NOTCH1 and SF3B1 mutations have been previously reported to have prognostic significance in chronic lymphocytic leukemia but to date they have not been validated in a prospective, controlled clinical trial. We have assessed the impact of these mutations in a cohort of 494 patients treated within the randomized phase 3 United Kingdom Leukaemia Research Fund Chronic Lymphocytic Leukemia 4 (UK LRF CCL4) trial that compared chlorambucil and fludarabine with and without cyclophosphamide in previously untreated patients. We investigated the relationship of mutations in NOTCH1 (exon 34) and SF3B1 (exon 14-16) to treatment response, survival and a panel of established biologic variables. NOTCH1 and SF3B1 mutations were found in 10% and17% of patients, respectively. NOTCH1 mutations correlated with unmutated IGHV genes, trisomy 12, high CD38/ ZAP-70 expression and were associated with reduced overall (median 54.8 vs 74.6 months, P = .02) and progression-free (median 22.0 vs 26.4 months, P = .02) survival. SF3B1 mutations were significantly associated with high CD38 expression and with shorter overall survival (median 54.3 vs 79.0 months, P < .001). Furthermore, multivariate analysis, including baseline clinical variables, treatment, and adverse prognostic factors demonstrated that although TP53 alterations remained the most informative marker of dismal survival in this cohort, NOTCH1 (HR 1.58, P = .03) and SF3B1 (HR 1.52, P = .01) mutations have added independent prognostic value. Key Points This is the first study to validate the importance of NOTCH1 and SF3B1 gene mutations in the context of a randomized, prospective clinical trial. Mutations in both genes are independent prognostic biomarkers, and therefore have clinical utility in the accurate risk-adapted stratification of CLL patients.

Purpose: Mounting evidence supports the clinical significance of gene mutations and immunogenetic features in common mature B-cell malignancies. Experimental Design: We undertook a detailed characterization of the genetic background of splenic marginal zone lymphoma (SMZL), using targeted resequencing and explored potential clinical implications in a multinational cohort of 175 patients with SMZL. Results: We identified recurrent mutations in TP53 (16%), KLF2 (12%), NOTCH2 (10%), TNFAIP3 (7%), MLL2 (11%), MYD88 (7%), and ARID1A (6%), all genes known to be targeted by somatic mutation in SMZL. KLF2 mutations were early, clonal events, enriched in patients with del(7q) and IGHV1-2*04 B-cell receptor immunoglobulins, and were associated with a short median time to first treatment (0.12 vs. 1.11 years; P = 0.01). In multivariate analysis, mutations in NOTCH2 [HR, 2.12; 95% confidence interval (CI), 1.02–4.4; P = 0.044] and 100% germline IGHV gene identity (HR, 2.19; 95% CI, 1.05–4.55; P = 0.036) were independent markers of short time to first treatment, whereas TP53 mutations were an independent marker of short overall survival (HR, 2.36; 95 % CI, 1.08–5.2; P = 0.03). Conclusions: We identify key associations between gene mutations and clinical outcome, demonstrating for the first time that NOTCH2 and TP53 gene mutations are independent markers of reduced treatment-free and overall survival, respectively. Clin Cancer Res; 21(18); 4174–83. ©2015 AACR.

Copy number variants (CNVs) play important roles in a number of human diseases and in pharmacogenetics. Powerful methods exist for CNV detection in whole genome sequencing (WGS) data, but such data are costly to obtain. Many disease causal CNVs span or are found in genome coding regions (exons), which makes CNV detection using whole exome sequencing (WES) data attractive. If reliably validated against WGS-based CNVs, exome-derived CNVs have potential applications in a clinical setting. Several algorithms have been developed to exploit exome data for CNV detection and comparisons made to find the most suitable methods for particular data samples. The results are not consistent across studies. Here, we review some of the exome CNV detection methods based on depth of coverage profiles and examine their performance to identify problems contributing to discrepancies in published results. We also present a streamlined strategy that uses a single metric, the likelihood ratio, to compare exome methods, and we demonstrated its utility using the VarScan 2 and eXome Hidden Markov Model (XHMM) programs using paired normal and tumour exome data from chronic lymphocytic leukaemia patients. We use array-based somatic CNV (SCNV) calls as a reference standard to compute prevalence-independent statistics, such as sensitivity, specificity and likelihood ratio, for validation of the exome-derived SCNVs. We also account for factors known to influence the performance of exome read depth methods, such as CNV size and frequency, while comparing our findings with published results.