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

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.

Metrics

  • 2,574
    views
  • 297
    downloads
  • 14
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Computational and Systems Biology
    Qianmu Yuan, Chong Tian, Yuedong Yang
    Tools and Resources

    Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

    1. Cell Biology
    2. Computational and Systems Biology
    Thomas Grandits, Christoph M Augustin ... Alexander Jung
    Research Article

    Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.