A statistical framework for assessing pharmacological response and biomarkers using uncertainty estimates

Drug high-throughput screenings across large molecular-characterised cancer cell line panels enable the discovery of biomarkers, and thereby, cancer precision medicine. The ability to experimentally generate drug response data has accelerated. However, this data is typically quantified by a summary statistic from a best-fit dose response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage this uncertainty for identifying associated biomarkers with a new statistical framework based on Bayesian testing. Applied to the Genomics of Drug Sensitivity in Cancer, in vitro screening data on 265 compounds across 1,074 cell lines, our uncertainty models identified 24 clinically established drug response biomarkers, and in addition provided evidence for 6 novel biomarkers. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to drug high-throughput screens without replicates, and enables robust biomarker discovery for new cancer therapies.