DECIPHERING THE METHYLATION SIGNATURE OF CIRCULATING EXTRACELLULAR VESICLE DNA FOR CNS TUMOR CLASSIFICATION

Abstract Genome-wide methylation profiling has recently been developed into a tool that allows subtype tumor classification in central nervous system (CNS) tumors. We previously showed that extracellular vesicle (EV) DNA faithfully reflects the tumor methylation class, including information on the IDH mutation and MGMT promoter methylation status. Furthermore we showed that circulating plasma EVs are elevated in CNS tumor patients in comparison to non-tumor donors (HD) controls with tumor related protein profiles. We now investigated, whether the methylation signatures of circulating DNA (both EV and cfNDA) can be used in liquid biopsy approaches for CNS tumor detection and classification. We isolated DNA from circulating EVs (n=27), cfDNA (n=27) and tumor tissue DNA (n=90) of patients with glioblastoma (GBM), meningioma (MGN) and cerebral metastases (CM). Patients undergoing epilepsy surgery as well as aneurysm clipping were used as non-tumor controls (HD, n= 7). EVs were classified by nanoparticle analysis, immunoblotting, imaging flow cytometry and electron microscopy. Isolated EV-DNA comprised many sorts of molecular weight (up tp >10Kb) in comparison to cfDNA (130-140bp). Healthy donors and tumor patients showed not differences in their DNA size profiles. We performed genome-wide methylation profiling by 850k Illumina EPIC arrays for all DNA analytes and tumor entities. Linear models and empirical Bayes methods identified significant differentially methylated CpGs (GBM vs. HD, MGN, vs HD, CM vs. HD), that revealed tumor specific signatures to detect and discriminate different CNS tumor entities. Visualization of differentially methylated CPGs by dimension reduction (PCA, t-SNE, Umap) verified tumor specific clusters. cfDNA and EV-DNA exhibited distinctive individual CpG profiles. Our study shows that the methylation signature of circulating EV DNA and cfDNA can be used to separate healthy individuals from tumor patients and could potentially complement standard-of-care imaging to improve tumor detection, classification and surveillance.