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Published on: June 16, 2017
Validation and next-generation update of a DNA methylation-based recurrence predictor for meningioma: A multicenter
Alexander P Landry1,2,3, Justin Z Wang1,2,3, Vikas Patil1,3
1Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
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