Direct molecular mimicry enables off-target cardiovascular toxicity by an enhanced affinity TCR designed for cancer immunotherapy

Zooming across the Alchemical Space

Alchemical transformations, whereby chemical species are modified seamlessly, represent a powerful tool in molecular simulations and free-energy calculations, with a broad range of applications. A general-extent, or alchemical parameter, λ ∈ [0,1], describes the gradual transition between the initial and final states of the transformation, and its discretization critically affects the reliability and efficiency of the free-energy calculations. For transformations involving large moieties, free-energy perturbation (FEP) and thermodynamic integration (TI) require numerous intermediates, or λ-states, to ensure appropriate overlap of the configurational ensembles and suitable convergence of the simulation, each state demanding extensive sampling, which burdens computational feasibility. To address this limitation, we combine λ-dynamics─treating λ as a dynamic variable─with the enhanced-sampling approach well-tempered metadynamics-extended adaptive biasing force (WTM-eABF), forming the basis of WTM-λABF. By handling λ as a continuously varying collective variable (CV) and applying a bin-discretized bias, WTM-λABF efficiently explores the λ-space, even when the latter is stratified in numerous intermediates. Calculations of free-energies of hydration, of protein-ligand binding, and of amino-acid mutations in proteins reveal that WTM-λABF consistently converges faster than standard FEP or λ-ABF, with its advantages becoming more pronounced as the number of intermediates rises. We find that WTM-λABF can handle alchemical transformations efficiently with as many as 1,000 intermediates, allowing transformations involving large moieties, or significant potential-energy changes, to be tackled with utmost accuracy. Additionally, its rapid exploration of the continuous λ-space accelerates sampling in the orthogonal space. We are confident that WTM-λABF has the potential to serve as a foundational method for routine applications relevant to chemistry and biophysics, ranging from drug discovery to protein engineering and design.

A naturally selected αβ T cell receptor binds HLA-DQ2 molecules without co-contacting the presented peptide

αβ T cell receptors (TCR) co-recognise peptide (p) antigens that are presented by major histocompatibility complex (MHC) molecules. While marked variations in TCR-p-MHC docking topologies have been observed from structural studies, the co-recognition paradigm has held fast. Using HLA-DQ2.5-peptide tetramers, here we identify a TRAV12-1+-TRBV5-1+ G9 TCR from human peripheral blood that binds HLA-DQ2.5 in a peptide-agnostic manner. The crystal structures of TCR-HLA-DQ2.5-peptide complexes show that the G9 TCR binds HLA-DQ2.5 in a reversed docking topology without contacting the peptide, with the TCR contacting the β1 region of HLA-DQ2.5 and distal from the peptide antigen binding cleft. High-throughput screening of HLA class I and II molecules finds the G9 TCR to be pan-HLA-DQ2 reactive, with leucine-55 of HLA-DQ2.5 being a key determinant underpinning G9 TCR specificity excluding other HLA-II allomorphs. Consistent with the functional assays, the interactions of the G9 TCR and HLA-DQ2.5 precludes CD4 binding, thereby impeding T cell activation. Collectively, we describe a naturally selected αβTCR from human peripheral blood that deviates from the TCR-p-MHC co-recognition paradigm.

TCR catch bonds nonlinearly control CD8 cooperation to shape T cell specificity

Naturally evolved T-cell receptors (TCRs) exhibit remarkably high specificity in discriminating non-self antigens from self-antigens under dynamic biomechanical modulation. In contrast, engineered high-affinity TCRs often lose this specificity, leading to cross-reactivity with self-antigens and off-target toxicity. The underlying mechanism for this difference remains unclear. Our study reveals that natural TCRs exploit mechanical force to form optimal catch bonds with their cognate antigens. This process relies on a mechanically flexible TCR-pMHC binding interface, which enables force-enhanced CD8 coreceptor binding to MHC-α1α2 domains through sequential conformational changes induced by force in both the MHC and CD8. Conversely, engineered high-affinity TCRs create rigid, tightly bound interfaces with cognate pMHCs of their parental TCRs. This rigidity prevents the force-induced conformational changes necessary for optimal catch-bond formation. Paradoxically, these high-affinity TCRs can form moderate catch bonds with non-stimulatory pMHCs of their parental TCRs, leading to off-target cross-reactivity and reduced specificity. We have also developed comprehensive force-dependent TCR-pMHC kinetics-function maps capable of distinguishing functional and non-functional TCR-pMHC pairs and identifying toxic, cross-reactive TCRs. These findings elucidate the mechano-chemical basis of the specificity of natural TCRs and highlight the critical role of CD8 in targeting cognate antigens. This work provides valuable insights for engineering TCRs with enhanced specificity and potency against non-self antigens, particularly for applications in cancer immunotherapy and infectious disease treatment, while minimizing the risk of self-antigen cross-reactivity.

T-cell receptor specificity landscape revealed through de novo peptide design

T-cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. An effective binding between T-cell receptors (TCRs) and pathogen-derived peptides presented on Major Histocompatibility Complexes (MHCs) mediate an immune response. However, predicting these interactions remains challenging due to limited functional data on T-cell reactivities. Here, we introduce a computational approach to predict TCR interactions with peptides presented on MHC class I alleles, and to design novel immunogenic peptides for specified TCR-MHC complexes. Our method leverages HERMES, a structure-based, physics-guided machine learning model trained on the protein universe to predict amino acid preferences based on local structural environments. Despite no direct training on TCR-pMHC data, the implicit physical reasoning in HERMES enables us to make accurate predictions of both TCR-pMHC binding affinities and T-cell activities across diverse viral epitopes and cancer neoantigens, achieving up to 72% correlation with experimental data. Leveraging our TCR recognition model, we develop a computational protocol for de novo design of immunogenic peptides. Through experimental validation in three TCR-MHC systems targeting viral and cancer peptides, we demonstrate that our designs-with up to five substitutions from the native sequence-activate T-cells at success rates of up to 50%. Lastly, we use our generative framework to quantify the diversity of the peptide recognition landscape for various TCR-MHC complexes, offering key insights into T-cell specificity in both humans and mice. Our approach provides a platform for immunogenic peptide and neoantigen design, opening new computational paths for T-cell vaccine development against viruses and cancer.

Design of high specificity binders for peptide-MHC-I complexes

Class I MHC molecules present peptides derived from intracellular antigens on the cell surface for immune surveillance, and specific targeting of these peptide-MHC (pMHC) complexes could have considerable utility for treating diseases. Such targeting is challenging as it requires readout of the few outward facing peptide antigen residues and the avoidance of extensive contacts with the MHC carrier which is present on almost all cells. Here we describe the use of deep learning-based protein design tools to denovo design small proteins that arc above the peptide binding groove of pMHC complexes and make extensive contacts with the peptide. We identify specific binders for ten target pMHCs which when displayed on yeast bind the on-target pMHC tetramer but not closely related peptides. For five targets, incorporation of designs into chimeric antigen receptors leads to T-cell activation by the cognate pMHC complexes well above the background from complexes with peptides derived from proteome. Our approach can generate high specificity binders starting from either experimental or predicted structures of the target pMHC complexes, and should be widely useful for both protein and cell based pMHC targeting.

Broadening alloselectivity of T cell receptors by structure guided engineering

Specificity of a T cell receptor (TCR) is determined by the combination of its interactions to the peptide and human leukocyte antigen (HLA). TCR-based therapeutic molecules have to date targeted a single peptide in the context of a single HLA allele. Some peptides are presented on multiple HLA alleles, and by engineering TCRs for specific recognition of more than one allele, there is potential to expand the targetable patient population. Here, as a proof of concept, we studied two TCRs, S2 and S8, binding to the PRAME peptide antigen (ELFSYLIEK) presented by HLA alleles HLA-A*03:01 and HLA-A*11:01. By structure-guided affinity maturation targeting a specific residue on the HLA surface, we show that the affinity of the TCR can be modulated for different alleles. Using a combination of affinity maturation and functional T cell assay, we demonstrate that an engineered TCR can target the same peptide on two different HLA alleles with similar affinity and potency. This work highlights the importance of engineering alloselectivity for designing TCR based therapeutics suitable for differing global populations.

High Resolution HLA-A, HLA-B, and HLA-C Allele Frequencies in Romanian Hematopoietic Stem Cell Donors

The HLA genes are associated with various autoimmune pathologies, with the control of the immune response also being significant in organs and cells transplantation. The aim of the study is to identify the HLA-A, HLA-B, and HLA-C alleles frequencies in the analyzed Romanian cohort. We performed HLA typing using next-generation sequencing (NGS) in a Romanian cohort to estimate class I HLA allele frequencies up to a six-digit resolution. A total of 420 voluntary donors from the National Registry of Voluntary Hematopoietic Stem Cell Donors (RNDVCSH) were included in the study for HLA genotyping. Peripheral blood samples were taken and brought to the Fundeni Clinical Institute during 2020-2021. HLA genotyping was performed using the Immucor Mia Fora NGS MFlex kit. A total of 109 different alleles were detected in 420 analyzed samples, out of which 31 were for HLA-A, 49 for HLA-B, and 29 for HLA-C. The most frequent HLA-A alleles were HLA-A*02:01:01 (26.11%), HLA-A*01:01:01 (12.5%), HLA-A*24:02:01 (11.67%), HLA-A*03:01:01 (9.72%), HLA-A*11:01:01, and HLA-A*32:01:01 (each with 8.6%). For the HLA-B locus, the most frequent allele was HLA-B*18:01:01 (11.25%), followed by HLA-B*51:01:01 (10.83%) and HLA-B*08:01:01 (7.78%). The most common HLA-C alleles were HLA-C*07:01:01 (17.36%), HLA-C*04:01:01 (13.47%), and HLA-C*12:03:01 (10.69%). Follow-up studies are ongoing for confirming the detected results.

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have emerged as promising tools for accelerating antigen peptide screening. However, most of these models solely rely on one-dimensional amino acid sequences, overlooking crucial information required for the three-dimensional (3-D) space binding process. In this study, we propose TransfIGN, a structure-based DL model that is inspired by our previously developed framework, Interaction Graph Network (IGN), and incorporates sequence information from transformers to predict the interactions between HLA-A*02:01 and antigen peptides. Our model, trained on a comprehensive data set containing 61,816 sequences with 9051 binding affinity labels and 56,848 eluted ligand labels, achieves an area under the curve (AUC) of 0.893 on the binary data set, better than state-of-the-art sequence-based models trained on larger data sets such as NetMHCpan4.1, ANN, and TransPHLA. Furthermore, when evaluated on the IEDB weekly benchmark data sets, our predictions (AUC = 0.816) are better than those of the recommended methods like the IEDB consensus (AUC = 0.795). Notably, the interaction weight matrices generated by our method highlight the strong interactions at specific positions within peptides, emphasizing the model's ability to provide physical interpretability. This capability to unveil binding mechanisms through intricate structural features holds promise for new immunotherapeutic avenues.

The recent advancement of TCR-T cell therapies for cancer treatment

Adoptive cell therapies involve infusing engineered immune cells into cancer patients to recognize and eliminate tumor cells. Adoptive cell therapy, as a form of living drug, has undergone explosive growth over the past decade. The recognition of tumor antigens by the T-cell receptor (TCR) is one of the natural mechanisms that the immune system used to eliminate tumor cells. TCR-T cell therapy, which involves introducing exogenous TCRs into patients' T cells, is a novel cell therapy strategy. TCR-T cell therapy can target the entire proteome of cancer cells. Engineering T cells with exogenous TCRs to help patients combat cancer has achieved success in clinical trials, particularly in treating solid tumors. In this review, we examine the progress of TCR-T cell therapy over the past five years. This includes the discovery of new tumor antigens, protein engineering techniques for TCR, reprogramming strategies for TCR-T cell therapy, clinical studies on TCR-T cell therapy, and the advancement of TCR-T cell therapy in China. We also propose several potential directions for the future development of TCR-T cell therapy.

Tuning the potency and selectivity of ImmTAC molecules by affinity modulation

T-cell-engaging bispecifics have great clinical potential for the treatment of cancer and infectious diseases. The binding affinity and kinetics of a bispecific molecule for both target and T-cell CD3 have substantial effects on potency and specificity, but the rules governing these relationships are not fully understood. Using immune mobilizing monoclonal TCRs against cancer (ImmTAC) molecules as a model, we explored the impact of altering affinity for target and CD3 on the potency and specificity of the redirected T-cell response. This class of bispecifics binds specific target peptides presented by human leukocyte antigen on the cell surface via an affinity-enhanced T-cell receptor and can redirect T-cell activation with an anti-CD3 effector moiety. The data reveal that combining a strong affinity TCR with an intermediate affinity anti-CD3 results in optimal T-cell activation, while strong affinity of both targeting and effector domains significantly reduces maximum cytokine release. Moreover, by optimizing the affinity of both parts of the molecule, it is possible to improve the selectivity. These results could be effectively modelled based on kinetic proofreading with limited signalling. This model explained the experimental observation that strong binding at both ends of the molecules leads to reduced activity, through very stable target-bispecific-effector complexes leading to CD3 entering a non-signalling dark state. These findings have important implications for the design of anti-CD3-based bispecifics with optimal biophysical parameters for both activity and specificity.

Innate TCRβ-chain engagement drives human T cells toward distinct memory-like effector phenotypes with immunotherapeutic potentials

Clonotypic αβ T cell responses to cargoes presented by major histocompatibility complex (MHC), MR1, or CD1 proteins underpin adaptive immunity. Those responses are mostly mediated by complementarity-determining region 3 motifs created by quasi-random T cell receptor (TCR) gene rearrangements, with diversity being highest for TCRγδ. Nonetheless, TCRγδ also displays nonclonotypic innate responsiveness following engagement of germline-encoded Vγ-specific residues by butyrophilin (BTN) or BTN-like (BTNL) proteins that uniquely mediate γδ T cell subset selection. We now report that nonclonotypic TCR engagement likewise induces distinct phenotypes in TCRαβ+ cells. Specifically, antibodies to germline-encoded human TCRVβ motifs consistently activated naïve or memory T cells toward core states distinct from those induced by anti-CD3 or superantigens and from others commonly reported. Those states combined selective proliferation and effector function with activation-induced inhibitory receptors and memory differentiation. Thus, nonclonotypic TCRVβ targeting broadens our perspectives on human T cell response modes and might offer ways to induce clinically beneficial phenotypes in defined T cell subsets.

T Cell Receptor Chain Centricity: The Phenomenon and Potential Applications in Cancer Immunotherapy

T cells are crucial players in adaptive anti-cancer immunity. The gene modification of T cells with tumor antigen-specific T cell receptors (TCRs) was a milestone in personalized cancer immunotherapy. TCR is a heterodimer (either α/β or γ/δ) able to recognize a peptide antigen in a complex with self-MHC molecules. Although traditional concepts assume that an α- and β-chain contribute equally to antigen recognition, mounting data reveal that certain receptors possess chain centricity, i.e., one hemi-chain TCR dominates antigen recognition and dictates its specificity. Chain-centric TCRs are currently poorly understood in terms of their origin and the functional T cell subsets that express them. In addition, the ratio of α- and β-chain-centric TCRs, as well as the exact proportion of chain-centric TCRs in the native repertoire, is generally still unknown today. In this review, we provide a retrospective analysis of studies that evidence chain-centric TCRs, propose patterns of their generation, and discuss the potential applications of such receptors in T cell gene modification for adoptive cancer immunotherapy.

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.

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.

PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure

Introduction

Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe.

Methods

Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs.

Results and discussion

We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.

Structural analysis of cancer-relevant TCR-CD3 and peptide-MHC complexes by cryoEM

The recognition of antigenic peptide-MHC (pMHC) molecules by T-cell receptors (TCR) initiates the T-cell mediated immune response. Structural characterization is key for understanding the specificity of TCR-pMHC interactions and informing the development of therapeutics. Despite the rapid rise of single particle cryoelectron microscopy (cryoEM), x-ray crystallography has remained the preferred method for structure determination of TCR-pMHC complexes. Here, we report cryoEM structures of two distinct full-length α/β TCR-CD3 complexes bound to their pMHC ligand, the cancer-testis antigen HLA-A2/MAGEA4 (230-239). We also determined cryoEM structures of pMHCs containing MAGEA4 (230-239) peptide and the closely related MAGEA8 (232-241) peptide in the absence of TCR, which provided a structural explanation for the MAGEA4 preference displayed by the TCRs. These findings provide insights into the TCR recognition of a clinically relevant cancer antigen and demonstrate the utility of cryoEM for high-resolution structural analysis of TCR-pMHC interactions.

Contemplating immunopeptidomes to better predict them

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

MatchTope: A tool to predict the cross reactivity of peptides complexed with Major Histocompatibility Complex I

The therapeutic targeting of the immune system, for example in vaccinology and cancer treatment, is a challenging task and the subject of active research. Several in silico tools used for predicting immunogenicity are based on the analysis of peptide sequences binding to the Major Histocompatibility Complex (pMHC). However, few of these bioinformatics tools take into account the pMHC three-dimensional structure. Here, we describe a new bioinformatics tool, MatchTope, developed for predicting peptide similarity, which can trigger cross-reactivity events, by computing and analyzing the electrostatic potentials of pMHC complexes. We validated MatchTope by using previously published data from in vitro assays. We thereby demonstrate the strength of MatchTope for similarity prediction between targets derived from several pathogens as well as for indicating possible cross responses between self and tumor peptides. Our results suggest that MatchTope can enhance and speed up future studies in the fields of vaccinology and cancer immunotherapy.

Structural insights into engineering a T-cell receptor targeting MAGE-A10 with higher affinity and specificity for cancer immunotherapy

BACKGROUND

T-cell receptor (TCR) immunotherapy is becoming a viable modality in cancer treatment with efficacy in clinical trials. The safety of patients is paramount, so innovative cell engineering methods are being employed to exploit adaptive immunity while controlling the factors governing antigen receptor (ie, TCR) specificity and cross-reactivity. We recently reported a TCR engineering campaign and selectivity profiling assay (X-scan) targeting a melanoma antigen gene (MAGE)-A10 peptide. This helped to distinguish between two well-performing TCRs based on cross-reactivity potential during preclinical drug evaluation, allowing one to be advanced to T-cell immunotherapeutic clinical trials. Here, we present three-dimensional structural information on those TCRs, highlighting engineering improvements and molecular mechanisms likely underpinning differential selectivity.

METHODS

Parental and engineered TCRs were purified and crystallized either alone or complexed to human leucocyte antigen (HLA)-A*02:01 presenting the MAGE-A10 9-mer peptide, GLYDGMEHL (pHLA/MAGE-A10-9). Using X-ray diffraction, we solved four high-resolution crystal structures and evaluated them relative to previously reported functional results.

RESULTS

The unligated parental TCR displayed similar complementarity-determining region (CDR) loop conformations when bound to pHLA/MAGE-A10-9; a rigid-body movement of TCR beta chain variable domain (TRBV) relative to TCR alpha chain variable domain helped optimal pHLA engagement. This first view of an HLA-bound MAGE-A10 peptide revealed an intrachain non-covalent 'staple' between peptide Tyr3 and Glu7. A subtle Glu31-Asp mutation in βCDR1 of the parental TCR generated a high-affinity derivative. Its pHLA-complexed structure shows that the shorter Asp leans toward the pHLA with resulting rigid-body TRBV shift, creating localized changes around the peptide's C-terminus. Structural comparison with a less selective TCR indicated that differential cross-reactivity to MAGE-A10 peptide variants is most readily explained by alterations in surface electrostatics, and the size and geometry of TCR-peptide interfacial cavities.

CONCLUSIONS

Modest changes in engineered TCRs targeting MAGE-A10 produced significantly different properties. Conformational invariance of TCR and antigen peptide plus more space-filling CDR loop sequences may be desirable properties for clinically relevant TCR-pHLA systems to reduce the likelihood of structurally similar peptide mimics being tolerated by a TCR. Such properties may partially explain why the affinity-enhanced, in vitro-selected TCR has been generally well tolerated in patients.

Tuning T cell receptor sensitivity through catch bond engineering

Adoptive cell therapy using engineered T cell receptors (TCRs) is a promising approach for targeting cancer antigens, but tumor-reactive TCRs are often weakly responsive to their target ligands, peptide-major histocompatibility complexes (pMHCs). Affinity-matured TCRs can enhance the efficacy of TCR-T cell therapy but can also cross-react with off-target antigens, resulting in organ immunopathology. We developed an alternative strategy to isolate TCR mutants that exhibited high activation signals coupled with low-affinity pMHC binding through the acquisition of catch bonds. Engineered analogs of a tumor antigen MAGE-A3-specific TCR maintained physiological affinities while exhibiting enhanced target killing potency and undetectable cross-reactivity, compared with a high-affinity clinically tested TCR that exhibited lethal cross-reactivity with a cardiac antigen. Catch bond engineering is a biophysically based strategy to tune high-sensitivity TCRs for T cell therapy with reduced potential for adverse cross-reactivity.

Engineering the T cell receptor for fun and profit: Uncovering complex biology, interrogating the immune system, and targeting disease

T cell receptors (TCRs) orchestrate cellular immunity by recognizing peptide antigens bound and presented by major histocompatibility complex (MHC) proteins. Due to the TCR's central role in immunity and tight connection with human health, there has been significant interest in modulating TCR properties through protein engineering methods. Complicating these efforts is the complexity and vast diversity of TCR-peptide/MHC interfaces, the interdependency between TCR affinity, specificity, and cross-reactivity, and the sophisticated relationships between TCR binding properties and T cell function, many aspects of which are not well understood. Here we review TCR engineering, starting with a brief historical overview followed by discussions of more recent developments, including new efforts and opportunities to engineer TCR affinity, modulate specificity, and develop novel TCR-based constructs.

Generalizable design parameters for soluble T cell receptor-based T cell engagers

While most biological and cellular immunotherapies recognize extracellular targets, T cell receptor (TCR) therapeutics are unique in their ability to recognize the much larger pool of intracellular antigens found on virus-infected or cancerous cells. Recombinant T cell receptor (rTCR)-based therapeutics are gaining momentum both preclinically and clinically highlighted by recent positive phase III human clinical trial results for a TCR/CD3 bifunctional protein in uveal melanoma. Unlike antibody-based T cell engagers whose molecular formats have been widely and extensively evaluated, little data exist describing the putative activities of varied bifunctional formats using rTCRs. Here we generate rTCR/anti-CD3 bifunctionals directed toward NY-ESO-1 or MAGE-A3 with a variety of molecular formats. We show that inducing strong redirected lysis activity against tumors displaying either NY-ESO-1 or MAGE-A3 is highly restricted to small, tandem binding formats with an rTCR/antiCD3 Fab demonstrating the highest potency, rTCR/anti-CD3 single chain variable domain fragment showing similar but consistently weaker potency, and IgG-like or IgG-Fc-containing molecules demonstrating poor activity. We believe this is a universal trait of rTCR bifunctionals, given the canonical TCR/human leukocyte antigen structural paradigm.

Criteria to make animal studies more relevant to treating human cancer

Certain aspects of experimental tumor models in mice most accurately reflect the biology and immunology of cancer in patients. A survey of experimental cancer immunotherapy papers published in 2020 shows most do not achieve cancer shrinkage although treatment is initiated at an early time point after cancer cell injection, which does not reflect cancer immunotherapy in patients. Even then, few current experimental approaches eradicate the injected malignant cells, most only delay outgrowth. The value of targeting mutation-encoded tumor-specific antigens becomes increasingly evident while problems of finding normal gene-encoded tumor-associated antigens as safe, effective targets persist. It might be time to refocus on realistic experimental settings and truly cancer-specific targets. These antigens are associated with the least risk of side effects.

OUP accepted manuscript

The study and manipulation of T cell receptors (TCRs) is central to multiple fields across basic and translational immunology research. Produced by V(D)J recombination, TCRs are often only recorded in the literature and data repositories as a combination of their V and J gene symbols, plus their hypervariable CDR3 amino acid sequence. However, numerous applications require full-length coding nucleotide sequences. Here we present Stitchr, a software tool developed to specifically address this limitation. Given minimal V/J/CDR3 information, Stitchr produces complete coding sequences representing a fully spliced TCR cDNA. Due to its modular design, Stitchr can be used for TCR engineering using either published germline or novel/modified variable and constant region sequences. Sequences produced by Stitchr were validated by synthesizing and transducing TCR sequences into Jurkat cells, recapitulating the expected antigen specificity of the parental TCR. Using a companion script, Thimble, we demonstrate that Stitchr can process a million TCRs in under ten minutes using a standard desktop personal computer. By systematizing the production and modification of TCR sequences, we propose that Stitchr will increase the speed, repeatability, and reproducibility of TCR research. Stitchr is available on GitHub.

Engineering soluble T-cell receptors for therapy

Immunotherapy approaches that target peptide-human leukocyte antigen (pHLA) complexes are becoming highly attractive because of their potential to access virtually all foreign and cellular proteins. For this reason, there has been considerable interest in the development of the natural ligand for pHLA, the T-cell receptor (TCR), as a soluble drug to target disease-associated pHLA presented at the cell surface. However, native TCR stability is suboptimal for soluble drug development, and natural TCRs generally have weak affinities for pHLAs, limiting their potential to reach efficacious receptor occupancy levels as soluble drugs. To overcome these limitations and make full use of the TCR as a soluble drug platform, several protein engineering solutions have been applied to TCRs to enhance both their stability and affinity, with a focus on retaining target specificity and selectivity. Here, we review these advances and look to the future for the next generation of soluble TCR-based therapies that can target monomorphic HLA-like proteins presenting both peptide and nonpeptide antigens.

Potent, Selective CARs as Potential T-Cell Therapeutics for HPV-positive Cancers

Next-generation T-cell therapies will likely continue to utilize T-cell receptors (TCRs) and chimeric antigen receptors (CARs) because each receptor type has advantages. TCRs often possess exceptional properties even when tested unmodified from patients' T cells. CARs are generally less sensitive, possibly because their ligand-binding domains are grafted from antibodies selected for binding affinity or avidity and not broadly optimized for a functional response. Because of the disconnect between binding and function among these receptor types, the ultimate potential of CARs optimized for sensitivity and selectivity is not clear. Here, we focus on a thoroughly studied immuno-oncology target, the HLA-A*02/HPV-E629-38 complex, and show that CARs can be optimized by a combination of high-throughput binding screens and low-throughput functional assays to have comparable activity to clinical TCRs in acute assays in vitro. These results provide a case study for the challenges and opportunities of optimizing high-performing CARs, especially in the context of targets utilized naturally by TCRs.

Synthetic Peptides with Inadvertent Chemical Modifications Can Activate Potentially Autoreactive T Cells

The human CD8+ T cell clone 6C5 has previously been shown to recognize the tert-butyl-modified Bax161-170 peptide LLSY(3-tBu)FGTPT presented by HLA-A*02:01. This nonnatural epitope was likely created as a by-product of fluorenylmethoxycarbonyl protecting group peptide synthesis and bound poorly to HLA-A*02:01. In this study, we used a systematic approach to identify and characterize natural ligands for the 6C5 TCR. Functional analyses revealed that 6C5 T cells only recognized the LLSYFGTPT peptide when tBu was added to the tyrosine residue and did not recognize the LLSYFGTPT peptide modified with larger (di-tBu) or smaller chemical groups (Me). Combinatorial peptide library screening further showed that 6C5 T cells recognized a series of self-derived peptides with dissimilar amino acid sequences to LLSY(3-tBu)FGTPT. Structural studies of LLSY(3-tBu)FGTPT and two other activating nonamers (IIGWMWIPV and LLGWVFAQV) in complex with HLA-A*02:01 demonstrated similar overall peptide conformations and highlighted the importance of the position (P) 4 residue for T cell recognition, particularly the capacity of the bulky amino acid tryptophan to substitute for the tBu-modified tyrosine residue in conjunction with other changes at P5 and P6. Collectively, these results indicated that chemical modifications directly altered the immunogenicity of a synthetic peptide via molecular mimicry, leading to the inadvertent activation of a T cell clone with unexpected and potentially autoreactive specificities.

Re-examination of MAGE-A3 as a T-cell Therapeutic Target

In 2013, an innovative MAGE-A3-directed cancer therapeutic of great potential value was terminated in the clinic because of neurotoxicity. The safety problems were hypothesized to originate from off-target T-cell receptor activity against a closely related MAGE-A12 peptide. A combination of published and new data led us to test this hypothesis with current technology. Our results call into question MAGE-A12 as the source of the neurotoxicity. Rather, the data imply that an alternative related peptide from EPS8L2 may be responsible. Given the qualities of MAGE-A3 as an onco-testis antigen widely expressed in tumors and largely absent from normal adult tissues, these findings suggest that MAGE-A3 may deserve further consideration as a cancer target. As a step in this direction, the authors isolated 2 MAGE-A3 peptide-major histocompatibility complex-directed chimeric antigen receptors, 1 targeting the same peptide as the clinical T-cell receptor. Both chimeric antigen receptors have improved selectivity over the EPS8L2 peptide that represents a significant risk for MAGE-A3-targeted therapeutics, showing that there may be other options for MAGE-A3 cell therapy.

TCR β chain-directed bispecific antibodies for the treatment of T cell cancers

Immunotherapies such as chimeric antigen receptor (CAR) T cells and bispecific antibodies redirect healthy T cells to kill cancer cells expressing the target antigen. The pan-B cell antigen-targeting immunotherapies have been remarkably successful in treating B cell malignancies. Such therapies also result in the near-complete loss of healthy B cells, but this depletion is well tolerated by patients. Although analogous targeting of pan-T cell markers could, in theory, help control T cell cancers, the concomitant healthy T cell depletion would result in severe and unacceptable immunosuppression. Thus, therapies directed against T cell cancers require more selective targeting. Here, we describe an approach to target T cell cancers through T cell receptor (TCR) antigens. Each T cell, normal or malignant, expresses a unique TCR β chain generated from 1 of 30 TCR β chain variable gene families (TRBV1 to TRBV30). We hypothesized that bispecific antibodies targeting a single TRBV family member expressed in malignant T cells could promote killing of these cancer cells, while preserving healthy T cells that express any of the other 29 possible TRBV family members. We addressed this hypothesis by demonstrating that bispecific antibodies targeting TRBV5-5 (α-V5) or TRBV12 (α-V12) specifically lyse relevant malignant T cell lines and patient-derived T cell leukemias in vitro. Treatment with these antibodies also resulted in major tumor regressions in mouse models of human T cell cancers. This approach provides an off-the-shelf, T cell cancer selective targeting approach that preserves enough healthy T cells to maintain cellular immunity.

Targeting a neoantigen derived from a common TP53 mutation

TP53 (tumor protein p53) is the most commonly mutated cancer driver gene, but drugs that target mutant tumor suppressor genes, such as TP53, are not yet available. Here, we describe the identification of an antibody highly specific to the most common TP53 mutation (R175H, in which arginine at position 175 is replaced with histidine) in complex with a common human leukocyte antigen-A (HLA-A) allele on the cell surface. We describe the structural basis of this specificity and its conversion into an immunotherapeutic agent: a bispecific single-chain diabody. Despite the extremely low p53 peptide-HLA complex density on the cancer cell surface, the bispecific antibody effectively activated T cells to lyse cancer cells that presented the neoantigen in vitro and in mice. This approach could in theory be used to target cancers containing mutations that are difficult to target in conventional ways.

Specificity of bispecific T cell receptors and antibodies targeting peptide-HLA

Tumor-associated peptide-human leukocyte antigen complexes (pHLAs) represent the largest pool of cell surface-expressed cancer-specific epitopes, making them attractive targets for cancer therapies. Soluble bispecific molecules that incorporate an anti-CD3 effector function are being developed to redirect T cells against these targets using 2 different approaches. The first achieves pHLA recognition via affinity-enhanced versions of natural TCRs (e.g., immune-mobilizing monoclonal T cell receptors against cancer [ImmTAC] molecules), whereas the second harnesses an antibody-based format (TCR-mimic antibodies). For both classes of reagent, target specificity is vital, considering the vast universe of potential pHLA molecules that can be presented on healthy cells. Here, we made use of structural, biochemical, and computational approaches to investigate the molecular rules underpinning the reactivity patterns of pHLA-targeting bispecifics. We demonstrate that affinity-enhanced TCRs engage pHLA using a comparatively broad and balanced energetic footprint, with interactions distributed over several HLA and peptide side chains. As ImmTAC molecules, these TCRs also retained a greater degree of pHLA selectivity, with less off-target activity in cellular assays. Conversely, TCR-mimic antibodies tended to exhibit binding modes focused more toward hot spots on the HLA surface and exhibited a greater degree of crossreactivity. Our findings extend our understanding of the basic principles that underpin pHLA selectivity and exemplify a number of molecular approaches that can be used to probe the specificity of pHLA-targeting molecules, aiding the development of future reagents.

Empirical and Rational Design of T Cell Receptor-Based Immunotherapies

The use of T cells reactive with intracellular tumor-associated or tumor-specific antigens has been a promising strategy for cancer immunotherapies in the past three decades, but the approach has been constrained by a limited understanding of the T cell receptor's (TCR) complex functions and specificities. Newer TCR and T cell-based approaches are in development, including engineered adoptive T cells with enhanced TCR affinities, TCR mimic antibodies, and T cell-redirecting bispecific agents. These new therapeutic modalities are exciting opportunities by which TCR recognition can be further exploited for therapeutic benefit. In this review we summarize the development of TCR-based therapeutic strategies and focus on balancing efficacy and potency versus specificity, and hence, possible toxicity, of these powerful therapeutic modalities.

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.

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.

Immunological and functional aspects of MAGEA3 cancer/testis antigen

Identification of ectopic gene activation in cancer cells serves as a basis for both gene signature-guided tumor targeting and unearthing of oncogenic mechanisms to expand the understanding of tumor biology/oncogenic process. Proteins expressed only in germ cells of testis and/or placenta (immunoprivileged organs) and in malignancies are called cancer testis antigens; they are antigenic because of the lack of antigen presentation by those specific cell types (germ cells), which limits the exposure of the proteins to the immune cells. Since the Cancer Testis Antigens (CTAs) are immunogenic and expressed in a wide variety of cancer types, CT antigens have become interesting target for immunotherapy against cancer. Among CT antigens MAGEA family is reported to have 12 members (MAGEA1 to MAGEA12). The current review highlights the studies on MAGEA3 which is a CT antigen and reported in almost all types of cancer. MAGEA3 is well tried for cancer immunotherapy. Recent advances on its functional and immunological aspect warranted much deliberation on effective therapeutic approach, thus making it a more interesting target for cancer therapy.

Molecular Rules Underpinning Enhanced Affinity Binding of Human T Cell Receptors Engineered for Immunotherapy

Immuno-oncology approaches that utilize T cell receptors (TCRs) are becoming highly attractive because of their potential to target virtually all cellular proteins, including cancer-specific epitopes, via the recognition of peptide-human leukocyte antigen (pHLA) complexes presented at the cell surface. However, because natural TCRs generally recognize cancer-derived pHLAs with very weak affinities, efforts have been made to enhance their binding strength, in some cases by several million-fold. In this study, we investigated the mechanisms underpinning human TCR affinity enhancement by comparing the crystal structures of engineered enhanced affinity TCRs with those of their wild-type progenitors. Additionally, we performed molecular dynamics simulations to better understand the energetic mechanisms driving the affinity enhancements. These data demonstrate that supra-physiological binding affinities can be achieved without altering native TCR-pHLA binding modes via relatively subtle modifications to the interface contacts, often driven through the addition of buried hydrophobic residues. Individual energetic components of the TCR-pHLA interaction governing affinity enhancements were distinct and highly variable for each TCR, often resulting from additive, or knock-on, effects beyond the mutated residues. This comprehensive analysis of affinity-enhanced TCRs has important implications for the future rational design of engineered TCRs as efficacious and safe drugs for cancer treatment.

Adaptive T cell immunotherapy in cancer

Impaired tumor-specific effector T cells contribute to tumor progression and unfavorable clinical outcomes. As a compensatory T cell-dependent cancer immunoediting strategy, adoptive T cell therapy (ACT) has achieved encouraging therapeutic results, and this strategy is now on the center stage of cancer treatment and research. ACT involves the ex vivo stimulation and expansion of tumor-infiltrating lymphocytes (TILs) with inherent tumor reactivity or T cells that have been genetically modified to express the cognate chimeric antigen receptor or T cell receptor (CAR/TCR), followed by the passive transfer of these cells into a lymphodepleted host. Primed T cells must provide highly efficient and long-lasting immune defense against transformed cells during ACT. Anin-depth understanding of the basic mechanisms of these living drugs can help us improve upon current strategies and design better next-generation T cell-based immunotherapies. From this perspective, we provide an overview of current developments in different ACT strategies, with a focus on frontier clinical trials that offer a proof of principle. Meanwhile, insights into the determinants of ACT are discussed, which will lead to more rational, potent and widespread applications in the future.

Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests

Prediction of stable peptide binding to Class I HLAs is an important component for designing immunotherapies. While the best performing predictors are based on machine learning algorithms trained on peptide-HLA (pHLA) sequences, the use of structure for training predictors deserves further exploration. Given enough pHLA structures, a predictor based on the residue-residue interactions found in these structures has the potential to generalize for alleles with little or no experimental data. We have previously developed APE-Gen, a modeling approach able to produce pHLA structures in a scalable manner. In this work we use APE-Gen to model over 150,000 pHLA structures, the largest dataset of its kind, which were used to train a structure-based pan-allele model. We extract simple, homogenous features based on residue-residue distances between peptide and HLA, and build a random forest model for predicting stable pHLA binding. Our model achieves competitive AUROC values on leave-one-allele-out validation tests using significantly less data when compared to popular sequence-based methods. Additionally, our model offers an interpretation analysis that can reveal how the model composes the features to arrive at any given prediction. This interpretation analysis can be used to check if the model is in line with chemical intuition, and we showcase particular examples. Our work is a significant step toward using structure to achieve generalizable and more interpretable prediction for stable pHLA binding.

Cancer Antigen Discovery Is Enabled by RNA Sequencing of Highly Purified Malignant and Nonmalignant Cells

PURPOSE

Broadly expressed, highly differentiated tumor-associated antigens (TAA) can elicit antitumor immunity. However, vaccines targeting TAAs have demonstrated disappointing clinical results, reflecting poor antigen selection and/or immunosuppressive mechanisms.

EXPERIMENTAL DESIGN

Here, a panel of widely expressed, novel colorectal TAAs were identified by performing RNA sequencing of highly purified colorectal tumor cells in comparison with patient-matched colonic epithelial cells; tumor cell purification was essential to reveal these genes. Candidate TAA protein expression was confirmed by IHC, and preexisting T-cell immunogenicity toward these antigens tested.

RESULTS

The most promising candidate for further development is DNAJB7 [DnaJ heat shock protein family (Hsp40) member B7], identified here as a novel cancer-testis antigen. It is expressed in many tumors and is strongly immunogenic in patients with cancers originating from a variety of sites. DNAJB7-specific T cells were capable of killing colorectal tumor lines in vitro, and the IFNγ+ response was markedly magnified by control of immunosuppression with cyclophosphamide in patients with cancer.

CONCLUSIONS

This study highlights how prior methods that sequence whole tumor fractions (i.e., inclusive of alive/dead stromal cells) for antigen identification may have limitations. Through tumor cell purification and sequencing, novel candidate TAAs have been identified for future immunotherapeutic targeting.

T cell receptor interactions with human leukocyte antigen govern indirect peptide selectivity for the cancer testis antigen MAGE-A4

T cell-mediated immunity is governed primarily by T cell receptor (TCR) recognition of peptide-human leukocyte antigen (pHLA) complexes and is essential for immunosurveillance and disease control. This interaction is generally stabilized by interactions between the HLA surface and TCR germline-encoded complementarity-determining region (CDR) loops 1 and 2, whereas peptide selectivity is guided by direct interactions with the TCR CDR3 loops. Here, we solved the structure of a newly identified TCR in complex with a clinically relevant peptide derived from the cancer testis antigen melanoma antigen-A4 (MAGE-A4). The TCR bound pHLA in a position shifted toward the peptide's N terminus. This enabled the TCR to achieve peptide selectivity via an indirect mechanism, whereby the TCR sensed the first residue of the peptide through HLA residue Trp-167, which acted as a tunable gateway. Amino acid substitutions at peptide position 1 predicted to alter the HLA Trp-167 side-chain conformation abrogated TCR binding, indicating that this indirect binding mechanism is essential for peptide recognition. These findings extend our understanding of the molecular rules that underpin antigen recognition by TCRs and have important implications for the development of TCR-based therapies.

High-throughput peptide-MHC complex generation and kinetic screenings of TCRs with peptide-receptive HLA-A*02:01 molecules

Major histocompatibility complex (MHC) class I molecules present short peptide ligands on the cell surface for interrogation by cytotoxic CD8⁺ T cells. MHC class I complexes presenting tumor-associated peptides such as neoantigens represent key targets of cancer immunotherapy approaches currently in development, making them important for efficacy and safety screenings. Without peptide ligand, MHC class I complexes are unstable and decay quickly, making the production of soluble monomers for analytical purposes labor intensive. We have developed a disulfide-stabilized HLA-A*02:01 molecule that is stable without peptide but can form peptide-MHC complexes (pMHCs) with ligands of choice in a one-step loading procedure. We illustrate the similarity between the engineered mutant and the wild-type molecule with respect to affinity of wild-type or affinity-matured T cell receptors (TCRs) and present a crystal structure corroborating the binding kinetics measurements. In addition, we demonstrate a high-throughput binding kinetics measurement platform to analyze the binding characteristics of bispecific TCR (bsTCR) molecules against diverse pMHC libraries produced with the disulfide-stabilized HLA-A*02:01 molecule. We show that bsTCR affinities for pMHCs are indicative of in vitro function and generate a bsTCR binding motif to identify potential off-target interactions in the human proteome. These findings showcase the potential of the platform and the engineered HLA-A*02:01 molecule in the emerging field of pMHC-targeting biologics.

Computational stabilization of T cell receptors allows pairing with antibodies to form bispecifics

Recombinant T cell receptors (TCRs) can be used to redirect naïve T cells to eliminate virally infected or cancerous cells; however, they are plagued by low stability and uneven expression. Here, we use molecular modeling to identify mutations in the TCR constant domains (Cα/Cβ) that increase the unfolding temperature of Cα/Cβ by 20 °C, improve the expression of four separate α/β TCRs by 3- to 10-fold, and improve the assembly and stability of TCRs with poor intrinsic stability. The stabilizing mutations rescue the expression of TCRs destabilized through variable domain mutation. The improved stability and folding of the TCRs reduces glycosylation, perhaps through conformational stabilization that restricts access to N-linked glycosylation enzymes. The Cα/Cβ mutations enables antibody-like expression and assembly of well-behaved bispecific molecules that combine an anti-CD3 antibody with the stabilized TCR. These TCR/CD3 bispecifics can redirect T cells to kill tumor cells with target HLA/peptide on their surfaces in vitro.

TCRs with Distinct Specificity Profiles Use Different Binding Modes to Engage an Identical Peptide-HLA Complex

The molecular rules driving TCR cross-reactivity are poorly understood and, consequently, it is unclear the extent to which TCRs targeting the same Ag recognize the same off-target peptides. We determined TCR-peptide-HLA crystal structures and, using a single-chain peptide-HLA phage library, we generated peptide specificity profiles for three newly identified human TCRs specific for the cancer testis Ag NY-ESO-1_157-165-HLA-A2. Two TCRs engaged the same central peptide feature, although were more permissive at peripheral peptide positions and, accordingly, possessed partially overlapping peptide specificity profiles. The third TCR engaged a flipped peptide conformation, leading to the recognition of off-target peptides sharing little similarity with the cognate peptide. These data show that TCRs specific for a cognate peptide recognize discrete peptide repertoires and reconciles how an individual's limited TCR repertoire following negative selection in the thymus is able to recognize a vastly larger antigenic pool.

Single variable domains from the T cell receptor β chain function as mono- and bifunctional CARs and TCRs

Cell therapy using T cell receptors (TCRs) and chimeric antigen receptors (CARs) represents a new wave of immunotherapies garnering considerable attention and investment. Further progress in this area of medicine depends in part on improving the functional capabilities of the engineered components, while maintaining the overall size of recombinant constructs to ensure their compatibility with existing gene delivery vehicles. We describe a single-variable-domain TCR (svd TCR) that utilizes only the variable domain of the β chain (Vβ). This Vβ module not only works in TCR and CAR formats, but also can be used to create single-chain bispecific CARs and TCRs. Comparison of individual ligand-binding Vβ domains in different formats suggests that the lone Vβ sequence controls the sensitivity and a major part of the specificity of the CAR or TCR construct, regardless of signaling format, in Jurkat and primary T cells.

Heterologous Immunity: Role in Natural and Vaccine-Induced Resistance to Infections

The central paradigm of vaccination is to generate resistance to infection by a specific pathogen when the vacinee is re-exposed to that pathogen. This paradigm is based on two fundamental characteristics of the adaptive immune system, specificity and memory. These characteristics come from the clonal specificity of T and B cells and the long-term survival of previously-encountered memory cells which can rapidly and specifically expand upon re-exposure to the same specific antigen. However, there is an increasing awareness of the concept, as well as experimental documentation of, heterologous immunity and cross-reactivity of adaptive immune lymphocytes in protection from infection. This awareness is supported by a number of human epidemiological studies in vaccine recipients and/or individuals naturally-resistant to certain infections, as well as studies in mouse models of infections, and indeed theoretical considerations regarding the disproportional repertoire of available T and B cell clonotypes compared to antigenic epitopes found on pathogens. Heterologous immunity can broaden the protective outcomes of vaccinations, and natural resistance to infections. Besides exogenous microbes/pathogens and/or vaccines, endogenous microbiota can also impact the outcomes of an infection and/or vaccination through heterologous immunity. Moreover, utilization of viral and/or bacterial vaccine vectors, capable of inducing heterologous immunity may also influence the natural course of many infections/diseases. This review article will briefly discuss these implications and redress the central dogma of specificity in the immune system.

Stabilization of soluble high-affinity T-cell receptor with de novo disulfide bonds

Soluble T‐cell receptors (TCRs) have recently gained visibility as target‐recognition units of anticancer immunotherapeutic agents. Here, we improved the thermal stability of the well‐expressed high‐affinity A6 TCR by introducing pairs of cysteines in the invariable parts of the α‐ and β‐chain. A mutant with a novel intradomain disulfide bond in each chain also tested superior to the wild‐type in the accelerated stability assay. Binding of the mutant to the soluble cognate peptide (cp)–MHC and to the peptide‐loaded T2 cell line was equal to the wild‐type A6 TCR. The same stabilization motif worked efficiently in TCRs with different specificities, such as DMF5 and 1G4. Altogether, the biophysical properties of the soluble TCR molecule could be improved, without affecting its expression level and antigen‐binding specificity.

A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours

Spatial interactions between cancer and immune cells, as well as the recognition of tumour antigens by cells of the immune system, play a key role in the immune response against solid tumours. The existing mathematical models generally focus only on one of these key aspects. We present here a spatial stochastic individual-based model that explicitly captures antigen expression and recognition. In our model, each cancer cell is characterised by an antigen profile which can change over time due to either epimutations or mutations. The immune response against the cancer cells is initiated by the dendritic cells that recognise the tumour antigens and present them to the cytotoxic T cells. Consequently, T cells become activated against the tumour cells expressing such antigens. Moreover, the differences in movement between inactive and active immune cells are explicitly taken into account by the model. Computational simulations of our model clarify the conditions for the emergence of tumour clearance, dormancy or escape, and allow us to assess the impact of antigenic heterogeneity of cancer cells on the efficacy of immune action. Ultimately, our results highlight the complex interplay between spatial interactions and adaptive mechanisms that underpins the immune response against solid tumours, and suggest how this may be exploited to further develop cancer immunotherapies.

TCR-induced alteration of primary MHC peptide anchor residue

The HLA-A*02:01-restricted decapeptide EAAGIGILTV, derived from melanoma antigen recognized by T-cells-1 (MART-1) protein, represents one of the best-studied tumor associated T-cell epitopes, but clinical results targeting this peptide have been disappointing. This limitation may reflect the dominance of the nonapeptide, AAGIGILTV, at the melanoma cell surface. The decapeptide and nonapeptide are presented in distinct conformations by HLA-A*02:01 and TCRs from clinically relevant T-cell clones recognize the nonapeptide poorly. Here, we studied the MEL5 TCR that potently recognizes the nonapeptide. The structure of the MEL5-HLA-A*02:01-AAGIGILTV complex revealed an induced fit mechanism of antigen recognition involving altered peptide-MHC anchoring. This "flexing" at the TCR-peptide-MHC interface to accommodate the peptide antigen explains previously observed incongruences in this well-studied system and has important implications for future therapeutic approaches. Finally, this study expands upon the mechanisms by which molecular plasticity can influence antigen recognition by T cells.