DNA methylation-based classification of hematolymphoid neoplasms

Accurate pathological diagnosis of hematolymphoid neoplasms (HLN) is often challenging due to their complexity and heterogeneity. Genome-wide DNA methylation profiling has emerged as a valuable tool for tumor classification and diagnosis across malignancies, like central nervous system neoplasms. In this study, we explored the role of DNA methylation-based profiling in HLN. We generated the largest cross-platform HLN methylome cohort to date (1,156 samples) and identified 44 reproducible methylation classes (MCs) that aligned closely with WHO 5th edition/ICC entities, 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 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, while few, the methylation classifier demonstrated its potential utility as an adjunct where on additional review the diagnosis was revised in favor of the methylation prediction in the majority of 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 (X2 = 11.7, p<0.0008). 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.

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Publication Details

Journal
Blood Advances
Published
2026-04-23
DOI
https://doi.org/10.1182/bloodadvances.2024015275
Primary Topic
Epigenetics and DNA Methylation
Type
article
Field-Weighted Citation Impact
0.00

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article

DNA methylation-based classification of hematolymphoid neoplasms

Stefania Pittaluga, Manel Esteller, Martí Duran‐Ferrer, Michael Hummel et al.
Blood Advances
Epigenetics and DNA Methylation
article

DNA methylation-based classification of hematolymphoid neoplasms

Stefania Pittaluga, Manel Esteller, Martí Duran‐Ferrer, Michael Hummel, Valerie Zgonc, F Fend, Mark Raffeld, Elı́as Campo, Leticia Quintanilla-Martı́nez, Benjamin Englert, Liqiang Xi, A Rosenwald, Shannon Skarshaug, David Capper, Rust Turakulov, Christin Siewert, Marion Wobser, Zied Abdullaev, Annapurna Saksena, Andreas von Deimling, Gerardo Ferrer, Kenneth Aldape, Miguel Á. Piris, Elaine S. Jaffe, Ioannis Anagnostopoulos, Julia Richter, Ruifeng Teng, Wolfgang Hiddemann, João Victor Alves de Castro, Lana Sultanaeva, Jose Ignacio Martin-Subero, Omkar Singh, Ting Zhou, Robert J. Kreitman, Jeremiah X. Karrs, Deanna Riley
article en

Abstract

Accurate pathological diagnosis of hematolymphoid neoplasms (HLN) is often challenging due to their complexity and heterogeneity. Genome-wide DNA methylation profiling has emerged as a valuable tool for tumor classification and diagnosis across malignancies, like central nervous system neoplasms. In this study, we explored the role of DNA methylation-based profiling in HLN. We generated the largest cross-platform HLN methylome cohort to date (1,156 samples) and identified 44 reproducible methylation classes (MCs) that aligned closely with WHO 5th edition/ICC entities, 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 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, while few, the methylation classifier demonstrated its potential utility as an adjunct where on additional review the diagnosis was revised in favor of the methylation prediction in the majority of 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 (X2 = 11.7, p<0.0008). 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.

Blood Advances
Institució Catalana de Recerca i Estudis Avançats (ES), Dartmouth–Hitchcock Medical Center (US), National Institutes of Health (US), University of Helsinki (FI), German Cancer Research Center (DE), Heidelberg University (DE), University of Würzburg (DE), AC Camargo Hospital (BR), Christian-Albrechts-Universität zu Kiel (DE), Josep Carreras Leukaemia Research Institute (ES), Austin Health & Human Services Department (US), Hospital Clínic de Barcelona (ES), Universitätsklinikum Würzburg (DE), Hospital Universitario Fundación Jiménez Díaz (ES), National Cancer Institute (US), Centro de Investigación Biomédica en Red de Cáncer (ES), Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (ES), Clinical Research Center Kiel (DE), Franklin University (US), University of Tübingen (DE)
U.S. Department of Health and Human Services, Deutschen Konsortium für Translationale Krebsforschung, National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 10%
Epigenetics and DNA Methylation
DOIOpen Access PDFOpenAlex
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