Home > Publications database > The effect of TERT promoter mutation on predicting meningioma outcomes: a multi-institutional cohort analysis. > print

001     304432
005     20250907022545.0
024 7 _ |a 10.1016/S1470-2045(25)00422-X
|2 doi
024 7 _ |a pmid:40907515
|2 pmid
024 7 _ |a 1470-2045
|2 ISSN
024 7 _ |a 1474-5488
|2 ISSN
024 7 _ |a altmetric:180905756
|2 altmetric
037 _ _ |a DKFZ-2025-01845
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Groff, Karenna J
|b 0
245 _ _ |a The effect of TERT promoter mutation on predicting meningioma outcomes: a multi-institutional cohort analysis.
260 _ _ |a London
|c 2025
|b The Lancet Publ. Group
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1757078209_960
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Molecular aberrations have been incorporated into tumour classification guidelines of meningioma. TERT-promoter (TERTp) mutation is associated with worse prognosis and is designated a WHO grade 3 biomarker. However, it remains unclear whether TERTp mutation is context-dependent, with other co-occurring genetic alterations potentially driving its association with prognosis. We sought to characterise the role of TERTp mutation in meningioma and guide TERTp sequencing.We identified 1492 patients of all ages who had previously received surgery for meningioma across 14 medical centres in the USA, Canada, and Germany. Patients were eligible if they had post-surgical clinical or radiographical assessment of the resection site, and TERTp status evaluated by Nov 1, 2024. Multi-modal profiling was used to assess TERTp mutation, focal gene alterations-including CDKN2A/B loss-and copy number alterations. An adjusted WHO grade was calculated for TERTp-mutant meningiomas, incorporating all WHO criteria except TERTp status. Kaplan-Meier curves and multivariable Cox proportional hazards models were used to quantify the effect of TERTp mutation on the endpoints of overall survival and recurrence-free survival across adjusted WHO grade and co-occurring molecular alterations.64 (4·3%) of 1492 meningiomas were TERTp-mutant and 1428 (95·7%) were TERTp-wildtype. Of the TERTp-mutant meningiomas, 33 (51·6%) were from female patients and 31 (48·4%) were from male patients, and the overall median age was 67 years (IQR 60-75). Of the wildtype meningiomas, 965 (67·6%) were from female patients and 463 (32·4%) were from male patients, and the overall median age of the patients was 59 years (IQR 48-70). Data on race was inconsistently reported and thus excluded. The TERTp-mutant patients had a 5-year overall survival (49·4% [95% CI 33·7-72·4]) and 5-year recurrence-free survival (27·6% [95% CI 16·8-45·5]) resembling that of patients with WHO grade 3 TERTp-wildtype tumours (5-year overall survival 32·3% [95% CI 17·2-60·5], p=0·28, 5-year recurrence-free survival 14·3% [5·8-35·2], p=0·28). However, the TERTp-mutant group had heterogenous histological grading and was enriched for aggressive molecular features, with 1p loss present in 44 (77·2%) of 57 profiled tumours and CDKN2A/B loss in 24 (41·4%) of the 58 profiled tumours. Adjusting tumour grade revealed a subset of TERTp-mutant meningiomas that were more molecularly and clinically benign. Among TERTp-mutant tumours, CDKN2A/B loss played a defining role in stratifying tumour behaviour. Multivariable analysis confirmed this, with CDKN2A/B loss being significantly associated with shorter overall survival (HR 3·04 [95% CI 1·67-5·52], p=0·00026) and faster time to recurrence (HR 5·22 [95% CI 3·10-8·79], p<0·0001), while TERTp-mutation did not independently affect overall survival (HR 1·00 [95% CI 0·53-1·87], p=0·99) or recurrence-free survival (1·17 [95% CI 0·75-1·83], p=0·49). Sequencing for TERTp-mutation demonstrated clinical impact only among histologically WHO grade 2 meningiomas.The indolent behaviour of certain TERTp-mutant meningiomas suggests that TERTp mutation is not sufficient to assign the most aggressive meningioma grade. Instead, TERT sequencing might offer prognostic utility in identifying high-risk cases among WHO grade 2 meningiomas.National Institutes of Health, National Institute of Neurological Disorders and Stroke, Friedberg Charitable Foundation, Courtney Meningioma Research Fund, Fleming Meningioma Research Fund, and the Gray Family Foundation.
536 _ _ |a 312 - Funktionelle und strukturelle Genomforschung (POF4-312)
|0 G:(DE-HGF)POF4-312
|c POF4-312
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a Telomerase
|0 EC 2.7.7.49
|2 NLM Chemicals
650 _ 7 |a TERT protein, human
|0 EC 2.7.7.49
|2 NLM Chemicals
650 _ 7 |a Biomarkers, Tumor
|2 NLM Chemicals
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Telomerase: genetics
|2 MeSH
650 _ 2 |a Meningioma: genetics
|2 MeSH
650 _ 2 |a Meningioma: pathology
|2 MeSH
650 _ 2 |a Meningioma: mortality
|2 MeSH
650 _ 2 |a Meningioma: surgery
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Middle Aged
|2 MeSH
650 _ 2 |a Mutation
|2 MeSH
650 _ 2 |a Promoter Regions, Genetic
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Meningeal Neoplasms: genetics
|2 MeSH
650 _ 2 |a Meningeal Neoplasms: pathology
|2 MeSH
650 _ 2 |a Meningeal Neoplasms: mortality
|2 MeSH
650 _ 2 |a Meningeal Neoplasms: surgery
|2 MeSH
650 _ 2 |a Prognosis
|2 MeSH
650 _ 2 |a Adult
|2 MeSH
650 _ 2 |a Biomarkers, Tumor: genetics
|2 MeSH
650 _ 2 |a Canada
|2 MeSH
700 1 _ |a Patel, Ruchit V
|b 1
700 1 _ |a Feng, Yang
|b 2
700 1 _ |a Ghosh, Hia S
|b 3
700 1 _ |a Millares Chavez, Miguel A
|b 4
700 1 _ |a O'Brien, Joseph
|b 5
700 1 _ |a Chen, William C
|b 6
700 1 _ |a Nitturi, Vijay
|b 7
700 1 _ |a Save, Akshay V
|b 8
700 1 _ |a Youngblood, Mark W
|b 9
700 1 _ |a Horbinski, Craig M
|b 10
700 1 _ |a Chandler, James P
|b 11
700 1 _ |a Ehret, Felix
|0 P:(DE-HGF)0
|b 12
700 1 _ |a Gui, Chloe
|b 13
700 1 _ |a Wang, Justin Z
|b 14
700 1 _ |a Park, Kristen
|b 15
700 1 _ |a Ajmera, Sonia
|b 16
700 1 _ |a Rosenblum, Marc
|b 17
700 1 _ |a Suwala, Abigail K
|0 P:(DE-He78)c77fccff3e6c42b017b2e8a09813590c
|b 18
|u dkfz
700 1 _ |a Kresbach, Catena
|b 19
700 1 _ |a Mount, Christopher W
|b 20
700 1 _ |a Schüller, Ulrich
|b 21
700 1 _ |a Santagata, Sandro
|b 22
700 1 _ |a Sahm, Felix
|0 P:(DE-He78)a1f4b408b9155beb2a8f7cba4d04fe88
|b 23
|u dkfz
700 1 _ |a Bale, Tejus A
|b 24
700 1 _ |a Jackson, Christina
|b 25
700 1 _ |a Richardson, Timothy E
|b 26
700 1 _ |a Cai, Chunyu
|b 27
700 1 _ |a Nassiri, Farshad
|b 28
700 1 _ |a Zadeh, Gelareh
|b 29
700 1 _ |a Kaul, David
|b 30
700 1 _ |a Capper, David
|0 P:(DE-He78)51bf9ae9cb5771b30c483e5597ef606c
|b 31
|u dkfz
700 1 _ |a Magill, Stephen T
|b 32
700 1 _ |a Golfinos, John G
|b 33
700 1 _ |a Sen, Chandra
|b 34
700 1 _ |a Patel, Akash J
|b 35
700 1 _ |a Raleigh, David R
|b 36
700 1 _ |a Moliterno, Jennifer
|b 37
700 1 _ |a Pacione, Donato
|b 38
700 1 _ |a Snuderl, Matija
|b 39
700 1 _ |a Bi, Wenya Linda
|b 40
773 _ _ |a 10.1016/S1470-2045(25)00422-X
|g Vol. 26, no. 9, p. 1178 - 1190
|0 PERI:(DE-600)2035574-9
|n 9
|p 1178 - 1190
|t The lancet / Oncology
|v 26
|y 2025
|x 1470-2045
909 C O |o oai:inrepo02.dkfz.de:304432
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 12
|6 P:(DE-HGF)0
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 18
|6 P:(DE-He78)c77fccff3e6c42b017b2e8a09813590c
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 23
|6 P:(DE-He78)a1f4b408b9155beb2a8f7cba4d04fe88
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 31
|6 P:(DE-He78)51bf9ae9cb5771b30c483e5597ef606c
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-312
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Funktionelle und strukturelle Genomforschung
|x 0
914 1 _ |y 2025
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2024-12-17
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-17
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b LANCET ONCOL : 2022
|d 2024-12-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2024-12-17
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2024-12-17
915 _ _ |a IF >= 50
|0 StatID:(DE-HGF)9950
|2 StatID
|b LANCET ONCOL : 2022
|d 2024-12-17
920 1 _ |0 I:(DE-He78)BE01-20160331
|k BE01
|l DKTK Koordinierungsstelle Berlin
|x 0
920 1 _ |0 I:(DE-He78)B300-20160331
|k B300
|l KKE Neuropathologie
|x 1
920 1 _ |0 I:(DE-He78)HD01-20160331
|k HD01
|l DKTK HD zentral
|x 2
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)BE01-20160331
980 _ _ |a I:(DE-He78)B300-20160331
980 _ _ |a I:(DE-He78)HD01-20160331
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21