Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2022-11-02 eCollection Date: 2022-01-01 DOI:10.1177/11769351221131124
Bridget Neary, Shuting Lin, Peng Qiu
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引用次数: 0

Abstract

Background: 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.

Method: 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.

Results: 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.

Conclusion: 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.

CpG位点甲基化作为预测癌症患者药物特异性生存的生物标志物。
背景:虽然靶向癌症药物的发展持续加速,但医生仍然缺乏可靠的方法来预测大多数癌症患者对标准治疗的反应。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希望的来源,但药物疗效和DNA甲基化之间的关系在很大程度上仍未被探索。方法: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,以发现CpG位点,其中二甲基化状态与接受特定药物治疗的患者的差异生存相关,而与未接触该药物的同一癌症患者的差异生存无关。我们还基于患者的共甲基化对这些药物特异性CpG位点进行了聚类,以确定可能与药物疗效相关的更广泛的甲基化模式,我们利用基因集富集分析研究了转录因子结合位点的富集。结果:我们确定了CpG位点是15种癌症和20种药物的38种癌症药物患者组的药物特异性生存预测因子。其中包括11个在多种癌症中具有类似药物特异性生存效应的CpG位点。我们还发现了76个与患者药物反应有较强关联的CpG位点簇,其中许多CpG位点位于含有转录因子结合位点的基因启动子中。结论:这些发现是多种药物反应的有希望的生物标志物,有助于我们了解癌症中药物甲基化相互作用。对这些结果的调查和验证可能会导致针对控制甲基化的靶向联合疗法的发展,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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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