Department of Bioinformatics & Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA.
Ben May Department for Cancer Research, University of Chicago, Chicago, IL 60637, USA.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab260.
Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.
细胞系药物筛选数据集可用于多种不同的药物发现应用,从药物生物标志物发现到建立药物反应的转化模型。此前,我们描述了三种独立的方法来(1)纠正药物敏感性的一般水平,以实现药物特异性生物标志物发现,(2)预测患者的临床药物反应,以及(3)将这些预测与临床特征相关联,以进行体内药物生物标志物发现。在这里,我们将这些方法整合并更新到一个 R 包(oncoPredict)中,以促进这些工具的开发和采用。这个新的 OncoPredict R 包可应用于各种体外和体内环境中的药物和生物标志物发现。