Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures: A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures

Ed Schwalbe, D. Hicks, Gholamreza Rafiee, Matthew Bashton, Henning Gohlke, Amir Enshaei, Sandeep Potluri, J. Matthiesen, M. Mather, P. Taleongpong, R. Chaston, A. Silmon, A. Curtis, J. C. Lindsey, S. Crosier, A. J. Smith, T. Goschzik, F. Doz, S. Rutkowski, B. LanneringT. Pietsch, S. Bailey, D. Williamson, S. C. Clifford

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Abstract

Rapid and reliable detection of disease-associated DNA methylation patterns has major potential to advance molecular diagnostics and underpin research investigations. We describe the development and validation of minimal methylation classifier (MIMIC), combining CpG signature design from genome-wide datasets, multiplex-PCR and detection by single-base extension and MALDI-TOF mass spectrometry, in a novel method to assess multi-locus DNA methylation profiles within routine clinically-applicable assays. We illustrate the application of MIMIC to successfully identify the methylation-dependent diagnostic molecular subgroups of medulloblastoma (the most common malignant childhood brain tumour), using scant/low-quality samples remaining from the most recently completed pan-European medulloblastoma clinical trial, refractory to analysis by conventional genome-wide DNA methylation analysis. Using this approach, we identify critical DNA methylation patterns from previously inaccessible cohorts, and reveal novel survival differences between the medulloblastoma disease subgroups with significant potential for clinical exploitation.
Original languageEnglish
Article number13421
JournalScientific Reports
Volume7
Issue number1
Early online date18 Oct 2017
DOIs
Publication statusPublished - Dec 2017

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