Computational Modelling of Metastasis Development in Renal Cell CarcinomaReport as inadecuate

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* Corresponding author 1 MONC - Modélisation Mathématique pour l-Oncologie IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest, Institut Bergonié - CRLCC Bordeaux 2 LAMC - Laboratoire Angiogenèse et Micro-environnement des Cancers 3 UB - Université de Bordeaux 4 RMSB - Résonance magnétique des systèmes biologiques

Abstract : To improve our understanding of the biology of the metastatic colonization process, weconducted a modelling study based on multi-modal data from an orthotopic murine experimentalsystem of metastatic renal cell carcinoma. The standard theory of metastatic colonization usuallyassumes that secondary tumours, once established at a distant site, grow independently from eachother and from the primary tumour. Using a mathematical model describing the metastaticpopulation dynamics under this assumption, we challenged the theory against our data thatincluded: 1 dynamics of primary tumour cells in the kidney and metastatic cells in the lungs,retrieved by green fluorescent protein tracking, and 2 magnetic resonance images MRI informingon the number and size of macroscopic lesions. While the model could fit the primary tumour andtotal metastatic burden, the predicted size distribution was not in agreement with the MRIobservations. Moreover, the model was incompatible with the growth rates of individual metastatictumours.To explain the observed metastatic patterns, we hypothesised that metastatic foci derivedfrom one or a few cells could aggregate, resulting in a similar total mass but a smaller number ofmetastases. This was indeed observed in our data and led us to investigate the effect of spatialinteractions on the dynamics of the global metastatic burden. We derived a novel mathematicalmodel for spatial tumour growth, where the intra-tumour increase in pressure is responsible for theslowdown of the growth rate. The model could fit the growth of lung metastasis visualized bymagnetic resonance imaging. As a non-trivial outcome from this analysis, the model predicted thatthe net growth of two neighbouring tumour lesions that enter in contact is considerably impaired of31% ± 1.5%, mean ± standard deviation, as compared to the growth of two independent tumours.Together, our results have implications for theories of metastatic development and suggest thatglobal dynamics of metastasis development is dependent on spatial interactions between metastaticlesions.

Keywords : metastasis cancer modelling Renal cell carcinoma

Author: Etienne Baratchart - Sébastien Benzekry - Andreas Bikfalvi - Thierry Colin - Lindsay S. Cooley - Raphäel Pineau - Emeline J Rib



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