RADIO-IBAG: RADIOMICS-BASED INTEGRATIVE BAYESIAN ANALYSIS OF MULTIPLATFORM GENOMIC DATA

• 成果类型: Article
• 署名作者: Zhang, Youyi; Morris, Jeffrey S.; Aerry, Shivali Narang; Rao, Arvind U. K.; Baladandayuthapani, Veerabhadran
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; Johns Hopkins 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: 10.1214/19-AOAS1238
• 发表日期: 2019
• 页码: 1957-1988
• 关键词: glioblastoma-multiforme; variable selection; patient survival; texture analysis; copy number; gene; expression; features; Heterogeneity; cancer

摘要

Technological innovations have produced large multi-modal datasets that include imaging and multi-platform genomics data. Integrative analyses of such data have the potential to reveal important biological and clinical insights into complex diseases like cancer. In this paper, we present Bayesian approaches for integrative analysis of radiological imaging and multi-platform genomic data, where-in our goals are to simultaneously identify genomic and radiomic, that is, radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. For this task, we propose Radio-iBAG: Radiomics-based Integrative Bayesian Analysis of Multi-platform Genomic Data, a multi-scale Bayesian hierarchical model that involves several innovative strategies: it incorporates integrative analysis of multi-platform genomic data sets to capture fundamental biological relationships; explores the associations between radiomic markers accompanying genomic information with clinical outcomes; and detects genomic and radiomic markers associated with clinical prognosis. We also introduce the use of sparse Principal Component Analysis (sPCA) to extract a sparse set of approximately orthogonal meta-features each containing information from a set of related individual radiomic features, reducing dimensionality and combining like features. Our methods are motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, where-in we integrate magnetic resonance imaging-based biomarkers along with genomic, epigenomic and transcriptomic data. Our model identifies important magnetic resonance imaging features and the associated genomic platforms that are related with patient survival times.