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Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach

hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBENZEKRY, Sébastien
hal.structure.identifierDepartment of Cancer Genetics [Buffalo]
dc.contributor.authorTRACZ, Amanda
hal.structure.identifierDepartment of Cancer Genetics [Buffalo]
dc.contributor.authorMASTRI, Michalis
hal.structure.identifierDepartment of Cancer Genetics [Buffalo]
dc.contributor.authorCORBELLI, R.
hal.structure.identifierSimulation & Modelling : Adaptive Response for Therapeutics in Cancer [SMARTc unit]
dc.contributor.authorBARBOLOSI, Dominique
hal.structure.identifierDepartment of Medicine [Buffalo]
hal.structure.identifierDepartment of Cancer Genetics [Buffalo]
dc.contributor.authorEBOS, John
dc.date.accessioned2024-04-04T03:17:02Z
dc.date.available2024-04-04T03:17:02Z
dc.date.created2015
dc.date.issued2016-02-01
dc.identifier.issn0008-5472
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194272
dc.description.abstractEnRapid improvements in the detection and tracking of early-stage tumor progression aim to guide decisions regarding cancer treatments as well as predict metastatic recurrence in patients following surgery. Mathematical models may have the potential to further assist in estimating metastatic risk, particularly when paired with in vivo tumor data that faithfully represent all stages of disease progression. Herein we describe mathematical analysis that uses data from mouse models of spontaneous metastasis developing after surgical removal of orthotopically implanted primary tumors. Both presurgical (primary tumor) and postsurgical (metastatic) growth was quantified using bioluminescence and was then used to generate a mathematical formalism based on general laws of the disease (i.e. dissemination and growth). The model was able to fit and predict pre-/post-surgical data at the level of the individual as well as the population. Our approach also enabled retrospective analysis of clinical data describing the probability of metastatic relapse as a function of primary tumor size. In these data-based models, inter-individual variability was quantified by a key parameter of intrinsic metastatic potential. Critically, our analysis identified a highly nonlinear relationship between primary tumor size and postsurgical survival, suggesting possible threshold limits for the utility of tumor size as a predictor of metastatic recurrence. These findings represent a novel use of clinically relevant models to assess the impact of surgery on metastatic potential and may guide optimal timing of treatments in neoadjuvant (presurgical) and adjuvant (postsurgical) settings to maximize patient benefit.
dc.description.sponsorshipInitiative d'excellence de l'Université de Bordeaux - ANR-10-IDEX-0003
dc.language.isoen
dc.publisherAmerican Association for Cancer Research
dc.rights.urihttp://creativecommons.org/licenses/by-nc/
dc.subject.enSurgery
dc.subject.enBreast cancer
dc.subject.enCancer modeling
dc.subject.enNonlinear mixed-effects
dc.subject.enMetastatic relapse
dc.title.enModeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach
dc.typeArticle de revue
dc.identifier.doi10.1158/0008-5472.CAN-15-1389
dc.subject.halSciences du Vivant [q-bio]/Cancer
bordeaux.journalCancer Research
bordeaux.page535 - 547
bordeaux.volume76
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue3
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01222046
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01222046v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Cancer%20Research&rft.date=2016-02-01&rft.volume=76&rft.issue=3&rft.spage=535%20-%20547&rft.epage=535%20-%20547&rft.eissn=0008-5472&rft.issn=0008-5472&rft.au=BENZEKRY,%20S%C3%A9bastien&TRACZ,%20Amanda&MASTRI,%20Michalis&CORBELLI,%20R.&BARBOLOSI,%20Dominique&rft.genre=article


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