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Updated: Mar 29, 2026

Establishment and Maintenance of Patient-derived Prostate Cancer Organoids: A Detailed Experimental Protocol
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A Mechanistic Digital Twin of uPAR-Driven Prostate Cancer Invasion Integrating ODE Signalling and Agent-Based

Radosław Dzik1, Joanna Chwał1, Ewaryst J Tkacz1

  • 1Department of Clinical Engineering, Academy of Silesia, ul. Rolna 43, 40-555 Katowice, Poland.

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Summary

In silico modeling reveals that inhibiting urokinase-type plasminogen activator receptor (uPAR) significantly reduces prostate cancer invasion and growth. This digital twin approach links molecular changes to spatial tumor behavior.

Keywords:
ODEagent based modellingdigital twinprostate canceruPAR

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Area of Science:

  • Computational Biology
  • Cancer Research
  • Systems Biology

Background:

  • Aberrant urokinase-type plasminogen activator receptor (uPAR) signaling drives prostate cancer invasion.
  • Linking molecular changes to spatial invasion phenotypes is a significant challenge.

Purpose of the Study:

  • To investigate uPAR-driven invasion dynamics in prostate cancer using a multiscale in silico framework.
  • To connect molecular-level perturbations to emergent spatial invasion phenotypes.

Main Methods:

  • Developed a multiscale framework integrating molecular docking, ordinary differential equation (ODE) modeling, and agent-based modeling (ABM).
  • Prioritized caffeic acid phenethyl ester (CAPE) as a potential uPA/uPAR modulator.
  • Mapped steady-state signaling outputs to proliferation and motility rates for spatial ABM.

Main Results:

  • uPAR inhibition significantly reduced spatial invasion and tumor growth in simulations.
  • Enhanced uPA signaling showed only modest, non-significant trends in invasion.
  • Demonstrated translation of intracellular signaling changes to population-level invasion phenotypes.

Conclusions:

  • Subtle intracellular signaling perturbations can lead to pronounced spatial invasion phenotypes.
  • The digital twin framework effectively connects molecular prioritization with quantitative predictions of tumor invasion.
  • The approach offers a coherent and extensible method for prostate cancer research.