Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence

Hayley P Ellis, Mark Greenslade, Ben Powell, Inmaculada Spiteri, Andrea Sottoriva, Kathreena M Kurian

Research output: Contribution to journalArticle (Academic Journal)peer-review

60 Citations (Scopus)
297 Downloads (Pure)

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.
Original languageEnglish
Article number251
Number of pages9
JournalFrontiers in Oncology
Volume5
DOIs
Publication statusPublished - 16 Nov 2015

Keywords

  • GBM
  • intratumor heterogeneity
  • neural networks
  • residual disease
  • Bayesian models

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