通过贝叶斯表观等位基因检测定量肿瘤演变和异质性
Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection
摘要：
动机：肿瘤内的表观遗传异质性可以在肿瘤演变和对治疗的耐药性的出现中发挥重要作用。人们越来越认识到，沿着基因组的DNA甲基化（DNAm）模式的研究-所谓的“表观等位基因”-比单独检查DNAm标记的常规分析提供了对表观遗传动力学更深入的了解。结果：我们已经开发了一个贝叶斯模型来推断哪些表观等位基因存在于同一肿瘤的多个区域。我们将我们的方法应用于一个肺癌肿瘤和一个匹配的正常样本的多个区域的减少代表性亚硫酸氢盐测序（RRBS）数据。该模型从所有肿瘤区域借用信息，以利用更大的统计能力。表观等位基因的总数、表观等位基因DNAm模式和噪声超参数都是从数据自动推断的。通过在适当的后验密度上边缘化来明确地并入关于观察到的测序读数源自哪个表观等位基因的不确定性。肿瘤样本被正常组织污染的程度可以被估计和校正。通过追踪表观等位基因在整个肿瘤中的分布，我们可以推断肿瘤的系统发育史，识别正常组织和癌症组织之间不同的表观等位基因，并定义全球表观遗传疾病的量度。
英文摘要：
Motivation: Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome -- so-called `epialleles' -- offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually. Results: We have developed a Bayesian model to infer which epialleles are present in multiple regions of the same tumour. We apply our method to reduced representation bisulfite sequencing (RRBS) data from multiple regions of one lung cancer tumour and a matched normal sample. The model borrows information from all tumour regions to leverage greater statistical power. The total number of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all automatically inferred from the data. Uncertainty as to which epiallele an observed sequencing read originated from is explicitly incorporated by marginalising over the appropriate posterior densities. The degree to which tumour samples are contaminated with normal tissue can be estimated and corrected for. By tracing the distribution of epialleles throughout the tumour we can infer the phylogenetic history of the tumour, identify epialleles that differ between normal and cancer tissue, and define a measure of global epigenetic disorder.
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