作者
Martí Duran-Ferrer,Guillem Clot,Ferran Nadeu,Renée Beekman,Tycho Baumann,Jessica Nordlund,Yanara Marincevic-Zuniga,Gudmar Lönnerholm,Julio Delgado,Silvia Martin,Raquel Ordoñez,Giancarlo Castellano,Marta Kulis,Ana M. Queirós,Lee Seung-Tae,Joseph L. Wiemels,Romina Royo,Montserrat Puiggròs,Junyan Lu,Eva Giné,Sílvia Beà,Pedro Jares,Xabier Agirre,Felipe Prosper,Carlos López-Otín,Xose S. Puente,Christopher C. Oakes,Thorsten Zenz,Armando López-Guillermo,Elias Campo,José I. Martín-Subero
摘要
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.