Abstract
A remarkable feature of chronic lymphocytic leukemia (CLL) is the existence of quasi-identical, stereotyped B-cell receptor immunoglobulins (BcR IGs), strongly supporting an antigen-driven pathway to CLL development. Subsets of cases with distinct stereotyped BcRs collectively account for almost one-third of all CLL. Furthermore, just a handful of major stereotyped subsets represent a substantial fraction of the entire cohort and, perhaps more importantly, an even larger fraction of clinically aggressive CLL. In several major subsets, stereotypy extends from shared primary IG sequences to shared clinical and biological features, including immune signaling, mRNA and miRNA expression, DNA methylation, and genomic aberrations. Regarding the latter, recent evidence indicates that different subsets display distinct profiles of recurrent gene mutations, even when limiting the analysis to subsets with similar IGHV gene mutational status. However, it should be emphasized that even the largest subsets account for only ∼3% of the cases with available IGHV-D-J sequence information, indicating that for meaningful conclusions to be reached, large patient cohorts are essential. Here, taking advantage of a series of 2482 CLL cases consolidated in the context of a multicenter collaboration coordinated by ERIC, the European Research Initiative on CLL, we systematically explored the genetic background of stereotyped subsets. Our main focus was on recurrent mutations in the NOTCH1 (entire exon 34 or targeted analysis for del7544-45), TP53 (exons 4-9), SF3B1 (exons 14-16), BIRC3 (exons 6-9) and MYD88 (exon 5) genes. Overall, 1313 cases (52.9%) carried mutated IGHV genes (M-CLL), whereas the remaining 1169 cases (47.1%) carried unmutated IGHV genes (U-CLL). Cases were sub-classified into the following major subsets: (i) U-CLL: #1, n=72; #3, n=25; #5, n=11; #6, n=22; #7, n=37; #8, n=20; (ii) M-CLL: #4, n=32; #77, n=12; #148, n=20; and, (iii) subset #2 (IGHV3-21, variable mutational status), n=57. Mutations in the MYD88 and BIRC3 genes were relatively rare, with no clear bias to any subset. With regards to the other three genes, only a single mutation in the TP53 gene was identified in a total of 80 M-CLL subset cases. Among U-CLL subsets and clinically aggressive subset #2, we noted asymmetric mutation frequencies, summarized as follows. (1) TP53 mutations were (a) enriched in subsets #3 and #7 (frequency >10%) and, in contrast, absent in subsets #5 and #6, though all these subsets utilize the IGHV1-69 gene; (b) enriched in subset #1 (9%) and, interestingly, subset #99, a less populated subset that is highly similar to subset #1 (2/4 cases positive for TP53 mutations); (c) absent in subset #2; and, (d) relatively infrequent in subset #8 (5%), the latter known to display the highest risk for Richter’s transformation among all CLL. Differences between these subsets showed a trend for statistical significance (p=0.09). (2) NOTCH1 mutations exhibited (a) increased frequency in subsets #1 (28%) and #8 (25%); (b) among IGHV1-69 expressing subsets, lower frequencies in subsets #3 (8%), #5 (10%) and #7 (3%) compared to subset #6 (25%); and (c) intermediate frequency (9%) in subset #2 (p=0.0078 for comparison between subsets). (3) SF3B1 mutations were (a) significantly (p<0.001) enriched in subsets #2 (47.6%), #3 (40%) and #7 (25%) compared to all other major subsets (subset #1: 7.5%; subset #5: 0%; subset #6: 7.7%; subset #8: 0%); and, (b) positive in 3/7 cases (42.8%) of subset #169, a minor subset sharing remarkable IG sequence similarities to subset #2. In conclusion, in the largest study thus far conducted, we confirm and significantly extend recent observations indicating that different CLL stereotyped subsets display distinct genetic makeup. On these grounds, we propose that differential modes of immune signaling in the context of subset-biased antigen-IG interactions may be associated with the acquisition and/or selection of certain genomic aberrations in various stereotyped subsets, ultimately underlying clinical aggressiveness through distinct mechanisms. Finally, our findings suggest that sequence similarities between different subsets (e.g. #1 and #99, #2 and #169) likely reflect similar pathobiology, underscoring the relevance of the molecular sub-classification of CLL based on BcR stereotypy.
Disclosures:
Stamatopoulos: Roche: Research Funding.
Understanding Monoclonal B Cell Lymphocytosis: An Interplay of Genetic and Microenvironmental Factors
