Investigating tumor-host response dynamics in preclinical immunotherapy experiments using a stepwise mathematical...

Area of Science:

• Oncology
• Immunology
• Mathematical Biology

Background:

• Immunotherapies targeting PD1 and CTLA4 show variable efficacy across different tumors.
• Understanding the dynamics of immunotherapy response and failure is crucial for improving cancer treatment.

Purpose of the Study:

• To develop a mathematical modeling strategy to quantify immunotherapy successes and failures.
• To investigate the biological drivers of immunotherapy failure, including tumor vasculature, immune response, and drug dosing.

Main Methods:

• Applied a stepwise mathematical modeling strategy to mouse models of colorectal and breast cancer.
• Utilized longitudinal tumor volume data and an exponential growth model to define response groups.
• Integrated [18F] fluoromisonidazole (FMISO)-positron emission tomography (PET) data to model tumor hypoxia and vasculature quality over time.
• Expanded the model to incorporate immune response and drug dosing for systematic investigation of hypotheses.

Main Results:

• Quantified biological drivers of tumor vasculature deterioration by calibrating the mathematical model to PET data.
• Model simulations identified elevated immune response fractions in non-responsive tumors.
• Results suggest that immunotherapy failure is associated with a functional immune response that diminishes over time.

Conclusions:

• The experimental-mathematical approach enables exploration of system dynamics not discernible from data alone.
• Identified key factors contributing to immunotherapy failure, including tumor aggressiveness, immune exhaustion, and immune cell functionality.
• Provides a framework for generating experimentally testable predictions regarding immune response to immunotherapy.