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: 8246510
• 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/8246510
• 关键词: Aberrant DNA Methylation; Affect; Algorithms; Alu Elements; Binding; Biological Markers; Biological Process; Cancer cell line; Cell Culture Techniques; ChIP-on-chip; 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 Profile; 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; 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.

描述(申请人提供)：表观遗传沉默涉及启动子区DNA甲基化和染色质结构的改变，CpG岛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。一个悬而未决的问题是，为什么在人类癌症中，一些基因是异常甲基化的靶标，而其他基因从未受到影响？在初步工作中，我们已经表明，即使在细胞从头开始甲基化的能力增加的背景下，CpG岛在其异常甲基化的潜力上也不同。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个短序列模式的算法，该算法能够准确地区分易甲基化和抗甲基化的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局部序列背景赋予的潜在易感性。该提案的目的是定义与异常甲基化相关的基因组签名。长期目标是(I)识别和功能表征有助于异常甲基化倾向或防止异常甲基化的局部序列属性，以及(Ii)开发和测试能够预测基因组异常甲基化风险的新的肿瘤特异性分类器。具体地说，我们将确定在计算机中识别的序列特征是在顺式基因中起作用，还是在体内使用表体转基因方法促进或防止从头甲基化。在初步工作中，我们已经确定了易于甲基化的CpG岛和由多梳抑制物复合体结合的基因组区域之间的关系。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。作为该项目的第三部分，我们将完善我们的计算模型，1)确定被预测为易于甲基化的CpG岛是否实际上是人类癌症异常甲基化的靶标，2)使用这些信息来重新训练预测模型，以及3)基于来自原发肺癌的大规模CpG岛甲基化数据开发和测试一种新的肺癌特异性分类器。我们预计，从这些研究中获得的信息将有助于更好地理解伴随癌变而来的肿瘤抑制基因表观遗传沉默的机制。此外，预测全基因组CpG岛甲基化状态的能力将为进一步研究潜在的癌症生物标志物的新基因靶点的识别提供重要的资源。