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Epigenomic evolution in diffuse large B-cell lymphomas.

作者信息

Pan Heng, Jiang Yanwen, Boi Michela, Tabbò Fabrizio, Redmond David, Nie Kui, Ladetto Marco, Chiappella Annalisa, Cerchietti Leandro, Shaknovich Rita, Melnick Ari M, Inghirami Giorgio G, Tam Wayne, Elemento Olivier

机构信息

1] Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medical College, 1300 York Avenue, New York, New York 10021, USA [2] Institute for Precision Medicine, Weill Cornell Medical College, 1300 York Avenue, New York, New York 10021, USA.

1] Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medical College, 1300 York Avenue, New York, New York 10021, USA [2] Institute for Precision Medicine, Weill Cornell Medical College, 1300 York Avenue, New York, New York 10021, USA [3] Hematology/Oncology Division, Department of Medicine, Weill Cornell Medical College, 1300 York Avenue, New York, New York 10021, USA.

出版信息

Nat Commun. 2015 Apr 20;6:6921. doi: 10.1038/ncomms7921.

Abstract

The contribution of epigenomic alterations to tumour progression and relapse is not well characterized. Here we characterize an association between disease progression and DNA methylation in diffuse large B-cell lymphoma (DLBCL). By profiling genome-wide DNA methylation at single-base pair resolution in thirteen DLBCL diagnosis-relapse sample pairs, we show that DLBCL patients exhibit heterogeneous evolution of tumour methylomes during relapse. We identify differentially methylated regulatory elements and determine a relapse-associated methylation signature converging on key pathways such as transforming growth factor-β (TGF-β) receptor activity. We also observe decreased intra-tumour methylation heterogeneity from diagnosis to relapsed tumour samples. Relapse-free patients display lower intra-tumour methylation heterogeneity at diagnosis compared with relapsed patients in an independent validation cohort. Furthermore, intra-tumour methylation heterogeneity is predictive of time to relapse. Therefore, we propose that epigenomic heterogeneity may support or drive the relapse phenotype and can be used to predict DLBCL relapse.

摘要

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