Epigenetic Drivers of Cancer (PQ 10)

癌症的表观遗传驱动因素 (PQ 10)

• 批准号: 8384811
• 负责人: STEPHEN B. BAYLIN
• 金额: $ 61.45万
• 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/8384811
• 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; 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. PUBLIC HEALTH RELEVANCE: Cancer arises from loss of the growth control of cells. Although most attention has focused on genetic mutations as the origin of these defects, in recent years, it has become increasingly appreciated that changes in the epigenetic control of gene activity also contribute to this loss of growth control. This application addresses an unmet need to develop methods of finding out which epigenetic changes contribute directly to cancer formation.

描述（由申请人提供）：在本申请中，我们提出解决PQ10：“随着我们改进识别肿瘤发展过程中发生的表观遗传变化的方法，我们能否开发区分“驾驶员”和“乘客”表观遗传事件的方法？”表观遗传机制对基因表达潜力有很强的影响，并在大多数人类癌症中显示出广泛的变化。与大多数突变事件相反，表观遗传事件通常表现出高度的相关性，大量已定义的改变似乎是对癌症过程没有功能贡献的乘客事件。我们建议开发一个集成的计算和实验验证管道来识别癌症中的表观遗传驱动事件。在目标1中，我们将开发一个概率框架来预测和优先考虑候选表观遗传驱动位点。这种方法的独特之处在于，它充分整合了丰富的可用数据，使用来自主要基因组数据、实验数据和支持策划信息的互补数据类型，产生复合表观遗传驱动评分（EDS），反映每个基因是表观遗传驱动的后验概率。Aim 2将提供表观遗传成瘾的实验数据，使用DNA甲基转移酶耗尽的细胞系，因此选择仅保留最基本的沉默事件，以及从胚胎和成体干细胞和祖细胞获得的数据。这些实验数据集将用于补充我们在TCGA背景下生成的初级表观基因组数据，使用Aim 1中开发的方法为每种肿瘤类型的每个位点提供表观遗传驱动评分。在Aim 3a中，我们将通过将候选基因的表达重新引入含有相关沉默事件的适当的人类癌细胞系，在体外对结肠癌、乳腺癌和肺癌的顶级候选表观遗传驱动因素进行功能测试。这些实验将通过shRNA方法在细胞系中进行补充，以调节候选表观遗传驱动因子的功能表达。体外增殖和凋亡试验将用于评估表型效应。在Aim 3b中，我们将在异种移植小鼠模型中使用Aim 3a中创建的稳定细胞系，评估候选表观遗传驱动因子在体内的功能贡献。这些验证实验的结果将用于迭代训练EDS模型。考虑到学习算法对训练数据的敏感性，我们预计随着训练样本数量的增加，性能会有所提高。通过在概率框架和计算导向实验中执行数据驱动建模，可用数据将得到最大程度的利用，同时允许添加新的数据类型和专家管理。表观遗传事件在癌症中的作用越来越受到重视，但区分司机和乘客的挑战尚未得到充分解决。这里提出的系统验证管道将解决大量未满足的需求，并深入了解表观遗传和遗传事件在驱动肿瘤发生的关键信号通路中的互补作用。