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.

我们的目标是开发用于分析人类结直肠癌DNA甲基化数据的统计方法和软件 癌症样本[CRC]和匹配的正常组织。我们假设因为表观遗传学决定了 哺乳动物细胞表型，将有可能重建肿瘤及其创始细胞的表型， 比较从相同CRC的相对侧取样的表观基因组。因此，我们将对基因或细胞 根据它们所表现出的甲基化状态的保守程度， 在肿瘤生长过程中是最重要的。 我们提出了三个创新来实现这些目标。首先，我们开发了一种新的Illumina微阵列 它可以测量约850，000个CpG位点的甲基化，可以广泛覆盖大多数人类基因， 增强剂。我们将制定方法，使我们能够对这些数据进行分析。我们将证明这一点 使用其中我们已经收集了多区域采样数据的测试数据集（即，我们从这些数据中 同一肿瘤的多个不同部分），沿着的配对样本， 其中6名患者和其他9名结肠的正常组织。其次，我们提出了一个双管齐下的攻击， 评估每个CpG位点是否应该根据CpG的程度被分类为“稳定”或“不稳定”， 允许有变化。在目标1中，我们提出了纯统计性质的方法;在目标2中，我们提出了 这些方法将建立在肿瘤演变的明确数学模型上。我们将进行比较和对比 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 创始细胞的表观基因组。 第三，我们将评估基因或途径内变异的保守性，以评估哪些是最重要的。 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 更重要的是，在选择性的压力下。 这项研究的意义在于，我们将开发新的方法来提取表观遗传 来自多区域肿瘤取样的信息。这样的数据目前还很少见，但很快就会成为惯例。 收集。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Shiny。 应用. 我们的长期目标是促进更个性化和更有效的治疗， 对个体CRC生长最重要的途径或基因。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，并且使用基因/途径获得表观遗传信息相对容易。 甲基化阵列应该允许广泛翻译到其他肿瘤类型。