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A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates
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 cu...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746236/ https://www.ncbi.nlm.nih.gov/pubmed/33274713 http://dx.doi.org/10.7554/eLife.60352 |
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author | Wang, Dennis Hensman, James Kutkaite, Ginte Toh, Tzen S Galhoz, Ana Dry, Jonathan R Saez-Rodriguez, Julio Garnett, Mathew J Menden, Michael P Dondelinger, Frank |
author_facet | Wang, Dennis Hensman, James Kutkaite, Ginte Toh, Tzen S Galhoz, Ana Dry, Jonathan R Saez-Rodriguez, Julio Garnett, Mathew J Menden, Michael P Dondelinger, Frank |
author_sort | Wang, Dennis |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7746236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-77462362020-12-21 A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates Wang, Dennis Hensman, James Kutkaite, Ginte Toh, Tzen S Galhoz, Ana Dry, Jonathan R Saez-Rodriguez, Julio Garnett, Mathew J Menden, Michael P Dondelinger, Frank eLife Computational and Systems Biology 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. eLife Sciences Publications, Ltd 2020-12-04 /pmc/articles/PMC7746236/ /pubmed/33274713 http://dx.doi.org/10.7554/eLife.60352 Text en © 2020, Wang et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Computational and Systems Biology Wang, Dennis Hensman, James Kutkaite, Ginte Toh, Tzen S Galhoz, Ana Dry, Jonathan R Saez-Rodriguez, Julio Garnett, Mathew J Menden, Michael P Dondelinger, Frank A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates |
title | A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates |
title_full | A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates |
title_fullStr | A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates |
title_full_unstemmed | A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates |
title_short | A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates |
title_sort | statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746236/ https://www.ncbi.nlm.nih.gov/pubmed/33274713 http://dx.doi.org/10.7554/eLife.60352 |
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