B0841
Title: Personalized integrated network modeling
Authors: Sayantan Banerjee - Indian Institute of Management (India)
Rehan Akbani - UT MD Anderson Cancer Center (United States)
Han Liang - UT MD Anderson Cancer Center (United States)
Gordon Mills - UT MD Anderson Cancer Center (United States)
Kim-Anh Do - The University of Texas MD Anderson Cancer Center (United States)
Veerabhadran Baladandayuthapani - UT MD Anderson Cancer Center (United States)
Min Jin Ha - UT MD Anderson Cancer Center (United States) [presenting]
Abstract: Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We assessed pan-cancer pathway activities across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. PRECISE is a general framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures. PRECISE-based pathway signatures, can delineate pan-cancer commonalities and differences in proteomic network biology within and across tumors, demonstrates robust tumor stratification that is both biologically and clinically informative and superior prognostic power compared to existing approaches.
