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Multiparameter Computational Modeling of Tumor Invasion

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

Requests for reprints: Vittorio Cristini, University of Texas HSC-SHIS, 7000 Fannin no. 600, Houston, TX 77030. Phone: 713-500-3965; Fax: 713-500-3929; E-mail: vittorio.cristini{at}uth.tmc.edu.

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

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):4493–501]

Major Findings: We apply a biologically founded, multiscale, mathematical model to identify and quantify tumor biologic and molecular properties relating to clinical and morphological phenotype and to demonstrate that tumor growth and invasion are predictable processes governed by biophysical laws, and regulated by heterogeneity in phenotypic, genotypic, and microenvironmental parameters. This heterogeneity drives migration and proliferation of more aggressive clones up cell substrate gradients within and beyond the central tumor mass, while often also inducing loss of cell adhesion. The model predicts that this process triggers a gross morphologic instability that leads to tumor invasion via individual cells, cell chains, strands, or detached clusters infiltrating into adjacent tissue producing the typical morphologic patterns seen, e.g., in the histopathology of glioblastoma multiforme. The model further predicts that these different morphologies of infiltration correspond to different stages of tumor progression regulated by heterogeneity.