Mathematical Analysis of Glioma Growth in a Murine Model

Abstract Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and develop...

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Autores principales:	Erica M. Rutter, Tracy L. Stepien, Barrett J. Anderies, Jonathan D. Plasencia, Eric C. Woolf, Adrienne C. Scheck, Gregory H. Turner, Qingwei Liu, David Frakes, Vikram Kodibagkar, Yang Kuang, Mark C. Preul, Eric J. Kostelich
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/49c26a5cb0574efa92425851a3c1b035
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spelling	oai:doaj.org-article:49c26a5cb0574efa92425851a3c1b0352021-12-02T11:53:06ZMathematical Analysis of Glioma Growth in a Murine Model10.1038/s41598-017-02462-02045-2322https://doaj.org/article/49c26a5cb0574efa92425851a3c1b0352017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02462-0https://doaj.org/toc/2045-2322Abstract Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.Erica M. RutterTracy L. StepienBarrett J. AnderiesJonathan D. PlasenciaEric C. WoolfAdrienne C. ScheckGregory H. TurnerQingwei LiuDavid FrakesVikram KodibagkarYang KuangMark C. PreulEric J. KostelichNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-16 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Erica M. Rutter Tracy L. Stepien Barrett J. Anderies Jonathan D. Plasencia Eric C. Woolf Adrienne C. Scheck Gregory H. Turner Qingwei Liu David Frakes Vikram Kodibagkar Yang Kuang Mark C. Preul Eric J. Kostelich Mathematical Analysis of Glioma Growth in a Murine Model
description	Abstract Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.
format	article
author	Erica M. Rutter Tracy L. Stepien Barrett J. Anderies Jonathan D. Plasencia Eric C. Woolf Adrienne C. Scheck Gregory H. Turner Qingwei Liu David Frakes Vikram Kodibagkar Yang Kuang Mark C. Preul Eric J. Kostelich
author_facet	Erica M. Rutter Tracy L. Stepien Barrett J. Anderies Jonathan D. Plasencia Eric C. Woolf Adrienne C. Scheck Gregory H. Turner Qingwei Liu David Frakes Vikram Kodibagkar Yang Kuang Mark C. Preul Eric J. Kostelich
author_sort	Erica M. Rutter
title	Mathematical Analysis of Glioma Growth in a Murine Model
title_short	Mathematical Analysis of Glioma Growth in a Murine Model
title_full	Mathematical Analysis of Glioma Growth in a Murine Model
title_fullStr	Mathematical Analysis of Glioma Growth in a Murine Model
title_full_unstemmed	Mathematical Analysis of Glioma Growth in a Murine Model
title_sort	mathematical analysis of glioma growth in a murine model
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/49c26a5cb0574efa92425851a3c1b035
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Mathematical Analysis of Glioma Growth in a Murine Model

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