title: Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence.
abstract: Glioblastoma (GB) is the most common primary malignant brain tumor, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumor recurrence. Technological advances are continually improving our understanding of the disease, and in particular, our knowledge of clonal evolution, intratumor heterogeneity, and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modeling (including neural networks) and strategies such as multiple sampling during tumor 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.
authors.affiliation: Brain Tumour Research Group, Institute of Clinical Neurosciences, University of Bristol , Bristol , UK.
authors.forename: Hayley P
authors.initials: HP
authors.lastname: Ellis
authors.affiliation: Bristol Genetics Laboratory, North Bristol NHS Trust , Bristol , UK.
authors.forename: Mark
authors.initials: M
authors.lastname: Greenslade
authors.affiliation: School of Mathematics, University of Bristol , Bristol , UK.
authors.forename: Ben
authors.initials: B
authors.lastname: Powell
authors.affiliation: Centre for Evolution and Cancer, The Institute of Cancer Research , London , UK.
authors.forename: Inmaculada
authors.initials: I
authors.lastname: Spiteri
authors.affiliation: Centre for Evolution and Cancer, The Institute of Cancer Research , London , UK.
authors.forename: Andrea
authors.initials: A
authors.lastname: Sottoriva
authors.affiliation: Brain Tumour Research Group, Institute of Clinical Neurosciences, University of Bristol , Bristol , UK.
authors.forename: Kathreena M
authors.initials: KM
authors.lastname: Kurian
keywords: Bayesian models; GBM; intratumor heterogeneity; neural networks; residual disease