The overall goal ofthis proposal is to build a multi-scale modeling platform for investigation ofthe breast cancer, with special emphasis on the roles ofthe tumor-initiating cells (TIC). This modeling platform will mainly consist of two closely related components: biological experiments and mathematical computational modeling. For the experiment component, we seek to use newly developed experimental and imaging methodologies to identify, localize, purify and characterize TIC. Further experiments will be designed to discover the spatial localization and movement, and specific changes in gene expression and cellular signaling of breast cancer TIC. Combined functional genomics and data mining strategies will allow us to characterize novel growth regulators. Further, our combined experimental and systems biology approach will allow us to evaluate responses to experimental therapeutics that may inhibit or kill TIC specifically in a manner not possible before. For the mathematical modeling component, we will develop bioinformatics and bio-imaging models to integrate and analyze the data generated from biological experiments, and make use ofthe information obtained from data analysis, biological knowledge to build in silico models to model TIC behavior, cancer cell apoptosis, cell migration, cycle and drug treatment response. Besides providing a basic framework for understanding the mechanism underlying breast cancer stem cell evolution, the models can also give birth to hypotheses or experimental design. More important, these models will allow one to predict the biological state under investigation and predict how the natural process will behave in various circumstances. Iterative feedback between these two components will refine our proposed platform further. The ultimate goal is an integrated modeling platform of breast cancer biology that can mimic in vivo processes faithfully enough to serve as a h5TDOthesis-generation and screening tool, and in the distant future, as a tool for evaluating clinical procedures and their expected outcomes.

Public Health Relevance

This project will be a substantial contribution to the public health by understanding the mechanism of cancer initial cells or cancer stem cells. More importantly, the completion ofthis screening project will help to answer some critical questions related to breast caner. Such understanding will in turn advance our knowledge in tumor biology and open up the possibility of novel treatments in the future.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA149196-04
Application #
8505400
Study Section
Special Emphasis Panel (ZCA1-SRLB-C (J1))
Program Officer
Gallahan, Daniel L
Project Start
2010-05-01
Project End
2015-02-28
Budget Start
2013-03-01
Budget End
2014-02-28
Support Year
4
Fiscal Year
2013
Total Cost
$2,029,886
Indirect Cost
$346,497
Name
Methodist Hospital Research Institute
Department
Type
DUNS #
185641052
City
Houston
State
TX
Country
United States
Zip Code
77030
Sinha, Vidya C; Qin, Lan; Li, Yi (2015) A p53/ARF-dependent anticancer barrier activates senescence and blocks tumorigenesis without impacting apoptosis. Mol Cancer Res 13:231-8
Wang, Zhihui; Deisboeck, Thomas S; Cristini, Vittorio (2014) Development of a sampling-based global sensitivity analysis workflow for multiscale computational cancer models. IET Syst Biol 8:191-7
Tang, Lei; van de Ven, Anne L; Guo, Dongmin et al. (2014) Computational modeling of 3D tumor growth and angiogenesis for chemotherapy evaluation. PLoS One 9:e83962
Haricharan, S; Hein, S M; Dong, J et al. (2014) Contribution of an alveolar cell of origin to the high-grade malignant phenotype of pregnancy-associated breast cancer. Oncogene 33:5729-39
Wei, Wei; Tweardy, David J; Zhang, Mei et al. (2014) STAT3 signaling is activated preferentially in tumor-initiating cells in claudin-low models of human breast cancer. Stem Cells 32:2571-82
Brocato, Terisse; Dogra, Prashant; Koay, Eugene J et al. (2014) Understanding Drug Resistance in Breast Cancer with Mathematical Oncology. Curr Breast Cancer Rep 6:110-120
Yu, Peng; Shaw, Chad A (2014) An efficient algorithm for accurate computation of the Dirichlet-multinomial log-likelihood function. Bioinformatics 30:1547-54
Dave, Bhuvanesh; Granados-Principal, Sergio; Zhu, Rui et al. (2014) Targeting RPL39 and MLF2 reduces tumor initiation and metastasis in breast cancer by inhibiting nitric oxide synthase signaling. Proc Natl Acad Sci U S A 111:8838-43
Jin, Guangxu; Wong, Stephen T C (2014) Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today 19:637-44
Yin, Zheng; Sailem, Heba; Sero, Julia et al. (2014) How cells explore shape space: a quantitative statistical perspective of cellular morphogenesis. Bioessays 36:1195-203

Showing the most recent 10 out of 44 publications