Abstract
Glioblastoma (GBM) is the most aggressive and most common primary brain tumour in adults and is uniformly fatal, with a poor median survival time of 15. Standard of care for GBM consist of radiotherapy either alone or following surgical resection, despite this, radio-resistance almost always occurs making recurrence inevitable. Failure of the current standard of care has been partly attributed to a special sub-population, the glioma stem cells (GSCs), which initiate and drive tumour growth. Treatment cannot be successful unless all GSCs are eliminated. However, GSCs are known to be highly resistant to radiotherapy, and complete surgical removal is impossible in GBM. Therefore, new treatments that specifically target GSCs could have a potentially large benefit. BMP4 has been shown to induce differentiation of GSCs towards a less malignant, astrocytic-like (ALCs) lineage reversing the GSC state and reducing radio-resistance. We develop a data driven mechanistic mathematical model that accounts for the GSCs, tumour cells (TCs) and ALCs as well as their response to both radiotherapy and BMP4 induced differentiation therapy. We parameterise our model based on data collected from twelve GSC cell lines, that underwent various BMP4 and radiotherapy treatments. Through virtual clinical trials we determine an optimal dosing strategy for BMP4 in combination with radiotherapy. We identify several key parameters that impact the efficacy of BMP4 therapy including radiosensitivity and proliferation rate. These parameters can be used to strategically select candidates for real clinical trials that will likely have the largest benefit.
Slides
Citation
@misc{harbour2025,
author = {Harbour, Nicholas and Curtin, Lee and Quinones-Hinojosa,
Alfredo and Hubbard, Matthew and Swanson, Kristin and Owen, Markus},
title = {Mathematical Modelling of Differentiation Therapy for Glioma
Stem Cells},
date = {2025-06-24},
langid = {en}
}