Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations

Bethan Morris; Lee Curtin; Andrea Hawkins-Daarud; Matthew E. Hubbard; Ruman Rahman; Stuart J. Smith; Dorothee Auer; Nhan L. Tran; Leland S. Hu; Jennifer M. Eschbacher; Kris A. Smith; Ashley Stokes; Kristin R. Swanson; Markus R. Owen; Bethan Morris; Lee Curtin; Andrea Hawkins-Daarud; Matthew E. Hubbard; Ruman Rahman; Stuart J. Smith; Dorothee Auer; Nhan L. Tran; Leland S. Hu; Jennifer M. Eschbacher; Kris A. Smith; Ashley Stokes; Kristin R. Swanson; Markus R. Owen

doi:10.3934/mbe.2020267

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 5: 4905-4941. doi: 10.3934/mbe.2020267

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Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations

1.
School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
2.
Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, 85054, USA
3.
School of Medicine and Health Sciences, University of Nottingham, Nottingham, NG7 2UH, UK
4.
Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona 85054, USA
5.
Department of Radiology, Mayo Clinic, Phoenix, Arizona 85054, USA
6.
Department of Pathology, Barrow Neurological Institute - St. Joseph’s Hospital and Medical Center, Phoenix, Arizona 85013, USA
7.
Department of Neurosurgery, Barrow Neurological Institute - St. Joseph’s Hospital and Medical Center, Phoenix, Arizona 85013, USA
8.
Department of Imaging Research, Barrow Neurological Institute - St. Joseph’s Hospital and Medical Center, Phoenix, Arizona 85013, USA
9.
Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona 85054, USA

Received: 01 April 2020 Accepted: 01 July 2020 Published: 16 July 2020

Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.
- glioblastoma,
- EGFR,
- PDGFRA,
- interactions,
- mathematical oncology
Citation: Bethan Morris, Lee Curtin, Andrea Hawkins-Daarud, Matthew E. Hubbard, Ruman Rahman, Stuart J. Smith, Dorothee Auer, Nhan L. Tran, Leland S. Hu, Jennifer M. Eschbacher, Kris A. Smith, Ashley Stokes, Kristin R. Swanson, Markus R. Owen. Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 4905-4941. doi: 10.3934/mbe.2020267

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Abstract

Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.

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