Metabolite Profile Differences in Childhood Brain Tumors: 1H Magic Angle Spinning NMR Spectroscopy

Abstract

Background: Brain and nervous system tumours are the most common solid cancers in children. Molecular characterisation of these tumours is important for providing novel biomarkers of disease and identifying molecular pathways which may provide putative targets for new therapies. 1H High Resolution Magic Angle Spinning NMR spectroscopy (HR-MAS) is an emerging technique for determining metabolite profiles from small pieces of intact tissue and whole cells grown in culture, making it ideal for molecular characterisation of disease.Method: 22 tissue samples from children with brain tumours and 7 cell lines originating from medulloblastoma (2), neuroblastoma (2), supertentorial PNET (1) and retinoblastoma (2) were analysed using HR-MAS. Spectra were fitted to a library of individual metabolite spectra to provide metabolite values, Principle Component Analysis (PCA) was used to investigate the metabolic relationship between the tumour types.Results: Primitive neuroectodermal and glial based tumours were linearly separable using PCA, demonstrating that histological features of the tissue were closely related to their metabolite profiles. Each cultured cell line was found to have a distinct metabolite profile. The desmoplastic and classic variants of medulloblastoma were particularly distinct, with large differences in the distribution of choline containing metabolites.Conclusion: HR-MAS identified key differences in the metabolite profiles of childhood brain tumour tissue and cells grown in culture, improving the molecular characterisation of these tumours and showing the promise of HR-MAS as a rapid diagnostic aid. Further investigation of the underlying molecular pathways is required to assess their potential as targets for new agents.KeywordsMetabolite ProfilePaediatric Brain TumourChildhood Brain TumourMedulloblastoma Cell LineBrain Tumour TissueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.