DNA methylation provides diagnostic value for meningioma recurrence in clinical practice.

BACKGROUND

Meningiomas are the most common intracranial tumors. Recent advancements in the genetic profiling of tumors have allowed information including DNA copy number analysis, mutational analysis, and RNA sequencing to be more frequently reported, in turn allowing better characterization of meningiomas. In recent years, analysis of tumor methylomes that reflects both cell-origin methylation signatures and somatically acquired DNA methylation changes has been utilized to better classify meningiomas with great success.

METHOD

We report DNA methylation profiling on meningiomas from 17 patients. Formalin-fixed paraffin-embedded (FFPE) meningioma tumor samples were processed, loaded onto the Infinium Methylation EPIC array, and scanned using the Illumina IScan system. Raw IDAT files were processed through the the CNS tumor classifier developed by the Molecular Neuropathology group at the German Cancer Research Center (DKFZ). Corresponding genomics were captured using targeted sequencing panels.

RESULT

Among the meningioma samples, 13 samples were classified as "benign," two samples as "intermediate," and the remaining three samples (from two patients) as "malignant," based on previously validated classification algorithms. In addition to tumor methylation profiling, we also present information that includes patient demographics, clinical presentations, tumor characteristics (including size and location), surgical approaches, and mutational analysis. The two patients who provided the samples with "malignant" methylation classifications had tumor recurrence, reflecting a more aggressive disease course.

CONCLUSION

In accordance with prior reports, our case series provides support that tumor DNA methylation profiling adds meaningful classification information and may be beneficial to incorporate in clinical practice. Our report also reveals that DNA methylation combined with WHO histology classification can more accurately predict tumor behavior than WHO classification alone.