TransPRECISE: Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome

• Activity Number: 46 - New Advances in Cancer Genomics
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Rupam Bhattacharyya* and Min Jin Ha and Qingzhi Liu and Rehan Akbani and Han Liang and Veera Baladandayuthapani
• Companies: University of Michigan and UT MD Anderson Cancer Center and University of Michigan and The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center and University of Michigan
• Keywords: Data integration; Cancer proteomics; Network analysis; Bayesian graphical regression; Drug response prediction; Pan-cancer

Abstract

A systemic approach in precision medicine has been to bridge anticancer pharmacological data to large-scale molecular tumor profiles using cell lines proxies for cancer patients. However, samples from different tumor microenvironments in the two model systems may exhibit distinct patterns of molecular activities – the architecture of cancer modulation through cumulative effects from interacting genes in functional/signaling pathways may vary. In this paper, we attempt to address these challenges by developing a multi-level analytical framework called TransPRECISE: Translational Personalized Cancer-specific Integrated Network Estimation. TransPRECISE uses Bayesian graphical regression models to infer on cancer-specific pathway circuitry which are then de-convolved to sample-specific pathway aberration scores. We use TransPRECISE to analyze proteomic data across 16 cell line lineages and 31 patient cancers. Through pan-cancer analysis, we investigate differential and conserved aspects of cancer-specific pathway networks across model systems and cancer lineages, identify matching avatar cell lines for patient tumor profiles and train prediction models for drug sensitivity in patients.