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，表观遗传标记越来越被认为在诊断、分类和预测癌症和癌症前体病变方面具有巨大潜力。因此，一个技术挑战是开发和应用高效和高覆盖率的方法来描述人类癌症和这些癌症的正常前体组织的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Affymetrix单核苷酸多态性DNA微阵列来表征DNA甲基化全基因组。除了分析净DNA甲基化的得失（GOM， LOM）外，MSNP的一个特殊优势在于它还可以查询等位基因特异性DNA甲基化（ASM）。在这里，我们假设MSNP可以进一步发展和优化，作为一种高分辨率的方法来揭示甲基化模式的差异，不仅在癌症和正常组织之间，而且在正常组织和最终导致癌症的早期非典型或发育不良前体组织之间。在这个合作R21提案中，我们将与来自癌症遗传学研究所和病理学和生物统计学部门的经验丰富的研究团队一起，从几个方面推进MSNP的方法和应用。目的1是优化非常高密度Affymetrix 1.8M（6.0阵列）SNP芯片的MSNP，通过分析网络和人类乳腺癌和正常乳腺上皮中等位基因特异性DNA甲基化来审查该方法。结果将通过独立分析验证，包括高通量亚硫酸盐测序。在这个目标中，我们将开发从MSNP数据中预测肿瘤类别的生物信息学方法，并开发数据注释和数据共享的格式。目标2是使MSNP方法小型化，以便从激光捕获显微解剖（LCM）或手动显微解剖（MM）中获得的少量基因组DNA中获得高质量的遗传和表观遗传数据。我们将建立条件，允许使用LCM或MM从正常和癌性乳腺上皮中获得的基因组DNA来测定ASM和净DNA甲基化。在这个目标中，我们将特别评估乳腺癌前体病变，即非典型导管上皮增生（ADH），它与随后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候选DNA序列列表，包括启动子相关的和非启动子相关的，这些序列可能作为控制正常和癌症组织中基因表达的新型甲基化敏感调控元件。