Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data

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Author(s)Ferrato, Mauricio H.
Author(s)Marsh, Adam G.
Author(s)Franke, Karl R.
Author(s)Huang, Benjamin J.
Author(s)Kolb, E. Anders
Author(s)DeRyckere, Deborah
Author(s)Grahm, Douglas K.
Author(s)Chandrasekaran, Sunita
Author(s)Crowgey, Erin L.
Date Accessioned2023-06-08T20:41:18Z
Date Available2023-06-08T20:41:18Z
Publication Date2023-03-24
Description© The Author(s) 2023. Published by Oxford University Press. This article was originally published in Bioinformatics Advances. The version of record is available at: https://doi.org/10.1093/bioadv/vbad034
AbstractMotivation: The application of machine learning (ML) techniques in the medical field has demonstrated both successes and challenges in the precision medicine era. The ability to accurately classify a subject as a potential responder versus a nonresponder to a given therapy is still an active area of research pushing the field to create new approaches for applying machine-learning techniques. In this study, we leveraged publicly available data through the BeatAML initiative. Specifically, we used gene count data, generated via RNA-seq, from 451 individuals matched with ex vivo data generated from treatment with RTK-type-III inhibitors. Three feature selection techniques were tested, principal component analysis, Shapley Additive Explanation (SHAP) technique and differential gene expression analysis, with three different classifiers, XGBoost, LightGBM and random forest (RF). Sensitivity versus specificity was analyzed using the area under the curve (AUC)-receiver operating curves (ROCs) for every model developed. Results: Our work demonstrated that feature selection technique, rather than the classifier, had the greatest impact on model performance. The SHAP technique outperformed the other feature selection techniques and was able to with high accuracy predict outcome response, with the highest performing model: Foretinib with 89% AUC using the SHAP technique and RF classifier. Our ML pipelines demonstrate that at the time of diagnosis, a transcriptomics signature exists that can potentially predict response to treatment, demonstrating the potential of using ML applications in precision medicine efforts. Availability and implementation: https://github.com/UD-CRPL/RCDML Supplementary information: Supplementary data are available at Bioinformatics Advances online at: https://doi.org/10.1093/bioadv/vbad034
SponsorWe would like to thank the University of Delaware and NSF award 1919839 (PI Dr Eigenmann) for the use of the DARWIN compute resource. This work was supported by the Lisa Dean Moseley Foundation (PI Crowgey) and the Nemours Center for Cancer and Blood Disorders (NCCBD). Conflict of Interest: none declared.
CitationMauricio H Ferrato and others, Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data, Bioinformatics Advances, Volume 3, Issue 1, 2023, vbad034, https://doi.org/10.1093/bioadv/vbad034
ISSN2635-0041
URLhttps://udspace.udel.edu/handle/19716/32841
Languageen_US
PublisherBioinformatics Advances
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
TitleMachine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data
TypeArticle
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