Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire

Quantifiable TCR repertoire changes in prediagnostic blood specimens among patients with high-grade ovarian cancer

High-grade ovarian cancer (HGOC) is a major cause of death in women. Early detection of HGOC usually leads to a cure, yet it remains a clinical challenge with over 90% HGOCs diagnosed at advanced stages. This is mainly because conventional biomarkers are not sensitive enough to detect the microscopic yet metastatic early lesions. In this study, we sequence the blood T cell receptor (TCR) repertoires of 466 patients with ovarian cancer and controls and systematically investigate the immune repertoire signatures in HGOCs. We observe quantifiable changes of selected TCRs in HGOCs that are reproducible in multiple independent cohorts. Importantly, these changes are stronger during stage I. Using pre-diagnostic patient blood samples from the Nurses' Health Study, we confirm that HGOC signals can be detected in the blood TCR repertoire up to 4 years preceding conventional diagnosis. Our findings may provide the basis for future immune-based HGOC early detection criteria.

Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis

T-cell receptor (TCR) detection can examine the extent of T-cell immune responses. Therefore, the article analyzed characteristic data of glioma obtained by DNA-based TCR high-throughput sequencing, to predict the disease with fewer biomarkers and higher accuracy. We downloaded data online and obtained six TCR-related diversity indices to establish a multidimensional classification system. By comparing actual presence of the 602 correlated sequences, we obtained two-dimensional and multidimensional datasets. Multiple classification methods were utilized for both datasets with the classification accuracy of multidimensional data slightly less to two-dimensional datasets. This study reduced the TCR β sequences through feature selection methods like RFECV (Recursive Feature Elimination with Cross-Validation). Consequently, using only the presence of these three sequences, the classification AUC value of 96.67% can be achieved. The combination of the three correlated TCR clones obtained at a source data threshold of 0.1 is: CASSLGGNTEAFF_TRBV12_TRBJ1-1, CASSYSDTGELFF_TRBV6_TRBJ2-2, and CASSLTGNTEAFF_TRBV12_TRBJ1-1. At 0.001, the combination is: CASSLGETQYF_TRBV12_TRBJ2-5, CASSLGGNQPQHF_TRBV12_TRBJ1-5, and CASSLSGNTIYF_TRBV12_TRBJ1-3. This method can serve as a potential diagnostic and therapeutic tool, facilitating diagnosis and treatment of glioma and other cancers.

A multi-bin rarefying method for evaluating alpha diversities in TCR sequencing data

MOTIVATION

T cell receptors (TCRs) constitute a major component of our adaptive immune system, governing the recognition and response to internal and external antigens. Studying the TCR diversity via sequencing technology is critical for a deeper understanding of immune dynamics. However, library sizes differ substantially across samples, hindering the accurate estimation/comparisons of alpha diversities. To address this, researchers frequently use an overall rarefying approach in which all samples are sub-sampled to an even depth. Despite its pervasive application, its efficacy has never been rigorously assessed.

RESULTS

In this paper, we develop an innovative "multi-bin" rarefying approach that partitions samples into multiple bins according to their library sizes, conducts rarefying within each bin for alpha diversity calculations, and performs meta-analysis across bins. Extensive simulations using real-world data highlight the inadequacy of the overall rarefying approach in controlling the confounding effect of library size. Our method proves robust in addressing library size confounding, outperforming competing normalization strategies by achieving better-controlled type-I error rates and enhanced statistical power in association tests.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/mli171/MultibinAlpha. The datasets are freely available at https://doi.org/10.21417/B7001Z and https://doi.org/10.21417/AR2019NC.

Differences in Tumor-Associated T cell receptor repertoires between Early-Onset and Average-Onset colorectal cancer

The incidence of colorectal cancer (CRC) among individuals younger than age 50 (early onset CRC; EOCRC) has substantially increased, yet the etiology and molecular mechanisms underlying this alarming rise remain unclear. We compared tumor-associated T cell repertoires between EOCRC and average-onset CRC (AOCRC) to uncover potentially unique immune microenvironment-related features by age of onset. Our discovery cohort included 242 patients who underwent surgical resection at Cleveland Clinic from 2000 to 2020. EOCRC was defined as age < 50 years at diagnosis (N = 126), and AOCRC as age ≥ 60 years (N = 116). T cell receptor (TCR) abundance and clonality were measured by immunosequencing of tumors. Logistic regression models were used to evaluate the associations between TCR repertoire features and age of onset, adjusting for sex, race, tumor location, and stage. Findings were replicated in 152 EOCRC and 1,984 AOCRC cases from the Molecular Epidemiology of Colorectal Cancer Study. EOCRC tumors had significantly higher TCR diversity compared to AOCRC tumors in the discovery cohort (Odds Ratio (OR):0.44, 95% Confidence Interval (CI):0.32-0.61, p < .0001). This association was also observed in the replication cohort (OR : 0.74, 95% CI : 0.62-0.89, p = .0013). No significant differences in TCR abundance were observed between EOCRC and AOCRC in either cohort. Higher TCR diversity, suggesting a more diverse intratumoral T cell response, is more frequently observed in EOCRC than AOCRC. Further studies are warranted to investigate the role of T cell diversity and the adaptive immune response more broadly in the etiology and outcomes of EOCRC.

The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood

T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen specificity of T cells is determined by the T-cell receptor (TCR). Therefore, monitoring changes in the TCR repertoire in peripheral blood may offer a strategy to detect various cancers at a relatively early stage. Here, we developed the deep learning framework iCanTCR to identify patients with cancer based on the TCR repertoire. The iCanTCR framework uses TCRβ sequences from an individual as an input and outputs the predicted cancer probability. The model was trained on over 2,000 publicly available TCR repertoires from 11 types of cancer and healthy controls. Analysis of several additional publicly available datasets validated the ability of iCanTCR to distinguish patients with cancer from noncancer individuals and demonstrated the capability of iCanTCR for the accurate classification of multiple cancers. Importantly, iCanTCR precisely identified individuals with early-stage cancer with an AUC of 86%. Altogether, this work provides a liquid biopsy approach to capture immune signals from peripheral blood for noninvasive cancer diagnosis.

SIGNIFICANCE

Development of a deep learning-based method for multicancer detection using the TCR repertoire in the peripheral blood establishes the potential of evaluating circulating immune signals for noninvasive early cancer detection.

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.

RNA aggregates harness the danger response for potent cancer immunotherapy

Cancer immunotherapy remains limited by poor antigenicity and a regulatory tumor microenvironment (TME). Here, we create "onion-like" multi-lamellar RNA lipid particle aggregates (LPAs) to substantially enhance the payload packaging and immunogenicity of tumor mRNA antigens. Unlike current mRNA vaccine designs that rely on payload packaging into nanoparticle cores for Toll-like receptor engagement in immune cells, systemically administered RNA-LPAs activate RIG-I in stromal cells, eliciting massive cytokine/chemokine response and dendritic cell/lymphocyte trafficking that provokes cancer immunogenicity and mediates rejection of both early- and late-stage murine tumor models. In client-owned canines with terminal gliomas, RNA-LPAs improved survivorship and reprogrammed the TME, which became "hot" within days of a single infusion. In a first-in-human trial, RNA-LPAs elicited rapid cytokine/chemokine release, immune activation/trafficking, tissue-confirmed pseudoprogression, and glioma-specific immune responses in glioblastoma patients. These data support RNA-LPAs as a new technology that simultaneously reprograms the TME while eliciting rapid and enduring cancer immunotherapy.

Early acquisition of S-specific Tfh clonotypes after SARS-CoV-2 vaccination is associated with the longevity of anti-S antibodies

SARS-CoV-2 vaccines have been used worldwide to combat COVID-19 pandemic. To elucidate the factors that determine the longevity of spike (S)-specific antibodies, we traced the characteristics of S-specific T cell clonotypes together with their epitopes and anti-S antibody titers before and after BNT162b2 vaccination over time. T cell receptor (TCR) αβ sequences and mRNA expression of the S-responded T cells were investigated using single-cell TCR- and RNA-sequencing. Highly expanded 199 TCR clonotypes upon stimulation with S peptide pools were reconstituted into a reporter T cell line for the determination of epitopes and restricting HLAs. Among them, we could determine 78 S epitopes, most of which were conserved in variants of concern (VOCs). After the 2nd vaccination, T cell clonotypes highly responsive to recall S stimulation were polarized to follicular helper T (Tfh)-like cells in donors exhibiting sustained anti-S antibody titers (designated as 'sustainers'), but not in 'decliners'. Even before vaccination, S-reactive CD4+ T cell clonotypes did exist, most of which cross-reacted with environmental or symbiotic microbes. However, these clonotypes contracted after vaccination. Conversely, S-reactive clonotypes dominated after vaccination were undetectable in pre-vaccinated T cell pool, suggesting that highly responding S-reactive T cells were established by vaccination from rare clonotypes. These results suggest that de novo acquisition of memory Tfh-like cells upon vaccination may contribute to the longevity of anti-S antibody titers.

T-cell receptor sequencing reveals selected donor-reactive CD8+ T cell clones resist antithymocyte globulin depletion after kidney transplantation

High frequencies of donor-reactive memory T cells in the periphery of transplant candidates prior to transplantation are linked to the development of posttransplant acute rejection episodes and reduced allograft function. Rabbit antithymocyte globulin (rATG) effectively depletes naïve CD4+ and CD8+ T cells for >6 months posttransplant, but rATG's effects on human donor-reactive T cells have not been carefully determined. To address this, we performed T cell receptor β-chain sequencing on peripheral blood mononuclear cells aliquots collected pretransplant and serially posttransplant in 7 kidney transplant recipients who received rATG as induction therapy. We tracked the evolution of the donor-reactive CD4+ and CD8+ T cell repertoires and identified stimulated pretransplant, CTV-(surface dye)-labeled, peripheral blood mononuclear cells from each patient with donor cells or third-party cells. Our analyses showed that while rATG depleted CD4+ T cells in all tested subjects, a subset of donor-reactive CD8+ T cells that were present at high frequencies pretransplant, consistent with expanded memory cells, resisted rATG depletion, underwent posttransplant expansion and were functional. Together, our data support the conclusion that a subset of human memory CD8+ T cells specifically reactive to donor antigens expand in vivo despite induction therapy with rATG and thus have the potential to mediate allograft damage.

A comparative analysis of TCR immune repertoire in COVID-19 patients

The coronavirus disease 2019 (COVID-19) has merged as a global health threat since its outbreak in December 2019. Despite widespread recognition, there has been a paucity of studies focusing on the T cell receptor (TCR) bias in adaptive immunity induced by SARS-CoV-2. This research conducted a comparative analysis of the TCR immune repertoire to identify notable αβ TCR bias sequences associated with the SARS-CoV-2 virus antigen. The present study encompassed 73 symptomatic COVID-19 patients, categorized as moderate/mild or severe/critical, along with 9 healthy controls. Our findings revealed specific TCR chains prominently utilized by moderate and severe patients, identified as TRAV30-J34-TRBV3-1-J2-7 and TRAV12-3-J6-TRBV28-J1-1, respectively. Additionally, our research explored critical TCR preferences in the bronchoalveolar lavage fluid (BALF) of COVID-19 patients at various disease stages. Indeed, monitoring the dynamics of immune repertoire changes in COVID-19 patients could serve as a crucial biomarker for predicting disease progression and recovery. Furthermore, the study explored TCR bias in both peripheral blood mononuclear cells (PBMCs) and BALF. The most common αβ VJ pair observed in BALF was TRAV12-3-J18-TRBV7-6-J2-7. In addition, a comparative analysis with the VDJdb database indicated that the HLA-A*02:01 allele exhibited the widest distribution and highest frequency in COVID-19 patients across different periods. This comprehensive examination provided a global characterization of the TCR immune repertoire in COVID-19 patients, contributing significantly to our understanding of TCR bias induced by SARS-CoV-2.

Self-supervised learning of T cell receptor sequences exposes core properties for T cell membership

The T cell receptor (TCR) repertoire is an extraordinarily diverse collection of TCRs essential for maintaining the body's homeostasis and response to threats. In this study, we compiled an extensive dataset of more than 4200 bulk TCR repertoire samples, encompassing 221,176,713 sequences, alongside 6,159,652 single-cell TCR sequences from over 400 samples. From this dataset, we then selected a representative subset of 5 million bulk sequences and 4.2 million single-cell sequences to train two specialized Transformer-based language models for bulk (CVC) and single-cell (scCVC) TCR repertoires, respectively. We show that these models successfully capture TCR core qualities, such as sharing, gene composition, and single-cell properties. These qualities are emergent in the encoded TCR latent space and enable classification into TCR-based qualities such as public sequences. These models demonstrate the potential of Transformer-based language models in TCR downstream applications.

Disease diagnostics using machine learning of immune receptors

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID) , an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

Integrative systems biology reveals NKG2A-biased immune responses correlate with protection in infectious disease, autoimmune disease, and cancer

Infection, autoimmunity, and cancer are principal human health challenges of the 21st century. Often regarded as distinct ends of the immunological spectrum, recent studies hint at potential overlap between these diseases. For example, inflammation can be pathogenic in infection and autoimmunity. T resident memory (TRM) cells can be beneficial in infection and cancer. However, these findings are limited by size and scope; exact immunological factors shared across diseases remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune/post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation and increased humoral immunity and that they resemble TRM cells. Our results suggest NKG2A+ biases as a cross-disease factor of protection, supporting suggestions of immunological overlap between infection, autoimmunity, and cancer.

Identification of apolipoprotein B-reactive CDR3 motifs allows tracking of atherosclerosis-related memory CD4+T cells in multiple donors

Introduction

Atherosclerosis is a major pathological condition that underlies many cardiovascular diseases (CVDs). Its etiology involves breach of tolerance to self, leading to clonal expansion of autoreactive apolipoprotein B (APOB)-reactive CD4+T cells that correlates with clinical CVD. The T-cell receptor (TCR) sequences that mediate activation of APOB-specific CD4+T cells are unknown.

Methods

In a previous study, we had profiled the hypervariable complementarity determining region 3 (CDR3) of CD4+T cells that respond to six immunodominant APOB epitopes in most donors. Here, we comprehensively analyze this dataset of 149,065 APOB-reactive and 199,211 non-reactive control CDR3s from six human leukocyte antigen-typed donors.

Results

We identified 672 highly expanded (frequency threshold > 1.39E-03) clones that were significantly enriched in the APOB-reactive group as compared to the controls (log10 odds ratio ≥1, Fisher's test p < 0.01). Analysis of 114,755 naïve, 91,001 central memory (TCM) and 29,839 effector memory (TEM) CDR3 sequences from the same donors revealed that APOB+ clones can be traced to the complex repertoire of unenriched blood T cells. The fraction of APOB+ clones that overlapped with memory CDR3s ranged from 2.2% to 46% (average 16.4%). This was significantly higher than their overlap with the naïve pool, which ranged from 0.7% to 2% (average 1.36%). CDR3 motif analysis with the machine learning-based in-silico tool, GLIPHs (grouping of lymphocyte interactions by paratope hotspots), identified 532 APOB+ motifs. Analysis of naïve and memory CDR3 sequences with GLIPH revealed that ~40% (209 of 532) of these APOB+ motifs were enriched in the memory pool. Network analysis with Cytoscape revealed extensive sharing of the memory-affiliated APOB+ motifs across multiple donors. We identified six motifs that were present in TCM and TEM CDR3 sequences from >80% of the donors and were highly enriched in the APOB-reactive TCR repertoire.

Discussion

The identified APOB-reactive expanded CD4+T cell clones and conserved motifs can be used to annotate and track human atherosclerosis-related autoreactive CD4+T cells and measure their clonal expansion.

Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes

BACKGROUND

In rheumatoid arthritis (RA), the activation of T and B cell clones specific for self-antigens leads to the chronic inflammation of the synovium. Here, we perform an in-depth quantitative analysis of the seven chains that comprise the adaptive immune receptor repertoire (AIRR) in RA.

RESULTS

In comparison to controls, we show that RA patients have multiple and strong differences in the B cell receptor repertoire including reduced diversity as well as altered isotype, chain, and segment frequencies. We demonstrate that therapeutic tumor necrosis factor inhibition partially restores this alteration but find a profound difference in the underlying biochemical reactivities between responders and non-responders. Combining the AIRR with HLA typing, we identify the specific T cell receptor repertoire associated with disease risk variants. Integrating these features, we further develop a molecular classifier that shows the utility of the AIRR as a diagnostic tool.

CONCLUSIONS

Simultaneous sequencing of the seven chains of the human AIRR reveals novel features associated with the disease and clinically relevant phenotypes, including response to therapy. These findings show the unique potential of AIRR to address precision medicine in immune-related diseases.

DeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention strategy on motifs

Introduction

T cell receptor (TCR) repertoires provide valuable insights into complex human diseases, including cancers. Recent advancements in immune sequencing technology have significantly improved our understanding of TCR repertoire. Some computational methods have been devised to identify cancer-associated TCRs and enable cancer detection using TCR sequencing data. However, the existing methods are often limited by their inadequate consideration of the correlations among TCRs within a repertoire, hindering the identification of crucial TCRs. Additionally, the sparsity of cancer-associated TCR distribution presents a challenge in accurate prediction.

Methods

To address these issues, we presented DeepLION2, an innovative deep multi-instance contrastive learning framework specifically designed to enhance cancer-associated TCR prediction. DeepLION2 leveraged content-based sparse self-attention, focusing on the top k related TCRs for each TCR, to effectively model inter-TCR correlations. Furthermore, it adopted a contrastive learning strategy for bootstrapping parameter updates of the attention matrix, preventing the model from fixating on non-cancer-associated TCRs.

Results

Extensive experimentation on diverse patient cohorts, encompassing over ten cancer types, demonstrated that DeepLION2 significantly outperformed current state-of-the-art methods in terms of accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the curve (AUC). Notably, DeepLION2 achieved impressive AUC values of 0.933, 0.880, and 0.763 on thyroid, lung, and gastrointestinal cancer cohorts, respectively. Furthermore, it effectively identified cancer-associated TCRs along with their key motifs, highlighting the amino acids that play a crucial role in TCR-peptide binding.

Conclusion

These compelling results underscore DeepLION2's potential for enhancing cancer detection and facilitating personalized cancer immunotherapy. DeepLION2 is publicly available on GitHub, at https://github.com/Bioinformatics7181/DeepLION2, for academic use only.

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.

Stem-like progenitor and terminally differentiated TFH-like CD4+ T cell exhaustion in the tumor microenvironment

Immune checkpoint inhibitors exert clinical efficacy against various types of cancer through reinvigoration of exhausted CD8+ T cells that attack cancer cells directly in the tumor microenvironment (TME). Using single-cell sequencing and mouse models, we show that CXCL13, highly expressed in tumor-infiltrating exhausted CD8+ T cells, induces CD4+ follicular helper T (TFH) cell infiltration, contributing to anti-tumor immunity. Furthermore, a part of the TFH cells in the TME exhibits cytotoxicity and directly attacks major histocompatibility complex-II-expressing tumors. TFH-like cytotoxic CD4+ T cells have high LAG-3/BLIMP1 and low TCF1 expression without self-renewal ability, whereas non-cytotoxic TFH cells express low LAG-3/BLIMP1 and high TCF1 with self-renewal ability, closely resembling the relationship between terminally differentiated and stem-like progenitor exhaustion in CD8+ T cells, respectively. Our findings provide deep insights into TFH-like CD4+ T cell exhaustion with helper progenitor and cytotoxic differentiated functions, mediating anti-tumor immunity orchestrally with CD8+ T cells.

Diagnosing Viral Infections Through T-Cell Receptor Sequencing of Activated CD8+ T Cells

T-cell-based diagnostic tools identify pathogen exposure but lack differentiation between recent and historical exposures in acute infectious diseases. Here, T-cell receptor (TCR) RNA sequencing was performed on HLA-DR+/CD38+CD8+ T-cell subsets of hospitalized coronavirus disease 2019 (COVID-19) patients (n = 30) and healthy controls (n = 30; 10 of whom had previously been exposed to severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]). CDR3α and CDR3β TCR regions were clustered separately before epitope specificity annotation using a database of SARS-CoV-2-associated CDR3α and CDR3β sequences corresponding to >1000 SARS-CoV-2 epitopes. The depth of the SARS-CoV-2-associated CDR3α/β sequences differentiated COVID-19 patients from the healthy controls with a receiver operating characteristic area under the curve of 0.84 ± 0.10. Hence, annotating TCR sequences of activated CD8+ T cells can be used to diagnose an acute viral infection and discriminate it from historical exposure. In essence, this work presents a new paradigm for applying the T-cell repertoire to accomplish TCR-based diagnostics.

Profiling of T cell repertoire in peripheral blood of patients from type 2 diabetes with complication

PURPOSE

More than 90% of patients with diabetes worldwide are type 2 diabetes (T2D), which is caused by insulin resistance or impaired producing insulin by pancreatic β cells. T2D and its complications, mainly large cardiovascular (LCV) and kidney (Ne) complications, are the major cause of death in diabetes patients. Recently, the dysregulation of peripheral T cell immune homeostasis was found in most T2D patients. However, the characteristics of T-cell receptors (TCR) remain largely unexplored in T2D patients.

PATIENTS AND METHODS

Here we investigated the TCR repertoire using high-throughput sequencing in peripheral blood collected from T2D patient with (8 LCV and 7 Ne) or without complications.

RESULTS

Our analysis of TCR repertoires in peripheral blood samples showed that TCR profiles in T2D patients with complications tended to be single and specific compared to controls, according to the characteristics of TCR repertoire in V-J combination number, diversity, principal component analysis (PCA) and differential genes. And we identified some differentially expressed V-J gene segments and amino acid clonotypes, which had the potential to contribute to distinguishing T2D patient with or without complications. As the progression of the disease, we found that the profiling of TCR repertoire was also differential between T2D patients with LVD and Ne complications base on this pilot analysis.

CONCLUSION

This study demonstrated the protentional unique property of TCR repertoire in peripheral blood of T2D patient with and without complications, or T2D patients with LVD and Ne complications, which provided the possibility for future improvements in immune-related diagnosis and therapy for T2D complications.

Characterisation of T cell receptor repertoires in coeliac disease

AIMS

Characterise T-cell receptor gene (TR) repertoires of small intestinal T cells of patients with newly diagnosed (active) coeliac disease (ACD), refractory CD type I (RCD I) and patients with CD on a gluten-free diet (GFD).

METHODS

Next-generation sequencing of complementarity-determining region 3 (CDR3) of rearranged T cell receptor β (TRB) and γ (TRG) genes was performed using DNA extracted from intraepithelial cell (IEC) and lamina propria cell (LPC) fractions and a small subset of peripheral blood mononuclear cell (PBMC) samples obtained from CD and non-CD (control) patients. Several parameters were assessed, including relative abundance and enrichment.

RESULTS

TRB and TRG repertoires of CD IEC and LPC samples demonstrated lower clonality but higher frequency of rearranged TRs compared with controls. No CD-related differences were detected in the limited number of PBMC samples. Previously published LP gliadin-specific TRB sequences were more frequently detected in LPC samples from patients with CD compared with non-CD controls. TRG repertoires of IECs from both ACD and GFD patients demonstrated increased abundance of certain CDR3 amino acid (AA) motifs compared with controls, which were encoded by multiple nucleotide variants, including one motif that was enriched in duodenal IECs versus the PBMCs of CD patients.

CONCLUSIONS

Small intestinal TRB and TRG repertoires of patients with CD are more diverse than individuals without CD, likely due to mucosal recruitment and accumulation of T cells because of protracted inflammation. Enrichment of the unique TRG CDR3 AA sequence in the mucosa of patients with CD may suggest disease-associated changes in the TCRγδ IE lymphocyte (IEL) landscape.

Single-cell analysis of immune recognition in chronic myeloid leukemia patients following tyrosine kinase inhibitor discontinuation

Immunological control of residual leukemia cells is thought to occur in patients with chronic myeloid leukemia (CML) that maintain treatment-free remission (TFR) following tyrosine kinase inhibitor (TKI) discontinuation. To study this, we analyzed 55 single-cell RNA and T cell receptor (TCR) sequenced samples (scRNA+TCRαβ-seq) from patients with CML (n = 13, N = 25), other cancers (n = 28), and healthy (n = 7). The high number and active phenotype of natural killer (NK) cells in CML separated them from healthy and other cancers. Most NK cells in CML belonged to the active CD56dim cluster with high expression of GZMA/B, PRF1, CCL3/4, and IFNG, with interactions with leukemic cells via inhibitory LGALS9-TIM3 and PVR-TIGIT interactions. Accordingly, upregulation of LGALS9 was observed in CML target cells and TIM3 in NK cells when co-cultured together. Additionally, we created a classifier to identify TCRs targeting leukemia-associated antigen PR1 and quantified anti-PR1 T cells in 90 CML and 786 healthy TCRβ-sequenced samples. Anti-PR1 T cells were more prevalent in CML, enriched in bone marrow samples, and enriched in the mature, cytotoxic CD8 + TEMRA cluster, especially in a patient maintaining TFR. Our results highlight the role of NK cells and anti-PR1 T cells in anti-leukemic immune responses in CML.

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.

Tracking DNA-based antigen-specific T cell receptors during progression to type 1 diabetes

T cells targeting self-proteins are important mediators in autoimmune diseases. T cells express unique cell-surface receptors (TCRs) that recognize peptides presented by major histocompatibility molecules. TCRs have been identified from blood and pancreatic islets of individuals with type 1 diabetes (T1D). Here, we tracked ~1700 known antigen-specific TCR sequences, islet antigen or viral reactive, in bulk TCRβ sequencing from longitudinal blood DNA samples in at-risk cases who progressed to T1D, age/sex/human leukocyte antigen-matched controls, and a new-onset T1D cohort. Shared and frequent antigen-specific TCRβ sequences were identified in all three cohorts, and viral sequences were present across all ages. Islet sequences had different patterns of accumulation based upon antigen specificity in the at-risk cases. Furthermore, 73 islet-antigen TCRβ sequences were present in higher frequencies and numbers in T1D samples relative to controls. The total number of these disease-associated TCRβ sequences inversely correlated with age at clinical diagnosis, indicating the potential to use disease-relevant TCR sequences as biomarkers in autoimmune disorders.

HLA variants and TCR diversity against SARS-CoV-2 in the pre-COVID-19 era

HLA antigen presentation and T-cell mediated immunity are critical to control acute viral infection such as COVID-19 caused by SARS-CoV-2. Recent data suggest that both the depth of peptide presentation and the breadth of the T-cell repertoire are associated with disease outcome. It has also been shown that unexposed subjects can develop strong T-cell responses against SARS-CoV-2 due to heterologous immunity. In this study, we explored the anti-SARS-CoV-2 T-cell repertoire by analyzing previously published T-cell receptor (TCR) CDR3β immunosequencing data in a cohort of 116 healthy donors and in the context of immune reconstitution after allogeneic hematopoietic stem cell transplantation in 116 recipients collected during the pre-COVID-19 era. For this, 143,310 publicly available SARS-CoV-2 specific T-cell sequences were investigated among the 3.5 million clonotypes in the cohort. We also performed HLA class I peptide binding predictions using the reference proteome of the virus and high resolution genotyping data in these patients. We could demonstrate that individuals are fully equipped at the genetic level to recognize SARS-CoV-2. This is evidenced by the 5% median cumulative frequency of clonotypes having their sequence matched to a SARS-CoV-2 specific T-cell. In addition, any combination of HLA class I variants in this cohort is associated with a broad capacity of presenting hundreds of SARS-CoV-2 derived peptides. These results could be explained by heterologous immunity and random somatic TCR recombination. We speculate that these observations could explain the efficacy of the specific immune response against SARS-CoV-2 in individuals without risk factors of immunodeficiency and infected prior to vaccination.

Integrated antibody and cellular immunity monitoring are required for assessment of the long term protection that will be essential for effective next generation vaccine development

The COVID pandemic exposed the critical role T cells play in initial immunity, the establishment and maintenance of long term protection, and of durable responsiveness against novel viral variants. A growing body of evidence indicates that adding measures of cellular immunity will fill an important knowledge gap in vaccine clinical trials, likely leading to improvements in the effectiveness of the next generation vaccines against current and emerging variants. In depth cellular immune monitoring in Phase II trials, particularly for high risk populations such as the elderly or immune compromised, should result in better understanding of the dynamics and requirements for establishing effective long term protection. Such analyses can result in cellular immunity correlates that can then be deployed in Phase III studies using appropriate, scalable technologies. Measures of cellular immunity are less established than antibodies as correlates of clinical immunity, and some misconceptions persist about cellular immune monitoring usefulness, cost, complexity, feasibility, and scalability. We outline the currently available cellular immunity assays, review their readiness for use in clinical trials, their logistical requirements, and the type of information each assay generates. The objective is to provide a reliable source of information that could be leveraged to develop a rational approach for comprehensive immune monitoring during vaccine development.

Newborn and child-like molecular signatures in older adults stem from TCR shifts across human lifespan

CD8+ T cells provide robust antiviral immunity, but how epitope-specific T cells evolve across the human lifespan is unclear. Here we defined CD8+ T cell immunity directed at the prominent influenza epitope HLA-A*02:01-M158-66 (A2/M158) across four age groups at phenotypic, transcriptomic, clonal and functional levels. We identify a linear differentiation trajectory from newborns to children then adults, followed by divergence and a clonal reset in older adults. Gene profiles in older adults closely resemble those of newborns and children, despite being clonally distinct. Only child-derived and adult-derived A2/M158+CD8+ T cells had the potential to differentiate into highly cytotoxic epitope-specific CD8+ T cells, which was linked to highly functional public T cell receptor (TCR)αβ signatures. Suboptimal TCRαβ signatures in older adults led to less proliferation, polyfunctionality, avidity and recognition of peptide mutants, although displayed no signs of exhaustion. These data suggest that priming T cells at different stages of life might greatly affect CD8+ T cell responses toward viral infections.

Neural network models for sequence-based TCR and HLA association prediction

T cells rely on their T cell receptors (TCRs) to discern foreign antigens presented by human leukocyte antigen (HLA) proteins. The TCRs of an individual contain a record of this individual's past immune activities, such as immune response to infections or vaccines. Mining the TCR data may recover useful information or biomarkers for immune related diseases or conditions. Some TCRs are observed only in the individuals with certain HLA alleles, and thus characterizing TCRs requires a thorough understanding of TCR-HLA associations. The extensive diversity of HLA alleles and the rareness of some HLA alleles present a formidable challenge for this task. Existing methods either treat HLA as a categorical variable or represent an HLA by its alphanumeric name, and have limited ability to generalize to the HLAs that are not seen in the training process. To address this challenge, we propose a neural network-based method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We demonstrate that DePTH is capable of making reasonable predictions for TCR-HLA associations, even when neither the HLA nor the TCR have been included in the training dataset. Furthermore, we establish that DePTH can be used to quantify the functional similarities among HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatments.

Better safe than sorry: Naive T-cell dynamics in healthy ageing

It is well-known that the functioning of the immune system gradually deteriorates with age, and we are increasingly confronted with its consequences as the life expectancy of the human population increases. Changes in the T-cell pool are among the most prominent features of the changing immune system during healthy ageing, and changes in the naive T-cell pool in particular are generally held responsible for its gradual deterioration. These changes in the naive T-cell pool are thought to be due to involution of the thymus. It is commonly believed that the gradual loss of thymic output induces compensatory mechanisms to maintain the number of naive T cells at a relatively constant level, and induces a loss of diversity in the T-cell repertoire. Here we review the studies that support or challenge this widely-held view of immune ageing and discuss the implications for vaccination strategies.

Quantifiable TCR repertoire changes in pre-diagnostic blood specimens among high-grade ovarian cancer patients

High grade serous ovarian cancer (HGOC) is a major cause of death in women. Early detection of HGOC usually leads to a cure, yet it remains a clinical challenge with over 90% HGOCs diagnosed at advanced stages. This is mainly because conventional biomarkers are not sensitive to detect the microscopic yet metastatic early HGOC lesions. In this study, we sequenced the blood T cell receptor (TCR) repertoires of 466 ovarian cancer patients and controls, and systematically investigated the immune repertoire signatures in HGOCs. We observed quantifiable changes of selected TCRs in HGOCs that are reproducible in multiple independent cohorts. Importantly, these changes are stronger during stage I. Using pre-diagnostic patient blood samples from the Nurses' Health Study, we confirmed that HGOC signals can be detected in the blood TCR repertoire up to 4 years proceeding conventional diagnosis. Our findings may provide the basis of an immune-based HGOC early detection criterion.

An NKG2A biased immune response confers protection for infection, autoimmune disease, and cancer

Infection, autoimmunity, and cancer are the principal human health challenges of the 21st century and major contributors to human death and disease. Often regarded as distinct ends of the immunological spectrum, recent studies have hinted there may be more overlap between these diseases than appears. For example, pathogenic inflammation has been demonstrated as conserved between infection and autoimmune settings. T resident memory (TRM) cells have been highlighted as beneficial for infection and cancer. However, these findings are limited by patient number and disease scope; exact immunological factors shared across disease remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune and post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation, increased humoral immunity, and resemble TRM cells. Our results suggest that an NKG2A+ bias is a pan-disease immunological factor of protection and thus supports recent suggestions that there is immunological overlap between infection, autoimmunity, and cancer. Our findings underscore the promotion of an NKG2A+ biased response as a putative therapeutic strategy.

Altered T-cell receptor β repertoire in adults with SARS CoV-2 inactivated vaccine of BBIBP-CorV

OBJECTIVE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the prolonged and widespread epidemic of coronavirus disease 2019 (COVID-19). The inactivated BBIBP-CorV vaccine has shown safety, efficacy and immunogenicity against COVID-19 in in-vitro studies and clinical trials. However, the characteristics changes of the TCRβ repertoire in patients receiving BBIBP-CorV remain unclear.

METHODS

TCRβ repertoire difference were analyzed between 54 uninfected subjects who received a third dose of the enhanced BBIBP-CorV vaccine and the 16 healthy donors who did not receive the vaccine and 44 COVID-19 patients with different courses of disease (asymptomatic, symptomatic and convalescent). Furthermore, antibody response, anti-inflammatory and pro-inflammatory cytokines also were examined.

RESULTS

We found that the third dose inactivated coronavirus vaccine induced widespread changes including the increased TCRβ repertoire diversity, a much shorter CDR3 length and high usage of V-J genes segments. Meanwhile, the vaccine-responding clones were also predicted. The results of the antibody response showed that 90.7 % of the vaccinated individuals were positive for NAb seroconversion and 88.9 % for IgG antibody about 60 days after the third dose. The concentration of IL-2 increased significantly compared to baseline inoculation.

CONCLUSION

Altered TCRβ repertoire in adults with SARS CoV-2 inactivated vaccine of BBIBP-CorV clarified the specific immunity induced by inactivated vaccines. Our research provides insights into the adaptive immune response induced by the new inactivated SARS-CoV-2 vaccine and strengthens the development of immunotherapy.

Immunosequencing and Profiling of T Cells at the Maternal-Fetal Interface of Women with Preterm Labor and Chronic Chorioamnionitis

T cells are implicated in the pathophysiology of preterm labor and birth, the leading cause of neonatal morbidity and mortality worldwide. Specifically, maternal decidual T cells infiltrate the chorioamniotic membranes in chronic chorioamnionitis (CCA), a placental lesion considered to reflect maternal anti-fetal rejection, leading to preterm labor and birth. However, the phenotype and TCR repertoire of decidual T cells in women with preterm labor and CCA have not been investigated. In this study, we used phenotyping, TCR sequencing, and functional assays to elucidate the molecular characteristics and Ag specificity of T cells infiltrating the chorioamniotic membranes in women with CCA who underwent term or preterm labor. Phenotyping indicated distinct enrichment of human decidual effector memory T cell subsets in cases of preterm labor with CCA without altered regulatory T cell proportions. TCR sequencing revealed that the T cell repertoire of CCA is characterized by increased TCR richness and decreased clonal expansion in women with preterm labor. We identified 15 clones associated with CCA and compared these against established TCR databases, reporting that infiltrating T cells may possess specificity for maternal and fetal Ags, but not common viral Ags. Functional assays demonstrated that choriodecidual T cells can respond to maternal and fetal Ags. Collectively, our findings provide, to our knowledge, novel insight into the complex processes underlying chronic placental inflammation and further support a role for effector T cells in the mechanisms of disease for preterm labor and birth. Moreover, this work further strengthens the contribution of adaptive immunity to the syndromic nature of preterm labor and birth.

TAPIR: a T-cell receptor language model for predicting rare and novel targets

T-cell receptors (TCRs) are involved in most human diseases, but linking their sequences with their targets remains an unsolved grand challenge in the field. In this study, we present TAPIR (T-cell receptor and Peptide Interaction Recognizer), a T-cell receptor (TCR) language model that predicts TCR-target interactions, with a focus on novel and rare targets. TAPIR employs deep convolutional neural network (CNN) encoders to process TCR and target sequences across flexible representations (e.g., beta-chain only, unknown MHC allele, etc.) and learns patterns of interactivity via several training tasks. This flexibility allows TAPIR to train on more than 50k either paired (alpha and beta chain) or unpaired TCRs (just alpha or beta chain) from public and proprietary databases against 1933 unique targets. TAPIR demonstrates state-of-the-art performance when predicting TCR interactivity against common benchmark targets and is the first method to demonstrate strong performance when predicting TCR interactivity against novel targets, where no examples are provided in training. TAPIR is also capable of predicting TCR interaction against MHC alleles in the absence of target information. Leveraging these capabilities, we apply TAPIR to cancer patient TCR repertoires and identify and validate a novel and potent anti-cancer T-cell receptor against a shared cancer neoantigen target (PIK3CA H1047L). We further show how TAPIR, when extended with a generative neural network, is capable of directly designing T-cell receptor sequences that interact with a target of interest.

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.

Human immune phenotyping reveals accelerated aging in type 1 diabetes

The proportions and phenotypes of immune cell subsets in peripheral blood undergo continual and dramatic remodeling throughout the human life span, which complicates efforts to identify disease-associated immune signatures in type 1 diabetes (T1D). We conducted cross-sectional flow cytometric immune profiling on peripheral blood from 826 individuals (stage 3 T1D, their first-degree relatives, those with ≥2 islet autoantibodies, and autoantibody-negative unaffected controls). We constructed an immune age predictive model in unaffected participants and observed accelerated immune aging in T1D. We used generalized additive models for location, shape, and scale to obtain age-corrected data for flow cytometry and complete blood count readouts, which can be visualized in our interactive portal (ImmScape); 46 parameters were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with accelerated immunological aging in T1D included increased CXCR3+ and programmed cell death 1-positive (PD-1+) frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels on memory T cells. Phenotypes associated with T1D after age correction were predictive of T1D status. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions.

Immune Repertoire Profiling Reveals Its Clinical Application Potential and Triggers for Neuromyelitis Optica Spectrum Disorders

BACKGROUND AND OBJECTIVES

Neuromyelitis optica spectrum disorders (NMOSD) is widely recognized as a CNS demyelinating disease associated with AQP4-IgG (T cell-dependent antibody), and its trigger is still unclear. In addition, although the treatment of NMOSD currently can rely on traditional immunosuppressive and modulating agents, effective methods to predict the efficacy of these therapeutics are lacking.

METHODS

In this study, high-throughput T-cell receptor (TCR) sequencing was performed on peripheral blood from 151 pretreatment patients with AQP4-IgG⁺ NMOSD and 151 healthy individuals. We compared the TCR repertoire of those with NMOSD with that of healthy individuals and identified TCR clones that were significantly enriched in NMOSD. In addition, we treated 28 patients with AQP4-IgG⁺ NMOSD with immunosuppressants and followed up for 6 months to compare changes in NMOSD-specific TCRs (NMOSD-TCRs) before and after treatment. Moreover, we analyzed transcriptome and single-cell B-cell receptor (BCR) data from public databases and performed T-cell activation experiments using antigenic epitopes of cytomegalovirus (CMV) to further explore the triggers of AQP4-IgG⁺ NMOSD.

RESULTS

Compared with healthy controls, patients with AQP4-IgG⁺ NMOSD had significantly reduced diversity and shorter CDR3 length of TCRβ repertoire. Furthermore, we identified 597 NMOSD-TCRs with a high sequence similarity that have the potential to be used in the diagnosis and prognosis of NMOSD. The characterization of NMOSD-TCRs and pathology-associated clonotype annotation indicated that the occurrence of AQP4-IgG⁺ NMOSD may be associated with CMV infection, which was further corroborated by transcriptome and single-cell BCR analysis results from public databases and T-cell activation experiments.

DISCUSSION

Our findings suggest that the occurrence of AQP4-IgG⁺ NMOSD may be associated with CMV infection. In conclusion, our study provides new clues to uncover the causative factors of AQP4-IgG⁺ NMOSD and provides a theoretical foundation for treating and monitoring the disease.

Polyclonal lymphoid expansion drives paraneoplastic autoimmunity in neuroblastoma

Neuroblastoma is a lethal childhood solid tumor of developing peripheral nerves. Two percent of children with neuroblastoma develop opsoclonus myoclonus ataxia syndrome (OMAS), a paraneoplastic disease characterized by cerebellar and brainstem-directed autoimmunity but typically with outstanding cancer-related outcomes. We compared tumor transcriptomes and tumor-infiltrating T and B cell repertoires from 38 OMAS subjects with neuroblastoma to 26 non-OMAS-associated neuroblastomas. We found greater B and T cell infiltration in OMAS-associated tumors compared to controls and showed that both were polyclonal expansions. Tertiary lymphoid structures (TLSs) were enriched in OMAS-associated tumors. We identified significant enrichment of the major histocompatibility complex (MHC) class II allele HLA-DOB∗01:01 in OMAS patients. OMAS severity scores were associated with the expression of several candidate autoimmune genes. We propose a model in which polyclonal auto-reactive B lymphocytes act as antigen-presenting cells and drive TLS formation, thereby supporting both sustained polyclonal T cell-mediated anti-tumor immunity and paraneoplastic OMAS neuropathology.

Can we predict T cell specificity with digital biology and machine learning?

Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR-ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR-antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.

BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing

MOTIVATION

The advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides.

RESULTS

We prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors' T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training.

AVAILABILITY AND IMPLEMENTATION

The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.

T-cell tolerant fraction as a predictor of immune-related adverse events

BACKGROUND

Immune checkpoint inhibitor (ICI) therapies may cause unpredictable and potentially severe autoimmune toxicities termed immune-related adverse events (irAEs). Because T cells mediate ICI effects, T cell profiling may provide insight into the risk of irAEs. Here we evaluate a novel metric-the T-cell tolerant fraction-as a predictor of future irAEs.

METHODS

We examined T-cell receptor beta (TRB) locus sequencing from baseline pretreatment samples from an institutional registry and previously published studies. For each patient, we used TRB sequences to calculate the T-cell tolerant fraction, which was then assessed as a predictor of future irAEs (classified as Common Terminology Criteria for Adverse Event grade 0-1 vs grade ≥2). We then compared the tolerant fraction to TRB clonality and diversity. Finally, the tolerant fraction was assessed on (1) T cells enriched against napsin A, a potential autoantigen of irAEs; (2) thymic versus peripheral blood T cells; and (3) TRBs specific for various infections and autoimmune diseases.

RESULTS

A total of 77 patients with cancer (22 from an institutional registry and 55 from published studies) receiving ICI therapy (43 CTLA4, 19 PD1/PDL1, 15 combination CTLA4+PD1/PDL1) were included in the study. The tolerant fraction was significantly lower in cases with clinically significant irAEs (p<0.001) and had an area under the receiver operating curve (AUC) of 0.79. The tolerant fraction was lower for each ICI treatment category, reaching statistical significance for CTLA4 (p<0.001) and demonstrating non-significant trends for PD1/PDL1 (p=0.21) and combination ICI (p=0.18). The tolerant fraction for T cells enriched against napsin A was lower than other samples. The tolerant fraction was also lower in thymic versus peripheral blood samples, and lower in some (multiple sclerosis) but not other (type 1 diabetes) autoimmune diseases. In our study cohort, TRB clonality had an AUC of 0.62, and TRB diversity had an AUC of 0.60 for predicting irAEs.

CONCLUSIONS

Among patients receiving ICI, the baseline T-cell tolerant fraction may serve as a predictor of clinically significant irAEs.

Tissue-specific features of the T cell repertoire after allogeneic hematopoietic cell transplantation in human and mouse

T cells are the central drivers of many inflammatory diseases, but the repertoire of tissue-resident T cells at sites of pathology in human organs remains poorly understood. We examined the site-specificity of T cell receptor (TCR) repertoires across tissues (5 to 18 tissues per patient) in prospectively collected autopsies of patients with and without graft-versus-host disease (GVHD), a potentially lethal tissue-targeting complication of allogeneic hematopoietic cell transplantation, and in mouse models of GVHD. Anatomic similarity between tissues was a key determinant of TCR repertoire composition within patients, independent of disease or transplant status. The T cells recovered from peripheral blood and spleens in patients and mice captured a limited portion of the TCR repertoire detected in tissues. Whereas few T cell clones were shared across patients, motif-based clustering revealed shared repertoire signatures across patients in a tissue-specific fashion. T cells at disease sites had a tissue-resident phenotype and were of donor origin based on single-cell chimerism analysis. These data demonstrate the complex composition of T cell populations that persist in human tissues at the end stage of an inflammatory disorder after lymphocyte-directed therapy. These findings also underscore the importance of studying T cell in tissues rather than blood for tissue-based pathologies and suggest the tissue-specific nature of both the endogenous and posttransplant T cell landscape.

Creating an Innovative Artificial Intelligence-Based Technology (TCRact) for Designing and Optimizing T Cell Receptors for Use in Cancer Immunotherapies: Protocol for an Observational Trial

BACKGROUND

Cancer continues to be the leading cause of mortality in high-income countries, necessitating the development of more precise and effective treatment modalities. Immunotherapy, specifically adoptive cell transfer of T cell receptor (TCR)-engineered T cells (TCR-T therapy), has shown promise in engaging the immune system for cancer treatment. One of the biggest challenges in the development of TCR-T therapies is the proper prediction of the pairing between TCRs and peptide-human leukocyte antigen (pHLAs). Modern computational immunology, using artificial intelligence (AI)-based platforms, provides the means to optimize the speed and accuracy of TCR screening and discovery.

OBJECTIVE

This study proposes an observational clinical trial protocol to collect patient samples and generate a database of pHLA:TCR sequences to aid the development of an AI-based platform for efficient selection of specific TCRs.

METHODS

The multicenter observational study, involving 8 participating hospitals, aims to enroll patients diagnosed with stage II, III, or IV colorectal cancer adenocarcinoma.

RESULTS

Patient recruitment has recently been completed, with 100 participants enrolled. Primary tumor tissue and peripheral blood samples have been obtained, and peripheral blood mononuclear cells have been isolated and cryopreserved. Nucleic acid extraction (DNA and RNA) has been performed in 86 cases. Additionally, 57 samples underwent whole exome sequencing to determine the presence of somatic mutations and RNA sequencing for gene expression profiling.

CONCLUSIONS

The results of this study may have a significant impact on the treatment of patients with colorectal cancer. The comprehensive database of pHLA:TCR sequences generated through this observational clinical trial will facilitate the development of the AI-based platform for TCR selection. The results obtained thus far demonstrate successful patient recruitment and sample collection, laying the foundation for further analysis and the development of an innovative tool to expedite and enhance TCR selection for precision cancer treatments.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04994093; https://clinicaltrials.gov/ct2/show/NCT04994093.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45872.

T cell receptor β repertoires in patients with COVID-19 reveal disease severity signatures

Background

The immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are crucial in maintaining a delicate balance between protective effects and harmful pathological reactions that drive the progression of coronavirus disease 2019 (COVID-19). T cells play a significant role in adaptive antiviral immune responses, making it valuable to investigate the heterogeneity and diversity of SARS-CoV-2-specific T cell responses in COVID-19 patients with varying disease severity.

Methods

In this study, we employed high-throughput T cell receptor (TCR) β repertoire sequencing to analyze TCR profiles in the peripheral blood of 192 patients with COVID-19, including those with moderate, severe, or critical symptoms, and compared them with 81 healthy controls. We specifically focused on SARS-CoV-2-associated TCR clonotypes.

Results

We observed a decrease in the diversity of TCR clonotypes in COVID-19 patients compared to healthy controls. However, the overall abundance of dominant clones increased with disease severity. Additionally, we identified significant differences in the genomic rearrangement of variable (V), joining (J), and VJ pairings between the patient groups. Furthermore, the SARS-CoV-2-associated TCRs we identified enabled accurate differentiation between COVID-19 patients and healthy controls (AUC > 0.98) and distinguished those with moderate symptoms from those with more severe forms of the disease (AUC > 0.8). These findings suggest that TCR repertoires can serve as informative biomarkers for monitoring COVID-19 progression.

Conclusions

Our study provides valuable insights into TCR repertoire signatures that can be utilized to assess host immunity to COVID-19. These findings have important implications for the use of TCR β repertoires in monitoring disease development and indicating disease severity.

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.

Immunologic Characterization and T cell Receptor Repertoires of Expanded Tumor-infiltrating Lymphocytes in Patients with Renal Cell Carcinoma

The successful use of expanded tumor-infiltrating lymphocytes (TIL) in adoptive TIL therapies has been reported, but the effects of the TIL expansion, immunophenotype, function, and T cell receptor (TCR) repertoire of the infused products relative to the tumor microenvironment (TME) are not well understood. In this study, we analyzed the tumor samples (n = 58) from treatment-naïve patients with renal cell carcinoma (RCC), "pre-rapidly expanded" TILs (pre-REP TIL, n = 15) and "rapidly expanded" TILs (REP TIL, n = 25) according to a clinical-grade TIL production protocol, with single-cell RNA (scRNA)+TCRαβ-seq (TCRαβ sequencing), TCRβ-sequencing (TCRβ-seq), and flow cytometry. REP TILs encompassed a greater abundance of CD4⁺ than CD8⁺ T cells, with increased LAG-3 and low PD-1 expressions in both CD4⁺ and CD8⁺ T cell compartments compared with the pre-REP TIL and tumor T cells. The REP protocol preferentially expanded small clones of the CD4⁺ phenotype (CD4, IL7R, KLRB1) in the TME, indicating that the largest exhausted T cell clones in the tumor do not expand during the expansion protocol. In addition, by generating a catalog of RCC-associated TCR motifs from >1,000 scRNA+TCRαβ-seq and TCRβ-seq RCC, healthy and other cancer sample cohorts, we quantified the RCC-associated TCRs from the expansion protocol. Unlike the low-remaining amount of anti-viral TCRs throughout the expansion, the quantity of the RCC-associated TCRs was high in the tumors and pre-REP TILs but decreased in the REP TILs. Our results provide an in-depth understanding of the origin, phenotype, and TCR specificity of RCC TIL products, paving the way for a more rationalized production of TILs.

Significance

TILs are a heterogenous group of immune cells that recognize and attack the tumor, thus are utilized in various clinical trials. In our study, we explored the TILs in patients with kidney cancer by expanding the TILs using a clinical-grade protocol, as well as observed their characteristics and ability to recognize the tumor using in-depth experimental and computational tools.

Familial Clonal Hematopoiesis in a Long Telomere Syndrome

BACKGROUND

Telomere shortening is a well-characterized cellular aging mechanism, and short telomere syndromes cause age-related disease. However, whether long telomere length is advantageous is poorly understood.

METHODS

We examined the clinical and molecular features of aging and cancer in persons carrying heterozygous loss-of-function mutations in the telomere-related gene POT1 and noncarrier relatives.

RESULTS

A total of 17 POT1 mutation carriers and 21 noncarrier relatives were initially included in the study, and a validation cohort of 6 additional mutation carriers was subsequently recruited. A majority of the POT1 mutation carriers with telomere length evaluated (9 of 13) had long telomeres (>99th percentile). POT1 mutation carriers had a range of benign and malignant neoplasms involving epithelial, mesenchymal, and neuronal tissues in addition to B- and T-cell lymphoma and myeloid cancers. Five of 18 POT1 mutation carriers (28%) had T-cell clonality, and 8 of 12 (67%) had clonal hematopoiesis of indeterminate potential. A predisposition to clonal hematopoiesis had an autosomal dominant pattern of inheritance, as well as penetrance that increased with age; somatic DNMT3A and JAK2 hotspot mutations were common. These and other somatic driver mutations probably arose in the first decades of life, and their lineages secondarily accumulated a higher mutation burden characterized by a clocklike signature. Successive generations showed genetic anticipation (i.e., an increasingly early onset of disease). In contrast to noncarrier relatives, who had the typical telomere shortening with age, POT1 mutation carriers maintained telomere length over the course of 2 years.

CONCLUSIONS

POT1 mutations associated with long telomere length conferred a predisposition to a familial clonal hematopoiesis syndrome that was associated with a range of benign and malignant solid neoplasms. The risk of these phenotypes was mediated by extended cellular longevity and by the capacity to maintain telomeres over time. (Funded by the National Institutes of Health and others.).

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.