DNA methylation-based classification of malformations of cortical development in the human brain

Samir Jabari, Katja Kobow, Tom Pieper, Till Hartlieb, Manfred Kudernatsch, Tilman Polster, Christian G. Bien, Thilo Kalbhenn, Matthias Simon, Hajo Hamer, Karl Rössler, Martha Feucht, Angelika Mühlebner, Imad Najm, José Eduardo Peixoto-Santos, Antonio Gil-Nagel, Rafael Toledano Delgado, Angel Aledo-Serrano, Yanghao Hou, Roland CorasAndreas von Deimling, Ingmar Blümcke

Research output: Contribution to JournalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

Malformations of cortical development (MCD) comprise a broad spectrum of structural brain lesions frequently associated with epilepsy. Disease definition and diagnosis remain challenging and are often prone to arbitrary judgment. Molecular classification of histopathological entities may help rationalize the diagnostic process. We present a retrospective, multi-center analysis of genome-wide DNA methylation from human brain specimens obtained from epilepsy surgery using EPIC 850 K BeadChip arrays. A total of 308 samples were included in the study. In the reference cohort, 239 formalin-fixed and paraffin-embedded (FFPE) tissue samples were histopathologically classified as MCD, including 12 major subtype pathologies. They were compared to 15 FFPE samples from surgical non-MCD cortices and 11 FFPE samples from post-mortem non-epilepsy controls. We applied three different statistical approaches to decipher the DNA methylation pattern of histopathological MCD entities, i.e., pairwise comparison, machine learning, and deep learning algorithms. Our deep learning model, which represented a shallow neuronal network, achieved the highest level of accuracy. A test cohort of 43 independent surgical samples from different epilepsy centers was used to test the precision of our DNA methylation-based MCD classifier. All samples from the test cohort were accurately assigned to their disease classes by the algorithm. These data demonstrate DNA methylation-based MCD classification suitability across major histopathological entities amenable to epilepsy surgery and age groups and will help establish an integrated diagnostic classification scheme for epilepsy-associated MCD.
Original languageEnglish
Pages (from-to)93-104
Number of pages12
JournalActa Neuropathologica
Volume143
Issue number1
Early online date2021
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Brain development
  • Cortical malformation
  • Deep learning
  • Epigenetic
  • Epilepsy

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