A new compact set of biomarkers for distinguishing among ten breast cancer subtypes

Abstract

World-wide, one in nine women are diagnosed with breast cancer in their lifetime and breast cancer is the second leading cause of death among women. Accurate diagnosis of the specific subtypes of this disease is vital to ensure that the patients will have the best possible response to therapy. Using the newly proposed ten subtypes of breast cancer we hypothesized that machine learning techniques would offer many benefits for selecting the most informative biomarkers. Unlike existing gene selection approaches, we use a hierarchical classification approach that selects genes and builds the classifier concurrently. Our results support that this modified approach to gene selection yields a small subset of 82 genes that can predict each of these ten subtypes with accuracies ranging from 92% to 99%.