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

Lookup NU author(s): Dr Ed Schwalbe, Dr Debbie Hicks, Dr Reza Rafiee, Dr Matthew Bashton, Dr Amir Enshaei, Dr Michael Mather, Dr Angela Silmon, Dr Janet Lindsey, Dr Stephen Crosier, Amanda Smith, Dr Daniel Williamson, Professor Steven Clifford

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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.


Publication metadata

Author(s): Schwalbe EC, Hicks D, Rafiee G, Bashton M, Gohlke H, Enshaei A, Potluri S, Matthiesen J, Mather M, Taleongpong P, Chaston R, Silmon A, Curtis A, Lindsey JC, Crosier S, Smith AJ, Goschzik T, Doz F, Rutkowski S, Lannering B, Pietsch T, Bailey S, Williamson D, Clifford SC

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2017

Volume: 7

Online publication date: 18/10/2017

Acceptance date: 26/09/2017

Date deposited: 03/11/2017

ISSN (electronic): 2045-2322

Publisher: Nature Publishing Group

URL: https://doi.org/10.1038/s41598-017-13644-1

DOI: 10.1038/s41598-017-13644-1


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