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The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives
Aymara Sancho-Araiz1,2, Victor Mangas-Sanjuan3,4, Iñaki F Trocóniz1,2
1Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31009 Pamplona, Spain.
Abstract:
Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.
Insights
Mathematical models can enhance cancer immunotherapy by identifying biomarkers and optimal treatment strategies. Middle-out models are particularly effective for evaluating new immuno-oncology (IO) approaches and improving patient response.
Area of Science:
- Oncology
- Immunology
- Mathematical Modeling
Background:
- Immuno-oncology (IO) leverages the immune system to combat cancer, with immunotherapies becoming a cornerstone of modern cancer treatment.
- Despite advancements, a significant portion of patients exhibit resistance to current immunotherapies, necessitating novel therapeutic strategies.
- Mathematical modeling presents a powerful tool to address challenges in IO, including biomarker discovery and treatment optimization.
Purpose of the Study:
- To review key therapeutic targets in immuno-oncology.
- To describe various mathematical modeling approaches (top-down, middle-out, bottom-up) for integrating the cancer immunity cycle with immunotherapeutic agents.
- To highlight the utility of mathematical models in optimizing clinical scenarios and overcoming treatment resistance.
Main Methods:
- Literature review of immuno-oncology targets and therapeutic strategies.
- Description of mathematical modeling frameworks: top-down, middle-out, and bottom-up.
- Integration of the cancer immunity cycle with immunotherapeutic agents within mathematical models.
Main Results:
- Identified key therapeutic targets in immuno-oncology.
- Detailed the characteristics and applications of different mathematical modeling approaches.
- Demonstrated the potential of mathematical models to predict patient response and guide treatment decisions.
Conclusions:
- Mathematical models offer a promising avenue for advancing immuno-oncology research and clinical practice.
- Middle-out models, integrating theoretical and empirical data, provide an optimal framework for evaluating novel IO strategies.
- Further development and application of mathematical models can lead to improved patient outcomes in cancer immunotherapy.
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