DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis

Felix Sahm, Daniel Schrimpf, Damian Stichel, David T W Jones, Thomas Hielscher, Sebastian Schefzyk, Konstantin Okonechnikov, Christian Koelsche, David E Reuss, David Capper, Dominik Sturm, Hans-Georg Wirsching, Anna Sophie Berghoff, Peter Baumgarten, Annekathrin Kratz, Kristin Huang, Annika K Wefers, Volker Hovestadt, Martin Sill, Hayley P EllisKathreena M Kurian, Ali Fuat Okuducu, Christine Jungk, Katharina Drueschler, Matthias Schick, Melanie Bewerunge-Hudler, Christian Mawrin, Marcel Seiz-Rosenhagen, Ralf Ketter, Matthias Simon, Manfred Westphal, Katrin Lamszus, Albert Becker, Arend Koch, Jens Schittenhelm, Elisabeth J Rushing, V Peter Collins, Stefanie Brehmer, Lukas Chavez, Michael Platten, Daniel Hänggi, Andreas Unterberg, Werner Paulus, Wolfgang Wick, Stefan M Pfister, Michel Mittelbronn, Matthias Preusser, Christel Herold-Mende, Michael Weller, Andreas von Deimling

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

SummaryBackground The WHO classification of brain tumours describes 15 subtypes of meningioma. Nine of these subtypes are allotted to WHO grade I, and three each to grade II and grade III. Grading is based solely on histology, with an absence of molecular markers. Although the existing classification and grading approach is of prognostic value, it harbours shortcomings such as ill-defined parameters for subtypes and grading criteria prone to arbitrary judgment. In this study, we aimed for a comprehensive characterisation of the entire molecular genetic landscape of meningioma to identify biologically and clinically relevant subgroups. Methods In this multicentre, retrospective analysis, we investigated genome-wide DNA methylation patterns of meningiomas from ten European academic neuro-oncology centres to identify distinct methylation classes of meningiomas. The methylation classes were further characterised by DNA copy number analysis, mutational profiling, and RNA sequencing. Methylation classes were analysed for progression-free survival outcomes by the Kaplan-Meier method. The DNA methylation-based and WHO classification schema were compared using the Brier prediction score, analysed in an independent cohort with WHO grading, progression-free survival, and disease-specific survival data available, collected at the Medical University Vienna (Vienna, Austria), assessing methylation patterns with an alternative methylation chip. Findings We retrospectively collected 497 meningiomas along with 309 samples of other extra-axial skull tumours that might histologically mimic meningioma variants. Unsupervised clustering of DNA methylation data clearly segregated all meningiomas from other skull tumours. We generated genome-wide DNA methylation profiles from all 497 meningioma samples. DNA methylation profiling distinguished six distinct clinically relevant methylation classes associated with typical mutational, cytogenetic, and gene expression patterns. Compared with WHO grading, classification by individual and combined methylation classes more accurately identifies patients at high risk of disease progression in tumours with WHO grade I histology, and patients at lower risk of recurrence among WHO grade II tumours (p=0·0096) from the Brier prediction test). We validated this finding in our independent cohort of 140 patients with meningioma. Interpretation DNA methylation-based meningioma classification captures clinically more homogenous groups and has a higher power for predicting tumour recurrence and prognosis than the WHO classification. The approach presented here is potentially very useful for stratifying meningioma patients to observation-only or adjuvant treatment groups. We consider methylation-based tumour classification highly relevant for the future diagnosis and treatment of meningioma. Funding German Cancer Aid, Else Kröner-Fresenius Foundation, and DKFZ/Heidelberg Institute of Personalized Oncology/Precision Oncology Program.
Original languageEnglish
Pages (from-to)682-694
Number of pages13
JournalLancet Oncology
Volume18
Issue number5
Early online date15 Mar 2017
DOIs
Publication statusPublished - 1 May 2017

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