A BAYESIAN GROUP SELECTION WITH COMPOSITIONAL RESPONSES FOR ANALYSIS OF RADIOLOGIC TUMOR PROPORTIONS AND THEIR GENOMIC DETERMINANTS

• 成果类型: Article
• 署名作者: Chekouo, Thierry; Stingo, Francesco C.; Mohammed, Shariq; Rao, Arvind; Baladandayuthapani, Eerabhadran
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; University of Florence; Boston University; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1749
• 发表日期: 2023
• 页码: 3013-3034
• 关键词: variable-selection; glioblastoma-multiforme; integrative approach; signaling pathways; model selection; group lasso; regression; reveals; identification; INFORMATION

摘要

Volumetric imaging features are used in cancer research to determine the size and the composition of a tumor and have been shown to be prog-nostic of overall survival. In this paper we focus on the analysis of tumor component proportions of brain cancer patients collected through The Can-cer Genome Atlas (TCGA) project. Our main goal is to identify pathways and corresponding genes that can explain the heterogeneity of the composition of a brain tumor. In particular, we focus on the glioblastoma multiform (GBM), as it is the most common malignant brain neoplasm, accounting for 23% of all primary brain tumors for which it still has very poor prognosis. We propose a Bayesian hierarchical model for variable selection with a group structure in the context of correlated multivariate compositional response variables. More specifically, we model the proportions of the tumor components within the tu-mor using a Dirichlet model by allowing for straightforward incorporation of available high-dimensional covariate information within a log-linear regres-sion framework. We impose prior distributions that account for the overlap-ping structure between groups of covariates. Simulations and application to GBM disease show the importance of our approach. We have identified asso-ciations between tumor component volume-based features and several impor-tant pathways and genes. Some of these genes have previously been shown to be prognostic indicators of overall survival time in GBM.