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Published May 15, 2009 | public
Journal Article

Multiparameter computational modeling of tumor invasion


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. 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.

Additional Information

© 2009 American Association for Cancer Research. Received 10/7/08; revised 3/23/09; accepted 3/27/09; published Online First 4/14/09. The Cullen Trust of Health Care, NSF-DMS 0818104, National Cancer Institute, Department of Defense (V. Cristini);NSF Division of Mathematical Sciences and NIH-P50GM76516 for a Center of Excellence in Systems Biology at University of California, Irvine ( J.S. Lowengrub);NIGMS-G M47368 and NINDSNS046810 (E.L. Bearer);and NCI U54 Center for Cancer Nanotechnology Excellence-TR CA119367 (D.B. Agus). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. We acknowledge Robert Gatenby (Moffitt Cancer Center) for useful discussions, Xiangrong Li (UC-Irvine) for Fig. 2A, Ed Stopa and the Pathology Department (Rhode Island Hospital) for autopsied specimens, Aleksey Novikov and Bryan Kinney (E.L. Bearer's lab) for technical assistance, and Henry Hirschberg (UC-Irvine) for information about recent results (20).

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