Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated

Abstract

PURPOSE 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 ( CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS 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. RESULTS 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 P = .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 (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION 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.

An Integrated Epigenomic and Genomic View on Phyllodes and Phyllodes-like Breast Tumors

“…As opposed to supervised machine learning-based static classifiers [5,7,10,[18][19][20], unsupervised approaches are able to place data series extraordinarily rare tumors [21] in the context of a magnitude of neoplastic and non-neoplastic differentiation based on the raw data alone [12,17]. In addition to fine-tuned supervised machine learning [22], integrated interpretation of copy number alterations, genetic changes, and histology can significantly increase disease course prediction granularity [23,24].…”

Identification and Management of Aggressive Meningiomas

“…and Bayley et al. have similarly created meningioma classification systems that integrate a combination of methylation array data, copy number alterations, DNA mutations, and histopathological findings to better stratify patients ( Table 2 ) ( 8 , 10 ). Importantly, Maas et al demonstrated that copy number alteration data can readily be inferred from methylation arrays, thus streamlining the molecular diagnostic workup of meningiomas, although they also provide alternative assays (targeted gene analysis or FISH) for stratification depending on resource availability ( 8 ).…”

The Discrepancy Between Standard Histologic WHO Grading of Meningioma and Molecular Profile: A Single Institution Series

“…Copy number data have been shown to predict tumor behavior, and several groups have proposed molecular grading systems based on copy number data due to the accessibility of this data in a non-research clinical setting ( 6 , 10 , 12 , 23 , 24 ). We determined whether meningiomas in our cohort had copy number profiles consistent with low-grade or high-grade meningiomas – thereby classified as “low” or “high” risk profiles – based on prior literature.…”

“…In recent years, there has also been a rapidly growing body of literature supporting the use of advanced molecular profiling in classifying meningiomas. Classification schemes based on methylation, sequence alterations, and copy number data have been introduced and have been shown to be superior to WHO grading in predicting tumour behaviour (10,(12)(13)(14). Nevertheless, such molecular methods are not widely incorporated in clinical practice where histopathologic WHO grade remains the standard that guides management of patients with meningioma.…”

“…A developing understanding of the molecular landscape of meningioma suggests that WHO grade alone may not provide an adequate prediction of tumor behavior, for surveillance or adjuvant treatment planning considerations. Several recent publications have proposed alternative grading systems that incorporate genomic and/or epigenomic data in order to better predict meningioma behavior, particularly targeting those that do not behave in concordance with their assigned WHO grade (10)(11)(12)(13)(14). These grading systems incorporate molecular data including sequence alterations, methylation data and copy number alterations, among other modalities, and have shown a compelling ability to predict meningioma recurrence and progression-free survival when compared with the WHO histologic-based schema alone.…”