Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groupsResearch in context
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
Affiliations
Sarah Kohe
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Birmingham Children’s Hospital, Birmingham, UK
Christopher Bennett
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Birmingham Children’s Hospital, Birmingham, UK
Florence Burté
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
Magretta Adiamah
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
Heather Rose
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Birmingham Children’s Hospital, Birmingham, UK
Lara Worthington
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Birmingham Children’s Hospital, Birmingham, UK; RRPPS, University Hospital Birmingham, Birmingham, UK
Fatma Scerif
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
Lesley MacPherson
Birmingham Children’s Hospital, Birmingham, UK
Simrandip Gill
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Birmingham Children’s Hospital, Birmingham, UK
Debbie Hicks
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
Edward C. Schwalbe
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK; Department of Applied Sciences, Northumbria University, Newcastle upon Tyne, UK
Stephen Crosier
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
Lisa Storer
Children’s Brain Tumour Research Centre, Queen’s Medical Centre, University of Nottingham, Nottingham, UK
Ambarasu Lourdusamy
Children’s Brain Tumour Research Centre, Queen’s Medical Centre, University of Nottingham, Nottingham, UK
Dipyan Mitra
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
Paul S. Morgan
Children’s Brain Tumour Research Centre, Queen’s Medical Centre, University of Nottingham, Nottingham, UK
Robert A. Dineen
Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK; Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
Shivaram Avula
Alder Hey Children’s Hospital, Liverpool, UK
Barry Pizer
University of Liverpool, Liverpool, UK
Martin Wilson
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Birmingham Children’s Hospital, Birmingham, UK
Nigel Davies
RRPPS, University Hospital Birmingham, Birmingham, UK
Daniel Tennant
Institute of Metabolism and Systems Research, University of Birmingham, UK
Simon Bailey
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
Daniel Williamson
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
Theodoros N. Arvanitis
Department of Electronic, Electrical and Systems Engineering, University of Birmingham, UK
Richard G. Grundy
Children’s Brain Tumour Research Centre, Queen’s Medical Centre, University of Nottingham, Nottingham, UK
Steven C. Clifford
Wolfson Childhood Cancer Research Centre, Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK; Corresponding author.
Andrew C. Peet
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Birmingham Children’s Hospital, Birmingham, UK; Corresponding author. Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.
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.
