Artigo Acesso aberto Revisado por pares

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

2014; Springer Nature; Volume: 29; Issue: 3 Linguagem: Inglês

10.1038/leu.2014.252

ISSN

1476-5551

Autores

Ana C. Queirós, Neus Villamor, Guillem Clot, Alejandra Martínez‐Trillos, Marta Kulis, Alba Navarro, Eva Maria Murga Penas, Sandrine Jayne, Aneela Majid, Johanna Richter, Anke K. Bergmann, Julia Kolarova, Cristina Royo, Núria Russiñol, Giancarlo Castellano, Magda Pinyol, Sı́lvia Beà, Itziar Salaverría, Mònica López‐Guerra, Dolors Colomer, Marta Aymerich, Marı́a Rozman, Julio Delgado, Eva Giné, Marcos González, Xosé S. Puente, Reiner Siebert, Martin J.S. Dyer, Carlos López-Otı́n, C Rozmán, Elı́as Campo, Armando López‐Guillermo, José I. Martín‐Subero,

Tópico(s)

Pancreatic function and diabetes

Resumo

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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