Integrating biology and mathematics to understand checkpoint immunotherapy

Immune checkpoint inhibitors (ICIs) elicit durable antitumor responses in immunogenic tumors but still fail in most patients. Extensive research has elucidated how ICIs trigger a wide array of antitumor responses, yet, unexpectedly, also fuel tumor immunosuppression, a major driver of therapy failure. We argue that the sheer number of variables at play will hinder upcoming research to improve ICI performance, making the classic experimental approach exceedingly time- and resource-intensive. We posit that this problem can be solved through a bidirectional feedback loop of mathematical modeling and experimental validation. This review consolidates foundational knowledge in cancer immunology and mathematical modeling and proposes strategies that enable mathematicians and immunologists to collaborate fruitfully and expedite breakthroughs in ICI therapy.