Optimizing MSNP for profiling DNA methylation in cancers and precursor lesions

描述（由申请人提供）：DNA甲基化的改变是癌细胞的标志，表观遗传标记物越来越被视为在诊断、分类和预测癌症和癌症前体病变方面具有巨大潜力。因此，技术挑战是开发和应用有效和高覆盖率的方法来分析人类癌症和这些癌症的正常前体组织中的DNA甲基化全基因组。我们已经开发了这样一种方法，称为MSNP，使用Affyssin单核苷酸多态性DNA微阵列来表征全基因组DNA甲基化。除了分析净DNA甲基化（GOM，LOM）的获得和损失之外，MSNP的一个特别优势是它还查询等位基因特异性DNA甲基化（ASM）。在这里，我们假设MSNP可以进一步开发和优化为高分辨率方法，以揭示甲基化模式的差异，不仅在癌症和正常组织之间，而且在正常组织和早期非典型或发育异常的前体组织，最终引起癌症。在这项合作R21提案中，来自癌症遗传学研究所和病理学和生物统计学系的经验丰富的研究人员团队将以多种方式推进MSNP的方法和应用。目的1：优化用于超高密度Affyellow 1.8M（6.0 array）SNP芯片的MSNP，并通过人类乳腺癌和正常乳腺上皮细胞中的基因组甲基化和基因组图谱分析对该方法进行验证。结果将通过独立的试验进行验证，包括高通量亚硫酸氢盐测序。在这个目标中，我们将开发用于从MSNP数据预测肿瘤类别的生物信息学方法，并开发用于数据注释和数据共享的格式。目的2是验证MSNP方法，以便从激光捕获显微切割（LCM）或手动显微切割（MM）获得的少量基因组DNA中获得高质量的遗传和表观遗传数据。我们将建立允许ASM和净DNA甲基化的条件，使用从正常和癌性乳腺上皮中通过LCM或MM获得的基因组DNA进行测定。在这个目标中，我们将特别评估乳腺癌前病变，即非典型导管上皮增生（ADH），这与随后的乳腺癌发展的高风险相关。目的3是将MSNP数据与表达谱数据相关联，以确定MSNP是否可以在人类基因组中产生启动子相关和非启动子相关的候选DNA序列列表，其可以充当控制正常和癌症组织中基因表达的新型甲基化敏感性调控元件。

DESCRIPTION (provided by applicant): Alterations in DNA methylation are hallmarks of cancer cells, and epigenetic markers are increasingly viewed as having great potential for diagnosing, classifying and prognosticating cancers and cancer precursor lesions. Thus, a technical challenge is to develop and apply efficient and high-coverage methods to profile DNA methylation genome-wide in human cancers and in the normal precursor tissues of these cancers. We have developed such a method, called MSNP, to characterize DNA methylation genome-wide using Affymetrix single nucleotide polymorphism DNA microarrays. In addition to profiling gains and losses of net DNA methylation (GOM, LOM), a particular strength of MSNP is that it also queries allele-specific DNA methylation (ASM). Here we hypothesize that MSNP can be further developed and optimized as a high resolution method to reveal differences in methylation patterns not only between cancer and normal tissues, but also between normal tissues and the early atypical or dysplastic precursor tissues which eventually give rise to cancers. In this collaborative R21 proposal, with an experienced team of investigators from the Institute for Cancer Genetics and the Departments of Pathology and Biostatistics, we will advance the methodology and applications of MSNP in several ways. Aim 1 is to optimize MSNP for very high density Affymetrix 1.8M (6.0 array) SNP chips, vetting this method by profiling net and allele-specific DNA methylation in human breast cancers and normal breast epithelium. The results of will be verified by independent assays, including high throughput bisulfite sequencing. In this Aim we will develop bioinformatics approaches for tumor class prediction from MSNP data, and develop formats for data annotation and data sharing. Aim 2 is to miniaturize the MSNP method so that high quality genetic and epigenetic data can be obtained from the small amounts of genomic DNA available from laser-capture microdissection (LCM) or manual microdissection (MM). We will establish conditions allowing ASM and net DNA methylation to be determined using genomic DNA obtained by LCM or MM from normal and cancerous breast epithelium. In this aim we will particularly evaluate breast cancer precursor lesions, namely atypical duct epithelial hyperplasias (ADH), which are associated with a high risk for subsequent breast cancer development. Aim 3 is to correlate MSNP data with expression profiling data, to determine whether MSNP can produce a list of candidate DNA sequences, both promoter-associated and non- promoter-associated, in the human genome that may act as novel methylation-sensitive regulatory elements controlling gene expression in normal and cancer tissues.