Sangam: A Confluence of Knowledge Streams

DNA methylation profiling to predict recurrence risk in meningioma: development and validation of a nomogram to optimize clinical management

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dc.creator Nassiri, F
dc.creator Mamatjan, Y
dc.creator Suppiah, S
dc.creator Badhiwala, JH
dc.creator Mansouri, S
dc.creator Karimi, S
dc.creator Saarela, O
dc.creator Poisson, L
dc.creator Gepfner-Tuma, I
dc.creator Schittenhelm, J
dc.creator Ng, H-K
dc.creator Noushmehr, H
dc.creator Harter, P
dc.creator Baumgarten, P
dc.creator Weller, M
dc.creator Preusser, M
dc.creator Herold-Mende, C
dc.creator Tatagiba, M
dc.creator Tabatabai, G
dc.creator Sahm, F
dc.creator von Deimling, A
dc.creator Aldape, K
dc.creator Au, K
dc.creator Barnhartz-Sloan, J
dc.creator Bi, WL
dc.creator Brastianos, PK
dc.creator Butowski, N
dc.creator Carlotti, C
dc.creator Cusimano, MD
dc.creator DiMeco, F
dc.creator Drummond, K
dc.creator Dunn, IF
dc.creator Galanis, E
dc.creator Giannini, C
dc.creator Goldbrunner, R
dc.creator Griffith, B
dc.creator Hashizume, R
dc.creator Hanemann, CO
dc.creator Herold-Mende, C
dc.creator Horbinski, C
dc.creator Huang, RY
dc.creator James, D
dc.creator Jenkinson, MD
dc.creator Jungk, C
dc.creator Kaufman, TJ
dc.creator Krischek, B
dc.creator Lachance, D
dc.creator Lafougère, C
dc.creator Lee, I
dc.creator Liu, JC
dc.creator Mamatjan, Y
dc.creator Malta, TM
dc.creator Mawrin, C
dc.creator McDermott, M
dc.creator Munoz, D
dc.creator Nassiri, F
dc.creator Noushmehr, H
dc.creator Ng, H-K
dc.creator Perry, A
dc.creator Pirouzmand, F
dc.creator Poisson, LM
dc.creator Pollo, B
dc.creator Raleigh, D
dc.creator Sahm, F
dc.creator Saladino, A
dc.creator Santarius, T
dc.creator Schichor, C
dc.creator Schultz, D
dc.creator Schmidt, NO
dc.creator Selman, W
dc.creator Sloan, A
dc.creator Spears, J
dc.creator Snyder, J
dc.creator Suppiah, S
dc.creator Tabatabai, G
dc.creator Tatagiba, M
dc.creator Tirapelli, D
dc.creator Tonn, JC
dc.creator Tsang, D
dc.creator Vogelbaum, MA
dc.creator von Deimling, A
dc.creator Wen, PY
dc.creator Walbert, T
dc.creator Westphal, M
dc.creator Workewych, AM
dc.creator Zadeh, G
dc.creator Zadeh, G
dc.creator Aldape, KD
dc.date 2022-03-04T10:08:17Z
dc.date 2022-03-04T10:08:17Z
dc.date 2019-07
dc.date.accessioned 2022-05-26T21:09:23Z
dc.date.available 2022-05-26T21:09:23Z
dc.identifier 1522-8517
dc.identifier http://hdl.handle.net/10026.1/18881
dc.identifier 10.1093/neuonc/noz061
dc.identifier 1523-5866
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/228920
dc.description <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Variability in standard-of-care classifications precludes accurate predictions of early tumor recurrence for individual patients with meningioma, limiting the appropriate selection of patients who would benefit from adjuvant radiotherapy to delay recurrence. We aimed to develop an individualized prediction model of early recurrence risk combining clinical and molecular factors in meningioma.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>DNA methylation profiles of clinically annotated tumor samples across multiple institutions were used to develop a methylome model of 5-year recurrence-free survival (RFS). Subsequently, a 5-year meningioma recurrence score was generated using a nomogram that integrated the methylome model with established prognostic clinical factors. Performance of both models was evaluated and compared with standard-of-care models using multiple independent cohorts.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The methylome-based predictor of 5-year RFS performed favorably compared with a grade-based predictor when tested using the 3 validation cohorts (ΔAUC = 0.10, 95% CI: 0.03–0.018) and was independently associated with RFS after adjusting for histopathologic grade, extent of resection, and burden of copy number alterations (hazard ratio 3.6, 95% CI: 1.8–7.2, P &lt; 0.001). A nomogram combining the methylome predictor with clinical factors demonstrated greater discrimination than a nomogram using clinical factors alone in 2 independent validation cohorts (ΔAUC = 0.25, 95% CI: 0.22–0.27) and resulted in 2 groups with distinct recurrence patterns (hazard ratio 7.7, 95% CI: 5.3–11.1, P &lt; 0.001) with clinical implications.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The models developed and validated in this study provide important prognostic information not captured by previously established clinical and molecular factors which could be used to individualize decisions regarding postoperative therapeutic interventions, in particular whether to treat patients with adjuvant radiotherapy versus observation alone.</jats:p> </jats:sec>
dc.format 901 - 910
dc.language en
dc.language en
dc.publisher Oxford University Press
dc.relation ISSN:1522-8517
dc.relation E-ISSN:1523-5866
dc.rights 2022-03-05
dc.rights Not known
dc.title DNA methylation profiling to predict recurrence risk in meningioma: development and validation of a nomogram to optimize clinical management
dc.type Journal Article


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