From fitting the average to fitting the individual: A cautionary tale for mathematical modelers

Abstract

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.

Myeloid-derived suppressor cell dynamics control outcomes in the metastatic niche

“…This has direct clinical implications: while it may not yet be possible to directly modulate this parameter in a clinical setting, it highlights the importance of interventions targeting properties of MDSCs in and around the tumor site. Moreover, successfully fitting of various tumor responses to tumor-MDSC dynamics and the stratification of rate parameters that resulted demonstrates our ability to build and fit patient-specific tumor growth models (83), with which to predict metastatic outcomes.…”

“…In particular, drug treatments that block MDSC recruitment to tumor sites and/or target MDSCs in the lymphoid organs seem to be most highly effective in preventing metastasis, but their effects are lessened if the level of circulating MDSCs is low, or if MDSCs are less effective at suppressing anti-tumor populations. Since the level of circulating MDSCs (as well as the level of MDSC-immunosuppresion) is likely to be highly variable within patients (20, 59), effective treatment strategies ought to be informed by patient-specific biomarkers (83, 89). In addition, evaluation of the phentoype of circulating MDSCs may not fully reflect the immunosuppressed state within tumors enough to predict potential response to immunotherapy, which may be determined in part by further mathematical and data-driven modeling.…”

“…However, the relative importances of parameters that this data fitting revealed were completely unexpected. and the stratification of rate parameters that resulted demonstrates our ability to build and fit patient-specific tumor growth models (83), with which to predict metastatic outcomes.…”