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

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.