An evidence-based workflow for integrating molecular testing with histopathology in the workup of meningiomas
Harrshavasan Congivaram, Vineeth Thirunavu, Lucas Santana-Santos, Pouya Jamshidi, Jared T Arendhsen, Rudolph J Castellani, Daniel J Brat, Lee A D Cooper, Marina A Ayad, William C Chen, Khizar R Nandoliya, Rahul K Chaliparambil, Rishi Jain, Mateo Gomez, Drew Duckett, , Madina Sukhanova, Heather Smith, Matthew McCord, Alicia Steffens, Jordain Walshon, Kathleen McCortney, Wenxia Wang, Xinyan Lu, Amy B Heimberger, David R Raleigh, Stephen T Magill, Mark W Youngblood, Craig M HorbinskiAbstract
Background
Meningiomas are graded using histologic and molecular criteria according to the World Health Organization (WHO) classification. However, there is scant evidence considering the specific value of each individual atypical histological feature that contributes to WHO grading. There is also little evidence-based guidance as to which cases should receive molecular testing.
Methods
Detailed histological information was collected for 1058 resected meningiomas, including cases with genomic DNA methylation and copy number variant (CNV) data, next-generation sequencing, and RNA risk score. Integrated analyses were conducted using known prognostic variables, including WHO Grade, Ki-67, mitotic index (MI), and atypical histologic features (necrosis, hypercellularity, macronucleoli, sheeting, and small cells).
Results
Necrosis, elevated MI, and increased WHO grade correlated with specific DNA methylation patterns, RNA risk groups, and chromosomal CNVs. Predictive analysis identified MI as the strongest single correlate, and suggested the presence of ≥ 2 atypical features as an optimal threshold for recurrence prediction. Sheeting architecture was the only independent histologic correlate of recurrence. Integration of histologic and molecular features suggests that tumors with high MI have poor prognosis regardless of molecular profile. Further analysis of low MI tumors revealed specific predictors of unfavorable molecular profiles.
Conclusions
These data indicate that some atypical histopathologic features are more powerful than others, and can be used on initial screening to increase the yield of molecular profiling of meningiomas. Furthermore, we propose that molecular testing may actually have greatest added value in cases with low, not high, MI.