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Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

DNA methylation-based classification of hematolymphoid neoplasms.

Annapurna Saksena¹, Christin Siewert^2,3, Rust Turakulov¹

¹Laboratory of Pathology, National Cancer Institute, Bethesda, MD.

|April 23, 2026

Summary

DNA methylation profiling accurately classifies hematolymphoid neoplasms (HLN), identifying 44 distinct methylation classes. A machine learning classifier showed high concordance with diagnoses, aiding in challenging cases.

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Last Updated: Apr 25, 2026

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

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Published on: June 16, 2017

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Published on: November 3, 2018

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Area of Science:

Oncology
Genomics
Computational Biology

Background:

Accurate pathological diagnosis of hematolymphoid neoplasms (HLN) is challenging due to complexity and heterogeneity.
Genome-wide DNA methylation profiling is a powerful tool for tumor classification across various cancers.

Purpose of the Study:

To explore the utility of DNA methylation profiling for classifying hematolymphoid neoplasms.
To develop and validate a machine learning-based DNA methylation classifier for HLN.

Main Methods:

Generated the largest cross-platform HLN methylome cohort (1,156 samples).
Identified 44 reproducible methylation classes (MCs) aligned with WHO entities.
Developed and validated a machine learning classifier, assessing performance and impact of tumor purity.

Main Results:

44 MCs were identified, correlating with WHO 5th edition/ICC entities and revealing clinically relevant subgroups.
The DNA methylation classifier achieved 97% concordance with original diagnoses in high-confidence cases.
In discrepant high-confidence cases, the classifier aided in revising diagnoses in the majority (5/8).
Tumor purity significantly impacted classifier performance in low-confidence samples.

Conclusions:

Distinct methylation signatures characterize hematolymphoid tumor types, enabling accurate classification.
DNA methylation profiling serves as a valuable adjunct diagnostic tool for HLN.
This pilot study lays the groundwork for a future clinical methylation-based classifier for HLN.