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Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma

Distant metastasis-free survival (DMFS) curves are widely used in oncology. They are classically analyzed using the Kaplan-Meier estimator or agnostic statistical models from survival analysis. Here we report on a method to extract more information from DMFS curves using a mathematical model of prim...

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Autores principales: Álvarez-Arenas, Arturo, Souleyreau, Wilfried, Emanuelli, Andrea, Cooley, Lindsay S., Bernhard, Jean-Christophe, Bikfalvi, Andreas, Benzekry, Sebastien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9451098/
https://www.ncbi.nlm.nih.gov/pubmed/36007057
http://dx.doi.org/10.1371/journal.pcbi.1010444
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author Álvarez-Arenas, Arturo
Souleyreau, Wilfried
Emanuelli, Andrea
Cooley, Lindsay S.
Bernhard, Jean-Christophe
Bikfalvi, Andreas
Benzekry, Sebastien
author_facet Álvarez-Arenas, Arturo
Souleyreau, Wilfried
Emanuelli, Andrea
Cooley, Lindsay S.
Bernhard, Jean-Christophe
Bikfalvi, Andreas
Benzekry, Sebastien
author_sort Álvarez-Arenas, Arturo
collection PubMed
description Distant metastasis-free survival (DMFS) curves are widely used in oncology. They are classically analyzed using the Kaplan-Meier estimator or agnostic statistical models from survival analysis. Here we report on a method to extract more information from DMFS curves using a mathematical model of primary tumor growth and metastatic dissemination. The model depends on two parameters, α and μ, respectively quantifying tumor growth and dissemination. We assumed these to be lognormally distributed in a patient population. We propose a method for identification of the parameters of these distributions based on least-squares minimization between the data and the simulated survival curve. We studied the practical identifiability of these parameters and found that including the percentage of patients with metastasis at diagnosis was critical to ensure robust estimation. We also studied the impact and identifiability of covariates and their coefficients in α and μ, either categorical or continuous, including various functional forms for the latter (threshold, linear or a combination of both). We found that both the functional form and the coefficients could be determined from DMFS curves. We then applied our model to a clinical dataset of metastatic relapse from kidney cancer with individual data of 105 patients. We show that the model was able to describe the data and illustrate our method to disentangle the impact of three covariates on DMFS: a categorical one (Führman grade) and two continuous ones (gene expressions of the macrophage mannose receptor 1 (MMR) and the G Protein-Coupled Receptor Class C Group 5 Member A (GPRC5a) gene). We found that all had an influence in metastasis dissemination (μ), but not on growth (α).
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spelling pubmed-94510982022-09-08 Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma Álvarez-Arenas, Arturo Souleyreau, Wilfried Emanuelli, Andrea Cooley, Lindsay S. Bernhard, Jean-Christophe Bikfalvi, Andreas Benzekry, Sebastien PLoS Comput Biol Research Article Distant metastasis-free survival (DMFS) curves are widely used in oncology. They are classically analyzed using the Kaplan-Meier estimator or agnostic statistical models from survival analysis. Here we report on a method to extract more information from DMFS curves using a mathematical model of primary tumor growth and metastatic dissemination. The model depends on two parameters, α and μ, respectively quantifying tumor growth and dissemination. We assumed these to be lognormally distributed in a patient population. We propose a method for identification of the parameters of these distributions based on least-squares minimization between the data and the simulated survival curve. We studied the practical identifiability of these parameters and found that including the percentage of patients with metastasis at diagnosis was critical to ensure robust estimation. We also studied the impact and identifiability of covariates and their coefficients in α and μ, either categorical or continuous, including various functional forms for the latter (threshold, linear or a combination of both). We found that both the functional form and the coefficients could be determined from DMFS curves. We then applied our model to a clinical dataset of metastatic relapse from kidney cancer with individual data of 105 patients. We show that the model was able to describe the data and illustrate our method to disentangle the impact of three covariates on DMFS: a categorical one (Führman grade) and two continuous ones (gene expressions of the macrophage mannose receptor 1 (MMR) and the G Protein-Coupled Receptor Class C Group 5 Member A (GPRC5a) gene). We found that all had an influence in metastasis dissemination (μ), but not on growth (α). Public Library of Science 2022-08-25 /pmc/articles/PMC9451098/ /pubmed/36007057 http://dx.doi.org/10.1371/journal.pcbi.1010444 Text en © 2022 Álvarez-Arenas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Álvarez-Arenas, Arturo
Souleyreau, Wilfried
Emanuelli, Andrea
Cooley, Lindsay S.
Bernhard, Jean-Christophe
Bikfalvi, Andreas
Benzekry, Sebastien
Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma
title Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma
title_full Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma
title_fullStr Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma
title_full_unstemmed Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma
title_short Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma
title_sort practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9451098/
https://www.ncbi.nlm.nih.gov/pubmed/36007057
http://dx.doi.org/10.1371/journal.pcbi.1010444
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