Abstract:
A first embodiment is a breast cancer prognosticator comprising a detection mechanism consisting a 15-gene signature. In addition there are embodiments comprised of 23-gene signatures and 28-gene signatures. The 28-gene signature may also be used for the prognosis of ovarian cancer. A second embodiment is a method to determine metastatic potential, relapse potential, or both in breast cancer patients comprising collecting a sample from an individual, removing marker-derived polynucleotide from said sample, using a detection mechanism to search for positive matches of said polynucleotides and either the 15, 23, or 28-gene signatures, and developing a quantitative expression profile. Utilizing risk analysis the individual can be placed into one of two or more groups predicting risk and/or clincopathogic variables. Another embodiment is a method to determine relapse free potential in breast cancer patients comprising collecting a sample from an individual, removing marker-derived polynucleotide from said sample, using a detection mechanism to search for positive matches of said polynucleotides and a 24-gene signature, and developing a quantitative expression profile.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority of U.S. provisional patent application numbered 60/919,369 filed on the date Mar. 22, 2007. 
    
    
     REFERENCE TO SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM LISTING COMPACT DISC APPENDIX 
     This application contains a Sequence Listing submitted on compact disk containing file name 387.5 eq. The sequence listing on the compact disc is incorporated by reference herein in its entirety. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The following figures are not drawn to scale and are for illustrative purposes only. 
         FIG. 1  is a Time dependent ROC analyses of the 28-gene signature in disease-free survival prediction in three breast cancer patient cohorts.  FIG. 1  is a Time dependent ROC (t=5 years) curve of the 28-gene signature on the training set from Sotiriou et al. (8) The area under the ROC curve (AUC)=0.983. 
         FIG. 2  is a Time dependent ROC analyses of the 28-gene signature in disease-free survival prediction in three breast cancer patient cohorts.  FIG. 2  is a AUC in year 1 to year 11 during follow-up after surgery in the patient cohort from Sotiriou et al. (8). 
         FIG. 3  is a Time dependent ROC analyses of the 28-gene signature in disease-free survival prediction in three breast cancer patient cohorts.  FIG. 3  is a Time-dependent ROC (t=5 years) curves of the 28-gene signature on two validation sets. AUC=0.843 with 25 overlapping genes on data from van&#39;t Veer et al. (27) AUC=0.764 with 8 overlapping genes on data from Sorlie et al. (10). 
         FIG. 4  is a Time dependent ROC analyses of the 28-gene signature in disease-free survival prediction in three breast cancer patient cohorts.  FIG. 4  is a AUC in year 1 to year 13 during follow-up after surgery on two independent patient cohorts (10;28). 
         FIG. 5  is a Time-dependent ROC analyses of the 28-gene signature in overall survival prediction in there breast cancer patient cohorts.  FIG. 5  is a Time-dependent ROC curves at time=5 years. AUC=0.927 on data from Sotiriou et al. (8) AUC=0.808 on data from Sorlie et al. (10) 
         FIG. 6  is a Time-dependent ROC analyses of the 28-gene signature in overall survival prediction in there breast cancer patient cohorts.  FIG. 6  is the area under the ROC curve (AUC) of overall survival prediction during the follow-up after surgery. 
         FIG. 7  is a Time-dependent ROC analyses of 15 genes within the 28-gene signature in relapse-free survival prediction in three breast cancer patient cohorts.  FIG. 7  are Time-dependent ROC curves at time=5 years. AUC=0.92 on data from Sotiriou et al. (8) 
         FIG. 8  is a Time-dependent ROC analyses of 15 genes within the 28-gene signature in relapse-free survival prediction in three breast cancer patient cohorts.  FIG. 8  are Time-dependent ROC curves at time=5 years. AUC=0.87 on data from Sorlie et al. (10) 
         FIG. 9  is a Time-dependent ROC analyses of 15 genes within the 28-gene signature in relapse-free survival prediction in three breast cancer patient cohorts.  FIG. 9  are Time-dependent ROC curves at time=5 years. AUC=0.79 on data from van&#39;t Veer et al. (26). 
         FIG. 10  is a Time-dependent ROC analyses of 24 genes within the 28-gene signature in relapse-free survival prediction in one ovarian cancer patient cohort from Bild et al. (29) 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     A first embodiment in this application can be an expression profile-defined prognostic model able to predict the recurrence and metastases of breast cancer and ovarian cancer by using unique gene expression patterns in tumors. Additionally, the expression profile-defined prognostic model may be used to predict the relapse-free interval and metastases-free interval. The expression based profile-defined prognostic model has been developed and is a highly accurate predictor of disease-free survival as well as overall survival in individual breast cancer patients. The expression based profile-defined prognostic model can be a gene signature such as a 15-gene signature, a 23-gene signature, or a 28-gene signature comprised of a combination of the following genes (Table 1). 
     
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 28 genes that quantifies disease-free survival 
               
               
                 and overall survival of breast cancer 
               
             
          
           
               
                   
                 Clone_ 
                 UniGene 
                   
                   
               
               
                 Gene 
                 IMAGE 
                 Cluster ID 
                 Sequence ID 
                 Seq. ID No 
               
               
                   
               
             
          
           
               
                 FAM134C 
                 198917 
                 Hs.463079 
                 NM_178126 
                 Seq. ID No 30 
               
               
                 TOMM70A 
                 198312 
                 Hs.227253 
                 NM_014820 
                 Seq. ID No 7 
               
               
                 MCF2 
                 268412 
                 Hs.387262 
                 NM_001099855 
                 Seq. ID No 8 
               
               
                   
                   
                   
                 NM_005369 
                 Seq. ID No 9 
               
               
                 RAD52 
                 1377154 
                 Hs.552577 
                 NM_134424 
                 Seq. ID No 10 
               
               
                 Pseudogene 
                   
                   
                   
                   
               
               
                 MCM2 
                 239799 
                 Hs.477481 
                 NM_004526 
                 Seq. ID No 11 
               
               
                 C18B11 
                 131988 
                 Hs.173311 
                 NM_152260 
                 Seq. ID No 12 
               
               
                 SEC13L 
                 757210 
                 Hs.301048 
                 NM_031216 
                 Seq. ID No 13 
               
               
                   
                   
                   
                 NM_001013437 
                 Seq. ID No 14 
               
               
                 SLC25A5 
                 291660 
                 Hs.522767 
                 NM_001152 
                 Seq. ID No 15 
               
               
                 PLSCR1 
                 268736 
                 Hs.130759 
                 NM_021105 
                 Seq. ID No 16 
               
               
                 TXNRD1 
                 789376 
                 Hs.434367 
                 NM_003330 
                 Seq. ID No 17 
               
               
                   
                   
                   
                 NM_001093771 
                 Seq. ID No 18 
               
               
                   
                   
                   
                 NM_182742 
                 Seq. ID No 19 
               
               
                   
                   
                   
                 NM_182729 
                 Seq. ID No 20 
               
               
                   
                   
                   
                 NM_182743 
                 Seq. ID No 21 
               
               
                 RAD50 
                 261828 
                 Hs.242635 
                 NM_005732 
                 Seq. ID No 22 
               
               
                   
                   
                   
                 NM_133482 
                 Seq. ID No 23 
               
               
                 — 
                 46196 
                   
                 BX100884 
                 Seq. ID No 4 
               
               
                   
                   
                   
                 H09243 
                 Seq. ID No 5 
               
               
                   
                   
                   
                 H09242 
                 Seq. ID No 6 
               
               
                 INPPL1 
                 703964 
                 Hs.523875 
                 NM_001567 
                 Seq. ID No 24 
               
               
                 — 
                 501651 
                 Hs.439445 
                 AK025546 
                 Seq. ID No 3 
               
               
                 PBX2 
                 80549 
                 Hs.509545 
                 NM_002586 
                 Seq. ID No 25 
               
               
                 SSBP1 
                 125183 
                 Hs.490394 
                 NM_003143 
                 Seq. ID No 26 
               
               
                 — 
                 34396 
                 Hs.448229 
                 BE870371 
                 Seq. ID No 1 
               
               
                 PDGFRA 
                 376499 
                 Hs.74615 
                 NM_006206 
                 Seq. ID No 27 
               
               
                 ACOT4 
                 488202 
                 Hs.49433 
                 NM_152331 
                 Seq. ID No 28 
               
               
                 DDOST 
                 50666 
                 Hs.523145 
                 NM_005216 
                 Seq. ID No 29 
               
               
                 IGHA1 
                 182930 
                 Hs.497723 
                 AK128652 
                 Seq. ID No 30 
               
               
                 S100P 
                 135221 
                 Hs.2962 
                 NM_005980 
                 Seq. ID No 31 
               
               
                 FAT 
                 591266 
                 Hs.481371 
                 NM_005245 
                 Seq. ID No 32 
               
               
                 FGF2 
                 324383 
                 Hs.284244 
                 NM_002006 
                 Seq. ID No 33 
               
               
                 INSM1 
                 22895 
                 Hs.89584 
                 NM_002196 
                 Seq. ID No 34 
               
               
                 IRF5 
                 260035 
                 Hs.521181 
                 NM_001098629 
                 Seq. ID No 35 
               
               
                   
                   
                   
                 NM_002200 
                 Seq. ID No 36 
               
               
                   
                   
                   
                 NM_001098627 
                 Seq. ID No 37 
               
               
                   
                   
                   
                 NM_001098630 
                 Seq. ID No 38 
               
               
                   
                   
                   
                 NM_001098628 
                 Seq. ID No 39 
               
               
                   
                   
                   
                 NM_032643 
                 Seq. ID No 40 
               
               
                   
                   
                   
                 NM_001098631 
                 Seq. ID No 41 
               
               
                 SMARCD2 
                 741067 
                 Hs.250581 
                 NM_001098426 
                 Seq. ID No 42 
               
               
                   
                   
                   
                 NM_003077 
                 Seq. ID No 43 
               
               
                 MAP2K2 
                 769579 
                 Hs.465627 
                 NM_030662 
                 Seq. ID No 44 
               
               
                   
               
             
          
         
       
     
     There is no overlap between the disclosed gene signature and previously reported gene signatures. Of the 28 genes in Table 1, 17 are related to tumorigenesis (Table 2) and 9 genes are linked to breast cancer pathogenesis (Table 3). Furthermore, among the nine breast cancer-related genes, five genes are established breast cancer biomarkers ((MCM2, Rad50, PDGFRA, S100P, and FGF2) (Table 3)). 
     
       
         
               
             
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Genes that are related to tumorigenesis 
               
             
          
           
               
                 Gene 
                 Gene Name 
                 Function 
               
               
                   
               
               
                 MCF2 
                 Mcf.2 cell line derived 
                 Guanine nucleotide 
               
               
                   
                 transforming sequence 
                 exchange factor 
               
               
                 MCM2 
                 Mcm2 minichromosome 
                 DNA replication 
               
               
                   
                 maintenance deficient 
                   
               
               
                   
                 2, mitotin 
                   
               
               
                 SEC13L 
                 Seh1-like 
                 mRNA export, nuclear 
               
               
                   
                   
                 pore distribution and 
               
               
                   
                   
                 cell division 
               
               
                 PLSCR1 
                 Phospholipid scramblase 1 
                 Lipid transfer signaling 
               
               
                 RAD50 
                 RAD50 homolog 
                 DNA repair 
               
               
                 INPPL1 
                 Inositol polyphosphate 
                 Lipid metabolism 
               
               
                   
                 phosphatase-like 1 
                   
               
               
                 TXNRD1 
                 Thioredoxin reductase 1 
                 Antioxidant and redox 
               
               
                   
                   
                 regulator 
               
               
                 PBX2 
                 Pre-b-cell leukemia 
                 Transcriptional repressor 
               
               
                   
                 transcription factor 2 
                 and tumor suppressor 
               
               
                 SSBP1 
                 Single-stranded dna 
                 DNA binding protein 
               
               
                   
                 binding protein 1 
                   
               
               
                 PDGFRA 
                 Platelet-derived growth 
                 Growth factor receptor 
               
               
                   
                 factor receptor 
                   
               
               
                 S100P 
                 S100 calcium binding protein p 
                 Cell differentiation 
               
               
                 FAT 
                 Fat tumor suppressor homolog 1 
                 Cell signaling suppressor 
               
               
                 FGF2 
                 Fibroblast growth factor 2 
                 Signaling tranduction 
               
               
                 INSM1 
                 Insulinoma-associated 1 
                 Transcriptional repressor 
               
               
                 IRF5 
                 Interferon regulatory factor 5 
                 Tumor suppressor gene 
               
               
                 SMARCD2 
                 Swi/snf related, matrix 
                 chromatin remodelling 
               
               
                   
                 associated, actin dependent 
                   
               
               
                   
                 regulator of chromatin, 
                   
               
               
                   
                 subfamily d, member 2 
                   
               
               
                 MAP2K2 
                 Mitogen-activated protein 
                 Signaling transduction 
               
               
                   
                 kinase kinase 2 
               
               
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Genes that are linked to breast cancer pathogenesis 
               
             
          
           
               
                   
                   
                   
                 Breast Cancer 
               
               
                 Gene 
                 Gene Name 
                 Function 
                 Involvement 
               
               
                   
               
               
                 MCF2 
                 Mcf.2 cell line derived 
                 Guanine nucleotide 
                 (+) 
               
               
                   
                 transforming sequence 
                 exchange factor 
                   
               
               
                 MCM2 
                 Mcm2 minichromosome 
                 DNA replication 
                 (+) 
               
               
                   
                 maintenance deficient 2, mitotin 
                   
                 biomarker (1) 
               
               
                 RAD50 
                   
                 DNA repair 
                 (+) 
               
               
                   
                   
                   
                 biomarker (2) 
               
               
                 TXNRD1 
                 Thioredoxin reductase 1 
                 Antioxidant and 
                 (+) 
               
               
                   
                   
                 redox regulator 
                   
               
               
                 PDGFRA 
                 Platelet-derived growth factor 
                 Growth factor 
                 (+) 
               
               
                   
                 receptor 
                 receptor 
                 biomarker (3; 4) 
               
               
                 S100P 
                 S100 calcium binding protein p 
                 Cell differentiation 
                 (+) 
               
               
                   
                   
                   
                 biomarker (5; 6) 
               
               
                 FGF2 
                 Fibroblast growth factor 2 
                 Signaling 
                 (+) 
               
               
                   
                   
                 tranduction 
                 biomarker (7) 
               
               
                 SMARCD2 
                 Swi/snf related, matrix associated, 
                 chromatin 
                 (+) 
               
               
                   
                 actin dependent regulator of 
                 remodelling 
                   
               
               
                   
                 chromatin, subfamily d, member 2 
                   
                   
               
               
                 MAP2K2 
                 Mitogen-activated protein kinase 
                 Signaling 
                 (+) 
               
               
                   
                 kinase 2 
                 transduction 
               
               
                   
               
             
          
         
       
     
     Based upon the expression profiles of these 28 genes in the data from Sotiriou et al. (8), a Linear Discriminant Analysis function classified 5-year relapse status for patients provided an accuracy of 0.92, a sensitivity of 0.90, and a specificity of 0.95. To evaluate relapse-free survival prediction, a Cox proportional hazards model was built on the 28-gene signature and the risk score was used to construct the time-dependent receiver operating curve (ROC). The area under the ROC curve (AUC) during year five was 0.983 ( FIG. 1 ), and remained 0.92 between years 8 and 11 during the follow up ( FIG. 2 ). 
     To evaluate the prognostic power of the identified gene signature, two independent validation sets were used (9;10). Using the signature genes, time-dependent ROC analyses were performed to evaluate relapse/metastases prediction on two independent patient cohorts ( FIGS. 3 and 4 ). The area under the ROC (5-year) curve on the data from van&#39;t Veer et al. (11) was 0.843 with 25 signature genes in predicting metastatic potential. The AUC (5-year) was 0.764 on the data from Sorlie et al. (10) with eight overlapped genes in the relapse-free survival prediction ( FIG. 3 ). 
     Time dependent ROC analysis showed that the 28-gene signature was also predictive of overall survival (P&lt;0.001;  FIGS. 5 and 6 ). In the prediction of overall survival, the AUC (5-year) was 0.927 on data from Sotiriou et al. (Sotiriou C. et al., Breast Cancer classification and prognosis based on gene expression profiles from a population-based study,  Proc. Natl. Acad. Sci ., USA 2003; 100:10393-8) and 0.808 on data from Sorlie et al. (Sorlie T. et al., Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications,  Proc. Natl. Acad. Sci ., USA 2001; 98:10869-74). 
     Among the 28-gene signature, 11 genes had significant association with relapse-free survival in Cox modeling (Table 4). 
     
       
         
               
             
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 Genes that are significantly associated with breast cancer relapse. 
               
             
          
           
               
                   
                 GENE 
                 P-value 
               
               
                   
               
               
                   
                 FGF2 
                 0.0039 
               
               
                   
                 SLC25A5 
                 0.0051 
               
               
                   
                 C18B11 
                 0.0062 
               
               
                   
                 SMARCD2 
                 0.0087 
               
               
                   
                 TOMM70A 
                 0.0250 
               
               
                   
                 PBX2 
                 0.0330 
               
               
                   
                 SEC13L 
                 0.0350 
               
               
                   
                 Clone ID: 501651 
                 0.0350 
               
               
                   
                 IRF5 
                 0.0350 
               
               
                   
                 DDOST 
                 0.0470 
               
               
                   
                 Clone ID: 182930 
                 0.0520 
               
               
                   
               
             
          
         
       
     
     Among the 28-gene set, 15 genes (Table 5) predict disease-free survival with an accuracy ranging from 0.79 to 0.92 in three patient cohorts from Sotiriou et al. (8), van&#39;t Veer et al. (12), and Sorlie et al. (10) ( FIGS. 7 ,  8 , and  9 ). These 15 genes can be used as a 15-gene signature prognostic model for breast cancer. In addition, the 8 unique genes from Table 4 may be added to form a 23-gene signature prognostic model for breast cancer. The remaining 5 unique genes from Table 1 form a 28-gene signature prognostic model for both breast and ovarian cancer. Together, genes in Tables 4 and 5 can predict both breast cancer relapse and metastases. 
     
       
         
               
             
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 5 
               
             
             
               
                   
               
               
                 Genes that predicts breast cancer relapse. 
               
             
          
           
               
                   
                 GENE 
                 CLONE ID 
                 Seq. ID No 
               
               
                   
               
             
          
           
               
                   
                 MAP2K2 
                 769579 
                 Seq. ID No 44 
               
               
                   
                 SMARCD2 
                 741067 
                 Seq. ID No 44-43 
               
               
                   
                 S100P 
                 2060823 
                 Seq. ID No 31 
               
               
                   
                 FAT 
                 591266 
                 Seq. ID No 32 
               
               
                   
                 DDOST 
                 50666 
                 Seq. ID No 29 
               
               
                   
                 SSBP1 
                 125183 
                 Seq. ID No 26 
               
               
                   
                 PDGFRA 
                 1643186 
                 Seq. ID No 27 
               
               
                   
                 INPPL1 
                 703964 
                 Seq. ID No 24 
               
               
                   
                 RAD50 
                 261828 
                 Seq. ID No 22-23 
               
               
                   
                 PLSCR1 
                 268736 
                 Seq. ID No 16 
               
               
                   
                 RAD52 
                 140004 
                 Seq. ID No 10 
               
               
                   
                 C18B11 
                 131988 
                 Seq. ID No 12 
               
               
                   
                 MCM2 
                 239799 
                 Seq. ID No 11 
               
               
                   
                 MCF2L 
                 1781388 
                 Seq. ID No 8-9 
               
               
                   
                 TXNRD1 
                 630625 
                 Seq. ID No 17-21 
               
               
                   
               
             
          
         
       
     
     To assess a breast cancer patient&#39;s relapse and metastatic potential, risk scores can be generated by using a Cox model of the 28-gene signature, independent of clinical-pathological parameters although any standard risk evaluation could be used. In this application large value of the risk scores indicates a high risk of relapse/metastases, while a small value indicates a lower risk of breast cancer relapse. The 28-gene signature obtained from the training set (8) was fitted into a Cox regression model as covariates. To avoid overfitting, the data set are randomly partitioned into two subsets—one was used to define risk groups by fitting the model and obtaining the risk score cutoffs; the other subset was used to validate the cutoffs for defining the risk groups. The distribution of the risk scores can be categorized into groups of two or more. If two groups, patients could be labeled as high risk at the 65 th  percentile or above and low risk at 64 th  percentile and below. Alternatively, the patients could be categorized into high, low, or intermediate risk group is 39%, 26%, and 35%, respectively in the training set. The cutoffs defined in the training subset can be used to separate the patients in the test subset into high, low and intermediate risk groups. 
     A further embodiment is the ability to evaluate clincopathogic variables for cancer patients. Clincopathogic variable includes, but is not limited to, average metastases-free days, ER and PR status, age, tumor size, and tumor grade. Table 6 displays the clinical characteristics of each risk group, including average relapse-free days, ER status, Her2/neu overexpression, nodal status, age, tumor size, and treatment received on the data from Sotiriou et al. (8). Risk scores were generated for patients in Cox modeling using the gene expression profiles, without including clinicopathologic parameters. The 39 th  and 65 th  percentile of the risk scores were used to partition patients into high, intermediate, and low risk groups. Same analysis is applied to the two validation sets. Table 7 summarizes the clinical characteristics of each risk group, including average metastases-free days, ER and PR status, age, tumor size, and tumor grade on the data from van&#39;t Veer et al. (13). Table 8 summarizes the clinical characteristics of each risk group, including average relapse-free days, ER status, age, and tumor grade on the data from Sorlie et al. (10). 
     
       
         
               
             
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 6 
               
             
             
               
                   
               
               
                 Clinical characteristics of each risk group on the patients from Sotiriou et al.(8) 
               
             
          
           
               
                   
                   
                   
                 # of 
                   
                 % of 
                   
                   
                   
               
               
                   
                 Average 
                   
                 Her-2\neu 
                 % of 
                 Positive 
               
               
                 Risk 
                 RFS 
                 % of 
                 positive 
                 Tumor 
                 Nodal 
                 % of 
                 % of 
                 % of 
               
               
                 Group 
                 (days) 
                 Age ≧50 yrs 
                 cases 
                 Size &gt;2 cm 
                 Status 
                 Chemo 
                 Hormone 
                 ER+ 
               
               
                   
               
             
          
           
               
                 High 
                 969 
                 82% 
                 6 
                 82% 
                 67% 
                 38% 
                 79% 
                 54% 
               
               
                 Inter. 
                 2407 
                 73% 
                 1 
                 58% 
                 50% 
                 35% 
                 85% 
                 58% 
               
               
                 Low 
                 2781 
                 65% 
                 0 
                 47% 
                 41% 
                 24% 
                 74% 
                 85% 
               
               
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
             
           
               
                 TABLE 7 
               
             
             
               
                   
               
               
                 Clinical characteristics of each risk group on 
               
               
                 the patients from van&#39;t Veer et al. (14) 
               
             
          
           
               
                   
                   
                 Average 
                   
                   
                 % of 
                 % of 
               
               
                 Risk 
                 % of 
                 RFS 
                 % of 
                 % of 
                 Tumor 
                 T3/T4 
               
               
                 Group 
                 Patients 
                 (days) 
                 Age ≧50 
                 ER+ 
                 Grade 3 
                 Tumors 
               
               
                   
               
             
          
           
               
                 High 
                 28% 
                 553 
                 50% 
                 69% 
                 81% 
                 94% 
               
               
                 Intermediate 
                 32% 
                 801 
                 84% 
                 89% 
                 26% 
                 89% 
               
               
                 Low 
                 40% 
                 1376 
                 70% 
                 73% 
                 32% 
                 77% 
               
               
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
             
           
               
                 TABLE 8 
               
             
             
               
                   
               
               
                 Clinical characteristics of each risk group (Sorlie et al. (10)) 
               
             
          
           
               
                   
                   
                 Average 
                   
                   
                 % of 
                 % of 
               
               
                 Risk 
                 % of 
                 RFS 
                 % of 
                 % of 
                 Tumor 
                 T3/T4 
               
               
                 Group 
                 Patients 
                 (days) 
                 Age ≧50 
                 ER+ 
                 Grade 3 
                 Tumors 
               
               
                   
               
             
          
           
               
                 High 
                 28% 
                 553 
                 50% 
                 69% 
                 81% 
                 94% 
               
               
                 Intermediate 
                 32% 
                 801 
                 84% 
                 89% 
                 26% 
                 89% 
               
               
                 Low 
                 40% 
                 1376 
                 70% 
                 73% 
                 32% 
                 77% 
               
               
                   
               
             
          
         
       
     
     Clinical variables such as nodal status, tumor size, tumor grade, ER status and HER2/neu overexpression in breast cancer patients affect the disease outcomes. The clinical characteristics of each risk group in the studied cohorts are analyzed including average disease-free survival days, ER and PR status, HER2/neu overexpression, nodal status, age, tumor size, grade, and treatment received. The 28-gene signature is strongly associated with the clincopathogic variables, including tumor size, tumor grade, ER and PR status, and HER2/neu overexpression (P&lt;0.05; Table 9). 
     
       
         
               
             
               
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 9 
               
             
             
               
                   
               
               
                 Association of gene expression-defined risk 
               
               
                 groups and clinicopathologic parameters 
               
             
          
           
               
                   
                 P-Values 
               
             
          
           
               
                   
                   
                 Sotiriou 
                 van&#39;t Veer 
                 Sorlie 
               
               
                   
                 Risk Groups vs. 
                 et al.(8) 
                 et al.(15) 
                 et al.(10) 
               
               
                   
               
             
          
           
               
                   
                 Age  1   
                 0.243 
                 0.458 
                 0.095 
               
               
                   
                 (&lt;50 yrs or ≧50 yrs) 
                   
                   
                   
               
               
                   
                 Tumor size 
                 0.006* 
                 0.047* 
                   
               
               
                   
                 (&lt;2 cm or &gt;2 cm) 
                   
                   
                   
               
               
                   
                 Tumor grade 
                 0.041* 
                 0.004* 
                 0.001* 
               
               
                   
                 (1/2 vs. 3) 
                   
                   
                   
               
               
                   
                 ER status 
                 0.011* 
                 0.004* 
                 0.296 
               
               
                   
                 PR status 
                   
                 0.001* 
                   
               
               
                   
                 Her2/neu 
                 0.020* 
               
               
                   
               
               
                   1  The percentage of patients who were at least 50 years old was 74%, 28%, and 69% in the cohorts from Sotiriou et al. (8), van&#39;t Veer et al. (16), and Sorlie et al. (10), respectively. 
               
             
          
         
       
     
     The 28-gene signature also predicts tumor recurrence in ovarian cancer with an accuracy of 0.89 ( FIG. 10 ). Table 10 listed the genes that are predictive of ovarian cancer relapse. 
     
       
         
               
             
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 10 
               
             
             
               
                   
               
               
                 24 genes that quantifies relapse-free survival of breast cancer 
               
             
          
           
               
                   
                   
                 UniGene 
                   
               
               
                 Gene 
                 Clone_IMAGE 
                 Cluster ID 
                 Sequence ID 
               
               
                   
               
             
          
           
               
                 FAM134C 
                 198917 
                 Hs.463079 
                 NM_178126 
               
               
                 TOMM70A 
                 198312 
                 Hs.227253 
                 NM_014820 
               
               
                 MCF2 
                 268412 
                 Hs.387262 
                 NM_001099855 
               
               
                   
                   
                   
                 NM_005369 
               
               
                 RAD52 Pseudogene 
                 1377154 
                 Hs.552577 
                 NM_134424 
               
               
                 MCM2 
                 239799 
                 Hs.477481 
                 NM_004526 
               
               
                 C18B11 
                 131988 
                 Hs.173311 
                 NM_152260 
               
               
                 SEC13L 
                 757210 
                 Hs.301048 
                 NM_031216 
               
               
                   
                   
                   
                 NM_001013437 
               
               
                 SLC25A5 
                 291660 
                 Hs.522767 
                 NM_001152 
               
               
                 PLSCR1 
                 268736 
                 Hs.130759 
                 NM_021105 
               
               
                 TXNRD1 
                 789376 
                 Hs.434367 
                 NM_003330 
               
               
                   
                   
                   
                 NM_001093771 
               
               
                   
                   
                   
                 NM_182742 
               
               
                   
                   
                   
                 NM_182729 
               
               
                   
                   
                   
                 NM_182743 
               
               
                 RAD50 
                 261828 
                 Hs.242635 
                 NM_005732 
               
               
                   
                   
                   
                 NM_133482 
               
               
                 INPPL1 
                 703964 
                 Hs.523875 
                 NM_001567 
               
               
                 PBX2 
                 80549 
                 Hs.509545 
                 NM_002586 
               
               
                 SSBP1 
                 125183 
                 Hs.490394 
                 NM_003143 
               
               
                 PDGFRA 
                 376499 
                 Hs.74615 
                 NM_006206 
               
               
                 DDOST 
                 50666 
                 Hs.523145 
                 NM_005216 
               
               
                 IGHA1 
                 182930 
                 Hs.497723 
                 AK128652 
               
               
                 S100P 
                 135221 
                 Hs.2962 
                 NM_005980 
               
               
                 FAT 
                 591266 
                 Hs.481371 
                 NM_005245 
               
               
                 FGF2 
                 324383 
                 Hs.284244 
                 NM_002006 
               
               
                 INSM1 
                 22895 
                 Hs.89584 
                 NM_002196 
               
               
                 IRF5 
                 260035 
                 Hs.521181 
                 NM_001098629 
               
               
                   
                   
                   
                 NM_002200 
               
               
                   
                   
                   
                 NM_001098627 
               
               
                   
                   
                   
                 NM_001098630 
               
               
                   
                   
                   
                 NM_001098628 
               
               
                   
                   
                   
                 NM_032643 
               
               
                   
                   
                   
                 NM_001098631 
               
               
                 MAP2K2 
                 769579 
                 Hs.465627 
                 NM_030662 
               
               
                   
               
             
          
         
       
     
     In the present invention, target polynucleotide molecules are extracted from a sample taken from an individual afflicted with breast cancer or ovarian cancer. The sample may be collected in any clinically acceptable manner, but must be collected such that marker-derived polynucleotides (i.e., RNA) are preserved. mRNA or nucleic acids derived there from (i.e., cDNA or amplified DNA) can be labeled distinguishably from standard or control polynucleotide molecules, and both are simultaneously or independently hybridized to a detection mechanism. A detection mechanism can be any standard comparison mechanism such as a microarray or an assay of reverse transcription polymerase chain reaction (RT-PCR) comprising some or all of the markers or marker sets or subsets described above. This process identifies positive matches. Alternatively, mRNA or nucleic acids derived therefrom may be labeled with the same label as the standard or control polynucleotide molecules to identify positive matches, wherein the intensity of hybridization of each at a particular probe or primer is compared for such an identification. A sample may comprise any clinically relevant tissue sample, such as a tumor biopsy or fine needle aspiration, or a sample of bodily fluid, such as blood, plasma, serum, lymph, ascetic fluid, cystic fluid, urine, or nipple exudate. The sample may be taken from a human, or from non-human animals such as horses, mice, ruminants, swine or sheep. Patients&#39; gene expression levels may be quantified by any means known in the art based on the marker sets defined above. Patients may be classified based on the quantitative expression profiles using any means known in the art. For example, the risk scores of a patient cohort may be generated using a Cox proportional hazard model. Patients with a risk score greater than the median is defined as high risk, whereas patients with a risk score less than the median is classified as low risk. Alternatively, a patient may be classified as high risk if this patient&#39;s gene expression profile is correlated with the high risk signature, or classified as low risk if this patient&#39;s gene expression profile is correlated with the low risk signature. A patient&#39;s prognostic categorization can also be determined by using a statistical model or a machine learning algorithm, which computes the probability of recurrence based on this patient&#39;s gene expression profiles. Cutoffs can be defined for patient stratification based on specific clinical setting. In addition, patients may be defined into three risk groups in the prognostic categorization based on the marker sets defined above. 
     Methods for preparing total and poly(A)+RNA are well known and are described in (17). RNA may be isolated from eukaryotic cells by procedures that involve cell lysis and denaturation of the proteins contained therein. Cells of interest include wide-type cells (i.e., no mutation), drug-treated wild-type cells, tumor- or tumor-derived cells, modified cells, normal or tumor cell lines cells, and drug-treated modified cells. Total RNA may also be extracted from samples using commercially available kits such as the RNeasy mini kit according the manufacturer&#39;s protocol (Qiagen, USA). 
     Additional steps may be performed to remove DNA (17). If desired, RNase inhibitors may be added to the lysis buffer. Likewise, a protein denaturation/digestion step may be added to the protocol. mRNA may be purified by means such as magnetic separation using Dynabeads (Dynal) or the Invitrogen FastTrack 2.0 kit (10). 
     For many applications, it is desirable to preferentially enrich mRNA with respect to other cellular RNAs, such as transfer RNA (tRNA) and ribosomal RNA (rRNA). Total RNA may also be linearly amplified using the original or modified Eberwine method (18) and be used as a reference for cDNA analysis (8). 
     The sample of RNA can comprise a plurality of different mRNA molecules, each different mRNA molecular having a different nucleotide sequence. In a specific embodiment, the RNA sample has not been functionally annotated. 
     The present invention provides a set of biomarkers for the identification of conditions of indications associated with breast cancer. Generally, the markers sets were identified by determining which of 25,000 human genes had expression patterns that correlated with the conditions or indications. 
     In one embodiment, the expression of all markers in a sample X is compared to the expression of all markers in the 28-gene signature or subsets as described above derived from tumor samples. The comparison may be accomplished by any means known in the art. The expression level may be determined by isolating and determining the level (i.e., the abundance) of nucleic acid transcribed from each marker gene. Alternatively, or additionally, the level of specific proteins translated from mRNA transcribed from a marker gene may be determined. For example, expression levels of various markers may be measured by separation of target nucleotide molecules (e.g., RNA or cDNA) derived from the markers in agarose or polyacrylamide gels, followed by hybridization with marker-specific oligonucleotide probes. Alternatively, the comparison may be accomplished by the labeling of target polynucleotide molecules followed by separation on a sequence gel. The comparison may also be accomplished by measuring the gene expression level using real-time reverse transcription polymerase chain reaction with marker-specific primers/probes. Patients may be classified based on the quantitative expression profiles using any means known in the art. For example, the risk scores of a patient cohort may be generated using a Cox proportional hazard model. Patients with a risk score greater than the median is defined as high risk, whereas patients with a risk score less than the median is classified as low risk. Alternatively, a patient may be classified as high risk if this patient&#39;s gene expression profile is correlated with the high risk signature, or classified as low risk if this patient&#39;s gene expression profile is correlated with the low risk signature. A patient&#39;s prognostic categorization can also be determined by using a statistical model or a machine learning algorithm, which computes the probability of recurrence based on this patient&#39;s gene expression profiles. Cutoffs can be defined for patient stratification based on specific clinical setting. In addition, patients may be defined into three risk groups in the prognostic categorization based on the marker sets defined above. 
     A marker is selected based on its predictive power of breast cancer recurrence, including local recurrence and distant metastasis. A combination of Random Forests (19) and Linear Discriminant Analysis (LDA) is used to identify gene signatures for predicting breast cancer recurrence/metastases. Random forests of software R is first used to identify a small subset of genes from the original microarray data. Linear Discriminant Analysis of software SAS is used to further refine the gene signature. 
     Random forests are a generalization of the standard tree algorithms (20). The basic step of random forests is to form diverse tree classifiers from a single training set. Each tree is built upon a bootstrap sample from the training set. The variables used for splitting the tree nodes are a random subset of the whole variables set. The classification decision of a new case is obtained by majority voting (unless the cutoff value is user defined) over all trees. In random forests, about one-third of the cases in the bootstrap sample are not used in growing the tree. These cases are called “out-of-bag” (OOB) cases and are used to evaluate the algorithm performance. A very important function of random forests is variable importance evaluation. The importance of a variable is defined in terms of its contribution to classification accuracy. Based on the variable importance measure, backward elimination was used to identify the gene subset with the smallest OOB error rate. Here, the OOB error rate was not used to assess the prediction accuracy of the identified gene subsets. Instead, it served as a stopping rule for feature selection. The varSelRF package of software R (21) was used according to the following steps: 
     1. Build a forest with N trees and obtain a ranking of variable importance 
     2. Remove 20% of the least important variables 
     3. Construct a new forest with K trees 
     4. Repeat steps 2 and 3 until two genes are left 
     5. Select the gene subset with the smallest OOB error rate 
     In the experiments, N=3,000 and K=1,000 are chosen because the large number of trees in the initial forests are likely to produce stable importance measures (21). The “0-Standard Error (0-SE) rule” is used, which identifies the gene subset with the smallest OOB error rate. The “0-SE rule” usually selects more genes than the “1-SE rule” does. Since further gene filtering would be performed by using Linear Discriminant Analysis, the gene subsets are selected with the lowest prediction error using random forests. 
     Discriminant analysis is used to determine which variables discriminate two or more naturally occurring groups in prognosis. Given a number of variables as the data representation, each class is modeled as multivariate normal distribution with a covariance matrix and a mean vector. Instances are classified to the label of the nearest mean vector based on Mahalanobis distance. The decision surfaces between classes become linear if the classes have a common covariance matrix. When the distribution within each group is assumed to be multivariate normal, a parametric method can be used to develop a discriminant function. Such function is determined by a measure of generalized square distance which is based on the pooled covariance matrix as well as the prior probabilities of group membership. The generalized squared distance D i   2 (x) from input x to class i is:
 
 D   i   2 ( x )= d   i   2 ( x )+ g ( i )
 
where d i   2 (x)=(x−m i )′V −1 (x−m i ) is the squared distance from x to group I, m i  is the p-dimensional mean vector for group I; V is the pooled covariance matrix and g(i) depends on the prior probability of class i. In practice, the prior probability can be assumed as equal for all groups (refer to SAS Users&#39; Manual). In this study, we assumed equal prior probability and thus g(i)=0. x is classified into class I, if D i   2 (x) is the smallest among all the distance measures. We selected the gene markers using backward selection of stepwise discriminant analysis with software SAS.
 
     Linear Discriminant Analysis (LDA) is used to refine the gene signature obtained from random forests and assess the classification accuracy of models in predicting 5-year relapse-free survival based on the identified gene signatures. Leave-one-out cross-validation is used in the evaluation to identify the optimal marker subset (22). 
     Once a marker set is identified, validation of the marker set may be accomplished by a survival analysis. To evaluate the accuracy of survival prediction, time-dependent receiver operating characteristic (ROC) analysis for censored data (23;24) was performed with software R. Time-dependent ROC analysis extends the concepts of sensitivity, specificity, and ROC curves for time-dependent binary disease variables in censored data. In this embodiment, the binary disease variable R i (t)=1, if patient i has recurrent or metastatic breast cancer prior to time t; otherwise, R i (t)=0. For a diagnostic marker M, both sensitivity and specificity are defined as a function of time t:
 
sensitivity( c,t )= P{M&gt;c|R ( t )=1}
 
specificity( c,t )= P{M≦c|R ( t )=0}
 
A ROC(t) is a function of t at different cutoffs c. A time-dependent ROC curve is a plot of sensitivity(c, t) vs. 1−specificity(c, t). The area under the ROC curve (AUC) can be used as an accuracy measure of the ROC curve. A higher prediction accuracy is evidenced by a larger AUC(t) (23;24).
 
     The prediction of patient outcome may be accomplished with any means known in the art. For example, to estimate a patient&#39;s recurrent and metastatic potential, risk scores are generated by fitting the identified gene predictors in a Cox proportional hazard model as covariates. A higher risk score represents a higher probability of tumor recurrence. The distribution of the risk scores can be used to classify the patients into three groups: high-risk, low-risk, and intermediate-risk. Alternatively, patients may be stratified into two groups: high- or low-risk. Kaplan-Meier analysis may be used to assess the disease-free survival probability of three risk groups in the studied patient cohorts (8;10;25). Similarly, a Cox proportional hazard model may be developed to estimate a patient&#39;s overall survival probability. A higher survival risk score represents a higher risk for death from breast cancer. Alternatively, a Linear Discriminant Analysis (LDA) function may be determined by a measure of generalized square distance which is based on the pooled covariance matrix based on the marker sets described above as well as the prior probabilities of group membership for prognostic categorization. 
     For prognostic predictions in clinic, the expression levels of the markers can be measured with any means known in the art such as cDNA microarrays (8; 10;26), various generations of Affymetrix gene chips (Affymetrix, Santa Clara, Calif.), and real-time reverse transcription polymerase chain reactions. The present invention further provides for kits comprising the marker sets above. The analytical methods described above can be implemented by use of following computer systems. For example, a computer system can be an Intel 8086-, 80386-, 80486-, or Pentium-based process with preferably 64 MB or more of main memory. The computer system can be linked to an external component, including mass storage. This mass storage can be one or more hard disks, preferably of 1 GB or more storage capacity. Other external components include regular accessories for a computer such as a monitor, a mouse, or a printer. 
     The software program described in above sections can be implemented with software packages R and SAS. The software to be included in the kit comprises the data analysis methods for this invention as disclosed herein. In particular, the software algorithms may include mathematical procedures for biomarker discovery, including the computation of the Mahalanobis distance between clinical categories (i.e., relapse status) and marker expression. The software may also include mathematical procedures for computing the regression coefficients between the marker expression and patient survival. 
     Alternative computer systems and software for implementing the analytical methods of this invention will be apparent to one of skill in the art and are intended to be comprehended within the accompanying claims. 
     These terms and specifications, including the examples, serve to describe the invention by example and not to limit the invention. It is expected that others will perceive differences, which, while differing from the forgoing, do not depart from the scope of the invention herein described and claimed. In particular, any of the function elements described herein may be replaced by any other known element having an equivalent function.