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Updated: Jul 11, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Investigating tumor-host response dynamics in preclinical immunotherapy experiments using a stepwise mathematical
Angela M Jarrett1, Patrick N Song2, Kirsten Reeves3
1Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, USA; Livestrong Cancer Institutes, The University of Texas at Austin, USA.
Abstract:
Immunotherapies such as checkpoint blockade to PD1 and CTLA4 can have varied effects on individual tumors. To quantify the successes and failures of these therapeutics, we developed a stepwise mathematical modeling strategy and applied it to mouse models of colorectal and breast cancer that displayed a range of therapeutic responses. Using longitudinal tumor volume data, an exponential growth model was utilized to designate response groups for each tumor type. The exponential growth model was then extended to describe the dynamics of the quality of vasculature in the tumors via [18F] fluoromisonidazole (FMISO)-positron emission tomography (PET) data estimating tumor hypoxia over time. By calibrating the mathematical system to the PET data, several biological drivers of the observed deterioration of the vasculature were quantified. The mathematical model was then further expanded to explicitly include both the immune response and drug dosing, so that model simulations are able to systematically investigate biological hypotheses about immunotherapy failure and to generate experimentally testable predictions of immune response. The modeling results suggest elevated immune response fractions (> 30 %) in tumors unresponsive to immunotherapy is due to a functional immune response that wanes over time. This experimental-mathematical approach provides a means to evaluate dynamics of the system that could not have been explored using the data alone, including tumor aggressiveness, immune exhaustion, and immune cell functionality.
Insights
Mathematical modeling reveals that immunotherapy failure in cancer is linked to a waning immune response over time. This approach quantifies tumor aggressiveness and immune cell functionality to predict treatment outcomes.
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.
