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

Study ID Alternative Stable ID Type
EGAS00001004640 Other

Study Description

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 ... (Show More)

Study Datasets 3 datasets.

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
DLBCL DNA methylation data measured by 450k and EPIC Illumina arrays
450k and EPIC Illumina arrays 67
DNA methylation of ICGC CLL patients measured by Illumina 450k array
Illumina 450k 490
DLBCL gene expression data using Affymetrix array 43

Who archives the data?

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