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Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups

Lookup NU author(s): Dr Florence Burté, Magretta Adiamah, Dr Fatma Scerif, Dr Debbie Hicks, Dr Ed Schwalbe, Dr Stephen Crosier, Dr Dipayan Mitra, Professor Simon BaileyORCiD, Dr Daniel Williamson, Professor Steven CliffordORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2024 The AuthorsBackground: The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification ‘gold-standard’, typically delivered 3–4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS). Methods: Metabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival. Findings: Group-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4–8.1, p = 0.025). Interpretation: Tissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis. Funding: Children with Cancer UK, Cancer Research UK, Children's Cancer North and a Newcastle University PhD studentship.

Publication metadata

Author(s): Kohe S, Bennett C, Burte F, Adiamah M, Rose H, Worthington L, Scerif F, MacPherson L, Gill S, Hicks D, Schwalbe EC, Crosier S, Storer L, Lourdusamy A, Mitra D, Morgan PS, Dineen RA, Avula S, Pizer B, Wilson M, Davies N, Tennant D, Bailey S, Williamson D, Arvanitis TN, Grundy RG, Clifford SC, Peet AC

Publication type: Article

Publication status: Published

Journal: eBioMedicine

Year: 2024

Volume: 100

Print publication date: 01/02/2024

Online publication date: 06/01/2024

Acceptance date: 06/01/2024

Date deposited: 23/01/2024

ISSN (electronic): 2352-3964

Publisher: Elsevier B.V.


DOI: 10.1016/j.ebiom.2023.104958


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Funder referenceFunder name
Cancer Research UK
Children with Cancer UK
Children’s Cancer North
Newcastle University