T cell receptors (TCRs) employ diverse strategies to target a p53 cancer neoantigen

Preclinical studies show that Co-STARs combine the advantages of chimeric antigen and T cell receptors for the treatment of tumors with low antigen densities

Two types of engineered T cells have been successfully used to treat patients with cancer, one with an antigen recognition domain derived from antibodies [chimeric antigen receptors (CARs)] and the other derived from T cell receptors (TCRs). CARs use high-affinity antigen-binding domains and costimulatory domains to induce T cell activation but can only react against target cells with relatively high amounts of antigen. TCRs have a much lower affinity for their antigens but can react against target cells displaying only a few antigen molecules. Here, we describe a new type of receptor, called a Co-STAR (for costimulatory synthetic TCR and antigen receptor), that combines aspects of both CARs and TCRs. In Co-STARs, the antigen-recognizing components of TCRs are replaced by high-affinity antibody fragments, and costimulation is provided by two modules that drive NF-κB signaling (MyD88 and CD40). Using a TCR-mimic antibody fragment that targets a recurrent p53 neoantigen presented in a common human leukocyte antigen (HLA) allele, we demonstrate that T cells equipped with Co-STARs can kill cancer cells bearing low densities of antigen better than T cells engineered with conventional CARs and patient-derived TCRs in vitro. In mouse models, we show that Co-STARs mediate more robust T cell expansion and more durable tumor regressions than TCRs similarly modified with MyD88 and CD40 costimulation. Our data suggest that Co-STARs may have utility for other peptide-HLA antigens in cancer and other targets where antigen density may limit the efficacy of engineered T cells.

TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes

The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.

How does TCR-T cell therapy exhibit a superior anti-tumor efficacy

TCR-engineered T cells have achieved great progress in solid tumor therapy, some of which have been applicated in clinical trials. Deep knowledge about the current progress of TCR-T in tumor therapy would be beneficial to understand the direction. Here, we classify tumor antigens into tumor-associated antigens, tumor-specific antigens, tumor antigens expressed by oncogenic viruses, and tumor antigens caused by abnormal protein modification; Then we detail the TCR-T cell therapy effects targeting those tumor antigens in clinical or preclinical trials, and propose that neoantigen specific TCR-T cell therapy is expected to be a promising approach for solid tumors; Furthermore, we summarize the optimization strategies, such as tumor microenvironment, TCR pairing and affinity, to improve the therapeutic effect of TCR-T. Overall, this review provides inspiration for the antigen selection and therapy strategies of TCR-T in the future.

Structural basis for T cell recognition of cancer neoantigens and implications for predicting neoepitope immunogenicity

Adoptive cell therapy (ACT) with tumor-specific T cells has been shown to mediate durable cancer regression. Tumor-specific T cells are also the basis of other therapies, notably cancer vaccines. The main target of tumor-specific T cells are neoantigens resulting from mutations in self-antigens over the course of malignant transformation. The detection of neoantigens presents a major challenge to T cells because of their high structural similarity to self-antigens, and the need to avoid autoimmunity. How different a neoantigen must be from its wild-type parent for it to induce a T cell response is poorly understood. Here we review recent structural and biophysical studies of T cell receptor (TCR) recognition of shared cancer neoantigens derived from oncogenes, including p53R175H, KRASG12D, KRASG12V, HHATp8F, and PIK3CAH1047L. These studies have revealed that, in some cases, the oncogenic mutation improves antigen presentation by strengthening peptide-MHC binding. In other cases, the mutation is detected by direct interactions with TCR, or by energetically driven or other indirect strategies not requiring direct TCR contacts with the mutation. We also review antibodies designed to recognize peptide-MHC on cell surfaces (TCR-mimic antibodies) as an alternative to TCRs for targeting cancer neoantigens. Finally, we review recent computational advances in this area, including efforts to predict neoepitope immunogenicity and how these efforts may be advanced by structural information on peptide-MHC binding and peptide-MHC recognition by TCRs.

SARS-CoV-2 infection establishes a stable and age-independent CD8+ T cell response against a dominant nucleocapsid epitope using restricted T cell receptors

The resolution of SARS-CoV-2 replication hinges on cell-mediated immunity, wherein CD8+ T cells play a vital role. Nonetheless, the characterization of the specificity and TCR composition of CD8+ T cells targeting non-spike protein of SARS-CoV-2 before and after infection remains incomplete. Here, we analyzed CD8+ T cells recognizing six epitopes from the SARS-CoV-2 nucleocapsid (N) protein and found that SARS-CoV-2 infection slightly increased the frequencies of N-recognizing CD8+ T cells but significantly enhanced activation-induced proliferation compared to that of the uninfected donors. The frequencies of N-specific CD8+ T cells and their proliferative response to stimulation did not decrease over one year. We identified the N222-230 peptide (LLLDRLNQL, referred to as LLL thereafter) as a dominant epitope that elicited the greatest proliferative response from both convalescent and uninfected donors. Single-cell sequencing of T cell receptors (TCR) from LLL-specific CD8+ T cells revealed highly restricted Vα gene usage (TRAV12-2) with limited CDR3α motifs, supported by structural characterization of the TCR-LLL-HLA-A2 complex. Lastly, transcriptome analysis of LLL-specific CD8+ T cells from donors who had expansion (expanders) or no expansion (non-expanders) after in vitro stimulation identified increased chromatin modification and innate immune functions of CD8+ T cells in non-expanders. These results suggests that SARS-CoV-2 infection induces LLL-specific CD8+ T cell responses with a restricted TCR repertoire.

Hydrophobic interactions dominate the recognition of a KRAS G12V neoantigen

Specificity remains a major challenge to current therapeutic strategies for cancer. Mutation associated neoantigens (MANAs) are products of genetic alterations, making them highly specific therapeutic targets. MANAs are HLA-presented (pHLA) peptides derived from intracellular mutant proteins that are otherwise inaccessible to antibody-based therapeutics. Here, we describe the cryo-EM structure of an antibody-MANA pHLA complex. Specifically, we determine a TCR mimic (TCRm) antibody bound to its MANA target, the KRASG12V peptide presented by HLA-A*03:01. Hydrophobic residues appear to account for the specificity of the mutant G12V residue. We also determine the structure of the wild-type G12 peptide bound to HLA-A*03:01, using X-ray crystallography. Based on these structures, we perform screens to validate the key residues required for peptide specificity. These experiments led us to a model for discrimination between the mutant and the wild-type peptides presented on HLA-A*03:01 based exclusively on hydrophobic interactions.

Exploiting big data survival information to unify risk-stratification related, adaptive immune receptor parameters for multiple myeloma

With the improvement of treatment options, multiple myeloma related life expectancy has been prolonged, but the disease remains largely incurable. Immunotherapy is a growing field that shows promise in advancements for treatment, and recent work has demonstrated an opportunity to use immune receptor, complementarity determining region-3 (CDR3)-candidate antigen chemical complementarity scores to identify survival distinctions among subgroups of patients. Here, we have applied the complementarity scoring algorithm to identify multiple myeloma related, CDR3-cancer testis antigen (CTA) relationships associated with survival distinctions. Furthermore, we have overlapped these immune receptor features with a previous study that showed a dramatic survival distinction based on T-cell receptor, V- and J-gene segment usage, HLA allele combinations, whereby 100% of the patients in certain combination groups had no mortality related to multiple myeloma, during the study period. This overlap evaluation was consistent with the idea that there are likely considerable constraints on productive TRB-antigen-HLA combinations but more flexibility, and unpredictability, for the TRA-antigen-HLA combinations. Also, the approaches in this reported indicated the potential importance of the CTA, IGSF11, as a multiple myeloma antigen, an antigen previously, independently considered as a vaccine candidate in other settings.

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.

TRB CDR3-cancer testis antigen chemical complementarity scoring for identifying productive immune responses in renal cell carcinoma

BACKGROUND

Immunogenomics approaches to the characterization of renal cell carcinoma (RCC) have helped to better our understanding of the features of RCC immune dysfunction. However, much is still unknown with regard to specific immune interactions and their impact in the tumor microenvironment.

OBJECTIVE

This study applied chemical complementarity scoring for the TRB complementarity determining region-3 (CDR3) amino acid sequences and cancer testis antigens (CTAs) to determine whether such complementarity correlated with survival and the expression of immune marker genes.

METHODS

TRB recombination reads from RCC tumor samples from RNAseq files obtained from two separate databases, Moffitt Cancer Center and The Cancer Genome Atlas (TCGA), were evaluated. Chemical complementarity scores (CSs) were calculated for TRB CDR3-CTA pairs and survival assessments based on those CSs were performed.

RESULTS

Moffitt Cancer Center and TCGA cases representing the upper 50th percentile of chemical CSs for TRB CDR3 amino acid sequences and the CTA POTEA were found to be associated with a better overall survival (OS) Also, greater tumor RNA expression of multiple immune signature genes, including granzyme A, granzyme B, and interferon-gamma were correlated with the higher chemical CSs.

CONCLUSIONS

These results indicate that TRB CDR3-CTA chemical complementarity scoring may be useful in distinguishing RCC cases with a productive, anti-tumor immune response from cases where basic immune parameter assessments are inconsistent with a productive immune response.