December 2023 A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinants
Thierry Chekouo, Francesco C. Stingo, Shariq Mohammed, Arvind Rao, Veerabhadran Baladandayuthapani
Author Affiliations +
Ann. Appl. Stat. 17(4): 3013-3034 (December 2023). DOI: 10.1214/23-AOAS1749


Volumetric imaging features are used in cancer research to determine the size and the composition of a tumor and have been shown to be prognostic of overall survival. In this paper we focus on the analysis of tumor component proportions of brain cancer patients collected through The Cancer 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 tumor using a Dirichlet model by allowing for straightforward incorporation of available high-dimensional covariate information within a log-linear regression framework. We impose prior distributions that account for the overlapping structure between groups of covariates. Simulations and application to GBM disease show the importance of our approach. We have identified associations between tumor component volume-based features and several important pathways and genes. Some of these genes have previously been shown to be prognostic indicators of overall survival time in GBM.

Funding Statement

Thierry Chekouo was supported by NSERC Discovery Grants number RGPIN-2019-04810 and start-up funds from the University of Calgary and the University of Minnesota.
Shariq Mohammed and Arvind Rao were supported through CCSG P30 CA046592, Institutional Research Grants and startup funds from The University of Michigan, NCI R37CA214955-01A1 and a Research Scholar Grant from the American Cancer Society (RSG-16-005-01).
Shariq Mohammed was partially supported by Precision Health at The University of Michigan. Veerabhadran Baladandayuthapani was supported by NIH grants: R01-CA160736, R37CA214955-01A1, R21-CA220299, P30 CA46592 and NSF grant 1463233, and start-up funds from the University of Michigan Rogel Cancer Center and School of Public Health.
Arvind Rao is also affiliated with the Departments of Radiation Onology, Biostatistics, and Biomedical Engineering, as well as the Rogel Cancer Center and the Michigan Institute of Data Science (MIDAS), at the University of Michigan Ann Arbor.


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Thierry Chekouo. Francesco C. Stingo. Shariq Mohammed. Arvind Rao. Veerabhadran Baladandayuthapani. "A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinants." Ann. Appl. Stat. 17 (4) 3013 - 3034, December 2023.


Received: 1 August 2021; Revised: 1 April 2022; Published: December 2023
First available in Project Euclid: 30 October 2023

MathSciNet: MR4661686
Digital Object Identifier: 10.1214/23-AOAS1749

Keywords: Bayesian hierarchical model , Dirichlet regression , glioblastoma , group selection , Overlap

Rights: Copyright © 2023 Institute of Mathematical Statistics


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Vol.17 • No. 4 • December 2023
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