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@ARTICLE{Taylor:306869,
      author       = {A. M. Taylor and J. T. Lombardi and A. J. Patel$^*$ and A.
                      Tamariz and J. Martin and M. J. Bookland and D. S. Hersh and
                      E. Cantor and X. Song and F. Sahm$^*$ and P. K. Ng and J. J.
                      Gell and C. C. Lau},
      title        = {{A} feasibility study of enzymatic methylation sequencing
                      of cell-free {DNA} from cerebrospinal fluid of pediatric
                      central nervous system tumor patients for molecular
                      classification.},
      journal      = {Neuro-oncology advances},
      volume       = {7},
      number       = {1},
      issn         = {2632-2498},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-02846},
      pages        = {vdaf159},
      year         = {2025},
      abstract     = {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.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.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.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.},
      keywords     = {CNS tumor classification (Other) / MNP-flex (Other) /
                      cell-free DNA (Other) / enzymatic methylation sequencing
                      (Other) / molecular diagnosis (Other)},
      cin          = {B062 / B300},
      ddc          = {610},
      cid          = {I:(DE-He78)B062-20160331 / I:(DE-He78)B300-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40746948},
      pmc          = {pmc:PMC12311925},
      doi          = {10.1093/noajnl/vdaf159},
      url          = {https://inrepo02.dkfz.de/record/306869},
}