A Mechanistic Digital Twin of uPAR-Driven Prostate Cancer Invasion Integrating ODE Signalling and Agent-Based Modelling.

Background

Aberrant signalling through the urokinase-type plasminogen activator receptor (uPAR) is a key driver of tumour invasion and progression in prostate cancer, yet linking molecular-level perturbations to emergent spatial invasion phenotypes remains challenging.

Methods

In this study, we developed a multiscale in silico framework combining molecular docking, mechanistic ordinary differential equation (ODE) modelling, and agent-based modelling (ABM) to investigate uPAR-driven invasion dynamics.

Results

Molecular docking and MM-GBSA analyses were used to prioritise caffeic acid phenethyl ester (CAPE) as a candidate uPA/uPAR modulator, while uPAR inhibition was implemented mechanistically at the signalling level within the ODE model rather than through direct energetic parametrisation. Steady-state signalling outputs were mapped to effective proliferation and motility rates, which served as inputs to a spatial ABM of tumour invasion. The integrated simulations showed that uPAR inhibition results in statistically significant reductions in spatial invasion and tumour growth compared with baseline conditions, whereas enhanced uPA signalling produced only modest, non-significant trends.

Conclusions

These findings demonstrate how subtle intracellular signalling perturbations can translate into pronounced population-level invasion phenotypes when embedded in a spatial context. Overall, the proposed digital-twin framework provides a coherent and extensible approach for connecting molecular prioritisation with quantitative predictions of tumour invasion behaviour in prostate cancer.