Conservation and functional-characterization of tumor methylation sites

肿瘤甲基化位点的保护和功能表征

• 批准号: 9883761
• 负责人: Paul Marjoram
• 金额: $ 17.94万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9883761
• 关键词: Area; Bayesian Analysis; Bioconductor; Cancer Etiology; Cells; Cessation of life; Classification; Clonal Evolution; Code; Colon; Colorectal Cancer; Colorectal Neoplasms; Computer software; DNA Methylation; DNA analysis; DNA sequencing; Data; Data Set; Enhancers; Epigenetic Process; Evolution; Exhibits; Genes; Goals; Growth; Human; Individual; Internet; Malignant Neoplasms; Mammalian Cell; Measures; Methods; Methylation; Modeling; Mutation; Nature; Normal tissue morphology; Pathway interactions; Patients; Pattern; Phenotype; Process; Replication Error; Sample Size; Sampling; Side; Site; Statistical Methods; Testing; Translating; Translations; Variant; Work; adenoma; bead chip; colorectal cancer progression; design; effective therapy; epigenome; human data; innovation; mathematical model; method development; methylation pattern; neoplastic cell; personalized medicine; pressure; software development; success; tumor; tumor growth; tumorigenesis

We aim to develop statistical methods and software for analysis of DNA methylation data from human colorectal cancer samples [CRCs] and matched normal tissue. We hypothesize that because epigenetics determines mammalian cell phenotypes, it will be possible to reconstruct the phenotype of the tumor and its founder cell by comparing epigenomes sampled from opposite sides of the same CRC. Therefore, we will rank genes or cell pathways according to the degree of conservation of methylation status they exhibit, indicating which are likely to be most important during tumor growth. We propose three innovations to accomplish these goals. First, we exploit a new Illumina microarray which can measure methylation at ~850,000 CpG sites, allowing broad coverage of most human genes and enhancers. We will develop methods to enable us to conduct an analysis of such data. We will demonstrate this using a test dataset in which we have collected multi-regional sampling data (i.e., data in which we sample from a number of different parts of the same tumor) for 26 human colorectal tumors, along with paired samples of normal tissue for 6 of those patients and 9 other colons. Second, we propose a two-pronged attack designed to assess whether each CpG site should be classified as ‘stable’ or ‘unstable’ with respect to the degree of CpG variation permitted there. In Aim 1 we propose methods that are purely statistical in nature; In Aim 2 we propose methods that will be built upon an explicit mathematical model for tumor evolution. We will compare and contrast their results. An additional advantage of the second approach is that it will also allow us to reconstruct the epigenome of the founder cell. Third, we will assess conservation of variation within genes or pathways to assess which are most important during growth---pathways with smaller methylation differences between tumor sides are likely to be more important and under selective pressures. The significance of the proposed studies is that we will develop new methods to extract epigenetic information from multi-regional tumor sampling. Such data are rare at the moment, but will soon be routinely collected. For that reason, in our fourth aim we propose to produce and freely distributed software and Shiny applications. Our long-term goal is to facilitate more personalized and effective therapies that specifically target pathways or genes most important to the growth of individual CRCs. The development of methods and software to characterize variation in methylation patterns from multi-regional tumor sampling, and relate that to genes/pathways will facilitate this process, and the relative ease of obtaining epigenetic information using methylation arrays should allow widespread translation to other tumor types.

我们旨在开发统计方法和软件，以分析人类结直肠的DNA甲基化数据 癌症样品[CRC]并匹配正常组织。我们假设这是因为表观遗传学认识 哺乳动物细胞表型，可以通过 比较从同一CRC的相对侧采样的表观人体工学。因此，我们将对基因或细胞进行排名 根据他们暴露的甲基化状态的保存程度，途径可能表明 在肿瘤生长中最重要。 我们提出三项创新来实现这些目标。首先，我们利用新的Illumina微阵列 它可以在约850,000个CpG位点测量甲基化，从而可以广泛覆盖大多数人类基因和 增强剂。我们将开发方法以使我们能够对此类数据进行分析。我们将证明这一点 使用我们收集的多区域采样数据的测试数据集（即，我们从中采样了 26种人类有色肿瘤的同一肿瘤的许多不同部分）以及配对的样品 其中6例患者和其他9个结肠的正常组织。其次，我们提出了一场两管齐的攻击 评估每个CPG站点应将CPG程度归类为“稳定”还是“不稳定” 那里允许的变化。在目标1中，我们提出了纯粹是统计本质的方法；在AIM 2中，我们提出了 将建立在肿瘤进化的显式数学模型上的方法。我们将比较和对比 他们的结果。第二种方法的另一个优点是，它还允许我们重建 创始人细胞的表观基因组。 第三，我们将评估基因或评估途径内的变异的保护，这是最多的 在生长过程中很重要 - 肿瘤侧之间甲基化差异较小的途径可能是 更重要的和选择性压力。 拟议的研究的意义在于，我们将开发提取表观遗传的新方法 来自多区域肿瘤采样的信息。此类数据目前很少见，但很快将是常规 集。因此，在我们的第四个目标中，我们建议生产和自由分发软件和闪亮 申请。 我们的长期目标是促进更个性化和有效的疗法，专门针对 途径或基因对单个CRC的生长最重要。方法和软件的开发 表征来自多区域肿瘤采样的甲基化模式的变化，并将其与 基因/途径将促进这一过程，并相对易于使用表观遗传信息 甲基化阵列应允许宽度转换为其他肿瘤类型。