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.
Highlights
Altered DNA methylation patterns have emerged as a common feature of disease pathogenesis, showing clear potential in diagnostics, sub-classification and prediction of therapeutic response/ disease course[1,2,3,4,5,6,7]
Following design and validation of a minimal methylation classifier (MIMIC) assay for molecular subgrouping, we assessed its efficacy in limited archival tumour biopsies previously refractory to subgrouping using current research methods, taken from the pan-European HIT-SIOP-PNET4 medulloblastoma clinical trial (2000–2006)[17,18]
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 polymerase chain reaction (PCR) was assessed in silico (Supplemental experimental methods), and (iii) in vitro PCR validation (Fig. 1a; Fig. 3)
Summary
Altered DNA methylation patterns have emerged as a common feature of disease pathogenesis, showing clear potential in diagnostics, sub-classification and prediction of therapeutic response/ disease course[1,2,3,4,5,6,7]. In contrast to current high-throughput, genome-wide research methodologies (e.g. whole-genome bisulfite sequencing[8], DNA methylation arrays9), particular challenges exist in the clinical application of disease-associated methylation patterns These include derivation and validation of representative DNA methylation signatures from genome-scale datasets, and their assessment using platform-independent assays that can be applied rapidly to single samples, including low quality and/or quantity biopsies, in routine diagnostics. Following design and validation of a MIMIC assay for molecular subgrouping, we assessed its efficacy in limited archival tumour biopsies previously refractory to subgrouping using current research methods, taken from the pan-European HIT-SIOP-PNET4 medulloblastoma clinical trial (2000–2006)[17,18] This trial enrolled patients negative for all established clinico-molecular risk-factors (termed ‘standard-risk (SR)’ disease12), a group for which there is an urgent unmet need to develop biomarker-driven treatment strategies
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