Publikation
Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated
Wissenschaftlicher Artikel/Review - 07.10.2021
German Consortium on Aggressive Meningiomas (KAM), Ratliff Miriam, Neidert Marian Christoph, Wirsching Hans-Georg, Harter Patrick N, Unterberg Andreas, Herold-Mende Christel, Jungwirth Gerhard, Etminan Nima, Lamszus Katrin, Westphal Manfred, Hänggi Daniel, Jungk Christine, Mawrin Christian, Platten Michael, Snuderl Matija, Sahm Felix, von Deimling Andreas, Preusser Matthias, Weller Michael, Wick Wolfgang, Acker Till, Reifenberger Guido, Jones David T W, Pfister Stefan M, Frank Stephan, Hench Jürgen, Brandner Sebastian, Aldape Kenneth D, Sen Chandra, Golfinos John, Serrano Jonathan, Stein Marco, Dohmen Hildegard, Reuss David, Dogan Helin, Patel Areeba, Blume Christina, Euskirchen Philipp, Sill Martin, Schrimpf Daniel, Berghoff Anna S, Sievers Philipp, Hielscher Thomas, Stichel Damian, Reinhardt Annekathrin, Suwala Abigail K, Grady Conor, Jones Timothy, Bridges Leslie R, Greenway Fay E A, Leu Severina, Jaunmuktane Zane, Schittenhelm Jens, Ketter Ralf, Bewerunge-Hudler Melanie, Rushing Elisabeth J, Ricklefs Franz, Baumgarten Peter, Wefers Annika K, Maas Sybren L N
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PURPOSE
Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established ( and ), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma.
METHODS
DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases.
RESULTS
Both CNV- and methylation family-based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively).
CONCLUSION
Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction.