Motivation: Breast cancer is the most commonly diagnosed malignancy in women and the second cause of cancer death in developed countries. While advancements in early detection and therapeutic options have led to a significant decrease in mortality, response to treatment is affected by the genetic heterogeneity of the disease. Recent genome-wide DNA mutation analyses revealed the existence of hundreds of low-frequency mutated genes, in addition to known cancer drivers: a finding that is prompting research into the impact of these genes on the pathogenesis of the disease.
Results: Herein, we describe a strategy towards the characterization of the role of low-frequency mutated genes in breast cancer. Through the combined analyses of publicly available gene expression and mutational datasets, we identified several Cancer Gene Modules (CMs) that we re-organized in Gene Regulatory Networks (GRN) enriched in low-frequency mutated genes. Importantly, these low-frequency mutated genes were mutually exclusive with known cancer drivers. Finally, we provide evidence that gene expression analysis of these mutated GRNs can predict resistance/sensitivity to chemotherapeutic drugs for breast cancer treatment.
Availability and implementation: Datasets are available at https://www.ncbi.nlm.nih.gov/geo/ and at https://www.ebi.ac.uk/ega/datasets/. Molecular signatures and GSEA software are available at http://www.gsea-msigdb.org/gsea/index.jsp. Source codes are available at https://github.com/EleonoraLusito/Reverse_Engineering_BC_GRNs.
Supplementary information: Supplementary data are available at Bioinformatics online.