Interrogating Epigenetic Changes in Cancer Genomes

探究癌症基因组的表观遗传变化

• 批准号: 7904698
• 负责人: Tim H.-M. Huang
• 金额: $ 72.39万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-30 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7904698
• 关键词: Algorithms; Applications Grants; Base Sequence; Binding; Biological Neural Networks; Cancer Biology; Cancer cell line; Characteristics; Chromatin; Complex; DMA-methyltransferase; DNA Methylation; DNA-Protein Interaction; Databases; Deposition; Drug resistance; Ensure; Epigenetic Process; Epithelium; Future; Gene Expression; Gene Mutation; Gene Silencing; Genes; Genetic; Goals; Histones; Indiana; Intervention; Lasso; Learning; Logistic Regressions; Loss of Heterozygosity; Malignant Neoplasms; Methylation; Modeling; Neoplasms; Ohio; Pathway interactions; Pattern Recognition; Phylogenetic Analysis; Process; Recruitment Activity; Research; Research Personnel; System; Techniques; Testing; Training; Universities; cancer cell; cancer genome; data modeling; histone modification; human female; improved; mathematical model; neoplastic; novel; outcome forecast; programs; response; success; tool; transcription factor; tumor progression

DESCRIPTION (provided by applicant): The investigators of the Ohio State University submit a center grant application in response to the RFA announcement of the Integrated Cancer Biology Program (ICBP). In addition, our colleagues at the neighboring Indiana University will join this endeavor. The proposed research will tackle a unique problem, i.e., epigenetic alteration, now being considered as important as genetic mutations in cancer. This phenomenon can be defined as a heritable change that modulates chromatin organization and gene expression without altering nucleotide sequences. Bringing together experimental and computational biologists, our overall goals of this ICBP are 1) to increase our understanding of complex epigenetic interactions in neoplasms and 2) to use high-end information for improved prognosis, intervention, and treatment of human female cancers. Experimental biologists will use novel microarray platforms to interrogate DNA methylation, histone modifications, loss of heterozygosity, and transcription factor binding in cancer cell lines and neoplastic epithelium and the surrounding stroma. Computational biologists will use these experimental data for model building and refinement. Empirical Bayesian models will be used to predict how repressors, histone deacetylases, and DMA methyltransferases are recruited to establish epigenetic gene silencing (Project 1). Phylogenetic clustering algorithms will be developed to recapitulate genetic and epigenetic pathways in cancer stroma as they relate to tumor progression (Project 2). LASSO logistic regression and neural network approaches will be used to model the synergistic DNA-protein interactions and the resulting change of chromatin landscape in cancer cells (Project 3). Pattern recognition and supervised learning techniques will be used to select genes that contain the characteristics of methylation-prone sequences in drug-resistant cancer cells (Project 4). These mathematical models will generate the first- or second-level hypotheses for experimental testing. The iterative process of model refinement and experimental verification will continue until models are derived that accurately predict specific epigenetic alterations in the interrogating cancer genome. All experimental data and modeling tools will be deposited in a centralized database and will be used for training future systems cancer biologists. The progress of these integrated studies will be evaluated by advisors and administrative leaders to ensure the success of the proposed ICBP.

描述（由申请人提供）：俄亥俄州立大学的研究人员根据 RFA 宣布的综合癌症生物学计划 (ICBP) 提交了一份中心拨款申请。 此外，我们在邻近印第安纳大学的同事也将加入这一努力。 拟议的研究将解决一个独特的问题，即表观遗传改变，现在被认为与癌症中的基因突变一样重要。 这种现象可以定义为一种可遗传的变化，可以在不改变核苷酸序列的情况下调节染色质组织和基因表达。 此次ICBP将实验生物学家和计算生物学家聚集在一起，我们的总体目标是：1）增进我们对肿瘤中复杂的表观遗传相互作用的理解；2）利用高端信息来改善人类女性癌症的预后、干预和治疗。 实验生物学家将使用新型微阵列平台来检测癌细胞系、肿瘤上皮和周围基质中的 DNA 甲基化、组蛋白修饰、杂合性丢失以及转录因子结合。 计算生物学家将使用这些实验数据来构建和完善模型。 经验贝叶斯模型将用于预测如何招募阻遏蛋白、组蛋白脱乙酰酶和 DMA 甲基转移酶来建立表观遗传基因沉默（项目 1）。 将开发系统发育聚类算法来概括癌症基质中与肿瘤进展相关的遗传和表观遗传途径（项目 2）。 LASSO 逻辑回归和神经网络方法将用于模拟 DNA-蛋白质协同相互作用以及由此产生的癌细胞染色质景观变化（项目 3）。 模式识别和监督学习技术将用于选择含有耐药癌细胞中易甲基化序列特征的基因（项目4）。 这些数学模型将生成用于实验测试的第一级或第二级假设。模型细化和实验验证的迭代过程将持续下去，直到导出能够准确预测癌症基因组中特定表观遗传改变的模型。 所有实验数据和建模工具将存放在一个集中数据库中，并将用于培训未来的系统癌症生物学家。 这些综合研究的进展将由顾问和行政领导进行评估，以确保拟议的 ICBP 取得成功。