Human thymic T cell repertoire is imprinted with strong convergence to shared sequences

Human TCR repertoire in cancer

BACKGROUND

T cells, the "superstar" of the immune system, play a crucial role in antitumor immunity. T-cell receptors (TCR) are crucial molecules that enable T cells to identify antigens and start immunological responses. The body has evolved a unique method for rearrangement, resulting in a vast diversity of TCR repertoires. A healthy TCR repertoire is essential for the particular identification of antigens by T cells.

METHODS

In this article, we systematically summarized the TCR creation mechanisms and analysis methodologies, particularly focusing on the application of next-generation sequencing (NGS) technology. We explore the TCR repertoire in health and cancer, and discuss the implications of TCR repertoire analysis in understanding carcinogenesis, cancer progression, and treatment.

RESULTS

The TCR repertoire analysis has enormous potential for monitoring the emergence and progression of malignancies, as well as assessing therapy response and prognosis. The application of NGS has dramatically accelerated our comprehension of TCR diversity and its role in cancer immunity.

CONCLUSIONS

To substantiate the significance of TCR repertoires as biomarkers, more thorough and exhaustive research should be conducted. The TCR repertoire analysis, enabled by advanced sequencing technologies, is poised to become a crucial tool in the future of cancer diagnosis, monitoring, and therapy evaluation.

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.

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.

Analysis of thymic generation of shared T-cell receptor α repertoire associated with recognition of tumor antigens shows no preference for neoantigens over wild-type antigens

BACKGROUND

The number of mutations in cancer cells is an important predictor of a positive response to cancer immunotherapy. It has been suggested that the neoantigens produced by these mutations are more immunogenic than nonmutated tumor antigens, which are likely to be protected by immunological tolerance. However, the mechanisms of tolerance as regards tumor antigens are incompletely understood.

METHODS

Here, we have analyzed the impact of thymic negative selection on shared T-cell receptor (TCR) repertoire associated with the recognition of either mutated or nonmutated tumor antigens by comparing previously known TCR-antigen-pairs to TCR repertoires of 21 immunologically healthy individuals.

RESULTS

Our results show that TCRα chains associated with either type of tumor antigens are readily generated in the thymus, at a frequency similar to TCRα chains associated with nonself. In the peripheral repertoire, the relative clone size of nonself-associated chains is higher than that of the tumor antigens, but importantly, there is no difference between TCRα chains associated with mutated or nonmutated tumor antigens.

CONCLUSION

This suggests that the tolerance mechanisms protecting nonmutated tumor antigens are non-deletional and therefore potentially reversible. As unmutated antigens are, unlike mutations, shared by a large number of patients, they may offer advantages in designing immunological approaches to cancer treatment.

Characterization of T-cell receptors and immunoglobulin heavy chains loci and identification of T/B cell clusters in teleost

Bacterial disease is one of the important factors leading to economic losses in the turbot (Scophthalmus maximus) cultivation industry. T lymphocytes are major components of cellular immunity, whereas B lymphocytes produce immunoglobulins (Ig) that are key elements of humoral immune responses against infection. However, the genomic organization of genes encoding T-cell receptors (TCR) and immunoglobulin heavy chains (IgHs) in turbot remains largely unknown. In this study, abundant full-length transcripts of TCRs and IgHs were sequenced by Isoform-sequencing (Iso-seq), and we investigated and annotated the V, D, J and C gene loci of TCRα, TCRβ, IgT, IgM and IgD in turbot. Furthermore, through single-cell RNA sequencing (scRNA-seq) of blood leukocytes, we confirmed that these identified TCRs and IgHs were highly expressed in T/B cell clusters, respectively. Meanwhile, we also identified the IgM⁺IgD⁺ B and IgT⁺ B cells with differential gene expression profiles and potential functions. Taken together, our results provide a comprehensive understanding of TCRs and IgHs loci in turbot, which will contribute to evolutionary and functional characterization of T and B lymphocytes in teleost.

Peripheral differentiation patterns of human T cells

Long-term T-cell memory is dependent on the maintenance of memory T cells in the lymphoid tissues, and at the surface interfaces that provide entry routes for pathogens. However, much of the current information on human T-cell memory is based on analyzing circulating T cells. Here, we have studied the distribution and age-related changes of memory T-cell subsets in samples from blood, mesenteric LNs, spleen, and ileum, obtained from donors ranging in age from 5 days to 67 years of age. Our data show that the main reservoir of polyclonal naive cells is found in the LNs, and the resting memory subsets capable of self-renewal are also prominent there. In contrast, nondividing but functionally active memory subsets dominate the spleen, and especially the ileum. In general, the replacement of naive cells with memory subsets continues throughout our period of observation, with no apparent plateau. In conclusion, the analysis of lymphoid and nonlymphoid tissues reveals a dynamic pattern of changes distinct to each tissue, and with substantial differences between CD4⁺ and CD8⁺ compartments.

Clonotype pattern in T-cell lymphomas map the cell of origin to immature lymphoid precursors

Mature T-cell lymphomas (TCLs) are rare, clinically heterogeneous hematologic cancers of high medical need. TCLs have inferior prognosis which is attributed to poor understanding of their pathogenesis. Based on phenotypic similarities between normal and neoplastic lymphocytes it has been assumed that TCLs develop in the periphery, directly from various subtypes of normal T-cells. To address the debated question of the cell of origin in TCLs we analyzed to identify the highly variable complementarity determining regions (CDR3) regions of T-cell receptor (TCR) to trace the clonal history of the T-cells. We have collected previously published whole genome -exome, and -transcriptome sequencing data from 574 TCL patients. TCR clonotypes were identified by de novo assembly of CDR3 regions of TCR γ, β and α. We have found that the vast majority of TCLs are clonotypically oligoclonal, although the pattern oligoclonality varied. Anaplastic large cell lymphoma was most diverse comprising multiple clonotypes of TCRγ, β and α whereas adult T-cell lymphoma/leukemia and peripheral T-cell lymphomas often showed monoclonality for TCRγ and β but had diverse TCRα clonotypes. These patterns of rearrangements indicated that TCLs are initiated at the level of the lymphoid precursor. In keeping with this hypothesis, TCR rearrangements in TCLs resembled the pattern seen in the human thymus showing biased usage of V and J segments of high combinatorial probability resulting in recurrent, "public" CDR3 sequences shared across unrelated patients and different clinical TCL entities. Clonotypically diverse initiating cells may seed target tissues being responsible for disease relapses after therapy.

High Throughput Human T Cell Receptor Sequencing: A New Window Into Repertoire Establishment and Alloreactivity

Recent advances in high throughput sequencing (HTS) of T cell receptors (TCRs) and in transcriptomic analysis, particularly at the single cell level, have opened the door to a new level of understanding of human immunology and immune-related diseases. In this article, we discuss the use of HTS of TCRs to discern the factors controlling human T cell repertoire development and how this approach can be used in combination with human immune system (HIS) mouse models to understand human repertoire selection in an unprecedented manner. An exceptionally high proportion of human T cells has alloreactive potential, which can best be understood as a consequence of the processes governing thymic selection. High throughput TCR sequencing has allowed assessment of the development, magnitude and nature of the human alloresponse at a new level and has provided a tool for tracking the fate of pre-transplant-defined donor- and host-reactive TCRs following transplantation. New insights into human allograft rejection and tolerance obtained with this method in combination with single cell transcriptional analyses are reviewed here.

Reconstituting T cell receptor selection in-silico

Each T cell receptor (TCR) gene is created without regard for which substances (antigens) the receptor can recognize. T cell selection culls developing T cells when their TCRs (i) fail to recognize major histocompatibility complexes (MHCs) that act as antigen presenting platforms or (ii) recognize with high affinity self-antigens derived from healthy cells and tissue. While T cell selection has been thoroughly studied, little is known about which TCRs are retained or removed by this process. Therefore, we develop an approach using TCR gene sequencing and machine learning to identify patterns in TCR protein sequences influencing the outcome of T cell receptor selection. We verify the trained models classify TCRs from developing T cells as being before selection and TCRs from mature T cells as being after selection. Our approach may provide future avenues for studying the relationship between T cell selection and conditions like autoimmune diseases.

Generation of self-reactive, shared T-cell receptor α chains in the human thymus

The T-cell receptor (TCR) repertoire is generated in a semistochastic process of gene recombination and pairing of TCRα to TCRβ chains with the estimated total TCR diversity of >10⁸. Despite this high diversity, similar or identical TCR chains are found to recur in immune responses. Here, we analyzed the thymic generation of TCR sequences previously associated with recognition of self- and nonself-antigens, represented by sequences associated with autoimmune diabetes and HIV, respectively. Unexpectedly, in the CD4⁺ compartment TCRα chains associated with the recognition of self-antigens were generated in significantly higher numbers than TCRα chains associated with the recognition of nonself-antigens. The analysis of the circulating repertoire further showed that these chains are not lost in negative selection nor predominantly converted to the regulatory T-cell lineage. The high abundance of self-reactive TCRα chains in multiple individuals suggests that the human thymus has a predilection to generate self-reactive TCRα chains independently of the HLA-type and that the individual risk of autoimmunity may be modulated by the TCRβ repertoire associated with these chains.

Characterization of human T cell receptor repertoire data in eight thymus samples and four related blood samples

T cell receptor (TCR) is a heterodimer consisting of TCRα and TCRβ chains that are generated by somatic recombination of multiple gene segments. Nascent TCR repertoire undergoes thymic selections where non-functional and potentially autoreactive receptors are removed. During the last years, the development of high-throughput sequencing technology has allowed a large scale assessment of TCR repertoire and multiple analysis tools are now also available.

In our recent manuscript, Human thymic T cell repertoire is imprinted with strong convergence to shared sequences[1], we show highly overlapping thymic TCR repertoires in unrelated individuals. In the current Data in Brief article, we provide a more detailed characterization of the basic features of these thymic and related peripheral blood TCR repertoires. The thymus samples were collected from eight infants undergoing corrective cardiac surgery, two of whom were monozygous twins [2]. In parallel with the surgery, a small aliquot of peripheral blood was drawn from four of the donors. Genomic DNA was extracted from mechanically released thymocytes and circulating leukocytes. The sequencing of TCRα and TCRβ repertoires was performed at ImmunoSEQ platform (Adaptive Biotechnologies). The obtained repertoire data were analysed applying relevant features from immunoSEQ® 3.0 Analyzer (Adaptive Biotechnologies) and a freely available VDJTools software package for programming language R [3].

The current data analysis displays the basic features of the sequenced repertoires including observed TCR diversity, various descriptive TCR diversity measures, and V and J gene usage. In addition, multiple methods to calculate repertoire overlap between two individuals are applied. The raw sequence data provide a large database of reference TCRs in healthy individuals at an early developmental stage. The data can be exploited to improve existing computational models on TCR repertoire behaviour as well as in the generation of new models.