Classification of DNA methylation patterns in tumor cell genomes using a CpG island microarray
Classification of DNA methylation patterns in tumor cell genomes using a CpG island microarray
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DOI：
10.1159/000091923
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发表时间：
2006-01-01
影响因子：
1.7
通讯作者：
Belbin, T. J.
中科院分区：
文献类型：
--
作者：
Adrien, L. R.;Schlecht, N. F.;Belbin, T. J.
我们的小组已启动实验，使用 12,288 个 CpG 岛克隆的微阵列对头颈鳞状细胞癌 (HNSCC) 组织样本的基因组 DNA 中的 CpG 岛高甲基化进行表观遗传学分析。我们的技术称为甲基化特异性限制性酶 (MSRE) 分析，是差异甲基化杂交 (DMH) 技术的一种变体，因为它不是使用甲基化特异性限制性酶对两个 DNA 样本进行阵列比较。相反，它是单个 DNA 样本对甲基化敏感的限制性酶 (HpaII) 及其相应的甲基化不敏感的同裂酶 (MspI) 的响应的比较。通过组内相关性 (ICC) 对该微阵列测定的再现性进行估计，结果表明，在三个肿瘤样本的四次重复实验中，在没有过滤数据的情况下，针对给定肿瘤样本观察到的 ICC 范围为 0.68 至 0.85。按荧光强度过滤阵列数据后，所有肿瘤的重复测定达到 87% 或更高的一致性。我们对 37 个 HNSCC 样本进行分层聚类，对具有相似 DNA 甲基化谱的肿瘤样本进行聚类。现在，我们利用监督学习技术来识别特定表观遗传特征与临床参数之间的关联。这些技术将使我们能够识别出可用作 HNSCC 诊断和预后的表观遗传标记的 CpG 岛位点组。版权所有 (c) 2006 S. Karger AG，巴塞尔。
Our group has initiated experiments to epigenetically profile CpG island hypermethylation in genomic DNA from tissue specimens of head and neck squamous cell carcinoma (HNSCC) using a microarray of 12,288 CpG island clones. Our technique, known as a methylation-specific restriction enzyme (MSRE) analysis, is a variation of the differential methylation hybridization (DMH) technique, in that it is not an array comparison of two DNA samples using methylation-specific restriction enzymes. Instead, it is a comparison of a single DNA sample's response to a methylation-sensitive restriction enzyme (HpaII) and its corresponding methylation-insensitive isoschizomer (MspI). Estimation of the reproducibility of this microarray assay by intraclass correlation (ICC) demonstrated that in four replicate experiments for three tumor specimens, the ICC observed for a given tumor specimen ranged from 0.68 to 0.85 without filtering of data. Repeated assays achieved 87% concordance or greater for all tumors after filtering of array data by fluorescence intensity. We utilized hierarchical clustering on a population of 37 HNSCC samples to cluster tumor samples with similar DNA methylation profiles. Supervised learning techniques are now being utilized to allow us to identify associations between specific epigenetic signatures and clinical parameters. Such techniques will allow us to identify select groups of CpG island loci that could be used as epigenetic markers for both diagnosis and prognosis in HNSCC. Copyright (c) 2006 S. Karger AG, Basel.