Cortes-Ciriano Isidro, Mervin Lewis H, Bender Andreas
Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States.
Curr Pharm Des. 2016;22(46):6918-6927. doi: 10.2174/1381612822666161026154430.
Cancer cell line panels have proved useful disease models to, among others, identify genomic markers of drug sensitivity and to develop new anticancer drugs. The increasing availability of in vitro sensitivity and cell line profiling data sets raises the question of whether this information could be used, and to which extent, to predict the activity of drugs in cancer cell lines and, ultimately, in patients tumors. Drug sensitivity prediction embraces those approaches aiming at predicting in vitro drug activity on cancer cell lines by integrating genomic and/or chemical information using machine learning models. In this review, we summarize the cytotoxicity assays generally used to determine in vitro activity on cultured cell lines, and revisit the drug sensitivity prediction studies that have leveraged chemical and cell line profiling data from the NCI60, Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) projects. A section outlining current limitations and future perspectives in the field closes the review.
癌细胞系面板已被证明是有用的疾病模型，可用于识别药物敏感性的基因组标记等，以及开发新的抗癌药物。体外敏感性和细胞系分析数据集的可用性不断提高，引发了一个问题，即这些信息是否可以以及在多大程度上用于预测药物在癌细胞系以及最终在患者肿瘤中的活性。药物敏感性预测包括那些旨在通过使用机器学习模型整合基因组和/或化学信息来预测癌细胞系体外药物活性的方法。在本综述中，我们总结了通常用于确定对培养细胞系体外活性的细胞毒性测定方法，并回顾了利用来自NCI60、癌细胞系百科全书（CCLE）和癌症药物敏感性基因组学（GDSC）项目的化学和细胞系分析数据的药物敏感性预测研究。综述最后一部分概述了该领域当前的局限性和未来展望。
