Network-based Prediction of Antiestrogen Resistance in Breast Cancer (R21CA139246)
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 ER-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 ER 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.
©2004, Computational Bioinformatics and Bioimaging Laboratory
(CBIL), Advanced Research Institute, Virginia Tech.
Updated: 03/03/2009. Suggestions/Comments