Defining Genomic Signatures for Aberrant DNA Methylation in Human Cancers

定义人类癌症中异常 DNA 甲基化的基因组特征

• 批准号: 8069622
• 负责人: Paula M. Vertino
• 金额: $ 30.61万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8069622
• 关键词: Aberrant DNA Methylation; Affect; Algorithms; Alu Elements; Binding; Biological Markers; Biological Process; Cancer cell line; Cell Culture Techniques; Chromatin Structure; Classification; Code; Complex; Computer Simulation; CpG Islands; Cytosine; DNA; DNA Methylation; DNA Methyltransferase; DNA Modification Methylases; DNA Sequence; Data; Defect; Deletion Mutation; Ectopic Expression; Employee Strikes; Epigenetic Process; Event; Frequencies; Gene Expression; Gene Silencing; Gene Targeting; Generations; Genes; Genome; Genomics; Goals; Growth; Health; Histones; Human; Human Genome; Hypermethylation; Intervention; Lead; Lung Neoplasms; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Mediating; Methylation; Modeling; Molecular; Molecular Target; Mutation; Non-Small-Cell Lung Carcinoma; Pattern; Pattern Recognition; Play; Polycomb; Post-Translational Protein Processing; Predisposition; Primary Neoplasm; Process; Promoter Regions; Proteins; Publishing; Recruitment Activity; Regulator Genes; Repression; Research; Resistance; Resources; Risk; Role; Shapes; Site; Techniques; Testing; Training; Transgenes; Tumor Suppressor Genes; Tumor Suppressor Proteins; Tumor-Suppressor Gene Inactivation; Work; base; cancer cell; cancer diagnosis; cancer therapy; carcinogenesis; genome-wide; histone modification; improved; in vivo; novel; overexpression; prevent; promoter; sound; treatment planning; tumor

DESCRIPTION (provided by applicant): Epigenetic silencing involving alterations in DNA methylation and chromatin structure at promoter region CpG islands is a common mechanism of tumor suppressor gene inactivation in human cancers. However, the mechanisms underlying this event remain poorly understood. An unresolved question is why are some genes targets of aberrant methylation in human cancers while others are never affected? In preliminary work, we have shown that even in the context of an increased cellular capacity for de novo methylation, CpG islands differ in their potential for aberrant methylation. By applying DNA pattern recognition and machine learning techniques, we have developed an algorithm based on several short sequence patterns that is capable of accurately discriminating methylation-prone and methylation-resistant CpG islands. These studies indicate that the epigenetic status of a CpG island can be predicted based on DNA sequence features, and lead us to propose that one factor contributing to the non-random patterns of CpG island methylation observed in human tumors is an underlying susceptibility conferred by local sequence context. The goal of this proposal is to define the genomic signature associated with aberrant methylation. The long term objectives are (i) to identify and to functionally characterize local sequence attributes that contribute to the propensity towards, or protection from, aberrant methylation, and (ii) to develop and to test novel tumor specific classifiers capable of predicting genomic loci at risk of aberrant methylation. Specifically, we will determine whether sequence features identified in silico act in cis to promote or to prevent de novo methylation in vivo using an episomal transgene approach. In preliminary work, we have identified a relationship between methylation-prone CpG islands and genomic regions bound by the polycomb repressor complex. As a second component of the project, we will determine the role of PRC2 in methylation susceptibility. As a third component of this project we will refine our computational models by 1) determining whether CpG islands predicted to be methylation-prone are in fact targets of aberrant methylation in human cancers, 2) using this information to re-train the prediction model, and 3) developing and testing a novel lung cancer specific classifier based on large-scale CpG island methylation data from primary lung tumors. We anticipate that the information gained from these studies will allow for a better understanding of the mechanisms underlying the epigenetic silencing of tumor suppressor genes that accompanies carcinogenesis. Moreover, the ability to predict the methylation status of CpG islands genome-wide will provide an important resource for the identification of novel gene targets for further study as potential cancer biomarkers. PUBLIC HEALTH RELEVANCE: The overarching goal of this research is to better understand the mechanisms by which important growth regulatory genes are targeted for epigenetic silencing in human cancers. Although the consequences of this event are the same as a mutation, there is no molecular defect in the DNA sequence itself. Rather, there is a problem with methylation marks on the DNA and the histone proteins it is wrapped around that render certain genes abnormally and permanently silent. We plan to use a computational approach to identify DNA features that put certain genes at risk of aberrant methylation. Because genes that are silenced by methylation are otherwise structurally sound, the potential for reactivating these genes by blocking or reversing the methylation process represents an exciting molecular target for chemotherapeutic intervention. A better understanding of the factors that contribute to aberrant methylation, including the identification of sequence features that attract or repel DNA methylation, will be an important step in achieving this long-term goal. Moreover, the ability to identify those genes at risk of epigenetic silencing will provide novel molecular targets for further study as potential biomarkers for improved cancer diagnosis and treatment planning.

描述（由申请人提供）：涉及启动子区域CpG岛处DNA甲基化和染色质结构改变的表观遗传沉默是人类癌症中肿瘤抑制基因失活的常见机制。然而，这一事件背后的机制仍然知之甚少。一个尚未解决的问题是，为什么一些基因是人类癌症中异常甲基化的靶基因，而另一些基因从未受到影响？在初步工作中，我们已经表明，即使在从头甲基化的细胞能力增加的情况下，CpG岛在异常甲基化的潜力方面也有所不同。通过应用DNA模式识别和机器学习技术，我们开发了一种基于几种短序列模式的算法，该算法能够准确区分甲基化倾向和甲基化抗性CpG岛。这些研究表明，CpG岛的表观遗传状态可以根据DNA序列特征进行预测，并使我们提出，一个因素有助于在人类肿瘤中观察到的CpG岛甲基化的非随机模式是由局部序列背景赋予的潜在易感性。该提议的目标是定义与异常甲基化相关的基因组特征。长期目标是（i）鉴定和功能性表征有助于异常甲基化倾向或保护免于异常甲基化的局部序列属性，和（ii）开发和测试能够预测处于异常甲基化风险的基因组位点的新型肿瘤特异性分类器。具体而言，我们将确定是否在计算机上识别的序列特征在顺式作用，以促进或防止在体内使用附加型转基因方法的从头甲基化。在初步工作中，我们已经确定了甲基化倾向的CpG岛和基因组区域的polycomb抑制复合物之间的关系。作为该项目的第二个组成部分，我们将确定PRC2在甲基化易感性中的作用。作为该项目的第三个组成部分，我们将通过以下方式改进我们的计算模型：1）确定预测为甲基化倾向的CpG岛是否实际上是人类癌症中异常甲基化的目标，2）使用此信息重新训练预测模型，以及3）基于来自原发性肺肿瘤的大规模CpG岛甲基化数据开发和测试新型肺癌特异性分类器。我们预计，从这些研究中获得的信息将允许更好地了解伴随致癌作用的肿瘤抑制基因的表观遗传沉默的机制。此外，预测CpG岛全基因组甲基化状态的能力将为鉴定新的基因靶标作为潜在的癌症生物标志物进行进一步研究提供重要资源。公共卫生关系：这项研究的首要目标是更好地了解重要的生长调节基因在人类癌症中靶向表观遗传沉默的机制。虽然这一事件的后果与突变相同，但DNA序列本身并没有分子缺陷。相反，DNA上的甲基化标记和它所包裹的组蛋白质存在一个问题，使某些基因异常并永久沉默。我们计划使用计算方法来识别使某些基因处于异常甲基化风险中的DNA特征。因为被甲基化沉默的基因在结构上是健全的，所以通过阻断或逆转甲基化过程来重新激活这些基因的潜力代表了化疗干预的令人兴奋的分子靶点。更好地了解导致异常甲基化的因素，包括识别吸引或排斥DNA甲基化的序列特征，将是实现这一长期目标的重要一步。此外，鉴定那些有表观遗传沉默风险的基因的能力将为进一步研究提供新的分子靶点，作为改善癌症诊断和治疗计划的潜在生物标志物。