Ex vivo metabolite profiling of paediatric central nervous system tumours reveals prognostic markers

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作者
Christopher D. Bennett
Simrandip K. Gill
Sarah E. Kohe
Martin P. Wilson
Nigel P. Davies
Theodoros N. Arvanitis
Daniel A. Tennant
Andrew C. Peet
机构
[1] University of Birmingham,Institute of Cancer and Genomic Sciences
[2] Birmingham Women’s and Children’s Hospital NHS Foundation Trust,Birmingham University Imaging Centre, School of Psychology
[3] University of Birmingham,Institute of Digital Healthcare
[4] University Hospitals Birmingham NHS Foundation Trust,Institute of Metabolism and Systems Research
[5] WMG,undefined
[6] University of Warwick,undefined
[7] University of Birmingham,undefined
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Brain tumours are the most common cause of cancer death in children. Molecular studies have greatly improved our understanding of these tumours but tumour metabolism is underexplored. Metabolites measured in vivo have been reported as prognostic biomarkers of these tumours but analysis of surgically resected tumour tissue allows a more extensive set of metabolites to be measured aiding biomarker discovery and providing validation of in vivo findings. In this study, metabolites were quantified across a range of paediatric brain tumours using 1H-High-Resolution Magic Angle Spinning nuclear magnetic resonance spectroscopy (HR-MAS) and their prognostic potential investigated. HR-MAS was performed on pre-treatment frozen tumour tissue from a single centre. Univariate and multivariate Cox regression was used to examine the ability of metabolites to predict survival. The models were cross validated using C-indices and further validated by splitting the cohort into two. Higher concentrations of glutamine were predictive of a longer overall survival, whilst higher concentrations of lipids were predictive of a shorter overall survival. These metabolites were predictive independent of diagnosis, as demonstrated in multivariate Cox regression models. Whilst accurate quantification of metabolites such as glutamine in vivo is challenging, metabolites show promise as prognostic markers due to development of optimised detection methods and increasing use of 3 T clinical scanners.
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