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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

hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorÁLVAREZ-ARENAS, Arturo
hal.structure.identifierLaboratoire Angiogenèse et Micro-environnement des Cancers [LAMC]
dc.contributor.authorSOULEYREAU, Wilfried
hal.structure.identifierLaboratoire Angiogenèse et Micro-environnement des Cancers [LAMC]
dc.contributor.authorEMANUELLI, Andrea
hal.structure.identifierLaboratoire Angiogenèse et Micro-environnement des Cancers [LAMC]
dc.contributor.authorCOOLEY, Lindsay
hal.structure.identifierservice d'urologie [CHU Bordeaux]
dc.contributor.authorBERNHARD, Jean-Christophe
hal.structure.identifierLaboratoire Angiogenèse et Micro-environnement des Cancers [LAMC]
dc.contributor.authorBIKFALVI, Andreas
hal.structure.identifierMéthodes computationnelles pour la prise en charge thérapeutique en oncologie : Optimisation des stratégies par modélisation mécaniste et statistique [COMPO]
hal.structure.identifierCentre de Recherche en Cancérologie de Marseille [CRCM]
dc.contributor.authorBENZEKRY, Sébastien
dc.date.accessioned2024-04-04T02:36:44Z
dc.date.available2024-04-04T02:36:44Z
dc.date.issued2022-08-25
dc.identifier.issn1553-734X
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190744
dc.description.abstractEnDistant 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 leastsquares 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 (Fuhrman 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).
dc.language.isoen
dc.publisherPublic Library of Science
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.title.enPractical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma
dc.typeArticle de revue
dc.identifier.doi10.1371/journal.pcbi.1010444
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halPhysique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an]
dc.subject.halSciences du Vivant [q-bio]/Cancer
dc.subject.halSciences du Vivant [q-bio]/Sciences pharmaceutiques/Pharmacologie
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologie
dc.subject.halStatistiques [stat]/Applications [stat.AP]
bordeaux.journalPLoS Computational Biology
bordeaux.volume18
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03921339
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03921339v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=PLoS%20Computational%20Biology&rft.date=2022-08-25&rft.volume=18&rft.eissn=1553-734X&rft.issn=1553-734X&rft.au=%C3%81LVAREZ-ARENAS,%20Arturo&SOULEYREAU,%20Wilfried&EMANUELLI,%20Andrea&COOLEY,%20Lindsay&BERNHARD,%20Jean-Christophe&rft.genre=article


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