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

Unlocking cancer vaccine potential: What are the key factors?

Cancer is a global health challenge, with changing demographics and lifestyle factors producing an increasing burden worldwide. Screening advancements are enabling earlier diagnoses, but current cancer immunotherapies only induce remission in a small proportion of patients and come at a high cost. Cancer vaccines may offer a solution to these challenges, but they have been mired by poor results in past decades. Greater understanding of tumor biology, coupled with the success of vaccine technologies during the COVID-19 pandemic, has reinvigorated cancer vaccine development. With the first signs of efficacy being reported, cancer vaccines may be beginning to fulfill their potential. Solid tumors, however, present different hurdles than infectious diseases. Combining insights from previous cancer vaccine clinical development and contemporary knowledge of tumor immunology, we ask: who are the 'right' patients, what are the 'right' targets, and which are the 'right' modalities to maximize the chances of cancer vaccine success?

TIRTL-seq: Deep, quantitative, and affordable paired TCR repertoire sequencing

α/β T cells are key players in adaptive immunity. The specificity of T cells is determined by the sequences of the hypervariable T cell receptor (TCR) α and β chains. Although bulk TCR sequencing offers a cost-effective approach for in-depth TCR repertoire profiling, it does not provide chain pairings, which are essential for determining T cell specificity. In contrast, single-cell TCR sequencing technologies produce paired chain data, but are limited in throughput to thousands of cells and are cost-prohibitive for cohort-scale studies. Here, we present TIRTL-seq (Throughput-Intensive Rapid TCR Library sequencing), a novel approach that generates ready-to-sequence TCR libraries from live cells in less than 7 hours. The protocol is optimized for use with non-contact liquid handlers in an automation-friendly 384-well plate format. Reaction volume miniaturization reduces library preparation costs to <$0.50 per well. The core principle of TIRTL-seq is the parallel generation of hundreds of libraries providing multiple biological replicates from a single sample that allows precise inference of both frequencies of individual clones and TCR chain pairings from well-occurrence patterns. We demonstrate scalability of our approach up to 1 million unique paired αβTCR clonotypes corresponding to over 30 million T cells per sample at a cost of less than $2000. For a sample of 10 million cells the cost is ~$200. We benchmarked TIRTL-seq against state-of-the-art 5'RACE bulk TCR-seq and 10x Genomics Chromium technologies on longitudinal samples. We show that TIRTL-seq is able to quantitatively identify expanding and contracting clonotypes between timepoints while providing accurate TCR chain pairings, including distinct temporal dynamics of SARS-CoV-2-specific and EBV-specific CD8+ T cell responses after infection. While clonal expansion was followed by sharp contraction for SARS-CoV-2 specific TCRs, EBV-specific TCRs remained stable once established. The sequences of both α and β TCR chains are essential for determining T cell specificity. As the field moves towards greater applications in diagnostics and immunotherapy that rely on TCR specificity, we anticipate that our scalable paired TCR sequencing methodology will be instrumental for collecting large paired-chain datasets and ultimately extracting therapeutically relevant information from the TCR repertoire.

Noncanonical microprotein regulation of immunity

The immune system is highly regulated but, when dysregulated, suboptimal protective or overly robust immune responses can lead to immune-mediated disorders. The genetic and molecular mechanisms of immune regulation are incompletely understood, impeding the development of more precise diagnostics and therapeutics for immune-mediated disorders. Recently, thousands of previously unrecognized noncanonical microprotein genes encoded by small open reading frames have been identified. Many of these microproteins perform critical functions, often in a cell- and context-specific manner. Several microproteins are now known to regulate immunity; however, the vast majority are uncharacterized. Therefore, illuminating what is often referred to as the "dark proteome," may present opportunities to tune immune responses more precisely. Here, we review noncanonical microprotein biology, highlight recently discovered examples regulating immunity, and discuss the potential and challenges of modulating dysregulated immune responses by targeting microproteins.

Epitope landscape in autoimmune neurological disease and beyond

Autoantibody binding has a central role in autoimmune diseases and has also been linked to cancer, infections, and behavioral disorders. Autoimmune neurological diseases remain misclassified also due to an incomplete understanding of the underlying disease-specific epitopes. Such epitopes are crucial for both pathology and diagnosis, but have historically been overlooked. Recent technological advancements have enabled the exploration of these epitopes, potentially opening novel clinical avenues. The precise identification of novel B and T cell epitopes and their autoreactivity has led to the discovery of autoantigen-specific biomarkers for patients at high risk of autoimmune neurological diseases. In this review, we propose utilizing newly available synthetic and cellular-surface display technologies and guide epitope-focused studies to unlock the potential of disease-specific epitopes for improving diagnosis and treatments. Additionally, we offer recommendations to guide emerging epitope-focused studies to broaden the current landscape.

Systems vaccinology studies - achievements and future potential

Human immune responses to vaccination are variable both within and between populations. Systems vaccinology, which is the application of multi-omics technologies to vaccine studies, seeks to understand such variation and predict responses to optimise vaccine strategies. Here, we outline new approaches to systems vaccinology, focusing on the incorporation of additional cohorts, endpoints and technologies.

A synthetic cytotoxic T cell platform for rapidly prototyping TCR function

Current tools for functionally profiling T cell receptors with respect to cytotoxic potency and cross-reactivity are hampered by difficulties in establishing model systems to test these proteins in the contexts of different HLA alleles and against broad arrays of potential antigens. We have implemented a granzyme-activatable sensor of T cell cytotoxicity in a universal prototyping platform which enables facile recombinant expression of any combination of TCR-, peptide-, and class I MHC-coding sequences and direct assessment of resultant responses. This system consists of an engineered cell platform based on the immortalized natural killer cell line, YT-Indy, and the MHC-null antigen-presenting cell line, K562. These cells were engineered to furnish the YT-Indy/K562 pair with appropriate protein domains required for recombinant TCR expression and function in a non-T cell chassis, integrate a fluorescence-based target-centric early detection reporter of cytotoxic function, and deploy a set of protective genetic interventions designed to preserve antigen-presenting cells for subsequent capture and downstream characterization. Our data show successful reconstitution of the surface TCR complex in the YT-Indy cell line at biologically relevant levels. We also demonstrate successful induction and highly sensitive detection of antigen-specific response in multiple distinct model TCRs. Additionally, we monitored destruction of targets in co-culture and found that our survival-optimized system allowed for complete preservation after 24 h exposure to cytotoxic effectors. With this bioplatform, we anticipate investigators will be empowered to rapidly express and characterize T cell receptor responses, generate knowledge regarding the patterns of T cell receptor recognition, and optimize therapeutic T cell receptors.

Development of a highly sensitive platform for protein-protein interaction detection and regulation of T cell function

We developed a highly sensitive assay for detecting protein-protein interaction using chimeric receptors comprising two molecules of interest in the extracellular domain and interferon alpha and beta receptor subunit 1 or 2 (IFNAR1/2) in the intracellular domain. This intracellular IFNAR1/2 reconstitution system (IFNARRS) proved markedly more sensitive than the NanoBiT system, currently considered one of the best detection systems for protein interaction. Employing chimeric receptors with extracellular domains from the IFNγ or IL-2 receptor and the intracellular domains of IFNAR1/2, the IFNARRS system effectively identifies low IFNγ or IL-2 levels. Cells stably expressing these chimeric receptors responded to IFNγ secreted by activated T cells following various stimuli, including a specific peptide-antigen. The activation signals were further enhanced by the expression of relevant genes, such as costimulators, via IFN-stimulated response elements in the promoters. Besides IFNγ or IL-2, the IFNARRS system demonstrated the capability to detect other cytokines by using the corresponding extracellular domains from these target cytokine receptors.

High-throughput discovery of MHC class I- and II-restricted T cell epitopes using synthetic cellular circuits

Antigen discovery technologies have largely focused on major histocompatibility complex (MHC) class I-restricted human T cell receptors (TCRs), leaving methods for MHC class II-restricted and mouse TCR reactivities relatively undeveloped. Here we present TCR mapping of antigenic peptides (TCR-MAP), an antigen discovery method that uses a synthetic TCR-stimulated circuit in immortalized T cells to activate sortase-mediated tagging of engineered antigen-presenting cells (APCs) expressing processed peptides on MHCs. Live, tagged APCs can be directly purified for deconvolution by sequencing, enabling TCRs with unknown specificity to be queried against barcoded peptide libraries in a pooled screening context. TCR-MAP accurately captures self-reactivities or viral reactivities with high throughput and sensitivity for both MHC class I-restricted and class II-restricted TCRs. We elucidate problematic cross-reactivities of clinical TCRs targeting the cancer/testis melanoma-associated antigen A3 and discover targets of myocarditis-inciting autoreactive T cells in mice. TCR-MAP has the potential to accelerate T cell antigen discovery efforts in the context of cancer, infectious disease and autoimmunity.

Oligonucleotide library screening for identification of virus-specific T-cell receptors

We demonstrate an optimized oligonucleotide library-based approach for the identification of virus-reactive T-cell receptors using Epstein-Barr virus as an example. HEK293T served as antigen-presenting cells and were co-cultured with human T cells that were transduced with T-cell receptors in question. T-cell activation was detected by CD137 expression.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

De novo identification of CD4+ T cell epitopes

CD4+ T cells recognize peptide antigens presented on class II major histocompatibility complex (MHC-II) molecules to carry out their function. The remarkable diversity of T cell receptor sequences and lack of antigen discovery approaches for MHC-II make profiling the specificities of CD4+ T cells challenging. We have expanded our platform of signaling and antigen-presenting bifunctional receptors to encode MHC-II molecules presenting covalently linked peptides (SABR-IIs) for CD4+ T cell antigen discovery. SABR-IIs can present epitopes to CD4+ T cells and induce signaling upon their recognition, allowing a readable output. Furthermore, the SABR-II design is modular in signaling and deployment to T cells and B cells. Here, we demonstrate that SABR-IIs libraries presenting endogenous and non-contiguous epitopes can be used for antigen discovery in the context of type 1 diabetes. SABR-II libraries provide a rapid, flexible, scalable and versatile approach for de novo identification of CD4+ T cell ligands from single-cell RNA sequencing data using experimental and computational approaches.

Construction and efficacy testing of DNA vaccines containing HLA-A*02:01-restricted SARS-CoV-2 T-cell epitopes predicted by immunoinformatics

Vaccines play essential roles in the fight against the COVID-19 pandemic. The development and assessment of COVID-19 vaccines have generally focused on the induction and boosting of neutralizing antibodies targeting the SARS-CoV-2 spike (S) protein. Due to rapid and continuous variation in the S protein, such vaccines need to be regularly updated to match newly emerged dominant variants. T-cell vaccines that target MHC I- or II-restricted epitopes in both structural and non-structural viral proteins have the potential to induce broadly cross-protective and long-lasting responses. In this work, the entire proteome encoded by SARS-CoV-2 (Wuhan-hu-1) is subjected to immunoinformatics-based prediction of HLA-A*02:01-restricted epitopes. The immunogenicity of the predicted epitopes is evaluated using peripheral blood mononuclear cells from convalescent Wuhan-hu-1-infected patients. Furthermore, predicted epitopes that are conserved across major SARS-CoV-2 lineages and variants are used to construct DNA vaccines expressing multi-epitope polypeptides. Most importantly, two DNA vaccine constructs induce epitope-specific CD8 + T-cell responses in a mouse model of HLA-A*02:01 restriction and protect immunized mice from challenge with Wuhan-hu-1 virus after hACE2 transduction. These data provide candidate T-cell epitopes useful for the development of T-cell vaccines against SARS-CoV-2 and demonstrate a strategy for quick T-cell vaccine candidate development applicable to other emerging pathogens.

The physiological interactome of TCR-like antibody therapeutics in human tissues

Selective binding of TCR-like antibodies that target a single tumour-specific peptide antigen presented by human leukocyte antigens (HLA) is the absolute prerequisite for their therapeutic suitability and patient safety. To date, selectivity assessment has been limited to peptide library screening and predictive modeling. We developed an experimental platform to de novo identify interactomes of TCR-like antibodies directly in human tissues using mass spectrometry. As proof of concept, we confirm the target epitope of a MAGE-A4-specific TCR-like antibody. We further determine cross-reactive peptide sequences for ESK1, a TCR-like antibody with known off-target activity, in human liver tissue. We confirm off-target-induced T cell activation and ESK1-mediated liver spheroid killing. Off-target sequences feature an amino acid motif that allows a structural groove-coordination mimicking that of the target peptide, therefore allowing the interaction with the engager molecule. We conclude that our strategy offers an accurate, scalable route for evaluating the non-clinical safety profile of TCR-like antibody therapeutics prior to first-in-human clinical application.

Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy

Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from  laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.

Deciphering Membrane-Protein Interactions and High-Throughput Antigen Identification with Cell Doublets

Deciphering cellular interactions is essential to both understand the mechanisms underlying a broad range of human diseases, but also to manipulate therapies targeting these diseases. Here, the formation of cell doublets resulting from specific membrane ligand-receptor interactions is discovered. Based on this phenomenon, the study developed DoubletSeeker, a novel high-throughput method for the reliable identification of ligand-receptor interactions. The study shows that DoubletSeeker can accurately identify T cell receptor (TCR)-antigen interactions with high sensitivity and specificity. Notably, DoubletSeeker effectively captured paired TCR-peptide major histocompatibility complex (pMHC) information during a highly complex library-on-library screening and successfully identified three mutant TCRs that specifically recognize the MART-1 epitope. In turn, DoubletSeeker can act as an antigen discovery platform that allows for the development of novel immunotherapy targets, making it valuable for investigating fundamental tumor immunology.

Circular RNA as a source of neoantigens for cancer vaccines

BACKGROUND

The effectiveness of somatic neoantigen-based immunotherapy is often hindered by the limited number of mutations in tumors with low to moderate mutation burden. Focusing on microsatellite-stable colorectal cancer (CRC), this study investigates the potential of tumor-associated circular RNAs (circRNAs) as an alternative source of neoepitopes in CRC.

METHODS

Tumor-associated circRNAs in CRC were identified using the MiOncoCirc database and ribo-depletion RNA sequencing of paired clinical normal and tumor samples. Candidate circRNA expression was validated by quantitative real-time PCR (RT-qPCR) using divergent primers. TransCirc database was used for translation prediction. Human leukocyte antigen binding affinity of open reading frames from potentially translatable circRNA was predicted using pVACtools. Strong binders from messenger RNA-encoded proteins were excluded using BlastP. The immunogenicity of the candidate antigens was functionally validated through stimulation of naïve CD8+ T cells against the predicted neoepitopes and subsequent analysis of the T cells through enzyme-linked immunospot (ELISpot) assay, intracellular cytokine staining (ICS) and granzyme B (GZMB) reporter. The cytotoxicity of T cells trained with antigen peptides was further tested using patient-derived organoids.

RESULTS

We identified a neoepitope from circRAPGEF5 that is upregulated in CRC tumor samples from MiOncoCirc database, and two neoepitopes from circMYH9, which is upregulated across various tumor samples from our matched clinical samples. The translation potential of candidate peptides was supported by Clinical Proteomic Tumor Analysis Consortium database using PepQuery. The candidate peptides elicited antigen-specific T cells response and expansion, evidenced by various assays including ELISpot, ICS and GZMB reporter. Furthermore, T cells trained with circMYH9 peptides were able to specifically target and eliminate tumor-derived organoids but not match normal organoids. This observation underscores the potential of circRNAs as a source of immunogenic neoantigens. Lastly, circMYH9 was enriched in the liquid biopsies of patients with CRC, thus enabling a detection-to-vaccination treatment strategy for patients with CRC.

CONCLUSIONS

Our findings underscore the feasibility of tumor-associated circRNAs as an alternative source of neoantigens for cancer vaccines targeting tumors with moderate mutation levels.

BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database encompassing complete single amino acid mutational assays of 10,750 TCR-peptide pairs, centered around 14 immunogenic peptides against 66 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. When validated on our database, BATMAN outperforms existing methods by 20% and reveals important biochemical predictors of TCR-peptide interactions.

T-FINDER: A highly sensitive, pan-HLA platform for functional T cell receptor and ligand discovery

Effective, unbiased, high-throughput methods to functionally identify both class II and class I HLA-presented T cell epitopes and their cognate T cell receptors (TCRs) are essential for and prerequisite to diagnostic and therapeutic applications, yet remain underdeveloped. Here, we present T-FINDER [T cell Functional Identification and (Neo)-antigen Discovery of Epitopes and Receptors], a system to rapidly deconvolute CD4 and CD8 TCRs and targets physiologically processed and presented by an individual's unmanipulated, complete human leukocyte antigen (HLA) haplotype. Combining a highly sensitive TCR signaling reporter with an antigen processing system to overcome previously undescribed limitations to target expression, T-FINDER both robustly identifies unknown peptide:HLA ligands from antigen libraries and rapidly screens and functionally validates the specificity of large TCR libraries against known or predicted targets. To demonstrate its capabilities, we apply the platform to multiple TCR-based applications, including diffuse midline glioma, celiac disease, and rheumatoid arthritis, providing unique biological insights and showcasing T-FINDER's potency and versatility.

CD8+ Trms against malaria liver-stage: prospects and challenges

Attenuated sporozoites provide a valuable model for exploring protective immunity against the malarial liver stage, guiding the design of highly efficient vaccines to prevent malaria infection. Liver tissue-resident CD8+ T cells (CD8+ Trm cells) are considered the host front-line defense against malaria and are crucial to developing prime-trap/target strategies for pre-erythrocytic stage vaccine immunization. However, the spatiotemporal regulatory mechanism of the generation of liver CD8+ Trm cells and their responses to sporozoite challenge, as well as the protective antigens they recognize remain largely unknown. Here, we discuss the knowledge gap regarding liver CD8+ Trm cell formation and the potential strategies to identify predominant protective antigens expressed in the exoerythrocytic stage, which is essential for high-efficacy malaria subunit pre-erythrocytic vaccine designation.

Re-centauring T cell antigen discovery around CD4+ T cells

In a recent issue of Cell, Dezfulian et al. develop a genome-scale platform to enable high-throughput identification of CD4+ T cell epitopes. This platform enables unbiased screens to discover antigens recognized by CD4+ T cells in cancer, infectious diseases, and autoimmunity.

Transposon-Based Manufacturing of Human CAR-T Cells

In this chapter, the methodologies are outlined for generating CAR-T from PBMCs using transposon engineering. Additionally, some methods and guidance related to basic functional and phenotypic analysis are described. This methodology can be applied to manufacture and assess chimeric antigen receptors for preclinical applications targeting a variety of molecules.

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αβ T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy

Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.

GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response

Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.

TScan-II: A genome-scale platform for the de novo identification of CD4+ T cell epitopes

CD4+ T cells play fundamental roles in orchestrating immune responses and tissue homeostasis. However, our inability to associate peptide human leukocyte antigen class-II (HLA-II) complexes with their cognate T cell receptors (TCRs) in an unbiased manner has hampered our understanding of CD4+ T cell function and role in pathologies. Here, we introduce TScan-II, a highly sensitive genome-scale CD4+ antigen discovery platform. This platform seamlessly integrates the endogenous HLA-II antigen-processing machinery in synthetic antigen-presenting cells and TCR signaling in T cells, enabling the simultaneous screening of multiple HLAs and TCRs. Leveraging genome-scale human, virome, and epitope mutagenesis libraries, TScan-II facilitates de novo antigen discovery and deep exploration of TCR specificity. We demonstrate TScan-II's potential for basic and translational research by identifying a non-canonical antigen for a cancer-reactive CD4+ T cell clone. Additionally, we identified two antigens for clonally expanded CD4+ T cells in Sjögren's disease, which bind distinct HLAs and are expressed in HLA-II-positive ductal cells within affected salivary glands.

T cell receptor therapeutics: immunological targeting of the intracellular cancer proteome

The T cell receptor (TCR) complex is a naturally occurring antigen sensor that detects, amplifies and coordinates cellular immune responses to epitopes derived from cell surface and intracellular proteins. Thus, TCRs enable the targeting of proteins selectively expressed by cancer cells, including neoantigens, cancer germline antigens and viral oncoproteins. As such, TCRs have provided the basis for an emerging class of oncology therapeutics. Herein, we review the current cancer treatment landscape using TCRs and TCR-like molecules. This includes adoptive cell transfer of T cells expressing endogenous or engineered TCRs, TCR bispecific engagers and antibodies specific for human leukocyte antigen (HLA)-bound peptides (TCR mimics). We discuss the unique complexities associated with the clinical development of these therapeutics, such as HLA restriction, TCR retrieval, potency assessment and the potential for cross-reactivity. In addition, we highlight emerging clinical data that establish the antitumour potential of TCR-based therapies, including tumour-infiltrating lymphocytes, for the treatment of diverse human malignancies. Finally, we explore the future of TCR therapeutics, including emerging genome editing methods to safely enhance potency and strategies to streamline patient identification.

Neoantigen-targeted TCR-engineered T cell immunotherapy: current advances and challenges

Adoptive cell therapy using T cell receptor-engineered T cells (TCR-T) is a promising approach for cancer therapy with an expectation of no significant side effects. In the human body, mature T cells are armed with an incredible diversity of T cell receptors (TCRs) that theoretically react to the variety of random mutations generated by tumor cells. The outcomes, however, of current clinical trials using TCR-T cell therapies are not very successful especially involving solid tumors. The therapy still faces numerous challenges in the efficient screening of tumor-specific antigens and their cognate TCRs. In this review, we first introduce TCR structure-based antigen recognition and signaling, then describe recent advances in neoantigens and their specific TCR screening technologies, and finally summarize ongoing clinical trials of TCR-T therapies against neoantigens. More importantly, we also present the current challenges of TCR-T cell-based immunotherapies, e.g., the safety of viral vectors, the mismatch of T cell receptor, the impediment of suppressive tumor microenvironment. Finally, we highlight new insights and directions for personalized TCR-T therapy.

Neo-Antigen-Reactive T Cells Immunotherapy for Colorectal Cancer: A More Personalized Cancer Therapy Approach

Colorectal cancer (CRC) is the second most common malignancy in women and the third most frequent cancer in men. Evidence has revealed that the survival of patients with metastatic CRC is very low, between one and three years. Neoantigens are known proteins encoded by mutations in tumor cells. It is theorized that recognizing neoantigens by T cells leads to T cell activation and further antitumor responses. Neoantigen-reactive T cells (NRTs) are designed against the mentioned neoantigens expressed by tumor cells. NRTs selectively kill tumor cells without damage to non-cancerous cells. Identifying patient-specific and high immunogen neoantigens is important in NRT immunotherapy of patients with CRC. However, the main challenges are the side effects and preparation of NRTs, as well as the effectiveness of these cells in vivo. This review summarized the properties of neoantigens as well as the preparation and therapeutic outcomes of NRTs for the treatment of CRC.

Updates in SJS/TEN: collaboration, innovation, and community

Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis (SJS/TEN) is a predominantly drug-induced disease, with a mortality rate of 15-20%, that engages the expertise of multiple disciplines: dermatology, allergy, immunology, clinical pharmacology, burn surgery, ophthalmology, urogynecology, and psychiatry. SJS/TEN has an incidence of 1-5/million persons per year in the United States, with even higher rates globally. One of the challenges of SJS/TEN has been developing the research infrastructure and coordination to answer questions capable of transforming clinical care and leading to improved patient outcomes. SJS/TEN 2021, the third research meeting of its kind, was held as a virtual meeting on August 28-29, 2021. The meeting brought together 428 international scientists, in addition to a community of 140 SJS/TEN survivors and family members. The goal of the meeting was to brainstorm strategies to support the continued growth of an international SJS/TEN research network, bridging science and the community. The community workshop section of the meeting focused on eight primary themes: mental health, eye care, SJS/TEN in children, non-drug induced SJS/TEN, long-term health complications, new advances in mechanisms and basic science, managing long-term scarring, considerations for skin of color, and COVID-19 vaccines. The meeting featured several important updates and identified areas of unmet research and clinical need that will be highlighted in this white paper.

Spying on SARS-CoV-2 with Fluorescent Tags and Protease Reporters

The SARS-CoV-2 coronavirus has caused worldwide disruption through the COVID-19 pandemic, providing a sobering reminder of the profound impact viruses can have on human well-being. Understanding virus life cycles and interactions with host cells lays the groundwork for exploring therapeutic strategies against virus-related diseases. Fluorescence microscopy plays a vital role in virus imaging, offering high spatiotemporal resolution, sensitivity, and spectroscopic versatility. In this opinion piece, we first highlight two recent techniques, SunTag and StayGold, for the in situ imaging of viral RNA translation and viral assembly. Next, we discuss a new class of genetically encoded fluorogenic protease reporters, such as FlipGFP, which can be customized to monitor SARS-CoV-2's main (Mpro) or papain-like (PLpro) protease activity. These assays have proven effective in identifying potential antivirals through high-throughput screening, making fluorogenic viral protease reporters a promising platform for viral disease diagnostics and therapeutics.

Massively-multiplexed epitope mapping techniques for viral antigen discovery

Following viral infection, viral antigens bind specifically to receptors on the surface of lymphocytes thereby activating adaptive immunity in the host. An epitope, the smallest structural and functional unit of an antigen, binds specifically to an antibody or antigen receptor, to serve as key sites for the activation of adaptive immunity. The complexity and diverse range of epitopes are essential to study and map for the diagnosis of disease, the design of vaccines and for immunotherapy. Mapping the location of these specific epitopes has become a hot topic in immunology and immune therapy. Recently, epitope mapping techniques have evolved to become multiplexed, with the advent of high-throughput sequencing and techniques such as bacteriophage-display libraries and deep mutational scanning. Here, we briefly introduce the principles, advantages, and disadvantages of the latest epitope mapping techniques with examples for viral antigen discovery.

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

A systematic safety pipeline for selection of T-cell receptors to enter clinical use

Cancer immunotherapy using T cell receptor-engineered T cells (TCR-Ts) represents a promising treatment option. However, technologies for pre-clinical safety assessment are incomplete or inaccessible to most laboratories. Here, TCR-T off-target reactivity was assessed in five steps: (1) Mapping target amino acids necessary for TCR-T recognition, followed by (2) a computational search for, and (3) reactivity screening against, candidate cross-reactive peptides in the human proteome. Natural processing and presentation of recognized peptides was evaluated using (4) short mRNAs, and (5) full-length proteins. TCR-Ts were screened for recognition of unintended HLA alleles, and as proxy for off-target reactivity in vivo, a syngeneic, HLA-A*02:01-transgenic mouse model was used. Validation demonstrated importance of studying recognition of full-length candidate off-targets, and that the clinically applied 1G4 TCR has a hitherto unknown reactivity to unintended HLA alleles, relevant for patient selection. This widely applicable strategy should facilitate evaluation of candidate therapeutic TCRs and inform clinical decision-making.

Defining and Studying B Cell Receptor and TCR Interactions

BCRs (Abs) and TCRs (or adaptive immune receptors [AIRs]) are the means by which the adaptive immune system recognizes foreign and self-antigens, playing an integral part in host defense, as well as the emergence of autoimmunity. Importantly, the interaction between AIRs and their cognate Ags defies a simple key-in-lock paradigm and is instead a complex many-to-many mapping between an individual's massively diverse AIR repertoire, and a similarly diverse antigenic space. Understanding how adaptive immunity balances specificity with epitopic coverage is a key challenge for the field, and terms such as broad specificity, cross-reactivity, and polyreactivity remain ill-defined and are used inconsistently. In this Immunology Notes and Resources article, a group of experimental, structural, and computational immunologists define commonly used terms associated with AIR binding, describe methodologies to study these binding modes, as well as highlight the implications of these different binding modes for therapeutic design.

MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations

MOTIVATION

The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs.

RESULTS

To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR-pMHC interactions using amino acid sequences and physicochemical properties. Based on convolutional neural networks, MIX-TPI incorporates sequence-based and physicochemical-based extractors to refine the representations of TCR-pMHC interactions. Each modality is projected into modality-invariant and modality-specific representations to capture the uniformity and diversities between different features. A self-attention fusion layer is then adopted to form the classification module. Experimental results demonstrate the effectiveness of MIX-TPI in comparison with other state-of-the-art methods. MIX-TPI also shows good generalization capability on mutual exclusive evaluation datasets and a paired TCR dataset.

AVAILABILITY AND IMPLEMENTATION

The source code of MIX-TPI and the test data are available at: https://github.com/Wolverinerine/MIX-TPI.

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

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

Construction and Applications of Mammalian Cell-Based DNA-Encoded Peptide/Protein Libraries

DNA-encoded peptide/protein libraries are the starting point for protein evolutionary modification and functional peptide/antibody selection. Different display technologies, protein directed evolution, and deep mutational scanning (DMS) experiments employ DNA-encoded libraries to provide sequence variations for downstream affinity- or function-based selections. Mammalian cells promise the inherent post-translational modification and near-to-natural conformation of exogenously expressed mammalian proteins and thus are the best platform for studying transmembrane proteins or human disease-related proteins. However, due to the current technical bottlenecks of constructing mammalian cell-based large size DNA-encoded libraries, the advantages of mammalian cells as screening platforms have not been fully exploited. In this review, we summarize the current efforts in constructing DNA-encoded libraries in mammalian cells and the existing applications of these libraries in different fields.

Pre-existing T Cell Memory to Novel Pathogens

Immunological experiences lead to the development of specific T and B cell memory, which readies the host for a later pathogen rechallenge. Currently, immunological memory is best understood as a linear process whereby memory responses are generated by and directed against the same pathogen. However, numerous studies have identified memory cells that target pathogens in unexposed individuals. How "pre-existing memory" forms and impacts the outcome of infection remains unclear. In this review, we discuss differences in the composition of baseline T cell repertoire in mice and humans, factors that influence pre-existing immune states, and recent literature on their functional significance. We summarize current knowledge on the roles of pre-existing T cells in homeostasis and perturbation and their impacts on health and disease.

High-throughput, targeted MHC class I immunopeptidomics using a functional genetics screening platform

Identification of CD8+ T cell epitopes is critical for the development of immunotherapeutics. Existing methods for major histocompatibility complex class I (MHC class I) ligand discovery are time intensive, specialized and unable to interrogate specific proteins on a large scale. Here, we present EpiScan, which uses surface MHC class I levels as a readout for whether a genetically encoded peptide is an MHC class I ligand. Predetermined starting pools composed of >100,000 peptides can be designed using oligonucleotide synthesis, permitting large-scale MHC class I screening. We exploit this programmability of EpiScan to uncover an unappreciated role for cysteine that increases the number of predicted ligands by 9-21%, reveal affinity hierarchies by analysis of biased anchor peptide libraries and screen viral proteomes for MHC class I ligands. Using these data, we generate and iteratively refine peptide binding predictions to create EpiScan Predictor. EpiScan Predictor performs comparably to other state-of-the-art MHC class I peptide binding prediction algorithms without suffering from underrepresentation of cysteine-containing peptides. Thus, targeted immunopeptidomics using EpiScan will accelerate CD8+ T cell epitope discovery toward the goal of individual-specific immunotherapeutics.

A microfluidics-enabled automated workflow of sample preparation for MS-based immunopeptidomics

Mass spectrometry (MS)-based immunopeptidomics is an attractive antigen discovery method with growing clinical implications. However, the current experimental approach to extract HLA-restricted peptides requires a bulky sample source, which remains a challenge for obtaining clinical specimens. We present an innovative workflow that requires a low sample volume, which streamlines the immunoaffinity purification (IP) and C18 peptide cleanup on a single microfluidics platform with automated liquid handling and minimal sample transfers, resulting in higher assay sensitivity. We also demonstrate how the state-of-the-art data-independent acquisition (DIA) method further enhances the depth of tandem MS spectra-based peptide sequencing. Consequently, over 4,000 and 5,000 HLA-I-restricted peptides were identified from as few as 0.2 million RA957 cells and a melanoma tissue of merely 5 mg, respectively. We also identified multiple immunogenic tumor-associated antigens and hundreds of peptides derived from non-canonical protein sources. This workflow represents a powerful tool for identifying the immunopeptidome of sparse samples.

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases

T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

Rapid TCR:Epitope Ranker (RAPTER): a primary human T cell reactivity screening assay pairing epitope and TCR at single cell resolution

Identifying epitopes that T cells respond to is critical for understanding T cell-mediated immunity. Traditional multimer and other single cell assays often require large blood volumes and/or expensive HLA-specific reagents and provide limited phenotypic and functional information. Here, we present the Rapid TCR:Epitope Ranker (RAPTER) assay, a single cell RNA sequencing (scRNA-SEQ) method that uses primary human T cells and antigen presenting cells (APCs) to assess functional T cell reactivity. Using hash-tag oligonucleotide (HTO) coding and T cell activation-induced markers (AIM), RAPTER defines paired epitope specificity and TCR sequence and can include RNA- and protein-level T cell phenotype information. We demonstrate that RAPTER identified specific reactivities to viral and tumor antigens at sensitivities as low as 0.15% of total CD8⁺ T cells, and deconvoluted low-frequency circulating HPV16-specific T cell clones from a cervical cancer patient. The specificities of TCRs identified by RAPTER for MART1, EBV, and influenza epitopes were functionally confirmed in vitro. In summary, RAPTER identifies low-frequency T cell reactivities using primary cells from low blood volumes, and the resulting paired TCR:ligand information can directly enable immunogenic antigen selection from limited patient samples for vaccine epitope inclusion, antigen-specific TCR tracking, and TCR cloning for further therapeutic development.

An mRNA-based T-cell-inducing antigen strengthens COVID-19 vaccine against SARS-CoV-2 variants

Herd immunity achieved through mass vaccination is an effective approach to prevent contagious diseases. Nonetheless, emerging SARS-CoV-2 variants with frequent mutations largely evaded humoral immunity induced by Spike-based COVID-19 vaccines. Herein, we develop a lipid nanoparticle (LNP)-formulated mRNA-based T-cell-inducing antigen, which targeted three SARS-CoV-2 proteome regions that enriched human HLA-I epitopes (HLA-EPs). Immunization of HLA-EPs induces potent cellular responses to prevent SARS-CoV-2 infection in humanized HLA-A*02:01/DR1 and HLA-A*11:01/DR1 transgenic mice. Of note, the sequences of HLA-EPs are highly conserved among SARS-CoV-2 variants of concern. In humanized HLA-transgenic mice and female rhesus macaques, dual immunization with the LNP-formulated mRNAs encoding HLA-EPs and the receptor-binding domain of the SARS-CoV-2 B.1.351 variant (RBD_beta) is more efficacious in preventing infection of SARS-CoV-2 Beta and Omicron BA.1 variants than single immunization of LNP-RBD_beta. This study demonstrates the necessity to strengthen the vaccine effectiveness by comprehensively stimulating both humoral and cellular responses, thereby offering insight for optimizing the design of COVID-19 vaccines.

Single-Cell Analysis in Immuno-Oncology

The complexity of the cellular and non-cellular milieu surrounding human tumors plays a decisive role in the course and outcome of disease. The high variability in the distribution of the immune and non-immune compartments within the tumor microenvironments (TME) among different patients governs the mode of their response or resistance to current immunotherapeutic approaches. Through deciphering this diversity, one can tailor patients' management to meet an individual's needs. Single-cell (sc) omics technologies have given a great boost towards this direction. This review gathers recent data about how multi-omics profiling, including the utilization of single-cell RNA sequencing (scRNA-seq), assay for transposase-accessible chromatin with sequencing (scATAC-seq), T-cell receptor sequencing (scTCR-seq), mass, tissue-based, or microfluidics cytometry, and related bioinformatics tools, contributes to the high-throughput assessment of a large number of analytes at single-cell resolution. Unravelling the exact TCR clonotype of the infiltrating T cells or pinpointing the classical or novel immune checkpoints across various cell subsets of the TME provide a boost to our comprehension of adaptive immune responses, their antigen specificity and dynamics, and grant suggestions for possible therapeutic targets. Future steps are expected to merge high-dimensional data with tissue localization data, which can serve the investigation of novel multi-modal biomarkers for the selection and/or monitoring of the optimal treatment from the current anti-cancer immunotherapeutic armamentarium.

Leveraging deep learning to improve vaccine design

Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however, the revolutionary results seen in other fields are only now beginning to be realized. Vaccine research and development efforts represent an application with high public health significance. Protein structure prediction, immune repertoire analysis, and phylogenetics are three principal areas in which deep learning is poised to provide key advances. Here, we opine on some of the current challenges with deep learning and how they are being addressed. Despite the nascent stage of deep learning applications in immunological studies, there is ample opportunity to utilize this new technology to address the most challenging and burdensome infectious diseases confronting global populations.

Dynamics and specificities of T cells in cancer immunotherapy

Recent advances in cancer immunotherapy - ranging from immune-checkpoint blockade therapy to adoptive cellular therapy and vaccines - have revolutionized cancer treatment paradigms, yet the variability in clinical responses to these agents has motivated intense interest in understanding how the T cell landscape evolves with respect to response to immune intervention. Over the past decade, the advent of multidimensional single-cell technologies has provided the unprecedented ability to dissect the constellation of cell states of lymphocytes within a tumour microenvironment. In particular, the rapidly expanding capacity to definitively link intratumoural phenotypes with the antigen specificity of T cells provided by T cell receptors (TCRs) has now made it possible to focus on investigating the properties of T cells with tumour-specific reactivity. Moreover, the assessment of TCR clonality has enabled a molecular approach to track the trajectories, clonal dynamics and phenotypic changes of antitumour T cells over the course of immunotherapeutic intervention. Here, we review the current knowledge on the cellular states and antigen specificities of antitumour T cells and examine how fine characterization of T cell dynamics in patients has provided meaningful insights into the mechanisms underlying effective cancer immunotherapy. We highlight those T cell subsets associated with productive T cell responses and discuss how diverse immunotherapies might leverage the pre-existing tumour-reactive T cell pool or instruct de novo generation of antitumour specificities. Future studies aimed at elucidating the factors associated with the elicitation of productive antitumour T cell immunity are anticipated to instruct the design of more efficacious treatment strategies.

Clonal dynamics of alloreactive T cells in kidney allograft rejection after anti-PD-1 therapy

Kidney transplant recipients are at particular risk for developing tumors, many of which are now routinely treated with immune checkpoint inhibitors (ICIs); however, ICI therapy can precipitate transplant rejection. Here, we use TCR sequencing to identify and track alloreactive T cells in a patient with melanoma who experienced kidney transplant rejection following PD-1 inhibition. The treatment was associated with a sharp increase in circulating alloreactive CD8+ T cell clones, which display a unique transcriptomic signature and were also detected in the rejected kidney but not at tumor sites. Longitudinal and cross-tissue TCR analyses indicate unintended expansion of alloreactive CD8+ T cells induced by ICI therapy for cancer, coinciding with ICI-associated organ rejection.

Multiple sclerosis: Neuroimmune crosstalk and therapeutic targeting

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system afflicting nearly three million individuals worldwide. Neuroimmune interactions between glial, neural, and immune cells play important roles in MS pathology and offer potential targets for therapeutic intervention. Here, we review underlying risk factors, mechanisms of MS pathogenesis, available disease modifying therapies, and examine the value of emerging technologies, which may address unmet clinical needs and identify novel therapeutic targets.