Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience

David Capper,Daniel Hänggi,Anne Schöler,Annika K Wefers,David Jones ,David Reuß ,Daniel Teichmann,Andreas Unterberg,Wolfgang Wick,Till Milde,Volker Hovestadt,Olaf Witt,Arend Koch,Michael Platten,Damian Stichel,Azadeh Ebrahimi,Martin Sill,Andrey Korshunov,Annekathrin Reinhardt,Simone Schmid,Philipp Sievers,Kristin Huang,Daniel Schrimpf,Stefan M Pfister,Felix Sahm,Christian Koelsche,Andreas Von Deimling

doi:10.1007/s00401-018-1879-y

Abstract

Recently, we described a machine learning approach for classification of central nervous system tumors based on the analysis of genome-wide DNA methylation patterns [6]. Here, we report on DNA methylation-based central nervous system (CNS) tumor diagnostics conducted in our institution between the years 2015 and 2018. In this period, more than 1000 tumors from the neurosurgical departments in Heidelberg and Mannheim and more than 1000 tumors referred from external institutions were subjected to DNA methylation analysis for diagnostic purposes. We describe our current approach to the integrated diagnosis of CNS tumors with a focus on constellations with conflicts between morphological and molecular genetic findings. We further describe the benefit of integrating DNA copy-number alterations into diagnostic considerations and provide a catalog of copy-number changes for individual DNA methylation classes. We also point to several pitfalls accompanying the diagnostic implementation of DNA methylation profiling and give practical suggestions for recurring diagnostic scenarios.

Highlights

DNA methylation-based tumor classification has emerged as a promising tool to dissect tumor classes and to improve diagnostic accuracy [6, 32, 45, 46]
Application of this method has shown that many accepted WHO central nervous system (CNS) tumor entities can be more precisely defined by DNA methylation profiling than by morphological features: medulloblastomas are separated into four major clinically relevant sub-groups [48], ependymomas grouped by DNA methylation profiles are clinically more homogenous than what WHO classification and grading can accomplish [32], and supratentorial primitive
The potential of methylation-based characterization has been expanded to other CNS tumor entities and the overarching concept and feasibility has just been published [6]. We introduced this technique in our routine diagnostic workup of CNS tumors in 2015

Summary

Introduction

DNA methylation-based tumor classification has emerged as a promising tool to dissect tumor classes and to improve diagnostic accuracy [6, 32, 45, 46]. Analysis of DNA methylation can be performed by readily available tools Application of this method has shown that many accepted WHO central nervous system (CNS) tumor entities can be more precisely defined by DNA methylation profiling than by morphological features: medulloblastomas are separated into four major clinically relevant sub-groups [48], ependymomas grouped by DNA methylation profiles are clinically more homogenous than what WHO classification and grading can accomplish [32], and supratentorial primitive. The potential of methylation-based characterization has been expanded to other CNS tumor entities and the overarching concept and feasibility has just been published [6]. We introduced this technique in our routine diagnostic workup of CNS tumors in 2015. We expect that many of these suggestions will be replaced by more sound recommendations in the future

Materials and methods

Methodology background

DNA methylation class