Bayesian Multilayered Mediation Analysis for Cancer Pharmacogenomics

Abstract

Multiomic data from multilevel biological systems are becoming common and motivate integrative modelling approaches to decipher within- and cross-platform dependencies. Mediation analysis aims to identify mediating mechanisms that regulate the effect of an exposure on an outcome. In multiomic contexts, identification of genomic mediators of disease outcomes provides a deeper understanding of mechanisms of disease and corresponding therapeutic targets. While there has been significant work on joint modelling of high-dimensional potential mediators, approaches that can identify individual mediators in presence of high-dimensional potential mediators are lacking. We posit that the multiomic data are interrelated following multilayered Gaussian graphical models that include undirected and directed acyclic graphs as special cases. We develop a Bayesian inferential framework for multilayered mediation analysis with continuous, binary, and ordinal outcomes using probit models. As opposed to existing approaches focusing on identifying joint mediation effects, we decompose the joint effect into effects attributable to individual mediators in the framework of interventional mediation analysis. Simulations demonstrate our method outperforms other existing approaches to identify mediators that have nonzero indirect effects to the outcome. We apply our method to multiomic analysis on drug sensitivity outcomes of palbociclib and agents for endocrine therapy, standard care for breast cancer.