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The proliferative history shapes the DNA methylome of B-cell tumor and predicts clinical outcome

Here, we provide access to CLL and DLBCL DNA methylation and gene expression data in the context of a systematic analysis of the DNA methylation variability in 1,595 samples of normal cell subpopulations and 14 tumor subtypes spanning the entire human B-cell lineage. This approach showed that differential methylation among tumor entities relates to differences in cellular origin and to de novo epigenetic alterations, which allowed us to build an accurate machine learning-based diagnostic algorithm. We identify extensive patient-specific methylation variability in silenced chromatin associated with the proliferative history of normal and neoplastic B cells. Mitotic activity generally leaves both hyper- and hypomethylation imprints, but some B-cell neoplasms preferentially gain or lose DNA methylation. Subsequently, we construct a DNA methylation-based mitotic clock called epiCMIT, whose lapse magnitude represents a strong independent prognostic variable in B-cell tumors and is associated with particular driver genetic alterations. Our findings reveal DNA methylation as a holistic tracer of B-cell tumor developmental history, with implications in the differential diagnosis and prediction of clinical outcome.

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00010001974 450k and EPIC Illumina arrays 67
EGAD00010001975 Illumina 450k 490
EGAD00010001976 43
Publications Citations
The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome.
Nat Cancer 1: 2020 1066-1081
Molecular map of chronic lymphocytic leukemia and its impact on outcome.
Nat Genet 54: 2022 1664-1674