Calibration of a Voronoi cell-based model for tumour growth using approximate Bayesian computation

Abstract

AbstractAgent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models can pose a challenge in terms of their ability to be validated with experimental measurements. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. The ability of this type of model to capture tumour growth accurately remains unknown. In this paper, a likelihood-free method is employed to calibrate a VCBM to breast, ovarian and pancreatic cancer measurements in vivo. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering the output from the model based on estimated parameters so that the outputs match the experimental data. Our results show that the VCBM can accurately model monophasic tumour growth but lacks the ability to capture biphasic or multiphasic tumour growth. Furthermore, we find that the time taken for a daughter cell to become a mature adult cell is highly informed by the data. Moving forward, this allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.