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.
Subject(s)
B-Lymphocytes/metabolism , Biomarkers, Tumor/genetics , Epigenesis, Genetic , Immunoglobulin Heavy Chains/genetics , Leukemia, Lymphocytic, Chronic, B-Cell/genetics , Transcriptome , Adult , Aged , Aged, 80 and over , Antineoplastic Agents/therapeutic use , B-Lymphocytes/classification , B-Lymphocytes/pathology , DNA Methylation , Disease Progression , Female , High-Throughput Nucleotide Sequencing , Humans , Leukemia, Lymphocytic, Chronic, B-Cell/classification , Leukemia, Lymphocytic, Chronic, B-Cell/drug therapy , Leukemia, Lymphocytic, Chronic, B-Cell/mortality , Male , Middle Aged , Proportional Hazards Models , Support Vector Machine , Survival Analysis , Time-to-Treatment , Treatment OutcomeABSTRACT
We analyzed the prognostic impact of the most frequent genetic abnormalities detected by fluorescence in situ hybridization in 101 patients with multiple myeloma, who underwent allogeneic hematopoietic stem cell transplantation (HSCT) after melphalan/fludarabine-based reduced conditioning. The incidences of abnormalities in the present analysis were as follows: del(13q14) (61%), t(11;14)(q13;q32) (14%), t(4;14)(p16.3;q32) (19%), MYC-gain gains (8q24) (21%), del(17p13) (16%) and t(14;16)(q32;q23) (5%). None of the patients had t(6;14)(p25;q32). The overall complete remission (CR) rate was 50% with no differences between the genetic abnormalities except for patients with del(17p13) who achieved less CR (7 vs 56%; P=0.001). Univariate analysis revealed a higher relapse rate in patients aged >50 years (P=0.002), patients with del(13q14) (P=0.006) and patients with del(17p13) (P=0.003). In multivariate analyses, only del(13q14) (HR: 2.34, P=0.03) and del(17p13) (HR: 2.24; P=0.04) significantly influenced the incidence of relapse, whereas for event-free survival, only age (HR 2.8; P=0.01) and del(17p13) (HR: 2.05; P=0.03) retained their negative prognostic value. These data show that del(17p13) is a negative prognostic factor for achieving CR as well as for event-free survival after HSCT. Translocation t(4;14) might be overcome by allogeneic HSCT, which will have implication for risk-adapted strategies.
