Interrogating Epigenetic Changes in Cancer Genomes

在我们提议的U54中心，我们将继续进行癌症的表观基因组分析。虽然重点是 之前的奖项是关于与正常细胞的肿瘤转化相关的表观遗传过程。 在这个相互竞争的应用中，我们将向前迈进一步，研究前列腺、乳房、 卵巢癌细胞向侵袭性表型发展，即荷尔蒙耐药。基座 根据我们的初步发现，我们假设雄激素受体、雌激素的表观遗传去调控 受体a，或转化生长因子-β/Smad4信号，是激素敏感的激素向激素敏感的转变的基础。 癌症中对锌化疗不敏感的表型。不同的信号介导转录模式，包括 配基依赖和独立的功能，将使用整合的表观基因组数据来定义。我们会 开发概率算法来预测染色体环路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 混合和隐马尔可夫模型(用于对信号中的目标基因的时空模式进行分类 网络)、转录“中枢”的交互建模、许可和非许可的随机建模 表观遗传标记和模式识别算法预测转录因子结合位点和 易甲基化或耐甲基化的序列。对这些计算预测的测试和验证将是 在癌细胞系中进行。包括关键转录中心的功能性敲入或敲除在内的分析将 确定癌细胞在体外分别获得或失去对激素的敏感性 操纵。在转化性研究中，将使用原发肿瘤与临床病理相关联。 与表观遗传变化的相关性。通过采取综合的“经济学”方法，我们预计将推动 表观基因组学领域至少朝着三个新方向前进：1)远程染色质环化可能是一种 癌症转录调控的共同表观遗传学机制；2)组蛋白修饰ZDNA 远距离转录结合位点的甲基化是以前未表征的生物标记物 预测癌症亚型的激素-锌耐药；以及3)计算模型可能支持 最近的观点认为，抑制性组蛋白修饰，而不是DNA甲基化，是关键的表观遗传学 基因可遗传沉默的因素。重要的是，这些最先进的计算方法和 庞大的组学数据将用于我们的教育Z扩展努力，以培训年轻的系统科学家和 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.