Multiparameter Computational Modeling of Tumor Invasion

¹Department of Pathology and Laboratory Medicine, and Division of Engineering, Brown University, Providence, Rhode Island; ²Department of Biology, California Institute of Technology, Pasadena, California; Departments of ³Mathematics and ⁴Biomedical Engineering, University of California, Irvine, California; ⁵School of Health Information Sciences, ⁶Division of Nanomedicine, and ⁷Department of Biomedical Engineering, University of Texas Health Science Center; Departments of ⁸Experimental Therapeutics and ⁹Systems Biology, The University of Texas M. D. Anderson Cancer Center; ¹⁰Department of Bioengineering, Rice University, Houston, Texas; ¹¹Department of Biomedical Engineering, The University of Texas, Austin, Texas; ¹²Department of Mathematics, University of Tennessee, Knoxville, Tennessee; and ¹³USC Center for Applied Molecular Medicine, University of Southern California, Los Angeles, California

^* To whom correspondence should be addressed. E-mail: vittorio.cristini{at}uth.tmc.edu.

	Abstract

Clinical outcome prognostication in oncology is a guiding principle in therapeutic choice. A wealth of qualitative empirical evidence links disease progression with tumor morphology, histopathology, invasion, and associated molecular phenomena. However, the quantitative contribution of each of the known parameters in this progression remains elusive. Mathematical modeling can provide the capability to quantify the connection between variables governing growth, prognosis, and treatment outcome. By quantifying the link between the tumor boundary morphology and the invasive phenotype, this work provides a quantitative tool for the study of tumor progression and diagnostic/prognostic applications. This establishes a framework for monitoring system perturbation towards development of therapeutic strategies and correlation to clinical outcome for prognosis. [Cancer Res 2009;69(10):OF1–9]

Key Words: tumor invasion, clinical outcome prognostication, computer simulation

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