Specificity, Privacy, and Degeneracy in the CD4 T Cell Receptor Repertoire Following Immunization

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.

Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm

Changes in the T cell receptor (TCR) repertoires have become important markers for monitoring disease or therapy progression. With the rise of immunotherapy usage in cancer, infectious and autoimmune disease, accurate assessment and comparison of the "state" of the TCR repertoire has become paramount. One important driver of change within the repertoire is T cell proliferation following immunisation. A way of monitoring this is by investigating large clones of individual T cells believed to bind epitopes connected to the disease. However, as a single target can be bound by many different TCRs, monitoring individual clones cannot fully account for T cell cross-reactivity. Moreover, T cells responding to the same target often exhibit higher sequence similarity, which highlights the importance of accounting for TCR similarity within the repertoire. This complexity of binding relationships between a TCR and its target convolutes comparison of immune responses between individuals or comparisons of TCR repertoires at different timepoints. Here we propose TCRDivER algorithm (T cell Receptor Diversity Estimates for Repertoires), a global method of T cell repertoire comparison using diversity profiles sensitive to both clone size and sequence similarity. This approach allowed for distinction between spleen TCR repertoires of immunised and non-immunised mice, showing the need for including both facets of repertoire changes simultaneously. The analysis revealed biologically interpretable relationships between sequence similarity and clonality. These aid in understanding differences and separation of repertoires stemming from different biological context. With the rise of availability of sequencing data we expect our tool to find broad usage in clinical and research applications.

Anti-tumor memory CD4 and CD8 T-cells quantified by bulk T-cell receptor (TCR) clonal analysis

Simple, reliable methods to detect anti-tumor memory T-cells are necessary to develop a clinical tumor vaccination program. A mouse model of curative viral onco-immunotherapy found that peritoneal tumor challenge following cure identified an oligoclonal anti-tumor memory CD4 and CD8 T-cell response. Clonotypes differed among the challenged animals but were congruent in blood, spleen and peritoneal cells (PC) of the same animal. Adoptive transfer demonstrated that the high-frequency responding T-cells were tumor specific. Tetramer analysis confirmed that clonotype frequency determined by T-cell receptor (TCR)- chain (TRB) analysis closely approximated cell clone frequency. The mean frequency of resting anti-tumor memory CD4 T-cells in unchallenged spleen was 0.028% and of memory CD8 T-cells was 0.11% which was not high enough to distinguish them from background. Stimulation produced a mean ~10-fold increase in splenic and 100-fold increase in peritoneal anti-tumor memory T-cell clonotypes. This methodology can be developed to use blood and tissue sampling to rapidly quantify the effectiveness of a tumor vaccine or any vaccine generating therapeutic T-cells.

Quantifying changes in the T cell receptor repertoire during thymic development

One of the feats of adaptive immunity is its ability to recognize foreign pathogens while sparing the self. During maturation in the thymus, T cells are selected through the binding properties of their antigen-specific T-cell receptor (TCR), through the elimination of both weakly (positive selection) and strongly (negative selection) self-reactive receptors. However, the impact of thymic selection on the TCR repertoire is poorly understood. Here, we use transgenic Nur77-mice expressing a T-cell activation reporter to study the repertoires of thymic T cells at various stages of their development, including cells that do not pass selection. We combine high-throughput repertoire sequencing with statistical inference techniques to characterize the selection of the TCR in these distinct subsets. We find small but significant differences in the TCR repertoire parameters between the maturation stages, which recapitulate known differentiation pathways leading to the CD4⁺ and CD8⁺ subtypes. These differences can be simulated by simple models of selection acting linearly on the sequence features. We find no evidence of specific sequences or sequence motifs or features that are suppressed by negative selection. These results favour a collective or statistical model for T-cell self non-self discrimination, where negative selection biases the repertoire away from self recognition, rather than ensuring lack of self-reactivity at the single-cell level.

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.

Comparative Study of Repertoire Classification Methods Reveals Data Efficiency of k -mer Feature Extraction

The repertoire of T cell receptors encodes various types of immunological information. Machine learning is indispensable for decoding such information from repertoire datasets measured by next-generation sequencing (NGS). In particular, the classification of repertoires is the most basic task, which is relevant for a variety of scientific and clinical problems. Supported by the recent appearance of large datasets, efficient but data-expensive methods have been proposed. However, it is unclear whether they can work efficiently when the available sample size is severely restricted as in practical situations. In this study, we demonstrate that their performances can be impaired substantially below critical sample sizes. To complement this drawback, we propose MotifBoost, which exploits the information of short k-mer motifs of TCRs. MotifBoost can perform the classification as efficiently as a deep learning method on large datasets while providing more stable and reliable results on small datasets. We tested MotifBoost on the four small datasets which consist of various conditions such as Cytomegalovirus (CMV), HIV, α-chain, β-chain and it consistently preserved the stability. We also clarify that the robustness of MotifBoost can be attributed to the efficiency of k-mer motifs as representation features of repertoires. Finally, by comparing the predictions of these methods, we show that the whole sequence identity and sequence motifs encode partially different information and that a combination of such complementary information is necessary for further development of repertoire analysis.

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.

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.

Genomic and TCR profiling data reveal the distinct molecular traits in epithelial ovarian cancer histotypes

Epithelial ovarian cancer (EOC) is classified into five major histotypes: high-grade serous (HGSOC), low-grade serous (LGSOC), clear cell (CCOC), endometrioid (ENOC), and mucinous (MOC). However, the landscape of molecular and immunological alterations in these histotypes, especially LGSOC, CCOC, ENOC, and MOC, is largely uncharacterized. We collected 101 treatment-naive EOC patients. The resected tumor tissues and paired preoperative peripheral blood samples were collected and subjected to target sequencing of 1021 cancer-associated genes and T cell repertoire sequencing. Distinct characteristics of mutations were identified among the five histotypes. Furthermore, tumor mutation burden (TMB) was found to be higher in CCOC and ENOC, but lower in LGSOC and HGSOC. Alterations associated with DNA damage repair (DDR) pathways and homologous recombination deficiencies (HRD) were prevalent in five histotypes. CCOC demonstrated increased level of T cell clonality compared with HSGOC. Interestingly, the proportion of the 100 most common T cell clones was associated with TMB and tumor neoantigen burden in CCOC, highlighting more sensitive anti-tumor responses in this histotype, which was also evidenced by the enhanced convergent recombination of T cell clones. These findings shed light on the molecular traits of genomic alteration and T cell repertoire in the five major EOC histotypes and may help optimize clinical management of EOC with different histotypes.

DeepLION: Deep Multi-Instance Learning Improves the Prediction of Cancer-Associated T Cell Receptors for Accurate Cancer Detection

Recent studies highlight the potential of T cell receptor (TCR) repertoires in accurately detecting cancers via noninvasive sampling. Unfortunately, due to the complicated associations among cancer antigens and the possible induced T cell responses, currently, the practical strategy for identifying cancer-associated TCRs is the computational prediction based on TCR repertoire data. Several state-of-the-art methods were proposed in recent year or two; however, the prediction algorithms were still weakened by two major issues. To facilitate the computational processes, the algorithms prefer to decompose the original TCR sequences into length-fixed amino acid fragments, while the first dilemma comes as the lengths of cancer-associated motifs are suggested to be various. Moreover, the correlations among TCRs in the same repertoire should be further considered, which are often ignored by the existing methods. We here developed a deep multi-instance learning method, named DeepLION, to improve the prediction of cancer-associated TCRs by considering these issues. First, DeepLION introduced a deep learning framework with alternative convolution filters and 1-max pooling operations to handle the amino acid fragments with different lengths. Then, the multi-instance learning framework modeled the TCR correlations and assigned adjusted weights for each TCR sequence during the predicting process. To validate the performance of DeepLION, we conducted a series of experiments on several cohorts of patients from nine cancer types. Compared to the existing methods, DeepLION achieved, on most of the cohorts, higher prediction accuracies, sensitivities, specificities, and areas under the curve (AUCs), where the AUC reached notably 0.97 and 0.90 for thyroid and lung cancer cohorts, respectively. Thus, DeepLION may further support the detection of cancers from TCR repertoire data. DeepLION is publicly available on GitHub, at https://github.com/Bioinformatics7181/DeepLION, for academic usage only.

Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers.

Increased Immune-Regulatory Receptor Expression on Effector T Cells as Early Indicators of Relapse Following Autologous Stem Cell Transplantation for Multiple Myeloma

The benefit of autologous stem cell transplantation (ASCT) in newly diagnosed myeloma patients, apart from supporting high dose chemotherapy, may include effects on T cell function in the bone marrow (BM). We report our exploratory findings on marrow infiltrating T cells early post-ASCT (day+100), examining phenotype and T cell receptor (TCR) repertoire, seeking correlations with timing of relapse. Compared to healthy donors (HD), we observed an increase in regulatory T cells (CD4+FoxP3+, Tregs) with reduction in CD4 T cells, leading to lower CD4:8 ratios. Compared to paired pre-treatment marrow, both CD4 and CD8 compartments showed a reduction in naïve, and increase in effector memory subsets, suggestive of a more differentiated phenotype. This was supported by increased levels of several immune-regulatory and activation proteins (ICOS, PD-1, LAG-3, CTLA-4 and GzmB) when compared with HD. Unsupervised analysis identified a patient subgroup with shorter PFS (p=0.031) whose BM contained increased Tregs, and higher immune-regulatory markers (ICOS, PD-1, LAG-3) on effector T cells. Using single feature analysis, higher frequencies of marrow PD-1+ on CD4+FoxP3- cells and Ki67+ on CD8 cells were independently associated with early relapse. Finally, studying paired pre-treatment and post-ASCT BM (n=5), we note reduced abundance of TCR sequences at day+100, with a greater proportion of expanded sequences indicating a more focused persistent TCR repertoire. Our findings indicate that, following induction chemotherapy and ASCT, marrow T cells demonstrate increased activation and differentiation, with TCR repertoire focusing. Pending confirmation in larger series, higher levels of immune-regulatory proteins on T cell effectors at day+100 may indicate early relapse.

The clonal structure and dynamics of the human T cell response to an organic chemical hapten

Diphenylcyclopropenone (DPC) is an organic chemical hapten which induces allergic contact dermatitis and is used in the treatment of warts, melanoma, and alopecia areata. This therapeutic setting therefore provided an opportunity to study T cell receptor (TCR) repertoire changes in response to hapten sensitization in humans. Repeated exposure to DPC induced highly dynamic transient expansions of a polyclonal diverse T cell population. The number of TCRs expanded early after sensitization varies between individuals and predicts the magnitude of the allergic reaction. The expanded TCRs show preferential TCR V and J gene usage and consist of clusters of TCRs with similar sequences, two characteristic features of antigen-driven responses. The expanded TCRs share subtle sequence motifs that can be captured using a dynamic Bayesian network. These observations suggest the response to DPC is mediated by a polyclonal population of T cells recognizing a small number of dominant antigens.

A Conserved TCRβ Signature Dominates a Highly Polyclonal T-Cell Expansion During the Acute Phase of a Murine Malaria Infection

CD4+ αβ T-cells are key mediators of the immune response to a first Plasmodium infection, undergoing extensive activation and splenic expansion during the acute phase of an infection. However, the clonality and clonal composition of this expansion has not previously been described. Using a comparative infection model, we sequenced the splenic CD4+ T-cell receptor repertoires generated over the time-course of a Plasmodium chabaudi infection. We show through repeat replicate experiments, single-cell RNA-seq, and analyses of independent RNA-seq data, that following a first infection - within a highly polyclonal expansion - T-effector repertoires are consistently dominated by TRBV3 gene usage. Clustering by sequence similarity, we find the same dominant clonal signature is expanded across replicates in the acute phase of an infection, revealing a conserved pathogen-specific T-cell response that is consistently a hallmark of a first infection, but not expanded upon re-challenge. Determining the host or parasite factors driving this conserved response may uncover novel immune targets for malaria therapeutic purposes.

Methods for sequence and structural analysis of B and T cell receptor repertoires

B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.

MHC-II alleles shape the CDR3 repertoires of conventional and regulatory naïve CD4+ T cells

T cell maturation and activation depend upon T cell receptor (TCR) interactions with a wide variety of antigenic peptides displayed in a given major histocompatibility complex (MHC) context. Complementarity-determining region 3 (CDR3) is the most variable part of the TCRα and -β chains, which govern interactions with peptide-MHC complexes. However, it remains unclear how the CDR3 landscape is shaped by individual MHC context during thymic selection of naïve T cells. We established two mouse strains carrying distinct allelic variants of H2-A and analyzed thymic and peripheral production and TCR repertoires of naïve conventional CD4⁺ T (T_conv) and naïve regulatory CD4⁺ T (T_reg) cells. Compared with tuberculosis-resistant C57BL/6 (H2-A^b) mice, the tuberculosis-susceptible H2-A^j mice had fewer CD4⁺ T cells of both subsets in the thymus. In the periphery, this deficiency was only apparent for T_conv and was compensated for by peripheral reconstitution for T_reg We show that H2-A^j favors selection of a narrower and more convergent repertoire with more hydrophobic and strongly interacting amino acid residues in the middle of CDR3α and CDR3β, suggesting more stringent selection against a narrower peptide-MHC-II context. H2-A^j and H2-A^b mice have prominent reciprocal differences in CDR3α and CDR3β features, probably reflecting distinct modes of TCR fitting to MHC-II variants. These data reveal the mechanics and extent of how MHC-II shapes the naïve CD4⁺ T cell CDR3 landscape, which essentially defines adaptive response to infections and self-antigens.

Selected before selection: A case for inherent antigen bias in the T cell receptor repertoire

T cell receptor recombination is frequently described as a random or semi-random process that belies the now well-established principle that recombination is extremely biased towards the generation of particular receptor chains. Here we describe the experimental and theoretical work arising from new TCR repertoire sequencing approaches, including the recognition of high rates of public receptors across individuals, estimates of the size and distribution of receptors in the naive repertoire, and the roles of evolutionary, thymic, and peripheral selection in generating public, pathogen-specific responses. Molecular studies of recombination that have identified epigenetic, transcriptional, and topological contributions to variable segment usage are presented as examples of possible mechanisms shaped by natural selection to bias the TCR repertoire. Lastly, we suggest experimental approaches that might contribute to resolving some of the controversies in these areas.

Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer

Somatic mutations together with immunoediting drive extensive heterogeneity within non-small-cell lung cancer (NSCLC). Herein we examine heterogeneity of the T cell antigen receptor (TCR) repertoire. The number of TCR sequences selectively expanded in tumors varies within and between tumors and correlates with the number of nonsynonymous mutations. Expanded TCRs can be subdivided into TCRs found in all tumor regions (ubiquitous) and those present in a subset of regions (regional). The number of ubiquitous and regional TCRs correlates with the number of ubiquitous and regional nonsynonymous mutations, respectively. Expanded TCRs form part of clusters of TCRs of similar sequence, suggestive of a spatially constrained antigen-driven process. CD8+ tumor-infiltrating lymphocytes harboring ubiquitous TCRs display a dysfunctional tissue-resident phenotype. Ubiquitous TCRs are preferentially detected in the blood at the time of tumor resection as compared to routine follow-up. These findings highlight a noninvasive method to identify and track relevant tumor-reactive TCRs for use in adoptive T cell immunotherapy.

Pregnancy Enables Expansion of Disease-Specific Regulatory T Cells in an Animal Model of Multiple Sclerosis

Disease activity of autoimmune disorders such as multiple sclerosis and its mouse model experimental autoimmune encephalomyelitis (EAE) is temporarily suppressed by pregnancy. However, whether disease amelioration is due to nonspecific immunomodulation or mediated by Ag-specific regulation of disease-causing conventional T cells (Tcon) and immunosuppressive regulatory T cells (Tregs) remains elusive. In the current study, we systematically analyzed changes of the TCRβ repertoire driven by EAE and pregnancy using TCR sequencing. We demonstrate that EAE, but not pregnancy, robustly increased TCR repertoire clonality in both peripheral Tcon and Treg. Notably, pregnancy was required for the expansion of Treg harboring the dominant EAE-associated TRBV13-2 chain and increased the frequency of EAE-associated clonotypes within the Treg compartment. Our findings indicate that pregnancy supports the expansion of Treg clonotypes that are equipped to recognize EAE-associated Ags. These Treg are thereby particularly suited to control corresponding encephalitogenic Tcon responses and likely contribute to pregnancy-associated protection in autoimmunity.

Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity

Background

Repertoire sequencing is enabling deep explorations into the cellular immune response, including the characterization of commonalities and differences among T cell receptor (TCR) repertoires from different individuals, pathologies, and antigen specificities. In seeking to understand the generality of patterns observed in different groups of TCRs, it is necessary to balance how well each pattern represents the diversity among TCRs from one group (sensitivity) vs. how many TCRs from other groups it also represents (specificity). The variable complementarity determining regions (CDRs), particularly the third CDRs (CDR3s) interact with major histocompatibility complex (MHC)-presented epitopes from putative antigens, and thus encode the determinants of recognition.

Results

We here systematically characterize the predictive power that can be obtained from CDR3 sequences, using representative, readily interpretable methods for evaluating CDR sequence similarity and then clustering and classifying sequences based on similarity. An initial analysis of CDR3s of known structure, clustered by structural similarity, helps calibrate the limits of sequence diversity among CDRs that might have a common mode of interaction with presented epitopes. Subsequent analyses demonstrate that this same range of sequence similarity strikes a favorable specificity/sensitivity balance in distinguishing twins from non-twins based on overall CDR3 repertoires, classifying CDR3 repertoires by antigen specificity, and distinguishing general pathologies.

Conclusion

We conclude that within a fairly broad range of sequence similarity, matching CDR3 sequences are likely to share specificities.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2864-8) contains supplementary material, which is available to authorized users.

Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition

Adaptive immune recognition is mediated by antigen receptors on B and T cells generated by somatic recombination during lineage development. The high level of diversity resulting from this process posed technical limitations that previously limited the comprehensive analysis of adaptive immune recognition. Advances over the last ten years have produced data and approaches allowing insights into how T cells develop, evolutionary signatures of recombination and selection, and the features of T cell receptors that mediate epitope-specific binding and T cell activation. The size and complexity of these data have necessitated the generation of novel computational and analytical approaches, which are transforming how T cell immunology is conducted. Here we review the development and application of novel biological, theoretical, and computational methods for understanding T cell recognition and discuss the potential for improved models of receptor:antigen interactions.

Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue

Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRβ chains. To develop each classifier, we extracted 4-mers from every TCRβ CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays. SIGNIFICANCE: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.See related commentary by Zoete and Coukos, p. 1299.

The expanding role of systems immunology in decoding the T cell receptor repertoire

T cells play a crucial role in the immune system's defense against many infectious diseases, including persistent infections for which no effective vaccines currently exist. The T cell component of the adaptive immune system is highly complex involving a constantly evolving landscape of various inter-related T cell populations. These T cell populations are characterized by their phenotypic and functional properties as well as the collection, or repertoire, of T cell receptors (TCR) that mediate T cell recognition of antigenic peptides derived from pathogens. Understanding the various processes and factors that impact the development and evolution of the broader T cell repertoire available to recognize and respond to pathogens and the characteristics of antigen-experienced T cell repertoires associated with effective immune control of pathogens is critical to the rational design of T cell-based vaccines and therapies. In this article we discuss, using examples of recent research, the promise that systems immunology approaches, involving quantitative analysis and mathematical and computational modeling of immunological data, hold for decoding the complex TCR repertoire system in the current era of advancing technologies.

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.

On the feasibility of mining CD8+ T cell receptor patterns underlying immunogenic peptide recognition

Current T cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T cell receptor (TCR) is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T cell receptor and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of TCRs that each bind to a known peptide and (2) retrieving TCRs that bind to a given peptide from a large pool of TCRs. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particular importance as they show that prediction of T cell epitope and T cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T cell epitopes but also paves the way for more general and high-performing models.

T cell receptor repertoires of mice and humans are clustered in similarity networks around conserved public CDR3 sequences

Diversity of T cell receptor (TCR) repertoires, generated by somatic DNA rearrangements, is central to immune system function. However, the level of sequence similarity of TCR repertoires within and between species has not been characterized. Using network analysis of high-throughput TCR sequencing data, we found that abundant CDR3-TCRβ sequences were clustered within networks generated by sequence similarity. We discovered a substantial number of public CDR3-TCRβ segments that were identical in mice and humans. These conserved public sequences were central within TCR sequence-similarity networks. Annotated TCR sequences, previously associated with self-specificities such as autoimmunity and cancer, were linked to network clusters. Mechanistically, CDR3 networks were promoted by MHC-mediated selection, and were reduced following immunization, immune checkpoint blockade or aging. Our findings provide a new view of T cell repertoire organization and physiology, and suggest that the immune system distributes its TCR sequences unevenly, attending to specific foci of reactivity.

DOI:http://dx.doi.org/10.7554/eLife.22057.001