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.
We have provided a blueprint for defining minimal, multiply-redundant disease-associated DNA methylation signatures from genome-wide datasets, and have developed MS-MIMIC as a validated assay for their assessment, including open-source classification tools for data interpretation (http://medulloblastomadiagnostics.ncl.ac.uk). Unlike research methodologies (e.g. Illumina 450k and MethylationEPIC arrays) which require batched assessments (≥8 samples per run), MS-MIMIC exploits detection technologies in common clinical use (MALDI-TOF) to enable rapid (<3 days from DNA extraction to result), low-cost (<$200 per sample in 2017), routine assessment in single or multiple samples. The assay format allows a flexible, modular approach, in which multiplex PCRs can be straightforwardly added or removed, offering the ability to adapt or extend panels to evolving clinical needs. Moreover, its low DNA input requirements and applicability to archival sample collections has the potential to unlock previously inaccessible molecular information from informative cohorts, as demonstrated for HIT-SIOP-PNET4. This assessment of DNA methylation signatures in the clinical setting holds rich promise for molecular sub-classification and prognostication across diverse diseases.