Understanding the Interplay of CAR-NK Cells and Triple-Negative Breast Cancer: Insights from Computational Modeling

Area of Science:

• Immunology
• Computational Biology
• Oncology

Background:

• Triple-negative breast cancer (TNBC) exhibits limited response to conventional therapies and immunotherapies.
• Chimeric antigen receptor (CAR)-natural killer (NK) cells offer a safer alternative to CAR-T cells for solid tumor treatment.
• CAR-NK cells targeting epidermal growth factor receptor (EGFR) show potential for TNBC immunotherapy.

Purpose of the Study:

• To develop a computational model simulating CAR-NK cell immunotherapy dynamics for TNBC.
• To identify key factors influencing CAR-NK immunotherapy efficacy, including vaccination parameters and tumor microenvironment.
• To determine optimal vaccination strategies for maximizing therapeutic outcomes in TNBC.

Main Methods:

• Integration of an individual-based model (IBM) for tumor-immune system interactions.
• Incorporation of an ordinary differential equation (ODE) model for inflammatory cytokine dynamics.
• Application of computational modeling and global sensitivity analysis to experimental data and immunological theories.

Main Results:

• The model elucidates conditions required for initiating effective anti-tumor responses against TNBC.
• Low in vivo persistence of CAR-NK cells is identified as a critical challenge for clinical application.
• Optimal vaccination time, dose, and injection intervals were computationally determined.

Conclusions:

• CAR-NK cell immunotherapy presents a viable strategy for TNBC treatment.
• Computational modeling is crucial for understanding and optimizing CAR-NK immunotherapy parameters.
• Addressing CAR-NK cell persistence is essential for successful clinical translation in TNBC treatment.