one or more of chemotherapy, targeted therapy (e.g., VEGF / VEGFR inhibitors, EGF / EGFR inhibitors, PARP inhibitors etc.), immunotherapy, radiation therapy, or surgery. Additionally or alternatively, in some embodiments, the cancer patient is non- responsive or resistant to chemotherapy. The chemotherapy may comprise one or more of alkylating agents, topoisomerase inhibitors, endoplasmic reticulum stress inducing agents, antimetabolites, mitotic inhibitors, nitrogen mustards, nitrosoureas, alkyl sulfonates, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, cytostatic alkaloids, cytotoxic antibiotics, antimetabolites, endocrine / hormonal agents, and bisphosphonate therapy agents.

[0079] In any of the preceding embodiments, the methods of the present technology further comprise sequentially, simultaneously, or separately administering to the cancer patient an effective amount of a TGF-β inhibitor. Examples of TGF-β inhibitor include, but are not limited to, vactosertib (TEW-7197), saracatinib (AZD0530), AVID200, fresolimumab (GC1008), and galunisertib (LY2157299). Examples of TGF-β inhibitor may include therapeutic agents that target TGF-β ligands (e.g., fresolimumab (GC1008)) and therapeutic agents that target TGF-β receptor type I (e.g., galunisertib (LY2157299)).

[0080] In one aspect, the present disclosure provides a method for selecting a triple negative breast cancer patient for treatment with an immune checkpoint inhibitor comprising: (a) detecting levels of tumor-specific total mRNA (TmS) expression at or above a predetermined threshold in a biological sample obtained from the cancer patient; and (b) administering to the cancer patient an effective amount of the immune checkpoint inhibitor. The predetermined threshold may be determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC cancer patients that are responsive to chemotherapy. In some embodiments, the immune checkpoint inhibitor comprises one or more of an anti-PD- 1 antibody, an anti-PD-L1antibody, an anti-PD-L2 antibody, an anti-CTLA-4 antibody, an anti-TIM3 antibody, an anti-4-1BB antibody, an anti-CD73 antibody, an anti-GITR antibody, an anti-LAG-3 antibody, an anti-OX40 antibody, an anti-TIGIT antibody, an anti- B7-H3 antibody, an anti-B7-H4 antibody, or an anti-BTLA antibody. Additionally orAtty. Dkt. No.: 642631-0061 alternatively, in some embodiments, the immune checkpoint inhibitor comprises, or consists essentially of, or yet further consists of an antibody or an equivalent thereof recognizing and binding to an immune checkpoint protein, such as an antibody or an equivalent thereof recognizing and binding to CTLA4 (for example, Yervoy (ipilimumab), CP-675,206 (tremelimumab), AK104 (cadonilimab), or AGEN1884 (zalifrelimab)), or an antibody or an equivalent thereof recognizing and binding to PD-1 (for example, Keytruda (pembrolizumab), Opdivo (nivolumab), Libtayo (cemiplimab), Tyvyt (sintilimab), BGB- A317 (tislelizumab), JS001 (toripalimab), SHR1210 (camrelizumab), GB226 (geptanolimab), JS001 (toripalimab), AB122 (zimberelimab), AK105 (penpulimab), HLX10 (serplulimab), BCD-100 (prolgolimab), AGEN2034 (balstilimab), MGA012 (retifanlimab), AK104 (cadonilimab), HX008 (pucotenlimab), PF-06801591 (sasanlimab), JNJ-63723283 (cetrelimab), MGD013 (tebotelimab), CT-011 (pidilizumab), or Jemperli (dostarlimab)), or an antibody or an equivalent thereof recognizing and binding to PD-L1 (for example, Tecentriq (atezolizumab), Imfinzi (durvalumab), Bavencio (avelumab), CS1001 (sugemalimab), or KN035 (envafolimab)).

[0081] In another aspect, the present disclosure provides a method for prolonging survival of a triple negative breast cancer (TNBC) patient comprising: administering to the cancer patient an effective amount of an immune checkpoint inhibitor, wherein tumor-specific total mRNA expression levels in a biological sample obtained from the cancer patient are at or above a predetermined threshold determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC patients that are responsive to chemotherapy. In certain embodiments, the cancer patient exhibits stage I or stage II TNBC, and the predetermined threshold is determined based on a cohort of stage I or stage II TNBC patients that are responsive to chemotherapy. In other embodiments, the cancer patient exhibits stage III or stage IV TNBC, and the predetermined threshold is determined based on a cohort of stage III or stage IV TNBC patients that are responsive to chemotherapy. The tumor-specific total mRNA expression may be detected via next-generation sequencing, single nucleotide polymorphism (SNP) array, microarray, or a combination of two or more thereof.

[0082] Additionally or alternatively, in some embodiments, the cancer patient has not received a prior anti-cancer therapy, optionally wherein the anti-cancer therapy comprises one or more of chemotherapy, targeted therapy (e.g., VEGF / VEGFR inhibitors, EGF / EGFR inhibitors, PARP inhibitors etc.), immunotherapy, radiation therapy, or surgery.Atty. Dkt. No.: 642631-0061 Additionally or alternatively, in some embodiments, the methods of the present technology further comprise sequentially, simultaneously, or separately administering to the cancer patient an effective amount of a chemotherapeutic agent. The chemotherapeutic agent may comprise one or more of alkylating agents, topoisomerase inhibitors, endoplasmic reticulum stress inducing agents, antimetabolites, mitotic inhibitors, nitrogen mustards, nitrosoureas, alkyl sulfonates, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, cytostatic alkaloids, cytotoxic antibiotics, antimetabolites, endocrine / hormonal agents, and bisphosphonate therapy agents.

[0083] Specific chemotherapeutic agents include, but are not limited to, cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, edatrexate (10- ethyl-10-deaza-aminopterin), thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein- bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, gemcitabine, irinotecan, ixabepilone, temozolmide, topotecan, vincristine, vinblastine, eribulin, mutamycin, capecitabine, anastrozole, exemestane, letrozole, leuprolide, abarelix, buserlin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, denosumab, zoledronate, trastuzumab, tykerb, anthracyclines (e.g., daunorubicin and doxorubicin), cladribine, midostaurin, bevacizumab, oxaliplatin, melphalan, etoposide, mechlorethamine, bleomycin, microtubule poisons, annonaceous acetogenins, chlorambucil, ifosfamide, streptozocin, carmustine, lomustine, busulfan, dacarbazine, temozolomide, altretamine, 6-mercaptopurine (6-MP), cytarabine, floxuridine, fludarabine, hydroxyurea, pemetrexed, epirubicin, idarubicin, SN-38, ARC, NPC, campothecin, 9-nitrocamptothecin, 9-aminocamptothecin, rubifen, gimatecan, diflomotecan, BN80927, DX-8951f, MAG-CPT, amsacnne, etoposide phosphate, teniposide, azacitidine (Vidaza), decitabine, accatin III, 10- deacetyltaxol, 7-xylosyl-10-deacetyltaxol, cephalomannine, 10-deacetyl-7-epitaxol, 7- epitaxol, 10-deacetylbaccatin III, 10-deacetyl cephalomannine, streptozotocin, nimustine, ranimustine, bendamustine, uramustine, estramustine, mannosulfan, camptothecin, exatecan, lurtotecan, lamellarin D9-aminocamptothecin, amsacrine, ellipticines, aurintricarboxylic acid, HU-331, or combinations thereof.

[0084] Examples of antimetabolites include 5-fluorouracil (5-FU), 6-mercaptopurine (6- MP), capecitabine, cytarabine, floxuridine, fludarabine, gemcitabine, hydroxyurea, methotrexate, pemetrexed, and mixtures thereof.Atty. Dkt. No.: 642631-0061

[0085] Examples of taxanes include accatin III, 10-deacetyltaxol, 7-xylosyl-10- deacetyltaxol, cephalomannine, 10-deacetyl-7-epitaxol, 7-epitaxol, 10-deacetylbaccatin III, 10-deacetyl cephalomannine, and mixtures thereof.

[0086] Examples of DNA alkylating agents include cyclophosphamide, chlorambucil, melphalan, bendamustine, uramustine, estramustine, carmustine, lomustine, nimustine, ranimustine, streptozotocin; busulfan, mannosulfan, and mixtures thereof.

[0087] Examples of topoisomerase I inhibitor include SN-38, ARC, NPC, camptothecin, topotecan, 9-nitrocamptothecin, exatecan, lurtotecan, lamellarin D9-aminocamptothecin, rubifen, gimatecan, diflomotecan, BN80927, DX-8951f, MAG-CPT, and mixtures thereof. Examples of topoisomerase II inhibitors include amsacrine, etoposide, etoposide phosphate, teniposide, daunorubicin, mitoxantrone, amsacrine, ellipticines, aurintricarboxylic acid, doxorubicin, and HU-331 and combinations thereof.

[0088] Additionally or alternatively, in some embodiments, the immune checkpoint inhibitor comprises a programmed cell death protein 1 (PD-1) inhibitor, a programmed death-ligand 1 (PD-L1) inhibitor, a cytotoxic T-lymphocyte associated protein 4 (CTLA-4) inhibitor, or any combination thereof. Additionally or alternatively, in some embodiments, the immune checkpoint inhibitor comprises one or more of ipilimumab, tremelimumab, cadonilimab, zalifrelimab, pembrolizumab, nivolumab, cemiplimab, sintilimab, tislelizumab, toripalimab), camrelizumab, geptanolimab, toripalimab, zimberelimab, penpulimab, serplulimab, prolgolimab, balstilimab, retifanlimab, cadonilimab, pucotenlimab, sasanlimab, cetrelimab, tebotelimab, pidilizumab, dostarlimab, atezolizumab, durvalumab, avelumab, sugemalimab, or envafolimab.

[0089] In any and all embodiments of the methods disclosed herein, the biological sample comprises plasma, blood, serum, or biopsied tissue. Where the TNBC patient has a localized tumor, the biological sample may comprise solid tumor tissue. Where the TNBC patient has metastatic tumors, the biological sample may additionally comprise solid tumor tissue from a metastatic tumor site and / or blood. In any of the foregoing embodiments of the methods disclosed herein where the TNBC patient has metastatic tumors, the tumor- specific total mRNA (TmS) may comprise circulating tumor RNA (ctRNA). EXAMPLES

[0090] The present technology is further illustrated by the following Examples, which should not be construed as limiting in any way. The examples herein are provided toAtty. Dkt. No.: 642631-0061 illustrate advantages of the present technology and to further assist a person of ordinary skill in the art with preparing or using the methods of the present technology. The examples should in no way be construed as limiting the scope of the present technology, as defined by the appended claims. The examples can include or incorporate any of the variations, aspects, or embodiments of the present technology described above. The variations, aspects, or embodiments described above may also further each include or incorporate the variations of any or all other variations, aspects, or embodiments of the present technology. Example 1: Tumor cell total mRNA level predicts chemotherapy response across multi-ethnic patient cohorts with triple-negative breast cancer

[0091] In this study, the aim was to assess the application of TmS by characterizing it in 582 chemotherapy-treated TNBC patients from four ethnically diverse cohorts: The Cancer Genome Atlas (TCGA), the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), Fudan University Shanghai Cancer Center (FUSCC), and the Sweden Cancerome Analysis Network - Breast (SCAN-B). The association of TmS with clinical features was assessed across these populations and subsequently correlated TmS with clinical outcomes, underscoring its prognostic utility irrespective of ethnic background. The analyses revealed distinct biological mechanisms indicated by variable TmS levels in TNBC patients, providing insights into the molecular underpinnings that may influence therapeutic responses. TmS-indicated signatures prevalent in chemo-resistant tumors were investigated, and alternative targets for therapeutic intervention were proposed. The investigation provided a comprehensive framework that not only validated the prognostic value of TmS but also highlighted potential pathways for personalized therapeutic strategies in TNBC. Methods Patient Data

[0092] The bioinformatic analysis pipeline in this study was executed using transcriptomic and genomic data derived from publicly available databases encompassing several distinct breast cancer datasets as follows.

[0093] The Cancer Genome Atlas (TCGA) includes RNA sequencing raw read counts, copy number variations, mutation data, and relevant clinical information for a total of 134Atty. Dkt. No.: 642631-0061 TCGA TNBC tumor samples retrieved from the Genomic Data Commons Data Portal. The dataset is accessible under the TCGA BRCA subtype.

[0094] The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) includes a primary dataset of samples, including Affymetrix SNP 6.0 CEL files and Illumina HT-12 gene expression arrays. This dataset can be accessed from the European Genome-phenome Archive (accession number: EGAS00000000083).

[0095] The Fudan University Shanghai Cancer Center (FUSCC) includes omics data and metadata for 465 FUSCC TNBC tumor samples. The dataset is accessible via the National Omics Data Encyclopedia (NODE) under the accession number OEP000155. Sequence data were deposited in the NCBI Gene Expression Omnibus and Sequence Read Archive under the identifiers OncoScan array (GEO: GSE118527) and WES and RNAseq (SRA: SRP157974), respectively.

[0096] The Sweden Cancerome Analysis Network - Breast (SCAN-B) includes somatic mutational data of 237 SCAN-B TNBC tumor samples. The data include raw counts of RNA sequencing data from 235 SCAN-B TNBC tumor samples. Tumor-specific total mRNA expression (TmS) estimation in multi-ethnic TNBC patient cohorts

[0097] The mathematical model for TmS estimation from Cao S, Wang JR, Ji S, et al; Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression; Nat Biotechnol.2022;40(11):1624-1633. doi:10.1038 / s41587-022-01342-x was applied to the data to estimate TmS.

[0098] The mathematical model for TmS estimation is as follows. For any group of cells, S was used to denote the average global mRNA transcript level per cell per haploid genome, which follows ^^ ^ ∑^ ୀ^ ^∑ீ ^ ^ୀ^^^^^ / ^^^^ / ^^. Here, ugc denotes the number of mRNA transcripts of gene g in cell c; G is the total number of genes; C is the number of cells; and pcis the ploidy—that is, the number of copies of the haploid genome in cell c . However, the cell-level ploidy pcis usually not measurable. Hence, in practice, average ploidy Ψ of the corresponding cell group is used to approximate it:Atty. Dkt. No.: 642631-0061 For non-tumor cells, which are commonly diploid, this assumption is assured.

[0099] In the analysis of bulk RNA sequencing data from mixed tumor samples, tumor cell groups are compared to non-tumor cell groups. Let T denote tumor cells and N denote non-tumor cells. Therefore, a TmS is defined to reflect the ratio of total mRNA transcript level per haploid genome of tumor cells to that of the surrounding non-tumor cells—that is, TmStumor= ST / SN, simplified as TmS from here forward. This ratio is calculated to cancel out technical effects presented in sequencing data that confound with both ST and SN. Letanddenote the total number of mRNA transcripts of gene g across all cells from tumor and non- tumor cells; letand CN denotes the total number of tumor and non-tumor cells; and let ΨT and ΨN represent the average ploidy of tumor and non-tumor cells, respectively. Under the assumption that the tumor cells have a similar ploidy, TmS can be derived without using single-cell-specific parameters as ^^^^^^ ൌ ^^^ା / ^^^்Ψ்^ / ^^^ା / ^^^ேΨே^^ ൌ ^^^ା / ^^ା^ / ^^^^்Ψ்^ / ^^^ேΨே^^ (1)

[0100] We further introduce the proportion of total bulk mRNA expression derived from tumor cells (herein ‘tumor-specific mRNA proportion’)and the tumor cell proportion (herein ‘tumor purity’) ρ = CT / (CT+ CN). Thus, ^^^^^^ ൌ ^^^ / ^1 െ ^^^^ / ^^^^ / ^1 െ ^^^^^ Ψ் / Ψே^^^^^^^^ ൌ ^^^^1 െ ^^^Ψே^ / ^^^^ / ^1 െ ^^^^^ Ψ்^^ (2)

[0101] The tumor-specific mRNA proportion π derived from the tumor can be estimated using the DeMixT model as ^^^; the tumor purity ρ and ploidy ΨTcan be estimated usingAtty. Dkt. No.: 642631-0061 algorithms, such as ASCAT, ABSOLUTE or Sequenza based on the matched DNAsequencing data as ^^^ and ^^^T, respectively; and the ploidy of non-tumor cells ΨN wasassumed to be 2. Hence,Here, TmS is used to represent T^mS for simplicity.

[0102] In the DeMixT model, for sample i ∈ (1, 2, …, M) and gene g ∈ (1, 2, …, G),where Yig represents the scale-normalized expression count matrix observed from mixed tumor samples, and T′igand N′igrepresent the normalized relative expression of gene g within tumor and surrounding non-tumor cells, respectively. The estimated tumor-specific mRNA proportion ^^^ is the desirable quantity for equation 3. Each hidden component is assumed to follow the log2-normal distribution—that is,Notation T and N drop the ′ sign for simplicity. The identifiability of a gene k in the DeMixT model is measured by the CIା^^்^] around the mean expression μTk. The definition of the profile likelihood function of μTk iswhere ^^^^^^^|^^^,^^்^,^^்^^is the likelihood function of the DeMixT model.Atty. Dkt. No.: 642631-0061

[0103] To acquire the input for the TmS model, DNA sequencing deconvolution was performed to estimate tumor purity and ploidy, and RNA sequencing deconvolution was performed to derive tumor-specific mRNA proportion. Details of the analysis for each cohort is described below.

[0104] TmS values from TCGA TNBC tumor samples were estimated using the mathematical model for TmS estimation described herein. All TNBC samples were included in the TCGA BRCA cohort. Triple-negative status of the samples was identified following the criteria established by Koboldt, D. C. et al. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012). Early stage cancer was defined as pathological stages I, IA, IB, IC, II, IIA, IIB, and IIC.

[0105] TmS values from METABRIC samples were estimated using the mathematical model for TmS estimation described herein. The triple-negative status of the samples was ascertained from negative entries for 'ER. status', 'PR. status' and 'HER2. status' in the clinical metadata. Only METABRIC TNBC patients (n=118) treated with chemotherapy were selected for this study, and their treatment information was sourced from the clinical metadata.

[0106] In the cohort of FUSCC TNBC, bulk RNA sequencing data, whole-exome sequencing data, OncoScan CNV array data, and clinical data including biochemical recurrence, treatment information, and molecular subtypes was available for 465 TNBC patients. Among these 465 patients, 279 had whole exome sequencing (WES) data on primary tumor tissue and paired blood samples, 401 had copy-number alteration (CNA) data, and 360 had RNA sequencing data on primary tumor tissue. For DNA-based deconvolution, Affymetrix OncoScan CNV SNP assays (n=401) were processed with OncoScan Console (v1.3) software (Affymetrix, Inc.). Tumor purity and ploidy were estimated by ASCAT (v2.4.3) using probe-level output from the OncoScan Console. For RNA sequencing deconvolution, fastq files of 360 primary tumors and 88 matched normal tissue from SRA with accession number SRP157974 were considered. Raw read counts were acquired via the standard RNA alignment pipeline from NCI GDC documentation, where the fastq files were mapped to a human reference genome (Hg19, GRCh37_snp_tran). The DeMixT model, described herein was applied to the deconvolution pipeline to estimate tumor-specific mRNA proportions using the 88 adjacent normal samples as the reference. DeMixT parameters were set as the top 1,500 genes and 50 spike-ins for accuracy in proportion estimation. Samples (n=245) were kept thatAtty. Dkt. No.: 642631-0061 contained tumor purity, tumor ploidy, and tumor-specific mRNA proportions to estimate the TmS value.

[0107] For SCAN-B TNBC samples, a total of 235 tumor samples, accompanied by matched RNA sequencing data and whole-genome sequencing data, were obtained from the SCAN-B clinical study conducted by Staaf J, et al., Whole-genome sequencing of triple- negative breast cancers in a population-based clinical study, Nat Med.2019; 25(10):1526- 1533, doi:10.1038 / s41591-019-0582-4. Tumor purity and ploidy were gauged from whole genome sequencing (WGS) data using ASCAT (v2.4.3) according to Van Loo P, et al., Allele-specific copy number analysis of tumors, Proc Natl Acad Sci.2010;107(39):16910- 16915, doi:10.1073 / pnas.1009843107. To estimate TmS values for SCAN-B TNBC tumors that lacked corresponding adjacent normal samples, DeMixT deconvolution pipeline was applied to two different sets of normal references: (1) Genotype-Tissue Expression (GTEx) breast tissue samples without significant pathology, and (2) TCGA breast cancer adjacent normal samples. Only tumor samples (n=208) with a tumor purity range of ≥ 0.2 and ≤ 0.85 were chosen for TmS calculation. The two sets of TmS estimates were highly correlated (Spearman r=0.94). To gauge the robustness of the consensus TmS estimation, a linear regression model was subsequently fitted using log2-transformed TmS values calculated by GTEx and log2-transformed TmS calculated by TCGA adjacent normal. Samples with a Cook’s distance ≥ 4 / n (n=10) were discarded, and, for the remaining samples (n=198), thefinal TmS values were calculated as: ^^^^^^ ൌ ^^^^^^^ீ்ா௫ ൈ ^^^^^^்^ீ^.Statistical Analysis

[0108] Batch effect correction. For RNA sequencing datasets from two batches, the METABRIC cohort or in the absence of matched adjacent normal samples the SCAN-B cohort, the ComBat algorithm according to Johnson, W. E., et al., Adjusting batch effects in microarray expression data using empirical Bayes methods, Biostatistics 8, 118–127 (2007), was employed to remove batch effects. This enabled a harmonization of the METABRIC tumor datasets and facilitated an integration of SCAN-B data with TCGA. For the SCAN-B cohort, the adjacent normal samples from TCGA are effectively leveraged for deconvolution purposes.

[0109] Association with clinical variables. Kruskal–Wallis tests were used to compare the distribution of TmS between subgroups defined by each clinical variable. The P valuesAtty. Dkt. No.: 642631-0061 from the Kruskal–Wallis tests were adjusted using Benjamin–Hochberg correction across all available clinical variables within each TNBC cohort.

[0110] Survival analyses. The association between TmS and survival outcomes was examined in the context of either Progression-Free Interval (PFI) or Recurrence-Free Survival (RFS), contingent upon the available survival metrics within each respective cohort. The employment of PFI and RFS as primary clinical endpoints adheres to methodological recommendations. PFI was used as the primary clinical outcome in the TCGA cohort, whereas RFS was used as the primary clinical outcome in the METABRIC, SCAN-B, and FUSCC cohorts. For all association analyses with clinical outcomes across all cohorts, a recursive partitioning survival tree model was used to find the optimal TmS cutoff (high versus low) separating different survival outcomes. Splits were assessed using the Gini index, and the maximum tree depth was set to 2. Survival disparities between the high- and low-TmS groups were statistically assessed using log-rank tests.

[0111] Cox regression analyses. Analysis was extended to assess the impact of TmS on survival outcomes along with various patient characteristics by utilizing two multivariate Cox proportional hazard models in our study. In each model, age (categorized as ≥50 years vs. <50 years), lymph node (LN) status (positive vs. negative), tumor size (categorized as >20 mm vs. ≤20 mm), and tumor grade (graded as 3 vs.2) were considered as covariates, along with either TmS as a categorical (high vs. low) or continuous variable. Both models were employed to interpret PFI (or DFS when PFI was not available) for TNBC. TNBC Molecular Subtyping

[0112] All samples from the four TNBC cohorts were assigned to Lehman’s TNBC type- 6 using the TNBCType online subtyping tool (http: / / cbc.mc.vanderbilt.edu / tnbc / ). Briefly, each of the Lehmann molecular subtypes was characterized by the top 20% of genes with the highest and lowest expressions. Each TNBC sample was assigned to a TNBC molecular subtype based on the highest Pearson correlation (centroid) and lowest p-value. Because IM and MSL subtype calls are strongly weighted by stromal cell gene expression and subtype correlations are independent of one another, IM and MSL subtypes were re- assigned to the second highest correlated centroid, which was renamed as TNBCtype-4.

[0113] Breast cancer intrinsic subtype prediction was performed using the R ‘genefu’ package based on ‘PAM50’ subtyping signature. In addition, Burstein’s TNBC subtype was assessed through a non-supervise clustering method - Non-negative MatrixAtty. Dkt. No.: 642631-0061 Factorization (NMF). Burstein MD, Tsimelzon A, Poage GM, et al. Comprehensive Genomic Analysis Identifies Novel Subtypes and Targets of Triple-negative Breast Cancer, Clin Cancer Res Off J Am Assoc Cancer Res.2015;21(7):1688-1698, doi:10.1158 / 1078- 0432.CCR-14-0432. The ideal rank basis and factorization algorithm were determined using the NMF R package before taking the 1000-iteration consensus for a final clustering basis of 4. Gaujoux R, Seoighe C. A flexible R package for nonnegative matrix factorization, BMC Bioinformatics, 2010;11:367, doi:10.1186 / 1471-2105-11-367. Pathway and Gene Module Analyses

[0114] The association between TmS with transcriptomic alteration was assessed. For each TNBC cohort, GSEAs was conducted on the HALLMARK, KEGG, and REACTOME pathways. Subramanian, A., et al. PNAS, 2005, 102:43; Liberzon, A., et al. Cell Syst. 2015;1(6):417-425, doi:10.1016 / j.cels.2015.12.004. All genes were ranked by the Spearman correlation coefficient between their expression levels and TmS across samples within each cohort; they were then put through GSEA in the ‘pre-ranked’ mode. For GSEA, permutation tests (1,000 times) were adopted to generate a normalized enrichment score (NES) for each candidate pathway.

[0115] Gene expression modules were obtained consistent with Fredlund E, et al., The gene expression landscape of breast cancer is shaped by tumor protein p53 status and epithelial-mesenchymal transition. Breast Cancer Res BCR, 2012;14(4):R113, doi:10.1186 / bcr3236. The association of TmS and gene modules was evaluated by calculating the Spearman correlation coefficient between the aggregated gene expression for each gene module and the TmS across samples within each cohort. Cell Type Deconvolution (CIBERSORTx)

[0116] The cellular composition was estimated using the impute cell fractions module of CIBERSORTx (https: / / cibersortx.stanford.edu / ). The reference matrix of cell-type-specific expression was derived from a single-cell atlas for breast cancers, which included 10 primary untreated TNBC samples. In the reference matrix, nine distinct cell types were annotated: myeloid cells, plasmablast cells, T cells, B cells, cancer epithelial cells, normal epithelial cells, perivascular-like cells (PVLs), cancer-associated fibroblasts (CAFs), and endothelial cells. A maximum of 500 cells per cell type was selected, and all available cells were utilized for cell types with fewer than 500 cells. Subsequently, CIBERSORTx's batchAtty. Dkt. No.: 642631-0061 correction method (S-mode batch correction) was applied for the deconvolution of bulk samples. The deconvolution mode was set to ‘absolute’ with 1000 permutations. Cell Dysfunction and Exclusion Analysis

[0117] To evaluate the immunomodulatory roles in the high / low TmS group, T Cell Dysfunction and Exclusion (TIDE) scores of TCGA and METABRIC cohorts were obtained. TIDE first used the average expression level of CD8A, CD8B, GZMA, GZMB, and PRF1 to estimate the cytotoxic T-lymphocyte (CTL) level in each sample within each cohort. Patients with a higher and lower CTL level compared to the mean CTL level within the cohort were stratified into high and low CTL groups, respectively. For T cell dysfunction analysis, TIDE may evaluate whether gene signatures from each cohort influence the beneficial effect of CTL levels on patient prognosis. This was performed using the interaction coefficient d from the Cox proportional hazard (Cox-PH) model to evaluate how the interaction between a candidate gene and the CTL affects the death hazard. For T cell exclusion analysis, TIDE evaluated the correlation between the CTL level and the expression profiles of three cell types that have been reported to restrict T cell infiltration in tumors – cancer-associated fibroblasts (CAFs), myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs). In each cohort, a Wilcoxon rank-sum test was used to compare the distributions of dysfunction and exclusion scores between the two TmS groups. Differential Gene Expression and Integrative Enrichment Analysis

[0118] Differential gene expression analysis was conducted using the DESeq2 package in R, following its recommended workflow for differential gene expression analysis. Adjusted p-values were computed using the Benjamini-Hochberg procedure to control the false discovery rate (FDR), with an αlevel set at 0.05 for statistical significance. The fold-change thresholds were established at ≥ log2(1.5) for upregulated genes and ≤ -log2(1.5) for downregulated genes. Given the intrinsic heterogeneity and potential batch effects between the TCGA and SCAN-B cohorts, each dataset was analyzed independently before the results were integrated. Intersection analyses were performed to identify consistently differentially expressed genes (DEGs) across both cohorts.

[0119] To gain biological insights into the genes found to be differentially expressed across the two cohorts, we performed integrative pathways analysis using the ActivePathways package in R. DEGs were kept consistent and ranked the genes byAtty. Dkt. No.: 642631-0061 adjusted p-values from both TCGA and SCAN-B. The input for ActivePathways included a matrix of adjusted p-values of genes as rows and datasets as columns and a list of gene sets in the form of a GMT (Gene Matrix Transposed) file, the gene sets were acquired from Gene Ontology and Reactome databases. Tumor microenvironment segmentation and classification on H&E images

[0120] A deep learning model was trained on H&E images resected from tumors of squamous cell lung carcinoma, and applied to segment tumor-bearing tissue into reactive stroma, inactive stroma, inflamed stroma, necrosis, and tumor. Inactive stroma was identified as a relatively sparse and less cellular tissue matrix surrounding the tumor. Reactive stroma was of high cellularity and contained fibroblasts and myofibroblasts with elongated shapes. The inflamed stroma was identified as stroma regions enriched with immune cells. The whole slide imaging (WSI) was divided into tiles of 2000 x 2000 pixels with the magnification at 20× (about 0.5 µm / pixel). Each tile was then normalized to a target image to align the color with the training data before feeding to the well-trained deep learning model, which, in turn, generated corresponding masks for the five tissue types detected at the pixel level, and unrecognizable tissue regions were assigned as background. The tile masks were then stitched and further down sampled to 1.25× (about 8 µm / pixel) for the analysis efficiency purpose. Image processing methods were used to remove the peripheral tissue, while preserving the tumor bed.

[0121] Example 1.2: Characterization of TmS on multi-ethnic TNBC cohorts

[0122] To reveal the pronounced etiological variations across ethnic populations, notably between Western and Asian groups, TmS values were quantitatively assessed across four patient cohorts of TNBC patients from distinct ethnicities, encompassing the TCGA, METABRIC, FUSCC, and SCAN-B studies (FIGS.1A and 1B, Methods). To elucidate the variations in TmS and explore its potential impact on chemotherapy interventions, the focus remained on TNBC patients treated with adjuvant chemotherapy. TmS values varied within and between each cohort. In TCGA and METABRIC cohorts, TmS values were more comparable (FIG.1B, TCGA TmS: 3.74 ± 3.18; METABRIC TmS: 3.68 ± 2.38). However, patients from the FUSCC and SCAN-B cohorts (FIG.1B, FUSCC TmS: 2.74 ± 4.34; SCAN-B TmS: 2.13 ± 1.17) presented lower TmS (FIG.1B, P value < 0.001), highlighting a broad range of TmS values across diverse ethnicities. Additionally, the variability of TmS was investigated concerning clinical pathological characteristics andAtty. Dkt. No.: 642631-0061 molecular subtypes of TNBC across all cohorts. A significant association was found between TmS and Lehmann’s TNBC subtypes, namely TNBCType-6 and TNBCType-4, across all TNBC cohorts. Tumors with prognostic favorable subtype such as BL1 demonstrated enrichment in higher TmS, and vice versa (TCGA: adjusted P < 0.001, FIG. 2A, METABRIC: adjusted P < 0.05, FIG.2B, SCAN-B: adjusted P < 0.001, FIG.2C, FUSCC: adjusted P < 0.001, FIG.2D). In the SCAN-B TNBC cohort, where accurate tumor-infiltrating lymphocytes (TiLs) were assessed on whole section FFPE H&E slides by a pathologist, higher TmS samples corresponded with an increase in TiLs (FIG.2E), aligning with improved relapse-free survival (RFS) outcomes. Further examination of other conventional clinical parameters such as lymph node status (LN_status), proliferation rate (Ki67), BRCA mutation status (BRCA1 / 2 status), tumor mutation burden (TMB), and homologous recombination deficiency (HRD) revealed no significant correlation with TmS. The findings emphasized the potential of TmS as an ethnically nuanced biomarker in TNBC, distinguishing it from conventional clinical markers and underscoring its singular significance within the TNBC landscape.

[0123] Example 1.3: Low TmS is associated with poor progression / relapse-free survival across multi-ethnic TNBC cohorts

[0124] Given the lack of effective biomarkers for TNBC, chemotherapy remains the primary conventional therapeutic option, despite varying patient resistance. To assess the potential clinical relevance of TmS as a prognostic biomarker of therapeutic responses in TNBC patients, the progression-free interval (PFI) was evaluated in the TCGA cohort and the relapse-free survival (RFS) in the remaining three cohorts. All TNBC patients’ prognostic outcomes were stratified independently, based on either the TmS category (high versus low) or the classification as per Lehmann’s TNBCType-4 taxonomy. There was a significant association between lower TmS and decreased PFI in the TCGA cohort (FIG. 2F), and reduced RFS in the METABRIC, SCAN-B, and FUSCC cohorts (FIGS.2G-2I). Contrarily, the TNBCType-4 classification did not manifest a significant impact on the separation of these prognostic outcomes for TNBC patients (FIG.2F-2I). Further investigation into the distribution pattern of TmS stratification within each TNBCType-4 category revealed an equitable dispersion across each TNBCType-4 subtype (FIG.2F-2I). This suggests the independence of TmS as a prognostic factor. Subsequent implementation of a multivariate Cox proportional hazards regression model, with adjustments for additional clinical parameters including age, lymph node status, tumor size, and tumorAtty. Dkt. No.: 642631-0061 grade across TNBC cohorts, indicated the robust prognostic value of TmS (TCGA: n = 89, hazard ratio (HR) = 0.1, 95% confidence interval (CI): 0.01-0.77, log-rank P = 0.03; METABRIC: n = 118, hazard ratio (HR) = 0.53, 95% confidence interval (CI): 0.3-0.89, log-rank P = 0.02; SCAN-B: n = 145, hazard ratio (HR) = 0.43, 95% confidence interval (CI): 0.2-0.93, log-rank P = 0.03; FUSCC: n = 230, hazard ratio (HR) = 0.34, 95% confidence interval (CI): 0.13-0.89, log-rank P = 0.03, FIG.2J). The results suggest TmS as an independent and robust prognostic biomarker for TNBC, with particular relevance for stratifying patients' responses to chemotherapy.

[0125] Example 1.4: Unveiling the distinct biological mechanisms identified by TmS across TNBC patients

[0126] Expanding TmS as a prognostic biomarker, a multi-layered analysis was conducted to elucidate the underlying biological complexities and cellular dynamics associated with varying TmS levels, further indicating its relevance in therapeutic decision- making. Gene set enrichment analyses (GSEA) were performed on HALLMARK, KEGG and REACTOME pathways in chemotherapy treated TNBC patients across all four cohorts. The genes were ordered based on the Spearman correlation coefficient indicating the relationship between their expression levels and TmS across the sample set. Comparing the GSEA results from the four TNBC cohorts revealed substantial enrichment of immune- related hallmark gene sets — including allograft rejection, inflammatory response, complement, and IL2-STAT5 signaling pathways — in patients with elevated TmS in the three Western TNBC cohorts, a trend that was not observed in the FUSCC TNBC cohort (FIG.3A). In parallel, immune-related KEGG pathways also revealed enrichment in Western TNBC patients with high TmS, encompassing pathways like antigen processing and presentation, cytokine-cytokine receptor interaction, chemokine signaling, T-cell receptor signaling, and B-cell receptor signaling (FIG.3B). Intriguingly, certain pro- oncogenic gene sets such as KRAS signaling and P53 pathways demonstrated positive enrichment in Western TNBC patients with high TmS, correlating with a more favorable prognosis. These findings suggested that an elevated TmS is linked with both a more aggressive cancer biology and an amplified immune response. Conversely, TNBC samples with lower TmS values across all cohorts, including FUSCC TNBCs, demonstrated significant enrichment in hallmark pathways known to promote tumor invasions and metastasis, such as TGF-beta signaling and epithelial-mesenchymal transition pathways, consistent across all cohorts (FIGS.3A and 3B). Overall, these findings indicate that pro-Atty. Dkt. No.: 642631-0061 inflammatory and antigen-specific immune responses are predominantly activated in high TmS TNBC tumors within Western TNBC cohorts, while low TmS TNBCs across all cohorts exhibited a consistent pattern of enrichment in pro-angiogenesis and metastatic pathways.

[0127] These findings suggest that patients with high TmS TNBC tumors may benefit from treatments targeting the immune-related pathways, since these pathways were upregulated in these patients. Treatments targeting immune-related pathways include therapeutic agents inhibiting immune checkpoint proteins.

[0128] These findings also suggest that patients with low TmS TNBC tumors may benefit from treatments that target tumor extracellular matrix receptor pathways, since these pathways were upregulated in these patients. Treatments targeting tumor extracellular matrix receptor pathways include anti-vascular endothelial growth factor (anti-VEGF) therapeutic agents.

[0129] Beyond the realm of cancer-agnostic pathways, the relationship between TmS and gene expression was examined in the context of breast cancer-specific gene modules. A pattern emerged across three western TNBC cohorts consistent with eight functional gene modules proposed by Fredlund E, et al., The gene expression landscape of breast cancer is shaped by tumor protein p53 status and epithelial-mesenchymal transition, Breast Cancer Res BCR, 2012;14(4):R113, doi:10.1186 / bcr3236. Tumors with elevated TmS demonstrated a pronounced association with the immune response module, whereas tumors characterized by lower TmS closely aligned with the stroma module, which describes a more mesenchymal-like subtype (FIG.3C). For the FUSCC cohort, only patients with lower TmS values showed a similar pattern as Western cohorts. This finding indicated the unique biological underpinnings of TmS phenotypes at the transcriptomic level and increased understanding of distinct molecular signatures between Western and Asian TNBCs.

[0130] To substantiate the hypothesis regarding TmS and its potential correlation with diverse cell type composition in bulk tumor samples, CIBERSORTx was employed to estimate the presence of nine major cell types across each TNBC cohort (FIG.3D). The signature matrix for CIBERSORTx was generated from single-cell RNA sequencing (scRNA-seq) data of 10 TNBC samples obtained from a publicly available human breast cancer atlas. Across Western TNBC cohorts, the proportions of primary immune cell types,Atty. Dkt. No.: 642631-0061 including myeloid cells, T cells, and plasma blast cells, exhibited a consistent positive correlation with TmS. Specifically, in the SCAN-B cohort, extreme positive correlation was observed with myeloid and T cells, consistent with results that indicated increased TmS is associated with TiLs percentage measured in whole hematoxylin and eosin (H&E) stained slides. In contrast, three major cell types within the stromal compartment—cancer- associated fibroblasts (CAFs), perivascular-like cells (PVLs), and endothelial cells— revealed a negative association with TmS. These observations were consistent with findings at the transcriptomic level, suggesting that a higher TmS is correlated with heightened immune activities in the Western cohort, whereas a lower TmS signified enhanced stromal activities. Without being bound by any theory, although the signals in the METABRIC cohort were relatively subdued, this may be attributable to the low expression level captured by the microarray platform. Notably, the FUSCC cohort exhibited a strong correlation between TmS and cancer epithelial cells, but not with other cell types. This aligns with previous observations suggesting a unique biological mechanism in the Asian population as captured by TmS alterations.

[0131] Based on the evidence that low TmS values are suggestive of a more immunosuppressive tumor microenvironment (TME), such patients may exhibit suboptimal responsiveness to Immune-Checkpoint-Blockade (ICB) therapies. To further investigate the relationship between TmS and immune activity, the Tumor Immune Dysfunction and Exclusion (TIDE) scores, comprising the T cell dysfunction and exclusion scores (FIG.3E- 3H), were evaluated in both high and low TmS groups to predict potential responsiveness to immunotherapy. There was no significant variance in the T-cell dysfunction scores between the high and low TmS groups. However, a higher T-cell exclusion score was consistently noted in low-TmS patients across all three Western cohorts (TCGA: adjusted P = 0.13, FIG.3E; METABRIC: adjusted P < 0.01, FIG.3F, SCAN-B: adjusted P < 0.001, FIG.3G), pointing towards a restriction in T-cell infiltration. Without being by any theory, this may imply that Western low TmS TNBC patients, due to their limited T-cell mediated immune responses, are less likely to derive substantial benefits from ICB treatments. In contrast, high TmS TNBC patients may derive substantial benefits from ICB treatments.

[0132] Whole H&E stained slides from the TCGA BRCA dataset were analyzed to delineate the spatial distribution and organization of cellular populations within high and low TmS tumors. A deep learning algorithm, tumor microenvironment segmentation (TMEseg), facilitated the annotation of nine representative H&E slides, six from the lowAtty. Dkt. No.: 642631-0061 TmS group and three from the high TmS group, which were subsequently corroborated by a breast cancer pathologist (FIG.4A-4F). The algorithm indicated five cellular populations - tumor, inflamed, necrotic, reactive stroma, and inactive stroma - and quantified the estimated percentage of each tissue type. Low TmS samples distinctly presented a heightened total stroma percentage (FIG.4J) and ‘reactive to tumor’ ratio (FIG.4K), emblematic of a denser, more rigid extracellular matrix (ECM). An analysis of the segmented masks from H&E slides revealed tumor cells in low TmS samples to be ensconced within stroma, with visibly clear stromal barriers, indicative of a potentially immune-suppressive tumor microenvironment (FIG.4A-4F). In contrast, high TmS samples demonstrated a more discernible separation between tumor and stromal tissue (FIG.4G-4I). These observations further substantiated the phenotypic variability that underpins TmS classifications in TNBC.

[0133] Example 1.5: Unveiling the distinct biological mechanisms identified by TmS across TNBC patients

[0134] Given the observed association between low TmS and ECM activities, the transcriptomic changes in this group were evaluated in order to determine potential opportunities for alternative therapeutic interventions. To obtain a detailed snapshot of the transcriptional shifts correlated with TmS levels, a differential expression analysis was conducted on bulk transcriptomic datasets from the TCGA and SCAN-B cohorts. The METABRIC and FUSCC cohorts were excluded from this analysis due to suboptimal read depth in the former and intrinsic ethnicity-related variations in the latter. The intersection of differentially expressed genes (DEGs) from both the TCGA and SCAN-B TNBC cohorts revealed 164 DEGs (≥ 1.5 fold change, adjusted P < 0.05; FIG.5A and 5B), consisting of 95 upregulated and 69 downregulated genes in high TmS versus low TmS comparison. Predominantly, the low TmS signature genes (downregulated ones) were associated with extracellular matrix (ECM) activities and the epithelial-to-mesenchymal transition process. Gene ontology (GO) enrichment was incorporated into the analysis, further elucidating the preferential enrichment of genes responsible for ECM processes and cell-cell adhesion pathways within low TmS tumors (FIG.5C). This observation highlighted that a major difference unveiled by TmS in tumor stroma is related to ECM production and remodeling. Conversely, the high TmS signature is implicated immune-related activities (FIG.5D). Consistent with this, the GO network analysis of high TmS signature genes further indicated the enrichment of immune-related processes, spanning innate and adaptive immuneAtty. Dkt. No.: 642631-0061 responses, chemokine signaling, and immune cell activation pathways. In summary, the analysis, encompassing differential expression and cross-cohort enrichment tests, robustly established the link between high TmS and an activated intratumor immune, providing a foundation for immunotherapeutic exploration in high TmS patients, while, notably, low TmS tumors unveiled a distinct transcriptomic profile rich in ECM-related activities, offering a promising pathway for investigating alternative therapeutic strategies focused on modulating and remodeling the ECM. Example 2: Efficacy of immune checkpoint inhibitor therapy in xenograft mouse model bearing high TmS TNBC tumors

[0135] The 4T1 orthotopic syngeneic murine model is used to probe the efficacy of immune checkpoint inhibitor therapy on high TmS TNBC tumors. 20,0004T1 cells in 100 µL media are injected into the mammary fat pad of female BALB / c immunocompetent mice that are about 6 weeks old. Subcutaneous tumor growth is assessed by monitoring tumor volume (V = 1 / 2 × length × width2) every two days. Upon formation of palpable tumors, mice are treated with atezolimumab anti-PD-L1 antibody (10mg / kg) or IgG2a control (1.2 mg / kg intraperitoneally) twice a week for a period of 3 weeks. At the end of 3 weeks, mice are sacrificed and tumors, lungs, blood (cardiac puncture) are harvested. Lung metastases and circulating tumor cells (CTC) colonies are counted. Tumors are analyzed for numbers and localization of immune cells. Single cells are isolated from tumors for further analysis of immune cell subsets via flowcytometry. Additionally, tumors are sectioned and immunostained for a panel of immune markers via imaging mass cytometry to accurately assess the complex phenotypes and immune spatial interactions in the tissue microenvironment. Macrophages (CD11b+, F4 / 80+) are isolated for phenotypic (M1 / M2) and functional (phagocytic potential) analysis via fluorescence-activated cell sorting (FACS) and Immunofluorescence. Phagocytic macrophages are identified as F4 / 80+ macrophages positive for epithelial cellular adhesion molecule (EpCAM) tumor cells using confocal microscopy. Since cytotoxic T cells are part of an immunostimulatory response, CD8+T cell number and functionality are assessed by measuring interferon γ, perforin and granzyme B levels via flowcytometry. The composition of CD4+T cells, regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), dendritic cells, and natural killer cells are also assessed. This demonstrates the therapeutic utility of immune checkpoint therapy through a TME switch from immunosuppressive to immunomodulatory.Atty. Dkt. No.: 642631-0061 Example 3: Efficacy of anti-VEGFA therapy in xenograft mouse model bearing low TmS TNBC tumors

[0136] The 4T1 orthotopic syngeneic murine model is used to probe the efficacy of anti- VEGFA therapy in low TmS TNBC tumors. 20,0004T1 cells in 100 µL media are injected into the mammary fat pad of female BALB / c immunocompetent mice that are about 6 weeks old. Subcutaneous tumor growth is assessed by monitoring tumor volume (V = 1 / 2 × length × width2) every two days. Upon formation of palpable tumors, mice are treated with bevacizumab (intraperitoneal injection, 5 mg / kg, weekly for 2 weeks). At the end of 2 weeks, mice are sacrificed and tumors, lungs, blood (cardiac puncture) are harvested. Lung metastases and CTC colonies are counted. Tumors are analyzed for the presence of angiogenic markers such as VEGF-A, Ang1, Ang2, and CD105. This demonstrates the therapeutic utility of anti-VEGFA therapy in low TmS TNBC tumors. Example 4: Validation of Using TmS to Classify Patients with TNBC For Alternative Target Therapies as Compared to Standard of Care Treatment

[0137] This clinical trial assesses whether TmS, which characterizes tumor-cell total mRNA expression, can predict a patient’s tumor sensitivity to standard of care treatment as compared to being placed on a personally designed treatment trial including either immune checkpoint therapy or anti-VEGFA therapy. The clinical trial indicates improved responses in patients with newly diagnosed TNBC using personally designed treatments.

[0138] The primary objective of the trial is to conduct a prospective single arm, non- randomized trial that aims to determine the impact of implementation of TmS to assess response to chemotherapy, immune checkpoint therapy, anti-VEGFA therapy. TmS is implemented to subtype TNBC in order to select the appropriate targeted therapy trial to complete.

[0139] Secondary objectives include conducting measurements as defined by standardized definitions for efficacy end points (STEEP) criteria using the following prioritization: distant recurrence free interval (DRFI), recurrence free survival (RFS), distant relapse-free survival (DRFS), overall survival (OS), invasive disease free survival (IDFS), disease free survival including ductal carcinoma in situ (DFS-DCIS). Secondary objectives also include evaluating the rates of enrollment into clinical trials for patients identified as having chemotherapy insensitive disease. Secondary objectives also include evaluating the frequency of pathologic response rates (pCR, RCB I-III residual disease) inAtty. Dkt. No.: 642631-0061 patients identified as chemotherapy sensitive versus insensitive. Secondary objectives also include determining the estimates of DRFI, RFS, DRFS, IDFS and DFS-DCIS at 3 years, and OS at 5 and 10 years in all patients. Secondary objectives also include determining the pathologic response based on molecular characterization. Secondary objectives also include conducting subset analyses of pathologic response and 3-year DRFI, RFS, DRFS, IDFS, and DFS-DCIS. Secondary objectives also include comparing the results of pathologic node-negative status (sentinel and / or non-sentinel nodes) after neoadjuvant chemotherapy according to a genomic predictor of nodal response to NACT, in subsets defined by pre- treatment clinical nodal status.

[0140] Patients are assigned to one of three arms. In Arm A, Patients undergo baseline molecular and immunohistochemistry (IHC) evaluation of their tumor biopsy, and receive the results. Patients then receive standard anthracycline-based chemotherapy and undergo standard ultrasound at baseline, after 2 cycles, after 4 cycles of treatment, and after completion of treatment (before surgery). Patients and physicians are notified of the results of the molecular evaluation. Patients may then choose to continue with standard taxane + / - platinum-based chemotherapy or participate in an experimental clinical trial designed to match the molecular profile and triple-negative subtype. Patients with tumors predicted to be insensitive to chemotherapy are advised to participate in a clinical trial treating their tumor subtype.

[0141] In Arm B, patients undergo baseline molecular and IHC evaluation of their tumor biopsy, and receive the results. Patients then receive standard anthracycline-based chemotherapy and undergo standard ultrasound at baseline, after 2 cycles, and after 4 cycles of treatment. Patients and physicians are notified of the results of the molecular evaluation. Patients may then choose to continue with standard taxane + / - platinum-based chemotherapy or participate in an experimental clinical trial designed to match the molecular profile and triple-negative subtype. Patients with tumors predicted to be insensitive to chemotherapy are advised to participate in a clinical trial treating their tumor subtype.

[0142] In Arm C, patients undergo baseline molecular and IHC evaluation of their tumor biopsy, and receive the results. Patients then receive standard anthracycline-based chemotherapy with immunotherapy and undergo standard ultrasound at baseline, after 2 cycles, and after 4 cycles of treatment. Patients and physicians are notified of the results of the molecular evaluation. Patients may then choose to continue with standard taxane + / -Atty. Dkt. No.: 642631-0061 platinum-based chemotherapy or participate in an experimental clinical trial designed to match the molecular profile and triple-negative subtype. Patients with tumors predicted to be insensitive to chemotherapy are advised to participate in a clinical trial treating their tumor subtype.

[0143] After completion of study treatment, patients are followed up for up to 5 years.

[0144] Patient eligibility criteria include the following. The patient can undergo biopsy or surgery of a primary tumor site for suspected or proven invasive breast cancer of clinical stage I to III and plan to receive neoadjuvant therapy with anthracycline / taxane based regimens (Arm A and Arm B) or chemotherapy / immunotherapy-based regimens (Arm C). The patient is proven to have TNBC, defined from standard pathologic assays as negative for ER and PR (< 10% tumor staining) and negative for HER2 (immunohistochemistry [IHC] score < 3, gene copy number not amplified). The patients must have left ventricular ejection fraction (LVEF) >= 50% by multi gated acquisition scan (MUGA) or echocardiogram (ECHO) within 12 weeks prior to starting adriamycin. The patients have leukocytes > 3,000 / mcL, absolute neutrophil count > 1,500 / mcL, platelets > 100,000 / mcL, total bilirubin ≤ 1.5 × upper limit of normal (ULN), aspartate aminotransferase (AST) (serum glutamic oxaloacetic transaminase [SGOT]) / alanine aminotransferase (ALT) (serum glutamate pyruvate transaminase [SGPT]) < 2.5 x institutional upper limit of normal, creatinine within 1.5 × the upper limits of normal or creatinine clearance > 60 mL / min / 1.73 m^2 for patients with creatinine levels above institutional normal. For Arms A and B, patients must be medically ineligible to receive immunotherapy in combination with anthracycline / taxane-based chemotherapy as part of standard care. For Arm C, patients must be medically eligible to receive immunotherapy in combination with chemotherapy as part of standard of care.

[0145] Patients are excluded if they have any of the following criteria. The patient has a diagnosis of stage IV disease or is found to have stage IV disease prior to initiation of chemotherapy. The patient has a prior history of invasive cancer within 5 years of study entry or history of metastatic cancer (exceptions include non-metastatic, curatively treated basal and squamous cell carcinoma of the skin). The patient has prior excisional biopsy of the primary invasive breast cancer. The patients have hematomas or biopsy site changes that limit response assessment of the primary tumor by diagnostic imaging. The patients are not eligible for chemotherapy with taxane and / or anthracycline based chemotherapy regimens. The patients have had prior therapy with chemotherapy and / or immunotherapy.Atty. Dkt. No.: 642631-0061 The patients have grade II or higher neuropathy. The patients have Zubrod performance status of > 2. The patients have a history of serious cardiac events defined as: patients with a history of New York Heart Association class 3 or 4 heart failure, or history of myocardial infarction, unstable angina, or cerebrovascular accident (CVA) within 6 months of protocol registration; or patients who have history of PR prolongation (grade 2 or higher) or atrioventricular (AV) block.

[0146] Example 5: TmS reveals distinct tumor niche status across TNBC patients

[0147] TmS with known driver genomic events in breast cancer were compared. Specifically, the distribution of TmS in relation to driver mutations in TP53, BRCA1, PTEN, PIK3CA, RAD51D, and ATM genes were investigated, as well as copy number alterations in MYC and PTEN. No systematic differences in the distribution of TmS were identified regardless of the presence or absence of known driver mutations or copy number status (FIG.7A). For instance, the TmS distributions were similar between wild-type and mutated samples for each gene across all cohorts. Similarly, copy number alterations (amplification, loss, or neutral) in MYC and PTEN showed no difference with TmS values. This lack of correlation suggested that TmS captured aspects of tumor biology not reflected by genomic alterations.

[0148] To explore the transcriptomic change captured by TmS within the TME, gene set enrichment analyses (GSEA) were performed on HALLMARK and KEGG pathways in chemotherapy-treated TNBC patients across all four cohorts (TCGA, METABRIC, SCAN- B, and FUSCC). Genes were ordered based on the Spearman correlation coefficient indicating the relationship between their expression levels and TmS across the sample set. GSEA results indicated substantial enrichment of immune-related hallmark gene sets, including allograft rejection, inflammatory response, complement, and IL2-STAT5 signaling pathways, in patients with higher TmS across three Western TNBC cohorts, but not in the FUSCC TNBC cohort (heatmap of normalized enrichment scores (NES) of top cancer hallmark pathways across four TNBC cohorts with BH adjusted p-value < 0.05). Immune-related KEGG pathways, encompassing pathways like antigen processing and presentation, cytokine-cytokine receptor interaction, chemokine signaling, T-cell receptor signaling, and B-cell receptor signaling, were enriched in Western TNBC patients with higher TmS. Certain pro-oncogenic gene sets, such as KRAS signaling and P53 pathways, demonstrated positive enrichment in Western TNBC patients with high TmS, correlating with a more favorable prognosis. Conversely, patients with lower TmS consistentlyAtty. Dkt. No.: 642631-0061 exhibited significant enrichment in hallmark pathways such as TGF-beta signaling and epithelial-mesenchymal transition, which are known to promote tumor invasion, metastasis, formation of a desmoplastic stroma, and creation of an immunosuppressive microenvironment, consistently across all patient cohorts.

[0149] The relationship between TmS and breast cancer-specific gene modules were examined. Consistent with the GSEA results, the gene module analysis revealed distinct patterns associated with high and low TmS across the cohorts(heatmap of normalized enrichment scores (NES) of top cancer hallmark pathways across four TNBC cohorts with BH adjusted p-value < 0.05 and heatmap of z-score scaled Pearson correlation between TmS and the average gene expression level of breast cancer gene modules within each cohort). In the three Western TNBC cohorts (TCGA, SCAN-B, and METABRIC), high TmS tumors demonstrated strong associations with the “immune response” module, which is linked to favorable prognosis. The FUSCC cohort exhibited a unique pattern: high TmS tumors in this cohort showed strong correlations specifically with cell cycle-related modules, particularly 'mitotic checkpoint' and 'mitotic progression'. These modules may represent distinct aspects of the cell cycle regulation and execution. Consistently across all four cohorts, low TmS tumors showed a strong alignment with the stroma module, indicative of a mesenchymal-like phenotype. This finding suggests that regardless of ethnic background, low TmS is associated with an immune-excluded stroma in TNBC. Taken together, both GSEA and gene module analyses indicate that the TME of high TmS TNBC patients was characterized by enhanced immune activation in Western cohorts, while the FUSCC cohort was primarily associated with upregulated cell cycle processes. In contrast, the TME of low TmS TNBC patients consistently represented an immune-excluded stroma across all cohorts. These findings highlight the importance of considering ethnic-specific variations in tumor biology when interpreting TmS-based classifications, while also underscoring the consistent stromal characteristics of low TmS tumors across diverse populations.

[0150] CIBERSORTx was employed to estimate the presence of nine major cell types across each TNBC cohort (heatmap of Pearson correlation between TmS and the CIBERSORTx deconvolved cell type proportion within each cohort). The signature matrix for CIBERSORTx was generated using single-cell RNA sequencing (scRNA-seq) data from 10 TNBC samples obtained from a public human breast cancer atlas. Western TNBC cohorts showed a positive correlation between TmS and the proportions of primary immuneAtty. Dkt. No.: 642631-0061 cell types, including myeloid cells, T cells, and plasma blast cells. This correlation was particularly strong in the SCAN-B cohort for myeloid and T cells. Conversely, across all four cohorts, there was a negative association between TmS and three major stromal cell types: cancer-associated fibroblasts (CAFs), perivascular-like cells (PVLs), and endothelial cells. This finding further supported the notion of an immune-excluded stroma in low TmS tumors. Without being bound by any theory, subdued signals in the METABRIC cohort may be due to the low expression levels captured by the microarray platform. The FUSCC cohort showed a strong correlation between TmS and cancer epithelial cells only, with no such correlations observed in other cell types. Collectively, these cell type deconvolution results indicated that a higher TmS represented a TME characterized by cancer cell proliferation and heightened immune activity, particularly in Western cohorts, whereas a lower TmS signified an immune-excluded stroma.

[0151] Tumors with lower TmS were consistently characterized as immune-cold. They may exhibit suboptimal responsiveness to Immune-Checkpoint-Blockade (ICB) therapies due to tumor immune evasion. To test this hypothesis, two mechanisms of tumor immune escape were evaluated: T cell dysfunction and T cell exclusion. The T cell dysfunction score may reflect the intrinsic inactive state of the tumor immune microenvironment. It is derived by systematically identifying genes that interact with cytotoxic T lymphocyte (CTL) infiltration levels to influence patient survival. A higher dysfunction score suggests a more exhausted or suppressed state of T cells within the tumor. On the other hand, the T cell exclusion score may identify factors that prevent T cell infiltration in tumors with low CTL levels. A higher exclusion score may indicate a greater barrier to T cell entry into the tumor microenvironment. This analysis revealed that low-TmS patients exhibited significantly higher T-cell exclusion scores across all three Western cohorts (FIG.7B, TCGA: adjusted P < 0.01; METABRIC: adjusted P < 0.01; SCAN-B: adjusted P < 0.001). This finding suggests that the activated stroma creates physical and biochemical barriers that restrict T-cell infiltration. Contrasting trends in T-cell dysfunction scores were observed between cohorts (FIG.7C). In the SCAN-B cohort, low-TmS patients showed significantly lower T-cell dysfunction scores (adjusted P < 0.001), indicating less T-cell exhaustion despite reduced infiltration. While not statistically significant, TCGA and METABRIC cohorts displayed a similar trend. However, the FUSCC cohort displayed an opposite trend: low-TmS patients exhibited significantly higher dysfunction scores (adjusted P < 0.001), suggesting a more immunosuppressive microenvironment even withAtty. Dkt. No.: 642631-0061 higher CTL levels. This divergence indicates potential ethnic-specific differences in the immune microenvironment of TNBC tumors. These findings indicate that Western low- TmS TNBC patients may face challenges with ICB treatments primarily due to T-cell exclusion, which physically prevents T cells from entering the tumor. In contrast, low-TmS patients in the FUSCC cohort may face a dual challenge of both T-cell exclusion and heightened T-cell dysfunction, potentially limiting the effectiveness of immunotherapies through different mechanisms. EQUIVALENTS

[0152] The present technology is not to be limited in terms of the particular embodiments described in this application, which are intended as single illustrations of individual aspects of the present technology. Many modifications and variations of this present technology can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the present technology, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the present technology. It is to be understood that this present technology is not limited to particular methods, reagents, compounds compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0153] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0154] As will be understood by one skilled in the art, for any and all purposes, particularly in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as “up to,” “at least,” “greater than,” “less than,” and the like, include the number recited and refer to ranges which can be subsequently broken down into subranges as discussed above.Atty. Dkt. No.: 642631-0061 Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells, and so forth.

[0155] All patents, patent applications, provisional applications, and publications referred to or cited herein are incorporated by reference in their entirety, including all figures and tables, to the extent they are not inconsistent with the explicit teachings of this specification.

Claims

Atty. Dkt. No.: 642631-0061 WHAT IS CLAIMED:

1. A method for selecting a triple negative breast cancer patient for treatment with a tumor extracellular matrix (ECM) targeting therapy comprising: (a) detecting levels of tumor-specific total mRNA (TmS) expression below a predetermined threshold in a biological sample obtained from the cancer patient; and (b) administering to the cancer patient an effective amount of the tumor ECM targeting therapy.

2. The method of claim 1, wherein the tumor ECM targeting therapy comprises an anti- vascular endothelial growth factor (anti-VEGF) therapeutic agent.

3. The method of claim 2, wherein the anti-VEGF therapeutic agent comprises one or more of bevacizumab, aflibercept, ranibizumab, sorafenib, dasatinib, sunitinib malate, nilotinib, pazopanib, or any combination thereof.

4. The method of any one of claims 1-3, wherein the tumor-specific total mRNA expression is detected via next-generation sequencing, single nucleotide polymorphism (SNP) array, or microarray.

5. The method of any one of claims 1-4, wherein the predetermined threshold is determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC cancer patients that are responsive to chemotherapy.

6. A method for prolonging survival of a triple negative breast cancer (TNBC) patient comprising: administering to the cancer patient an effective amount of an anti-VEGF therapeutic agent, wherein tumor-specific total mRNA expression levels in a biological sample obtained from the cancer patient are below a predetermined threshold determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC patients that are responsive to chemotherapy.Atty. Dkt. No.: 642631-0061 7. The method of claim 6, wherein the cancer patient exhibits stage I or stage II TNBC, and the predetermined threshold is determined based on a cohort of stage I or stage II TNBC patients that are responsive to chemotherapy.

8. The method of claim 6, wherein the cancer patient exhibits stage III or stage IV TNBC, and the predetermined threshold is determined based on a cohort of stage III or stage IV TNBC patients that are responsive to chemotherapy.

9. The method of any one of claims 6-8, wherein tumor-specific total mRNA expression is detected via next-generation sequencing, single nucleotide polymorphism (SNP) array, or microarray.

10. The method of any one of claims 6-9, wherein the anti-VEGF therapeutic agent comprises one or more of bevacizumab, aflibercept, ranibizumab, sorafenib, dasatinib, sunitinib malate, nilotinib, pazopanib, or any combination thereof.

11. The method of any one of claims 1-10, wherein the cancer patient has not received a prior anti-cancer therapy, optionally wherein the anti-cancer therapy comprises one or more of chemotherapy, targeted therapy, immunotherapy, radiation therapy, or surgery.

12. The method of any one of claims 1-10, wherein the cancer patient is non-responsive or resistant to chemotherapy.

13. The method of claim 12, wherein the chemotherapy comprises one or more of alkylating agents, topoisomerase inhibitors, endoplasmic reticulum stress inducing agents, antimetabolites, mitotic inhibitors, nitrogen mustards, nitrosoureas, alkyl sulfonates, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, cytostatic alkaloids, cytotoxic antibiotics, antimetabolites, endocrine / hormonal agents, and bisphosphonate therapy agents.

14. The method of any one of claims 1-10, further comprising sequentially, simultaneously, or separately administering to the cancer patient an additional anti-cancer therapy, optionally wherein the anti-cancer therapy comprises one or more of chemotherapy, targeted therapy, immunotherapy, radiation therapy, or surgery, optionally wherein the targeted therapy comprises a VEGF / VEGFR inhibitor, EGF / EGFR inhibitor, PARP inhibitor, or a combination thereof, or optionally wherein the immunotherapy comprises an immune checkpoint inhibitor therapy.Atty. Dkt. No.: 642631-0061 15. The method of any one of claims 1-14, further comprising sequentially, simultaneously, or separately administering to the cancer patient an effective amount of a TGF-β inhibitor, optionally wherein the TGF-β inhibitor is selected from among vactosertib (TEW-7197), saracatinib (AZD0530), AVID200, fresolimumab (GC1008), and galunisertib (LY2157299).

16. A method for selecting a triple negative breast cancer patient for treatment with an immune checkpoint inhibitor comprising: (a) detecting levels of tumor-specific total mRNA (TmS) expression at or above a predetermined threshold in a biological sample obtained from the cancer patient; and (b) administering to the cancer patient an effective amount of the immune checkpoint inhibitor.

17. The method of claim 16, wherein the immune checkpoint inhibitor comprises a programmed cell death protein 1 (PD-1) inhibitor, a programmed death-ligand 1 (PD-L1) inhibitor, a cytotoxic T-lymphocyte associated protein 4 (CTLA-4) inhibitor, or any combination thereof.

18. The method of claim 16 or 17, wherein the immune checkpoint inhibitor comprises one or more of ipilimumab, tremelimumab, cadonilimab, zalifrelimab, pembrolizumab, nivolumab, cemiplimab, sintilimab, tislelizumab, toripalimab), camrelizumab, geptanolimab, toripalimab, zimberelimab, penpulimab, serplulimab, prolgolimab, balstilimab, retifanlimab, cadonilimab, pucotenlimab, sasanlimab, cetrelimab, tebotelimab, pidilizumab, dostarlimab, atezolizumab, durvalumab, avelumab, sugemalimab, or envafolimab.

19. The method of any one of claims 16-18, wherein the predetermined threshold is determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC cancer patients that are responsive to chemotherapy.

20. A method for prolonging survival of a triple negative breast cancer (TNBC) patient comprising: administering to the cancer patient an effective amount of an immune checkpoint inhibitor,Atty. Dkt. No.: 642631-0061 wherein tumor-specific total mRNA expression levels in a biological sample obtained from the cancer patient are at or above a predetermined threshold determined using recursive partitioning survival tree model comparing survival outcome to tumor-specific total mRNA (TmS) expression in a cohort of TNBC patients that are responsive to chemotherapy.

21. The method of claim 20, wherein the cancer patient exhibits stage I or stage II TNBC, and the predetermined threshold is determined based on a cohort of stage I or stage II TNBC patients that are responsive to chemotherapy.

22. The method of claim 20, wherein the cancer patient exhibits stage III or stage IV TNBC, and the predetermined threshold is determined based on a cohort of stage III or stage IV TNBC patients that are responsive to chemotherapy.

23. The method of any one of claims 20-22, wherein tumor-specific total mRNA expression is detected via next-generation sequencing, single nucleotide polymorphism (SNP) array, or microarray.

24. The method of any one of claims 20-23, wherein the immune checkpoint inhibitor comprises a programmed cell death protein 1 (PD-1) inhibitor, a programmed death-ligand 1 (PD-L1) inhibitor, a cytotoxic T-lymphocyte associated protein 4 (CTLA-4) inhibitor, or any combination thereof.

25. The method of any one of claims 20-24, wherein the immune checkpoint inhibitor comprises one or more of ipilimumab, tremelimumab, cadonilimab, zalifrelimab, pembrolizumab, nivolumab, cemiplimab, sintilimab, tislelizumab, toripalimab), camrelizumab, geptanolimab, toripalimab, zimberelimab, penpulimab, serplulimab, prolgolimab, balstilimab, retifanlimab, cadonilimab, pucotenlimab, sasanlimab, cetrelimab, tebotelimab, pidilizumab, dostarlimab, atezolizumab, durvalumab, avelumab, sugemalimab, or envafolimab 26. The method of any one of claims 16-25, wherein the cancer patient has not received a prior anti-cancer therapy, optionally wherein the anti-cancer therapy comprises one or more of chemotherapy, targeted therapy, immunotherapy, radiation therapy, or surgery.

27. The method of any one of claims 16-26, further comprising sequentially, simultaneously, or separately administering to the cancer patient an effective amount of a chemotherapeutic agent.Atty. Dkt. No.: 642631-0061 28. The method of claim 27, wherein the chemotherapeutic agent comprises one or more of alkylating agents, topoisomerase inhibitors, endoplasmic reticulum stress inducing agents, antimetabolites, mitotic inhibitors, nitrogen mustards, nitrosoureas, alkyl sulfonates, platinum agents, taxanes, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, cytostatic alkaloids, cytotoxic antibiotics, antimetabolites, endocrine / hormonal agents, and bisphosphonate therapy agents.

29. The method of claim 27 or 28, wherein the chemotherapeutic agent comprises one or more of cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, edatrexate (10-ethyl-10-deaza-aminopterin), thiotepa, carboplatin, cisplatin, taxanes, paclitaxel, protein-bound paclitaxel, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, gemcitabine, irinotecan, ixabepilone, temozolmide, topotecan, vincristine, vinblastine, eribulin, mutamycin, capecitabine, anastrozole, exemestane, letrozole, leuprolide, abarelix, buserlin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, denosumab, zoledronate, trastuzumab, tykerb, anthracyclines (e.g., daunorubicin and doxorubicin), cladribine, midostaurin, bevacizumab, oxaliplatin, melphalan, etoposide, mechlorethamine, bleomycin, microtubule poisons, annonaceous acetogenins, chlorambucil, ifosfamide, streptozocin, carmustine, lomustine, busulfan, dacarbazine, temozolomide, altretamine, 6-mercaptopurine (6-MP), cytarabine, floxuridine, fludarabine, hydroxyurea, pemetrexed, epirubicin, idarubicin, SN-38, ARC, NPC, campothecin, 9-nitrocamptothecin, 9-aminocamptothecin, rubifen, gimatecan, diflomotecan, BN80927, DX-8951f, MAG-CPT, amsacnne, etoposide phosphate, teniposide, azacitidine (Vidaza), decitabine, accatin III, 10-deacetyltaxol, 7-xylosyl-10- deacetyltaxol, cephalomannine, 10-deacetyl-7-epitaxol, 7-epitaxol, 10-deacetylbaccatin III, 10-deacetyl cephalomannine, streptozotocin, nimustine, ranimustine, bendamustine, uramustine, estramustine, mannosulfan, camptothecin, exatecan, lurtotecan, lamellarin D9- aminocamptothecin, amsacrine, ellipticines, aurintricarboxylic acid, HU-331, or combinations thereof.

30. The method of any one of claims 16-25, further comprising sequentially, simultaneously, or separately administering to the cancer patient an additional anti-cancer therapy, optionally wherein the anti-cancer therapy comprises one or more of chemotherapy, targeted therapy, immunotherapy, radiation therapy, or surgery, optionally wherein the targeted therapy comprises a VEGF / VEGFR inhibitor, EGF / EGFR inhibitor,Atty. Dkt. No.: 642631-0061 PARP inhibitor, or a combination thereof, or optionally wherein the immunotherapy comprises an immune checkpoint inhibitor therapy.

31. The method of any one of claims 1-30, wherein the biological sample comprises plasma, blood, serum, or biopsied tissue.

32. The method of any one of claims 1-31, wherein the tumor-specific total mRNA (TmS) comprises circulating tumor RNA (ctRNA).

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