Year: 2026
Type: article
Abstract: ABSTRACT: Accurate pathologic diagnosis of hematolymphoid neoplasms (HLN) is often challenging because of their complexity and heterogeneity. Genome-wide DNA methylation profiling has emerged as a valuable tool for tumor classification and diagnosis across malignancies, such as central nervous system neoplasms. In this study, we explored the role of DNA methylation-based profiling in HLN. We generated the largest crossplatform HLN methylome cohort to date (1156 samples) and identified 44 reproducible methylation classes (MCs) that aligned closely with entities defined by the 5th edition of the World Health Organization (WHO) Classification/the International Consensus Classification (ICC), including subgroups with clinical and biological relevance. Copy number alterations were also inferred for all MCs. Additionally, a machine learning-based DNA methylation classifier was developed and validated on an independent test set, demonstrating a modest 58% high-confidence score rate, nevertheless, with a robust 97% concordance with the original diagnosis in these high-confidence score cases. Additionally, in discrepant high-confidence score cases, although few, the methylation classifier demonstrated its potential utility as an adjunct, in which, on additional review, the diagnosis was revised in favor of the methylation prediction in most of the cases (5/8 discrepant cases). Tumor purity was a significant contributor for a substantial proportion of low-confidence score samples (scores below predetermined thresholds, 42%), affecting the classifier performance (χ2 = 11.7; P< .0008). Our findings suggest that specific hematolymphoid tumor types exhibit distinct methylation signatures that can be leveraged to accurately classify these tumors. As a pilot study, our results provide a foundation for the development of a comprehensive and clinically valuable methylation-based classifier for hematolymphoid tumors in the future.
Source: Blood Advances
Authors: Annapurna Saksena, Christin Siewert, Rust Turakulov, Omkar Singh, Zied Abdullaev, Ting Zhou, Jeremiah Karrs, Joao Victor Alves De Castro, Deanna Riley, Shannon Skarshaug, Valerie Zgonc, Lana Sultanaeva, Ruifeng Teng, Liqiang Xi, Robert J. Kreitman, Andreas von Deimling, Martí Duran-Ferrer, José Ignacio Martín-Subero, Elias Campo, Benjamin Englert, Julia Richter, Wolfram Klapper, Ioannis Anagnostopoulos, Andreas Rosenwald, Marion Wobser, Falko Fend, Leticia Quintanilla-Martinez, Gerardo Ferrer, Manel Esteller, Miguel A. Piris, Michael Hummel, Stefania Pittaluga, Mark Raffeld, Elaine S. Jaffe, David Capper, Kenneth Aldape
Institutions: National Cancer Institute, German Cancer Research Center, Humboldt-Universität zu Berlin, Charité – Universitätsmedizin Berlin, University Hospital Heidelberg, Consorci Institut D'Investigacions Biomediques August Pi I Sunyer, Institució Catalana de Recerca i Estudis Avançats, Hospital Clínic de Barcelona, Universitat de Barcelona, University of Helsinki, Christian-Albrechts-Universität zu Kiel, University Hospital Schleswig-Holstein, University of Würzburg, Universitätsklinikum Würzburg, Universitätsklinikum Tübingen, University of Tübingen, Josep Carreras Leukaemia Research Institute, Centro de Investigación Biomédica en Red de Cáncer, Hospital Universitario Fundación Jiménez Díaz
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,800
OpenAlex ID: W7155353099