Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection.
Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection.
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DOI：
10.1186/s12859-017-1753-2
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发表时间：
2017-07-25
影响因子：
3
通讯作者：
Beck S
中科院分区：
文献类型：
--
作者：
Barrett JE;Feber A;Herrero J;Tanic M;Wilson GA;Swanton C;Beck S
肿瘤内的表观遗传异质性在肿瘤进化和治疗耐药性的出现中发挥重要作用。人们越来越认识到，与单独检查 DNAm 标记的传统分析相比，对基因组 DNA 甲基化 (DNAm) 模式（即所谓的“表观基因”）的研究可以更深入地了解表观遗传动态。我们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来自一个肺癌肿瘤和匹配的正常样本的多个区域的简化代表性亚硫酸氢盐测序（RRBS）数据。该模型借用了所有肿瘤区域的信息来利用更大的统计能力。表观等位基因总数、表观等位基因 DNAm 模式和噪声超参数都是从数据中自动推断的。通过边缘化适当的后验密度，可以明确地合并观察到的测序读数源自哪个表观等位基因的不确定性。可以估计和校正肿瘤样本被正常组织污染的程度。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历史，识别正常组织和癌症组织之间不同的表观等位基因，并定义整体表观遗传疾病的衡量标准。多个肿瘤区域内表观等位基因的检测和比较能够进行系统发育分析、差异表达表观等位基因的鉴定，并提供表观遗传异质性的测量。 R 代码可在 github.com/james-e-barrett 获取。本文的在线版本 (doi:10.1186/s12859-017-1753-2) 包含补充材料，可供授权用户使用。
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. 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. Detection and comparison of epialleles within multiple tumour regions enables phylogenetic analyses, identification of differentially expressed epialleles, and provides a measure of epigenetic heterogeneity. R code is available at github.com/james-e-barrett. The online version of this article (doi:10.1186/s12859-017-1753-2) contains supplementary material, which is available to authorized users.
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DOI：
10.1093/bioinformatics/bts231
发表时间：
2012-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Peng Q;Ecker JR
通讯作者：
Ecker JR
影响因子：
16.6
作者：
Pan, Heng;Jiang, Yanwen;Boi, Michela;Tabbo, Fabrizio;Redmond, David;Nie, Kui;Ladetto, Marco;Chiappella, Annalisa;Cerchietti, Leandro;Shaknovich, Rita;Melnick, Ari M.;Inghirami, Giorgio G.;Tam, Wayne;Elemento, Olivier
通讯作者：
Elemento, Olivier
影响因子：
30.8
作者：
Landan, Gilad;Cohen, Netta Mendelson;Tanay, Amos
通讯作者：
Tanay, Amos
DNA 甲基化模式变异的全基因组定量评估。
影响因子：
14.9
作者：
Xie H;Wang M;de Andrade A;Bonaldo Mde F;Galat V;Arndt K;Rajaram V;Goldman S;Tomita T;Soares MB
通讯作者：
Soares MB
影响因子：
82.9
作者：
Sheffield NC;Pierron G;Klughammer J;Datlinger P;Schönegger A;Schuster M;Hadler J;Surdez D;Guillemot D;Lapouble E;Freneaux P;Champigneulle J;Bouvier R;Walder D;Ambros IM;Hutter C;Sorz E;Amaral AT;de Álava E;Schallmoser K;Strunk D;Rinner B;Liegl-Atzwanger B;Huppertz B;Leithner A;de Pinieux G;Terrier P;Laurence V;Michon J;Ladenstein R;Holter W;Windhager R;Dirksen U;Ambros PF;Delattre O;Kovar H;Bock C;Tomazou EM
通讯作者：
Tomazou EM