Cross-scale, cross-pathway evaluation using an agent-based non-small cell lung cancer model

Area of Science:

• Computational biology
• Cancer research
• Multiscale modeling

Background:

• Non-small cell lung cancer (NSCLC) progression involves complex molecular signaling.
• Epidermal growth factor (EGF) and transforming growth factor beta (TGFbeta) are key signaling molecules influencing cancer cell behavior.

Purpose of the Study:

• To investigate how molecular-level changes in EGF and TGFbeta concentrations impact NSCLC tumor growth dynamics.
• To determine the effects of individual and combined EGF/TGFbeta signaling on tumor volume and expansion rate.

Main Methods:

• Development of a multiscale agent-based in silico model of NSCLC.
• Integration of EGF and TGFbeta as extrinsic molecular inputs.
• Simulation of varying EGF and TGFbeta concentrations to observe multi-cellular responses.

Main Results:

• Individual EGF and TGFbeta fluctuations induce competing phenotypes; synchronous signaling results in a more aggressive, EGF-driven tumor.
• A specific range of EGF-TGFbeta concentrations promotes phenotypic stability, minimizing changes in tumor volume and expansion rate.
• Outside this stable region, molecular alterations significantly affect tumor growth dynamics.

Conclusions:

• Tumor growth dynamics are scale-dependent, influenced by molecular signaling pathways.
• Targeting only one signaling pathway (EGF or TGFbeta) may be insufficient for therapeutic efficacy in NSCLC.
• Findings have implications for developing targeted cancer therapies and understanding clinico-pharmacological applications.