Multiple studies over the last decade analyzed the DNA methylome of normal and neoplasitc cells, but none of them studied a whole developmental cell lineage and derived neoplasms. Here, we report 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. 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. Here, I provide the diagnostic algorithm and the epiCMIT mitotic clock calculator.
If you use any data or code derived from this study, please cite:
Duran-Ferrer, M. et al. The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome. Nat Cancer (2020). https://doi.org/10.1038/s43018-020-00131-2.
The pdf of the article can be found here.
Please, also check the comment of our manuscipt by Paolo Strati and Michael R. Green.
LICENSE terms can be found here
The current code supports 450k and EPIC Illumina arrays, as well as next generation sequencing (NGS) approaches, including single- and paired-end RRBS, ERRB and WGBS.
Complete tutorial to estimate the epiCMIT mitotic clock in DNA methylation data..
The pan B-cell tumor classifier is able to classify ALL, MCL, CLL, DLBCL and MM and their main subtypes. In addition, a previous step checking the tumor cell contetn is applied.
Complete tutorial for the pan Bcell tumor classifier.
DNA methylation and gene expression data that support the findings of this study have been deposited at the European Genome-phenome Archive (EGA) under accession number EGAS00001004640.
Previously published DNA methylation data re-analyzed in this study can be found under accession codes: B cells, EGAS00001001196; ALL, GSE56602, GSE49032, GSE76585, GSE69229; MCL, EGAS00001001637, EGAS00001004165; CLL, EGAD00010000871, EGAD00010000948; MM, EGAS00001000841; In vitro B-cell differentiation model of naïve B cells from human primary samples, GSE72498.
Normalized DNA methylation matrices used for all the analyses in this study are available here.
Published gene expression datasets can be found under the accession codes: B cells, EGAS00001001197; ALL, GSE49032; MCL, GSE36000; CLL, EGAS00000000092, EGAD00010000252; MM, GSE19784; In vitro B-cell differentiation model of naïve B cells from human primary samples, GSE72498.
ChIP-seq datasets that were re-analyzed here can be found under the accession codes: GSE109377 (NALM6 ALL cell line, n=1) and EGAS00001000326 (15 normal B cells donors, and 5 MCL, 7 CLL and 4 MM patients) available from Blueprint.
If you have any question, comment or suggestions please contact me at: maduran@clinic.cat :-)