Epigenetic Drivers of Cancer (PQ 10)

癌症的表观遗传驱动因素 (PQ 10)

• 批准号: 8538911
• 负责人: STEPHEN B. BAYLIN
• 金额: $ 55.32万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8538911
• 关键词: Address; Adopted; Algorithms; Apoptosis; Attention; Behavior; Bioinformatics; Biological Assay; Cancer cell line; Candidate Disease Gene; Cell Line; Cell model; Cells; Chromatin; Colon Carcinoma; Complement; CpG Islands; DNA; DNA Methyltransferase; DNA Modification Methylases; Data; Data Set; Defect; Development; Dreams; Embryo; Epigenetic Process; Event; Frequencies; Gene Expression; Gene Mutation; Gene Silencing; Gene Targeting; Genes; Genetic; Genome; Genomics; Growth; Human; Hypermethylation; In Vitro; Learning; Malignant Neoplasms; Malignant neoplasm of lung; Methodology; Methods; Modeling; Mutation; Performance; Phenotype; Probability; Process; Production; Recurrence; Role; Scheme; Signal Pathway; Specimen; Technology; Testing; The Cancer Genome Atlas; Training; Validation; Xenograft procedure; addiction; adult stem cell; cancer genomics; cancer type; candidate identification; epigenomics; experience; improved; in vivo; inhibitor/antagonist; insight; malignant breast neoplasm; mouse model; next generation sequencing; progenitor; promoter; research study; small hairpin RNA; stable cell line; stem cell division; tumor; tumorigenesis

DESCRIPTION (provided by applicant): In this application, we propose to address PQ10: "As we improve methods to identify epigenetic changes that occur during tumor development, can we develop approaches to discriminate between "driver" and "passenger" epigenetic events?". Epigenetic mechanisms exert a strong effect on gene expression potential, and have been shown to undergo widespread change in most human cancers. In contrast to most mutational events, epigenetic events often display a high degree of correlation, with a large number of defined alterations that appear to be passenger events without functional contribution to the cancer process. We propose to develop an integrated computational and experimental validation pipeline to identify epigenetic driver events in cancer. In Aim 1 we will develop a probabilistic framework for predicting and prioritizing candidate epigenetic driver loci. This approach is unique in that it fully integrates the wealth of available data, using complementary data types derived from primary genomic data, experimental data, and supporting curated information, resulting in a composite Epigenetic Driver Score (EDS), reflecting the posterior probability that each gene is an epigenetic driver. Aim 2 will provide experimental data on epigenetic addiction, using cell lines depleted of DNA methyltransferases, and thus selected to retain only the most essential silencing events, in addition to data obtained with embryonic and adult stem-cell and progenitors. These experimental data sets will be used to complement primary epigenomic data we have generated in the context of TCGA, to provide Epigenetic Driver Scores for each locus in each tumor type, using the methodology developed in Aim 1. In Aim 3a we will functionally test the top-ranked candidate epigenetic drivers of colon, breast, and lung cancer in vitro, by reintroducing expression of candidate genes into appropriate human cancer cells lines containing the relevant silencing events. These experiments will be complemented by shRNA approaches in cell lines to modulate the functional expression of the candidate epigenetic drivers. In vitro proliferation and apoptosis assays will be used to assess phenotypic effects. In Aim 3b we will assess the functional contributions of the candidate epigenetic drivers in vivo, using the stable cell lines created in Aim 3a in xenograft mouse models. The results of these validation experiments will be used to iteratively train the EDS model. Given the sensitivity of learning algorithms to their training data, we anticipate an improvement in performance as the number of training examples increases. By performing data-driven modeling in a probabilistic framework and computationally- directed experimentation the available data will be utilized to the fullest extent, while allowing for the addition of new data types and expert curation. The role of epigenetic events in cancer is increasingly appreciated, but the challenge of distinguishing drivers from passengers has not yet been adequately addressed. The systematic validation pipeline proposed here will address a large unmet need, and yield insights into the complementary roles of epigenetic and genetic events in key signaling pathways that drive tumorigenesis.

描述（由申请人提供）：在本申请中，我们建议解决PQ10：“随着我们改进识别肿瘤发育过程中发生的表观遗传变化的方法，我们是否可以开发方法以区分“驱动程序”和“乘客”和“乘客”表观遗传事件？”。表观遗传机制对基因表达潜力产生了强大的影响，并且已证明在大多数人类癌症中会发生广泛的变化。与大多数突变事件相反，表观遗传事件通常显示出高度的相关性，大量定义的变化似乎是乘客事件，而没有对癌症过程的功能贡献。我们建议开发一个集成的计算和实验验证管道，以识别癌症中的表观遗传驱动器事件。在AIM 1中，我们将开发一个概率框架，用于预测和优先考虑候选表观遗传驱动器基因座。这种方法是独一无二的，因为它使用源自主要基因组数据，实验数据和支持的策划信息的互补数据类型充分整合了可用数据，从而导致了复合表观遗传驱动程序评分（EDS），这反映了每个基因都是表观遗传驱动程序的后验概率。 AIM 2将使用DNA甲基转移酶耗尽的细胞系提供有关表观遗传成瘾的实验数据，因此除了用胚胎和成人的干细胞和祖细胞获得的数据外，还选择仅保留最重要的沉默事件。 These experimental data sets will be used to complement primary epigenomic data we have generated in the context of TCGA, to provide Epigenetic Driver Scores for each locus in each tumor type, using the methodology developed in Aim 1. In Aim 3a we will functionally test the top-ranked candidate epigenetic drivers of colon, breast, and lung cancer in vitro, by reintroducing expression of candidate genes into appropriate human cancer cells lines containing the relevant沉默事件。这些实验将通过细胞系中的shRNA方法补充，以调节候选表观遗传驱动因素的功能表达。体外增殖和凋亡测定法将用于评估表型作用。在AIM 3B中，我们将使用异种移植小鼠模型中的AIM 3A中创建的稳定细胞系评估体内候选表观遗传驱动因素的功能贡献。这些验证实验的结果将用于迭代训练EDS模型。鉴于学习算法对他们的培训数据的敏感性，我们预计随着训练示例的数量的增加，表现会有所提高。通过在概率框架和计算指导的实验中执行数据驱动的建模，将在最大程度上使用可用的数据，同时允许添加新的数据类型和专家策划。表观遗传事件在癌症中的作用得到了越来越多的赞赏，但是将驾驶员与乘客区分开的挑战尚未得到充分解决。此处提出的系统验证管道将满足巨大的未满足需求，并在驱动肿瘤发生的关键信号通路中对表观遗传和遗传事件的互补作用产生见解。