Systems biology approach in understanding metabolic reprogramming in breast cancer.

Cancer is increasingly being viewed as a metabolic disease. Research shows the importance of metabolism to cancerous traits such as metastasis, invasion, drug resistance, growth, evasion of apoptosis or immune system. Therefore, understanding how metabolism adapts to support the growth of tumours could lead towards the optimization of current therapeutic approaches and the development of new treatment options, which would help to overcome multi-drug resistance traits. In this study we represent a systems biology approach to identify biomarkers and therapeutic options of breast cancer through the analysis of breast cancer metabolism. The metabolism of breast cancer was explored in the context of personalized genome scale metabolic models (GSMNs) by combing gene expression data with currently the most comprehensive GSMN Recon2 by using the GEBRA algorithm. GEBRA algorithm ensures maximum congruency between gene expression data and metabolic genes present in the GSMN while satisfying various constraints (stoichiometry, mass balance, thermodynamic) to generate metabolic landscapes. We performed analysis using microarray data derived from the Metabric study, consisting of 2,000 individual breast tumours and 131 matched normal breast tissue samples. In addition, this study was complemented with publicly available gene expression datasets of breast cancer cell lines (MCF-7, MDA-MB-231, and MCF-10a), and breast cancer stem cells. The performed metabolic analysis of the Metabric Discovery set (997 patients) identified a novel poor prognosis group, which was reproduced in the validation set (995 samples). We further explored the hypothetical metabolic adaptations elicited by the poor prognosis tumours and established serotonin production to be an important trait of the poor prognosis group. In addition, the analysis of breast cancer stem cells suggested the prostaglandin synthesis pathway to play a major role in breast cancer stem cell maintenance and development. Our data supports the reconsideration of the synergistic use of selective serotonin uptake inhibitors (SSRIs) and prostaglandin synthase inhibitors along with the chemotherapeutic regimens used to treat breast cancers