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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
Abstract #326694 Presentation
Title: Pathway and Network-Based Integrative Bayesian Modeling of Multiplatform Genomics Data
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

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.

Authors who are presenting talks have a * after their name.

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