Addressing current challenges in cancer immunotherapy with mathematical and computational modelling

Area of Science:

• Oncology
• Computational Biology
• Immunology

Background:

• Cancer immunotherapy aims to enhance the patient's immune response against tumors.
• Designing effective immunotherapies is challenging due to immunosuppressive tumor microenvironments, conventional treatment effects, and toxicity.
• Mathematical and computational models offer a framework to integrate these complexities for rational therapy design.

Purpose of the Study:

• To review modeling approaches for cancer immunotherapy development.
• To highlight how models address key challenges in immunotherapy, including tumor classification, treatment scheduling, and combination therapy.
• To showcase the dynamic interplay between model complexity and advancements in tumor-immune biology.

Main Methods:

• Survey of mathematical and computational modeling approaches in cancer immunotherapy.
• Analysis of model outcomes using analytical and numerical methods.
• Application of optimization algorithms for therapy design.

Main Results:

• Models are increasingly complex, incorporating more biological information.
• Modeling approaches have provided insights into tumor classification, optimal treatment scheduling, and combination therapy design.
• Model development is intrinsically linked to rapid advances in tumor-immune biology.

Conclusions:

• Mathematical modeling is crucial for advancing cancer immunotherapy.
• Recommendations are provided for methodological and biological directions in modeling.
• Continued integration of biological data and advanced modeling techniques will drive future immunotherapy development.