Data from 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.[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.