Identification and Validation of a New Set of Five Genes for Prediction of Risk in Early Breast Cancer

Molecula tests predicting the outcome of breast cancer patients based on gene expression levels can be used to assist in making treatment decisions after considerations of conventional markers. In this study we identified a subset of 20 mRNA differentially regulated in breast cancer analyzing several publicly available array gene expression data using R/Bioconductor package. Using RTqPCR we evaluate 261 consecutive invasive breast cancers cases not selected for age, adjuvant treatment, nodal and estrogen receptor status from paraffin embedded sections.The biological samples dataset was split into a trainig (137 cases) and a validation set (124 cases). The gene signature was developed on the training set and a multivariate stepwise Cox analysis selected five genes indipendently associated with DFS. These five genes were combined into a linear score (signature) weighted according to the coefficients of the Cox Model. Our signature could be proposed as a prognostic signature for disease free survival in breast cancer patients where the indication for adjuvant chemotherapy added to endocrine treatment is uncertain