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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21CA139246-02
Application #
7779475
Study Section
Drug Discovery and Molecular Pharmacology Study Section (DMP)
Program Officer
Forry, Suzanne L
Project Start
2009-03-03
Project End
2012-02-28
Budget Start
2010-03-01
Budget End
2012-02-28
Support Year
2
Fiscal Year
2010
Total Cost
$166,162
Indirect Cost
Name
Virginia Polytechnic Institute and State University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
003137015
City
Blacksburg
State
VA
Country
United States
Zip Code
24061
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