Assessment of modelling strategies for drug response prediction in cell lines and xenografts

• Verfasst von: Kurilov, Roman [VerfasserIn]
• Verfasst von: Haibe-Kains, Benjamin [VerfasserIn]
• Verfasst von: Brors, Benedikt [VerfasserIn]
• Titel: Assessment of modelling strategies for drug response prediction in cell lines and xenografts
• Verf.angabe: Roman Kurilov, Benjamin Haibe-Kains & Benedikt Brors
• E-Jahr: 2020
• Jahr: 18 February 2020
• Fussnoten: Gesehen am 18.09.2020
• Titel Quelle: Enthalten in: Scientific reports
• Ort Quelle: [London] : Macmillan Publishers Limited, part of Springer Nature, 2011
• Jahr Quelle: 2020
• Band/Heft Quelle: 10(2020) Artikel-Nummer 2849, 11 Seiten
• ISSN Quelle: 2045-2322
• DOI: doi:10.1038/s41598-020-59656-2
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1733306277

Abstract

Data from several large high-throughput drug response screens have become available to the scientific community recently. Although many efforts have been made to use this information to predict drug sensitivity, our ability to accurately predict drug response based on genetic data remains limited. In order to systematically examine how different aspects of modelling affect the resulting prediction accuracy, we built a range of models for seven drugs (erlotinib, pacliatxel, lapatinib, PLX4720, sorafenib, nutlin-3 and nilotinib) using data from the largest available cell line and xenograft drug sensitivity screens. We found that the drug response metric, the choice of the molecular data type and the number of training samples have a substantial impact on prediction accuracy. We also compared the tasks of drug response prediction with tissue type prediction and found that, unlike for drug response, tissue type can be predicted with high accuracy. Furthermore, we assessed our ability to predict drug response in four xenograft cohorts (treated either with erlotinib, gemcitabine or paclitaxel) using models trained on cell line data. We could predict response in an erlotinib-treated cohort with a moderate accuracy (correlation ≈ 0.5), but were unable to correctly predict responses in cohorts treated with gemcitabine or paclitaxel.