Publicaciones científicas

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

01-nov-2020 | Revista: Nature Cancer

Martí Duran-Ferrer  1   2 , Guillem Clot  3   4 , Ferran Nadeu  3   4 , Renée Beekman  3   4 , Tycho Baumann  4   5 , Jessica Nordlund  6 , Yanara Marincevic-Zuniga  6 , Gudmar Lönnerholm  7 , Alfredo Rivas-Delgado  3   5 , Silvia Martín  3   4 , Raquel Ordoñez  4   8 , Giancarlo Castellano  3 , Marta Kulis  3 , Ana C Queirós  3 , Seung-Tae Lee  9 , Joseph Wiemels  10 , Romina Royo  11 , Montserrat Puiggrós  11 , Junyan Lu  12 , Eva Giné  3   4   5 , Sílvia Beà  3   4   13 , Pedro Jares  3   4   13 , Xabier Agirre  4   8 , Felipe Prosper  4   8   14 , Carlos López-Otín  4   15 , Xosé S Puente  4   15 , Christopher C Oakes  13 , Thorsten Zenz  13   16 , Julio Delgado  3   4   5 , Armando López-Guillermo  3   4   5 , Elías Campo  3   4   17 , José Ignacio Martín-Subero  18   19   20   21


Abstract

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.

CITA DEL ARTÍCULO  Nat Cancer. 2020 Nov;1(11):1066-1081.  doi: 10.1038/s43018-020-00131-2.  Epub 2020 Nov 2