Abstract
Abstract BACKGROUND Meningiomas are the most common intracranial tumors. According to the World Health Organization’s (WHO) classification guidelines, they can be classified into 15 subtypes with three grades of malignancy based on histology alone. 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: In our case series, 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 then processed through the v12b6 version of the CNS tumor classifier developed by the Molecular Neuropathology group at the University of Heidelberg. Classifier output consisted of assigned methylation classifications along with confidence scores, with scores ≥0.84 classified as “high confidence.” Result: Among our meningioma samples from 17 patients (18 samples total), 14 samples are classified as “benign,” one sample is classified as “intermediate,” and the remaining three samples from two patients are classified as “malignant.” In addition to tumor methylation profiling, we also present information that includes patient demographics, clinical presentations, tumor characteristics (size, location), surgical approaches, and mutational analysis. Genomic analysis has revealed variants in NF2, SLX4, ARID1A, KDM4C, ERCC2, EP300, ERBB2, KMT2D, LRP1B, FANCE, CRKL, and SPEN in multiple patients. The three meningioma samples from the two patients that are classified as “malignant” all have a high MIB1 indexes consistent with WHO Grade II meningiomas. Both patients had tumor recurrence reflecting the aggressiveness of the tumor in their disease course. CONCLUSION In accordance with prior reports, our case series provides support that tumor DNA methylation profiling can provide useful classification information and can be correlated to both tumor pathology and patient clinical presentation.
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