On the importance of the submicrovascular network in a computational model of tumour growth.

A computational model is potentially a powerful tool to apprehend complex phenomena like solid tumour growth and to predict the outcome of therapies. To that end, the confrontation of the model with experiments is essential to validate this tool. In this study, we develop a computational model specifically dedicated to the interpretation of tumour growth as observed in a mouse model with a dorsal skinfold chamber. Observation of the skin vasculature at the dorsal window scale shows a sparse network of a few main vessels of several hundreds micrometers in diameter. However observation at a smaller scale reveals the presence of a dense and regular interconnected network of capillaries about ten times smaller. We conveniently designate this structure as the submicrovascular network (SMVN).(1) The question that we wish to answer concerns the necessity of explicitly taking into account the SMVN in the computational model to describe the tumour evolution observed in the dorsal chamber. For that, simulations of tumour growth realised with and without the SMVN are compared and lead to two distinct scenarios. Parameters that are known to strongly influence the tumour evolution are then tested in the two cases to determine to which extent those parameters can be used to compensate the observed differences between these scenarios. Explicit modelling of the smallest vessels appears mandatory although not necessarily under the form of a regular grid. A compromise between the two investigated cases can thus be reached.