PATENT ABSTRACT
A method of evaluating a cancer patient comprising evaluating gene expression levels in a patient sample, calculating a predictor score using the gene expression levels, and assessing the likelihood of a therapeutic outcome using the predictor score is disclosed.

PATENT DESCRIPTION
[0001]    This application claims priority to U.S. Provisional application Ser. No. 61/324,166 filed Apr. 14, 2010, which is incorporated herein by reference in its entirety. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    I. Field of the Invention 
         [0003]    Embodiments of this invention are directed generally to biology and medicine. In certain aspects the invention relates to a gene set whose levels of expression are evaluated and used to prognose and/or derive a survival indicator for a patient who has undergone therapy, who is undergoing therapy, or who is a candidate for therapy. 
         [0004]    II. Background 
         [0005]    There are four main approaches to improving the ability to predict responsiveness to therapies. One approach is a standard predictive or chemopredictive study focused on treatment, in which a sufficiently powered discovery population of subjects is used to define a predictive test that must then be proven to be accurate in a similarly sized validation population (Ransohoff, 2005; Ransohoff 2004). Several studies have used this approach to define predictive genes for adjuvant tamoxifen therapy (Ma et al., 2004; Jansen et al., 2005; Loi et al., 2005). There are advantages to this approach, particularly when samples are available from mature studies for retrospective analysis. But two disadvantages are that the study design is empirical and that adjuvant (post surgery) treatment introduces surgery as a confounding variable, because it is impossible to ever know which patients were cured by their surgery and would never relapse, irrespective of their sensitivity to systemic therapy. Neoadjuvant chemotherapy trials enable a direct comparison of tumor characteristics with pathologic response to the specific therapy (Ayers et al., 2004). 
       SUMMARY OF THE INVENTION 
       [0006]    In medicine today, doctors search for methods of predicting how a patient (given their condition) may respond to treatment. Symptoms and tests may indicate favorable treatment with standard therapies. Likewise, a number of symptoms, health factors, and tests may indicate a less favorable treatment result with standard treatment—this may indicate that a more aggressive treatment plan may be desired. Prognostic scoring is also used for cancer outcome predictions. 
         [0007]    Although pathologic complete response (pCR) has been adopted as the primary endpoint for neoadjuvant trials because it is associated with long-term survival, it has not been uniformly or consistently defined (Bear, 2006; Carey, 2005; Hennessy, 2005; 
         [0008]    Kaufmann, 2006; Kuroi, 2005; Kurosumi, 2004; Rajan, 2004; von Minckwitz, 2005). While it is generally agreed that a definition of pCR should include patients without residual invasive carcinoma in the breast (pT0), the presence of nodal metastasis, minimal residual cellularity, and residual in situ carcinoma are not consistently stated as either pCR or residual disease (RD) (Bear, 2006; Kaufmann, 2006; Hennessy, 2005; Rajan, 2004). Therefore, dichotomization of response as pCR or residual disease (RD) may be simplistic for the objective of assay discovery and validation, particularly because residual disease (RD) after neoadjuvant treatment includes a broad range of actual tumor shrinkage. In some patients who are categorized as RD but actually show minimal residual disease, the response outcome blurs the prognostic distinction between pCR and RD. On the other hand, it should be possible to clearly identify patients within RD who are resistant to treatment in order to develop management strategies for this adverse outcome. 
         [0009]    Expression markers are chosen for the ability to classify and/or identify patients as to probability for response (or non response) to therapy. Response to therapy is commonly classified by the RECIST criteria established by the World Health Organization, the National Cancer Institute and the European Organization for Research and Treatment of Cancer. The RECIST criteria classify response as progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR). A good response is typically considered to include PR+CR (collectively referred to herein as Objective Response). 
         [0010]    Certain aspects of the invention include methods of evaluating a cancer patient comprising one or more of the steps of (a) evaluating gene expression levels in a patient sample comprising cancer cells or an RNA sample isolated from one or more a patient samples, wherein a plurality of genes to be evaluated are selected from 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 150, or all of the genes identified in Table 2, Table 3, and Table 4, including all ranges and values there between and all subsets and combinations thereof (5, 10, 15, 20, 25, 100 or more such genes can be specifically excluded, including all values and ranges there between); (b) calculating a predictor score using a gene expression profile index; and (c) assessing the likelihood of a therapeutic outcome using the predictor score. The method may further comprise classifying a patient prior to evaluation. In certain aspects classification can include identifying a cancer patient with a disease state classified as a residual disease state or other clinically defined state prior to evaluation. In certain aspects, a predictor includes but is not limited to a measure for distant relapse-free survival (DRFS). 
         [0011]    In still a further aspect, a gene expression index comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 150 or all of the genes identified in Table 2, Table 3, and Table 4 including all values and ranges there between as well as a number of subsets of these genes which may include some genes from one or more tables and exclude others from the same table or other tables. 
         [0012]    In other aspects, a patient may be stratified or analyzed by using other factors such as protein expression, demographic information, family history, and other biological or medical states. The method may include determining Her2-neu and/or estrogen receptor status of the patient sample and/or evaluation of tumor size, cellularity of tumor bed, and/or nodal burden to name a few. 
         [0013]    The methods may also provide a treatment recommendation depending on the assessment derived from analysis of the gene expression profile as well as other factors. In certain aspects the recommendation may be based on residual cancer burden (RCB) classification or the like. A treatment is typically a standard treatment or a more aggressive non-standard treatment depending on the analysis. For example a treatment may be combination of one or more cancer therapies, such as hormonal therapy and/or chemotherapy. Hormonal therapy includes, but is not limited to tamoxifen therapy, aromatase inhibitor therapy, or SERM therapy. 
         [0014]    In other aspects, preparing a gene expression index can include one or more of the following steps: (a) obtaining data associated with a plurality of cancer patients, such as breast cancer, melanoma, ovarian cancer, testicular cancer or the like comprising measuring expression levels of a plurality of genes in samples from a plurality of patients; (b) partitioning the data into a first and second dataset; (c) evaluating the data and identifying data associated with a particular treatment outcome; (d) selecting a set of genes whose expression levels are indicative of therapeutic outcome. In one aspect, the index includes evaluation of survival of the patient population sampled for all or part of the reference population of tumor samples such as the distant relapse-free survival (DRFS) of the patient population. 
         [0015]    Other aspects of the invention include kits to determine responsiveness of a cancer or cancer patient to a treatment or therapy comprising one or more of (a) reagents for determining expression levels of a plurality of genes selected from Table 2, Table 3, and Table 4 or combinations thereof, such as probe sets that identify and measure the levels of gene transcripts, transcription, or protein levels; and software encoding methods for designing, gathering, inputting, analyzing and/or assessing various data, which includes an algorithm for calculating a predictor score based on the analysis of the gene expression levels. 
         [0016]    In still other aspects the invention includes an apparatus, or system for providing assessment of a sample relative to a gene expression index, the system comprising (a) an application server comprising an input manager to receive expression data from a user for a plurality of genes selected from Table 2, Table 3, and Table 4 or combinations thereof obtained from a patient sample or an RNA sample from such patient sample; and (b) a network server comprising an output manager constructed and arranged to provide an assessment to the user. 
         [0017]    In yet another aspect the invention includes a computer readable medium having software modules for performing the one or more of the methods described herein comprising the acts of: (a) comparing gene expression data obtained from a patient sample for a plurality of genes selected from Table 2, Table 3, and Table 4 or combinations thereof with a reference; and (b) providing a predictor score to a physician for use in determining an appropriate therapeutic regimen for a patient. 
         [0018]    In still yet another aspect the invention includes a computer system, having a processor, memory, external data storage, input/output mechanisms, a display, for performing the method of the invention, comprising (a) a database; (b) logic mechanisms in the computer for generating the transcriptional profile index; and (c) a comparing mechanism in the computer for comparing the gene expression reference to expression data from a patient sample or an RNA sample from such a patient sample to calculate a predictor score. 
         [0019]    An internet accessible portal may be use to provide biological information constructed and arranged to execute a computer-implemented methods for providing: (a) a comparison of gene expression data of a plurality of genes of claim  1  in a patient sample with a transcriptional profile index; and (b) providing a predictor score to a physician for use in determining an appropriate therapeutic regime for a patient. 
         [0020]    Other embodiments of the invention are discussed throughout this application. Any embodiment discussed with respect to one aspect of the invention applies to other aspects of the invention as well and vice versa. The embodiments in the Example section are understood to be embodiments of the invention that are applicable to all aspects of the invention. 
         [0021]    The terms “inhibiting,” “reducing,” or “prevention,” or any variation of these terms, when used in the claims and/or the specification includes any measurable decrease or complete inhibition to achieve a desired result. 
         [0022]    The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” 
         [0023]    Throughout this application, the term “about” is used to indicate that a value includes the standard deviation of error for the device or method being employed to determine the value. 
         [0024]    The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and/or.” 
         [0025]    As used in this specification and claim(s), the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps. 
         [0026]    Other objects, features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating specific embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description. 
     
    
     
       DESCRIPTION OF THE DRAWINGS 
         [0027]    The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein. 
           [0028]      FIG. 1  Plot of relapse-free survival in predicted responders and non-responders using the relapse-based predictor of Example 2 in the validation cohort of patients. 
           [0029]      FIG. 2  Plot of distant relapse-free survival outcomes in predicted responders and non-responders using response-based endpoint of RCB0/I of Example 4 in the validation cohort of patients. 
           [0030]      FIG. 3  Prediction of responders to chemotherapy in ER-positive tumors (A) and ER-negative tumors (B) using the response-based predictor in the validation cohort of patients. 
           [0031]      FIG. 4  Prediction of responders to chemotherapy using a combination of relapse- and response-based predictors in the validation cohort of patients. 
           [0032]      FIG. 5  Prediction of responders to chemotherapy in ER-positive tumors (A) and ER-negative tumors (B) using the combination of relapse- and response-based predictors in the validation cohort of patients. 
           [0033]      FIG. 6  Endocrine sensitivity index in the validation cohort of patients. 
           [0034]      FIG. 7  Plot of combined predictions in the validation cohort to identify responders and non-responders to chemotherapy. 
           [0035]      FIG. 8  Plot of distant relapse-free survival within ER-specific subsets of the validation cohort, (A) ER-positive patients stratified by predicted responders and non-responders, (B) ER-negative patients stratified by predicted responders and non-responders. 
           [0036]      FIG. 9  The decision algorithm that was used in the genomic test to predict a patient&#39;s sensitivity to adjuvant chemotherapy or chemo-endocrine therapy from a biopsy of newly diagnosed invasive breast cancer. (*) predicted sensitivity to endocrine therapy was defined as high or intermediate genomic sensitivity to endocrine therapy (SET) index; (**) predicted resistance to chemotherapy was defined as predicted extensive residual cancer burden (RCB-III) or predicted distant relapse or death within 3 years of diagnosis; (***) predicted sensitivity to chemotherapy was defined as predicted pathologic complete response (pCR) or minimal residual cancer burden (RCB-I). 
           [0037]      FIG. 10  Plot of responders and non-responders in the validation cohort of patients predicted by using a combination of predictors of relapse, response as RCB-0/I, resistance as RCB-III, and SET. Kaplan-Meier estimates of distant relapse-free survival according to genomic predictions (before treatment) as treatment-sensitive (Rx Sensitive) or treatment-insensitive (Rx Insensitive) in the discovery (A) and independent validation (B) cohorts. For comparison, the prognosis of the groups stratified by actual pathologic response (pathologic complete response vs. residual disease) after completion of all chemotherapy is shown for the validation cohort (C). P-values are from the log-rank test. Vertical ticks on the curves indicate censored observations. 
           [0038]      FIG. 11  Subset analysis of genomic predictions in the validation cohort: ER+/HER2-(A), ER-/HER2-(B), taxane chemotherapy administered as 12 cycles of weekly paclitaxel (C) or 4 cycles of 3-weekly docetaxel (D). P-values are from the log-rank test. Vertical ticks on the curves indicate censored observations. 
           [0039]      FIG. 12  Kaplan-Meier estimates of distant relapse-free survival in the discovery cohort (A-D) and the independent validation cohort (E-H) of patients treated with sequential taxane-anthracycline chemotherapy, then endocrine therapy if hormone receptor-positive, stratified by other signatures reported to be predictive of response to neoadjuvant taxane-anthracycline chemotherapy. A prognostic signature for genomic grade index predicts pathologic response if high GGI versus low GGI (A, E); the intrinsic subtype classifier predicts pathologic response if basal-like or luminal B versus other subtypes (B, F); a genomic predictor of pathologic complete response (pCR) versus residual disease following taxane-anthracycline chemotherapy (C, G); and the genomic predictor of excellent pathologic response (pCR or RCB-I) versus other residual disease, according to ER status, that we incorporated in the last step of our prediction algorithm (D, H). P-values are from the log-rank test. Vertical ticks on the curves indicate censored observations. 
           [0040]      FIG. 13  Schematic of use of the predictor assay to guide decisions in therapy outcome. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0041]    Despite the critical importance of selecting the most effective adjuvant/neoadjuvant chemotherapy for an individual, diagnostic tests to guide selection of the optimal regimen for a particular patient continue to be inadequate (Carlson, 2000; Goldhirsch, 2003). Estrogen receptor (ER) negative status, high grade and high proliferative activity are histological characteristics that tend to indicate more chemotherapy sensitive cancer (Bast, 2001; Ross, 2003; Rouzier, 2005). However, although these clinicopathologic variables may identify eligibility or predict general chemotherapy sensitivity, they have little potential to guide selection of a specific treatment regimen in standard-of-care practice. 
         [0042]    The limited utility of individual markers to predict clinical outcome of cancer may be due to the incomplete understanding of the function of these markers. In addition, biologically important molecules act in concert and form complex, interactive pathways where an individual molecule may only contribute limited information on the functional activity of a whole pathway. The promise of microarray technology is that, by assessing the transcriptional activity of a large number of genes, the complex gene-expression profile may contain more information than any individual marker that contributes to it. 
         [0043]    There are examples indicating that the molecular classification of cancer based on gene-expression profiles could be important in framing patient management strategies. Unsupervised clustering of breast cancer specimens consistently separated tumors into ER +  and ER −  clusters (Gruvberger, 2001; Perou, 2000; Pusztai, 2003). Analysis of gene-expression profiles also distinguished sporadic breast cancers from breast cancer gene, BRCA, mutant cases (Hedenfalk, 2001). Transcriptional profiles have also revealed previously unrecognized molecular subgroups within existing histological categories in breast cancer (Perou, 2000), diffuse large-B-cell lymphoma, and soft tissue and central nervous system embryonal tumors (Nielsen, 2002; Pomeroy, 2002). In addition, gene-expression profiles have been shown to predict survival of patients with node-negative breast cancer (van de Vijver, 2002; van&#39;t Veer, 2002), lymphoma (Alizadeh, 2000; Rosenwald, 2002), renal cancer (Takahashi, 2001), and lung cancer (Beer, 2002). 
         [0044]    Previous efforts into applying gene expression-based predictors in breast cancer have focused largely on predicting a patient&#39;s risk of cancer recurrence in the event of either receiving no systemic treatment after surgery (van de Vijver, 2002; van&#39;t Veer, 2002; Wang, 2005) or receiving tamoxifen, a hormonal therapy agent, for 5 years after surgery (Paik, 2006; Paik, 2004; Ma, 2006; Davis, 2007). These gene-based predictors do not directly address the need or the responsiveness to chemotherapy although a high risk of recurrence may indirectly suggest the general consideration of chemotherapy among the available options for patient management. 
         [0045]    Other research efforts have also reported gene-based predictors of response to standard breast cancer treatments (Ayers, 2004; Bild, 2006; Chang, 2003; Hess, 2006; Modlich, 2006) although these are not commercially marketed yet as assays. Some of these predictors are developed using patient tissue samples treated clinically with a specific chemotherapy regimen and subsequently comparing genomic profiles of responders versus non-responders using survival-driven endpoints (Ayers, 2004; Chang, 2003; Hess, 2006; Modlich, 2006) whereas others are focused on analyses of changes in genes within breast cancer cell lines that are treated in vitro with single standard therapeutic agents (Bild, 2006). 
         [0046]    As an in vivo model for marker development and validation, neoadjuvant (preoperative) chemotherapy provides an opportunity to gain access to samples that directly describe tumor response to therapy. Furthermore, complete eradication of all invasive cancer from the breast and regional lymph nodes, called pathologic complete response (pCR), is associated with excellent long-term cancer-free survival (Fisher, 1998; Kuerer, 1999). Therefore, the goal in developing treatment-directed response markers is to evaluate gene expression profiles in order to predict who may achieve pCR versus residual disease (RD). Pathologic CR is a meaningful clinical end-point to predict because these patients experience prolonged disease-free and overall survival compared to patients with lesser response (Cleator, 2005; Fisher, 1998; Kaufmann, 2006; Wolmark, 2001). Good survival in these patients reflects benefit from chemotherapy since most clinical and gene expression variables that are associated with pCR high grade, ER-negative status, high OncotypeDX recurrence score) tend to predict worse prognosis in the absence of chemotherapy (Paik, 2006; Paik, 2004). 
         [0047]    Previous work has demonstrated the development and validation of a 30-probe genomic predictor for response to a taxane-containing chemotherapy (Ayers, 2004; Hess, 2006). The treatment administered in the neoadjuvant setting was sequential paclitaxel anthracycline preoperative chemotherapy (T/FAC). A complex multidrug regimen was selected for study because combination chemotherapy represents the current clinical standard for patients who require systemic cytotoxic treatment. Also, studies that explore gene signatures for response to individual drugs may not fully capture sensitivity to combination chemotherapy as practiced in standard-of-care. 
         [0048]    A cohort of 82 patients was used for predictor discovery of pCR to preoperative T/FAC chemotherapy using fine needle biopsies taken before treatment and by analyzing gene profiles generated from a commercially available standard gene expression profiling technology (Affymetrix, Santa Clara, Calif.). Although several analytic techniques and resulting gene sets for response prediction were studied, the nominally best predictor for pCR with the least number of genes, called DLDA-30, was selected for independent validation in 51 additional patients. The predictor showed substantially higher sensitivity (a measure of how well a predictor identifies responsiveness or non-responsiveness to a therapy, e.g., true positives/(true positives+false negatives)) (92% vs. 61%) and slightly better negative predictive value (NPV, the proportion of patients with negative test results who are correctly diagnosed.) (96% vs. 86%) than a clinical predictor based on ER, grade and age (Hess, 2006). The positive predictive value (PPV, is the proportion of patients with positive test results who are correctly diagnosed.) of the genomic predictor at 52% (95 CI: 30%-73%), was significantly higher than the baseline 26% pCR rate in unselected patients. A sensitivity of 100% means that the test recognizes all patient as either responsive to therapy or non-responsive to therapy. Typically, sensitivity alone does not tell us how well the test predicts other classes (that is, about the negative cases). Sensitivity is not the same as the positive predictive value (ratio of true positives to combined true and false positives), which is as much a statement about the proportion of actual positives in the population being tested as it is about the test. The calculation of sensitivity typically does not take into account indeterminate test results. If a test cannot be repeated, the options are to exclude indeterminate samples from analyses (but the number of exclusions should be stated when quoting sensitivity), or, alternatively, indeterminate samples can be treated as false negatives (which gives the worst-case value for sensitivity and may therefore underestimate it). 
         [0049]    Although this predictor and others described in literature (Chang, 2003; Modlich, 2006) may help define a patient population that is more likely to achieve pCR than the general patient population, further developments can help refine prediction of treatment response considerably. Although pCR as a response endpoint is strongly correlated with high treatment-related survival, patients with residual disease (RD) after treatment encompass a wide range of outcomes ranging from very good prognosis (“near-pCR”) to drug resistance. Predictors that can better classify response outcomes to capture and differentiate the high responders and non-responders within the spectrum of residual disease could significantly benefit patient management. 
         [0050]    Although pathologic complete response (pCR) has been adopted as the primary endpoint for neoadjuvant trials because it is associated with long-term survival, it has not been uniformly or consistently defined (Bear, 2006; Carey, 2005; Hennessy, 2005; Kaufmann, 2006; Kuroi, 2005; Kurosumi, 2004; Rajan, 2004; von Minckwitz, 2005). While it is generally agreed that a definition of pCR should include patients without residual invasive carcinoma in the breast (pT0), the presence of nodal metastasis, minimal residual cellularity, and residual in situ carcinoma are not consistently stated as either pCR or residual disease (RD) (Bear, 2006; Kaufmann, 2006; Hennessy, 2005; Rajan, 2004). Therefore, dichotomization of response as pCR or residual disease (RD) may be simplistic for the objective of assay discovery and validation, particularly because residual disease (RD) after neoadjuvant treatment includes a broad range of actual tumor shrinkage. In some patients who are categorized as RD but actually show minimal residual disease, the response outcome blurs the prognostic distinction between pCR and RD. On the other hand, it should be possible to clearly identify patients within RD who are resistant to treatment in order to develop management strategies for this adverse outcome. 
         [0051]    A measure of residual disease or residual cancer burden (RCB), previously developed and reported, may be useful as a variable to characterize response to treatment (Symmans et al., 2007). This measure is derived from the primary tumor dimensions, cellularity of the tumor bed, and axillary nodal burden. Each component contributes meaningful pathologic information and can be obtained using routine pathologic materials and methods of interpretation that could easily be implemented in routine diagnostic practice. RCB measurements can provide a continuous parameter of residual disease and thus of response or resistance, so that all subject responses contribute to the analysis. 
         [0052]    RCB is divided into four survival-related classes (RCB-0 to RCB-III) where patients with minimal residual disease (RCB-I) have the same 5-year relapse-free survival as those with pCR (RCB-0), irrespective of the type of neoadjuvant chemotherapy administered, adjuvant hormonal therapy or the pathologic stage of RD. Therefore, the combination of RCB-0 (pCR) and RCB-I expands the subset of patients who can be identified as having “good response” and to have benefited from the chemotherapy. Extensive residual disease (RCB-III), on the other hand, is associated with poor prognosis, irrespective of the type of neoadjuvant chemotherapy administered, adjuvant hormonal therapy, or the pathologic stage of RD. In particular, all patients with RCB-III after T/FAC chemotherapy, who did not receive adjuvant hormonal therapy, suffered distant relapse within 3 years (Symmans et al., 2007). This identifies an important subset of patients who are not responsive to chemotherapy, or with residual disease (after surgery) that is too extensive to be controlled by hormonal therapy alone. 
         [0053]    Therefore, residual cancer burden (RCB) is an informative tool and a metric to help develop response predictors based on better characterization of likely treatment outcomes. RCB categories can be employed with existing methods to define surrogate endpoints from neoadjuvant trials. As a metric correlated with survival, RCB is strongly and independently prognostic and the classes of RCB capture distinct sets of survival-based outcomes. Development of a predictor that reports likelihood of a patient&#39;s tumor post-treatment to belong to one of the RCB classes, rather than simply pCR as an endpoint, can yield valuable diagnostic information for efficient treatment management. In certain aspects, predictors specific to RCB-0 (pCR or complete response), RCB-0/I (pCR+near-pCR called good response) and RCB-III (resistance) are developed. In certain aspects of the methods described, the inventors have also accounted for tumor sub-types based on the status of two receptors, Her2-neu and ER, allowing for the predictors to capture heterogeneity within breast cancers and achieve acceptable diagnostic performance. 
       III. Predictors of Response or Resistance to Therapy 
       [0054]    Sets of genes are defined that are prognostic, diagnostic, or predictive or indicative of the outcome for a cancer patient. These genes can be incorporated into an index or predictor of such an outcome and used in the management of the treatment for a given patient. Prognosis is a medical term denoting the doctor&#39;s prediction of how a patient&#39;s disease will progress, and whether there is chance of recovery. 
         [0055]    Outcome can be represented in various forms to indicate probability of survival or likely survival outcome. In biostatistics, survival rate is a part of survival analysis, indicating the percentage of people in a study or treatment group who are alive for a given period of time after diagnosis. Survival rates are important for prognosis; for example, whether a type of cancer has a good or bad prognosis can be determined from its survival rate or survival outcome. 
         [0056]    Patients with a certain disease can die directly from that disease or from an unrelated cause such as a car accident. When the precise cause of death is not specified, this is called the overall survival rate or observed survival rate. Doctors often use mean overall survival rates to estimate the patient&#39;s prognosis. This is often expressed over standard time periods, like one, five, and ten years. For example, prostate cancer has a much higher one year overall survival rate than pancreatic cancer, and thus has a better prognosis. 
         [0057]    When someone is more interested in how survival is affected by the disease, there is also the net survival rate, which filters out the effect of mortality from other causes than the disease. Typically, the two main ways to calculate net survival arc relative survival and cause specific survival or disease specific survival. 
         [0058]    Relative survival is calculated by dividing the overall survival after diagnosis of a disease by the survival as observed in a similar population that was not diagnosed with that disease. A similar population is composed of individuals with at least age and gender similar to those diagnosed with the disease. Cause-specific survival is calculated by treating deaths from other causes than the disease as withdrawals from the population that don&#39;t lower survival, comparable to patients who are not observed any longer, e.g. due to reaching the end of the study period. Relative survival has the advantage that it does not depend on accuracy of the reported cause of death; cause-specific survival has the advantage that it does not depend on the ability to find a similar population of people without the disease. 
         [0059]    Survival is not the only endpoint that can be used as a metric in developing predictors such as those described herein. Endpoints or therapeutic outcomes can include survival or distant relapse-free survival (DRFS). Other endpoints are discussed in Cooper and Kaanders, Biological surrogate end-points in cancer trials: Potential uses, benefits and pitfalls, European Journal of Cancer, Volume 41, Issue 9, Pages 1261-1266, which is incorporated herein by reference. A “surrogate marker” or “surrogate endpoint” or “secondary endpoint” typically will refer to a biological or clinical parameter that is measured in place of the biologically definitive or clinically most meaningful parameter, i.e., survival. Primary endpoints may also include limitation of pharmacologic therapies, reduction of time to death, or reduction in the progression of the disease, disorder, or condition. Surrogate markers are pathophysiologic parameters determined by medical or clinical laboratory diagnosis that arc associated and have been correlated with the prognosis, progression, predisposition, or risk analysis with a disease, disorder, or condition that are not directly related to the primary diagnosed pathophysiologic condition. Secondary endpoints are those that supplement the primary endpoint. For example, secondary endpoints include reduction in pharmacologic therapy, reduction in requirement of a medical device, or alteration of the progression of the disease disorder, or condition. Typically, a clinical endpoint may refer to a disease, symptom, or sign that constitutes one of the target outcomes of the therapy or clinical trial. The results of a therapy or clinical trial generally indicate the number of people enrolled who reached the pre-determined clinical endpoint during the study interval, compared with the overall number of people who were enrolled. Once a patient reaches the endpoint, he or she is generally excluded from further experimental intervention (the origin of the term endpoint). For example, a clinical trial investigating the ability of a medication to prevent heart attack might use chest pain as a clinical endpoint. Any patient enrolled in the trial who develops chest pain over the course of the trial, then, would be counted as having reached that clinical endpoint. The results would ultimately reflect the fraction of patients who reached the endpoint of having developed chest pain, compared with the overall number of people enrolled. When an experiment involves a control group, the fraction of individuals who reach the clinical endpoint after an intervention is compared with the fraction of individuals in the control group who reached the same clinical endpoint, thus reflecting the ability of the intervention to prevent the endpoint in question. Some studies will examine the incidence of a combined endpoint, which can merge a variety of outcomes into one group. 
         [0060]    When building prediction rules of treatment response or disease state in general from gene expression data can be selected from a small subset of informative genes that will be used as prognostic features in the predictor. Most predictors employ univariate filtering to rank the candidate genes according to the p-value of a two-sample unequal variance t-test comparing the mean expression values of each gene in the two response classes (e.g., pCR and RD). Univariate filtering methods have the disadvantage that they do not deal well with redundant features (genes that have similar expression profiles) and therefore the resulting predictors tend to be less robust (Lai, 2006). 
         [0061]    The method used to identify predictive genes involved first, applying a filter to the gene expression data of all probes on an array to select the top probe sets to be used in signature development using the above described algorithm. Gene filtering can be based on the regularized t-test for the selected response endpoint such as pCR or RCB-0 (complete response), RCB-0/I (good response), or RCB-III (poor response). Other methods for gene filtering include methods that utilize non-specific global filtering criteria. These include, but are not limited to intensity-based filtering, which aims to remove genes that are not expressed at all in the samples studied or variability-based filtering, which aims to remove genes with low variability across samples. 
         [0062]    A multivariate method was used to simultaneously select the signature genes and to calculate the classification score. The predictor is determined by level of penalization, which determines the number of genes included in the predictive signature, and the choice of a decision threshold to dichotomize the classification score. As one example, the inventors selected the maximum level of penalization resulting in the smallest signatures that yield significant cross-validated predictor or outcome predictor, each of these terms can be used interchangeably, performance—this step determines the signature probe sets and their weights. Then, a decision threshold is selected in order to optimize the predictive values of the predictor. Evaluation of the predictors was based on the joint confidence interval of the positive predictive value (PPV) and the negative predictive value (NPV) of the predictor at 5% significance level (low 95% confidence limit of PPV≧baseline response rate &amp; low 95% confidence limit of NPV≧1—baseline response rate). 
         [0063]    In developing the RCB-based predictor, the inventors used an approach that combines feature selection and model discovery using a multivariate penalized approach, an example of which is Gradient Directed Regularization developed by Prof. J. Friedman at Stanford University, a description of which can be found on the World Wide Web at stat.stanford.edu/˜jhf/ftp/pathlite.pdf. Typically, the informative genes are selected with penalization using the maximization of the area under the receiver operating characteristic (ROC) curve (AUC) as the optimization criterion. Ma and Huang have previously used a similar approach for disease classification (Ma, 2006). A receiver operating characteristic (ROC), or simply ROC curve, is a graphical plot of the sensitivity vs. (1—specificity) for a binary classifier system as its discrimination threshold is varied. The ROC can also be represented equivalently by plotting the fraction of true positives (TPR=true positive rate) vs. the fraction of false positives (FPR=false positive rate). The best possible prediction method would yield a point in the upper left corner or coordinate (0,1) of the ROC space, representing 100% sensitivity (all true positives are found) and 100% specificity (no false positives are found). The (0,1) point is also called a perfect classification. A completely random guess would give a point along a diagonal line (the so-called line of no-discrimination) from the left bottom to the top right corners. The diagonal line divides the ROC space in areas of good or bad classification/diagnostic. Points above the diagonal line indicate good classification results, while points below the line indicate wrong results. 
         [0064]    As an example of predictor discovery and evaluation the protocol suggested by Wessels et al. was followed (Wessels, 2005). The methodology is briefly explained below. First, the input dataset is randomly partitioned into a training set and a test set. A 3-fold cross-validation based on Dudoit et al. recommendation of a 2:1 split between training and test sets was used (Dudoit, 2002). The training set consisting of ⅔ of the original data is used to develop a predictor. To account for bias in the several data-dependent decisions involved in building the predictor, a 5-fold internal cross-validation can be used to select the optimal set of genes for the predictor and to tune the parameters of the predictor, e.g., the degree of penalization. Since different optimal reporter gene sets might result from the different internal cross-validation folds, the number of times each gene is selected is tracked to provide a measure of its importance or its reliability. The trained predictor is then tested on the ⅕ hold-out part of the training dataset and its performance is evaluated based on the AUC. 
         [0065]    To obtain a less biased estimate of classification performance, the trained predictor or outcome predictor can be evaluated on the test set (⅓ of the original data) that was not used in training the predictor. To assess the significance of the predictive performance of the trained predictor, the permutation predictive performance of the predictor was estimated by randomly scrambling the outcome labels in the test dataset. The entire process of randomly splitting the data to a training and a test set was repeated a number of times to obtain the distributions and summary statistics of the performance metrics. 
         [0066]    Typically, under cross-validation the decision threshold is varied along all possible values and for each value predictor performance (accuracy, positive predictive value (PPV), negative predictive value (NPV)) is determined. The threshold is selected that yields the best compromise between PPV and NPV, as typically increasing PPV results in decreasing NPV. Typically, the objective is to maximize both. 
         [0067]    In certain aspects, other measurements or determinations can be made in conjunction the nucleic acid analysis, for example determination of protein expression and/or histology of a sample. Protein expression can be detected in tumor tissue, cell material obtained by biopsy and the like. For example, a biopsy sample can be immobilized and contacted with an antibody, an antibody fragment or an aptamer that binds selectively to the protein to be detected. The sample can be assayed to determine whether the antibody, fragment or aptamer has bound to the protein by techniques well known in the art. Protein expression can be measured by a variety of methods including but not limited to Western blot, immunoblot, enzyme-linked immunosorbant assay (ELISA), radioimmunoassay (RIA), immunoprecipitation, surface plasmon resonance, immunohistochemical (IHC) analysis, mass spectrometry, fluorescence activated cell sorting (FACS) and flow cytometry. 
         [0068]    In a further aspect, IHC analysis is used to measure protein expression. The level of expression for a sample is determined by IHC by staining the sample for a particular expression marker and developing a score for the staining. For example, monoclonal antibodies can be used to stain for the expression of a marker of interest. Mouse antibodies are known for use in the staining of the marker PTEN. Samples can be evaluated for the frequency of cells stained for each sample and the intensity of the stain. Typically, a score based on the frequency (rated from 0-4) and intensity (rated from 0-4) of the stained sample is developed as a measure of overall expression. Exemplary but non-limiting methods for IHC and criteria for scoring expression are described in detail in Handbook of Immunohistochemistry and In Situ Hybridization in Human Carcinomas, M. Hayat Ed., 2004, Academic Press. 
       IV. Use of Predictor for Patient Evaluation 
       [0069]    In one aspect of the invention, a predictor or transcriptional profile index is used to measure the expression of many genes that provide predictive information about a likely outcome for a particular patient. The invention includes the methods for standardizing the expression values of future samples to a normalization standard that will allow direct comparison of the results to past samples, such as from a clinical trial. The invention also includes the biostatistical methods to calculate and report such results. A sample as used herein can comprise any number of cells that is sufficient for a clinical diagnosis or prognosis, and typically contain at least, at most or about 100 target cells. 
         [0070]    The microarrays provide a suitable method to measure gene expression from clinical samples. mRNA levels measured by microarrays, such as Affymetrix U133A gene chips, in fine needle aspirates (FNA), core needle biopsy, and/or frozen tumor tissue samples of breast cancer correlated closely with protein expression by enzyme immunoassay and by routine immunohistochemistry. 
         [0071]    Estrogen receptor and Her2-neu status. ER-positive breast cancer includes a continuum of ER expression that might reflect a continuum of biologic behavior and endocrine sensitivity. Others have reported that some breast cancers are difficult to predict as ER-positive based on transcriptional profile and described non-estrogenic growth effects, such as HER-2, more frequently in this small subset of tumors with aggressive natural history (Kun et al., 2003). Indeed, ER mRNA levels are lower in breast cancers that are positive for both ER and HER2 (Konecny et al., 2003). 
       V. Cancer Therapies 
       [0072]    Diagnostic tools are needed not merely for prognosis, but, for providing a biological rationale and to demonstrate clinical benefit when they are used to guide the selection and duration of therapies, particularly in light of the cost, complexity, toxicity, benefits and other factors related to such therapies. An index or predictor can be used to predict the likelihood of response rather than intrinsic prognosis. 
         [0073]    In addition to other know methods of cancer therapy, hormone therapies may be employed in the treatment of patients identified as having hormone sensitive cancers. Hormones, or other compounds that stimulate or inhibit these pathways, can bind to hormone receptors, blocking a cancer&#39;s ability to get the hormones it needs for growth. By altering the hormone supply, hormone therapy can inhibit growth of a tumor or shrink the tumor. Typically, these cancer treatments only work for hormone-sensitive cancers. If a cancer is hormone sensitive, a patient might benefit from hormone therapy as part of cancer treatment. Sensitive to hormones is usually determined by taking a sample of a tumor (biopsy) and conducting analysis in a laboratory. 
         [0074]    A. Chemotherapy 
         [0075]    Chemotherapy is the use of chemical substances to treat disease. In its modern-day use, it refers to cytotoxic drugs used to treat cancer or the combination of these drugs into a standardized treatment regimen. There are a number of strategies in the administration of chemotherapeutic drugs used today. Chemotherapy may be given with a curative intent or it may aim to prolong life or to palliate symptoms. 
         [0076]    Combined modality chemotherapy is the use of drugs with other cancer treatments, such as radiation therapy or surgery. Combination chemotherapy is a similar practice which involves treating a patient with a number of different drugs simultaneously, e.g., T/FAC therapy. Typically, the drugs differ in their mechanism and side effects. The biggest advantage is minimizing the chances of resistance developing to any one agent. 
         [0077]    In neoadjuvant chemotherapy (preoperative treatment) initial chemotherapy is aimed for shrinking the primary tumor, thereby rendering local therapy (surgery or radiotherapy) less destructive or more effective. 
         [0078]    Adjuvant chemotherapy (postoperative treatment) can be used when there is little evidence of cancer present, but there is risk of recurrence. This can help reduce chances of resistance developing if the tumor does develop. It is also useful in killing any cancerous cells which have spread to other parts of the body. This is often effective as the newly growing tumors are fast-dividing, and therefore very susceptible. 
         [0079]    Palliative chemotherapy is given without curative intent, but simply to decrease tumor load and increase life expectancy. For these regimens, a better toxicity profile is generally expected. 
         [0080]    All chemotherapy regimens require that the patient be capable of undergoing the treatment. Performance status is often used as a measure to determine whether a patient can receive chemotherapy, or whether dose reduction is required. 
         [0081]    B. Hormone Therapy 
         [0082]    Several malignancies respond to hormonal therapy. Strictly speaking, this is not chemotherapy. Cancer arising from certain tissues, including the mammary and prostate glands, may be inhibited or stimulated by appropriate changes in hormone balance. Cancers that are most likely to be hormone-receptive include: Breast cancer, Prostate cancer, Ovarian cancer, and Endometrial cancer. Not every cancer of these types is hormone-sensitive, however. That is why the cancer must be analyzed to determine if hormone therapy is appropriate. 
         [0083]    Breast cancer cells often highly express the estrogen and/or progesterone receptor. Inhibiting the production (with aromatase inhibitors) or action (with tamoxifen) of these hormones can often be used as an adjunct to therapy. 
         [0084]    Hormone therapy may be used in combination with other types of cancer treatments, including surgery, radiation and chemotherapy. A hormone therapy can be used before a primary cancer treatment, such as before surgery to remove a tumor. This is called neoadjuvant therapy. Hormone therapy can sometimes shrink a tumor to a more manageable size so that it&#39;s easier to remove during surgery. 
         [0085]    Hormone therapy is sometimes given in addition to the primary treatment—usually after—in an effort to prevent the cancer from recurring (adjuvant therapy). In some cases of advanced (metastatic) cancers, such as in advanced prostate cancer and advanced breast cancer, hormone therapy is sometimes used as a primary treatment. 
         [0086]    The most common types of drugs for hormone-receptive cancers include: (1) Anti-hormones that block the cancer cell&#39;s ability to interact with the hormones that stimulate or support cancer growth. Though these drugs do not reduce the production of hormones, anti-hormones block the ability to use these hormones. Anti-hormones include the anti-estrogens tamoxifen (Nolvadex) and toremifene (Fareston) for breast cancer, and the anti-androgens flutamide (Eulexin) and bicalutamide (Casodex) for prostate cancer. (2) Aromatase inhibitors —Aromatase inhibitors (AIs) target enzymes that produce estrogen in postmenopausal women, thus reducing the amount of estrogen available to fuel tumors. AIs are only used in postmenopausal women because the drugs can&#39;t prevent the production of estrogen in women who haven&#39;t yet been through menopause. Approved AIs include letrozole (Femara), anastrozole (Arimidex) and exemestane (Aromasin). (3) Luteinizing hormone-releasing hormone (LH-RH) agonists and antagonists—LH-RH agonists—sometimes called analogs —and LH-RH antagonists reduce the level of hormones by altering the mechanisms in the brain that tell the body to produce hormones. LH-RH agonists are essentially a chemical alternative to surgery for removal of the ovaries for women, or of the testicles for men. Depending on the cancer type, one might choose this route if they hope to have children in the future and want to avoid surgical castration. In most cases the effects of these drugs are reversible. Examples of LH-RH agonists include: Leuprolide (Lupron, Viadur, Eligard) for prostate cancer, Goserelin (Zoladex) for breast and prostate cancers, Triptorelin (Trelstar) for ovarian and prostate cancers and abarelix (Plenaxis). 
         [0087]    One class of pharmaceuticals is the Selective Estrogen Receptor Modulators or SERMs. SERMs block the action of estrogen in the breast and certain other tissues by occupying estrogen receptors inside cells. SERMs include, but are not limited to tamoxifen (the brand name is Nolvadex, generic tamoxifen citrate); Raloxifene (brand name: Evista), and toremifene (brand name: Fareston). 
       VI. Kits 
       [0088]    Further embodiments of the invention include kits for the measurement, analysis, and reporting of gene expression and transcriptional output. A kit may include, but is not limited to microarray, quantitative RT-PCR, antibodies, labeling or other reagents and materials, as well as hardware and/or software for performing at least a portion of the methods described. For example, custom microarrays or analysis methods for existing microarrays are contemplated. Also, methods of the invention include methods of accessing and using a reporting system that compares a single result to a scale of clinical trial results. In yet still further aspects of the invention, a digital standard for data normalization is contemplated so that the assay result values from future samples would be able to be directly compared with the assay value results from past samples, such as from specific clinical trials. 
       EXAMPLES 
       [0089]    The following examples are given for the purpose of illustrating various embodiments of the invention and are not meant to limit the present invention in any fashion. One skilled in the art will appreciate readily that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those objects, ends and advantages inherent herein. The present examples, along with the methods described herein are presently representative of preferred embodiments, are exemplary, and are not intended as limitations on the scope of the invention. Changes therein and other uses which are encompassed within the spirit of the invention as defined by the scope of the claims will occur to those skilled in the art. 
       Example 1 
     Materials and Methods 
       [0090]    Needle biopsy samples (fine needle aspirates—FNAs or core biopsies—CBX) were analyzed in order to examine genes correlated with the selected endpoint. The genes were identified by this method using these samples and methods to standardize data were done in order to facilitate calculation of the predictor indices consistently in different sample types such as biopsies, resected tissue from an excised tumor, and frozen tumor tissue. 
         [0091]    Patients and samples—Patients prospectively consented to an Institutional Research Board approved research protocol (LAB99-402, USO-02-103, 2003-0321, I-SPY-1) to obtain a tumor biopsy by fine needle aspiration (FNA) or core biopsy (CBX) prior to any systemic therapy for genomic studies to develop and test predictors of treatment outcome. Clinical nodal status was determined before treatment from physical examination, with or without axillary ultrasound, with diagnostic FNA as required. Pathologic HER2 status was defined as negative according to the ASCO/CAP guidelines. Patients with any nuclear immunostaining for ER in the tumor cells were considered as eligible for adjuvant endocrine therapy. During this research, patients were consented to undergo pretreatment biopsy as fine needle aspiration (FNA) (Ayers, 2004; Hess, 2006) or core needle biopsy, of the primary breast tumor or ipsilateral axillary metastasis before starting chemotherapy as part of an ongoing pharmacogenomic marker discovery program. Gene expression data generated from the biopsies captures the molecular characteristics of the invasive cancer including the molecular class (Pusztai, 2003). At least 70% of all aspirations yielded at least 1 μg total RNA that is required for the gene expression profiling. The main reason for failure to obtain sufficient RNA was acellular aspirations. Three hundred and ten (310) patients with at least 1 μg RNA were included in this analysis. All patients received neoadjuvant chemotherapy consisting of a combination of either paclitaxel or docetaxel with anthracycline. At the completion of neoadjuvant chemotherapy all patients had modified radical mastectomy or lumpectomy and sentinel lymph node biopsy or axillary node dissection as determined appropriate by the surgeon. Patients who were ER-positive also received endocrine therapy as tamoxifen or aromatase inhibitor. Clinical characteristics of the patients are in Table 1A.
   Discovery of predictor of relapse after therapy: Table 1B describes the breakdown of samples between FNAs and core biopsies and the treatments administered to the patients. Validation of predictors of response and relapse after therapy: Table 1A and 1B also describe the patients whose samples were used to validate the predictors developed for outcome of chemotherapy. Patient samples were collected at University of Texas M. D. Anderson Cancer Center (MDACC), LBJ Hospital, and US Oncology, in Houston, Tex. and at cancer centers in Peru, Mexico and Spain. During this research, patients were consented to undergo pretreatment biopsy as fine needle aspiration (FNA) (Ayers, 2004; Hess, 2006) or core needle biopsy, of the primary breast tumor or ipsilateral axillary metastasis before starting chemotherapy as part of an ongoing pharmacogenomic marker discovery program. One hundred and ninety eight (198) patients with at least 1 μg RNA and data on relapse-free survival to perform survival analysis were included in this analysis. All patients received either neoadjuvant chemotherapy, or in a small group, adjuvant chemotherapy, consisting of a combination of either paclitaxel or docetaxel with anthracycline. At the completion of neoadjuvant chemotherapy all patients had modified radical mastectomy or lumpectomy and sentinel lymph node biopsy or axillary node dissection as determined appropriate by the surgeon. Patients who were ER-positive also received endocrine therapy as tamoxifen or aromatase inhibitor. This study was approved by the institutional review boards (IRB) of the respective institutions and all patients signed an informed consent for voluntary participation.   
 
         [0000]    
       
         
               
             
               
               
               
             
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 1A 
               
             
             
               
                   
               
               
                 Patient characteristics in development and validation of the predictors 
               
             
          
           
               
                   
                 Discovery Population 
                 Validation Population 
               
             
          
           
               
                   
                 MDACC a   
                 I-SPY b   
                 Total 
                 MDACC 
                 LBJ c /IN d /GEI e   
                 USO f   
                 Total 
               
               
                   
                   
               
             
          
           
               
                 Patients 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
             
          
           
               
                 Age 
                 227  
                 83  
                 310 
                 86  
                 58  
                 54  
                 198  
               
             
          
           
               
                 &lt;=50 
                 112 
                 (49%) 
                 30 
                 (36%) 
                 142 
                 (46%) 
                 48 
                 (56%) 
                 30 
                 (52%) 
                 31 
                 (57%) 
                 109 
                 (55%) 
               
               
                 &gt;50 
                 115 
                 (51%) 
                 53 
                 (64%) 
                 168 
                 (54%) 
                 38 
                 (44%) 
                 28 
                 (48%) 
                 23 
                 (43%) 
                 89 
                 (45%) 
               
               
                 Mean (SD) 
                 51 
                 (11) 
                 47 
                 (8) 
                 50 
                 (10) 
                 49 
                 (11) 
                 51 
                 (11) 
                 48 
                 (9) 
                 49 
                 (11) 
               
               
                 Nodal status 
               
               
                 Pos 
                 165 
                 (73%) 
                 58 
                 (70%) 
                 223 
                 (72%) 
                 52 
                 (60%) 
                 42 
                 (72%) 
                 34 
                 (63%) 
                 128 
                 (65%) 
               
               
                 Neg 
                 62 
                 (27%) 
                 25 
                 (30%) 
                 87 
                 (28%) 
                 34 
                 (40%) 
                 16 
                 (28%) 
                 20 
                 (37%) 
                 70 
                 (35%) 
               
               
                 T stage 
               
             
          
           
               
                 0 
                 2 
                 (1%) 
                 0 
                 2 
                 (1%) 
                 1 
                 (1%) 
                 0 
                 0 
                 1 
                 (1%) 
               
             
          
           
               
                 1 
                 19 
                 (8%) 
                 1 
                 (1%) 
                 20 
                 (6%) 
                 8 
                 (9%) 
                 1 
                 (1%) 
                 1 
                 (2%) 
                 10 
                 (5%) 
               
               
                 2 
                 131 
                 (58%) 
                 34 
                 (41%) 
                 165 
                 (53%) 
                 52 
                 (61%) 
                 19 
                 (33%) 
                 19 
                 (35%) 
                 90 
                 (45%) 
               
               
                 3 
                 35 
                 (15%) 
                 39 
                 (47%) 
                 74 
                 (24%) 
                 18 
                 (21%) 
                 19 
                 (33%) 
                 34 
                 (63%) 
                 71 
                 (36%) 
               
             
          
           
               
                 4 
                 40 
                 (18%) 
                 9 
                 (11%) 
                 49 
                 (16%) 
                 7 
                 (8%) 
                 19 
                 (33%) 
                 0 
                 26 
                 (13%) 
               
             
          
           
               
                 Grade 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 1 
                 13 
                 (6%) 
                 6 
                 (7%) 
                 19 
                 (6%) 
                 7 
                 (8%) 
                 5 
                 (8%) 
                 1 
                 (2%) 
                 13 
                 (7%) 
               
               
                 2 
                 92 
                 (40%) 
                 25 
                 (30%) 
                 117 
                 (38%) 
                 28 
                 (33%) 
                 19 
                 (33%) 
                 16 
                 (30%) 
                 63 
                 (32%) 
               
               
                 3 
                 122 
                 (54%) 
                 29 
                 (35%) 
                 151 
                 (49%) 
                 51 
                 (59%) 
                 23 
                 (40%) 
                 34 
                 (63%) 
                 108 
                 (54%) 
               
             
          
           
               
                 Unknown 
                 0 
                 23 
                 (28%) 
                 23 
                 (7%) 
                 0 
                 11 
                 (19%) 
                 3 
                 (5%) 
                 14 
                 (7%) 
               
             
          
           
               
                 AJCC g   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 Stage 
               
             
          
           
               
                 I 
                 6 
                 (3%) 
                 0 
                 6 
                 (2%) 
                 2 
                 (2%) 
                 0 
                 0 
                 2 
                 (1%) 
               
             
          
           
               
                 II 
                 126 
                 (55%) 
                 39 
                 (47%) 
                 165 
                 (53%) 
                 57 
                 66%) 
                 18 
                 (31%) 
                 32 
                 (59%) 
                 107 
                 (54%) 
               
               
                 III 
                 95 
                 (42%) 
                 44 
                 (53%) 
                 139 
                 (45%) 
                 27 
                 (32%) 
                 40 
                 (69%) 
                 22 
                 (41%) 
                 89 
                 (45%) 
               
               
                 ER h  Status 
               
               
                 Pos 
                 131 
                 (58%) 
                 43 
                 (52%) 
                 174 
                 (56%) 
                 60 
                 (70%) 
                 37 
                 (64%) 
                 27 
                 (50%) 
                 124 
                 (63%) 
               
               
                 Neg 
                 96 
                 (42%) 
                 35 
                 (42%) 
                 131 
                 (42%) 
                 26 
                 (30%) 
                 21 
                 (36%) 
                 27 
                 (50%) 
                 74 
                 (37%) 
               
             
          
           
               
                 Indeterminate 
                 0 
                 5 
                 (6%) 
                 5 
                 (2%) 
                 0 
                 0 
                 0 
                 0 
               
             
          
           
               
                 PR i  Status 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 Pos 
                 102 
                 (45%) 
                 40 
                 (48%) 
                 142 
                 (46%) 
                 43 
                 (50%) 
                 31 
                 (53%) 
                 28 
                 (52%) 
                 102 
                 (52%) 
               
               
                 Neg 
                 125 
                 (55%) 
                 37 
                 (45%) 
                 162 
                 (52%) 
                 43 
                 (50%) 
                 27 
                 (47%) 
                 26 
                 (48%) 
                 96 
                 (48%) 
               
             
          
           
               
                 Indeterminate 
                 0 
                 6 
                 (7%) 
                 6 
                 (2%) 
                 0 
                 0 
                 0 
                 0 
               
               
                   
               
               
                   a M. D. Anderson Cancer Center; 
               
               
                   b I-SPY-1 clinical trial; 
               
               
                   c Lyndon B. Johnson Hospital; 
               
               
                   d Instituto Nacional de Enfermedades Neoplásicas (INEN); 
               
               
                   e Grupo Español de Investigación en Cáncer de Mama (GEICAM); 
               
               
                   f US Oncology; 
               
               
                   g American Joint Committee on Cancer; 
               
               
                   h Estrogen receptor; 
               
               
                   i Progesterone receptor. 
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 1B 
               
             
             
               
                   
               
               
                 Chemotherapy And Pre-treatment Biopsy 
               
               
                 Details for the Study Cohorts 
               
             
          
           
               
                   
                 Discovery 
                 Validation 
               
               
                   
                 Cohort 
                 Cohort 
               
               
                   
                 (N = 310) 
                 (N = 198) 
               
               
                   
                   
               
             
          
           
               
                   
                 Needle Biopsy for Genomic Testing 
                   
                   
               
               
                   
                 FNA 
                 227 
                 157 
               
               
                   
                 CBX 
                 83 
                 41 
               
               
                   
                 Chemotherapy Regimen 
               
               
                   
                 Entirely Neoadjuvant 
               
               
                   
                 T × 12 → FAC × 4 → Sx 1   
                 227 
                 73 
               
               
                   
                 AC × 4 → T/Tx × 4 → Sx 2   
                 83 
                 — 
               
               
                   
                 TxX × 4 → FEC × 4 → Sx 3   
                 — 
                 92 
               
               
                   
                 Partial Neoadjuvant 
               
               
                   
                 FAC/FEC × 6 → Sx → T × 12 4   
                 — 
                 18 
               
               
                   
                 Entirely Adjuvant 
               
               
                   
                 Sx → T × 12 → FAC/FEC × 4 5   
                 — 
                 12 
               
               
                   
                 Sx → TxX × 4 → FEC × 4 6   
                 — 
                 2 
               
               
                   
                 Sx → Tx × 4 → FEC × 4 7   
                 — 
                 1 
               
               
                   
                   
               
               
                   
                 FNA: fine needle aspiration 
               
               
                   
                 CBX: core needle biopsy 
               
               
                   
                 Sx: surgery 
               
               
                   
                   1 12 weekly doses of paclitaxel (T) followed by four cycles of fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) and then surgery. 
               
               
                   
                   2 Four cycles of doxorubicin (A) and cyclophosphamide (C) followed by four cycles of paclitaxel (T) (N = 60) or docetaxel (Tx) (N = 18) or taxane not specified (N = 5) and then surgery. 
               
               
                   
                   3 Four cycles of docetaxel (Tx) with capecitabine (X) followed by four cycles of fluorouracil (F), epirubicin (E) and cyclophosphamide (C) and then surgery. 
               
               
                   
                   4 Six cycles of fluorouracil (F), doxorubicin (A) or epirubicin (E), and cyclophosphamide (C) followed by surgery and then by 12 weekly doses of paclitaxel (T). 
               
               
                   
                   5 Surgery followed by 12 weekly doses of paclitaxel (T) and then by four cycles of fluorouracil (F), doxorubicin (A) or epirubicin (E), and cyclophosphamide (C). 
               
               
                   
                   6 Surgery followed by four cycles of docetaxel (Tx) with capecitabine (X) and then followed by four cycles of fluorouracil (F), epirubicin (E) and cyclophosphamide (C). 
               
               
                   
                   7 Surgery followed by four cycles of docetaxel (Tx) and then by four cycles of fluorouracil (F), epirubicin (E) and cyclophosphamide (C). 
               
             
          
         
       
     
         [0093]    RNA extraction and gene expression profiling—Biopsy samples were either collected in 1.5 ml RNAlater™ (Qiagen, Valencia, Calif.) and stored locally at −70° C. and transported to the laboratory on dry ice (MDACC, INEN, LBJ, GEICAM) or couriered overnight in a cooler pack from clinics to the laboratory (USO), or were frozen, cryosectioned and an aliquot of RNA sent to the laboratory on dry ice (I-SPY). Details of our methods for RNA purification and microarray hybridization have been reported previously Rouzier, 2005; Stec, 2005; Symmans, 2003). Briefly, a single-round T7 amplification was used to generate biotin-labeled cRNA for hybridization to oligonucleotide microarrays (U133A GeneChip™, Affymetrix, Santa Clara, Calif.). Gene expression levels were derived from multiple oligonucleotide probes on the microarray that hybridize to different sequence sites of a gene transcript (probe sets). 
         [0094]    Microarray quality control—Quality control (QC) checks are performed at 3 levels (i) RNA yield, (ii) cRNA yield, and (ii) chip hybridization signal) and samples that fail at any level are not processed further. The amount and quality of RNA is assessed with NanoDrop ND-1000 Spectrophotometer (Thermo Fisher scientific In, Wilmington, Del., USA) and is generally considered adequate for further analysis if the OD 260/280 ratio is between 1.8-2.1 and the total RNA yield is ≧1.0 microgram. If total RNA yield is &lt;1.0 microgram all remaining samples (if available) from that patient are used for RNA extraction. At least 10 μg of biotin-labeled cRNA need to be generated from a single-round in vitro transcription protocol to proceed with hybridized to U133A chips. 
         [0095]    Microarray data normalization—Raw intensity files (.CEL) from each microarray were processed using MAS5.0 (R/Bioconductor, www.bioconductor.org) 1  to normalize to a mean array intensity of 600 and to generate probe set-level expression values. Expression values were then log2-transformed and subsequently scaled by the expression levels of 1322 breast cancer reference genes to reference values that had been established as the median expression of these genes in an independent reference cohort of invasive breast cancer (N=444). The quality of hybridization and microarray profiling was assessed based on a set of 8 metrics that compare the expression level of the reference genes in each sample to the historical reference values before and after scaling. Metrics include the median deviation, the inter-quartile range (IQR) of deviations, the Kolmogorov-Smirnov statistic for equality of the distributions and the p-value of the K-S statistic. Dimensionality was reduced through a principal component analysis (PCA) model of the 8 metrics which were further summarized in two multivariate statistics, the Hotteling T2 and the sum of squares of the residuals or Q statistic (Jackson &amp; Mudholkar, 1979). Control limits for Q and T2 for sample acceptance were established from historical in-control samples. Prior to analysis for predictor development, 2,522 probe sets that either had low specificity (extensions _xfri_ in their name), were housekeeping probes (starting with AFFX) or were not adequately expressed (log2-transformed intensity of at least 5 in at least 75% of the arrays) were removed. A total of 16,289 probe sets (73% of all) were retained for further analysis. 
       Example 2 
     Predictor of Distant Relapse After Therapy or of Resistance to Therapy 
       [0096]    Methods for building predictor of survival outcomes as a result of therapy—Distant relapse-free survival (DRFS) was used as the endpoint of favorable outcome of therapy to build the predictor genes. Prior to analysis, probes that either had low specificity (those that include extensions _xfri_ in their name) or housekeeping probes (those starting with AFFX) were selected and removed from the candidate probesets. This process removed 2522 probesets. Subsequently, a non-specific filter was applied to retain probesets that has log2-transformed intensity of at least 5 in at least 75% of the arrays. A total of 16289 probesets (73% of all) were retained for further analysis. 
         [0097]    The samples in the development cohort were subdivided in ER+ and ER− subsets and in lymph node negative (N0) and lymph positive (NP) subsets within each ER group. Means and standard deviations (SDs) of the 16289 genes were computed for each of the 4 subsets of cases. Within each ER cohort, the means and SDs for N0 and NP subsets were averaged to yield nodal-status adjusted statistics. These means and SDs were then used to scale the expression values of all probesets using the corresponding statistics for ER+ or ER− cases. 
         [0098]    Each probeset was evaluated in a univariate Cox regression model for the significance of its association with risk of distant relapse. For this analysis, distant relapses or breast-cancer related deaths were considered as events, whereas local relapses were censored at the time of occurrence. Time to event was determined since the time of initial diagnosis. The significance of the association of each probeset to distant relapse risk was assessed based on the likelihood ratio test, which compares the log-likelihood of the model having the given probeset as the only covariate to the null model. The likelihood ratio statistic is distributed according to a chi-squared with one degree of freedom. P-values for the significance of each probeset were calculated from this distribution. 
         [0099]    To account for sampling variability in the training dataset, Cox regression models for each probeset were fit repeatedly using a bootstrap procedure in which cases were sampled with replacement to generate bootstrapped datasets of the same size as the original dataset. This process was repeated 499 times, thus generating 500 estimates for the p-values of each probeset. The association of each probeset with distant relapse risk was assessed within each bootstrapped dataset at a critical significance level of 0.001 or 0.0005 to account for multiple testing. Those probesets that were called significant in at least 20% of the bootstrap replicates were selected as candidate probesets. This process was applied separately to the 
         [0100]    ER-positive and ER-negative cases in the training dataset and resulted in 235 and 268 candidate probesets in the ER+ and ER− subsets. 
         [0101]    Final multivariate prediction models were built from the candidate probesets in the ER+ and ER− cohorts. Maximization of the partial likelihood associated with Cox proportional hazards models becomes problematic and non-unique if the number of covariates exceeds the number of available samples or if there is a high degree of colinearity between the predictors. To prevent this pathologic behavior, some sort of regularization or shrinkage needs to be applied to the regression coefficients to allow efficient estimation of the remaining ones. The Cox univariate shrinkage (CUS) approach was used for this purpose (Tibshirani, 2009), which is equivalent to the lasso estimate in standard regression analysis. The level of penalization is an adjustable parameter in the algorithm, with higher penalization resulting in smaller signatures. The optimal level of penalization was determined under 5-fold cross-validation as the penalization level that resulted in the shortest list of genes that yielded the highest incremental improvement in the Cox model&#39;s deviance. 
         [0102]    The final predictors for ER+ and ER− subsets used 33 probesets and 27 probesets respectively to make the predictions. The probesets, genes that they encode for, and their weights (Cox coefficients) are shown in Table 2. The risk score is calculated by multiplying the scaled log2-transformed expression level of each gene in a given sample by its corresponding weight and then adding up the weighted expression values for all genes in the signature. The following formula describes the score calculation for sample i: 
         [0000]    
       
         
           
             
               y 
               i 
             
             = 
             
               { 
               
                 
                   
                     
                       
                         
                           ∑ 
                           
                             j 
                             = 
                             1 
                           
                           
                             K 
                             + 
                           
                         
                          
                         
                           
                             w 
                             j 
                             + 
                           
                            
                           
                             z 
                             ij 
                             + 
                           
                         
                       
                       , 
                     
                   
                   
                     
                       if 
                        
                       
                           
                       
                        
                       ER 
                        
                       
                           
                       
                        
                       positive 
                     
                   
                 
                 
                   
                     
                       
                         
                           ∑ 
                           
                             j 
                             = 
                             1 
                           
                           
                             K 
                             - 
                           
                         
                          
                         
                           
                             w 
                             j 
                             - 
                           
                            
                           
                             z 
                             ij 
                             - 
                           
                         
                       
                       , 
                     
                   
                   
                     
                       if 
                        
                       
                           
                       
                        
                       ER 
                        
                       
                           
                       
                        
                       negative 
                     
                   
                 
               
             
           
         
       
       
         where w j  is the weight of gene j in the signature, z ij  is the log2-transformed and scaled expression value of gene j in sample i, K is the number of genes in the signature, and the + or − symbols refer to the ER+ and ER− signatures. 
       
     
         [0104]    A cut point was selected to dichotomize the risk score and predict two risk classes. The optimal cutoff was selected in order to maximize the accuracy of the prediction of 5-yr distant relapse outcome by the risk classes. A cutoff of 0 was selected for both the ER+ and ER− scores. Positive scores signify “High risk” class, i.e. higher risk of distant relapse and a zero or negative score signifies “Low risk”. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
               
               
             
               
             
               
               
               
               
               
               
               
               
             
               
             
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Genes used for prediction of distant relapse risk in ER-stratified patient subsets 
               
             
          
           
               
                   
                 Probe Set 
                 Symbol 
                 Description 
                 GeneID 
                 Chromosome 
                 Cytoband 
                 Weight 
               
               
                   
                   
               
             
          
           
               
                 ER-Positive 
               
             
          
           
               
                 1 
                 212174_at 
                 AK2 
                 adenylate kinase 2 
                 204 
                 1 
                 1p34 
                 0.0011 
               
               
                 2 
                 215407_s_at 
                 ASTN2 
                 astrotactin 2 
                 23245 
                 9 
                 9q33.1 
                 −0.0131 
               
               
                 3 
                 205626_s_at 
                 CALB1 
                 calbindin 1, 28 kDa 
                 793 
                 8 
                 8q21.3-q22.1 
                 0.008 
               
               
                 4 
                 212816_s_at 
                 CBS 
                 cystathionine-beta- 
                 875 
                 21 
                 21q22.3 
                 0.0116 
               
               
                   
                   
                   
                 synthase 
               
               
                 5 
                 216923_at 
                 CDLK5 
                 cyclin-dependent kinase- 
                 6792 
                 X 
                 Xp22.13 
                 −0.0084 
               
               
                   
                   
                   
                 like 5 
               
               
                 6 
                 205471_s_at 
                 DACH1 
                 dachshund homolog 1 
                 1602 
                 13 
                 13q22 
                 −0.0043 
               
               
                   
                   
                   
                 ( Drosophila ) 
               
               
                 7 
                 221681_s_at 
                 DSPP 
                 dentin 
                 1834 
                 4 
                 4q21.3 
                 −0.0285 
               
               
                   
                   
                   
                 sialophosphoprotein 
               
               
                 8 
                 201539_s_at 
                 FHL1 
                 four and a half LIM 
                 2273 
                 X 
                 Xq26 
                 0.0142 
               
               
                   
                   
                   
                 domains 1 
               
               
                 9 
                 215744_at 
                 FUS 
                 fusion (involved in t(12; 16) 
                 2521 
                 16 
                 16p11.2 
                 −0.0016 
               
               
                   
                   
                   
                 in malignant liposarcoma) 
               
               
                 10 
                 209604_s_at 
                 GATA3 
                 GATA binding protein 3 
                 2625 
                 10 
                 10p15 
                 −0.0414 
               
               
                 11 
                 209602_s_at 
                 GATA3 
                 GATA binding protein 3 
                 2625 
                 10 
                 10p15 
                 −0.0285 
               
               
                 12 
                 209603_at 
                 GATA3 
                 GATA binding protein 3 
                 2625 
                 10 
                 10p15 
                 −0.0067 
               
               
                 13 
                 203821_at 
                 HBEGF 
                 heparin-binding EGF-like 
                 1839 
                 5 
                 5q23 
                 −0.0126 
               
               
                   
                   
                   
                 growth factor 
               
               
                 14 
                 219976_at 
                 HOOK1 
                 hook homolog 1 
                 51361 
                 1 
                 1p32.1 
                 −0.0136 
               
               
                   
                   
                   
                 ( Drosophila ) 
               
               
                 15 
                 212531_at 
                 LCN2 
                 lipocalin 2 
                 3934 
                 9 
                 9q34 
                 0.0411 
               
               
                 16 
                 220906_at 
                 LDB2 
                 LIM domain binding 2 
                 9079 
                 4 
                 p15.32 
                 −0.0358 
               
               
                 17 
                 217506_at 
                 LOC339290 
                 hypothetical LOC339290 
                 339290 
                 18 
                 18p11.31 
                 −0.0171 
               
               
                 18 
                 204058_at 
                 ME1 
                 malic enzyme 1, 
                 4199 
                 6 
                 6q12 
                 0.0002 
               
               
                   
                   
                   
                 NADP(+)-dependent, 
               
               
                   
                   
                   
                 cytosolic 
               
               
                 19 
                 200899_s_at 
                 MGEA5 
                 meningioma expressed 
                 10724 
                 10 
                 10q24.1-q24.3 
                 −0.0023 
               
               
                   
                   
                   
                 antigen 5 (hyaluronidase) 
               
               
                 20 
                 203419_at 
                 MLL4 
                 myeloid/lymphoid or 
                 9757 
                 19 
                 19q13.1 
                 −0.0097 
               
               
                   
                   
                   
                 mixed-lineage leukemia 4 
               
               
                 21 
                 211874_s_at 
                 MYST4 
                 MYST histone 
                 23522 
                 10 
                 10q22.2 
                 −0.0336 
               
               
                   
                   
                   
                 acetyltransferase 
               
               
                   
                   
                   
                 (monocytic leukemia) 4 
               
               
                 22 
                 40569_at 
                 MZF1 
                 myeloid zinc finger 1 
                 7593 
                 19 
                 19q13.4 
                 −0.0349 
               
               
                 23 
                 203621_at 
                 NDUFB5 
                 NADH dehydrogenase 
                 4711 
                 3 
                 3q26.33 
                 0.0448 
               
               
                   
                   
                   
                 (ubiquinone) 1 beta 
               
               
                   
                   
                   
                 subcomplex, 5, 16 kDa 
               
               
                 24 
                 202886_s_at 
                 PPP2R1B 
                 protein phosphatase 2 
                 5519 
                 11 
                 11q23.2 
                 0.0061 
               
               
                   
                   
                   
                 (formerly 2A), regulatory 
               
               
                   
                   
                   
                 subunit A, beta isoform 
               
               
                 25 
                 201834_at 
                 PRKAB1 
                 protein kinase, AMP- 
                 5564 
                 12 
                 12q24.1 
                 −0.0341 
               
               
                   
                   
                   
                 activated, beta 1 non- 
               
               
                   
                   
                   
                 catalytic subunit 
               
               
                 26 
                 212743_at 
                 RCHY1 
                 ring finger and CHY zinc 
                 25898 
                 4 
                 4q21.1 
                 −0.0127 
               
               
                   
                   
                   
                 finger domain containing 1 
               
               
                 27 
                 219869_s_at 
                 SLC39A8 
                 solute carrier family 39 
                 64116 
                 4 
                 4q22-q24 
                 0.0262 
               
               
                   
                   
                   
                 (zinc transporter), member 
               
               
                   
                   
                   
                 8 
               
               
                 28 
                 210692_s_at 
                 SLC43A3 
                 solute carrier family 43, 
                 29015 
                 11 
                 11q11 
                 0.0075 
               
               
                   
                   
                   
                 member 3 
               
               
                 29 
                 213103_at 
                 STARD13 
                 StAR-related lipid transfer 
                 90627 
                 13 
                 13q12-q13 
                 −0.0185 
               
               
                   
                   
                   
                 (START) domain 
               
               
                   
                   
                   
                 containing 13 
               
               
                 30 
                 202342_s_at 
                 TRIM2 
                 tripartite motif-containing 2 
                 23321 
                 4 
                 4q31.3 
                 0.0088 
               
               
                 31 
                 212534_at 
                 ZNF24 
                 zinc finger protein 24 
                 7572 
                 18 
                 18q12 
                 −0.0025 
               
               
                 32 
                 219635_at 
                 ZNF606 
                 zinc finger protein 606 
                 80095 
                 19 
                 19q13.4 
                 −0.0198 
               
               
                 33 
                 214202_at 
                 — 
                 — 
                 — 
                 5 
                 5q22.3 
                 −0.0421 
               
             
          
           
               
                 ER-Negative 
               
             
          
           
               
                 1 
                 200982_s_at 
                 ANXA6 
                 annexin A6 
                 309 
                 5 
                 5q32-q34 
                 0.0136 
               
               
                 2 
                 212136_at 
                 ATP2B4 
                 ATPase, Ca++ transporting, plasma membrane 
                 493 
                 1 
                 1q32.1 
                 0.0123 
               
               
                   
                   
                   
                 4 
               
               
                 3 
                 205379_at 
                 CBR3 
                 carbonyl reductase 3 
                 874 
                 21 
                 21q22.2 
                 −0.0067 
               
               
                 4 
                 219755_at 
                 CBX8 
                 chromobox homolog 8 (Pc class homolog, 
                 57332 
                 17 
                 17q25.3 
                 −0.0023 
               
               
                   
                   
                   
                   Drosophila ) 
               
               
                 5 
                 204720_s_at 
                 DNAJC6 
                 DnaJ (Hsp40) homolog, subfamily C, member 
                 9829 
                 1 
                 1pter-q31.3 
                 0.0022 
               
               
                   
                   
                   
                 6 
               
               
                 6 
                 203303_at 
                 DYNLT3 
                 dynein, light chain, Tctex-type 3 
                 6990 
                 X 
                 Xp21 
                 0.0041 
               
               
                 7 
                 216682_s_at 
                 FAM48A 
                 family with sequence similarity 48, member A 
                 55578 
                 13 
                 13q13.3 
                 0.0044 
               
               
                 8 
                 206847_s_at 
                 HOXA7 
                 homeobox A7 
                 3204 
                 7 
                 7p15-p14 
                 −0.0323 
               
               
                 9 
                 219284_at 
                 HSPBAP1 
                 HSPB (heat shock 27 kDa) associated protein 1 
                 79663 
                 3 
                 3q21.1 
                 0.0068 
               
               
                 10 
                 210036_s_at 
                 KCNH2 
                 potassium voltage-gated channel, subfamily H 
                 3757 
                 7 
                 7q35-q36 
                 0.0158 
               
               
                   
                   
                   
                 (eag-related), member 2 
               
               
                 11 
                 217929_s_at 
                 KIAA0319L 
                 KIAA0319-like 
                 79932 
                 1 
                 1p34.2 
                 −0.0044 
               
               
                 12 
                 201932_at 
                 LRRC41 
                 leucine rich repeat containing 41 
                 10489 
                 1 
                 1p34.1 
                 −0.0089 
               
               
                 13 
                 205301_s_at 
                 OGG1 
                 8-oxoguanine DNA glycosylase 
                 4968 
                 3 
                 3p26.2 
                 −0.0025 
               
               
                 14 
                 208393_s_at 
                 RAD50 
                 RAD50 homolog ( S. cerevisiae ) 
                 10111 
                 5 
                 5q31 
                 0.0404 
               
               
                 15 
                 203286_at 
                 RNF44 
                 ring finger protein 44 
                 22838 
                 5 
                 5q35.2 
                 0.0061 
               
               
                 16 
                 213044_at 
                 ROCK1 
                 Rho-associated, coiled-coil containing protein 
                 6093 
                 18 
                 18q11.1 
                 0.0138 
               
               
                   
                   
                   
                 kinase 1 
               
               
                 17 
                 203889_at 
                 SCG5 
                 secretogranin V (7B2 protein) 
                 6447 
                 15 
                 15q13-q14 
                 0.0065 
               
               
                 18 
                 221053_s_at 
                 TDRKH 
                 tudor and KH domain containing 
                 11022 
                 1 
                 1q21 
                 0.0107 
               
               
                 19 
                 203254_s_at 
                 TLN1 
                 talin 1 
                 7094 
                 9 
                 9p13 
                 0.0167 
               
               
                 20 
                 210180_s_at 
                 TRA2B 
                 transformer 2 beta homolog ( Drosophila ) 
                 6434 
                 3 
                 3q26.2-q27 
                 −0.001 
               
               
                 21 
                 221836_s_at 
                 TRAPPC9 
                 trafficking protein particle complex 9 
                 83696 
                 8 
                 8q24.3 
                 0.0165 
               
               
                 22 
                 208349_at 
                 TRPA1 
                 transient receptor potential cation channel, 
                 8989 
                 8 
                 8q13 
                 0.025 
               
               
                   
                   
                   
                 subfamily A, member 1 
               
               
                 23 
                 216374_at 
                 TSPY1 
                 testis specific protein, Y-linked 1 
                 7258 
                 Y 
                 Yp11.2 
                 0.0079 
               
               
                 24 
                 218715_at 
                 UTP6 
                 UTP6, small subunit (SSU) processome 
                 55813 
                 17 
                 17q11.2 
                 0.0142 
               
               
                   
                   
                   
                 component, homolog (yeast) 
               
               
                 25 
                 208453_s_at 
                 XPNPEP1 
                 X-prolyl aminopeptidase (aminopeptidase P) 1, 
                 7511 
                 10 
                 10q25.3 
                 −0.0208 
               
               
                   
                   
                   
                 soluble 
               
               
                 26 
                 214900_at 
                 ZKSCAN1 
                 zinc finger with KRAB and SCAN domains 1 
                 7586 
                 7 
                 7q21.3-q22.1 
                 −0.0032 
               
               
                 27 
                 215298_at 
                 — 
                 — 
                 — 
                 4 
                 4q8.3 
                 0.0039 
               
               
                   
               
             
          
         
       
     
       Example 3 
     Performance of Relapse-Based Predictor in Chemotherapy Outcomes Prediction 
       [0105]      FIG. 1  shows the survival outcome of patients from the validation cohort (Table 1A) predicted as good and poor responders by the ER-stratified outcomes predictor described in Example 2. Survival is defined by distant relapse-free survival (DRFS) over a period of about 60 months since the initial biopsy. These patients have undergone surgery where it was considered appropriate and the ER-positive patients received hormonal therapy (tamoxifen or aromatase inhibitor) for 5 years after the surgery. ER-negative patients did not receive any additional treatment post-surgery. 
         [0106]    The plot shows that predicted good and poor responders to taxane-chemotherapy ( FIG. 1 ) have distinctly separated relapse-free survival curves (p=0.008). The good responders (51%) or “low-risk” patients show a fewer number of distant relapse events (˜85% relapse-free after 60 months) whereas the remaining patients show considerably higher relapse rates among the patients (˜60% DRFS after 60 months). 
       Example 4 
     Predictor of Response to Chemotherapy 
       [0107]    Patients and samples—Patient samples used were those shown in Table 1A. All other laboratory analytic methods were the same as in Example 1. 
         [0108]    Methods for building predictors of response to chemotherapy—The inventors used the response endpoint RCB0/I, representing no residual disease or minimal residual disease measured at the completion of neoadjuvant chemotherapy, to identify genes that differentiated patients who responded to chemotherapy versus all others in the discovery cohort of Table 1A. Prior to analysis, probes that either had low specificity (those that include extensions _xfri_ in their name) or housekeeping probes (those starting with AFFX) were selected and removed from the candidate probesets. This process removed 2522 probesets. Subsequently, a non-specific filter was applied to retain probesets that has log2-transformed intensity of at least 5 in at least 75% of the arrays. A total of 16289 probesets (73% of all) were retained for further analysis. 
         [0109]    The samples in the development cohort were subdivided in ER+ and ER− subsets and in lymph node negative (N0) and lymph positive (NP) subsets within each ER group. Means and standard deviations (SDs) of the 16289 genes were computed for each of the 4 subsets of cases. Within each ER cohort, the means and SDs for N0 and NP subsets were averaged to yield nodal-status adjusted statistics. These means and SDs were then used to scale the expression values of all probesets using the corresponding statistics for ER+ or ER− cases. 
         [0110]    Each probeset was evaluated for differential expression in the two responder groups (RCB-0/I vs rest) using an unequal variance t-statistic based on the trimmed means and trimmed standard deviations in the two groups using a trim fraction of 0.025 (i.e. the lowest 2.5% and highest 2.5% values were eliminated and the statistics were calculated on the remaining 95% of the observations in each group). Degrees of freedom for the unequal variance t-statistic were estimated based on Satterthwaite&#39;s approximation (Armitage, Berry &amp; Matthews, 2002). The significance of association of each probe set with response was assessed based on the unequal variance t-statistic. P-values for the significance of each probeset were calculated from the t-distribution with the corresponding degrees of freedom. 
         [0111]    To account for sampling variability in the training dataset, the differential expression analysis for each probeset described in the previous paragraph was performed repeatedly using a bootstrap procedure in which cases were sampled with replacement to generate bootstrapped datasets of the same size as the original dataset. This process was repeated 499 times, thus generating 500 estimates for the p-values of each probeset. The association of each probeset with distant relapse risk was assessed within each bootstrapped dataset at a critical significance level of 0.0005 to account for multiple testing. Those probesets that were called significant in at least 30% of the bootstrap replicates were selected as candidate probesets. This process was applied separately to the ER-positive and ER-negative cases in the training dataset and resulted in 209 and 244 candidate probesets in the ER+ and ER− subsets. 
         [0112]    In developing the RCB-based chemotherapy response predictor, the inventors used an approach that combines feature selection and model discovery using a multivariate penalized approach called Gradient Directed Regularization developed by Prof. J. Friedman at Stanford University, a description of which can be found on the World Wide Web at stat.stanford.edu/˜jhf/ftp/pathlite.pdf. The informative genes are selected through penalization using the maximization of the area under the ROC curve (AUC) as the optimization criterion. Ma and Huang have previously used a similar approach for disease classification (Ma, 2006). 
         [0113]    For predictor discovery and evaluation the inventors followed a cross-validation protocol. First, the input dataset is randomly partitioned into a training set and a test set. A 5-fold cross-validation for a 4:1 split stratified by response group between training and test sets was used (Dudoit, 2002). The training set consisting of ⅘ of the original data is used to develop the predictor. The algorithm starts with the same initial list of candidate genes that were determined through the bootstrap procedure and iteratively refines the predictor by selecting genes that contribute in maximizing the AUC of the candidate predictor. The maximum level of penalization is used to derive the most parsimonious predictors. Since different optimal reporter gene sets might result from the different internal cross-validation folds, the number of times each gene is selected is tracked to provide a measure of its importance or its reliability. The trained predictor is then tested on the ⅕ hold-out part of the training dataset and its performance is evaluated based on the AUC. 
         [0114]    The entire process of randomly splitting the data to a training- and a test-set was repeated 499 times to obtain the distributions and summary statistics of the performance metrics from the cross-validated replicates. 
         [0115]    The final predictors for ER+ and ER− subsets used 39 probesets and 55 probesets respectively to make the predictions. The probesets, genes that they encode for, and their weights (coefficients) are shown in Table 3. The risk score is calculated by multiplying the scaled log2-transformed expression level of each gene in a given sample by its corresponding weight and then adding up the weighted expression values for all genes in the signature. The following formula describes the score calculation for sample i: 
         [0000]    
       
         
           
             
               y 
               i 
             
             = 
             
               { 
               
                 
                   
                     
                       
                         
                           ∑ 
                           
                             j 
                             = 
                             1 
                           
                           
                             K 
                             + 
                           
                         
                          
                         
                           
                             w 
                             j 
                             + 
                           
                            
                           
                             z 
                             ij 
                             + 
                           
                         
                       
                       , 
                     
                   
                   
                     
                       if 
                        
                       
                           
                       
                        
                       ER 
                        
                       
                           
                       
                        
                       positive 
                     
                   
                 
                 
                   
                     
                       
                         
                           ∑ 
                           
                             j 
                             = 
                             1 
                           
                           
                             K 
                             - 
                           
                         
                          
                         
                           
                             w 
                             j 
                             - 
                           
                            
                           
                             z 
                             ij 
                             - 
                           
                         
                       
                       , 
                     
                   
                   
                     
                       if 
                        
                       
                           
                       
                        
                       ER 
                        
                       
                           
                       
                        
                       negative 
                     
                   
                 
               
             
           
         
       
       
         where w j  is the weight of gene j in the signature, z ij  is the log2-transformed and scaled expression value of gene j in sample i, K is the number of genes in the signature, and the + or − symbols refer to the ER+ and ER− signatures. 
       
     
         [0117]    A cut point was selected to dichotomize the risk score and predict two risk classes. The optimal cutoff was selected in order to maximize the accuracy of the prediction. A cutoff of 0 was selected for both the ER+ and ER− scores. Positive scores signify “responders” and a zero or negative score signifies “non-responders”. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
               
               
             
               
             
               
               
               
               
               
               
               
               
             
               
             
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Genes used for prediction of response, RCB-0/1, in ER-stratified patient subsets 
               
             
          
           
               
                   
                 Probe Set 
                 Symbol 
                 Description 
                 GeneID 
                 Chromosome 
                 Cytoband 
                 Weight 
               
               
                   
                   
               
             
          
           
               
                 ER-Positive 
               
             
          
           
               
                 1 
                 204332_s_at 
                 AGA 
                 aspartylglucosaminidase 
                 175 
                 4 
                 4q32-q33 
                 1.023626 
               
               
                 2 
                 36865_at 
                 ANGEL1 
                 angel homolog 1 
                 23357 
                 14 
                 14q24.3 
                 0.538063 
               
               
                   
                   
                   
                 ( Drosophila ) 
               
               
                 3 
                 219437_s_at 
                 ANKRD11 
                 ankyrin repeat domain 
                 29123 
                 16 
                 16q24.3 
                 0.26952 
               
               
                   
                   
                   
                 11 
               
               
                 4 
                 205865_at 
                 ARID3A 
                 AT rich interactive 
                 1820 
                 19 
                 19p13.3 
                 0.832093 
               
               
                   
                   
                   
                 domain 3A (BRIGHT- 
               
               
                   
                   
                   
                 like) 
               
               
                 5 
                 215407_s_at 
                 ASTN2 
                 astrotactin 2 
                 23245 
                 9 
                 9q33.1 
                 1.081851 
               
               
                 6 
                 204493_at 
                 BID 
                 BH3 interacting domain 
                 637 
                 22 
                 22q11.1 
                 0.351295 
               
               
                   
                   
                   
                 death agonist 
               
               
                 7 
                 205557_at 
                 BPI 
                 bactericidal/permeability- 
                 671 
                 20 
                 20q11.23-q12 
                 −1.05657 
               
               
                   
                   
                   
                 increasing protein 
               
               
                 8 
                 42361_g_at 
                 CCHCR1 
                 coiled-coil alpha-helical 
                 54535 
                 6 
                 6p21.3 
                 −0.19308 
               
               
                   
                   
                   
                 rod protein 1 
               
               
                 9 
                 205937_at 
                 CGREF1 
                 cell growth regulator 
                 10669 
                 2 
                 2p23.3 
                 0.616448 
               
               
                   
                   
                   
                 with EF-hand domain 1 
               
               
                 10 
                 208817_at 
                 COMT 
                 catechol-O- 
                 1312 
                 22 
                 22q11.21 
                 0.964167 
               
               
                   
                   
                   
                 methyltransferase 
               
               
                 11 
                 202250_s_at 
                 DCAF8 
                 DDB1 and CUL4 
                 50717 
                 1 
                 1q22-q23 
                 0.438059 
               
               
                   
                   
                   
                 associated factor 8 
               
               
                 12 
                 202570_s_at 
                 DLGAP4 
                 discs, large ( Drosophila ) 
                 22839 
                 20 
                 20q11.23 
                 −0.03735 
               
               
                   
                   
                   
                 homolog-associated 
               
               
                   
                   
                   
                 protein 4 
               
               
                 13 
                 218103_at 
                 FTSJ3 
                 FtsJ homolog 3 ( E. coli ) 
                 117246 
                 17 
                 17q23.3 
                 0.902969 
               
               
                 14 
                 216651_s_at 
                 GAD2 
                 glutamate 
                 2572 
                 10 
                 10p11.23 
                 1.191928 
               
               
                   
                   
                   
                 decarboxylase 2 
               
               
                   
                   
                   
                 (pancreatic islets and 
               
               
                   
                   
                   
                 brain, 65 kDa) 
               
               
                 15 
                 205505_at 
                 GCNT1 
                 glucosaminyl (N-acetyl) 
                 2650 
                 9 
                 9q13 
                 0.635989 
               
               
                   
                   
                   
                 transferase 1, core 2 
               
               
                   
                   
                   
                 (beta-1,6-N- 
               
               
                   
                   
                   
                 acetylglucosaminyl 
               
               
                   
                   
                   
                 transferase) 
               
               
                 16 
                 213020_at 
                 GOSR1 
                 golgi SNAP receptor 
                 9527 
                 17 
                 17q11 
                 0.041002 
               
               
                   
                   
                   
                 complex member 1 
               
               
                 17 
                 212597_s_at 
                 HMGXB4 
                 HMG box domain 
                 10042 
                 22 
                 22q13.1 
                 0.241141 
               
               
                   
                   
                   
                 containing 4 
               
               
                 18 
                 212898_at 
                 KIAA0406 
                 KIAA0406 
                 9675 
                 20 
                 20q11.23 
                 −0.37731 
               
               
                 19 
                 220652_at 
                 KIF24 
                 kinesin family member 
                 347240 
                 9 
                 9p13.3 
                 −0.85991 
               
               
                   
                   
                   
                 24 
               
               
                 20 
                 218486_at 
                 KLF11 
                 Kruppel-like factor 11 
                 8462 
                 2 
                 2p25 
                 0.145703 
               
               
                 21 
                 202057_at 
                 KPNA1 
                 karyopherin alpha 1 
                 3836 
                 3 
                 3q21 
                 0.047619 
               
               
                   
                   
                   
                 (importin alpha 5) 
               
               
                 22 
                 209204_at 
                 LMO4 
                 LIM domain only 4 
                 8543 
                 1 
                 1p22.3 
                 0.906757 
               
               
                 23 
                 201818_at 
                 LPCAT1 
                 lysophosphatidylcholine 
                 79888 
                 5 
                 5p15.33 
                 0.602505 
               
               
                   
                   
                   
                 acyltransferase 1 
               
               
                 24 
                 208328_s_at 
                 MEF2A 
                 myocyte enhancer 
                 4205 
                 15 
                 15q26 
                 0.196532 
               
               
                   
                   
                   
                 factor 2A 
               
               
                 25 
                 215491_at 
                 MYCL1 
                 v-myc 
                 4610 
                 1 
                 1p34.2 
                 1.199616 
               
               
                   
                   
                   
                 myelocytomatosis viral 
               
               
                   
                   
                   
                 oncogene homolog 1, 
               
               
                   
                   
                   
                 lung carcinoma derived 
               
               
                   
                   
                   
                 (avian) 
               
               
                 26 
                 202944_at 
                 NAGA 
                 N- 
                 4668 
                 22 
                 22q11 
                 0.053596 
               
               
                   
                   
                   
                 acetylgalactosaminidase, 
               
               
                   
                   
                   
                 alpha- 
               
               
                 27 
                 218886_at 
                 PAK1IP1 
                 PAK1 interacting protein 
                 55003 
                 6 
                 6p24.2 
                 −0.39992 
               
               
                   
                   
                   
                 1 
               
               
                 28 
                 207081_s_at 
                 PI4KA 
                 phosphatidylinositol 4- 
                 5297 
                 22 
                 22q11.21 
                 0.879705 
               
               
                   
                   
                   
                 kinase, catalytic, alpha 
               
               
                 29 
                 210771_at 
                 PPARA 
                 peroxisome proliferator- 
                 5465 
                 22 
                 22q12-q13.1 
                 0.771244 
               
               
                   
                   
                   
                 activated receptor alpha 
               
               
                 30 
                 203096_s_at 
                 RAPGEF2 
                 Rap guanine nucleotide 
                 9693 
                 4 
                 4q32.1 
                 0.645585 
               
               
                   
                   
                   
                 exchange factor (GEF) 
               
               
                   
                   
                   
                 2 
               
               
                 31 
                 218593_at 
                 RBM28 
                 RNA binding motif 
                 55131 
                 7 
                 7q32.1 
                 0.533325 
               
               
                   
                   
                   
                 protein 28 
               
               
                 32 
                 211678_s_at 
                 RNF114 
                 ring finger protein 114 
                 55905 
                 20 
                 20q13.13 
                 1.178185 
               
               
                 33 
                 202762_at 
                 ROCK2 
                 Rho-associated, coiled- 
                 9475 
                 2 
                 2p24 
                 1 
               
               
                   
                   
                   
                 coil containing protein 
               
               
                   
                   
                   
                 kinase 2 
               
               
                 34 
                 206239_s_at 
                 SPINK1 
                 serine peptidase 
                 6690 
                 5 
                 5q32 
                 0.620242 
               
               
                   
                   
                   
                 inhibitor, Kazal type 1 
               
               
                 35 
                 221276_s_at 
                 SYNC 
                 syncoilin, intermediate 
                 81493 
                 1 
                 1p34.3-p33 
                 −0.38482 
               
               
                   
                   
                   
                 filament protein 
               
               
                 36 
                 213155_at 
                 WSCD1 
                 WSC domain containing 
                 23302 
                 17 
                 17p13.2 
                 −0.31573 
               
               
                   
                   
                   
                 1 
               
               
                 37 
                 37117_at 
                 PRR5 
                 proline rich 5 (renal) 
                 55615 
                 22 
                 22q13 
                 0.106363 
               
               
                 38 
                 220855_at 
                 AC091271.1 
                 no-protein transcript 
                 — 
                 17 
                 17q23.2 
                 −0.3595 
               
               
                 39 
                 222275_at 
                 — 
                 — 
                 — 
                 5 
                 5p12 
                 0.03155 
               
             
          
           
               
                 ER-Negative 
               
             
          
           
               
                 1 
                 202442_at 
                 AP3S1 
                 adaptor-related protein 
                 1176 
                 5 
                 5q22 
                 −0.19044 
               
               
                   
                   
                   
                 complex 3, sigma 1 
               
               
                   
                   
                   
                 subunit 
               
               
                 2 
                 212135_s_at 
                 ATP2B4 
                 ATPase, Ca++ 
                 493 
                 1 
                 1q32.1 
                 −0.3245 
               
               
                   
                   
                   
                 transporting, plasma 
               
               
                   
                   
                   
                 membrane 4 
               
               
                 3 
                 217911_s_at 
                 BAG3 
                 BCL2-associated 
                 9531 
                 10 
                 10q25.2-q26.2 
                 −0.23225 
               
               
                   
                   
                   
                 athanogene 3 
               
               
                 4 
                 210214_s_at 
                 BMPR2 
                 bone morphogenetic 
                 659 
                 2 
                 2q33-q34 
                 0.814841 
               
               
                   
                   
                   
                 protein receptor, type II 
               
               
                   
                   
                   
                 (serine/threonine kinase) 
               
               
                 5 
                 202048_s_at 
                 CBX6 
                 chromobox homolog 6 
                 23466 
                 22 
                 22q13.1 
                 −1.02907 
               
               
                 6 
                 203653_s_at 
                 COIL 
                 coilin 
                 8161 
                 17 
                 17q22-q23 
                 0.078687 
               
               
                 7 
                 203633_at 
                 CPT1A 
                 carnitine 
                 1374 
                 11 
                 11q13.1-q13.2 
                 −0.06407 
               
               
                   
                   
                   
                 palmitoyltransferase 1A 
               
               
                   
                   
                   
                 (liver) 
               
               
                 8 
                 210096_at 
                 CYP4B1 
                 cytochrome P450, family 
                 1580 
                 1 
                 1p34-p12 
                 −0.39651 
               
               
                   
                   
                   
                 4, subfamily B, 
               
               
                   
                   
                   
                 polypeptide 1 
               
               
                 9 
                 212838_at 
                 DNMBP 
                 dynamin binding protein 
                 23268 
                 10 
                 10q24.2 
                 −0.07158 
               
               
                 10 
                 219850_s_at 
                 EHF 
                 ets homologous factor 
                 26298 
                 11 
                 11p12 
                 0.115972 
               
               
                 11 
                 201936_s_at 
                 EIF4G3 
                 eukaryotic translation 
                 8672 
                 1 
                 1p36.12 
                 −0.08341 
               
               
                   
                   
                   
                 initiation factor 4 gamma, 
               
               
                   
                   
                   
                 3 
               
               
                 12 
                 217254_s_at 
                 EPO 
                 erythropoietin 
                 2056 
                 7 
                 7q22 
                 −0.9403 
               
               
                 13 
                 205774_at 
                 F12 
                 coagulation factor XII 
                 2161 
                 5 
                 5q33-qter 
                 −0.21253 
               
               
                   
                   
                   
                 (Hageman factor) 
               
               
                 14 
                 218532_s_at 
                 FAM134B 
                 family with sequence 
                 54463 
                 5 
                 5p15.1 
                 −0.12462 
               
               
                   
                   
                   
                 similarity 134, member B 
               
               
                 15 
                 200709_at 
                 FKBP1A 
                 FK506 binding protein 
                 2280 
                 20 
                 20p13 
                 −0.06741 
               
               
                   
                   
                   
                 1A, 12 kDa 
               
               
                 16 
                 212294_at 
                 GNG12 
                 guanine nucleotide 
                 55970 
                 1 
                 1p31.3 
                 −0.2595 
               
               
                   
                   
                   
                 binding protein (G 
               
               
                   
                   
                   
                 protein), gamma 12 
               
               
                 17 
                 211525_s_at 
                 GP5 
                 glycoprotein V (platelet) 
                 2814 
                 3 
                 3q29 
                 −0.52858 
               
               
                 18 
                 212090_at 
                 GRINA 
                 glutamate receptor, 
                 2907 
                 8 
                 8q24.3 
                 −0.02213 
               
               
                   
                   
                   
                 ionotropic, N-methyl D- 
               
               
                   
                   
                   
                 aspartate-associated 
               
               
                   
                   
                   
                 protein 1 (glutamate 
               
               
                   
                   
                   
                 binding) 
               
               
                 19 
                 213053_at 
                 HAUS5 
                 HAUS augmin-like 
                 23354 
                 19 
                 19q13.12 
                 0.395212 
               
               
                   
                   
                   
                 complex, subunit 5 
               
               
                 20 
                 214537_at 
                 HIST1H1D 
                 histone cluster 1, H1d 
                 3007 
                 6 
                 6p21.3 
                 0.029003 
               
               
                 21 
                 206194_at 
                 HOXC4 
                 homeobox C4 
                 3221 
                 12 
                 12q13.3 
                 −0.10183 
               
               
                 22 
                 204544_at 
                 HPS5 
                 Hermansky-Pudlak 
                 11234 
                 11 
                 11p14 
                 0.203156 
               
               
                   
                   
                   
                 syndrome 5 
               
               
                 23 
                 205700_at 
                 HSD17B6 
                 hydroxysteroid (17-beta) 
                 8630 
                 12 
                 12q13 
                 −0.88741 
               
               
                   
                   
                   
                 dehydrogenase 6 
               
               
                   
                   
                   
                 homolog (mouse) 
               
               
                 24 
                 209575_at 
                 IL10RB 
                 interleukin 10 receptor, 
                 3588 
                 21 
                 21q22.1-q22.2 
                 0.162807 
               
               
                   
                   
                   
                 beta 
               
               
                 25 
                 215177_s_at 
                 ITGA6 
                 integrin, alpha 6 
                 3655 
                 2 
                 2q31.1 
                 0.34206 
               
               
                 26 
                 221986_s_at 
                 KLHL24 
                 kelch-like 24 ( Drosophila ) 
                 54800 
                 3 
                 3q27.1 
                 1 
               
               
                 27 
                 208107_s_at 
                 LOC81691 
                 exonuclease NEF-sp 
                 81691 
                 16 
                 16p12.3 
                 0.618062 
               
               
                 28 
                 221650_s_at 
                 MED18 
                 mediator complex 
                 54797 
                 1 
                 1p35.3 
                 −0.05596 
               
               
                   
                   
                   
                 subunit 18 
               
               
                 29 
                 218251_at 
                 MID1IP1 
                 MID1 interacting protein 
                 58526 
                 X 
                 Xp11.4 
                 −0.41753 
               
               
                   
                   
                   
                 1 (gastrulation specific 
               
               
                   
                   
                   
                 G12 homolog 
               
               
                   
                   
                   
                 (zebrafish)) 
               
               
                 30 
                 215563_s_at 
                 MSTP9 
                 macrophage stimulating, 
                 11223 
                 1 
                 1p36.13 
                 −0.02463 
               
               
                   
                   
                   
                 pseudogene 9 
               
               
                 31 
                 221207_s_at 
                 NBEA 
                 neurobeachin 
                 26960 
                 13 
                 13q13 
                 −0.45289 
               
               
                 32 
                 208926_at 
                 NEU1 
                 sialidase 1 (lysosomal 
                 4758 
                 6 
                 6p21.3 
                 0.27621 
               
               
                   
                   
                   
                 sialidase) 
               
               
                 33 
                 204107_at 
                 NFYA 
                 nuclear transcription 
                 4800 
                 6 
                 6p21.3 
                 −0.10057 
               
               
                   
                   
                   
                 factor Y, alpha 
               
               
                 34 
                 218410_s_at 
                 PGP 
                 phosphoglycolate 
                 283871 
                 16 
                 16p13.3 
                 −0.13051 
               
               
                   
                   
                   
                 phosphatase 
               
               
                 35 
                 211159_s_at 
                 PPP2R5D 
                 protein phosphatase 2, 
                 5528 
                 6 
                 6p21.1 
                 0.218826 
               
               
                   
                   
                   
                 regulatory subunit B′, 
               
               
                   
                   
                   
                 delta isoform 
               
               
                 36 
                 205617_at 
                 PRRG2 
                 proline rich Gla (G- 
                 5639 
                 19 
                 19q13.33 
                 0.752739 
               
               
                   
                   
                   
                 carboxyglutamic acid) 2 
               
               
                 37 
                 203038_at 
                 PTPRK 
                 protein tyrosine 
                 5796 
                 6 
                 6q22.2-q22.3 
                 0.268374 
               
               
                   
                   
                   
                 phosphatase, receptor 
               
               
                   
                   
                   
                 type, K 
               
               
                 38 
                 203831_at 
                 R3HDM2 
                 R3H domain containing 2 
                 22864 
                 12 
                 12q13.3 
                 −0.04695 
               
               
                 39 
                 201779_s_at 
                 RNF13 
                 ring finger protein 13 
                 11342 
                 3 
                 3q25.1 
                 0.247392 
               
               
                 40 
                 203286_at 
                 RNF44 
                 ring finger protein 44 
                 22838 
                 5 
                 5q35.2 
                 −0.07864 
               
               
                 41 
                 221524_s_at 
                 RRAGD 
                 Ras-related GTP binding 
                 58528 
                 6 
                 6q15-q16 
                 0.616503 
               
               
                   
                   
                   
                 D 
               
               
                 42 
                 212416_at 
                 SCAMP1 
                 secretory carrier 
                 9522 
                 5 
                 5q13.3-q14.1 
                 −0.96624 
               
               
                   
                   
                   
                 membrane protein 1 
               
               
                 43 
                 207707_s_at 
                 SEC13 
                 SEC13 homolog ( S . 
                 6396 
                 3 
                 3p25-p24 
                 0.706684 
               
               
                   
                   
                   
                   cerevisiae ) 
               
               
                 44 
                 201915_at 
                 SEC63 
                 SEC63 homolog ( S.   
                 11231 
                 6 
                 6q21 
                 0.383853 
               
               
                   
                   
                   
                   cerevisiae ) 
               
               
                 45 
                 203580_s_at 
                 SLC7A6 
                 solute carrier family 7 
                 9057 
                 16 
                 16q22.1 
                 −0.16415 
               
               
                   
                   
                   
                 (cationic amino acid 
               
               
                   
                   
                   
                 transporter, y+ system), 
               
               
                   
                   
                   
                 member 6 
               
               
                 46 
                 212257_s_at 
                 SMARCA2 
                 SWI/SNF related, matrix 
                 6595 
                 9 
                 9p22.3 
                 0.152197 
               
               
                   
                   
                   
                 associated, actin 
               
               
                   
                   
                   
                 dependent regulator of 
               
               
                   
                   
                   
                 chromatin, subfamily a, 
               
               
                   
                   
                   
                 member 2 
               
               
                 47 
                 201794_s_at 
                 SMG7 
                 Smg-7 homolog, 
                 9887 
                 1 
                 1q25 
                 −0.33961 
               
               
                   
                   
                   
                 nonsense mediated 
               
               
                   
                   
                   
                 mRNA decay factor ( C.   
               
               
                   
                   
                   
                 
                   elegans) 
                 
               
               
                 48 
                 202991_at 
                 STARD3 
                 StAR-related lipid 
                 10948 
                 17 
                 17q11-q12 
                 0.579916 
               
               
                   
                   
                   
                 transfer (START) domain 
               
               
                   
                   
                   
                 containing 3 
               
               
                 49 
                 210294_at 
                 TAPBP 
                 TAP binding protein 
                 6892 
                 6 
                 6p21.3 
                 0.04522 
               
               
                   
                   
                   
                 (tapasin) 
               
               
                 50 
                 217711_at 
                 TEK 
                 TEK tyrosine kinase, 
                 7010 
                 9 
                 9p21 
                 −0.06112 
               
               
                   
                   
                   
                 endothelial 
               
               
                 51 
                 212638_s_at 
                 WWP1 
                 WW domain containing 
                 11059 
                 8 
                 8q21 
                 −0.37266 
               
               
                   
                   
                   
                 E3 ubiquitin protein 
               
               
                   
                   
                   
                 ligase 1 
               
               
                 52 
                 213081_at 
                 ZBTB22 
                 zinc finger and BTB 
                 9278 
                 6 
                 6p21.3 
                 −0.16771 
               
               
                   
                   
                   
                 domain containing 22 
               
               
                 53 
                 216738_at 
                 — 
                 — 
                 — 
                 3 
                 3p25.3 
                 −0.10674 
               
               
                 54 
                 220820_at 
                 — 
                 — 
                 — 
                 10 
                 10q11.23 
                 −0.3542 
               
               
                 55 
                 222312_s_at 
                 — 
                 — 
                 — 
                 1 
                 1p22.3 
                 −0.11559 
               
               
                   
               
             
          
         
       
     
       Example 5  
     Performance of Response-Based Predictor in Validation Cohort 
       [0118]      FIG. 2  shows the survival outcomes of patients from the independent validation cohort (Table 1A) that were predicted as good responders by the ER-stratified predictor of response (RCB0/I) described in Example 4. Survival is defined by distant relapse-free survival (DRFS) over a period of about 80 months after the initial diagnostic biopsy. These patients have undergone surgery where it was considered appropriate and the ER-positive patients received hormonal therapy (tamoxifen) for 5 years after the surgery. ER-negative patients did not receive any treatment post-surgery. 
         [0119]    The plot shows that predicted responders to taxane-containing chemotherapy ( FIG. 2 ) show fewer events resulting in lower distant relapse rate (˜20% relapse rate after 60 months) whereas the remainder show considerably higher relapse rate among the patients (˜40% relapse rate in after 60 months). The overall separation of the two curves, poor responders corresponding to lower survival and good responders corresponding to higher survival, however, are not statistically significant (log-rank test p=0.143). This indicates that the response-based predictor facilitates some separation according to outcomes after therapy but is not strongly predictive enough on its own to distinctly differentiate survival after therapy in this particular validation cohort. 
         [0120]      FIG. 3  shows plots of the prediction of the response predictor versus relapse-free survival in ER-positive and ER-negative subsets of the independent validation cohort of Table 1A. The plot shows that predicted responders in ER-positive tumors are not well separated from non-responders over the first 3 years ( FIG. 3A ), although the predicted non-responders accumulate more events after 3 years, whereas there is a reasonably good separation between responders to taxane-therapy versus non-responders in ER-negative tumors (p=0.094,  FIG. 3B ). The response-based predictor, therefore, shows a potentially stronger predictive power in ER-negative tumors for outcomes after chemotherapy. 
       Example 6 
     Prediction of Chemotherapy Outcome Using a Combination of Relapse-Based and Response-Based Predictors 
       [0121]    Based on the performance of the relapse-based or resistance predictor of Example 2 and the response-based predictor of Example 4, combined prediction using the two predictors was studied in the validation cohort (Table 1A). The relapse-based predictor was applied first to the cohort as described in  FIG. 1  to obtain low-risk and high-risk patients. The response-based predictor was then applied to the low-risk patients to further stratify them into two groups—called High responders and Intermediate responders. The patients previously identified as high-risk by the relapse-based predictor were labeled here as Low responders. 
         [0122]      FIG. 4  shows K-M plots of the cohorts defined by the combined predictor based on relapse (resistance) and response. The plot shows about 29% of patients with an excellent 5-year survival (average 92% DRFS at 60 months) versus the Intermediate and Low responders who show approximately 65% or lower DRFS at 60 months. The separation of the curves is statistically significant (p=0.003). The Intermediate and Low responders may be combined into a single group as non-responders since they had very similar DRFS profiles. 
         [0123]      FIG. 5  shows plots of the prediction of the combined predictor versus relapse-free survival in ER-positive ( FIG. 5A ) and ER-negative ( FIG. 5B ) subsets of the validation cohort. In both subsets, the High responders as one group are distinctly separated from the Intermediate and Low responders, which together can be considered as Non-responders in both subsets. The responders for the ER-positive tumors have excellent survival (˜100% DRFS at 60 months) versus the non-responders have about 73% DRFS in that time period. The ER-negative tumors, known to have poorer prognosis relative to ER-positive tumors, have an 85% DRFS at 60 months among responders but a much lower DRFS of ˜50% among non-responders. Identifying patients who would be at such high risk despite aggressive chemotherapy would be clinically useful since they can be considered for more advanced therapies or in clinical trials of new therapeutic agents. 
       Example 7 
     Chemotherapy Outcomes Prediction Using an Index of Endocrine Sensitivity 
       [0124]    The prediction of breast cancer sensitivity to endocrine therapy such as tamoxifen and aromatase inhibitors has been described earlier by measurement of gene expression levels (U.S. Provisional Patent Application, 61/174706). We examined the combination of the sensitivity to endocrine therapy (SET) index with prediction of chemosensitivity using the combined predictor genes described in Example 6. 
         [0125]    In this example, the endocrine sensitivity index (as described in U.S. 61/174706) was applied first to the validation cohort of patients shown in Table 1A. The High and Intermediate classes (8.9%) of endocrine sensitivity showed good relapse-free survival ( FIG. 6 ). Therefore, patients who show high and intermediate values of the endocrine sensitivity index will have a good outcome when chemotherapy is combined with endocrine therapy for these patients. The remaining patients (91.1%) need to be evaluated additionally for benefit of chemotherapy using other methods, such as the predictors described in Examples 2 and 4. 
         [0126]    The relapse-based predictor (Example 2) and response-based predictor (Example 4), combined as described in Example 6, were applied to the patient samples classified with a low endocrine sensitivity index. Patients identified for chemosensitivity by the predictors of Example 2 and 4 together were then combined with patients with high and intermediate endocrine sensitivity index as responders.  FIG. 7  shows the predicted good and poor responders identified by these combined predictors. The poor responders (64.1% of patients) show a larger number of events resulting in lower DRFS (˜60% relapse-free after 60 months) whereas the responder patients (35.9% of total) show considerably higher relapse-free survival among the patients (˜95% relapse-free after 60 months). The two curves, poor responders corresponding to lower survival and good responders corresponding to higher survival, are statistically distinct (p&lt;0.001). This shows that the synergistic use of genomic indices such as the SET index along with the predictor genes in Tables 2 and 3 can very effectively identify patients who will have a good outcome or a poor outcome as a result of chemotherapy. 
         [0127]      FIG. 8  shows the performance of the combined predictor separately ER positive and ER negative patients. In ER-positive patients ( FIG. 8A ), the predicted responders have an excellent outcome as ˜98%% relapse-free survival over 5 years and represent about 35% of the patients whereas the poor responders have a relapse-free survival of 65% in comparison. In ER-negative patients ( FIG. 8B ), the identified responders have about an 80% relapse-free survival rate in contrast to poor responders who do much worse at 45% relapse-free survival. In both sets of patients, whether ER-positive or ER-negative, the responder and non-responder curves are distinctly separated with statistical significance (p=0.005 for ER-positive and p=0.004 for ER-negative subsets, respectively). 
       Example 8 
     Predictor of Poor Response to Chemotherapy 
       [0128]    Patients and samples - Patient samples used were those shown in Table 1A. All other laboratory analytic methods were the same as in Example 1. 
         [0129]    Methods for building predictors of poor response to chemotherapy—The inventors used the response endpoint RCB-III, representing extensive residual disease after the completion of neoadjuvant chemotherapy, to identify genes that differentiated patients who failed to respond to chemotherapy versus all others in the discovery cohort (Table 1A). Prior to analysis, probes that either had low specificity (those that include extensions _xfri_ in their name) or housekeeping probes (those starting with AFFX) were selected and removed from the candidate probesets. This process removed 2522 probesets. Subsequently, a non-specific filter was applied to retain probesets that has log2-transformed intensity of at least 5 in at least 75% of the arrays. A total of 16289 probesets (73% of all) were retained for further analysis. 
         [0130]    The samples in the development cohort were subdivided in ER+ and ER− subsets and in lymph node negative (N0) and lymph positive (NP) subsets within each ER group. Means and standard deviations (SDs) of the 16289 genes were computed for each of the 4 subsets of cases. Within each ER cohort, the means and SDs for N0 and NP subsets were averaged to yield nodal-status adjusted statistics. These means and SDs were then used to scale the expression values of all probesets using the corresponding statistics for ER+ or ER− cases. 
         [0131]    Each probeset was evaluated for differential expression in the two responder groups (RCB-III vs rest) using an unequal variance t-statistic based on the trimmed means and trimmed standard deviations in the two groups using a trim fraction of 0.025 (i.e. the lowest 2.5% and highest 2.5% values were eliminated and the statistics were calculated on the remaining 95% of the observations in each group). Degrees of freedom for the unequal variance t-statistic were estimated based on Satterthwaite&#39;s approximation (Armitage, Berry &amp; Matthews, 2002). The significance of association of each probe set with response was assessed based on the unequal variance t-statistic. P-values for the significance of each probeset were calculated from the t-distribution with the corresponding degrees of freedom. 
         [0132]    To account for sampling variability in the training dataset, the differential expression analysis for each probeset described in the previous paragraph was performed repeatedly using a bootstrap procedure in which cases were sampled with replacement to generate bootstrapped datasets of the same size as the original dataset. This process was repeated 499 times, thus generating 500 estimates for the p-values of each probeset. The association of each probeset with distant relapse risk was assessed within each bootstrapped dataset at a critical significance level of 0.00075 to account for multiple testing. Those probesets that were called significant in at least 30% of the bootstrap replicates were selected as candidate probesets. This process was applied separately to the ER-positive and ER-negative cases in the training dataset and resulted in 256 and 202 candidate probesets in the ER+ and ER− subsets. 
         [0133]    In developing the RCB-based chemotherapy response predictor, the inventors used an approach that combines feature selection and model discovery using a multivariate penalized approach called Gradient Directed Regularization developed by Prof. J. Friedman at Stanford University, a description of which can be found on the World Wide Web at stat.stanford.edu/˜jhf/ftp/pathlite.pdf. The informative genes are selected through penalization using the maximization of the area under the ROC curve (AUC) as the optimization criterion. Ma and Huang have previously used a similar approach for disease classification (Ma, 2006). 
         [0134]    For predictor discovery and evaluation the inventors followed a cross-validation protocol. First, the input dataset is randomly partitioned into a training set and a test set. A 5-fold cross-validation for a 4:1 split stratified by response group between training and test sets was used (Dudoit, 2002). The training set consisting of ⅘ of the original data is used to develop the predictor. The algorithm starts with the same initial list of candidate genes that were determined through the bootstrap procedure and iteratively refines the predictor by selecting genes that contribute in maximizing the AUC of the candidate predictor. The maximum level of penalization is used to derive the most parsimonious predictors. Since different optimal reporter gene sets might result from the different internal cross-validation folds, the number of times each gene is selected is tracked to provide a measure of its importance or its reliability. The trained predictor is then tested on the ⅕ hold-out part of the training dataset and its performance is evaluated based on the AUC. 
         [0135]    The entire process of randomly splitting the data to a training- and a test-set was repeated 499 times to obtain the distributions and summary statistics of the performance metrics from the cross-validated replicates. 
         [0136]    The final predictors for ER+ and ER− subsets used 73 probesets and 54 probesets respectively to make the predictions. The probesets, genes that they encode for, and their weights (coefficients) are shown in Table 4. The risk score is calculated by multiplying the scaled log2-transformed expression level of each gene in a given sample by its corresponding weight and then adding up the weighted expression values for all genes in the signature. The following formula describes the score calculation for sample i: 
         [0000]    
       
         
           
             
               y 
               i 
             
             = 
             
               { 
               
                 
                   
                     
                       
                         
                           ∑ 
                           
                             j 
                             = 
                             1 
                           
                           
                             K 
                             + 
                           
                         
                          
                         
                           
                             w 
                             j 
                             + 
                           
                            
                           
                             z 
                             ij 
                             + 
                           
                         
                       
                       , 
                     
                   
                   
                     
                       if 
                        
                       
                           
                       
                        
                       ER 
                        
                       
                           
                       
                        
                       positive 
                     
                   
                 
                 
                   
                     
                       
                         
                           ∑ 
                           
                             j 
                             = 
                             1 
                           
                           
                             K 
                             - 
                           
                         
                          
                         
                           
                             w 
                             j 
                             - 
                           
                            
                           
                             z 
                             ij 
                             - 
                           
                         
                       
                       , 
                     
                   
                   
                     
                       if 
                        
                       
                           
                       
                        
                       ER 
                        
                       
                           
                       
                        
                       negative 
                     
                   
                 
               
             
           
         
       
       
         where w j  is the weight of gene j in the signature, z ij  is the log2-transformed and scaled expression value of gene j in sample i, K is the number of genes in the signature, and the + or − symbols refer to the ER+ and ER− signatures. 
       
     
         [0138]    A cut point was selected to dichotomize the risk score and predict two risk classes. The optimal cutoff was selected in order to maximize the accuracy of the prediction. A cutoff of 0 was selected for both the ER+ and ER− scores. Positive scores signify “resistant” or poor-responder and a zero or negative score signifies “non-resistant”. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
               
               
             
               
             
               
               
               
               
               
               
               
               
             
               
             
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 Genes used for prediction of poor response, RCB-III, in ER-stratified patient subsets 
               
             
          
           
               
                   
                 Probe Set 
                 Symbol 
                 Description 
                 GeneID 
                 Chromosome 
                 Cytoband 
                 Weight 
               
               
                   
                   
               
             
          
           
               
                 ER-Positive 
               
             
          
           
               
                 1 
                 200045_at 
                 ABCF1 
                 ATP-binding cassette, 
                 23 
                 6 
                 6p21.33 
                 −0.1287 
               
               
                   
                   
                   
                 sub-family F (GCN20), 
               
               
                   
                   
                   
                 member 1 
               
               
                 2 
                 218868_at 
                 ACTR3B 
                 ARP3 actin-related 
                 57180 
                 7 
                 7q36.1 
                 −0.073 
               
               
                   
                   
                   
                 protein 3 homolog B 
               
               
                   
                   
                   
                 (yeast) 
               
               
                 3 
                 213532_at 
                 ADAM17 
                 ADAM 
                 6868 
                 2 
                 2p25 
                 −0.3194 
               
               
                   
                   
                   
                 metallopeptidase 
               
               
                   
                   
                   
                 domain 17 
               
               
                 4 
                 217090_at 
                 ADAM3A 
                 ADAM 
                 1587 
                 8 
                 8p11.23 
                 0.3763 
               
               
                   
                   
                   
                 metallopeptidase 
               
               
                   
                   
                   
                 domain 3A (cyritestin 
               
               
                   
                   
                   
                 1) 
               
               
                 5 
                 205013_s_at 
                 ADORA2A 
                 adenosine A2a 
                 135 
                 22 
                 22q11.23 
                 0.1786 
               
               
                   
                   
                   
                 receptor 
               
               
                 6 
                 208042_at 
                 AGGF1 
                 angiogenic factor with 
                 55109 
                 5 
                 5q13.3 
                 0.1425 
               
               
                   
                   
                   
                 G patch and FHA 
               
               
                   
                   
                   
                 domains 1 
               
               
                 7 
                 215789_s_at 
                 AJAP1 
                 adherens junctions 
                 55966 
                 1 
                 1p36.32 
                 −0.111 
               
               
                   
                   
                   
                 associated protein 1 
               
               
                 8 
                 221825_at 
                 ANGEL2 
                 angel homolog 2 
                 90806 
                 1 
                 1q32.3 
                 0.5463 
               
               
                   
                   
                   
                 ( Drosophila ) 
               
               
                 9 
                 202631_s_at 
                 APPBP2 
                 amyloid beta 
                 10513 
                 17 
                 17q21-q23 
                 −0.1027 
               
               
                   
                   
                   
                 precursor protein 
               
               
                   
                   
                   
                 (cytoplasmic tail) 
               
               
                   
                   
                   
                 binding protein 2 
               
               
                 10 
                 200011_s_at 
                 ARF3 
                 ADP-ribosylation 
                 377 
                 12 
                 12q13 
                 0.3083 
               
               
                   
                   
                   
                 factor 3 
               
               
                 11 
                 202492_at 
                 ATG9A 
                 ATG9 autophagy 
                 79065 
                 2 
                 2q35 
                 −0.6807 
               
               
                   
                   
                   
                 related 9 homolog A 
               
               
                   
                   
                   
                 ( S. cerevisiae ) 
               
               
                 12 
                 212930_at 
                 ATP2B1 
                 ATPase, Ca++ 
                 490 
                 12 
                 12q21.3 
                 0.0737 
               
               
                   
                   
                   
                 transporting, plasma 
               
               
                   
                   
                   
                 membrane 1 
               
               
                 13 
                 218789_s_at 
                 C11orf71 
                 chromosome 11 open 
                 54494 
                 11 
                 11q14.2-q14.3 
                 0.2824 
               
               
                   
                   
                   
                 reading frame 71 
               
               
                 14 
                 219022_at 
                 C12orf43 
                 chromosome 12 open 
                 64897 
                 12 
                 12q 
                 0.3528 
               
               
                   
                   
                   
                 reading frame 43 
               
               
                 15 
                 214322_at 
                 CAMK2G 
                 calcium/calmodulin- 
                 818 
                 10 
                 10q22 
                 −0.0176 
               
               
                   
                   
                   
                 dependent protein 
               
               
                   
                   
                   
                 kinase II gamma 
               
               
                 16 
                 218384_at 
                 CARHSP1 
                 calcium regulated 
                 23589 
                 16 
                 16p13.2 
                 0.5253 
               
               
                   
                   
                   
                 heat stable protein 1, 
               
               
                   
                   
                   
                 24 kDa 
               
               
                 17 
                 212586_at 
                 CAST 
                 calpastatin 
                 831 
                 5 
                 5q15 
                 −0.0498 
               
               
                 18 
                 218592_s_at 
                 CECR5 
                 cat eye syndrome 
                 27440 
                 22 
                   
                 −0.4437 
               
               
                   
                   
                   
                 chromosome region, 
               
               
                   
                   
                   
                 candidate 5 
               
               
                 19 
                 218439_s_at 
                 COMMD10 
                 COMM domain 
                 51397 
                 5 
                 5q23.1 
                 0.1117 
               
               
                   
                   
                   
                 containing 10 
               
               
                 20 
                 211808_s_at 
                 CREBBP 
                 CREB binding protein 
                 1387 
                 16 
                 16p13.3 
                 0.1494 
               
               
                 21 
                 209164_s_at 
                 CYB561 
                 cytochrome b-561 
                 1534 
                 17 
                 17q11-qter 
                 −0.3429 
               
               
                 22 
                 203979_at 
                 CYP27A1 
                 cytochrome P450, 
                 1593 
                 2 
                 2q33-qter 
                 −0.3785 
               
               
                   
                   
                   
                 family 27, subfamily A, 
               
               
                   
                   
                   
                 polypeptide 1 
               
               
                 23 
                 216874_at 
                 DKFZp686O1327 
                 hypothetical gene 
                 401014 
                 2 
                 2q22.3 
                 1 
               
               
                   
                   
                   
                 supported by 
               
               
                   
                   
                   
                 BC043549; BX648102 
               
               
                 24 
                 204797_s_at 
                 EML1 
                 echinoderm 
                 2009 
                 14 
                 14q32 
                 0.2037 
               
               
                   
                   
                   
                 microtubule 
               
               
                   
                   
                   
                 associated protein like 
               
               
                   
                   
                   
                 1 
               
               
                 25 
                 218692_at 
                 GOLSYN 
                 Golgi-localized protein 
                 55638 
                 8 
                 8q23.2 
                 0.174 
               
               
                 26 
                 202453_s_at 
                 GTF2H1 
                 general transcription 
                 2965 
                 11 
                 11p15.1-p14 
                 −0.3144 
               
               
                   
                   
                   
                 factor IIH, polypeptide 
               
               
                   
                   
                   
                 1, 62 kDa 
               
               
                 27 
                 221046_s_at 
                 GTPBP8 
                 GTP-binding protein 8 
                 29083 
                 3 
                 3q13.2 
                 −0.118 
               
               
                   
                   
                   
                 (putative) 
               
               
                 28 
                 208886_at 
                 H1F0 
                 H1 histone family, 
                 3005 
                 22 
                 22q13.1 
                 0.028 
               
               
                   
                   
                   
                 member 0 
               
               
                 29 
                 205426_s_at 
                 HIP1 
                 huntingtin interacting 
                 3092 
                 7 
                 7q11.23 
                 0.4815 
               
               
                   
                   
                   
                 protein 1 
               
               
                 30 
                 202983_at 
                 HLTF 
                 helicase-like 
                 6596 
                 3 
                 3q25.1-q26.1 
                 −0.1866 
               
               
                   
                   
                   
                 transcription factor 
               
               
                 31 
                 217145_at 
                 IGKC 
                 immunoglobulin kappa 
                 3514 
                 2 
                 2p12 
                 −0.035 
               
               
                   
                   
                   
                 constant 
               
               
                 32 
                 204863_s_at 
                 IL6ST 
                 interleukin 6 signal 
                 3572 
                 5 
                 5q11 
                 −0.6475 
               
               
                   
                   
                   
                 transducer (gp130, 
               
               
                   
                   
                   
                 oncostatin M receptor) 
               
               
                 33 
                 211817_s_at 
                 KCNJ5 
                 potassium inwardly- 
                 3762 
                 11 
                 11q24 
                 0.3023 
               
               
                   
                   
                   
                 rectifying channel, 
               
               
                   
                   
                   
                 subfamily J, member 
               
               
                   
                   
                   
                 5 
               
               
                 34 
                 201776_s_at 
                 KIAA0494 
                 KIAA0494 
                 9813 
                 1 
                 1pter-p22.1 
                 −0.3831 
               
               
                 35 
                 209212_s_at 
                 KLF5 
                 Kruppel-like factor 5 
                 688 
                 13 
                 13q22.1 
                 −0.1623 
               
               
                   
                   
                   
                 (intestinal) 
               
               
                 36 
                 212271_at 
                 MAPK1 
                 mitogen-activated 
                 5594 
                 22 
                 22q11.2 
                 0.1979 
               
               
                   
                   
                   
                 protein kinase 1 
               
               
                 37 
                 206904_at 
                 MATN1 
                 matrilin 1, cartilage 
                 4146 
                 1 
                 1p35 
                 −0.4397 
               
               
                   
                   
                   
                 matrix protein 
               
               
                 38 
                 206961_s_at 
                 MED20 
                 mediator complex 
                 9477 
                 6 
                 6p21.1 
                 0.1547 
               
               
                   
                   
                   
                 subunit 20 
               
               
                 39 
                 213403_at 
                 MFSD9 
                 major facilitator 
                 84804 
                 2 
                 2q12.1 
                 0.3304 
               
               
                   
                   
                   
                 superfamily domain 
               
               
                   
                   
                   
                 containing 9 
               
               
                 40 
                 209733_at 
                 MID2 
                 midline 2 
                 11043 
                 X 
                 Xq22.3 
                 0.1227 
               
               
                 41 
                 218205_s_at 
                 MKNK2 
                 MAP kinase 
                 2872 
                 19 
                 19p13.3 
                 0.1801 
               
               
                   
                   
                   
                 interacting 
               
               
                   
                   
                   
                 serine/threonine 
               
               
                   
                   
                   
                 kinase 2 
               
               
                 42 
                 209973_at 
                 NFKBIL1 
                 nuclear factor of 
                 4795 
                 6 
                 6p21.3 
                 −0.0068 
               
               
                   
                   
                   
                 kappa light 
               
               
                   
                   
                   
                 polypeptide gene 
               
               
                   
                   
                   
                 enhancer in B-cells 
               
               
                   
                   
                   
                 inhibitor-like 1 
               
               
                 43 
                 217963_s_at 
                 NGFRAP1 
                 nerve growth factor 
                 27018 
                 X 
                 Xq22.2 
                 0.201 
               
               
                   
                   
                   
                 receptor (TNFRSF16) 
               
               
                   
                   
                   
                 associated protein 1 
               
               
                 44 
                 207400_at 
                 NPY5R 
                 neuropeptide Y 
                 4889 
                 4 
                 4q31-q32 
                 0.0984 
               
               
                   
                   
                   
                 receptor Y5 
               
               
                 45 
                 202097_at 
                 NUP153 
                 nucleoporin 153 kDa 
                 9972 
                 6 
                 6p22.3 
                 −0.1197 
               
               
                 46 
                 220631_at 
                 OSGEPL1 
                 O-sialoglycoprotein 
                 64172 
                 2 
                 2q32.2 
                 0.2148 
               
               
                   
                   
                   
                 endopeptidase-like 1 
               
               
                 47 
                 205077_s_at 
                 PIGF 
                 phosphatidylinositol 
                 5281 
                 2 
                 2p21-p16 
                 0.5495 
               
               
                   
                   
                   
                 glycan anchor 
               
               
                   
                   
                   
                 biosynthesis, class F 
               
               
                 48 
                 220811_at 
                 PRG3 
                 proteoglycan 3 
                 10394 
                 11 
                 11q12 
                 0.2689 
               
               
                 49 
                 208733_at 
                 RAB2A 
                 RAB2A, member RAS 
                 5862 
                 8 
                 8q12.1 
                 0.581 
               
               
                   
                   
                   
                 oncogene family 
               
               
                 50 
                 206066_s_at 
                 RAD51C 
                 RAD51 homolog C ( S.   
                 5889 
                 17 
                 17q22-q23 
                 −0.0517 
               
               
                   
                   
                   
                   cerevisiae ) 
               
               
                 51 
                 206290_s_at 
                 RGS7 
                 regulator of G-protein 
                 6000 
                 1 
                 1q23.1 
                 0.0092 
               
               
                   
                   
                   
                 signaling 7 
               
               
                 52 
                 214519_s_at 
                 RLN2 
                 relaxin 2 
                 6019 
                 9 
                 9p24.1 
                 0.103 
               
               
                 53 
                 206805_at 
                 SEMA3A 
                 sema domain, 
                 10371 
                 7 
                 7p12.1 
                 0.1132 
               
               
                   
                   
                   
                 immunoglobulin 
               
               
                   
                   
                   
                 domain (Ig), short 
               
               
                   
                   
                   
                 basic domain, 
               
               
                   
                   
                   
                 secreted, 
               
               
                   
                   
                   
                 (semaphorin) 3A 
               
               
                 54 
                 208941_s_at 
                 SEPHS1 
                 selenophosphate 
                 22929 
                 10 
                 10p14 
                 −0.6301 
               
               
                   
                   
                   
                 synthetase 1 
               
               
                 55 
                 213755_s_at 
                 SKI 
                 v-ski sarcoma viral 
                 6497 
                 1 
                 1q22-q24 
                 −0.1078 
               
               
                   
                   
                   
                 oncogene homolog 
               
               
                   
                   
                   
                 (avian) 
               
               
                 56 
                 202667_s_at 
                 SLC39A7 
                 solute carrier family 
                 7922 
                 6 
                 6p21.3 
                 −0.1376 
               
               
                   
                   
                   
                 39 (zinc transporter), 
               
               
                   
                   
                   
                 member 7 
               
               
                 57 
                 216611_s_at 
                 SLC6A2 
                 solute carrier family 6 
                 6530 
                 16 
                 16q12.2 
                 −0.0064 
               
               
                   
                   
                   
                 (neurotransmitter 
               
               
                   
                   
                   
                 transporter, 
               
               
                   
                   
                   
                 noradrenalin), 
               
               
                   
                   
                   
                 member 2 
               
               
                 58 
                 211805_s_at 
                 SLC8A1 
                 solute carrier family 8 
                 6546 
                 2 
                 2p23-p22 
                 −0.248 
               
               
                   
                   
                   
                 (sodium/calcium 
               
               
                   
                   
                   
                 exchanger), member 
               
               
                   
                   
                   
                 1 
               
               
                 59 
                 205596_s_at 
                 SMURF2 
                 SMAD specific E3 
                 64750 
                 17 
                 17q22-q23 
                 −0.1446 
               
               
                   
                   
                   
                 ubiquitin protein ligase 
               
               
                   
                   
                   
                 2 
               
               
                 60 
                 203054_s_at 
                 TCTA 
                 T-cell leukemia 
                 6988 
                 3 
                 3p21 
                 0.2818 
               
               
                   
                   
                   
                 translocation altered 
               
               
                   
                   
                   
                 gene 
               
               
                 61 
                 218099_at 
                 TEX2 
                 testis expressed 2 
                 55852 
                 17 
                 17q23.3 
                 −0.0149 
               
               
                 62 
                 217121_at 
                 TNKS 
                 tankyrase, TRF1- 
                 8658 
                 8 
                 8p23.1 
                 −0.5943 
               
               
                   
                   
                   
                 interacting ankyrin- 
               
               
                   
                   
                   
                 related ADP-ribose 
               
               
                   
                   
                   
                 polymerase 
               
               
                 63 
                 220415_at 
                 TNNI3K 
                 TNNI3 interacting 
                 51086 
                 1 
                 1p31.1 
                 0.3122 
               
               
                   
                   
                   
                 kinase 
               
               
                 64 
                 209593_s_at 
                 TOR1B 
                 torsin family 1, 
                 27348 
                 9 
                 9q34 
                 −0.0834 
               
               
                   
                   
                   
                 member B (torsin B) 
               
               
                 65 
                 215796_at 
                 TRD@ 
                 T cell receptor delta 
                 6964 
                 14 
                 14q11.2 
                 0.4491 
               
               
                   
                   
                   
                 locus 
               
               
                 66 
                 210541_s_at 
                 TRIM27 
                 tripartite motif- 
                 5987 
                 6 
                 6p22 
                 −0.0174 
               
               
                   
                   
                   
                 containing 27 
               
               
                 67 
                 213563_s_at 
                 TUBGCP2 
                 tubulin, gamma 
                 10844 
                 10 
                 10q26.3 
                 −0.169 
               
               
                   
                   
                   
                 complex associated 
               
               
                   
                   
                   
                 protein 2 
               
               
                 68 
                 221839_s_at 
                 UBAP2 
                 ubiquitin associated 
                 55833 
                 9 
                 9p13.3 
                 −0.0133 
               
               
                   
                   
                   
                 protein 2 
               
               
                 69 
                 213822_s_at 
                 UBE3B 
                 ubiquitin protein ligase 
                 89910 
                 12 
                 12q24.11 
                 −0.4683 
               
               
                   
                   
                   
                 E3B 
               
               
                 70 
                 221746_at 
                 UBL4A 
                 ubiquitin-like 4A 
                 8266 
                 X 
                 Xq28 
                 −0.0227 
               
               
                 71 
                 219740_at 
                 VASH2 
                 vasohibin 2 
                 79805 
                 1 
                 1q32.3 
                 −0.1995 
               
               
                 72 
                 205877_s_at 
                 ZC3H7B 
                 zinc finger CCCH-type 
                 23264 
                 22 
                 22q13.2 
                 −0.9818 
               
               
                   
                   
                   
                 containing 7B 
               
               
                 73 
                 218413_s_at 
                 ZNF639 
                 zinc finger protein 639 
                 51193 
                 3 
                 3q26.33 
                 −0.1572 
               
             
          
           
               
                 ER-Negative 
               
             
          
           
               
                 1 
                 214919_s_at 
                 ANKHD1- 
                 ANKHD1- 
                 404734 
                 5 
                 5q31.3 
                 0.1134 
               
               
                   
                   
                 EIF4EBP3 
                 EIF4EBP3 
               
               
                   
                   
                   
                 readthrough 
               
               
                 2 
                 202955_s_at 
                 ARFGEF1 
                 ADP-ribosylation 
                 10565 
                 8 
                 8q13 
                 0.0616 
               
               
                   
                   
                   
                 factor guanine 
               
               
                   
                   
                   
                 nucleotide- 
               
               
                   
                   
                   
                 exchange factor 
               
               
                   
                   
                   
                 1(brefeldin A- 
               
               
                   
                   
                   
                 inhibited) 
               
               
                 3 
                 203576_at 
                 BCAT2 
                 branched chain 
                 587 
                 19 
                 19q13 
                 −0.1544 
               
               
                   
                   
                   
                 aminotransferase 
               
               
                   
                   
                   
                 2, mitochondrial 
               
               
                 4 
                 202047_s_at 
                 CBX6 
                 chromobox 
                 23466 
                 22 
                 22q13.1 
                 0.0673 
               
               
                   
                   
                   
                 homolog 6 
               
               
                 5 
                 220674_at 
                 CD22 
                 CD22 molecule 
                 933 
                 19 
                 19q13.1 
                 0.1582 
               
               
                 6 
                 208022_s_at 
                 CDC14B 
                 CDC14 cell 
                 8555 
                 9 
                 9q22.32-q22.33 
                 −0.4312 
               
               
                   
                   
                   
                 division cycle 14 
               
               
                   
                   
                   
                 homolog B ( S.   
               
               
                   
                   
                   
                   cerevisiae ) 
               
               
                 7 
                 204250_s_at 
                 CEP164 
                 centrosomal 
                 22897 
                 11 
                 11q23.3 
                 0.1607 
               
               
                   
                   
                   
                 protein 164 kDa 
               
               
                 8 
                 218597_s_at 
                 CISD1 
                 CDGSH iron sulfur 
                 55847 
                 10 
                 10q21.1 
                 0.6177 
               
               
                   
                   
                   
                 domain 1 
               
               
                 9 
                 206073_at 
                 COLQ 
                 collagen-like tail 
                 8292 
                 3 
                 3p25 
                 −0.033 
               
               
                   
                   
                   
                 subunit (single 
               
               
                   
                   
                   
                 strand of 
               
               
                   
                   
                   
                 homotrimer) of 
               
               
                   
                   
                   
                 asymmetric 
               
               
                   
                   
                   
                 acetylcholinesterase 
               
               
                 10 
                 208303_s_at 
                 CRLF2 
                 cytokine receptor- 
                 64109 
                 X, Y 
                 Xp22.3 
                 −0.0413 
               
               
                   
                   
                   
                 like factor 2 
               
               
                 11 
                 217047_s_at 
                 FAM13A 
                 family with 
                 10144 
                 4 
                 4q22.1 
                 −0.1603 
               
               
                   
                   
                   
                 sequence 
               
               
                   
                   
                   
                 similarity 13, 
               
               
                   
                   
                   
                 member A 
               
               
                 12 
                 212484_at 
                 FAM89B 
                 family with 
                 23625 
                 11 
                 11q23 
                 −0.0232 
               
               
                   
                   
                   
                 sequence 
               
               
                   
                   
                   
                 similarity 89, 
               
               
                   
                   
                   
                 member B 
               
               
                 13 
                 204437_s_at 
                 FOLR1 
                 folate receptor 1 
                 2348 
                 11 
                 11q13.3-q14.1 
                 −1 
               
               
                   
                   
                   
                 (adult) 
               
               
                 14 
                 203314_at 
                 GTPBP6 
                 GTP binding 
                 8225 
                 X, Y 
                 Xp22.33 
                 0.0926 
               
               
                   
                   
                   
                 protein 6 (putative) 
               
               
                 15 
                 210964_s_at 
                 GYG2 
                 glycogenin 2 
                 8908 
                 X 
                 Xp22.3 
                 −0.364 
               
               
                 16 
                 212431_at 
                 HMGXB3 
                 HMG box domain 
                 22993 
                 5 
                 5q32 
                 0.0875 
               
               
                   
                   
                   
                 containing 3 
               
               
                 17 
                 211616_s_at 
                 HTR2A 
                 5- 
                 3356 
                 13 
                 13q14-q21 
                 0.1049 
               
               
                   
                   
                   
                 hydroxytryptamine 
               
               
                   
                   
                   
                 (serotonin) 
               
               
                   
                   
                   
                 receptor 2A 
               
               
                 18 
                 204990_s_at 
                 ITGB4 
                 integrin, beta 4 
                 3691 
                 17 
                 17q25 
                 −0.2769 
               
               
                 19 
                 207012_at 
                 MMP16 
                 matrix 
                 4325 
                 8 
                 8q21.3 
                 0.1034 
               
               
                   
                   
                   
                 metallopeptidase 
               
               
                   
                   
                   
                 16 (membrane- 
               
               
                   
                   
                   
                 inserted) 
               
               
                 20 
                 212251_at 
                 MTDH 
                 Metadherin 
                 92140 
                 8 
                 8q22.1 
                 0.7935 
               
               
                 21 
                 202039_at 
                 MYO18A 
                 myosin XVIIIA 
                 399687 
                 17 
                 17q11.2 
                 0.1596 
               
               
                 22 
                 222018_at 
                 NACA 
                 nascent 
                 4666 
                 12 
                 12q23-q24.1 
                 0.1843 
               
               
                   
                   
                   
                 polypeptide- 
               
               
                   
                   
                   
                 associated 
               
               
                   
                   
                   
                 complex alpha 
               
               
                   
                   
                   
                 subunit 
               
               
                 23 
                 209519_at 
                 NCBP1 
                 nuclear cap 
                 4686 
                 9 
                 9q34.1 
                 −0.4186 
               
               
                   
                   
                   
                 binding protein 
               
               
                   
                   
                   
                 subunit 1, 80 kDa 
               
               
                 24 
                 213032_at 
                 NFIB 
                 nuclear factor I/B 
                 4781 
                 9 
                 9p24.1 
                 0.1829 
               
               
                 25 
                 215818_at 
                 NUDT7 
                 nudix (nucleoside 
                 283927 
                 16 
                 16q23.1 
                 −0.1766 
               
               
                   
                   
                   
                 diphosphate linked 
               
               
                   
                   
                   
                 moiety X)-type 
               
               
                   
                   
                   
                 motif 7 
               
               
                 26 
                 218271_s_at 
                 PARL 
                 presenilin 
                 55486 
                 3 
                 3q27.1 
                 −0.0708 
               
               
                   
                   
                   
                 associated, 
               
               
                   
                   
                   
                 rhomboid-like 
               
               
                 27 
                 204049_s_at 
                 PHACTR2 
                 phosphatase and 
                 9749 
                 6 
                 6q24.2 
                 0.1352 
               
               
                   
                   
                   
                 actin regulator 2 
               
               
                 28 
                 217806_s_at 
                 POLDIP2 
                 polymerase (DNA- 
                 26073 
                 17 
                 17q11.2 
                 0.3128 
               
               
                   
                   
                   
                 directed), delta 
               
               
                   
                   
                   
                 interacting protein 
               
               
                   
                   
                   
                 2 
               
               
                 29 
                 206653_at 
                 POLR3G 
                 polymerase (RNA) 
                 10622 
                 5 
                 5q14.3 
                 −0.3632 
               
               
                   
                   
                   
                 III (DNA directed) 
               
               
                   
                   
                   
                 polypeptide G 
               
               
                   
                   
                   
                 (32 kD) 
               
               
                 30 
                 210831_s_at 
                 PTGER3 
                 prostaglandin E 
                 5733 
                 1 
                 1p31.2 
                 −0.0066 
               
               
                   
                   
                   
                 receptor 3 
               
               
                   
                   
                   
                 (subtype EP3) 
               
               
                 31 
                 213933_at 
                 PTGER3 
                 prostaglandin E 
                 5733 
                 1 
                 1p31.2 
                 0.0187 
               
               
                   
                   
                   
                 receptor 3 
               
               
                   
                   
                   
                 (subtype EPS) 
               
               
                 32 
                 208393_s_at 
                 RAD50 
                 RAD50 homolog 
                 10111 
                 5 
                 5q31 
                 −0.1057 
               
               
                   
                   
                   
                 ( S. cerevisiae ) 
               
               
                 33 
                 221705_s_at 
                 SIKE1 
                 suppressor of 
                 80143 
                 1 
                 1p13.2 
                 −0.2882 
               
               
                   
                   
                   
                 IKBKE 1 
               
               
                 34 
                 211112_at 
                 SLC12A4 
                 solute carrier 
                 6560 
                 16 
                 16q22.1 
                 −0.1596 
               
               
                   
                   
                   
                 family 12 
               
               
                   
                   
                   
                 (potassium/chloride 
               
               
                   
                   
                   
                 transporters), 
               
               
                   
                   
                   
                 member 4 
               
               
                 35 
                 215294_s_at 
                 SMARCA1 
                 SWI/SNF related, 
                 6594 
                 X 
                 Xq25 
                 0.056 
               
               
                   
                   
                   
                 matrix associated, 
               
               
                   
                   
                   
                 actin dependent 
               
               
                   
                   
                   
                 regulator of 
               
               
                   
                   
                   
                 chromatin, 
               
               
                   
                   
                   
                 subfamily a, 
               
               
                   
                   
                   
                 member 1 
               
               
                 36 
                 215458_s_at 
                 SMURF1 
                 SMAD specific E3 
                 57154 
                 7 
                 7q22.1 
                 −0.1767 
               
               
                   
                   
                   
                 ubiquitin protein 
               
               
                   
                   
                   
                 ligase 1 
               
               
                 37 
                 215860_at 
                 SYT12 
                 synaptotagmin XII 
                 91683 
                 11 
                 11q13.2 
                 −0.023 
               
               
                 38 
                 222173_s_at 
                 TBC1D2 
                 TBC1 domain 
                 55357 
                 9 
                 9q22.33 
                 −0.124 
               
               
                   
                   
                   
                 family, member 2 
               
               
                 39 
                 204147_s_at 
                 TFDP1 
                 transcription factor 
                 7027 
                 13 
                 13q34 
                 0.1725 
               
               
                   
                   
                   
                 Dp-1 
               
               
                 40 
                 206260_at 
                 TGM4 
                 transglutaminase 
                 7047 
                 3 
                 3p22-p21.33 
                 0.2701 
               
               
                   
                   
                   
                 4 (prostate) 
               
               
                 41 
                 212963_at 
                 TM2D1 
                 TM2 domain 
                 83941 
                 1 
                 1p31.3 
                 0.1779 
               
               
                   
                   
                   
                 containing 1 
               
               
                 42 
                 213882_at 
                 TM2D1 
                 TM2 domain 
                 83941 
                 1 
                 1p31.3 
                 0.1487 
               
               
                   
                   
                   
                 containing 1 
               
               
                 43 
                 219182_at 
                 TMEM231 
                 transmembrane 
                 79583 
                 16 
                 16q23.1 
                 −0.2436 
               
               
                   
                   
                   
                 protein 231 
               
               
                 44 
                 209344_at 
                 TPM4 
                 tropomyosin 4 
                 7171 
                 19 
                 19p13.1 
                 0.3404 
               
               
                 45 
                 217056_at 
                 TRD@ 
                 T cell receptor 
                 6964 
                 14 
                 14q11.2 
                 0.0697 
               
               
                   
                   
                   
                 delta locus 
               
               
                 46 
                 217065_at 
                 TRD@ 
                 T cell receptor 
                 6964 
                 14 
                 14q11.2 
                 0.0128 
               
               
                   
                   
                   
                 delta locus 
               
               
                 47 
                 203701_s_at 
                 TRMT1 
                 TRM1 tRNA 
                 55621 
                 19 
                 19p13.2 
                 0.187 
               
               
                   
                   
                   
                 methyltransferase 
               
               
                   
                   
                   
                 1 homolog ( S.   
               
               
                   
                   
                   
                   cerevisiae ) 
               
               
                 48 
                 201797_s_at 
                 VARS 
                 valyl-tRNA 
                 7407 
                 6 
                 6p21.3 
                 −0.5888 
               
               
                   
                   
                   
                 synthetase 
               
               
                 49 
                 208453_s_at 
                 XPNPEP1 
                 X-prolyl 
                 7511 
                 10 
                 10q25.3 
                 0.5107 
               
               
                   
                   
                   
                 aminopeptidase 
               
               
                   
                   
                   
                 (aminopeptidase 
               
               
                   
                   
                   
                 P) 1, soluble 
               
               
                 50 
                 213081_at 
                 ZBTB22 
                 zinc finger and 
                 9278 
                 6 
                 6p21.3 
                 0.3968 
               
               
                   
                   
                   
                 BTB domain 
               
               
                   
                   
                   
                 containing 22 
               
               
                 51 
                 206448_at 
                 ZNF365 
                 zinc finger protein 
                 22891 
                 10 
                 10q21.2 
                 0.3809 
               
               
                   
                   
                   
                 365 
               
               
                 52 
                 212867_at 
                 — 
                 — 
                 — 
                 8 
                 8q13.3 
                 0.4115 
               
               
                 53 
                 213879_at 
                 — 
                 — 
                 — 
                 17 
                 17q25.1 
                 −0.4574 
               
               
                 54 
                 222174_at 
                 — 
                 — 
                 — 
                 14 
                 — 
                 0.017 
               
               
                   
               
             
          
         
       
     
       Example 9 
     Prediction of Chemotherapy Outcomes Combining Poor Response as Endpoint 
       [0139]    Survival outcomes of patients predicted as responders and non-responders were assessed by using the predictor of RCB-III described in Example 8 used as a combined algorithm with predictors of Examples 2 and 4 and the sensitivity to endocrine therapy (SET) index of Example 7. Survival is defined by distant relapse-free survival (DRFS) over a period of about 80 months. These patients have undergone surgery where it was considered appropriate and the ER-positive patients received hormonal therapy (tamoxifen) for 5 years after the surgery. ER-negative patients did not receive any treatment post-surgery. We combined the individual predictions into a testing algorithm ( FIG. 9 ) for predicted sensitivity to adjuvant treatment of HER2-negative breast cancer with taxane-anthracycline chemotherapy: 1) sensitivity to endocrine therapy (SET) assessed based on the published 165-gene index of the most ER-correlated genes (high or intermediate SET index) that independently predicts survival following adjuvant endocrine or chemoendocrine therapy 13 ; 2) resistance to chemotherapy predicted either by early distant relapse events or by extensive residual disease after neoadjuvant chemotherapy; and 3) sensitivity (pathologic response) to chemotherapy. 
         [0140]    The predictive test (algorithm) was applied to the discovery cohort of 310 samples ( FIG. 10A ) and then evaluated in the independent validation cohort of 198 patients (99% clinical Stage II-III) who received sequential taxane-anthracycline chemotherapy then endocrine therapy (if ER+). The validation cohort had a pathologic response rate of pCR 25% and of pCR or RCB-I 30%, median follow up of 3 years, and an average 3-year baseline DRFS of 79% (95% CI 74 to 85). The 3-year DRFS (NPV) was 92% (95% CI 85 to 100), and there was significant absolute risk reduction (ARR) of 18% (95% CI 6 to 28), in 28% of patients who were predicted to be treatment-sensitive. The 3-year point estimate of DRFS for those predicted to be treatment-insensitive was 75% (95% CI 67 to 82). Overall, we observed a significant association between predicted sensitivity to treatment and DRFS (p=0.002;  FIG. 10B ). In 91 tumors with low SET and evaluated for RCB, excellent response from chemotherapy (pCR or RCB-I) was observed in 56% (95% CI 31 to 78) of those predicted to be treatment-sensitive. 
         [0141]    Of note, 3-year DRFS in patients predicted to be treatment-sensitive at the time of diagnosis was similar to the 3-year DRFS of 93% (95% CI 85 to 100) in the 21% of patients in the validation cohort who achieved pathologic complete response (pCR) after completion of neoadjuvant chemotherapy. Also, 3-year DRFS for predicted treatment-insensitive was identical to the 3-year DRFS of 75% (95% CI 68 to 83) in those who had residual disease (RD) ( FIG. 10C ). Furthermore, DRFS estimates for the predicted treatment-sensitive and the actual pCR groups were unchanged at 5 years, and were identical at 65% (95% CI 56 to 75) for the predicted treatment-insensitive and for the actual RD groups: 
         [0142]    Treatment Sensitivity According to ER Status: There were 30% and 26% of patients with predicted sensitivity to treatment in the ER+/HER2- and ER−/HER2-subsets, respectively, and both had significantly favorable prognosis ( FIG. 11A-B ). The treatment sensitive patients identified by test in the ER+/HER2-subset had excellent DRFS (NPV) of 97% (95% CI 91 to 100) and a significant ARR of 11% (95% CI 0.1 to 21) at 3 years of follow up. In the low SET subset of ER+/HER2-, PPV for pathologic response was 42% (95% CI 15 to 72) in 20% who were predicted treatment-sensitive. For ER−/HER2-patients, the PPV for 3-year relapse was 43% (95% CI 28 to 55) if predicted treatment-insensitive. Patients predicted to be treatment-sensitive had considerably improved 3-year DRFS (NPV 83% (95% CI 68 to 100)) and significant ARR of 26% (95% CI 4 to 48) overall, and PPV for pathologic response of 83% (95% CI 36-100). 
         [0143]    Performance of the Predictive Test in Other Relevant Subsets The association between predicted treatment sensitivity and DRFS appears to be unrelated to the type of taxane therapy administered ( FIG. 11C-D ). The 3-year DRFS was 90% (95% CI 80 to 100) in the subset who received 12 cycles of weekly paclitaxel, and 96% (95% CI 88 to 100) for 4 cycles of 3-weekly docetaxel with capecitabine. Also, the 3-year DRFS was 93% (95% CI 84 to 100) in 128 clinically node-positive patients, with significantly improved DRFS compared to those predicted to be insensitive (p=0.003). The 3-year DRFS was 91% (95% CI 81 to 100) in 70 clinically node-negative patients, but was not significantly different from predicted insensitivity. 
         [0144]    Comparison of the Predictive Test with Clinical-Pathologic Parameters Genomic predictions were independently and significantly associated with risk of distant relapse or death (sensitive versus insensitive; HR 0.19; 95% CI 0.07 to 0.55; p=0.002), after adjusting for standard clinical-pathologic parameters (Table 5). Addition of the genomic prediction to a multivariate Cox model of the clinical-pathologic factors significantly increased the model&#39;s predictive utility (likelihood ratio of complete model versus clinical model 13.8, p&lt;0.001). In this model, higher clinical tumor stage (tumor stage T3 or T4 versus T1 or T2; HR 2.13; 
         [0145]    95% CI 1.13 to 4.02; p=0.02) and ER-negative status (ER status positive versus negative; HR 0.34; 95% CI 0.18 to 0.65; p=0.001) were associated with statistically significant greater risk of distant relapse or death. 
         [0000]    
       
         
               
             
               
               
               
             
               
               
               
             
           
               
                 TABLE 5 
               
             
             
               
                   
               
               
                 MULTIVARIATE Cox Regression Analysis 
               
               
                 of Association with DRFS 
               
             
          
           
               
                   
                 Validation Cohort 
                   
               
               
                   
                 (N = 183)* 
                 P 
               
               
                 Factor 
                 Hazard Ratio (95% CI) 
                 value 
               
               
                   
               
             
          
           
               
                 Age (&gt;50 vs ≦50) 
                 0.53 (0.27 to 1.04) 
                 0.063 
               
               
                 Clinical Nodal Status (pos vs neg) 
                 1.76 (0.84 to 3.67) 
                 0.134 
               
               
                 Clinical Tumor Stage (T3 or T4 vs T1 
                 2.13 (1.13 to 4.02) 
                 0.020 
               
               
                 or T2) 
               
               
                 Histologic Grade (3 vs 1 or 2) 
                 0.64 (0.32 to 1.29) 
                 0.208 
               
               
                 ER Status (THC positive vs negative) 
                 0.34 (0.18 to 0.65) 
                 0.001 
               
               
                 Taxane (docetaxel vs paclitaxel) 
                 0.92 (0.49 to 1.73) 
                 0.795 
               
               
                 Prediction (Rx Sensitive vs Insensitive) 
                 0.19 (0.07 to 0.56) 
                 0.002 
               
               
                   
               
               
                 *Fifteen cases were excluded from the multivariate analysis due to incomplete data. Likelihood ratio test for the addition of Genomic Prediction to the model was 13.8 on one degree of freedom, p = 0.0002. 
               
               
                 The Hazard Ratio is a measure of the risk of distant relapse or death; vs, versus; ER, estrogen receptor. 
               
             
          
         
       
     
       Example 10 
     Comparison with Other Predictive Genomic Signatures 
       [0146]    The entire predictive test algorithm described in  FIG. 9  had PPV of 56% (95% CI 31 to 78) for pathologic response prediction in the validation cohort (Table 6) after excluding patients with predicted endocrine sensitivity (high or intermediate SET). We also evaluated other phenotypic predictors that have published association with higher probability of pCR to neoadjuvant chemotherapy, have a pre-defined threshold for prediction of pCR that was based on Affymetrix microarray data, and that we have confirmed to be correctly calculated in our hands: the 96-gene genomic grade index (GGI) to define high versus low grade (high GGI predicted pCR) (Liedtke et al., 2009), a 52-gene signature (PAM50) to assign intrinsic subtype (basal-like, HER2 and luminal B subtypes predicted pCR) (Parker et al., 2009), and a 30-gene signature (DLDA30) developed to predict pCR versus residual disease (Hess, Anderson et al., 2006). These tests were significantly predictive of pathologic response in the discovery cohort (lower 95% confidence limit of the PPV greater than the baseline pCR rate of 19% and pCR or RCB-I rate of 29%), and the tests had NPV of 84% or greater (Table 6). Performance in the validation cohort was similar, but not all tests had PPV and NPV that was significantly greater than the baseline response rates (pCR rate of 25% and pCR or RCB-I rate of 30%). The entire prediction algorithm ( FIGS. 9 ), demonstrated significantly better DRFS for patients who were predicted to be treatment-sensitive (Table 6). The other tests (GGI, PAM50, DLDA30) demonstrated worse DRFS for patients who were predicted to have chemosensitive breast cancer ( FIG. 12 ), as indicated by their negative ARR (Table 6). 
         [0147]    The performance of the different genomic signatures for predicting 3-year DRFS was compared on the basis of the diagnostic likelihood ratio (DLR), which is clinically useful statistic for summarizing the diagnostic accuracy of tests (Deeks and Altman, 2004). The DLR+ summarizes how many times a positive test (predicted distant relapse or treatment insensitive) is more likely among patients who experience distant metastasis within 3 years, compared to those who do not. The DLR− is a similar metric for a negative test (predicted absence of relapse or treatment sensitive), which is more relevant in the context of this test. A clinically useful test associated with the presence of relapse should have DLR+&gt;1, whereas a test associated with the absence of relapse should have DLR−&lt;1. Another useful property of the DLR is that it allows calculation of the post-test odds of relapse, simply by multiplying the pre-test odds of relapse by the DLR. The odds ratio (OD), defined as DLR+/DLR−, is also related to the coefficient of a logistic regression model of the binary genomic test for predicting the binary relapse outcome. The values summarized in Table 7 were calculated from the K-M estimates of DRFS for the two predicted groups from each genomic predictor, for the overall validation cohort and for the ER-positive and ER-negative subsets. 
         [0148]    The predictive test of Example 9 (last entry in Table 7) is the only test with a significant DLR− (0.33, 0.27, 0.35 in the overall validation cohort and ER+, ER− subsets), indicating a 3-fold reduction in the odds of distant relapse in the presence of a negative test result (predicted treatment sensitive). The DLR+ of the genomic predictor was &gt;1 in all 3 cohorts, but was not significant. The ER-stratified predictor of pCR/RCB-I showed consistent but not significant metrics. The first three genomic predictors showed paradoxical statistics (DLR+&lt;1 and DLR−&gt;1), i.e. a positive test result (predicted relapse) was associated with lower odds of relapse and vice versa. 
         [0000]    
       
         
               
             
               
               
               
             
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 6 
               
             
             
               
                   
               
               
                 Performance of Genomic Signatures for Predicting Pathologic Response and 3-year DRFS 
               
             
          
           
               
                   
                 Prediction of Pathologic Response 
                 Prediction of Distant Relapse or Death Within 3 Years 
               
             
          
           
               
                   
                 Discovery Cohort 
                 Validation Cohort 
                 Discovery Cohort (N = 310) 
                 Validation Cohort (N = 198) 
               
             
          
           
               
                   
                 N 
                   
                   
                 N 
                   
                 % 
                 % NPV 
                   
                   
                 % NPV 
                   
                   
               
               
                 Predictor 
                 (% Resp) 
                 % PPV 
                 % NPV 
                 (% Resp) 
                 % PPV 
                 NPV 
                 (DRFS) 
                 % PPV 
                 % ARR 
                 (DRFS) 
                 % PPV 
                 % ARR 
               
               
                   
               
               
                 Genomic 
                 301 
                 36 
                 88 
                 101  
                 40 
                 84 
                 72 
                 14 
                 −14 
                 72 
                  7 
                 −21 
               
               
                 Grade 
                  (29) 
                 (30 to 
                 (79 to 
                 (30) 
                 (28 to 
                 (70 to 
                 (65 to 
                 (6 to 
                 (−25 to 
                 (64 to 
                 (1 to 
                 (−30 to 
               
               
                 Index 
                   
                 43) 
                 93) 
                   
                 54) 
                 93) 
                 79) 
                 22) 
                 −3) 
                 80) 
                 13) 
                 −10) 
               
               
                 (High) 
               
               
                 Genomic 
                 301 
                 40 
                 85 
                 101  
                 40 
                 78 
                 66 
                 13 
                 −20 
                 72 
                 12 
                 −16 
               
               
                 Subtype 
                  (29) 
                 (32 to 
                 (78 to 
                 (30) 
                 (25 to 
                 (65 to 
                 (58 to 
                 (7 to 
                 (−31 to 
                 (62 to 
                 (6 to 
                 (−27 to 
               
               
                 Classifier 
                   
                 48) 
                 90) 
                   
                 56) 
                 87) 
                 76) 
                 19) 
                 −10) 
                 81) 
                 20) 
                 −5) 
               
               
                 (Luminal B 
               
               
                 or Basal- 
               
               
                 like) 
               
               
                 Genomic 
                 301 
                 46 
                 83 
                 101  
                 40 
                 75 
                 62 
                 15 
                 −24 
                 62 
                 10 
                 −28 
               
               
                 Predictor 
                  (29) 
                 (37 to 
                 (77 to 
                 (30) 
                 (24 to 
                 (63 to 
                 (52 to 
                 (9 to 
                 (−36 to 
                 (50 to 
                 (4 to 
                 (−41 to 
               
               
                 of pCR 
                   
                 55) 
                 88) 
                   
                 58) 
                 85) 
                 73) 
                 20) 
                 −12) 
                 73) 
                 16) 
                 −16) 
               
               
                 ER- 
                 301 
                 69 
                 100  
                 101  
                 42 
                 81 
                 85 
                 30 
                   15 
                 82 
                 24 
                    5 
               
               
                 stratified 
                  (29) 
                 (60 to 
                 (98 to 
                 (30) 
                 (28 to 
                 (68 to 
                 (78 to 
                 (22 to 
                 (4 to 
                 (74 to 
                 (14 to 
                 (−7 to 
               
               
                 Genomic 
                   
                 77) 
                 100) 
                   
                 57) 
                 91) 
                 93) 
                 37) 
                 25) 
                 90) 
                 32) 
                 16) 
               
               
                 Predictor of 
               
               
                 pCR/RCB-I  §   
               
               
                 Predictive 
                 256 
                 78 
                 84 
                 91 
                 56 
                 73 
                 95 
                 36 
                   31 
                 92 
                 25 
                   18 
               
               
                 Test (Rx 
                  (31) 
                 (66 to 
                 78 to 
                 (33) 
                 (31 to 
                 (61 to 
                 (91 to 
                 (27 to 
                 (22 to 
                 (85 to 
                 (18 to 
                 (6 to 
               
               
                 Sensitive)  §   ¶   #   
                   
                 88) 
                 89) 
                   
                 78) 
                 82) 
                 100) 
                 44) 
                 41) 
                 100) 
                 33) 
                 28) 
               
               
                   
               
               
                 N, number or patients evaluated; %, percent; Resp, pathologic response rate; PPV, positive predictive value; NPV, negative predictive value; DRFS, distant relapse-free survival estimate at 3 years; ARR, absolute risk reduction for event within 3 years if predicted to be treatment-sensitive (−, any negative risk reduction was in favor of predicted treatment-insensitive). The 95% confidence intervals (parentheses) for PPV and NPV for prediction of pathologic response were based on binomial approximation. 
               
               
                   §  Performance of the pCR predictor on the discovery cohort is optimistically biased because the predictor was trained on a subset of these samples. Performance of the pCR/RCB-I predictor and of the overall genomic prediction test on the discovery cohort represents resubstitution performance, since the predictors were trained on the same cohort. 
               
               
                   ¶  Genomic prediction of pathologic response was evaluated in the SET-Low subset in both cohorts. 
               
               
                   #  Performance of the predictive test is optimistically biased in the discovery cohort because a component of the test was trained on DRFS events to define resistance. 
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 7 
               
             
             
               
                   
               
               
                 Comparison of Genomic Signatures Performance for Predicting 3-year DRFS 
               
             
          
           
               
                   
                 Validation Cohort (N = 198) 
                 ER-positive Subset (N = 123) 
                 ER-negative Subset (N = 74) 
               
             
          
           
               
                   
                 DLR+ 
                 DLR− 
                 OR 
                 DLR+ 
                 DLR− 
                 OR 
                 DLR+ 
                 DLR− 
                 OR 
               
               
                 Predictor 
                 (95% CI) 
                 (95% CI) 
                 (95% CI) 
                 (95% CI) 
                 (95% CI) 
                 (95% CI) 
                 (95% CI) 
                 (95% CI) 
                 (95% CI) 
               
               
                   
               
               
                 Genomic 
                 .30 
                 1.50 
                 .2  
                 .62 
                 1.41 
                 .44 
                 .17 
                 1.20 
                 .14 
               
               
                 Grade 
                 (.06 to 
                 (.97 to 
                 (.04 to 
                 (.13 to 
                 (.45 to 
                 (.06 to 
                 (.0 to 
                 (.73 to 
                 (.0 to 
               
               
                 Index 
                 0.63) 
                 2.20) 
                 .47) 
                 1.33) 
                 2.88) 
                 1.71) 
                 .77) 
                 2.01) 
                 .70) 
               
               
                 (High) 
               
               
                 Genomic 
                 .55 
                 1.53 
                 .36 
                 .81 
                 1.32 
                 .62 
                 .60 
                 1.21 
                 .50 
               
               
                 Subtype 
                 (.24 to 
                 (.97 to 
                 (.14 to 
                 (.23 to 
                 (.36 to 
                 (.15 to 
                 (.15 to 
                 (.69 to 
                 (.12 to 
               
               
                 Classifier 
                 .96) 
                 2.33) 
                 .74) 
                 1.60) 
                 2.64) 
                 2.80) 
                 1.56) 
                 2.14) 
                 1.50) 
               
               
                 (Luminal B 
               
               
                 or Basal- 
               
               
                 like) 
               
               
                 Genomic 
                 .43 
                 2.39 
                 .18 
                 .86 
                 1.98 
                 .43 
                 .28 
                 1.31 
                 .21 
               
               
                 Predictor 
                 (.18 to 
                 (1.41 to 
                 (.07 to 
                 (.33 to 
                 (.01 to 
                 (.10 to 
                 (.0 to 
                 (.77 to 
                 (.0 to 
               
               
                 of pCR 
                 .73) 
                 3.96) 
                 .37) 
                 1.50) 
                 6.37) 
                 88.1) 
                 .92) 
                 2.25) 
                 .77) 
               
               
                 ER- 
                 1.18  
                  .85 
                 1.39  
                 1.07  
                  .93 
                 1.15  
                 1.70  
                  .68 
                 2.50  
               
               
                 stratified 
                 (.65 to 
                 (.46 to 
                 (.66 to 
                 (.35 to 
                 (.16 to 
                 (.31 to 
                 (.79 to 
                 (.32 to 
                 (.88 to 
               
               
                 Genomic 
                 1.83) 
                 1.36) 
                 2.93) 
                 2.11) 
                 2.03) 
                 6.61) 
                 3.65) 
                 1.27) 
                 7.03) 
               
               
                 Predictor of 
               
               
                 pCR/RCB-I 
               
               
                 Predictive 
                 1.32  
                  .33 
                 4.01  
                 1.33  
                  .27 
                 4.88  
                 1.33  
                  .35 
                 3.78  
               
               
                 Test (Rx 
                 (.84 to 
                 (.07 to 
                 (1.55 to 
                 (.56 to 
                 (.01 to 
                 (1.05 to 
                 (.76 to 
                 (.01 to 
                 (1.16 to 
               
               
                 Sensitive) 
                 1.93) 
                 .78) 
                 21.6) 
                 2.34) 
                 0.98) 
                 206) 
                 2.30) 
                 .99) 
                 138) 
               
               
                   
               
               
                 DLR: Diagnostic likelihood ratio; DLR+: DLR given a positive test result (predicted treatment insensitive); DLR−: DLR given a negative test result (predicted treatment sensitive); OR: odd ratio of a positive test result over a negative test result (DLR+/DLR−); CI: confidence interval. Confidence intervals were calculated through bootstrap with 999 iterations 
               
             
          
         
       
     
       Example 11 
     Analysis of Patient Samples Using Predictor for Assessing Outcome of Therapy 
       [0149]      FIG. 13  shows a schematic of how a patient sample may be collected at the time of biopsy or at the time of surgery, and analyzed in a laboratory to produce a result from the predictor to be used to assess likely outcome of chemotherapy. A tumor sample, collected as a needle biopsy or a fresh tumor sample from the excised tumor after surgery is added to a pre-supplied tube containing RNA preservative solution. The tube is shipped overnight to a qualified laboratory for analysis of gene expression. 
         [0150]    RNA is extracted in a manner described in Example 1. A gene chip such as Affymetrix U133A (Affymetrix, Inc., Santa Clara, CA) is used to analyze the expression levels of genes of Tables 2, 3 and 4. The resulting expression values are then normalized as described in Examples 2, 4, and 8, and weighted according to their respective coefficients to calculate the predictor score. Using cut-off values for the predictor score, a patient&#39;s tumor can be classified as either a High Score (good outcome from therapy) or a Low Score (poor outcome of therapy). The analyses could be completed within 5-7 days from receipt of a tumor sample to provide a report on results to the requesting physician. Decisions may be made by physicians regarding the inclusion of a certain therapy if the likely outcome is good or alternatively, to consider additional aggressive therapy regimens for the patient in the likely event of a poor outcome. 
       REFERENCES 
       [0151]    The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference.
   Armitage, P., G. Berry &amp; J. N. S. Matthews (2002). Statistical Methods In Medical Research, Fourth Edition. Blackwell Science.   Ayers, M., W. F. Symmans, et al. (2004). “Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer.” J Clin Oncol 22(12): 2284-93.   Bear, H. D., S. Anderson, et al. (2006). “Sequential preoperative or postoperative docetaxel added to preoperative doxorubicin plus cyclophosphamide for operable breast cancer: National Surgical Adjuvant Breast and Bowel Project Protocol B-27.” J Clin Oncol 24(13): 2019-27.   Bild, A. H., G. Yao, et al. (2006). “Oncogenic pathway signatures in human cancers as a guide to targeted therapies.” Nature 439(7074): 353-7.   Carey, L. A., R. Metzger, et al. (2005). “American Joint Committee on Cancer tumor-node-metastasis stage after neoadjuvant chemotherapy and breast cancer outcome.” J Natl Cancer Inst 97(15): 1137-42.   Carlson, R. W., B. O. Anderson, et al. (2000). “NCCN practice guidelines for breast cancer.” Oncology (Williston Park) 14(11A): 33-49.   Chang, J. C., E. C. Wooten, et al. (2003). Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362(9381): 362-9.   Deeks J J, Altman D G. Diagnostic tests 4: likelihood ratios.  BMJ . Jul 17 2004;329(7458):168-169   Dudoit, S., J. Fridlyand, et al. (2002). “Comparison of discrimination methods for the classification of tumors using gene expression data.” J Am Stat Assoc 97: 77-87.   Fisher, B., J. Bryant, et al. (1998). “Effect of preoperative chemotherapy on the outcome of women with operable breast cancer.” J Clin Oncol 16(8): 2672-85.   Goldhirsch, A., W. C. Wood, et al. (2003). “Meeting highlights: updated international expert consensus on the primary therapy of early breast cancer.” J Clin Oncol 21(17): 3357-65.   Hennessy, B. T., G. N. Hortobagyi, et al. (2005). “Outcome after pathologic complete eradication of cytologically proven breast cancer axillary node metastases following primary chemotherapy.” J Clin Oncol 23(36): 9304-11.   Hennessy, B. T. and L. Pusztai (2005). “Adjuvant therapy for breast cancer.” Minerva Ginecol 57(3): 305-26.   Hess, K. R., K. Anderson, et al. (2006). “Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer.” J Clin Oncol 24(26): 4236-44.   Jackson J E, Mudholkar, G S. (1979). “Control procedures for residuals associated with principal component analysis.” Technometrics 21:341-349.   Kaufmann, M., G. N. Hortobagyi, et al. (2006). “Recommendations from an international expert panel on the use of neoadjuvant (primary) systemic treatment of operable breast cancer: an update.” J Clin Oncol 24(12): 1940-9.   Kuerer, H. M., L. A. Newman, et al. (1999). “Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy.” J Clin Oncol 17(2): 460-9.   Kuroi, K., M. Toi, et al. (2005). “Unargued issues on the pathological assessment of response in primary systemic therapy for breast cancer.” Biomed Pharmacother 59 Suppl 2: S387-92.   Kurosumi, M. (2004). “Significance of histopathological evaluation in primary therapy for breast cancer—recent trends in primary modality with pathological complete response (pCR) as endpoint.” Breast Cancer 11(2): 139-47.   Lai, C., M. J. Reinders, et al. (2006). “A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets.” BMC Bioinformatics 7(1): 235.   Liedtke C, Hatzis C, Symmans W F, et al. Genomic grade index is associated with response to chemotherapy in patients with breast cancer.  J Clin Oncol . Jul 1 2009;27(19):3185-3191.   Ma, S., X. Song, et al. (2006). “Regularized binormal ROC method in disease classification using microarray data.” BMC Bioinformatics 7: 253.   Parker J S, Mullins M, Cheang MC, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes.  J. Clin Oncol . Mar 10 2009;27(8):1160-1167.   Perou, C. M., T. Sorlie, et al. (2000). “Molecular portraits of human breast tumours.” Nature 406(6797): 747-52.   Pusztai, L., M. Ayers, et al. (2003). “Gene expression profiles obtained from fine-needle aspirations of breast cancer reliably identify routine prognostic markers and reveal large-scale molecular differences between estrogen-negative and estrogen-positive tumors.” Clin Cancer Res 9(7): 2406-15.   Pusztai, L., M. Ayers, et al. (2003). “Clinical application of cDNA microarrays in oncology.” Oncologist 8(3): 252-8.   Pusztai, L., C. Sotiriou, et al. (2003). “Molecular profiles of invasive mucinous and ductal carcinomas of the breast: a molecular case study.” Cancer Genet Cytogenet 141(2): 148-53.   Rajan, R., A. Poniecka, et al. (2004). “Change in tumor cellularity of breast carcinoma after neoadjuvant chemotherapy as a variable in the pathologic assessment of response.” Cancer 100(7): 1365-73.   Ross, J. S., J. A. Fletcher, et al. (2003). “HER-2/neu testing in breast cancer.” Am J Clin Pathol 120 Suppl: S53-71.   Ross, J. S., J. A. Fletcher, et al. (2003). “The Her-2/neu gene and protein in breast cancer 2003: biomarker and target of therapy.” Oncologist 8(4): 307-25.   Ross, J. S., G. P. Linette, et al. (2003). “Breast cancer biomarkers and molecular medicine.” Expert Rev Mol Diagn 3(5): 573-85.   Rouzier, R., C. M. Perou, et al. (2005). “Breast cancer molecular subtypes respond differently to preoperative chemotherapy.” Clin Cancer Res 11(16): 5678-85.   Rouzier, R., L. Pusztai, et al. (2005). “Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer.” J Clin Oncol 23(33): 8331-9.   Rouzier, R., R. Rajan, et al. (2005). “Microtubule-associated protein tau: a marker of paclitaxel sensitivity in breast cancer.” Proc Natl Acad Sci USA 102(23): 8315-20.   Rouzier, R., P. Wagner, et al. (2005). “Gene expression profiling of primary breast cancer.” Curr Oncol Rep 7(1): 38-44.   Stec, J., J. Wang, et al. (2005). “Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix GeneChips.” J Mol Diagn 7(3): 357-67.   Symmans, W. F., M. Ayers, et al. (2003). “Total RNA yield and microarray gene expression profiles from fine-needle aspiration biopsy and core-needle biopsy samples of breast carcinoma.” Cancer 97(12): 2960-71.   Symmans, W. F., F. Peintinger, et al. (2007). “Measurement of Residual Breast Cancer Burden to Predict Survival After Neoadjuvant Chemotherapy.” J Clin Oncol.   Tibshirani R.J. (2009) Univaraite shrinkage in the Cox model for high dimensional data. Statistical Applications in Genetics and Molecular Biology 8(1): article 21.   van&#39;t Veer, L. J., H. Dai, et al. (2002). “Gene expression profiling predicts clinical outcome of breast cancer.” Nature 415(6871): 530-6.   van de Vijver, M. J., Y. D. He, et al. (2002). “A gene-expression signature as a predictor of survival in breast cancer.” N Engl J Med 347(25): 1999-2009.   Wang, Y., J. G. Klijn, et al. (2005). “Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.” Lancet 365(9460): 671-9