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.
|Wang, Yao; Jadhav, Rohit Ramakant; Liu, Joseph et al. (2016) Roles of Distal and Genic Methylation in the Development of Prostate Tumorigenesis Revealed by Genome-wide DNA Methylation Analysis. Sci Rep 6:22051|
|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|
|Ruan, Jianhua; Jahid, Md Jamiul; Gu, Fei et al. (2016) A novel algorithm for network-based prediction of cancer recurrence. Genomics :|
|Hsiao, Tzu-Hung; Chiu, Yu-Chiao; Hsu, Pei-Yin et al. (2016) Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers. Sci Rep 6:23035|
|Ã–zeÅŸ, A R; Miller, D F; Ã–zeÅŸ, O N et al. (2016) NF-ÎºB-HOTAIR axis links DNA damage response, chemoresistance and cellular senescence in ovarian cancer. Oncogene :|
|Cui, Xiaodong; Meng, Jia; Zhang, Shaowu et al. (2016) A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data. Bioinformatics 32:i378-i385|
|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|
|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|
|Horning, Aaron M; Awe, Julius A; Wang, Chiou-Miin et al. (2015) DNA methylation screening of primary prostate tumors identifies SRD5A2 and CYP11A1 as candidate markers for assessing risk of biochemical recurrence. Prostate 75:1790-801|
|Meng, Nan; Machiraju, Raghu; Huang, Kun (2015) Identify Critical Genes in Development with Consistent H3K4me2 Patterns across Multiple Tissues. IEEE/ACM Trans Comput Biol Bioinform 12:1104-11|
Showing the most recent 10 out of 137 publications