Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes.
Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes.
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DOI：
10.1038/srep43381
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发表时间：
2017-02-24
影响因子：
4.6
通讯作者：
Deasy JO
中科院分区：
文献类型：
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作者：
Oh JH;Kerns S;Ostrer H;Powell SN;Rosenstein B;Deasy JO
临床观察到的放射治疗后正常组织损伤的变异性的生物学原因知之甚少。我们假设使用基于单核苷酸多态性（SNP）的全基因组关联研究（GWAS）的机器/统计学习方法将识别具有不同并发症风险的患者组，并且还可用于识别变异性的关键生物学来源。我们开发了一种新的学习算法，称为预条件随机森林回归（PRFR），使用数百个SNP构建多基因风险模型，从而捕获赋予小差异风险的基因组特征。在368名前列腺癌患者的队列中训练和验证了两个放疗后临床终点：晚期直肠出血和勃起功能障碍。与现有的计算方法相比，该方法具有更好的预测性能。基因本体富集分析和蛋白质-蛋白质相互作用网络分析用于识别基于其他已发表研究的可能的关键生物过程和蛋白质。总之，我们证实，新的机器学习方法可以产生大型预测模型（数百个SNP），产生临床有用的风险分层模型，以及识别辐射损伤和组织修复过程中重要的潜在生物学过程。这些方法通常适用于GWAS数据，而不是特定于放射治疗终点。
The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.