From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.793908/full |
| _version_ | 1818011619654893568 |
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| author | Michael C. Luo Elpiniki Nikolopoulou Jana L. Gevertz |
| author_facet | Michael C. Luo Elpiniki Nikolopoulou Jana L. Gevertz |
| author_sort | Michael C. Luo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T06:09:54Z |
| format | Article |
| id | doaj.art-f2eea593943d4abfb90126bedd6e0d76 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-14T06:09:54Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-f2eea593943d4abfb90126bedd6e0d762022-12-22T02:08:24ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-04-011210.3389/fonc.2022.793908793908From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical ModelersMichael C. Luo0Elpiniki Nikolopoulou1Jana L. Gevertz2Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, United StatesSchool of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United StatesDepartment of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, United StatesAn 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.https://www.frontiersin.org/articles/10.3389/fonc.2022.793908/fullcancermathematical modelingpersonalized therapyimmunotherapynonlinear mixed effects modeling |
| spellingShingle | Michael C. Luo Elpiniki Nikolopoulou Jana L. Gevertz From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers Frontiers in Oncology cancer mathematical modeling personalized therapy immunotherapy nonlinear mixed effects modeling |
| title | From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers |
| title_full | From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers |
| title_fullStr | From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers |
| title_full_unstemmed | From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers |
| title_short | From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers |
| title_sort | from fitting the average to fitting the individual a cautionary tale for mathematical modelers |
| topic | cancer mathematical modeling personalized therapy immunotherapy nonlinear mixed effects modeling |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.793908/full |
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