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

The ability to decode antigen specificities encapsulated in the sequences of rearranged T-cell receptor (TCR) genes is critical for our understanding of the adaptive immune system and promises significant advances in the field of translational medicine. Recent developments in high-throughput sequencing methods (immune repertoire sequencing technology, or RepSeq) and single-cell RNA sequencing technology have allowed us to obtain huge numbers of TCR sequences from donor samples and link them to T-cell phenotypes. However, our ability to annotate these TCR sequences still lags behind, owing to the enormous diversity of the TCR repertoire and the scarcity of available data on T-cell specificities. In this paper, we present VDJdb, a database that stores and aggregates the results of published T-cell specificity assays and provides a universal platform that couples antigen specificities with TCR sequences. We demonstrate that VDJdb is a versatile instrument for the annotation of TCR repertoire data, enabling a concatenated view of antigen-specific TCR sequence motifs. VDJdb can be accessed at https://vdjdb.cdr3.net and https://github.com/antigenomics/vdjdb-db.

Chimeric antigen receptor T-cell (CAR-T) therapy for acute myeloid leukaemia (AML) has thus far been elusive, in part due to target restriction and phenotypic heterogeneity of AML cells. Mutations of the FMS-like tyrosine kinase 3 (FLT3) and DNA methyltransferase 3A (DNMT3A) genes are common driver mutations that present with a poor prognosis in AML patients. We found that AML patients with FLT3 or DNMT3A mutations had higher expression of CD44 isoform 6 (CD44v6) compared to normal specimens. Therefore, we intended to demonstrate CD44v6 could be a specific option for AML with FLT3 or DNMT3A mutations.

The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

T cell receptor (TCR) antigen-specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide-major histocompatibility complex) interactions and a neural network-based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen-specific interactions for basic immunological research and clinical immune monitoring.

We evaluated the association of disease outcome with T cell immune-related characteristics and T cell receptor (TCR) repertoire in malignant ascites from patients with high-grade epithelial ovarian cancer. Ascitic fluid samples were collected from 47 high-grade epithelial ovarian cancer patients and analyzed using flow cytometry and TCR sequencing to characterize the complementarity determining region 3 TCR β-chain. TCR functions were analyzed using the McPAS-TCR and VDJ databases. TCR clustering was implemented using Grouping of Lymphocyte Interactions by Paratope Hotspots software. Patients with poor prognosis had ascites characterized by an increased ratio of CD8+ T cells to regulatory T cells, which correlated with an increased productive frequency of the top 100 clones and decreased productive entropy. TCRs enriched in patients with an excellent or good prognosis were more likely to recognize cancer antigens and contained more TCR reads predicted to recognize epithelial ovarian cancer antigens. In addition, a TCR motif that is predicted to bind the TP53 neoantigen was identified, and this motif was enriched in patients with an excellent or good prognosis. Ascitic fluid in high-grade epithelial ovarian cancer patients with an excellent or good prognosis is enriched with TCRs that may recognize ovarian cancer-specific neoantigens, including mutated TP53 and TEAD1. These results suggest that an effective antigen-specific immune response in ascites is vital for a good outcome in high-grade epithelial ovarian cancer.

Adoptive T cell therapies rely on the production of T cells with an antigen receptor that directs their specificity toward tumor-specific antigens. Methods for identifying relevant T cell receptor (TCR) sequences, predominantly achieved through the enrichment of antigen-specific T cells, represent a major bottleneck in the production of TCR-engineered cell therapies. Fluctuation of intracellular calcium is a proximal readout of TCR signaling and candidate marker for antigen-specific T cell identification that does not require T cell expansion; however, calcium fluctuations downstream of TCR engagement are highly variable. We propose that machine learning algorithms may allow for T cell classification from complex datasets such as polyclonal T cell signaling events. Using deep learning tools, we demonstrate accurate prediction of TCR-transgenic CD8+ T cell activation based on calcium fluctuations and test the algorithm against T cells bearing a distinct TCR as well as polyclonal T cells. This provides the foundation for an antigen-specific TCR sequence identification pipeline for adoptive T cell therapies.

The emergence of immune escape is a significant roadblock to developing effective chimeric antigen receptor (CAR) T cell therapies against hematological malignancies, including acute myeloid leukemia (AML). Here, we demonstrate feasibility of targeting two antigens simultaneously by combining a GRP78-specific peptide antigen recognition domain with a CD123-specific scFv to generate a peptide-scFv bispecific antigen recognition domain (78.123). To achieve this, we test linkers with varying length and flexibility and perform immunophenotypic and functional characterization. We demonstrate that bispecific CAR T cells successfully recognize and kill tumor cells that express GRP78, CD123, or both antigens and have improved antitumor activity compared to their monospecific counterparts when both antigens are expressed. Protein structure prediction suggests that linker length and compactness influence the functionality of the generated bispecific CARs. Thus, we present a bispecific CAR design strategy to prevent immune escape in AML that can be extended to other peptide-scFv combinations.

Rationale: Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies in the world. Apart from traditional surgical resection, radiotherapy, and chemotherapy, more recent techniques such as nano-photothermal therapy and biotherapy are gradually being adopted for the treatment of HCC. This project intends to combine the advantages of nanoscale drug delivery systems with the targeting ability of CAR-T cells. Method: Based on cell membrane-coated nanoparticles and cell membrane-targeting modifications, a novel nanomaterial was prepared by coating CAR-T cell membranes specifically recognizing GPC3+ HCC cells onto mesoporous silica containing IR780 nanoparticles. Subsequently, the physical properties were characterized, and the in vitro and in vivo targeting abilities of this nanoparticle were verified. Results: CAR-T cells were constructed which could recognize GPC3 expressed on the cell surface of HCC cells. Then the isolated CAR-T cell membrane was successfully coated on the IR780 loaded mesoporous silica materials, as verified by transmission electron microscopy. The superior targeting ability of CAR-T cell membrane coated nanoparticles compared to IR780 loaded mesoporous silica nanoparticles was verified, both in vitro and in vivo. Conclusion: This new nanomaterial exhibits photothermal antitumor abilities along with enhanced targeting abilities, suggesting a promising strategy for the treatment of HCC.

T cell receptor crossreactivity with different peptide ligands and biased recognition of MHC are coupled features of antigen recognition that are necessary for the T cell's diverse functional repertoire. In the crystal structure between an autoreactive, EAE T cell clone 172.10 and myelin basic protein (1-11) presented by class II MHC I-Au, recognition of the MHC is dominated by the Vbeta domain of the TCR, which interacts with the MHC alpha chain in a manner suggestive of a germline-encoded TCR/MHC "anchor point." Strikingly, there are few specific contacts between the TCR CDR3 loops and the MBP peptide. We also find that over 1,000,000 different peptides derived from combinatorial libraries can activate 172.10, yet the TCR strongly prefers the native MBP contact residues. We suggest that while TCR scanning of pMHC may be degenerate due to the TCR germline bias for MHC, recognition of structurally distinct agonist peptides is not indicative of TCR promiscuity, but rather highly specific alternative solutions to TCR engagement.

Although CD8+ T cells play a critical role in the control of HIV-1 infection,their antiviral efficacy can be limited by antigenic variation and immune exhaustion.The latter phenomenon is characterized by the upregulation of multiple inhibitory receptors, such as programmed death-1 (PD-1), CD244 and lymphocyte activation gene-3 (LAG-3), which modulate the functional capabilities of CD8+ T cells.

Over the last several years, there has been considerable progress in the treatment of cancer using gene modified adoptive T cell therapies. Two approaches have been used, one involving the introduction of a conventional αβ T cell receptor (TCR) against a pepMHC cancer antigen, and the second involving introduction of a chimeric antigen receptor (CAR) consisting of a single-chain antibody as an Fv fragment linked to transmembrane and signaling domains. In this review, we focus on one aspect of TCR-mediated adoptive T cell therapies, the impact of the affinity of the αβ TCR for the pepMHC cancer antigen on both efficacy and specificity. We discuss the advantages of higher-affinity TCRs in mediating potent activity of CD4 T cells. This is balanced with the potential disadvantage of higher-affinity TCRs in mediating greater self-reactivity against a wider range of structurally similar antigenic peptides, especially in synergy with the CD8 co-receptor. Both TCR affinity and target selection will influence potential safety issues. We suggest pre-clinical strategies that might be used to examine each TCR for possible on-target and off-target side effects due to self-reactivities, and to adjust TCR affinities accordingly.

T cell receptors (TCRs) have emerged as a new class of immunological therapeutics. However, though antigen specificity is a hallmark of adaptive immunity, TCRs themselves do not possess the high specificity of monoclonal antibodies. Although a necessary function of T cell biology, the resulting cross-reactivity presents a significant challenge for TCR-based therapeutic development, as it creates the potential for off-target recognition and immune toxicity. Efforts to enhance TCR specificity by mimicking the antibody maturation process and enhancing affinity can inadvertently exacerbate TCR cross-reactivity. Here we demonstrate this concern by showing that even peptide-targeted mutations in the TCR can introduce new reactivities against peptides that bear similarity to the original target. To counteract this, we explored a novel structure-guided approach for enhancing TCR specificity independent of affinity. Tested with the MART-1-specific TCR DMF5, our approach had a small but discernible impact on cross-reactivity toward MART-1 homologs yet was able to eliminate DMF5 cross-recognition of more divergent, unrelated epitopes. Our study provides a proof of principle for the use of advanced structure-guided design techniques for improving TCR specificity, and it suggests new ways forward for enhancing TCRs for therapeutic use.

Mature T cells bearing αβ T cell receptors react with foreign antigens bound to alleles of major histocompatibility complex proteins (MHC) that they were exposed to during their development in the thymus, a phenomenon known as positive selection. The structural basis for positive selection has long been debated. Here, using mice expressing one of two different T cell receptor β chains and various MHC alleles, we show that positive selection-induced MHC bias of T cell receptors is affected both by the germline encoded elements of the T cell receptor α and β chain and, surprisingly, dramatically affected by the non germ line encoded portions of CDR3 of the T cell receptor α chain. Thus, in addition to determining specificity for antigen, the non germline encoded elements of T cell receptors may help the proteins cope with the extremely polymorphic nature of major histocompatibility complex products within the species.

T cells recognize antigen using a large and diverse set of antigen-specific receptors created by a complex process of imprecise somatic cell gene rearrangements. In response to antigen-/receptor-binding-specific T cells then divide to form memory and effector populations. We apply high-throughput sequencing to investigate the global changes in T cell receptor sequences following immunization with ovalbumin (OVA) and adjuvant, to understand how adaptive immunity achieves specificity. Each immunized mouse contained a predominantly private but related set of expanded CDR3β sequences. We used machine learning to identify common patterns which distinguished repertoires from mice immunized with adjuvant with and without OVA. The CDR3β sequences were deconstructed into sets of overlapping contiguous amino acid triplets. The frequencies of these motifs were used to train the linear programming boosting (LPBoost) algorithm LPBoost to classify between TCR repertoires. LPBoost could distinguish between the two classes of repertoire with accuracies above 80%, using a small subset of triplet sequences present at defined positions along the CDR3. The results suggest a model in which such motifs confer degenerate antigen specificity in the context of a highly diverse and largely private set of T cell receptors.

Glioblastoma multiforme (GBM) is the most common malignant brain cancer that invades normal brain tissue and impedes surgical eradication, resulting in early local recurrence and high mortality. In addition, most therapeutic agents lack permeability across the blood brain barrier (BBB), further reducing the efficacy of chemotherapy. Thus, effective treatment against GBM requires tumor specific targets and efficient intracranial drug delivery. With the most recent advances in immunotherapy, genetically engineered T cells with chimeric antigen receptors (CARs) are becoming a promising approach for treating cancer. By transducing T lymphocytes with CAR constructs containing a tumor-associated antigen (TAA) recognition domain linked to the constant regions of a signaling T cell receptor, CAR T cells may recognize a predefined TAA with high specificity in a non-MHC restricted manner, and is independent of antigen processing. Active T cells can travel across the BBB, providing additional advantage for drug delivery and tumor targeting. Here we review the CAR design and technical innovations, the major targets that are in pre-clinical and clinical development with a focus on GBM, and multiple strategies developed to improve CAR T cell efficacy.

Chimeric antigen receptor (CAR)-engineered T cells (CAR-T cells) have been shown to have unprecedented efficacy in B cell malignancies, most notably in B cell acute lymphoblastic leukemia (B-ALL) with up to a 90% complete remission rate using anti-CD19 CAR-T cells. However, CAR T-cell therapy for solid tumors currently is faced with numerous challenges such as physical barriers, the immunosuppressive tumor microenvironment and the specificity and safety. The clinical results in solid tumors have been much less encouraging, with multiple cases of toxicity and a lack of therapeutic response. In this review, we will discuss the current stats and challenges of CAR-T cell therapy for solid tumors, and propose possibl e solutions and future perspectives.

T cell receptors (TCRs) are key to antigen-specific immunity and are increasingly being explored as therapeutics, most visibly in cancer immunotherapy. As TCRs typically possess only low-to-moderate affinity for their peptide/MHC (pMHC) ligands, there is a recognized need to develop affinity-enhanced TCR variants. Previous in vitro engineering efforts have yielded remarkable improvements in TCR affinity, yet concerns exist about the maintenance of peptide specificity and the biological impacts of ultra-high affinity. As opposed to in vitro engineering, computational design can directly address these issues, in theory permitting the rational control of peptide specificity together with relatively controlled increments in affinity. Here we explored the efficacy of computational design with the clinically relevant TCR DMF5, which recognizes nonameric and decameric epitopes from the melanoma-associated Melan-A/MART-1 protein presented by the class I MHC HLA-A2. We tested multiple mutations selected by flexible and rigid modeling protocols, assessed impacts on affinity and specificity, and utilized the data to examine and improve algorithmic performance. We identified multiple mutations that improved binding affinity, and characterized the structure, affinity, and binding kinetics of a previously reported double mutant that exhibits an impressive 400-fold affinity improvement for the decameric pMHC ligand without detectable binding to non-cognate ligands. The structure of this high affinity mutant indicated very little conformational consequences and emphasized the high fidelity of our modeling procedure. Overall, our work showcases the capability of computational design to generate TCRs with improved pMHC affinities while explicitly accounting for peptide specificity, as well as its potential for generating TCRs with customized antigen targeting capabilities.

T-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. While a large number of TCR sequences specific to different antigenic peptides are known to date, the structural data describing the conformation and contacting residues for TCR-peptide-MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of these complexes using TCR sequences with known specificity.

The transduction of T cells to express chimeric T-cell antigen receptor (CAR) is an attractive strategy for adaptive immunotherapy for cancer, because the CAR can redirect the recognition specificity of T cells to tumor-associated antigens (TAAs) on the surface of target cells, thereby avoiding the limitations of HLA restriction. However, there are considerable problems with the clinical application of CAR, mostly due to its xenogeneic components, which could be immunogenic in humans. Moreover, while extensive studies on the CARs have been performed, the detailed molecular mechanisms underlying the activation of CAR-grafted T cells remain unclear. In order to eliminate potential immunogenicity and investigate the molecular basis of the CAR-mediated T-cell activation, we constructed a novel CAR (CAR57-28ζ) specific for one of the most important TAAs, epithelial cell adhesion molecule (EpCAM), using only human-derived genes. We revealed that in Jurkat T cells, lentivirally expressed CAR57-28ζ can transmit the T-cell-activating signals sufficient to induce IL-2 production upon EpCAM stimulation. An immunofluorescent analysis clearly showed that the CAR57-28ζ induces the formation of signaling clusters containing endogenous CD3ζ at the CAR/EpCAM interaction interface. These results suggest that this CAR gene may be safely and effectively applied for adaptive T-cell immunotherapy.