Neary Bridget, Lin Shuting, Qiu Peng
School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
Cancer Inform. 2022 Nov 2;21:11769351221131124. doi: 10.1177/11769351221131124. eCollection 2022.
Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored.
In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis.
We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites.
These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction.
尽管靶向抗癌药物的研发仍在加速，但对于大多数癌症，医生仍缺乏可靠的方法来预测患者对标准治疗方案的反应。DNA甲基化与肿瘤药物反应有关，是药物疗效预测生物标志物的一个有前景的来源，但药物疗效与DNA甲基化之间的关系在很大程度上仍未得到探索。
在本分析中，我们对按癌症和药物暴露分组的患者进行对数秩生存分析，以找到那些二元甲基化状态与接受特定药物治疗的患者的差异生存相关，但与未接触该药物的相同癌症患者的生存无关的CpG位点。我们还根据患者之间的共甲基化对这些药物特异性CpG位点进行聚类，以识别可能与药物疗效相关的更广泛的甲基化模式，并使用基因集富集分析对其进行转录因子结合位点富集研究。
我们在15种癌症和20种药物的38个癌症-药物患者组中确定了作为生存的药物特异性预测因子的CpG位点。其中包括11个在多种癌症中具有相似药物特异性生存效应的CpG位点。我们还确定了76个与患者药物反应有更强关联的CpG位点簇，其中许多在含有转录因子结合位点的基因启动子中包含CpG位点。
这些发现是多种药物反应的有前景的生物标志物，有助于我们理解癌症中的药物-甲基化相互作用。对这些结果的研究和验证可能会导致开发旨在操纵甲基化的靶向联合疗法，以提高常用疗法的疗效，并通过进一步努力预测药物反应来提高患者的生存率和生活质量。
