0000000000882239
AUTHOR
Henning Leske
Intraoperative DNA methylation classification of brain tumors impacts neurosurgical strategy
Abstract Background Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms. Met…
EPCT-15. RAPID EPIGENOMIC CLASSIFICATION OF BRAIN TUMORS ENABLES INTRAOPERATIVE NEUROSURGICAL RISK MODULATION
Abstract 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 y…