Key Points
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Brain tumours, such as gliomas, in adults and children are morphologically similar, but harbour different genomic alterations, both across and within histological subtypes, that affect prognosis and treatment response
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Many of these genomic alterations lead to reprogramming of cellular metabolism, including glucose, amino acid, and lipid metabolism
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The metabolic reprogramming in brain tumours can be visualized and assessed using various non-invasive clinical imaging modalities
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Metabolic imaging shows great promise as a means to non-invasively evaluate some of the genomic alterations in order to guide the clinical management of patients
Abstract
The revolution in cancer genomics has uncovered a variety of clinically relevant mutations in primary brain tumours, creating an urgent need to develop non-invasive imaging biomarkers to assess and integrate this genetic information into the clinical management of patients. Metabolic reprogramming is a central hallmark of cancer, including brain tumours; indeed, many of the molecular pathways implicated in the pathogenesis of brain tumours result in reprogramming of metabolism. This relationship provides the opportunity to devise in vivo metabolic imaging modalities to improve diagnosis, patient stratification, and monitoring of treatment response. Metabolic phenomena, such as the Warburg effect and altered mitochondrial metabolism, can be leveraged to image brain tumours using techniques including PET and MRI. Moreover, genetic alterations, such as mutations affecting isocitrate dehydrogenase, are associated with unique metabolic signatures that can be detected using magnetic resonance spectroscopy. The need to translate our understanding of the molecular features of brain tumours into imaging modalities with clinical utility is growing; metabolic imaging provides a unique platform to achieve this objective. In this Review, we examine the molecular basis for metabolic reprogramming in brain tumours, and examine current non-invasive metabolic imaging strategies that can be used to interrogate these molecular characteristics with the ultimate goal of guiding and improving patient care.
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Acknowledgements
We thanks Prof. Yue Cao of the Functional Imaging Group in the Department of Radiation Oncology at the University of Michigan Health System, Ann Arbor, Michigan, USA, for providing the imaging scans included in Fig. 6. The work of S.V. is supported by the US NIH National Cancer Institute (grant K08CA181475A), the Mathew Larson Foundation, the Sidney Kimmel Foundation and the Doris Duke Foundation.
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M.M.K. and S.V. researched the data for the article and wrote the manuscript M.M.K., A.P., and S.V. contributed substantially to discussions of the content. M.P.D. contributed figures. All authors reviewed and/or edited the manuscript before submission.
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Kim, M., Parolia, A., Dunphy, M. et al. Non-invasive metabolic imaging of brain tumours in the era of precision medicine. Nat Rev Clin Oncol 13, 725–739 (2016). https://doi.org/10.1038/nrclinonc.2016.108
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DOI: https://doi.org/10.1038/nrclinonc.2016.108
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