A statistical framework for assessing pharmacological response and biomarkers using uncertainty estimates
- University of Sheffield, United Kingdom
- PROWLER.io, United Kingdom
- Helmholtz Zentrum Muenchen, Germany
- AstraZeneca, United States
- Heidelberg University, Germany
- Wellcome Sanger Institute, United Kingdom
- Helmholtz Zentrum München, Germany
- Lancaster University, United Kingdom
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 1,074 cancer cell lines, our models identified 24 clinically established drug response biomarkers, and provided evidence for 6 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.
Data availability
All data is available through the GDSC downloads portal (ftp://ftp.sanger.ac.uk/pub4/cancerrxgene/releases)Raw dose response data have been deposited in GDSC under v17a_public_raw_data.csvSigmoid fitted dose-response curves have been deposited in GDSC under v17_fitted_dose_response.csvCell line genomics data have been deposited in GDSC under GDSCtools_mobems.zipCell line identity details have been deposited in GDSC under Cell_Lines_Details.xlsxDrug compound details have been been deposited in GDSC under screened_compunds_rel_8.2.csv
Article and author information
Author details
Funding
NIHR Sheffield Biomedical Research Centre (BRC - IS-BRC-1215-20017)
- Dennis Wang
- Tzen S Toh
Rosetrees Trust (A2501)
- Dennis Wang
- Tzen S Toh
Academy of Medical Sciences (SBF004/1052)
- Dennis Wang
Wellcome Trust (206194)
- Mathew J Garnett
European Union's Horizon 2020 Research and Innovation Programme (Grant agreement No. 950293 - COMBAT-RES)
- Michael P Menden
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Joseph Lehár, Boston University, United States
Version history
- Received: June 24, 2020
- Accepted: December 4, 2020
- Accepted Manuscript published: December 4, 2020 (version 1)
- Accepted Manuscript updated: December 7, 2020 (version 2)
- Version of Record published: December 17, 2020 (version 3)
Copyright
© 2020, Wang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A statistical framework for assessing pharmacological response and biomarkers using uncertainty estimates
eLife 9:e60352.
https://doi.org/10.7554/eLife.60352
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