MBRS-29. IN-VIVO METABOLITE PROFILES FOR THE NON-INVASIVE AND RAPID IDENTIFICATION OF MOLECULAR SUBGROUP IN MEDULLOBLASTOMA. Issue 2 (22nd June 2018)

• Record Type: Journal Article
• Authors: Kohe, Sarah E; Babourina-Brooks, Ben; Scerif, Fatma; Hicks, Debbie; Schwalbe, Ed C; Crosier, Stephen; Lindsey, Janet; Adiamah, Magretta; Storer, Lisa C D; Lourdusamy, Anbarasu; Gill, Simrandip K; Bennett, Christopher D; Wilson, Martin; Avula, Shivaram; Mitra, Dipayan; Dineen, Rob; Bailey, Simon; Williamson, Daniel; Grundy, Richard G; Clifford, Steven C; Peet, Andrew C
• Is Part Of: Neuro-oncology. Volume 20:Issue 2(2018)supplement 2
• Page Start: i134
• Page End: i134
• Publication Date: 2018-06-22
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noy059.474
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Molecular subgroup is now influencing risk stratification and disease management in medulloblastoma. Tissue metabolite profiles have shown promise in identifying the four consensus subgroups. A smaller number of metabolites can be measured non-invasively in patients using magnetic resonance spectroscopy (MRS). We investigated if a classifier constructed from tissue metabolites could be applied to in-vivo metabolite profiles to accurately predict subgroup non-invasively. Machine learning was used to construct a classifier with tissue concentrations from 10 metabolites reliably detected in-vivo. Retrospectively acquired diagnostic in-vivo MRS was available for 37 cases from four treatment centres. Although we identified WNT tumours by the presence of GABA with 100% accuracy in tissue, GABA was not reliability quantified in this in-vivo dataset. Therefore the classifier was developed using tissue profiles of known molecular subgroup (determined using DNA methylation array) from group 3(n=20), group 4(n=34) and SHH(n=23). The cross-validated accuracy of the tissue classifier was 86%. When applied to in-vivo metabolite profiles, subgroup was predicted non-invasively with an overall accuracy of 76%. Group 3 had the highest proportion of incorrectly classified cases (4/11), followed by SHH (2/10), and group 4 (3/16), largely due to differences in measuring lipids, glutamate, glutamine and hypotaurine in-vivo. We have established the feasibility of non-invasive metabolite profiling to identify medulloblastoma subgroups. With the ongoing optimization of MRS to target specific metabolites including GABA, we can further improve accuracy. Rapid, non-invasive preoperative diagnosis of subgroup will offer opportunities to stratify early therapeutic intervention especially surgery, avoiding long-term sequalae and improving quality of survival.