Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes

SARS-CoV-2 genome-wide T cell epitope mapping reveals immunodominance and substantial CD8+ T cell activation in COVID-19 patients

T cells are important for effective viral clearance, elimination of virus-infected cells and long-term disease protection. To examine the full-spectrum of CD8+ T cell immunity in COVID-19, we experimentally evaluated 3141 major histocompatibility (MHC) class I-binding peptides covering the complete SARS-CoV-2 genome. Using DNA-barcoded peptide-MHC complex (pMHC) multimers combined with a T cell phenotype panel, we report a comprehensive list of 122 immunogenic and a subset of immunodominant SARS-CoV-2 T cell epitopes. Substantial CD8+ T cell recognition was observed in COVID-19 patients, with up to 27% of all CD8+ lymphocytes interacting with SARS-CoV-2-derived epitopes. Most immunogenic regions were derived from open reading frame (ORF) 1 and ORF3, with ORF1 containing most of the immunodominant epitopes. CD8+ T cell recognition of lower affinity was also observed in healthy donors toward SARS-CoV-2-derived epitopes. This pre-existing T cell recognition signature was partially overlapping with the epitope landscape observed in COVID-19 patients and may drive the further expansion of T cell responses to SARS-CoV-2 infection. Importantly the phenotype of the SARS-CoV-2-specific CD8+ T cells, revealed a strong T cell activation in COVID-19 patients, while minimal T cell activation was seen in healthy individuals. We found that patients with severe disease displayed significantly larger SARS-CoV-2-specific T cell populations compared to patients with mild diseases and these T cells displayed a robust activation profile. These results further our understanding of T cell immunity to SARS-CoV-2 infection and hypothesize that strong antigen-specific T cell responses are associated with different disease outcomes.

Applications of Machine and Deep Learning in Adaptive Immunity

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.

Proinsulin-Reactive CD4 T Cells in the Islets of Type 1 Diabetes Organ Donors

Proinsulin is an abundant protein that is selectively expressed by pancreatic beta cells and has been a focus for development of antigen-specific immunotherapies for type 1 diabetes (T1D). In this study, we sought to comprehensively evaluate reactivity to preproinsulin by CD4 T cells originally isolated from pancreatic islets of organ donors having T1D. We analyzed 187 T cell receptor (TCR) clonotypes expressed by CD4 T cells obtained from six T1D donors and determined their response to 99 truncated preproinsulin peptide pools, in the presence of autologous B cells. We identified 14 TCR clonotypes from four out of the six donors that responded to preproinsulin peptides. Epitopes were found across all of proinsulin (insulin B-chain, C-peptide, and A-chain) including four hot spot regions containing peptides commonly targeted by TCR clonotypes derived from multiple T1D donors. Of importance, these hot spots overlap with peptide regions to which CD4 T cell responses have previously been detected in the peripheral blood of T1D patients. The 14 TCR clonotypes recognized proinsulin peptides presented by various HLA class II molecules, but there was a trend for dominant restriction with HLA-DQ, especially T1D risk alleles DQ8, DQ2, and DQ8-trans. The characteristics of the tri-molecular complex including proinsulin peptide, HLA-DQ molecule, and TCR derived from CD4 T cells in islets, provides an essential basis for developing antigen-specific biomarkers as well as immunotherapies.

The Current Lung Cancer Neoantigen Landscape and Implications for Therapy

All tumors harbor unique mutant peptides, some of which are able to elicit T-cell-mediated immune responses. These are known as neoantigens. Lung cancers bear a heavy mutational burden and hence many potential neoantigens. Neoantigens are increasingly recognized as key mediators of tumor-specific immune activation and have been identified as potential targets for personalized cancer therapies. In this review, we discuss the current data on neoantigens in lung cancer and provide an overview of the recent advances in neoantigen-based immunotherapy. Furthermore, we look ahead to highlight the major opportunities and challenges for the clinical application of neoantigen-based treatment strategies for thoracic and other malignancies.

Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting

CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors. However, the rules that govern which peptides are truly recognized by existing T cell receptors (TCRs) remain poorly understood, precluding accurate predictions of neo-epitopes for cancer immunotherapy. Here, we capitalize on recent (neo-)epitope data to train a predictor of immunogenic epitopes (PRIME), which captures molecular properties of both antigen presentation and TCR recognition. PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition that we experimentally validate. Analysis of cancer genomics data reveals that recurrent mutations tend to be less frequent in patients where they are predicted to be immunogenic, providing further evidence for immunoediting in human cancer. PRIME will facilitate identification of pathogen epitopes in infectious diseases and neo-epitopes in cancer immunotherapy.

An Empirical Antigen Selection Method Identifies Neoantigens That Either Elicit Broad Antitumor T-cell Responses or Drive Tumor Growth

Neoantigens are critical targets of antitumor T-cell responses. The ATLAS bioassay was developed to identify neoantigens empirically by expressing each unique patient-specific tumor mutation individually in Escherichia coli, pulsing autologous dendritic cells in an ordered array, and testing the patient's T cells for recognition in an overnight assay. Profiling of T cells from patients with lung cancer revealed both stimulatory and inhibitory responses to individual neoantigens. In the murine B16F10 melanoma model, therapeutic immunization with ATLAS-identified stimulatory neoantigens protected animals, whereas immunization with peptides associated with inhibitory ATLAS responses resulted in accelerated tumor growth and abolished efficacy of an otherwise protective vaccine. A planned interim analysis of a clinical study testing a poly-ICLC adjuvanted personalized vaccine containing ATLAS-identified stimulatory neoantigens showed that it is well tolerated. In an adjuvant setting, immunized patients generated both CD4⁺ and CD8⁺ T-cell responses, with immune responses to 99% of the vaccinated peptide antigens. SIGNIFICANCE: Predicting neoantigens in silico has progressed, but empirical testing shows that T-cell responses are more nuanced than straightforward MHC antigen recognition. The ATLAS bioassay screens tumor mutations to uncover preexisting, patient-relevant neoantigen T-cell responses and reveals a new class of putatively deleterious responses that could affect cancer immunotherapy design.This article is highlighted in the In This Issue feature, p. 521.

High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides

MOTIVATION

The binding of T cell receptors (TCRs) to their target peptide MHC (pMHC) ligands initializes the cell-mediated immune response. In autoimmune diseases such as multiple sclerosis, the TCR erroneously recognizes self-peptides as foreign and activates an immune response against healthy cells. Such responses can be triggered by cross-recognition of the autoreactive TCR with foreign peptides. Hence, it would be desirable to identify such foreign-antigen triggers to provide a mechanistic understanding of autoimmune diseases. However, the large sequence space of foreign antigens presents an obstacle in the identification of cross-reactive peptides.

RESULTS

Here, we present an in silico modeling and scoring method which exploits the structural properties of TCR-pMHC complexes to predict the binding of cross-reactive peptides. We analyzed three mouse TCRs and one human TCR isolated from a patient with multiple sclerosis. Cross-reactive peptides for these TCRs were previously identified via yeast display coupled with deep sequencing, providing a robust dataset for evaluating our method. Modeling query peptides in their associated TCR-pMHC crystal structures, our method accurately selected the top binding peptides from sets containing more than a hundred thousand unique peptides.

AVAILABILITY AND IMPLEMENTATION

Analyses were performed using custom Python and R scripts available at https://github.com/tborrman/antigen-predict.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TCR Recognition of Peptide-MHC-I: Rule Makers and Breakers

T cells are a critical part of the adaptive immune system that are able to distinguish between healthy and unhealthy cells. Upon recognition of protein fragments (peptides), activated T cells will contribute to the immune response and help clear infection. The major histocompatibility complex (MHC) molecules, or human leukocyte antigens (HLA) in humans, bind these peptides to present them to T cells that recognise them with their surface T cell receptors (TCR). This recognition event is the first step that leads to T cell activation, and in turn can dictate disease outcomes. The visualisation of TCR interaction with pMHC using structural biology has been crucial in understanding this key event, unravelling the parameters that drive this interaction and their impact on the immune response. The last five years has been the most productive within the field, wherein half of current unique TCR-pMHC-I structures to date were determined within this time. Here, we review the new insights learned from these recent TCR-pMHC-I structures and their impact on T cell activation.

Thermostability profiling of MHC-bound peptides: a new dimension in immunopeptidomics and aid for immunotherapy design

The features of peptide antigens that contribute to their immunogenicity are not well understood. Although the stability of peptide-MHC (pMHC) is known to be important, current assays assess this interaction only for peptides in isolation and not in the context of natural antigen processing and presentation. Here, we present a method that provides a comprehensive and unbiased measure of pMHC stability for thousands of individual ligands detected simultaneously by mass spectrometry (MS). The method allows rapid assessment of intra-allelic and inter-allelic differences in pMHC stability and reveals profiles of stability that are broader than previously appreciated. The additional dimensionality of the data facilitated the training of a model which improves the prediction of peptide immunogenicity, specifically of cancer neoepitopes. This assay can be applied to any cells bearing MHC or MHC-like molecules, offering insight into not only the endogenous immunopeptidome, but also that of neoepitopes and pathogen-derived sequences.

Changing the landscape of tumor immunology: novel tools to examine T cell specificity

Immunotherapy has established itself as a stalwart arm in patient care and with precision medicine forms the new paradigm in cancer treatment. T cells are an important group of immune cells capable of potent cancer immune surveillance and immunity. The advent of bioinformatics, particularly more recent advances incorporating algorithms employing machine learning, provide a seemingly limitless ability for T cell analysis and hypothesis generation. Such endeavors have become indispensable to research efforts accelerating and evolving to such an extent that there exists an appreciable gap between knowledge and proof of function and application. Exciting new technologies such as DNA barcoding, cytometry by time-of-flight (CyTOF), and peptide-exchangeable pHLA multimers inclusive of rare and difficult HLA alleles offer high-throughput cell-by-cell analytical capabilities. These outstanding recent contributions to T cell research will help close this gap and potentially bring practical benefit to patients.

Antigen discovery tools for adaptive immune receptor repertoire research

The adaptive immune system has evolved to recognize with incredible precision a large diversity of molecules. Innovations in high-throughput sequencing and bioinformatics have accelerated large-scale immune repertoire analyses and given us important insights into the behavior of the adaptive immune system. However, establishing a connection between receptor sequence and its antigen-specificity remains a challenge despite its central role in determining T and B cell fate. We discuss recent large-scale antigen discovery technologies which can be combined with adaptive immune receptor repertoire (AIRR) studies. We highlight important discoveries made using repertoire analyses in the field of host-microbe interactions.

Recurrent bladder cancer in aging societies: Importance of major histocompatibility complex class I antigen presentation

Aging is associated with an insufficient immune response that may lead to the initiation and progression of various malignancies. Bladder cancer (BC), prevalent in elderly patients, predominantly presents as recurrent nonmuscle invasive BC that requires further treatment. There is much interest in the activation of patients' immune cells with the focus on CD8+ T cells. Successful therapy should also ensure the efficient presentation of BC antigens by major histocompatibility complex (MHC) class I molecules. The purpose of this systematic review is to present the existing literature on the role of MHC class I in BC research and therapy. The bibliographic databases PubMed and Web of Science were searched for articles published between January 2009 and September 2020 that addressed MHC class I relationship to BC. We searched for available relevant publications on MHC class I and its role and regulation in BC, aging and MHC class I importance in BC immunotherapy. Based on the provided evidence, we propose that the loss of MHC class I expression in BC may lead to its recurrence after the transurethral resection and unresponsiveness to Bacillus Calmette-Guerin immunotherapy. We discuss different ways to enhance MHC class I antigen presentation to CD8+ T cells in BC treatment. The immune status characterized by MHC class I expression patterns and cancer-infiltrating immune cells may provide valuable prognostic information about which patients may benefit from transurethral resection of BC and additional immunotherapy.

Structure-Based Modeling of SARS-CoV-2 Peptide/HLA-A02 Antigens

SARS-CoV-2-specific CD4 and CD8 T cells have been shown to be present in individuals with acute, mild, and asymptomatic Coronavirus disease (COVID-19). Toward the development of diagnostic and therapeutic tools to fight COVID-19, it is important to predict and characterize T cell epitopes expressed by SARS-CoV-2. Here, we use RosettaMHC, a comparative modeling approach which leverages existing structures of peptide/MHC complexes available in the Protein Data Bank, to derive accurate 3D models for putative SARS-CoV-2 CD8 epitopes. We outline an application of our method to model 8-10 residue epitopic peptides predicted to bind to the common allele HLA-A*02:01, and we make our models publicly available through an online database (https://rosettamhc.chemistry.ucsc.edu). We further compare electrostatic surfaces with models of homologous peptide/HLA-A*02:01 complexes from human common cold coronavirus strains to identify epitopes which may be recognized by a shared pool of cross-reactive TCRs. As more detailed studies on antigen-specific T cell recognition become available, RosettaMHC models can be used to understand the link between peptide/HLA complex structure and surface chemistry with immunogenicity, in the context of SARS-CoV-2 infection.

Human endogenous retroviruses form a reservoir of T cell targets in hematological cancers

Human endogenous retroviruses (HERV) form a substantial part of the human genome, but mostly remain transcriptionally silent under strict epigenetic regulation, yet can potentially be reactivated by malignant transformation or epigenetic therapies. Here, we evaluate the potential for T cell recognition of HERV elements in myeloid malignancies by mapping transcribed HERV genes and generating a library of 1169 potential antigenic HERV-derived peptides predicted for presentation by 4 HLA class I molecules. Using DNA barcode-labeled MHC-I multimers, we find CD8+ T cell populations recognizing 29 HERV-derived peptides representing 18 different HERV loci, of which HERVH-5, HERVW-1, and HERVE-3 have more profound responses; such HERV-specific T cells are present in 17 of the 34 patients, but less frequently in healthy donors. Transcriptomic analyses reveal enhanced transcription of the HERVs in patients; meanwhile DNA-demethylating therapy causes a small and heterogeneous enhancement in HERV transcription without altering T cell recognition. Our study thus uncovers T cell recognition of HERVs in myeloid malignancies, thereby implicating HERVs as potential targets for immunotherapeutic therapies.

Needle in a Haystack: The Naïve Repertoire as a Source of T Cell Receptors for Adoptive Therapy with Engineered T Cells

T cell engineering with antigen-specific T cell receptors (TCRs) has allowed the generation of increasingly specific, reliable, and versatile T cell products with near-physiological features. However, a broad applicability of TCR-based therapies in cancer is still limited by the restricted number of TCRs, often also of suboptimal potency, available for clinical use. In addition, targeting of tumor neoantigens with TCR-engineered T cell therapy moves the field towards a highly personalized treatment, as tumor neoantigens derive from somatic mutations and are extremely patient-specific. Therefore, relevant TCRs have to be de novo identified for each patient and within a narrow time window. The naïve repertoire of healthy donors would represent a reliable source due to its huge diverse TCR repertoire, which theoretically entails T cells for any antigen specificity, including tumor neoantigens. As a challenge, antigen-specific naïve T cells are of extremely low frequency and mostly of low functionality, making the identification of highly functional TCRs finding a "needle in a haystack." In this review, we present the technological advancements achieved in high-throughput mapping of patient-specific neoantigens and corresponding cognate TCRs and how these platforms can be used to interrogate the naïve repertoire for a fast and efficient identification of rare but therapeutically valuable TCRs for personalized adoptive T cell therapy.

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close inter-relationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications.

Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening

CD4+ T cells are critical to fighting pathogens, but a comprehensive analysis of human T-cell specificities is hindered by the diversity of HLA alleles (>20,000) and the complexity of many pathogen genomes. We previously described GLIPH, an algorithm to cluster T-cell receptors (TCRs) that recognize the same epitope and to predict their HLA restriction, but this method loses efficiency and accuracy when >10,000 TCRs are analyzed. Here we describe an improved algorithm, GLIPH2, that can process millions of TCR sequences. We used GLIPH2 to analyze 19,044 unique TCRβ sequences from 58 individuals latently infected with Mycobacterium tuberculosis (Mtb) and to group them according to their specificity. To identify the epitopes targeted by clusters of Mtb-specific T cells, we carried out a screen of 3,724 distinct proteins covering 95% of Mtb protein-coding genes using artificial antigen-presenting cells (aAPCs) and reporter T cells. We found that at least five PPE (Pro-Pro-Glu) proteins are targets for T-cell recognition in Mtb.

TCR Redirected T Cells for Cancer Treatment: Achievements, Hurdles, and Goals

Adoptive T cell therapy (ACT) is a rapidly evolving therapeutic approach designed to harness T cell specificity and function to fight diseases. Based on the evidence that T lymphocytes can mediate a potent anti-tumor response, initially ACT solely relied on the isolation, in vitro expansion, and infusion of tumor-infiltrating or circulating tumor-specific T cells. Although effective in a subset of cases, in the first ACT clinical trials several patients experienced disease progression, in some cases after temporary disease control. This evidence prompted researchers to improve ACT products by taking advantage of the continuously evolving gene engineering field and by improving manufacturing protocols, to enable the generation of effective and long-term persisting tumor-specific T cell products. Despite recent advances, several challenges, including prioritization of antigen targets, identification, and optimization of tumor-specific T cell receptors, in the development of tools enabling T cells to counteract the immunosuppressive tumor microenvironment, still need to be faced. This review aims at summarizing the major achievements, hurdles and possible solutions designed to improve the ACT efficacy and safety profile in the context of liquid and solid tumors.

Antitumour dendritic cell vaccination in a priming and boosting approach

Mobilizing antitumour immunity through vaccination potentially constitutes a powerful anticancer strategy but has not yet provided robust clinical benefits in large patient populations. Although major hurdles still exist, we believe that currently available strategies for vaccines that target dendritic cells or use them to present antitumour antigens could be integrated into existing clinical practice using prime-boost approaches. In the priming phase, these approaches capitalize on either standard treatment modalities to trigger in situ vaccination and release tumour antigens or vaccination with dendritic cells loaded with tumour lysates or patient-specific neoantigens. In a second boost phase, personalized synthetic vaccines specifically boost T cells that were triggered during the priming phase. This immunotherapy approach has been enabled by the substantial recent improvements in dendritic cell vaccines. In this Perspective, we discuss these improvements, highlight how the prime-boost approach can be translated into clinical practice and provide solutions for various anticipated hurdles.

Predicting antigen specificity of single T cells based on TCR CDR3 regions

It has recently become possible to simultaneously assay T-cell specificity with respect to large sets of antigens and the T-cell receptor sequence in high-throughput single-cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T-cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell-to-dextramer binding strength and receptor-to-pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single-cell RNA-seq studies on T cells without the need for MHC staining.

Progress in Neoantigen Targeted Cancer Immunotherapies

Immunotherapies that harness the immune system to kill cancer cells have showed significant therapeutic efficacy in many human malignancies. A growing number of studies have highlighted the relevance of neoantigens in recognizing cancer cells by intrinsic T cells. Cancer neoantigens are a direct consequence of somatic mutations presenting on the surface of individual cancer cells. Neoantigens are fully cancer-specific and exempt from central tolerance. In addition, neoantigens are important targets for checkpoint blockade therapy. Recently, technological innovations have made neoantigen discovery possible in a variety of malignancies, thus providing an impetus to develop novel immunotherapies that selectively enhance T cell reactivity for the destruction of cancer cells while leaving normal tissues unharmed. In this review, we aim to introduce the methods of the identification of neoantigens, the mutational patterns of human cancers, related clinical trials, neoantigen burden and sensitivity to immune checkpoint blockade. Moreover, we focus on relevant challenges of targeting neoantigens for cancer treatment.

T-Cell Receptor-Based Immunotherapy for Hematologic Malignancies

Adoptive immunotherapy with engineered T cells is at the forefront of cancer treatment. T cells can be engineered to express T-cell receptors (TCRs) specific for tumor-associated antigens (TAAs) derived from intracellular or cell surface proteins. T cells engineered with TCRs (TCR-T) allow for targeting diverse types of TAAs, including proteins overexpressed in malignant cells, those with lineage-restricted expression, cancer-testis antigens, and neoantigens created from abnormal, malignancy-restricted proteins. Minor histocompatibility antigens can also serve as TAAs for TCR-T to treat relapsed hematologic malignancies after allogeneic hematopoietic cell transplantation. Moreover, TCR constructs can be modified to improve safety and enhance function and persistence of TCR-T. Transgenic T-cell receptor therapies targeting 3 different TAAs are in early-phase clinical trials for treatment of hematologic malignancies. Preclinical studies of TCR-T specific for many other TAAs are underway and offer great promise as safe and effective therapies for a wide range of cancers.

Assessment of TCR signal strength of antigen-specific memory CD8+ T cells in human blood

Assessment of the quality and the breadth of antigen (Ag)-specific memory T cells in human samples is of paramount importance to elucidate the pathogenesis and to develop new treatments in various diseases. T-cell receptor (TCR) signal strength, primarily controlled by TCR affinity, affects many fundamental aspects of T-cell biology; however, no current assays for detection of Ag-specific CD8⁺ T cells can assess their TCR signal strength in human samples. Here, we provide evidence that interferon regulatory factor 4 (IRF4), a transcription factor rapidly upregulated in correlation with TCR signal strength, permits the assessment of the TCR signal strength of Ag-specific CD8⁺ T cells in human peripheral blood mononuclear cells (PBMCs). Coexpression of IRF4 and CD137 sensitively detected peptide-specific CD8⁺ T cells with extremely low background in PBMCs stimulated for 18 hours with MHC class I peptides. Our assay revealed that human memory CD8⁺ T cells with high-affinity TCRs have an intrinsic ability to highly express CD25. Furthermore, HIV-specific CD8⁺ T cells in chronic HIV⁺ subjects were found to display primarily low-affinity TCRs with low CD25 expression capacity. Impairment in the functions of HIV-specific CD8⁺ T cells might be associated with their suboptimal TCR signals, as well as impaired responsiveness to interleukin-2.

T cell antigen discovery

T cells respond to threats in an antigen-specific manner using T cell receptors (TCRs) that recognize short peptide antigens presented on major histocompatibility complex (MHC) proteins. The TCR-peptide-MHC interaction mediated between a T cell and its target cell dictates its function and thereby influences its role in disease. A lack of approaches for antigen discovery has limited the fundamental understanding of the antigenic landscape of the overall T cell response. Recent advances in high-throughput sequencing, mass cytometry, microfluidics and computational biology have led to a surge in approaches to address the challenge of T cell antigen discovery. Here, we summarize the scope of this challenge, discuss in depth the recent exciting work and highlight the outstanding questions and remaining technical hurdles in this field.

Breathing fresh air into respiratory research with single-cell RNA sequencing

The complex cellular heterogeneity of the lung poses a unique challenge to researchers in the field. While the use of bulk RNA sequencing has become a ubiquitous technology in systems biology, the technique necessarily averages out individual contributions to the overall transcriptional landscape of a tissue. Single-cell RNA sequencing (scRNA-seq) provides a robust, unbiased survey of the transcriptome comparable to bulk RNA sequencing while preserving information on cellular heterogeneity. In just a few years since this technology was developed, scRNA-seq has already been adopted widely in respiratory research and has contributed to impressive advancements such as the discoveries of the pulmonary ionocyte and of a profibrotic macrophage population in pulmonary fibrosis. In this review, we discuss general technical considerations when considering the use of scRNA-seq and examine how leading investigators have applied the technology to gain novel insights into respiratory biology, from development to disease. In addition, we discuss the evolution of single-cell technologies with a focus on spatial and multi-omics approaches that promise to drive continued innovation in respiratory research.

T-Cell Gene Therapy in Cancer Immunotherapy: Why It Is No Longer Just CARs on The Road

T-cells have a natural ability to fight cancer cells in the tumour microenvironment. Due to thymic selection and tissue-driven immunomodulation, these cancer-fighting T-cells are generally low in number and exhausted. One way to overcome these issues is to genetically alter T-cells to improve their effectiveness. This process can involve introducing a receptor that has high affinity for a tumour antigen, with two promising candidates known as chimeric-antigen receptors (CARs), or T-cell receptors (TCRs) with high tumour specificity. This review focuses on the editing of immune cells to introduce such novel receptors to improve immune responses to cancer. These new receptors redirect T-cells innate killing abilities to the appropriate target on cancer cells. CARs are modified receptors that recognise whole proteins on the surface of cancer cells. They have been shown to be very effective in haematological malignancies but have limited documented efficacy in solid cancers. TCRs recognise internal antigens and therefore enable targeting of a much wider range of antigens. TCRs require major histocompatibility complex (MHC) restriction but novel TCRs may have broader antigen recognition. Moreover, there are multiple cell types which can be used as targets to improve the “off-the-shelf” capabilities of these genetic engineering methods.

Neoantigen-Specific Adoptive Cell Therapies for Cancer: Making T-Cell Products More Personal

Mutation-derived neoantigens are taking central stage as a determinant in eliciting effective antitumor immune responses following adoptive T-cell therapies. These mutations are patient-specific, and their targeting calls for highly personalized pipelines. The promising clinical outcomes of tumor-infiltrating lymphocyte (TIL) therapy have spurred interest in generating T-cell infusion products that have been selectively enriched in neoantigen (or autologous tumor) reactivity. The implementation of an isolation step, prior to T-cell in vitro expansion and reinfusion, may provide a way to improve the overall response rates achieved to date by adoptive T-cell therapies in metastatic cancer patients. Here we provide an overview of the main technologies [i.e., peptide major histocompatibility complex (pMHC) multimers, cytokine capture, and activation markers] to enrich infiltrating or circulating T-cells in predefined neoantigen specificities (or tumor reactivity). The unique technical and regulatory challenges faced by such highly specialized and patient-specific manufacturing T-cell platforms are also discussed.

Engineering Strategies to Enhance TCR-Based Adoptive T Cell Therapy

T cell receptor (TCR)-based adoptive T cell therapies (ACT) hold great promise for the treatment of cancer, as TCRs can cover a broad range of target antigens. Here we summarize basic, translational and clinical results that provide insight into the challenges and opportunities of TCR-based ACT. We review the characteristics of target antigens and conventional αβ-TCRs, and provide a summary of published clinical trials with TCR-transgenic T cell therapies. We discuss how synthetic biology and innovative engineering strategies are poised to provide solutions for overcoming current limitations, that include functional avidity, MHC restriction, and most importantly, the tumor microenvironment. We also highlight the impact of precision genome editing on the next iteration of TCR-transgenic T cell therapies, and the discovery of novel immune engineering targets. We are convinced that some of these innovations will enable the field to move TCR gene therapy to the next level.

Breakthrough concepts in immune-oncology: Cancer vaccines at the bedside

Clinical approval of the immune checkpoint blockade (ICB) agents for multiple cancer types has reinvigorated the long-standing work on cancer vaccines. In the pre-ICB era, clinical efforts focused on the Ag, the adjuvants, the formulation, and the mode of delivery. These translational efforts on therapeutic vaccines range from cell-based (e.g., dendritic cells vaccine Sipuleucel-T) to DNA/RNA-based platforms with various formulations (liposome), vectors (Listeria monocytogenes), or modes of delivery (intratumoral, gene gun, etc.). Despite promising preclinical results, cancer vaccine trials without ICB have historically shown little clinical activity. With the anticipation and expansion of combinatorial immunotherapeutic trials with ICB, the cancer vaccine field has entered the personalized medicine arena with recent advances in immunogenic neoantigen-based vaccines. In this article, we review the literature to organize the different cancer vaccines in the clinical space, and we will discuss their advantages, limits, and recent progress to overcome their challenges. Furthermore, we will also discuss recent preclinical advances and clinical strategies to combine vaccines with checkpoint blockade to improve therapeutic outcome and present a translational perspective on future directions.

VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium

Here, we report an update of the VDJdb database with a substantial increase in the number of T-cell receptor (TCR) sequences and their cognate antigens. The update further provides a new database infrastructure featuring two additional analysis modes that facilitate database querying and real-world data analysis. The increased yield of TCR specificity identification methods and the overall increase in the number of studies in the field has allowed us to expand the database more than 5-fold. Furthermore, several new analysis methods are included. For example, batch annotation of TCR repertoire sequencing samples allows for annotating large datasets on-line. Using recently developed bioinformatic methods for TCR motif mining, we have built a reduced set of high-quality TCR motifs that can be used for both training TCR specificity predictors and matching against TCRs of interest. These additions enhance the versatility of the VDJdb in the task of exploring T-cell antigen specificities. The database is available at https://vdjdb.cdr3.net.

Therapeutic Opportunities in Inflammatory Bowel Disease: Mechanistic Dissection of Host-Microbiome Relationships

The current understanding of inflammatory bowel disease (IBD) pathogenesis implicates a complex interaction between host genetics, host immunity, microbiome, and environmental exposures. Mechanisms gleaned from genetics and molecular pathogenesis offer clues to the critical triggers of mucosal inflammation and guide the development of therapeutic interventions. A complex network of interactions between host genetic factors, microbes, and microbial metabolites governs intestinal homeostasis, making classification and mechanistic dissection of involved pathways challenging. In this Review, we discuss these challenges, areas of active translation, and opportunities for development of next-generation therapies.

T-Scan: A Genome-wide Method for the Systematic Discovery of T Cell Epitopes

T cell recognition of specific antigens mediates protection from pathogens and controls neoplasias, but can also cause autoimmunity. Our knowledge of T cell antigens and their implications for human health is limited by the technical limitations of T cell profiling technologies. Here, we present T-Scan, a high-throughput platform for identification of antigens productively recognized by T cells. T-Scan uses lentiviral delivery of antigen libraries into cells for endogenous processing and presentation on major histocompatibility complex (MHC) molecules. Target cells functionally recognized by T cells are isolated using a reporter for granzyme B activity, and the antigens mediating recognition are identified by next-generation sequencing. We show T-Scan correctly identifies cognate antigens of T cell receptors (TCRs) from viral and human genome-wide libraries. We apply T-Scan to discover new viral antigens, perform high-resolution mapping of TCR specificity, and characterize the reactivity of a tumor-derived TCR. T-Scan is a powerful approach for studying T cell responses.

Identification of the Targets of T-cell Receptor Therapeutic Agents and Cells by Use of a High-Throughput Genetic Platform

T-cell receptor (TCR)-based therapeutic cells and agents have emerged as a new class of effective cancer therapies. These therapies work on cells that express intracellular cancer-associated proteins by targeting peptides displayed on MHC receptors. However, cross-reactivities of these agents to off-target cells and tissues have resulted in serious, sometimes fatal, adverse events. We have developed a high-throughput genetic platform (termed "PresentER") that encodes MHC-I peptide minigenes for functional immunologic assays and determines the reactivities of TCR-like therapeutic agents against large libraries of MHC-I ligands. In this article, we demonstrated that PresentER could be used to identify the on-and-off targets of T cells and TCR-mimic (TCRm) antibodies using in vitro coculture assays or binding assays. We found dozens of MHC-I ligands that were cross-reactive with two TCRm antibodies and two native TCRs and that were not easily predictable by other methods.

Landscape mapping of shared antigenic epitopes and their cognate TCRs of tumor-infiltrating T lymphocytes in melanoma

HLA-restricted T cell responses can induce antitumor effects in cancer patients. Previous human T cell research has largely focused on the few HLA alleles prevalent in a subset of ethnic groups. Here, using a panel of newly developed peptide-exchangeable peptide/HLA multimers and artificial antigen-presenting cells for 25 different class I alleles and greater than 800 peptides, we systematically and comprehensively mapped shared antigenic epitopes recognized by tumor-infiltrating T lymphocytes (TILs) from eight melanoma patients for all their class I alleles. We were able to determine the specificity, on average, of 12.2% of the TILs recognizing a mean of 3.1 shared antigen-derived epitopes across HLA-A, B, and C. Furthermore, we isolated a number of cognate T cell receptor genes with tumor reactivity. Our novel strategy allows for a more complete examination of the immune response and development of novel cancer immunotherapy not limited by HLA allele prevalence or tumor mutation burden.

The immune system is the body’s way of defending itself, offering protection against diseases such as cancer. But to remove the cancer cells, the immune system must be able to identify them as different from the rest of the body. All cells break down proteins into shorter fragments, known as peptides, that are displayed on the cell surface by a protein called human leukocyte antigen, HLA for short. Cancer cells display distinctive peptides on their surface as they generate different proteins to those of healthy cells. Immune cells called T cells use these abnormal peptides to identify the cancer so that it can be destroyed.

Sometimes T cells can lack the right equipment to detect abnormal peptides, allowing cancer cells to hide from the immune system. However, T cells can be trained through a treatment called immunotherapy, which provides T cells with new tools so that they can spot the peptides displayed by HLA on the previously ‘hidden’ cancer cells.

There are many different forms of HLA, each of which can display different peptides. Current research in immunotherapy commonly targets only a subset of HLA forms, and not all cancer patients have these types. This means that immunotherapy research is only likely to be of most benefit to a limited number of patients. Immunotherapy could be made effective for more people if new cancer peptides that are displayed by the other ‘under-represented’ forms of HLA were identified.

Murata, Nakatsugawa et al. have now used T cells that were taken from tumors in eight patients with melanoma, which is a type of skin cancer. A library of fluorescent HLA-peptides was generated – using a new, simplified methodology – with 25 forms of HLA that displayed over 800 peptides. T cells were then mixed with the library to identify which HLA-peptides they can target. As a result, Murata, Nakatsugawa et al. found the cancer targets of around 12% of the tumor-infiltrating T cells tested, including those from under-represented forms of HLA. Consequently, these findings could be used to develop new immunotherapies that can treat more patients.

High throughput pMHC-I tetramer library production using chaperone-mediated peptide exchange

Peptide exchange technologies are essential for the generation of pMHC-multimer libraries used to probe diverse, polyclonal TCR repertoires in various settings. Here, using the molecular chaperone TAPBPR, we develop a robust method for the capture of stable, empty MHC-I molecules comprising murine H2 and human HLA alleles, which can be readily tetramerized and loaded with peptides of choice in a high-throughput manner. Alternatively, catalytic amounts of TAPBPR can be used to exchange placeholder peptides with high affinity peptides of interest. Using the same system, we describe high throughput assays to validate binding of multiple candidate peptides on empty MHC-I/TAPBPR complexes. Combined with tetramer-barcoding via a multi-modal cellular indexing technology, ECCITE-seq, our approach allows a combined analysis of TCR repertoires and other T cell transcription profiles together with their cognate antigen specificities in a single experiment. The new approach allows TCR/pMHC interactions to be interrogated easily at large scale.

Antibody-mediated delivery of viral epitopes to tumors harnesses CMV-specific T cells for cancer therapy

Several cancer immunotherapy approaches, such as immune checkpoint blockade and adoptive T-cell therapy, boost T-cell activity against the tumor, but these strategies are not effective in the absence of T cells specific for displayed tumor antigens. Here we outline an immunotherapy in which endogenous T cells specific for a noncancer antigen are retargeted to attack tumors. The approach relies on the use of antibody-peptide epitope conjugates (APECs) to deliver suitable antigens to the tumor surface for presention by HLA-I. To retarget cytomegalovirus (CMV)-specific CD8+ T cells against tumors, we used APECs containing CMV-derived epitopes conjugated to tumor-targeting antibodies via metalloprotease-sensitive linkers. These APECs redirect pre-existing CMV immunity against tumor cells in vitro and in mouse cancer models. In vitro, APECs activated specifically CMV-reactive effector T cells whereas a bispecific T-cell engager activated both effector and regulatory T cells. Our approach may provide an effective alternative in cancers that are not amenable to checkpoint inhibitors or other immunotherapies.

Structure-based modeling of SARS-CoV-2 peptide/HLA-A02 antigens

As a first step toward the development of diagnostic and therapeutic tools to fight the Coronavirus disease (COVID-19), it is important to characterize CD8+ T cell epitopes in the SARS-CoV-2 peptidome that can trigger adaptive immune responses. Here, we use RosettaMHC, a comparative modeling approach which leverages existing high-resolution X-ray structures from peptide/MHC complexes available in the Protein Data Bank, to derive physically realistic 3D models for high-affinity SARS-CoV-2 epitopes. We outline an application of our method to model 439 9mer and 279 10mer predicted epitopes displayed by the common allele HLA-A*02:01, and we make our models publicly available through an online database ( https://rosettamhc.chemistry.ucsc.edu ). As more detailed studies on antigen-specific T cell recognition become available, RosettaMHC models of antigens from different strains and HLA alleles can be used as a basis to understand the link between peptide/HLA complex structure and surface chemistry with immunogenicity, in the context of SARS-CoV-2 infection.

Adoptive Cell Therapy-Harnessing Antigen-Specific T Cells to Target Solid Tumours

In recent years, much research has been focused on the field of adoptive cell therapies (ACT) that use native or genetically modified T cells as therapeutic tools. Immunotherapy with T cells expressing chimeric antigen receptors (CARs) demonstrated great success in the treatment of haematologic malignancies, whereas adoptive transfer of autologous tumour infiltrating lymphocytes (TILs) proved to be highly effective in metastatic melanoma. These encouraging results initiated many studies where ACT was tested as a treatment for various solid tumours. In this review, we provide an overview of the challenges of T cell-based immunotherapies of solid tumours. We describe alternative approaches for choosing the most efficient T cells for cancer treatment in terms of their tumour-specificity and phenotype. Finally, we present strategies for improvement of anti-tumour potential of T cells, including combination therapies.

Tumor-Infiltrating T Cells From Clear Cell Renal Cell Carcinoma Patients Recognize Neoepitopes Derived From Point and Frameshift Mutations

Mutation-derived neoantigens are important targets for T cell-mediated reactivity toward tumors and, due to their unique tumor expression, an attractive target for immunotherapy. Neoepitope-specific T cells have been detected across a number of solid cancers with high mutational burden tumors, but neoepitopes have been mostly selected from single nucleotide variations (SNVs), and little focus has been given to neoepitopes derived from in-frame and frameshift indels, which might be equally important and potentially highly immunogenic. Clear cell renal cell carcinomas (ccRCCs) are medium-range mutational burden tumors with a high pan-cancer proportion of frameshift mutations. In this study, the mutational landscape of tumors from six RCC patients was analyzed by whole-exome sequencing (WES) of DNA from tumor fragments (TFs), autologous tumor cell lines (TCLs), and tumor-infiltrating lymphocytes (TILs, germline reference). Neopeptides were predicted using MuPeXI, and patient-specific peptide–MHC (pMHC) libraries were created for all neopeptides with a rank score < 2 for binding to the patient's HLAs. T cell recognition toward neoepitopes in TILs was evaluated using the high-throughput technology of DNA barcode-labeled pMHC multimers. The patient-specific libraries consisted of, on average, 258 putative neopeptides (range, 103–397, n = 6). In four patients, WES was performed on two different sources (TF and TCL), whereas in two patients, WES was performed only on TF. Most of the peptides were predicted from both sources. However, a fraction was predicted from one source only. Among the total predicted neopeptides, 16% were derived from frameshift indels. T cell recognition of 52 neoepitopes was detected across all patients (range, 4–18, n = 6) and spanning two to five HLA restrictions per patient. On average, 21% of the recognized neoepitopes were derived from frameshift indels (range, 0–43%, n = 6). Thus, frameshift indels are equally represented in the pool of immunogenic neoepitopes as SNV-derived neoepitopes. This suggests the importance of a broad neopeptide prediction strategy covering multiple sources of tumor material, and including different genetic alterations. This study, for the first time, describes the T cell recognition of frameshift-derived neoepitopes in RCC and determines their immunogenic profile.

Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes

Efforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides derived from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass spectrometry (MS)-based proteogenomics approaches are required to robustly identify these non-canonical peptides. We present an MS-based analytical approach that characterizes the non-canonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single-cell transcriptomics, ribosome profiling, and two MS/MS search tools in combination. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides, including an immunogenic peptide derived from an open reading frame downstream of the melanoma stem cell marker gene ABCB5. These findings hold great promise for the discovery of previously unknown tumor antigens for cancer immunotherapy.

Development of tumour peptide vaccines: From universalization to personalization

In recent years, relying on the human immune system to kill tumour cells has become an effective means of cancer treatment. The development of peptide vaccines, which not only break the immune tolerance of a tumour but also attack malignant cells via specific antitumour immunity, has received increased attention in tumour immunization therapy due to their safety and easy preparation. The use of large-scale sequencing technology enables the continuous discovery of new tumour antigens. With improved accuracy of epitope prediction by computer simulation and the usage of a tetramer assay, cytotoxic lymphocyte epitopes can be screened and identified more easily. Transmembrane peptide and nanoparticle technologies promote more effective intake and delivery of antigens. Consequently, considerable evolution from universal to personalized peptide vaccines has taken place, and such vaccines induce an efficient and specific immune response targeting tumour neoantigens. Recently, genomic analysis and bioinformatics approaches have greatly facilitated the breakthrough of personalized peptide vaccines targeting neoantigens, resulting in a renewed interest in this field. Further, the combination of tumour peptide vaccines with checkpoint blockades may improve patient outcomes. In this review, we discuss the development of tumour peptide vaccines and the new technological progress, from universalization to personalization, to highlight the substantial promise of tumour peptide vaccines in clinical cancer immunotherapy.

Identification of Antigenic Targets

The ideal cancer target antigen (Ag) is expressed at high copy numbers on neoplastic cells, absent on normal tissues, and contributes to the survival of cancer cells. Despite significant investments in the identification of cell surface Ags, there is a paucity of targets that meet such ideal cancer target criteria. Recent clinical trials in patients with cancer treated with immune checkpoint inhibitors (ICIs) indicate that cluster of differentiation (CD)8⁺ T cells, by means of their T cell receptors (TCRs) recognizing intracellular targets presented as peptides in the context of human leukocyte antigen (peptide-human leukocyte antigen complex; pHLA) molecules on tumor cells, can mediate deep and long-lasting antitumor responses in patients with solid tumors. Therefore, pHLA-target Ags may represent the long sought-after, ideal targets for solid tumor targeting by high-potency oncology compounds.

Peptide-MHC class I and class II tetramers: From flow to mass cytometry

To develop better vaccines and more targeted treatments for cancer and autoimmune disorders, the disease-specific T cells and their cognate antigens need to be better characterized. For more than two decades, peptide-major histocompatibility complex (pMHC) tetramers and flow cytometry have been the gold standard for detection of CD8+ and CD4+ T cells specific to antigens in the context of MHC class I and class II, respectively. Nonetheless, more recent studies combining such reagents with mass cytometry, that is, cytometry by time of flight (CyTOF), have offered far more comprehensive profiling of antigen-specific T-cell responses. In addition, mass cytometry has enabled ex vivo screening of CD8+ T-cell reactivities against hundreds of MHC class I restricted candidate epitopes. MHC class II molecules, on the other hand, have been challenging to combine with mass cytometry as they are more complex and bind with lower affinities to cognate T-cell receptors than MHC class I molecules. In this review, I discuss how techniques originally developed to improve the staining capacity of pMHC tetramers in flow cytometry led to the successful combination of such reagents with mass cytometry. Especially, I will highlight very recent advances facilitating the combination with pMHC class II tetramers. Together, these mass cytometry-based studies can help develop more targeted treatments for cancer and autoimmune disorders.

Analysis of Immunological Treatment Effects of Virotherapy in Tumor Tissue

In addition of being directly tumoricidal, oncolytic viruses have emerged as potent partners for established and investigational immunotherapies due to their immune-stimulatory effects. The shifting focus on virus-mediated immune modulation calls for a comprehensive analysis of the tumor microenvironment (TME) and the factors orchestrating the antiviral and antitumor immune response. The oncolytic VSV-GP studied in our lab is a safe and potent antitumor agent with a fast replication cycle and killing of a broad range of different cancer types. It induces a robust local inflammatory conversion of the TME and drives a strong adaptive immune response toward the tumor. Here we present our multidisciplinary approach to study VSV-GP treatment effects in tumors by assessing both immune cells (tumor-infiltrating lymphocytes and tumor-associated macrophages) and immune-regulatory factors (cytokines) as well as characterizing immune signatures using an immune-targeted NanoString gene expression system.

Tracking the immune response with single-cell genomics

The immune system is composed of a diverse array of cell types, each with a specialized role in orchestrating the immune response to pathogens or cancer. Even within a single cell 'type,' individual cells can access a wide spectrum of differentiation and activation states, which reflect the physiological response of each cell to the tissue environment and immune stimuli. Thus, the cellular diversity of the immune system is inherently quite complex and understanding this complexity has greatly benefited from technologies that measure immune responses at single-cell resolution, in addition to the systems-level response as a whole. In this Commentary, we focus on recent work at the interface of immunology and single-cell genomics and highlight advances in technologies and their application to immune cells. In particular, we highlight recent single-cell genomic profiling studies of T cells, since somatic rearrangements in the T cell receptor (TCR) loci enable the tracking of clonal T cell responses through space and time. Finally, we discuss opportunities for future use of these technologies in understanding vaccination and the basis for effective vaccine-induced immunity.

Single-cell sequencing and its applications in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC), like many tumors, is characterized by significant intra-tumoral heterogeneity, namely transcriptional, genetic, and epigenetic differences that define distinct cellular subpopulations. While it has been established that intra-tumoral heterogeneity may have prognostic significance in HNSCC, we are only beginning to describe and define such heterogeneity at a cellular resolution. Recent advances in single-cell sequencing technologies have been critical in this regard, opening new avenues in our understanding of more nuanced tumor biology by identifying distinct cellular subpopulations, dissecting signaling within the tumor microenvironment, and characterizing cellular genomic mutations and copy number aberrations. The combined effect of these insights is likely to be robust and meaningful changes in existing diagnostic and treatment algorithms through the application of novel biomarkers as well as targeted therapeutics. Here, we review single-cell technological and computational advances at the genomic, transcriptomic, and epigenomic levels, and discuss their applications in cancer research and clinical practice, with a specific focus on HNSCC.

Peptide-Major Histocompatibility Complex Class I Binding Prediction Based on Deep Learning With Novel Feature

Peptide-based vaccine development needs accurate prediction of the binding affinity between major histocompatibility complex I (MHC I) proteins and their peptide ligands. Nowadays more and more machine learning methods have been developed to predict binding affinity and some of them have become the popular tools. However most of them are designed by the shallow neural networks. Bengio said that deep neural networks can learn better fits with less data than shallow neural networks. In our case, some of the alleles only have dozens of peptide data. In addition, we transform each peptide into a characteristic matrix and input it into the model. As we know when dealing with the problem that the input is a matrix, convolutional neural network (CNN) can find the most critical features by itself. Obviously, compared with the traditional neural network model, CNN is more suitable for predicting binding affinity. Different from the previous studies which are based on blocks substitution matrix (BLOSUM), we used novel feature to do the prediction. Since we consider that the order of the sequence, hydropathy index, polarity and the length of the peptide could affect the binding affinity and the properties of these amino acids are key factors for their binding to MHC, we extracted these information from each peptide. In order to make full use of the data we have obtained, we have integrated different lengths of peptides into 15mer based on the binding mode of peptide to MHC I. In order to demonstrate that our method is reliable to predict peptide-MHC binding, we compared our method with several popular methods. The experiments show the superiority of our method.

In vivo clonal expansion and phenotypes of hypocretin-specific CD4+ T cells in narcolepsy patients and controls

Individuals with narcolepsy suffer from abnormal sleep patterns due to loss of neurons that uniquely supply hypocretin (HCRT). Previous studies found associations of narcolepsy with the human leukocyte antigen (HLA)-DQ6 allele and T-cell receptor α (TRA) J24 gene segment and also suggested that in vitro-stimulated T cells can target HCRT. Here, we present evidence of in vivo expansion of DQ6-HCRT tetramer⁺/TRAJ24⁺/CD4⁺ T cells in DQ6⁺ individuals with and without narcolepsy. We identify related TRAJ24⁺ TCRαβ clonotypes encoded by identical α/β gene regions from two patients and two controls. TRAJ24-G allele⁺ clonotypes only expand in the two patients, whereas a TRAJ24-C allele⁺ clonotype expands in a control. A representative tetramer⁺/G-allele⁺ TCR shows signaling reactivity to the epitope HCRT_87–97. Clonally expanded G-allele⁺ T cells exhibit an unconventional effector phenotype. Our analysis of in vivo expansion of HCRT-reactive TRAJ24⁺ cells opens an avenue for further investigation of the autoimmune contribution to narcolepsy development.

T cells from narcolepsy patients were recently reported to recognize hypocretin, a wakefulness-promoting neurohormone, suggesting autoimmune origin of the disease. Here the authors show that hypocretin-specific T cells expand both in healthy controls and in narcolepsy patients, and identify preliminary features that may distinguish them.