Interrogating Epigenetic Changes in Cancer Genomes

在我们提议的U 54中心，我们将继续进行癌症的表观基因组分析。虽然重点是 上一个奖项是关于与正常细胞的肿瘤转化有关的表观遗传过程。 在这个竞争性的应用中，我们将向前迈进一步，研究前列腺、乳腺、 和卵巢癌细胞进展为侵袭性表型，即，抗化疗药物基于 根据我们的初步发现，我们假设雄激素受体、雌激素 受体a或TGF-β/SMAD 4信号转导是细胞对激素敏感的转变的基础， 癌症中的Zchemo不敏感表型。不同模式的信号传导介导的转录，包括 配体依赖性和非依赖性功能，将使用整合的表观基因组数据来定义。我们将 开发概率算法来预测染色体成环和染色质重塑的影响（即， 组蛋白标记和DNA甲基化）对靶基因转录的影响，包括经验贝叶斯 混合和隐马尔可夫建模（用于对信号转导中的靶基因的时空模式进行分类） 网络），转录“枢纽”的交互式建模，许可和非许可的随机建模 表观遗传标记和用于预测转录因子结合位点的模式识别算法， 易甲基化或耐甲基化序列。这些计算预测的测试和验证将是 在癌细胞系中进行。包括关键转录枢纽的功能性敲入或敲出的测定将 确定癌细胞是否获得或失去化疗敏感性，分别作为体外结果， 操纵对于转化研究，原发性肿瘤将用于与临床病理学相关性。 与表观遗传变化的相关性。通过采取综合的“组学”方法，我们希望将 表观基因组学领域至少有三个新的方向：1）长距离染色质环可能是一个 癌症转录调控的常见表观遗传机制; 2）组蛋白修饰ZDNA 远端转录结合位点的甲基化代表了以前未表征的生物标志物， 预测癌症亚型的耐药;和3）计算建模可以支持 最近的观点认为，抑制性组蛋白修饰，而不是DNA甲基化，是关键的表观遗传 基因沉默的遗传因素。重要的是，这些最先进的计算方法和 大量的组学数据将用于我们的教育，培训年轻的系统科学家， 与CCSB-ICBP网络中的其他研究人员进行合作研究。

At our proposed U54 center, we will continue to conduct epigenomic analysis in cancer. While the focus of the previous award was on epigenetic processes associated with neoplastic transformation of normal cells. In this competing application, we will move a step forward to study epigenetic changes in prostate, breast, and ovarian cancer cells progressing to an aggressive phenotype, i.e., hormone-Zchemo-resistance. Based on our preliminary findings, we hypothesize that epigenetic deregulation of androgen receptor, estrogen receptor a, or TGF-p/SMAD4 signaling underlies the transition of a hormone-Zchemo-sensitive to a hormone- Zchemo-insensitive phenotype in cancer. Different modes of signaling-mediated transcription, including ligand-dependent and -independent functions, will be defined using integrated epigenomic data. We will develop probabilistic algorithms to predict the effect of chromosome looping and chromatin remodeling (i.e., changes of histone marks and DNA methylation) on target gene transcription, including empirical Bayesian mixture and hidden Markov modeling (for classifying spatiotemporal patterns of target genes in a signaling network), interactive modeling of transcription "hubs", stochastic modeling of permissive and non-permissive epigenetic marks, and pattern recognition algorithms for predicting transcription factor binding sites and methylation-prone or -resistant sequences. Testing and validation of these computational predictions will be performed in cancer cell lines. Assays including functional knock-in or -out of key transcription hubs will determine whether cancer cells gain or lose hormone-Zchemo-sensitivity, respectively, as a result of in vitro manipulation. For translational studies, primary tumors will be used to correlate clinicopathological correlations with epigenetic changes. By taking an integrative "omics" approach, we expect to move the epigenomics field forward in at least three new directions: 1) long-range chromatin looping may be a common epigenetic mechanism of transcriptional regulation in cancer; 2) histone modificationsZDNA methylation of distant transcription binding sites represent previously uncharacterized biomarkers for predicting hormone-Zchemo-resistance in cancer subtypes; and 3) computational modeling may support the recent notion that repressive histone modifications, rather than DNA methylation, are critical epigenetic factors in the heritable silencing of genes. Importantly, these state-of-the-art computational approaches and the vast omics data will be used for our educationZoutreach efforts to train young systems scientists and for collaborative studies with other researchers in the CCSB-ICBP network.