文章基本信息

标题：Treatment Stratification of Patients with Metastatic Castration-Resistant Prostate Cancer by Machine Learning
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作者：Kaiwen Deng ; Hongyang Li ; Yuanfang Guan 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2020
卷号：23
期号：2
页码：1-22
DOI：10.1016/j.isci.2019.100804
语种：English
出版社：Elsevier
摘要：SummaryProstate cancer is the most common cancer in men in the Western world. One-third of the patients with prostate cancer will develop resistance to hormonal therapy and progress into metastatic castration-resistant prostate cancer (mCRPC). Currently, docetaxel is a preferred treatment for mCRPC. However, about 20% of the patients will undergo early therapeutic failure owing to adverse events induced by docetaxel-based chemotherapy. There is an emergent need for a computational model that can accurately stratify patients into docetaxel-tolerable and docetaxel-intolerable groups. Here we present the best-performing algorithm in the Prostate Cancer DREAM Challenge for predicting adverse events caused by docetaxel treatment. We integrated the survival status and severity of adverse events into our model, which is an innovative way to complement and stratify the treatment discontinuation information. Critical stratification biomarkers were further identified in determining the treatment discontinuation. Our model has the potential to improve future personalized treatment in mCRPC.Graphical AbstractDisplay OmittedHighlights•Predicting the docetaxel treatment discontinuation in prostate cancer•The winning solution in the DREAM Prostate Cancer Challenge•Integrating survival status and adverse events to stratify treatment discontinuationBioinformatics; Cancer; Algorithms; Artificial Intelligence