Publication

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated

Journal Paper/Review - Oct 7, 2021

Units
PubMed
Doi

Citation
German Consortium on Aggressive Meningiomas (KAM), Ratliff M, Neidert M, Wirsching H, Harter P, Unterberg A, Herold-Mende C, Jungwirth G, Etminan N, Lamszus K, Westphal M, Hänggi D, Jungk C, Mawrin C, Platten M, Snuderl M, Sahm F, von Deimling A, Preusser M, Weller M, Wick W, Acker T, Reifenberger G, Jones D, Pfister S, Frank S, Hench J, Brandner S, Aldape K, Sen C, Golfinos J, Serrano J, Stein M, Dohmen H, Reuss D, Dogan H, Patel A, Blume C, Euskirchen P, Sill M, Schrimpf D, Berghoff A, Sievers P, Hielscher T, Stichel D, Reinhardt A, Suwala A, Grady C, Jones T, Bridges L, Greenway F, Leu S, Jaunmuktane Z, Schittenhelm J, Ketter R, Bewerunge-Hudler M, Rushing E, Ricklefs F, Baumgarten P, Wefers A, Maas S. Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated. J Clin Oncol 2021; 39:3839-3852.
Type
Journal Paper/Review (English)
Journal
J Clin Oncol 2021; 39
Publication Date
Oct 7, 2021
Issn Electronic
1527-7755
Pages
3839-3852
Brief description/objective

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.