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Ellis HP, Greenslade M, Powell B, et al. Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence. Front Oncol. 2015;5:251doi: 10.3389/fonc.2015.00251.
Ellis, H. P., Greenslade, M., Powell, B., Spiteri, I., Sottoriva, A., & Kurian, K. M. (2015). Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence. Frontiers in oncology, 5251. https://doi.org/10.3389/fonc.2015.00251
Ellis, Hayley P, et al. "Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence." Frontiers in oncology vol. 5 (2015): 251. doi: https://doi.org/10.3389/fonc.2015.00251
Ellis HP, Greenslade M, Powell B, Spiteri I, Sottoriva A, Kurian KM. Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence. Front Oncol. 2015 Nov 16;5:251. doi: 10.3389/fonc.2015.00251. eCollection 2015. PMID: 26636033; PMCID: PMC4644939.
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Front Oncol. 2015 Nov 16;5:251. doi: 10.3389/fonc.2015.00251. eCollection 2015.

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

Frontiers in oncology

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

Affiliations

  1. Brain Tumour Research Group, Institute of Clinical Neurosciences, University of Bristol , Bristol , UK.
  2. Bristol Genetics Laboratory, North Bristol NHS Trust , Bristol , UK.
  3. School of Mathematics, University of Bristol , Bristol , UK.
  4. Centre for Evolution and Cancer, The Institute of Cancer Research , London , UK.

PMID: 26636033 PMCID: PMC4644939 DOI: 10.3389/fonc.2015.00251

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.

Keywords: Bayesian models; GBM; intratumor heterogeneity; neural networks; residual disease

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