Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach
BENZEKRY, Sébastien
Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]
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Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]
BENZEKRY, Sébastien
Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]
BARBOLOSI, Dominique
Simulation & Modelling : Adaptive Response for Therapeutics in Cancer [SMARTc unit]
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Simulation & Modelling : Adaptive Response for Therapeutics in Cancer [SMARTc unit]
Idioma
en
Article de revue
Este ítem está publicado en
Cancer Research. 2016-02-01, vol. 76, n° 3, p. 535 - 547
American Association for Cancer Research
Resumen en inglés
Rapid 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 ...Leer más >
Rapid 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.< Leer menos
Palabras clave en inglés
Surgery
Breast cancer
Cancer modeling
Nonlinear mixed-effects
Metastatic relapse
Proyecto ANR
Initiative d'excellence de l'Université de Bordeaux - ANR-10-IDEX-0003
Orígen
Importado de HalCentros de investigación