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ÀÛ¼ºÀÚ °ü¸®ÀÚ ÀÛ¼ºÀÏ 2021-09-14
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¿¬»ç: ÇϹÎÁø ¿¬»ç´Ô (MD Anderson Cancer Center)

Title:¡¡Integrative Network Modeling in Cancer Pharmacogenomics

Abstract
Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multilayered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multilevel genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative pharmacogenomic analysis of breast cancer cell lines for key signaling pathways highlights major mechanistic drivers for drug actions in Palbociclib and endocrine therapy including Tamoxifen and Fulvestrant.