Document Type
Article
Publication Date
1-1-2025
Original Citation
Taylor A,
Lombardi J,
Patel A,
Tamariz A,
Martin J,
Bookland M,
Hersh D,
Cantor E,
Song X,
Sahm F,
Ng P,
Gell J,
Lau C.
A feasibility study of enzymatic methylation sequencing of cell-free DNA from cerebrospinal fluid of pediatric central nervous system tumor patients for molecular classification. Neurooncol Adv. 2025;7(1):vdaf159.
Keywords
JGM, SS1
JAX Source
Neurooncol Adv. 2025;7(1):vdaf159.
ISSN
2632-2498
PMID
40746948
DOI
https://doi.org/10.1093/noajnl/vdaf159
Grant
Martin J. Gavin Endowment and Start-up Fund (to C.C.L.); Department of Defense Cancer Research Program Career Development Award (W81XWH-22-1-0177-PRCRP to J.J.G.); CureSearch for Children’s Cancer Young Investigator Award in Pediatric Cancer Drug Development (685676 to J.J.G.); The Shanfield Family Fund (to J.J.G); Jackson Lab Cancer Center Fast Forward Award (TJL JAX CC FF Lau FY24 to C.C.L.); The Nick Strong Foundation (to C.C.L.)
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.
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