Computational Cellular Mathematical Model Aids Understanding the cGAS-STING in NSCLC Pathogenicity

Area of Science:

• Systems biology
• Computational oncology
• Immunology

Background:

• Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality worldwide.
• Understanding complex tumor microenvironment interactions is crucial for developing effective therapies.
• The cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS/STING) pathway has a complex role in cancer immunity.

Purpose of the Study:

• To develop a mathematical model to elucidate the dual role of the cGAS/STING pathway in NSCLC.
• To identify key regulators and potential therapeutic targets for NSCLC precision therapy.
• To integrate multiple signaling axes for a comprehensive understanding of cancer progression.

Main Methods:

• Ordinary differential equations (ODEs) were used for mathematical modeling of cancer growth and immune interactions.
• Model validation involved local sensitivity analysis, principal component analysis, metabolite flow analysis, and model reduction.
• Simulation and analysis of integrated signaling pathways including cGAS-STING, NF-κB, p53, PD-L1, PI3K, and AKT.

Main Results:

• The mathematical model successfully captured the dual role of the cGAS/STING pathway in promoting or inhibiting NSCLC.
• Key regulators of cancer progression were identified through model simulation and validation.
• Integration of signaling pathways identified cGAS-STING, PI3K, and AKT as potential therapeutic targets.

Conclusions:

• Mathematical modeling provides mechanistic insights into complex biological systems like NSCLC.
• The cGAS/STING pathway exhibits context-dependent roles in NSCLC, influencing tumor regression or growth.
• Systems biology approaches can identify novel immunotherapeutic targets for NSCLC treatment.