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Pan-omics, pan-cancer analysis has advanced our understanding of themolecular heterogeneity of cancer, expanding what was known from single-canceror single-omics studies. However, pan-cancer, pan-omics analyses have beenlimited in their ability to use information from multiple sources of data(e.g., omics platforms) and multiple sample sets (e.g., cancer types) topredict important clinical outcomes, like overall survival. We address theissue of prediction across multiple high-dimensional sources of data andmultiple sample sets by using exploratory results from BIDIFAC+, a method forintegrative dimension reduction of bidimensionally-linked matrices, in apredictive model. We apply a Bayesian hierarchical model that performs variableselection using spike-and-slab priors which are modified to allow for theborrowing of information across clustered data. This method is used to predictoverall patient survival from the Cancer Genome Atlas (TCGA) using data from 29cancer types and 4 omics sources. Our model selected patterns of variationidentified by BIDIFAC+ that differentiate clinical tumor subtypes with markedlydifferent survival outcomes. We also use simulations to evaluate theperformance of the modified spike-and-slab prior in terms of its variableselection accuracy and prediction accuracy under different underlyingdata-generating frameworks. Software and code used for our analysis can befound at https://github.com/sarahsamorodnitsky/HierarchicalSS_PanCanPanOmics/ .

A Hierarchical Spike-and-Slab Model for Pan-Cancer Survival Using Pan-Omic Data