In 2008, over 41,000 American women will die of breast cancer. In the same period, there will be almost 173,000 newly diagnosed cases of invasive breast cancer, approximately 70% of which will be estrogen receptor-1 positive (ER+). However, it is evident that we still do not fully understand either the nature of ER-driven molecular signaling or how this differs in endocrine sensitive and resistant breast tumors. In this project, we hypothesize that new insights into ER1-signaling can be discovered in the context of hormone responsiveness. We will develop, optimize, and apply innovative new computational methods to breast cancer gene expression microarray data sets. We will discover new knowledge of ER signaling and construct, test, and validate computational models of antiestrogen (AE) resistance. We also hypothesize that model predictions will have clinical/functional relevance and will identify new targets for drug development.
Specific aims of this application include: (1) to develop new computational methods, integrative network analyses (INA), and use these to build and test computational models of ER1 signaling in the context of hormone responsiveness;(2) to develop a network motif-based prediction (NMP) scheme to integrate network information and gene expression profiles to identify signaling components of ER-mediated signaling associated with AE resistance;(3) to assess and validate the functional relevance of key genes in mechanistic studies in breast cancer models, and ultimately use this information to identify new therapeutic targets for drug discovery. The development of new therapies for endocrine resistant disease should have a major impact on breast cancer mortality and improve quality of life for breast cancer survivors. The proposed project will be carried out by an interdisciplinary team of computer scientists, molecular biologists, and medical oncologists at Virginia Tech and Georgetown University Medical Center, and represents a continuation of the long and productive collaboration.
While antiestrogen (AE) therapies induce a high proportion of objective responses and will have a significant survival benefit for some women, many of these cancers will recur due to AE resistance. In this project, we will develop, optimize, and apply innovative new computational methods to breast cancer gene expression microarray data sets for AE resistance prediction. We will discover new knowledge of ER signaling and construct, test, and validate computational models of AE resistance, and ultimately use this information to identify new therapeutic targets for drug discovery.
|Chen, Xi; Shi, Xu; Shajahan, Ayesha N et al. (2014) BSSV: Bayesian based somatic structural variation identification with whole genome DNA-seq data. Conf Proc IEEE Eng Med Biol Soc 2014:3937-40|
|Chen, Li; Xuan, Jianhua; Riggins, Rebecca B et al. (2013) Identifying protein interaction subnetworks by a bagging Markov random field-based method. Nucleic Acids Res 41:e42|
|Zhang, Yuji; Xuan, Jianhua; Clarke, Robert et al. (2013) Module-based breast cancer classification. Int J Data Min Bioinform 7:284-302|
|Chen, Li; Xuan, Jianhua; Gu, Jinghua et al. (2012) Integrative network analysis to identify aberrant pathway networks in ovarian cancer. Pac Symp Biocomput :31-42|
|Chen, Xi; Xuan, Jianhua; Wang, Chen et al. (2012) Reconstruction of Transcriptional Regulatory Networks by Stability-based Network Component Analysis. IEEE/ACM Trans Comput Biol Bioinform :|
|Wang, Chen; Ha, Sook; Xuan, Jianhua et al. (2012) Computational Analysis of Muscular Dystrophy Sub-types Using A Novel Integrative Scheme. Neurocomputing 92:9-17|
|Wang, Chen; Xuan, Jianhua; Shih, Ie-Ming et al. (2012) Regulatory component analysis: a semi-blind extraction approach to infer gene regulatory networks with imperfect biological knowledge. Signal Processing 92:1902-1915|
|Gu, Jinghua; Xuan, Jianhua; Riggins, Rebecca B et al. (2012) Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic. Bioinformatics 28:1990-7|
|Clarke, Robert; Shajahan, Ayesha N; Wang, Yue et al. (2011) Endoplasmic reticulum stress, the unfolded protein response, and gene network modeling in antiestrogen resistant breast cancer. Horm Mol Biol Clin Investig 5:35-44|
|Chen, Li; Xuan, Jianhua; Riggins, Rebecca B et al. (2011) Identifying cancer biomarkers by network-constrained support vector machines. BMC Syst Biol 5:161|
Showing the most recent 10 out of 18 publications