Despite the great benefit of endocrine therapy for breast cancer patients, its application is greatly limited by both de novo and acquired resistance. Only 50% of all estrogen receptor-positive (ER+) tumors are responsive at first presentation to antiestrogens such as tamoxifen, and many initially responsive tumors eventually become resistant to endocrine treatment, leading to tumor recurrence and death. Thus, it is imperative to better understand the mechanisms responsible for endocrine resistance. Evidence has begun to accumulate in our studies and others that ER-signaling can contribute, at least in part, to endocrine resistance. In this project we hypothesize that new insights into ER-signaling can be discovered to circumvent endocrine-resistant tumor growth. We will develop novel computational methods to uncover ER-signaling networks by integrating protein-protein interaction data and breast cancer gene expression data. We will use the identified ER-signaling networks to define novel predictors of endocrine resistance. We also hypothesize that the ER-signaling networks will have clinical/functional relevance and will identify putative new targets for drug development.
Specific aims of this application include: (1) to develop a novel computational approach, integrative signaling network analysis (iSNA), for signaling network identification;(2) to optimize and apply the iSNA approach to identify ER-signaling networks from tumor samples, and then to construct novel predictors of endocrine resistance;(3) to validate the identified ER-signaling networks by establishing their functional relevance to endocrine resistance using biological experiments. By achieving these aims, we will discover new knowledge of ER-signaling, identify novel mechanisms associated with endocrine resistance, and ultimately use this information to identify new therapeutic targets for drug discovery. New therapies targeting to overcome endocrine resistance should have a major impact on breast cancer mortality and improve quality of life for breast cancer survivors. Notably, this application represents the continuing effort of a long and productive collaboration between computational scientists at Virginia Tech, and cancer biologists &medical oncologists at Georgetown University Medical Center.
Resistance to endocrine therapy is a major impediment in breast cancer therapeutics. In this project, we will develop novel methods to uncover ER-signaling networks so as to overcome endocrine resistance. We will discover new knowledge of ER-signaling and ultimately use this information to identify new therapeutic targets for drug discovery.
|Chen, Xi; Jung, Jin-Gyoung; Shajahan-Haq, Ayesha N et al. (2016) ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles. Nucleic Acids Res 44:e65|
|Wang, Niya; Hoffman, Eric P; Chen, Lulu et al. (2016) Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues. Sci Rep 6:18909|
|Shi, Xu; Barnes, Robert O; Chen, Li et al. (2015) BMRF-Net: a software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method. Bioinformatics 31:2412-4|
|Fu, Yi; Yu, Guoqiang; Levine, Douglas A et al. (2015) BACOM2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumor. Sci Rep 5:13955|
|Shi, Xu; Wang, Xiao; Shajahan, Ayesha et al. (2015) BMRF-MI: integrative identification of protein interaction network by modeling the gene dependency. BMC Genomics 16 Suppl 7:S10|
|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|
|Shajahan-Haq, Ayesha N; Cook, Katherine L; Schwartz-Roberts, Jessica L et al. (2014) MYC regulates the unfolded protein response and glucose and glutamine uptake in endocrine resistant breast cancer. Mol Cancer 13:239|
|Gu, Jinghua; Wang, Xiao; Halakivi-Clarke, Leena et al. (2014) BADGE: a novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data. BMC Bioinformatics 15 Suppl 9:S6|
|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|
|Shi, Xu; Gu, Jinghua; Chen, Xi et al. (2013) mAPC-GibbsOS: an integrated approach for robust identification of gene regulatory networks. BMC Syst Biol 7 Suppl 5:S4|
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