  Cancer is a disease driven by random DNA mutations and the interaction of
many complex phenomena. To improve the understanding and ultimately find more
effective treatments, researchers leverage computer simulations mimicking the
tumor growth in silico. The challenge here is to account for the many phenomena
influencing the disease progression and treatment protocols. This work
introduces a computational model to simulate vascular tumor growth and the
response to drug treatments in 3D. It consists of two agent-based models for
the tumor cells and the vasculature. Moreover, partial differential equations
govern the diffusive dynamics of the nutrients, the vascular endothelial growth
factor, and two cancer drugs. The model focuses explicitly on breast cancer
cells over-expressing HER2 receptors and a treatment combining standard
chemotherapy (Doxorubicin) and monoclonal antibodies with anti-angiogenic
properties (Trastuzumab). However, large parts of the model generalize to other
scenarios. We show that the model qualitatively captures the effects of the
combination therapy by comparing our simulation results with previously
published pre-clinical data. Furthermore, we demonstrate the scalability of the
model and the associated C++ code by simulating a vascular tumor occupying a
volume of 400mm3 using a total of 92.5 million agents.
