PATH-11. FEASIBILITY OF BRAIN TUMOR CLASSIFICATION BY ENZYMATIC DNA METHYLATION SEQUENCING ANALYSIS OF CELL-FREE DNA OBTAINED FROM CEREBROSPINAL FLUID

Abstract

Abstract BACKGROUND Array-based DNA methylation profiling is the gold standard for molecular classification of brain tumors. However, it relies on significant amount of input DNA extracted from surgical tissue, thus limiting its use for tumors where biopsy is challenging or limited in quantity. Cell-free tumor DNA (cfDNA) in cerebrospinal fluid (CSF) presents alternative opportunities for brain tumor diagnosis and disease monitoring following treatment. Novel enzymatic DNA methylation sequencing (EM-seq) methods may allow us to overcome input DNA limitations and accurately quantify methylation from cfDNA. METHODS We performed methylation sequencing using the NEBNext EM-seq kit on cfDNA from archival CSF samples collected from three brain tumor patients with confirmed histopathological diagnoses. Variable amounts of input cfDNA (0.1ng-10ng) were tested. We utilized the methylseq pipeline for data processing. For tumor classification, data was limited to CpG sites overlapping the MethylationEPIC array before analysis using MNP-Flex, a modified platform-agnostic version of the Heidelberg methylation classifier RESULTS Using the EM-seq method, genomic coverage for 10 and 1ng input DNA samples (average: 46x and 26x, respectively) was sufficient for generating global methylation profiles. Samples with 0.1ng input showed an average coverage of only 5.32x due to high levels of read duplication. However, methylation levels for CpG sites with at least 5x coverage were highly correlated across varying input DNA amounts, suggesting that lower input cfDNA could still be used for tumor classification based on relevant CpG sites. CONCLUSIONS Using the MNP-Flex classifier, which was originally trained with methylation array data from tumor tissue, we successfully predicted brain tumor types (both high-grade glioma and medulloblastoma) with cfDNA methylation data down to only 0.1ng of input cfDNA, matching diagnosis based on tissue methylation and histopathology in this pilot study. Further classification of additional tumor types using CSF cfDNA is required to confirm the clinical utility of this platform.