Duran-Ferrer M, Clot G, Nadeu F, Beekman R, Baumann T, Nordlund J, Marincevic-Zuniga Y, Lönnerholm G, Rivas-Delgado A, Martín S, Ordoñez R, Castellano G, Kulis M, Queirós AC, Lee S, Wiemels J, Royo R, Puiggrós M, Lu J, Giné E, Beà S, Jares P, Agirre X, Prosper F, López-Otín C, Puente XS, Oakes CC, Zenz T, Delgado J, López-Guillermo A, Campo E, Martín-Subero JI
Nat Cancer 1 (11) 1066-1081 [2020-11-00; online 2020-11-02]
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.