Abstract
BACKGROUND: Array-based DNA methylation profiling is the gold standard for central nervous system (CNS) tumor molecular classification, but requires over 100 ng input DNA from surgical tissue. Cell-free tumor DNA (cfDNA) in cerebrospinal fluid (CSF) offers an alternative for diagnosis and disease monitoring. This study aimed to test the utilization of enzymatic DNA methylation sequencing (EM-seq) methods to overcome input DNA limitations. METHODS: We used the NEBNext EM-seq v2 kit on various amounts of cfDNA, as low as 0.1 ng, extracted from archival CSF samples of 10 patients with CNS tumors. Tumor classification was performed via MNP-Flex using CpG sites overlapping those on the MethylationEPIC array. RESULTS: EM-seq provided sufficient genomic coverage for 10 and 1 ng input DNA samples to generate global DNA methylation profiles. Samples with 0.1 ng input showed lower coverage due to read duplication. Methylation levels for CpG sites with at least 5× coverage were highly correlated across various input DNA amounts, indicating that lower input cfDNA can still be used for tumor classification. The MNP-Flex classifier, trained on tissue DNA methylation data, successfully predicted CNS tumor types for 7 out of 10 CSF samples using EM-seq methylation data with only 1 ng of input cfDNA, consistent with diagnoses based on tissue MethylationEPIC classification and/or histopathology. Additionally, we detected focal and arm-level copy number alterations previously identified via clinical cytogenetics of tumor tissue. CONCLUSIONS: This study demonstrated the feasibility of CNS tumor molecular classification based on CSF using the EM-seq approach, and establishes potential sample quality limitations for future studies.