ArticlePLoS computational biology2012
The impact of phenotypic switching on glioblastoma growth and invasion.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed, 131 citations in OpenAlex.
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- Modelling microtube driven invasion of glioma.Journal of mathematical biology · 2023Article
- Uptake of H-ferritin by Glioblastoma stem cells and its impact on their invasion capacity.Journal of cancer research and clinical oncology · 2023Article
- Traveling wave speed and profile of a "go or grow" glioblastoma multiforme model.Communications in nonlinear science & numerical simulation · 2023Article
- Cellular states are coupled to genomic and viral heterogeneity in HPV-related oropharyngeal carcinoma.Nature genetics · 2023Article
- Data-driven spatio-temporal modelling of glioblastoma.Royal Society open science · 2023Review
- Quantification of spatial subclonal interactions enhancing the invasive phenotype of pediatric glioma.Cell reports · 2022Article
- Understanding glioblastoma invasion using physically-guided neural networks with internal variables.PLoS computational biology · 2022Article
- Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity.Bulletin of mathematical biology · 2021Article
- Modeling glioblastoma heterogeneity as a dynamic network of cell states.Molecular systems biology · 2021Article
- Genealogical structure changes as range expansions transition from pushed to pulled.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The brain tumour glioblastoma is characterised by diffuse and infiltrative growth into surrounding brain tissue. At the macroscopic level, the progression speed of a glioblastoma tumour is determined by two key factors: the cell proliferation rate and the cell migration speed. At the microscopic level, however, proliferation and migration appear to be mutually exclusive phenotypes, as indicated by recent in vivo imaging data. Here, we develop a mathematical model to analyse how the phenotypic switching between proliferative and migratory states of individual cells affects the macroscopic growth of the tumour. For this, we propose an individual-based stochastic model in which glioblastoma cells are either in a proliferative state, where they are stationary and divide, or in motile state in which they are subject to random motion. From the model we derive a continuum approximation in the form of two coupled reaction-diffusion equations, which exhibit travelling wave solutions whose speed of invasion depends on the model parameters. We propose a simple analytical method to predict progression rate from the cell-specific parameters and demonstrate that optimal glioblastoma growth depends on a non-trivial trade-off between the phenotypic switching rates. By linking cellular properties to an in vivo outcome, the model should be applicable to designing relevant cell screens for glioblastoma and cytometry-based patient prognostics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.