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: 7742918
• 负责人: Paula M. Vertino
• 金额: $ 32.94万
• 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/7742918
• 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; Fibrinogen; Frequencies; Gene Expression; Gene Silencing; Gene Targeting; Generations; Genes; Genome; Genomics; Goals; Growth; 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; carcinogenesis; genome-wide; histone modification; improved; in vivo; novel; overexpression; prevent; promoter; public health relevance; 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）：表观遗传沉默涉及启动子区域CpG岛的DNA甲基化和染色质结构的改变是人类癌症中肿瘤抑制基因失活的常见机制。但是，该事件的基础机制仍然很少理解。一个尚未解决的问题是，为什么在人类癌症中某些异常甲基化的基因靶标，而另一些从未受到影响？在初步工作中，我们已经表明，即使在从头开始甲基化的细胞能力增加的背景下，CpG岛的异常甲基化潜力也有所不同。通过应用DNA模式识别和机器学习技术，我们基于几种短序列模式开发了一种算法，该算法能够准确区分易甲基化和抗甲基化的CPG岛。这些研究表明，可以根据DNA序列特征预测CpG岛的表观遗传状态，并导致我们提出，在人类肿瘤中观察到的CpG岛甲基化的非随机模式的一个因素是由局部序列上下文赋予的潜在敏感性。该提案的目的是定义与异常甲基化相关的基因组特征。长期目标是（i）识别和在功能上表征有助于或保护异常甲基化倾向或保护的局部序列属性，以及（ii）开发和测试能够预测具有异常甲基化风险的基因组基因座的新型肿瘤特异性分类器。具体而言，我们将确定在CIS中鉴定出在CIS中鉴定出的序列特征，以使用偶发转基因方法在体内促进或防止从头开始。在初步工作中，我们已经确定了容易发生甲基化的CpG岛与受Polycomb抑制剂复合物约束的基因组区域之间的关系。作为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1）确定预测为甲基化的CpG岛是否实际上是人类癌症中异常甲基化的靶标，2）使用此信息来重新培养预测模型，3）基于大型CPG Islant甲基化数据的新型肺癌特异性分类和测试新型肺癌特异性分类。我们预计从这些研究中获得的信息将可以更好地理解伴随癌变的肿瘤抑制基因表观遗传沉默的基础机制。此外，预测全基因组CpG岛的甲基化状态的能力将为鉴定新的基因靶标的进一步研究作为潜在的癌症生物标志物提供重要资源。公共卫生相关性：这项研究的总体目标是更好地了解重要的生长调节基因针对人类癌症的表观遗传沉默的机制。尽管该事件的后果与突变相同，但在DNA序列本身中没有分子缺陷。相反，DNA上的甲基化痕迹和组蛋白蛋白的甲基化标记存在一个问题，它被包裹在异常和永久性的某些基因上。我们计划使用一种计算方法来识别将某些基因面临异常甲基化风险的DNA特征。由于被甲基化沉默的基因在结构上是合理的，因此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学治疗干预的令人兴奋的分子靶标。更好地了解导致异常甲基化的因素，包括吸引或驱除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标，作为改善癌症诊断和治疗计划的潜在生物标志物。