Towards cellular digital twins of in vivo tumors.

Abstract

To this day, cancer remains an insufficiently understood disease plaguing humanity. In particular, the mechanisms driving tumor invasion still require extensive study. Current investigations address collective cellular behavior within tumors, which leads to solid or fluid tissue dynamics. Furthermore, the extracellular matrix (ECM) has come into focus as a driving force facilitating invasion. Large scale tumor simulations at subcellular resolution represent a promising computational tool to complement the experimental studies, and advances in computational power within HPC systems have enabled the simulation of such macroscopic tissue arrangements. We hereby present our work using Cells in Silico (CiS), a high performance framework for large-scale tissue simulation previously developed by us. Combining a cellular Potts model and an agent-based layer, CiS is capable of simulating tissues composed of tens of millions of cells, while accurately representing many physical and biological properties. However, in order to realistically represent tumor dynamics, CiS needs to be parameterized. We present strategies and pitfalls in investigating multiple experimental data sources for this task. In particular, we highlight recent success utilizing a feature-extraction based deviation score method for the comparison of simulated and experimental tumor spheroids.