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-B/SMAD4 signaling underlies the transition of a hormone-/chemo-sensitive to a hormone-/chemo-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-/chemo-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 modifications/DNA methylation of distant transcription binding sites represent previously uncharacterized biomarkers for predicting hormone-/chemo-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 education/outreach efforts to train young systems scientists and for collaborative studies with other researchers in the CCSB-ICBP network.
Epigenetic assays will be used to determine whether differential histone modifications and DNA methylation occur in distant transcription factor binding sites and nearby promoter regions in hormone-/chemo-insensitive cells. These analyses will be extended to a panel of cancer cell lines and primary tumors. The final objective is to identify a panel of epigenetic biomarkers for predicting responsiveness to anti-hormone treatments and chemotherapies in cancer patients.
|Cui, Xiaodong; Zhang, Lin; Meng, Jia et al. (2018) MeTDiff: A Novel Differential RNA Methylation Analysis for MeRIP-Seq Data. IEEE/ACM Trans Comput Biol Bioinform 15:526-534|
|Martin, Elizabeth C; Conger, Adrienne K; Yan, Thomas J et al. (2017) MicroRNA-335-5p and -3p synergize to inhibit estrogen receptor alpha expression and promote tamoxifen resistance. FEBS Lett 591:382-392|
|Miller, David F B; Yan, Pearlly; Fang, Fang et al. (2017) Complete Transcriptome RNA-Seq. Methods Mol Biol 1513:141-162|
|Mitsuya, Kohzoh; Parker, Ashley N; Liu, Lu et al. (2017) Alterations in the placental methylome with maternal obesity and evidence for metabolic regulation. PLoS One 12:e0186115|
|Hsu, Ya-Ting; Osmulski, Pawel; Wang, Yao et al. (2016) EpCAM-Regulated Transcription Exerts Influences on Nanomechanical Properties of Endometrial Cancer Cells That Promote Epithelial-to-Mesenchymal Transition. Cancer Res 76:6171-6182|
|Cui, Xiaodong; Wei, Zhen; Zhang, Lin et al. (2016) Guitar: An R/Bioconductor Package for Gene Annotation Guided Transcriptomic Analysis of RNA-Related Genomic Features. Biomed Res Int 2016:8367534|
|Cui, Xiaodong; Meng, Jia; Zhang, Shaowu et al. (2016) A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data. BMC Genomics 17 Suppl 7:520|
|Ye, Zhenqing; Chen, Zhong; Sunkel, Benjamin et al. (2016) Genome-wide analysis reveals positional-nucleosome-oriented binding pattern of pioneer factor FOXA1. Nucleic Acids Res 44:7540-54|
|Öze?, A R; Miller, D F; Öze?, O N et al. (2016) NF-?B-HOTAIR axis links DNA damage response, chemoresistance and cellular senescence in ovarian cancer. Oncogene 35:5350-5361|
|Sunkel, Benjamin; Wu, Dayong; Chen, Zhong et al. (2016) Integrative analysis identifies targetable CREB1/FoxA1 transcriptional co-regulation as a predictor of prostate cancer recurrence. Nucleic Acids Res 44:4105-22|
Showing the most recent 10 out of 154 publications