Abstract:
Disclosed are methods of predicting the likelihood of long-term survival without recurrence of breast cancer for a subject having estrogen receptor-positive (ER+) breast cancer treated with adjuvant endocrine monotherapy. In various embodiments, these methods comprise performing a gene expression profile of a breast tissue sample of substantially all of the genes of the “CADER set” described herein; calculating a risk score using a regression model; and applying a double median cutoff classification to assign the subject to a sensitive, indeterminate or resistant group, wherein assignment to a sensitive group predicts longer relapse-free survival compared to the median relapse-free survival of ER+ breast cancer patients treated with adjuvant endocrine monotherapy.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
       [0001]    This application claims priority from U.S. Provisional Application Ser. No. 61/658,517 filed Jun. 12, 2012, which is incorporated herein by reference in its entirety. 
     
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH 
       [0002]    This work received support from NCI grants U01CA114722, U01CA114722S1 and R01 CA095614. The government may have certain rights in the invention. 
     
    
     FIELD 
       [0003]    This work relates generally to breast cancer and, more particularly, to clinically useful methods and devices for assessing breast cancer prognosis. 
       INTRODUCTION 
       [0004]    Genomic models based upon gene-expression signatures can provide clinically useful information for patient management. Nevertheless, patients are often assigned to intermediate risk groups in which the clinical decisions cannot be made confidently (Paik, S., et al. N. Engl. J. Med. 351:2817-26, 2004; Nielsen, T. O., et al. Clin. Cancer Res. 16:5222-32, 2010). Furthermore, besides HER2 and ER, most genes in these signatures are not amplicon-driven. 
         [0005]    There are multiple studies that use gene expression markers for diagnosis, prognosis, determining treatment options, and monitoring relapse free survival. 
         [0006]    Cobleigh et al., U.S. Pat. No. 7,569,345 (&#39;345) provides a gene set for diagnosis and/or prognosis of breast cancer. This patent discloses a method of predicting the likelihood of long-term survival without recurrence of breast cancer for a patient having ER-positive breast cancer. 
         [0007]    Bertucci et al., US Patent Application No. US 2011/0014191, discloses 16 serine/threonine kinases for determining poor clinical outcome or increased risk of recurrence following treatment when these genes are overexpressed. 
         [0008]    Fuqua et al., US Patent Application No. US 2007/0059720, discloses RNA profiling for predicting resistance to chemotherapy in breast cancer. 
         [0009]    Erlander et al., U.S. Pat. No. 7,504,214 (&#39;214), discloses a gene signature of 149 genes that predicts tamoxifen treatment outcome in ER-positive breast cancer. 
         [0010]    Perou et al., US Patent Application No. US 2011/0145176 (“Perou”) discloses methods for classifying and evaluating the prognosis of a subject having breast cancer. Perou further provides methods for predicting outcome or response to therapy of a subject diagnosed with breast cancer using the PAM50 classification model. 
         [0011]    Some of these tests are used in a clinical setting such as OncotypeDx Assay (Genomic Health, Inc.) and PAM50 assay (University Genomics, St. Louis, Mo.). OncotypeDx predicts recurrence of tamoxifen-treated, node-negative breast cancer (Paik, S., et al. N. Engl. J. Med. 351:2817-26, 2004), while PAM50 assay predicts variable response to chemotherapy (Parker, J. S., et al. J. Clin. Oncol. 27:1160-7, 2009). 
         [0012]    While these studies predict and provide clinically useful information in certain groups of subjects, they are still limited in their ability to predict risk of relapse, i.e., many patients will be deemed to be of indeterminate risk of relapse using existing prognostic models. Thus, there is a need for methods for assigning greater percentages of subjects to either high risk of relapse or low risk of relapse in view of present prognostic models or methods that further dissect intermediate risk of relapse deemed by an existing model, for example, PAM50. 
       SUMMARY 
       [0013]    Accordingly, the present inventors have succeeded in integrating gene expression and gene copy data in estrogen receptor positive (ER+) breast cancer to develop a new prognostic model that is superior to models derived from gene expression data alone. The mRNA expression from genes that are both prognostic in ER+ disease and modulated by copy number aberration (CNA) provide superior assay performance as well as new biological and clinical insights. 
         [0014]    Thus, the present teachings are drawn to methods to determine relapse risk in a human subject afflicted with estrogen receptor-positive breast cancer treated with adjuvant endocrine monotherapy. In various embodiments, the methods comprise obtaining a breast cancer tissue sample from a human subject, determining gene expression levels in the sample of most, substantially all, or all the genes listed in the Copy number Aberration Driven Endocrine Response (CADER) set. The CADER set comprises, consists essentially of or consists of 27 treatment sensitivity genes and 27 treatment resistant genes listed in Table 1. The endocrine therapy sensitivity genes include PARP3, AZGP1, ZNF18, EPHX2, IGFBP4, NUDT18, FM05, C1orf66, COL14A1, PLAT, PCM1, PHYHD1, ZBTB20, NFKB1, TK2, ABAT, ACP6, TSPAN7, TNFRSF10B, GSTM1, CHDH, KCTD9, EVL, MAP2K4, RPL21 and STC2 and the endocrine therapy resistance genes include VDAC2, KIFC1, EIF2S2, EIF2C2, CCNB1, RAD54B, RACGAP1, CDC2, CDCA5, BIRC5, C8orf76, MCM10, TDG, UBE2C, TPX2, C20orf24, FBXO45, KIF4A, NUP107, DSC2, KIF18A, ZWINT, TMPO, CCT6A, TOP2A, CENPE and XPOT. The methods further comprise scaling the expression levels to have similar distribution of a matching prototype dataset and assigning the subject to a risk group for relapse based on the measured gene expression values. In various embodiments of the present teachings, a subject can be deemed to be at low risk of relapse if the subject has an up-regulated sensitive gene centroid value (relative to median zero) and a down-regulated resistant gene centroid value. In addition, in various embodiments, the subject can be deemed to be at high risk of relapse if the subject has a down-regulated sensitive gene centroid value (relative to median zero) and an up-regulated resistant gene centroid value. In various embodiments, a subject having an expression profile that does not lead to a determination of either low risk of relapse or high risk of relapse can be deemed to be at indeterminate risk of relapse. 
         [0015]    In various embodiments, assigning the subject to a sensitive, indeterminate or resistant group comprises representing the expression levels as a coordinate in a quadrant in a 1-dimensional space by the resistant gene centroid and the sensitive gene centroid, and determining the Euclidean distance of the expression levels to the gene centroid of each of sensitive, resistant and indeterminate response groups, wherein the subject is assigned to the group with the shortest distance. 
         [0016]    In some embodiments, determining the gene centroid of the treatment-resistant endocrine-response modifier genes and the gene centroid of the treatment-sensitive endocrine-response modifier genes can include calculating the double median cutoff classification scheme to categorize the risk score. In some embodiments, determining the gene centroid of the treatment-resistant endocrine-response modifier genes and the gene centroid of the treatment-sensitive endocrine-response modifier genes can comprise calculating the average of the expression levels of treatment-resistant genes and treatment-sensitive genes separately, and comparing them to the median zero (if genes are each median-centered) to give the risk score response categories. 
         [0017]    In various embodiments, the adjuvant endocrine monotherapy can be tamoxifen treatment. In some embodiments, the adjuvant endocrine monotherapy can be aromatase inhibitor treatment. 
         [0018]    In various embodiments, the CADER gene set can be significantly associated with copy number aberration. In some embodiments, a copy number aberration can be a copy number loss. In other embodiments, a copy number aberration can be a copy number gain, such as, in non-limiting example, a tandem duplication. In other embodiments, the copy number aberration can be copy number amplification. 
         [0019]    Various embodiments of the present teachings include a microarray comprising, consisting essentially of, or consisting of a solid support and probe sets for each gene of the CADER set. In some configurations, the solid support can be, in non-limiting example, an Agilent 4*44 K platform (Agilent Technologies, Inc., Santa Clara Calif.) or a NanoString nCounter platform (NanoString Technologies, Inc., Seattle, Wash.). In some configurations, the probe sets can comprise, consist of or consist essentially of probes for each gene of the CADER set. In some configurations, the probe sets can comprise, consist essentially of, or consist of probes for each gene of the CADER set, plus probe sets for one or more housekeeping genes. In various embodiments, a microarray consisting essentially of a solid support and probe sets for each gene of the CADER set can also include additional probes and probe sets such as positive and negative controls, as well as proteins, buffers and salts that may be needed to conduct sample analysis. 
         [0020]    In various embodiments, the present teachings include a microarray-based methods of predicting the likelihood of long-term survival without recurrence of cancer for a subject having estrogen receptor-positive breast cancer treated with endocrine monotherapy. The methods comprise obtaining a breast cancer tissue sample from a human subject, determining gene expression levels in the sample of most, substantially all, or all the genes listed in the CADER set. The CADER set comprises, consists essentially of or consists of 27 treatment sensitivity genes and 27 treatment resistant genes listed in Table 1. The endocrine therapy sensitivity genes include PARP3, AZGP1, ZNF18, EPHX2, IGFBP4, NUDT18, FMO5, C1orf66, COL14A1, PLAT, PCM1, PHYHD1, ZBTB20, NFKB1, TK2, ABAT, ACP6, TSPAN7, TNFRSF10B, GSTM1, CHDH, KCTD9, EVL, MAP2K4, RPL21 and STC2 and the endocrine therapy resistance genes include VDAC2, KIFC1, EIF2S2, EIF2C2, CCNB1, RAD54B, RACGAP1, CDC2, CDCA5, BIRC5, C8orf76, MCM10, TDG, UBE2C, TPX2, C20orf24, FBXO45, KIF4A, NUP107, DSC2, KIF18A, ZWINT, TMPO, CCT6A, TOP2A, CENPE and XPOT. The methods further comprise scaling the expression levels to have similar distribution of a matching prototype dataset and assigning the subject to a risk group for relapse based on the measured gene expression values. In various embodiments of the present teachings, a subject can be deemed to be at low risk of relapse if the subject has an up-regulated sensitive gene centroid value (relative to median zero) and a down-regulated resistant gene centroid value. In addition, in various embodiments, the subject can be deemed to be at high risk of relapse if the subject has a down-regulated sensitive gene centroid value (relative to median zero) and an up-regulated resistant gene centroid value. In various embodiments, a subject having an expression profile that does not lead to a determination of either low risk of relapse or high risk of relapse can be deemed to be at indeterminate risk of relapse. 
         [0021]    In some embodiments, determining the gene centroid of the treatment-resistant endocrine-response modifier genes and the gene centroid of the treatment-sensitive endocrine-response modifier genes can include calculating the double median cutoff classification scheme to categorize the risk score. In some embodiments, determining the gene centroid of the treatment-resistant endocrine-response modifier genes and the gene centroid of the treatment-sensitive endocrine-response modifier genes can comprise calculating the average of the expression levels of treatment-resistant genes and treatment-sensitive genes separately, and comparing them to the median zero (if genes are each median-centered) to give the risk score response categories. 
         [0022]    In various embodiments, the adjuvant endocrine monotherapy can be tamoxifen treatment. In some embodiments, the adjuvant endocrine monotherapy can be aromatase inhibitor treatment. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0023]      FIG. 1  illustrates the combined remark diagram for the POL and Z1031 cohort and analysis flow chart. 
           [0024]      FIG. 2  illustrates Kaplan Meier (KM) curves of the CADER categories. 
           [0025]      FIG. 3  illustrates KM curves of RFS on CADER response groups based on the 54-ERMGs in each of the three public cohorts: Symmans (A), Pawitan (B) and Zhang (C). 
           [0026]      FIG. 4  illustrates a quadrant in a 2-dimensional space by the resistant and the sensitive gene centroid. 
           [0027]      FIG. 5  illustrates KM curves for sensitive/indeterminate/resistance groups using eighteen chromosome 8q resistance genes and six chromosome 8p sensitivity genes. 
       
    
    
     DETAILED DESCRIPTION 
       [0028]    The present inventors disclose putative endocrine-response-modifier genes (ERMGs) that were identified by statistical associations between mRNA expression, copy number aberrations (CNA) and the anti-proliferative effects of neoadjuvant aromatase inhibitor (AI) therapy. Twenty-seven “treatment-sensitivity” and 27 “treatment-resistance” ERMGs were further selected through association with relapse-free survival (RFS) in patients uniformly treated with adjuvant tamoxifen monotherapy. The present teachings include 54 ERMGs and the derived copy number aberration driven endocrine response signature (CADER) that were further validated in three independent public datasets and compared with an established prognostic model (PAM50 ROR subtype-based score). 
         [0029]    Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present teachings belong. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley &amp; Sons (New York, N.Y. 1994), and March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley &amp; Sons (New York, N.Y. 1992), provide a person of skill in the art with general guides to many of the terms used in the present application. 
         [0030]    One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present teachings. The present teachings are in no way limited to the methods and materials described. For purposes of the present teachings, the following terms are defined below. 
         [0031]    As used herein, “microarray” refers to an ordered arrangement of hybridizable array elements such as polynucleotide probes, on a substrate. 
         [0032]    As used herein, “treatment sensitive genes” are genes that are up-regulated in patients who are sensitive to endocrine treatment. 
         [0033]    As used herein, “treatment resistance genes” are genes that are up-regulated in patients who are resistant to endocrine treatment. 
         [0034]    As used herein, “gene amplification” refers to abnormal multiple copies of a gene or gene fragment comprised by a cell or cell line. 
         [0035]    As used herein, “amplicon” refers to an amplified stretch of DNA. 
         [0036]    As used herein, “prediction” refers to the likelihood that a subject will respond either favorably or unfavorably to a drug or set of drugs, and also the extent of those responses, or that a patient will survive, following surgical removal or the primary tumor and/or chemotherapy for a certain period of time without cancer recurrence. 
         [0037]    As used herein, “long-term” survival means survival for at least 5 years following surgery or other treatment. 
         [0038]    As used herein, “tumor” refers to cancerous tumors. 
         [0039]    As used herein, “pathology” of cancer refers to phenomena that compromise the health of a cancer patient. These include, without limitation, abnormal cell growth, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immunological responses, neoplasia, premalignancy, malignancy, and invasion of surrounding or distant tissues or organs. 
         [0040]    As used herein, “biological sample” is any sampling of cells, tissues, or bodily fluids containing cells. Examples of biological samples include, but are not limited to, biopsies, smears, and bodily fluids such as blood, lymph, urine, saliva, nipple aspirates, and gynecological fluids. 
         [0041]    As used herein, “blood” includes whole blood, plasma, and serum. 
         [0042]    Methods and compositions described herein utilize laboratory techniques well known to skilled artisans. Such techniques can be found in laboratory manuals such as Sambrook, J., et al., Molecular Cloning: A Laboratory Manual, 3rd ed. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 2001; Spector, D. L. et al., Cells: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 1998; Methods in Enzymology (Academic Press, Inc.); Handbook of Experimental Immunology, 4th edition (D. M. Weir &amp; C. C. Blackwell, eds., Blackwell Science Inc., 1987); Current Protocols in Molecular Biology (F. M. Ausubel et al., eds., 1987); and PCR: The Polymerase Chain Reaction, (Mullis et al., eds., 1994). 
       Gene Expression Profiling 
       [0043]    In general, methods of gene expression profiling can be divided into two large groups: methods based on hybridization analysis of polynucleotides, and methods based on sequencing of polynucleotides. Methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization (Parker &amp; Barnes, Methods in Molecular Biology 106:247-283 (1999)); RNAse protection assays (Hod, Biotechniques 13:852-854 (1992)); and reverse transcription-polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8:263-264 (1992)). Alternatively, antibodies can be employed that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS). For example, RT-PCR can be used to compare mRNA levels in different sample populations, in normal and tumor tissues, with or without drug treatment, to characterize patterns of gene expression, to discriminate between closely related mRNAs, and/or to analyze RNA structure. 
         [0044]    A first step for an RT-PCR analysis can be extraction and/or isolation of mRNA from a sample. In some embodiments, starting material can be total RNA isolated from a human tumor, a tumor cell line, and/or corresponding normal tissues or cell lines. RNA can be isolated from a variety of primary tumors, such as, without limitation, breast, lung, colon, prostate, brain, liver, kidney, pancreas, spleen, thymus, testis, ovary, uterus, etc., tumor, or tumor cell lines. If the source of mRNA is a primary tumor, mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples. 
         [0045]    Methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987), and De Andres et al., BioTechniques 18:42044 (1995). In particular, RNA isolation can be performed using a purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer&#39;s instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy® mini-columns. Other commercially available RNA isolation kits include MasterPure™ Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation. 
         [0046]    A first step in gene expression profiling by RT-PCR can be the reverse transcription of the RNA template into cDNA, followed by amplification in a PCR reaction. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, Calif., USA), following the manufacturer&#39;s instructions. The cDNA can then be used as template in a subsequent PCR amplification and quantitative analysis using, for example, a TaqMan® (Life Technologies, Inc., Grand Island, N.Y.) assay. 
         [0047]    TaqMan® RT-PCR can be performed using commercially available equipment, such as, for example, an ABI PRISM 7700™ Sequence Detection System™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). 
         [0048]    RT-PCR can be performed using an internal standard such as mRNA for glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and/or β-actin as a control (see, e.g., Held et al., Genome Research 6: 986-994, 1996). 
         [0049]    Representative protocols for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA isolation, purification, primer extension and amplification are given in various published journal articles (for example: T. E. Godfrey et al., J. Molec. Diagnostics 2: 84-91, 2000; K. Specht et al., Am. J. Pathol. 158: 419-429, 2001). In various configurations, a representative process can start with cutting about 10 μm-thick sections of paraffin-embedded tumor tissue samples. The RNA can be extracted, and protein and DNA can be removed. RNA repair and/or amplification steps can be included. 
         [0050]    In some aspects of the present teachings, PCR primers and probes can be designed based upon intron sequences present in the gene to be amplified. In such aspects, a first step in the primer/probe design can be the delineation of intron sequences within the genes. This can be accomplished using publicly available software, such as the DNA BLAST software (Kent, W. J., Genome Res. 12(4): 656-664, 2002). Subsequent steps can follow well established methods of PCR primer and probe design. 
         [0051]    In some configurations, in order to avoid non-specific signals, repetitive sequences within the introns can be masked when designing the primers and probes. This can be accomplished by using software such as the Repeat Masker program available on-line through the Baylor College of Medicine. This program can be used to screen DNA sequences against a library of repetitive elements and returns a query sequence in which the repetitive elements are masked. The masked intron sequences can then be used to design primer and probe sequences using a commercially or otherwise publicly available primer/probe design package, such as Primer Express (Applied Biosystems); MGB assay-by-design (Applied Biosystems); Primer3 (Steve Rozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, N.J., pp 365-386). 
         [0052]    Factors considered in PCR primer design include primer length, melting temperature (Tm), and G/C content, specificity, complementary primer sequences, and 3′-end sequence. In general, optimal PCR primers are generally 17-30 bases in length, and contain about 20% from 10% to 80%, or about 80% G+ C bases, such as, for example, about 50%, from 50 to 60%, or about 60% G+C bases. In various configurations, Tm&#39;s between 50 and 80° C., e.g. about 50 to 70° C. can be preferred. 
         [0053]    Further guidelines for PCR primer and probe design can be found in various published sources, e.g. Dieffenbach, C. W. et al., “General Concepts for PCR Primer Design” in: PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press, New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs” in: PCR Protocols, A Guide to Methods and Applications, CRC Press, London, 1994, pp. 5-11; and Plasterer, T. N. Primerselect: Primer and probe design. Methods Mol. Biol. 70:520-527 (1997), the entire disclosures of which are hereby expressly incorporated by reference. 
       Microarrays 
       [0054]    In some embodiments, differential gene expression can also be identified, or confirmed using a microarray technique. Thus, the expression profile of breast cancer-associated genes can be measured in either fresh or paraffin-embedded tumor tissue, using microarray technology. In these methods, polynucleotide sequences of interest (including cDNAs and oligonucleotides) can be plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. Just as in the RT-PCR method, the source of mRNA typically can be total RNA isolated from human tumors or tumor cell lines, and corresponding normal tissues or cell lines. Thus RNA can be isolated from a variety of primary tumors or tumor cell lines. If the source of mRNA is a primary tumor, mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples. 
         [0055]    In an embodiment of the microarray technique, PCR-amplified inserts of cDNA clones can be applied to a substrate in a dense array. The microarrayed genes, immobilized on the microchip, can be suitable for hybridization under stringent conditions. 
         [0056]    In some embodiments, fluorescently labeled cDNA probes can be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip can hybridize with specificity to loci of DNA on the array. After washing to remove non-specifically bound probes, the chip can be scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantification of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. 
         [0057]    In some configurations, dual color fluorescence can be used. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pairwise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. In various configurations, the miniaturized scale of the hybridization can afford a convenient and rapid evaluation of the expression pattern for large numbers of genes. In various configurations, such methods can have sensitivity required to detect rare transcripts, which are expressed at fewer than 1000, fewer than 100, or fewer than 10 copies per cell. In various configurations, such methods can detect at least approximately two-fold differences in expression levels (Schena et al., Proc. Natl. Acad. Sci. USA 93(2): 106-149 (1996)). In various configurations, microarray analysis can be performed by commercially available equipment, following manufacturer&#39;s protocols, such as by using the Affymetrix GenChip technology, or Incyte&#39;s microarray technology. 
       Serial Analysis of Gene Expression (SAGE) 
       [0058]    SAGE is a method that allows simultaneous and quantitative analysis of a large number of gene transcripts, without the need of providing an individual hybridization probe for each transcript, hi various configurations of these methods, a short sequence tag (about 10-14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags, and identifying the gene corresponding to each tag (Velculescu et al., Science 270:484-487 (1995); Velculescu et al., Cell 88:243-51 (1997). 
       MassARRAY Technology 
       [0059]    The MassARRAY (Sequenom, San Diego, Calif.) technology is an automated, high-throughput method of gene expression analysis using mass spectrometry (MS) for detection. According to this method, following the isolation of RNA, reverse transcription and PCR amplification, the cDNAs are subjected to primer extension. The cDNA-derived primer extension products are purified, and dispensed on a chip array that is pre-loaded with the components needed for MALTI-TOF MS sample preparation. The various cDNAs present in the reaction are quantitated by analyzing the peak areas in the mass spectrum obtained. 
       Gene Expression Analysis by Massively Parallel Signature Sequencing (MPSS) 
       [0060]    This method, described by Brenner et al., Nature Biotechnology 18:630-634 (2000), is a sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 μm diameter microbeads. First, a microbead library of DNA templates is constructed by in vitro cloning. This is followed by the assembly of a planar array of the template-containing microbeads in a flow cell at a high density (typically greater than 3.times.10 6  microbeads/cm 2 ). The free ends of the cloned templates on each microbead are analyzed simultaneously, using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to simultaneously and accurately provide, in a single operation, hundreds of thousands of gene signature sequences from a yeast cDNA library. 
         [0000]    General Description of the mRNA Isolation, Purification and Amplification 
         [0061]    The steps of a representative protocol for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA isolation, purification, primer extension and amplification are given in various published journal articles {for example: T. E. Godfrey et al. J. Molec. Diagnostics 2: 84-91 [2000]; K. specht et al., Am. J. Pathol. 158: 419-29 [2001]}. Briefly, a representative process starts with cutting about 10 urn thick sections of paraffin-embedded tumor tissue samples. The RNA is then extracted, and protein and DNA are removed. After analysis of the RNA concentration, RNA repair and/or amplification steps may be included, if necessary, and RNA is reverse transcribed using gene specific promoters followed by RT-PCR. Finally, the data are analyzed to identify the best treatment option(s) available to the patient on the basis of the characteristic gene expression pattern identified in the tumor sample examined. 
       Breast Cancer Gene Set, Assayed Gene Subsequences, and Clinical Application of Gene Expression Data 
       [0062]    Some embodiments of the present teachings comprise measuring expression levels of certain genes by breast cancer tissue to provide prognostic information. In various configurations, expression levels can be normalized regarding both differences in the amount of RNA assayed and variability in the quality of the RNA used. Therefore, an assay can involve measurement of the expression of certain “normalizing” genes, including housekeeping genes such as, for example, GAPDH and/or Cyp1. In some configurations, normalization can be based on the mean or median signal (Ct) of all of the assayed genes or a subset thereof (global normalization approach). On a gene-by-gene basis, measured normalized amount of a patient tumor mRNA can be compared to the amount found in a breast cancer tissue reference set. The number (N) of breast cancer tissues in this reference set can be sufficiently high to ensure that different reference sets (as a whole) behave essentially the same way. In various configurations, if this condition is met, the identity of the individual breast cancer tissues present in a particular set can have no significant impact on the relative amounts of the genes assayed. In some configurations, the breast cancer tissue reference set can consist of or consist essentially of at least about 30, preferably at least about 40, more preferably at least about 50, different FPE breast cancer tissue specimens. Unless noted otherwise, normalized expression levels for each mRNA/tested tumor/patient can be expressed as a percentage of the expression level measured in the reference set. More specifically, the reference set of a sufficiently high number (e.g. at least 40) of tumors can yield a distribution of normalized levels of each mRNA species. The level measured in a particular tumor sample to be analyzed falls at some percentile within this range, which can be determined by methods well known in the art. Below, unless noted otherwise, reference to expression levels of a gene assume normalized expression relative to a reference set although unless stated otherwise. 
         [0063]    In some embodiments, gene expression data can be pre-processed, by addressing for example but without limitation, missing data, translation, scaling, normalization, and/or weighting. In some configurations, multivariate projection methods, such as principal component analysis (PCA) and partial least squares analysis (PLS), can be used as scaling-sensitive methods. In these configurations, by using prior knowledge and experience about the type of data studied, the quality of the data prior to multivariate modeling can be enhanced by scaling and/or weighting. In some embodiments, scaling and weighting can be used to place the data in the correct metric, thereby revealing patterns inherent in the data. 
         [0064]    In some configurations, missing data, for example gaps in column values, can be replaced or “filled” with, for example but without limitation, the mean value of a column (“mean fill”); a random value (“random fill”); or a value based on a principal component analysis (“principal component fill”). 
         [0065]    As used herein, “translation” of descriptor coordinate axes can include normalization and mean centering. In some configurations, “normalization” can be used to remove sample-to-sample variation. In various embodiments, for microarray data, the process of normalization can be used to remove systematic errors by balancing the fluorescence intensities of the two labeling dyes. In various configurations, methods for calculating normalization factor can include: (i) global normalization that uses all genes on the array; (ii) housekeeping genes normalization that uses constantly expressed housekeeping/invariant genes; and (iii) internal controls normalization that uses known amount of exogenous control genes added during hybridization (Quackenbush (2002) Nat. Genet. 32 (Suppl.), 496-501). In one embodiment, intrinsic genes disclosed herein can be normalized to control housekeeping genes. For example, the housekeeping genes described in U.S. Patent Publication 2008/0032293, which is herein incorporated by reference in its entirety, can be used for normalization. Exemplary housekeeping genes include MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, and TFRC. It will be understood by one of skill in the art that the methods disclosed herein are not bound by normalization to any particular housekeeping genes, and that any suitable housekeeping gene(s) known in the art can be used. 
         [0066]    A gene centroid represents the average expression levels of a set of predefined genes for an individual relative to control levels. The CADER classification centers on two centroids, the centroid of the 27 CADER resistant genes and the centroid of the 27 CADER sensitive genes. 
         [0067]    In various configurations, many normalization approaches are possible, which can be applied at any of several points in the analysis. In a configuration, microarray data can be normalized using the LOWESS method (Yang, Y. H., et al. Nucleic Acids Res. 30:e15, 2002). In another embodiment, qPCR data can be normalized to the geometric mean of a set of housekeeping genes. 
         [0068]    In some embodiments, “mean centering” can also be used to simplify interpretation. In these embodiments, for each descriptor, the average value of that descriptor for all samples can be subtracted. In this way, the mean of a descriptor coincides with the origin, and all descriptors can be “centered” at zero. In “unit variance scaling,” data can be scaled to equal variance. In various configurations, the value of each descriptor can be scaled by 1/StDev, where StDev is the standard deviation for that descriptor for all samples. 
         [0069]    In some embodiments, “Pareto scaling” can also be used to simplify interpretation (van den Berg, R. A., et al., BMC Genomics. 7:142, 2006). In pareto scaling, the value of each descriptor is scaled by 1/sqrt (StDev), where StDev is the standard deviation for that descriptor for all samples. In this way, each descriptor has a variance numerically equal to its initial standard deviation. In various configurations, pareto scaling can be performed, for example, on raw data or mean centered data. 
         [0070]    In some embodiments, “logarithmic scaling” can be used to assist interpretation when data have a positive skew and/or when data spans a large range, e.g., several orders of magnitude. In these embodiments, a value is replaced by the logarithm of that value. 
         [0071]    In some embodiments, “equal range scaling” can be used to assist interpretation. In these embodiments, each descriptor is divided by the range of that descriptor for all samples. In this way, all descriptors have the same range, that is, 0-1. However, this method can be sensitive to the presence of outlier points. 
         [0072]    In some embodiments, “autoscaling,” can be used to assist interpretation. In these embodiments, each data vector is mean centered and unit variance scaled. This technique can be used to weigh each descriptor equally. 
         [0073]    In various embodiments, a double median cut-off scheme can be used to classify subjects into the three response groups (sensitive, resistant and indeterminate). In various configurations, each gene can be normalized to have a zero median and a unit inter-quartile range (IQR). The gene centroid of 27 CADER resistant genes and the gene centroid of the 27 CADER sensitive genes can be separately calculated. Because of this normalization, the double median cut-off can be equivalent to double zero cut-off since all genes and centroids have a zero median. Patients with a greater-than-zero resistant gene centroid value and a less-than-zero sensitive gene centroid can be assigned into the therapy resistant group while the patients with less-than-zero resistant gene centroid value and a greater-than-zero sensitive gene centroid can be assigned into the therapy sensitive group. In various configurations, detection of up-regulation of resistant genes can lead to a bad prognosis, and detection of up-regulation of the sensitive genes can lead to good prognosis. The remaining patients can have indeterminate responses to therapy. ( FIG. 4 ) 
       Methods 
       [0074]    The methods and compositions described herein utilize laboratory techniques well known to skilled artisans, and can be found in references such as Sambrook and Russel (2006), Condensed Protocols from Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, ISBN 0879697717; Sambrook and Russel (2001) Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, ISBN 0879695773; Ausubel et al. (2002) Short Protocols in Molecular Biology, Current Protocols, ISBN 0471250929; Spector et al. (1998) Cells: A Laboratory Manual, Cold Spring Harbor Laboratory Press, ISBN 0879695226. 
       EXAMPLES 
       [0075]    The present teachings including descriptions provided in the Examples that are not intended to limit the scope of any claim or aspect. Unless specifically presented in the past tense, an example can be a prophetic or an actual example. The following non-limiting examples are provided to further illustrate the present teachings. Those of skill in the art, in light of the present disclosure, will appreciate that many changes can be made in the specific embodiments that are disclosed and still obtain a like or similar result without departing from the spirit and scope of the present teachings. 
       Example 1 
       [0076]    This example illustrates the identification of Endocrine Response Modifier Genes (ERMG). 
         [0077]    Study samples to identify ERMG were used from previously described neoadjuvant endocrine therapy studies, preoperative letrozole Phase 2 (POL) trial and ACOSOG Z1031 neoadjuvant aromatase inhibitor (AI) trial (Van Tine, B. A., et al. 2011; Olson, J.A., Jr., et al. 2009). A 10% Ki67 cut-point in the surgical specimen was used to define AI sensitivity and resistance, as this cut point is a reliable surrogate for relapse-free survival (RFS). Fifty-two and 179 patients from the POL and Z1031 cohort respectively were used for discovery ( FIG. 1A ). Total sample size (sample size for POL/sample size for Z1031) are noted in each box. Baseline PAM50 subtype and baseline and surgical specimen ER and Ki67 levels were similarly distributed between the two cohorts. Public gene expression data were accessed on 263 patients from the Loi study (Loi, S., et al. 2007, referred to herein as “Loi”), 195 from the Symmans study (Symmans, W. F., et al. 2010, referred to herein as “Symmans”), 136 from the Zhang study (Zhang, Y., et al. 2009, referred to herein as “Zhang”) and 87 from the Pawitan study (Pawitan, Y., et al. 2005, referred to herein as “Pawitan”), restricting to ER+ breast cancer patients treated with adjuvant endocrine monotherapy (i.e. no chemotherapy) ( FIG. 1B ). Sequential analysis steps that lead to the CADER signature are shown in this flow chart ( FIG. 1B ). In each step, analyzed cohorts, analysis end points, genomic profiling and statistical methods, and number of retained treatment resistance and sensitivity signature genes are presented. 
         [0078]    ERMGs were identified in study samples by significance analysis of microarrays (SAM) as genes differentially expressed between the sensitive and resistant tumors (defined by the 10% Ki67 cut point) at the 5% false discovery rate (FDR). There were 1927 putative ERMGs that fit these criteria. Of these, 1047 are up-regulated in the resistant tumors with a fold-change (relative to sensitive) ranging from 1.29˜1.80 while 780 ERMGs were up-regulated in the sensitive tumors with a fold change (relative to resistant) between 1.32 and 1.81. 
         [0079]    Two exercises were conducted in parallel to focus verification and validation efforts on the most relevant genes. First, genes were excluded whose mRNA levels were not significantly associated with CNA, based on 137 POL-Z1031 cases with paired gene-expression and aCGH, comparative genomic hybrid, data. This analysis revealed that 617 (460 resistant and 157 sensitive) of the original 1927 ERMGs were differentially expressed between copy number gain/amplification versus neutral/LOH samples. Correlation matrixes demonstrated that copy number data in contiguous chromosomal locations were highly correlated. Chromosomes (Chr) 6, 1, 20, 3 and 8 harbored the greatest number of CNA-associated resistance-ERMGs (n=64, 60, 43, 40, 40 respectively) while sensitivity-ERMGs were largely located on Chr17, 4, 1, 5, 11 and 8 (n=25, 18, 15, 14, 13, and 12 respectively), hi general, the sensitivity ERMGs were predominantly affected by LOH whereas resistance ERMGs were dominantly affected by gene gain and amplification (Table 1). Table 1 presents the list of the 54 ERMGs including official gene symbols, gene descriptions, Cox survival P-values from Loi, copy number P values are shown for each ERMG. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
             
               
               
               
               
               
             
               
             
               
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Listing of the 54 ERMGs. 
               
             
          
           
               
                   
                 CNV Percentage 
                   
                   
                   
               
               
                 Symbol 
                 (del/neutral/gain/amp) 
                 Locus 
                 CNV P 
                 Cox P 
               
               
                   
               
             
          
           
               
                 Endocrine Therapy Sensitivity Genes 
               
             
          
           
               
                 PARP3 
                 36.1/60.6/3.2/— 
                 3p21.31 
                 0.0418 
                 1.80E−05 
               
               
                 AZGP1 
                 31.2/58.4/10.4/— 
                 7q22.1 
                 0.0013 
                 3.81E−05 
               
               
                 ZNF18 
                 54.2/44.5/1.3/— 
                 17p11.2 
                 0.0416 
                 1.13E−04 
               
               
                 EPHX2 
                 48.0/40.9/11.0/— 
                 8p21 
                 0.0004 
                 6.57E−04 
               
               
                 IGFBP4 
                 34.6/45.8/17.0/2.6 
                 17q12 
                 0.0239 
                 0.0010 
               
               
                 NUDT18 
                 53.6/36.6/9.8/— 
                 8p21.3 
                 0.0003 
                 0.0010 
               
               
                 FMO5           
                 0.6/38.1/55.5/5.8 
                 1q21.1 
                 0.0008 
                 0.0018 
               
               
                 C1orf66           
                 2.6/34.8/57.4/5.2 
                 1q23.1 
                 2.68E−09 
                 0.0021 
               
               
                 COL14A1 
                 2.6/47.7/27.4/22.2 
                 8q23 
                 0.0003 
                 0.0031 
               
               
                 PLAT 
                 16.2/50.6/22.7/10.4 
                 8p12 
                 0.0001 
                 0.0033 
               
               
                 PCM1 C   
                 43.2/45.8/10.3/0.6 
                 8p22 
                 1.8E−05 
                 0.0051 
               
               
                 PHYHD1 
                 44.0/52.0/4.0/— 
                 9q34.11 
                 0.0013 
                 0.0072 
               
               
                 ZBTB20 
                 4.7/82.4/12.2/0.7 
                 3q13.2 
                 0.0344 
                 0.0081 
               
               
                 NFKB1 
                 6.5/84.5/8.4/0.6 
                 4q24 
                 0.0214 
                 0.0081 
               
               
                 TK2 
                 67.3/26.1/6.5/— 
                 16q22 
                 0.0019 
                 0.0081 
               
               
                 ABAT 
                 4.5/49.7/43.9/1.9 
                 16p13.2 
                 1.56E−05 
                 0.0083 
               
               
                 ACP6 
                 0.6/38.1/54.8/6.5 
                 1q21 
                 0.0023 
                 0.0094 
               
               
                 TSPAN7 
                 17.4/66.5/14.8/1.3 
                 Xp11.4 
                 0.0482 
                 0.0097 
               
               
                 TNFRSF10B 
                 50.3/40.6/9.0/— 
                 8p22 
                 0.0004 
                 0.0131 
               
               
                 GSTM1           
                 27.1/69.0/3.2/0.6 
                 1p13.3 
                 0.0245 
                 0.0196 
               
               
                 CHDH 
                 23.4/73.4/3.2/— 
                 3p21.1 
                 0.0115 
                 0.0244 
               
               
                 OSBPL1A 
                 25.2/66.5/7.7/0.6 
                 18q11.1 
                 0.0423 
                 0.0272 
               
               
                 KCTD9 
                 45.8/43.9/10.3/— 
                 8p21.1 
                 0.0051 
                 0.0285 
               
               
                 EVL 
                 21.9/65.6/11.3/1.3 
                 14q32.2 
                 0.0001 
                 0.0294 
               
               
                 MAP2K4 
                 54.5/44.2/1.3/— 
                 17p11.2 
                 0.0170 
                 0.0308 
               
               
                 RPL21 
                 27.7/61.9/8.4/1.9 
                 13q12.2 
                 0.0006 
                 0.0333 
               
               
                 STC2           
                 9.0/70.3/20.6/— 
                 5q35.1 
                 0.0070 
                 0.0466 
               
             
          
           
               
                 Endocrine Therapy Resistance Genes 
               
             
          
           
               
                 VDAC2 
                 5.2/78.7/14.2/1.9 
                 10q22 
                 3.2E−07 
                 3.44E−08 
               
               
                 KIFC1 
                 17.4/73.5/9.0/— 
                 6p21.3 
                 0.0039 
                 1.63E−06 
               
               
                 EIF2S2 
                 11.1/63.4/25.5/— 
                 20q11.2 
                 2.3E−04 
                 3.57E−06 
               
               
                 EIF2C2 
                 7.2/48.4/26.8/17.6 
                 8q24 
                 5.4E−14 
                 4.80E−06 
               
               
                 CCNB1           
                 24.5/58.1/17.4/— 
                 5q12 
                 4.5E−04 
                 7.55E−06 
               
               
                 RAD54B 
                 2.0/47.1/33.3/17.6 
                 8q22.1 
                 2.4E−15 
                 1.12E−05 
               
               
                 RACGAP1 
                 11.6/76.1/12.3/— 
                 12q13.12 
                 0.0187 
                 2.23E−05 
               
               
                 CDC2           
                 5.2/80.0/14.2/0.6 
                 10q21.1 
                 0.0014 
                 2.27E−05 
               
               
                 CDCA5 
                 30.3/58.1/10.3/1.3 
                 11q12.1 
                 6.2E−04 
                 2.29E−05 
               
               
                 BIRC5           
                 33.5/38.1/27.1/1.3 
                 17q25 
                 7.2E−07 
                 2.67E−05 
               
               
                 C8orf76 
                 5.8/46.1/28.6/19.5 
                 8q24.13 
                 1.4E−18 
                 3.27E−05 
               
               
                 MCM10 
                 9.7/78.1/11.0/1.3 
                 10p13 
                 1.2E−04 
                 3.52E−05 
               
               
                 TDG 
                 11.0/72.9/15.5/0.6 
                 12q24.1 
                 1.4E−04 
                 3.68E−05 
               
               
                 UBE2C           
                 4.5/54.5/36.4/4.5 
                 20q13.12 
                 4.6E−04 
                 3.70E−05 
               
               
                 TPX2 
                 11.0/63.9/25.2/— 
                 20q11.2 
                 7.0E−03 
                 3.71E−05 
               
               
                 C20orf24 
                 13.6/57.4/25.2/3.9 
                 20q11.23 
                 5.2E−08 
                 4.45E−05 
               
               
                 FBXO45 
                 13.6/70.3/14.8/1.3 
                 3q29 
                 3.6E−05 
                 4.46E−05 
               
               
                 KIF4A 
                 22.2/69.9/7.8/— 
                 Xq13.1 
                 3.5E−02 
                 4.51E−05 
               
               
                 NUP107 
                 2.0/69.5/25.3/3.2 
                 12q15 
                 2.6E−07 
                 6.62E−05 
               
               
                 DSC2 
                 14.2/71.6/13.6/0.6 
                 18q12.1 
                 2.5E−02 
                 6.91E−05 
               
               
                 KIF18A 
                 6.6/77.0/15.1/1.3 
                 11p14.1 
                 3.5E−07 
                 7.09E−05 
               
               
                 ZWINT 
                 5.2/80.7/13.6/0.6 
                 10q21 
                 3.2E−02 
                 7.24E−05 
               
               
                 TMPO 
                 12.6/68.9/17.2/1.3 
                 12q22 
                 1.0E−02 
                 7.71E−05 
               
               
                 CCT6A 
                 14.2/69.7/16.1/— 
                 7p11.2 
                 2.6E−04 
                 8.91E−05 
               
               
                 TOP2A 
                 43.8/40.5/15.0/0.6 
                 17q21 
                 2.7E−07 
                 9.03E−05 
               
               
                 CENPE 
                 6.5/85.2/7.7/0.6 
                 4q24 
                 1.9E−04 
                 1.31E−04 
               
               
                 XPOT 
                 3.9/68.4/25.2/2.6 
                 12q14.2 
                 2.0E−03 
                 1.32E−04 
               
               
                   
               
               
                             indicates data missing or illegible when filed 
               
             
          
         
       
     
         [0080]    In the second exercise, the 1927 putative ERMGs were simultaneously screened for association with RFS using the independent Loi cohort (Loi, S., et al. 2007). In univariate survival analysis, a total of 431 ERMGs (317 resistant and 114 sensitive) were significantly associated with RFS in the same resistance/sensitivity direction (by hazard ratio estimation) as observed in POL-Z1031. Only fifty two of these genes (12%) overlapped with those contained in the five gene signatures previously published as prognostic in ER+ breast cancer (Paik, S., et al. N. Engl. J. Med. 351:2817-26, 2004; Nielsen, T. O., et al. Clin. Cancer. Res. 16:5222-32, 2010; Chanrion, M., et al. Clin. Cancer. Res. 14:1744-52, 2008; van de Vijver, M. J., et al. N. Engl. J. Med. 347:1999-2009, 2002; Wang, Y., et al. Lancet. 365:671-9, 2005). Takingthe results from both exercises, two hundred and twenty four ERMGs (197 resistant and 27 sensitivity) survived. 
       Example 2 
       [0081]    This example illustrates the prognostic power of the 54 ERMGs individually. 
         [0082]    Three independent public microarray data sets on ER+ breast cancer tumors treated with adjuvant tamoxifen only were accessed (Symmans, Zhang and Pawitan). The hazard ratios (HRs), 95% CIs and P-values estimated from univariate Cox regression models were calculated for each ERMG within each public cohort. By meta-analysis, 25 among the 48 genes present in at least one cohort showed significant association with RFS and in the expected direction as from the discovery. Hierarchical cluster analyses on the 54 ERMGs subsequently demonstrated that the sensitivity and resistant genes separated consistently and cleanly in all cohorts. Based on the joint expression pattern of treatment sensitivity and resistance ERMGs, three groups of samples were definable: a group with high-expression of resistance-ERMGs and low-expression of sensitivity-ERMGs (‘resistant’ group), a second group with the opposite expression pattern (‘sensitive’ group) and a third with a mixed pattern (‘indeterminate’ group). 
         [0083]    A CADER signature through application of a double median cutoff was developed to classify patients into the three groups. Categorical CADER calls (sensitive/indeterminate/resistant) can be separately made on each cohort. The three categories of CADER calls exhibited a balanced distribution on the three public cohorts (Chi-square p-value p=0.48), especially between Symmans and Pawitan (Chi-square p-value p=0.83). The association between CADER and RFS can be tested individually within each cohort. Assembled together, the CADER calls from all the three cohorts can also be tested for a combined association analysis with RFS. The CADER categories successfully produced differing risk of relapse predictions in the combined data ( FIG. 2A , Log-rank test P=1.84e-07) as well as within each cohort separately ( FIG. 3A-C  Log-rank tests, Symmans p=0.004, Pawitan P=0.001, Zhang P=0.044). Survival probabilities (FIG.) of the CADER-sensitive group were nearly 25% higher compared to the resistance group at year 8 (94% vs. 70%) in the combined dataset with an estimated HR (resistant vs. sensitive) of 5.18 (95% CI:2.67˜10.08) while the HR of indeterminate to sensitive was 2.31 (95% CI: 1.06˜5.02). Kaplan Meier (KM) curves of RFS in the combined public cohorts are displayed in  FIG. 2 , showing data from all patients (A), the patient subset of intermediate-risk designated by the PAM50 ROR-S model (B), node positive subset of patients (C), and node negative subset of patients (D). CADER groups are indicated as follows: sensitive indicated by solid lines, indeterminate indicated by dashed lines and resistant indicated by dotted lines. The P-values are based on log rank tests. This illustrates that patient stratification into endocrine therapy response groups using the CADER model is significantly associated with survival, even by stratification of node status and within the ROR-S median risk subset. 
         [0084]    The CADER assignments were highly concordant with the PAM50 ROR-S (Fisher exact test P=3.50E-44) and the PAM50 intrinsic subtype calls (see Parker, J. S., et al. 2009, referred to herein as “PAM50”) (Fisher exact test P=8.22E-37) in the combined dataset and individually (Table 2). 
         [0000]                                                                                                                                                      TABLE 2               Concordance between CADER response groups and PAM50-based ROR-S and breast       cancer intrinsic subtypes.                                        CADER                               Response           Set   Group   N   Node−   Node+   Node.P                       POL-Z1031   Sensitive   86           —               Indeterminate   53               Resistant   92           Loi (N = 263)   Sensitive   99   47   48   0.5228               Indeterminate   69   28   38               Resistant   95   39   54           MDACC (N = 195)   Sensitive   79   52   27   0.1371               Indeterminate   55   33   22               Resistant   61   30   31           Zhang (N = 136)   Sensitive   54   54   0   —               Indeterminate   28   28   0               Resistant   54   54   0           Pawitan (N = 87)   Sensitive   35   26   8   0.2592               Indeterminate   22   11   9               Resistant   30   20   9           All (N = 418)   Sensitive   168   132   35   0.1881               Indeterminate   105   72   31               Resistant   145   104   40                            CADER                   Response   ROR_S                Set   Group   Low   Med   High   RORS.P                       POL-Z1031   Sensitive   57   25   0   1.73E−23               Indeterminate   14   32   4               Resistant   4   55   31           Loi (N = 263)   Sensitive   47   18   0   3.26E−22               Indeterminate   17   28   4               Resistant   1   31   30           MDACC (N = 195)   Sensitive   67   12   0   1.07E−21               Indeterminate   26   26   3               Resistant   6   32   23           Zhang (N = 136)   Sensitive   45   9   0   2.72E−16               Indeterminate   7   19   2               Resistant   6   30   18           Pawitan (N = 87)   Sensitive   26   9   0   1.85E−11               Indeterminate   12   10   0               Resistant   2   11   17           All (N = 418)   Sensitive   138   30   0   3.50E−44               Indeterminate   44   55   5               Resistant   14   73   58                            CADER                   Response   Subtype            Set   Group   LumA   LumB   Her2   Basal   Normal   Subtype.P               POL-Z1031   Sensitive   68   18               1.16E−22           Indeterminate   19   34           Resistant   5   87       Loi (N = 263)   Sensitive   70   15   2   3   9   1.44E−26           Indeterminate   15   36   3   5   10           Resistant   2   58   17   14   4       MDACC (N = 195)   Sensitive   61   4   0   0   14   2.59E−19           Indeterminate   26   17   1   0   11           Resistant   10   33   11   6   1       Zhang (N = 136)   Sensitive   43   4   0   0   7   2.15E−11           Indeterminate   13   11   0   1   3           Resistant   10   33   5   3   3       Pawitan (N = 87)   Sensitive   27   6   0   0   2   6.21E−08           Indeterminate   10   8   0   0   4           Resistant   3   17   5   4   1       All (N = 418)   Sensitive   131   14   0   0   23   8.22E−37           Indeterminate   49   36   1   1   18           Resistant   23   83   21   13   5                    
Analyzed cohorts include POL-Z1031, Loi, Symmans, Zhang, Pawitan and “All” (combining Symmans, Zhang and Pawitan) in Table 2. High agreement between CADER response groups, ROR-S and breast cancer subtypes can be observed within each individual cohort, as well as the combination of the three public cohorts. The association between CADER classification and RFS was still significant after stratification by nodal status in the combined analysis ( FIG. 2C-D , Log rank test P=0.038 in node positive and P=1.82e-05 in node negative). Despite the high concordance with the PAM50-defined groups, the CADER classification stratified the risk of relapse within the subset of patients who were assigned medium risk by the ROR-S model ( FIG. 2B , Log rank test P=0.0005). To confirm the independent prognostic ability of CADER, multivariate Cox analysis can be applied. The results (Table 3) show a strong independent prognostic ability of CADER to classic clinical variables (likelihood ratio test P=5.62E-05), with the predicted resistant patients experiencing a HR of 5.32 (95% CI: 2.41˜11.76) relative to sensitive patients.
 
         [0000]    
       
         
               
             
               
               
               
             
               
             
               
               
               
             
               
               
             
               
             
               
               
               
             
               
               
             
               
             
               
               
               
             
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Multivariate survival analysis of the combined Public cohort 
               
               
                 (Symmans and Pawitan) for RFS. 
               
             
          
           
               
                 Variable 
                 HR (95% CI) 
                 P 
               
               
                   
               
             
          
           
               
                 CADER (N = 238) 
               
             
          
           
               
                 Age (≧65 vs. &lt;65) 
                  2.4 (1.33~4.35) 
                 0.0038 
               
               
                 Grade (3 vs. 1~2) 
                 0.62 (0.32~1.19) 
                 0.1508 
               
               
                 Node (Positive vs. Negative) 
                  2.1 (1.17~3.75) 
                 0.0126 
               
               
                 Tumor Size (≧2.0 cm vs. &lt;2.0 cm) 
                 2.73 (1.15~6.46) 
                 0.0227 
               
               
                   
                   
                 5.62E-05 
               
               
                 CADER 
                 1.91 (0.81~4.51) 
                 0.138 
               
               
                 indeterminate vs. sensitive 
                  5.32 (2.41~11.76) 
                 3.59E-05 
               
               
                 resistant vs. sensitive 
               
             
          
           
               
                 Harrell&#39;s C-index 
                 0.7851 
               
             
          
           
               
                 ROR-S (N = 238) 
               
             
          
           
               
                 Age (≧65 vs. &lt;65) 
                 2.11 (1.15~3.87) 
                 0.016 
               
               
                 Grade (3 vs. 1~2) 
                 0.92 (0.47~1.8)  
                 0.8054 
               
               
                 Node (Positive vs. Negative) 
                 2.52 (1.42~4.48) 
                 0.0015 
               
               
                 Tumor Size (3 vs. 1~2) 
                 2.59 (1.12~5.98) 
                 0.0263 
               
               
                 ROR_S 
                   
                 0.1104 
               
               
                 med vs. low 
                 1.84 (0.99~3.4)  
                 0.0537 
               
               
                 high vs. low 
                 1.94 (0.83~4.55) 
                 0.1283 
               
             
          
           
               
                 Harrell&#39;s C-index 
                 0.7252 
               
             
          
           
               
                 CADER + ROR-S (N = 238) 
               
             
          
           
               
                 Age (≧65 vs. &lt;65) 
                 2.27 (1.24~4.15) 
                 0.0078 
               
               
                 Grade (3 vs. 1~2) 
                 0.66 (0.34~1.31) 
                 0.2349 
               
               
                 Node (Positive vs. Negative) 
                  2.1 (1.17~3.77) 
                 0.0132 
               
               
                 Tumor Size (≧2.0 cm vs. &lt;2.0 cm) 
                 2.78 (1.17~6.6)  
                 0.0201 
               
               
                   
                   
                 0.6284 
               
               
                 ROR_S 
                  0.9 (0.44~1.81) 
                 0.765 
               
               
                 med vs. low 
                 0.64 (0.25~1.64) 
                 0.3498 
               
               
                 high vs. low 
                   
                 0.0002 
               
               
                 CADER 
                   2 (0.82~4.86) 
                 0.1279 
               
               
                 indeterminate vs. sensitive 
                 6.31 (2.51~15.9) 
                 9.23E−05 
               
               
                 resistant vs. sensitive 
               
             
          
           
               
                 Harrell&#39;s C-index 
                 0.7857 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                 Survival Probability estimation of CADER and ROR-S 
               
               
                 in Public cohorts. 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                   
                 5 yr survival 
                 10 yr survival prob. 
               
               
                 Set 
                 CADER 
                 prob. 
                 (8 yr for all cohort) 
               
               
                   
               
               
                 All cohort 
                 sensitive 
                 0.96 (0.93-0.99) 
                 0.94 (0.91-0.98) 
               
               
                   
                 (N = 168) 
               
               
                 (Symmans, 
                 indeterminate 
                 0.90 (0.85-0.96) 
                 0.85 (0.78-0.93) 
               
               
                 Zhang, 
                 (N = 105) 
                 0.77 (0.7-0.84)  
                  0.7 (0.63-0.78) 
               
               
                 Pawitan) 
                 resistant 
               
               
                   
                 (N = 145 
               
               
                   
               
               
                   
                   
                 5 yr survival 
                 10 yr survival prob. 
               
               
                 CADER 
                 ROR S 
                 prob. 
                 (8 yr for all cohort) 
               
               
                   
               
               
                 sensitive 
                 low (N = 63) 
                 0.94 (0.91-0.98) 
                 0.91 (0.87-0.95) 
               
               
                 (N = 168) 
               
               
                 indeterminate 
                 med (N = 197) 
                 0.85 (0.80-0.91) 
                 0.79 (0.73-0.86) 
               
               
                 (N = 105) 
               
               
                 resistant 
                 high (N = 158) 
                 0.74 (0.64-0.86) 
                 0.73 (0.62-0.85) 
               
               
                 (N = 145) 
               
               
                   
               
             
          
         
       
     
         [0085]    When both CADER and ROR-S were included in the multivariate model, CADER remained significant (likelihood ratio test P-value=0.0002) but ROR-S did not. These results illustrate the prognostic effect of CADER response groups is independent of classic clinical variables and ROR-S. 
       Example 3 
       [0086]    This example illustrates a single sample predictor. 
         [0087]    A single sample predictor for CADER classification can proceed as the following. Patient can provide a breast cancer tissue sample. The sample can be subject to measurement of the microarray gene expression on the 54 CADER genes using either the Agilent 4*44 K platform or the NanoString nCounter platform. Next, the patient&#39;s 54 CADER genes&#39; expression values (along with other patients&#39;) can be scaled to have similar distribution as the matching prototype dataset by using the “distance-weighted-discrimination-single sample predictor (DWD-SSP)” tool (for example, https://genome.unc.edu/dwd/dwd-java.pdf) or other software developed for this purpose. Then, the patient&#39;s gene expression data can be adjusted toward the prototype data, and the patient can be assigned to the one of the three groups based on the nearest neighbor principle. More specifically, a patient in each of the three groups in the prototype dataset can be represented as a coordinate in a quadrant in a 2-dimensional space by the resistant and the sensitive gene centroid ( FIG. 4 ). Subsequently, the Euclidean distance of the new patient&#39;s gene expression data can be compared to the gene centroid of each of the three groups. The patient can then be assigned to the group with the shortest distance. 
       Example 4 
       [0088]    This example illustrates the complex interplay between gene expression, gene copy and prognostic effects. 
         [0089]    Examination of Chromosome 8 (Chr8) can show the complex interplay between gene expression, gene copy and prognostic effects. Multiple Chr8 genes were identified as ERMGs. Loss of a large portion of Chr8p and gene copy gain of both the remaining fragment of Chr8p and often concomitant gain of the entire Chr8q arm had a strong influence on gene expression and prognosis. A CADER classification focused only on 24 Chr8 ERMGs in the public data sets produced significant risk stratification in the merged data ( FIG. 5 , log-rank P=0.005). Notably neither the 21 gene recurrence score (Paik, S., et al. 2004) nor the PAM50 model (Parker, J. S., et al. 2009) includes genes on Chr8. The CADER gene expression signature can successfully parse this complexity and can translate the information into a prognostic test. 
       Example 5 
       [0090]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0091]    In this example, a patient was diagnosed at age 69 as grade 1 node negative with a tumor size of 1.2 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 5. 
         [0000]                                                            TABLE 5                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   9.089031301   CENPE   5.714622364       AZGP1   11.35771279   CCNB1   8.154402347       EPHX2   9.070143362   KIFC1   6.484647472       IGFBP4   12.32944125   CCT6A   9.620321527       NUDT18   8.382194264   EIF2C2   8.044701081       FMO5   8.142263278   RAD54B   5.756407083       C1orf66   9.247242841   VDAC2   11.7508889       COL14A1   7.416577951   MCM10   6.069573765       PLAT   9.831019817   ZWINT   9.805901534       PCM1   11.28875579   KIF18A   6.147591126       ZBTB20   9.688345972   RACGAP1   8.457413969       NKFB1   9.990544209   TDG   9.108421015       TK2   8.328267011   NUP107   9.937813942       ABAT   10.16352859   TMPO   8.183558895       ACP6   10.39248656   XPOT   9.450319913       TSPAN7   8.854489277   BIRC5   8.129717848       TNFRSF10B   7.479798286   TOP2A   8.069509653       GSTM1   10.09219086   DSC2   6.432188782       OSBPL1A   10.03427376   EIF2S2   7.637794024       KCTD9   7.904455623   UBE2C   9.62721325       EVL   12.75017457   TPX2   8.319388569       MAP2K4   9.256905695   C20orf24   11.46484768       RPL21   13.97244515   KIF4A   7.598866178       STC2   12.7561502               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk. This patient exhibited a relapse-free survival time of 10.22 years.
 
       Example 6 
       [0092]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0093]    In this example, patient was diagnosed at age 78 as grade 2 node positive. Gene expression profile of patient&#39;s CADER gene signature is presented in table 6. 
         [0000]                                                            TABLE 6                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   7.706061643   CENPE   5.047042347       AZGP1   12.80261593   CCNB1   9.559859894       EPHX2   4.993816896   KIFC1   6.711240139       IGFBP4   10.79447333   CCT6A   9.205403494       NUDT18   9.07085503   EIF2C2   8.538651152       FMO5   7.532417562   RAD54B   4.523797903       C1orf66   8.820325116   VDAC2   12.01035138       COL14A1   6.679366713   MCM10   5.446321834       PLAT   11.65087922   ZWINT   11.22262799       PCM1   11.07051999   KIF18A   3.237846496       ZBTB20   8.711204359   RACGAP1   9.157539202       NKFB1   11.27566382   TDG   8.564709433       TK2   9.063859097   NUP107   9.936110469       ABAT   9.086053214   TMPO   8.40781558       ACP6   9.703784979   XPOT   8.679222508       TSPAN7   9.435145555   BIRC5   8.666361558       TNFRSF10B   5.94832483   TOP2A   8.640228015       GSTM1   9.75245794   DSC2   7.280523855       OSBPL1A   9.601367587   EIF2S2   8.042112414       KCTD9   7.885565166   UBE2C   10.55005182       EVL   12.93028836   TPX2   9.792737142       MAP2K4   10.1175172   C20orf24   11.34355343       RPL21   15.49653705   KIF4A   8.988270499       STC2   11.67771198               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk. This patient exhibited a relapse-free survival time of 13.28 years.
 
       Example 7 
       [0094]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0095]    In this example, patient was diagnosed at age 82.6 as grade 1 node negative with a tumor size of 2.5 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 7. 
         [0000]                                                            TABLE 7                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   8.174546595   CENPE   5.183835158       AZGP1   13.16491313   CCNB1   9.329909721       EPHX2   8.94232765   KIFC1   8.159754674       IGFBP4   10.73512082   CCT6A   10.11615574       NUDT18   7.614608355   EIF2C2   6.716000941       FMO5   9.016646233   RAD54B   6.687388038       C1orf66   9.343330199   VDAC2   12.02040261       COL14A1   5.412112391   MCM10   4.450809873       PLAT   9.611437635   ZWINT   10.05941182       PCM1   11.00268588   KIF18A   6.517618437       ZBTB20   8.412640663   RACGAP1   9.053757653       NKFB1   10.31368648   TDG   8.283186412       TK2   9.063293162   NUP107   10.06027055       ABAT   11.24702673   TMPO   8.534868246       ACP6   10.6358907   XPOT   8.725274164       TSPAN7   7.772079521   BIRC5   8.690569453       TNFRSF10B   7.675838903   TOP2A   9.108931542       GSTM1   9.794034203   DSC2   5.869174656       OSBPL1A   9.989288869   EIF2S2   8.030215231       KCTD9   8.509371583   UBE2C   9.983460223       EVL   12.56118174   TPX2   9.441747358       MAP2K4   10.41218633   C20orf24   11.19135574       RPL21   15.08960134   KIF4A   9.086006485       STC2   10.7705123               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.
 
This patient exhibited a relapse-free survival time of 4.62 years.
 
       Example 8 
       [0096]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0097]    In this example, patient was diagnosed at age 50 as grade 1 node negative with a tumor size of 2 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 8. 
         [0000]                                                            TABLE 8                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   8.423019389   CENPE   5.208923128       AZGP1   12.14689386   CCNB1   9.067919062       EPHX2   7.179973326   KIFC1   7.803930949       IGFBP4   10.96270429   CCT6A   10.10034748       NUDT18   8.093034532   EIF2C2   8.355983071       FMO5   8.509914885   RAD54B   5.578315416       C1orf66   9.268922232   VDAC2   11.98112025       COL14A1   7.402107354   MCM10   5.096041201       PLAT   10.2412839   ZWINT   9.400614412       PCM1   10.80954268   KIF18A   4.320711476       ZBTB20   8.216998421   RACGAP1   8.426501711       NKFB1   10.37867582   TDG   8.424581655       TK2   8.642152449   NUP107   10.34239592       ABAT   10.99556805   TMPO   8.238047033       ACP6   9.716986412   XPOT   9.722447929       TSPAN7   8.5971143   BIRC5   8.154255818       TNFRSF10B   7.789496624   TOP2A   8.426551678       GSTM1   9.07138847   DSC2   7.916892991       OSBPL1A   9.709580369   EIF2S2   8.062037251       KCTD9   8.12069555   UBE2C   9.351723036       EVL   11.57919319   TPX2   8.857336669       MAP2K4   9.476622768   C20orf24   11.54843739       RPL21   14.8847084   KIF4A   7.999559725       STC2   11.89892093               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.
 
This patient exhibited a relapse-free survival time of 9.48 years.
 
       Example 9 
       [0098]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0099]    In this example, patient was diagnosed at age 72 as grade 2 node negative with a tumor size of 2.5 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 9. 
         [0000]                                                            TABLE 9                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   8.771670864   CENPE   3.651962054       AZGP1   14.12907741   CCNB1   8.842648054       EPHX2   8.224530958   KIFC1   9.934962727       IGFBP4   13.25201221   CCT6A   8.541236904       NUDT18   9.132402527   EIF2C2   7.812369431       FMO5   8.573554651   RAD54B   3.771356866       C1orf66   10.38687407   VDAC2   11.48336871       COL14A1   6.226066377   MCM10   6.289920626       PLAT   9.894345609   ZWINT   9.856885203       PCM1   6.610539309   KIF18A   4.323664316       ZBTB20   9.658834161   RACGAP1   8.678725078       NKFB1   9.515187228   TDG   8.78362627       TK2   9.00909589   NUP107   9.291523701       ABAT   8.774114776   TMPO   8.070476677       ACP6   10.57558001   XPOT   8.035070874       TSPAN7   9.544470732   BIRC5   9.867229559       TNFRSF10B   5.75209336   TOP2A   8.268887116       GSTM1   10.61241092   DSC2   8.0597748       OSBPL1A   9.063677782   EIF2S2   7.677085789       KCTD9   6.62967452   UBE2C   11.33116967       EVL   12.21472727   TPX2   9.862771273       MAP2K4   7.983384073   C20orf24   12.76892279       RPL21   13.50547562   KIF4A   8.868607027       STC2   10.77093214               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.
 
This patient exhibited a relapse-free survival time of 10.37 years.
 
       Example 10 
       [0100]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0101]    In this example, patient was diagnosed at age 60 as grade 1 node negative with a tumor size of 1.5 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in the table 10. 
         [0000]                                                            TABLE 10                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   8.195331371   CENPE   5.632172111       AZGP1   13.74984022   CCNB1   8.055483336       EPHX2   6.696227011   KIFC1   6.489111331       IGFBP4   12.09832399   CCT6A   10.38135864       NUDT18   8.270420346   EIF2C2   8.755586257       FMO5   7.021118002   RAD54B   6.958867796       C1orf66   9.662189619   VDAC2   11.30180572       COL14A1   7.293557331   MCM10   5.917221042       PLAT   9.920697333   ZWINT   9.7205269       PCM1   10.02811491   KIF18A   3.444838554       ZBTB20   10.19466269   RACGAP1   8.419747507       NKFB1   9.603847806   TDG   9.456320243       TK2   8.924649251   NUP107   9.984285983       ABAT   8.896633344   TMPO   7.949108266       ACP6   9.285473956   XPOT   9.54292892       TSPAN7   8.695560913   BIRC5   7.063289261       TNFRSF10B   6.320138873   TOP2A   7.086963896       GSTM1   9.681219679   DSC2   8.003530478       OSBPL1A   9.970328855   EIF2S2   7.436955491       KCTD9   7.911145356   UBE2C   9.291612864       EVL   12.0387619   TPX2   8.426273521       MAP2K4   8.50845927   C20orf24   11.48997945       RPL21   14.66438421   KIF4A   6.462304872       STC2   10.34574607               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.
 
This patient exhibited a relapse-free survival time of 9.82 years.
 
       Example 11 
       [0102]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0103]    In this example, patient was diagnosed at age 76 as grade 2 node negative with a tumor size of 2 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 11. 
         [0000]                                                            TABLE 11                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   9.220276473   CENPE   4.890147366       AZGP1   13.63396186   CCNB1   9.357511666       EPHX2   7.63440817   KIFC1   9.213362515       IGFBP4   10.90988711   CCT6A   8.961784315       NUDT18   9.043535413   EIF2C2   8.739433283       FMO5   7.432979371   RAD54B   6.007633509       C1orf66   9.643784067   VDAC2   11.81497055       COL14A1   7.599356611   MCM10   6.617340391       PLAT   11.32894871   ZWINT   10.78246017       PCM1   7.474273878   KIF18A   5.558831023       ZBTB20   9.525264933   RACGAP1   8.939562764       NKFB1   9.83374884   TDG   8.734711926       TK2   8.840877327   NUP107   9.38846082       ABAT   10.29856415   TMPO   8.103172514       ACP6   10.5667799   XPOT   8.466124368       TSPAN7   8.74103966   BIRC5   9.011353969       TNFRSF10B   7.94094245   TOP2A   8.059448642       GSTM1   11.16294174   DSC2   6.733968514       OSBPL1A   9.990399182   EIF2S2   7.133801494       KCTD9   7.503238991   UBE2C   10.34231341       EVL   9.296777305   TPX2   8.956665888       MAP2K4   8.788801129   C20orf24   12.14857435       RPL21   13.81407231   KIF4A   7.816111893       STC2   12.93997443               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.
 
This patient exhibited a relapse-free survival time of 8.82 years.
 
       Example 12 
       [0104]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0105]    In this example, patient was diagnosed at age 71 as grade 2 node negative with a tumor size of 3.8 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 12. 
         [0000]                                                            TABLE 12                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   7.225232241   CENPE   5.356683467       AZGP1   11.10551416   CCNB1   8.69014881       EPHX2   7.608820719   KIFC1   6.627439879       IGFBP4   10.14052865   CCT6A   9.503117742       NUDT18   8.790224261   EIF2C2   8.276676063       FMO5   8.352492368   RAD54B   7.296602326       C1orf66   9.095903836   VDAC2   12.07803136       COL14A1   6.801220806   MCM10   4.90426628       PLAT   12.1254784   ZWINT   9.963178365       PCM1   10.80643214   KIF18A   5.297509956       ZBTB20   8.655693668   RACGAP1   8.675716832       NKFB1   9.950598064   TDG   8.856305995       TK2   8.142178994   NUP107   9.825879761       ABAT   12.07425458   TMPO   7.898770909       ACP6   9.934738407   XPOT   9.409422485       TSPAN7   8.615270886   BIRC5   7.920768251       TNFRSF10B   5.930584997   TOP2A   8.274852202       GSTM1   10.48712907   DSC2   7.740213072       OSBPL1A   9.748346575   EIF2S2   7.64317594       KCTD9   7.917730095   UBE2C   9.971378017       EVL   11.05681921   TPX2   8.827280345       MAP2K4   9.304427564   C20orf24   11.53656485       RPL21   14.34440973   KIF4A   7.655959994       STC2   12.86487322               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.
 
This patient exhibited a relapse-free survival time of 9.77 years.
 
       Example 13 
       [0106]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0107]    In this example, patient was diagnosed at age 71 as grade 2 node negative with a tumor size of 1.1 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 13. 
         [0000]                                                            TABLE 13                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   9.291704969   CENPE   5.382882804       AZGP1   12.78955459   CCNB1   8.440426299       EPHX2   9.635858316   KIFC1   6.829062874       IGFBP4   11.53492614   CCT6A   9.324394803       NUDT18   8.580021659   EIF2C2   8.891052037       FMO5   9.367852626   RAD54B   5.385472494       C1orf66   9.531665745   VDAC2   11.42239554       COL14A1   7.198780142   MCM10   6.408723154       PLAT   8.965633637   ZWINT   10.00026781       PCM1   11.73299647   KIF18A   3.071083725       ZBTB20   9.995356409   RACGAP1   9.064077065       NKFB1   10.39137003   TDG   8.955158771       TK2   8.610248982   NUP107   9.991926076       ABAT   10.16325203   TMPO   8.339618599       ACP6   9.899745943   XPOT   9.205077535       TSPAN7   8.232730392   BIRC5   8.003442603       TNFRSF10B   8.125270272   TOP2A   8.044071277       GSTM1   9.57469389   DSC2   5.813682345       OSBPL1A   9.742077303   EIF2S2   7.924900627       KCTD9   8.122019716   UBE2C   9.793971887       EVL   12.81661697   TPX2   8.571855639       MAP2K4   8.464554411   C20orf24   11.58041449       RPL21   13.78632737   KIF4A   7.9808711       STC2   9.597783917               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.
 
This patient exhibited a relapse-free survival time of 11.03 years.
 
       Example 14 
       [0108]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0109]    In this example, patient was diagnosed at age 64 as grade 2 node negative with a tumor size of 4 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 14. 
         [0000]                                                            TABLE 14                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   8.672237007   CENPE   5.698169245       AZGP1   10.03974419   CCNB1   8.499274857       EPHX2   7.997469912   KIFC1   8.528740811       IGFBP4   9.855627665   CCT6A   9.52592378       NUDT18   8.405623241   EIF2C2   8.373179934       FMO5   9.240328855   RAD54B   6.843287473       C1orf66   8.995817157   VDAC2   11.54992641       COL14A1   7.932292908   MCM10   4.90418841       PLAT   8.852188372   ZWINT   10.28260367       PCM1   11.08708954   KIF18A   6.428181192       ZBTB20   9.793639913   RACGAP1   9.049262721       NKFB1   9.967037219   TDG   9.118005266       TK2   8.642951231   NUP107   9.94434022       ABAT   9.276640765   TMPO   8.588766178       ACP6   10.29122674   XPOT   9.116196563       TSPAN7   7.719768947   BIRC5   8.143036369       TNFRSF10B   7.640641107   TOP2A   8.619855173       GSTM1   9.281695426   DSC2   7.808699225       OSBPL1A   10.05216708   EIF2S2   7.959857094       KCTD9   8.239843842   UBE2C   9.996023676       EVL   11.57660733   TPX2   8.505614443       MAP2K4   8.738524985   C20orf24   11.67011271       RPL21   15.28974011   KIF4A   7.810522036       STC2   11.64642431               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.
 
This patient exhibited a relapse-free survival time of 9.45 years.
 
       Example 15 
       [0110]    This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0111]    In this example, patient was diagnosed at age 62 as grade 1 node negative with a tumor size of 2 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 15. 
         [0000]                                                            TABLE 15                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   7.95404528   CENPE   5.889816181       AZGP1   12.96281502   CCNB1   8.977058202       EPHX2   3.925829388   KIFC1   6.860790579       IGFBP4   13.98951479   CCT6A   10.07424594       NUDT18   7.342338206   EIF2C2   8.819670918       FMO5   8.755710391   RAD54B   6.850474828       C1orf66   8.502083292   VDAC2   10.70361673       COL14A1   6.885825359   MCM10   4.556044807       PLAT   8.958129179   ZWINT   9.90352554       PCM1   9.263851333   KIF18A   2.43819653       ZBTB20   8.760094598   RACGAP1   8.91534784       NKFB1   9.944393215   TDG   9.143939972       TK2   8.550001609   NUP107   10.22940838       ABAT   10.65823825   TMPO   8.625364575       ACP6   9.717129716   XPOT   10.04425538       TSPAN7   8.234606874   BIRC5   8.244907696       TNFRSF10B   6.783398571   TOP2A   9.529961049       GSTM1   10.27668464   DSC2   7.958312793       OSBPL1A   11.07866444   EIF2S2   7.901577226       KCTD9   7.495327704   UBE2C   10.12534313       EVL   11.12621928   TPX2   9.347041926       MAP2K4   8.92875982   C20orf24   12.26651445       RPL21   14.3166023   KIF4A   7.967112657       STC2   14.76346489               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.
 
This patient exhibited a relapse-free survival time of 9.4 years.
 
       Example 16 
       [0112]    This example refers to examples 5-15 of patients that were classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0113]    In the Symmans dataset, twelve patients were classified as medium risk by PAM50 but low risk by CADER. Relapse free survival was observed in 11 out of the 12 patients that were re-characterized by CADER therefore the present teaching are able to predict relapse free survival. 
       Example 17 
       [0114]    This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0115]    In this example, patient was diagnosed at age 61 as grade 2 node negative with a tumor size of 2.2 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 16. 
         [0000]                                                            TABLE 16                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   7.744600437   CENPE   7.544588013       AZGP1   10.66904972   CCNB1   9.47591913       EPHX2   4.805292308   KIFC1   9.506767063       IGFBP4   11.29608728   CCT6A   10.21427538       NUDT18   8.002058823   EIF2C2   8.7960344       FMO5   6.155200313   RAD54B   6.375895604       C1orf66   9.333179208   VDAC2   12.00687248       COL14A1   6.288615109   MCM10   7.501949976       PLAT   9.537949138   ZWINT   10.95987061       PCM1   9.588582125   KIF18A   6.051795069       ZBTB20   8.790479168   RACGAP1   9.24241408       NKFB1   9.725030872   TDG   9.575761169       TK2   8.531978461   NUP107   10.32938969       ABAT   8.159354409   TMPO   8.790588323       ACP6   9.584543206   XPOT   10.49669192       TSPAN7   8.693409574   BIRC5   9.336064812       TNFRSF10B   6.091732191   TOP2A   9.421322801       GSTM1   9.06286858   DSC2   7.776095411       OSBPL1A   9.151000584   EIF2S2   8.190102297       KCTD9   7.87786112   UBE2C   10.78982172       EVL   10.55251978   TPX2   10.15418118       MAP2K4   8.27281105   C20orf24   11.90394725       RPL21   13.95732984   KIF4A   8.570767398       STC2   9.01194817               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.
 
This patient exhibited distant relapse and had survived without distant relapse for 6.98 years.
 
       Example 18 
       [0116]    This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0117]    In this example, patient was diagnosed at age 53 as grade 2 node positive with a tumor size of 3.5 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 17. 
         [0000]                                                            TABLE 17                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   6.804051671   CENPE   5.37205207       AZGP1   10.39856245   CCNB1   8.991964499       EPHX2   5.513231806   KIFC1   6.771636713       IGFBP4   11.7797415   CCT6A   10.3483913       NUDT18   7.862417652   EIF2C2   8.904665998       FMO5   5.658509257   RAD54B   7.079699025       C1orf66   8.564056693   VDAC2   13.68322287       COL14A1   8.03407683   MCM10   6.122997443       PLAT   10.11372685   ZWINT   9.949184652       PCM1   9.443642359   KIF18A   6.485956244       ZBTB20   8.531888877   RACGAP1   8.632956402       NKFB1   10.66976642   TDG   8.135705715       TK2   8.928895236   NUP107   10.29069575       ABAT   8.16839595   TMPO   7.526540447       ACP6   9.360656124   XPOT   9.91958533       TSPAN7   8.640596844   BIRC5   7.847795729       TNFRSF10B   6.051866381   TOP2A   8.010053062       GSTM1   9.84184812   DSC2   8.152131803       OSBPL1A   9.022350499   EIF2S2   8.122990969       KCTD9   8.251784271   UBE2C   10.09858581       EVL   11.45450604   TPX2   9.211925163       MAP2K4   8.798858799   C20orf24   12.24436801       RPL21   14.78763383   KIF4A   8.680433487       STC2   11.53819169               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.
 
This patient exhibited distant relapse and had survived without distant relapse for 5.22 years.
 
       Example 19 
       [0118]    This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0119]    In this example, patient was diagnosed at age 68 as grade 1 node positive with a tumor size of 9.9 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 18. 
         [0000]                                                            TABLE 18                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   7.941656208   CENPE   5.820982523       AZGP1   11.91844546   CCNB1   9.080828313       EPHX2   11.09221285   KIFC1   8.443053537       IGFBP4   11.09921988   CCT6A   9.912108147       NUDT18   7.291000984   EIF2C2   9.014789327       FMO5   5.507258787   RAD54B   7.373821752       C1orf66   8.052387123   VDAC2   12.24316711       COL14A1   6.619255837   MCM10   5.481657234       PLAT   9.179447346   ZWINT   9.928994375       PCM1   9.999879743   KIF18A   7.056975017       ZBTB20   8.664983154   RACGAP1   9.481396294       NKFB1   9.962166703   TDG   8.90556312       TK2   8.363256706   NUP107   10.45249963       ABAT   8.00208168   TMPO   7.768463425       ACP6   9.287822071   XPOT   9.563085024       TSPAN7   8.75268169   BIRC5   8.48871999       TNFRSF10B   7.582667467   TOP2A   9.319611162       GSTM1   10.13714896   DSC2   6.847939442       OSBPL1A   10.21907084   EIF2S2   8.340508305       KCTD9   8.544638002   UBE2C   9.971599409       EVL   10.3354868   TPX2   9.405812825       MAP2K4   7.800937453   C20orf24   12.00273341       RPL21   14.15711663   KIF4A   8.013343028       STC2   10.27067709               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.
 
This patient exhibited distant relapse and had survived without distant relapse for 4.66 years.
 
       Example 20 
       [0120]    This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0121]    In this example, patient was diagnosed at age 66 as grade 2 node negative with a tumor size of 4 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 19. 
         [0000]                                                            TABLE 19                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   8.575552586   CENPE   5.702899308       AZGP1   11.37810078   CCNB1   9.954936246       EPHX2   5.625112756   KIFC1   7.746900849       IGFBP4   11.49885025   CCT6A   9.318846576       NUDT18   8.252384776   EIF2C2   9.173067959       FMO5   5.640726866   RAD54B   6.204679593       C1orf66   9.838671483   VDAC2   12.33336871       COL14A1   7.175991287   MCM10   4.978675499       PLAT   11.14152372   ZWINT   9.799826718       PCM1   8.676624207   KIF18A   5.066342911       ZBTB20   9.359683135   RACGAP1   9.958406158       NKFB1   9.804029895   TDG   9.03871544       TK2   8.627974937   NUP107   9.704097868       ABAT   7.87688799   TMPO   8.42129297       ACP6   10.06691172   XPOT   9.736253402       TSPAN7   8.465600958   BIRC5   8.988490932       TNFRSF10B   5.909433537   TOP2A   9.10676166       GSTM1   12.05894345   DSC2   8.097231854       OSBPL1A   10.15966192   EIF2S2   8.45169494       KCTD9   6.895008503   UBE2C   11.9116952       EVL   12.22035136   TPX2   10.42247756       MAP2K4   8.843494166   C20orf24   13.05330118       RPL21   14.22740117   KIF4A   7.893017097       STC2   12.64230526               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.
 
This patient exhibited distant relapse and had survived without distant relapse for 5.68 years.
 
       Example 21 
       [0122]    This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0123]    In this example, patient was diagnosed at age 61 as grade 3 node positive with a tumor size of 3 cm. Gene expression profile of patient&#39;s CADER gene signature is presented in table 20. 
         [0000]                                                            TABLE 20                   Endocrine Therapy   Endocrine Therapy       Sensitivity Genes   Resistance Genes            Gene Symbol   Expression Level   Gene Symbol   Expression Level                    PARP3   7.381832762   CENPE   7.085017638       AZGP1   10.64694672   CCNB1   8.638235879       EPHX2   7.549320403   KIFC1   8.129562479       IGFBP4   12.1897795   CCT6A   8.923314956       NUDT18   7.802321466   EIF2C2   8.834816089       FMO5   7.751416609   RAD54B   5.881147478       C1orf66   8.943595771   VDAC2   11.87057155       COL14A1   7.984848322   MCM10   7.072996991       PLAT   10.83283341   ZWINT   9.926258074       PCM1   10.86560731   KIF18A   5.780102567       ZBTB20   10.32743519   RACGAP1   8.843596679       NKFB1   10.14861856   TDG   9.445600595       TK2   8.511326627   NUP107   9.962609964       ABAT   8.19708022   TMPO   8.355612197       ACP6   9.416616338   XPOT   9.200544014       TSPAN7   8.395774951   BIRC5   8.314366606       TNFRSF10B   7.110230031   TOP2A   8.881611607       GSTM1   9.235942407   DSC2   7.863153004       OSBPL1A   9.309367829   EIF2S2   7.712268984       KCTD9   7.720045847   UBE2C   10.07626817       EVL   12.13976416   TPX2   8.850223895       MAP2K4   8.417635977   C20orf24   12.08210918       RPL21   14.25344008   KIF4A   8.257021285       STC2   8.652446485               *missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.            
Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.
 
This patient exhibited distant relapse and had survived without distant relapse for 10.8 years.
 
       Example 22 
       [0124]    This example refers to examples 17-21 of patients that were classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings. 
         [0125]    In the Symmans dataset, six patients were classified as low risk for relapse by PAM50 but high risk for relapse by CADER. Distant relapse was observed in 5 out of the 6 patients that were re-characterized by CADER therefore the present teachings are able to predict distant relapse. 
         [0000]    All references cited are hereby incorporated by reference, each in its entirety.