  Precision cancer medicine aims to determine the optimal treatment for each
patient. In-vitro cancer drug sensitivity screens combined with multi-omics
characterization of the cancer cells have become an important tool to achieve
this aim. Analyzing such pharmacogenomic studies requires flexible and
efficient joint statistical models for associating drug sensitivity with
high-dimensional multi-omics data. We propose a multivariate Bayesian
structured variable selection model for sparse identification of omics features
associated with multiple correlated drug responses. Since many anti-cancer
drugs are designed for specific molecular targets, our approach makes use of
known structure between responses and predictors, e.g. molecular pathways and
related omics features targeted by specific drugs, via a Markov random field
(MRF) prior for the latent indicator variables of the coefficients in sparse
seemingly unrelated regression. The structure information included in the MRF
prior can improve the model performance, i.e. variable selection and response
prediction, compared to other common priors. In addition, we employ random
effects to capture heterogeneity between cancer types in a pan-cancer setting.
The proposed approach is validated by simulation studies and applied to the
Genomics of Drug Sensitivity in Cancer data, which includes pharmacological
profiling and multi-omics characterization of a large set of heterogeneous cell
lines.
