Speed Switch in Glioblastoma Growth Rate due to Enhanced Hypoxia-Induced Migration

Curtin, Lee; Hawkins-Daarud, Andrea; Zee, Kristoffer G. van der; Swanson, Kristin R.; Owen, Markus R.

doi:10.1007/s11538-020-00718-x

Cited by 10 publications

(19 citation statements)

References 35 publications

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“…These parameters were chosen as they represent the tumor's response to hypoxic stress. In previous work, we have observed that all of these parameters (except for β) influence the outward growth rate of PIHNA simulations, which is another consideration of the effects of hypoxia on GBM [12]. We note that the varying tumor kinetics (migration rates D c , D h and proliferation rate ρ) affect the nodularity of the simulated tumors.…”

Section: Virtual Experimentsmentioning

confidence: 69%

“…The effect of an increase in D h /D c is more pronounced for tumors that are more sensitive to the hypoxic environment caused by the ischemia, see Appendix Figures 9 -11. In previous work, we have shown that an increase in D h /D c increases the outward growth rate of PIHNAsimulated GBM [12]. With faster hypoxic migration rates, the simulated tumor cell densities are able to travel through the hypoxic region faster.…”

Section: Faster Hypoxic Cell Migration Rates Promote Distant Recurrencementioning

confidence: 75%

“…To simulate glioblastoma growth and spread, we have adapted a previously established tumor growth model -the PIHNA model [12,43]. The PIHNA model describes the growth of GBM with the interactions of vasculature in the process of angiogenesis.…”

Section: The Pihna Modelmentioning

confidence: 99%

“…Angiogenic factors migrate with rate D a , are created by the presence of normoxic and hypoxic tumor cells (with rates δ c and δ h , respectively), decay naturally (λ) and are consumed through the creation and presence of vascular cells. For a more in depth justification of these model parameters, see Curtin et al and Swanson et al [12,43]. The parameter values used in this work, as well as their units, can all be found in Table 1.…”

Section: The Pihna Modelmentioning

confidence: 99%

“…Simulated hypoxic events have shown an increase in glioma growth rates in spatiotemporal models of GBM [26,28]. We have recently found the parameters of the PIHNA model that drive faster outward growth of these simulated tumors and found that those relating to hypoxia were in some cases extremely influential [12].…”

Section: Introductionmentioning

confidence: 99%

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A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma

et al. 2020

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Glioblastoma (GBM) is the most aggressive primary brain tumor with a short median survival. Tumor recurrence is a clinical expectation of this disease and usually occurs along the resection cavity wall. However, previous clinical observations have suggested that in cases of ischemia following surgery, tumors are more likely to recur distally. Through the use of a previously established mechanistic model of GBM, the Proliferation Invasion Hypoxia Necrosis Angiogenesis (PIHNA) model, we explore the phenotypic drivers of this observed behavior. We have extended the PIHNA model to include a new nutrient-based vascular efficiency term that encodes the ability of local vasculature to provide nutrients to the simulated tumor. The extended model suggests sensitivity to a hypoxic microenvironment and the inherent migration and proliferation rates of the tumor cells are key factors that drive distal recurrence.

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Section: Virtual Experimentsmentioning

confidence: 69%

Section: Faster Hypoxic Cell Migration Rates Promote Distant Recurrencementioning

confidence: 75%

Section: The Pihna Modelmentioning

confidence: 99%

Section: The Pihna Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma

et al. 2020

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An adaptive semi‐implicit finite element solver for brain cancer progression modeling

Tzirakis

Papanikas

Sakkalis

et al. 2023

Numer Methods Biomed Eng

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Glioblastoma is the most aggressive and infiltrative glioma, classified as Grade IV, with the poorest survival rate among patients. Accurate and rigorously tested mechanistic in silico modeling offers great value to understand and quantify the progression of primary brain tumors. This paper presents a continuum‐based finite element framework that is built on high performance computing, open‐source libraries to simulate glioblastoma progression. We adopt the established proliferation invasion hypoxia necrosis angiogenesis model in our framework to realize scalable simulations of cancer, and has demonstrated to produce accurate and efficient solutions in both two‐ and three‐dimensional brain models. The in silico solver can successfully implement arbitrary order discretization schemes and adaptive remeshing algorithms. A model sensitivity analysis is conducted to test the impact of vascular density, cancer cell invasiveness and aggressiveness, the phenotypic transition potential, including that of necrosis, and the effect of tumor‐induced angiogenesis in the evolution of glioblastoma. Additionally, individualized simulations of brain cancer progression are carried out using pertinent magnetic resonance imaging data, where the in silico model is used to investigate the complex dynamics of the disease. We conclude by arguing how the proposed framework can deliver patient‐specific simulations of cancer prognosis and how it could bridge clinical imaging with modeling.

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Simulating the behaviour of glioblastoma multiforme based on patient MRI during treatments

2022

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Glioblastoma Multiforme is a brain cancer that still shows poor prognosis for patients despite the active research for new treatments. In this work, the goal is to model and simulate the evolution of tumour associated angiogenesis and the therapeutic response to Glioblastoma Multiforme. Multiple phenomena are modelled in order to fit different biological pathways, such as the cellular cycle, apoptosis, hypoxia or angiogenesis. This leads to in a nonlinear system with 4 equations and 4 unknowns: the density of tumour cells, the O 2 concentration, the density of endothelial cells and the vascular endothelial growth factor concentration. This system is solved numerically on a mesh fitting the geometry of the brain and the tumour of a patient based on a 2D slice of MRI. We show that our numerical scheme is positive, and we give the energy estimates on the discrete solution to ensure its existence. The numerical scheme uses nonlinear control volume finite elements in space and is implicit in time. Numerical simulations have been done using the different standard treatments: surgery, chemotherapy and radiotherapy, in order to conform to the behaviour of a Funding: F.A is a recipient of a PhD fellowship from the Minist Ã¨re de la recherche, A.A.S is supported by chaire INSERM-Ecole Centrale de Nantes, we are also supported by IEA-CNRS and HIPHOP project.

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Speed Switch in Glioblastoma Growth Rate due to Enhanced Hypoxia-Induced Migration

Cited by 10 publications

References 35 publications

A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma

A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma

An adaptive semi‐implicit finite element solver for brain cancer progression modeling

Simulating the behaviour of glioblastoma multiforme based on patient MRI during treatments

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