  Cancer pathology is unique to a given individual, and developing personalized
diagnostic and treatment protocols are a primary concern. Mathematical modeling
and simulation is a promising approach to personalized cancer medicine. Yet,
the complexity, heterogeneity and multiscale nature of cancer present severe
challenges. One of the major barriers to use mathematical models to predict the
outcome of therapeutic regimens in a particular patient lies in their
initialization and parameterization in order to reflect individual cancer
characteristics accurately. Here we present a study where we used multitype
measurements acquired routinely on a single breast tumor, including
histopathology, magnetic resonance imaging (MRI), and molecular profiling, to
personalize a multiscale hybrid cellular automaton model of breast cancer
treated with chemotherapeutic and antiangiogenic agents. We model drug
pharmacokinetics and pharmacodynamics at the tumor tissue level but with
cellular and subcellular resolution. We simulate those spatio-temporal dynamics
in 2D cross-sections of tumor portions over 12-week therapy regimes, resulting
in complex and computationally intensive simulations. For such computationally
demanding systems, formal statistical inference methods to estimate individual
parameters from data have not been feasible in practice to until most recently,
after the emergence of machine learning techniques applied to likelihood-free
inference methods. Here we use the inference advances provided by Bayesian
optimization to fit our model to simulated data of individual patients. In this
way, we investigate if some key parameters can be estimated from a series of
measurements of cell density in the tumor tissue, as well as how often the
measurements need to be taken to allow reliable predictions.
