Gene signature for diagnosis and prognosis of breast cancer and ovarian cancer

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.

1 . 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 the markers in Table 5, and developing a quantitative expression profile. 
     
     
         2 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 1  further comprising the addition of unique markers in Table 4 for said search of positive matches. 
     
     
         3 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 2  further comprising the addition of unique markers in Table 1 for said search of positive matches. 
     
     
         4 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 3  further comprising evaluating said quantitative expression profile using risk analysis. 
     
     
         5 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 4  wherein said risk analysis is a statistical model or machine learning algorithm. 
     
     
         6 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 4  further, comprising placing an individual in two or more categories. 
     
     
         7 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 6  wherein said categories are high risk or lower risk based on said statistical model or machine learning algorithm. 
     
     
         8 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 6  wherein said categories are high risk, intermediate risk, or lower risk based on said statistical model or machine learning algorithm. 
     
     
         9 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 4  wherein said risk analysis is a Cox proportional hazard model. 
     
     
         10 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 14  wherein said risk analysis is a Kaplan Meier analysis for disease free survival. 
     
     
         11 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 4  wherein said risk analysis is a Linear Discriminate Analysis. 
     
     
         12 . The method to determine metastatic potential, relapse potential, or both in breast cancer patients of  claim 4  further comprising assessing clincopathogic variables. 
     
     
         13 . 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 the markers in Table 10, and developing a quantitative expression profile. 
     
     
         14 . The method to determine relapse free potential in breast cancer patients of  claim 13  further comprising evaluating said quantitative expression profile using risk analysis. 
     
     
         15 . The method to determine relapse free potential in breast cancer patients of  claim 14  wherein said risk analysis is a statistical model or machine learning algorithm.