Predictability of B cell clonal persistence and immunosurveillance in breast cancer.

B cells and T cells are important components of the adaptive immune system and mediate anticancer immunity. The T cell landscape in cancer is well characterized, but the contribution of B cells to anticancer immunosurveillance is less well explored. Here we show an integrative analysis of the B cell and T cell receptor repertoire from individuals with metastatic breast cancer and individuals with early breast cancer during neoadjuvant therapy. Using immune receptor, RNA and whole-exome sequencing, we show that both B cell and T cell responses seem to coevolve with the metastatic cancer genomes and mirror tumor mutational and neoantigen architecture. B cell clones associated with metastatic immunosurveillance and temporal persistence were more expanded and distinct from site-specific clones. B cell clonal immunosurveillance and temporal persistence are predictable from the clonal structure, with higher-centrality B cell antigen receptors more likely to be detected across multiple metastases or across time. This predictability was generalizable across other immune-mediated disorders. This work lays a foundation for prioritizing antibody sequences for therapeutic targeting in cancer.

AbDiver: a tool to explore the natural antibody landscape to aid therapeutic design.

MOTIVATION: Rational design of therapeutic antibodies can be improved by harnessing the natural sequence diversity of these molecules. Our understanding of the diversity of antibodies has recently been greatly facilitated through the deposition of hundreds of millions of human antibody sequences in next-generation sequencing (NGS) repositories. Contrasting a query therapeutic antibody sequence to naturally observed diversity in similar antibody sequences from NGS can provide a mutational roadmap for antibody engineers designing biotherapeutics. Because of the sheer scale of the antibody NGS datasets, performing queries across them is computationally challenging. RESULTS: To facilitate harnessing antibody NGS data, we developed AbDiver (http://naturalantibody.com/abdiver), a free portal allowing users to compare their query sequences to those observed in the natural repertoires. AbDiver offers three antibody-specific use-cases: (i) compare a query antibody to positional variability statistics precomputed from multiple independent studies, (ii) retrieve close full variable sequence matches to a query antibody and (iii) retrieve CDR3 or clonotype matches to a query antibody. We applied our system to a set of 742 therapeutic antibodies, demonstrating that for each use-case our system can retrieve relevant results for most sequences. AbDiver facilitates the navigation of vast antibody mutation space for the purpose of rational therapeutic antibody design. AVAILABILITY AND IMPLEMENTATION: AbDiver is freely accessible at http://naturalantibody.com/abdiver. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Inferring B cell specificity for vaccines using a Bayesian mixture model.

BACKGROUND: Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. RESULTS: Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity. CONCLUSION: Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells.

B-cell receptor repertoire sequencing in patients with primary immunodeficiency: a review.

The advent of next-generation sequencing (NGS) now allows a detailed assessment of the adaptive immune system in health and disease. In particular, high-throughput B-cell receptor (BCR) repertoire sequencing provides detailed information about the functionality and abnormalities of the B-cell system. However, it is mostly unknown how the BCR repertoire is altered in the context of primary immunodeficiencies (PID) and whether findings are consistent throughout phenotypes and genotypes. We have performed an extensive literature search of the published work on BCR repertoire sequencing in PID patients, including several forms of predominantly antibody disorders and combined immunodeficiencies. It is somewhat surprising that BCR repertoires, even from severe clinical phenotypes, often show only mild abnormalities and that diversity or immunoglobulin gene segment usage is generally preserved to some extent. Despite the great variety of wet laboratory and analytical methods that were used in the different studies, several findings are common to most investigated PIDs, such as the increased usage of gene segments that are associated with self-reactivity. These findings suggest that BCR repertoire characteristics may be used to assess the functionality of the B-cell compartment irrespective of the underlying defect. With the use of NGS approaches, there is now the opportunity to apply BCR repertoire sequencing to multiple patients and explore the PID BCR repertoire in more detail. Ultimately, using BCR repertoire sequencing in translational research could aid the management of PID patients by improving diagnosis, estimating functionality of the immune system and improving assessment of prognosis.

LLT1 and CD161 Expression in Human Germinal Centers Promotes B Cell Activation and CXCR4 Downregulation.

Germinal centers (GCs) are microanatomical structures critical for the development of high-affinity Abs and B cell memory. They are organized into two zones, light and dark, with coordinated roles, controlled by local signaling. The innate lectin-like transcript 1 (LLT1) is known to be expressed on B cells, but its functional role in the GC reaction has not been explored. In this study, we report high expression of LLT1 on GC-associated B cells, early plasmablasts, and GC-derived lymphomas. LLT1 expression was readily induced via BCR, CD40, and CpG stimulation on B cells. Unexpectedly, we found high expression of the LLT1 ligand, CD161, on follicular dendritic cells. Triggering of LLT1 supported B cell activation, CD83 upregulation, and CXCR4 downregulation. Overall, these data suggest that LLT1-CD161 interactions play a novel and important role in B cell maturation within the GC in humans.

Studying the antibody repertoire after vaccination: practical applications.

Nearly all licensed vaccines have been developed to confer protection against infectious diseases by stimulating the production of antibodies by B cells, but the nature of a successful antibody response has been difficult to capture. Recent advances in next-generation sequencing (NGS) technology have allowed high-resolution characterization of the antibody repertoire, and of the changes that occur following vaccination. These approaches have yielded important insights into the B cell response, and have raised the possibility of using specific antibody sequences as measures of vaccine immunogenicity. Here, we review recent findings based on antibody repertoire sequencing, and discuss potential applications of these new technologies and of the analyses of the increasing volume of antibody sequence data in the context of vaccine development.

Identification of antigen-specific B cell receptor sequences using public repertoire analysis.

High-throughput sequencing allows detailed study of the BCR repertoire postimmunization, but it remains unclear to what extent the de novo identification of Ag-specific sequences from the total BCR repertoire is possible. A conjugate vaccine containing Haemophilus influenzae type b (Hib) and group C meningococcal polysaccharides, as well as tetanus toxoid (TT), was used to investigate the BCR repertoire of adult humans following immunization and to test the hypothesis that public or convergent repertoire analysis could identify Ag-specific sequences. A number of Ag-specific BCR sequences have been reported for Hib and TT, which made a vaccine containing these two Ags an ideal immunological stimulus. Analysis of identical CDR3 amino acid sequences that were shared by individuals in the postvaccine repertoire identified a number of known Hib-specific sequences but only one previously described TT sequence. The extension of this analysis to nonidentical, but highly similar, CDR3 amino acid sequences revealed a number of other TT-related sequences. The anti-Hib avidity index postvaccination strongly correlated with the relative frequency of Hib-specific sequences, indicating that the postvaccination public BCR repertoire may be related to more conventional measures of immunogenicity correlating with disease protection. Analysis of public BCR repertoire provided evidence of convergent BCR evolution in individuals exposed to the same Ags. If this finding is confirmed, the public repertoire could be used for rapid and direct identification of protective Ag-specific BCR sequences from peripheral blood.

Identification of Antigen-Specific B-Cell Receptor Sequences from the Total B-Cell Repertoire.

Advances in next-generation sequencing now allow characterization of the global B-cell receptor (BCR) heavy-chain repertoire at a level that reflects its huge diversity. This technology has provided great insight into the structure of the BCR repertoire and how it responds to specific antigen stimuli. There are numerous potential clinical and research applications of BCR repertoire sequencing, but a major hurdle in the realization of these applications is the identification of the antigen-specific sequences of interest from within the total repertoire. To deconvolute the antigen-specific sequences from total repertoire, either a source of antigen-enriched sequence data is required with which to annotate the total repertoire, or de novo annotation methods must be used based on preconceptions of the features of antigen-specific sequences and their behavior following antigen-specific immune stimulation. We present a review of how these different methods can be applied to identify antigen-specific BCR sequences from the total BCR repertoire.

BCR repertoire sequencing: different patterns of B-cell activation after two Meningococcal vaccines.

Next-generation sequencing was used to investigate the B-cell receptor heavy chain transcript repertoire of different B-cell subsets (naive, marginal zone (MZ), immunoglobulin M (IgM) memory and IgG memory) at baseline, and of plasma cells (PCs) 7 days following administration of serogroup ACWY meningococcal polysaccharide and protein-polysaccharide conjugate vaccines. Baseline B-cell subsets could be distinguished from each other using a small number of repertoire properties (clonality, mutation from germline and complementarity-determining region 3 (CDR3) length) that were conserved between individuals. However, analyzing the CDR3 amino-acid sequence (which is particularly important for antigen binding) of the baseline subsets showed few sequences shared between individuals. In contrast, day 7 PCs demonstrated nearly 10-fold greater sequence sharing between individuals than the baseline subsets, consistent with the PCs being induced by the vaccine antigen and sharing specificity for a more limited range of epitopes. By annotating PC sequences based on IgG subclass usage and mutation, and also comparing them with the sequences of the baseline cell subsets, we were able to identify different signatures after the polysaccharide and conjugate vaccines. PCs produced after conjugate vaccination were predominantly IgG1, and most related to IgG memory cells. In contrast, after polysaccharide vaccination, the PCs were predominantly IgG2, less mutated and were equally likely to be related to MZ, IgM memory or IgG memory cells. High-throughput B-cell repertoire sequencing thus provides a unique insight into patterns of B-cell activation not possible from more conventional measures of immunogenicity.

Building Representation Learning Models for Antibody Comprehension.

Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling.

Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.

Becoming fluent in proteins.

Large language models have emerged as a new compass for navigating the complex landscapes of protein engineering. This issue of Cell Systems features ProGen2 and IgLM-two protein language models (PLMs) that use subtly different approaches to design proteins.

Characterisation of the immune repertoire of a humanised transgenic mouse through immunophenotyping and high-throughput sequencing.

Immunoglobulin loci-transgenic animals are widely used in antibody discovery and increasingly in vaccine response modelling. In this study, we phenotypically characterised B-cell populations from the Intelliselect Transgenic mouse (Kymouse) demonstrating full B-cell development competence. Comparison of the naïve B-cell receptor (BCR) repertoires of Kymice BCRs, naïve human, and murine BCR repertoires revealed key differences in germline gene usage and junctional diversification. These differences result in Kymice having CDRH3 length and diversity intermediate between mice and humans. To compare the structural space explored by CDRH3s in each species' repertoire, we used computational structure prediction to show that Kymouse naïve BCR repertoires are more human-like than mouse-like in their predicted distribution of CDRH3 shape. Our combined sequence and structural analysis indicates that the naïve Kymouse BCR repertoire is diverse with key similarities to human repertoires, while immunophenotyping confirms that selected naïve B cells are able to go through complete development.

Structural modeling of antibody variable regions using deep learning-progress and perspectives on drug discovery.

AlphaFold2 has hallmarked a generational improvement in protein structure prediction. In particular, advances in antibody structure prediction have provided a highly translatable impact on drug discovery. Though AlphaFold2 laid the groundwork for all proteins, antibody-specific applications require adjustments tailored to these molecules, which has resulted in a handful of deep learning antibody structure predictors. Herein, we review the recent advances in antibody structure prediction and relate them to their role in advancing biologics discovery.

B cell clonal expansion and mutation in the immunoglobulin heavy chain variable domain in response to Pfs230 and Pfs25 malaria vaccines.

Malaria transmission-blocking vaccines induce antibodies that target Plasmodium in the mosquito vector. We recently reported that Pfs230 vaccine achieves activity superior to Pfs25 in humans. Here, we describe clonal expansion in the variable region of immunoglobulin heavy chains (VH) of antigen-specific single B cells collected from humans immunised with Pfs230D1-EPA or Pfs25-EPA conjugate vaccines formulated in Alhydrogel®. Based on studies of CD27+ memory B cells following Pfs230 vaccination, clonal expansion and somatic hypermutation was seen in four of five subjects. Pfs25 did not induce sufficient CD27+ cells for sorting; based instead on CD19+ Pfs25-reactive B cells, clonal expansion was only seen in two of five subjects. Clonal expansions and mutations in Pfs230-specific single B cells combined with the enhanced activity of Pfs230 antibodies by complement, might justify the outstanding activity of Pfs230D1 as a TBV candidate.

Deciphering the language of antibodies using self-supervised learning.

An individual's B cell receptor (BCR) repertoire encodes information about past immune responses and potential for future disease protection. Deciphering the information stored in BCR sequence datasets will transform our understanding of disease and enable discovery of novel diagnostics and antibody therapeutics. A key challenge of BCR sequence analysis is the prediction of BCR properties from their amino acid sequence alone. Here, we present an antibody-specific language model, Antibody-specific Bidirectional Encoder Representation from Transformers (AntiBERTa), which provides a contextualized representation of BCR sequences. Following pre-training, we show that AntiBERTa embeddings capture biologically relevant information, generalizable to a range of applications. As a case study, we fine-tune AntiBERTa to predict paratope positions from an antibody sequence, outperforming public tools across multiple metrics. To our knowledge, AntiBERTa is the deepest protein-family-specific language model, providing a rich representation of BCRs. AntiBERTa embeddings are primed for multiple downstream tasks and can improve our understanding of the language of antibodies.

Different B cell subpopulations show distinct patterns in their IgH repertoire metrics.

Several human B cell subpopulations are recognised in the peripheral blood, which play distinct roles in the humoral immune response. These cells undergo developmental and maturational changes involving VDJ recombination, somatic hypermutation and class switch recombination, altogether shaping their immunoglobulin heavy chain (IgH) repertoire. Here, we sequenced the IgH repertoire of naïve, marginal zone, switched and plasma cells from 10 healthy adults along with matched unsorted and in silico separated CD19+ bulk B cells. Using advanced bioinformatic analysis and machine learning, we show that sorted B cell subpopulations are characterised by distinct repertoire characteristics on both the individual sequence and the repertoire level. Sorted subpopulations shared similar repertoire characteristics with their corresponding in silico separated subsets. Furthermore, certain IgH repertoire characteristics correlated with the position of the constant region on the IgH locus. Overall, this study provides unprecedented insight over mechanisms of B cell repertoire control in peripherally circulating B cell subpopulations.

A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies.

Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However, current computational methods cannot identify epitope convergence between antibodies from different clonotypes, limiting the sequence diversity of antigen-specific antibodies that can be identified. We describe how the antibody binding site, the paratope, can be used to cluster antibodies with common antigen reactivity from different clonotypes. Our method, paratyping, uses the predicted paratope to identify these novel cross clonotype matches. We experimentally validated our predictions on a pertussis toxoid dataset. Our results show that even the simplest abstraction of the antibody binding site, using only the length of the loops involved and predicted binding residues, is sufficient to group antigen-specific antibodies and provide additional information to conventional clonotype analysis. Abbreviations: BCR: B-cell receptor; CDR: complementarity-determining region; PTx: pertussis toxoid.

A human monoclonal antibody blocks malaria transmission and defines a highly conserved neutralizing epitope on gametes.

Malaria elimination requires tools that interrupt parasite transmission. Here, we characterize B cell receptor responses among Malian adults vaccinated against the first domain of the cysteine-rich 230 kDa gamete surface protein Pfs230, a key protein in sexual stage development of P. falciparum parasites. Among nine Pfs230 human monoclonal antibodies (mAbs) that we generated, one potently blocks transmission to mosquitoes in a complement-dependent manner and reacts to the gamete surface; the other eight show only low or no blocking activity. The structure of the transmission-blocking mAb in complex with vaccine antigen reveals a large discontinuous conformational epitope, specific to domain 1 of Pfs230 and comprising six structural elements in the protein. The epitope is conserved, suggesting the transmission-blocking mAb is broadly functional. This study provides a rational basis to improve malaria vaccines and develop therapeutic antibodies for malaria elimination.

Cross-Reactive SARS-CoV-2 Neutralizing Antibodies From Deep Mining of Early Patient Responses.

Passive immunization using monoclonal antibodies will play a vital role in the fight against COVID-19. The recent emergence of viral variants with reduced sensitivity to some current antibodies and vaccines highlights the importance of broad cross-reactivity. This study describes deep-mining of the antibody repertoires of hospitalized COVID-19 patients using phage display technology and B cell receptor (BCR) repertoire sequencing to isolate neutralizing antibodies and gain insights into the early antibody response. This comprehensive discovery approach has yielded a panel of potent neutralizing antibodies which bind distinct viral epitopes including epitopes conserved in SARS-CoV-1. Structural determination of a non-ACE2 receptor blocking antibody reveals a previously undescribed binding epitope, which is unlikely to be affected by the mutations in any of the recently reported major viral variants including B.1.1.7 (from the UK), B.1.351 (from South Africa) and B.1.1.28 (from Brazil). Finally, by combining sequences of the RBD binding and neutralizing antibodies with the B cell receptor repertoire sequencing, we also describe a highly convergent early antibody response. Similar IgM-derived sequences occur within this study group and also within patient responses described by multiple independent studies published previously.