ArticleCommunications medicine2026
Continuous DNA methylation deconvolution-based surrogate for B-cell differentiation state in chronic lymphocytic leukemia.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Continuous DNA methylation deconvolution-based surrogate for B-cell differentiation state in chronic lymphocytic leukemia.Communications medicine · 2026Article
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9 authors.
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Abstract
backgroundChronic lymphocytic leukemia (CLL) is clinically divided categorically into IGHV mutated (M-CLL) and IGHV unmutated (U-CLL) subtypes. We aimed to generate a continuous metric of CLL epigenetic state to correspond to the many steps of B-cell differentiation from which CLL neoplasms may arise.
methodsWe measured genome-scale DNA methylation in purified CLL samples (n = 89) and utilized reference-based cell deconvolution techniques to develop a continuous metric of epigenetic similarity across a B-naive-like to B-memory-like scale (B-Index). We used B-Index to find epigenome-wide changes across CLL differentiation states. We also analyzed the epigenetic similarity of U-CLL to normal B-cell types, and epigenetic correlates of tumor burden.
resultsWe find that B-Index accurately classifies CLL into clinical subtypes (98.8%), has a stronger epigenetic signal than IGHV gene percent identity, and demonstrates additional epigenetic signal within clinical subgroups. We demonstrate that U-CLL is epigenetically more similar to B-memory than B-naive cells and reconcile previous reports of a B-naive-like epigenetic signal. The B-memory-like program of U-CLL is enriched for binding sites of transcription factors related to the germinal center activation pathway. We also identified an epigenetic signal associated with tumor burden, which was enriched for binding sites of Epstein-Barr-Virus transcription factors.
conclusionsOur analyses show that more epigenetic states are present among CLL neoplasms than are captured by the clinical classification system. Our approach can be used to generate continuous metrics of epigenetic states for other neoplasms.
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