Modelling Therapy Resistance for the Identification of Treatment-Refractory Cell Population(s) in Human Glioblastoma Academic Article uri icon

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abstract

  • Despite aggressive multimodal therapy, human glioblastoma (hGBM), a highly malignant grade IV astrocytic tumour, remains incurable and inevitably relapses. Recent data has implicated intratumoral heterogeneity as the driver of therapy resistance and tumour relapse in hGBM. Thus models that capture the evolving hGBM biology in response to chemoradiotherapy will allow for the identification of cellular pathways that govern GBM therapy failure. In this study, we have developed a novel model to profile the clonal evolution of treatment na├»ve brain tumour initiating cell (BTIC) enriched hGBMs through chemoradiotherapy using: stem cell assays, BTIC marker expression and transcriptome analysis, immunohistochemistry, and cellular DNA barcoding technology. We report that treatment of hGBM BTICs leads to increased self-renewal capacity and higher transcript expression of stem cell genes Bmi1 and Sox2. Based on global transcriptome analysis of the in vitro treated hGBM, we also identify a hyper-aggressive form of glioma. Using our therapy-adapted hGBM-mouse xenograft model, we discover that despite tumour regression and increased mouse survival post-therapy, tumour relapse remains inevitable. The treatment-refractory cells again have increased self-renewal capacity and higher expression of Bmi1 and Sox2. Furthermore, by combining cellular DNA barcoding technology, which barcodes hGBM at single cell resolution, with our novel in vitro and in vivo therapy models, we are able to determine whether a pre-existing or a therapy driven subpopulation(s) seeds hGBM tumour relapse. Profiling the dynamic nature of heterogeneous hGBM subpopulations through disease progression and treatment may lead to the identification of novel therapeutic targets for the treatment of recurrent hGBM.

authors

  • Qazi, MA
  • Vora, P
  • Venugopal, C
  • Bakhshinyan, D
  • Nixon, A
  • McFarlane, N
  • Subapanditha, MK
  • Murty, NK
  • Hallett, RM
  • Moffat, J
  • Singh, Sheila

publication date

  • October 2016