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Molecular Subgroups of Medulloblastoma Identification Using Noninvasive Magnetic Resonance Spectroscopy

Abstract

Background: Medulloblastomas in children can be categorized into 4 molecular subgroups with differing clinical characteristics, such that subgroup determination aids in prognostication and risk-adaptive treatment strategies. Magnetic resonance spectroscopy (MRS) is a widely available, noninvasive tool that is used to determine the metabolic characteristics of tumors and provide diagnostic information without the need for tumor tissue. In this study, we investigated the hypothesis that metabolite concentrations measured by MRS would differ between molecular subgroups of medulloblastoma and allow accurate subgroup determination.

Methods: MRS was used to measure metabolites in medulloblastomas across molecular subgroups (SHH = 12, Groups 3/4 = 17, WNT = 1). Levels of 14 metabolites were analyzed to determine those that were the most discriminant for medulloblastoma subgroups in order to construct a multivariable classifier for distinguishing between combined Group 3/4 and SHH tumors.

Results: Medulloblastomas across molecular subgroups revealed distinct spectral features. Group 3 and Group 4 tumors demonstrated metabolic profiles with readily detectable taurine, lower levels of lipids, and high levels of creatine. SHH tumors showed prominent choline and lipid with low levels of creatine and little or no evidence of taurine. A 5-metabolite subgroup classifier inclusive of creatine, myo-inositol, taurine, aspartate, and lipid 13a was developed that could discriminate between Group 3/4 and SHH medulloblastomas with excellent accuracy (cross-validated area under the curve [AUC] = 0.88).

Conclusions: The data show that medulloblastomas of Group 3/4 differ metabolically as measured using MRS when compared with SHH molecular subgroups. MRS is a useful and accurate tool to determine medulloblastoma molecular subgroups.

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References
1.
Kunder R, Jalali R, Sridhar E, Moiyadi A, Goel N, Goel A . Real-time PCR assay based on the differential expression of microRNAs and protein-coding genes for molecular classification of formalin-fixed paraffin embedded medulloblastomas. Neuro Oncol. 2013; 15(12):1644-51. PMC: 3829591. DOI: 10.1093/neuonc/not123. View

2.
Schwalbe E, Williamson D, Lindsey J, Hamilton D, Ryan S, Megahed H . DNA methylation profiling of medulloblastoma allows robust subclassification and improved outcome prediction using formalin-fixed biopsies. Acta Neuropathol. 2013; 125(3):359-71. PMC: 4313078. DOI: 10.1007/s00401-012-1077-2. View

3.
Margol A, Robison N, Gnanachandran J, Hung L, Kennedy R, Vali M . Tumor-associated macrophages in SHH subgroup of medulloblastomas. Clin Cancer Res. 2014; 21(6):1457-65. PMC: 7654723. DOI: 10.1158/1078-0432.CCR-14-1144. View

4.
Braga-Neto U, Dougherty E . Is cross-validation valid for small-sample microarray classification?. Bioinformatics. 2004; 20(3):374-80. DOI: 10.1093/bioinformatics/btg419. View

5.
Hanley J, McNeil B . The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982; 143(1):29-36. DOI: 10.1148/radiology.143.1.7063747. View