Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics

• 成果类型: Article
• 署名作者: Ni, Yang; Stingo, Francesco C.; Ha, Min Jin; Akbani, Rehan; Baladandayuthapani, Veerabhadran
• 署名单位: University of Texas System; University of Texas Austin; University of Florence; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2018.1434529
• 发表日期: 2019
• 页码: 48-60
• 关键词: tumor heterogeneity; variable-selection; rna-seq; protein; carcinoma; suppression; progression; mutations; pathways; insights

摘要

Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, Bayesian hierarchical varying-sparsity regression (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein-gene relationships as well as induces sparsity in both protein-gene and protein-survival relationships, to select genomically driven prognostic protein markers at the patient-level. Simulation studies demonstrate the superior performance of BEHAVIOR against competing method in terms of both protein marker selection and survival prediction. We apply BEHAVIOR to The Cancer Genome Atlas (TCGA) proteogenomic pan-cancer data and find several interesting prognostic proteins and pathways that are shared across multiple cancers and some that exclusively pertain to specific cancers. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available online.