Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model
Fig 5
Spatial distributions of cell densities under different tumor growth conditions and immunotherapy.
Cancer cell, CD8+ T cell, and FoxP3+ T cell densities are normalized and represented in turquoise, purple, and blue scale bars, respectively. CSC density distributions are represented in the first column of contour plots as yellow-to-blue lines. ξp,y = y/yref and ξp,x = x/xref are non-dimensional spatial coordinates with yref = xref = 2up/rp. Four different scenarios are presented: similar migration and proliferation effects, R ~ 1, 10% of the grid occupied by voxels with cell recruitment sources, and dmax = 9 (panel A); fast migration of CSCs and proliferation of PCs, R < 1, 10% of the grid occupied by voxels with cell recruitment sources, and dmax = 9 (panel B); R ~ 1, 10% of the grid occupied by voxels with cell recruitment sources, and dmax = 4 (panel C); R < 1, 30% of the grid occupied by voxels with cell recruitment sources, and dmax = 9 (panel D). The spatial QSP algorithm calculated the evolution of three-dimensional tumors starting from a normal distribution of cancer cells located at the center of the grid. QSP model and ABM are coupled before reaching the point where T cells are recruited and also before the initial tumor diameter condition from the QSP model is met. Thus, no initial T cell spatial distribution is enforced. The figures show the cancer cell density of a two-dimensional slice at the center of the tumor 10 months after the initial tumor diameter condition is met and 3 mg/kg nivolumab is administered every two weeks. The scaling factor is γ=50000 in all cases.