Nguyen, Minh

Abstract The prevalence of asymptomatic, incidental meningiomas is increasing due to greater frequency of brain imaging. This retrospective cohort study of adult patients presenting at a single institution from 1996-2020 integrated clinical data, serial magnetic resonance imaging (MRI) volumetrics, DNA methylation profiling, and targeted gene expression profiling to define the natural history and molecular architecture of incidental meningiomas. Volumetrics were calculated from contrast-enhanced brain MRIs at time of diagnosis compared to serial surveillance MRIs (average 3 MRIs per patient pre-intervention, range: 1-12) DNA methylation groups and gene expression risk scores were defined for 123 samples that underwent resection. Time-to-event analyses were performed for progression-free survival (PFS), symptom-free survival (SFS), and treatment-free survival (TFS). The cohort included 238 meningiomas from 207 patients. Median age at diagnosis was 59 years and 81.5% of patients were female. Median clinical and imaging follow-up were 9.1 and 6.7 years, respectively, during which time 50.8% progressed. Median maximum diameter and volume at diagnosis were 2.20 cm and 4.25 cm3, respectively, and median pre-treatment volumetric growth rate was 19.2% per year. Median pre-treatment PFS and TFS were 3.10 and 1.27 years, respectively, and median SFS and post-treatment PFS were not reached. Cox proportional hazards regression identified maximum diameter at diagnosis as a significant predictor of worse pre-treatment PFS (multivariate HR 1.65 per 1 cm [95% CI 1.19–2.28], p=0.0027) and worse TFS (multivariate HR 1.44 per 1 cm [95% CI 1.17–1.76], p<0.001). Skull base tumors had with worse SFS (multivariate HR 2.48 [95% CI 1.02–6.00], p=0.045). Molecular and histological features among incidental meningiomas that ultimately underwent resection (n=176, 74.0%) were reassuring (89.2% WHO grade 1, 10.2% WHO grade 2). These findings suggest maximum diameter at diagnosis of incidental meningiomas is significantly associated with earlier progression and eventual treatment, and skull base tumors have earlier symptom development.

AbstractMeningeal solitary fibrous tumors (SFT) are rare and have a high frequency of local recurrence and distant metastasis. In a cohort of 126 patients (57 female, 69 male; mean age at surgery 53.0 years) with pathologically confirmed meningeal SFTs with extended clinical follow‐up (median 9.9 years; range 15 days–43 years), we performed extensive molecular characterization including genome‐wide DNA methylation profiling (n = 80) and targeted TERT promoter mutation testing (n = 98). Associations were examined with NAB2::STAT6 fusion status (n = 101 cases; 51 = ex5‐7::ex16‐17, 26 = ex4::ex2‐3; 12 = ex2‐3::exANY/other and 12 = no fusion) and placed in the context of 2021 Central Nervous System (CNS) WHO grade. NAB2::STAT6 fusion breakpoints (fusion type) were significantly associated with metastasis‐free survival (MFS) (p = 0.03) and, on multivariate analysis, disease‐specific survival (DSS) when adjusting for CNS WHO grade (p = 0.03). DNA methylation profiling revealed three distinct clusters: Cluster 1 (n = 38), Cluster 2 (n = 22), and Cluster 3 (n = 20). Methylation clusters were significantly associated with fusion type (p < 0.001), with Cluster 2 harboring ex4::ex2‐3 fusion in 16 (of 20; 80.0%), nearly all TERT promoter mutations (7 of 8; 87.5%), and predominantly an “SFT” histologic phenotype (15 of 22; 68.2%). Clusters 1 and 3 were less distinct, both dominated by tumors having ex5‐7::ex16‐17 fusion (respectively, 25 of 33; 75.8%, and 12 of 18; 66.7%) and with variable histological phenotypes. Methylation clusters were significantly associated with MFS (p = 0.027), but not overall survival (OS). In summary, NAB2::STAT6 fusion type was significantly associated with MFS and DSS, suggesting that tumors with an ex5::ex16‐17 fusion may have inferior patient outcomes. Methylation clusters were significantly associated with fusion type, TERT promoter mutation status, histologic phenotype, and MFS.

Abstract Background Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and current indications for postoperative radiotherapy are controversial. Recent studies have proposed prognostic meningioma classification systems using DNA methylation profiling, copy number variants, DNA sequencing, RNA sequencing, histology, or integrated models based on multiple combined features. Targeted gene expression profiling has generated robust biomarkers integrating multiple molecular features for other cancers, but is understudied for meningiomas. Methods Targeted gene expression profiling was performed on 173 meningiomas and an optimized gene expression biomarker (34 genes) and risk score (0 to 1) was developed to predict clinical outcomes. Clinical and analytical validation was performed on independent meningiomas from 12 institutions across 3 continents (N = 1856), including 103 meningiomas from a prospective clinical trial. Gene expression biomarker performance was compared to 9 other classification systems. Results The gene expression biomarker improved discrimination of postoperative meningioma outcomes compared to all other classification systems tested in the independent clinical validation cohort for local recurrence (5-year area under the curve [AUC] 0.81) and overall survival (5-year AUC 0.80). The increase in area under the curve compared to the current standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval [CI] 0.07–0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% CI 0.37–0.78, P = 0.0001) and re-classified up to 52.0% meningiomas compared to conventional clinical criteria, suggesting postoperative management could be refined for 29.8% of patients. Conclusions A targeted gene expression biomarker improves discrimination of meningioma outcomes compared to recent classification systems and predicts postoperative radiotherapy responses.