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Mantle cell lymphoma (MCL), a malignancy of B-lymphocytes, has a poor prognosis. It is thus necessary to improve the understanding of the pathobiology of MCL and identify factors contributing to its aggressiveness. Our studies, based on Affymetrix data from 17 MCL biopsies, real-time quantitative polymerase chain reaction data from 18 sorted primary MCL cells and 108 MCL biopsies compared to non-malignant tissue, reveals that GNAZ expression predicts poor clinical outcome of MCL patients (Cox regression, P = 0 center dot 014) and lymphocytosis (Mann-Whitney, P = 0 center dot 011). We show that GNAZ translates to G alpha(z) protein - a signalling molecule within the G-protein coupled receptor network. Our findings suggest that GNAZ/G alpha(z) contribute to the MCL pathobiology.

Key Points:

Microenvironmental stimuli affect EZH2 expression and function in CLL.Combined B-cell signaling and EZH2 inhibition showed synergistic effects on primary CLL cells.

Chronic lymphocytic leukemia (CLL) stereotyped subsets #6 and #8 include cases expressing unmutated B cell receptor immunoglobulin (BcR IG) (U-CLL). Subset #6 (IGHV1-69/IGKV3-20) is less aggressive compared to subset #8 (IGHV4-39/IGKV1(D)-39) which has the highest risk for Richter's transformation among all CLL. The underlying reasons for this divergent clinical behavior are not fully elucidated. To gain insight into this issue, here we focused on epigenomic signatures and their links with gene expression, particularly investigating genome-wide DNA methylation profiles in subsets #6 and #8 as well as other U-CLL cases not expressing stereotyped BcR IG. We found that subset #8 showed a distinctive DNA methylation profile compared to all other U-CLL cases, including subset #6. Integrated analysis of DNA methylation and gene expression revealed significant correlation for several genes, particularly highlighting a relevant role for the TP63 gene which was hypomethylated and overexpressed in subset #8. This observation was validated by quantitative PCR, which also revealed TP63 mRNA overexpression in additional nonsubset U-CLL cases. BcR stimulation had distinct effects on p63 protein expression, particularly leading to induction in subset #8, accompanied by increased CLL cell survival. This pro-survival effect was also supported by siRNA-mediated downregulation of p63 expression resulting in increased apoptosis. In conclusion, we report that DNA methylation profiles may vary even among CLL patients with similar somatic hypermutation status, supporting a compartmentalized approach to dissecting CLL biology. Furthermore, we highlight p63 as a novel prosurvival factor in CLL, thus identifying another piece of the complex puzzle of clinical aggressiveness. What's new? In chronic lymphocytic leukemia (CLL), cases with unmutated immunoglobulin receptors (U-CLL) are generally associated with inferior outcome, albeit still displaying considerable heterogeneity. Might such differences in CLL progression be explained by epigenetics? In this study, the authors found that an unusually aggressive subset of CLLs called subset #8 has a distinctive DNA-methylation profile. They also found that p63 is a novel pro-survival factor for CLL cells. These molecular studies may lead to new prognostic biomarkers, and possibly new therapeutic targets, for CLL.

Background: Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. Results: Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. Conclusions: The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.

Deregulated transcriptional control caused by aberrant DNA methylation and/or histone modifications is a hallmark of cancer cells. In chronic lymphocytic leukemia (CLL), the most common adult leukemia, the epigenetic 'landscape' has added a new layer of complexity to our understanding of this clinically and biologically heterogeneous disease. Early studies identified aberrant DNA methylation, often based on single gene promoter analysis with both biological and clinical impact. Subsequent genome-wide profiling studies revealed differential DNA methylation between CLLs and controls and in prognostics subgroups of the disease. From these studies, it became apparent that DNA methylation in regions outside of promoters, such as enhancers, is important for the regulation of coding genes as well as for the regulation of non-coding RNAs. Although DNA methylation profiles are reportedly stable over time and in relation to therapy, a higher epigenetic heterogeneity or 'burden' is seen in more aggressive CLL subgroups, albeit as non-recurrent 'passenger' events. More recently, DNA methylation profiles in CLL analyzed in relation to differentiating normal B-cell populations revealed that the majority of the CLL epigenome reflects the epigenomes present in the cell of origin and that only a small fraction of the epigenetic alterations represents truly CLL-specific changes. Furthermore, CLL patients can be grouped into at least three clinically relevant epigenetic subgroups, potentially originating from different cells at various stages of differentiation and associated with distinct outcomes. In this review, we summarize the current understanding of the DNA methylome in CLL, the role of histone modifying enzymes, highlight insights derived from animal models and attempts made to target epigenetic regulators in CLL along with the future directions of this rapidly advancing field.

In chronic lymphocytic leukemia (CLL), TP53 gene defects, due to deletion of the 17p13 locus and/or mutation(s) within the TP53 gene, are associated with resistance to chemoimmunotherapy and a particularly dismal clinical outcome. On these grounds, analysis of TP53 aberrations has been incorporated into routine clinical diagnostics to improve patient stratification and optimize therapeutic decisions. The predictive implications of TP53 aberrations have increasing significance in the era of novel targeted therapies, i.e., inhibitors of B-cell receptor (BcR) signaling and anti-apoptotic BCL2 family members, owing to their efficacy in patients with TP53 defects. In this report, the TP53 Network of the European Research Initiative on Chronic Lymphocytic Leukemia (ERIC) presents updated recommendations on the methodological approaches for TP53 mutation analysis. Moreover, it provides guidance to ensure that the analysis is performed in a timely manner for all patients requiring treatment and that the data is interpreted and reported in a consistent, standardized, and accurate way. Since next-generation sequencing technologies are gaining prominence within diagnostic laboratories, this report also offers advice and recommendations for the interpretation of TP53 mutation data generated by this methodology.

The clustering of different types of B-cell malignancies in families raises the possibility of shared aetiology. To examine this, we performed cross-trait linkage disequilibrium (LD)-score regression of multiple myeloma (MM) and chronic lymphocytic leukaemia (CLL) genome-wide association study (GWAS) data sets, totalling 11,734 cases and 29,468 controls. A significant genetic correlation between these two B-cell malignancies was shown (R-g = 0.4, P = 0.0046). Furthermore, four of the 45 known CLL risk loci were shown to associate with MM risk and five of the 23 known MM risk loci associate with CLL risk. By integrating eQTL, Hi-C and ChlP-seq data, we show that these pleiotropic risk loci are enriched for B-cell regulatory elements and implicate B-cell developmental genes. These data identify shared biological pathways influencing the development of CLL and, MM and further our understanding of the aetiological basis of these B-cell malignancies.

Despite the recent discovery of recurrent driver mutations in chronic lymphocytic leukemia, the genetic factors involved in disease onset remain largely unknown. To address this issue, we per-formed whole-genome sequencing in 11 individuals with monoclonal B-cell lymphocytosis, both of the low-count and high-count subtypes, and 5 patients with ultra-stable chronic lymphocytic leukemia (>10 years without progression from initial diagnosis). All three entities were indistinguishable at the genomic level exhibiting low genomic complexity and similar types of somatic mutations. Exonic mutations were not frequently identified in putative chronic lymphocytic leukemia driver genes in all settings, including low-count monoclonal B-cell lymphocytosis. To corroborate these findings, we also performed deep sequencing in 11 known frequently mutated genes in an extended cohort of 28 monoclonal B-cell lym phocytosis/chronic lymphocytic leukemia cases. Interestingly, shared mutations were detected between clonal B cells and paired polymorphonuclear cells, strengthening the notion that at least a fraction of somatic mutations may occur before disease onset, likely at the hematopoietic stem cell level. Finally, we identified previously unreported non-coding variants targeting pathways relevant to B-cell and chronic lymphocytic leukemia development, likely associated with the acquisition of the characteristic neoplastic phenotype typical of both monoclonal B-cell lymphocytosis and chronic lymphocytic leukemia.