Novel Targets in Endocrine Responsiveness. While Project 1 takes a focused analysis of specific signaling around cellular stress and cell fate decisions. Project 2 will take a functional genomics approach and study cell cycle progression in endocrine sensifive and resistant cells. Drs. Weiner and Golemis will lead the team in Project 2 and apply a powerful synthetic lethal screen approach that we have previously used to study EGFR-mediated signaling. Using a focused siRNA library targeting known ER-driven genes implicated in endocrine responsiveness, we will determine which genes, when their expression is inhibited, affect the ability of E2, TAM, and ICI to regulate cell cycle progression. Importanfiy. the siRNA library will be applied to the same cell models used in Proiect 1. The team will validate candidate genes that primarily affect cell proliferation {i.e., cell cycle), whereas candidate genes associated with specific cellular stress pathways and cell survival {e.g., apoptosis, autophagy, necrosis) will be studied in Project 1.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center--Cooperative Agreements (U54)
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Special Emphasis Panel (ZCA1-SRLB-C)
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Georgetown University
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Little, J L; Serzhanova, V; Izumchenko, E et al. (2014) A requirement for Nedd9 in luminal progenitor cells prior to mammary tumorigenesis in MMTV-HER2/ErbB2 mice. Oncogene 33:411-20
Chen, Chun; Baumann, William T; Xing, Jianhua et al. (2014) Mathematical models of the transitions between endocrine therapy responsive and resistant states in breast cancer. J R Soc Interface 11:20140206
Hatzis, Christos; Bedard, Philippe L; Birkbak, Nicolai J et al. (2014) Enhancing reproducibility in cancer drug screening: how do we move forward? Cancer Res 74:4016-23
Tian, Ye; Zhang, Bai; Hoffman, Eric P et al. (2014) Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks. BMC Syst Biol 8:87
Chen, Li; Choyke, Peter L; Wang, Niya et al. (2014) Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics. PLoS One 9:e112143
Cook, Katherine L; Wärri, Anni; Soto-Pantoja, David R et al. (2014) Hydroxychloroquine inhibits autophagy to potentiate antiestrogen responsiveness in ER+ breast cancer. Clin Cancer Res 20:3222-32
Xiao, Junfeng; Zhao, Yi; Varghese, Rency S et al. (2014) Evaluation of metabolite biomarkers for hepatocellular carcinoma through stratified analysis by gender, race, and alcoholic cirrhosis. Cancer Epidemiol Biomarkers Prev 23:64-72
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
Liu, Hanqing; Beck, Tim N; Golemis, Erica A et al. (2014) Integrating in silico resources to map a signaling network. Methods Mol Biol 1101:197-245
Heckler, Mary M; Thakor, Hemang; Schafer, Cara C et al. (2014) ERK/MAPK regulates ERR? expression, transcriptional activity and receptor-mediated tamoxifen resistance in ER+ breast cancer. FEBS J 281:2431-42

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