A Network-based Approach to Breast Cancer Systems Medicine.

Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer death in women. Although recent improvements in the prevention, early detection, and treatment of breast cancer have led to a significant decrease in the mortality rate, the identification of an optimal therapeutic strategy for each patient remains a difficult task because of the heterogeneous nature of the disease. Clinical heterogeneity of breast cancer is in part explained by the vast genetic and molecular heterogeneity of this disease, which is now emerging from large-scale screening studies using “-omics” technologies (e.g. microarray gene expression profiling, next-generation sequencing). This genetic and molecular heterogeneity likely contributes significantly to therapy response and clinical outcome. The recent advances in our understanding of the molecular nature of breast cancer due, in particular, to the explosion of high-throughput technologies, is driving a shift away from the “one-dose-fits-all” paradigm in healthcare, to the new “Personalized Cancer Care” paradigm. The aim of “Personalized Cancer Care” is to select the optimal course of clinical intervention for individual patients, maximizing the likelihood of effective treatment and reducing the probability of adverse drug reactions, according to the molecular features of the patient. In light to this medical scenario, the aim of this project is to identify novel molecular mechanisms that are altered in breast cancer through the development of a computational pipeline, in order to propose putative biomarkers and druggable target genes for the personalized management of patients. Through the application of a Systems Biology approach to reverse engineer Gene Regulatory Networks (GRNs) from gene expression data, we built GRNs around “hub” genes transcriptionally correlating with clinical-pathological features associated with breast tumor expression profiles. The relevance of the GRNs as putative cancer-related mechanisms was reinforced by the occurrence of mutational events related to breast cancer in the “hub” genes, as well as in the neighbor genes. Moreover, for some networks, we observed mutually exclusive mutational patterns in the neighbors genes, thus supporting their predicted role as oncogenic mechanisms. Strikingly, a substantial fraction of GRNs were overexpressed in Triple Negative Breast Cancer patients who acquired resistance to therapy, suggesting the involvement of these networks in mechanisms of chemoresistance. In conclusion, our approach allowed us to identify cancer molecular mechanisms frequently altered in breast cancer and in chemorefractory tumors, which may suggest novel cancer biomarkers and potential drug targets for the development of more effective therapeutic strategies in metastatic breast cancer patients.