ARResT/AssignSubsets: a novel application for robust subclassification of chronic lymphocytic leukemia based on B cell receptor IG stereotypy.

Abstract Motivation: An ever-increasing body of evidence supports the importance of B cell receptor immunoglobulin (BcR IG) sequence restriction, alias stereotypy, in chronic lymphocytic leukemia (CLL). This phenomenon accounts for ∼30% of studied cases, one in eight of which belong to major subsets, and extends beyond restricted sequence patterns to shared biologic and clinical characteristics and, generally, outcome. Thus, the robust assignment of new cases to major CLL subsets is a critical, and yet unmet, requirement. Results: We introduce a novel application, ARResT/AssignSubsets, which enables the robust assignment of BcR IG sequences from CLL patients to major stereotyped subsets. ARResT/AssignSubsets uniquely combines expert immunogenetic sequence annotation from IMGT/V-QUEST with curation to safeguard quality, statistical modeling of sequence features from more than 7500 CLL patients, and results from multiple perspectives to allow for both objective and subjective assessment. We validated our approach on the learning set, and evaluated its real-world applicability on a new representative dataset comprising 459 sequences from a single institution. Availability and implementation: ARResT/AssignSubsets is freely available on the web at http://bat.infspire.org/arrest/assignsubsets/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

An entity evolving into a community: defining the common ancestor and evolutionary trajectory of chronic lymphocytic leukemia stereotyped subset #4.

Work was completed during previous post-I joined DMU on January 2016Patients with chronic lymphocytic leukemia (CLL) assigned to stereotyped subset #4 express highly homologous B-cell receptor immunoglobulin (BcR IG) sequences with intense intraclonal diversification (ID) in the context of ongoing somatic hypermutation (SHM). Their remarkable biological and clinical similarities strongly support derivation from a common ancestor. We here revisited ID in subset #4 CLL to reconstruct their evolutionary history as a community of related clones. To this end, using specialized bioinformatics tools we assessed both IGHV-IGHD-IGHJ rearrangements (n = 511) and IGKV-IGKJ rearrangements (n = 397) derived from eight subset #4 cases. Due to high sequence relatedness, a number of subclonal clusters from different cases lay very close to one another, forming a core from which clusters exhibiting greater variation stemmed. Minor subclones from individual cases were mutated to such an extent that they now resembled the sequences of another patient. Viewing the entire subset #4 data set as a single entity branching through diversification enabled inference of a common sequence representing the putative ancestral BcR IG expressed by their still elusive common progenitor. These results have implications for improved understanding of the ontogeny of CLL subset #4, as well as the design of studies concerning the antigenic specificity of the clonotypic BcR IGs

Disease-biased and shared characteristics of the immunoglobulin gene repertoires in marginal zone B cell lymphoproliferations.

The B cell receptor immunoglobulin (BcR 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 World Health Organization 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 spleen 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.This work was supported in part by H2020 “AEGLE, An analytics framework for integrated and personalized healthcare services in Europe”, by the European Union (EU); H2020 No. 692298 project “MEDGENET, Medical Genomics and Epigenomics Network” by the EU; grant AZV 15-30015A from the Ministry of Health of the Czech Republic, and the project CEITEC2020 LQ1601 from the Ministry of Education, Youth, and Sports of the Czech Republic; Bloodwise Research Grant (15019); the Swedish Cancer Society, the Swedish Research Council, the Knut and Alice Wallenberg Foundation, Karolinska Institutet, Stockholm, the Lion’s Cancer Research Foundation, Uppsala, the Marcus Borgström Foundation and Selander’s Foundation, Uppsala

Integrating multiple immunogenetic data sources for feature extraction and mining somatic hypermutation patterns : the case of "towards analysis" in chronic lymphocytic leukaemia

Background: Somatic Hypermutation (SHM) refers to the introduction of mutations within rearranged V(D)J genes, a process that increases the diversity of Immunoglobulins (IGs). The analysis of SHM has offered critical insight into the physiology and pathology of B cells, leading to strong prognostication markers for clinical outcome in chronic lymphocytic leukaemia (CLL), the most frequent adult B-cell malignancy. In this paper we present a methodology for integrating multiple immunogenetic and clinocobiological data sources in order to extract features and create high quality datasets for SHM analysis in IG receptors of CLL patients. This dataset is used as the basis for a higher level integration procedure, inspired form social choice theory. This is applied in the Towards Analysis, our attempt to investigate the potential ontogenetic transformation of genes belonging to specific stereotyped CLL subsets towards other genes or gene families, through SHM. Results: The data integration process, followed by feature extraction, resulted in the generation of a dataset containing information about mutations occurring through SHM. The Towards analysis performed on the integrated dataset applying voting techniques, revealed the distinct behaviour of subset #201 compared to other subsets, as regards SHM related movements among gene clans, both in allele-conserved and non-conserved gene areas. With respect to movement between genes, a high percentage movement towards pseudo genes was found in all CLL subsets. Conclusions: This data integration and feature extraction process can set the basis for exploratory analysis or a fully automated computational data mining approach on many as yet unanswered, clinically relevant biological questions

ARResT/AssignSubsets: a novel application for robust subclassification of chronic lymphocytic leukemia based on B cell receptor IG stereotypy

Abstract Motivation: An ever-increasing body of evidence supports the importance of B cell receptor immunoglobulin (BcR IG) sequence restriction, alias stereotypy, in chronic lymphocytic leukemia (CLL). This phenomenon accounts for ∼30% of studied cases, one in eight of which belong to major subsets, and extends beyond restricted sequence patterns to shared biologic and clinical characteristics and, generally, outcome. Thus, the robust assignment of new cases to major CLL subsets is a critical, and yet unmet, requirement. Results: We introduce a novel application, ARResT/AssignSubsets, which enables the robust assignment of BcR IG sequences from CLL patients to major stereotyped subsets. ARResT/AssignSubsets uniquely combines expert immunogenetic sequence annotation from IMGT/V-QUEST with curation to safeguard quality, statistical modeling of sequence features from more than 7500 CLL patients, and results from multiple perspectives to allow for both objective and subjective assessment. We validated our approach on the learning set, and evaluated its real-world applicability on a new representative dataset comprising 459 sequences from a single institution. Availability and implementation: ARResT/AssignSubsets is freely available on the web at http://bat.infspire.org/arrest/assignsubsets/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online