OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs

Limits on inferring T cell specificity from partial information

A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T cell receptor (TCR) sequence features provide about antigen specificity. We identify informative features by their degree of conservation among antigen-specific receptors relative to null expectations. We find that TCR specificity synergistically depends on the hypervariable regions of both receptor chains, with a degree of synergy that strongly depends on the ligand. Using a coincidence-based approach to measuring information enables us to directly bound the accuracy with which TCR specificity can be predicted from partial matches to reference sequences. We anticipate that our statistical framework will be of use for developing machine learning models for TCR specificity prediction and for optimizing TCRs for cell therapies. The proposed coincidence-based information measures might find further applications in bounding the performance of pairwise classifiers in other fields.

Exploring the potential of structure-based deep learning approaches for T cell receptor design

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively underexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF1, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.

Computational detection of antigen-specific B cell receptors following immunization

B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.

The TCR assigns naive T cells to a preferred lymph node

Naive T cells recirculate between the spleen and lymph nodes where they mount immune responses when meeting dendritic cells presenting foreign antigen. As this may happen anywhere, naive T cells ought to visit all lymph nodes. Here, deep sequencing almost-complete TCR repertoires led to a comparison of different lymph nodes within and between individual mice. We find strong evidence for a deterministic CD4/CD8 lineage choice and a consistent spatial structure. Specifically, some T cells show a preference for one or multiple lymph nodes, suggesting that their TCR interacts with locally presented (self-)peptides. These findings are mirrored in TCR-transgenic mice showing localized CD69 expression, retention, and cell division. Thus, naive T cells intermittently sense antigenically dissimilar niches, which is expected to affect their homeostatic competition.

EBV T-cell immunotherapy generated by peptide selection has enhanced effector functionality compared to LCL stimulation

Adoptive immunotherapy with Epstein-Barr virus (EBV)-specific T cells is an effective treatment for relapsed or refractory EBV-induced post-transplant lymphoproliferative disorders (PTLD) with overall survival rates of up to 69%. EBV-specific T cells have been conventionally made by repeated stimulation with EBV-transformed lymphoblastoid cell lines (LCL), which act as antigen-presenting cells. However, this process is expensive, takes many months, and has practical risks associated with live virus. We have developed a peptide-based, virus-free, serum-free closed system to manufacture a bank of virus-specific T cells (VST) for clinical use. We compared these with standard LCL-derived VST using comprehensive characterization and potency assays to determine differences that might influence clinical benefits. Multi-parameter flow cytometry revealed that peptide-derived VST had an expanded central memory population and less exhaustion marker expression than LCL-derived VST. A quantitative HLA-matched allogeneic cytotoxicity assay demonstrated similar specific killing of EBV-infected targets, though peptide-derived EBV T cells had a significantly higher expression of antiviral cytokines and degranulation markers after antigen recall. High-throughput T cell receptor-beta (TCRβ) sequencing demonstrated oligoclonal repertoires, with more matches to known EBV-binding complementary determining region 3 (CDR3) sequences in peptide-derived EBV T cells. Peptide-derived products showed broader and enhanced specificities to EBV nuclear antigens (EBNAs) in both CD8 and CD4 compartments, which may improve the targeting of highly expressed latency antigens in PTLD. Importantly, peptide-based isolation and expansion allows rapid manufacture and significantly increased product yield over conventional LCL-based approaches.

Germline-like TCR-α chains shared between autoreactive T cells in blood and pancreas

Human type 1 diabetes (T1D) is caused by autoimmune attack on the insulin-producing pancreatic beta cells by islet antigen-reactive T cells. How human islet antigen-reactive (IAR) CD4+ memory T cells from peripheral blood affect T1D progression in the pancreas is poorly understood. Here, we aim to determine if IAR T cells in blood could be detected in pancreas. We identify paired αβ (TRA/TRB) T cell receptors (TCRs) in IAR T cells from the blood of healthy, at-risk, new-onset, and established T1D donors, and measured sequence overlap with TCRs in pancreata from healthy, at risk and T1D organ donors. We report extensive TRA junction sharing between IAR T cells and pancreas-infiltrating T cells (PIT), with perfect-match or single-mismatch TRA junction amino acid sequences comprising ~29% total unique IAR TRA junctions (942/3,264). PIT-matched TRA junctions were largely public and enriched for TRAV41 usage, showing significant nucleotide sequence convergence, increased use of germline-encoded versus non-templated residues in epitope engagement, and a potential for cross-reactivity. Our findings thus link T cells with distinctive germline-like TRA chains in the peripheral blood with T cells in the pancreas.

Unbiased estimation of sampling variance for Simpson's diversity index

Quantification of measurement uncertainty is crucial for robust scientific inference, yet accurate estimates of this uncertainty remain elusive for ecological measures of diversity. Here, we address this longstanding challenge by deriving a closed-form unbiased estimator for the sampling variance of Simpson's diversity index. In numerical tests the estimator consistently outperforms existing approaches, particularly for applications in which species richness exceeds sample size. We apply the estimator to quantify biodiversity loss in marine ecosystems and to demonstrate ligand-dependent contributions of T-cell-receptor chains to specificity, illustrating its versatility across fields. The novel estimator provides researchers with a reliable method for comparing diversity between samples, essential for quantifying biodiversity trends and making informed conservation decisions.

Multi-omic profiling reveals the endogenous and neoplastic responses to immunotherapies in cutaneous T cell lymphoma

Cutaneous T cell lymphomas (CTCLs) are skin cancers with poor survival rates and limited treatments. While immunotherapies have shown some efficacy, the immunological consequences of administering immune-activating agents to CTCL patients have not been systematically characterized. We apply a suite of high-dimensional technologies to investigate the local, cellular, and systemic responses in CTCL patients receiving either mono- or combination anti-PD-1 plus interferon-gamma (IFN-γ) therapy. Neoplastic T cells display no evidence of activation after immunotherapy. IFN-γ induces muted endogenous immunological responses, while anti-PD-1 elicits broader changes, including increased abundance of CLA+CD39+ T cells. We develop an unbiased multi-omic profiling approach enabling discovery of immune modules stratifying patients. We identify an enrichment of activated regulatory CLA+CD39+ T cells in non-responders and activated cytotoxic CLA+CD39+ T cells in leukemic patients. Our results provide insights into the effects of immunotherapy in CTCL patients and a generalizable framework for multi-omic analysis of clinical trials.

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.

Principles and therapeutic applications of adaptive immunity

Adaptive immunity provides protection against infectious and malignant diseases. These effects are mediated by lymphocytes that sense and respond with targeted precision to perturbations induced by pathogens and tissue damage. Here, we review key principles underlying adaptive immunity orchestrated by distinct T cell and B cell populations and their extensions to disease therapies. We discuss the intracellular and intercellular processes shaping antigen specificity and recognition in immune activation and lymphocyte functions in mediating effector and memory responses. We also describe how lymphocytes balance protective immunity against autoimmunity and immunopathology, including during immune tolerance, response to chronic antigen stimulation, and adaptation to non-lymphoid tissues in coordinating tissue immunity and homeostasis. Finally, we discuss extracellular signals and cell-intrinsic programs underpinning adaptive immunity and conclude by summarizing key advances in vaccination and engineering adaptive immune responses for therapeutic interventions. A deeper understanding of these principles holds promise for uncovering new means to improve human health.

Enhancing comparative T cell receptor repertoire analysis in small biological samples through pooling homologous cell samples from multiple mice

Accurate characterization and comparison of T cell receptor (TCR) repertoires from small biological samples present significant challenges. The main challenge is the low material input, which compromises the quality of bulk sequencing and hinders the recovery of sufficient TCR sequences for robust analyses. We aimed to address this limitation by implementing a strategic approach to pool homologous biological samples. Our findings demonstrate that such pooling indeed enhances the TCR repertoire coverage, particularly for cell subsets of constrained sizes, and enables accurate comparisons of TCR repertoires at different levels of complexity across T cell subsets with different sizes. This methodology holds promise for advancing our understanding of T cell repertoires in scenarios where sample size constraints are a prevailing concern.

Innate-like T cell subset commitment in the murine thymus is independent of TCR characteristics and occurs during proliferation

How T-cell receptor (TCR) characteristics determine subset commitment during T-cell development is still unclear. Here, we addressed this question for innate-like T cells, mucosal-associated invariant T (MAIT) cells, and invariant natural killer T (iNKT) cells. MAIT and iNKT cells have similar developmental paths, leading in mice to two effector subsets, cytotoxic (MAIT1/iNKT1) and IL17-secreting (MAIT17/iNKT17). For iNKT1 vs iNKT17 fate choice, an instructive role for TCR affinity was proposed but recent data argue against this model. Herein, we examined TCR role in MAIT and iNKT subset commitment through scRNAseq and TCR repertoire analysis. In our dataset of thymic MAIT cells, we found pairs of T-cell clones with identical amino acid TCR sequences originating from distinct precursors, one of which committed to MAIT1 and the other to MAIT17 fates. Quantitative in silico simulations indicated that the number of such cases is best explained by lineage choice being independent of TCR characteristics. Comparison of TCR features of MAIT1 and MAIT17 clonotypes demonstrated that the subsets cannot be distinguished based on TCR sequence. To pinpoint the developmental stage associated with MAIT sublineage choice, we demonstrated that proliferation takes place both before and after MAIT fate commitment. Altogether, we propose a model of MAIT cell development in which noncommitted, intermediate-stage MAIT cells undergo a first round of proliferation, followed by TCR characteristics-independent commitment to MAIT1 or MAIT17 lineage, followed by an additional round of proliferation. Reanalyzing a published iNKT TCR dataset, we showed that this model is also relevant for iNKT cell development.

A comparison of clustering models for inference of T cell receptor antigen specificity

The vast potential sequence diversity of TCRs and their ligands has presented an historic barrier to computational prediction of TCR epitope specificity, a holy grail of quantitative immunology. One common approach is to cluster sequences together, on the assumption that similar receptors bind similar epitopes. Here, we provide the first independent evaluation of widely used clustering algorithms for TCR specificity inference, observing some variability in predictive performance between models, and marked differences in scalability. Despite these differences, we find that different algorithms produce clusters with high degrees of similarity for receptors recognising the same epitope. Our analysis strengthens the case for use of clustering models to identify signals of common specificity from large repertoires, whilst highlighting scope for improvement of complex models over simple comparators.

Reversing immunosuppression in the tumor microenvironment of fibrolamellar carcinoma via PD-1 and IL-10 blockade

Fibrolamellar carcinoma (FLC) is a rare liver tumor driven by the DNAJ-PKAc fusion protein that affects healthy young patients. Little is known about the immune response to FLC, limiting rational design of immunotherapy. Multiplex immunohistochemistry and gene expression profiling were performed to characterize the FLC tumor immune microenvironment and adjacent non-tumor liver (NTL). Flow cytometry and T cell receptor (TCR) sequencing were performed to determine the phenotype of tumor-infiltrating immune cells and the extent of T cell clonal expansion. Fresh human FLC tumor slice cultures (TSCs) were treated with antibodies blocking programmed cell death protein-1 (PD-1) and interleukin-10 (IL-10), with results measured by cleaved caspase-3 immunohistochemistry. Immune cells were concentrated in fibrous stromal bands, rather than in the carcinoma cell compartment. In FLC, T cells demonstrated decreased activation and regulatory T cells in FLC had more frequent expression of PD-1 and CTLA-4 than in NTL. Furthermore, T cells had relatively low levels of clonal expansion despite high TCR conservation across individuals. Combination PD-1 and IL-10 blockade signficantly increased cell death in human FLC TSCs. Immunosuppresion in the FLC tumor microenvironment is characterized by T cell exclusion and exhaustion, which may be reversible with combination immunotherapy.

T cell help shapes B cell tolerance

T cell help is a crucial component of the normal humoral immune response, yet whether it promotes or restrains autoreactive B cell responses remains unclear. Here, we observe that autoreactive germinal centers require T cell help for their formation and persistence. Using retrogenic chimeras transduced with candidate TCRs, we demonstrate that a follicular T cell repertoire restricted to a single autoreactive TCR, but not a foreign antigen-specific TCR, is sufficient to initiate autoreactive germinal centers. Follicular T cell specificity influences the breadth of epitope spreading by regulating wild-type B cell entry into autoreactive germinal centers. These results demonstrate that TCR-dependent T cell help can promote loss of B cell tolerance and that epitope spreading is determined by TCR specificity.

Repeated mRNA vaccination sequentially boosts SARS-CoV-2-specific CD8+ T cells in persons with previous COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hybrid immunity is more protective than vaccination or previous infection alone. To investigate the kinetics of spike-reactive T (TS) cells from SARS-CoV-2 infection through messenger RNA vaccination in persons with hybrid immunity, we identified the T cell receptor (TCR) sequences of thousands of index TS cells and tracked their frequency in bulk TCRβ repertoires sampled longitudinally from the peripheral blood of persons who had recovered from coronavirus disease 2019 (COVID-19). Vaccinations led to large expansions in memory TS cell clonotypes, most of which were CD8+ T cells, while also eliciting diverse TS cell clonotypes not observed before vaccination. TCR sequence similarity clustering identified public CD8+ and CD4+ TCR motifs associated with spike (S) specificity. Synthesis of longitudinal bulk ex vivo single-chain TCRβ repertoires and paired-chain TCRɑβ sequences from droplet sequencing of TS cells provides a roadmap for the rapid assessment of T cell responses to vaccines and emerging pathogens.

Immune profiling after allogeneic hematopoietic cell transplantation in pediatric acute myeloid leukemia

Although allogeneic hematopoietic cell transplant (allo-HCT) is curative for high-risk pediatric acute myeloid leukemia (AML), disease relapse remains the primary cause of posttransplant mortality. To identify pressures imposed by allo-HCT on AML cells that escape the graft-versus-leukemia effect, we evaluated immune signatures at diagnosis and posttransplant relapse in bone marrow samples from 4 pediatric patients using a multimodal single-cell proteogenomic approach. Downregulation of major histocompatibility complex class II expression was most profound in progenitor-like blasts and accompanied by correlative changes in transcriptional regulation. Dysfunction of activated natural killer cells and CD8+ T-cell subsets at relapse was evidenced by the loss of response to interferon gamma, tumor necrosis factor α signaling via NF-κB, and interleukin-2/STAT5 signaling. Clonotype analysis of posttransplant relapse samples revealed an expansion of dysfunctional T cells and enrichment of T-regulatory and T-helper cells. Using novel computational methods, our results illustrate a diverse immune-related transcriptional signature in posttransplant relapses not previously reported in pediatric AML.

Immunophenotypic correlates of sustained MRD negativity in patients with multiple myeloma

The role of the immune microenvironment in maintaining disease remission in patients with multiple myeloma (MM) is not well understood. In this study, we comprehensively profile the immune system in patients with newly diagnosed MM receiving continuous lenalidomide maintenance therapy with the aim of discovering correlates of long-term treatment response. Leveraging single-cell RNA sequencing and T cell receptor β sequencing of the peripheral blood and CyTOF mass cytometry of the bone marrow, we longitudinally characterize the immune landscape in 23 patients before and one year after lenalidomide exposure. We compare patients achieving sustained minimal residual disease (MRD) negativity to patients who never achieved or were unable to maintain MRD negativity. We observe that the composition of the immune microenvironment in both the blood and the marrow varied substantially according to both MRD negative status and history of autologous stem cell transplant, supporting the hypothesis that the immune microenvironment influences the depth and duration of treatment response.

Response to mRNA SARS-CoV-2 vaccination evaluated by B-cell receptor repertoire after tixagevimab/cilgavimab administration

The use of anti-SARS-CoV-2 antibody products like tixagevimab/cilgavimab represents an important strategy to protect immunocompromised patients with haematological malignancies from COVID-19. Although patients who receive these agents should still be vaccinated, the use of tixagevimab/cilgavimab can mask the production of anti-spike antibody after vaccination, making it hard to assess vaccine response. We have newly established a quantification method to assess the response to SARS-CoV-2 vaccination at the mRNA level using B-cell receptor (BCR) repertoire assay and the Coronavirus Antibody Database (CoV-AbDab). Repeated blood samples before and after vaccination were analysed for the BCR repertoire, and BCR sequences were searched in the database. We analysed the number and percentage frequency of matched sequences. We found that the number of matched sequences increased 2 weeks after the first vaccination and quickly decreased. Meanwhile, the number of matched sequences more rapidly increased after the second vaccination. These results show that the postvaccine immune response can be assessed at the mRNA level by analysing the fluctuation in matching sequences. Finally, BCR repertoire analysis with CoV-AbDab clearly demonstrated the response to mRNA SARS-CoV-2 vaccination even after tixagevimab/cilgavimab administration in haematological malignancy patients who underwent allogeneic haematopoietic stem cell transplantation.

HIV-1 neutralizing antibodies elicited in humans by a prefusion-stabilized envelope trimer form a reproducible class targeting fusion peptide

Elicitation of antibodies that neutralize the tier-2 neutralization-resistant isolates that typify HIV-1 transmission has been a long-sought goal. Success with prefusion-stabilized envelope trimers eliciting autologous neutralizing antibodies has been reported in multiple vaccine-test species, though not in humans. To investigate elicitation of HIV-1 neutralizing antibodies in humans, here, we analyze B cells from a phase I clinical trial of the "DS-SOSIP"-stabilized envelope trimer from strain BG505, identifying two antibodies, N751-2C06.01 and N751-2C09.01 (named for donor-lineage.clone), that neutralize the autologous tier-2 strain, BG505. Though derived from distinct lineages, these antibodies form a reproducible antibody class that targets the HIV-1 fusion peptide. Both antibodies are highly strain specific, which we attribute to their partial recognition of a BG505-specific glycan hole and to their binding requirements for a few BG505-specific residues. Prefusion-stabilized envelope trimers can thus elicit autologous tier-2 neutralizing antibodies in humans, with initially identified neutralizing antibodies recognizing the fusion-peptide site of vulnerability.

Inhibitory IL-10-producing CD4+ T cells are T-bet-dependent and facilitate cytomegalovirus persistence via coexpression of arginase-1

Inhibitory CD4+ T cells have been linked with suboptimal immune responses against cancer and pathogen chronicity. However, the mechanisms that underpin the development of these regulatory cells, especially in the context of ongoing antigen exposure, have remained obscure. To address this knowledge gap, we undertook a comprehensive functional, phenotypic, and transcriptomic analysis of interleukin (IL)-10-producing CD4+ T cells induced by chronic infection with murine cytomegalovirus (MCMV). We identified these cells as clonally expanded and highly differentiated TH1-like cells that developed in a T-bet-dependent manner and coexpressed arginase-1 (Arg1), which promotes the catalytic breakdown of L-arginine. Mice lacking Arg1-expressing CD4+ T cells exhibited more robust antiviral immunity and were better able to control MCMV. Conditional deletion of T-bet in the CD4+ lineage suppressed the development of these inhibitory cells and also enhanced immune control of MCMV. Collectively, these data elucidated the ontogeny of IL-10-producing CD4+ T cells and revealed a previously unappreciated mechanism of immune regulation, whereby viral persistence was facilitated by the site-specific delivery of Arg1.

NAIR: Network Analysis of Immune Repertoire

T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.

A novel approach to T-cell receptor beta chain (TCRB) repertoire encoding using lossless string compression

MOTIVATION

T-cell receptor beta chain (TCRB) repertoires are crucial for understanding immune responses. However, their high diversity and complexity present significant challenges in representation and analysis. The main motivation of this study is to develop a unified and compact representation of a TCRB repertoire that can efficiently capture its inherent complexity and diversity and allow for direct inference.

RESULTS

We introduce a novel approach to TCRB repertoire encoding and analysis, leveraging the Lempel-Ziv 76 algorithm. This approach allows us to create a graph-like model, identify-specific sequence features, and produce a new encoding approach for an individual's repertoire. The proposed representation enables various applications, including generation probability inference, informative feature vector derivation, sequence generation, a new measure for diversity estimation, and a new sequence centrality measure. The approach was applied to four large-scale public TCRB sequencing datasets, demonstrating its potential for a wide range of applications in big biological sequencing data.

AVAILABILITY AND IMPLEMENTATION

Python package for implementation is available https://github.com/MuteJester/LZGraphs.

Large clones of pre-existing T cells drive early immunity against SARS-COV-2 and LCMV infection

T cell responses precede antibody and may provide early control of infection. We analyzed the clonal basis of this rapid response following SARS-COV-2 infection. We applied T cell receptor (TCR) sequencing to define the trajectories of individual T cell clones immediately. In SARS-COV-2 PCR+ individuals, a wave of TCRs strongly but transiently expand, frequently peaking the same week as the first positive PCR test. These expanding TCR CDR3s were enriched for sequences functionally annotated as SARS-COV-2 specific. Epitopes recognized by the expanding TCRs were highly conserved between SARS-COV-2 strains but not with circulating human coronaviruses. Many expanding CDR3s were present at high frequency in pre-pandemic repertoires. Early response TCRs specific for lymphocytic choriomeningitis virus epitopes were also found at high frequency in the preinfection naive repertoire. High-frequency naive precursors may allow the T cell response to respond rapidly during the crucial early phases of acute viral infection.

T cell repertoire breadth is associated with the number of acute respiratory infections in the LoewenKIDS birth cohort

We set out to gain insight into peripheral blood B and T cell repertoires from 120 infants of the LoewenKIDS birth cohort to investigate potential determinants of early life respiratory infections. Low antigen-dependent somatic hypermutation of B cell repertoires, as well as low T and B cell repertoire clonality, high diversity, and high richness especially in public T cell clonotypes reflected the immunological naivety at 12 months of age when high thymic and bone marrow output are associated with relatively few prior antigen encounters. Infants with inadequately low T cell repertoire diversity or high clonality showed higher numbers of acute respiratory infections over the first 4 years of life. No correlation of T or B cell repertoire metrics with other parameters such as sex, birth mode, older siblings, pets, the onset of daycare, or duration of breast feeding was noted. Together, this study supports that-regardless of T cell functionality-the breadth of the T cell repertoire is associated with the number of acute respiratory infections in the first 4 years of life. Moreover, this study provides a valuable resource of millions of T and B cell receptor sequences from infants with available metadata for researchers in the field.

Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is lethal in 88% of patients1, yet harbours mutation-derived T cell neoantigens that are suitable for vaccines 2,3. Here in a phase I trial of adjuvant autogene cevumeran, an individualized neoantigen vaccine based on uridine mRNA-lipoplex nanoparticles, we synthesized mRNA neoantigen vaccines in real time from surgically resected PDAC tumours. After surgery, we sequentially administered atezolizumab (an anti-PD-L1 immunotherapy), autogene cevumeran (a maximum of 20 neoantigens per patient) and a modified version of a four-drug chemotherapy regimen (mFOLFIRINOX, comprising folinic acid, fluorouracil, irinotecan and oxaliplatin). The end points included vaccine-induced neoantigen-specific T cells by high-threshold assays, 18-month recurrence-free survival and oncologic feasibility. We treated 16 patients with atezolizumab and autogene cevumeran, then 15 patients with mFOLFIRINOX. Autogene cevumeran was administered within 3 days of benchmarked times, was tolerable and induced de novo high-magnitude neoantigen-specific T cells in 8 out of 16 patients, with half targeting more than one vaccine neoantigen. Using a new mathematical strategy to track T cell clones (CloneTrack) and functional assays, we found that vaccine-expanded T cells comprised up to 10% of all blood T cells, re-expanded with a vaccine booster and included long-lived polyfunctional neoantigen-specific effector CD8+ T cells. At 18-month median follow-up, patients with vaccine-expanded T cells (responders) had a longer median recurrence-free survival (not reached) compared with patients without vaccine-expanded T cells (non-responders; 13.4 months, P = 0.003). Differences in the immune fitness of the patients did not confound this correlation, as responders and non-responders mounted equivalent immunity to a concurrent unrelated mRNA vaccine against SARS-CoV-2. Thus, adjuvant atezolizumab, autogene cevumeran and mFOLFIRINOX induces substantial T cell activity that may correlate with delayed PDAC recurrence.

Viral infection reveals hidden sharing of TCR CDR3 sequences between individuals

The T cell receptor is generated by a process of random and imprecise somatic recombination. The number of possible T cell receptors which this process can produce is enormous, greatly exceeding the number of T cells in an individual. Thus, the likelihood of identical TCRs being observed in multiple individuals (public TCRs) might be expected to be very low. Nevertheless such public TCRs have often been reported. In this study we explore the extent of TCR publicity in the context of acute resolving Lymphocytic choriomeningitis virus (LCMV) infection in mice. We show that the repertoire of effector T cells following LCMV infection contains a population of highly shared TCR sequences. This subset of TCRs has a distribution of naive precursor frequencies, generation probabilities, and physico-chemical CDR3 properties which lie between those of classic public TCRs, which are observed in uninfected repertoires, and the dominant private TCR repertoire. We have named this set of sequences "hidden public" TCRs, since they are only revealed following infection. A similar repertoire of hidden public TCRs can be observed in humans after a first exposure to SARS-COV-2. The presence of hidden public TCRs which rapidly expand following viral infection may therefore be a general feature of adaptive immunity, identifying an additional level of inter-individual sharing in the TCR repertoire which may form an important component of the effector and memory response.

Statistical inference reveals the role of length, GC content, and local sequence in V(D)J nucleotide trimming

To appropriately defend against a wide array of pathogens, humans somatically generate highly diverse repertoires of B cell and T cell receptors (BCRs and TCRs) through a random process called V(D)J recombination. Receptor diversity is achieved during this process through both the combinatorial assembly of V(D)J-genes and the junctional deletion and insertion of nucleotides. While the Artemis protein is often regarded as the main nuclease involved in V(D)J recombination, the exact mechanism of nucleotide trimming is not understood. Using a previously published TCRβ repertoire sequencing data set, we have designed a flexible probabilistic model of nucleotide trimming that allows us to explore various mechanistically interpretable sequence-level features. We show that local sequence context, length, and GC nucleotide content in both directions of the wider sequence, together, can most accurately predict the trimming probabilities of a given V-gene sequence. Because GC nucleotide content is predictive of sequence-breathing, this model provides quantitative statistical evidence regarding the extent to which double-stranded DNA may need to be able to breathe for trimming to occur. We also see evidence of a sequence motif that appears to get preferentially trimmed, independent of GC-content-related effects. Further, we find that the inferred coefficients from this model provide accurate prediction for V- and J-gene sequences from other adaptive immune receptor loci. These results refine our understanding of how the Artemis nuclease may function to trim nucleotides during V(D)J recombination and provide another step toward understanding how V(D)J recombination generates diverse receptors and supports a powerful, unique immune response in healthy humans.

Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires

T-cell receptors (TCRs) play an essential role in the adaptive immune system. Probabilistic models for TCR repertoires can help decipher the underlying complex sequence patterns and provide novel insights into understanding the adaptive immune system. In this work, we develop TCRpeg, a deep autoregressive generative model to unravel the sequence patterns of TCR repertoires. TCRpeg largely outperforms state-of-the-art methods in estimating the probability distribution of a TCR repertoire, boosting the average accuracy from 0.672 to 0.906 measured by the Pearson correlation coefficient. Furthermore, with promising performance in probability inference, TCRpeg improves on a range of TCR-related tasks: profiling TCR repertoire probabilistically, classifying antigen-specific TCRs, validating previously discovered TCR motifs, generating novel TCRs and augmenting TCR data. Our results and analysis highlight the flexibility and capacity of TCRpeg to extract TCR sequence information, providing a novel approach for deciphering complex immunogenomic repertoires.

T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy

T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.

Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals

Adaptive immunity's success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population-wide vaccines and therapeutics. Using probabilistic modeling, we show many of these public receptors are shared by chance in healthy individuals. This predictable overlap is driven not only by biases in the random generation process of receptors, as previously reported, but also by their common functional selection. However, the model underestimates sharing between repertoires of individuals infected with SARS-CoV-2, suggesting strong specific antigen-driven convergent selection. We exploit this discrepancy to identify COVID-associated receptors, which we validate against datasets of receptors with known viral specificity. We study their properties in terms of sequence features and network organization, and use them to design an accurate diagnostic tool for predicting SARS-CoV-2 status from repertoire data.

Compartmentalization and persistence of dominant (regulatory) T cell clones indicates antigen skewing in juvenile idiopathic arthritis

Autoimmune inflammation is characterized by tissue infiltration and expansion of antigen-specific T cells. Although this inflammation is often limited to specific target tissues, it remains yet to be explored whether distinct affected sites are infiltrated with the same, persistent T cell clones. Here, we performed CyTOF analysis and T cell receptor (TCR) sequencing to study immune cell composition and (hyper-)expansion of circulating and joint-derived Tregs and non-Tregs in juvenile idiopathic arthritis (JIA). We studied different joints affected at the same time, as well as over the course of relapsing-remitting disease. We found that the composition and functional characteristics of immune infiltrates are strikingly similar between joints within one patient, and observed a strong overlap between dominant T cell clones, especially Treg, of which some could also be detected in circulation and persisted over the course of relapsing-remitting disease. Moreover, these T cell clones were characterized by a high degree of sequence similarity, indicating the presence of TCR clusters responding to the same antigens. These data suggest that in localized autoimmune disease, there is autoantigen-driven expansion of both Teffector and Treg clones that are highly persistent and are (re)circulating. These dominant clones might represent interesting therapeutic targets.

Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.

Counting is almost all you need

The immune memory repertoire encodes the history of present and past infections and immunological attributes of the individual. As such, multiple methods were proposed to use T-cell receptor (TCR) repertoires to detect disease history. We here show that the counting method outperforms two leading algorithms. We then show that the counting can be further improved using a novel attention model to weigh the different TCRs. The attention model is based on the projection of TCRs using a Variational AutoEncoder (VAE). Both counting and attention algorithms predict better than current leading algorithms whether the host had CMV and its HLA alleles. As an intermediate solution between the complex attention model and the very simple counting model, we propose a new Graph Convolutional Network approach that obtains the accuracy of the attention model and the simplicity of the counting model. The code for the models used in the paper is provided at: https://github.com/louzounlab/CountingIsAlmostAllYouNeed.

simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods

BACKGROUND

Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires.

RESULTS

We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets.

CONCLUSIONS

This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.

Clonal diversity predicts persistence of SARS-CoV-2 epitope-specific T-cell response

T cells play a pivotal role in reducing disease severity during SARS-CoV-2 infection and formation of long-term immune memory. We studied 50 COVID-19 convalescent patients and found that T cell response was induced more frequently and persisted longer than circulating antibodies. We identified 756 clonotypes specific to nine CD8+ T cell epitopes. Some epitopes were recognized by highly similar public clonotypes. Receptors for other epitopes were extremely diverse, suggesting alternative modes of recognition. We tracked persistence of epitope-specific response and individual clonotypes for a median of eight months after infection. The number of recognized epitopes per patient and quantity of epitope-specific clonotypes decreased over time, but the studied epitopes were characterized by uneven decline in the number of specific T cells. Epitopes with more clonally diverse TCR repertoires induced more pronounced and durable responses. In contrast, the abundance of specific clonotypes in peripheral circulation had no influence on their persistence.

Amino acids at position 5 in the peptide/MHC binding region of a public virus-specific TCR are completely inter-changeable without loss of function

Anti-viral T-cell responses are usually directed against a limited set of antigens, but often contain many T cells expressing different T-cell receptors (TCRs). Identical TCRs found within virus-specific T-cell populations in different individuals are known as public TCRs, but also TCRs highly-similar to these public TCRs, with only minor variations in amino acids on specific positions in the Complementary Determining Regions (CDRs), are frequently found. However, the degree of freedom at these positions was not clear. In this study, we used the HLA-A*02:01-restricted EBV-LMP2^FLY -specific public TCR as model and modified the highly-variable position 5 of the CDR3β sequence with all 20 amino acids. Our results demonstrate that amino acids at this particular position in the CDR3β region of this TCR are completely inter-changeable, without loss of TCR function. We show that the inability to find certain variants in individuals is explained by their lower recombination probability rather than by steric hindrance.

Functional antibodies exhibit light chain coherence

The vertebrate adaptive immune system modifies the genome of individual B cells to encode antibodies that bind particular antigens1. In most mammals, antibodies are composed of heavy and light chains that are generated sequentially by recombination of V, D (for heavy chains), J and C gene segments. Each chain contains three complementarity-determining regions (CDR1-CDR3), which contribute to antigen specificity. Certain heavy and light chains are preferred for particular antigens2-22. Here we consider pairs of B cells that share the same heavy chain V gene and CDRH3 amino acid sequence and were isolated from different donors, also known as public clonotypes23,24. We show that for naive antibodies (those not yet adapted to antigens), the probability that they use the same light chain V gene is around 10%, whereas for memory (functional) antibodies, it is around 80%, even if only one cell per clonotype is used. This property of functional antibodies is a phenomenon that we call light chain coherence. We also observe this phenomenon when similar heavy chains recur within a donor. Thus, although naive antibodies seem to recur by chance, the recurrence of functional antibodies reveals surprising constraint and determinism in the processes of V(D)J recombination and immune selection. For most functional antibodies, the heavy chain determines the light chain.

Memory persistence and differentiation into antibody-secreting cells accompanied by positive selection in longitudinal BCR repertoires

The stability and plasticity of B cell-mediated immune memory ensures the ability to respond to the repeated challenges. We have analyzed the longitudinal dynamics of immunoglobulin heavy chain repertoires from memory B cells, plasmablasts, and plasma cells from the peripheral blood of generally healthy volunteers. We reveal a high degree of clonal persistence in individual memory B cell subsets, with inter-individual convergence in memory and antibody-secreting cells (ASCs). ASC clonotypes demonstrate clonal relatedness to memory B cells, and are transient in peripheral blood. We identify two clusters of expanded clonal lineages with differing prevalence of memory B cells, isotypes, and persistence. Phylogenetic analysis revealed signs of reactivation of persisting memory B cell-enriched clonal lineages, accompanied by new rounds of affinity maturation during proliferation and differentiation into ASCs. Negative selection contributes to both persisting and reactivated lineages, preserving the functionality and specificity of B cell receptors (BCRs) to protect against current and future pathogens.

CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching

MOTIVATION

Adaptive immune receptor (AIR) repertoires (AIRRs) record past immune encounters with exquisite specificity. Therefore, identifying identical or similar AIR sequences across individuals is a key step in AIRR analysis for revealing convergent immune response patterns that may be exploited for diagnostics and therapy. Existing methods for quantifying AIRR overlap scale poorly with increasing dataset numbers and sizes. To address this limitation, we developed CompAIRR, which enables ultra-fast computation of AIRR overlap, based on either exact or approximate sequence matching.

RESULTS

CompAIRR improves computational speed 1000-fold relative to the state of the art and uses only one-third of the memory: on the same machine, the exact pairwise AIRR overlap of 104 AIRRs with 105 sequences is found in ∼17 min, while the fastest alternative tool requires 10 days. CompAIRR has been integrated with the machine learning ecosystem immuneML to speed up commonly used AIRR-based machine learning applications.

AVAILABILITY AND IMPLEMENTATION

CompAIRR code and documentation are available at https://github.com/uio-bmi/compairr. Docker images are available at https://hub.docker.com/r/torognes/compairr. The code to replicate the synthetic datasets, scripts for benchmarking and creating figures, and all raw data underlying the figures are available at https://github.com/uio-bmi/compairr-benchmarking.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

Motivation

Single-cell sequencing now enables the recovery of full-length immune receptor repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate single-cell datasets incorporating immune receptor repertoires and gene expression.

Results

We developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, transcriptional phenotypes and spatial location. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. We demonstrated the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Finally, we simulated immune repertoire information onto existing spatial transcriptomic experiments, thereby generating novel datasets that could be used to develop and integrate methods to profile clonal selection in a spatially resolved manner. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies.

Availability and implementation

The R package and code used in this manuscript can be found at github.com/alexyermanos/echidna and also in the R package Platypus (Yermanos et al., 2021). Installation instructions and the vignette for Echidna is described in the Platypus Computational Ecosystem (https://alexyermanos.github.io/Platypus/index.html). Publicly available data and corresponding sample accession numbers can be found in Supplementary Tables S2 and S3.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Vaccine-elicited murine antibody WS6 neutralizes diverse beta-coronaviruses by recognizing a helical stem supersite of vulnerability

Immunization with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike elicits diverse antibodies, but it is unclear if any of the antibodies can neutralize broadly against other beta-coronaviruses. Here, we report antibody WS6 from a mouse immunized with mRNA encoding the SARS-CoV-2 spike. WS6 bound diverse beta-coronavirus spikes and neutralized SARS-CoV-2 variants, SARS-CoV, and related sarbecoviruses. Epitope mapping revealed WS6 to target a region in the S2 subunit, which was conserved among SARS-CoV-2, Middle East respiratory syndrome (MERS)-CoV, and hCoV-OC43. The crystal structure at 2 Å resolution of WS6 revealed recognition to center on a conserved S2 helix, which was occluded in both pre- and post-fusion spike conformations. Structural and neutralization analyses indicated WS6 to neutralize by inhibiting fusion and post-viral attachment. Comparison of WS6 with other recently identified antibodies that broadly neutralize beta-coronaviruses indicated a stem-helical supersite-centered on hydrophobic residues Phe1148, Leu1152, Tyr1155, and Phe1156-to be a promising target for vaccine design.

Inactivated tick-borne encephalitis vaccine elicits several overlapping waves of T cell response

The development and implementation of vaccines have been growing exponentially, remaining one of the major successes of healthcare over the last century. Nowadays, active regular immunizations prevent epidemics of many viral diseases, including tick-borne encephalitis (TBE). Along with the generation of virus-specific antibodies, a highly effective vaccine should induce T cell responses providing long-term immune defense. In this study, we performed longitudinal high-throughput T cell receptor (TCR) sequencing to characterize changes in individual T cell repertoires of 11 donors immunized with an inactivated TBE vaccine. After two-step immunization, we found significant clonal expansion of both CD4+ and CD8+ T cells, ranging from 302 to 1706 vaccine-associated TCRβ clonotypes in different donors. We detected several waves of T cell clonal expansion generated by distinct groups of vaccine-responding clones. Both CD4+ and CD8+ vaccine-responding T cell clones formed 17 motifs in TCRβ sequences shared by donors with identical HLA alleles. Our results indicate that TBE vaccination leads to a robust T cell response due to the production of a variety of T cell clones with a memory phenotype, which recognize a large set of epitopes.

Two types of human TCR differentially regulate reactivity to self and non-self antigens

Based on analyses of TCR sequences from over 1,000 individuals, we report that the TCR repertoire is composed of two ontogenically and functionally distinct types of TCRs. Their production is regulated by variations in thymic output and terminal deoxynucleotidyl transferase (TDT) activity. Neonatal TCRs derived from TDT-negative progenitors persist throughout life, are highly shared among subjects, and are reported as disease-associated. Thus, 10%–30% of most frequent cord blood TCRs are associated with common pathogens and autoantigens. TDT-dependent TCRs present distinct structural features and are less shared among subjects. TDT-dependent TCRs are produced in maximal numbers during infancy when thymic output and TDT activity reach a summit, are more abundant in subjects with AIRE mutations, and seem to play a dominant role in graft-versus-host disease. Factors decreasing thymic output (age, male sex) negatively impact TCR diversity. Males compensate for their lower repertoire diversity via hyperexpansion of selected TCR clonotypes.

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Over 10⁸ TCR CDR3 sequences from ∼10³ individuals and 7 cohorts were analyzed

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The TCR repertoire is composed of two layers: neonatal and TDT-dependent layer

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∼70% of frequent cord blood TCRs are associated with common pathogens

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Acute graft-vs-host disease correlates with a high proportion of TDT-dependent TCRs

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

Homologous recombination repair deficient prostate cancer represents an immunologically distinct subtype

Homologous recombination repair deficiency (HRD) is observed in 10% of patients with castrate-resistant prostate cancer (PCa). Preliminary data suggest that HRD-PCa might be more responsive to immune checkpoint inhibitors (ICIs). In this study, we compare the tumor immune landscape and peripheral T cell receptor (TCR) repertoire of patients with and without HRD-PCa to gain further insight into the immunogenicity of HRD-PCa. Immunohistochemistry was performed on tumor tissue of 81 patients, including 15 patients with HRD-PCa. Peripheral TCR sequencing was performed in a partially overlapping cohort of 48 patients, including 16 patients with HRD-PCa. HRD patients more frequently had intratumoral CD3⁺, CD3⁺CD8⁻FoxP3⁻ or Foxp3⁺ TILs above median compared to patients without DNA damage repair alterations (DDRwt; CD3⁺ and Foxp3⁺: 77% vs 35%, p = .013; CD3⁺CD8⁻FoxP3⁻: 80% vs 44%, p = .031). No significant difference in CD8⁺ TILs or PD-L1 expression was observed. In peripheral blood, HRD patients displayed a more diverse TCR repertoire compared to DDRwt patients (p = .014). Additionally, HRD patients shared TCR clusters with low generation probability, suggesting patient-overlapping T cell responses. A pooled analysis of clinical data from 227 patients with molecularly characterized PCa suggested increased efficacy of ICIs in HRD-PCa. In conclusion, patients with HRD-PCa display increased TIL density and an altered peripheral TCR repertoire. Further research into the efficacy of ICIs and the presence of shared neoantigens in HRD-PCa is warranted.

Clonal evolution and TCR specificity of the human TFH cell response to Plasmodium falciparum CSP

T follicular helper (T_FH) cells play a crucial role in the development of long-lived, high-quality B cell responses after infection and vaccination. However, little is known about how antigen-specific T_FH cells clonally evolve in response to complex pathogens and what guides the targeting of different epitopes. Here, we assessed the cell phenotype, clonal dynamics, and T cell receptor (TCR) specificity of human circulating T_FH (cT_FH) cells during successive malaria immunizations with radiation-attenuated Plasmodium falciparum (Pf) sporozoites. Repeated parasite exposures induced a dynamic, polyclonal cT_FH response with high frequency of cells specific to a small number of epitopes in Pf circumsporozoite protein (PfCSP), the primary sporozoite surface protein and well-defined vaccine target. Human leukocyte antigen (HLA) restrictions and differences in TCR generation probability were associated with differences in the epitope targeting frequency and indicated the potential of amino acids 311 to 333 in the Th2R/T* region as a T cell supertope. But most of vaccine-induced anti-amino acid 311 to 333 TCRs, including convergent TCRs with high sequence similarity, failed to tolerate natural polymorphisms in their target peptide sequence, thus demonstrating that the T_FH cell response was limited to the vaccine strain. These data suggest that the high parasite diversity in endemic areas will limit boosting of the vaccine-induced T_FH cell response by natural infections. Our findings may guide the further design of PfCSP-based malaria vaccines able to induce potent T helper cell responses for broad, long-lasting antibody responses.

Divergent Characteristics of T-Cell Receptor Repertoire Between Essential Hypertension and Aldosterone-Producing Adenoma

Aldosterone-producing adenoma (APA) is a benign adrenal tumor that results in persistent hyperaldosteronism. As one major subtype of primary aldosteronism, APA leads to secondary hypertension that is associated with immune dysregulation. However, how the adaptive immune system, particularly the T-cell population, is altered in APA patients remains largely unknown. Here, we performed TCR sequencing to characterize the TCR repertoire between two age-matched groups of patients: one with APA and the other one with essential hypertension (EH). Strikingly, we found a significant reduction of TCR repertoire diversity in the APA group. Analyses on TCR clustering and antigen annotation further showed that the APA group possessed lower diversity in TCR clonotypes with non-common antigen-specific features, compared with the EH group. In addition, our results indicated that the strength of correlation between generation probabilities and frequencies of TCR clonotypes was significantly higher in the APA group than that in the EH group. Finally, we observed that clinical features, including plasma aldosterone level, aldosterone–renin ratio, and blood sodium level, were positively associated with the strength of correlation between generation and abundance of TCR clonotypes in the APA group. Our findings unveiled the correlation between T-cell immune repertoire and APA, suggesting a critical role of such adrenal adenoma in the T-cell immunity of patients with hypertension.

Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification

BACKGROUND

Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where method development of more sophisticated ML approaches may be required.

RESULTS

To identify those scenarios where a baseline ML method is able to perform well for AIRR classification, we generated a collection of synthetic AIRR benchmark data sets encompassing a wide range of data set architecture-associated and immune state-associated sequence patterns (signal) complexity. We trained ≈1,700 ML models with varying assumptions regarding immune signal on ≈1,000 data sets with a total of ≈250,000 AIRRs containing ≈46 billion TCRβ CDR3 amino acid sequences, thereby surpassing the sample sizes of current state-of-the-art AIRR-ML setups by two orders of magnitude. We found that L1-penalized logistic regression achieved high prediction accuracy even when the immune signal occurs only in 1 out of 50,000 AIR sequences.

CONCLUSIONS

We provide a reference benchmark to guide new AIRR-ML classification methodology by (i) identifying those scenarios characterized by immune signal and data set complexity, where baseline methods already achieve high prediction accuracy, and (ii) facilitating realistic expectations of the performance of AIRR-ML models given training data set properties and assumptions. Our study serves as a template for defining specialized AIRR benchmark data sets for comprehensive benchmarking of AIRR-ML methods.

How Naive T-Cell Clone Counts Are Shaped By Heterogeneous Thymic Output and Homeostatic Proliferation

The specificity of T cells is that each T cell has only one T cell receptor (TCR). A T cell clone represents a collection of T cells with the same TCR sequence. Thus, the number of different T cell clones in an organism reflects the number of different T cell receptors (TCRs) that arise from recombination of the V(D)J gene segments during T cell development in the thymus. TCR diversity and more specifically, the clone abundance distribution, are important factors in immune functions. Specific recombination patterns occur more frequently than others while subsequent interactions between TCRs and self-antigens are known to trigger proliferation and sustain naive T cell survival. These processes are TCR-dependent, leading to clone-dependent thymic export and naive T cell proliferation rates. We describe the heterogeneous steady-state population of naive T cells (those that have not yet been antigenically triggered) by using a mean-field model of a regulated birth-death-immigration process. After accounting for random sampling, we investigate how TCR-dependent heterogeneities in immigration and proliferation rates affect the shape of clone abundance distributions (the number of different clones that are represented by a specific number of cells, or “clone counts”). By using reasonable physiological parameter values and fitting predicted clone counts to experimentally sampled clone abundances, we show that realistic levels of heterogeneity in immigration rates cause very little change to predicted clone-counts, but that modest heterogeneity in proliferation rates can generate the observed clone abundances. Our analysis provides constraints among physiological parameters that are necessary to yield predictions that qualitatively match the data. Assumptions of the model and potentially other important mechanistic factors are discussed.