An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. In this work, we employ two fitting methodologies to personalize treatment in a mathematical model of murine cancer immunotherapy. Unexpectedly, we found that the predicted personalized treatment response is sensitive to the fitting methodology utilized. This raises concerns about the ability of mathematical models, even relatively simple ones, to make reliable predictions about individual treatment response. Our analyses shed light onto why it can be challenging to make personalized treatment recommendations from a model, but also suggest ways we can increase our confidence in personalized mathematical predictions.Author summaryAs we enter the era of healthcare where personalized medicine becomes a more common approach to treating cancer patients, harnessing the power of mathematical models will only become more essential. Using a preclinical dataset on cancer immunotherapy, we explore the challenges and limitations that arise when trying to move from a one-size-fits-all approach to treatment design towards personalized therapeutic design. These challenges lead to actionable suggestions on how to ascertain when we have enough data to personalize treatment, or how to determine when we can have confidence that an optimal-for-the-average prediction will have a comparable impact on an individual. We also show how mathematical modeling can suggest what data is needed to increased confidence in personalized predictions.

An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.