Towards personalized computer simulations of breast cancer treatment

TL;DR

This paper develops a personalized multiscale breast cancer simulation model using patient-specific data and Bayesian optimization to estimate parameters, aiming to improve prediction of treatment outcomes.

Contribution

It introduces a novel approach combining multiscale modeling with machine learning-based parameter inference for personalized cancer treatment simulations.

Findings

Model can be personalized using routine measurements

Bayesian optimization effectively estimates key parameters

Measurement frequency impacts prediction reliability

Abstract

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.