Accurately predicting the response of a cancer patient to a therapeutic agent remains an important challenge in precision medicine. With the rise of data science, researchers have applied computational models to study the drug inhibition effects on cancers based on cancer genomics and transcriptomics. Moreover, a common epigenetic modification, DNA methylation, has been related to the occurrence and development of cancer, as well as drug effectiveness. Therefore, it is helpful for improvement of drug response prediction through exploring the relationship between DNA methylation and drug effectiveness. Here, we proposed a computational model to predict drug responses in cancers through integration of cancer genomics, transcriptomics, epigenomics, and compound chemical properties. Meanwhile, we applied a regularized regression model (Least Absolute Shrinkage and Selection Operator, lasso) to detect the methylation sites that were closely related to drug effectiveness. The prediction models were trained on a well-known pharmacogenomics data resource, Genomics of Drug Sensitivity in Cancer (GDSC). The cross-validation indicates that the performance of the prediction model using DNA methylation is comparable to that of using other cancer omics, including oncogene mutation and gene expression data. It indicates the important role of DNA methylation in prediction of drug responses. Encyclopedia of DNA Elements (ENCODE) and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST2) database analyses suggest that the methylation sites associated with drug effectiveness are mainly located in the transcription factor (TF) binding region. Therefore, we hypothesized that the sensitivity of cancer cells to drugs could be regulated by changing the methylation modification of TF binding region. In conclusion, we confirmed the important role of DNA methylation in prediction of drug responses, and provided some methylation sites that closely related to the drug effectiveness, which may be a great regulatory target for improvement of drug treatment effects on cancer patients.

精确预测癌症患者对治疗剂的反应仍然是精密医学中的重要挑战。随着数据科学的兴起，研究人员已基于癌症基因组学和转录组学应用计算模型来研究药物对癌症的抑制作用。此外，常见的表观遗传修饰，DNA甲基化，与癌症的发生和发展以及药物有效性有关。因此，通过探索DNA甲基化与药物有效性之间的关系，有助于改善药物反应的预测。在这里，我们提出了一种计算模型，通过整合癌症基因组学，转录组学，表观基因组学和化合物的化学特性来预测癌症中的药物反应。与此同时，我们应用正则化回归模型（最小绝对收缩和选择算子，套索）来检测与药物疗效密切相关的甲基化位点。在著名的药物基因组学数据资源“癌症药物敏感性的基因组学”（GDSC）中对预测模型进行了训练。交叉验证表明，使用DNA甲基化的预测模型的性能与使用其他癌基因组学（包括癌基因突变和基因表达数据）的性能可比。它表明了DNA甲基化在预测药物反应中的重要作用。DNA元素百科全书（ENCODE）和基于句子的文本挖掘（TRRUST2）数据库揭示的转录调控关系表明，与药物有效性相关的甲基化位点主要位于转录因子（TF）结合区。因此，我们假设可以通过改变TF结合区的甲基化修饰来调节癌细胞对药物的敏感性。总之，我们确认了DNA甲基化在预测药物反应中的重要作用，并提供了一些与药物有效性密切相关的甲基化位点，这可能是改善对癌症患者的药物治疗效果的重要调控目标。我们假设可以通过改变TF结合区的甲基化修饰来调节癌细胞对药物的敏感性。总之，我们确认了DNA甲基化在预测药物反应中的重要作用，并提供了一些与药物有效性密切相关的甲基化位点，这可能是改善对癌症患者的药物治疗效果的重要调控目标。我们假设可以通过改变TF结合区的甲基化修饰来调节癌细胞对药物的敏感性。总之，我们确认了DNA甲基化在预测药物反应中的重要作用，并提供了一些与药物有效性密切相关的甲基化位点，这可能是改善对癌症患者的药物治疗效果的重要调控目标。