Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma.
Arup K BagSapan MandloiSaulius JarmalaviciusSusmita MondalKrishna KumarChhabinath MandalPeter WaldenSaikat ChakrabartiChitra MandalPublished in: PLoS computational biology (2019)
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.
Keyphrases
- wild type
- protein protein
- electronic health record
- signaling pathway
- induced apoptosis
- growth factor
- big data
- small cell lung cancer
- endothelial cells
- small molecule
- poor prognosis
- emergency department
- case report
- epidermal growth factor receptor
- papillary thyroid
- cell proliferation
- data analysis
- oxidative stress
- pi k akt
- cell cycle arrest
- single cell
- artificial intelligence
- social media
- induced pluripotent stem cells
- endoplasmic reticulum stress