期刊
LEUKEMIA
卷 29, 期 3, 页码 598-605出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/leu.2014.252
关键词
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资金
- Spanish Ministry of Economy and Competitiveness (MINECO) through Instituto de Salud Carlos III (ISCIII)
- Red Tematica de Investigacion del Cancer (RTICC) of the ISCIII [RD12/0036/0036, RD12/0036/0023, RD12/0036/0004, RD12/0036/0067]
- UK Medical Research Council
- European Union's Seventh Framework Programme through the Blueprint Consortium [282510]
- Ramon y Cajal contract of the MINECO
- Portuguese Fundacao para a Ciencia e a Tecnologia
- Agenda de Gestio d'Ajuts Universitaris i de Recerca (Generalitat de Catalunya)
- Junior Excellence Research Group of the Jackstadt foundation
- [SAF2009-08663]
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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