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Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groupsResearch in context

التفاصيل البيبلوغرافية
العنوان: Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groupsResearch in context
المؤلفون: Sarah Kohe, Christopher Bennett, Florence Burté, Magretta Adiamah, Heather Rose, Lara Worthington, Fatma Scerif, Lesley MacPherson, Simrandip Gill, Debbie Hicks, Edward C. Schwalbe, Stephen Crosier, Lisa Storer, Ambarasu Lourdusamy, Dipyan Mitra, Paul S. Morgan, Robert A. Dineen, Shivaram Avula, Barry Pizer, Martin Wilson, Nigel Davies, Daniel Tennant, Simon Bailey, Daniel Williamson, Theodoros N. Arvanitis, Richard G. Grundy, Steven C. Clifford, Andrew C. Peet
المصدر: EBioMedicine, Vol 100, Iss , Pp 104958- (2024)
بيانات النشر: Elsevier, 2024.
سنة النشر: 2024
المجموعة: LCC:Medicine
LCC:Medicine (General)
مصطلحات موضوعية: Medulloblastoma, Groups, Metabolites, Metabolomics, Mass spectrometry, Radiology, Medicine, Medicine (General), R5-920
الوصف: Summary: Background: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2352-3964
Relation: http://www.sciencedirect.com/science/article/pii/S2352396423005248; https://doaj.org/toc/2352-3964
DOI: 10.1016/j.ebiom.2023.104958
URL الوصول: https://doaj.org/article/7ea57b301d434c53922cf4908ce48606
رقم الأكسشن: edsdoj.7ea57b301d434c53922cf4908ce48606
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:23523964
DOI:10.1016/j.ebiom.2023.104958