Author summary A core tenant of precision medicine is that treatment should be tailored to the patient. In the context of cancer, large-scale screens, assaying the sensitivity of many cell-lines to panels of drugs, have the potential to enable discovery of biomarkers of sensitivity to specific therapeutics. However, existing computational approaches have not taken full advantage of these data. We develop a novel multi-task regression model, Lacrosse, which uses a Bayesian non-parametric prior to model latent characteristics of cell-lines that confer sensitivity to specific drugs and are predictable from genomic features. The resulting algorithm improves upon existing work by: a) jointly modeling multiple drugs to share statistical signal b) incorporating prior knowledge in terms of known inhibition targets c) using a sparse latent variable regression approach giving interpretable summaries of detected gene-drug associations. In particular, our analysis uncovers groups of drugs whose efficacy depends on genomic features in a similar way. We find new potential biomarkers of drug sensitivity, one of which we validate experimentally: that panobinostat is less effective when C/EBP is over-expressed.
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