Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.
Mines Saint-Etienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Centre CIS, Saint-Etienne, France.
PLoS One. 2021 Sep 10;16(9):e0238757. doi: 10.1371/journal.pone.0238757. eCollection 2021.
Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.
癌细胞系是从肿瘤样本中培养出来的细胞系，是药物开发中最廉价和最受研究的临床前模型之一。根据分子特征准确预测给定细胞系的药物反应有助于优化药物开发管道，并解释治疗反应背后的机制。在这项研究中，我们专注于 DNA 甲基化谱作为一种已知驱动肿瘤发生和调节治疗反应的分子特征。我们使用癌症药物敏感性基因组学数据库中 987 个细胞系的全基因组 DNA 甲基化谱，使用机器学习算法评估预测八种抗癌药物细胞毒性反应的潜力。我们比较了五种分类算法和四种回归算法的性能，这些算法代表了不同的方法，包括树状、概率、核、集成和基于距离的方法。我们对数据进行了不同程度的人工抽样，旨在了解基于相对极端结果进行训练是否会提高性能。当使用分类或回归算法分别预测离散或连续响应时，当训练和测试集由细胞系数据组成时，我们一致观察到出色的预测性能。当我们使用相对极端药物反应值的细胞系来训练模型时，分类算法的表现最佳，达到了高达 0.97 的接收器操作特征曲线下面积值。当我们使用全范围的药物反应值来训练模型时，回归算法的表现最佳，尽管这取决于我们使用的性能指标。最后，我们使用来自癌症基因组图谱的患者数据来评估基于细胞系衍生模型对人类肿瘤临床反应进行分类的可行性。一般来说，这些算法无法可靠地识别出可预测患者反应的模式；然而，随机森林算法的预测与低级别神经胶质瘤的替莫唑胺反应显著相关。
