Chronic lymphocytic leukemia (CLL) patients assigned to stereotyped subset #4 (mutated IGHV4-34/IGKV2-30 BCR Ig) display a particularly indolent disease course. Immunogenetic studies of the clonotypic BCR Ig of CLL subset #4 suggested a resemblance with B cells rendered anergic through chronic autoantigenic stimulation. In this article, we provide experimental evidence that subset #4 CLL cells show low IgG levels, constitutive ERK1/2 activation, and fail to either release intracellular Ca2+ or activate MAPK signaling after BCR cross-linking, thus displaying a signature of B cell anergy at both biochemical and functional levels. Interestingly, TLR1/2 triggering restored BCR functionality, likely breaching the anergic state, and this was accompanied by induction of the miR-17∼92 cluster, whose members target critical BCR-associated molecules, including MAPKs. In conclusion, we demonstrate BCR anergy in CLL subset #4 and implicate TLR signaling and the miR-17∼92 cluster in the regulation of the anergic state. This detailed signaling profiling of subset #4 has implications for advanced understanding of the complex regulation of intracellular signaling pathways in CLL, currently a major therapeutic target of the disease.

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The B cell receptor immunoglobulin (Ig) gene repertoires of marginal zone (MZ) lymphoproliferations were analyzed in order to obtain insight into their ontogenetic relationships. Our cohort included cases with MZ lymphomas (n = 488), i.e. splenic (SMZL), nodal (NMZL) and extranodal (ENMZL), as well as provisional entities (n = 76), according to the WHO classification. The most striking Ig gene repertoire skewing was observed in SMZL. However, restrictions were also identified in all other MZ lymphomas studied, particularly ENMZL, with significantly different Ig gene distributions depending on the primary site of involvement. Cross‐entity comparisons of the MZ Ig sequence dataset with a large dataset of Ig sequences (MZ‐related or not; n = 65 837) revealed four major clusters of cases sharing homologous (‘public’) heavy variable complementarity‐determining region 3. These clusters included rearrangements from SMZL, ENMZL (gastric, salivary gland, ocular adnexa), chronic lymphocytic leukemia, but also rheumatoid factors and non‐malignant splenic MZ cells. In conclusion, different MZ lymphomas display biased immunogenetic signatures indicating distinct antigen exposure histories. The existence of rare public stereotypes raises the intriguing possibility that common, pathogen‐triggered, immune‐mediated mechanisms may result in diverse B lymphoproliferations due to targeting versatile progenitor B cells and/or operating in particular microenvironments. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Background Antigen receptors are characterized by an extreme diversity of specificities, which poses major computational and analytical challenges, particularly in the era of high-throughput immunoprofiling by next generation sequencing (NGS). The T cell Receptor/Immunoglobulin Profiler (TRIP) tool offers the opportunity for an in-depth analysis based on the processing of the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling, through a number of interoperable modules. These provide detailed information about antigen receptor gene rearrangements, including variable (V), diversity (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins (BcR IG), and characteristics of the somatic hypermutation within the BcR IG genes. TRIP is a web application implemented in R shiny. Results Two sets of experiments have been performed in order to evaluate the efficiency and performance of the TRIP tool. The first used a number of synthetic datasets, ranging from 250k to 1M sequences, and established the linear response time of the tool (about 6 h for 1M sequences processed through the entire BcR IG data pipeline). The reproducibility of the tool was tested comparing the results produced by the main TRIP workflow with the results from a previous pipeline used on the Galaxy platform. As expected, no significant differences were noted between the two tools; although the preselection process seems to be stricter within the TRIP pipeline, about 0.1% more rearrangements were filtered out, with no impact on the final results. Conclusions TRIP is a software framework that provides analytical services on antigen receptor gene sequence data. It is accurate and contains functions for data wrangling, cleaning, analysis and visualization, enabling the user to build a pipeline tailored to their needs. TRIP is publicly available at https://bio.tools/TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler.