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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.

Author information

1
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.
2
Servicio de Hematología, Hospital Clínic de Barcelona, IDIBAPS, Barcelona, Spain.
3
Institute of Human Genetics, Christian-Albrechts-University of Kiel, University Hospital Schleswig-Holstein, Campus Kiel, Germany.
4
MRC Toxicology Unit Leicester, Leicester, UK.
5
Servicio de Hematología, Hospital Universitario, Centro de Investigación del Cáncer-IBMCC (USAL-CSIC), Universidad de Salamanca, Salamanca, Spain.
6
Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología (IUOPA), Universidad de Oviedo, Oviedo, Spain.
7
Ernest and Helen Scott Haematological Research Institute, Leicester University, Leicester, UK.
8
Josep Carreras Leukemia Research Institute, Barcelona, Spain.

Abstract

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.

PMID:
25151957
DOI:
10.1038/leu.2014.252
[Indexed for MEDLINE]
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