Open Access

Alexander P. Landry, Justin Z. Wang, Vikas Patil, Chloe Gui, Yasin Mamatjan, Zeel Patel, Rebecca Yakubov, Ramneet Kaloti, Parnian Habibi, Mark Wilson, Andrew Ajisebutu, Yosef Ellenbogen, Qingxia Wei, Olivia Singh, Julio Sosa, Sheila Mansouri, Christopher Wilson, Aaron A. Cohen-Gadol, Piiamaria Virtanen, Noah Burket, Matthew Blackwell, Jenna Koenig, Anthony Alfonso, Joseph Davis, Mohamed A. Zaazoue, Ghazaleh Tabatabai, Marcos Tatagiba, Felix Behling, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Silky Chotai, Lola B. Chambless, Alireza Mansouri, Felix Ehret, David Capper, Derek S. Tsang, Kenneth Aldape, Andrew Gao, Farshad Nassiri, Gelareh Zadeh

• We previously developed a DNA methylation-based risk predictor for meningioma, which has been used locally in a prospective fashion since its original publication. As a follow-up, we validate this model using a large prospective cohort and introduce a streamlined next-generation predictor compatible with newer methylation arrays. Genome-wide methylation profiles were generated with the Illumina EPICArray. The performance of our next-generation predictor was compared with our original model and standard-of-care 2021 WHO grade using time-dependent receiver operating characteristic curves. An nomogram was generated by incorporating our methylation predictor with WHO grade and the extent of resection. A total of 1347 meningioma cases were utilized in the study, including 469 prospective cases from 3 institutions and an external cohort of 100 WHO grade 2 cases for model validation. Both the original and next-generation models significantly outperform the 2021 WHO grade in predicting earlyWe previously developed a DNA methylation-based risk predictor for meningioma, which has been used locally in a prospective fashion since its original publication. As a follow-up, we validate this model using a large prospective cohort and introduce a streamlined next-generation predictor compatible with newer methylation arrays. Genome-wide methylation profiles were generated with the Illumina EPICArray. The performance of our next-generation predictor was compared with our original model and standard-of-care 2021 WHO grade using time-dependent receiver operating characteristic curves. An nomogram was generated by incorporating our methylation predictor with WHO grade and the extent of resection. A total of 1347 meningioma cases were utilized in the study, including 469 prospective cases from 3 institutions and an external cohort of 100 WHO grade 2 cases for model validation. Both the original and next-generation models significantly outperform the 2021 WHO grade in predicting early postoperative recurrence. Dichotomizing patients into grade-specific risk subgroups was predictive of outcomes within both WHO grades 1 and 2 tumors (P < .05), whereas all WHO grade 3 tumors were considered high-risk. Multivariable Cox regression demonstrated the benefit of adjuvant radiotherapy (RT) in high-risk cases specifically, reinforcing its informative role in clinical decision-making. Finally, our next-generation predictor contains nearly 10-fold fewer features than the original model, allowing for targeted arrays. This next-generation DNA methylation-based meningioma outcome predictor significantly outperforms the 2021 WHO grading in predicting time to recurrence. We make this available as a point-and-click tool that will improve prognostication, inform patient selection for RT, and allow for molecularly stratified clinical trials.…