Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma

• Published in: PLoS computational biology Vol. 15; no. 8; p. e1007090
• Main Authors: Bag, Arup K, Mandloi, Sapan, Jarmalavicius, Saulius, Mondal, Susmita, Kumar, Krishna, Mandal, Chhabinath, Walden, Peter, Chakrabarti, Saikat, Mandal, Chitra
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.08.2019; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Brain Neoplasms - etiology; Brain Neoplasms - genetics; Brain Neoplasms - metabolism; Carcinogenesis; Cell Line, Tumor; Cell metabolism; Cellular signal transduction; Computational Biology; Computer and Information Sciences; Computer Simulation; Control; ErbB Receptors - genetics; ErbB Receptors - metabolism; Genetic aspects; Glioblastoma - etiology; Glioblastoma - genetics; Glioblastoma - metabolism; Glioblastoma multiforme; Humans; Metabolic Networks and Pathways; Methods; Models, Biological; Mutation; Protein Interaction Maps; Research and Analysis Methods; Signal Transduction; Systems Biology; India
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007090

As malignant transformation requires synchronization of growth-driving signaling (S) and metabolic (M) pathways, defining cancer-specific S-M interconnected networks (SMINs) could lead to better understanding of oncogenic processes. In a systems-biology approach, we developed a mathematical model for SMINs in mutated EGF receptor (EGFRvIII) compared to wild-type EGF receptor (EGFRwt) expressing glioblastoma multiforme (GBM). Starting with experimentally validated human protein-protein interactome data for S-M pathways, and incorporating proteomic data for EGFRvIII and EGFRwt GBM cells and patient transcriptomic data, we designed a dynamic model for EGFR-driven GBM-specific information flow. Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model. This demonstrated capacity of the model to identify unknown connections between signaling and metabolic pathways, explain the robustness of oncogenic SMINs, predict drug escape, and assist identification of drug targets and the development of combination therapies.