Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School
The clinical behavior of meningiomas often belies their histopathologic grade. Increasing data suggest that molecular features augment classic World Health Organization (WHO) classification in predicting recurrent risk. We investigated the influence of histopathology, chromosome copy number, and treatment on meningioma outcome to construct a simple, scalable, molecularly integrated classifier. Methods: We analyzed 684 meningiomas with genome-wide chromosomal copy-number profiling for clinical features, pre-/post-operative tumor volume, histopathology, and recurrence. We devised a point-based molecularly-integrated classification system (IC 1-3), incorporating mitotic count and nine common molecular alterations consistently associated with risk of recurrence across four independent statistical tests. We used brier curves, time-dependent area-under-the-curve, and average precision to assess model performance. We added treatment variables, including primary or recurrent status, tumor size, and extent of resection, to the Integrated class to formulate a nomogram of recurrence risk at 5 years. Results: The Integrated Class significantly associated with recurrence in a multivariate model (IC Class 2 vs 1: HR 3.60, 95% CI 2.19-5.91; Class 3 vs 1: HR 5.15, 95% CI 3.28-8.09) and outperformed WHO grade in predicting recurrence (by integrated brier score, 0.093 vs 0.178, internally and independently validated). WHO Grade I and IC-1 exhibited 86% concordance, 31.5% between Grade II and IC-2, and 64% between Grade III and IC-3. WHO grade I meningiomas with IC 2-3 fared significantly worse than WHO grade II-III meningiomas with IC-1 designation. Each additional molecular feature incrementally strengthens the classifier, allowing for application of single-arm FISH or combinatorial genome-wide signatures based on available resources. Intriguingly, receipt of adjuvant radiation in newly diagnosed WHO Grade II-III or IC 2-3 meningiomas was associated with greater propensity for recurrence, after controlling for extent of resection. Conclusion: We present a scalable, molecularly-integrated classification for meningioma that better predicts recurrence compared to classic histopathologic grades to aid in clinical management.