Interrogating Epigenetic Changes in Cancer Genomes

描述(由申请人提供)：俄亥俄州立大学的研究人员提交了一份中心拨款申请，以回应RFA宣布的综合癌症生物学计划(ICBP)。此外，我们在邻近的印第安纳大学的同事将加入这一努力。这项拟议的研究将解决一个独特的问题，即表观遗传改变，目前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的可遗传变化。将实验生物学家和计算生物学家聚集在一起，我们ICBP的总体目标是1)增加我们对肿瘤复杂的表观遗传相互作用的理解，2)利用高端信息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的微阵列平台来询问癌细胞系和肿瘤上皮及其周围基质中的DNA甲基化、组蛋白修饰、杂合性丢失和转录因子结合。计算生物学家将使用这些实验数据来建立模型和改进。经验贝叶斯模型将被用来预测阻遏物、组蛋白脱乙酰酶和DMA甲基转移酶如何被招募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，以概括癌症间质中与肿瘤进展相关的遗传和表观遗传途径(项目2)。套索Logistic回归和神经网络方法将被用来模拟协同的DNA-蛋白质相互作用以及由此导致的癌细胞染色质景观的变化(项目3)。模式识别和监督学习技术将被用于选择包含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或第二级假设。模型改进和实验验证的迭代过程将继续下去，直到得出准确预测询问癌症基因组中特定表观遗传学变化的模型为止。所有实验数据和建模工具将存放在一个中央数据库中，并将用于培训未来的系统癌症生物学家。顾问和行政领导人将评估这些综合研究的进展情况，以确保拟议的ICBP取得成功。

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.