Development of a scoring function for comparing simulated and experimental tumor spheroids.

Area of Science:

• Cancer Biology and Biophysics
• Computational Modeling and Simulation
• Biomedical Engineering and Imaging

Background:

• Understanding cancer invasion mechanisms, particularly tumor cell remodeling of the extracellular matrix (ECM), is crucial but complex.
• Tumor spheroids in 3D collagen offer a reproducible model for studying cell-ECM interactions during invasion.
• Integrating high-resolution imaging of experimental spheroids with computational modeling presents a significant challenge.

Purpose of the Study:

• To present a novel method for comparing spatial features of 3D tumor spheroids.
• To establish a framework for extracting spheroid features and defining metrics for quantitative comparison.
• To enable a more robust comparison between experimental and in silico (simulated) spheroid data.

Main Methods:

• Spheroid point cloud data was simulated using the Cells in Silico (CiS) framework.
• Key spatial features were extracted from the simulated spheroid data.
• Metrics were defined to compare these features, combined into an overall deviation score, and applied to experimental data.

Main Results:

• A novel method for extracting and comparing spatial features of 3D spheroids was successfully developed.
• The method was validated by comparing simulated spheroids and experimental data across varying collagen densities.
• The approach provides a quantitative basis for comparing complex 3D spheroid datasets.

Conclusions:

• The developed feature extraction and metric definition approach offers a foundation for improved comparison of large 3D datasets.
• This method facilitates the detailed analysis of spheroids from various origins.
• It enables better integration of in vitro experimental data with in silico modeling, advancing cancer research.