"From Fitting the Average to Fitting the Individual: A Cautionary Tale " by Michael C. Luo, Elpiniki Nikolopoulou et al.
 

From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers

Document Type

Article

Publication Date

4-28-2022

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. 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.

Identifier

85130187530 (Scopus)

Publication Title

Frontiers in Oncology

External Full Text Location

https://doi.org/10.3389/fonc.2022.793908

e-ISSN

2234943X

Volume

12

Grant

OAC-1826915

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS