• Biologie

  • Progression et métastases

Mathematical modeling of tumor growth and metastatic spreading: validation in tumor-bearing mice

A partir de données provenant d'expériences menées à l'aide de xénogreffes de tumeurs du sein, cette étude évalue les performances d'une modélisation mathématique du processus métastatique

Defining tumor stage at diagnosis is a pivotal point for clinical decisions regarding patient treatment strategies. In this respect, early detection of occult metastasis invisible to current imaging methods would have a major impact on best care and long-term survival. Mathematical models that describe metastatic spreading might estimate the risk of metastasis when no clinical evidence is available. In this study, we adapted a top-down model to make such estimates. The model was constituted by a transport equation describing metastatic growth and endowed with a boundary condition for metastatic emission. Model predictions were compared to experimental results from orthotopic breast tumor xenograft experiments conducted in Nod/Scid gamma mice. Primary tumor growth, metastatic spread and growth were monitored by 3D bioluminescence tomography. A tailored computational approach allowed the use of Monolix software for mixed-effects modeling with a partial differential equation model. Primary tumor growth was described best by Bertalanffy, West and Gompertz models which involve an initial exponential growth phase. All other tested models were rejected. The best metastatic model involved two parameters describing metastatic spreading and growth, respectively. Visual Predictive Check, analysis of residuals and a bootstrap study validated the model. Coefficients of determination were R²=0.94 for primary tumor growth and R²=0.57 for metastatic growth. The data-based model development revealed several biologically significant findings. First, information on both growth and spreading can be obtained from measures of total metastatic burden. Second, the postulated link between primary tumor size and emission rate is validated. Finally, fast growing peritoneal metastases can only be described by such a complex partial differential equation model and not by ordinary differential equation models. This work advances efforts to predict metastatic spreading during the earliest stages of cancer.

Cancer Research

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