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Cancer is a disease driven by random DNA mutations and the interaction ofmany complex phenomena. To improve the understanding and ultimately find moreeffective treatments, researchers leverage computer simulations mimicking thetumor growth in silico. The challenge here is to account for the many phenomenainfluencing the disease progression and treatment protocols. This workintroduces a computational model to simulate vascular tumor growth and theresponse to drug treatments in 3D. It consists of two agent-based models forthe tumor cells and the vasculature. Moreover, partial differential equationsgovern the diffusive dynamics of the nutrients, the vascular endothelial growthfactor, and two cancer drugs. The model focuses explicitly on breast cancercells over-expressing HER2 receptors and a treatment combining standardchemotherapy (Doxorubicin) and monoclonal antibodies with anti-angiogenicproperties (Trastuzumab). However, large parts of the model generalize to otherscenarios. We show that the model qualitatively captures the effects of thecombination therapy by comparing our simulation results with previouslypublished pre-clinical data. Furthermore, we demonstrate the scalability of themodel and the associated C++ code by simulating a vascular tumor occupying avolume of 400mm3 using a total of 92.5 million agents.

Bridging Scales: a Hybrid Model to Simulate Vascular Tumor Growth and Treatment Response