In silico purification improves DNA methylation-based classification rates of pediatric low-grade gliomas.

ABSTRACT: DNA methylation-based classification using the Heidelberg Classifier is a state-of-the-art data-driven method for molecular diagnosis of central nervous system (CNS) tumors. However, many pediatric low-grade glioma (pLGG) samples fail to yield a confident methylation-based classification, often suspected due to low tumor cell content. Here, we present a rapid, reference-based in silico purification framework that systematically removes the epigenetic signatures of five non-malignant cell types-microglia, monocytes, neutrophils, T cells, and neurons-from tumor profiles to enable classification of previously non-classifiable pLGG samples. To validate our approach, we analyzed paired DNA methylation profiles from the same biopsy, where one was initially classifiable and the other was not. Aft

SUBMITTER: Jurgensen L 

PROVIDER: S-EPMC12476408 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature