Wang, D. orcid.org/0000-0003-0068-1005, Hensman, J., Kutkaite, G. et al. (18 more authors) (2020) A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates. eLife, 9. e60352. ISSN 2050-084X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Wang et al. This article is distributed under the terms of the Creative Commons Attribution License, (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use and redistribution provided that the original author and source are credited. |
Keywords: | biomarkers; computational biology; drug prediction; human; machine learning; pharmacogenomics; statistical inference; systems biology; uncertainty estimation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Neuroscience (Sheffield) |
Funding Information: | Funder Grant number ACADEMY OF MEDICAL SCIENCES SBF004\1052 ROSETREES TRUST nan Engineering and Physical Sciences Research Council 2132030 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Jan 2021 17:26 |
Last Modified: | 21 Jan 2021 17:27 |
Status: | Published |
Publisher: | eLife Sciences Publications, Ltd |
Refereed: | Yes |
Identification Number: | 10.7554/elife.60352 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169449 |