PATH-39. INTEGRATED MOLECULAR-MORPHOLOGICAL MENINGIOMA CLASSIFICATION: A MULTICENTER RETROSPECTIVE ANALYSIS, RETRO- AND PROSPECTIVELY VALIDATED. (12th November 2021)
- Record Type:
- Journal Article
- Title:
- PATH-39. INTEGRATED MOLECULAR-MORPHOLOGICAL MENINGIOMA CLASSIFICATION: A MULTICENTER RETROSPECTIVE ANALYSIS, RETRO- AND PROSPECTIVELY VALIDATED. (12th November 2021)
- Main Title:
- PATH-39. INTEGRATED MOLECULAR-MORPHOLOGICAL MENINGIOMA CLASSIFICATION: A MULTICENTER RETROSPECTIVE ANALYSIS, RETRO- AND PROSPECTIVELY VALIDATED
- Authors:
- Maas, Sybren
Stichel, Damian
Hielscher, Thomas
Sievers, Philipp
Berghoff, Anna
Schrimpf, Daniel
Sill, Martin
Euskirchen, Philipp
Reuss, David
Dohmen, Hildegard
Stein, Marco
Baumgarten, Peter
Ricklefs, Franz
Rushing, Elizabeth
Bewerunge-Hudler, Melanie
Ketter, Ralf
Schittenhelm, Jens
Jaunmuktane, Zane
Leu, Severina
Grady, Conor
Serrano, Jonathan
Golfinos, John
Sen, Chandra
Mawrin, Christian
Jungk, Christine
Hänggi, Daniel
Westphal, Manfred
Lamszus, Katrin
Etminan, Nima
Unterberg, Andreas
Harter, Patrick
Wirsching, Hans-Georg
Neidert, Marian Christoph
Ratliff, Miriam
Platten, Michael
Snuderl, Matija
Aldape, Kenneth
Brandner, Sebastian
Hench, Jürgen
Frank, Stephan
Pfister, Stefan
Jones, David
Reifenberger, Guido
Acker, Till
Wick, Wolfgang
Weller, Michael
Preusser, Matthias
von Deimling, Andreas
Sahm, Felix
… (more) - Abstract:
- Abstract: PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from cases with benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for the individual patient is of pivotal importance in clinical management. However, only biomarkers for highly aggressive tumors are established at present ( CDKN2A/B and TERT ), while no molecularly-based stratification exists for the broad spectrum of low- and intermediate-risk meningioma patients. PATIENTS AND METHODS: DNA methylation data and copy-number information were generated for 3, 031 meningiomas of 2, 868 individual patients, with mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNV), mutations and WHO grading were comparatively analyzed. Prediction power for outcome of these parameters was assessed in an initial retrospective cohort of 514 patients, and validated on a retrospective cohort of 184, and on a prospective cohort of 287 multi-center cases, respectively. RESULTS: Both CNV and methylation family- (MF)-based subgrouping independently resulted in an increase in prediction accuracy of risk of recurrence compared to the WHO classification (c-indexes WHO 2016, CNV, and MF 0.699, 0.706 and 0.721, respectively). Merging all independently powerful risk stratification approaches into an integrated molecular-morphological score resulted in a further, substantial increase in accuracy (c-index 0.744). ThisAbstract: PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from cases with benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for the individual patient is of pivotal importance in clinical management. However, only biomarkers for highly aggressive tumors are established at present ( CDKN2A/B and TERT ), while no molecularly-based stratification exists for the broad spectrum of low- and intermediate-risk meningioma patients. PATIENTS AND METHODS: DNA methylation data and copy-number information were generated for 3, 031 meningiomas of 2, 868 individual patients, with mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNV), mutations and WHO grading were comparatively analyzed. Prediction power for outcome of these parameters was assessed in an initial retrospective cohort of 514 patients, and validated on a retrospective cohort of 184, and on a prospective cohort of 287 multi-center cases, respectively. RESULTS: Both CNV and methylation family- (MF)-based subgrouping independently resulted in an increase in prediction accuracy of risk of recurrence compared to the WHO classification (c-indexes WHO 2016, CNV, and MF 0.699, 0.706 and 0.721, respectively). Merging all independently powerful risk stratification approaches into an integrated molecular-morphological score resulted in a 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=0.005). 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 (HR 4.56 [2.97;7.00], 4.34 [2.48;7.57] and 3.34 [1.28; 8.72] for discovery, retrospective, and prospective validation cohort, respectively). CONCLUSIONS: Merging these layers of histological 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 meningioma patients on the basis of robust outcome prediction. … (more)
- Is Part Of:
- Neuro-oncology. Volume 23: Supplement 6(2021)
- Journal:
- Neuro-oncology
- Issue:
- Volume 23: Supplement 6(2021)
- Issue Display:
- Volume 23, Issue 6 (2021)
- Year:
- 2021
- Volume:
- 23
- Issue:
- 6
- Issue Sort Value:
- 2021-0023-0006-0000
- Page Start:
- vi123
- Page End:
- vi124
- Publication Date:
- 2021-11-12
- Subjects:
- Brain Neoplasms -- Periodicals
Brain -- Tumors -- Periodicals
Brain -- Cancer -- Periodicals
Nervous system -- Cancer -- Periodicals
616.99481 - Journal URLs:
- http://neuro-oncology.dukejournals.org/ ↗
http://neuro-oncology.oxfordjournals.org/ ↗
http://www.oxfordjournals.org/content?genre=journal&issn=1522-8517 ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/neuonc/noab196.491 ↗
- Languages:
- English
- ISSNs:
- 1522-8517
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6081.288000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20208.xml