Patent Publication Number: US-2023143295-A1

Title: Methods and kits for determining the risk of breast cancer recurrence

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of priority to E.P. Application No. 20173612.1, filed May 8, 2020, which is incorporated by reference herein in its entirety. 
     FIELD OF THE INVENTION 
     This disclosure relates generally to the field of breast cancer biology, and specifically, to refined prognostic clinical tools, methods, and kits for the evaluation of risk and management of distant recurrence in ER+/HER2− breast cancer patients. 
     BACKGROUND OF THE INVENTION 
     Endocrine receptor-positive (ER+)/HER2-negative (HER2−) breast cancers constitute the majority (approximately &gt;75%) of breast cancer cases. Due to the high level of molecular and clinical heterogeneity displayed by these cancers, prognosis and therapy response are often difficult to predict. This makes the clinical management of the ER+/HER2− breast cancer patients challenging, particularly, in terms of the type and the duration of the adjuvant systemic therapy an individual should receive. Based on the intrinsic risk of recurrence (typically assessed using standard clinico-pathological parameters), ER+/HER2− breast cancer patients may be offered adjuvant chemotherapy in addition to hormonal therapy or prolonged hormonal therapy beyond the five years standard of care. However, since standard clinico-pathological parameters are often insufficient to accurately predict risk of recurrence in these patients, a significant proportion of patients are, consequently, either over- or under-treated. 
     Accordingly, an unmet need exists for more refined prognostic clinical tools for the evaluation of risk and management of distant recurrence in ER+/HER2− breast cancer patients. 
     SUMMARY OF THE INVENTION 
     The present disclosure provides a method for predicting a risk of breast cancer recurrence in a subject having at least one breast cancer tumor, the method comprising: (a) determining, in a sample from the subject, the expression level of at least one gene from Table 1; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; and (d) calculating a risk score based upon the expression level of the at least one gene, the pT and the pN. 
     The present disclosure also provides a method for stratifying a subject having at least one breast cancer tumor into a low or high risk group of breast cancer recurrence, the method comprising: (a) determining, in a sample from the subject, the expression level of at least one gene from Table 1; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least one gene, the pT and the pN; and (e) stratifying the subject based upon the calculated risk score. 
     The present disclosure also provides a method for stratifying a subject having at least one breast cancer tumor into a low, intermediate, or high risk group of breast cancer recurrence, the method comprising: (a) determining, in a sample from the subject, the expression level of at least one gene from Table 1; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least one gene, the pT and the pN; and (e) stratifying the subject based upon the calculated risk score. 
     In some aspects of the preceding methods, step (a) can comprise determining, in a sample from the subject, the expression level of each of the twenty genes from Table 1. 
     In some aspects of the preceding methods, step (d) can comprise determining a risk score based upon the expression level of each of the twenty genes, the pT and the pN. 
     In some aspects of the preceding methods, step (a) can comprise determining, in a sample from the subject, the expression level of at least three genes from Table 1, wherein the at least three genes: a) comprises at least EIF4EBP1, MRPS23, and TOP2A; or b) consist of EIF4EBP1, MRPS23, and TOP2A. 
     In some aspects of the preceding methods, step (a) can comprise determining, in a sample from the subject, the expression level of at least nine genes from Table 1, wherein the at least nine genes: a) comprises at least APOBEC3B, CENPW, EIF4EBP1, EXOSC4, LY6E, MMP1, MRPS23, NDUFB10, and TOP2A; or b) consist of APOBEC3B, CENPW, EIF4EBP1, EXOSC4, LY6E, MMP1, MRPS23, NDUFB10, and TOP2A. 
     In some aspects of the preceding methods, step (a) can comprise determining, in a sample from the subject, the expression level of at least sixteen genes from Table 1, wherein the at least sixteen genes: a) comprises at least ALYREF, APOBEC3B, CDK1, CENPW, EIF4EBP1, EXOSC4, H2AFJ, LY6E, MIEN1, MMP1, MRPS23, NDUFB10, NOL3, RACGAP1, SFN, and TOP2A; or b) consist of ALYREF, APOBEC3B, CDK1, CENPW, EIF4EBP1, EXOSC4, H2AFJ, LY6E, MIEN1, MMP1, MRPS23, NDUFB10, NOL3, RACGAP1, SFN, and TOP2A. 
     In some aspects of the preceding methods, step (d) can comprise determining a risk score based upon the expression level of the at least three, or the at least nine, or the at least sixteen genes, the pT and the pN. 
     In some aspects of the preceding methods, stratifying a subject based upon the calculated risk score can comprise stratifying the subject as having a low risk for breast cancer recurrence when the risk score is less than a predetermined cutoff value. In some aspects of the preceding methods, a predetermined cutoff value can be 49. 
     In some aspects of the preceding methods, stratifying a subject based upon the calculated risk score can comprise stratifying the subject as having a high risk for breast cancer recurrence when the risk score is greater than a predetermined cutoff value. In some aspects of the preceding methods, a predetermined cutoff value can be 59. 
     In some aspects of the preceding methods, stratifying a subject based upon the calculated risk score can comprise stratifying the subject as having an intermediate risk for breast cancer recurrence when the risk score is greater than or equal to a first predetermined cutoff value and less than or equal to a second predetermined cutoff value. In some aspects of the preceding methods, a first predetermined cutoff value can be 49 and the second predetermined cutoff value can be 59. 
     In some aspects of the preceding methods, the method can further comprise administering at least one treatment to the subject based on the risk score. 
     In some aspects of the preceding methods, the method can further comprise administering to the subject stratified as having a high risk of breast cancer recurrence a cancer treatment that is more aggressive than a cancer treatment provided to a subject stratified as having a low risk of breast cancer recurrence. 
     In some aspects of the preceding methods, the method can further comprise administering to the subject stratified as having a high risk of breast cancer recurrence a cancer treatment that is more aggressive than a cancer treatment provided to a subject stratified as having an intermediate risk of breast cancer recurrence. 
     In some aspects of the preceding methods, the method can further comprise administering to the subject stratified as having an intermediate risk of breast cancer recurrence a cancer treatment that is more aggressive than a cancer treatment provided to a subject stratified as having a low risk of breast cancer recurrence. 
     In some aspects of the preceding methods, an at least one treatment can comprise adjuvant chemotherapy. 
     In some aspects of the preceding methods, a more aggressive treatment can comprise adjuvant chemotherapy. 
     In some aspects of the preceding methods, a risk score can be calculated according to the following formula: 
     
       
         
           
             
               Risk 
               ⁢ 
                   
               Score 
             
             = 
             
               
                 
                   
                     ( 
                     
                       
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                         . 
                         0 
                       
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                       ⁢ 
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                     ) 
                   
                   × 
                   
                     ( 
                     
                       
                         ∑ 
                         i 
                       
                       
                         
                           β 
                           i 
                         
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                         C 
                         ⁢ 
                         
                           q 
                           
                             n 
                             ⁢ 
                             o 
                             ⁢ 
                             r 
                             ⁢ 
                             m 
                             ⁢ 
                             a 
                             ⁢ 
                             l 
                             ⁢ 
                             ized 
                           
                         
                       
                     
                     ) 
                   
                 
                 + 
                 
                   p 
                   ⁢ 
                   T 
                 
                 + 
                 
                   p 
                   ⁢ 
                   N 
                 
               
               
                 
                   5 
                   . 
                   6 
                 
                 ⁢ 
                 4 
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                 ⁢ 
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                 ⁢ 
                 2 
               
             
           
         
       
     
     wherein
         i is the summation index for the genes for which the expression level was measured;   β is the ridge penalized Cox model coefficient for each gene as put forth in Table 1, Table 2, Table 3 or Table 4; and Cq normalized  is the normalized average Cq for each of the genes for which the expression level was measured;       

     wherein
         pT is 0.45542 if the tumor size of the at least one breast cancer tumor is determined to be pT1c; or   pT is 0.96572 if the tumor size of the breast cancer tumor is determined to be any of pT2, pT3 and pT4; and       

     wherein
         pN is 0.46293 if the nodal status of the at least one breast cancer tumor is determined to be pN1; or   pN is 1.24922 if the nodal status of the at least one breast cancer tumor is determined to be pN2.       

     In some aspects of the preceding methods, Cqnormalized can be normalized to the expression of at least one reference gene. An at least one reference gene can be a housekeeping gene. An at least one reference gene can be selected from the group consisting of GAPDH, GUSB, HPRT1, and TBP. An at least one reference gene can comprise at least each of GAPDH, GUSB, HPRT1, and TBP. 
     In some aspects of the preceding methods, Cq normalized  can be calculated according to the following formula: 
         Cq   normalized =AVG  Cq−SF    
     wherein SF is the difference between the AVG Cq value of the at least one reference gene for each subject and a constant reference value K. 
     In some aspects of the preceding methods, K can be the mean of the AVG Cq of the at least one reference gene calculated across a plurality of training samples. In some aspects, K can be 25.012586069. 
     In some aspects of the preceding methods, gene expression can be determined using one or more techniques selected from the group consisting of analysis of single strand conformation polymorphism, capillary electrophoresis, denaturing high performance liquid chromatography, digital molecular barcoding technology, e.g., Nanostring&#39;s nCounter system, direct sequencing, DNA mismatch-binding protein assays, dynamic allele-specific hybridization, Fluorescent in situ hybridization (FISH), high-density oligonucleotide SNP arrays, high-resolution melting analysis, microarray, next generation sequencing (NGS), e.g., using the Illumina Genome Analyzer, ABI Solid instrument, Roche 454 instrument, Heliscope instrument, Northern blot analysis, nuclease protection analysis, oligonucleotide ligase assays, polymerase chain reaction (PCR), primer extension assays, Quantigene analysis, quantitative nuclease-protection assay (qNPA), reporter gene detection, restriction fragment length polymorphism (RFLP) assays, reverse transcription and real-time quantitative polymerase chain reaction (RT-qPCR), reverse transcription-polymerase chain reaction (RT-PCR), RNA sequencing (RNA-seq), Serial analysis of gene expression (SAGE), Single Molecule Real Time (SMRT) DNA sequencing technology, SNPLex, Southern blot analysis, Sybr Green chemistry, TaqMan-based assays, temperature gradient gel electrophoresis (TGGE), Tiling array, Western blot analysis, and immunohistochemistry. 
     In some aspects of the preceding methods, gene expression can be determined using reverse transcription and real-time quantitative polymerase chain reaction (RT-qPCR). 
     In some aspects of the preceding methods, gene expression can be determined using microarray analysis. 
     In some aspects of the preceding methods, a sample can be a tumor obtained from the subject, a cancerous cell obtained from the subject, or a cancer stem cell obtained from the subject; or wherein the sample is a primary cell line derived from a tumor obtained from the subject, from a cancerous cell obtained from the subject, or from a cancer stem cell obtained from the subject. 
     In some aspects of the preceding methods, a subject can have a ER+/HER2− breast cancer. 
     The present disclosure provides a kit for use in the methods of the present disclosure. A kit can comprise comprising reagents sufficient for determining the expression levels of the at least one, at least three, at least nine, at least sixteen or each of the twenty genes from Table 1. 
     In some aspects, the disclosure provides a method for treating a breast cancer in a subject in need thereof, the method comprising the steps of: (a) predicting a risk of breast cancer recurrence in the subject; or (b) stratifying the subject into a low or high risk group of breast cancer recurrence; and (c) administering a treatment to the subject, wherein the treatment is based on (a) or (b). In some embodiments, predicting a risk of breast cancer recurrence in the subject comprises the methods described herein. In some embodiments, stratifying the subject into a low or high risk group of breast cancer recurrence comprises the methods described herein. In some embodiments, the breast cancer is ER+/HER2− breast cancer. In some embodiments, the treatment is a breast cancer treatment. In some embodiments, the breast cancer treatment comprises surgery, radiation, anthracycline agents, alkylating agents, nucleoside analogs, platinum agents, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, endocrine/hormonal agents, bisphophonate therapy agents and targeted biological therapy agents (e.g., antibodies), or combinations thereof. In some embodiments, the breast cancer treatment comprises cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, gemcitabine, anthracycline, taxanes, paclitaxel, protein-bound paclitaxel, doxorubicin, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, irinotecan, ixabepilone, temozolmide, topotecan, vincristine, vinblastine, eribulin, mutamycin, capecitabine, capecitabine, anastrozole, exemestane, letrozole, leuprolide, abarelix, buserlin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, denosumab, zoledronate, trastuzumab, tykerb or bevacizumab, or combinations thereof. 
     Any of the above aspects can be combined with any other aspect. 
     Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. In the Specification, the singular forms also include the plural unless the context clearly dictates otherwise; as examples, the terms “a,” “an,” and “the” are understood to be singular or plural and the term “or” is understood to be inclusive. By way of example, “an element” means one or more element. Throughout the specification the word “comprising,” or variations such as “comprises” or “comprising,” will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. About can be understood as within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated value. Unless otherwise clear from the context, all numerical values provided herein are modified by the term “about.” 
     Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The references cited herein are not admitted to be prior art to the claimed invention. In the case of conflict, the present Specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be limiting. Other features and advantages of the disclosure will be apparent from the following detailed description and claim. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and further features will be more clearly appreciated from the following detailed description when taken in conjunction with the accompanying drawings. 
         FIG.  1    is a graph showing the 10-year risk of distant recurrence (%) vs. the recurrence score as calculated using the SPARE methods of the present disclosure. 
         FIG.  2    is a series of graphs showing the results of the calibration prediction plot analysis of observed versus predicted 10-year distant metastasis rate according to the SPARE methods of the present disclosure in both the training and validation datasets. 
         FIG.  3    is a series of graphs showing the cumulative incidence of distant recurrences according to the SPARE methods of the present disclosure in both the training and validation datasets. 
         FIG.  4    is a series of graphs showing the cumulative incidence of distant recurrences according to SPARE methods of the present disclosure and the use of chemotherapy in the combined dataset. 
         FIG.  5    is forest plot analysis showing 10-year distant recurrence rates according to the SPARE methods of the present disclosure in the different risk groups and stratified according to clinicopathological characteristics. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention relates to improved prognostic clinical tools, methods, and kits for the evaluation of risk and management of distant recurrence in ER+/HER2− breast cancer patients. The present invention also relates to improved methods of treating ER+/HER2− breast cancer patients with the risk of distant recurrence. 
     The present invention is based in part through a retrospective analysis of a consecutive cohort of 1,827 ER+/HER2− breast cancer patients with long-term follow-up (˜15 years). The retrospective analysis yielded a 20-gene signature (and subsets thereof) that is, in combination with the tumor size (pT) and nodal status (pN), able to stratify breast cancer patients according to risk of early and late recurrence. The methods of the present disclosure, herein referred to as the StemPrintER risk score (SPRS) for Personalized Adjuvant therapy in Receptor-Expressing patients (SPARE) methods, function as prognostic-predictive clinical tools in ER+/HER2− breast cancer patients that may be used to guide clinical decision-making on the selection of adjuvant systemic therapies. The 20-gene signature can be further partitioned into 3, 9, 16, and 20-gene signatures, i.e., the “StemPrintER3 genomic predictor”, “StemPrintER5 genomic predictor”, “StemPrintER9 genomic predictor”, “StemPrintER16 genomic predictor”, and “StemPrintER20 genomic predictor”, which, in combination with the tumor size (pT) and nodal status (pN) can function as prognostic-predictive clinical tools in ER+/HER2− breast cancer patients that may be used to guide clinical decision-making on the selection of adjuvant systemic therapies. 
     The StemPrintER risk score, StemPrintER3 genomic predictor, StemPrintER9 genomic predictor, StemPrintER16 genomic predictor, and StemPrintER20 genomic predictor are described in further detail in US Patent Application Publication No. 2019-0161809 and International Patent Application Publication No. WO2017/220492, both of which are incorporated by reference in their entireties. The present disclosure is based in part on the surprising and unexpected finding that combining any of the StemPrintER risk score, StemPrintER3 genomic predictor, StemPrintER9 genomic predictor StemPrintER16 genomic predictor, and StemPrintER20 genomic predictor with clinicopathological parameters including, but not limited to, the subject&#39;s tumor size (pT) and nodal status (pN) provides a more accurate and powerful prognostic tool that can be used, for example, in personalized therapeutic decision making in ER+/HER2− breast cancer patients. In fact, the methods of the present disclosure outperform the Clinical Treatment Score (CTS) algorithm, which is widely accepted in the field as one of the most powerful predictive algorithms for distant recurrence of breast cancer. Methods for deriving the CTS are well known in the art (see Cuzick J, et al., J Clin Oncol. 2011; 29 (32): 4273-4278). 
     The present disclosure provides a method of predicting a risk of breast cancer recurrence in a subject having at least one breast cancer tumor, the method comprising: (a) determining, in a sample from the subject, the expression level of at least one gene from Table 1; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; and (d) calculating a risk score based upon the expression level of the at least one gene, the pT and the pN. 
     The present disclosure provides a method of stratifying a subject having at least one breast cancer tumor into a low or high risk group for breast cancer recurrence, the method comprising: (a) determining, in a sample from the subject, the expression level of at least one gene from Table 1; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least one gene, the pT and the pN; and (e) stratifying the subject based upon the calculated risk score. 
     The present disclosure provides a method of stratifying a subject having at least one breast cancer tumor into a low, intermediate or high risk group for breast cancer recurrence, the method comprising: (a) determining, in a sample from the subject, the expression level of at least one gene from Table 1; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least one gene, the pT and the pN; and (e) stratifying the subject based upon the calculated risk score. 
     The present disclosure provides a method of stratifying a subject having at least one breast cancer tumor according to risk of breast cancer recurrence, the method comprising: (a) determining, in a sample from the subject, the expression level of at least one gene from Table 1; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least one gene, the pT and the pN; and (e) stratifying the subject based upon the calculated risk score. 
     The present disclosure provides a method of treating breast cancer in a subject having at least one breast cancer tumor, the method comprising: (a) determining, in a sample from the subject, the expression level of at least one gene from Table 1; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least one gene, the pT and the pN; and (e) administering at least one treatment based on the calculated risk score. 
     In some aspects of any of the preceding methods, step (a) can comprise determining, in a sample from the subject, the expression level of at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen genes from Table 1. Accordingly, calculating the risk score can comprise calculating a risk score based upon the expression level of the at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, or at least nineteen genes, the pT and the pN. 
     In some aspects of any of the preceding methods, step (a) can comprise determining, in a sample from the subject, the expression level of each of the twenty genes from Table 1. Accordingly, calculating the risk score can comprise calculating a risk score based upon the expression level of the each of the twenty genes, the pT and the pN. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Gene 
                 Ridge penalized Cox model coefficient (β) 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 H2AFZ 
                 −0.03833591325196550 
               
               
                   
                 CDK1 
                 −0.06132455806571770 
               
               
                   
                 EXOSC4 
                 −0.02105976326055420 
               
               
                   
                 PHLDA2 
                 −0.06295739658169650 
               
               
                   
                 APOBEC3B 
                 0.02341881674020150 
               
               
                   
                 EIF4EBP1 
                 −0.13911217901125500 
               
               
                   
                 SFN 
                 0.05788269046891110 
               
               
                   
                 PHB 
                 −0.03538557745953510 
               
               
                   
                 EPB41L5 
                 −0.04675539403890050 
               
               
                   
                 RACGAP1 
                 −0.05097505893853430 
               
               
                   
                 MRPS23 
                 −0.14201022110072700 
               
               
                   
                 TOP2A 
                 −0.11290078348786600 
               
               
                   
                 H2AFJ 
                 −0.04975471358452700 
               
               
                   
                 NOL3 
                 −0.04193802459521500 
               
               
                   
                 MIEN1 
                 0.01133668644106850 
               
               
                   
                 CENPW 
                 −0.03717918353187610 
               
               
                   
                 LY6E 
                 −0.02829256296234230 
               
               
                   
                 ALYREF 
                 −0.09541915699494330 
               
               
                   
                 MMP1 
                 −0.00911370427072023 
               
               
                   
                 NDUFB10 
                 0.00626166874136819 
               
               
                   
                   
               
            
           
         
       
     
     The present disclosure provides a method of predicting a risk of breast cancer recurrence in a subject having at least one breast cancer tumor, the method comprising: (a) determining, in a sample from the subject, the expression level of at least three genes from Table 1, wherein the at least three genes comprise at least EIF4EBP1, MRPS23, and TOP2A; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; and (d) calculating a risk score based upon the expression level of the at least three genes, the pT and the pN. 
     The present disclosure provides a method of stratifying a subject having at least one breast cancer tumor into a low or high risk group for breast cancer recurrence, the method comprising: (a) determining, in a sample from the subject, the expression level of at least three genes from Table 1, wherein the at least three genes comprise at least EIF4EBP1, MRPS23, and TOP2A; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least three genes, the pT and the pN; and (e) stratifying the subject based upon the calculated risk score. 
     The present disclosure provides a method of stratifying a subject having at least one breast cancer tumor into a low, intermediate or high risk group for breast cancer recurrence, the method comprising: (a) determining, in a sample from the subject, the expression level of at least three genes from Table 1, wherein the at least three genes comprise at least EIF4EBP1, MRPS23, and TOP2A; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least three genes, the pT and the pN; and (e) stratifying the subject based upon the calculated risk score. 
     The present disclosure provides a method of stratifying a subject having at least one breast cancer tumor according to risk of breast cancer recurrence, the method comprising: (a) determining, in a sample from the subject, the expression level of at least three genes from Table 1, wherein the at least three genes comprise at least EIF4EBP1, MRPS23, and TOP2A; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least three genes, the pT and the pN; and (e) stratifying the subject based upon the calculated risk score. 
     The present disclosure provides a method of treating breast cancer in a subject having at least one breast cancer tumor, the method comprising: (a) determining, in a sample from the subject, the expression level of at least three genes from Table 1, wherein the at least three genes comprise at least EIF4EBP1, MRPS23, and TOP2A; (b) determining the tumor size (pT) of the at least one breast cancer tumor; (c) determining the nodal status (pN) of the at least one breast cancer tumor; (d) calculating a risk score based upon the expression level of the at least three genes, the pT and the pN; and (e) administering at least one treatment based on the calculated risk score. 
     In some aspects, the at least three genes can comprise at least EIF4EBP1, MRPS23, and TOP2A. In some aspects, the at least three genes can comprise at least APOBEC3B, CENPW, EIF4EBP1, EXOSC4, LY6E, MMP1, MRPS23, NDUFB10, and TOP2A. In some aspects, the at least three genes can comprise at least ALYREF, APOBEC3B, CDK1, CENPW, EIF4EBP1, EXOSC4, H2AFJ, LY6E, MIEN1, MMP1, MRPS23, NDUFB10, NOL3, RACGAP1, SFN, and TOP2A. 
     In some aspects, the at least three genes can consist of EIF4EBP1, MRPS23, and TOP2A. In some aspects, the at least three genes can consist of APOBEC3B, CENPW, EIF4EBP1, EXOSC4, LY6E, MMP1, MRPS23, NDUFB10, and TOP2A. In some aspects, the at least three genes can consist of ALYREF, APOBEC3B, CDK1, CENPW, EIF4EBP1, EXOSC4, H2AFJ, LY6E, MIEN1, MMP1, MRPS23, NDUFB10, NOL3, RACGAP1, SFN, and TOP2A. 
     In aspects wherein the at least three genes comprise at least EIF4EBP1, MRPS23, and TOP2A or consist of EIF4EBP1, MRPS23, and TOP2A, the ridge penalized Cox model coefficient for each gene (β) can be the coefficients put forth in Table 2. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                   
                 Ridge penalized Cox 
               
               
                   
                 Gene 
                 model coefficient (β) 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 EIF4EBP1 
                 −0.2661522777700890 
               
               
                   
                 MRPS23 
                 −0.4064807990811070 
               
               
                   
                 TOP2A 
                 −0.1898759903565910 
               
               
                   
                   
               
            
           
         
       
     
     In aspects wherein the at least three genes comprise at least APOBEC3B, CENPW, EIF4EBP1, EXOSC4, LY6E, M/MP1, MRPS23, NDUFB10, and TOP2A or consist of APOBEC3B, CENPW, EIF4EBP1, EXOSC4, LY6E, MMP1, MRPS23, NDUFB10, and TOP2A, the ridge penalized Cox model coefficients for each gene (0) can be the coefficient put forth in Table 3. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                   
                 Ridge penalized Cox 
               
               
                   
                 Gene 
                 model coefficient (β) 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 EXOSC4 
                 0.3440660812818810 
               
               
                   
                 APOBEC3B 
                 0.2110001016524630 
               
               
                   
                 EIF4EBP1 
                 −0.2223133616036320 
               
               
                   
                 MRPS23 
                 −0.4788624802373240 
               
               
                   
                 TOP2A 
                 −0.2406479942640310 
               
               
                   
                 CENPW 
                 −0.2053740290368180 
               
               
                   
                 LY6E 
                 −0.2897586785096140 
               
               
                   
                 MMP1 
                 −0.0824402740633608 
               
               
                   
                 NDUFB10 
                 0.3085837868590590 
               
               
                   
                   
               
            
           
         
       
     
     In aspects wherein the at least three genes comprise at least ALYREF, APOBEC3B, CDK1, CENPW, EIF4EBP1, EXOSC4, H2AFJ, LY6E, MIEN1, MMP1, MRPS23, NDUFB10, NOL3, RACGAP1, SFN, and TOP2A or consist of ALYREF, APOBEC3B, CDK1, CENPW, EIF4EBP1, EXOSC4, H2AFJ, LY6E, MIEN1, MMP1, MRPS23, NDUFB10, NOL3, RACGAP1, SFN, and TOP2A, the ridge penalized Cox model coefficient for each gene (β) can be the coefficients put forth in Table 4. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 4 
               
               
                   
                   
               
               
                   
                   
                 Ridge penalized Cox 
               
               
                   
                 Gene 
                 model coefficient (β) 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 CDK1 
                 −0.0777226352877493 
               
               
                   
                 EXOSC4 
                 0.2571414958102690 
               
               
                   
                 APOBEC3B 
                 0.2027825590936980 
               
               
                   
                 EIF4EBP1 
                 −0.2329055344285050 
               
               
                   
                 SFN 
                 0.0591955291393095 
               
               
                   
                 RACGAP1 
                 −0.0928937254771330 
               
               
                   
                 MRPS23 
                 −0.4265118770613120 
               
               
                   
                 TOP2A 
                 −0.1515759123771920 
               
               
                   
                 H2AFJ 
                 −0.0432973988579006 
               
               
                   
                 NOL3 
                 −0.1044402373747570 
               
               
                   
                 MIEN1 
                 0.0607555452253983 
               
               
                   
                 CENPW 
                 −0.1260999729121140 
               
               
                   
                 LY6E 
                 −0.2129142199263660 
               
               
                   
                 ALYREF 
                 −0.0867101647370881 
               
               
                   
                 MMP1 
                 −0.0499993277954095 
               
               
                   
                 NDUFB10 
                 0.2355873243333520 
               
               
                   
                   
               
            
           
         
       
     
     In aspects wherein the at least three genes comprise each of the genes in Table 1, the ridge penalized Cox model coefficient for each gene (β) can be the coefficients put forth in Table 1. 
     In some aspects of the methods of the present disclosure, stratifying the subject based upon the calculated risk score can comprise stratifying the subject as having a low risk for breast cancer recurrence if the risk score is less than a predetermined cutoff value. In some aspects, the predetermined cutoff value can be 49. Thus, in some aspects of the methods of the present disclosure, stratifying the subject based upon the calculated risk score can comprise stratifying the subject as having a low risk for breast cancer recurrence if the risk score is less than 49. 
     In some aspects of the methods of the present disclosure, stratifying the subject based upon the calculated risk score can comprise stratifying the subject as having a high risk for breast cancer recurrence if the risk score is greater than a predetermined cutoff value. In some aspects, the predetermined cutoff value can be 59. Thus, in some aspects of the methods of the present disclosure, stratifying the subject based upon the calculated risk score can comprise stratifying the subject as having a high risk for breast cancer recurrence if the risk score is greater than 59. 
     In some aspects of the methods of the present disclosure, stratifying the subject based upon the calculated risk score can comprise stratifying the subject has having an intermediate risk for breast cancer recurrence if the risk score is greater than or equal to a first predetermined cutoff value and less than or equal to a second predetermined cutoff value. In some aspects the first predetermined cutoff value can be 49 and the second predetermined cutoff value can be 59. Thus, in some aspects of the methods of the present disclosure, stratifying the subject based upon the calculated risk score can comprise stratifying the subject as having an intermediate risk for breast cancer recurrence if the risk score is greater than or equal to 49 and less than or equal to 59. 
     In some aspects, a method of the present disclosure can have a clinical treatment score (CTS) for 10-year risk of distant metastasis of at least about 141. 
     In some aspects, determining the expression level of a gene in a sample from a subject can comprise determining the quantification cycle (Cq) for the gene using quantitative PCR. 
     In some aspects, the risk score can be calculated according to the following formula: 
     
       
         
           
             
               Risk 
               ⁢ 
                   
               Score 
             
             = 
             
               
                 
                   
                     ( 
                     
                       
                         0 
                         . 
                         0 
                       
                       ⁢ 
                       3 
                       ⁢ 
                       6 
                       ⁢ 
                       0 
                       ⁢ 
                       4 
                     
                     ) 
                   
                   × 
                   
                     ( 
                     
                       
                         ∑ 
                         i 
                       
                       
                         
                           β 
                           i 
                         
                         × 
                         C 
                         ⁢ 
                         
                           q 
                           
                             n 
                             ⁢ 
                             o 
                             ⁢ 
                             r 
                             ⁢ 
                             m 
                             ⁢ 
                             a 
                             ⁢ 
                             l 
                             ⁢ 
                             ized 
                           
                         
                       
                     
                     ) 
                   
                 
                 + 
                 
                   p 
                   ⁢ 
                   T 
                 
                 + 
                 
                   p 
                   ⁢ 
                   N 
                 
               
               
                 
                   5 
                   . 
                   6 
                 
                 ⁢ 
                 4 
                 ⁢ 
                 0 
                 ⁢ 
                 3 
                 ⁢ 
                 2 
               
             
           
         
       
     
     wherein
         i is the summation index for the genes for which the expression level was measured;   β is the ridge penalized Cox model coefficient for each gene as put forth in any one of Tables 1-4; and   Cq normalized  is the normalized average Cq for each of the genes for which the expression level was measured;       

     wherein
         pT is 0.45542 if the tumor size of the at least one breast cancer tumor is determined to be pT1c; or   pT is 0.96572 if the tumor size of the breast cancer tumor is determined to be any of pT2, pT3 and pT4; and       

     wherein
         pN is 0.46293 if the nodal status of the at least one breast cancer tumor is determined to be pN1; or   pN is 1.24922 if the nodal status of the s at least one breast cancer tumor is determined to be pN2.       

     In some aspects, determining the tumor size of a breast cancer tumor can comprise determining whether the at least one tumor is characterized as pT1c, pT2, pT3 or pT4 using standards and techniques that are known in the art (see Zurrida and Veronesi,  Women&#39;s Health,  2011, 7(1), 41-49). 
     In a non-limiting example, a pT1c breast cancer tumor can be a tumor with a size more than 1.0 cm, but not more than 2.0 cm in the greatest dimension. 
     In a non-limiting example, a pT2 breast cancer tumor can be a tumor with a size more than 2.0 cm, but not more than 5.0 cm in the greatest dimension. 
     In a non-limiting example, a pT3 breast cancer tumor can be a tumor with a size more than 5.0 cm in the greatest dimension. 
     In a non-limiting example, a pT4 breast cancer tumor can be a tumor of any size with direct extension to the chest wall (pT4a), to an edema or ulceration of the skin of the breast or satellite skin nodules confined to the same breast (pT4b), to both the chest wall and an edema or ulceration of the skin of the breast or satellite skin nodules confined to the same breast (pT4c) or to an inflammatory carcinoma (pT4d). 
     In some aspects, determining the nodal status of a breast cancer tumor can comprise determining whether the at least one tumor is characterized as pN1 or pN2 using standards and techniques that are known in the art (see Zurrida and Veronesi,  Women&#39;s Health,  2011, 7(1), 41-49). 
     In a non-limiting example, a pN1 breast cancer tumor can be a tumor that has a metastasis in 1 to 3 axillary lymph nodes and/or in internal mammary nodes with microscopic disease detected by sentinel lymph node dissection. 
     In a non-limiting example, a pN2 breast cancer tumor can be a tumor that has a metastasis in 4-9 axillary lymph nodes or in clinically apparent internal mammary lymph nodes in the absence of axillary lymph node metastasis. 
     As used herein, the term Cq is used in its broadest sense to refer to the measured quantification cycle (Cq) of a particular gene as measured using quantitative PCR. In some aspects, a Cq can be defined as a limit of detection. In some aspects, a Cq of 35 can be defined as the limit of detection. 
     Any method of the present disclosure can further comprise determining the 10-year distant recurrence probability for the subject based on the calculated risk score. In some aspects, the 10-year distant recurrence probability (P 10 ) can be calculated based on the calculated risk score (R) using the following formula: 
         P   10 =1−(0.99514 e(005627×R)  
 
     In some aspects of the methods of the present disclosure, a subject stratified in a high risk group may be administered a treatment that is more aggressive than the treatment administered to a subject stratified in a low risk group. In some aspects of the methods of the present disclosure, a subject stratified in a high risk group may be administered a treatment that is more aggressive than the treatment administered to a subject stratified in an intermediate risk group. In some aspects of the methods of the present disclosure, a subject stratified in an intermediate risk group may be administered a treatment that is more aggressive than the treatment administered to a subject stratified in a low risk group. 
     In some aspects of the methods of the present disclosure, a subject who has a risk score of greater than 59 may be administered a treatment that is more aggressive than the treatment administered to a subject who has a risk score of less than 49. In some aspects of the methods of the present disclosure, a subject who has a risk score of greater than 59 may be administered a treatment that is more aggressive than the treatment administered to a subject who has a risk score greater than or equal to 49 but less than or equal to 59. In some aspects, a subject who has a risk score greater than 59 may be administered a treatment that is more aggressive than the treatment administered to a subject who has a risk score of less than or equal to 59. In some aspects, a subject who has a risk score greater than or equal 49 may be administered a treatment that is more aggressive than the treatment administered to a subject who has a risk score of less than 49. In some aspects, a subject who has a risk score greater than or equal to 49 and less than or equal to 59 may be administered a treatment that is more aggressive than the treatment administered to a subject who has a risk score of less than 49. 
     Stratification of subjects into risk groups may be influenced by other features of the subject. For example, risk models can also be derived. As examples, categorizations may be more appropriate for subsets of patients (e.g., pre- post-menopausal or NO N+, treatments). 
     In some aspects of the methods of the present disclosure, Cq normalized  is normalized to the expression of at least one reference gene; in some aspects the at least one referenced gene is a housekeeping gene, e.g., as recited herein. In some aspects, Cq normalized  is normalized to the expression of at least one reference gene (e.g., all four genes) selected from the group consisting of GAPDH, GUSB, HPRT1, and TBP. Cq normalized  may be calculated according to the following formula: Cq normalized =AVG Cq−SF, in which wherein SF is the difference between the AVG Cq value of the reference genes for each subject and a constant reference value K, wherein K=25.012586069, which represents the mean of the AVG Cq of the four reference genes calculated across a plurality of training samples. 
     Other risk models and formulae may be derived from the disclosure recited herein. 
     In some aspects of the methods of the present disclosure, a sample can be a biological sample comprising a cancer cell or cancerous tissue. In some aspects of the methods of the present disclosure, a sample can be a breast tissue sample comprising a cancerous cell and/or a cancer stem cell or a primary breast tumor tissue sample. A biological sample can be any sampling of cells, tissues, or bodily fluids in which expression of a breast cancer, stem cell, or stem cell-like gene can be detected. Examples of such biological samples include, but are not limited to, biopsies and smears. Bodily fluids may be useful in the present disclosure and include blood, lymph, urine, saliva, nipple aspirates, gynecological fluids, or any other bodily secretion or derivative thereof when the bodily fluid comprises a cancerous cell and/or a cancer stem cell. Blood can include whole blood, plasma, serum, or any derivative of blood. In some aspects, the biological sample includes breast cancer cells, particularly breast tissue from a biopsy, such as a breast tumor tissue sample, and any derivate thereof, such as three-dimensional structures generated in organotypic cultures in matrices or in suspension cultures (commonly regarded as to mammospheres). Biological samples may be obtained from a subject by a variety of techniques including, for example, by scraping or swabbing an area, by using a needle to aspirate cells or bodily fluids, or by removing a tissue sample (i.e., biopsy). Methods for collecting various biological samples are well known in the art. In some aspects, a breast tissue sample is obtained by, for example, fine needle aspiration biopsy, core needle biopsy, or excisional biopsy. Fixative and staining solutions may be applied to the cells or tissues for preserving the specimen and for facilitating examination. Biological samples, particularly breast tissue samples, may be transferred to a glass slide for viewing under magnification. In some aspects of the methods of the present disclosure, a biological sample can be a formalin-fixed, paraffin-embedded breast tissue sample, particularly a primary breast tumor sample or a cancerous cell. In some aspects of the methods of the present disclosure, a tissue sample can be obtained from a pathologist-guided tissue core sample. In some aspects of the methods of the present disclosure a tissue sample can be a “fresh”, i.e., unfixed and/or unfrozen tissue samples (e.g., obtained from a biopsy). In some aspects of the methods of the present disclosure, a tissue sample can be a frozen, unfixed tissue sample. 
     In some aspects, a sample may be obtained from the subject. A sample may be a tumor obtained from the subject, a cancerous cell obtained from the subject, or a cancer stem cell obtained from the subject. A sample may be a primary cell line derived from a tumor obtained from the subject, from a cancerous cell obtained from the subject, or from a cancer stem cell obtained from the subject. 
     Breast cancer includes all forms of cancer of the breast. Breast cancer can include primary epithelial breast cancers and any derivate thereof, such as three-dimensional structures generated in organotypic cultures in matrices or in suspension cultures (commonly regarded as to mammospheres). Breast cancer can include cancers in which the mammary tissue breast is involved. Breast cancer can include Stage I, II, IIIA, IIIB, IIIC and IV breast cancer. Ductal carcinoma of the breast can include invasive carcinoma, invasive carcinoma in situ with predominant intraductal component, inflammatory breast cancer, and a ductal carcinoma of the breast with a histologic type selected from the group consisting of comedo, mucinous (colloid), medullary, medullary with lymphocytic infiltrate, papillary, scirrhous, and tubular. Lobular carcinoma of the breast can include invasive lobular carcinoma with predominant in situ component, invasive lobular carcinoma, and infiltrating lobular carcinoma. Breast cancer can include Paget&#39;s disease, Paget&#39;s disease with intraductal carcinoma, and Paget&#39;s disease with invasive ductal carcinoma. Breast cancer can include breast neoplasms having histologic and ultrastructural heterogeneity (e.g., mixed cell types). A breast cancer that is relevant to the present invention may include familial and hereditary breast cancer. 
     In some aspects, the breast cancer is ER+/HER2− breast cancer. 
     A breast cancer relevant to the present invention (e.g., that is treated) can include a localized tumor of the breast. A breast cancer can include a tumor of the breast that is associated with a negative sentinel lymph node (SLN) biopsy. A breast cancer can include a tumor of the breast that is associated with a positive sentinel lymph node (SLN) biopsy. A breast cancer can include a tumor of the breast that is associated with one or more positive axillary lymph nodes, where the axillary lymph nodes have been staged by any applicable method. A breast cancer can include a tumor of the breast that has been typed as having nodal negative status (e.g., node-negative) or nodal positive status (e.g., node-positive). A breast cancer can include a tumor of the breast that has been characterized by receptor status. For example, the receptor status can be hormone receptor negative (e.g., estrogen receptor-positive or estrogen receptor-negative). A breast cancer can include a tumor of the breast that has metastasized to other locations in the body. A breast cancer can be classified as having metastasized to a location selected from the group consisting of bone, lung, liver, lymph nodes, and brain. A breast cancer can be classified according to a characteristic selected from the group consisting of metastatic, localized, regional, local-regional, locally advanced, distant, multicentric, bilateral, ipsilateral, contralateral, newly diagnosed, recurrent, and inoperable. 
     As used herein, a “subject in need thereof” is a subject having breast cancer or presenting with one or more symptoms of breast cancer, a subject suspected of having breast cancer, a subject having undiagnosed breast cancer, or a subject actually diagnosed with breast cancer. In some aspects, a subject in need thereof has a diagnosed breast cancer. The breast cancer can be primary breast cancer, locally advanced breast cancer or metastatic breast cancer. A “subject” includes a mammal. The mammal can be any mammal, e.g., a human, a primate, a mouse, a rat, a dog, a cat, a cow, a horse, a goat, a camel, a sheep and a pig. In some aspects, the subject is human. The subject may be a male or a female. The subject may have been diagnosed by a skilled artisan as having a breast cancer and is included in a subpopulation of humans who currently have breast cancer or had breast cancer. The subject that has breast cancer may be pre-mastectomy or post-mastectomy. 
     In some aspects, the methods of the present invention can further comprise determining at least one of, a combination of, or each of, the following: tumor grade, intrinsic subtype, histological type, perivascular infiltration, Ki-67 status, estrogen receptor (ER) status, progesterone receptor (PgR) status, and/or HER2/ERBB2 status. 
     In some aspects of the methods of the present disclosure, determining the expression level of a gene can comprise any method available in the art for detecting gene expression of the breast cancer, stem cell, or stem cell-like genes. By “detecting expression” is intended determining the quantity or presence of an RNA transcript or its expression product of a gene. Non-limiting examples of methods for detecting gene expression include but are not limited to analysis of single strand conformation polymorphism, capillary electrophoresis, denaturing high performance liquid chromatography, digital molecular barcoding technology, e.g., Nanostring&#39;s nCounter® system, direct sequencing, DNA mismatch-binding protein assays, dynamic allele-specific hybridization, Fluorescent in situ hybridization (FISH), high-density oligonucleotide SNP arrays, high-resolution melting analysis, microarray, next generation sequencing (NGS), e.g., using the Illumina Genome Analyzer, ABI Solid instrument, Roche 454 instrument, Heliscope instrument, Northern blot analysis, nuclease protection analysis, oligonucleotide ligase assays, polymerase chain reaction (PCR), primer extension assays, Quantigene analysis, quantitative nuclease-protection assay (qNPA), reporter gene detection, restriction fragment length polymorphism (RFLP) assays, reverse transcription and real-time quantitative polymerase chain reaction (RT-qPCR), reverse transcription-polymerase chain reaction (RT-PCR), RNA sequencing (RNA-seq), Serial analysis of gene expression (SAGE), Single Molecule Real Time (SMRT) DNA sequencing technology, SNPLex, Southern blot analysis, Sybr Green chemistry, TaqMan-based assays, temperature gradient gel electrophoresis (TGGE), Tiling array, Western blot analysis, and immunohistochemistry. 
     Methods for detecting expression of the genes of the disclosure, that is, gene expression profiling, include methods based on hybridization analysis of polynucleotides, methods based on sequencing of polynucleotides, immunohistochemistry methods, and proteomics-based methods. In some aspects, PCR-based methods, such as reverse transcription PCR (RT-PCR) (Weis et al., TIG 8:263-64, 1992), and array-based methods such as microarray (Schena et al., Science 270:467-70, 1995) can be used. By “microarray” is intended an ordered arrangement of hybridizable array elements, such as, for example, polynucleotide probes, on a substrate. The term “probe” refers to any molecule that is capable of selectively binding to a specifically intended target biomolecule, for example, a nucleotide transcript or a protein encoded by or corresponding to an intrinsic gene. Probes can be synthesized by one of skill in the art, or derived from appropriate biological preparations. Probes may be specifically designed to be labeled. Examples of molecules that can be utilized as probes include, but are not limited to, RNA, DNA, proteins, antibodies, and organic molecules. 
     Many expression detection methods use isolated RNA. The starting material is typically total RNA isolated from a biological sample, such as a tumor or cell line derived from a tumor (i.e., a primary cell line), and corresponding normal tissue or cell line (e.g., which may serve as a control), respectively. If the source of RNA is a primary tumor, RNA (e.g., mRNA) can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g., formalin-fixed) tissue samples (e.g., pathologist-guided tissue core samples) and “fresh”, i.e., unfixed and/or unfrozen tissue samples (e.g., obtained from a biopsy). 
     General methods for RNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley &amp; Sons, New York 1987-1999. 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 18:42-44, (1995). In particular, RNA isolation can be performed using a purification kit, a buffer set and protease from commercial manufacturers, such as Qiagen (Valencia, Calif.), 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, Austin, Tex.). Total RNA from tissue samples can be isolated, for example, using RNA Stat-60 (Tel-Test, Friendswood, Tex.). Total RNA from FFPE can be isolated, for example, using High Pure FFPE RNA Microkit, Cat No. 04823125001 (Roche Applied Science, Indianapolis, Ind.). RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation. Additionally, large numbers of tissue samples can readily be processed using techniques well known to those of skill in the art, such as, for example, the single-step RNA isolation process of Chomczynski (U.S. Pat. No. 4,843,155). 
     In some aspects, a method for determining the level of gene expression in a sample involves the process of nucleic acid amplification, for example, by RT-PCR (U.S. Pat. No. 4,683,202), ligase chain reaction (Barany, PNAS USA 88: 189-93, (1991)), self-sustained sequence replication (Guatelli et al., Proc. Natl. Acad. Sci USA 87: 1874-78, (1990)), transcriptional amplification system (Kwoh et al., Proc. Natl. Acad. Sci USA 86: 1173-77, (1989)), Q-Beta Replicase (Lizardi et al., Bio/Technology 6:1197, (1988)), rolling circle replication (U.S. Pat. No. 5,854,033), or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art. These detection schemes are especially useful for the detection of nucleic acid molecules if such molecules are present in very low numbers. 
     In some aspects, intrinsic gene expression is assessed by quantitative RT-PCR. Numerous different PCR or QPCR protocols are known in the art and exemplified herein below and can be directly applied or adapted for use using the presently-described compositions for the detection and/or quantification of the genes listed herein. Generally, in PCR, a target polynucleotide sequence is amplified by reaction with at least one oligonucleotide primer or pair of oligonucleotide primers. The primer(s) hybridize to a complementary region of the target nucleic acid and a DNA polymerase extends the primer(s) to amplify the target sequence. Under conditions sufficient to provide polymerase-based nucleic acid amplification products, a nucleic acid fragment of one size dominates the reaction products (the target polynucleotide sequence which is the amplification product). The amplification cycle is repeated to increase the concentration of the single target polynucleotide sequence. The reaction can be performed in any thermocycler commonly used for PCR. However, preferred are cyclers with real time fluorescence measurement capabilities, for example, SMARTCYCLER® (Cepheid, Sunnyvale, Calif.), ABI PRISM 7700® (Applied Biosystems, Foster City, Calif.), ROTOR-GENE™ (Corbett Research, Sydney, Australia), LIGHTCYCLER® (Roche Diagnostics Corp, Indianapolis, Ind.), ICYCLER® (Biorad Laboratories, Hercules, Calif.) and MX4000® (Stratagene, La Jolla, Calif.). 
     In some aspects, microarrays are used for expression profiling. Microarrays are particularly well suited for this purpose because of the reproducibility between different experiments. DNA microarrays provide one method for the simultaneous measurement of the expression levels of large numbers of genes. Each array consists of a reproducible pattern of capture probes attached to a solid support. Labeled RNA or DNA is hybridized to complementary probes on the array and then detected by laser scanning. Hybridization intensities for each probe on the array are determined and converted to a quantitative value representing relative gene expression levels. See, for example, U.S. Pat. Nos. 6,040,138, 5,800,992 and 6,020,135, 6,033,860, and 6,344,316. High-density oligonucleotide arrays are particularly useful for determining the gene expression for a large number of RNAs in a sample. 
     In methods of the present invention, gene expression is normalized to the expression of at least one reference gene. The at least one reference gene may be a housekeeping gene. Exemplary housekeeping genes include and are not limited to AAAS, AAGAB, AAMP, AAR2, AARS, AARS2, AARSD1, AASDHPPT, AATF, ABCB10, ABCB7, ABCD3, ABCE1, ABCF1, ABCF2, ABCF3, ABHD10, ABHD12, ABHD13, ABHD14A, ABHD16A, ABHD4, ABHD8, ABI1, ABT1, ACAD9, ACADVL, ACAP3, ACBD3, ACBD5, ACBD6, ACINI, ACLY, ACOT13, ACOT8, ACOT9, ACOXI, ACOX3, ACP1, ACSF3, ACSL3, ACSS2, ACTR10, ACTR1A, ACTR1B, ACTR5, ACTR8, ACVR1, ACVR1B, ADCK2, ADCK4, ADH5, ADI1, ADIPOR1, ADIPOR2, ADK, ADNP, ADO, ADPRH, ADPRHL2, ADPRM, ADSL, AES, AFF4, AFTPH, AGFG1, AGGF1, AGPAT1, AGPAT3, AGPAT6, AGPS, AHCY, AHSA1, AIMP1, AIP, AK2, AK3, AKAP8, AKAP9, AKIP1, AKIRIN1, AKIRIN2, AKR1A1, AKR7A2, AKT1, AKT1S1, AKTIP, ALAD, ALDH3A2, ALDH9A1, ALG11, ALG5, ALG8, ALG9, ALKBH1, ALKBH2, ALKBH3, ALKBH5, ALS2, ALYREF, AMBRA1, AMD1, ANAPC10, ANAPC11, ANAPC13, ANAPC15, ANAPC16, ANAPC2, ANAPC5, ANAPC7, ANKFY1, ANKH, ANKHD1, ANKHD1-EIF4EBP3, ANKRD10, ANKRD17, ANKRD28, ANKRD39, ANKRD46, ANO6, ANP32A, ANP32B, ANP32C, ANP32E, ANXA6, ANXA7, APIBI, APIGI, APIMI, AP2A1, AP2A2, AP2M1, AP2S1, AP3B1, AP3D1, AP3M1, AP3S1, AP3S2, AP4B1, AP5M1, APEH, APEX1, APEX2, APH1A, API5, APIP, APOA1BP, APOL2, APOOL, APOPTI, APPL2, APTX, ARAF, ARCN1, ARF1, ARF5, ARF6, ARFGAP2, ARFGAP3, ARFGEF2, ARFIP1, ARFIP2, ARFRP1, ARHGAP35, ARHGAP5, ARHGDIA, ARHGEF10L, ARHGEF11, ARHGEF40, ARIH1, ARIH2, ARIH2OS, ARL1, ARL14EP, ARL5A, ARL6IP4, ARL8A, ARL8B, ARMC1, ARMC10, ARMC5, ARMC6, ARMC7, ARMC8, ARMCX3, ARMCX5, ARNT, ARPC1A, ARPC2, ARPC5L, ARVI, ASBI, ASB6, ASB7, ASB8, ASCC1, ASCC3, ASFIA, ASH2L, ASNA1, ASNSD1, ASPSCR1, ASUN, ASXL1, ATADI, ATAD3A, ATE1, ATF1, ATF2, ATF4, ATF6, ATF7, ATF7IP, ATG12, ATG13, ATG16L1, ATG2A, ATG2B, ATG3, ATG4B, ATG4D, ATG5, ATG7, ATIC, ATL2, ATMIN, ATOX1, ATP2C1, ATP5A1, ATP5B, ATP5C1, ATP5D, ATP5F1, ATP5G2, ATP5G3, ATP5H, ATP5J, ATP5J2, ATP5J2-PTCD1, ATP5L, ATP50, ATP5S, ATP5SL, ATP6AP1, ATP6V0A2, ATP6V0B, ATP6V0C, ATP6V0D1, ATP6V0E1, ATP6V1C1, ATP6V1D, ATP6V1E1, ATP6V1F, ATP6V1G1, ATP6V1H, ATPAF2, ATPIF1, ATRAID, ATRN, ATXN10, ATXN1L, ATXN2, ATXN2L, ATXN7L3, ATXN7L3B, AUH, AUP1, AURKAIP1, AXIN1, AZI2, AZIN1, B3GALT6, B4GALT3, B4GALT5, B4GALT7, BABAMI, BAD, BAGI, BAG4, BAG6, BAHD1, BANF1, BAP1, BAZ1B, BBS4, BCAP29, BCAP31, BCAS2, BCAT2, BCCIP, BCKDHA, BCKDK, BCL2L1, BCL2L13, BCL2L2-PABPN1, BCL7B, BCLAF1, BCS1L, BECN1, BFAR, BIRC2, BIVM-ERCC5, BLMH, BLOCISI, BLOC1S2, BLOC1S3, BLOC1S4, BLOC1S6, BLZF1, BMI1, BMS1, BNIP1, BNIP2, BOD1, BOLA1, BOLA3, BPGM, BPNT1, BPTF, BRAT1, BRD2, BRD4, BRD7, BRD9, BRE, BRF1, BRF2, BRIX1, BRK1, BRMS1, BRPF1, BRPF3, BSDC1, BSG, BTBD2, BTD, BTF3, BUB3, BZW1, C10orf12, C10orf2, C10orf76, C10orf88, C11orf1, C11orf24, C11orf31, C11orf57, C11orf58, C11orf73, C11orf83, C12orf10, C12orf23, C12orf29, C12orf44, C12orf45, C12orf5, C12orf52, C12orf57, C12orf65, C12orf66, C14orf1, C14orf119, C14orf142, C14orf166, C14orf2, C14orf28, C15orf38-AP3S2, C15orf57, C16orf13, C16orf62, C16orf72, C16orf91, C17orf49, C17orf51, C17orf58, C17orf59, C17orf70, C17orf85, C18orf21, C18orf25, C18orf32, C18orf8, C19orf43, C19orf53, C19orf60, C19orf70, CIGALT1, C1QBP, C1orf109, C1orf122, C1orf123, C1orf174, C1orf43, C1orf50, C1orf52, C20orf111, C20orf24, C21orf2, C21orf33, C21orf59, C22orf28, C22orf29, C22orf32, C2orf47, C2orf49, C2orf69, C2orf74, C2orf76, C3orf17, C3orf37, C3orf38, C3orf58, C4orf27, C4orf3, C4orf52, C5orf15, C5orf24, C6orf1, C6orf106, C6orf120, C6orf136, C6orf226, C6orf47, C6orf57, C6orf62, C6orf89, C7orf25, C7orf26, C7orf49, C7orf50, C7orf55, C7orf55-LUC7L2, C7orf73, C8orf33, C8orf40, C8orf59, C8orf76, C8orf82, C9orf123, C9orf16, C9orf37, C9orf64, C9orf69, C9orf78, C9orf89, CAB39, CALCOCO2, CALM1, CALR, CALU, CAMTA1, CAMTA2, CANT1, CANX, CAPN1, CAPN7, CAPNS1, CAPRIN1, CAPZA2, CAPZB, CARKD, CARS, CARS2, CASC3, CASC4, CASP3, CASP7, CASP9, CBR4, CBX3, CBX5, CC2D1A, CC2D1B, CCAR1, CCBL1, CCDC12, CCDC124, CCDC127, CCDC130, CCDC137, CCDC149, CCDC174, CCDC22, CCDC23, CCDC25, CCDC47, CCDC50, CCDC51, CCDC59, CCDC71, CCDC86, CCDC90A, CCDC92, CCDC94, CCM2, CCNB1IP1, CCNDBP1, CCNG1, CCNH, CCNK, CCNL1, CCNL2, CCNY, CCPG1, CCT3, CCT4, CCT5, CCT6A, CCT7, CCT8, CD164, CD320, CD46, CD63, CD81, CD82, CD99L2, CDC123, CDC16, CDC23, CDC27, CDC37, CDC37L1, CDC40, CDC42, CDC5L, CDIP1, CDIPT, CDK12, CDK13, CDK16, CDK2AP1, CDK4, CDK5RAP1, CDK8, CDK9, CDS2, CDV3, CDYL, CEBPG, CEBPZ, CECR5, CELF1, CENPB, CENPT, CEP104, CEP57, CEP63, CERK, CERS2, CGGBP1, CHAMPI, CHCHD1, CHCHD2, CHCHD3, CHCHD4, CHCHD5, CHCHD7, CHD1L, CHD4, CHD8, CHERP, CHID1, CHKB, CHMP1A, CHMP2A, CHMP2B, CHMP4A, CHMP4B, CHMP5, CHMP6, CHP1, CHPT1, CHRAC1, CHST12, CHST7, CHTOP, CHUK, CHURC1, CHURC1-FNTB, CIAO1, CIB1, CIC, CINP, CIR1, CIRH1A, CISD1, CISD2, CISD3, CKAP4, CLCC1, CLCN3, CLCN7, CLINTI, CLK3, CLNS1A, CLOCK, CLP1, CLPP, CLPTM1, CLPTM1L, CLPX, CLTA, CLTB, CLTC, CMAS, CMC1, CMC2, CMC4, CMPK1, CNBP, CNIH, CNIH4, CNNM2, CNNM3, CNOT1, CNOT11, CNOT2, CNOT3, CNOT4, CNOT7, CNST, COA1, COA3, COA4, COA5, COA6, COASY, COG1, COG2, COG3, COG4, COG7, COG8, COMMD1, COMMD10, COMMD3, COMMD3-BMI1, COMMD5, COMMD6, COMMD7, COMMD9, COMT, COPA, COPB1, COPB2, COPE, COPG1, COPS2, COPS3, COPS4, COPS5, COPS6, COPS7A, COPS7B, COPS8, COPZ1, COQ10B, COQ2, COQ4, COQ5, COQ6, COROIC, COX11, COX14, COX15, COX16, COX19, COX20, COX4I1, COX5B, COX6B1, COX6C, COX7A2, COX7A2L, COX7C, COX8A, CPD, CPNE1, CPNE2, CPNE3, CPOX, CPSF2, CPSF3L, CPSF4, CPSF6, CPSF7, CRADD, CRBN, CRCP, CREB3, CREBZF, CREGI, CRELDI, CRIPAK, CRIPT, CRK, CRKL, CRLS1, CRNKL1, CRTC2, CRY2, CSGALNACT2, CSNK1A1, CSNK1A1L, CSNK1D, CSNK1G3, CSNK2A3, CSNK2B, CSRP2BP, CST3, CSTB, CSTF1, CSTF2T, CTAGE5, CTBP1, CTCF, CTDSP2, CTNNA1, CTNNB1, CTNNBIP1, CTNNBL1, CTNND1, CTSA, CTSD, CTTN, CTU2, CUEDC2, CUL1, CUL2, CUL4A, CUL4B, CUL5, CUTA, CUX1, CWC15, CWC22, CWC25, CXXC1, CXXC5, CXorf40A, CXorf40B, CXorf56, CYB5B, CYB5D2, CYB5R3, CYC1, CYFIPI, CYHR1, CYP2U1, D2HGDH, DAD1, DAGI, DAGLB, DALRD3, DAP3, DARS, DARS2, DAXX, DAZAP1, DBT, DCAF10, DCAF11, DCAF12, DCAF13, DCAF5, DCAF7, DCAF8, DCAKD, DCTD, DCTN2, DCTN3, DCTN4, DCTN5, DCTN6, DCTPP1, DCUN1D3, DCUN1D4, DCUN1D5, DDA1, DDB1, DDB2, DDOST, DDRGK1, DDX1, DDX10, DDX17, DDX18, DDX19A, DDX19B, DDX21, DDX23, DDX24, DDX27, DDX39B, DDX3X, DDX41, DDX42, DDX46, DDX47, DDX49, DDX54, DDX56, DDX59, DEDD, DEF8, DEGS1, DEK, DENND1A, DENND4A, DENR, DERA, DERL1, DERL2, DESI1, DEXI, DFFA, DGCR14, DGCR2, DGCR6L, DHPS, DHRS12, DHRS7B, DHX15, DHX16, DHX29, DHX30, DHX32, DHX33, DHX36, DHX38, DHX8, DHX9, DIABLO, DIDO1, DIEXF, DIMT1, DIRC2, DIS3, DIS3L2, DKC1, DLD, DLG1, DLGAP4, DLST, DMAP1, DNAAF2, DNAJA2, DNAJA3, DNAJB11, DNAJB12, DNAJB9, DNAJC10, DNAJC11, DNAJC14, DNAJC17, DNAJC19, DNAJC2, DNAJC21, DNAJC3, DNAJC4, DNAJC5, DNAJC7, DNAJC8, DNAJC9, DNASE2, DNLZ, DNM1L, DNM2, DNTTIP1, DNTTIP2, DOHH, DOLK, DPAGT1, DPH1, DPH2, DPH3, DPH5, DPM1, DPP7, DPY30, DRI, DRAM2, DRAP1, DRG2, DROSHA, DSCR3, DTWD1, DUSP11, DUSP14, DUSP16, DUSP22, DUT, DVL3, DYM, DYNCILI1, DYNLL2, DYNLRBI, DYNLTI, E2F4, E4F1, EAFI, EAPP, EARS2, EBAG9, EBNA1BP2, ECD, ECHI, ECHDC1, ECHS1, ECI1, ECI2, ECSIT, EDC3, EDC4, EDEM3, EDF1, EED, EEF1B2, EEF1E1, EEF2, EEFSEC, EFCAB14, EFHA1, EFR3A, EFTUD1, EFTUD2, EGLN2, EHMT1, E124, EID2, EIF1, EIF1AD, EIFIB, EIF2A, EIF2AK1, EIF2AK3, EIF2AK4, EIF2B2, EIF2B3, EIF2B4, EIF2B5, EIF2D, EIF2S1, EIF2S2, EIF3A, EIF3B, EIF3D, EIF3E, EIF3G, EIF3H, EIF3I, EIF3J, EIF3K, EIF3L, EIF3M, EIF4A1, EIF4A3, EIF4E2, EIF4G1, EIF4G2, EIF4G3, EIF4H, EIF5, EIF5A, EIF5AL1, EIF5B, EIF6, ELAC2, ELAVL1, ELF2, ELK1, ELK4, ELL2, ELMOD3, ELOVL1, ELP2, ELP3, ELP4, ELP6, EMC1, EMC10, EMC2, EMC3, EMC4, EMC6, EMC7, EMC8, EMC9, EMD, EMG1, ENDOG, ENOPH1, ENSA, ENTPD4, ENTPD6, ENY2, EPC1, EPM2AIP1, EPN1, EPRS, ERAL1, ERAP1, ERCC1, ERCC2, ERCC3, ERCC5, ERGIC2, ERGIC3, ERH, ERI3, ERICH1, ERLEC1, ERO1L, ERP44, ESD, ESF1, ETF1, ETFA, ETFB, ETV6, EWSR1, EXD2, EXOC1, EXOC2, EXOC3, EXOC4, EXOC7, EXOC8, EXOSC1, EXOSC10, EXOSC2, EXOSC4, EXOSC7, EXOSC8, EXT2, EXTL3, FADD, FAF1, FAF2, FAHD1, FAM104B, FAM108A1, FAM108B1, FAM114A2, FAM118B, FAM120A, FAM120AOS, FAM120B, FAM122A, FAM127B, FAM134A, FAM134C, FAM136A, FAM149B1, FAM160A2, FAM160B1, FAM160B2, FAM162A, FAM168B, FAM173A, FAM173B, FAM174A, FAM175B, FAM177A1, FAM178A, FAM192A, FAM199X, FAM200A, FAM204A, FAM206A, FAM208B, FAM20B, FAM210B, FAM32A, FAM35A, FAM3A, FAM50A, FAM50B, FAM58A, FAM63A, FAM73B, FAM8A1, FAM96A, FAM96B, FAM98A, FARS2, FARSA, FARSB, FASTK, FASTKD2, FASTKD5, FBRSL1, FBXL15, FBXL17, FBXL3, FBXL4, FBXL5, FBXL6, FBXO11, FBXO18, FBXO22, FBXO28, FBXO3, FBXO38, FBXO42, FBXO45, FBXO6, FBXO7, FBXW11, FBXW2, FBXW4, FBXW5, FBXW7, FCF1, FDFT1, FDPS, FDX1, FECH, FEM1C, FEN1, FEZ2, FGFR1OP2, FH, FIBP, FICD, FIP1L1, FIS1, FIZ1, FKBP3, FKBP8, FKBPL, FKRP, FLAD1, FLCN, FLOT1, FLOT2, FNDC3A, FNTA, FNTB, FOPNL, FOXK2, FOXP4, FOXRED1, FPGS, FPGT, FRA10AC1, FTO, FTSJ1, FTSJ2, FTSJ3, FTSJD1, FTSJD2, FUBP1, FUK, FUNDC2, FXN, FYTTD1, FZR1, G3BP1, GAA, GABARAP, GABARAPL2, GABPB1, GAPDH, GADD45GIP1, GALK2, GALNS, GALNT1, GALNT2, GALT, GANAB, GAPVD1, GARS, GART, GATAD2A, GATAD2B, GATC, GBA, GBA2, GBF1, GCC1, GCDH, GCLC, GCLM, GDE1, GDI2, GDPGP1, GEMIN7, GEMIN8, GET4, GFER, GFM1, GFOD2, GGCT, GGNBP2, GGT7, GHDC, GHITM, GID8, GINMI, GIPC1, GLCE, GLE1, GLG1, GLI4, GLO1, GLRX2, GLRX3, GLRX5, GLT8D1, GLTP, GLTPD1, GLYRI, GMPPA, GMPR2, GNB1, GNB2, GNE, GNL2, GNL3, GNPAT, GNPDA1, GNPNAT1, GNPTG, GNS, GOLGA1, GOLGA2, GOLGA3, GOLGA5, GOLGA7, GOLGB1, GOLPH3, GOLTIB, GOPC, GORASP1, GORASP2, GOSR1, GOSR2, GPAA1, GPANK1, GPATCH4, GPBP1, GPBP1L1, GPHN, GPI, GPKOW, GPN1, GPN2, GPN3, GPR107, GPR108, GPS1, GPS2, GPX4, GRAMD4, GRHPR, GRINA, GRIPAP1, GRPEL1, GRSF1, GRWD1, GSK3A, GSK3B, GSPT1, GSPT2, GSR, GSS, GSTK1, GSTM4, GSTO1, GTDC2, GTF2A1, GTF2B, GTF2F1, GTF2F2, GTF2H1, GTF2H4, GTF2H5, GTF2I, GTF3A, GTF3C1, GTF3C2, GTF3C3, GTF3C5, GTF3C6, GTPBP10, GTPBP4, GTPBP5, GTPBP8, GUK1, GUSB, GZF1, H1FX, H2AFV, H2AFX, H2AFY, H2AFZ, HADH, HADHA, HAGH, HARS, HARS2, HAT1, HAUS3, HAUS4, HAUS7, HAX1, HBP1, HBS1L, HCCS, HCFC1, HDAC2, HDAC3, HDAC6, HDAC8, HDDC3, HDGF, HDHD3, HDLBP, HEATR2, HEATR5A, HEBP1, HECTD3, HELZ, HEMK1, HERC4, HERPUD1, HERPUD2, HEXA, HEXDC, HEXIM1, HGS, HIAT1, HIATL1, HIBADH, HIGD1A, HIGD2A, HINFP, HINT1, HINT2, HIST1H2BC, HIVEPI, HMBS, HMG20A, HMG20B, HMGB1, HMGN3, HMGXB3, HMGXB4, HMOX2, HN1L, HNRNPAO, HNRNPA2B1, HNRNPAB, HNRNPC, HNRNPD, HNRNPF, HNRNPH1, HNRNPH2, HNRNPK, HNRNPL, HNRNPM, HNRNPR, HNRNPU, HNRNPUL1, HNRNPUL2, HNRPDL, HNRPLL, HPRT1, HP1BP3, HPS1, HPS6, HS1BP3, HS2ST1, HS6ST1, HSBP1, HSCB, HSD17B10, HSD17B12, HSD17B4, HSPA14, HSPA4, HSPA5, HSPA8, HSPA9, HSPBP1, HSPE1-MOB4, HTATIP2, HTRA2, HTT, HUS1, HUWE1, HYOU1, HYPK, IAH1, IARS, IARS2, IBA57, IBTK, ICK, ICMT, ICT1, IDE, IDH3A, IDH3B, IDH3G, IDI1, IER3IP1, IFNAR1, IFNGR1, IFRD1, IFT27, IKZF5, IL13RA1, IL6ST, ILF2, ILKAP, ILVBL, IMMT, IMP3, IMP4, IMPAD1, INF2, ING1, INO80B, INO80E, INPP5A, INPP5K, INSIG2, INTS1, INTS10, INTS12, INTS3, INTS4, INVS, IP6K1, IP6K2, IP07, IP08, IP09, IRAK1, IREB2, IRF2BP1, IRF2BP2, IRF2BPL, IRGQ, ISCU, ISOC2, IST1, ISY1, ISY1-RAB43, ITCH, ITFG1, ITFG3, ITGB1, ITGB1BP1, ITM2B, ITPA, ITPK1, ITPKC, ITPRIPL2, IVNS1ABP, IWS1, JAGN1, JAK1, JKAMP, JMJD4, JMJD6, JMJD7, JMJD8, JOSD2, JTB, JUND, KANSL2, KANSL3, KARS, KAT2B, KAT5, KAT8, KBTBD2, KBTBD4, KBTBD7, KCMF1, KCTD20, KCTD21, KCTD6, KDM2A, KDM4A, KDM5C, KDSR, KHDRBS1, KHNYN, KHSRP, KIAA0100, KIAA0141, KIAA0195, KIAA0196, KIAA0232, KIAA0319L, KIAA0391, KIAA0754, KIAA0947, KIAA1143, KIAA1191, KIAA1429, KIAA1430, KIAA1586, KIAA1704, KIAA1715, KIAA1919, KIAA1967, KIAA2013, KLC4, KLF3, KLF9, KLHDC2, KLHDC3, KLHL20, KLHL25, KLHL36, KLHL5, KLHL8, KPNA1, KPNB1, KRCC1, KRR1, KTI12, KTN1, KXD1, L3MBTL2, LACTB, LAGE3, LAMP1, LAMP2, LAMTOR1, LAMTOR2, LAMTOR3, LAMTOR4, LAMTOR5, LAP3, LAPTM4A, LARP1, LARP4, LARP7, LARS2, LCOR, LDHA, LEMD2, LENGI, LEPROT, LETMI, LETMDI, LGALSL, LHPP, LIAS, LIG3, LIG4, LIN37, LIN54, LIN7C, LINS, LIPT1, LMAN1, LMBRD1, LMF2, LMO4, LNX2, LOC100129361, LOC100289561, LOC441155, LOC729020, LONP1, LONP2, LPCAT3, LPIN1, LPPR2, LRFN3, LRPAP1, LRPPRC, LRRC14, LRRC24, LRRC28, LRRC40, LRRC41, LRRC42, LRRC47, LRRC57, LRRC59, LRRC8A, LRRFIP2, LRSAM1, LSG1, LSM1, LSM10, LSM14A, LSM14B, LSM2, LSM3, LSM4, LSM5, LSM6, LSM7, LSMD1, LSS, LTV1, LUC7L2, LUC7L3, LUZP6, LYRMI, LYRM4, LYRM5, LYSMDI, LYSMD3, LYSMD4, LZTR1, M6PR, MAD2L1BP, MAD2L2, MAEA, MAGEDI, MAGEF1, MAGOH, MAGT1, MAK16, MALSU1, MAN1A2, MAN1B1, MAN2A2, MAN2B2, MAN2C1, MAP1LC3B2, MAP2K1, MAP2K2, MAP2K5, MAP3K7, MAP4K4, MAPK1, MAPK1IP1L, MAPK6, MAPK8, MAPK9, MAPKAP1, MAPKAPK2, MAPKAPK5, MAPRE2, MARCH2, MARCH5, MARCH6, MARCH7, MARK3, MARK4, MARS, MARS2, MAT2B, MAVS, MAX, MAZ, MBD1, MBD2, MBD3, MBD4, MBLAC1, MBNL2, MBTPS1, MBTPS2, MCAT, MCCC1, MCEE, MCFD2, MCM3AP, MCM7, MCMBP, MCOLN1, MCPH1, MCRS1, MCTS1, MCU, MDC1, MDP1, ME2, MEAF6, MECP2, MED10, MED11, MED13, MED14, MED16, MED19, MED20, MED21, MED24, MED29, MED31, MED4, MED6, MED7, MED8, MEF2A, MEF2BNB, MEMO1, MEN1, MEPCE, METAP1, METAP2, METRN, METTL13, METTL14, METTL16, METTL17, METTL18, METTL20, METTL21A, METTL23, METTL2A, METTL2B, METTL3, METTL5, MFAP1, MFAP3, MFF, MFN1, MFSD11, MFSD12, MFSD3, MFSD5, MGAT2, MGAT4B, MGME1, MGMT, MGRN1, MGST3, MIA3, MIB1, MICALL1, MICU1, MID1IP1, MIDN, MIEN1, MIER1, MIF, MIF4GD, MIIP, MINOSI, MIS12, MITD1, MKI67IP, MKKS, MKLN1, MKNK1, MKRN2, MEEC, MLF2, MEH1, MELT1, MLLT10, MLST8, MEX, MMAA, MMADHC, MMS19, MNAT1, MNF1, MOB4, MOGS, MON1A, MON2, MORC2, MORF4L2, MOSPD1, MPC2, MPDU1, MPG, MPHOSPH10, MPI, MPLKIP, MPND, MPPE1, MPV17L2, MRFAP1, MRFAP1L1, MRI1, MRMI, MRP63, MRPL1, MRPL10, MRPLI1, MRPL12, MRPL13, MRPL14, MRPL15, MRPL16, MRPL17, MRPL18, MRPL19, MRPL2, MRPL20, MRPL21, MRPL22, MRPL23, MRPL24, MRPL27, MRPL28, MRPL3, MRPL30, MRPL32, MRPL33, MRPL35, MRPL36, MRPL37, MRPL38, MRPL4, MRPL40, MRPL41, MRPL42, MRPL43, MRPL44, MRPL45, MRPL46, MRPL47, MRPL48, MRPL49, MRPL50, MRPL51, MRPL52, MRPL53, MRPL54, MRPL55, MRPL9, MRPS10, MRPS11, MRPS12, MRPS14, MRPS15, MRPS16, MRPS17, MRPS18A, MRPS18B, MRPS18C, MRPS2, MRPS21, MRPS22, MRPS23, MRPS24, MRPS25, MRPS26, MRPS27, MRPS28, MRPS30, MRPS31, MRPS33, MRPS34, MRPS35, MRPS5, MRPS6, MRPS7, MRPS9, MRRF, MRS2, MRTO4, MSANTD3, MSH3, MSH6, MSL3, MSMP, MSRA, MSRB2, MTA2, MTCH1, MTCH2, MTDH, MTERFD1, MTERFD2, MTERFD3, MTFMT, MTFR1, MTFR1L, MTIF3, MTM1, MTMR1, MTMR3, MTMR6, MTO1, MTPAP, MTRR, MTSS1, MTX2, MUL1, MUS81, MUT, MVD, MXD4, MXI1, MYBBP1A, MYEOV2, MYL12B, MYNN, MYO1E, MYPOP, MZF1, MZT2A, MZT2B, N4BP1, N4BP2L2, NAA10, NAA15, NAA20, NAA38, NAA50, NAA60, NABP2, NACA, NACA2, NACC1, NACC2, NAE1, NAMPT, NANS, NAP1L4, NAPA, NARF, NARFL, NARG2, NARS, NARS2, NAT10, NBN, NBR1, NCAPH2, NCBP2, NCK1, NCKIPSD, NCL, NCLN, NCOA1, NCOA6, NCOR1, NCSTN, NDEL1, NDFIP1, NDNL2, NDST1, NDUFA10, NDUFA11, NDUFA12, NDUFA13, NDUFA2, NDUFA3, NDUFA4, NDUFA5, NDUFA6, NDUFA7, NDUFA8, NDUFA9, NDUFAF2, NDUFAF3, NDUFAF4, NDUFB10, NDUFB11, NDUFB2, NDUFB3, NDUFB4, NDUFB5, NDUFB6, NDUFB7, NDUFB8, NDUFB9, NDUFC1, NDUFC2, NDUFC2-KCTD14, NDUFS2, NDUFS3, NDUFS4, NDUFS5, NDUFS6, NDUFS7, NDUFS8, NDUFV1, NDUFV2, NECAP1, NEDD8, NEDD8-MDP1, NEIL2, NEK4, NEK9, NELFB, NELFCD, NELFE, NENF, NEU1, NF2, NFATC2IP, NFE2L2, NFIL3, NFKBIB, NFKBIL1, NFU1, NFX1, NFYB, NFYC, NGDN, NGLY1, NGRN, NHP2, NHP2L1, NIF3L1, NINJ1, NIP7, NIPA2, NIPBL, NISCH, NIT1, NIT2, NKAP, NKIRAS2, NMD3, NME1-NME2, NME2, NME3, NME6, NMRK1, NMT1, NOA1, NOB1, NOC2L, NOL10, NOL11, NOL12, NOL6, NOL7, NOL8, NOLC1, NOM1, NONO, NOP10, NOP14, NOP16, NOP2, NOP56, NOP58, NOP9, NPC1, NPC2, NPLOC4, NPRL2, NPRL3, NQO2, NR1H2, NR2C1, NR2C2AP, NR3C2, NRBP1, NRDE2, NRIP1, NSA2, NSD1, NSDHL, NSFL1C, NSMCE1, NSMCE2, NSMCE4A, NSRP1, NSUN2, NSUN5, NSUN6, NT5C, NT5C3, NT5DC1, NTAN1, NTMT1, NTPCR, NUB1, NUBP1, NUBP2, NUCB1, NUCKS1, NUDC, NUDCD1, NUDCD2, NUDT14, NUDT15, NUDT2, NUDT21, NUDT22, NUDT3, NUDT9, NUFIP2, NUP107, NUP133, NUP153, NUP54, NUP62, NUP85, NUPL2, NUTF2, NXF1, NXT1, OAT, OAZ1, OAZ2, OBFC1, OCEL1, OCIADI, ODC1, OGFOD1, OGFOD3, OGFR, OGGI, OGT, OLA1, OPA1, OPA3, ORC4, ORMDL1, ORMDL2, ORMDL3, OS9, OSBP, OSBPL2, OSBPL9, OSGEP, OSGIN2, OSTM1, OTUB1, OTUD5, OVCA2, OXA1L, OXNAD1, P4HTM, PA2G4, PABPN1, PACSIN2, PAF1, PAFAHIBI, PAGRI, PAICS, PAIP1, PAIP2, PAKIIPI, PAK2, PAM16, PANK2, PANK3, PANK4, PANX1, PAPD4, PAPD7, PAPOLA, PARK7, PARL, PARN, PARP1, PARP3, PARP9, PATL1, PATZ1, PAXBP1, PBDC1, PBX2, PCBP1, PCBP2, PCDHGB5, PCF11, PCGF1, PCGF5, PCID2, PCIF1, PCM1, PCMT1, PCNA, PCNX, PCNXL4, PCSK7, PCYOX1, PCYT1A, PDAP1, PDCD2, PDCD5, PDCD6, PDCD6IP, PDE12, PDE6D, PDGFC, PDHB, PDHX, PDK2, PDLIM5, PDP2, PDS5A, PDZD11, PDZD8, PEBP1, PEF1, PELO, PELP1, PEPD, PES1, PET100, PET117, PEX1, PEX11A, PEX11B, PEX12, PEX13, PEX14, PEX16, PEX19, PEX2, PEX26, PEX5, PEX6, PFDN2, PFDN4, PFDN5, PFDN6, PFN1, PGAM5, PGBD3, PGK1, PGLS, PGP, PGPEP1, PGRMC2, PHACTR4, PHAX, PHB, PHB2, PHC2, PHF10, PHF12, PHF20L1, PHF23, PHF5A, PHKB, PHPT1, PHRF1, PI4K2A, PI4KA, PI4 KB, PIAS1, PICALM, PICK1, PIGC, PIGF, PIGG, PIGH, PIGK, PIGP, PIGS, PIGT, PIGU, PIGW, PIGX, PIGY, PIH1D1, PIK3C3, PIK3CB, PIK3R1, PIK3R4, PIN1, PINK1, PINX1, PIP5K1A, PITHD1, PITPNA, PITPNB, PITRM1, PLA2G12A, PLAA, PLBD2, PLD3, PLEKHA1, PLEKHJ1, PLEKHM1, PLGRKT, PLIN3, PLOD1, PLOD3, PLRG1, PMF1, PMF1-BGLAP, PMPCA, PMPCB, PMS1, PMVK, PNISR, PNKD, PNKP, PNN, PNO1, PNPLA6, PNPLA8, PNPO, PNPT1, PNRC2, POFUT1, POLD2, POLDIP2, POLDIP3, POLE3, POLE4, POLG, POLH, POLK, POLL, POLM, POLR1C, POLR1D, POLR1E, POLR2A, POLR2B, POLR2C, POLR2D, POLR2E, POLR2F, POLR2G, POLR2H, POLR2I, POLR2J, POLR2K, POLR2L, POLR3C, POLR3E, POLR3GL, POLR3K, POM121, POM121C, POMGNT1, POMP, POMT1, POP4, POP5, POP7, PPA1, PPA2, PPAN, PPAN-P2RY11, PPARA, PPARD, PPCS, PPFIA1, PPHLN1, PPID, PPIE, PPIF, PPIG, PPIH, PPIL4, PPM1A, PPM1B, PPP1CA, PPP1CC, PPP1R10, PPP1R11, PPP1R15B, PPP1R37, PPP1R7, PPP1R8, PPP2CA, PPP2CB, PPP2R1A, PPP2R2A, PPP2R2D, PPP2R3C, PPP2R4, PPP2R5A, PPP2R5B, PPP2R5C, PPP2R5D, PPP2R5E, PPP4C, PPP4R1, PPP4R2, PPP5C, PPP6C, PPP6R2, PPP6R3, PPWD1, PQBP1, PQLC1, PQLC2, PRADC1, PRCC, PRDM4, PRDX1, PRDX2, PRDX3, PRDX5, PRDX6, PREB, PREP, PRKAA1, PRKAB1, PRKACA, PRKAG1, PRKAR1A, PRKRIP1, PRMT1, PRMT5, PRMT7, PROSC, PRPF18, PRPF19, PRPF3, PRPF31, PRPF4, PRPF40A, PRPF4B, PRPF6, PRPF8, PRPS1, PRPSAP1, PRR14, PRRC1, PRRC2A, PRRC2B, PRUNE, PSEN1, PSEN2, PSENEN, PSKH1, PSMA1, PSMA2, PSMA3, PSMA4, PSMA5, PSMA6, PSMA7, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB7, PSMC2, PSMC3, PSMC4, PSMC5, PSMC6, PSMD1, PSMD10, PSMD11, PSMD12, PSMD13, PSMD14, PSMD2, PSMD3, PSMD4, PSMD5, PSMD6, PSMD7, PSMD8, PSMD9, PSME1, PSME3, PSMF1, PSMG2, PSMG3, PSMG4, PSPC1, PTCD1, PTCD3, PTDSS1, PTEN, PTGES2, PTGES3, PTOV1, PTP4A2, PTPMT1, PTPN1, PTPN11, PTPN23, PTRH1, PTRH2, PTRHD1, PUF60, PUM1, PUM2,  PURA , PURB, PUS3, PUS7, PUSL1, PWP1, PWP2, PWWP2A, PXMP4, PYCR2, PYGO2, PYURF, QARS, QRICH1, QRSL1, QSOX1, QTRT1, R3HCC1, R3HDM2, RAB10, RAB11A, RAB11B, RAB14, RAB18, RAB1A, RAB1B, RAB21, RAB22A, RAB2A, RAB2B, RAB3GAP1, RAB3GAP2, RAB40C, RAB4A, RAB5A, RAB5B, RAB5C, RAB6A, RAB7A, RAB9A, RABEP1, RABEPK, RABGEF1, RABGGTA, RABGGTB, RAD1, RAD17, RAD23B, RAD50, RAD51C, RAF1, RALA, RALBP1, RALY, RAN, RANBP1, RANBP2, RANBP3, RANBP6, RANGAPI, RANGRF, RAP1A, RAPGEF1, RAPGEF2, RARS, RARS2, RB1CC1, RBAK, RBBP4, RBBP7, RBCK1, RBFA, RBM10, RBM12, RBM12B, RBM14, RBM14-RBM4, RBM15, RBM15B, RBM17, RBM18, RBM19, RBM23, RBM27, RBM28, RBM33, RBM34, RBM39, RBM4, RBM41, RBM42, RBM5, RBM6, RBM7, RBM8A, RBMX, RBMXL1, RBX1, RC3H2, RCAN1, RCHY1, RCN2, RDH14, RDX, REEP3, REEP5, RELA, REPINI, REPS1, RERI, REST, REXO1, RFC1, RFC2, RFC5, RFK, RFNG, RFT1, RFWD2, RFXANK, RGP1, RHBDD1, RHBDD3, RHOA, RHOB, RHOT1, RHOT2, RIC8A, RIN2, RINGI, RINT1, RIOK1, RIOK2, RIOK3, RIPK1, RMDN1, RMDN3, RMI1, RMND1, RMND5A, RMND5B, RNASEH1, RNASEH2C, RNASEK, RNF10, RNF103, RNF11, RNF111, RNF113A, RNF115, RNF121, RNF126, RNF13, RNF14, RNF141, RNF146, RNF167, RNF181, RNF185, RNF187, RNF216, RNF220, RNF25, RNF26, RNF31, RNF34, RNF4, RNF40, RNF5, RNF6, RNF7, RNH1, RNMTL1, RNPEP, ROMO1, RP9, RPA2, RPA3, RPAIN, RPAP3, RPF1, RPF2, RPL10A, RPL11, RPL14, RPL26L1, RPL27, RPL30, RPL31, RPL32, RPL34, RPL35, RPL35A, RPL36AL, RPL4, RPL6, RPL7L1, RPL8, RPN1, RPN2, RPP14, RPP25L, RPP30, RPP38, RPRD1B, RPS13, RPS19BP1, RPS23, RPS24, RPS27L, RPS5, RPS6, RPS6KA3, RPS6KB1, RPS6KB2, RPUSD3, RQCD1, RRAGA, RRM1, RRN3, RRNAD1, RRP1, RRP36, RRP7A, RRP8, RRS1, RSAD1, RSBN1L, RSC1A1, RSL1D1, RSPRY1, RSRC1, RSRC2, RTCA, RTFDC1, RTN4, RUFY1, RUVBL1, RWDD1, RWDD3, RXRA, RXRB, SAE1, SAMD1, SAMD4B, SAMD8, SAMM50, SAP18, SAP30, SAP30BP, SAP30L, SAR1A, SARNP, SARS, SART1, SART3, SAT2, SAV1, SBDS, SCAF1, SCAF11, SCAF4, SCAF8, SCAMP2, SCAMP3, SCANDI, SCAP, SCARB2, SCFD1, SCFD2, SCNM1, SCO1, SC02, SCOC, SCP2, SCRIB, SCRN3, SCYL1, SCYL2, SCYL3, SDAD1, SDCBP, SDCCAG3, SDCCAG8, SDE2, SDF2, SDF4, SDHA, SDHAF2, SDHB, SDHC, SDHD, SDR39U1, SEC11A, SEC13, SEC16A, SEC22B, SEC22C, SEC23A, SEC23IP, SEC24A, SEC24B, SEC24C, SEC31A, SEC61A1, SEC61B, SEC61G, SEC62, SEC63, SECISBP2, SEH1L, SEL1L, SELK, SELO, SELRC1, SELT, SENP2, SENP3, SENP5, SENP6, SEPHS1, SERBP1, SERF2, SERGEF, SERINCI, SERINC3, SERPINB6, SERTAD2, SET, SETD2, SETD3, SETD5, SETD6, SETD7, SETD8, SETDB1, SF1, SF3A1, SF3A3, SF3B1, SF3B14, SF3B2, SF3B3, SF3B4, SF3B5, SFSWAP, SGK196, SGMS1, SGPL1, SGSM3, SGTA, SH3BP5L, SH3GLB1, SHARPIN, SHOC2, SIAH1, SIAH2, SIGMARI, SIKE1, SIL1, SIRT2, SIRT3, SIRT5, SIRT6, SIVA1, SKIL, SKIV2L, SKIV2L2, SKP1, SLC15A4, SLC20A1, SLC25A11, SLC25A26, SLC25A28, SLC25A3, SLC25A32, SLC25A38, SLC25A39, SLC25A44, SLC25A46, SLC25A5, SLC27A4, SLC30A1, SLC30A5, SLC30A9, SLC35A2, SLC35A4, SLC35B1, SLC35B2, SLC35C2, SLC35E1, SLC35E3, SLC35F5, SLC38A2, SLC39A1, SLC39A3, SLC39A7, SLC41A3, SLC46A3, SLC48A1, SLIRP, SLMO2, SLTM, SMAD2, SMAD4, SMAD5, SMAP1, SMARCA2, SMARCA4, SMARCAL1, SMARCB1, SMARCE1, SMC1A, SMC5, SMCR7L, SMEK1, SMEK2, SMG5, SMG7, SMG8, SMIMI1, SMIM12, SMIM8, SMNDC1, SMPD1, SMPD4, SMU1, SMUGI, SNAP23, SNAP29, SNAP47, SNAPC3, SNAPC5, SNAPIN, SND1, SNF8, SNRNP200, SNRNP25, SNRNP27, SNRNP35, SNRNP40, SNRNP48, SNRNP70, SNRPA, SNRPB, SNRPB2, SNRPC, SNRPD1, SNRPD2, SNRPD3, SNRPG, SNUPN, SNW1, SNX12, SNX13, SNX17, SNX18, SNX19, SNX2, SNX25, SNX3, SNX4, SNX5, SNX6, SNX9, SOCS4, SOCS6, SOD1, SON, SPAG7, SPAG9, SPATA2, SPATA5L1, SPCS1, SPCS3, SPECC1L, SPEN, SPG11, SPG21, SPG7, SPHAR, SPNS1, SPOP, SPPL2B, SPPL3, SPRYD3, SPRYD7, SPSB3, SPTSSA, SPTY2D1, SRA1, SRD5A3, SREBF2, SREK1IP1, SRM, SRP14, SRP19, SRP54, SRP68, SRP72, SRP9, SRPR, SRPRB, SRR, SRRD, SRRM1, SRSF1, SRSF10, SRSF11, SRSF2, SRSF3, SRSF4, SRSF7, SRSF8, SS18L2, SSB, SSBP1, SSNA1, SSR1, SSR2, SSR3, SSRP1, SSSCA1, SSU72, ST3GAL2, ST6GALNAC6, ST7, STAM, STAM2, STAMBP, STARD3, STARD7, STAT3, STAU1, STAU2, STIMI, STIP1, STK11, STK16, STOM, STOML1, STOML2, STRAP, STRIP1, STRN3, STT3A, STT3B, STUB1, STX10, STX17, STX4, STX5, STX8, STXBP3, STYXL1, SUB1, SUCLA2, SUCLG1, SUCLG2, SUGP1, SUGT1, SUMO1, SUMO3, SUN2, SUPT4H1, SUPT5H, SUPT6H, SUPT7L, SUPV3L1, SURF1, SURF4, SURF6, SUV420H1, SUZ12, SYAP1, SYF2, SYMPK, SYNCRIP, SYNJ2BP, SYNJ2BP-COX16, SYPL1, SYS1, SYVN1, SZRD1, TAB1, TAB2, TACO1, TADA1, TADA3, TAF10, TAF11, TAF12, TAF13, TAF15, TAFID, TAF4, TAF5L, TAF8, TAF9, TALDO1, TAMM41, TANGO2, TANGO6, TANK, TAOK2, TAPBP, TAPT1, TARDBP, TARS, TATDN1, TATDN2, TAX1BP1, TAZ, TBC1D1, TBC1D14, TBC1D15, TBC1D20, TBC1D22A, TBC1D23, TBC1D7, TBC1D9B, TBCA, TBCB, TBCC, TBCCD1, TBCD, TBCE, TBK1, TBP, TBRG1, TBRG4, TCAIM, TCEANC2, TCEB1, TCEB2, TCEB3, TCERG1, TCF12, TCF20, TCF25, TCP1, TCTN3, TDP2, TDRD3, TECR, TEF, TEFM, TELO2, TERF2, TERF2IP, TEX2, TEX261, TEX264, TFAM, TFB1M, TFB2M, TFCP2, TFDP1, TFE3, TFG, TFIP11, TFPT, TGIF2-C20orf24, TGOLN2, THADA, THAP3, THAP4, THAP5, THAP7, THOC5, THOC7, THOPI, THRAP3, THTPA, THUMPD3, THYNI, TIA1, TIAL1, TICAM1, TIGD5, TIGD6, TIMM10, TIMMIOB, TIMM13, TIMM17A, TIMM17B, TIMM21, TIMM22, TIMM44, TIMM50, TIMM8B, TIMM9, TIMMDC1, TINF2, TIPRL, TJAP1, TLE1, TLK1, TM2D1, TM2D2, TM2D3, TM9SF1, TM9SF2, TM9SF3, TM9SF4, TMBIMI, TMBIM4, TMBIM6, TMCC1, TMCO1, TMCO3, TMED1, TMED10, TMED2, TMED4, TMED5, TMED7, TMED7-TICAM2, TMED9, TMEM101, TMEM106B, TMEM106C, TMEM115, TMEM120A, TMEM126A, TMEM127, TMEM128, TMEM129, TMEM131, TMEM134, TMEM141, TMEM147, TMEM14B, TMEM14C, TMEM161A, TMEM167B, TMEM168, TMEM177, TMEM179B, TMEM18, TMEM184C, TMEM185B, TMEM186, TMEM187, TMEM189, TMEM189-UBE2V1, TMEM19, TMEM192, TMEM199, TMEM203, TMEM205, TMEM214, TMEM219, TMEM222, TMEM223, TMEM230, TMEM242, TMEM248, TMEM251, TMEM256, TMEM258, TMEM259, TMEM30A, TMEM33, TMEM39A, TMEM41A, TMEM41B, TMEM42, TMEM5, TMEM50A, TMEM50B, TMEM55B, TMEM57, TMEM59, TMEM60, TMEM62, TMEM63B, TMEM64, TMEM66, TMEM69, TMEM70, TMEM81, TMEM87A, TMEM9, TMEM9B, TMF1, TMLHE, TMPO, TMUB1, TMUB2, TMX1, TMX2, TMX4, TNFAIP1, TNFAIP8L2-SCNM1, TNIP1, TNKS2, TNPO1, TNPO3, TNRC6A, TOB1, TOLLIP, TOMM20, TOMM22, TOMM40, TOMM5, TOMM6, TOMM7, TOMM70A, TOP1, TOP2B, TOPORS, TOR1A, TOR1AIP2, TOR1B, TOR3A, TOX4, TP53RK, TPCN1, TPD52L2, TPGS1, TPI1, TPP2, TPRA1, TPRG1L, TPRKB, TPRN, TPST2, TRA2A, TRA2B, TRAF6, TRAF7, TRAP1, TRAPPC1, TRAPPC10, TRAPPC11, TRAPPC12, TRAPPC13, TRAPPC2L, TRAPPC3, TRAPPC4, TRAPPC5, TRAPPC6B, TRAPPC8, TRAPPC9, TRIAPI, TRIM26, TRIM27, TRIM28, TRIM3, TRIM39, TRIM39-RPP21, TRIM41, TRIM44, TRIM56, TRIM65, TRIM8, TRIP12, TRIP4, TRMT1, TRMT10C, TRMT112, TRMT12, TRMT1L, TRMT2A, TRNAU1AP, TRNT1, TRPC4AP, TRPT1, TRUB2, TSC2, TSEN15, TSEN34, TSFM, TSG101, TSN, TSNAX, TSPAN17, TSPAN31, TSPYL1, TSR1, TSR2, TSR3, TSSC4, TSTA3, TSTD2, TTC1, TTC17, TTC19, TTC32, TTC33, TTC37, TTC4, TTC7B, TTC9C, TTI1, TTI2, TUBAIB, TUBAIC, TUBB, TUBD1, TUBGCP2, TUBGCP4, TUFM, TUSC2, TUT1, TVP23B, TXLNA, TXLNG, TXN2, TXNDC11, TXNDC12, TXNDC15, TXNDC17, TXNDC9, TXNL1, TXNL4A, TXNL4B, TXNRD1, TYK2, TYW1, U2AF1, U2AF1L4, U2AF2, UAP1, UBA1, UBA2, UBA3, UBA5, UBA52, UBAC2, UBALDI, UBAP1, UBAP2L, UBB, UBC, UBE2A, UBE2B, UBE2D2, UBE2D3, UBE2D4, UBE2E1, UBE2E2, UBE2E3, UBE2F, UBE2G2, UBE2H, UBE2I, UBE2J1, UBE2J2, UBE2K, UBE2L3, UBE2M, UBE2N, UBE2NL, UBE2Q1, UBE2R2, UBE2V1, UBE2V2, UBE2W, UBE2Z, UBE3A, UBE3B, UBE3C, UBE4A, UBE4B, UBFD1, UBIADI, UBL3, UBL4A, UBL5, UBL7, UBOX5, UBP1, UBQLN1, UBQLN2, UBQLN4, UBR2, UBR7, UBTD1, UBTF, UBXN2A, UBXN4, UBXN6, UCHL3, UCHL5, UCK1, UCK2, UCKL1, UEVLD, UFC1, UFD1L, UFL1, UFSP2, UGP2, UHRF1BP1L, ULK1, ULK3, UNC50, UNG, UPF1, UPF2, UPF3B, UPRT, UQCC, UQCR10, UQCR11, UQCRB, UQCRC1, UQCRC2, UQCRHL, UQCRQ, URGCP, URI1, URM1, UROD, UROS, USB1, USE1, USF1, USF2, USP10, USP14, USP16, USP19, USP22, USP25, USP27X, USP33, USP38, USP39, USP4, USP47, USP5, USP7, USP8, USP9X, UTP11L, UTP14A, UTP14C, UTP15, UTP23, UTP3, UTP6, UXS1, UXT, VAC14, VAMP3, VAMP5, VAPA, VAPB, VARS2, VBP1, VCP, VDAC3, VEZT, VIMP, VMA21, VPS16, VPS18, VPS25, VPS26A, VPS26B, VPS28, VPS29, VPS33A, VPS36, VPS37A, VPS4A, VPS51, VPS52, VPS53, VPS72, VRK2, VRK3, VTA1, VTI1A, VTI1B, WAC, WAPAL, WARS2, WBP11, WBP1L, WBP2, WBP4, WBSCR22, WDR1, WDR12, WDR13, WDR18, WDR20, WDR24, WDR25, WDR26, WDR3, WDR33, WDR36, WDR41, WDR43, WDR44, WDR45, WDR45B, WDR46, WDR55, WDR59, WDR5B, WDR6, WDR61, WDR70, WDR73, WDR74, WDR75, WDR77, WDR81, WDR83OS, WDR85, WDR89, WDTC1, WIBG, WIPI2, WIZ, WRAP53, WRB, WRNIP1, WSB2, WTAP, WTH3DI, WWP1, WWP2, XIAP, XPA, XPC, XPNPEP1, XPO1, XPO7, XPOT, XRCC5, XRCC6, XYLT2, YAF2, YARS, YARS2, YIF1A, YIF1B, YIPF1, YIPF3, YIPF4, YIPF5, YIPF6, YKT6, YME1L1, YPEL2, YRDC, YTHDC1, YTHDF1, YTHDF2, YTHDF3, YWHAB, YWHAE, YY1, YY1AP1, ZADH2, ZBED4, ZBED6, ZBTB1, ZBTB10, ZBTB11, ZBTB14, ZBTB17, ZBTB18, ZBTB21, ZBTB25, ZBTB33, ZBTB39, ZBTB44, ZBTB45, ZBTB5, ZBTB6, ZBTB7A, ZBTB8OS, ZC3H10, ZC3H11A, ZC3H13, ZC3H15, ZC3H18, ZC3H3, ZC3H7A, ZC3H7B, ZCCHC10, ZCCHC11, ZCCHC3, ZCCHC7, ZCCHC9, ZCRB1, ZDHHC14, ZDHHC16, ZDHHC2, ZDHHC3, ZDHHC4, ZDHHC5, ZDHHC8, ZFAND1, ZFAND2B, ZFAND3, ZFAND5, ZFAND6, ZFP91, ZFPL1, ZFR, ZFYVE1, ZFYVE19, ZFYVE27, ZGPAT, ZHX1, ZHX1-C80RF76, ZHX2, ZHX3, ZKSCAN1, ZMAT2, ZMAT3, ZMAT5, ZMPSTE24, ZMYM2, ZMYND11, ZNF121, ZNF131, ZNF134, ZNF138, ZNF142, ZNF143, ZNF146, ZNF174, ZNF181, ZNF189, ZNF195, ZNF197, ZNF207, ZNF22, ZNF226, ZNF232, ZNF24, ZNF259, ZNF274, ZNF277, ZNF280D, ZNF281, ZNF3, ZNF32, ZNF322, ZNF326, ZNF330, ZNF335, ZNF33A, ZNF343, ZNF347, ZNF37A, ZNF384, ZNF394, ZNF397, ZNF398, ZNF408, ZNF41, ZNF410, ZNF414, ZNF419, ZNF438, ZNF444, ZNF446, ZNF48, ZNF480, ZNF491, ZNF506, ZNF507, ZNF513, ZNF518A, ZNF526, ZNF561, ZNF574, ZNF576, ZNF579, ZNF580, ZNF592, ZNF593, ZNF598, ZNF620, ZNF622, ZNF623, ZNF638, ZNF639, ZNF641, ZNF644, ZNF649, ZNF654, ZNF655, ZNF664, ZNF668, ZNF672, ZNF687, ZNF688, ZNF691, ZNF7, ZNF706, ZNF721, ZNF740, ZNF76, ZNF764, ZNF770, ZNF777, ZNF787, ZNF805, ZNF814, ZNF830, ZNF865, ZNF91, ZNHIT1, ZNHIT3, ZNRD1, ZRANB1, ZRANB2, ZSCAN21, ZSCAN29, ZSCAN32, ZSWIM1, ZSWIM7, ZSWIM8, ZW10, ZXDA, ZXDB, and ZZZ3. 
     In some aspects, the at least one reference gene can comprise one or more of GAPDH, GUSB, HPRT1, and TBP. More preferably, the at least one reference gene comprises at least each of GAPDH, GUSB, HPRT1, and TBP. 
     The present disclosure also provides kits useful for determining gene expression of a breast cancer sample and/or providing prognostic information to identify risk of recurrence. These kits can comprise a set of probes and/or primers specific for least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least eleven, or at least twelve, or at least thirteen, or at least fourteen, or at least fifteen, or at least sixteen, or at least seventeen, or at least eighteen, at least nineteen genes or each of the twenty genes in Table 1. In some aspects, a kit can comprise a set of probes and/or primers specific for EIF4EBP1, MRPS23, and TOP2A. In some aspects, a kit can comprise a set of probes and/or primers specific for APOBEC3B, CENPW, EIF4EBP1, EXOSC4, LY6E, MMP1, MRPS23, NDUFB10, and TOP2A. In some aspects, a kit can comprise a set of probes and/or primers specific for ALYREF, APOBEC3B, CDK1, CENPW, EIF4EBP1, EXOSC4, H2AFJ, LY6E, MIEN1, MMP1, MRPS23, NDUFB10, NOL3, RACGAP1, SFN, and TOP2A. 
     In some aspects, a kit of the present disclosure can comprise a set of instructions for performing any method of the present disclosure. The instructions can be written instructions. In some aspects the instructions can be on a computer readable medium. 
     In one embodiment of the present disclosure, the capture probes are immobilized on an array. By “array” is intended a solid support or a substrate with peptide or nucleic acid probes attached to the support or substrate. Arrays typically comprise a plurality of different capture probes that are coupled to a surface of a substrate in different, known locations. The arrays of the disclosure comprise a substrate having a plurality of capture probes that can specifically bind an intrinsic gene expression product. The number of capture probes on the substrate varies with the purpose for which the array is intended. The arrays may be low-density arrays or high-density arrays. 
     Techniques for the synthesis of these arrays using mechanical synthesis methods are described in, e.g., U.S. Pat. No. 5,384,261, incorporated herein by reference in its entirety for all purposes. The array may be fabricated on a surface of virtually any shape or even a multiplicity of surfaces. Arrays may be probes (e.g., nucleic-acid binding probes) on beads, gels, polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate, see U.S. Pat. Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193 and 5,800,992, each of which is hereby incorporated in its entirety for all purposes. Arrays may be packaged in such a manner as to allow for diagnostics or other manipulation on the device. See, for example, U.S. Pat. Nos. 5,856,174 and 5,922,591, each of which is herein incorporated by reference. 
     In another embodiment, the kit comprises a set of oligonucleotide primers sufficient for the detection and/or quantitation of each of the 3, 9, 16, or 20 genes listed in Tables 1-4. The oligonucleotide primers may be provided in a lyophilized or reconstituted form, or may be provided as a set of nucleotide sequences. In one embodiment, the primers are provided in a microplate format, where each primer set occupies a well (or multiple wells, as in the case of replicates) in the microplate. The microplate may further comprise primers sufficient for the detection of one or more housekeeping genes as discussed infra. The kit may further comprise reagents and instructions sufficient for the amplification of expression products from the 3, 9, 16, or 20 genes listed in Tables 1-4. 
     In order to facilitate ready access, e.g., for comparison, review, recovery, and/or modification, the gene expressions are typically recorded in a database. Most typically, the database is a relational database accessible by a computational device, although other formats, e.g., manually accessible indexed files of expression profiles as photographs, analogue or digital imaging readouts, spreadsheets, etc. can be used. Regardless of whether the expression patterns initially recorded are analog or digital in nature, the expression patterns, expression profiles (collective expression patterns), and molecular signatures (correlated expression patterns) are stored digitally and accessed via a database. Typically, the database is compiled and maintained at a central facility, with access being available locally and/or remotely. 
     The methods described herein may be implemented and/or the results recorded using any device capable of implementing the methods and/or recording the results. Examples of devices that may be used include but are not limited to electronic computational devices, including computers of all types. When the methods described herein are implemented and/or recorded in a computer, the computer program that may be used to configure the computer to carry out the steps of the methods may be contained in any computer readable medium capable of containing the computer program. Examples of computer readable medium that may be used include but are not limited to diskettes, CD-ROMs, DVDs, ROM, RAM, and other memory and computer storage devices. The computer program that may be used to configure the computer to carry out the steps of the methods and/or record the results may also be provided over an electronic network, for example, over the internet, an intranet, or other network. 
     The present disclosure provides a method for treating a breast cancer in a subject in need thereof, the method comprising the steps of: (a) predicting a risk of breast cancer recurrence in the subject; or (b) stratifying the subject into a low or high risk group of breast cancer recurrence; and (c) administering a treatment to the subject, wherein the treatment is based on (a) or (b). In some embodiments, predicting a risk of breast cancer recurrence in the subject can comprise methods described herein. In some embodiments, stratifying the subject into a low or high risk group of breast cancer recurrence can comprise the methods described herein. In some embodiments, the breast cancer is ER+/HER2− breast cancer. In some embodiments, the treatment is a breast cancer treatment. 
     37. The method of claim  36 , wherein predicting a risk of breast cancer recurrence in the subject comprises the method of any one of claim  1  or  4  to  34 . 
     The present invention further comprises providing a subject in need a breast cancer treatment. The breast cancer treatment may include one or more anti-cancer or chemotherapeutic agents. Classes of anti-cancer or chemotherapeutic agents can include anthracycline agents, alkylating agents, nucleoside analogs, platinum agents, vinca agents, anti-estrogen drugs, aromatase inhibitors, ovarian suppression agents, endocrine/hormonal agents, bisphophonate therapy agents and targeted biological therapy agents (e.g., antibodies). Specific anti-cancer or chemotherapeutic agents include cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, thiotepa, carboplatin, cisplatin, gemcitabine, anthracycline, taxanes, paclitaxel, protein-bound paclitaxel, doxorubicin, docetaxel, vinorelbine, tamoxifen, raloxifene, toremifene, fulvestrant, irinotecan, ixabepilone, temozolmide, topotecan, vincristine, vinblastine, eribulin, mutamycin, capecitabine, capecitabine, anastrozole, exemestane, letrozole, leuprolide, abarelix, buserlin, goserelin, megestrol acetate, risedronate, pamidronate, ibandronate, alendronate, denosumab, zoledronate, trastuzumab, tykerb or bevacizumab, or combinations thereof. 
     The treatment may include radiation therapy. Preferably, the treatment that includes radiation also includes cyclophosphamide, fluorouracil (or 5-fluorouracil or 5-FU), methotrexate, or combinations thereof. One such combination is CMF which includes cyclophosphamide, methotrexate, and fluorouracil; another such combination is AC which includes doxorubicin and cyclophosphamide. 
     The treatment may include a surgical intervention. 
     A “more aggressive” cancer treatment can refer to a specific cancer treatment regimen. A “more aggressive” cancer treatment may comprise a higher dose of an anti-cancer or chemotherapeutic agent. A “more aggressive” cancer treatment may comprise more frequent dosing of an anti-cancer or chemotherapeutic agent. A “more aggressive” cancer treatment may comprise a more potent anti-cancer or chemotherapeutic agent. A “more aggressive” cancer treatment may comprise a plurality of anti-cancer or chemotherapeutic agents. A “more aggressive” cancer treatment may combine a plurality of treatment modalities, e.g., anti-cancer or chemotherapeutic agents along with surgical intervention, anti-cancer or chemotherapeutic agents along with radiation, radiation along with surgical intervention, and anti-cancer or chemotherapeutic agents, surgical intervention, and radiation. Any of the above-mentioned “more aggressive” cancer treatment may be combined with any other above-mentioned “more aggressive” cancer treatments or with other cancer treatments known in the art. A “more aggressive” cancer treatment may comprise administering to the subject adjuvant chemotherapy. 
     Example 1—the Methods of the Present Disclosure Outperform Existing Prognostic Algorithms 
     The following is an example that describes the development of the SPARE methods of the present disclosure and that demonstrates these methods outperform existing prognostic algorithms in the prediction of distant recurrence of breast cancer. 
     The SPARE methods of the present disclosure that integrates gene expression data with tumor size and nodal status to derive risk scores for recurrence were developed using a data from ER+/HER2− breast cancer patients (n=1,827) that included a 15-year complete follow-up from the European Institute of Oncology in Milan. The clinico-pathological characteristics and demographics of the patients are shown in Table 5. One third of the patients were randomly assigned to a training set and two-thirds to a validation set. The association between sets (training or validation) and the demographic, clinical, and pathological variables was evaluated using the chi-square test. The number (N) of patients and percentage (%) in each group are indicated in Table 5. 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 5 
               
               
                   
               
               
                   
                 All Patients 
                 Training Set 
                 Validation Set 
                   
               
               
                 Characteristic 
                 (N = 1827) 
                 (N = 609) 
                 (N = 1218) 
                 p value 
               
               
                   
               
             
            
               
                 Age at surgery 
                   
                   
                   
                 0.51 
               
               
                 &lt;50 
                 670 (36.7) 
                 217 (35.6) 
                 453 (37.2) 
                   
               
               
                 ≥50 
                 1157 (63.3) 
                 392 (64.4) 
                 765 (62.8) 
                   
               
               
                 Histology 
                   
                   
                   
                 0.91 
               
               
                 Ductal 
                 1407 (77.0) 
                 470 (77.2) 
                 937 (76.9) 
                   
               
               
                 Other subtypes 
                 420 (23.0) 
                 139 (22.8) 
                 281 (23.1) 
                   
               
               
                 pT 
                   
                   
                   
                 0.33 
               
               
                 pT1a/b 
                 266 (14.6) 
                 97 (15.9) 
                 169 (13.9) 
                   
               
               
                 pT1c 
                 989 (54.1) 
                 312 (51.2) 
                 677 (55.6) 
                   
               
               
                 pT2 
                 517 (28.3) 
                 182 (29.9) 
                 335 (27.5) 
                   
               
               
                 pT3/pT4 
                 55 (3.0) 
                 18 (3.0) 
                 37 (3.0) 
                   
               
               
                 pN 
                   
                   
                   
                 0.82 
               
               
                 pN0 
                 910 (49.8) 
                 303 (49.8) 
                 607 (49.8) 
                   
               
               
                 pN1 
                 573 (31.4) 
                 185 (30.4) 
                 388 (31.9) 
                   
               
               
                 pN2/pN3 
                 293 (16.0) 
                 102 (16.7) 
                 191 (15.7) 
                   
               
               
                 pNx 
                 51 (2.8) 
                 19 (3.1) 
                 32 (2.6) 
                   
               
               
                 Grade 
                   
                   
                   
                 0.57 
               
               
                 1 
                 418 (22.9) 
                 140 (23.0) 
                 278 (22.8) 
                   
               
               
                 2 
                 910 (49.8) 
                 291 (47.8) 
                 619 (50.8) 
                   
               
               
                 3 
                 453 (24.8) 
                 161 (26.4) 
                 292 (24.0) 
                   
               
               
                 n/a 
                 46 (2.5) 
                 17 (2.8) 
                 29 (2.4) 
                   
               
               
                 Lymphovascular 
                   
                   
                   
                 0.88 
               
               
                 invasion 
                   
                   
                   
                   
               
               
                 Absent 
                 1276 (69.8) 
                 424 (69.6) 
                 852 (70.0) 
                   
               
               
                 Present 
                 551 (30.2) 
                 185 (30.4) 
                 366 (30.0) 
                   
               
               
                 Ki-67 
                   
                   
                   
                 0.72 
               
               
                 &lt;14% 
                 627 (34.3) 
                 213 (35.0) 
                 414 (34.0) 
                   
               
               
                 ≥14% 
                 1199 (65.6) 
                 396 (65.0) 
                 803 (65.9) 
                   
               
               
                 n/a 
                 1 (0.1) 
                 0 (0.0) 
                 1 (0.1) 
                   
               
               
                 Surgery 
                   
                   
                   
                 0.29 
               
               
                 Quadrantectomy 
                 1524 (83.4) 
                 500 (82.1) 
                 1024 (84.1) 
                   
               
               
                 Mastectomy 
                 303 (16.6) 
                 109 (17.9) 
                 194 (15.9) 
                   
               
               
                 Hormone therapy 
                   
                   
                   
                 0.41 
               
               
                 No 
                 136 (7.4) 
                 41 (6.7) 
                 95 (7.8) 
                   
               
               
                 Yes 
                 1691 (92.6) 
                 568 (93.3) 
                 1123 (92.2) 
                   
               
               
                 Adjuvant 
                   
                   
                   
                 0.37 
               
               
                 chemotherapy 
                   
                   
                   
                   
               
               
                 No 
                 867 (47.5) 
                 298 (48.9) 
                 569 (46.7) 
                   
               
               
                 Yes 
                 960 (52.5) 
                 311 (51.1) 
                 960 (53.3) 
                   
               
               
                 Adjuvant 
                   
                   
                   
                 0.27 
               
               
                 radiotherapy 
                   
                   
                   
                   
               
               
                 No 
                 314 (17.2) 
                 113 (36.0) 
                 201 (64.0) 
                   
               
               
                 Yes 
                 1513 (82.8) 
                 496 (32.8) 
                 1017 (67.2) 
               
               
                   
               
            
           
         
       
     
     The SPARE methods of the present disclosure were generated by determining a continuous function in the training set with the use of a Cox Hazards regression model in which patients&#39; follow-up was censored after 15 years.  FIG.  1    shows the predicted risk of distant recurrence as a continuous function of the SPARE recurrence score, with 9500 confidence intervals. The dashed curves and shaded areas in  FIG.  1    indicate the 95 percent confidence interval. The cut-off values for the determination of the low risk and high risk groups corresponds respectively to the values of the recurrence score for which the 95% upper bound and 95% lower bound cross the horizontal 10% risk line. The rug plot on the top of the x axis shows the recurrence score for individual patients in the training set. 
     To measure how much the prognostic estimation of the SPARE model matches the real outcome probability, a calibration plot analysis was performed comparing observed versus predicted 10-year distant metastasis rate according to the SPARE methods of the present disclosure in both the training and validation datasets. As shown in  FIG.  2   , the SPARE method reliably predicts recurrence over the span of 10 years. 
     The performance of the SPARE methods of the present disclosure for predicting distant recurrence in the training and validation sets, as well as in groups treated with adjuvant chemotherapy or not treated with adjuvant chemotherapy, are summarized in Table 6. The results in Table 6 indicate that the SPARE methods of the present disclosure can accurately predict distant recurrence of breast cancer at 10 years. 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 6 
               
             
            
               
                   
               
               
                   
                   
                 Rate of distant 
                   
                   
               
               
                   
                 No. of 
                 recurrence at 
                   
                 Distant recurrences at 10 
               
               
                   
                 Patients 
                 10 years (95% 
                 Hazards Ratio 
                 years 
               
            
           
           
               
               
               
               
               
               
            
               
                 Class of Risk 
                 (%) 
                 CI) 
                 (95% CI) 
                 Observed 
                 Predicted 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 Training Set 
                 609 
                   
                   
                   
                   
               
               
                 Low risk 
                 275 (45.2) 
                 2.6 (1.3-5.3) 
                 1.00 
                 7 
                 11.5 
               
               
                 Intermediate risk 
                 122 (20.0) 
                 8.4 (4.3-14.2) 
                 3.11 (1.31-7.36) 
                 10 
                 12.0 
               
               
                 High risk 
                 212 (34.8) 
                 28.7 (22.7-35.0) 
                 11.3 (5.65-22.5) 
                 59 
                 55.0 
               
               
                 Validation Set 
                 1218 
                   
                   
                   
                   
               
               
                 Low risk 
                 579 (47.5) 
                 3.2 (2.0-4.9) 
                 1.00 
                 18 
                 25.5 
               
               
                 Intermediate risk 
                 260 (21.4) 
                 8.3 (5.2-12.2) 
                 2.29 (1.30-4.05) 
                 20 
                 25.1 
               
               
                 High risk 
                 379 (31.1) 
                 29.6 (25.0-34.4) 
                 8.57 (5.52-13.3) 
                 109 
                 99.3 
               
               
                 No adjuvant 
                 960 
                   
                   
                   
                   
               
               
                 chemotherapy 
                   
                   
                   
                   
                   
               
               
                 Low risk 
                 605 (69.8) 
                 2.7 (1.6-4.3) 
                 1.00 
                 16 
                 23.7 
               
               
                 Intermediate risk 
                 145 (16.7) 
                 8.7 (4.7-14.1) 
                 2.73 (1.37-5.46) 
                 12 
                 14.0 
               
               
                 High risk 
                 117 (13.5) 
                 24.7 (17.1-32.9) 
                 8.26 (4.72-14.4) 
                 28 
                 26.0 
               
               
                 Adjuvant 
                 867 
                   
                   
                   
                   
               
               
                 chemotherapy 
                   
                   
                   
                   
                   
               
               
                 Low risk 
                 249 (25.9) 
                 3.7 ( 1.8-6.4) 
                 1.00 
                 9 
                 13.3 
               
               
                 Intermediate risk 
                 237 (24.7) 
                 8.1 ( 5.0-12.1) 
                 2.05 (1.02-4.13) 
                 18 
                 23.1 
               
               
                 High risk 
                 474 (49.4) 
                 30.4 (26.3-34.7) 
                 7.89 (4.39-14.2) 
                 140 
                 128.4 
               
               
                   
               
            
           
         
       
     
     The Clinical Treatment Score (CTS) is a prognostic algorithm based on clinicopathological variables that is widely used by clinicians to predict late distant metastasis for women with ER+ breast cancer. CTS is regarded by those of CTS integrates information on nodal status, grade, proliferation status, HER2 status, tumor size, age, and endocrine treatment with tamoxifen or anastrozole. It was developed in the TransATAC cohort to allow head-to-head comparison of genomic predictors with clinico-pathological parameters (see Cuzick J, et al., Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor-2 immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer. J Clin Oncol. 2011; 29(32): 4273-4278). 
     The performance of the SPARE methods of the present disclosure were compared to the performance of CTS. A summary of the comparison is shown in Table 7 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                   
                   
                   
                   
                   
                 SPARE + CTS 
                 CTS + SPARE 
               
               
                   
                 # of 
                 No of 
                 SPARE alone 
                 CTS alone 
                 vs CTS 
                 vs SPARE 
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
            
               
                   
                 Patients 
                 DRs 
                 LRΔχ2 
                 P 
                 LRΔχ2 
                 P 
                 LRΔχ2 
                 P 
                 LRΔχ2 
                 P 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
            
               
                 All patients 
                 1827 
                 248 
                 218.40 
                 &lt;0.0001 
                 173.83 
                 &lt;0.0001 
                 46.17 
                 &lt;0.0001 
                 1.60 
                 0.21 
               
               
                 Data set 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 Training set 
                 609 
                 87 
                 76.77 
                 &lt;0.0001 
                 55.75 
                 &lt;0.0001 
                 21.05 
                 &lt;0.0001 
                 0.02 
                 0.88 
               
               
                 Validation set 
                 1218 
                 161 
                 141.17 
                 &lt;0.0001 
                 118.08 
                 &lt;0.0001 
                 25.24 
                 &lt;0.0001 
                 2.15 
                 0.14 
               
               
                 Chemotherapy 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 No 
                 867 
                 63 
                 55.63 
                 &lt;0.0001 
                 48.88 
                 &lt;0.0001 
                 9.48 
                 0.002 
                 2.73 
                 0.10 
               
               
                 Yes 
                 960 
                 185 
                 106.77 
                 &lt;0.0001 
                 74.30 
                 &lt;0.0001 
                 32.65 
                 &lt;0.0001 
                 0.18 
                 0.67 
               
               
                 pN 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 pN0/pNx 
                 961 
                 63 
                 47.58 
                 &lt;0.0001 
                 27.53 
                 &lt;0.0001 
                 21.30 
                 &lt;0.0001 
                 1.25 
                 0.26 
               
               
                 pN1 
                 573 
                 84 
                 30.56 
                 &lt;0.0001 
                 15.08 
                 0.0001 
                 15.53 
                 &lt;0.0001 
                 0.05 
                 0.82 
               
               
                 pN2/pN3 
                 293 
                 101 
                 16.22 
                 &lt;0.0001 
                 10.06 
                 0.002 
                 7.11 
                 0.008 
                 0.95 
                 0.33 
               
               
                   
               
            
           
         
       
     
     The LRΔχ2 values in Table 7 (for instance SPARE=218.4 vs. CTS=173.83 in all patients) are to an absolute quantitative measure of the prognostic power of both methods. Therefore, the LRΔχ2 values associated to SPARE or CTS are compared in univariate analysis one against the other. Thus, as shown in Table 7, the SPARE methods of the present disclosure have superior prognostic power compared to CTS (218.4 vs 173.83, all patients). Moreover, if the information derived from the CTS algorithm is added to the information derived from the SPARE methods, there is no increase in statistically significant information (LRΔχ2=1.60, which is below the significant LRΔχ2 cut-off of 3.84). Conversely, if the information from the SPARE methods are added to the information from the CTS algorithm, there is a significant gain of information (46.17), indicating that SPARE adds prognostic information to CTS. Taken together, these results indicate that the SPARE methods of the present disclosure outperform CTS and provide the most prognostic information for distant recurrence of breast cancer. 
       FIG.  3   . shows the cumulative incidence of distant metastasis over a 10-year period in the training and validation sets according to the SPARE methods of the present disclosure. 
     The SPARE methods of the present disclosure were also used to analyze patients in the dataset stratified according to treatments. The treatments were decided per the physician&#39;s choice based on the estimation of the intrinsic risk of the patients according to standard clinical guidelines (i.e. standard clinicopathological parameters). The results of this analysis are shown in  FIG.  4   . As shown in the left panel of  FIG.  4   , in patients that did not receive adjuvant chemotherapy because their physicians deemed them to be low risk based on their clinicopathological characteristics, the SPARE methods of the present disclosure correctly identifies not only patients who did not need adjuvant chemotherapy, but also identifies patients with a high likelihood of recurrence who might have benefited from more aggressive treatments. 
       FIG.  5    shows the results of a forest plot analysis showing 10-year distant recurrence rates, according to the SPARE methods of the present disclosure, in the different risk groups in the entire population and stratified according to clinicopathological characteristics. 
     The prognostic information provided by the SPARE methods of the present disclosure was also compared with the prognostic information provided by the StemPrintER risk score described in US Patent Application Publication No. 2019-0161809 and International Patent Application Publication No. WO2017/220492. The results of this comparison are summarized in Table 8. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 8 
               
             
            
               
                   
               
               
                   
                   
                   
                   
                   
                 SPARE + 
                 StemPrintER + 
               
               
                   
                   
                   
                 StemPrintER 
                 SPARE 
                 StemPrintER vs 
                 SPARE 
               
               
                   
                 No of 
                 No of 
                 alone 
                 alone 
                 StemPrintER 
                 vs SPARE 
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
            
               
                   
                 Patients 
                 DRs 
                 LRΔχ2 
                 P 
                 LRΔχ2 
                 P 
                 LRΔχ2 
                 P 
                 LRΔχ2 
                 P 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
            
               
                 All patients 
                 1827 
                 248 
                 106.58 
                 &lt;0.0001 
                 218.40 
                 &lt;0.0001 
                 113.46 
                 &lt;0.0001 
                 1.63 
                 0.20 
               
               
                 Data set 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 Training set 
                 609 
                 87 
                 40.22 
                 &lt;0.0001 
                 76.77 
                 &lt;0.0001 
                 36.56 
                 &lt;0.0001 
                 0.00 
                 1.00 
               
               
                 Validation set 
                 1218 
                 161 
                 66.20 
                 &lt;0.0001 
                 141.17 
                 &lt;0.0001 
                 77.23 
                 &lt;0.0001 
                 2.25 
                 0.13 
               
               
                 Chemotherapy 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 No 
                 867 
                 63 
                 21.00 
                 &lt;0.0001 
                 55.63 
                 &lt;0.0001 
                 37.48 
                 &lt;0.0001 
                 2.84 
                 0.091 
               
               
                 Yes 
                 960 
                 185 
                 56.51 
                 &lt;0.0001 
                 106.77 
                 &lt;0.0001 
                 50.32 
                 &lt;0.0001 
                 0.05 
                 0.82 
               
               
                 pN 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 pN0/pNx 
                 961 
                 63 
                 37.38 
                 &lt;0.0001 
                 47.58 
                 &lt;0.0001 
                 10.32 
                 0.001 
                 0.12 
                 0.73 
               
               
                 pN1 
                 573 
                 84 
                 23.01 
                 &lt;0.0001 
                 30.56 
                 &lt;0.0001 
                 7.73 
                 0.005 
                 0.18 
                 0.67 
               
               
                 pN2/pN3 
                 293 
                 101 
                 11.90 
                 0.0006 | 
                 16.22 
                 &lt;0.0001 
                 4.71 
                 0.030 
                 0.39 
                 0.53 
               
               
                   
               
            
           
         
       
     
     The results in Table 8 show that the SPARE methods of the present disclosure provide more prognostic information than the StemPrintER risk score on its own.