Pathway and Network-Based Integrative Bayesian Modeling of Multiplatform Genomics Data

• Activity Number: 99 - Single Cell Sequencing and Cancer Genomics
• Type: Invited
• Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Veera Baladandayuthapani* and Jeffrey S Morris and Elizabeth McGuffey and Raymond J. Carroll and Min Jin Ha
• Companies: UT MD Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center and United States Naval Academy and Texas A & M University and UT MD Anderson Cancer Center
• Keywords: Bayesian; Genomics; Graphical models; MCMC; data integration; big data

Abstract

The identification of gene pathways and networks involved in cancer development and progression and characterization of their activity in terms of multiplatform genomics can provide information leading to discovery of new targeted medications. We propose a two-step model that integrates multiple genomic platforms, as well as gene pathway membership information, to efficiently and simultaneously (a) identify the genes significantly related to a clinical outcome, (b) identify the genomic platform(s) regulating each important gene, and (c) rank the pathways by importance to clinical outcome. We propose hierarchical Bayesian pathway- and network-based frameworks, which allows us not only to identify the important pathways and the important genes within pathways, but also to gain insight as to the platform(s) driving the effects mechanistically. The approaches will be illustrated using several case examples integrating high-throughput pan-omic (e.g. genomic, epigenomic, transcriptomic, proteomic) across multiple tumor types.