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RADIOHEAD: Radiogenomic analysis incorporating tumor heterogeneity in imaging through densities

Mohammed, Shariq; Bharath, Karthik; Kurtek, Sebastian; Rao, Arvind; Baladandayuthapani, Veerabhadran

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Authors

Shariq Mohammed

Sebastian Kurtek

Arvind Rao

Veerabhadran Baladandayuthapani



Abstract

Recent technological advancements have enabled detailed investigation of associations between the molecular architecture and tumor heterogeneity through multisource integration of radiological imaging and genomic (radiogenomic) data. In this paper we integrate and harness radiogenomic data in patients with lower grade gliomas (LGG), a type of brain cancer, in order to develop a regression framework called RADIOHEAD (RADIOgenomic analysis incorporating tumor HEterogeneity in imAging through Densities) to identify radiogenomic associations. Imaging data is represented through voxel-intensity probability density functions of tumor subregions obtained from multimodal magnetic resonance imaging and genomic data through molecular signatures in the form of pathway enrichment scores corresponding to their gene expression profiles. Employing a Riemannian-geometric framework for principal component analysis on the set of probability density functions, we map each probability density to a vector of principal component scores which are then included as predictors in a Bayesian regression model with the pathway enrichment scores as the response. Variable selection compatible with the grouping structure amongst the predictors induced through the tumor subregions is carried out under a group spike-and-slab prior. A Bayesian false discovery rate mechanism is then used to infer significant associations based on the posterior distribution of the regression coefficients. Our analyses reveal several pathways relevant to LGG etiology (such as synaptic transmission, nerve impulse and neurotransmitter pathways) to have significant associations with the corresponding imaging-based predictors.

Citation

Mohammed, S., Bharath, K., Kurtek, S., Rao, A., & Baladandayuthapani, V. (2021). RADIOHEAD: Radiogenomic analysis incorporating tumor heterogeneity in imaging through densities. Annals of Applied Statistics, 15(4), 1808-1830. https://doi.org/10.1214/21-AOAS1458

Journal Article Type Article
Acceptance Date Mar 21, 2021
Online Publication Date Dec 21, 2021
Publication Date 2021-12
Deposit Date Apr 19, 2021
Publicly Available Date Mar 29, 2024
Journal Annals of Applied Statistics
Print ISSN 1932-6157
Electronic ISSN 1941-7330
Publisher Institute of Mathematical Statistics
Peer Reviewed Peer Reviewed
Volume 15
Issue 4
Pages 1808-1830
DOI https://doi.org/10.1214/21-AOAS1458
Keywords Statistics, Probability and Uncertainty; Modeling and Simulation; Statistics and Probability
Public URL https://nottingham-repository.worktribe.com/output/5483378
Publisher URL https://projecteuclid.org/journals/annals-of-applied-statistics/volume-15/issue-4/RADIOHEAD--Radiogenomic-analysis-incorporating-tumor-heterogeneity-in-imaging-through/10.1214/21-AOAS1458.full

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