Optimizing MSNP for profiling DNA methylation in cancers and precursor lesions

优化 MSNP 以分析癌症和癌前病变中的 DNA 甲基化

• 批准号: 7660795
• 负责人: Benjamin Tycko
• 金额: $ 17.64万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7660795
• 关键词: Alleles; Atypical hyperplasia; Bioinformatics; Biological Assay; Biometry; Breast; Cancerous; Classification; Classification Scheme; CpG Islands; DNA; DNA Fingerprinting; DNA Methylation; DNA Microarray Chip; DNA Sequence; Data; Data Analyses; Development; Diagnosis; Disease Progression; Duct (organ) structure; Epigenetic Process; Epithelial; Epithelium; Gene Expression; Genetic; Genome; Genomics; Human; Human Genome; Hyperplasia; Institutes; Lead; Lesion; Malignant Neoplasms; Manuals; Methodology; Methods; Methylation; Microdissection; Molecular Biology; Molecular Profiling; Normal tissue morphology; Outcome; Pathogenesis; Pathology; Pattern; Recurrence; Regulatory Element; Research Personnel; Resolution; Scanning; Single Nucleotide Polymorphism; Testing; Tissues; Work; bisulfite; cancer cell; cancer genetics; cancer initiation; cancer type; data format; data sharing; density; experience; genome-wide; high risk; laser capture microdissection; malignant breast neoplasm; miniaturize; novel; promoter; public health research; research study; response; tool; tumor

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.

描述（由申请人提供）：DNA甲基化的改变是癌细胞的标志，并且表观遗传标记越来越被视为具有诊断，分类和预测性癌症和癌症前体病变具有巨大潜力。因此，技术挑战是开发和应用高效，高覆盖的方法来介绍人类癌症和这些癌症的正常前体组织中的DNA甲基化基因组。我们已经开发了一种称为MSNP的方法，用于使用Affymetrix单核苷酸多态性DNA微阵列来表征DNA甲基化基因组。除了分析净DNA甲基化（GOM，LOM）的分析增长和损失外，MSNP的特殊强度还在于它还查询了等位基因特异性DNA甲基化（ASM）。在这里，我们假设MSNP可以进一步开发和优化，作为一种高分辨率方法，可以揭示甲基化模式的差异，不仅在癌症和正常组织之间，而且在正常组织与早期非典型或非典型前体前体组织之间，最终引起癌症。在这项合作R21提案中，与癌症遗传学研究所以及病理和生物统计学部门的经验丰富的研究人员团队一起，我们将通过多种方式推进MSNP的方法和应用。 AIM 1是针对非常高的密度Affymetrix 1.8M（6.0阵列）SNP芯片优化MSNP，通过在人乳腺癌和正常的乳腺癌上皮中分析网络和等位基因特异性DNA甲基化来审查此方法。结果将通过独立测定法（包括高吞吐量亚硫酸盐测序）来验证。在此目标中，我们将开发从MSNP数据的肿瘤类预测的生物信息学方法，并开发用于数据注释和数据共享的格式。 AIM 2是将MSNP方法微型化，以便从少量的基因组DNA（LASER捕获显微解剖（LCM））或手动显微解剖（MM）获得高质量的遗传和表观遗传学数据。我们将建立允许使用正常和癌性乳房上皮中LCM或MM获得的基因组DNA确定ASM和净DNA甲基化的条件。在此目的中，我们将特别评估乳腺癌前体病变，即非典型风管上皮增生（ADH），这与随后的乳腺癌发展的高风险有关。 AIM 3是将MSNP数据与表达分析数据相关联，以确定MSNP是否可以在人类基因组中产生候选DNA序列的列表，即启动子相关和与非启动子相关的启动子序列，该序列可以充当新型甲基化敏感的调控元件，从而控制正常和癌症组织中的基因表达。