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

Wang, D. and Hensman, J. and Kutkaite, G. and Toh, T.S. and Galhoz, A. and Dry, J.R. and Saez-Rodriguez, J. and Garnett, M.J. and Menden, M.P. and Dondelinger, F. (2020) A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates. eLife, 9. ISSN 2050-084X

Full text not available from this repository.

Abstract

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. 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. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. 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. 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 any dose-response data without replicates, and improves biomarker discovery for precision medicine.

Item Type:
Journal Article
Journal or Publication Title:
eLife
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2800
Subjects:
ID Code:
151586
Deposited By:
Deposited On:
15 Feb 2021 16:30
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Apr 2021 05:37