Multiscalar cellular automaton simulates in-vivo tumour-stroma patterns calibrated from in-vitro assay data

Area of Science:

• Computational biology
• Cancer research
• Pharmacology

Background:

• The tumor microenvironment (TME) significantly impacts solid cancer progression and drug efficacy.
• The complex, bidirectional tumor-stroma relationship is poorly understood quantitatively, hindering drug development translation.
• Existing research presents conflicting pro- and anti-tumor roles for the stroma.

Purpose of the Study:

• To develop a quantitative model of the tumor-stroma interaction within complex tumor morphologies.
• To investigate the contribution of microenvironmental factors to tumor physiology and oxygen distribution.
• To establish a platform for parameter extraction from in-vitro assays for in-vivo model calibration.

Main Methods:

• Developed a lattice-based multiscalar cellular automaton model simulating cytokine/oxygen diffusion and cell dynamics.
• Integrated principles of cell motility and plasticity within the tumor-stroma landscape.
• Proposed an innovative platform for extracting model parameters from in-vitro experimental data.

Main Results:

• The model successfully reproduced in-vivo stromal patterns observed in human lung cancer cell lines (Calu3, Calu6).
• Demonstrated the necessity of the opposing tumor-stroma relationship for accurate simulation of preclinical and clinical tumor topologies.
• Highlighted the model's relevance for understanding drugs targeting the tumor microenvironment, including antiangiogenics and immune checkpoint inhibitors.

Conclusions:

• The developed tumor-stroma automaton model quantifies complex in-vitro data for in-vivo applications.
• This approach facilitates a mechanistic understanding of drug responses within the tumor microenvironment.
• The model serves as a key platform for advancing preclinical to clinical drug development translation.