Journal Article

DKFZ-2022-02788

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Clinical implementation of integrated molecular-morphologic risk prediction for meningioma.

Hielscher, T. (First author)DKFZ* ; Sill, M.DKFZ* ; Sievers, P.DKFZ* ; Stichel, D.DKFZ* ; Brandner, S. ; Jones, D.DKFZ* ; von Deimling, A.DKFZ* ; Sahm, F. (Last author)DKFZ* ; Maas, S. L. N.

2023
Wiley-Blackwell Oxford

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Please use a persistent id in citations: doi:10.1111/bpa.13132

Abstract: Risk prediction for meningioma tumors was until recently almost exclusively based on morphological features of the tumor. To improve risk prediction, multiple models have been established that incorporate morphological and molecular features for an integrated risk prediction score. One such model is the integrated molecular-morphologic meningioma integrated score (IntS), which allocates points to the histological grade, epigenetic methylation family and specific copy-number variations. After publication of the IntS, questions arose in the neuropathological community about the practical and clinical implementation of the IntS, specifically regarding the calling of CNVs, the applicability of the newly available version (v12.5) of the brain tumor classifier and the need for incorporation of TERT-promoter and CDKN2A/B status analysis in the IntS calculation. To investigate and validate these questions additional analyses of the discovery (n = 514), retrospective validation (n = 184) and prospective validation (n = 287) cohorts used for IntS discovery and validation were performed. Our findings suggest that any loss over 5% of the chromosomal arm suffices for the calling of a CNV, that input from the v12.5 classifier is as good or better than the dedicated meningioma classifier (v2.4) and that there is most likely no need for additional testing for TERT-promoter mutations and/or homozygous losses of CDKN2A/B when defining the IntS for an individual patient. The findings from this study help facilitate the clinical implementation of IntS-based risk prediction for meningioma patients.

Keyword(s): brain tumors ; meningioma ; molecular biomarkers ; risk prediction ; tumor classification

Note: #EA:C060#LA:B300# / 2023 May;33(3):e13132

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Contributing Institute(s):

1. C060 Biostatistik (C060)
2. B062 Pädiatrische Neuroonkologie (B062)
3. KKE Neuropathologie (B300)
4. DKTK HD zentral (HD01)
5. Pediatric Glioma (B360)

Research Program(s):

1. 312 - Funktionelle und strukturelle Genomforschung (POF4-312) (POF4-312)

Appears in the scientific report 2022

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Database coverage:
; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DEAL Wiley ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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