The rapid development of technologies for deep molecular characterisation of clinical samples holds the promise to uncover molecular biomarkers that stratify patients towards more efficacious drugs, a cornerstone of precision medicine. The failure rate for new drugs entering clinical trials is in excess of 90%, with more than a quarter of drugs failing due to lack of efficacy ( Arrowsmith and Miller, 2013 Cook et al., 2014).
Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. However, the cells’ response to a drug 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. High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers.
Wellcome Sanger Institute, United Kingdom.The Medical School, University of Sheffield, United Kingdom.Department of Biology, Ludwig-Maximilians University Munich, Germany.Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Germany.Department of Computer Science, University of Sheffield, United Kingdom.Sheffield Institute for Translational Neuroscience, University of Sheffield, United Kingdom.