Current challenges in glioblastoma: intratumour heterogeneity, residual disease and models to predict disease recurrence

Glioblastoma (GB) is the most common malignant primary brain tumour, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumour recurrence. Technological advances are continually improving our understanding of the disease and in particular our knowledge of clonal evolution, intratumour heterogeneity and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modelling (including neural networks), and strategies such as multiple-sampling during tumour resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB.

Abstract licence: CC BY

• Zebrafish Models of Cancer—New Insights on Modeling Human Cancer in a Non-Mammalian Vertebrate · 2019

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