Development of a prognostic model for breast cancer survival in an open challenge environment

Abstract

The accuracy with which cancer phenotypes can be predicted by selecting and combining molecular features is compromised by the large number of potential features available. In an effort to design a robust prognostic model to predict breast cancer survival, we hypothesized that signatures consisting of genes that are coexpressed in multiple cancer types should correspond to molecular events that are prognostic in all cancers, including breast cancer. We previously identified several such signatures--called attractor metagenes--in an analysis of multiple tumor types. We then tested our attractor metagene hypothesis as participants in the Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge. Using a rich training data set that included gene expression and clinical features for breast cancer patients, we developed a prognostic model that was independently validated in a newly generated patient data set. We describe our model, which was based on three attractor metagenes associated with mitotic chromosomal instability, mesenchymal transition, or lymphocyte-based immune recruitment.