Cytosine methylation profiling of cancer cell lines
Cytosine methylation profiling of cancer cell lines
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DOI：
10.1073/pnas.0712251105
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发表时间：
2008-03-25
影响因子：
11.1
通讯作者：
van den Boom, Dirk
中科院分区：
文献类型：
--
作者：
Ehrich, Mathias;Turner, Julia;van den Boom, Dirk
人类癌症中的DNA甲基化变化是复杂的，并且在不同类型的癌症之间变化。在DNA甲基化变化图谱中捕获这种表观遗传变异将有利于基础研究和转化医学。研究全基因组甲基化模式的无假设方法已经产生了有希望的结果。然而，这些方法仍然受到其定量准确性和可以单独评估的CpG位点数量的限制。在这里，我们使用一种独特的方法来测量一组>400个候选基因中的定量甲基化模式。在这项高分辨率研究中，我们采用了由美国国家癌症研究所提供的59种癌细胞系和6种健康对照组织组成的细胞系模型，以发现癌症相关基因的甲基化差异。为了评估细胞培养的效果，我们通过使用临床结肠癌标本验证了来自结肠癌细胞系的结果。我们的研究结果表明，很大一部分基因（400个基因中的78个）在癌症中发生了表观遗传学改变。虽然大多数基因只在一种肿瘤类型（35个基因）中显示甲基化变化，但我们也发现了一组在许多不同形式的癌症中发生变化的基因（7个基因）。该数据集可以很容易地扩展，以开发更全面和最终完整的定量甲基化变化图。我们的甲基化数据还为进一步的翻译研究提供了理想的起点，其结果可以与现有的大规模数据集相结合，以开发一种整合表观遗传，转录和突变发现的方法。
DNA-methylation changes in human cancer are complex and vary between the different types of cancer. Capturing this epigenetic variability in an atlas of DNA-methylation changes will be beneficial for basic research as well as translational medicine. Hypothesis-free approaches that interrogate methylation patterns genome-wide have already generated promising results. However, these methods are still limited by their quantitative accuracy and the number of CpG sites that can be assessed individually. Here, we use a unique approach to measure quantitative methylation patterns in a set of >400 candidate genes. In this high-resolution study, we employed a cell-line model consisting of 59 cancer cell lines provided by the National Cancer Institute and six healthy control tissues for discovery of methylation differences in cancer-related genes. To assess the effect of cell culturing, we validated the results from colon cancer cell lines by using clinical colon cancer specimens. Our results show that a large proportion of genes (78 of 400 genes) are epigenetically altered in cancer. Although most genes show methylation changes in only one tumor type (35 genes), we also found a set of genes that changed in many different forms of cancer (seven genes). This dataset can easily be expanded to develop a more comprehensive and ultimately complete map of quantitative methylation changes. Our methylation data also provide an ideal starting point for further translational research where the results can be combined with existing large-scale datasets to develop an approach that integrates epigenetic, transcriptional, and mutational findings.