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Background Clear identification of tumor subtype is the main predictor of patient outcome and ultimately what is considered an adequate level of surgical risk. At brain tumor resection, imaging modalities and intraoperative histology often give an ambigious diagnosis, complicating intraoperative surgical decision-making. Here, we report a nanopore DNA methylation analysis (NDMA) sequencing approach combined with machine learning for classification of tumor entities that could be used intraoperatively. Methods We analyzed 50 biopsies obtained from biobanked tissue (43, prospective) or sampled at surgery (7, intraoperative) from 20 female and 30 male patients with a median age of 8 years. DNA was extracted using spin columns, quantified on a Qubit fluorometer and assessed for purity using NanoDrop spectrophotometer. DNA was then barcoded with the Rapid Barcoding kit from Oxford Nanopore technologies and loaded onto a MinION flow cell. Sequencing was performed for 3 hours (intraoperative) and 24 hours (prospective). Raw reads were basecalled using the Guppy algorithm, then fed into a snakemake workflow (nanoDx pipeline). This generated a report showing the copy number profile, genome-wide methylation status and subclassification of the tumor according to the Heidelberg reference cohort. Results Twelve different tumor classes were discovered within our cohort spanning from WHO Grade I to Grade IV. The results generated by NDMA were concordant with standard neuropathological diagnosis in 43 out of 50 cases (86%). Of the discordant cases, six were due to the biological complexity of the tumor and one case was misclassified by the pipeline. NDMA enabled correct subclassification of 6/7 intraop cases within a mean of 129 minutes. Conclusion NDMA can accurately subclassify tumor entities intraoperatively and guide surgical procedures when preoperative imaging and frozen section evaluation are unclear.

EPCT-15. RAPID EPIGENOMIC CLASSIFICATION OF BRAIN TUMORS ENABLES INTRAOPERATIVE NEUROSURGICAL RISK MODULATION