Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated.
Journal
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
ISSN: 1527-7755
Titre abrégé: J Clin Oncol
Pays: United States
ID NLM: 8309333
Informations de publication
Date de publication:
01 12 2021
01 12 2021
Historique:
pubmed:
8
10
2021
medline:
29
12
2021
entrez:
7
10
2021
Statut:
ppublish
Résumé
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 ( 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. 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 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.
Identifiants
pubmed: 34618539
doi: 10.1200/JCO.21.00784
pmc: PMC8713596
doi:
Types de publication
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3839-3852Subventions
Organisme : Medical Research Council
ID : MR/N004272/1
Pays : United Kingdom
Références
Nat Genet. 2016 Jan;48(1):59-66
pubmed: 26618343
Acta Neuropathol. 2017 Nov;134(5):691-703
pubmed: 28638988
Acta Neuropathol. 2019 Aug;138(2):295-308
pubmed: 31069492
Int J Cancer. 2007 Oct 1;121(7):1473-80
pubmed: 17557299
Cancer Discov. 2020 Nov;10(11):1722-1741
pubmed: 32703768
Nat Genet. 2013 Mar;45(3):285-9
pubmed: 23334667
Neuro Oncol. 2019 Jul 11;21(7):901-910
pubmed: 31158293
Lancet Oncol. 2017 May;18(5):682-694
pubmed: 28314689
J Clin Oncol. 2003 Sep 1;21(17):3285-95
pubmed: 12947064
Acta Neuropathol. 2017 Mar;133(3):431-444
pubmed: 28130639
Neuro Oncol. 2021 Aug 2;23(8):1231-1251
pubmed: 34185076
Brain Pathol. 2019 Jul;29(4):469-472
pubmed: 31038238
Acta Neuropathol. 2018 Nov;136(5):779-792
pubmed: 30123936
Neuro Oncol. 2021 Jun 1;23(6):990-998
pubmed: 33346835
Lancet Oncol. 2016 Sep;17(9):e383-91
pubmed: 27599143
PLoS One. 2013;8(1):e54114
pubmed: 23349797
J Natl Cancer Inst. 2015 Dec 13;108(5):
pubmed: 26668184
Nat Med. 2020 Jul;26(7):1044-1047
pubmed: 32572265
Science. 2013 Mar 1;339(6123):1077-80
pubmed: 23348505
Nat Commun. 2017 Feb 14;8:14433
pubmed: 28195122
Lancet Neurol. 2006 Dec;5(12):1045-54
pubmed: 17110285
Acta Neuropathol. 2018 Nov;136(5):805-810
pubmed: 30259105
Neuro Oncol. 2014 May;16(5):735-47
pubmed: 24536048
Acta Neuropathol. 2020 Mar;139(3):603-608
pubmed: 31996992
Acta Neuropathol. 2020 Sep;140(3):409-413
pubmed: 32642869
Nat Genet. 2016 Oct;48(10):1253-9
pubmed: 27548314
PLoS One. 2014 Apr 10;9(4):e94987
pubmed: 24722350