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Cancer Research 69, 7111, September 1, 2009. Published Online First August 25, 2009;
doi: 10.1158/0008-5472.CAN-08-3957
© 2009 American Association for Cancer Research

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Systems Biology and Emerging Technologies

The Role of Transforming Growth Factor-β–Mediated Tumor-Stroma Interactions in Prostate Cancer Progression: An Integrative Approach

David Basanta1, Douglas W. Strand3, Ralf B. Lukner6, Omar E. Franco3, David E. Cliffel5, Gustavo E. Ayala6, Simon W. Hayward2,3,4 and Alexander R.A. Anderson1

1 Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Departments of 2 Cancer Biology and 3 Urologic Surgery and 4 The Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center; 5 Department of Chemistry, Vanderbilt University, Nashville, Tennessee; and 6 Baylor College of Medicine, Houston, Texas

Requests for reprints: David Basanta, Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL 33612. Phone: 813-745-6433; Fax: 813-745-6433; E-mail: david.basanta{at}moffitt.org.

Key Words: mathematical modeling • reactive stroma • TGF-β

We have implemented a hybrid cellular automata model based on the structure of human prostate that recapitulates key interactions in nascent tumor foci between tumor cells and adjacent stroma. Model simulations show how stochastic interactions between tumor cells and stroma may lead to a structural suppression of tumor growth, modest proliferation, or unopposed tumor growth. The model incorporates key aspects of prostate tumor progression, including transforming growth factor-β (TGF-β), matrix-degrading enzyme activity, and stromal activation. It also examines the importance of TGF-β during tumor progression and the role of stromal cell density in regulating tumor growth. The validity of one of the key predictions of the model about the effect of epithelial TGF-β production on glandular stability was tested in vivo. These experimental results confirmed the ability of the model to generate testable biological predictions in addition to providing new avenues of experimental interest. This work underscores the need for more pathologically representative models to cooperatively drive computational and biological modeling, which together could eventually lead to more accurate diagnoses and treatments of prostate cancer. [Cancer Res 2009;69(17):7111–20]







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Copyright © 2009 by the American Association for Cancer Research.