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Leukemia. 2015 Mar;29(3):598-605. doi: 10.1038/leu.2014.252. Epub 2014 Aug 25.

A B-cell epigenetic signature defines three biologic subgroups of chronic lymphocytic leukemia with clinical impact.

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Unidad de Hematopatología, Servicio de Anatomía Patológica, Hospital Clínic, Departamento de Anatomía Patológica, Farmacología y Microbiología, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
Servicio de Hematología, Hospital Clínic de Barcelona, IDIBAPS, Barcelona, Spain.
Institute of Human Genetics, Christian-Albrechts-University of Kiel, University Hospital Schleswig-Holstein, Campus Kiel, Germany.
MRC Toxicology Unit Leicester, Leicester, UK.
Servicio de Hematología, Hospital Universitario, Centro de Investigación del Cáncer-IBMCC (USAL-CSIC), Universidad de Salamanca, Salamanca, Spain.
Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología (IUOPA), Universidad de Oviedo, Oviedo, Spain.
Ernest and Helen Scott Haematological Research Institute, Leicester University, Leicester, UK.
Josep Carreras Leukemia Research Institute, Barcelona, Spain.


Prospective identification of patients with chronic lymphocytic leukemia (CLL) destined to progress would greatly facilitate their clinical management. Recently, whole-genome DNA methylation analyses identified three clinicobiologic CLL subgroups with an epigenetic signature related to different normal B-cell counterparts. Here, we developed a clinically applicable method to identify these subgroups and to study their clinical relevance. Using a support vector machine approach, we built a prediction model using five epigenetic biomarkers that was able to classify CLL patients accurately into the three subgroups, namely naive B-cell-like, intermediate and memory B-cell-like CLL. DNA methylation was quantified by highly reproducible bisulfite pyrosequencing assays in two independent CLL series. In the initial series (n=211), the three subgroups showed differential levels of IGHV (immunoglobulin heavy-chain locus) mutation (P<0.001) and VH usage (P<0.03), as well as different clinical features and outcome in terms of time to first treatment (TTT) and overall survival (P<0.001). A multivariate Cox model showed that epigenetic classification was the strongest predictor of TTT (P<0.001) along with Binet stage (P<0.001). These findings were corroborated in a validation series (n=97). In this study, we developed a simple and robust method using epigenetic biomarkers to categorize CLLs into three subgroups with different clinicobiologic features and outcome.

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