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

Document Type

Article

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.

Keywords

EGFR, Glioblastoma, Interactions, Mathematical oncology, Pdgfra

Publication Date

7-16-2020

Publication Title

Mathematical Biosciences and Engineering

ISSN

15471063

E-ISSN

15510018

Volume

17

Issue

5

First Page

4905

Last Page

4941

PubMed ID

33120534

Digital Object Identifier (DOI)

10.3934/mbe.2020267

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