A Data-Enhanced Agent-Based Model for Simulating 3D Cancer Spheroid Growth: Integrating Metabolism and Mechanics

TL;DR

This paper introduces an agent-based model that integrates metabolism and mechanics to simulate 3D cancer spheroid growth, providing insights into tumor development and variability in experimental conditions.

Contribution

The study presents a novel integrated ABM that combines tumor metabolism and mechanics, calibrated and validated against experimental data, enhancing understanding of spheroid growth dynamics.

Findings

Model reproduces experimental spheroid growth accurately.

Reveals different cellular behaviors under identical conditions.

Demonstrates adaptability to various cell lines.

Abstract

Cancer research has shifted from a purely gene-centric view to a more holistic understanding that recognizes the critical role of the tumour microenvironment, where mechanics and metabolism are key drivers of disease progression. However, the intricate interplay between these multifactorial mechanisms remains poorly understood. To address this gap, we present an agent-based computational model (ABM) that integrates tumour metabolism and mechanics to study 3D cancer spheroid growth. Our approach unifies the metabolism and mechanical aspects of tumour development within an integral model for cancer spheroid formation and growth. In addition to that, we performed a computational calibration of the parameters and tested the model versatility to reproduce different cellular behaviours. Our model reproduced qualitatively and quantitatively the experimental results of spheroid growth obtained in the lab and also allowed to discern different dynamics that cancer cells can present under the same conditions, providing insight into the potential factors contributing to the variability in the size of spheroids. Furthermore, it also showed its adaptability to reproduce diferent cell lines and behaviours by tuning its parameters. This study highlights the significant potential and versatility of integrative modelling approaches in the field of cancer research, not only as a tool to complement in vitro studies, but also as independent tools to derive conclusions from the physical reality.