WO2016196002A1 - Triple negative breast cancer screen and methods of using same in patient treatment selection and risk management - Google Patents

Abstract

Disclosed are methods and kits useful for selecting a treatment for a triple negative breast cancer (TNBC) patient. The method involves assessing expression levels of a panel of TNBC biomarker genes that are specific for a gene set correlated to chemotherapeutic agent sensitivity to a mitosis-inhibiting chemotherapeutic agent (such as anthracycline-taxane (AT)-chemotherapy) in a TNBC patient, deriving a score from the gene expression values, and using the score to identify the level of sensitivity of the patient to the mitosis-inhibiting chemotherapeutic treatment. The most appropriate treatment for the TNBC patient may then be selected. Kits may include a set of TNBC biomarker gene molecular probes, and an instructional insert providing steps on calculating a TNBC patient response score, and classifying the TNBC patient as having a high sensitivity or low sensitivity for a mitosis-inhibiting chemotherapeutic regimen (such as AT-chemotherapy), based on the patient's TNBC response score.

Description

TRIPLE NEGATIVE BREAST CANCER SCREEN AND METHODS OF USING SAME IN PATIENT TREATMENT SELECTION AND RISK MANAGEMENT

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to U.S. Provisional Application Ser. Nos. 62/237,019, filed on Oct. 05, 2015, and 62/168,060, filed on May 29, 2015.

BACKGROUND

FIELD

[0002] The present disclosure relates generally to methods and diagnostic tools for identifying a patient with breast cancer that will benefit from a particular chemotherapy treatment or not, as well as a triple negative breast cancer (TNBC) patient score system that may be used in the clinical management of the patient.

DESCRIPTION OF RELATED ART

[0003] Several therapies exist for treating cancer. Current practices utilize different combinations of chemotherapeutic drugs. A serious problem faced by practitioners and their patients is that cancers can vary in their responsiveness to different treatments. Selection of the optimal therapy for a particular cancer, as early in the treatment cycle as possible, is key to achieving the best outcome.

[0004] Breast cancer is the most common cancer for women in the United States, and among the leading causes of cancer death. Around 15% to 20% of these cancers are termed "triple negative breast cancers" (TNBC), and are characterized by breast cancer cells that test negative for estrogen receptors (ER-), progesterone receptors (PR-), and HER2 (HER2-). Testing negative for all three of these means the cancer is triple-negative. Treatment with systemic chemotherapy is the standard of care for breast cancer patients with estrogen-receptor negative (ER-) disease, and thus, also for TNBC disease.

[0005] It is known in the art that amongst TNBC patients, chemotherapy sensitivity varies tremendously with the overall molecular profile of the tumor. For example, a significant number of TNBC patients achieve pathological complete response (pCR) and improved relapse- free survival from anthracycline-taxane (AT) based chemotherapy. However, the 5 -year distant metastasis free survival (DMFS) probability for AT-treated TNBC patients is only about 65% - 70%.

[0006] Studies have identified hundreds of genes that are linked to the behavior of tumor cells, including their susceptibility to chemotherapeutic drugs. Published studies utilizing microarray gene expression patterns of thousands of genes to classify different types of cancers also are available in the literature.

[0007] With regards to predicting a patient's outcome given a particular treatment, it has been demonstrated (Hatzis 2011) that the molecular profile of a tumor can be used to predict pCR in cancer. Furthermore, it is known in the art that pCR may be used as a clinical indication of chemotherapy sensitivity in TNBC; i.e., whether the selected treatment was effective for reducing risk of patient cancer relapse.

[0008] A shortcoming in the clinical management of breast cancer patients is that a reliable and predictive molecular profile and/or screening tool useful in tailoring a potential treatment modality, specifically as relates to chemosensitivity, has not been developed. Such a tool would be especially useful in managing TNBC breast cancer patients. While some attempts have been made to use molecular profiles to predict pCR in estrogen receptor negative breast cancer patients treated with AT chemotherapy, none are suitably precise to influence treatment decisions in the clinic. Improved methods are needed to identify the AT-insensitive TNBC patient, so as to provide this population of patients an alternative therapy and a higher probability of distant metastasis free survival (DMFS).

SUMMARY

[0009] The present invention, in a general and overall sense, relates to improved modalities and systems useful in managing and improving the therapeutic outcome of a TNBC patient. The personalized TNBC assessment modalities and tools present a tool for application of personalized medicine approaches for a specific TNBC patient, thus providing more effective treatment options to the patient. Methods for selecting an appropriate treatment plan personalized for a specific TNBC patient also provides for improved longer-term metastasis -free survival for the identified TNBC patient upon treatment with the identified, most appropriate patient-specific tailored treatment option.

[0010] A significant number of triple-negative breast cancer (TNBC) patients achieve pathological complete response (pCR) and improved relapse free survival from neoadjuvant and adjuvant anthracycline-taxane (AT) based chemotherapy. However, the 5-year distant metastasis free survival (DMFS) probability for AT-treated TNBC patients is only about 65% - 70%. Methods and tools are presented that identify an AT-insensitive patient who is a candidate for an alternative therapy based on the patient's personalized gene expression profile for a panel of TNBC biomarker genes. [0011] In some embodiments, a method of selecting a treatment for a triple negative breast cancer (TNBC) patient is provided, comprising assessing expression levels of a TNBC biomarker gene panel, said panel comprising five or more TNBC biomarker genes selected from the group consisting of ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, ΓΝΡΡ4Β, SCNN1B, MSH6, SYT17, EXOSC5, PODXL, ALMS1, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, MYOIC, MAST2, SPDEF, PSME3, CDC45, NFIB, AKAPl, PDK3, SMIM7, YIPF3, PPP1R2, EXOC7, ECEl, KAT6B, PRDX2, ALPKl, and GDF15; calculating a TNBC response score for said patient from said expression levels; comparing the patient TNBC response score to a threshold TNBC reference patient population response score; selecting a mitotic inhibitor chemotherapy regimen for a TNBC patient having a TNBC response score above the threshold TNBC reference population response score, or selecting a chemotherapy regimen other than a mitotic inhibitor chemotherapy regimen for a TNBC patient having TNBC response score that is equal to or below the threshold TNBC reference population response score. A TNBC biomarker gene panel comprising two or more TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, and UNC5B.

[0012] In some embodiments, the TNBC biomarker gene panel may comprise two or more TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, and UNC5B. In other embodiments, the TNBC biomarkers selected from the group consisting of: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, INPP4B, SCNN1B, MSH6, and SYT17. In yet other embodiments, TNBC biomarker gene panel comprising the first 20 genes identified in Table 4. A TNBC patient having a high sensitivity response score will have upregulated TNBC biomarker gene levels of one or more of the TNBC biomarker genes: ITGA6, GOLT1B, TPGS2, ACTR3B, ELF5, ABT1, EXOC5, MSH6, EXOSC5, PODXL, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, PSME3, CDC45, NFIB, AKAPl, PDK3, SMIM7, YIPF3, PPP1R2.

[0013] In some embodiments, the TNBC patient score having a high sensitivity response score will have downregulated TNBC biomarker gene levels of one or more of the TNBC biomarker genes: MZT2B, UNC5B, HEMK1, ΓΝΡΡ4Β, SCN 1B, SYT17, ALMS1, MYOIC, MAST2, SPDEF, EXOC7, ECEl, KAT6B, PRDX2, ALPKl, and GDF15. [0014] The level of each gene comprising the TNBC biomarker gene panel is identified with a cDNA, mRNA, cRNA or other nucleotide that is specific for the gene for each TNBC biomarker gene of the panel.

[0015] In other aspects, the method may be further described as comprising selecting a treatment for a triple negative breast cancer (TNBC) patient by assessing levels of nucleic acid indicator molecules, also known herein as biomarkers, in a frozen or fresh tissue sample of the TNBC patient's breast tumor tissue, calculating a patient response score from measurements of a TNBC biomarker indicator molecule panel, and comparing the patient response score to response scores from tissues of a TNBC reference population for the TNBC biomarker indicator molecule panel. The TNBC reference population response scores are known for each gene of the TNBC biomarker panel. The response score from the tissue sample is then used to classify the patient as having a low or high sensitivity level for a mitosis inhibiting chemotherapeutic agent, such as (AT)-chemotherapy. An informed decision can then be made to select a mitosis- inhibiting chemotherapeutic regimen (such as AT-chemotherapy treatment) for a TNBC patient having a high sensitivity level, or not selecting AT-chemotherapy treatment to a TNBC patient having a low sensitivity level for a AT-chemotherapy treatment.

[0016] The materials for use in the methods of the aforementioned test are suited for preparation of kits produced in accordance with well-known procedures. The kits could comprise a set of probes or a set of oligonucleotide primer pairs, wherein each probe or set of oligonucleotide primer pairs is a detectably labeled single-stranded polynucleotide having specific binding affinity for a panel of genes determined to positively correlate with increased likelihood of a beneficial response to a treatment with a mitosis inhibiting agent chemotherapy regimen (such as (AT)-chemotherapy). Additionally, kits could include a software program configured to categorize a TNBC patient as having high sensitivity or low sensitivity for (AT)- chemotherapy, or instead an instructional insert defining the TNBC gene probes included and how the expression levels of each shall be used to calculate an individual TNBC patient response score, and compared against a reference TNBC patient population score.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1. TNBC response score is a significant predictor of pCR by logistic regression (p = 1.3 x 10^"5). The curve exhibits a steady increase in probability of pCR with increasing TNBC response score. [0018] Figure 2. Significance of TNBC response score as a predictor of distant metastasis-free survival in chemotherapy-treated TNBC is exhibited in the Affymetrix TNBC validation as a plot of the 5-year DMFS probability versus TNBC response score. In a Cox proportional hazard model, TNBC response score is a significant (p = 0.016) predictor of 5-year DMFS. The hash marks on the x axis indicate individual score values.

[0019] Figure 3. Kaplan-Meier plot of the RespondR score risk strata in the Affymetrix TNBC validation set. The expected 5-year DMFS in RR-low (RespondR < 45) is 0.49 (95%CI 0.38 - 0.62) and for RR-high (RespondR > 45) it is 0.75 (95%CI 0.68 - 0.87).

[0020] Figure 4. Process by which a doctor will use RespondR to make a treatment decision for a TNBC patient.

[0021] Figure 5. Kaplan-Meier plot of the probability of relapse-free survival for 5 years for the high-response score (> 45 ) and low-response score (< 45) groups in the taxane-treated TNBC patients (n=84) in TCGA. The expected 5-year relapse-free survival probabilities for the groups are 0.92 (95%CI 0.82 - 1.0) for high-response score and 0.68 (95%CI 0.52 - 0.89) for low-response score.

[0022] Figure 6. Plots the gene risk score for ITGA6 versus gene expression measurements in (A) the Affymetrix cohorts, (B) the METABRIC cohort, and (C) TCGA.

DETAILED DESCRIPTION

[0023] While the present technology is susceptible to various modifications and alternative forms, exemplary embodiments thereof are shown by way of example in the drawings and are herein described in detail. It should be understood, however, that the description of exemplary embodiments is not intended to limit the technology to the particular forms disclosed, but on the contrary, the intention is to cover all advantages, effects, features and objects falling within the spirit and scope of the technology as defined by the embodiments above and the claims below. Reference should therefore be made to the embodiments above and claims below for interpreting the scope of the technology. As such, it should be noted that the embodiments described herein may have advantages, effects, features and objects useful in solving other problems.

[0024] The present disclosure provides a clinical tool useful in the management of a triple negative breast cancer (TNBC) patient. Some breast cancers, termed triple negative breast cancers, are characterized by breast cancer cells that test negative for estrogen receptors (ER-), progesterone receptors (PR-), and HER2 (HER2-). Testing negative for all three of these receptors means the cancer is triple-negative. These negative results mean that the growth of the cancer is not supported by the hormones estrogen and progesterone, and not by the growth factor HER2. Therefore, triple-negative breast cancer does not respond to hormonal therapy (such as tamoxifen or aromatase inhibitors) or therapies that target HER2 receptors, such as Herceptin (chemical name: trastuzumab). However, other medicines can be used to treat triple-negative breast cancer. The disclosed methods provide a measure of the likelihood that a TNBC patient will have a favorable outcome upon AT-chemotherapy treatment, providing critical information to patients and physicians deciding between AT and an alternative therapy.

[0025] About 15-20% of breast cancers are found to be triple-negative. For doctors and researchers, there is intense interest in finding the most effective medication to treat a particular TNBC patient. Treatment with systemic chemotherapy is the standard of care for breast cancer patients with estrogen-receptor negative (ER-) disease. However, in triple-negative breast cancer (TNBC) patients, chemotherapy sensitivity varies tremendously with the overall molecular profile of the tumor (Masuda et al. 2013). There is significant need for a diagnostic test that can identify the TNBC patients who are sensitive to anthracycline and taxane (AT) based chemotherapy, and perhaps more importantly, select patients who are unlikely to respond to AT- chemotherapy. A patient predicted to be AT-insensitive patients could be directed to other, perhaps more promising drugs, or participation in clinical trials for alternative therapies under development, with a greater probability of distant metastasis free survival (DMFS).

[0026] Several studies have confirmed the effectiveness of neoadjuvant and adjuvant chemotherapy in reducing the risk of relapse (Rastogi et al. 2008). Moreover, pCR is predictive of improved long-term relapse-free survival. For this reason, pCR may be used as a clinical indication of chemotherapy sensitivity, i.e., that the treatment was effective in reducing risk of patient cancer relapse.

[0027] In some embodiments, the present disclosure provides for a method of measuring expression levels of TNBC biomarker genes in a TNBC biomarker panel, chosen from the universal RespondR set of genes (Table 3) as an assessment to predict the probability of a TNBC patient responding favorably AT-chemotherapy. These patients are assessed using a TNBC response score, which is a predictive score that provides a measure of the likelihood that a TNBC patient will have a favorable outcome upon AT-chemotherapy treatment, wherein, a favorable outcome can be pCR or 5-year DMFS. A TNBC patient will be identified as insensitive to AT- chemotherapy treatment if the AT-chemotherapy treatment does not result in a pCR or the patient relapses.

[0028] In some embodiments, the TNBC response score gives a continuous measure of expected 5-year DMFS in an AT-treated TNBC patient ranging from below 0.40 to above 0.75.

[0029] In some embodiments, utilizing TNBC biomarkers from Table 3, the present disclosure stratifies TNBC patients into groups of low (RR-low, 58%), and high (RR-high, 42%) sensitivity to AT-based chemotherapy. The rate of pCR in RR-low pCR is 0.20 and in RR-high it is 0.52. The division of patients in RR-low and RR-high also separates patients into groups with widely different probabilities of 5-year DMFS: in R-low it is 0.49 (95%CI 0.38 - 0.62) and in RR-high it is 0.75 (95%CI 0.66 - 0.87). The RR-high group contains 65% of the pCR samples while the RR-low group contains only 35% of the pCR samples. The percentages of samples in RR-high and RR-low also provide a reference point from which a particular percentile within a given reference population of TNBC patients may be used to divide the reference population into RR-low and RR-high.. The reference population of TNBC patients may be described as a random population of TNBC patients having a known pathological outcome in response to AT-chemotherapy, for which TNBC biomarker gene expression levels of a TNBC biomarker panel have been collected and TNBC response score has been calculated.

[0030] In some embodiments, a TNBC patient's TNBC response score would be examined to determine if the TNBC patient has a low, moderate or high sensitivity level for AT- chemotherapy treatment. In this instance, a low sensitivity level would be indicated in a TNBC patient having a TNBC response score of below a 58th percentile of the reference TNBC population response scores, and a high sensitivity level would be indicated in a TNBC patient having a TNBC response score of above a 58th percentile of the reference TNBC population. A patient having a low sensitivity would not be directed to receive AT-chemotherapy treatment, while a high sensitivity patient would be directed to receive an AT-chemotherapy treatment or regimen including another mitotic inhibitor, such as paclitaxel or docetaxel.

[0031] TNBC biomarkers for use in kits and methods described herein include a panel of detectably labeled molecular probes that specifically bind under stringent conditions to identified TNBC biomarker genes, as identified here, to provide a specific indication that a TNBC patient is or is not likely to be sensitive to a treatment with a mitosis-inhibiting chemotherapeutic agent, such as AT-chemotherapy. [0032] In some embodiments, the detectably labeled molecular probes for the TNBC biomarker gene panel of the present products and methods, will have specific binding affinity under stringent conditions for a TNBC biomarker gene selected from those listed in Table 3. These TNBC biomarker genes are: ITGA6, MZT2B, GOLTIB, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, INPP4B, SCNN1B, MSH6, SYT17, EXOSC5, PODXL, ALMS1, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, MYOIC, MAST2, SPDEF, PSME3, CDC45, NFIB, AKAPl, PDK3, SMIM7, YIPF3, PPP1R2, EXOC7, ECE1, KAT6B, PRDX2, ALPK1, and GDF15. In some embodiments, the detectably labeled molecular probes will have specific binding affinity for a TNBC biomarker gene panel of any five TNBC biomarker genes selected from Table 3. Alternatively, the detectably labeled molecular probes will have specific binding affinity for a TNBC biomarker panel of at least 2 TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLTIB, TPGS2, ACTR3B, ELF5, UNC5B. In other embodiments, the detectably labeled molecular probes will have specific binding affinity for a TNBC biomarker panel of 3 TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLTIB, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, ΓΝΡΡ4Β, SCNN1B, MSH6, and SYT17. In another embodiment, the detectably labeled molecular probes will have specific binding affinity for a TNBC biomarker panel of 20 TNBC biomarker genes consisting of: ITGA6, MZT2B, GOLTIB, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, INPP4B, SCNN1B, MSH6, SYT17, EXOSC5, PODXL, ALMS1, SNAPC3, TANK, and TGFBR3.

[0033] The genes, ITGA6, GOLTIB, TPGS2, ACTR3B, ELF5, ABT1, EXOC5, MSH6, EXOSC5, PODXL, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, PSME3, CDC45, NFIB, AKAPl, PDK3, SMIM7, YIPF3, PPP1R2, are identified here to be upregulated in a patient identified as having a high sensitivity to a mitosis-inhibiting chemotherapeutic agent, such as AT, and this sensitivity will be reflected in a TNBC patient high sensitivity response score, when normalized to a control gene. Additionally, the genes MZT2B, UNC5B, HEMK1, ΓΝΡΡ4Β, SCNN1B, SYT17, ALMS1, MYOIC, MAST2, SPDEF, EXOC7, ECE1, KAT6B, PRDX2, ALPK1, GDF15, are identified herein to be downregulated in a TNBC patient with a high sensitivity response score when normalized to a control gene, and are positively correlated with increased likelihood of a beneficial response to a treatment with a mitosis inhibiting agent, such as an AT-chemotherapy. [0034] In some embodiments, a TNBC response score below a 58th percentile of the TNBC reference population response scores indicates a TNBC patient that has a low sensitivity level for AT-chemotherapy. Additionally, a TNBC response score higher than a 58th percentile of the TNBC reference population response scores indicates a patient that has a high sensitivity level for AT-chemotherapy.

[0035] In some embodiments, the detectably labeled molecular probes of the methods and products described herein have specific binding affinity under stringent binding conditions to TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF 5, U C5B, HEMKl, ABTl, EXOC5, INPP4B, SCNNIB, MSH6, S YT17, EXOSC5, PODXL, ALMS 1 , SNAPC3 , TANK, and TGFBR3.

[0036] In some embodiments, the method may first comprise collecting a human tissue sample from a TNBC patient. When the sample is a biopsy specimen, it can include, but is not limited to, breast cells, particularly breast tissue from a biopsy, such as a breast tumor tissue sample. Biopsy specimens can be obtained by a variety of techniques including, but not limited to, scraping or swabbing an area, using a needle to aspirate cells or bodily fluids, or removing a tissue sample. Methods for collecting various samples/biopsy specimens are well known in the art. In some embodiments, a breast tissue sample is obtained by, for example, fine needle aspiration biopsy, core needle biopsy, or excisional biopsy. Fixative and staining solutions can be applied to, for example, cells or tissues for preserving them and for facilitating examination. Samples, particularly breast tissue samples, can be transferred to a glass slide for viewing under magnification. For example, the sample is a breast tumor tissue sample, and can be a formalin fixed paraffin embedded (FFPE) breast tumor tissue sample, a fresh breast tumor tissue sample, or a fresh frozen breast tissue sample.

[0037] After collecting and preparing the sample from the TNBC patient, the sample will be assayed to detect the expression levels of particularly defined groups of genes, identified in the present disclosure to be TNBC biomarker genes. One can use any method available for detecting gene expression of a polynucleotide and polypeptide biomarkers. As used herein, "detecting expression" means determining the quantity or presence of an identified gene, biomarker polynucleotide or an expression product thereof. As such, detecting expression encompasses instances where a biomarker is determined not to be expressed, not to be detectably expressed, expressed at a low level, expressed at a normal level, or overexpressed. In addition, isolated RNA can be used to determine the level of biomarker transcripts (i.e., mRNA) in a tissue sample, as many expression detection methods use isolated RNA from the tissue sample. For example, the starting material may typically comprise total RNA isolated from the tumor tissue sample. If the source of mRNA is a primary tumor, mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g., formalin-fixed) tissue samples. A cDNA may then be prepared corresponding to the mRNA, and used in various of the applications described herein. The molecules used to quantify relative gene expression levels between a patient sample and a TNBC reference population can thus be identified with a cDNA, mRNA, cRNA or anther nucleotide sequence that is specific for the gene.

[0038] Methods of detecting and quantifying polynucleotide biomarkers in a sample are well known in the art. Such methods include, but are not limited to gene expression profiling, which are based on hybridization analysis of polynucleotides, and sequencing of polynucleotides. The most commonly used methods in the art for detecting and quantifying polynucleotide expression include northern blottmg and in situ hybridization, RNAse protection assays, PCR-based methods, such as RT-PCR, and array-based methods. Alternatively, antibodies may be employed that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes, or DNA- protein duplexes in, for example, an oligonucleotide-linked immunosorbent assay ("OLISA"). Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression ("SAGE") and gene expression analysis by massively parallel signature sequencing.

[0039] In a particular embodiment, expression of a TNBC biomarker can be determined by normalizing the level of a reference marker/control, which can be all measured transcripts (or their products) in the sample or a particular reference set of RNA transcripts (or their products). Normalization can be performed to correct for or normalize away both differences in the amount of biomarker assayed and variability in the quality of the biomarker type used. Therefore, an assay typically measures and incorporates the expression of certain normalizing polynucleotides or polypeptides, including well known housekeeping genes, such as, for example, GAPDH and/or actin. Alternatively, normalization can be based on the mean or median signal of all of the assayed biomarkers or a large subset thereof (global normalization approach). To determine overexpression, the sample can be compared with a corresponding sample that originates from a healthy individual. That is, the "normal" level of expression is the level of expression of the biomarker in, for example, a breast tissue sample from an individual not afflicted with breast cancer. Such a sample can be present in standardized form. Sometimes, determining biomarker overexpression requires no comparison between the sample and a corresponding sample that originated from a healthy individual. For example, detecting overexpression of a biomarker indicative of a poor prognosis in a breast tumor sample may preclude the need for comparison to a corresponding breast tissue sample that originates from a healthy individual.

[0040] The TNBC response score is determined by extracting mRNA from a sample from a patient with TNBC, measuring expression values of TNBC biomarker genes of a TNBC gene panel in the patient tissue specimen to provide a patient TNBC gene expression level for the each TNBC biomarker gene of the TNBC gene panel, normalizing each TNBC biomarker gene expression level against a control gene level to provide a normalized TNBC continuous risk score for each of the TNBC panel genes, and calculating an overall TNBC response score from the normalized TNBC continuous risk scores, and scaling the overall TNBC response score to provide a patient continuous response score from 0 to 100. In yet another aspect, the present invention provides computer implemented methods and computer compatible software for implementing the present methods and tissue sample processing, analysis, and/or report of analysis applications. Software suitable for providing the implementing functions associated with these methods and tissue sample processing, analysis, and/or report or analysis capabilities to a computer are also provided.

[0041] The materials for utilizing the present disclosure are suited for preparation of kits produced in accordance with well-known procedures. The kits could comprise agents, which may include gene-specific or gene-selective probes and/or primers, for quantitating the expression of the disclosed genes for predicting prognostic outcome or response to treatment. Such kits may optionally contain reagents for the extraction of RNA from tumor samples, in particular fixed paraffin-embedded tissue samples and/or reagents for RNA amplification. In addition, the kits could optionally comprise the reagent(s) with an identifying description or label or instructions relating to their use in the methods of the present technology. The kits could comprise containers, each with one or more of the various reagents (typically in concentrated form) utilized in the methods, including, for example, pre-fabricated microarrays, buffers, the appropriate nucleotide triphosphates (e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP), reverse transcriptase, DNA polymerase, RNA polymerase, and one or more probes and primers, wherein each probe or set of oligonucleotide primer pairs is a detectably labeled single-stranded polynucleotide having specific binding affinity TNBC biomarker genes. [0042] The kit could optionally comprise a software program configured to categorize a TNBC patient as having high sensitivity or low sensitivity for AT-chemotherapy. The software can generate a report summarizing the patient's biomarker expression levels and/or the patient's suitability for AT-chemotherapy treatment. Moreover, the computer program can perform any statistical analysis of the patient's data or a population of patient's data as described herein in order to generate the status of the patient as AT-sensitive or AT-insensitive. Further, the computer program also can normalize the patient's biomarker expression levels in view of a standard or control prior to comparison of the patient's biomarker expression levels to those of the reference patient population. The computer also can ascertain raw data of a patient's expression values from, for example, a microarray, or the raw data can be input into the computer.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of skill in the art to which the disclosure pertains. Although any methods and materials similar to or equivalent to those described herein can be used in the practice or testing of the present technology, the preferred methods and materials are described herein.

[0044] Definitions: The following terms are used throughout the description of the present invention.

[0045] As used herein, "Respond score" is a term that is used interchangeably with the term, "TNBC score". These terms relate to a numerical score that reflects a statistically significant measure or indicator of triple negative breast cancer response or lack of response to a therapeutic treatment that is characterized by a mode of action that is similar to taxane, such as taxane itself, ixabepilone, taxol (Paclitaxel), taxotere (docetaxel), or other therapeutic drug or regimen of drugs having a mode of action as a mitotic poison to impair/halt cell division, such as by disrupting microtubule function, and more specifically, by acting as a mitotic inhibitor. (Steve - the taxane "mode of action" is by disruption of microtubule formation, and therefore they are mitotic inhibitors. The other drugs above are also common mitotic inhibitors (ixabepilone, taxol (Paclitaxel), taxotere (docetaxel).

[0046] Reference to an element by the indefinite article "a" or "an" does not exclude the possibility that more than one element is present, unless the context clearly requires that there be one and only one element. The indefinite article "a" or "an" thus usually means "at least one." [0047] As used herein, "patient" means an individual having symptoms of, or at risk for, cancer or other malignancy. A patient may be human or non-human and may include, for example, animal strains or species used as "model systems" for research purposes, such a mouse model. Likewise, patient may include either adults or juveniles (e.g., children). Moreover, patient may mean any living organism, preferably a mammal (e.g., human or non-human) that may benefit from the administration of compositions contemplated herein.

[0048] As used herein, "prognose," "prognosing," "prognosticating" and the like mean predictions about or predicting a likely course or outcome of a disease or disease progression, particularly with respect to a likelihood of, for example, disease remission, disease relapse, disease progression including tumor recurrence, metastasis and cancer- attributable death (i.e., the outlook for chances of survival), as well as drug resistance of a neoplastic disease. As used herein, "good prognosis" or "favorable prognosis," or like terms, means a likelihood that a patient having cancer, particularly breast cancer, will remain disease-free (i.e., cancer-free). As used herein, "poor prognosis" or "bad prognosis," or like terms, means a likelihood of a relapse or recurrence of the underlying cancer or tumor, metastasis or death. As such, patients classified as having a good prognosis tend to remain free of the underlying cancer or tumor. Conversely, patients classified as having a bad prognosis tend to experience disease relapse, tumor recurrence, metastasis and/or death.

[0049] As used herein, "prediction" means a likelihood that a patient will respond favorably or unfavorably to a therapeutic or therapeutic combination, and also the extent of those responses, or that a patient will survive, following surgical removal of a primary tumor and/or chemotherapy for a certain period of time, without a significant risk of cancer recurrence. The predictive methods described herein can be used clinically to make treatment decisions by facilitating the most appropriate treatment modalities for an individual patient based on molecular genetic factors. They also can be valuable tools in predicting if a patient is likely to respond favorably to a treatment regimen, such as surgical intervention, chemotherapy with a given therapeutic or therapeutic combination, and/or radiation therapy, or whether long-term survival of the patient, following surgery and/or termination of chemotherapy or other treatment modalities is likely.

[0050] As used herein, "about" means within a statistically meaningful range of a value or values such as a stated concentration, length, molecular weight, pH, sequence identity, time frame, temperature or volume. Such a value or range can be within an order of magnitude, typically within 20%, more typically within 10%, and even more typically within 5% of a given value or range. The allowable variation encompassed by "about" will depend upon the particular system under study, and can be readily appreciated by one of skill in the art.

[0051] As used herein, "tumor" means neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.

[0052] As used herein, "cancer" and "cancerous" mean a physiological condition in mammals that typically is characterized by unregulated cell growth. Of particular interest is breast cancer.

[0053] As used herein, "biomarker" refers generally to a molecule, substance or genetic characteristic that is an indicator of a biologic state. As used herein, the biomarker can be a gene or gene product that serves as a predictive marker for patient condition, patient long-term metastatic characteristics, patient outcome response or patient resistance or response to a drug or treatment modlality.

[0054] As used herein, a TNBC biomarker refers to a polynucleotide or polynucleotide sequence comprising the entire or partial sequence of a nucleotide sequence encoding a TNBC biomarker, or a complementary genetic sequence to the nucleotide sequence encoding a TNBC biomarker. As used herein, "polynucleotide" means a polymer of nucleic acids or nucleotides that, unless otherwise limited, encompasses naturally occurring bases (i.e., adenine, guanine, cytosine, thymine and uracil), non-naturally occurring base-like moieties, or known base analogues having the essential nature of naturally occurring nucleotides in that they hybridize to single- stranded nucleic acid molecules in a manner similar to naturally occurring nucleotides. Although it may comprise any type of nucleotide units, the term generally applies to nucleic acid polymers of ribonucleotides ("RNA") or deoxyribonucleotides ("DNA"). The term includes single-stranded nucleic acid polymers, double-stranded nucleic acid polymers, and RNA and DNA made from nucleotide or nucleoside analogues that can be identified by their nucleic acid sequences, which are generally presented in the 5' to 3' direction (as the coding strand), where the 5' and 3' indicate the linkages formed between the 5' hydroxyl group of one nucleotide and the 3' -hydroxyl group of the next nucleotide. For a coding strand presented in the 5 '-3' direction, its complement (or non-coding strand) is the strand that hybridizes to that sequence according to Watson-Crick base pairing. Thus, as used herein, the complement of a nucleic acid is the same as the "reverse complement" and describes the nucleic acid that in its natural form, would be based paired with the nucleic acid in question. [0055] As used herein, a "nucleic acid," "nucleotide" or "nucleic acid residue" are used interchangeably to mean a nucleic acid that is incorporated into a molecule such as a gene or other polynucleotide. As noted above, the nucleic acid may be a naturally occurring nucleic acid and, unless otherwise limited, may encompass known analogues of natural nucleic acids that can function in a similar manner as naturally occurring nucleic acids. Examples of nucleic acids include any of the known base analogues of DNA and RNA such as, but not limited to, 4 acetylcytosine, 8-hydroxy-N6-methyladenosine, aziridinylcytosine, pseudoisocytosine, 5 (carboxyhydroxylmethyl) uracil, 5-fluorouracil, 5-bromouracil, 5 carboxymethylaminomethyl-2- thiouracil, 5-carboxymethylaminomethyluracil, dihydrouracil, inosine, N6-isopentenyladenine, 1-methyladenine, 1-methylpseudouracil, 1-methylguanine, 1 methylinosine, 2,2 dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6- methyladenine, 7-methylguanine, 5-methylaminomethyluracil, 5 methoxyaminomethyl-2- thiouracil, beta-D-mannosylqueosine, 5 methoxycarbonylmethyluracil, 5-methoxyuracil, 2 methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid, oxybutoxosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4- thiouracil, 5 methyluracil, uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid, pseudouracil, queosine, 2-thiocytosine, and 2,6-diaminopurine.

[0056] As such, the biomarkers can include DNA, RNA, cDNA, cRNA, or iRNA comprising an entire or partial nucleotide sequence suitable for use as an indicator molecule as provided herein. It is contemplated that in some instances, a native or non-native (modified) amino acid sequences of the biomarker as provided herein may be used.

[0057] The biomarkers can include not only the entire biomarker sequence but also fragments and/or variants thereof. As used herein, "fragment" or "fragments" means a portion of the nucleic or amino acid sequence of the biomarker. Polynucleotides that are fragments of a biomarker nucleic acid sequence generally comprise at least about 10, 15, 20, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 800, 900, 1,000, 1,200 or 1,500 contiguous nucleotides, or up to the number of nucleotides present in a full-length biomarker polynucleotide disclosed herein. Likewise, a fragment of a biomarker polypeptide comprises at least about 15, 25, 30, 50, 100, 150, 200 or 250 contiguous amino acids, or up to the total number of amino acids present in a full-length biomarker protein.

[0058] As used herein, "variant" or "variants" means substantially similar sequences. Generally, variants of a particular biomarker have at least about 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity (preferably over the full length) to a biomarker as determined by sequence alignment programs.

[0059] As used herein, the term "very low sensitivity TNBC response score" means a TNBC response score that is lower than about 40% of a TNBC reference population TNBC response score. The term "moderate sensitivity TNBC response score" means a TNBC response score between about a 40% and about a 60% response score of a TNBC reference population TNBC response score. The term "low sensitivity TNBC response score" means a TNBC response score that is less than about 60% of a TNBC reference population TNBC response score. The term "high sensitivity TNBC response score" means a TNBC response score that is higher than about 60% of a TNBC reference population TNBC response score.

[0060] One of skill in the art understands that variants can be constructed via modifications to either the polynucleotide or polypeptide sequence of the biomarker and can include substitutions, insertions (e.g., adding no more than ten nucleotides or amino acid) and deletions (e.g., deleting no more than ten nucleotides or amino acids). Methods of mutating and altering nucleic acid sequences, as well as DNA shuffling, are well known in the art. See, e.g., Crameri et al. (1997) Nature Biotech. 15:436-438; Crameri et al. (1998) Nature 391 :288-291 ; Kunkel (1985) Proc. Natl. Acad. Sci. USA 82:488-492; Kunkel et al. (1987) Methods in Enzymol. 154:367-382; Moore et al. (1997) J. Mol. Biol. 272:336-347; Stemmer (1994) Proc. Natl. Acad. Sci. USA 91 :10747-10751; Stemmer (1994) Nature 370:389-391; Zhang et al. (1997) Proc. Natl. Acad. Sci. USA 94:4504-4509; and Techniques in Molecular Biology (Walker & Gaastra eds., MacMillan Publishing Co. 1983) and the references cited therein; as well as US Patent Nos. 4,873,192; 5,605,793 and 5,837,458.

[0061] Methods of aligning sequences for comparison are well known in the art. Thus, the determination of percent sequence identity between any two sequences can be accomplished using a mathematical algorithm. Non-limiting examples of such mathematical algorithms are the algorithm of Myers & Miller (1988) CAB/OS 4:1 1-17; the local alignment algorithm of Smith et al. (1981) Adv. Appl. Math. 2:482; the global alignment algorithm of Needleman & Wunsch (1970) J. Mol. Biol. 48:443-453; the search-for-local alignment method of Pearson & Lipman (1988) Proc. Natl. Acad. Sci. USA 85:2444-2448; the algorithm of Karlin & Altschul (1990) Proc. Natl. Acad. Sci. USA 87:2264, modified as in Karlin & Altschul (1993) Proc. Natl. Acad. Sci. USA 90:5873-5877. [0062] As noted above, biomarkers for use in the kits and methods described herein include biomarkers that provide a specific indication that the particular TNBC patient will likely or will not likely benefit from an AT-chemotherapy regimen. In some embodiments, a TNBC patient will be considered to likely have benefited from AT-chemotherapy (demonstrating a pathological complete response (pCR) and improved relapse free survival from AT chemotherapy) where a score identified as high (TNBC response score above about the 58^th quantile) using the present kits, methods and compositions of the present technology. A TNBC patient will be considered to not benefit from an AT-chemotherapy regimen, or to be AT- insensitive, where a score identified as low (TNBC response score below about the 58^th quantile) using the present kits, methods and compositions.

[0063] Compositions of the technology can include kits for identifying a TNBC patient that is insensitive to AT-chemotherapy treatment. As used herein, "kit" or "kits" means any manufacture (e.g., a package or a container) including at least one reagent, such as a nucleic acid probe or the like, for specifically detecting the expression of the biomarkers described herein. As used herein, "probe" means any molecule that is capable of selectively binding to a specifically intended target biomolecule, for example, a nucleotide transcript or a protein encoded by or corresponding to a biomarker. Probes can be synthesized by one of skill in the art, or derived from appropriate biological preparations. Probes may be specifically designed to be labeled. Examples of molecules that can be utilized as probes include, but are not limited to, RNA, DNA, proteins, antibodies and organic molecules. The kit will, in some embodiments, include an instructional insert, or contain instructions for use on a label or other surface available for print on the product.

[0064] When making polynucleotides for use as probes to the biomarkers (e.g., hybridization probes or primer sets), one of skill in the art can be further guided by knowledge of redundancy in the genetic code as shown below in Table 1. Table 1: Redundancy in Genetic Code. Sadava, D. E., Hillis, D. M., Heller, H. C, & Berenbaum, M. (2014). Life: The science of biology. Sunderland, MA: Sinauer.

Residue Triplet Codons Encoding the

Residue

Ala (A) GCU, GCC, GCA, GCG

Arg (R) CGU, CGC, CGA, CGG, AGA,

AGG

Asn (N) AAU, AAC

Asp (D) GAU, GAC

Cys (C) UGU, UGC

Gin (Q) CAA, CAG

Glu (E) GAA, GAG

Gly (G) GGU, GGC, GGA, GGG

His (H) CAU, CAC

Lie (I) AUU, AUC, AUA

Leu (L) UUA, UUG, CUU, CUC, CUA,

CUG

Lys (K) AAA, AAG

Met (M) AUG

Phe (F) UUU, UUC

Pro (P) CCU, CCC, CCA, CCG

Ser (S) UCU, UCC, UCA, UCG, AGU,

AGC

Thr (T) ACU, ACC, ACA, ACG

Trp (W) UGG

Tyr (Y) UAU, UAC

Val (V) GUU, GUC, GUA, GUG

START AUG

STOP UAG, UGA, UAA

[0065] Methods of synthesizing polynucleotides are well known in the art, such as cloning and digestion of the appropriate sequences, as well as direct chemical synthesis (e.g., ink-jet deposition and electrochemical synthesis). Methods of cloning polynucleotides are described, for example, in Copeland et al. (2001) Nat. Rev. Genet. 2:769-779; Current Protocols in Molecular Biology (Ausubel et al. eds., John Wiley & Sons 1995); Molecular Cloning: A Laboratory Manual, 3rd ed. (Sambrook & Russell eds., Cold Spring Harbor Press 2001); and PCR Cloning Protocols, 2nd ed. (Chen & Janes eds., Humana Press 2002). Methods of direct chemical synthesis of polynucleotides include, but are not limited to, the phosphotriester methods of Reese (1978) Tetrahedron 34:3143-3179 and Narang et al. (1979) Methods Enzymol. 68:90-98; the phosphodiester method of Brown et al. (1979) Methods Enzymol. 68:109-151; the diethylphosphoramidate method of Beaucage et al. (1981) Tetrahedron Lett. 22:1859-1862; and the solid support methods of Fodor et al. (1991) Science 251 :767-773; Pease et al. (1994) Proc. Natl. Acad. Sci. USA 91:5022-5026; and Singh-Gasson et al. (1999) Nature Biotechnol. 17:974- 978; as well as US Patent No. 4,485,066. See also, Peattie (1979) Proc. Natl. Acad. Sci. USA 76:1760-1764; as well as EP Patent No. 1721908; lnt'I Patent Application Publication Nos. WO 2004/022770 and WO 2005/082923 ; US Patent Application Publication Nos. 2009/0062521 and 2011/0092685; and US Patent Nos. 6,521,427; 6,818,395; 7,521, 178 and 7,910,726 .

[0066] Kits can be promoted, distributed or sold as units for performing the methods described below. Additionally, the kits can contain a package insert describing the kit and methods for its use. For example, the insert can include instructions for correlating the level of biomarker expression measured with a patient's likelihood of cancer recurrence, long-term survival, and the like, and select the most appropriate treatment option accordingly.

[0067] The kits therefore can be used for identifying a TNBC patient with biomarkers at the nucleic acid level. Such kits are compatible with both manual and automated nucleic acid detection techniques (e.g., gene arrays, Northern blotting or Southern blotting). These kits can include a plurality of probes, for example, from 5 to 100 nucleic acid probes that specifically bind to distinct biomarkers, fragments or variants thereof. Alternatively, the kits can contain at 5 probes, 10 probes, 15 probes, 20 probes, 30, 40 probes, 50 probes, 80 probes, 90 probes, 100 probes, 110 probes, 120 probes, 150 probes, 200 probes, or more. For example, in some embodiments, the kits described herein will comprise at least 5 probes. By way of example, the probes may be any of the 5 or 10 probes, all of the first 10 probes, 15-20 probes from among the first 20 probes, or all of the probes identified in Table 3. In some embodiments, the kit and/or methods include a panel of Probes for the genes listed as 1-10 or Probes for the genes listed as 1- 20 listed in Table 3. The kit may also include instructional inserts that provide instruction on the specific TNBC probes included, along with how to calculate a TNBC patient response score, how to compare the patient score to a reference TNBC population response score, and how to categorize the TNBC patient as having a low sensitivity or high sensitivity to a mitosis-inhibiting chemotherapeutic agent (such as AT-chemotherapy).

[0068] Any or all of the kit reagents can be provided within containers that protect them from the external environment, such as in sealed containers. Positive and/or negative controls can be included in the kits to validate the activity and correct usage of reagents employed in accordance with the technology. Controls can include samples, such as tissue sections, cells fixed on glass slides, RNA preparations from tissues or cell lines, and the like, known to be either positive or negative for the presence of at least five different biomarkers. The design and use of controls is standard and well within the routine capabilities of one of skill in the art.

[0069] The methods generally begin by collecting a sample from a patient pre-determined to have cancer. As used herein "sample" means any collection of cells, tissues, organs or bodily fluids in which expression of a biomarker can be detected. Examples of such samples include, but are not limited to, biopsy specimens of cells, tissues or organs, bodily fluids and smears.

[0070] When the sample is a biopsy specimen, it can include, but is not limited to, breast cells, particularly breast tissue from a biopsy, such as a breast tumor tissue sample. Biopsy specimens can be obtained by a variety of techniques including, but not limited to, scraping or swabbing an area, using a needle to aspirate cells or bodily fluids, or removing a tissue sample. Methods for collecting various samples/biopsy specimens are well known in the art. In some embodiments, a breast tissue sample is obtained by, for example, fine needle aspiration biopsy, core needle biopsy, or excisional biopsy.

[0071] Fixative and staining solutions can be applied to, for example, cells or tissues for preserving them and for facilitating examination. Samples, particularly breast tissue samples, can be transferred to a glass slide for viewing under magnification. For example, the sample is a breast tumor tissue sample, and can be a FFPE breast tumor tissue sample, a fresh breast tumor tissue sample or a fresh frozen breast tissue sample. The breast tissue sample is in some embodiments particularly a primary breast tumor tissue cancer sample.

[0072] After collecting and preparing the specimen from the patient, the methods then include detecting expression of the biomarkers. One can use any method available for detectmg expression of polynucleotide and polypeptide biomarkers. As used herein, "detecting expression" means determining the quantity or presence of a biomarker polynucleotide or its expression product. As such, detecting expression encompasses instances where a biomarker is determined not to be expressed, not to be detectably expressed, expressed at a low level, expressed at a normal level, or overexpressed.

[0073] Expression of a biomarker can be determined by normalizing the level of a reference marker/control, which can be all measured transcripts (or their products) in the sample or a particular reference set of RNA transcripts (or their products). Normalization can be performed to correct for or normalize away both differences in the amount of biomarker assayed and variability in the quality of the biomarker type used. Therefore, an assay typically measures and incorporates the expression of certain normalizing polynucleotides or polypeptides, including well known housekeeping genes, such as, for example, GAPDH and/or actin. Alternatively, normalization can be based on the mean or median signal of all of the assayed biomarkers or a large subset thereof (global normalization approach).

[0074] To determine overexpression, the sample can be compared with a corresponding sample that originates from a healthy individual. That is, the "normal" level of expression is the level of expression of the biomarker in, for example, a breast tissue sample from an individual not afflicted with breast cancer. Such a sample can be present in standardized form.

[0075] Sometimes, determining biomarker overexpression requires no comparison between the sample and a corresponding sample that originated from a healthy individual. For example, detecting overexpression of a biomarker indicative of a poor prognosis in a breast tumor sample may preclude the need for comparison to a corresponding breast tissue sample that originates from a healthy individual.

[0076] Methods of detecting and quantifying polynucleotide biomarkers in a sample are well known in the art. Such methods include, but are not limited to gene expression profiling, which are based on hybridization analysis of polynucleotides, and sequencing of polynucleotides. The most commonly used methods art for detecting and quantifying polynucleotide expression in include northern blotting and in situ hybridization (Parker & Barnes (1999) Methods Mo/. Biol. 106:247-283), R Ase protection assays (Hod (1992) Biotechniques 13:852-854), PCR-based methods, such as RT-PCR (Weis et al. (1992) TIG 8:263-264), and array-based methods (Schena et al. (1995) Science 270:467-470). Alternatively, antibodies may be employed that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes, or DNA- protein duplexes in, for example, an oligonucleotide- linked immunosorbent assay ("OLISA"). See, Lee et al. (1985) FEBS Lett. 190:120-124; Han et al. (2010) Bioconjug. Chem. 21:2190-2196; Miura et al. (1987) Biochem. Biophys. Res. Commun. 144:930-935; and Tanha & Lee (1997) Nucleic Acids Res. 25:1442-1449. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression ("SAGE") and gene expression analysis by massively parallel signature sequencing. See, Velculescu et al. (1995) Science 270: 484-487.

[0077] Methods of isolating polynucleotides such as RNA from a sample are well known in the art. See, e.g., Molecular Cloning: A Laboratory Manual, 3rd ed. (Sambrook et al. eds., Cold Spring Harbor Press 2001); and Current Protocols in Molecular Biology (Ausubel et al. eds., John Wiley & Sons 1995). Methods for RNA extraction from paraffin-embedded tissues also are well known in the art. See, e.g., Rupp & Locker (1987) La.b Invest. 56:A67; and De Andres et al. (1995) Biotechniques 18:42-44. Moreover, isolation/purification kits are commercially available for isolating polynucleotides such as RNA (Qiagen; Valencia, CA). For example, total RNA from cells in culture can be isolated using Qiagen RNeasy® Mini-Columns. Other commercially available RNA isolation/purification kits include MasterPure TM Complete DNA and RNA Purification Kit (Epicentre; Madison, WI.) and Paraffin Block RNA Isolation Kit (Ambion; Austin, TX). Total RNA from tissue samples can be isolated, for example, using RNA Stat-60 (Tel-Test; Friendswood, TX). RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation. Additionally, large numbers of tissue samples readily can be processed using techniques well known to those of skill in the art, such as, for example, the single-step RNA isolation process of Chomczynski (US Patent No. 4,843, 155).

[0078] Once isolated, the polynucleotide, such as mRNA, can be used in hybridization or amplification assays including, but not limited to, Southern or Northern blotting, PCR and probe arrays. One method of detecting polynucleotide levels involves contacting the isolated polynucleotides with a nucleic acid molecule (probe) that can hybridize to the desired polynucleotide target. The nucleic acid probe can be, for example, a full-length DNA, or a portion thereof, such as an oligonucleotide of at least about 10, 15, 20, 30, 40, 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 400 or 500 nucleotides or more in length and sufficient to specifically hybridize under stringent conditions to a polynucleotide such as an mRNA or genomic DNA encoding a biomarker of interest. Hybridization of a polynucleotide encoding the biomarker of interest with the probe indicates that the biomarker in question is being expressed.

[0079] Stringent hybridization conditions typically include low ionic strength and high temperature for washing and can be defined as hybridizing at 68°C in 5x SSC/5x Denhardt's solution/1.0% SOS, and washing in 0.2x SSC/0.1 % SOS +/- 100 μ^πύ denatured salmon sperm DNA at room temperature (RT). Moderately stringent hybridization conditions include conditions less stringent than those described above (e.g., temperature, ionic strength and % SOS) and can be defined as washing in the same buffer at 42°C. One of skill in the art understands how to adjust the temperature, ionic strength, etc. as necessary to accommodate factors such as probe length and the like. Additional guidance regarding such conditions is readily available in the art, for example, in Molecular Cloning: A Laboratory Manual, 3rd ed. (Sambrook et al. eds., Cold Spring Harbor Press 2001); and Current Protocols in Molecular Biology (Ausubel et al. eds., John Wiley & Sons 1995).

[0080] Another method of detecting polynucleotide expression levels that involves immobilized polynucleotides on a solid surface such as a biochip or a microarray and contacting the immobilized polynucleotides with a probe, for example by running isolated mRNA on an agarose gel and transferring the mRNA from the gel to a membrane, such as nitrocellulose. Alternatively, the probes can be immobilized on a solid surface and isolated mRNA is contacted with the probes, for example, in an Agilent Gene Chip Array or Affymetrix GeneChip.

[0081] As used herein, "biochip" or "microarray" can be used interchangeably to mean a solid substrate comprising an attached probe or plurality of probes as described herein, wherein the probe(s) comprise at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 100, 150, 200 or more probes. The detectably labeled molecular probes are capable of hybridizing to a target sequence (identifying a TNBC biomarker gene, for example) under stringent hybridization conditions. The probes may be attached at spatially defined address on the substrate. More than one probe per target sequence may be used, with either overlapping probes or probes to different sections of a particular target sequence. The probes may be capable of hybridizing to target sequences associated with a single disorder. The probes may be attached to the biochip/microarray in a wide variety of ways, as will be appreciated by one of skill in the art. The probes may either be synthesized first, with subsequent attachment to the biochip, or may be directly synthesized on the biochip/microarray. The solid substrate may be a material that may be modified to contain discrete individual sites appropriate for the attachment or association of the probes and is amenable to at least one detection method. The probes may be labeled with any number of detectable labels known to those of skill in the molecular arts.

[0082] Examples of substrates include, but are not limited to, glass and modified or functionalized glass, plastics (including acrylics, polystyrene and copolymers of styrene and other materials, polypropylene, polyethylene, polybutylene, polyurethanes, Teflon®, etc.), polysaccharides, nylon or nitrocellulose, resins, silica or silica-based materials including silicon and modified silicon, carbon, metals, inorganic glasses and plastics. The substrates may allow optical detection without appreciably fluorescing. The substrate may be planar, although other configurations of substrates may be used as well. For example, probes may be placed on the inside surface of a tube, for flow-through sample analysis to minimize sample volume. Similarly, the substrate may be flexible, such as a flexible foam, including closed cell foams made of particular plastics.

[0083] The biochip/microarray and the probe can be derivatized with chemical functional groups for subsequent attachment of the two. For example, the biochip/microarray may be derivatized with a chemical functional group including, but not limited to, amino groups, carboxyl groups, oxo groups or thiol groups. Using these functional groups, the probes can be attached using functional groups on the probes either directly or indirectly using a linker. The probes may be attached to the solid support by either the 5' terminus, 3' terminus, or via an internal nucleotide. The probe may also be attached to the solid support noncovalently. For example, biotinylated oligonucleotides can be made, which may bind to surfaces covalently coated with streptavidin, resulting in attachment. Alternatively, probes can be synthesized on the surface using techniques such as photopolymerization and photolithography.

[0084] For example, microarrays can be used to detect polynucleotide expression. Microarrays are particularly well suited because of the reproducibility between different experiments. DNA microarrays provide one method for the simultaneous measurement of the expression levels of large numbers of polynucleotides. Each array consists of a reproducible pattern of capture probes attached to a solid support. Labeled RNA or DNA is hybridized to complementary probes on the array and then detected by laser scanning. Hybridization intensities for each probe on the array are determined and converted to a quantitative value representing relative gene expression levels. See, e.g., US Patent Nos. 6,040, 138; 5,800,992; 6,020, 135; 6,033,860 and 6,344,316. High-density oligonucleotide arrays are particularly useful for determining expression profiles for a large number of polynucleotides in a sample. For example, the methods described herein used a microarray and 4 or 5 probes including 212022_s_at (MKI67), 203145_at (SPAG5), 204817_at (ESPL1), 202240_at (PLK1).

[0085] Methods of synthesizing these arrays using mechanical synthesis methods are described in, for example, US Patent No. 5,384,261. Although a planar array surface generally is used, the array can be fabricated on a surface of virtually any shape or even a multiplicity of surfaces. Arrays can be nucleic acids (or peptides) on beads, gels, polymeric surfaces, fibers (such as fiber optics), glass or any other appropriate substrate. See, e.g., US Patent Nos. 5,770,358; 5,789,162; 5,708,153; 6,040,193 and 5,800,992. [0086] As such, PCR-amplified inserts of cDNA clones can be applied to a substrate in a dense array. For example, at least about 10,000 nucleotide sequences can be applied to the substrate. The microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes can be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mR A abundance.

[0087] With dual color fluorescence, separately labeled cDNA probes generated from two sources of polynucleotide can be hybridized pairwise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified molecule is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels. See, Schena et al. (1996) Proc. Natl. Acad. Sci. USA 93:106-149. Advantageously, microarray analysis can be performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix® GenChip Technology, or Agilent® Ink- Jet Microarray Technology. The development of microarray methods for large-scale analysis of gene expression makes it possible to search systematically for molecular markers of cancer classification and outcome prediction in a variety of tumor types.

[0088] Another method of detecting polynucleotide expression levels involves a digital technology developed by NanoString® Technologies (Seattle, WA) and based on direct multiplexed measurement of gene expression, which offers high levels of precision and sensitivity (<1 copy per cell). The method uses molecular "barcodes" and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. Each color- coded barcode is attached to a single target-specific probe corresponding to a gene of interest. Mixed together with controls, they form a multiplexed CodeSet. Two ~50 base probes per mRNA can be included for hybridization. The reporter probe carries the signal, and the capture probe allows the complex to be immobilized for data collection. After hybridization, the excess probes are removed and the probe/target complexes aligned and immobilized in an nCounter® Cartridge. Sample cartridges are placed in a digital analyzer for data collection. Color codes on the surface of the cartridge are counted and tabulated for each target molecule.

[0089] Another method of detecting polynucleotide expression levels involves nucleic acid amplification, for example, by RT-PCR (US Patent No. 4,683,202), ligase chain reaction (Barany (1991) Proc. Natl. Acad. Sci. USA 88:189-193), self-sustained sequence replication (Guatelli et al. (1990) Proc. Natl. Acad. Sci. USA 87:1874-1878), transcriptional amplification system (Kwoh et al. (1989) Proc. Natl. Acad. Sci. USA 86:1173-1177), Q-Beta Replicase (Lizardi et al., (1988) Bio/Technology 6:1197), rolling circle replication (US Patent No. 5,854,033), or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known in the art. Likewise, biomarker expression can be assessed by quantitative fluorogenic RT-PCR (i.e., the TaqMan® System). For PCR analysis, methods and software are available to determine primer sequences for use in the analysis. These methods are particularly useful for detecting polynucleotides present in very low numbers.

[0090] Additional methods of detecting polynucleotide expression levels of RNA may be monitored using a membrane blot (such as used in hybridization analysis such as Northern or Southern blotting, dot, and the like), or microwells, sample tubes, gels, beads or fibers (or any solid support comprising bound nucleic acids). See, e.g., US Patents Nos. 5,770,722; 5,874,219; 5,744,305; 5,677, 195 and 5,445,934. Polynucleotide biomarker expression also can include using nucleic acid probes in solution.

[0091] Another method of detecting polynucleotide expression levels involves SAGE, which is a method that allows the simultaneous and quantitative analysis of a large number of polynucleotides without the need of providing an individual hybridization probe for each transcript. First, a short sequence tag (about 10-14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags and identifying the gene corresponding to each tag. See, Velculescu et al. (1995), supra. [0092] Another method of detecting polynucleotide expression levels involves massively parallel signature sequencing ("MPSS"). See, Brenner et al. (2000) Nat. Biotech. 18:630-634. This sequencing combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate diameter microbeads. First, a microbead library of DNA templates can be constructed by in vitro cloning. This is followed by assembling a planar array of the template-containing microbeads in a flow cell at a high density (typically greater than 3.0 x 106 microbeads/cm2). The free ends of the cloned templates on each microbead are analyzed simultaneously, using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to simultaneously and accurately provide, in a single operation, hundreds of thousands of gene signature sequences from a yeast DNA library.

[0093] After measuring expression levels of the biomarkers, the method described herein then includes correlating the expression levels of the biomarkers in the patient sample to a reference/control set to determine the prognosis of the patient. One may use any method available for correlating expression levels of polynucleotide (or polypeptide) biomarkers.

[0094] The present method may also be implemented through the use of a computer. For example, present method may employ a computer running a software program that can analyze biomarker expression level data from a TNBC patient, compare that data to a distribution of expression levels from a population of TNBC patients that were insensitive to AT-chemotherapy treatment, and determine whether the TNBC patient's expression levels were below or above the level of each biomarker of interest in the reference population of TNBC-patients that did respond to AT-chemotherapy treatment.

[0095] The computer can generate a report summarizing the patient's biomarker expression levels and/or the patient's suitability for subsequent AT-chemotherapy treatment. Moreover, the computer can perform any statistical analysis of the patient's data or a population of patient's data as described herein in order to generate the status of the patient as AT-sensitive or AT-insensitive. Further, the computer program also can normalize the patient's biomarker expression levels in view of a standard or control prior to comparison of the patient's biomarker expression levels to those of the patient population. The computer also can ascertain raw data of a patient's expression values from, for example, a microarray, or the raw data can be input into the computer. [0096] Methods for assessing statistical significance are well known in the art and include, for example, using a log-rank test, Cox analysis and Kaplan-Meier curves. A p-value of less than 0.05 can be used to establish statistical significance.

[0097] Overexpression of a TNBC biomarker or combination of TNBC biomarkers can be indicative of a poor prognosis for AT-chemotherapy treatment as a viable promising option. As used herein, "indicative of a poor prognosis" is intended to mean that altered expression of particular biomarkers or combination of biomarkers is associated with an increased likelihood that an AT-chemotherapy regimen would be relatively ineffective, and suggest alternative therapeutic regimens be selected. As used herein, "indicative of a good prognosis" for AT- chemotherapy treatment refers to an increased likelihood that the TNBC patient will benefit from AT-chemotherapy treatment. For example, "indicative of a good prognosis" may refer to an increased likelihood that the TNBC patient will improve upon AT-chemotherapy treatment, and remain relapse and metastasis free for at least 3, 4, or 5 years.

[0098] Likewise, and as noted above, it is contemplated that the methods herein can be applied to polypeptide biomarkers, as methods of detecting and quantifying polypeptides in a sample are well known in the art and include, but are not limited to, immunohistochemistry and proteomics-based methods.

EXAMPLES

[0099] The technology will be more fully understood upon consideration of the following non-limiting examples, which are offered for purposes of illustration, not limitation.

EXAMPLE 1 -- CONSTRUCTION OF A PREDICTIVE SCORE FROM GENE

EXPRESSION MEASUREMENTS

[0100] A gene is considered multistate if its distribution of expression across a population is sufficiently bimodal, which is formalized with the statistical concept of a mixture model. The mixture model method identifies a threshold c and partitions samples into those with expression greater than c (the high component) and those with expression less than or equal to c (the low component). In identifying multistate genes for inclusion in the panel, we search for those in which one component is enriched with pCR cases. In building prognostic or predictive models, the vector of expression values for a multistate gene can be replaced by a vector of numbers (0 - 1) measuring the probability that a sample is in the component enriched with pCR cases. This probability is reported by the mixture model fit. This probability vector may be called the risk score of the gene since it expresses the risk that a sample will achieve the event in question, here, achieving pCR.

[0101] A predictive score for a panel of multistate genes is defined as the sum of the risk scores of these genes, scaled from 0 to 100. Samples considered unlikely to achieve pCR based on the risk scores of the panel genes will predictive score values near 0. The score increases with the number of genes that classify the sample as likely to achieve pCR.

[0102] While the continuous score gives the most accurate assessment of a patient's likelihood of achieving pCR, it is also useful to divide patients into discrete groups for the purpose of deciding on a specific treatment. Below, this will be done for the RespondR score using a further application of statistical mixture models.

EXAMPLE 2 -- STATISTICAL METHODS USED IN THE ANALYSIS

[0103] All statistical analyses were performed using R (http://www.r-project.org). Mixture models were fit using the package mclust (Fraley & Raftery 2002; Fraley & Raftery 2012) and survival analysis was performed with the survival package. The significance of a Cox proportional hazard (CPH) model is assessed with the P value of the logrank score test. The proportional hazard condition is tested with the cox.zph function. Expected survival curves were generated using the rms package (Harrell:2013). The Monte Carlo cross-validation (Kuhn & Johnson 2013) test was used to estimate parameters in the development of a predictive model.

[0104] A training- validation set framework will be used to derive and validate the TNBC (RespondR) predictive score. The genes included in the panel, along with certain parameters used in calculating the score, will be identified using only the training set. This final predictive score will be tested for significance in the validation set. In this study multiple validation sets will be used to establish the TNBC (RespondR) functionality when gene expression is measured with a variety of technologies, and for predicting the relative effectiveness of multiple chemotherapy treatment regimens.

EXAMPLE 3 -- TECHNOLOGIES USED TO MEASURE GENE EXPRESSION

[0105] Numerous technologies can be used to measure gene expression. The three major groups are microarrays, next-generation sequencing, and quantitative real-time PCR (qRT-PCR). Microarray analysis is traditionally done with a fresh-frozen tissue source, and next-generation sequencing and qRT-PCR and be effectively analyzed with fresh-frozen or formalin-fixed paraffin-embedded (FFPE) tissue source.

[0106] RespondR has been designed to function in an equivalent manner independent of the measurement technology and the tissue source. Here, we have shown that RespondR can be executed with Affymetrix microarrays (hgul33a, hgul33av2, hgul33plus2), Illumina microarrays (illuminaHumanv3), and next-generation sequencing (RNAseq using Illumina HiSeq RNAseqV2). Some of these datasets used fresh-frozen tissue, and others used FFPE tissue.

[0107] The test will be derived and principally validated with Affymetrix hgul33a datasets (Example 4). The test will be further validated with TNBC samples from The Cancer Genome Atlas (TCGA) (Koboldt et al. 2012), in which gene expression was measured with RNAseq. The utility of the score in samples with gene expression measured by an Illumina microarray was assessed using the METABRIC cohort (Chin et al. 2012).

EXAMPLE 4 ~ DATASETS USED IN TRAINING AND VALIDATION OF TNBC

RESPONSE SCORE ("RESPONDR")

[0108] The present example is provided to present the datasets used in the derivation RespondR and the initial validation using Affymetrix array technology.

[0109] A set of 295 chemotherapy treated TNBC patients were divided into training and validation sets balanced for clinical traits and pCR (Table 2). The validation set will be used to show that RespondR is prognostic of improved distant metastasis-free survival (DMFS), as well as an increased likelihood of pCR. In these datasets, gene expression was measured with the hgul33a Affymetrix microarray.

Table 2. Microarray cohorts used in this study

age (< 50/> 50/NA) 53/64/1 90/88/0

Grade (1/2/3/NA) 1/17/79/21 1/22/139/16

5-year distant metastasis- NA 117/61

free survival (no

event/event)

ER-, not TNBC, AT-chemotherapy treated

Validation (n=l 03)

Pathological complete 41/61/1

response (pCR/RD/NA)

TNBC, no chemotherapy

Validation (n=l 78)

Cohorts

Lymph node (-/+/NA) 165/11/2

5-year distant metastasis- 123/50/5

free survival (no

event/event)

Obtained from the Gene Expression Omnibus < (http://www.ncbi.nlm.nih.gov/gds/) >

' Duplicate samples from patients included in both GSE20194 and GSE20271 were eliminated

[0110] The study includes ER- patients that have been treated with AT-based chemotherapy and others untreated with chemotherapy used in other aspects of the study. All microarray data in the study were normalized together and we verified that there were no significant batch effects.

[0111] In three of the chemotherapy-treated cohorts (MDACC, USO, ISPY), the chemotherapy regimen included anthracyclines and taxane. However, in the SPAIN (GSE20271) cohort, included in the training set, patients were randomized to receive TFAC (paclitaxel, fluorouracil, doxorubicin, cyclophosphamide) or FAC chemotherapy (Fluorouracil, Doxorubicin, Cyclophosphamide). Receiving TFAC or FAC did not inhibit the identification of a significant panel predicting chemotherapy benefit. EXAMPLE 5 - DERIVATION OF THE UNIVERSAL RESPONDR PREDICTIVE

SCORE

[0112] The present example demonstrates that the TNBC (RespondR) system as described herein provides a universal TNBC (RespondR) patient score that is independent of the technology used to measure gene expression.

[0113] Following selection of training and validation sets, the following algorithm is applied within the training set with Monte Carlo cross-validation to select parameters n = the number of genes to use for the panel. After selecting this parameters, the following algorithm is executed in the training set, resulting in the RespondR gene panel and the score.

[0114] TNBC Response Score Derivation Algorithm

Objects and parameters on which the derivation process is executed:

• Disjoint training and validation sets with comparable rates of pCR

• A number n = the number of genes to use for the panel;

• A set of multistate genes from which the panel is selected.

Discovery process:

• For each candidate multistate variable, compute the chi-square statistic between the multistate gene's binary variable and the pCR event vector in the training set;

• Select as the panel variables P the genes with the n largest chi-square statistics;

• Form the pCR predictive score S by adding the individual risk scores of the genes in P and scaling for 0 to 100;

Output of the process:

• Report the p-value of the linear regression of S and the pCR event vector in the validation set;

• Export the panel of n genes and the score S.

As candidates for inclusion in the panel, we consider only probes that satisfy the following:

• The probe represents a gene annotated with an Entrez gene identifier;

• For all Affymetrix microarrays, Illumina microarrays, and for RNAseq data, expression values of this gene are available for all samples in our validation cohorts; • In each of the validation cohorts, the multistate methodology identifies components such that the percentage of samples in the high component in TNBC patients is within 15% of the same percentage in the training dataset.

[0115] To begin the process, a TNBC training and validation set for the study was selected (see Table 2), with comparable rates of pCR and other clinical traits. Application of the above algorithm requires a choice of the number "n" of genes to include in the panel. This number will be selected as the one yielding the best-performing score in the following Monte Carlo cross-validation step, executed within the training set.

[0116] A family of 100 training sets, Tj, i < 100, were randomly chosen so that each Tj consists of 2/3 of the TNBC biomarker gene panel training set, balanced for pCR rate. For each i < 100, the complement of Tj was chosen in the biomarker gene panel training set as the paired validation set, V;. Each Tj contains 85 samples with 25 pCR events. Candidate values of n, specifically 5, 10, 15, 20, 30, were tested by applying the TNBC Response Score Derivation Algorithm to each pair Ti-Vi, i < 100, and each candidate value of n. From each application, we collected the p-value of the linear regression of derived score S and the pCR event vector in the corresponding validation set. The suitability of the candidate parameter n was assessed using the median p-values ranging over all Tj-Vj. Assessment of the results of this Monte Carlo cross- validation analysis showed that continuous predictive scores using 20 genes performed the best.

[0117] Following the choice of 20 as the optimal number of genes to use in computing the score, the TNBC Response Score Derivation Algorithm created by the present inventors, was executed for the entire training set. This resulted in ranked list of candidate genes (Table 3). The universal TNBC (RespondR) score is computed from the top 20 genes on the list. The algorithm produces a ranked list of all genes significant predictive in the training set, which may contain considerably more than 20 genes. Sets of these genes can be used to compute alternative scores with nearly comparable performance to our preferred panel. This is discussed in Example 16.

Table 3. Panel of genes for the universal RespondR family of tests. (The preferred RespondR score is computed from the genes ranked 1 - 20.)

pCR enriched rank symbol gene id Affymetrix probe Illumina probe component

3 GOLT1B 51026 218193_s_at ILMNJ 767837 high

4 TPGS2 25941 213617_s_at ILMN_1671693 high

5 ACTR3B 57180 218868_at ILMN_1787513 high

6 ELF5 2001 220624_s_at ILMN_1813270 high

7 U C5B 219699 213100_at ILMNJ 176502 low

8 HEMK1 51409 218621_at ILMNJ 731014 low

9 ABT1 29777 218405_at ILMNJ 658083 high

10 EXOC5 10640 218748_s_at ILMNJ 662862 high

11 INPP4B 8821 205376_at ILMNJ 198878 low

12 SCNN1B 6338 205464_at ILMNJ 740917 low

13 MSH6 2956 211450_s_at ILMNJ 729051 high

14 SYT17 51760 205613_at ILMNJ 657760 low

15 EXOSC5 56915 218481_at ILMNJ 659725 high

16 PODXL 5420 201578_at ILMN 297511 high

17 ALMS1 7840 214707_x_at ILMNJ 709474 low

18 SNAPC3 6619 20400 l_at ILMN 224300 high

19 TANK 10010 210458_s_at ILMNJ 793849 high

20 TGFBR3 7049 20473 l_at ILMNJ 784287 high

21 DYRK2 8445 202968_s_at ILMNJ 684184 high

22 PPFIBP2 8495 212841_s_at ILMNJ 675656 high

23 MYOIC 4641 214656_x_at ILMNJ 812616 low

24 MAST4 375449 210958_s_at ILMNJ 738438 low

25 SPDEF 25803 213441_x_at ILMN_2161330 low

26 PSME3 10197 200987_x_at ILMNJ 800975 high

27 CDC45 8318 204126_s_at ILMNJ 670238 high

28 NFIB 4781 211467_s_at ILMNJ 778991 high

29 AKAPl 8165 210625_s_at ILMNJ712530 high

30 PDK3 5165 221957_at ILMNJ 776582 high pCR enriched rank symbol gene id Affymetrix probe Illumina probe component

31 SMIM7 79086 221988_at ILMN_1694759 high

32 YIPF3 25844 216338_s_at ILMN_1673604 high

33 PPP1R2 5504 202165_at ILMNJ 683044 high

34 EXOC7 23265 214802_at ILMNJ 750011 low

35 ECE1 1889 201749_at ILMN_1672174 low

36 KAT6B 23522 212452_x_at ILMNJ 698441 low

37 PRDX2 7001 215067_x_at ILMNJ 767766 low

38 ALPK1 80216 207133_x_at ILMN_2078697 low

39 GDF15 9518 221577_x_at ILMNJ 188862 low

EXAMPLE 6 ~ DISCOVERY OF A DISCRETE VERSION OF TNBC

(RESPONDR) SCORE

[0118] RespondR is a continuous score created so that the probability of a patient achieving pCR increases along with the score. However, pCR is a discrete event. A threshold that optimally separates patients by likelihood of pCR will provide doctors with useful information in designing a treatment strategy. RespondR is a continuous score that can assume any value between 0 and 100, however, in the training set, the RespondR score values of the samples cluster into two groups: a group with high RespondR values, and a group with low RespondR values. To form the 2 groups, we applied the statistical mixture model method to the RespondR score values in the training set. This method results in a choice of 45, the 58^th quantile, as the threshold at which to partition the dataset. Patients in the RR-low region (RespondR < 45) are predicted to be insensitive to AT chemotherapy, while those in RR-high region (RespondR > 45) are predicted to be sensitive to AT chemotherapy.

EXAMPLE 7 ~ VALIDATION OF RESPONDR AS A PREDICTOR OF PCR IN

THE PRIMARY AFFYMETRIX VALIDATION SET

[0119] The RespondR score, derived from a panel of 20 genes (Example 5), and the partition into RR-low and RR-high groups (Example 6), were evaluated as predictors of pCR following AT chemotherapy in the primary Affymetrix validation set (Table 2). [0120] Logistic regression showed that RespondR score is a significant predictor of pCR in the validation set (Fig 1, p = 1.3 x 10^"5). The discrete partition significantly stratifies the validation by likelihood of pCR (Table 4, p = 8.2 x 10^~6, by chi-squared test).

Table 4. RespondR stratification (RR-low, RR-high) as predictor of pCR and DMFS

EXAMPLE 8 -- VALIDATION OF RESPONDR AS A PREDICTOR OF IMPROVED 5-YEAR METASTASIS-FREE SURVIVAL IN THE PRIMARY

AFFYMETRIX VALIDATION SET

[0121] Pathological complete response (pCR) is a rapid indicator of a patient's positive response to neoadjuvant chemotherapy. However, a more important measure of the effectiveness of a drag is long-term remission of the cancer. Most, but not all, patients who achieve pCR do not relapse. Also, many patients who do not achieve pCR will not relapse following surgical removal of the tumor. Thus, the clinical utility of the RespondR scoring is demonstrated by the observation of the long-term prognostic significance of the score. Note that a high percentage of TNBC patients who eventually relapse do so within 5 years of initial diagnosis.

[0122] Expected 5 -year DMFS increases significantly (p = 0.016) along with RespondR score in the primary Affymetrix validation set (Fig 2). The stratification of patients into RR-low and RR-high is prognostic of 5-year DMFS (p = 0.0037, Fig 3). The 5-year probabilities of DMFS (Table 3) show a dramatic difference in prognosis in the two strata following AT chemotherapy.

EXAMPLE 9 - TNBC RESPONSE SCORE NOT PROGNOSTIC OF DMFS IN TNBC PATIENTS NOT TREATED WITH CHEMOTHERAPY IS [0123] Samples from TNBC patients who were not treated with chemotherapy were isolated from microarray datasets available through GEO (Table 2), and the TNBC response score as described in Example 5 was calculated in this set. The study was restricted to lymph node negative (LN-) samples because this sample set has few LN+ samples. In patients untreated with chemotherapy, TNBC response score was found not to be a significant factor in predicting 5-year DMFS (p = 0.59).

[0124] The significance of the effect of chemotherapy for samples with high TNBC response score values was not measured because the treated and untreated sets were not randomized.

EXAMPLE 10 - RESPONDR IS A CLINICALLY USEFUL DIAGNOSTIC TEST FOR DECIDING BETWEEN DIFFERENT CHEMOTHERAPY REGIMENS IN

TNBC TREATMENT

[0125] The treatment outcomes of patients with TNBC can be significantly improved through the use of RespondR. A doctor's use of RespondR will follow the process described in Fig 4. When the doctor receives the patient's RespondR score, the choice of the most effective treatment will be based on the score value. If the score is high, defined above as greater than 45 (Example 6), then the patient is likely to achieve pCR and a high probability of 5-year DMFS following AT treatment (Examples 7 & 8). Moreover, without chemotherapy, the patient can expect the same DMFS probability as all other TNBC patients (Example 9). Thus, a patient with high RespondR score should be administered an AT chemotherapy regimen. On the other hand, a patient with low RespondR score, if treated with an AT chemotherapy regimen has a higher than average chance of relapse. Thus, she should be administered an alternative form of chemotherapy. Some of the options for alternative chemotherapy regimens are described in Example 12.

EXAMPLE 11 -- RESPONDR WITH GENE EXPRESSION MEASURED BY RNASEQ IS PROGNOSTIC OF IMPROVED SURVIVAL IN TNBC FOLLOWING

TAXANE-BASED CHEMOTHERAPY

[0126] The Cancer Genome Atlas (TCGA) (Koboldt et al, 2012) includes 84 TNBC patients treated with a taxane-based chemotherapy. Gene expression was measured by RNA- sequencing. This technology reads strings of nucleotides, and software uses this data to estimate the number of molecules of each species of mRNA in the sample. These estimates are further translated to normalized counts of mRNA species, which are measurements of gene expression with this technology.

[0127] The full panel of 39 genes (Table 3) were chosen so that for each gene, expression measurements by RNAseq result in a risk score with a comparable distribution to that of the risk score obtained for the same gene in TNBC samples in the Affymetrix training set (Table 2). The RespondR score for TCGA was computed from the risk scores of the 20 highest ranked genes in Table 3 by adding these risk scores and scaling from 0 - 100. RespondR high and low groups (RR-high, RR-low) were computed here using the same method employed in the Affymetrix data (Example 6).

[0128] In this dataset, at least 50% of the patients were treated adjuvantly; i.e., after surgery (Koboldt et al. 2012). Also, for the patients who were treated with neoadjuvant therapy, pathological complete response status was not recorded. However, the most important indicator of outcome, relapse-free survival status (RFS), was recorded, along with follow-up time.

[0129] The RR-low and RR-high groups show significant differences in 5 -year expected RFS (Fig 5). This result validates the ability of RespondR to predict sensitivity to AT chemotherapy when gene expression values are computed with RNAseq.

[0130] This result also broadens the taxane-based chemotherapy regimens for which RespondR predicts sensitivity to include adjuvant therapy in addition to the neoadjuvant regimen used in the Affymetrix validation set.

EXAMPLE 12 ~ CHEMOTHERAPY REGIMENS THAT MAY BE EMPLOYED

TO TREAT TNBC

[0131] Chemotherapy is rarely given as a single drug, but is normally administered as a combination of multiple drags, given simultaneously or sequentially. A list of drugs used and a schedule for administering them is known as a "chemotherapy regimen". Moreover, drugs are normally grouped into classes based on their modes of action. For example, anthracyclines are a class of drugs that inhibit the action of the gene TOP2A. Taxanes are another class of drugs that disrupt mitosis by microtubule interference. In treating TNBC, the drugs in a regimen typically include one or more cytotoxic agents, and other supporting drugs. Anthracyclines and taxanes are both cytotoxic agents. Chemotherapy regimens that include taxanes and or anthracyclines often include supportive chemotherapy drugs that augment their activity such as cyclophosphamide and 5-fluorouracil. The regimen known as TFAC, used for the patients in the Affymetrix validation set, consists of a taxane, 5-fourouracil, an anthracycline and cyclophosphamide.

[0132] Numerous alternatives to taxane-based chemotherapy have been FDA-approved for use in metastatic breast cancer (Table 5). These are also candidates for use in TNBC patients that Respond predicts will be insensitive to AT.

Table 5. Chemotherapy drug alternatives to a taxane-based regimen (National Comprehensive Cancer Network Guidelines, nccn.org).

Platinum salt (cisplatin or carboplatin)

Ixabepilone

Capecitabine

Gemcitabine

Vinorelbine

Lapatinib

Bevacizumab

[0133] A drug in Table 5 is normally administered in a regimen that does not contain a taxane, however, for some patients, it may be combined with a taxane. In this case, we will also call the regimen a non-taxane based regimen because the taxane is not the predominant cytotoxic agent.

[0134] AT chemotherapy and some other regimens may be administered neoadjuvantly (presurgically) or adjuvantly (post-surgically). While the patients in the Affymetrix validation set (Table 2) were treated with neoadjuvant AT, most of those in TCGA were treated adjuvantly. Combined with the results reported in Example 13, this shows that RespondR is predictive of a positive response to neoadjuvant AT chemotherapy and to adjuvant chemotherapy.

EXAMPLE 13 -- RESPONDR PREDICTS FAVORABLE RESPONSE TO TREATMENT BY ADJUVANT TAXANE-BASED CHEMOTHERAPY AND NEOADJUVANT IXABEPILONE-BASED CHEMOTHERAPY

[0135] Based on analyses of additional microarray cohorts, we show that RespondR can be used to select an alternative chemotherapy regimen (Table 5) for some TNBC patients. Our assertions follow. The datasets referenced here are available from the Gene Expression Ontology (http://www.ncbi.nlm.nih.gov/).

1) The dataset GSE58812 (Jezequel et al. 2015) contains Affymetrix microarray expression data on 107 TNBC patients who were treated with adjuvant chemotherapy according to international guidelines of the time. Those guidelines recommended AT chemotherapy. Gene expression values were computed with the hgul33plus2 (Affymetrix) array. In this dataset, 5-year expected DMFS is 0.67 in RR-low and 0.80 in RR-high, showing significant stratification.

2) Ixabepilone is a cytotoxic form a chemotherapy that, like taxanes, interferes with microtubule activity during mitosis. It is recommended for use in metastatic or locally advanced breast cancer patients that have become resistant to taxanes. GSE41998 contains 140 samples from a clinical trial testing the efficacy of neoadjuvant AT versus neoadjuvant therapy of an anthracycline and Ixabepilone (Horak 2013). In the Ixabepilone arm of the study, the rate of complete or partial pathological response was 31% in RR-low and 53% in RR-high.

EXAMPLE 14 - PLATINUM-BASED CHEMOTHERAPY MAY IMPROVE THE OUTCOMES IN RR-LOW PATIENTS IN COMPARISON TO AT

CHEMOTHERAPY

[0136] In previous examples we showed that patients in RR-low have very high risk of recurrence and respond poorly to taxane-based chemotherapy. To improve the overall outcomes in TNBC disease requires finding effective treatments for RR-low patients. In GSE18864, 24 TNBC patients were treated with neoadjuvant cisplatin, a platinum salt form of chemotherapy (Silver 2010. Responses to the therapy were recorded using the Miller Payne response score (Ogston et al. 2013). In this dataset, 50% of the patients received partial or complete response to the chemotherapy, and 50% had stable or progressive disease. We showed that 50% of the patients in RR-low received partial or complete response to cisplatin. Thus, TNBC patients in RR-low can expect to receive significantly greater benefit from cisplatin chemotherapy than to AT chemotherapy.

EXAMPLE 15 -- SIGNIFICANTLY PREDICTIVE GENE PANEL

ALTERNATIVES FOR RESPONDR SCORE ASSESSMENT ARE DEFINED USING SELECTED COMBINATIONS OF THE GENES IN TABLE 3 [0137] The universal RespondR score, defined using the top 20 genes in Table 3 (Example 5), was selected for some embodiments as providing a preferred score because it was found to provide the most statistically significant results compared to alternative groups of the 39 total genes provided at Table 5. However, it has also been established herein that alternative groupings of genes from Table 3 may be used to generate a RespondR score that is also statistically significant. To verify this assertion, many alternative groupings of the genes in table 5 were selected for the creation of additional Affymetrix sample sets (Table 2), and their significances tested in the Affymetrix validation set as described herein. Based on this analysis, the following identified groups of predictive genes for TNBC biomarker panels were identified.

1) A predictive score formed from any 2 of the first 7 genes in Table 3 (ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B) is significant;

2) A predictive score formed from any 3 of the first 14 genes in Table 3 (ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, ΓΝΡΡ4Β, SCNN1B, MSH6, SYT17) is significant;

3) A predictive score formed from any 5 of the 39 genes in Table 3 has a 95% chance of being statistically significant.

[0138] To explain 3) further, there are 575,757 collections of 5 genes among those in Table 3. To assess the significance of a randomly chosen one, we randomly sampled 100,000 subsets and computed the significance of the resulting score. We found that 98% of these are statistically significant at the p = 0.05 level, and all are significant at the 0.1 level. Using sampling theory, we conclude that a randomly chosen set of 5 genes has over 95% probability of being significant.

EXAMPLE 16 ~ AN 11-GENE PREDICTIVE SCORE IS PREFERRED FOR GENE EXPRESSION VALUES COMPUTED WITH AFFYMETRIX

MICROARRAYS

[0139] The 20 gene panel identified in Example 5 was chosen in part because it is independent of the technology used to measure gene expression. Removing this restriction, but otherwise duplicating the derivation process in Example5, results in a preferred score computed from ITGA6, GOLT1B, MZT2B, INPP4B, TPGS2, ELF5, HEMK1, UNC5B, ACTR3B, CDC45, TANK. In the Affymetrix validation set, the resulting Affymetrix RespondR score is predictive of pCR (p = 1.9 x 10^"5). EXAMPLE 17 -- RESPONDR IS PREDICTIVE OF CHANGES IN COPY

NUMBERS FOR GENES CORRELATED WITH DRUG ACTIVITY

[0140] TCGA and the METABRIC distributions of tumor data include records of copy number changes in the tumor DNA. The distributions of these alterations were analyzed with respect to RespondR. Typical of tumor samples, numerous copy number alterations (CNA) were observed. In some instances, a CNA is more frequent in RR-high than in RR-low, or conversely. Here, we report on selected CNAs that are differentially represented in RR-high and RR-low and are associated with activity of some forms of chemotherapy. Here, we report differential alterations that are statistically significant in the TNBC samples in both TCGA and METABRIC.

1) Copy number gain of the gene PARP1 is significantly more frequent in RR-high than in RR-low. Based on this we can predict that targeted drugs that inhibit the PARP1 protein will be largely ineffective in RR-high. PARP-inhibitors may be an effective chemotherapeutic agent for patients in RR-low, however.

2) Copy number loss of multiple genes involved in DNA repair, e.g., XRCC4, RAD50, RAD51, MSH3, BRCA1, is significantly more likely in RR-high. Impairment of the DNA repair mechanism has been associated to effectiveness of platinum-based chemotherapy.

3) Copy number alterations of genes involved in immune response are significantly more common in RR-high. For example, gain in HFE, CR2, NCR3, and loss in THBS1 and TLR2, are more common in RR-high. The immune system impacts the effectiveness of chemotherapy in multiple ways.

EXAMPLE 18 -- COMPARISON OF TNBC MICRO ARRAY WITH OTHER

MEASUREMENT SYSTEMS

[0141] The present example demonstrates that the TNBC platform presented provides consistent analysis for identifying specific groups of TNBC patients consistently across measurement platforms beyond microarray analysis. The ITGA6 gene is used as an exemplary TNBC biomarker gene to illustrate this feature.

[0142] A fundamental feature of computation of the RespondR score from the expression values of the panel genes is that the raw expression values for a gene are first transformed to the gene risk score (Example 1). The gene risk score is a number between 0 and 1 that increases with the gene's expression values and higher values are associated with a greater probability of responding to the drug. The mathematical method used to calculate a risk score from the raw expression values is not dependent on the technology used to measure gene expression.

[0143] The independence of the gene risk score from the technology used to measure gene expression is best illustrated with an example. The Affymetrix cohorts (training and validation combined), the METABRIC cohort and the TCGA cohort. In the Affymetrix cohort, expression of ITGA6 was measured with the Affymetrix hgul33a microarray, in METABRIC it was measured with an Illumina microarray and in TCGA it was measured with RNAseq. While the technologies used for measuring gene expression are different in each cohort, they are all representative of the overall population and the relationships between ITGA6 and response to treatment should be the same in each. Indeed, this is reflected in the gene risk scores (Figure 6). The expression values for ITGA6 in these separate cohorts take on different values reflective of the different measurement technologies. However, the risk scores for ITGA6 in these 3 cohorts have very similar distributions (Figure 6).

[0144] The independence of individual gene risk scores to the technology used to measure raw gene expression extends to the RespondR score because the latter is calculated directly from the gene risk scores of the panel genes.

[0145] This example was executed using gene expression measurements calculated with two microarray platforms and RNAseq. The same assertions hold for any gene expression measurement technology, such as RT-PCR, that computes measurements closely correlated to n RNA concentration.

EXAMPLE 19 -- RT-PCR -BASED PLATFORM TESTS FOR TNBC PATIENT ASSESSMENT, SCREENING AND TREATMENT SELECTION

[0146] The present example illustrates the creation of a novel group of RT-PCR probes that may be created that are specific for the 39 genes identified in Table 3.

[0147] Here we describe how to construct a TaqMan qRT-PCR assay kit.

[0148] Using the Affymetrix probes for the 39 genes in Table 3, a specific target sequence for each probe will be obtained using NetAffx Analysis Center <(http://www.affymetrix.com/analysis/index.affx)>. Target sequences were aligned to the appropriate mRNA reference sequence (REFSEQ) accession number using NCBI BLAST (Basic Local Alignment Search Tool) (http://blast.ncbi.nlm.nih.gov/Blast.cgi), and accessed the consensus sequence through the NCBI Entrez nucleotide database. [0149] Form the target sequence, for each gene in Table 3, a TaqMan probe to measure the gene's expression with RT-PCR will be isentified as follows. Using UMapIt mapping tool of Applied Biosystems (ABI, Foster City, CA), the target sequences from the Affymetrix probe IDs will be mapped to TaqMan assays specific to each sequence. If a TaqMan probe for a particular target sequence does not already exist, a TaqMan probe will be custom-designed using Primer Express (Applied Biosystems), and tested for the amplification efficiency based on the ABI defined criteria. Control RNA (Universal Human Reference RNA; Stratagene) and FFPE samples will be used to test the efficiency of the probes. If probe efficiency is found to be inadequate for a particular gene, alternative probes will be considered. Those skilled in the art of molecular biology can identify a TaqMan probe with adequate efficiency for 90% of genes.

[0150] The panel of genes to be represented in the custom array microfluidics device will include the 20 highest ranking genes in Table 3 for which a TaqMan probe with adequate efficiency was identified. To these 20 discriminant genes, we add the five reference genes, ACTB, TFRC, GUS, RPLPO and GAPDH. A custom array microfluidics card will be constructed that is pre-loaded with TaqMan probes for the 20 discriminant genes and the 5 reference genes.

[0151] A TNBC relative risk score can be computed for a patient using this custom microfluidics device as follows. From an FFPE patient tumor sample, mRNA will be extracted following standard procedures for a clinical pathology laboratory. This mRNA will be assayed in triplicate using the custom array microfluidics card and a machine designed for the purpose, e.g., the ABI Prism 7900HT Fast Real-Time platform, according to the manufacturer's instructions. The Delta threshold cycle values for each of the 20 genes of interest will be normalized using these endogenous controls according to the method of Applied Biosystems DataAssist™ Software. This process will result in measurements of gene expression for all 20 panel genes in the Δ ACT format, the industry standard for quantitative RT-PCR. These panel gene expression values will be compared to corresponding expression values in a reference set of samples. A computer program will compute a TNBC relative risk score for this patient using data from the reference set comparison.

[0152] All publications and patents mentioned in the above specification are herein incorporated by reference. In particular, the following references are specifically incorporated herein:

Chin, S.-F. et al., 2012. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature.

Harrell, F.E., 2013. Regression Modeling Strategies, Springer Science & Business Media.

Hatzis, C. et al., 2011. A genomic predictor of response and survival following taxane- anthracycline chemotherapy for invasive breast cancer. JAMA : the journal of the American Medical Association, 305(18), pp.1873-1881.

Horak, C.E. et al., 2013. Biomarker analysis of neoadjuvant doxorubicin/cyclophosphamide followed by ixabepilone or Paclitaxel in early-stage breast cancer. Clinical cancer research : an official journal of the American Association for Cancer Research, 19(6), pp.1587— 1595.

Koboldt, D.C. et al., 2012. Comprehensive molecular portraits of human breast tumours. Nature, 490(7418), pp.61-70.

Kuhn, M. & Johnson, K., 2013. Applied Predictive Modeling, Springer Science & Business Media.

Masuda, H. et al., 2013. Differential Response to Neoadjuvant Chemotherapy Among 7 Triple- Negative Breast Cancer Molecular Subtypes. Clinical Cancer Research, 19(19), pp.5533- 5540.

Ogston, K.N. et al., 2003. A new histological grading system to assess response of breast cancers to primary chemotherapy: prognostic significance and survival. Breast (Edinburgh,

Scotland), 12(5), pp.320-327.

Rastogi, P. et al., 2008. Preoperative chemotherapy: updates of National Surgical Adjuvant Breast and Bowel Project Protocols B-18 and B-27. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 26(5), pp.778-785.

Silver, D.P. et al., 2010. Efficacy of Neoadjuvant Cisplatin in Triple-Negative Breast Cancer.

Journal of Clinical Oncology, 287) (, pp.1145-1153.

Claims

CLAIMS What is claimed is:

1. A method of selecting a treatment for a triple negative breast cancer (TNBC) patient comprising:

assessing expression levels of a TNBC biomarker gene panel in a TNBC patient tissue, said TNBC biomarker gene panel comprising five detectably labeled molecular probes having specific binding affinity for five or more TNBC biomarker genes selected from the group consisting of ITGA6, MZT2B, GOLTIB, TPGS2, ACTR3B, ELF5, UNC5B, HEMKl, ABTl, EXOC5, ΓΝΡΡ4Β, SCNNIB, MSH6, SYT17, EXOSC5, PODXL, ALMS1, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, MYOIC, MAST2, SPDEF, PSME3, CDC45, NFIB, AKAP1, PDK3, SMIM7, YIPF3, PPP1R2, EXOC7, ECE1, KAT6B, PRDX2, ALPK1, and GDF15;

calculating a TNBC response score for said patient from said expression levels;

comparing the patient TNBC response score to a threshold TNBC reference patient population response score;

selecting a mitotic inhibitor chemotherapy regimen for a TNBC patient having a TNBC response score above the threshold TNBC reference population response score, or selecting a chemotherapy regimen other than a mitotic inhibitor chemotherapy regimen for a TNBC patient having TNBC response score that is equal to or below the threshold TNBC reference population response score.

2. A TNBC biomarker gene panel comprising detectably labeled molecular probes having specific binding affinity under stringent conditions for TNBC biomarker genes ITGA6, MZT2B, GOLTIB, TPGS2, ACTR3B, ELF5, UNC5B, HEMKl, ABTl, EXOC5, INPP4B, SCNNIB, MSH6, SYT17, EXOSC5, PODXL, ALMS1, SNAPC3, TANK, and TGFBR3.

3. A TNBC biomarker gene panel comprising three or more TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLTIB, TPGS2, ACTR3B, ELF5, UNC5B, HEMKl, ABTl, EXOC5, ΓΝΡΡ4Β, SCNNIB, MSH6, and SYT17.

4. A TNBC biomarker gene panel comprising a set of detectably labeled molecular probes having specific binding affinity under stringent conditions for five or more TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, ΓΝΡΡ4Β, SCNN1B, MSH6, SYT17, EXOSC5, PODXL, ALMS1, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, MYOIC, MAST2, SPDEF, PSME3, CDC45, NFIB, AKAPl, PDK3, SMIM7, YIPF3, PPP1R2, EXOC7, ECE1, KAT6B, PRDX2, ALPK1, and GDF15.

5. The method according to claim 1, wherein a TNBC patient will be identified as having a high sensitivity response score where the TNBC biomarker gene levels of one or more of the TNBC biomarker genes selected from the group consisting of are upregulated in the TNBC patient sample: ITGA6, GOLT1B, TPGS2, ACTR3B, ELF5, ABT1, EXOC5, MSH6, EXOSC5, PODXL, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, PSME3, CDC45, NFIB, AKAPl, PDK3, SMIM7, YIPF3, and PPP1R2.

6. The method according to claim 1, wherein a TNBC patient will be identified as having a high sensitivity response score where the TNBC biomarker gene levels of one or more of the TNBC biomarker genes selected from the group consisting of the following are downregulated in the TNBC patient sample: MZT2B, UNC5B, HEMK1, ΓΝΡΡ4Β, SCNN1B, SYT17, ALMS1, MYOIC, MAST2, SPDEF, EXOC7, ECE1, KAT6B, PRDX2, ALPK1, and GDF15.

7. The method according to claim 1, wherein the level of each gene comprising the TNBC biomarker gene panel is identified with a cDNA, mRNA, cRNA or other nucleotide that is specific for the gene for each TNBC biomarker gene of the panel.

8. The method according to claim 1, wherein the TNBC response score is determined by: normalizing each TNBC biomarker gene expression level against a control gene level to provide a normalized TNBC continuous risk score for each of the TNBC panel genes; and

calculating an overall TNBC response score from the normalized TNBC continuous risk scores, and scaling the overall TNBC response score to provide a patient continuous response score from 0 to 100.

9. The method according to claim 1, wherein AT-chemotherapy is neoaduvant or adjuvant.

10. The method according to claim 1, wherein said chemotherapy regimen other than a mitosis inhibitor regimen comprises a platinum salt.

11. A method of selecting a treatment for a triple negative breast cancer (TNBC) patient, comprising:

assessing expression levels of a TNBC biomarker gene panel of 5 or more TNBC biomarker genes in a TNBC patient tissue sample, said TNBC biomarker genes being selected from the group consisting of: ITGA6, MZT2B, GOLTIB, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, ΓΝΡΡ4Β, SCNN1B, MSH6, SYT17, EXOSC5, PODXL, ALMS1, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, MYOIC, MAST2, SPDEF, PSME3, CDC45, NFIB, AKAP1, PDK3, SMIM7, YIPF3, PPP1R2, EXOC7, ECE1, KAT6B, PRDX2, ALPK1, and GDF15;

calculating a TNBC response score for said TNBC patient from said expression levels; comparing the TNBC patient response score to a threshold TNBC reference patient population response score;

selecting a mitosis-inhibiting agent chemotherapy regimen for a TNBC patient having a TNBC response score above the threshold TNBC reference population response score, or selecting a chemotherapy regimen with other than a mitosis-inhibiting agent for a TNBC patient having a TNBC response score that is below the threshold TNBC reference population response score.

12. The method of claim 11, wherein the TNBC biomarker gene panel comprises two or more TNBC biomarker genes selected from the group consisting of: ITGA6, GOLTIB, MZT2B, TPGS2, ELF5, ACTR3B, and UNC5B.

13. The method of claim 11, wherein the TNBC biomarker gene panel comprises three or more TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLTIB, TPGS2, ACTR3B, ELF5, UNC5B, HEMKl, ABTl, EXOC5, INPP4B, SCNNIB, MSH6, and SYT17.

14. The method of claim 11, wherein the TNBC biomarker gene panel is: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B, HEMKl, ABTl, EXOC5, INPP4B, SCNNIB, MSH6, SYT17, EXOSC5, PODXL, ALMS1, SNAPC3, TANK, and TGFBR3.

15. The method according to claim 11, wherein a TNBC patient having a high sensitivity response score will have upregulated TNBC biomarker gene levels of 1 or more of: ITGA6, GOLT1B, TPGS2, ACTR3B, ELF5, ABTl, EXOC5, MSH6, EXOSC5, PODXL, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, PSME3, CDC45, NFIB, AKAP1, PDK3, SMIM7, YIPF3, PPP1R2 TNBC biomarker genes.

16. The method according to claim 11, wherein a TNBC patient having a high sensitivity response score will have downregulated TNBC biomarker gene levels of 1 or more of the TNBC biomarker genes: MZT2B, UNC5B, HEMKl, ΓΝΡΡ4Β, SCNNIB, SYT17, ALMS1, MYOIC, MAST2, SPDEF, EXOC7, ECE1, KAT6B, PRDX2, ALPK1, and GDF15.

17. The method according to claim 11, wherein a TNBC patient response score below about 40 percent of the threshold TNBC reference population response score is a low sensitivity TNBC patient for a mitotic inhibiting agent chemotherapy regimen.

18. The method according to claim 11, wherein a TNBC patient score above about 60% of the threshold TNBC reference population response score is a high sensitivity TNBC patient for a mitotic inhibiting agent chemotherapy regimen.

19. The method according to claim 11, wherein the level of each gene comprising the TNBC gene panel is identified with a cDNA, mRNA, cRNA or other nucleotide that is specific for the gene.

20. The method according to claim 11 , wherein the TNBC response score is determined by: normalizing each TNBC biomarker gene expression level against a control gene level to provide a normalized TNBC continuous risk score for each of the TNBC panel genes; and

calculating an overall TNBC response score from the normalized TNBC continuous risk scores, and scaling the overall TNBC response score to provide a patient continuous response score from 0 to 100.

21. The method according to claim 11 , wherein AT-chemotherapy is neoaduvant or adjuvant.

22. The method according to claim 11, wherein said chemotherapy regimen other than a mitosis inhibiting agent includes a platinum salt.

23. A kit for assessing method of treatment for a patient with triple negative breast cancer, comprising:

a set of TNBC biomarker gene molecular probes on a solid substrate, said TNBC biomarker gene molecular probes being specific for TNBC biomarker genes: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, ΓΝΡΡ4Β, SCNNIB, MSH6, SYT17, EXOSC5, PODXL, ALMSl, SNAPC3, TANK, and TGFBR3; and

an instructional insert defining how to determine a TNBC patient response score from measurements of the TNBC biomarker genes against a TNBC reference population response score, and defining a category of a TNBC patient as having a high sensitivity or low sensitivity for a mitosis-inhibiting chemotherapeutic agent.

24. A method for administering an appropriate treatment regimen to a TNBC patient, comprising:

assessing expression levels of a TNBC biomarker gene panel in a TNBC patient tissue, wherein expression levels of TNBC biomarker genes in the patient tissue sample defines a TNBC patient response score, the TNBC biomarker gene panel comprising at least 5 of the genes: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B, HEMK1, ABT1, EXOC5, INPP4B, SCNNIB, MSH6, SYT17, EXOSC5, PODXL, ALMSl, SNAPC3, TANK, TGFBR3, DYRK2, PPFIBP2, MYOIC, MAST2, SPDEF, PSME3, CDC45, NFIB, AKAP1, PDK3, SMIM7, YIPF3, PPP1R2, EXOC7, ECE1, KAT6B, PRDX2, ALPKl, and GDF15;

comparing the patient TNBC response score to a TNBC reference population threshold response score; and

selecting a mitosis-inhibiting chemotherapeutic regimen for a TNBC patient having a TNBC response score above the TNBC reference population threshold response score, or selecting a chemotherapy regimen other than a mitosis-inhibiting chemotherapeutic regimen for a TNBC patient having TNBC response score that is equal to or below the TNBC reference population threshold response score.

25. The method of claim 24 wherein the TNBC biomarker gene panel comprises 2 of the TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, and UNC5B.

26. The method of claim 24 wherein the TNBC biomarker gene panel comprises 3 of the TNBC biomarker genes selected from the group consisting of: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B, HEMKl, ABTl, EXOC5, INPP4B, SCNNIB, MSH6, and SYT17.

27. The method of claim 24 wherein the TNBC biomarker gene panel is: ITGA6, MZT2B, GOLT1B, TPGS2, ACTR3B, ELF5, UNC5B, HEMKl, ABTl, EXOC5, ΓΝΡΡ4Β, SCNNIB, MSH6, SYT17, EXOSC5, PODXL, ALMS1, SNAPC3, TANK, and TGFBR35.