Genomic DNA methylation distinguishes subtypes of human focal cortical dysplasia

Abstract

Objectives Focal cortical dysplasia (FCD) is a major cause of drug‐resistant focal epilepsy in children, and the clinicopathological classification remains a challenging issue in daily practice. With the recent progress in DNA methylation–based classification of human brain tumors we examined whether genomic DNA methylation and gene expression analysis can be used to also distinguish human FCD subtypes. Methods DNA methylomes and transcriptomes were generated from massive parallel sequencing in 15 surgical FCD specimens, matched with 5 epilepsy and 6 nonepilepsy controls. Results Differential hierarchical cluster analysis of DNA methylation distinguished major FCD subtypes (ie, Ia, IIa, and IIb) from patients with temporal lobe epilepsy patients and nonepileptic controls. Targeted panel sequencing identified a novel likely pathogenic variant in DEPDC5 in a patient with FCD type IIa. However, no enrichment of differential DNA methylation or gene expression was observed in mechanistic target of rapamycin (mTOR) pathway–related genes. Significance Our studies extend the evidence for disease‐specific methylation signatures toward focal epilepsies in favor of an integrated clinicopathologic and molecular classification system of FCD subtypes incorporating genomic methylation.

Mosaic trisomy of chromosome 1q in human brain tissue associates with unilateral polymicrogyria, very early-onset focal epilepsy, and severe developmental delay

“…We performed unsupervised dimensionality reduction and hierarchical cluster analysis. In addition to previously described FCD and TLE specific methylation classes [36], PMG in our analysis formed a novel separate cluster in the UMAP dimensionality reduction (Fig. 1a).…”

“…It can further provide certain information on chromosomal imbalances, and inform about molecular pathways underlying disease development. This has been proven to variable extent in brain tumors and major subtypes of focal cortical dysplasia (FCD) [32,36,53,57]. We hypothesized that the determination of DNA methylation signatures might help distinguish PMG from other related hemispheric and focal MCD and identify PMG subtypes with specific molecular and clinical features.…”

2017 WONOEP appraisal: Studying epilepsy as a network disease using systems biology approaches

“…Data-driven (unsupervised) classification techniques are particularly useful for extracting information from unclassified patterns, or during an exploratory phase of pattern recognition. Kobow and colleagues were able to show that major FCD subtypes can be discriminated from each other as well as from non-FCD epilepsy (ie, patients with TLE) and nonepilepsy autopsy controls based on their epigenomic signature, 37 thereby recapitulating previous results obtained in experimental epilepsy models. 29,32 Thus, the approach appears promising with regard to the identification of diagnostic molecular biomarkers.…”

“…Thereby, one genome gives rise to several "epigenomes" and consequently phenotypes. 37 The aim was to identify potential molecular biomarkers based on DNA methylation that could help stratify patients according to seizure phenotype and associated pathology. [24][25][26][27][28][29][30][31][32] Massive parallel sequencing and improved molecular and computational analysis allow advanced profiling of the genome, exome, and transcriptome together with all different layers of the epigenome, opening new avenues to decipher complex disease pathomechanisms.…”

“…Kobow et al recently performed genome-wide DNA methylation profiling in tissue from patients with focal cortical dysplasia (FCD), patients with TLE, and no-seizure controls. 37 The aim was to identify potential molecular biomarkers based on DNA methylation that could help stratify patients according to seizure phenotype and associated pathology. To aid biomarker identification, they performed multidimensional scaling and hierarchical cluster analysis, to reduce the high dimensionality of the data and identify unbiased subgroups within the data.…”