| |  Online-Ressource |
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| Verfasst von: | Wang, Dennis [VerfasserIn]  |
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| | Hensman, James [VerfasserIn] |
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| | Kutkaite, Ginte [VerfasserIn] |
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| | Toh, Tzen S. [VerfasserIn] |
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| | Galhoz, Ana [VerfasserIn] |
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| | Dry, Jonathan R. [VerfasserIn] |
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| | Sáez Rodríguez, Julio [VerfasserIn] |
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| | Garnett, Mathew J. [VerfasserIn] |
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| | Menden, Michael [VerfasserIn] |
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| | Dondelinger, Frank [VerfasserIn] |
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| Titel: | A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates |
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| Verf.angabe: | Dennis Wang, James Hensman, Ginte Kutkaite, Tzen S Toh, Ana Galhoz, GDSC Screening Team, Jonathan R Dry, Julio Saez-Rodriguez, Mathew J Garnett, Michael P Menden, Frank Dondelinger |
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| E-Jahr: | 2020 |
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| Jahr: | 04 December 2020 |
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| Umfang: | 21 S. |
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| Fussnoten: | Gesehen am 23.02.2021 |
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| Titel Quelle: | Enthalten in: eLife |
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| Ort Quelle: | Cambridge : eLife Sciences Publications, 2012 |
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| Jahr Quelle: | 2020 |
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| Band/Heft Quelle: | 9(2020) Artikel-Nummer e60352, 21 Seiten |
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| ISSN Quelle: | 2050-084X |
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| 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. |
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| DOI: | doi:10.7554/eLife.60352 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.7554/eLife.60352 |
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| | DOI: https://doi.org/10.7554/eLife.60352 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | biomarkers |
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| | drug prediction |
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| | machine learning |
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| | pharmacogenomics |
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| | statistical inference |
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| | uncertainty estimation |
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| K10plus-PPN: | 1749168081 |
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| Verknüpfungen: | → Zeitschrift |
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¬A¬ statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates / Wang, Dennis [VerfasserIn]; 04 December 2020 (Online-Ressource)
