Markers Of Triple-negative Breast Cancer And Uses Thereof

MARKERS OF TRIPLE-NEGATIVE BREAST CANCER AND USES THEREOF

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application

No.61/561,743, filed on November 18, 2011.

The entire teachings of the above application are incorporated herein by reference.

GOVERNMENT SUPPORT

This invention was made with government support under grants CA95131, CA148375; CA105436 and CA070856, CA68485 and CA009385 awarded by the National Institute of Health (NIH). The government has certain rights in the invention.

BACKGROUND OF THE INVENTION

Treatment of patients with triple-negative breast cancer (TNBC), lacking estrogen receptor (ER) and progesterone receptor (PR) expression as well as human epidermal growth factor receptor 2 (HER2) amplification, has been challenging due to the heterogeneity of the disease and the absence of well-defined molecular targets (Pegram MD, et al. J Clin Oncol. 1998;16(8):2659-2671 ; Wiggans RG, et al. Cancer Chemother Pharmacol. 1979;3(l):45-48; Carey LA, et al. Clin Cancer Res.

2007;13(8):2329-2334). TNBCs constitute 10%-20% of all breast cancers, more frequently affect younger patients, and are more prevalent in African-American women (Morris GJ, et al. Cancer. 2007;110(4):876-884). TNBC tumors are generally larger in size, are of higher grade, have lymph node involvement at diagnosis, and are biologically more aggressive (Haffty BG, et al. J Clin Oncol. 2006;24(36):5652-5657). Despite having higher rates of clinical response to presurgical (neoadjuvant) chemotherapy, TNBC patients have a higher rate of distant recurrence and a poorer prognosis than women with other breast cancer subtypes (Haffty BG, et al. J Clin Oncol. 2006;24(36):5652-5657; Dent R, et al. Clin Cancer Res. 2007; 13(15 pt 1). -4429-4434). Less than 30% of women with metastatic TNBC survive 5 years, and almost all die of their disease despite adjuvant chemotherapy, which is the mainstay of treatment (Dent R, et al. Clin Cancer Res. 2007; 13(15 pt l):4429-4434).

One of the first molecular insights into TNBCs was the observation that they are likely to arise in BRCA1 mutation carriers and have gene expression (GE) profiles similar to those of B RCA 1 -deficient tumors (Haffty BG, et al. J Clin Oncol. 2006;24(36):5652-5657). BRCA1 plays an important role in DNA double-strand break repair, contributing to the maintenance of DNA stability (D 'Andrea AD, Grompe M. Nat Rev Cancer. 2003;3(l):23-34). Poly ADP-ribose polymerase (PARP) enzymes are critical for appropriate processing and repair of DNA breaks (Hoeijmakers JH. Nature. 2001 ; 41 1(6835):366-374). Tumor cell lines lacking functional BRCA1 or BRCA2 are sensitive to PARP inhibitors in preclinical studies (Farmer H, et al. Nature. 2005;434(7035):917-921). Clinical trials using both PARP inhibitors and DNA-damaging agents (e.g., cisplatin) in TNBC are currently underway and show promise in BRCAl/2-mutant tumors (Fong PC, et al. N Engl J Med. 2009;361(2): 123-134). Other studies identifying molecular markers of TNBC, such as VEGF (Burstein HJ, et al. J Clin Oncol. 2008;26(1 1): 1810-1816), EGFR (Nielsen TO, et al. Clin Cancer Res. 2004; 10(16):5367-5374), Src (Finn RS, et al. Breast Cancer Res Treat. 2007; 105(3):319-326), and mTOR (Ellard SL, et al. J Clin Oncol. 2009; 27(27):4536-4541) have been important for the design of clinical trials investigating targeted treatments.

Clearly, there is a major need to better understand the molecular basis of TNBC and to develop effective treatments for this aggressive type of breast cancer.

SUMMARY OF THE INVENTION

In one aspect provided herein are methods of determining a triple negative breast cancer (TNBC) subtype in an individual in need thereof comprising determining expression of one or more genes (e.g., presence of one or more mRNAs and/or protein encoded by gene) in one or more TNBC cells of the individual; and comparing the expression of the one or more genes in the TNBC cells with the expression of the one or more genes in a control. Increased expression of one or more genes comprising one or more cell cycle genes, cell division genes, proliferation genes, DNA damage response genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC BL1 subtype. Increased expression of one or more growth factor signaling genes, glycolysis genes, gluconeogenesis genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC BL2 subtype. Increased expression of one or more immune cell signaling genes, cytokine signaling genes, antigen processing and presentation genes, core immune signal transduction genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC IM subtype. Increased expression of one or more cell motility genes, extracellular matrix (ECM) receptor interaction genes, cell differentiation genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC M subtype. Increased expression of one or more cell motility genes, cellular differentiation genes, growth pathway genes, growth factor signaling genes, angiogenesis genes, immune signaling genes, stem cell genes, HOX genes, mesenchymal stem cell-specific marker genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC MSL subtype. Increased expression of one or more hormone regulated genes in the TNBC cells compared to the control indicates that the TNBC is a TNBC LAR type.

In another aspect, the methods are directed to methods of determining a treatment protocol for the triple negative breast cancer (TNBC) patient based on the TNBC subtype.

In another aspect, the methods are directed to predicting whether an individual {e.g., patient) will benefit from a (one or more) treatment for a particular TNBC subtype.

In yet another aspect, the invention is directed to a method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) subtype comprising contacting a cell line that is a model for the TNBC subtype of interest with an agent to be assessed; and determining viability of the cell line in the presence of the agent, wherein if the viability of the cell line decreases in the presence of the agent, then the agent can be used to treat the TNBC subtype of interest. The TNBC subtype of interest can be a TNBC BL-1 subtype, a TNBC BL-2 subtype, a TNBC IM subtype, a TNBC M subtype, a TNBC MSL subtype or a TNBC LAR subtype.

BRIEF DESCRIPTION OF THE DRAWINGS

Figures 1 A-1C: Filtering GE data sets to identify TNBCs. (1A) Flow chart of analysis. Human breast cancer GE profiles (training = 2353, validation = 894) were normalized within individual data sets and a bimodal filter was applied to select ER, PR, and HER2 negative samples by GE, resulting in 386 samples in the training set and 201 samples in the validation set with a triple-negative phenotype. k-means clustering was performed on the training set, and a GE signature representing the TNBC subtypes from the training set was used to predict the best-fit subtype for each TNBC profile in an independent validation set. GSE-A was performed on the training and validation sets to identify enriched canonical pathways for each TNBC subtype. (IB) Histograms show the distribution and frequency of tumors using relative ER, PR, and HER2 GE levels (log2) and bimodal fit to identify TN tumor samples. Dashed line indicates the expression value at the center of the positive expression peak used to select controls for C. (1C) Heat map representation of GE for 386 TNBCs relative to 5 IHC-validated controls for each ER, PR, and HER2.

Figures 2A-2E: Identification of TNBC subtypes. (2A) Silhouette plot showing the composition (n = number of tumors) and stability (AVG width) of k- means clustering on the TNBC training set. Clusters with s(I) > 0 were considered stable. (2B) Consensus clustering displaying the robustness of sample classification using multiple iterations (x l OOO) of k-means clustering. (2C) The CDF depicting the cumulative distribution from consensus matrices at a given cluster number (k). (2D) The optimal cluster number is 7 at the point in which the relative change in area (Δ) under the CDF plot does not change with increasing k. (2E) Principal component analysis graphically depicting fundamental differences in GE between the TNBC clusters. The cluster (subtypes) are named as shown.

Figure 3: GE patterns within TNBC subtypes are reproducible. Heat maps showing the relative GE (log2, -3 to 3) of the top differentially expressed genes (P < 0.05) in each subtype in the training set (left) and the same differentially expressed genes used to predict the best-fit TNBC subtype of the validation set (right).

Overlapping gene ontology (GO) terms for top canonical pathways in both the training and validation sets as determined by GSE-A are shown to the right of the heat maps.

Figures 4A-4F: Basal-like TNBC subtypes have differential sensitivity to DNA-damaging agents. IC50 values for TNBC cell lines treated with PARP inhibitors (4A) veliparib, (4C) olaparib, or (4E) cisplatin for 72 hours. Error bars reflect SEM for 3 independent experiments. Black horizontal lines above various bars in the plots indicate cell lines that failed to achieve an IC50 at the highest dose of veliparib (30 μΜ), olaparib (100 μΜ), or cisplatin (30 μΜ). Cell lines that carry BRCA1 or BRCA2 mutations (pink) are displayed below the graph. Dot plot shows the log distribution of drug sensitivity to PARP inhibitors (B) veliparib, (4D) olaparib, or (4F) cisplatin in the basal-like subtypes (BL = BL1 + BL2), the mesenchymal-like subtypes (ML = M + MSL), and the LAR subtype. Black horizontal bars in the dot plot indicate the mean IC50 for each of the subtypes.

* Statistically significant differences in IC50 values of BL compared with ML (P = 0.017) and LAR (P = 0.032), as determined by Mann- Whitney U test.

Figures 5A-5D: Differential sensitivity of the LAR TNBC subtype to AR and Hsp90 inhibitors. IC50 values for each TNBC cell line after treatment with ( A) bicalutamide or (5C) the Hsp90 inhibitor 17-DMAG for 72 hours. Black bar above bicalutamide indicates cell lines that failed to achieve an IC50. Heat map displays relative AR expression (log2) across TNBC cell lines. Dot plot shows log distribution of drug sensitivity to (5B) bicalutamide or (5D) 17-DMAG in the basal- like (BL = BL1 + BL2), mesenchymal-like (ML = M + MSL), and LAR subtypes. Black horizontal bars in the dot plot indicate the mean IC50 for each of the subtypes. * Statistically significant differences in IC50 values of LAR versus BL (P = 0.007) or ML (P = 0.038) after bicalutamide and LAR versus BL and ML (P = 0.05) after 17- DMAG treatments, as determined by Mann- Whitney U test.

Figures 6A-6D: Mesenchymal- like TNBC subtypes are sensitive to dasatinib and NVP-BEZ235. IC50 values for each TNBC cell lined treated with (6A) dasatinib or (6C) NVP-BEZ235 for 72 hours. Cell lines that have PIK3CA mutations (red) or are deficient in PTEN (blue, circle indicates mutated) are displayed below the NVP-BEZ235 graph. Dot plots show the log distribution of drug sensitivity to (6B) dasatinib or (6D) NVP-BEZ235 in the basal-like subtypes (BL = BL1 + BL2), mesenchymal-like subtypes (ML = M + MSL), and LAR. Black horizontal bars in the dot plots indicate the mean IC50 for each of the subtypes.

* Statistically significant differences in IC50 values of BL versus ML (P = 0.020) when treated with dasatinib and ML versus BL (P = 0.001) and LAR versus BL (P = 0.01) when treated with VP-BEZ235, as determined by Mann- Whitney U test.

Figure 7: Xenograft tumors established from TNBC subtypes display differential sensitivity to cisplatin, bicalutamide, and NVP-BEZ235. Nude mice bearing established tumors (25-50 mm3) from basal-like (HCC1806 and MDA-MB- 468), LAR (SUM185PE and CAL-148), or mesenchymallike (CAL-51 and

SUM159PT) were treated with cisplatin (red), bicalutamide (purple), NVP-BEZ235 (green), or vehicle (blue) for approximately 3 weeks. Serial tumor volumes (mm3) were measured at the indicated days. Each data point represents the mean tumor volume of 16 tumors; error bars represent SEM.

Figure 8: Random distribution of TNBC subtypes found within dalasels. The percent of tumors in each cluster is displayed across 14 studies that comprise the training dataset.

Figure 9: Consensus clustering of training and validation TNBC datasets (n= 587). Heat map displays consensus clustering results depicting the robustness of sample classification. Red areas indicate samples that frequently cluster with each other over multiple iterations (1000) of k-means clustering (k::c 2 to k= 9).

Figure 10: Gene expression profiles derived from tumors that were laser- capture microdissecled represent all TNBC subtypes. Bar graph depicts the percentage of tumors that were microdissected (blue bars) present in the GSE584 (n=17) and Vanderbilt (n=14) datasets relative to non-microdissected tumors in the combined dataset (red bars).

Figures 11A-11B: TNBC subtypes identified by IHC. (11A) Silhouette plot showing the composition (n== number of tumors) and stability (AVG width) of k- means clustering on the TNBC training sel. Clusters with a silhouette width, s(i)>0 were considered stable. (1 IB) Heatmap displays hierarchical clustering of ERBB2, PGR, ESRl and AR expression in the tumors identified by IHC. Color bar identifies the cluster associated with each tumor.

Figure 12: TNBC subtypes Identified by IHC display similar GE patterns to the six TNBC subtypes. IHC TNBC clusters are shown in silhouette plot with relative GE for genes unique to the six TNBC subtypes shown below.

Figure 13: Differential GE across TNBC subtypes. Heatmaps show relative GE (log2, -2 to 2) associated with proliferation, DNA damage response,

myoepithelial genes, immune signal transduction, TGFp signaling, growth factor receptors, EMT, WNT signaling, stem-like, claudin (CL), angiogenesis, AR and of AR target genes across TNBC subtypes (as in Figure 3).

Figures 14A-14B: TNBC tumor subtypes differentially stain for the proliferation marker Ki-67. (14A) Representative micrographs from 20 tumors showing IHC staining of the proliferation marker, Ki-67, in tumors from different TNBC subtypes. (14B) Dol plot showing the mean and distribution of Ki-67 staining within TNBC subtypes as scored by study pathologist. Subtypes : BL1= basal-like 1, BL2= basal-like 2, IM=immunomodulatory, M= mesenchymal-like, MSL= mesenchymal stem-like and LAR= luminal AR.

Figure 15: Response rates differ across TNBC subtypes in taxane-treated patients. Percent of patients achieving pathologic complete response (pCR) after taxane-bsed treatment was significantly (P=0.042, chi-squared analysis) different for patients whose tumors correlated to basal-like (n=19), mesenchymal-like (n=16) and luminal AR (n=7) subtypes, from studies in which response data were available.

Figure 16: 1M subtype Is enriched in Immune cell signaling. Heatmaps showing the relative GE (log2, -2 to 2) for genes involved in immune cell surface antigens, cytokines, immune cell signal transduction, complement, chemokine, and antigen presentation across TNBC subtypes (as in Figure 3).

Figure 17: The claudin-Iow predictor gene set identifies a sub-population of MSL tumors. Unsupervised hierarchical clustering was performed on the training TNBC tumors using genes (n= 770) unique to the claud in* low subgroup [26]. Displayed to the right of the heatmap are the genes that are most differentially expressed (either high or low) in the claudin-Iow tumor set. Colorbar displays the TNBC subtype and heatmaps show relative levels (Log 2) 01 claudins (3, 4 and 7) and CD24, markers of this subgroup.

Figures 18A-18B: TNBC tumor subtypes differentially stain for AR by IHC.

(18A) IHC staining for AR from 20 tumors with representative samples from each TNBC subtype shown. (18B) Dot plot showing the quantification of nuclear AR staining based on intensity and the percent of nuclei staining positive for AA in 20 tumors; Note, in some cases one dot represents overlapping dots from multiple tumors.

Figures 19A-19B:. An androgen-inducible gene signature segregates LAR tumors. Hierarchical clustering was performed on both the (19A) training and (19) validation TNBC tumor sel using a 559 androgen-inducible gene signature (Hayes MJ et al. Clin Cancer Res. 2008;14(13):4038^1044).

Figures 20A-20B: TNBC subtypes differentially correlate with the intrinsic molecular subtypes. (20A) Healmaps show relative GE (log2, -2 to 2) of luminal and basal markers of breast cancer across all TNBC subtypes. (20B) Bar graph shows the distribution of intrinsic molecular subtypes of breast cancer (luminal A and B, normal breast-like, HER2, basal-like or unclassified) within each TNBC subtype (UNS= unstable, BLl=basal-like 1, B12=basal-like 2, 1M= immunomodulatory, M=mesenchymal-like, MSL=mesenchymal stem-like and LAR=luminal AR) as determined by best- fit Spearman correlation to the intrinsic centroids.

Figures 21A-21D TNBC subtypes differ in relapse-free survival and distant metastasis-free survival. Kaplan-Meier plot showing (21 A) to-year RFS or (21C) DMFS in TNBC subtypes. RFS (21B) and DMFS (21D) hazard ratios (bold) and 95% CI (parentheses) for patients from TNBC subtypes. Shaded boxes indicate significant (P < 0.05) comparisons.

Figures 22A-22B: TNBC subtypes differ in age, but are similar size upon diagnosis. Box plots show the median (horizontal line), range (rectangle) and SD (error bars) of (22A) age at diagnosis and (22B) tumor size (mm) between TNBC subtypes (as in Figure 3); · P = 9.0e-6

Figures 23A-23B: Chromosomal Aberrations in TNBC cell lines. (23 A) Box plots depicting the average number of chromosomes (mode) in breast cancer cell lines correlating to basal-like (n=8) vs. mesenchymal-like (n=6) subtypes. (23B) Box plots depicting the average number of chromosomal rearrangements

(translocations, inversions, and deletions) in basal-like vs. mesenchymal-like subtypes. Chromosome mode and rearrangements were obtained from Departments of Pathology and Oncology, Univ of Cambridge

(www.path.cam.ac.uk/~pawefisri/cell%201ine%20catalogues/breast-cell-lines.htm). *P<0.01 by unpaired t-test.

Figure 24: TNBC cell lines cluster in three major groups: luminal AR, basal- like and mesenchymal-like. Unsupervised hierarchical clustering of TNBC cell lines performed on genes unique to TNBC subtypes (n= 2188). Colorbar shows the best correlation of cell lines to the TNBC subtypes.

Figures 25A-25C: LAR cell lines depend on AR expression for colony formation. (25A) Top panel depicts relative AR mRNA levels obtained from GE mlcroarrays performed on TNBC cell lines (log 2, centered on 0). Color bar identifies TNBC subtype classification for each cell line (immunomodulatory shown in orange and unclassified shown in red). Immunoblots showing relative expression AR protein in TNBC cell lines, Hsp90 serves as a loading control. (25B) AR expression 72h following transfection with siRNA pools of non-targeting (NT) or targeting AR in MFM-223, MDA-MB-453 and SUM185PE cells. GAPDH expression serves as a loading control. (25C) Colony formation of MFM-223, MDA-MB-453 and SUM185PE cells 14d following siRNA transfection of AR or non-targeting control (NT). Figures 26A-26B: Identification of PIK3CA mutations in AR-positive TNBC cell lines and tumors. (26A) Detection of PIK3CA 1047 mutation using digital droplet PCR. DNA from cell lines with varying PIK3CA status (HMEC = H1047R wt/wt, MDAMB453 = H1047R wt/mut, SUM185 = HI 047R mut/mut and

SUM 159= H1047L wt/mut) were simultaneously amplified with fluorescent- conjugated primers to wild-type (VIC) or mutant (FAM) and the percentage of wt vs. mut DNA was measured by digital droplet PCR. DNA that did not undergo PCR amplification is indicated by no reaction (NR). (26B) Heatmap displays the TNBC molecular subtype based of TCGA samples with corresponding levels of AR RNA (RNA-seq) and protein (RPPA) for AR and phosphorylated AKT (T308 and S473). Color bar indicates PIK3CA mutations (red) within TNBC from the TCGA.

Figure 27: AR-expressing TNBC cells display active PI3K pathway.

Heatmap displays relative protein levels (RPPA) of AR, AKT (S473 and T308), GSK3P (S9 and S21) and PTEN across a large panel of TNBC cell lines.

Hierarchical clustering was performed on cell lines and revel clusters of cell lines with active PI3K through PTEN loss or PIK3CA mutations.

Figures 28A-28C: AR-positive TNBC cell lines display active PI3K pathway and are sensitive to PI3K inhibitors. (28A) Immunoblot displays relative levels of AR, p-AKT, p-S6 in AR-dependent prostate cancer (LNCaP), human mammary epithelial cells (HMEC) and LAR TNBC, with GAPDH serving as a loading control. (28B) IHC-stained sections display cellular levels of AR, and p-AKT in cell lines from the same tissue microarray (TMA). (28C) Bar graphs display the 50% inhibitory concentration (IC50) for TNBC cell lines treated with single-agent pan PI3K inhibitors (BKM-120 and GDC-0941) or PI3K/mTOR inhibitors (NVP- BEZ235 or GDC-0980) for 72 h. Black bars above graphs indicate cell lines in which the IC50 was not reached at maximal dose. Bar below indicates normal HMEC cells and cell lines carrying PIK3CA mutations.

Figures 29A-29B: Genetic targeting of AR increases the efficacy of the PI3K inhibitor GDC-0941 and PI3K/mTOR inhibitor GDC-0980. (29A) Immunoblot displays decreased AR expression at 72h following infection with two shRNAs targeting AR (shARl and shAR2) compared to infection with nontargeting shRNA (shNT) in three AR-positive TNBC cell lines. Actin levels below serve as loading controls. (29B) Line graphs display relative viability of LAR cell lines infected with nontargeting (shNT) or shRNAs targeting AR (shARl and shAR2) following a 72h treatment with the pan-PI3K inhibitor GDC-0941 (top) or the dual PI3K/mTOR inhibitor GDC-0980.

Figures 30A-30B: Pharmacological targeting of AR with bicalutamide increases the efficacy of GDC0941 and GDC0980 in AR-expressing TNBC. (30A) Line graphs show relative viability of AR-expressing cell lines treated with increasing doses of GDC-0941 (top) or GDC-0980 (bottom) alone or in combination with 25 μΜ bicalutamide (CDX). Dashed black line depicts the theoretical line of additivity of both drugs determined from the effect of bicalutamide alone and either GDC-0941 or GDC-0980 alone. (30B) Immunoblots from three AR-expressing TNBC cell lines treated with either bicalutamide (CDX), GDC-0941, GDC0980 alone or bicalutamide in combination with either GDC-0941 or GDC-0980 for 24h or 48h were probed for AR, PARP, p-AKT, AKT, p-S6, S6 and GAPDH.

Figures 31A-31B: Combined inhibition of AR and PI3K increases apoptotic cell death in AR+ TNBC cell lines. (31 A) Bar graphs display relative caspase 3/7 activity (RLU) normalized to viable cell number 48 hrs after treatment with vehicle, positive control (3μΜ adriamycin, ADR), 50μΜ bicalutamide (CDX) and 3μΜ GDC-09 1 or 1 μΜ GDC-0980 alone or in combination with CDX. (3 IB)

Histograms show representative cell cycle analysis of MDA-MB-453 cells treated with similar conditions as above. Indicated percentages represent the fraction of hypodiploid DNA (sub-Gl), indicative of late stage apoptotic DNA fragmentation.

Figures 32A-32C: Simultaneous targeting of AR and PI3K decreases viability of AR-expressing cell lines grown in a 3-D forced suspension assay and inhibits in vivo xenograft growth. (31A) Representative images display 3-D cell aggregates of MDA-MB-453 cells treated with increasing doses of GDC-0941 or GDC-0980 in the absence or presence of 25μΜ bicalutamide (CDX). (32B) Line graphs display relative viability of 3-D cell aggregates treated with GDC-0941 or GDC-0980 alone or in combination with 25μΜ bicalutamide (CDX). Dashed black line depicts the theoretical line of additivity determined from the effect of bicalutamide alone and either GDC-0941 or GDC-0980 alone. (32C) Nude mice bearing established tumors from AR-positive TNBC cell lines (CAL-148 and MDA- MB-453) were treated with either bicalutamide (CDX, black hashed line), NVP- BEZ235, GDC-0980 or with the combination of bicalutamide and either NVP- BEZ235 (blue hashed line) or GDC-0980 (red hashed line). Serial tumor volumes (mmJ) were measured at the indicated days. Each data point represents mean tumor volume of 16 tumors; error bars represent SEM.

Figures 33A-33B: Detection of PIK3CA mutations by Sanger and digital droplet PCR. (33 A) Electropherograms display representative results from sanger sequencing of the E20 amplicon in a PIK3CA wild-type (top) or a H1047R PIK3CA mutant sample. (33B) Scatterplot shows the percent of cells with mutant PIK3CA detected by digital droplet compared to Sanger sequencing.

Figure 34: Genetic targeting of AR increases the efficacy of the PI3K inhibitor BKM-120 and PI3K/mTOR inhibitor NVP-BEZ235. Line graphs display relative viability of LAR cell lines infected with nontargeting (shNT) or shRNAs targeting AR (shARl and shAR2) following a 72h treatment with the pan-PI3K inhibitor BKM- 120 (top) or the dual PI3K/mTOR inhibitor NVP-BEZ235.

Figure 35: Pharmacological targeting of AR with bicalutamide increases the efficacy of BKM120 and NVP-BEZ235 in AR-expressing TNBC. Line graphs show relative viability of AR-expressing cell lines treated with increasing doses of BKM 120 (top) or NVP-BEZ235 (bottom) alone or in combination with 25 μΜ bicalutamide (CDX). Dashed black line depicts the theoretical line of additivity determined from the effect of bicalutamide alone and either BKM120 or NVP- BEZ235 alone.

Figures 36A-36B: Simultaneous targeting of AR and PI3K decreases viability of AR-expressing cell lines grown in a 3-D forced suspension assay. (36A) Line graphs display relative viability of 3-D cell aggregates treated with BKM120 or NVP-BEZ235 alone or in combination with 25uM bicalutamide (CDX). Dashed black line depicts the theoretical line of additivity determined from the effect of bicalutamide alone and either BKM120 or NVP-BEZ235 alone. (36B) Immunoblots from three AR-expressing TNBC cell lines treated with either bicalutamide (CDX), BKM-120, BEZ235 alone or in combination with bicalutamide for 24h or 48h. Western blots were probed for AR, PARP, p-AKT, AKT, p-S6, S6 and GAPDH.

Figure 37: Combined Targeting of PI3K and AR increase apoptosis detected by annexin V and propidium iodide (PI) staining. Scattergrams show the percentage of viable (lower left quadrant, annexin V and PI negative), early apoptotic (lower right quadrant, annexin V-positive and Pi-negative), or late apoptotic/necrotic (upper right quadrant, annevin V and PI positive) cells following 48 hrs of treatment with either GDC-0941 or GDC-0980 alone or in combination with 50μΜ bicalutamide (CDX).

Figures 38A-38C: Genetic and pharmacological targeting of AR increases p-

AKT.

Figure 39: PDGFR inhibition selectively inhibits viability of mesenchymal- like TNBC cell lines. Bar graph displays the half-maximal inhibitory concentration (nM) of the PDGFR inhibitor sorafenib for a panel of TNBC cell lines. Colorbar below designates the TNBC subtype (basal-like= black, mesenchymal- like= grey) or normal cells (white).

Figure 40: IGF1R inhibition selectively inhibits viability of mesenchymal- like TNBC cell lines. Bar graph displays the half-maximal inhibitory concentration (nM) of the IGF1R inhibitor OSI-906 for a panel of TNBC cell lines. Colorbar below designates the TNBC subtype (basal-like= black, mesenchymal-like=grey) or normal cells (white).

Figure 41 : IGF1R inhibition selectively inhibits viability of mesenchymal- like TNBC cell lines. Bar graph displays the half-maximal inhibitory concentration (nM) of the IGF 1R inhibitor BMS-754807 for a panel of TNBC cell lines. Colorbar below designates the TNBC subtype (basal-like= black, mesenchymal-like= grey) or normal cells (white).

Figure 42: MET inhibition selectively inhibits viability of mesenchymal-like TNBC cell lines. Bar graph displays the half-maximal inhibitory concentration (nM) of the MET inhibitor PF2341066 for a panel of TNBC cell lines. Colorbar below designates the TNBC subtype (basal-like= black, mesenchymal-like= grey) or normal cells (white). DETAILED DESCRIPTION OF THE INVENTION

Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, gene expression (GE) profiles from 21 breast cancer data sets were analyzed and 587 TNBC cases were identified. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem-like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed identification of TNBC cell line models representative of these subtypes. Predicted "driver" signaling pathways were pharmacologically targeted in these cell line models as proof of concept that analysis of distinct GE signatures can inform therapy selection. BL1 and BL2 subtypes had higher expression of cell cycle and DNA damage response genes, and representative cell lines preferentially responded to cisplatin. M and MSL subtypes were enriched in GE for epithelial-mesenchymal transition, and growth factor pathways and cell models responded to NVP-BEZ235 (a PI3K/mTOR inhibitor) and dasatinib (an abl/src inhibitor). The LAR subtype includes patients with decreased relapse-free survival and was characterized by androgen receptor (AR) signaling. LAR cell lines were uniquely sensitive to bicalutamide (an AR antagonist). These data provide biomarker selection, drug discovery, and clinical trial design that align TNBC patients to appropriate targeted therapies.

An extensive number of TNBC GE profiles was compiled with the intent of molecularly subtyping the disease. Six (6) TNBC subtypes were identified. Further, using GE signatures derived from each TNBC subtype, representative TNBC cell lines that serve as models for the different subtypes of the disease were aligned. Using the panel of cell lines, prominent signaling pathways revealed by GE signatures were pharmacologically targeted and it was found that the cell lines representing the various subtypes had different sensitivities to targeted therapies currently under laboratory and clinical investigation. The identification of diverse TNBC subtypes and the molecular drivers in corresponding cell line models provides great insight to the heterogeneity of this disease and provides preclinical platforms for the development of effective treatment (see Lehmann, B.D., et al, J Clin Invest, 121 (7):2750-2767 (July 2011), the entire teachings of which are incorporated herein by reference).

Also described herein is further investigation of a panel of AR-positive TNBC tumors which showed that PIK3CA kinase mutations are a frequent event in the LAR subtype (65.3% vs. 38.4%) and were not just selected for in vitro during establishment of the tumor-derived cell lines. Also shown herein is that PI3K pathway activation plays a role in resistance to AR antagonists, as there are elevated levels of activated AKT in residual tumors after bicalutamide treatment of xenograft tumors. Further, it was found that genetic or pharmacological targeting of AR synergizes with PDK/mTOR inhibition in both two- and three-dimensional cell culture models. Based on the cell culture-based results, the combination of these inhibitors in in vivo xenograft studies was examined using bicalutamide +/-

GDC0980 or NVP-BEZ235. The preclinical data herein provide the rationale for pre-selecting AR-positive TNBC patients for treatment with the combination of AR antagonists and/or PDK/mTOR inhibitors.

As discussed herein, triple negative breast cancer (TNBC) refers to breast cancers that are estrogen receptor (ER) negative, progesterone receptor (PR) negative and human epidermal growth factor receptor 2 (HER-2) negative. The invention is based, in part, of the discovery of six TNBC subtypes.

Accordingly, in one aspect provided herein are methods of determining a triple negative breast cancer (TNBC) subtype in an individual in need thereof comprising determining expression of one or more genes (e.g., presence of one or more mRNAs and/or protein encoded by gene) in one or more TNBC cells of the individual; and comparing the expression of the one or more genes in the TNBC cells with the expression of the one or more genes in a control. Increased expression of one or more genes comprising one or more cell cycle genes, cell division genes, proliferation genes, DNA damage response genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC BL1 subtype. Increased expression of one or more growth factor signaling genes, glycolysis genes, gluconeogenesis genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC BL2 subtype. Increased expression of one or more immune cell signaling genes, cytokine signaling genes, antigen processing and presentation genes, core immune signal transduction genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC IM subtype. Increased expression of one or more cell motility genes, extracellular matrix (ECM) receptor interaction genes, cell differentiation genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC M subtype. Increased expression of one or more cell motility genes, cellular differentiation genes, growth pathway genes, growth factor signaling genes, angiogenesis genes, immune signaling genes, stem cell genes, HOX genes, mesenchymal stem cell-specific marker genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC MSL subtype. Increased expression of one or more hormone regulated genes in the TNBC cells compared to the control indicates that the TNBC is a TNBC LAR type.

In a particular aspect, the invention is directed to a method of determining a TNBC BL1 subtype in an individual in need thereof comprising determining increased expression of one or more cell cycle genes, cell division genes, proliferation genes, DNA damage response genes or a combination thereof in the TNBC cells of the individual compared to the control. Increased expression of the one or more genes indicates that the TNBC is a BL1 subtype TNBC. In one aspect, the method of determining a TNBC BL1 subtype in an individual in need thereof comprises selectively detecting increased expression of a gene combination comprising A URKA, A URKB, CENPA, CENPF, BUB1, BUB IB, TTK, CCNA2, PLK1, PRC1, MYC, NBAS, PLK1, BIRC5, CHEK1, FANCA, FANCG, FOXM1, HNGA1, RAD54BP, RAD51, NEKS, NBN, EXOl, MSH2, MCM10, RAD21,SIX3, Z1C1, SOX4, SOX1 and MDC1 wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC BL1 subtype. Determination that the TNBC is a TNBC BL1 subtype can further comprise detecting increased expression of Ki-67 mRNA in the TNBC cells compared to the control. In addition, determination that the TNBC is a TNBC BL1 subtype can further comprise detecting one or more mutated genes. Examples of such mutated genes include mutated BRCA1, STAT4, UTX, BRCA2, TP53, CTNND1, TOP2B, CAMK1G, MAPK13, MDCl, PTEN, RBI, SMAD4, CDKN2A, ATM, ATR, CLSPN, HDAC4, NOTCH1, SMARCAL1 , and TIMELESS.

In another aspect, the invention is directed to a method of determining a TNBC BL2 subtype in an individual in need thereof comprising determining increased expression of one or more growth factor signaling genes, glycolysis genes, gluconeogenesis genes or a combination thereof in the TNBC cells compared to the control. Increased expression of the one or more genes indicates that the TNBC is a TNBC BL2 subtype. In one aspect, the method of determining a TNBC BL subtype in an individual in need thereof comprises selectively detecting increased expression of a gene combination comprising EGFR, MET, ELF4, MAF, NUAK1, JAG1, FOSL2, 1D1, ZIC1, SOX11, 1D3, FHL2, EPHA2, TP63 and MME wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC BL2 subtype. Determination that the TNBC is a TNBC BL2 subtype can further comprise detecting one or more mutated genes. Examples of such genes comprise mutated BRCA1, RBI, TP53, PTEN, CDKN2A, UTX, BRAC2, PTCH1, PTCH2, and RET.

In another aspect, the invention is directed to a method of determining a

TNBC M subtype in an individual in need thereof comprising determining increased expression of one or more cell motility genes, extracellular matrix (ECM) receptor interaction genes, cell differentiation genes or a combination thereof in the TNBC cells compared to the control. Increased expression of the one or more genes indicates that the TNBC is a TNBC M subtype. In one aspect, the method of determining a TNBC M subtype in an individual in need thereof comprises selectively detecting increased expression of a gene combination comprising TGFB1L1 , BGN, SMAD6, SMAD7, NOTCH1, TGFB1, TGFB2, TGFB3,

TGFBR1, TGFBR2, TGFBR3, MMP2, ACTA2, SNAI2, SPARC, SMAD7, PDGFRA, TAGLN, TCF4, TWIST1, ZEB1, COL3A1, JAG1, EN1, MYLK, STK38L, CDH11, ETV5, IGF1R, FGFR1, FGFR2, FGFR3, TBX3, COL5A2, GNG11, ZEB2, CTNNB 1, DKK2, DKK3, SFPR4, TCF4, TCF7L2, FZD4, CAV1, CA V2, CCND 1 and CCND2 wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC M subtype. Determination that the TNBC is a TNBC M subtype can further comprise detecting decreased E-cadherin (CDH1) expression compared to the control. Determination that the TNBC is a TNBC M subtype can further comprise detecting one or more mutated genes.

Examples of such mutated genes include mutated PTEN, RBI, TP53, PIK3CA, APC, BRAF, CTNNBl, FGFR1, GLI1, HRAS, KRAS, NOTCH1, and NOTCH4. Determination that the TNBC is a TNBC M subtype can further comprise detecting chromosomal amplifications in KRAS, IGF1R or MET.

In another aspect, the invention is directed to a method of determining a TNBC MSL subtype in an individual in need thereof comprising determining increased expression of one or more cell motility genes, cellular differentiation genes, growth pathway genes, growth factor signaling genes, angiogenesis genes, immune signaling genes, stem cell genes, HOX genes, mesenchymal stem cell- specific marker genes or a combination thereof in the TNBC cells compared to the control. Increased expression of the one or more genes indicates that the TNBC is a TNBC MSL subtype. In one aspect, the method of determining a TNBC MSL subtype in an individual in need thereof comprises selectively detecting increased expression of a gene combination comprising VEGFR2 (KDR), TEK, TIE1, EPAS1, ABCA8, PROCR, ENG, ALDHA1, PERI, ABCB1, TERF2IP, BCL2, BMP2, EPAS1, STAT4, PPARG, JAK1, ID1, SMAD3, TWIST1, THY1, HOXA5, HOXA10, GL13, HHEX, ZFHX4, HMBOX1 , FOS, PIK3R1, MAF, MAFB, RPS6KA2, TCF4, TGFBILI, MEISl, MEIS2, MEOXl, MEOX2, MSXl, ITGAV, KDR, NGFR, NT5E, PDGFRA, PDGFRB, POU2F 1 , and VCAM 1 wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC MSL subtype. Determination that the TNBC is a TNBC MSL subtype can further comprise detecting low expression of claudins 3, 4, 7 or a combination thereof compared to the control. Determination that the TNBC is a MSL subtype TNBC can further comprise detecting one or more mutated genes. Examples of such mutated genes include mutated CDKN2A, HRAS, TP53, NF1 , PIK3CA, BRCA1, BRAF, KRAS, NF2, PDGFRA, APC, CTNNB1, FGFR1, PDGFRB.

In another aspect, the invention is directed to a method of determining a TNBC IM subtype in an individual in need thereof comprising determining increased expression of one or more immune cell signaling genes, cytokine signaling genes, antigen processing and presentation genes, core immune signal transduction genes or a combination thereof in the TNBC cells compared to the control. Increased expression of the one or more genes indicates that the TNBC is a TNBC IM subtype. In one aspect, the method of determining a TNBC IM subtype in an individual in need thereof comprises selectively detecting increased expression of a gene combination comprising CCL19, CCL3, CCL4, CCL5, CCL8, CCRl, CCR2, CCR5, CCR7, CD2, CD37, CD38, CD3D, CD48, CD52, CD69, CD74, and CD 8 A wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC IM subtype. Determination that the TNBC is a TNBC IM subtype can further comprise detecting one or more mutated genes. Examples of such mutated genes include TP53, CTNNA1, DDX18, HUWE1, NFKBIA, APC, BRAF,

MAP2K4, RB I, STAT4, STAT1, and RET.

In another aspect, the invention is directed to a method of determining a TNBC LAR subtype in an individual in need thereof comprising determining increased expression of one or more hormone regulated genes in the TNBC cells compared to the control. Increased expression of the one or more genes indicates that the TNBC is a TNBC LAR type. In one aspect, the method of determining a TNBC LAR subtype in an individual in need thereof comprises selectively detecting increased expression of a gene combination comprising AR, DHCR24, ALCAM, GATA2, GATA3, IDIHl , IDIH2, CDHl 1 , ERBB3, CUX2, FGFR4, HOPX, FASN, FKBP5, APOD, PIP, SPDEF, CLDN8, FOXA1, KRT18,and XBP1 wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC LAR subtype. Determination that the TNBC is a LAR subtype TNBC can further comprise detecting one or more mutated genes. Examples of such mutated genes include PIK3CA, CDHl, PTEN, RB I, TP53, and MAP3K1.

1 A variety of methods (e.g. spectroscopy, colorometry, electrophoresis, chromatography) can be used to detect increased expression of a (one or more) gene. As is apparent to those of skill in the art, increased expression of one or more genes can be detected by measuring all or a portion of nucleic acid (e.g., DNA, mRNA) of the gene and/or protein (polypeptide) expressed by the gene. The level, expression and/or activity of a gene and or its encoded polypeptide can be measured. For example, nucleic acid such as genes, mRNA or a combination thereof, can be determined using PCR (e.g., RT-PCR, RT-qPCR), gene chips or microarray analysis (DNA arrays, gene expression arrays, nanostrings), next generation sequencing (e.g., next generation RNA sequencing), in situ hybridization, blotting techniques, and the like. Protein can be detected using immunoassays (e.g.,immunohistochemistry, immunofluorescence, immunoprecipitation), arrays (e.g., reverse phase protein microarrays), blotting techniques (e.g., Western blots), SDS-PAGE, and the like

As described herein, the methods can further comprise detecting one or more mutated genes. A variety of methods can be used to detect one or more mutated genes. Examples of such methods include a SNaPshot assay, exome sequencing, Sanger gene sequencing, resequencing array analysis, mRNA analysis/cDNA sequencing polymerase chain reaction (PCR), single-strand conformation polymorphism (sscp), heteroduplex analysis (het), allele-specific oligonucleotide (aso), restriction fragment analysis, allele-specific amplification (asa), single nucleotide primer extension, oligonucleotide ligation assay (ola), denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE) and single strand conformation polymorphism (SSCP).

The methods can further comprise comparing the expression of the one or more genes in the individual to the expression of the one or more genes in a control. As will be apparent to those of skill in the art, a variety of suitable controls can be used. Examples of such controls include one or more non-cancer cells (e.g., breast cells from one or more individuals that do not have cancer) and/or non-TNBC cells (e.g., from the individual being tested). The control can also be a (one or more) standard or reference control established by assaying one or more (e.g., a large sample of) individuals which do not have cancer (e.g., TNBC) and using a statistical model to obtain a control value (standard value; known standard). See, for example, models described in Knapp, R.G. and Miller M.C. (1992) Clinical Epidemiology and Bio statistics, William and Wilkins, Harual Publishing Co. Malvern, PA, which is incorporated herein by reference.

As used herein an "individual" refers to an animal, and in a particular aspect, a mammal. Examples of mammals include primates, a canine, a feline, a rodent, and the like. Specific examples include humans, dogs, cats, horses, cows, sheep, goats, rabbits, guinea pigs, rats and mice.

The term "individual in need thereof refers to an individual who is in need of treatment or prophylaxis as determined by a researcher, veterinarian, medical doctor or other clinician. In one aspect, an individual in need thereof is a mammal, such as a human. In other aspects, the individual has been diagnosed with breast cancer, the individual has not been diagnosed with breast cancer, or the individual is at risk of developing breast cancer.

The methods of the invention can further comprise detecting expression

(increased expression) of one or more genes in a sample (biological sample) of the individual. Thus, the method can further comprise obtaining a (one or more) sample from the individual. The sample can be, for example, a biological fluid, a biological tissue (e.g., a biopsy) and combinations thereof from the individual. In particular aspects, the sample is one or more TNBC cells (e.g., epithelial cells, mesenchymal cells, immune cells). Methods for obtaining such biological samples from an individual are known to those of skill in the art.

As also shown herein, the methods can further comprise determining a treatment protocol for the triple negative breast cancer (TNBC) patient based on the TNBC subtype. As described herein, a "cancer therapy", "treatment for cancer" or "cancer treatment" comprises administering one or more agents to the individual. In one embodiment, the agent is a chemotherapeutic agent which targets the TNBC cells. Examples of such agents include an alkylating agent (e.g., busulfan, cisplatin, carboplatin, chlorambucil, cyclophosphamide, ifosfamide, dacarbazine (DTIC), mechlorethamine (nitrogen mustard), melphalan, temozolomide); a nitrosoureas (e.g., carmustine (BCNU), lomustine (CCNU)); an antimetabolite (e.g., 5- fluorouracil, capecitabine, 6-mercaptopurine, methotrexate, gemcitabine, cytarabine (ara-C), fludarabine, pemetrexed); an anthracycline or related drug (e.g.,

daunorubicin, doxorubicin (adriamycin), epirubicin, idarubicin, mitoxatrone); a topoisomerase (topoisomerase I inhibitor; topoisomerase inhibitor II) inhibitor (e.g., topotecan, irinotecan, etoposide (VP- 16), te iposide); and a mitotic inhibitor (e.g., taxanes (paclitaxel, docetaxel, taxol) vinca alkaloids (vinblastine, vincristine, vinorelbine).

In another embodiment, the agent is a targeted therapy agent which attacks cancer cells more specifically than chemotherapeutic agents. Examples of such agents include imatinib (Gleevec), gefitinib (Iressa), erlotinib (Tarceva), rituximab (Rituxan), and bevacizumab (Avastin). In yet another embodiment, the agent is a sex hormone, or hormone-like drug that alters the action or production of female or male hormones and can be used to slow the growth of breast, prostate, and endometrial (uterine) cancers. Examples of such agents include anti-estrogens (e.g., tamoxifen, fulvestrant), aromatase inhibitors (e.g., anastrozole, exemestane, letrozole), progestins (e.g., megestrol acetate), anti-androgens (e.g., bicalutamide, flutamide) and LHRH agonists (leuprolide, goserelin). In addition, the agent can be a drug which is used to stimulate the immune system to more effectively recognize and attack cancer cells of an individual with cancer. Other examples of agents include a hormone such as a selective estrogen receptor modulator (SERM); an antibody or antigen binding fragment thereof (e.g., herceptin); a protein tyrosine kinase inhibitor; a combination of chemotherapeutic agents such as Cytoxan (C), methotrexate (M), fluorouracil (F), an anthracylcin such as adriamycin (A), epirubicin (E), doxorubicin, and/or daunorubicin; a targeted kinase inhibitor; a metalloprotemase inhibitor; and a proteosome inhibitor.

The one or more agents can be administered to the individual before (e.g., neoadjuvant therapy), during or after (e.g., adjuvant therapy) primary treatment of the cancer. As used herein "primary treatment of a cancer" generally refers to a treatment involving surgery (e.g., surgical removal of a tumor) and/or radiation (e.g., local radiation). In the methods of the present invention, the "treatment for cancer" can be a neoadjuvant treatment wherein an agent (e.g., a hormone, a

1 chemotherapeutic) is administered prior to surgical removal of a (residual) tumor, or prior to localized radiation (e.g., radiation used to shrink the tumor in order to simplify subsequent surgical removal of the tumor). In another embodiment, the "treatment for cancer" can be an adjuvant treatment wherein an agent (e.g., a hormone, a chemotherapeutic agent) is administered after surgical removal of a (residual) tumor, or after localized radiation. In yet another embodiment, the treatment can be a palliative treatment in which one or more chemotherapeutic agents are administered to an individual to treat metastatic cancer (e.g., to make the individual more comfortable and/or to prolong survival of the individual after the cancer has metastasized).

In particular aspects, the TNBC BL1 or BL2 subtype is detected in the individual and the individual is treated with one or more drugs that damages DNA. Examples of such drugs include an alkylating-like agent (e.g., cisplatin) and a PARP inhibitor (e.g., veliparib, olaparib).

In another aspect, the TNBC ML subtype is detected in the individual and the individual is treated with one or more drugs that inhibits Src (e.g., disatinib), IGF1R (e.g., OSI-906, BMS-754807), MET (e.g., PF2341066), PDGFR (e.g., sorafinib), P13K (e.g., BKM-120, GDC0941), P13K/mTOR (e.g., NVP-BEZ235, GDC0980) or a combination thereof.

In another aspect, the TNBC LAR subtype is detected in the individual and the individual is treated with one or more drugs that inhibit androgen receptor (AR). Examples of such drugs include bicalutamide, MVD3100, and abiraterone. The method can further comprise treating the individual with P13K/mTOR (e.g., NVP- BEZ235, GDC0980) or a combination thereof. The method can further comprise treating the individual with an inhibitor of HSP90 (e.g., DMAG).

In other aspects, the invention can further comprise predicting whether an individual (e.g., patient) will benefit from a (one or more) treatment for a particular TNBC subtype.

Also described herein are TNBC cell lines that serve as models for each of the six TNBC subtypes. Thus, in another aspect, the invention is directed to a method of determining whether an agent can be used to treat a triple negative breast i cancer (TNBC) subtype comprising contacting a cell line that is a model for the TNBC subtype of interest with an agent to be assessed; and determining viability of the cell line in the presence of the agent, wherein if the viability of the cell line decreases in the presence of the agent, then the agent can be used to treat the TNBC subtype of interest. The TNBC subtype of interest can be a TNBC BL-1 subtype, a TNBC BL-2 subtype, a TNBC IM subtype, a TNBC M subtype, a TNBC MSL subtype or a TNBC LAR subtype. In addition, as will be apparent to those of skill in the art, the viability of a cell decreases when e.g., the cell undergoes apoptosis, proliferation of the cell is inhibited or slowed and/or metastasis of the cell is inhibited to slowed.

Examples of agents (test agent; agent of interest) include nucleic acids, polypeptides, fusion proteins, peptidomimetics, prodrugs, drugs, receptors, binding agents {e.g., antibodies), small molecules, etc and libraries (e.g., combinatorial libraries) of such agents. Test agents can be obtained made or obtained from libraries of natural, synthetic, and/or semi-synthetic products (e.g., extracts). Those skilled in the field of drug discovery and development will understand the precise source of such agents.

In one aspect, the invention is directed to a method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) BL-1 subtype comprising contacting one or more TNBC BL-1 subtype cell lines with an agent to be assessed. The viability of the one or more cell lines in the presence of the agent is detected, wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC BL-1 subtype. Examples of TNBC BL-1 subtype cell lines include HCC2157, HCC1599,

HCC1937, HCC1143, HCC3153, MDA-MB-468, and HCC38.

In another aspect, the invention is directed to a method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) BL-2 subtype comprising contacting one or more TNBC BL-2 subtype cell lines with an agent to be assessed. The viability of the one or more cell lines in the presence of the agent is determined, wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC BL-2 subtype. Examples of TNBC BL-2 subtype cell lines include SUM149PT, CAL-851, HCC70, HCC1806 and HDQ-P1.

In another aspect, the invention is directed to a method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) EVl subtype comprising contacting one or more TNBC IM subtype cell lines with an agent to be assessed. The viability of the one or more cell lines in the presence of the agent is determined, wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC IM subtype. Examples of TNBC IM subtype cell lines include HCC1187 and DU4475.

In another aspect, the invention is directed to a method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) M subtype comprising contacting one or more TNBC M subtype cell lines with an agent to be assessed. The viability of the one or more cell lines in the presence of the agent is determeined, wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC M subtype. Examples of TNBC M subtype cell lines include BT-549, CAL-51 and CAL-120.

In another aspect, the invention is directed to a method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) MSL subtype comprising contacting one or more TNBC MSL subtype cell lines with an agent to be assessed. The viability of the one or more cell lines in the presence of the agent is determined, wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC MSL subtype. Examples of TNBC MSL subtype cell lines include HS578T, MDA-MB- 157, SUM159PT, MDA-MB-436, and MDA-MB-231.

In another aspect, the invention is directed to a method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) LAR subtype comprising contacting one or more TNBC LAR subtype cell lines with an agent to be assessed. The viability of the one or more cell lines in the presence of the agent is determined, wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC LAR subtype. Examples of TNBC LAR subtype cell lines inclulde MDA-MB,

SUM185PE, HCC2185, CAL-148, and MFM-223.

In the methods of the invention, viability of one of more of the cells can be determined using a variety of methods. Examples of such methods include a membrane leakage assay, a mitochondrial activity assay, a functional assay, a proteomic assay, a genomic assay or a combination thereof.

The methods can further comprise comparing the viability of the one or more cell lines in the presence of the agent to the viability of a control such as a noncancerous cell line (e.g., a culture of normal human and immortalized MCF210A).

Exemplification

Example 1 Identification of human triple negative breast cancer subtypes and models for selection of targeted therapies

Methods

Laser capture microdissection, RNA extraction, and GE profiling. Invasive tumor cells from serial sections of primary breast cancers (n = 112) were captured onto polymeric caps using the PixCell II lasercapture microdissection system (Arcturus) as previously described (Bauer JA, et al. Clin Cancer Res. 2010;

16(2):681-690). Areas of ductal carcinoma in situ and normal breast tissue were excluded and areas of inflammation and areas with tumor-associated fibroblasts were avoided. Total RNA was isolated from captured cells, quantified, and integrity analyzed, and microarray analyses were done using Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays as previously described (Bauer JA, et al. Clin Cancer Res. 2010; 16(2):681-690).

Data set collection and TNBC identification by bimodal filtering. 2353 human breast cancer GE profiles were compiled from 14 publicly available breast cancer microarray data sets (GEO, www.ncbi.nlm.nih.gov/gds; Array Express, www.ebi.ac.uk/microarray-as/ae/) including 1 12 primary breast cancer GE profiles from our institution (Vanderbilt University Medical Center). All Vanderbilt tissue samples were taken from individuals treated at Vanderbilt University with institutional review board approval, and all patients signed a protocol-specific consent. All GE profiles were generated on Affymetrix microarrays and collected for identification of T BCs for the training data set (Table 1). An additional 894 breast cancer GE profiles from 7 data sets were collected for the identification of TNBCs for the validation data set (Table 2). Raw GE values for each data set (n = 21) were normalized independently using RMA procedure. The Affymetrix probes 205225_at, 208305_at and 216836_s_at were chosen to represent ER, PR, and HER2 expression, respectively (Karn T, et al. Breast Cancer Res Treat.

2010;120(3):567-579). For each data set, empirical expression distributions of ER, PR, and HER2 were analyzed using a 2-component Gaussian mixture distribution model and parameters were estimated by maximum likelihood optimization, using op'tim function (R statistical software, www.r-project.org). The posterior probability of negative expression status for ER, PR, and HER2 was then estimated. A sample was classified as having negative expression if its posterior probability was less than 0.5. Upon initial hierarchical clustering, it was evident that some samples were from the same tumors, but in different data sets. These and additional outliers were removed after PCA. To ensure all ER/PR/HER2 -positive tumors were removed, a secondary filter was performed on the combined samples identified by bimodal filtering. TNBC tumors were renormalized along with 5 positive controls for each parameter (ER, PR, and HER2) from 4 data sets that were positive by IHC and expressed mRNA near the center of the positive bimodal peak (Figure IB). Only tumors that displayed a greater than 10-fold reduction in expression than the positive controls were considered negative in expression and used for further analysis, resulting in 386 TNBC tumors (training set) and 201 TNBC tumors (validation set).

Normalization, data reduction, and cross-platform batch effect removal. Training (n = 386) and validation (n = 201) TNBC data sets identified by the 2- component Gaussian distribution model were collectively RMA normalized, summarized and log-transformed using the combined raw GE from each data set. For genes containing multiple probes, the probe with the largest interquartile range across the samples was chosen to represent the gene. Batch effects were removed by fitting each gene to a linear model with 14 and 7 fixed effects for each data set, respectively. The residual genes from this model (n = 13,060) were used for subsequent clustering analysis.

GE-based identification of TNBC subtypes, k-means clustering and consensus clustering were used to determine the optimal number of stable TNBC subtypes. Cluster robustness was assessed by consensus clustering using

agglomerative k-means clustering (1,000 iterations), with average linkage on the 386 TNBC profiles using the most differentially expressed genes (SD > 0.8; n = 1510 genes) (Gene Pattern version 3.2.1, GSE-A, www. broadinstitute.org/gsea/) (19). The optimal number of clusters was determined from the CDF, which plots the corresponding empirical cumulative distribution, defined over the range [0,1], and from calculation of the proportion increase in the area under the CDF curve. The number of clusters is decided when any further increase in cluster number (k) does not lead to a corresponding marked increase in the CDF area. PCA and heat maps were generated using Genespring GX ver.lO software (Agilent).

Functional annotation. Each TNBC subtype was tested for gene enrichment compared with all other samples using GSE-A software (Subramanian A, et al. Proc Natl Acad Sci U S A. 2005;102(43):15545-15550). Genes were tested for enrichment in the C2 curated gene sets of canonical pathways. Using the GSE-A algorithm (1,000 permutations), the top significantly enriched canonical pathways were selected based on a normalized enrichment score (NES) greater than 0.4 and false discovery rate (FDR) q value of less than 0.60. The FDR cutoff of 0.60 was chosen because of the lack of coherence in the data set collection spanning 21 studies, and more stringent FDR cutoffs resulted in fewer results, potentially overlooking significant pathways.

TNBC subtype gene signature derivation. The 20% of genes with the highest and lowest expression levels in at least 50% of the samples for each subtype were chosen for further analysis. Within each cluster, for each selected gene, the nonparametric Kruskal-Wallis test was applied to identify genes significantly different from the median GE among all 6 groups. Significant genes with a

Bonferroni's adjusted P value of less than 0.05 were included in the combined gene signature (n = 2188) for the TNBC subtypes and used to predict an independent validation set as well as to classify TNBC cell lines. Each sample from the validation set was assigned to a TNBC subtype based on highest Pearson correlation and lowest P value to one of the subtypes derived from the training data set. Samples with correlations differing by a P value of less than 0.05 were considered unclassifiable.

TNBC cell-line classification. GE data for TNBC cell lines (GSE- 10890 and E-TABM-157) were correlated (Spearman) to the centroids of the GE signatures for each TNBC subtype. GE data from both the TNBC tumors and cell lines were combined so that each gene was standardized to have mean = 0 and SD = 1. GE profiles from the cell lines were correlated to the centroids for each of the 6 TNBC subtypes. To remove size effects of the 6 gene signatures, the empirical P values were estimated for the correlations using a resampling (bootstrap, n = 1000) approach and estimating correlation coefficients for each resample. Cell lines were assigned to the TNBC subtype with the highest correlation (Table 3), and those that had low correlations (<0.1) or were similar between multiple subtypes (P > 0.05) were considered unclassified.

Cell proliferation/viability assays and IC50 determinations. All cell lines and culture conditions used can be found in Supplemental Methods (Table 5). Short tandem repeat (STR) DNA fingerprinting analysis was performed on all TNBC cell lines used in this study (Cell Line Genetics). All cell lines matched STR databases (ATCC, DSMZ, and Asterand), and their identity was confirmed by a certified process at Cell Line Genetics. Breast cancer cell lines and HMECs were seeded (3,000-10,000 cells) in quadruplicate wells in 96-well plates. Cells were incubated in the presence of alamar-Blue (Invitrogen), and baseline (predrug) fluorescence (Ex/Em: 560/590 nm) measurements were obtained following overnight attachment. Medium was then replaced with either fresh medium (control) or medium containing half-log serial dilutions of the following drugs: 0.3-30 μΜ olaparib (ChemieTek), 0.3-30 uM veliparib (ChemieTek), 1-100 μΜ bicalutamide (IPR Pharmaceutical), 0.3-30 μΜ cisplatin (APP Pharmaceutical), 3-300 nM NVP-BEZ235 (Chemdea), 10-1000 nM 17-DMAG (ChemieTek), and 0.1-10 μΜ dasatinib (LC Laboratories). Viability was determined from measuring fluorescent intensity after metabolic reduction of alamarBlue in the presence/absence of the drug after 72 hours. Viability assays were performed in triplicate, and replicates were normalized to untreated wells. Inhibitory concentration (IC5o) values were determined after double-log transformation of dose response curves as previously described (Bauer JA, et al. Breast Cancer Res. 2010;12(3):R41). For analysis of cell-line drug assays, data generated from the different cell lines representative of TNBC subtypes were compared using the nonparametric Mann-Whitney U test. All analyses and graphic representations were performed using Prism software (version 4.0c; GraphPad Software), and values are represented as the mean ± SEM.

Kaplan-Meier survival analysis and Hr. Log-rank test was used to determine survival significance in TNBC subtypes from Kaplan-Meier survival curves, RFS, and DMFS. Cox proportional hazards model was used to calculate Hr,

demonstrating differences in survival by pairwise comparison between TNBC subtypes (P < 0.05).

Xenograft tumor studies. Five-week-old female athymic nude-Foxnlnu mice

(Harlan Sprague-Dawley) were injected (s.c.) with either approximately 5 x 106 (CAL-51, HCC1806, MDA-MB-468, and SUM185PE) or approximately 10 x 106 (CAL-148 and SUM159PT) cells suspended in medium (200 μΐ) into each flank using a 22-gauge needle. The protocols describing the process of xenograft tumor formation in athymic mice were reviewed and approved by the Vanderbilt

Institutional Review Board. Once tumors reached a volume of 25-50 mm , mice were randomly allocated to treatment or vehicle arms. Treatments included bicalutamide per oral (100 mg/kg/d), cisplatin i.p. (8 mg/kg/wk), or NVP-BEZ235 per oral (50 mg/kg/d) in 1 :9 N-methylpyrolidone: PEG300. Tumor diameters were serially measured at the indicated times with digital calipers, and tumor volumes were calculated as width2 x length/2. Data points reflect the means and SEM for 16 tumors per treatment.

Statistics. Description of the relevant statistical methods used and analyses performed are described in the relevant Methods sections above. Comparisons between cell lines representative of TNBC subtypes were performed using the nonparametric Mann- Whitney U test. Statistical significance of drug effects on tumor growth in athymic mice was determined by 2-tailed unpaired t test.

Supplemental Methods

Immunostaining. Both formalin fixed paraffin embedded (FFPE) and frozen tissue were used for immunohistochemical studies. When frozen tissue was used for immunohistochemistry, staining was performed on sections taken from the same tissue block from which RNA was isolated for microarray. AR, Ki67, EGFR and CK 5/6 expression were evaluated in frozen tissue using the DAKO (Carpinteria, CA) antibodies: AR (clone AR41 1) at a 1 :50 dilution, EGFR at 1 :200, CK5/6 at 1 :50 and Ki67 antibody (MIB 1 clone) at a 1 :200 dilution for 1 h at room temperature. FFPE tissue was subject to antigen retrieval with high pH buffer (pH 8.0) followed by overnight incubation with an AR (1:30) or Ki67 (1 :75) antibody dilution overnight. AR expression was scored as both the percentage of tumor cells with nuclear staining as well as the intensity of staining (scored as 0-3+). An AR intensity score was calculated as follows: (AR intensity x 100) + % AR positive cells. For Ki67 the percentage of cells demonstrating nuclear staining at any intensity was recorded.

Immunoblotting. Cells were trypsinized, lysed and relative protein expression was determined by Western blot as previously described with the following antibodies; HSP90 monoclonal antibody, clone F-l 8 (Santa Cruz

Biotechnology, Santa Cruz, CA) and the AR polyclonal antibody, SC-N20 (Santa Cruz Biotechnology).

Colony Formation. MFM-223, MDA-MB-453 and CAL-148 cells (3000 cells/well) were reverse-transfected with 1.25 pmole of siRNAs to AR

[ONTARGET plus SMARTpool, cat# L-003400-00 (Dharmacon, Lafayette, CO)] or non-targeting control (ON-TARGETplus Non-targeting Pool cat# D-001810-10-05) with 0.25 Dharmafect #3 (MFM-223), or 0.25 μΐ, Dharmafect #1 (MDAMB-453 and CAL-148). Colonies were stained and quantified 14 d following transfection using Cell Profiler 2.0 (Broad Institute, Cambridge, MA). Experiments were preformed in triplicate and error bars reflect standard deviation. Results

Analysis of human tumor GE profiles identifies TNBC subtypes. GE profiles were obtained from 21 publicly available data sets that contained 3,247 primary human breast cancers and processed according to the flow chart in Figure 1A. To allow for robust analysis, these data sets were further divided into training (Table 1; 14 data sets, cases n = 2353) and validation data sets (Table 2; 7 data sets, cases n = 894). Since the majority of these tumors lacked sufficient molecular analysis of ER, PR, and HER2, we filtered each data set for ER, PR, and HER2 mR A expression to identify triple-negative status (see Methods for bimodal filter description;

supplemental material available in Lehmann, B.D., et al, J Clin Invest,

121(7):2Ί '50-27 '67 (July 2011), the entire teachings of which are incorporated herein by reference). Previous studies have shown that ER and HER2 mRNA expression correlates with immunohistochemistry (IHC) and FISH analyses, respectively (Carmeci C. et al. Am J Pathol. 1997; 150(5): 1563-1570; Press MF, et al. Clin Cancer Res. 2008; 14(23):7861-7870). Using a bimodal filter on the GE data (Figure IB), 386 and 201 TNBC tumor samples were identified in the training and validation sets, respectively. The 386 TNBC GE profiles of the training set were robust multiarray average (RMA) normalized, summarized, transformed, and corrected for "batch effect," resulting in 13,060 identical probes representing unique genes across all platforms. The triple-negative GE pattern is shown compared with 5 positive controls for each parameter (ER, PR, and HER2) from data sets that were confirmed IHC positive and expressed mRNA near the center of the positive bimodal peak (Figure 1C). Of the 14 data sets in the training set, 4 (GSE-7904, E- TABM-158, MDA133, GSE-22513, GSE-28821, and GSE-28796) included IHC data for all 3 markers, while others lacked information on ER, PR, or HER2 status (Table 1). The IHC data provided were used to calculate false-positive rates for each study, defined as tumors that were predicted negative for ER, PR, or HER2 by bimodal filtering of mRNA expression, but were positive by IHC. The overall false- positive rates were 1.7%, 1.7%, and 0.9% for ER, PR, and HER2, respectively, demonstrating that bimodal filtering of data sets by mRNA expression is a reliable method for identifying TNBC tumors from data sets lacking IHC information (Table

1 1). The overall frequency of TNBC across the training data set was 16% and is consistent with the prevalence of TNBC previously reported in 2 other large studies performed on 3,744 cases (17%) (17) and 1,726 cases (16%) (18).

k-means and consensus clustering reveal 6 TNBC subtypes. To identify global differences in GE between TNBC subtypes, k-means clustering was performed on the most differentially expressed genes (SD>0.8). Using the silhouette width (s[i]) as a measure of relative closeness of individual samples to their clusters, k-means clustering classified 337 of the 386 TNBC tumors into 6 stable clusters (s[i] > 0) and 49 tumors into 1 unstable cluster (s[i] > 0) (Figure 2A). Clustering resulted in a distribution of samples in all 7 clusters independent of each data set, n = 14, indicating that confounding factors such as batch effect, RNA amplification, and sample quality did not influence cluster distribution (Figure 8). Sample classification robustness was analyzed by consensus clustering, which involves k-means clustering by resampling (1,000 iterations) randomly selected tumor profiles. The consensus matrix is a visual representation of the proportion of times in which 2 samples are clustered together across the resampling iterations (Figure 2B). Groups of samples that frequently cluster with one another are pictorially represented by darker shades of red. To determine the number of clusters present in the data, the area under the curve of the consensus distribution function (CDF) plot was examined (Figure 2C). The point at which the area under the curve ceases to show marked increases with additional cluster number (k) indicates the ideal number of clusters (Figure 2D; Monti S. Machine Learning. 2003;52(1-2):91-118). Therefore, the optimal number of clusters is 7, as defined by the consensus plots consistent with the k-means clustering (6 stable, 1 unstable). Unsupervised dimension reduction by principal component analysis demonstrated fundamental differences in GE between tumor subtypes identified by k-means and consensus clustering (Figure 2E).

To assess whether similar TNBC subtypes could be generated from an independent TNBC cohort, 201 TNBC samples were compiled from bimodal filtering of 7 additional publicly available data sets (Table 2). A GE signature was derived from the most differentially expressed genes found in the TNBC training set (see Methods) and used to predict which TNBC subtype was best-fit for each of the tumors in the validation set. Each sample from the validation set was assigned to 1 of the TNBC subtypes derived from the training data set based on the highest Pearson correlation and lowest P value. Samples with correlations differing by P < 0.05 were considered unclassifiable. The validation set resulted in proportioned subtypes similar to those of the initial k-means clustering of the training set. After the analysis was completed, an ad hoc analysis was performed by combining the training and validation data sets (587 tumors), which resulted in 7 subtypes identified by consensus clustering (Figure 9), with similar enrichment in gene ontologies. This further validates the stability of the subtypes and shows that increasing sample size does not change optimal cluster number. Evaluation of GE profiles of RNA obtained from laser-capture microdissection of tumor cells from 2 data sets (GSE-22513; GSE-28821 ; GES-28796, Table 1 ; GSE-5847, Table 2) showed a similar pattern of distribution across all 7 subtypes, suggesting that these subtypes are indeed representative primarily of GE resulting from epithelial tumor cells rather than stromal components surrounding the tumor (i.e., inflammatory cells, myofibroblasts, etc.) (Figure 10).

Distinct gene ontologies are associated with each TNBC subtype. As an independent method to analyze TNBC subtypes, Gene Set Enrichment Analysis (GSE-A) (Subramanian A, et al. Proc Natl Acad Sci U S A. 2005;102(43):15545- 15550) was performed on all genes from both the training and validation sets to determine the top canonical pathways associated with each TNBC subtype. The top enriched genes (P < 0.05), identified from the training set used to predict the validation set, are displayed in a heat map in Figure 3. The top canonical pathways substantially overlapped between the training and validation sets for each subtype, indicating that the subtypes were reproducibly enriched in unique pathways (Table 4). Our 7 TNBC subtypes were characterized on the basis of gene ontologies and differential GE and subsequently labeled as follows: basal-like 1 (BLl); basal-like 2 (BL2); immunomodulatory (IM); mesenchymal (M); mesenchymal stem-like (MSL); luminal androgen receptor (LAR); and unstable (UNS) (Figure 3).

Independent analysis of 5 data sets based on TNBCs identified by IHC staining (n = 724

- 35 -

183) resulted in 4 clusters with GE similar to that of basal-like, IM, mesenchymal- like, and LAR subtypes (Figures 11 A, 1 IB and 12).

BL1 and BL2 subtypes. The top gene ontologies for the BL1 subtype are heavily enriched in cell cycle and cell division components and pathways (cell cycle, DNA replication reactome, G2 cell-cycle pathway, RNA polymerase, and Gi to S cell cycle) (Figure 3). The annotations are supported by the expression of genes associated with proliferation, such as A URKA, AURKB, CENPA, CENPF, BUBl, TTK, CCNA2, PRC1, MYC, NBAS, PLK1, and BIRC5 (Figure 13). Elevated DNA damage response (ATR/BRCA) pathways accompany the proliferation pathways in the BL1 subtype (Figure 3). Increased proliferation and cell-cycle checkpoint loss are consistent with the elevated expression of the DNA damage response genes observed (CHEK1, FANCA, FANCG, RAD54BP, RAD51, NBN, EXOl, MSH2, MCM10, RAD21, and MDC1) (Figure 13).

The highly proliferative nature of this subtype is further supported by the finding of high Ki-67 mRNA expression (MKI67) (Figure 13) and nuclear Ki-67 staining as assessed by IHC analysis (BL1 + BL2 = 70% vs. other subtypes = 42%; P < 0.05) (Figures 14A-14B). Enrichment of proliferation genes and increased Ki-67 expression in basal-like TNBC tumors suggest that this subtype would preferentially respond to antimitotic agents such as taxanes (paclitaxel or docetaxel) (Chakravarthy AB, et al. Clin Cancer Res. 2006; 12(5): 1570-1576; Bauer JA, et al. Clin Cancer Res. 2010; 16(2):681-690). This is indeed the case when comparing the percentage of patients achieving a pathologic complete response (pCR) in 42 TNBC patients treated with neoadjuvant taxane in 2 studies (Bauer JA, et al. Clin Cancer Res. 2010; 16(2):681-690; Juul N, et al. Lancet Oncol. 2010;l l(4):358-365). In these combined studies, TNBC patients whose tumors correlated to the basal-like (BL1 and BL2) subtype had a significantly higher pCR (63%; P = 0.042) when treated with taxane-based therapies as compared with mesenchymal-like (31 %) or LAR (14%) svibtypes (Figure 15).

The BL2 subtype displays unique gene ontologies involving growth factor signaling (EGF pathway, NGF pathway, MET pathway, Wnt/ -catenin, and IGF 1R pathway) as well as glycolysis and gluconeogenesis (Figure 3). Likewise, the BL2

1 subtype is uniquely enriched in growth factor receptor GE such as EGFR, MET, and EPHA2 (Figure 13). This subtype has features suggestive of basal/myoepithelial origin as demonstrated by higher expression levels of TP63 and MME (CD 10) (Figure 13).

IM subtype. The IM subtype is enriched for gene ontologies in immune cell processes. These processes include immune cell signaling (TH1/TH2 pathway, NK cell pathway, B cell receptor [BCR] signaling pathway, DC pathway, and T cell receptor signaling), cytokine signaling (cytokine pathway, IL-12 pathway, and IL-7 pathway), antigen processing and presentation, and signaling through core immune signal transduction pathways (NFKB, TNF, and JA STAT signaling) (Figure 3). The IM signaling is evidenced by immune signal transduction GE (Figure 13), in addition to immune cell-surface antigens, cytokine signaling, complement cascade, chemokine receptors and ligands, and antigen presentation (Figure 16). Since a similar proportion of samples that were microdissected fell into the IM subtype, it is likely that the IM characteristics are unique to the tumor cells themselves and not a reflection of immune cell infiltrate (Figure 10). Immune signaling genes within the IM subtype substantially overlap with a gene signature for medullary breast cancer, a rare, histologically distinct form of TNBC that despite its high-grade histology is associated with a favorable prognosis (Bertucci F, et al. Cancer Res.

2006;66(9):4636-4644).

M and MSL subtypes. The M subtype displays a variety of unique gene ontologies that are heavily enriched in components and pathways involved in cell motility (regulation of actin by Rho), ECM receptor interaction, and cell differentiation pathways (Wnt pathway, anaplastic lymphoma kinase [ALK] pathway, and TGF-β signaling) (Figure 3). The MSL subtype shares enrichment of genes for similar biological processes with the M subtype, including cell motility (Rho pathway), cellular differentiation, and growth pathways (ALK pathway, TGF-β signaling and Wnt/p-catenin pathway). However, unique to the MSL are genes representing components and processes linked to growth factor signaling pathways that include inositol phosphate metabolism, EGFR, PDGF, calcium signaling, G-

1 protein coupled receptor, and ERK1/2 signaling as well as ABC transporter and adipocytokine signaling (Figure 3).

The prevalence of cell differentiation and growth factor signaling pathways is illustrated by expression of TGF-β signaling pathway components (TGFB1L1, BGNSMAD6, SMAD7, NOTCH1, TGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2, and TGFBR3), epithelial-mesenchymal transition-associated (EMT-associated) genes (MMP2, ACTA2, SNAI2, SPARC, TAGLN, TCF4, TWIST1, ZEB1, COL3A1, COL5A2, GNG11, ZEB2, and decreased E-cadherin [CDH1] expression), growth factors (FGF, IGF, and PDGF pathways), and Wnt/p-catenin signaling (CTNNB1, DKK2, DKK3, SFRP4, TCF4, TCF7L2, FZD4, CAV1, CAV2, and CCND2) (Figure 13). The MSL subtype is also uniquely enriched in genes involved in angiogenesis, including VEGFR2 (KDR), TEK, TIE1, and EPAS1 as well as immune signaling evidenced by an overlap in GE unique to the IM subtype (Figure 16).

One interesting difference between the M and MSL subtypes is that the MSL subtype expresses low levels of proliferation genes (Figure 13). This decreased proliferation is accompanied by enrichment in the expression of genes associated with stem cells (ABCA8, PROCR, ENG, ALDHAl, PERI, ABCBl, TERF2IP BCL2, BMP2, and THYl), numerous HOX genes (HOXA5, HOXA10, MEIS1, MEIS2, MEOXl, MEOX2, and MSXl), and mesenchymal stem cell-specific markers (BMP2, ENG, ITGA V, KDR, NGFR, NT5E, PDGFRB, THYl, and VCAMl) (Figure 13). The signaling pathway components differentially expressed in the M and MSL groups share similar features with a highly dedifferentiated type of breast cancer called metaplastic breast cancer, which is characterized by mesenchymal/sarcomatoid or squamous features and is chemoresistant (Gibson GR, et al. Am Surg.

2005;71(9):725-730).

The MSL subtype also displays low expression of claudins 3, 4, and 7, consistent with a recently identified claudin-low subtype of breast cancer (Figures 13 and 17; Prat et al, Breast Cancer Res, 2010; 12(5):R68). Hierarchical clustering of TNBC GE profiles using the claudin-low gene predictor set (n = 770) segregated a portion of the M and MSL subtypes with low claudin (3, 4, and 7), cytokeratin (KRT7, KRT8, KRT18, and KRT19), and CD24 expression (Figure 17; Prat A, et al. Breast Cancer Res. 2010;12(5):R68). This population of claudin-low-expressing tumors also had high expression of genes associated with EMT (FBN1, MMP2, PDGFRB, THY1, SPARC, TGFBR2, PDGFRA, TWIST, CAV1, CAV2, and

SERPINE1).

LAR subtype. GE in the LAR group was the most differential among TNBC subtypes. This subtype is ER negative, but gene ontologies are heavily enriched in hormonally regulated pathways including steroid synthesis, porphyrin metabolism, and androgen/estrogen metabolism (Figure 3). Whether other hormone-regulated pathways such as androgen receptor (AR) signaling, previously reported in ER- negative breast cancer (Hayes MJ et al. Clin Cancer Res. 2008;14(13):4038- 044), could be responsible for the GE patterns in this LAR subtype was investigated. Indeed, it was found that AR mRNA was highly expressed, on average at 9-fold greater than all other subtypes (Figure 13). Tumors within the LAR group also expressed numerous downstream AR targets and coactivators (DHCR24, ALCAM, FASN, FKBP5, APOD, PIP, SPDEF, and CLDN8) (Figure 13). In addition to AR mRNA expression, AR protein expression was investigated by IHC across all TNBC tumors from the Vanderbilt cohort (n = 20). The percentage of tumor cells scored with nuclear AR staining and the intensity of staining were significantly higher in the LAR subtype (>10-fold; P < 0.004) compared with all other TNBC subtypes (Figures 18A- 18B). Hierarchical clustering was performed using an AR-activated gene signature (Chen CD, et al. Nat Med. 2004; 10(l):33-39) on the training and validation TNBC tumor data sets. Hierarchical clustering with this signature segregated the majority of LAR tumor profiles from other subtypes (Figures 19A- 19B). Tumors in the LAR subtype display luminal GE patterns, with FOXA1, KRT18, and XBP1 among the most highly expressed genes (Figure 20A), Others have previously described a breast cancer subgroup expressing AR termed molecular apocrine (Chen CD, et al. Nat Med. 2004; 10(l):33-39). While we do not have detailed pathology reports for all of the LAR tumors in this study, the TNBC subgroup GE centroids were used to correlate the apocrine samples from GSE-1561 (Farmer P, et al. Oncogene. 2005;24(29):4660^1671 ; Table 1). The GE profiles of all 6 apocrine tumors (GSM26883, GSM26878, GSM26886, GSM26887,

1 GSM26903, and GSM26910) described in the study strongly correlate with LAR, suggesting that the LAR TNBC subtype is composed of AR-driven tumors that include the molecular apocrine subtype.

Intrinsic molecular breast cancer subtype classification of TNBC. Breast cancers can be classified as luminal or basal-like, dependent on their expression of different cytokeratins. TNBC tumor subtypes display differential expression of both basal-like cytokeratins (KRT5, KRT6A, KRT6B, KRT14, KRT16, KRT17, KRT23, and KRT81) and luminal cytokeratins (KRT7, KRT8, KRT18, and KRT19) across the subtypes (Figures 20A-20B). The UNS, BL1, BL2, and M subtypes expressed higher levels of basal cytokeratin expression, while tumors within the LAR subtype lacked basal cytokeratin expression and expressed high levels of luminal cytokeratins and other luminal markers (FOXA1 and XBP1) (Figure 20A). In addition to cytokeratin expression, breast cancers can be classified by an

"intrinsic/UNC" 306-gene set into 5 major subtypes (basal-like, HER2-like, normal breast-like, luminal A, and luminal B) (Hu Z, et al. BMC Genomics. 2006;7:96). Since TNBCs are largely considered basal-like, each of the 386 TNBC tumor profiles were correlated to the intrinsic gene set centroids of the 5 molecular subtypes, as previously described (Hu Z, et al. BMC Genomics. 2006;7:96). Tumors were assigned to 1 of the molecular subtypes based on the highest correlation coefficient between each TNBC expression profile and the 5 molecular subtype centroids. This analysis resulted in 49% (n = 188) of the TNBC training set classified as basal-like, 14% (n = 54) as luminal A, 11% (n = 42) as normal breastlike, 8% (n = 31) as luminal B, 5% (n = 19) as HER2, and 13% (n = 52) as unclassifiable (Figure 20B). This confirms that most TNBCs classify to the basal- like molecular subtype. Both the unstable tumors and BL1 tumors correlated strongly to the basal-like intrinsic molecular classification (76% and 85%, respectively). However, the BL2, IM, and M subtypes only moderately correlated to the basal-like molecular class (31%, 58%, and 47%, respectively), with a portion of tumors unclassified (22%, 17%, and 18%, respectively) (Figure 20B). The M and MSL subtypes displayed the largest portion of tumors classified as normal breastlike (25%o, and 46%, respectively). The BL2, M, and MSL subtypes were a mixture of classifications, suggesting the intrinsic classification system may not be suitable for characterizing these TNBC subtypes. The majority of TNBC tumors within the LAR subtype were classified as either luminal A or luminal B (82%), and none were classified as basal-like, further supporting the luminal origin of the LAR subtype (Figures 20A-20B). While only 49% of the tumors were classified as basal-like according to the intrinsic gene set, IHC staining performed on the Vanderbilt subset of tumors (n = 25) showed that the majority (88%) of TNBCs stained positive for the basal cytokeratins CK5/6. Additionally, 56% of the Vanderbilt tumors stained positive for EGFR, similar to a previous study that found 56% of n = 929 pooled from 34 studies that were positive for EGFR or CK5/6 (Yang XR, et al. J Natl

Cancer Inst. 201 1 ; 103(3):250-263). There were no statistical differences between CK5/6 and EGFR staining across TNBC subtypes. Thus, the majority of TNBCs display a basal-like phenotype by IHC, while only approximately half correlate to the basal-like intrinsic gene set.

Patient relapse-free survival and distant-metastasis-free survival differs among TNBC subtypes. Relapse-free survival (RFS) between TNBC subtypes was significantly different (log-rank test; P = 0.0083) (Figure 21A), despite variability of therapy and duration of treatment. RFS was significantly decreased in the LAR subtype compared with the BL1 (hazard ratio [Hr] = 2.9), IM (Hr = 3.2), and MSL (Hr = 10.5) subtypes (P < 0.05) (Figure 21B). RFS was significantly decreased in the M subtype compared with BL1 (Hr = 2.6) and IM (Hr = 2.9), while the MSL subtype had higher RFS than the M subtype (Figure 21B). Distant- metastasis-free survival (DMFS) did not vary between TNBC subtypes (log-rank test; P = 0.2176), but the M subtype had a significantly higher Hr compared with the BL1 (Hr = 2.4, P < 0.05) and IM (Hr = 1.9, P < 0.06) subtypes (Figures 21C and 21D). The increased RFS in the absence of increased DMFS for patients in the LAR subtype suggests that recurrence is due to local relapse. Tumor size and grade were not significantly different among TNBC subtypes, but age at diagnosis was greater in the LAR subtype (P < 0.0001), potentially contributing to decreased RFS of this subtype compared with other subtypes (Figures 22A-22B). TNBC cell line models for TNBC subtypes. Cell line models would facilitate preclinical experiments to define differential drug sensitivity of the distinct subtypes within this heterogeneous disease. Using the bimodal filtering approach on ER, PR, and HER2 GE levels from 2 independent breast cancer cell line data sets (GSE- 10890 and ETABM-157), 24 and 25 triple-negative cell lines were identified in the GSE-10890 and ETABM-157 GE data sets, respectively. Of the cell lines present in both data sets, nearly all had similar predictions of triple-negative status by bimodal filtering. Discrepancies in some cell lines (e.g., HCC1500 and HCC1937) may be the result of differences in culturing methods and/or loss of hormone receptor expression over time in culture. Analysis of these 2 data sets identified 30 nonoverlapping TNBC cell lines.

GE profiles from the cell lines were correlated to the centroids for each of the 6 TNBC subtypes. The majority of cell lines (27 of 30) were assigned to TNBC subtypes (Table 3), except for BT20, HCC1395, and SW527, which had low correlations (<0.1) or were similar between multiple subtypes (P > 0.05). Of the 7 cell lines that contained known BRCA1 and BRCA2 mutations, 5 correlated with the BL1 or BL2 subtypes (HCC1937, HCC1599, HCC2157, HCC3153, and

SUM149PT), consistent with this subtype containing tumors with defects in DNA repair pathways (Table 3) and gene ontologies enriched for GE involved in cell cycle and DNA repair functions (Figure 13). Additionally, cell lines in the BL1 and BL2 subtypes were more genomically unstable, displaying significantly more chromosomal rearrangements than those in the mesenchymal subtypes (M and MSL) (average translocations, inversions, deletions = 34 versus 14, respectively; P < 0.01), as determined by SKY-FISH (www.path.cam.ac.uk/~pawefish/index.html) (Figures 23A-23B). All cell lines that correlated to the basal-like intrinsic molecular subtype were in the BL1, BL2, or IM subtypes (Table 3). Only 2 cell lines (HCC1187 and DU4475) were placed into the IM subtype, suggesting that the IM subtype is underrepresented in cell culture.

Cell lines in the M and MSL subtypes were generated from highly dedifferentiated tumors derived from unique pathologies (e.g., HS578T,

carcinosarcoma; and SUM159PT, anaplastic carcinoma) and expressed both epithelial and mesenchymal components. All cell lines assigned to the M and MSL subtype have spindle-like morphology in 2D culture (CAL-120, CAL-51, MDA- MB-157, MDA-MB-231, MDA-MB-436, SUM159PT, HS578T, and BT549) or stellate-like morphology in 3D (Kenny PA et al., Mol Oncol, 2007; 1 :84-96). Five cell lines matched to the LAR subtype (MDA-MB-453, SUM185PE, HCC2185, CAL-148, and MFM-223).

Two distinct basal groups (A and B) have been identified by GE profiling of breast cancer cell lines (Neve RM, et al. Cancer Cell. 2006; 10(6) :515-527). Basal A cell lines display epithelial characteristics and are associated with BRCA1 gene signatures, while basal B cell lines are more invasive and display mesenchymal and stem/progenitor-like characteristics. The GE analyses revealed that the majority of basal A cell lines belong to the BLl and BL2 subtypes, while the majority of basal B cell lines fall into the M and MSL subtypes (Table 3). Hierarchical clustering analysis was tested on all TNBC cell lines using the most differentially expressed genes from the tumors to determine whether GE patterns of the cell lines are similar within TNBC subtypes (Figure 24). Three clusters were identified: LAR, containing all 4 LAR lines; basal-like, containing lines in the BLl and BL2 subtypes; and mesenchymal-like, containing lines in the M and MSL subtypes. This clustering analysis indicates that TNBC cell lines can be classified into 3 main groups: basal- like (BLl and BL2); mesenchymal-like (M and MSL); and LAR. This classification will be used in subsequent sections.

TNBC cell lines have differential sensitivity to therapeutic agents. There are a variety of targeted therapies undergoing clinical investigation in patients with TNBC including those targeting PARP (Farmer H, et al. Nature.

2005;434(7035):917-921), AR (Agoff NS et al. Am J Clin Pathol.

2003;120(5):725-731), Src (Finn RS, et al. Breast Cancer Res Treat.

2007;105(3):319^-326), and PI3K/mTOR (Marty B, et al. Breast Cancer Res.

2008;10(6):R101) signaling. The panel of TNBC cell lines was used to assess differential response to several agents targeting these pathways. For comparison primary human mammary epithelial cells (HMECs) were analyzed in cell viability assays. The half-maximal inhibitory concentration (IC50) values were determined for the following drugs [targets] : veliparib (ABT-888) [PARP 1/2]; olaparib (AZD2281) [PARP 1/2]; cisplatin [DNA]; bicalutamide [AR]; 17-DMAG [Hsp90]; dasatinib [Src/ Abl]; and NVP-BEZ235 [PI3K and mTOR].

Cell-cycle and DNA damage response genes are elevated in the BL1 and BL2 subtypes (Figure 13), and 5 of the 7 lines in the cell line panel that carry

BRCAl/2-mutations reside in these subtypes (Table 3). Given the previous clinical trial observations with PARP inhibitors and cisplatin (Bhattacharyya A et al. J Biol Chem. 2000;275(31):23899-23903; Jones P, et al. J Med Chem. 2009;52(22):7170- 7185; Evers B, et al. Clin Cancer Res. 2008;14(12):3916-3925), it was predicted that these agents would preferentially decrease viability in cell lines with DNA repair defects that are representative of the BL1 and BL2 subtypes. The TNBC cell line panel was treated with the PARP inhibitors veliparib and olaparib, but not all cell lines representative of the basal-like TNBC subtypes were sensitive to PARP inhibition (Figures 4A-4D). The BRCAl -null cell line HCC1937 was sensitive to veliparib (IC50 = 4 μΜ) but not olaparib (IC50 > 100 μΜ), while the BRCA1 -mutant MDA-MB-436 was sensitive to both PARP inhibitors (veliparib IC50 = 18 μΜ and olaparib IC50 =14 μΜ). The BRCA2-mutant cell line HCC 1599 lacked sensitivity to either PARP inhibitor (veliparib IC50 > 30 μΜ and olaparib IC5o> 100 μΜ). Thus, in addition to BRCAl/2 status, other properties of the tumor may dictate sensitivity to a given PARP inhibitor. Unlike PARP inhibitor sensitivity, basal-like lines were significantly more sensitive to cisplatin than mesenchymal-like lines (average IC50 = 8 μΜ vs. IC50 = 16 μΜ, P = 0.032) or LAR lines (average IC50 = 8 μΜ vs. IC50 = 15 μΜ, P = 0.017) (Figure 4, E and F). The BRCA1 -mutant cells (SUM149PT, HCC1937, and MDA-MB-436) and BRCA2-mutant cells (HCC1599) were among the most sensitive to cisplatin treatment (Figure 4E).

Since cell lines representative of the LAR subtype (MDA-MB-453, SUM185PE, CAL-148, and MFM-223) express high levels of AR mRNA and protein (Figure 25A), we compared the sensitivity of TNBC cell lines to the AR antagonist bicalutamide (Figures 5 A and 5B). IC50 values for the majority of TNBC cell lines were not achieved using the highest dose of 500 μΜ. However, all LAR cell lines tested (SUM185PE, CAL-148, MDA-MB-453, and MFM-223) and a subset of mesenchymal-like cell lines that express low levels of AR (HS578T, BT549, CAL-51 , and MDA-MB-231) were more sensitive to bicalutamide than basal-like cell lines (average IC50 = 227 μΜ vs. IC50 > 600 μΜ, P = 0.007; and average IC50 =361 μΜ vs. IC50 > 600 μΜ, P = 0.038, respectively) (Figure 5A).

Since AR requires the Hsp90 chaperone for proper protein folding and stability (Solit DB, et al. Clin Cancer Res. 2002; 8(5):986-993), the sensitivity of the cell line panel to an Hsp90 inhibitor, 17-dimethylaminoethylamino-17- demethoxy-geldanamycin (17-DMAG), was determined. Again, the LAR cell lines were more sensitive to 17-DMAG compared with the majority of basal-like (average IC50 = 16 nM vs. IC50 = 81 nM; P = 0.004) and mesenchymal-like (average IC50 = 16 nM vs. ICso = 1 17 nM; P = 0.05) cell lines (Figures 5C and 5D), albeit 17- DMAG has many other targets. These data strongly suggest that LAR tumors are driven by AR signaling and AR represents a therapeutic target for this subtype. Importantly, AR status in TNBC patients represents a molecular marker for preselection of patients for antiandrogen therapy.

To determine whether the growth of the LAR cell lines was AR dependent, MFM-223, MDA-MB-453, and SUM185PE cell lines were treated with control and AR-targeting siRNA. Knockdown of AR in the experimental samples at the protein level was verified (Figure 25B), colony growth assays were performed, and the number of colonies formed after 14 days of siRNA transfection were analyzed. The ability of all the LAR cell lines to form colonies was significantly reduced after knockdown of AR expression as compared with control samples (MFM-223 = 55%, P = 0.031 ; MDA-MB-453= 51%, P = 0.004; and SUM185PE= 42.3%, P = 0.002) (Figure 25C), indicating that AR expression is in part responsible for tumor cell viability/survival.

GE analysis of the mesenchymal-like subtypes demonstrated enrichment in the expression of genes that make up components and pathways associated with EMT (TGFP, ECM-receptor interaction, ALK, Wnt/p-catenin, and Racl) and those associated with cell motility (focal adhesion, integrin signaling, Racl , striated muscle contraction, and regulation of actin by Rho GTPase) Figure 3). Since the nonreceptor tyrosine kinase Src plays critical roles in cell migration and the mesenchymal-like subtypes are enriched in cell motility pathways, the effect of the Src inhibitor dasatinib on the panel of TNBC lines was analyzed. Cell lines belonging to the mesenchymal-like subtypes (M and MSL) were more sensitive to dasatinib than the LAR cell lines (average IC50 = 22 μΜ vs. IC50 =88 μΜ, P = 0.024) (Figures 6A and 6B).

Since activating mutations in PIK3CA are the most frequent genetic event in breast cancer (Samuels Y, et al. Science. 2004;304(5670):554), the TNBC cell lines were treated with the dual PI3K/mTOR inhibitor NVP-BEZ235 (Maira SM, et al. Mol Cancer Ther. 2008;7(7): 1851-1863). TNBC cell lines that have activated PI3K/AKT signaling due to PIK3CA mutations or PTEN deficiency (Table 3) were highly sensitive to NVPBEZ235 (Figure 6C). In addition, mesenchymal-like TNBC cell lines were more sensitive to NVP-BEZ235 compared with basallike cell lines (average IC50 = 44 nM vs. IC50 = 201 nM; P = 0.001) (Figures 6C and 6D), which may suggest that deregulation of the PI3K pathway is important for this subtype. LAR cell lines were also more sensitive to NVP-BEZ235 compared with basal-like cell lines (average IC50 = 37 nM vs. 116 nM; P = 0.01) (Figures 6C and 6D). This sensitivity can be explained by PIK3CA mutations, frequent in the LAR subtype, with all LAR cell lines containing PIK3CA-activating mutations (HCC2185, MDA- MB-453 CAL-148, MFM-223, and SUM185PE) (Table 3). While PIK3CA mutations predicted NVP-BEZ235 sensitivity, PTEN deficiencies (mutation or loss of protein expression) did not correlate with sensitivity.

Xenograft tumors derived from TNBC cell lines display differential sensitivity to therapeutic agents in vivo. In order to further analyze the susceptibility or resistance of TNBC subtypes to therapeutic agents in a more physiological setting than 2D culture, xenograft tumors were established in nude mice from cell lines representative of the basallike (HCC1806 and MDA-MB-468), mesenchymal-like (CAL-51 and SUM159PT), or LAR (SUM185PE and CAL-148) subtypes. After tumors reached an approximate volume of 25-50 mm3, the mice were treated with cisplatin, NVP-BEZ235 or bicalutamide. The sensitivity of the TNBC cell lines to the therapeutic agents when grown as 3D xenograft tumors in vivo was very similar to that seen with the cell lines grown in 2D monolayer culture. The xenograft tumors derived from the 2 cell lines representative of basal-like tumors (HCC1806 and MDA-MB-468) were highly and differentially sensitive to cisplatin and were significantly growth inhibited (P < 0.0001) relative to treatment with vehicle control or the other experimental treatments (bicalutamide and NVP-BEZ235) (Figure 7). The tumors derived from the LAR cell lines (SUM185PE and CAL-148) and the mesenchymal-like cell line that expresses low level AR protein (CAL-51) (Figures 25A-25C) displayed significant sensitivity to bicalutamide (Figure 7). The xenograft tumors derived from all the cell lines carrying activating PIK3CA mutations (SUM185PE, CAL-148, CAL-51, and SUM159PT) had partial response or complete compared with NVP-BEZ235 (Figure 7).

In summary, cell lines representative of the 6 TNBC subtypes display different sensitivities to a variety of agents, and importantly, these differences can be attributed to distinct expression of cellular components and presence of mutations in key oncogenes and tumor suppressors. The results have immediate clinical translation, as they provide a valuable platform and insights for ongoing and future clinical investigation. The data inidcate that patients with basal-like TNBC should be treated with agents that engage DNA damage signaling response pathways; those with tumors expressing AR should receive bicalutamide alone or in combination with PI3K inhibitors; and those with mesenchymallike TNBC should be considered for trials exploring the activities of an Src antagonist in combination with a PI3K inhibitor.

Supplemental Results

K-means Clustering of a subset of five datasets that were identified as TNBC by IHC. Since TNBCs are not identified by GE in the clinical setting, a similar GE analysis was performed on a dataset from TNBCs identified using IHC (n=183) complied from five datasets: GSE7904 (n= 22), GSE19615 (n= 34), GSE20194 (n= 67), E-TABM-158 (n= 30) and GSE22513, GSE-XXX (n= 30). This compilation included 136 tumors (74.3%) that were identified by our bimodal filtering analysis. K-means clustering performed on the most differentially expressed genes (SD> 0.8) resulted in the identification of 5 TNBC subtypes (Figures 1 lA-1 IB). One subtype contained only three tumors and analysis of GE showed these tumors to have high levels of ER and PR Figure 1 IB). These tumors were considered to be false negatives by IHC and were removed from further analysis. Comparison of these IHC-identified TNBC samples using genes differentially expressed from the six TNBC subtypes revealed similar

patterns of gene enrichment (Figure 12).

Discussion

Through GE analysis of 3247 breast cancers, demonstrate herein is that TNBCs can be reliably identified by filtering GE profiles for ER, PR, and HER2 mRNA levels. 587 TNBC GE profiles from 21 studies (training set = 386 and validation set = 201) were compiled. An 18% incidence rate of TNBC across the 21 independent studies (training and validation combined) was observed, similar to previously reported TNBC prevalence (Bertucci F, et al. Int J Cancer.

2008;123(l):236-240; Rakha EA, et al. Clin Cancer Res. 2009;15(7):2302-2310). k-means and consensus clustering of tumor profiles revealed that TNBC is composed of 6 stable subtypes enriched in distinct gene ontologies and GE patterns. Furthermore, using a GE signature derived from TNBC patient tumors, cell-line models for each of the TNBC subtypes were identified.

Previously, the majority (50%-90%) of TNBCs have been classified as basal-like either by IHC or by correlation to the intrinsic molecular breast cancer subtypes (Bertucci F, et al. Int J Cancer. 2008; 123(l):236-240; Rakha EA, et al. Clin Cancer Res. 2009; 15(7):2302-2310; Kreike B, et al. Breast Cancer Res. 2007;9(5):R65). A previous TNBC study identified 5 distinct hierarchical clusters in which 91% (88 of 97) of TNBCs identified by IHC coiTelated to the basal-like subtype (Kreike B, et al. Breast Cancer Res. 2007;9(5):R65). However, the study lacked molecular analysis of the tumors and conclusions were limited to clinical outcomes based on pathological markers. The relationship between TNBC and basal-like breast cancer remains controversial (Rakha E et al. Clin Cancer Res. 2008;14(2):618). The proportion of TNBCs with basal-like GE in our study was 47%, resulting in a higher proportion of TNBCs that correlate with other molecular subtypes: luminal A (17%), normal breast-like (12%), luminal B (6%), HER2 (6%), or unclassified (12%). The study herein indicates that TNBC is not limited to tumors with a basal-like phenotype; rather it is a heterogeneous collection of tumors with distinct

phenotypes, as evidenced by the diverse GE patterns and varying sensitivity of representative cell lines to the targeted therapies assessed in this study.

The BL1 and BL2 subtypes express high levels of genes involved in cell proliferation and DNA damage response, suggesting patients with basal-like tumors would benefit from agents that preferentially target highly proliferative tumors (e.g., anti-mitotic and DNA-damaging agents). Consistent with this notion, patients with basal-like tumors had up to a 4-fold higher pCR after taxane-based and radiation- based treatment as compared with patients with tumors that displayed characteristics of the ML or LAR subtypes (Bauer JA, et al. Clin Cancer Res. 2010; 16(2):681-690; Juul N, et al. Lancet Oncol. 2010;11(4):358-365).

Nearly all of the cell lines with known mutations in BRCA1 and BRCA2 had GE patterns that correlated with the basal-like subtype, which is in agreement with the current view that BRCA-mutant tumors display a basal-like phenotype

(Stefansson OA, et al. Breast Cancer Res. 2009; 11(4):R47). A number of non- BRCAmutant cell lines that correlated with the basal-like TNBC subtypes contained nearly 2-fold the number of chromosome rearrangements as all other subtypes. These findings suggest that a predominant characteristic of basal-like TNBC is genomic instability (Kwei KA et al.Mol Oncol. 2010;4(3):255-266). Because of GE similarities between basal-like TNBC and BRCA1 -mutation carriers, PARP inhibitors are currently being tested in clinical trials for TNBC (Silver DP, et al. J Clin Oncol. 2010;28(7):1145-1153). Despite success in BRCA-null cells, there is not sufficient evidence for PARP inhibitor efficacy in BRCAl/2-mutant breast cancer cells (Farmer H, et al. Nature. 2005;434(7035):917-921). PARP sensitivity across a panel of TNBC cell lines did not correlate with TNBC subtype alignment or BRCAl/2-status in this study using short-term cell viability assays (72 hours).

However, the synthetic lethal effects of PARP inhibition in homologous

recombination-deficient cells may require multiple cell divisions and may be more appropriately tested in long-term survival assays. Other studies show that a subset of BRCA1 -mutant tumors lack large-scale genomic alterations and these tumors represent a distinct disease entity and may not be susceptible to PARP inhibition (Stefansson OA, et al. Breast Cancer Res. 2009; 11(4) :R47). Previous analyses of PARP inhibitors in isogenic BRCA2+/+ and BRCA2-/- cell lines suggest that sensitivity to this targeted therapy is dependent on the molecular context of the DNA repair machinery and that DNA repair requiring homologous recombination involves multiple redundant pathways (Farmer H, et al. Nature. 2005;434(7035):917-921; Fong PC, et al. N Engl J Med. 2009;361(2):123-134). BRCA1 is a relatively large protein (1863 aa) that forms numerous complexes that may not be entirely disrupted when BRCA1 and BRCA2 are mutated, as opposed to BRCA-null cells. Despite an incomplete understanding of the molecular mechanism of the PARP inhibitors in vivo, these drugs have been proven to be highly effective in the clinical trial setting (41% objective response rate; 6.2 month progression-free survival) (Audeh MW, et al. J Clin Oncol. 2009;27:5500).

It was also found that BRCA1 -mutant and non-BRCA-mutant basal like cell lines had relatively higher sensitivity to cisplatin treatment compared with all other TNBC subtypes.The results are consistent with the observed 21% pCR in a clinical trial investigating neoadjuvant cisplatin as a single agent in a heterogeneous TNBC patient population (Garber JE, et al. Breast Cancer Res Treat. 2006;105:S149; Telli ML, Ford JM. Clin Breast Cancer. 2010;10 suppl 1 :E16-E22). These data collectively suggest that the use of proliferation biomarkers such as Ki-67 and development of markers identifying defects in DNA damage response signaling could provide patient selection and tailored treatments for basal-like TNBC. Use of cisplatin as a "targeted" agent alone or in combination with antimitotics (taxanes) and/or radiation may benefit patients with this subtype and a current trial is underway with these agents (Mayer IA, et al. J Clin Oncol. 2010;28(1):15).

The IM subtype is highly enriched in immune cell signaling. Other studies have described the presence of immune response gene signatures in ER-negative and medullary breast cancers (Bertucci F, et al. Cancer Res. 2006;66(9):4636-4644; Teschendorff AE et al. Genome Biol. 2007;8(8):R157). Similar to these studies, it was found elevated expression of T cell-associated genes, immune transcription factors, IFN regulatory factors, TNF, complement pathway, and antigen processing. It cannot be ruled out that the GE profile of the IM subtype comprises, at least in part, stromal components including immune cell infiltrate. However, the finding that the same proportion of microdissected tumors belongs to this group argues against stromal contamination.

The M and MSL subtypes share similar gene ontologies and GE profiles involving TGF-β, mTOR, Racl/Rho, Wnt/p-catenin, FGFR, PDGFR, and VEGF signaling pathways. These signaling pathways are prominent in processes of EMT and stem cell-like properties of a CD44+CD24- population of normal mammary cells (Mani SA, et al. Cell. 2008;133(4):704-715; Shipitsin M, et al. Cancer Cell. 2007; 11 (3):259— 273). Similarly, the MSL subtype is made up at least in part by the recently described claudin-low tumors, which lack luminal differentiation markers, high enrichment for EMT markers, immune response genes, and cancer stem celllike features (Prat A, et al. Breast Cancer Res. 2010;12(5):R68). Interestingly, the M and MSL subtypes differed clinically, with patients in the M subtype presenting with shorter RFS. This may be a reflection of differences in proliferation, as the M subtype displayed higher expression of proliferation-associated genes, including Ki- 67. Additionally, patients with the M and MSL subtypes had decreased 5-year DMFS, consistent with enrichment in pathways associated with metastasis and motility.

Tumors within the mesenchymal-like subtypes have GE profiles that are similar to those from mesenchymal cells and metaplastic breast cancers (Hennessy BT, et al. Cancer Res. 2009;69(10):4116-4124). Metaplastic breast cancers have lineage plasticity, including foci of spindle cell, as well as osseous or cartilaginous differentiation (Gibson GR, et al. Am Surg. 2005;71(9):725-730). A recent study found that 47% of metaplastic breast cancers sequenced have PIK3CA mutations and have higher phosho-AKT expression (Hennessy BT, et al. Cancer Res.

2009;69(10):4116-4124). It was found herein that TNBC mesenchymallike cell lines preferentially responded to the dual PBK/mTOR inhibitor NVP-BEZ235. This response to NVP-BEZ235 was demonstrated in cell lines that carry PIK3CA mutations in xenografts in addition to an MSL cell line (SUM159PT) that lacks PIK3CA mutation or PTEN deficiencies, suggesting that the PI3K/mTOR pathway is important in the mesenchymal-like subtype.

The mesenchymal-like subtypes were enriched in pathways associated with EMT and cell motility. There is evidence of a prominent role for Src in tumor cells that are highly invasive, such as those that have undergone EMT (Guarino M. J Cell Physiol. 2010;223(l):14-26). Accordingly, we found that mesenchymal-like TNBC cell lines had differential sensitivity to dasatinib. Markers of EMT may have clinical value for patient preselection for trials using dasatinib. In addition, Wnt-signaling pathways regulate EMT and may contribute to tumor cell invasion (Shin SY, et al. Cancer Res.2010;70(17):6715-6724). Mutations in the Wnt/p-catenin pathway

(CTN B 1, APC, and WISP3) occur frequently (52%) in metaplastic breast cancer, suggesting that deregulated Wnt/p-catenin pathway in these tumors may be a viable therapeutic target (Hayes MJ et al. Clin Cancer Res. 2008;14(13):4038^1044). Inhibitors of Wnt/p-catenin are of great interest and currently are in preclinical development (MacDonald BT, et al. Dev Cell. 2009;17(l):9-26). Drugs targeting this pathway could be of value for treating mesenchymal-like TNBC.

The LAR subtype was readily subclassified by an AR gene signature and high levels of luminal cytokeratin expression. GE analysis of the LAR subtype is consistent with a prior report of a subset of ER-negative tumors expressing AR- regulated genes (Doane AS, et al. Oncogene. 2006;25(28):3994-4008). In addition, Farmer et al. described an apocrine tumor subtype based on GE profiling that was characterized by AR expression distinguishing this tumor subtype from other basal- like tumors (Farmer P, et al. Oncogene. 2005;24(29):4660-4671). In the GE analysis of tumors from 21 studies described herein, the prevalence of the LAR tumors was 11% (62 of 587) of TNBCs or 2% (62 of 3247) of all breast cancers. Analysis of clinical data demonstrated that patients in the LAR subtype had higher RFS but no difference in DMFS compared with all other TNBC subtypes, suggesting these patients have local relapse. The higher RFS could imply that this group of patients received ineffective therapies (standard chemotherapy); however, patients in the LAR group were significantly older at diagnosis and the extent of disease or age- associated comorbidities that affect the ability to deliver treatment as planned may have contributed to relapse. Older age at diagnosis has previously been reported in patients with AR-positive TNBC and is associated with postmenopausal status (Agoff NS et al. Am J Clin Pathol. 2003;120(5):725-731). Whether this AR-driven subtype is arising from hormone-replacement therapy (HRT) merits further investigation; however, it is becoming clear that the risk of breast cancer increases with HRT, and synthetic progestins such as medroxyprogesterone acetate have been shown to bind and disrupt AR (Birrell SN et al.FASEB J. 2007;21(10):2285-2293). As described herein, five (5) cell lines were identified that represent the LAR subtype and it was shown that they are sensitive to bicalutamide and 17-DMAG, suggesting that therapies targeting AR may be effective against tumors that express this hormone receptor. In fact, there is a clinical trial (NCT00468715) underway testing the effect of bicalutamide in preselected patients with ER/PR-negative AR- positive tumors. This further supports using in silico-based approaches to provide leads for trials with other targeted therapies in TNBC subtypes.

In addition to agents that target AR function, LAR cell lines were also sensitive to PI3K inhibition. This sensitivity correlated with PIK3CA mutations. All 5 LAR cells lines have activating PIK3CA mutations and are sensitive to the PI3K inhibitor NVP-BEZ235, similar to ER-positive breast cancer in which PIK3CA mutations are common (Gonzalez-Angulo AM, et al. Clin Cancer Res.

2009;15(7):2472-2478; Stemke-Hale K, et al. Cancer Res. 2008; 68(15):6084- 6091). These findings suggest simultaneous targeting of AR and the PI3K/mTOR pathway may be of clinical benefit for LAR TNBC patients, as this combination has been shown to be synergistic in AR-dependent prostate cancer cells (Liu X et al. J Clin Oncol. 2010;28:(suppl; abstr el5049)).

The GE analysis of TNBC described herein demonstrates that with sufficient sample size, distinct subtypes of TNBC can be identified with putative molecular targets. The analyses provides biomarkers that can be used for patient selection in the design of clinical trials for TNBC as well as identification of potential markers of response to treatment. The identification of cell lines representing TNBC tumor subtypes provides key models for preclinical studies with newly developed targeted agents. Example 2 Frequent PIKCA mutations in AR-positive TNBC confer sensitivity to PI3K and AR inhibitors

Described herein is the further investigation of the molecular features of the LAR subtype to identify rationale combinations of drugs that would show robust efficacy against AR-positive TNBC cells with the added goal of generating preclinical data for rationale clinical trial design. It was discovered that PIK3CA kinase mutations were a frequent event in AR-positive TNBC tumors. It was found that genetic or pharmacological targeting of AR in LAR cells increased the therapeutic benefit of PI3K/mTOR inhibition. Further, it was discovered that after bicalutamide treatment of mice bearing LAR xeongraft tumors, the PI3K pathway signaling increased. Thus, the combination of AR antagonism and PDK/mTOR inhibition was examined and an additive or synergistic effect, depending on the TNBC cell line, was found. Given that AR expression is a robust biomarker for selection of LAR TNBC patients, the preclinical findings provide the rationale for preselecting AR- positive TNBC patients for treatment with AR inhibitors and/or P13K/mTOR inhibitors, and future clinical trials exploring the efficacy of combinations of drugs that target AR and PI3K signaling. Results and Discussion

AR-positive TNBC tumors are enriched for PIK3CA kinase domain mutations. High frequency PIK3CA mutations in AR-positive TNBC cell lines are described in Example 1. To determine if PIK3CA mutation was the result of in vitro selection during establishment of the tumor-derived cell lines or if PIK3CA mutations were frequent in AR-positive TNBC tumors, performed Sanger sequencing was performed on 26 AR-positive TNBC cases. PCR-amplified regions from exons 9 and 20 that harbor the most frequently occurring activating mutations in PIK3CA were sequenced. Consistent with the observations in cell lines, nearly all of the detected mutations occurred at amino acid HI 047 (20 of 21) with only one occurring at E545K. Mutation status was confirmed with digital droplet PCR on

DNA amplified from exon 20. Using both Sanger sequencing of exons 9 and 20 and digital droplet PCR, PIK3CA mutations were significantly (P<0.0056) enriched in AR-positive TNBC tumors (17 of 26, 65.3%) versus AR-negative TNBC tumors (10 of 26, 38.4%). Validation of PIK3CA mutations in AR-Expressing TNBC

To verify that PIK3CA mutations were enriched in AR-expressing TNBC, RNA-seq, DNA-seq and reverse-phase protein array (RPPA) data available from The Cancer Genome Atlas (TCGA) breast cohort (Cancer Genome Atlas Network, Nature, 490:61-70 (2012)) was analyzed. Following removal of false negatives for ER, 66 TNBC samples were identified and classified according to molecular subtypes (Chen et al., Cancer informatics, 1 1 : 147-156 (2012)). The distribution of samples across the TNBC molecular subtypes was similar to that previously published (Lehmann et al., J Clin Invest, 121 :2750-2767 (201 1)). (BL1= 14 (21.1%), BL2 = 6 (9.0%), IM= 15 (22.7%), M = 17 (25.7%), MSL=7 (10.6%) and LAR =7 (10.6%) (Figure 26B). As expected, RNA-seq data confirmed significant levels of AR RNA (6.8 vs. 0.3, pO.0001) and protein (1.7 vs. 0.35, pO.0001) in the LAR subtype. Overall, PIK3CA mutations were relatively rare in TNBC cases in the TCGA dataset (4 of 66, 6.0%). However, when sorted by subtype, PIK3CA mutations were significantly enriched in LAR TNBC (3 of 7, 42.8%) compared to all other subtypes (1 of 59, 1.7%, p<0.0001), supporting our analysis of 26 TNBC cases above (Figure 26B).

LAR TNBC cell lines have increased PI3K pathway activation and are sensitive to PI3K and PI3K/mTOR inhibition.

RPPA was used to investigate AR, PTEN, active AKT (pS473 and PT308) and active GSK3 (pS21/pS9) protein levels in LAR cell lines to determine if the PI3K pathway was active in AR expressing, PI3K mutant TNBC. Activated PI3K signaling correlated with PIK3CA mutation and PTEN loss. AR-expressing TNBC cell lines expressed higher levels of active AKT as evidenced by increased pS473 (2.18 vs. -0.23, p=0.007) and pS308 (1.45 vs. -0.10, p=0.0032) and increased downstream phosphorylation of GSK3P at pS9 (1.00 vs. -0.26, p=0.0094) and pS21/pS9 (1.12 vs. -0.19, p=0.0026) (Figure 27). Similarly, those cell lines that were null for PTEN (BT549, CAL51, HCC1395, HCC1937, HCC38, HCC70, MDA-MB- 436 and MDA-MB-468) displayed higher levels of active PI3K signaling (p-AKT S473 1.28 vs. -1.28, p=0.0001 , p-AKT T308 0.41 vs. -0.53, p=0.0018, p-GSK S9 0.37 vs. -0.76, p=0.0002 and pGSKap S9/S21 0.26 vs. -0.59, p=0.0009).

To validate the RPPA data, immunoblot analysis of the PI3K/mTOR pathway in four of the LAR cell lines, as well as primary cultures of normal mammary epithelial cells (HMEC) and the AR-dependent prostate cancer cell line LNCaP (Figure 27), was also performed. AR receptor protein in the LAR TNBC cell lines was expressed at levels equal to or greater than LNCaP cells. AR-expressing cell lines displayed higher levels of phosphorylated AKT (S473) and phosphorylated ribosomal S6, confirming an activated PI3K pathway and downstream mTOR activation, respectively (Figure 27).

To evaluate the clonality of AR expression, immunohistochemistry (IHC) for AR and p-AKT was performed on a cell line-based array (Figure 27). The AR- expressing cell lines displayed differing percentage of cells positive for AR protein expression (CAL148: 20%, MDA-MB-453: 50%, MFM-223 : 50% and SUM185: 90%). However, p-AKT expression was much more homogenous, indicating that activation of PI3K/AKT signaling was an earlier clonal event.

To determine the sensitivity of PIK3CA mutation-bearing LAR cell lines to

PI3K inhibitors relative to other TNBC cell lines, a large panel of TNBC cell lines was treated with pan-PI3K inhibitors BKM120 (Novartis) and GDC-0941

(Genentech) or with the dual PI3K/mTOR inhibitors NVP-BEZ235 (Novartis) and GDC-0980 (Genentech). AR-expressing and other TNBC cell lines containing PIK3CA mutations were among the most sensitive to PI3K inhibition, as indicated by low half maximal inhibitory concentration (IC50) values (Figures 28A-28C). In contrast, PTEN deficiencies, while conferring PI3K pathway activation, did not predict for sensitivity to PI3K inhibitors (Figures 28A-28C). Nearly all of the PIK3CA mutant cell lines were more sensitive to PI3K/mTOR inhibitors than primary cultures of human mammary epithelial cells (HMEC). Genetic and pharmacologic inhibition of AR increases the efficacy of PI3K inhibition.

Whether the combined targeting of AR and PI3K would be more effective than either agent alone was investigated. To determine the effectiveness of combinatorial treatment both genetic knockdown and pharmacological inhibition of AR with shRNA and bicalutamide, respectively, in the presence and absence of PI3K inhibitors were performed (Figures 29A-29B). Immunoblot analysis confirmed decreased AR protein levels in three AR-expressing TNBC cell lines infected with lentiviral particles containing non-targeting shRNA or two AR- targeting hairpins (Figure 29A). Both shRNA expression vectors targeting AR decreased cell viability across multiple doses of PI3K or PI3K7mTOR inhibitors (Figure 29B). Efficacy of AR-targeting in combination with PI3K or PI3K/mTOR inhibitors was independent of compound, as similar results were obtained with BKM-120 and NVP-BEZ235 (Figure 34).

Given that knockdown of AR increased the efficacy of PI3K inhibitors, pharmacological targeting of the AR was performed using bicalutamide. Both GDC-0941 and GDC-0980 displayed efficacy in AR-expressing TNBC cell lines alone and viability could be decreased further with the addition of 25 μΜ

bicalutamide (CDX) across multiple concentrations of PI3K inhibitors (Figure 30A). PI3K inhibition in combination with bicalutamide decreased cell viability in an additive or synergistic manner, as demonstrated by viabilities at, or below, the line of additivity. Immunoblot analysis was performed on cell lystates from untreated cells or cells treated alone or in combination with CDX, GDC-0941 and GDC-0980 to evaluate attenuation of the PI3K/mTOR pathway. Both GDC-0941 and GDC- 0980 decreased activated AKT (p-S473), however, GDC-0980 was more effective at decreasing mTOR activity as measured by the decrease in levels of phosphorylated S6 (Figure 30B). In contrast, bicalutamide (CDX) had minimal effect on either the PI3K/mTOR pathway or AR levels. Interestingly, GDC-0980 alone decreased AR levels and this decrease could be increased further by the addition of bicalutamide, suggesting that mTOR signaling contributes to the stability of AR protein, similar to crosstalk observed in prostate cancer (Figure 30B) (Carver et al., Cancer Cell, 19:575-586 (2011)).

To determine if the decreased viability observed in AR-positive TNBC cell lines that were treated with the combination of pathway inhibitors described above was attributed to apoptotic cell death, activated caspase 3/7 levels after drug treatments were measured (Figures 31 A-3 IB). Cells were treated with a high dose of adriamycin (ADR, 3 DM) to measure the ability of cells to engage the apoptotic pathway. With exception of MFM-223 cells, each of the cell lines underwent robust caspase activation after 48 h of ADR treatment. While bicalutamide treatment did not induce caspase activation, treatment with both GDC-0941 and GDC-0980 alone caused caspase activation and the levels of apoptosis could be increased in combination with bicalutamide (Figure 31 A).

In addition to caspase activation, the percentage of sub 2N DNA (sub-Gl)- containing cells (indicative of late stage apoptotic DNA fragmentation) was quantitated by FACs analysis. While bicalutamide treatment did not result in caspase activation, there was an increase in the sub-Gl fraction 48 h after treatment (5.25% to 18.44%) (Figure 3 IB). Treatment with GDC-0941 or GDC-0980 resulted in higher sub-Gl fraction that increased in combination with bicalutamide, consistent with the caspase activation described above (Figure 3 IB). Therefore, the decreased viability of AR-positive TNBC cell lines treated with a combination of AR and PI3K inhibitors can be, in part, attributed to apoptosis.

Simultaneous targeting of AR and PI3K decreases viability of AR-expressing cell lines grown in a 3-D forced suspension assay.

Since cell viability and drug sensitivity can be influenced by 2-D versus 3-D growth conditions, combined targeting of AR and PI3K mTOR was evaluated in a 3-D forced suspension assay devoid of matrix. Cell lines were grown in forced suspension in agarose coated 96-well plates and treated with GDC-0941 or

GDC0980 alone or in combination with bicalutamide (Figures 32A-32C).

Increasing doses of GDC-0941 and GDC-0980 alone decreased cell aggregate size and viability in AR-expressing TNBC cell lines to a lesser degree when compared to similar treatments in 2-D (Figures 32A and 32B). However, the addition of bicalutamide was either additive or synergistic with PI3K/mTOR inhibition (Figure 32B). Similar results were obtained when PI3K was inhibited with BKM120 or PI3K and mTOR were targeted with NVP-BEZ235 (Figure 35). Together, these results indicate that simultaneous targeting of AR and PI3K/mTOR is more effective than either agent alone in AR-expressing TNBC.

PI3K is activated after pharmacological inhibition of AR.

As described in Example 1, evaluation of bicalutamide in AR-expressing TNBC xenografts demonstrated significant reduction in tumor burden, however over time tumor growth occurred in the presence of drug, consistent with evolution of bicalutamide-resistant tumor cells. Since crosstalk between AR and PI3K has been demonstrated in prostate cancer, whether the level of PI3K activity changed between untreated and bicalutamide-resistant xenograft tumor cells was examined. Phospho- AKT (S473) levels were elevated in bicalutamide-resistant xenograft tumor cells (Figure 37). Consistent with these findings were the findings herein of dose- dependent increases in p-AKT in cell lines upon genetic silencing of AR by shRNA (Figure 38B). Simultaneous inhibition of AR and PI3K signaling decreases the growth of LAR cell line-derived xenograft tumors.

In order to further validate and investigate the combined targeting of AR and PI3K in AR-expressing TNBC in vivo, xenograft tumors in nude mice was established using two representative AR-positive TNBC cell lines (CAL-148 and MDA-MB-453). Following tumor establishment (25-50 mm3), mice were either treated with vehicle, bicalutamide, NVP-BEZ235, GDC-0980 alone or either NVP- BEZ235 or GDC-0980 in combination with bicalutamide. While each single agent alone significantly decreased tumor growth, both PI3K/mTOR inhibitors in combination with bicalutamide inhibited tumor growth to the greatest extent (Figure 32C). Thus, the combined inhibition of AR and PI3K appears to be a rational treatment strategy for AR-expressing TNBC and should be considered in future clinical trial design.

Discussion

Herein we show that activating mutations in PIK3CA are a frequent event in a subset of AR-expressing TNBC (LAR) that display luminal gene expression patterns. The data herein provide the preclinical rationale for targeting AR in combination with PI3K/mTOR inhibition.

Higher levels of p-AKT in cell line xenografts after prolonged treatment with bicalutamide and increased p-AKT after siR A knockdown of AR in cell lines is demonstrated herein. The data show that combined targeting of AR and PI3K pathway inhibition can overcome the PI3K pathway activation after AR inhibition. These studies demonstrate that both pathways cross-regulate each other by reciprocal feedback and coordinately support survival when either is repressed. The feedback between the AR and PI3K pathway may, in part, explain the enrichment of PIK3CA mutations observed in AR-positive TNBC, as this co-evolution may be necessary to overcome the increased PTEN levels driven by AR signaling.

Methods

PIK3CA mutation evaluation.

Two independent methods were used to identify PIK3CA mutations in tumor samples. Samples were identified as mutant if they had greater than 5% of the cells positive by microdroplet PCR, as there was complete concordance with Sanger sequencing calls, or called mutant by Sanger when microdroplet samples were <5% and >1% (Figures 33A-33B).

Sanger

To detect mutations in PIK3CA (HI 047), a 588 bp fragment was PCR- amplified from exon 20 of PIK3CA using the following amplification primers; foreword: TGACATTTGAGCAAAGACCTG (SEQ ID NO: 1), reverse:

CATAACATGAAATTGCGCATT (SEQ ID NO: 2). Sanger sequencing was performed on the PCR amplicon using the following internal sequencing primers; Exon Forward: ACATCATTTGCTCCAAACTGA (SEQ ID NO: 3)

Reverse: CCTATGCAATCGGTCTTTGC (SEQ ID NO: 4) (Samuels et al., Science, 304:554 (2004)).

Droplet Digital PCR

The TaqMan PCR reaction mixture was assembled from a 2x ddPCR Mastermix (Bio-Rad), 20x primer, and probes (final concentrations of 900 and 250 nM, respectively) and template (variable volume) in a final volume of 20 μί. Each assembled ddPCR reaction mixture was then loaded into the sample well of an eight- channel disposable droplet generator cartridge (Bio-Rad). A volume of 70 of droplet generation oil (Bio-Rad) was loaded into the oil well for each channel. The cartridge was placed into the droplet generator (Bio-Rad). The cartridge was removed from the droplet generator, where the droplets that collected in the droplet well were then manually transferred with a multichannel pipet to a 96-well PCR plate. The plate was heat-sealed with a foil seal and then placed on a conventional thermal cycler and amplified to the end-point (40 cycles). Thermal cycling conditions were 95°C x 10 min (1 cycle), 94°C x 30 s and 57°C x 60 s (40 cycles), and 12°C hold. After PCR, the 96-well PCR plate was loaded on the droplet reader (Bio-Rad), which automatically reads the droplets from each well of the plate.

Analysis of the ddPCR data was performed with QuantaSoft analysis software (Bio- Rad) that accompanied the droplet reader. Primer sequences: Forward:

5'GATAAAACTGAGCAAGAGGCTTTGG' (SEQ ID NO: 5), Reverse:

5'GCTGTTTAATTGTGTGGAAGATCCAA' (SEQ ID NO: 6), Wild type probe: 5'- VIC-CCACCATGATGTGCA-MGB-3 (SEQ ID NO: 7), 'H1047L probe:5*-FAM- CCACCATGATGAGCA-MGB-3' (SEQ ID NO: 8), HI 047R probe: 5'-FAM- CCACCATGATGCGCA-MGB-3' (SEQ ID NO: 9).

Subtyping TNBC cases and validating PIK3CA mutations in the TCGA.

RNA-seq (lvl 3), somatic mutations (lvl2) and reverse phase protein array

(RPPA, lvl3) data for breast cancer was downloaded from The Cancer Genome Atlas (tcga-data.nci.nih.gov/). RNA-seq data (RPKM) was combined and molecularly subtyped using the online TNBCtype software according to published methods (Chen et al., Cancer Informatics, 1 1 : 147-1556 (2012)). After removal of potential ER positive, 137 TNBC samples were identified and assigned a molecular subtype.

Reverse Phase Protein Array on TNBC cell lines.

Tumor or cell lysates were two-fold-serial diluted for 5 dilutions (from undiluted to 1 : 16 dilution) and arrayed on nitrocellulose-coated slide in 1 lxl 1 format. Samples were probed with antibodies by CSA amplification approach and visualized by DAB colorimetric reaction. Slides were scanned on a flatbed scanner to produce 16-bit tiff image and the density was quantified by Micro Vigene.

Relative protein levels for each sample were determined by interpolation of each dilution curves from the standard curve (supercurve) of the slide (antibody) as previously described (Zhang et al., Bioinformatics, 25 :650-654 (2009)). These values were Log2 transformed and normalized for protein loading by transformation a to linear value. Linear normalized data was then median-centered for heatmap comparisons. Cell proliferation/viability assays and IC50 determinations.

All cell lines were maintained in culture as previously described (JCI ref). Breast cancer cell lines and HMECs were seeded (3,000-10,000 cells) in quadruplicate wells in 96-well plates. Media was then replaced with either fresh media (control) or media containing half-log serial dilutions of the following drugs: GDC-0941 (10-nM-3000nM), GDC0980 (1ηΜ-300ηΜ), BKM120 (10-nM- 3000nM) and NVP-BEZ235 (lnM-300nM) purchased from Selleck Chemicals (Houston, TX) or in combination with bicalutamide (Sigma, St. Louis, MO).

Viability was determined from measuring fluorescent intensity after metabolic reduction of Alamar Blue in the presence/absence of drug after 72 hours. Viability assays were performed in triplicate and replicates were normalized to untreated wells. Inhibitory concentration (IC50) values were determined after double-log transformation of dose response curves as previously described (Lehmann et al., J Clin Invest, 121 :2750-2767 (2011)).

Quantification of apoptosis

Caspase 3/7 activity

Cell lines were seeded in triplicate in 96-well plates. Media was removed and replaced with media containing vehicle (control) or indicated drugs. After 48 hrs, apoptosis was determined by measuring luciferase from activated caspase 3/7 upon addition of Caspase-Glo reagent (Promega, Madison, WI). Relative levels of caspase activity were normalized to viable cell number determined by metabolic reduction of Alamar blue. shRNA knockdown of AR

293FT cells were transfected with Lipofectamine 2000 (Invitrogen, Grand Island, NY) the packaging vectors PAX2 and pMD2.g (Addgene, Cambridge, MA) along with pLKO.l-puro Misson shRNA constructs (Sigma) targeting AR or a non- targeting control. Viral media was harvested 48h post-transfection and added to target cells along with polybrene (10 g/mL). 72h post-infection MDA-MB-453 (8,000 cells/well), CAL-148 (5,000 cells/well) and MFM-223 (7,000 cells/well) seeded in quadruplicate in 96-well plates. Following overnight attachment media was replaced with fresh media (control) or media containing half-log dilutions of drugs. Following incubation in drug for 72h viability was measured with alamarBlue as previously described (Lehmann et al., J Clin Invest, 121 :2750-2767 (201 1)).

Forced Suspension Viability Assay

CAL-148, MFM-223 and MDA-MB-453 cells were plated (10,000 cells/well) in quadruplicate into 96-well plates coated with 0.9% agarose diluted in corresponding media. Following 5 d of growth, cells were then treated with increasing half-log concentrations of GDC-0941 (10 - 3000 nM), GDC0980 (1 nM - 300 nM), BKM120 (10 nM - 3000 nM) and NVP-BEZ235 (1 nM - 300 nM) alone or in combination with 25 μΜ bicalutamide (CDX) for an additional 72 h, upon which cells were imaged and viability determined by Alamar blue.

Immunostaining

Formalin fixed paraffin embedded (FFPE) tissue were used for

immunohistochemical studies. Androgen receptor (AR) expression and p-AKT expression were evaluated in FFPE tissue using the DAKO (Carpinteria, CA) antibody: AR (clone AR41 1) at a 1 :50 dilution and AKT p473 (Cell signaling, CA) at a 1 :200 dilution for 1 h at room temperature. FFPE tissue was subject to antigen retrieval with high pH buffer (pH 8.0) followed by overnight incubation with an AR (1 :30) or Ki67 (1 :75) antibody dilution overnight.

Immunoblotting

Cells were trypsinized, lysed and relative protein expression was determined by Western blot as previously described (JCI reference) with the following antibodies; the AR polyclonal antibody, SC-N20 (Santa Cruz Biotechnology), pS6- Ser235/236 (Cell Signaling #4856), S6 (Cell Signaling #2317), pAKT-S473 (Cell Signaling #9271), AKT (Cell Signaling #9272), PARP (Cell Signaling #9542) and GAPDH (Millipore).

Xenograft Tumor Studies.

Five-week-old female athymic nude-Foxnlnu mice (Harlan Sprague- Dawley) were injected (s.c.) with either ~4 x 106 (CAL-148) or -10 x 106 (MDA- MB-453) cells suspended in media (200 μί) into each flank using a 22-gauge needle. Once tumors reached a volume of 25-50 mm3, mice were randomly allocated to treatment or vehicle arms. Treatments included bicalutamide p.o. (100 mg/kg/d), GDC0980 p.o. (7.5mg/kg/d) in 5% DMSO, 0.5% carboxymethyl cellulose (MCT) or NVP-BEZ235 p.o. (50 mg/kg/d) in 0.5% MCT or the combination of bicalutamide and either GDC-0980 or NVP-BEZ235 according to the above concentrations. Tumor diameters were serially measured at the indicated times with digital calipers and tumor volumes were calculated as width2 x length/2. Supplemental Results

Validation of digital droplet PIK3CA mutant PCR primers

Primers were validated using on cell lines with known PIK3CA status cells. The percentage of DNA reflected the frequency of mutant PIK3CA alleles similar to expected genotype status; HMECwt/wt = 0.01%, MDA-MB-453wt/mut = 61.19% and SUM-185mut/mut = 97.09% (Figure 26A). The slightly higher mutant frequency in the heterozygous mutant cell line MDA-MB-453 is likely caused by chromosomal aneuploidy. Digital droplet PCR was far more quantitative than Sanger sequencing and both methods were 100% concordant when mutant DNA was present at levels greater than 5% (Figures 33A-33B).

Example 3

Cell proliferation/viability assays and IC50 determinations. All cell lines were maintained in culture as previously described herein. Breast cancer cell lines and HMECs were seeded (3,000-10,000 cells) in quadruplicate wells in 96-well plates. Media was then replaced with either fresh media (control) or media containing half- log serial dilutions of the following drugs: Sorafenib, OSi-906, BMS-754807 and PF2341066 purchased from Selleck Chemicals (Houston, TX) Viability was determined from measuring fluorescent intensity after metabolic reduction of

Alamar Blue in the presence/absence of drug after 72 hours. Viability assays were performed in triplicate and replicates were normalized to untreated wells. Inhibitory concentration (IC50) values were determined after double-log transformation of dose response curves.

PDGFR inhibition selectively inhibits viability of mesenchymal-like TNBC cell lines. Bar graph displays the half-maximal inhibitory concentration (nM) of the PDGFR inhibitor sorafenib for a panel of TNBC cell lines. Colorbar below designates the TNBC subtype (basal-like= black, mesenchymal-like= grey) or normal cells (white).

IGF1R inhibition selectively inhibits viability of mesenchymal-like TNBC cell lines. Bar graph displays the half-maximal inhibitory concentration (nM) of the IGF 1R inhibitor OSI-906 for a panel of TNBC cell lines. Colorbar below designates the TNBC subtype (basal-like= black, mesenchymal-like=grey) or normal cells (white).

IGFIR inhibition selectively inhibits viability of mesenchymal-like TNBC cell lines. Bar graph displays the half-maximal inhibitory concentration (nM) of the IGFIR inhibitor BMS-754807 for a panel of TNBC cell lines. Colorbar below designates the TNBC subtype (basal-like= black, mesenchymal-like= grey) or normal cells (white).

MET inhibition selectively inhibits viability of mesenchymal-like TNBC cell lines. Bar graph displays the half-maximal inhibitory concentration (nM) of the MET inhibitor PF2341066 for a panel of TNBC cell lines. Colorbar below designates the TNBC subtype (basal-like= black, mesenchymal-like= grey) or normal cells (white).

ASource: GEO. BGSE-5327 and GSE-16446 date sets are derived from ER-negalve tumors y . - aa se ncu es ony , an negative tumors by IHC.

Table 4

Top gene ontologies for TNBC subtypes in the training and validation datasets

Subtype Training Set (386) Validation Set (201)

BL1

'"¥N ETOGATI0N REACTOME ΊΒΕΤΧ" CYCLE KEGG

CELL CYCLE KEGG DMA REPLICATION REACTOME

fWA POLYMERASE Gi PATHWAY

CELL CYCLE METHIONINE METABOLISM

GLYCOSPHINGOLtPID BIOSYNTHESIS NEO CHOLESTEROL BIOSYNTHESIS

LACTOSERIES CELL CYCLE

ATRBRCAPATHWAY RNA POLYMERASE

UBIQUITIN MEDIATED PROTEOLYSIS ATRBRCAPATHWAY

©2 PATHWAY 01 TO S CELL CYCLE REACTOME

PROTEASOME CASPASE CASCADE

G1 TO S CELL CYCLE REACTOME GLUTAMATE METABOLISM

METHIONINE METABOLISM AMtNOACYL TRNA BIOSYNTHESIS

FASPATHWAY GLYCOSPHINGO LIPID BIOSYNTHESIS NEO LACTOSERIES

CHOLESTEROL BIOSYNTHESIS G1 AND S PHASES

PYRIMIDINE METABOLISM CYSTEINE METABOLISM

AMlNQACYL TRNA BtOSYNTHESIS ATMPATHWAY

DMA POLYMERASE MRNA PROCESSING REACTOME

CARBON FIXATION BIOSYNTHESIS OF STEROIDS

ALANINE AND ASPARTATE METABOLISM KREBS TCA CYCLE

PROTEASOME PYRIMIOINE METABOLISM

CELLCYCLEPATHWAY PROTEASOMEPATHWAY

CDMACPATHWAY SELENOAMINO ACID METABOLISM

KREBS TCA CYCLE CELLCYCLEPATH AY

ALANINE AND ASPARTATE METABOLISM

GAO PATHWAY

A INOACYLTRNA BIOSYNTHESIS

FASPATHWAY

PROTIASOME

Table 4

Top gene ontologies for TNBC subtypes in the training and validation datasets

Subtype Training Set (386) Validation Set (201)

BL2

PORPHYRIN AND CHLOROPHYLL METABOLISM BLADDER CANCER '

6ATA3PATHWAY GATA3PATHWAY

STEMPATHWAY EPITHELIAL CELL SIGNALING IN HELICOBACTER PYLORI

PARKINSONS DISEASE INFECTION

STARCH AND SUCROSE METABOLISM CARDIACEOFPATHWAY

EGFPATHWAY NIOOTINATE AND NICQTSNAMBI METABOLISM

INSULINPATHWAY ED01 PATHWAY

RENAL CELL CARCINOMA PANTOTHENATE AND COA BIOSYNTHESIS

WNT SIGNALING TOLLPATHWAY

AMINOACYL TRNA BIOSYNTHESIS GALACTOSE METABOLISM RENIN ANGIOTENSIN SYSTEM

0 GLYCAN BIOSYNTHESIS ECM RECEPTOR INTERACTION

AT PATHWAY PHOTOSYNTHESIS HUNTINOTONS DISEASE NTHIPATHWAY IL6PATHWAY AMINOSUOARS METABOUSM

Q1 TO S CELL CYCLE REACTO E EiCOSANOIO SYNTHESIS

FOCAL ADHESION DORSO VENTRAL AXIS FORMATION IGF1PATHWAY NQFPATHWAY

U6IQU1TIN MEDIATED PROTEOLYSIS SPRYPATHWAY

GLIOMA ECMPATHWAY

TPOPATHWAY METPATHWAY

02PATHWAY COMACPATHWAY

WNT BETA CATENIN PATHWAY ARFPATHWA

INSULIN RECEPTOR PATHWAY IN CARDIAC P38 MAPK PATHWAY

MYOCYTES CHOLERA INFECTION

METPATHWAY

HYPERTROPHY MODEL

PS3 SIGNALING PATHWAY

CARDIACEOFPATHWAY WNT BETA CATENIN PATHWA

RASPATHWAY ERKPATHWAY

NFKBPATHWAY F LPPATHWAY

UCALPAINPATHWAY GLYCOLYSIS

GS 3PATHWAY ATMPATHWAY

GLYCOLYSIS AND GLUCONEOGENESIS IGF1RPATHWAY

EPOPATHWAY 6LYC08AMINOQLYCAN DEGRADA ION

SPRYPATHWAY GALACTOSl METABOLISM

TRKA RECEPTOR ER 1 ER 2 MA PATHWAY

PHOSPHOINOSmDE 3 KINASE PATHWAY GLUCONEOGENESIS

PROTEASOME t AND 2 METH YLNAPHTHALBN E DEGRADATION

P38 MAPK PATHWAY

PATHOGENIC ESCHERICHIA COLI INFECTION BPEC VEOFPATHWAY

CHOLERA INFECTION ETSPATHWAY

TIDPATHWAY PROSTAGLANDIN SYNTHESIS REGULATION

ECMPATHWAY BENZOATE DEGRADATION VIA COA UQATION

POGFPATHWAY NFKBPATHWAY

ARGININE AND PROLINE METABOLISM GLYCOLYSIS HEPARAN SULFATE BIOSYNTHESIS PROTEASO EPATHWAY KTHIPATHWAY

EPITHELIAL CELL SIGNALING IN HELICOBACTER

PYLORI INFECTION

NQFPATHWAY

ETSPATHWAY

ERK1 ERK2 MAPK PATHWAY

GALACTOSE METABOLISM GAP JUNCTION ERYTHPATHWAY

GLYCOLYSIS ANO GLUCO EOGENESIS

MYOCYTE AD PATHWAY Table 4

Top gene ontologies for TNBC subtypes in the training and validation datasets

Subtype Training Set (386) Validation Set (201 )

IM

CTLA4PATHWAY N02IL12PATHWAY

N02IL12PATHWAY

TH 1 TH2PATHWAY CTLA4PATHWAY

CSKPATHWAY 1L12PATHWAY

NKTPATHWAY TYPE 1 DIABETES MELUTUS

COMPPATHWAY NKCELLSPATKWAY

lUrPATHWAY

PROTEASOME TH1TM2PATHWAY

NKCELLSFATHWAY TNFR1MTHWAY

TYPE I DIABETES MELUTUS CS PATH AY

AMIPATKWAY ILTPATHWAY

ANTIGEN PROCESSING AND PRESENTATION

1LI2PATHWAY AMIPATHWAY

LAIRPATHWAY ANTIGEN PROCESSING AND PRESENTATION

TNFR2PATMWAY NFKBPATHWAY

OCPATHWAY TUMOR NECROSIS FACTOR PATHWAY

TtDPATHWAY PROTEASOME

APOPTOSIS GENMAPP

NF BPATHWAY NKTPATHWAY

PROTEASOMEPATHWAY T CELL SIGNAL TRANSDUCTION

T CELL SIGNAL TRANSDUCTION DEATH PATHWAY

TUMOR NECROSIS FACTOR PATHWAY

CASPASEPATHWAY APOPTOSIS GENMAPP

APOPTOSIS OCPATHWAY

NATURAL KILLER CELL MEDIATED CYTOTOXICITY RELAPATHWAY TOB1PATKWAY TOB1 PATHWAY

B CELL RECEPTOR SIGNALING PATHWAY APOPTOSIS

T CELL RECEPTOR SIGNALING PATHWAY

CYTOKINEPATHWAY APOPf OSiS KEGG

CELL ADHESION MOLECULES T CELL RECEPTOR SIGNALING PATHWAY

DEATH PATHWAY CYTOWNEPATHWAY

APOPTOSIS EGQ CASPASEPATHWAY

TOLL LIKE RECEPTOR SIGNALING PATHWAY PROTEASOME TIOPATHWAY

HEMATOPOIETIC CELL LINEAGE ILZPATHWAY

RBLAPATHWAY NATURAL KILLER CELL ME DIATED CYTOTOXICITV

IL2PATHWAY CASPASE CASCADE

NUTOCHONDRIAPATHWAY

TOLLPATH AY LAIRPATHWAY

B CELL ANTIGEN RECEPTOR HEMATOPOIETIC CELL LINEAGE

INFLAMPATKWAY IL3PATHWAY

IL2RBPATHWAY COMPPATHWAY

IL3PATHVYAY

STEMPATH AY TCRPATHWAY

BCR SIGNALING PATHWAY B CELL RECEPTOR SIGNALING PATHWAY

HIVNEFPATHWAY STEMPATHWAY

TCRPATHWAY CASPASE CASCADE MITOCHONDRIA? ATHWAY

PIPS SIGNALING IM E LYMPHOCYTES CELL ADHESION MOLECULES NTHIPATHWAY TOLLPATHWAY

41BBPATHWAY STRESSPATHWAY

CC S PATHWAY t RBPATHWAY

CYT0KINI CYTOKlNf RECEPTOR INTERACTION INFLAMPATHWAY

INTERLEUKIN 4 PATHWAY CYTOKINE CYTOKINE RICEPTOR INTERACTION

STRESSPATHWAY PIP3 SIGNALING IN B LYMPHOCYTES

IL1RPATHWAY TOLL LIKE RECEPTOR SIGNALING PATHWAY

APOPTOSIS

EICOSAN0ID SYNTHESIS 41BBPATH AY

UB!QUmN MEDIATED PROTEOLYSIS B CELL ANTIGEN RECEPTOR

CELLCYCLEPATHWAY BCR SIGNALI G PATHWAY CALCINEURIN NF AT SIGNALING PROTEASOME FC EP8ILON Rl SIGNALING PATHWAY CERAMIDEPATHWAY

AMINOACYL TRNA BIOSYNTHESIS APOPTOSIS JAK STAT SIGNALING PATHWAY JAK STAT SIGNALING PATHWAY FASPATHWAY PTEN PATHWAY EPOPATHWAY

NTHIPATHWAY Table 4

Top gene ontologies for TNBC subtypes in the training and validation datasets

Subtype Training Set (386) Validation Set (201)

IM B CELL RECEPTOR COMPLEXES CD4QPATHWAYMAP

cont'd DNA REPLICATION REACTOME CALCINEU RIM NF AT SIGNALING

FOLATE BIOSYNTHESIS ATRBRCAPATHWAY HIWEFPATHWAY IL6PATHWAY eSDSANOfD SYNTHESIS

LEUKOCYTE TRANSENDOTHELIAL MIGRATION BCRPATHWAY

GLEEVECPATHWAY FC EPSILON l SIGNALING PATHWAY

TNFR1 PATHWAY KiRATmOCYTEPATHWAY

COMPLEMENT AND COAGULATION CASCADES

BCR PATHWAY AMI OACYL TR A IIOSYNTH ES1S

EPOPATHWAY 6ELLGYCLEPATM AY

GHPATHWAY B CELL RECEPTOR COMPLEXES

TPOPATHWAY IL6PATHWAY

AMINOACYL RNA BIOSYNTHESIS PROTEASOMEPATHWAY

PEPTIDE GPCRS

N CCRSPATHWAY GLYCAM BIOSYNTHESIS

AL2HBMERS DISEASE ATRBRCAPATHWAY

ACUTE MYELOID LEUKEMIA GHPATHWAY

GAQ PATHWAY FOLATE BIOSYNTHESIS

C D40PATH WAYMAP PEPTttJi GPCRS

METHIONINE METABOLISM GLEEVEC PATHWAY

GPCRDB CLASS B SECRETIN LIKE

IL4RECEPTOR IN B LYPHOCYTES FASPATMWAY

CERA IOEPATHWAY AlC 'TRANSPORTERS GENERAL,

FCERI PATHWAY INTERLEUK1N 4 PATHWAY

01 TO S CELL CYCLE REACTOME l RECIPTOR IN B LYPHOCYTlS

PARKINSONS DISEASE ADIPOCYTOKINE SIGNALING PATHWAY

CHEMICALPATHWAY

CELL CYCLE KEGG PROSTAQLANDiNi AND LEUKOTRIENE METABOLISM

SNARE INTERACTIONS IN VESICULAR TRANSPORT CDMAGFAWWAY AT PATMWAY TPOPATHWAY ICOTINATE AND NICOTINAMIDE METABOLISM HSP27PATHWAY

KERATINOCYTEPATH AY TNFR1 PATHWAY

HYPERTROPHY MODEL

ADIPOCYTOWNE SIGNALING PATHWAY INOSITOL PHOSPHATE METABOLISM

DENTATORUBROPALUDOLUY8IAN ATROPHY ACUTE MYELOID LEUKEMIA

F LPPATHWAY LEUKOCYTE TRANSENDOTHELIAL MIGRATION

D!CTYQSTELfUM DISCOIDEUM CAMP 0HEMOTAXIS IL1RPATHWAY

PATHWAY DINTATORUBROPALUDOLUYSIAN ATROPHY

BIOPEPTIOESPATHWAY OVARIAN INFERTILITY GENES PANCREATIC CANCER FAS SIGNALING PATHWAY PTEN PATHWAY RACCYCDPATHWAY AMINOSUGARS METABOLISM

Table 4

Top gene ontologies for TNBC subtypes in the training and validation datasets

Subtype Training Set (386) Validation Set (201)

M

CELL COMMUNICATION PT1MPATHWAY

HEPARAN SULFATE BIOSYNTHESIS DMA REPLICATION EACTOWE

ECM RECEPTOR INTERACTION RlBOSOME

ALKPATHWAY

TRANSLATIO FACTORS

UCALPAINPATHWAY RNA POLYMERASE

STRIATED MUSCLE CONTRACTION DMA POLYI RASI

BASAL CELL CARCINOMA BASAL TRANSCRIPTION FACTORS REGULATION OF THE ACTIN CYTOSKELETON BY IGF1 TORPATHWAY

RKO GTPASES R A TRANSCRIPTION REACTOME

TGF BETA SIGNALING PATHWAY CHOLESTEROL BIOSYNTHESIS HOACPATHWAY OtYCOSYLPHOBPHATlDYLINOSrrOL ANCHOR BIOSYNTHESIS

HEDGEHOG SIGNALING PATHWAY ECMPATHWAY

INOSITOL PHOSPHATE METABOLISM UCALPAINPATHWAY

IGF1 PATHWAY

EIF4PATHWAY

ECMPATHWAY

RIBOSOMAL PROTEINS

ERK5PATHWAY

FOCAL ADHESION CELL CYCLE KEGG

EDG1 PATHWAY MTORPATH AY

CAR ERPATHWAY REQULAT!ON OF THE ACTIN CYTOSKELETON BY RHO

INSUUNPATHWAY GTPASES

ETHER LIPID METABOLISM G1 TO S CELL CYCLE REACTOME .

TELPATHWAY MRNA PROCESSING REACTOME

NOTCH SIGNALING PATHWAY usiQurriN MEDIATED PROTEOLYSIS

WNT BETA CATENIM PATHWAY WNTPATHWAY

MEF2DPATHWAY AU PATHWAY

INTEGRINPATHWAY Q2PATHWAY

ELANOGENESIS TELPATHWAY

VEGFPATHWAY CELL CYCLE

WHTPATHWAY ATR6RCAPATH AY

ETSPATHWAY

OORSO VENTRAL AXIS FORMATION

Table 4

Top gene ontologies for TNBC subtypes in the training and validation datasets

Subtype Training Set (386) Validation Set (201)

MSL PROSTAGLANDIN SYNTHESIS REGULATION N02A12PATHWAY

BADPATKWAY CSKPATHWAY

LAIRPATHWAY LAIRPATHWAY

IL7PATKWAY TOB1PATHWAY

COM PPATH WAY CTLA4PATHWAY

RENIN ANGIOTENSIN SYSTEM AMIPATHWAY

AMIPATHWAY TH1TH2PATHWAY

CSKPATHWAY RENIN ANGIOTENSIN SYSTEM

ERYTHPATHWAY PARI PATHWAY

ECM RECEPTOR INTERACTION NKTPATHWAY

TOB1 PATHWAY IL7PATHWAY

COMPLEMENT AND COAGULATION CASCADES NKCELLSPATHWAY

CARDIACEGFPATHWAY

HISTICMNE METABOLISM OCPATHWAY

GATA3PATHWAY ERYTHPATHWAY

NDKDYNAMINPATNWAY - HEMATOPOIETIC CELL LINEAGE

CCR5PATHWAY TYPE 1 DIABETES MELLITUS

FOCAL ADHESION IL3PATHWAY

BETA ALANINE METABOLISM GLYCOSAMINOQLYCAN DEGRADATION

INTRINSICPATHWAY IL12PATHWAY

PARI PATHWAY IL2PATHWAY

ALKPATH AY PROSTAGLANDIN SYNTHESIS REGULATION

CALCI NEURINPATHWAY EICOSANOID SYNTHESIS

NTHIPATHWAY COMPPATHWAY

CTLA4PATHWAY CCR5PATHWAY

BETA ALANINE METABOLISM GLYCAN STRUCTURES DEGRADATION

GCRPATHWAY CALCIMEURINPATHWAY

HEMATOPOIETIC CELL LINEAGE COMPLEMENT AND COAGULATION CASCADES

N01 PATHWAY ECM RECEPTOR INTERACTIO

VIPPATHWAY TCRPATHWAY

UCALPAINPATHWAY

EDG1 PATHWAY W 2PATHWAY

N01PATHWAY

IL3PATHWAY

I GF1 PATHWAY INTRINSICPATHWAY

TCRPATHWAY STATIN PATHWAY PHAR G B

EICOSANOID SYNTHESIS ALKALOID BIOSYNTHES I S 11

N CELLSPATHWAY LEUKOCYTE TRANSENDOTHELIAL MIGRATION

VALINE LEUCINE AND fSOLEUCINE DEORADATtON HSA045H CELL ADHESION MOLECULES PPARAPATHWAY lNFLAMPATHWAY

SPRYPATHWAY MEF2DPATHWAY

HDACPATHWAY IL2RBPATHWAY

STATIN PATHWAY PHARMGKB WNT BETA CATENIN PATHWAY GLYCAN STRUCTURES DEGRADATION H SA04E10 FOCAL ADHESION

SMOOTH MUSCLE CONTRACTION NTHIPATHWAY TH1TH2PATHWAY BCRPATHWAY

FC PATHWAY . BCR SIGNALING PATHWAY

GPCRPATHWAY GCRPATHWAY PDGFPATHWAY GLYCEROLIPID METABOLISM

INTEGRIN MEDIATED CELL ADHESION KEOQ HlSTlD!NE METABOLISM

FATTY ACIO METABOLISM GATA3PATHWAY

NKTPATHWAY PEPTIDE GPCRS

CXCR4PATHWAY PIP3 SIGNALING IN B LYMPHOCYTES

TOLLPATHWAY CYTOKINE CYTOKINE RECEPTOR INTERACTION

TNFR2PATHWAY INOSITOL PHOSPHATE METABOLISM

LEUKOCYTE TRANSENOOTHS UAL MIGRATION BADPATHWAY PPAR SIGNALING PATHWAY MONOAMINE GPCRS AMYOTROPHIC LATERAL SCLEROSIS NFKBFATHWAY GLYCEROUPIO METABOLISM ECM PATHWAY QLYCOSPHINGOLIPID METABOLISM 1 AND 2 METHYLNAPHTHALENE DEGRADATION MTOR PATHWAY B CELL RECEPTOR SIGNALING PATHWAY

NICOT1NATE AND NICOTINAMIDE METABOLISM VIPPATHWAY

PGCIAPATHWAY CARDIACEGFPATHWAY

METPATHWAY ALKPATHWAY

WNT BETA CATENIN PATHWAY IWRECEPTOR IN B LYPHOCYTES TPOPATHWAY GHPATHWAY BUTANOATE METABOLISM NATURAL KILLER CELL MEDIATED CYTOTOXICITY Table 4

Top gene ontologies for TNBC subtypes in the training and validation datasets

Subtype Training Set (386) Validation Set (201)

MSL TGF BETA SIGNALING PATHWAY RAC1 PATHWAY

con'd GSK3PATHWAY EOG1PATHWAY

GLYCOSAMINOGLYCAN DEGRADATION

BETA ALANINE METABOLISM

PROPANOATE METABOLISM

CELL COMMUNICATION UCALPAINPATHWAY

OVARIAN INFERTILITY GENES GPCRPATHWAY

VALINE LEUCINE ΛΝΟ ISOLEUCINE DEGRADATION E K1 ER 2 MARK PATHWAY

BILE AGIO BIOSYNTHESIS INTBMHN MEDIATED CELL ADHESION KEQO

ILBPATHWAY ARACHIDONIC ACID METABOLISM '

BCR SIGNALING PATHWAY ABC TRANS ORTERS GENE AL

CELL ADHESION MOLECULES RHOPATHWAY

B CELL RECEPTOR SIGNALING PATHWAY

BCRPATHWAY JAK STAT SIGNALING PATHWAY

PTENPATHWAY HDACPATHWAY CALPAiNPATHWAY BILE ACID BIOSYNTHESIS

1 AND 2 METHYL NAPHTHALENE DEGRADATION TOLLPATHWAY

QPCRDB CLASS 8 SECRETIN LIKE SPPAPATHWAY

IL2PATHWAY NDKDYNA INPATHWAY

PROPANOATE METABOLISM

MELANOMA SMOOTH MUSCLE CONTRACTION

INTEGRiNPATHWAY CALCIUM: SIGNALING PATHWAY

ALKALOID BIOSYNTHESIS II QLYCEROURD METABOLISM

ETHER LIPID METABOLISM ANTK3EN PROCESSING AND PRESENTATION

NUCLEAR RECEPTORS ADIPOCYTOKINE SIGNALING PATHWAY

CK1 PATHWAY

TYPE 1 DIABETES MELUTUS

N021L12PATHWAY

RHOPATHWAY

CYTOKINE CYTOKINE RECEPTOR INTERACTION BLOOD CLOTTING CASCADE INFLAMPATHWAY

OICTYOSTELIUM DISCO !DEUM CAMP CHEMOTAX!S

PATHWAY

DIFFERENTIATION PATHWAY IN PC12 CELLS

Table 4

Top gene ontologies for TNBC subtypes in the training and validation datasets

Subtype Training Set (386) Validation Set (201)

LAR

CHOLESTEROL BIOSYNTHESIS GLUTATHIONE METABOLISM PENTOSE ANO GLUCURONATE PENTOSE AND GLUCURONATE INTERCONVERSIONS INTERCONVERStONS GLUTATHIONE METABOLISM

BIOSYNTHESIS OF STEROIDS CHOLESTEROL BIOSYNTHESIS

TYROS SHE METABOLISM TYROSINE METABOLISM

GAMMA HEXACHLQROCYCLOH EXANE BIOSYNTHESIS OF STEROIDS

DEGRADATION PORPHYRIN ANO CHLOROPHYLL METABOLISM

PORPHYRIN AND CHLOROPHYLL METABOLISM ANDROGEN AND ESTROGEN METABOLISM

PHENYLALANINE METABOLISM OLYCOSPHINGOLIPIO METABOLISM

GAMMA HEXACHLOROCYCLOHEXANE TYPE ill SECRETION SYSTEM

DEGRADATION GAMMA HEXACHLOROCYCLOHEXANE DEGRADATION

CHREBPPATHWAY FLAGELLAR ASSEMBLY

GLUTATHIONE METABOLISM CITRATE CYCLE TCA CYCLE

1 AND 2 METHYLN APHTHA LENE DEGRADATION PHENYLALANINE METABOLISM

SLYCOSPHINGOUPID METABOLISM ATP SYNTHESIS

PORPHYRIN AND CHLOROPHYLL METABOLISM PHOTOSYNTHESIS

SEN AND ESTROGEN METABOLISM STARCH ANO SUCROSE METABOLISM HENTLALANINE METABOLISM PORPHYRIN AND CHLOROPHYLL METABOLISM CITRATE CYCLE TCA CYCLE ARGININE AND PROLINE METABOUSM TYROSINE METABOLISM GAMMA HEXACHLOROCYCLOHEXANE DEGRADATION ETABOLISM OF XEN0BI0TIC5 BY CYTOCHROME METABOLISM OF XENOBJOTICS BY CYTOCHROME P450 P4SD FRUCTOSE AND MANNOSE METABOLISM

EICOSANOID SYNTHESIS CARBON FIXATION

VALINE LEUCINE; AND ISOLEUC1NE DEGRADATION GLYCAN STRUCTURES DEGRADATION

GLYCAN STRUCTURES DEGRADATION CITRATE CYCLE GLYCOSYLPHOSPHATIDYLINOSITOL ANCHOR CO PPATHWAY

BIOSYNTHESIS PARKINSONS DISEASE BUTANOATE METABOLISM ANDROGEN AND ESTROGEN METABOLISM ARGININE AND PROLINE METABOLISM PANTOTHENATE AND COA BIOSYNTHESIS GLUTATHIONE METABOLISM

C 1 PATHWAY FATTY ACID METABOLISM

CITRATE CYCLE RENIN ANGIOTENSIN SYSTEM

PANTOTHENATE AND COA BIOSYNTHESIS TYROSINE METABOLISM

PROPANOATE METABOLISM GLUTAMATE METABOUSM

FATTY ACID METABOLISM ALANINE AND ASPARTATE METABOLISM

ANDROGEN AND ESTROGEN METABOLISM

GLUTAUATE METABOLISM CARBON FIXATION

N GLYCAN BIOSYNTHESIS EICOSANOID SYNTHESIS

HISTtDlNE METABOLISM GLYCOSAM WOGLYCAN DEGRADATION

BILE ACID BIOSYNTHESIS PHENYLALANINE METABOLISM

PROPANOATE METABOLISM CHREBPPATHWAY

HCMVPATHWAY CK1PATHWAY

TRYPTOPHAN METABOLISM MONOAMINE (3PCR3

ALANINE AND ASPARTATE METABOLISM 3PHINQOUPID METABOLISM

VALINE LEUCINE AND ISOLEUCINE DEGRADATION PS3HYPOXIAPATHWAY

PPAR SIGNALING PATHWAY TRYPTOPHAN METABOLISM

CREBPATHWAY NICOTINATE AND NICOTINAMIDE METABOUSM

ATP SYNTHESIS GLUTAMATE METABOLISM

FLAGELLAR ASSEMBLY UOALP Al N PATHWAY

TYPE III SECRETION SYSTEM FRUCTOSE AND MANNOSE METABOLISM

SPRYPATHWAY

6RYTHPATHWAY KREBS TCA CYCLE

PHOTOSYNTHESIS OXIDATIVE PHOSPHORYIATIO N

HISTIDINE METABOUSU HISTtDlNE METABOLISM

MOALPAWPATHWAY GALACTOSE METABOUSM

FRUCTOSE AND MAMNOSE METABOLISM PENTOSE PHOSPHATE PATHWAY

GLYCOSA INOGLYCAN DEGRADATION OXIDATIVE PHOSPHORYLATION

8PHINGOLIPID METABOLISM H GLYCAN BIOSYNTHESIS

GLUTAUATE METABOLISM LINOLEIC ACID METABOUSM

LINOLEIC ACID METABOLISM BLOOD CLOTTING OASGADE

GLYCEROUPIO METABOLISM ARACHtDONIC ACID METABOLISM

ARGININE AND PROLINE METABOLISM MCALPAINPATHWAY

SNARE INTERACTIONS IN VESICULAR TRANSPORT ERYTHPATHWAY

SELENOAMINO ACID METABOLISM ABC TRANSPORTERS GENERAL

PENTOSE PHOSPHATE PATHWAY

FRUCTOSE AND MAN NOSE METABOLISM BUTANOATC METABOLISM

N01 PATHWAY 0 GLYCAN BIOSYNTHESIS

BILE ACID BIOSYNTHESIS PPAR SIGNALING PATHWAY

ALANINE AND ASPARTATE METABOUSM

VALINE LEUCINE AND ISOLEUCINE DEGRADATION Table 4

Top gene ontologies for TNBC subtypes in the training and validation datasets

Subtype Training Set (386) Validation Set (201)

LAR

cont'd STARCH ANB SUCROSE METABOLISM

CYSTEINE METABOUSM N01 PATHWAY

GATA3PATHWAY STARCH AMD SUCROSE METABOUSM

ALANINE AND ASPARTATE METABOLISM ARGININE AND PROLINE METABOLISM UMONENE AND PINENE DEGRADATION COMPLEMENT AND COAGULATION CASCADES

ABC TRANSPORTERS GENERAL GALACTOSE METABOLISM

UREA CYCLE AND METABOUSM OF AMINO

GROUPS PENTOSE PHOSPHATE PATHWAY

OXIDATIVE PHOSPHORYLATION

N GLYCAM BIOSYNTHESIS

GLYCEROLIPID METABOLISM REBS TCA CYCLE

A INOSUGARS METABOLISM

ARACHIDONIC ACID METABOLISM

TRYPTOPHAN METABOUSM

PENTOSE PHOSPHATE PATHWAY

NAPHTHALENE AND ANTHRACENE DEGRADATION

OXIDATIVE PHOSPHORYLATION

UNC

DNA REPLICATION RIACTOME ATRB CAPATHWAY DNA POLYMERASE CELL CYCLE KEQG CELLCYCLEPATH AY

G1 TO 8 CELL CYCLE REACTOME

CELL CYCLE

61 PATHWAY

G1 AND 5 PHASES

BASAL TRANSCRIPTION FACTORS RNA TRANSCRIPTION REACTOME AMINOACYL TRNA BIOSYNTHESIS RNA POLYMERASE PYR!MIDINE METABOLISM

CARM ERPATH AY PYRIMIDINE METABOUSM ALANINE AMD ASPARTATE METABOLISM ALANINE AND ASPARTATE METABOLISM AMINOACYL TRNA BIOSYNTHESIS

G2PATHWAY

PS3PATHWAY

SELENOAMINO ACID METABOLISM QLUTAMATE METABOLISM PENTOSE PHOSPHATE PATHWAY MRNA PROCESSING REACTOME PENTOSE PHOSPHATE PATHWAY

Bold- shared between training and validation set

Tumour Type (AC, adenocarcinoma; ANC, anaplastic carcinoma; ASCC, acantholytic squamous cell carcinoma; CS, Carcinosarcoma; DC ductal carcinoma; C, carcinoma; S, carc nosarcoma I , invasive d; Isolation (PB, primary breast; PE, pleural effusion; CN cutaneous nodule)

Table 6A

Gene Categories

BL1 BL2 IM M MSL LAR

IGKV4-1 COROIA NOTCH1 E CTNNB1* HGD

ITPKB CSF2RB PDGFRA CXCL12" HMGCS2

IVD CTSC PDGFRB CXCL13 u HSD17B11

LOC100130100 CTSS PMP22 CYTIP HTRAl

LOC339562 CXCL10H ROPN1 DKK2 F INPP4B

LOC652493 CXCL11 u S100A1 DKK3 IQGAP2

MCM10A CXCL13H SCRG1 DPYD KCNMA1

MDC1 L CXCL14" SFPR4*' ENG MO FGE8 CXCL9" SMAD6 E EPAS1 KRT18

MIA CXCR4H SMAD7 EV12A KYNU

MIC ALL 1 CXCR6 u SNA12 EV12B LRRC17

MSH2 CYBB SOX10 FB 1 K MLPH

MUC5B CYTIP SPARC" FCFL2 MSX2

MYBL1 DDX60 SPOCK1 FGL2 PIPU

MYC" DPYD SRPX FN1 E POU2AF1

NBNC EN1 SY M FYB RARRES3

NRASB EVI2A TAGLN E FZD4" S100A7

NTN3 EVI2B TCF4 * GALNT10 S100A8

PAD12 FGL2 TCF7L2 GALNT7 S100A9

PLK1 A FYB TGFB 1 GIMAP6 SERHL2

PMP22 GBP1 TGFB1L1 GNG11 SIDT1

POU2AF1 GBP2 TGFB 2 GZMA SPARCL1

PRC1 A GIMAP6 TGFB 3 E HCLS1 SPDEFU

PSMB9 GNLY TGFBR1 HLA-DMA SPOCK1

RAD21 A GZMA TGFBR2 E HLA-DPB1 TARP

RAD51 L GZMB TGFBR3 E HLA-DRA TFAP2B

RAD54BPC HCLS1 TRPS1 HLA-DRB 1 TNFSF10

ROPN1 HCP5 TWIST1 HOXA10MEIS TP53TG1

RSAD2 HERC5 VI E HOXA5 TRGC2

RTP4 HLA-DMA WWP2 HSD17B11 TRGV9

S100A1 HLA-DPB1 ZEB1 K HTRAl TRIM36

SCRG1 HLA-DRA ZEB2 E IGFl15 UGT2B28

SOX10 HLA-DRB 1 IGF2 XBPl^

Tabl e 6B

TNBC Subty pe Mutations

BL1 BL2 IM M MSL LAR

TOP2B BRCA1 TP53 APC APC MAP3K1

ATM BRCA2 STAT4 BRAF BRAF PIK3CA

ATR CDKN2A STAT1 CTNNB1 BRCA RBI

BRCA1 PTCH1 RET FGFR1 CDKN2A TP53

BRCA2 PTCH2 NFKBIA GLI1 CTNNB1

CAMK1G PTEN MAP2K4 HRAS FGFR1

CDKN2A RBI HUWE1 KRAS HRAS

CLSPN RET BRAF NOTCH 1 KRAS

CTNNDl TP53 NOTCH4 NF1

HDAC4 UTX PIK3CA NF2

MAPK13 PTEN PDGFRA

MDC1 RBI PDGFRB

NOTCH1 TP53 PH 3CA

PTEN TP53

RBI

SMAD4

SMARCALl

STAT4

TIMELESS

TP53

UTX

The teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety.

While this invention has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.

CLAI S

What is claimed is:

1. A method of determining a triple negative breast cancer (TNBC) subtype in an individual in need thereof comprising

a) determining expression of one or more genes in one or more TNBC cells of the individual; and

b) comparing the expression of the one or more genes in the TNBC cells with the expression of the one or more genes in a control,

wherein increased expression of one or more cell cycle genes, cell division genes, proliferation genes, DNA damage response genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC BL1 subtype;

increased expression of one or more growth factor signaling genes, glycolysis genes, gluconeogenesis genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC BL2 subtype;

increased expression of one or more immune cell signaling genes, cytokine signaling genes, antigen processing and presentation genes, core immune signal transduction genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC IM subtype;

increased expression of one or more cell motility genes, extracellular matrix (ECM) receptor interaction genes, cell differentiation genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC M subtype;

increased expression of one or more cell motility genes, cellular differentiation genes, growth pathway genes, growth factor signaling genes, angiogenesis genes, immune signaling genes, stem cell genes, HOX genes, mesenchymal stem cell-specific marker genes or a combination thereof in the TNBC cells compared to the control indicates that the TNBC is a TNBC MSL subtype; and

increased expression of one or more hormone regulated genes in the TNBC cells compared to the control indicates that the TNBC is a TNBC LAR subtype.

2, The method of Claim 1 wherein detection of the TNBC BL1 subtype comprises selectively detecting increased expression of a gene combination comprising AURKA, AURKB, CENPA, CENPF, BUB1, BUB1B, TTK, CCNA2, PLK1, PRC1, MYC, NRAS, PLK1, BIRC5, CHEK1, FANCA, FANCG, FOXMl, HNGAl, RAD54BP, RAD51, NEKS, NBN, EXOl, MSH2, MCMIO, RAD21,SIX3, Z1C1, SOX4, SOX1 and MDC1 wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC BL1 subtype. 3. The method of Claim 1 or 2 wherein determination that the TNBC is a TNBC BL1 subtype further comprises detecting increased expression of Ki-67 mRNA in the TNBC cells compared to the control.

4. The method of Claims 1 , 2 ,or 3 wherein determination that the TNBC is a TNBC BL1 subtype further comprises detecting one or more mutated genes.

5. The method of Claim 4 wherein the one or more mutated genes comprise mutated BRCA1, STAT4, UTX, BRCA2, TP53, CTNND1, TOP2B, CAMK1G, MAPK13, MDC1, PTEN, RBI, SMAD4, CDKN2A, ATM, ATR, CLSPN, HDAC4, NOTCH1, SMARCAL1, and TIMELESS.

6. The method of Claim 1 wherein detection of the TNBC BL2 subtype comprises selectively detecting increased expression of a gene combination comprising EGFR, MET, ELF4, MAF, NUAKl, JAGl, FOSL2, 1D1, ZICl, SOXll, 1D3, FHL2, EPHA2, TP63 and MME wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC BL2 subtype.

7. The method of Claim 1 or 6 wherein determination that the TNBC is a TNBC BL2 subtype further comprises detecting one or more mutated genes, 8. The method of Claim 7 wherein the one or more mutated genes comprise mutated BRCAl, RBI, TP53, PTEN, CDKN2A, UTX, BRAC2, PTCHl, PTCH2, and RET.

9. The method of Claim 1 wherein detection of the TNBC M subtype comprises selectively detecting increased expression of a gene combination comprising

TGFB1L1, BGN, SMAD6, SMAD7, NOTCH1, TGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2, TGFBR3, MMP2, ACTA2, SNAI2, SPARC, SMAD7, PDGFRA, TAGLN, TCF4, TWIST1, ZEB1, COL3A1, JAG1, EN1, MYLK, STK38L, CDH11, ETV5, IGF1R, FGFRl, FGFRl, FGFR3, TBX3, COL5A2, GNG11, ZEB2, CTNNBl, DKK2, DKK3, SFPR4, TCF4, TCF7L2, FZD4, CA V1, CA V2, CCND1 and CCND2 wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC M subtype.

10. The method of Claim 1 or 9 wherein determination that the TNBC is a TNBC M subtype further comprises detecting decreased E-cadherin (CDH1) expression compared to the control.

11. The method of Claims 1 , 9 or 10 wherein determination that the TNBC is a TNBC M subtype further comprises detecting one or more mutated genes.

12. The method of Claim 11 wherein the one or more mutated genes comprise mutated PTEN, RBI, TP53, PIK3CA, APC, BRAF, CTNNBl, FGFRl, GUI, HRAS, KRAS, NOTCH1, and NOTCH4.

13. The method of Claim 1 , 9, 10, 11 or 12 wherein determination that the TNBC is a TNBC M subtype further comprises detecting chromosomal

amplifications in KRAS, IGF1R or MET. 14. The method of Claim 1 wherein detection of the TNBC MSL subtype comprises selectively detecting increased expression of a gene combination comprising VEGFR2 (KDR), TEK, TIEl, EPASl, ABCA8, PROCR, ENG, ALDHAl, PERI, ABCB1, TERF2IP, BCL2, BMP2, EPASl, STAT4, PPARG, JAK1, 1D1, SMAD3, TWISTl, THY1, HOXA5, HOXAIO, GL13, HHEX, ZFHX4, HMBOX1, FOS, PIK3R1, MAF, MAFB, RPS6KA2, TCF4, TGFBILI, MEISl, MEIS2, MEOXl, MEOX2, MSX1, ITGA V, KDR, NGFR, NT5E, PDGFRA, PDGFRB, POU2F1, and VCAMl wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC MSL subtype. 15. The method of Claim 1 or 14 wherein determination that the TNBC is a TNBC MSL subtype further comprises detecting low expression of claudins 3, 4, 7 or a combination thereof compared to the control.

16. The method of Claims 1, 14 or 15 wherein determination that the TNBC is a MSL subtype TNBC further comprises detecting one or more mutated genes.

17. The method of Claim 16 wherein the one or more mutated genes comprise mutated CDKN2A, HRAS, TP53, NF1, PIK3CA, BRCA1, BRAF, KRAS, NF2, PDGFRA, APC, CTNNB1, FGFR1, PDGFRB.

18. The method of Claim 1 wherein detection of the TNBC IM subtype comprises selectively detecting increased expression of a gene combination comprising CCL19, CCL3, CCL4, CCL5, CCL8, CCR1, CCR2, CCR5, CCR7, CD2, CD37, CD38, CD3D, CD48, CD52, CD69, CD74, and CD8A wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC IM subtype.

19. The method of Claim 1 or 18 wherein determination that the TNBC is a TNBC IM subtype further comprises detecting one or more mutated genes. 20. The method of Claim 19 wherein the one or more mutated genes comprise TPS 3, CTNNAl, DDX18, HUWEl, NFKBIA, APC, BRAF, MAP2K4, RBI, STAT4, STATl. md RET.

21. The method of Claim 1 wherein detection of the TNBC LA subtype comprises selectively detecting increased expression of a gene combination comprising DHCR24, ALCAM, GATA2, GATA3, IDIH1, IDIH2, CDH11, ERBB3, CUX2, FGFR4, HOPX, FASN, FKBP5, APOD, PIP, SPDEF, CLDN8, FOXAl, KRT18, and XBP1 wherein detection of increased expression of the gene combination indicates that the TNBC is TNBC LAR subtype.

22. The method of Claim 1 or 21 wherein determination that the TNBC is a LAR subtype TNBC further comprises detecting one or more mutated genes.

23. The method of Claim 22 wherein the one or more mutated genes comprise PIK3CA, CDH1, PTEN, RBI, TP53, and MAP3K1.

24. The method of any one of Claims 2-23 wherein selective expression of the gene combination is detected using PCR, microarray analysis, next generation RNA sequencing, immunofluorescence, Western blot analysis or a combination thereof.

25. The method of any one of Claims 4, 5, 7, 8, 11, 12, 16, 17, 19 or 20 wherein the one or more mutated genes is detected using a SNaPshot assay, exome sequencing, Sanger gene sequencing, resequencing array analysis, mRNA analysis/cDNA sequencing polymerase chain reaction (PCR), single-strand conformation polymorphism (sscp), heteroduplex analysis (het), allele- specific oligonucleotide (aso), restriction fragment analysis, allele-specific amplification (asa), single nucleotide primer extension, oligonucleotide ligation assay (ola), denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE) and single strand conformation polymorphism (SSCP) or a combination thereof.

26. The method of any one of the preceding claims wherein the individual has been diagnosed with breast cancer, the individual has not been diagnosed with breast cancer, or the individual is at risk of developing breast cancer.

27. The method of any one of the preceding claims wherein the individual is a human.

28. The method of any preceding claim wherein the TNBC cells are epithelial cells, mesenchymal cells, immune cells or a combination thereof.

29. The method of any preceding claim wherein the control is a non-cancer cell.

30. The method of Claim 29 wherein the non-cancer cell is obtained from an individual that does not have TNBC, from non-cancerous tissue from an individual with TNBC or a combination thereof.

31. The method of any one of the preceding claims further comprising

determining a treatment protocol for the triple negative breast cancer (TNBC) patient based on the TNBC subtype.

32. The method of Claim 31 wherein the TNBC BLl or BL2 subtype is detected in the individual and the individual is treated with one or more drugs that damages DNA.

33. The method of Claim 32 wherein the one or more drugs is an alkylating-like agent, a PARP inhibitor or a combination thereof.

34. The method of Claim 33 wherein the alkylating-like drug is cisplatin and the PARP inhibitor is veliparib, olaparib or a combination thereof.

35. The method of Claim 31 wherein the TNBC ML subtype is detected in the individual and the individual is treated with one or more drugs that inhibits Src, IGF1R, MET, PDGFR or a combination thereof.

36. The method of Claim 35 wherein the one or more drugs is disatinib, OSI- 906, BMS-754807, PF2341066, sorafinib or a combination thereof.

37. The method of Claim 35 or 36 further comprising treating the individual with a P13K inhibitor, a P13K7mTOR inhibitor or a combination thereof.

38. The method of 37 wherein the P13K inhibitor is BKM-120, GDC0941 or a combination thereof, and the P13K/mTOR inhibitor is NVP-BEZ235, GDC0980 or a combination thereof.

39. The method of Claim 31 wherein the TNBC LAR subtype is detected in the individual and the individual is treated with one or more drugs that inhibit androgen receptor (AR). 40. The method of Claim 39 wherein the one or more drugs that inhibit AR is bicalutamide, MVD3100, abiraterone or a combination thereof.

41. The method of Claim 39 or 40 further comprising treating the individual with a P13K inhibitor a P13K/mTOR inhibitor or a combination thereof.

42. The method of 41 wherein the P13 inhibitor is BKM-120, GDC0941 or a combination thereof, and the P13K/mTOR inhibitor is VP-BEZ235, GDC0980 or a combination thereof. 43. The method of Claim 39, 40, 41 or 42 further comprising treating the individual with an inhibitor of HSP90.

44. The method of Claim 43 wherein the inhibitor of HSP90 is DMAG. 45. A method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) subtype comprising

a) contacting a cell line that is a model for the TNBC subtype of interest with an agent to be assessed; and

b) determining viability of the cell line in the presence of the agent, wherein if the viability of the cell line decreases in the presence of the agent, then the agent can be used to treat the TNBC subtype of interest.

46. The method of Claim 45 wherein the TNBC subtype of interest is a TNBC BL-1 subtype, a TNBC BL-2 subtype, a TNBC IM subtype, a TNBC M subtype, a TNBC MSL subtype or a TNBC LAR subtype.

47. A method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) BL-1 subtype comprising

a) contacting one or more TNBC BL-1 subtype cell lines with an agent to be assessed; and

b) determining viability of the one or more cell lines in the presence of the agent,

wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC BL-1 subtype.

48. The method of Claim 47 wherein the one or more TNBC BL-1 subtype cell lines comprises HCC2157, HCC1599, HCC1937, HCCl 143, HCC3153, MDA-MB- 468, and HCC38. 49. A method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) BL-2 subtype comprising

a) contacting one or more TNBC BL-2 subtype cell lines with an agent to be assessed; and

b) determining viability of the one or more cell lines in the presence of the agent,

wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC BL-2 subtype.

50. The method of Claim 49 wherein the one or more TNBC BL-2 subtype cell lines comprises SUM1 9PT, CAL-851, HCC70, HCCl 806 and HDQ-P1.

51. A method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) I subtype comprising

a) contacting one or more TNBC IM subtype cell lines with an agent to be assessed; and

b) determining viability of the one or more cell lines in the presence of the agent,

wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC IM subtype.

52. The method of Claim 51 wherein the one or more TNBC IM subtype cell lines comprises HCCl 187 and DU4475.

53. A method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) M subtype comprising a) contacting one or more TNBC M subtype cell lines with an agent to be assessed; and

b) determining viability of the one or more cell lines in the presence of the agent,

wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC M subtype.

54. The method of Claim 53 wherein the one or more TNBC M subtype cell lines comprises BT-549, CAL-51 and CAL-120.

55. A method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) MSL subtype comprising

a) contacting one or more TNBC MSL subtype cell lines with an agent to be assessed; and

b) determining viability of the one or more cell lines in the presence of the agent,

wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC MSL subtype. 56, The method of Claim 55 wherein the one or more TNBC MSL subtype cell lines comprises HS578T, MDA-MB-157, SUM159PT, MDA-MB-436, and MDA- MB-231.

57. A method of determining whether an agent can be used to treat a triple negative breast cancer (TNBC) LAR subtype comprising

a) contacting one or more TNBC LAR subtype cell lines with an agent to be assessed; and

b) determining viability of the one or more cell lines in the presence of the agent,

wherein if the viability of the one or more cell lines decreases in the presence of the agent, then the agent can be used to treat the TNBC LAR subtype.

58. The method of Claim 57 wherein the one or more TNBC LAR subtype cell lines comprises MDA-MB, SUM185PE, HCC2185, CAL-148, and MFM-223.

59. The method of any one of Claims 45-58 wherein the viability is determined using a membrane leakage assay, a mitochondrial activity assay, a functional assay, a proteomic assay, a genomic assay or a combination thereof.

60. The method of any one of Claims 45-59 further comprising comparing the viability of the one or more cell lines in the presence of the agent to the viability of a control.

61. The method of Claim 60 wherein the control is a non-cancerous cell line.

62. The method of Claim 61 wherein the control is a culture of normal human and immortalized MCF210A.

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