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

  1. Dennis Wang  Is a corresponding author
  2. James Hensman
  3. Ginte Kutkaite
  4. Tzen S Toh
  5. Ana Claudia Paulo Galhoz
  6. Jonathan R Dry
  7. Julio Saez-Rodriguez
  8. Mathew J Garnett
  9. Michael P Menden  Is a corresponding author
  10. Frank Dondelinger  Is a corresponding author
  1. University of Sheffield, United Kingdom
  2. PROWLER.io, United Kingdom
  3. Helmholtz Zentrum Muenchen, Germany
  4. AstraZeneca, United States
  5. Heidelberg University, Germany
  6. Wellcome Sanger Institute, United Kingdom
  7. Helmholtz Zentrum München, Germany
  8. 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

  1. Dennis Wang

    Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, United Kingdom
    For correspondence
    dennis.wang@sheffield.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0068-1005
  2. James Hensman

    PROWLER.io, Cambridge, United Kingdom
    Competing interests
    James Hensman, James Hensman is an employee of Amazon.com. The author has no competing financial interests to declare..
  3. Ginte Kutkaite

    Computational Biology, Helmholtz Zentrum Muenchen, Munich, Germany
    Competing interests
    No competing interests declared.
  4. Tzen S Toh

    The Medical School, University of Sheffield, Sheffield, United Kingdom
    Competing interests
    No competing interests declared.
  5. Ana Claudia Paulo Galhoz

    Computational Biology, Helmholtz Zentrum Muenchen, Munich, Germany
    Competing interests
    No competing interests declared.
  6. Jonathan R Dry

    AstraZeneca, Boston, United States
    Competing interests
    Jonathan R Dry, Jonathan Dry is affiliated with AstraZeneca. The author has no competing financial interests to declare..
  7. Julio Saez-Rodriguez

    Heidelberg University, Heidelberg, Germany
    Competing interests
    No competing interests declared.
  8. Mathew J Garnett

    Translational Cancer Genomics, Wellcome Sanger Institute, Hinxton, United Kingdom
    Competing interests
    No competing interests declared.
  9. Michael P Menden

    Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
    For correspondence
    michael.menden@helmholtz-muenchen.de
    Competing interests
    No competing interests declared.
  10. Frank Dondelinger

    Lancaster University, Lancaster, United Kingdom
    For correspondence
    fdondelinger.work@gmail.com
    Competing interests
    Frank Dondelinger, Frank Dondelinger is an employee of Roche. The author has no competing financial interests to declare..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1816-6300

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

  1. Joseph Lehár, Boston University, United States

Version history

  1. Received: June 24, 2020
  2. Accepted: December 4, 2020
  3. Accepted Manuscript published: December 4, 2020 (version 1)
  4. Accepted Manuscript updated: December 7, 2020 (version 2)
  5. 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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  1. Dennis Wang
  2. James Hensman
  3. Ginte Kutkaite
  4. Tzen S Toh
  5. Ana Claudia Paulo Galhoz
  6. Jonathan R Dry
  7. Julio Saez-Rodriguez
  8. Mathew J Garnett
  9. Michael P Menden
  10. Frank Dondelinger
(2020)
A statistical framework for assessing pharmacological response and biomarkers using uncertainty estimates
eLife 9:e60352.
https://doi.org/10.7554/eLife.60352

Share this article

https://doi.org/10.7554/eLife.60352

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