  Patient-derived cells (PDC) mouse xenografts are increasingly important tools
in glioblastoma (GBM) research, essential to investigate case-specific growth
patterns and treatment responses. Despite the central role of xenograft models
in the field, few good simulation models are available to probe the dynamics of
tumor growth and to support therapy design. We therefore propose a new
framework for the patient-specific simulation of GBM in the mouse brain. Unlike
existing methods, our simulations leverage a high-resolution map of the mouse
brain anatomy to yield patient-specific results that are in good agreement with
experimental observations. To facilitate the fitting of our model to
histological data, we use Approximate Bayesian Computation. Because our model
uses few parameters, reflecting growth, invasion and niche dependencies, it is
well suited for case comparisons and for probing treatment effects. We
demonstrate how our model can be used to simulate different treatment by
perturbing the different model parameters. We expect in silico replicates of
mouse xenograft tumors can improve the assessment of therapeutic outcomes and
boost the statistical power of preclinical GBM studies.
