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Derivation of minimal DNA methylation signatures to identify the four medulloblastoma molecular subgroups:

We first identified a DNA methylation signature of 17 CpG loci, established detection methods and developed a Support Vector Machine (SVM) classification model for distinction of the four medulloblastoma molecular subgroups (WNT, SHH, Grp3 and Grp4). Non-negative matrix factorisation (NMF) consensus clustering was used to identify subgroup membership of a training cohort comprising genome-scale Illumina 450k DNA methylation microarray data for 220 medulloblastomas. The 50 most discriminatory CpG loci for each subgroup (i.e. 200 in total) were considered as signature candidates. These were triaged using (i) a 10-fold cross validated classification fusion algorithm, (ii) a reiterative primer design process where amenability to primer design and multiplex bisulfite PCR was assessed in silico (Supplemental experimental methods), and (iii) in vitro PCR validation. Candidate signature CpG loci were assayed by the development of a novel application of the Agena iPlex assay, whereby methylation-dependent SNPs representative of CpG methylation status were induced by initial treatment of DNA with sodium bisulfite, followed by multiplexed PCR and single base extension of probe oligonucleotides. The resultant products were quantified by MALDI-TOF MS (Matrix-assisted laser desorption/ionization-time of flight mass spectrometry). The accuracy and precision of methylation estimates from multiplexed extension reactions were tested using incremental proportions of bisulfite-treated methylated:unmethylated DNA. Using these techniques, our optimal, multiply-redundant 17-CpG locus signature was generated. Finally, the training cohort was used to generate an optimised SVM classifier for the signature using 450k DNA methylation array data.