Clinical application of molecular therapeutics targeting signaling molecules holds tremendous promise for the management of patients with cancer. Within the cell, signals are routed through specific signal transduction pathways, many of which can interact to form networks. Targeting a node or molecule within this network can be challenging since signals can bypass the inhibition and continue to propagate. One of the challenges in the use of targeted therapeutics to inhibit cancer cell proliferation is to understand how signal flows through the network and what combination of molecules have to be blocked in order to effectively inhibit proliferation. My long-term goal is to understand the systems function of signaling networks in cancer and utilize this knowledge to develop and implement novel approaches to patient management. The objectives of this application are to determine the regulators of the EGFR signaling network in breast cancer to identify optimal combinations of targets for therapy, and to determine how differences in activity of regulatory molecules and homeostatic loops alters response to targeted therapies. We will integrate existing proteomic and computational modeling technology to address these important issues in the use of combination targeted therapy for cancer.
Aim 1. Determine rational approaches to targeting the EGFR signaling network in lapatinib resistant breast cancer cells.
Aim 2. Determine approaches to target regulatory molecules defining EGFR/AKT/MAPK network function in human breast cancer cells.
Aim 3. Determine the response to rational combinatorial targeted therapy of the EGFR/MAPK/AKT network on cell proliferation and tumor growth.
These aims will be addressed using a systems biology approach using experimental data from cell lines, functional proteomics and xenograft tumors in mice, integrated with computational modeling of the signaling network.

Public Health Relevance

The treatment and management of patients with cancer is entering a new and very exciting era. New drugs that target specific proteins are showing tremendous promise if they are given to the right patients. In this proposal we will determine what combinations of these new drugs work best for different tumors based on the aberrations within the cancer cells. With the idea being that different tumors will need a different combination of drugs and in the future we can give specific combinations of drugs to different individuals.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA125109-02
Application #
7614499
Study Section
Developmental Therapeutics Study Section (DT)
Program Officer
Forry, Suzanne L
Project Start
2008-04-16
Project End
2011-03-31
Budget Start
2009-04-01
Budget End
2010-03-31
Support Year
2
Fiscal Year
2009
Total Cost
$352,351
Indirect Cost
Name
University of Texas MD Anderson Cancer Center
Department
Biology
Type
Other Domestic Higher Education
DUNS #
800772139
City
Houston
State
TX
Country
United States
Zip Code
77030
Iadevaia, Sergio; Nakhleh, Luay K; Azencott, Robert et al. (2014) Mapping network motif tunability and robustness in the design of synthetic signaling circuits. PLoS One 9:e91743
Ram, Prahlad T; Mendelsohn, John; Mills, Gordon B (2012) Bioinformatics and systems biology. Mol Oncol 6:147-54
Komurov, Kakajan; Tseng, Jen-Te; Muller, Melissa et al. (2012) The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant ErbB2-positive breast cancer cells. Mol Syst Biol 8:596
Lu, Y; Muller, M; Smith, D et al. (2011) Kinome siRNA-phosphoproteomic screen identifies networks regulating AKT signaling. Oncogene 30:4567-77
Taube, Joseph H; Herschkowitz, Jason I; Komurov, Kakajan et al. (2010) Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes. Proc Natl Acad Sci U S A 107:15449-54
Iadevaia, Sergio; Lu, Yiling; Morales, Fabiana C et al. (2010) Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. Cancer Res 70:6704-14
Komurov, Kakajan; White, Michael A; Ram, Prahlad T (2010) Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data. PLoS Comput Biol 6:
Komurov, Kakajan; Ram, Prahlad T (2010) Patterns of human gene expression variance show strong associations with signaling network hierarchy. BMC Syst Biol 4:154