Epigenetic Drivers of Cancer (PQ 10)

描述（由申请人提供）：在本申请中，我们提出解决PQ10：“随着我们改进鉴定肿瘤发展期间发生的表观遗传变化的方法，我们能否开发区分“驱动”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产生强烈影响，并已显示在大多数人类癌症中发生广泛变化。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，其中大量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，以确定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，用于预测和优先考虑候选表观遗传驱动基因座。这种方法的独特之处在于它完全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，从而产生复合表观遗传驱动因子评分（EDS），反映每个基因是表观遗传驱动因子的后验概率。目的2将提供实验数据表观遗传成瘾，使用细胞系耗尽的DNA甲基转移酶，从而选择保留最重要的沉默事件，除了获得的数据与胚胎和成人干细胞和祖细胞。这些实验数据集将用于补充我们在TCGA背景下生成的主要表观基因组数据，以使用目标1中开发的方法提供每种肿瘤类型中每个基因座的表观遗传驱动因子评分。在目标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人类癌细胞系中，在体外功能上测试结肠癌、乳腺癌和肺癌的顶级候选表观遗传驱动因素。这些实验将通过细胞系中的shRNA方法来补充，以调节候选表观遗传驱动因子的功能表达。将使用体外增殖和凋亡试验评估表型效应。在目标3b中，我们将使用目标3a中创建的稳定细胞系在异种移植小鼠模型中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。鉴于学习算法对训练数据的敏感性，我们预计随着训练示例数量的增加，性能会有所提高。通过在概率框架中执行数据驱动的建模和以计算为导向的实验，将最大程度地利用可用数据，同时允许添加新的数据类型和专家策展。表观遗传事件在癌症中的作用越来越受到重视，但区分驾驶员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一个巨大的未满足的需求，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。

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.