Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer

• 成果类型: Article
• 署名作者: Wang, Zeya; Baladandayuthapani, Veerabhadran; Kaseb, Ahmed O.; Amin, Hesham M.; Hassan, Manal M.; Wang, Wenyi; Morris, Jeffrey S.
• 署名单位: Rice University; University of Texas System; UTMD Anderson Cancer Center; University of Michigan System; University of Michigan; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; University of Pennsylvania
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.2000866
• 发表日期: 2022
• 页码: 533-546
• 关键词: hepatocellular-carcinoma; variable-selection; joint estimation; inference; deconvolution; expression; needles; markers; straw

摘要

It is well established that interpatient heterogeneity in cancer may significantly affect genomic data analyses and in particular, network topologies. Most existing graphical model methods estimate a single population-level graph for genomic or proteomic network. In many investigations, these networks depend on patient-specific indicators that characterize the heterogeneity of individual networks across subjects with respect to subject-level covariates. Examples include assessments of how the network varies with patient-specific prognostic scores or comparisons of tumor and normal graphs while accounting for tumor purity as a continuous predictor. In this article, we propose a novel edge regression model for undirected graphs, which estimates conditional dependencies as a function of subject-level covariates. We evaluate our model performance through simulation studies focused on comparing tumor and normal graphs while adjusting for tumor purity. In application to a dataset of proteomic measurements on plasma samples from patients with hepatocellular carcinoma (HCC), we ascertain how blood protein networks vary with disease severity, as measured by HepatoScore, a novel biomarker signature measuring disease severity. Our case study shows that the network connectivity increases with HepatoScore and a set of hub proteins as well as important protein connections are identified under different HepatoScore, which may provide important biological insights to the development of precision therapies for HCC.