Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated
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Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated. / Maas, Sybren L N; Stichel, Damian; Hielscher, Thomas; Sievers, Philipp; Berghoff, Anna S; Schrimpf, Daniel; Sill, Martin; Euskirchen, Philipp; Blume, Christina; Patel, Areeba; Dogan, Helin; Reuss, David; Dohmen, Hildegard; Stein, Marco; Reinhardt, Annekathrin; Suwala, Abigail K; Wefers, Annika K; Baumgarten, Peter; Ricklefs, Franz; Rushing, Elisabeth J; Bewerunge-Hudler, Melanie; Ketter, Ralf; Schittenhelm, Jens; Jaunmuktane, Zane; Leu, Severina; Greenway, Fay E A; Bridges, Leslie R; Jones, Timothy; Grady, Conor; Serrano, Jonathan; Golfinos, John; Sen, Chandra; Mawrin, Christian; Jungk, Christine; Hänggi, Daniel; Westphal, Manfred; Lamszus, Katrin; Etminan, Nima; Jungwirth, Gerhard; Herold-Mende, Christel; Unterberg, Andreas; Harter, Patrick N; Wirsching, Hans-Georg; Neidert, Marian C; Ratliff, Miriam; Platten, Michael; Snuderl, Matija; Aldape, Kenneth D; Brandner, Sebastian; Hench, Jürgen; Frank, Stephan; Pfister, Stefan M; Jones, David T W; Reifenberger, Guido; Acker, Till; Wick, Wolfgang; Weller, Michael; Preusser, Matthias; von Deimling, Andreas; Sahm, Felix; German Consortium on Aggressive Meningiomas (KAM).
In: J CLIN ONCOL, Vol. 39, No. 34, 01.12.2021, p. 3839-3852.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated
AU - Maas, Sybren L N
AU - Stichel, Damian
AU - Hielscher, Thomas
AU - Sievers, Philipp
AU - Berghoff, Anna S
AU - Schrimpf, Daniel
AU - Sill, Martin
AU - Euskirchen, Philipp
AU - Blume, Christina
AU - Patel, Areeba
AU - Dogan, Helin
AU - Reuss, David
AU - Dohmen, Hildegard
AU - Stein, Marco
AU - Reinhardt, Annekathrin
AU - Suwala, Abigail K
AU - Wefers, Annika K
AU - Baumgarten, Peter
AU - Ricklefs, Franz
AU - Rushing, Elisabeth J
AU - Bewerunge-Hudler, Melanie
AU - Ketter, Ralf
AU - Schittenhelm, Jens
AU - Jaunmuktane, Zane
AU - Leu, Severina
AU - Greenway, Fay E A
AU - Bridges, Leslie R
AU - Jones, Timothy
AU - Grady, Conor
AU - Serrano, Jonathan
AU - Golfinos, John
AU - Sen, Chandra
AU - Mawrin, Christian
AU - Jungk, Christine
AU - Hänggi, Daniel
AU - Westphal, Manfred
AU - Lamszus, Katrin
AU - Etminan, Nima
AU - Jungwirth, Gerhard
AU - Herold-Mende, Christel
AU - Unterberg, Andreas
AU - Harter, Patrick N
AU - Wirsching, Hans-Georg
AU - Neidert, Marian C
AU - Ratliff, Miriam
AU - Platten, Michael
AU - Snuderl, Matija
AU - Aldape, Kenneth D
AU - Brandner, Sebastian
AU - Hench, Jürgen
AU - Frank, Stephan
AU - Pfister, Stefan M
AU - Jones, David T W
AU - Reifenberger, Guido
AU - Acker, Till
AU - Wick, Wolfgang
AU - Weller, Michael
AU - Preusser, Matthias
AU - von Deimling, Andreas
AU - Sahm, Felix
AU - German Consortium on Aggressive Meningiomas (KAM)
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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 (CDKN2A/B and TERT), 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 P = .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.
AB - 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 (CDKN2A/B and TERT), 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 P = .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.
U2 - 10.1200/JCO.21.00784
DO - 10.1200/JCO.21.00784
M3 - SCORING: Journal article
C2 - 34618539
VL - 39
SP - 3839
EP - 3852
JO - J CLIN ONCOL
JF - J CLIN ONCOL
SN - 0732-183X
IS - 34
ER -