Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes

Evoking the Cancer-immunity cycle by targeting the tumor-specific antigens in Cancer immunotherapy

Cancer-related deaths continue to rise, largely due to the suboptimal efficacy of current treatments. Fortunately, immunotherapy has emerged as a promising alternative, offering new hope for cancer patients. Among various immunotherapy approaches, targeting tumor-specific antigens (TSAs) has gained particular attention due to its demonstrated success in clinical settings. Despite these advancements, there are still gaps in our understanding of TSAs. Therefore, this review explores the life cycle of TSAs in cancer, the methods used to identify them, and recent advances in TSAs-targeted cancer therapies. Enhancing medical professionals' understanding of TSAs will help facilitate the development of more effective TSAs-based cancer treatments.

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

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

Comprehensive epitope mutational scan database enables accurate T cell receptor cross-reactivity prediction

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database, called BATCAVE, encompassing complete single amino acid mutational assays of more than 22,000 TCR-peptide pairs, centered around 25 immunogenic human and mouse epitopes, across both major histocompatibility complex classes, against 151 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. We also developed an active learning version of BATMAN, which can efficiently learn the binding profile of a novel TCR by selecting an informative yet small number of peptides to assay. When validated on our database, BATMAN outperforms existing methods and reveals important biochemical predictors of TCR-peptide interactions. Finally, we demonstrate the broad applicability of BATMAN, including for predicting off-target effects for TCR-based therapies and polyclonal T cell responses.

Droplet-based functional CRISPR screening of cell-cell interactions by SPEAC-seq

Cell-cell interactions are essential for the function and contextual regulation of biological tissues. We present a platform for high-throughput microfluidics-supported genetic screening of functional regulators of cell-cell interactions. Systematic perturbation of encapsulated associated cells followed by sequencing (SPEAC-seq) combines genome-wide CRISPR libraries, cell coculture in droplets and microfluidic droplet sorting based on functional read-outs determined by fluorescent reporter circuits to enable the unbiased discovery of interaction regulators. This technique overcomes limitations of traditional methods for characterization of cell-cell communication, which require a priori knowledge of cellular interactions, are highly engineered and lack functional read-outs. As an example of this technique, we describe the investigation of neuroinflammatory intercellular communication between microglia and astrocytes, using genome-wide CRISPR-Cas9 inactivation libraries and fluorescent reporters of NF-κB activation. This approach enabled the discovery of thousands of microglial regulators of astrocyte NF-κB activation important for the control of central nervous system inflammation. Importantly, SPEAC-seq can be adapted to different cell types, screening modalities, cell functions and physiological contexts, only limited by the ability to fluorescently report cell functions and by droplet cultivation conditions. Performing genome-wide screening takes less than 2 weeks and requires microfluidics capabilities. Thus, SPEAC-seq enables the large-scale investigation of cell-cell interactions.

Feature selection enhances peptide binding predictions for TCR-specific interactions

Introduction

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders.

Methods

This study presents a theoretical approach that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key properties contributing to binding affinity.

Results

Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data.

Discussion

Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a quantitative tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

Targeting cancer with precision: strategical insights into TCR-engineered T cell therapies

T cell receptor-engineered T (TCR-T) cell therapies are at the forefront of cancer immunotherapy, offering a transformative approach that significantly enhances the ability of T cells to recognize and eliminate cancer cells. This innovative method involves genetically modifying TCRs to increase their affinity for tumor-specific antigens. While these enhancements improve the ability of T cells to recognize and bind to antigens on cancer cells, rigorous assessment of specificity remains crucial to ensure safety and targeted responses. This dual focus on affinity and specificity holds significant promise for the treatment of solid tumors, enabling precise and efficient cancer cell recognition. Despite rapid advancements in TCR engineering and notable progress in TCR screening technologies, as evidenced by the growing number of specific TCRs entering clinical trials, several technical and clinical challenges remain. These challenges primarily pertain to the specificity, affinity, and safety of engineered TCRs. Moreover, the accurate identification and selection of TCRs that are both effective and safe are essential for the success of TCR-T cell therapies in cancer treatment. This review provides a comprehensive examination of the theoretical foundations of TCR therapy, explores strategies for screening specific TCRs and antigens, and highlights the ongoing challenges in this evolving therapeutic landscape.

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.

Antigen specificity of clonally-enriched CD8+ T cells in multiple sclerosis

CD8+ T cells are the dominant lymphocyte population in multiple sclerosis (MS) lesions where they are highly clonally expanded. The clonal identity, function, and antigen specificity of CD8+ T cells in MS are not well understood. Here we report a comprehensive single-cell RNA-seq and T cell receptor (TCR)-seq analysis of the cerebrospinal fluid (CSF) and blood from a cohort of treatment-naïve MS patients and control participants. A small subset of highly expanded and activated CD8+ T cells were enriched in the CSF in MS that displayed high activation, cytotoxicity and tissue-homing transcriptional profiles. Using a combination of unbiased and targeted antigen discovery approaches, MS-derived CD8+ T cell clonotypes recognizing Epstein-Barr virus (EBV) antigens and multiple novel mimotopes were identified. These findings shed vital insight into the role of CD8+ T cells in MS and pave the way towards disease biomarkers and therapeutic targets.

Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

Yeast-based screening platforms to understand and improve human health

Detailed molecular understanding of the human organism is essential to develop effective therapies. Saccharomyces cerevisiae has been used extensively for acquiring insights into important aspects of human health, such as studying genetics and cell-cell communication, elucidating protein-protein interaction (PPI) networks, and investigating human G protein-coupled receptor (hGPCR) signaling. We highlight recent advances and opportunities of yeast-based technologies for cost-efficient chemical library screening on hGPCRs, accelerated deciphering of PPI networks with mating-based screening and selection, and accurate cell-cell communication with human immune cells. Overall, yeast-based technologies constitute an important platform to support basic understanding and innovative applications towards improving human health.

The neoantigens derived from transposable elements - A hidden treasure for cancer immunotherapy

Neoantigen-based therapy is a promising approach that selectively activates the immune system of the host to recognize and eradicate cancer cells. Preliminary clinical trials have validated the feasibility, safety, and immunogenicity of personalized neoantigen-directed vaccines, enhancing their effectiveness and broad applicability in immunotherapy. While many ongoing oncological trials concentrate on neoantigens derived from mutations, these targets do not consistently provoke an immune response in all patients harboring the mutations. Additionally, tumors like ovarian cancer, which have a low tumor mutational burden (TMB), may be less amenable to mutation-based neoantigen therapies. Recent advancements in next-generation sequencing and bioinformatics have uncovered a rich source of neoantigens from non-canonical RNAs associated with transposable elements (TEs). Considering the substantial presence of TEs in the human genome and the proven immunogenicity of TE-derived neoantigens in various tumor types, this review investigates the latest findings on TE-derived neoantigens, examining their clinical implications, challenges, and unique advantages in enhancing tumor immunotherapy.

Encoding and display technologies for combinatorial libraries in drug discovery: The coming of age from biology to therapy

Drug discovery is a sophisticated process that incorporates scientific innovations and cutting-edge technologies. Compared to traditional bioactivity-based screening methods, encoding and display technologies for combinatorial libraries have recently advanced from proof-of-principle experiments to promising tools for pharmaceutical hit discovery due to their high screening efficiency, throughput, and resource minimization. This review systematically summarizes the development history, typology, and prospective applications of encoding and displayed technologies, including phage display, ribosomal display, mRNA display, yeast cell display, one-bead one-compound, DNA-encoded, peptide nucleic acid-encoded, and new peptide-encoded technologies, and examples of preclinical and clinical translation. We discuss the progress of novel targeted therapeutic agents, covering a spectrum from small-molecule inhibitors and nonpeptidic macrocycles to linear, monocyclic, and bicyclic peptides, in addition to antibodies. We also address the pending challenges and future prospects of drug discovery, including the size of screening libraries, advantages and disadvantages of the technology, clinical translational potential, and market space. This review is intended to establish a comprehensive high-throughput drug discovery strategy for scientific researchers and clinical drug developers.

Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen

T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR-peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR-peptide-major histocompatibility complex structure. Then a TCR-peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR-peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.

CRISPR-Cas9 screening reveals a distinct class of MHC-I binders with precise HLA-peptide recognition

Human leukocyte antigen (HLA) class-I molecules present fragments of the cellular proteome to the T cell receptor (TCR) of cytotoxic T cells to control infectious diseases and cancer. The large number of combinations of HLA class-I allotypes and peptides allows for highly specific and dedicated low-affinity interactions to a diverse array of TCRs and natural killer (NK) cell receptors. Whether the divergent HLA class-I peptide complex is exclusive for interactions with these proteins is unknown. Using genome-wide CRISPR-Cas9 activation and knockout screens, we identified peptide-specific HLA-C∗07 combinations that can interact with the surface molecules CD55 and heparan sulfate. These interactions closely resemble the HLA class-I interaction with the TCR regarding both the affinity range and the specificity of the peptide and HLA allele. These findings indicate that various proteins can specifically bind HLA class-I peptide complexes due to their polymorphic nature, which suggests there are more interactions like the ones we describe here.

Applications of self-assembled peptide hydrogels in anti-tumor therapy

Peptides are a class of active substances composed of a variety of amino acids with special physiological functions. The rational design of peptide sequences at the molecular level enables their folding into diverse secondary structures. This property has garnered significant attention in the biomedical sphere owing to their favorable biocompatibility, adaptable mechanical traits, and exceptional loading capabilities. Concurrently with advancements in modern medicine, the diagnosis and treatment of tumors have increasingly embraced targeted and personalized approaches. This review explores recent applications of self-assembled peptides derived from natural amino acids in chemical therapy, immunotherapy, and other adjunctive treatments. We highlighted the utilization of peptide hydrogels as delivery systems for chemotherapeutic drugs and other bioactive molecules and then discussed the challenges and prospects for their future application.

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

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

Single-cell RNA sequencing explores the evolution of the ecosystem from leukoplakia to head and neck squamous cell carcinoma

It has been found that progression from leukoplakia to head and neck squamous cell carcinoma (HNSCC) is a long-term process that may involve changes in the multicellular ecosystem. We acquired scRNA-seq samples information from gene expression omnibus and UCSC Xena database. The BEAM function was used to construct the pseudotime trajectory and analyze the differentially expressed genes in different branches. We used the ssGSEA method to explore the correlation between each cell subgroup and survival time, and obtained the cell subgroup related to prognosis. During the progression from leukoplakia to HNSCC, we found several prognostic cell subgroups, such as AURKB + epithelial cells, SFRP1 + fibroblasts, SLC7A8 + macrophages, FCER1A + CD1C + dendritic cells, and TRGC2 + NK/T cells. All cell subgroups had two different fates, one tending to cell proliferation, migration, and enhancement of angiogenesis capacity, and the other tending to inflammatory immune response, leukocyte chemotaxis, and T cell activation. Tumor-promoting genes such as CD163 and CD209 were highly expressed in the myeloid cells, and depletion marker genes such as TIGIT, LAG3 were highly expressed in NK/T cells. Our study may provide a reference for the molecular mechanism of HNSCC and theoretical basis for the development of new therapeutic strategies.

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

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

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

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

The Qi Yin San Liang San decoction enhances anti-CD19 CAR-T cell function in the treatment of B-cell lymphomas

ETHNOPHARMACOLOGICAL RELEVANCE

Adoptive T-cell therapy with anti-CD19 chimeric antigen receptor (CAR)-expressing T cells is a new approach for treating advanced B-cell malignancies. However, CAR-Tcell therapies for tumors are challenging due to tumor heterogeneity, cytokine release syndrome (CRS), and CAR-T cell exhaustion. The Qi Yin San Liang San (SLS) decoction has a significant curative effect in treating tumors and can improve clinical efficacy when combined with tumor immunotherapy. However, there has been no in vitro or in vivo pharmacodynamic evaluation of SLS in combination with immunotherapy, and the underlying immunological mechanism remains unclear.

AIM OF THE REVIEW

The study objective was to determine the auxiliary effect and potential mechanism of SLS as an adjuvant treatment with anti-CD19 CAR-T cells for B-cell lymphomas.

MATERIALS AND METHODS

Network pharmacology analyses, in vitro and in vivo studies, and transcriptome sequencing analyses were performed.

RESULTS

Forty-two components were detected in SLS by HPLC. Sixteen pharmacologically active ingredients were analyzed by searching the TCMSP database. The predicted targets included IL-2, IL-6, IL-10, TNF-α, CASP7, and CASP9. In vitro studies revealed that SLS can dose-dependently promote the killing effect of unmodified T and anti-CD19 CAR-T cells against Raji cell lines. Meanwhile, SLS inhibited unmodified T and anti-CD19 CAR-T cell exhaustion, promoted anti-CD19 CAR-T cell proliferation, reduced the levels of IL-6, IL-10, and TNF-α, and increased granzyme B levels. In vivo studies, SLS effectively improved the anti-tumor function of anti-CD19 CAR-T cells, prolonged the survival of the mice, and reduced the levels of IL-6, GM-CSF, and IL-17. Subsequently, the transcriptomic analysis showed that SLS inhibited the IL-17 signaling pathway and the apoptosis signaling pathway of T cells. In addition, SLS downregulated the expression of IL-17A, IL-6, TNF-α, GM-CSF, S100A8, CASP 7, CASP 9, and CASP 10 in anti-CD19 CAR-T cells. SLS regulated the IL-17 signaling pathway and apoptosis signaling pathway in anti-CD19 CAR-T cells.

CONCLUSION

SLS plays a potential auxiliary role in enhancing the function of anti-CD19 CAR T cells in the treatment of B-cell lymphoma, improving the killing ability of these cells, reducing the potential risk associated with inflammation, and providing synergistic and attenuating effects. The mechanism of SLS is partially mediated by the apoptosis and IL-17 signaling pathways (such as IL-17A, IL-6, TNF-α, GM-CSF, and Granzyme B).

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

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

An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator

The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.

Massively-multiplexed epitope mapping techniques for viral antigen discovery

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

Evolution by innovation as a driving force to improve TCR-T therapies

Adoptive cell therapies continually evolve through science-based innovation. Specialized innovations for TCR-T therapies are described here that are embedded in an End-to-End Platform for TCR-T Therapy Development which aims to provide solutions for key unmet patient needs by addressing challenges of TCR-T therapy, including selection of target antigens and suitable T cell receptors, generation of TCR-T therapies that provide long term, durable efficacy and safety and development of efficient and scalable production of patient-specific (personalized) TCR-T therapy for solid tumors. Multiple, combinable, innovative technologies are used in a systematic and sequential manner in the development of TCR-T therapies. One group of technologies encompasses product enhancements that enable TCR-T therapies to be safer, more specific and more effective. The second group of technologies addresses development optimization that supports discovery and development processes for TCR-T therapies to be performed more quickly, with higher quality and greater efficiency. Each module incorporates innovations layered onto basic technologies common to the field of immunology. An active approach of "evolution by innovation" supports the overall goal to develop best-in-class TCR-T therapies for treatment of patients with solid cancer.

TAPIR: a T-cell receptor language model for predicting rare and novel targets

T-cell receptors (TCRs) are involved in most human diseases, but linking their sequences with their targets remains an unsolved grand challenge in the field. In this study, we present TAPIR (T-cell receptor and Peptide Interaction Recognizer), a T-cell receptor (TCR) language model that predicts TCR-target interactions, with a focus on novel and rare targets. TAPIR employs deep convolutional neural network (CNN) encoders to process TCR and target sequences across flexible representations (e.g., beta-chain only, unknown MHC allele, etc.) and learns patterns of interactivity via several training tasks. This flexibility allows TAPIR to train on more than 50k either paired (alpha and beta chain) or unpaired TCRs (just alpha or beta chain) from public and proprietary databases against 1933 unique targets. TAPIR demonstrates state-of-the-art performance when predicting TCR interactivity against common benchmark targets and is the first method to demonstrate strong performance when predicting TCR interactivity against novel targets, where no examples are provided in training. TAPIR is also capable of predicting TCR interaction against MHC alleles in the absence of target information. Leveraging these capabilities, we apply TAPIR to cancer patient TCR repertoires and identify and validate a novel and potent anti-cancer T-cell receptor against a shared cancer neoantigen target (PIK3CA H1047L). We further show how TAPIR, when extended with a generative neural network, is capable of directly designing T-cell receptor sequences that interact with a target of interest.

BamQuery: a proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens

MHC-I-associated peptides deriving from non-coding genomic regions and mutations can generate tumor-specific antigens, including neoantigens. Quantifying tumor-specific antigens' RNA expression in malignant and benign tissues is critical for discriminating actionable targets. We present BamQuery, a tool attributing an exhaustive RNA expression to MHC-I-associated peptides of any origin from bulk and single-cell RNA-sequencing data. We show that many cryptic and mutated tumor-specific antigens can derive from multiple discrete genomic regions, abundantly expressed in normal tissues. BamQuery can also be used to predict MHC-I-associated peptides immunogenicity and identify actionable tumor-specific antigens de novo.

High-throughput characterization of HLA-E-presented CD94/NKG2x ligands reveals peptides which modulate NK cell activation

HLA-E is a non-classical class I MHC protein involved in innate and adaptive immune recognition. While recent studies have shown HLA-E can present diverse peptides to NK cells and T cells, the HLA-E repertoire recognized by CD94/NKG2x has remained poorly defined, with only a limited number of peptide ligands identified. Here we screen a yeast-displayed peptide library in the context of HLA-E to identify 500 high-confidence unique peptides that bind both HLA-E and CD94/NKG2A or CD94/NKG2C. Utilizing the sequences identified via yeast display selections, we train prediction algorithms and identify human and cytomegalovirus (CMV) proteome-derived, HLA-E-presented peptides capable of binding and signaling through both CD94/NKG2A and CD94/NKG2C. In addition, we identify peptides which selectively activate NKG2C+ NK cells. Taken together, characterization of the HLA-E-binding peptide repertoire and identification of NK activity-modulating peptides present opportunities for studies of NK cell regulation in health and disease, in addition to vaccine and therapeutic design.

BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing

MOTIVATION

The advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides.

RESULTS

We prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors' T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training.

AVAILABILITY AND IMPLEMENTATION

The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.

Multi-tiered approach to detect autoimmune cross-reactivity of therapeutic T cell receptors

T cell receptor (TCR)-engineered T cell therapy using high-affinity TCRs is a promising treatment modality for cancer. Discovery of high-affinity TCRs especially against self-antigens can require approaches that circumvent central tolerance, which may increase the risk of cross-reactivity. Despite the potential for toxicity, no standardized approach to screen cross-reactivity has been established in the context of preclinical safety evaluation. Here, we describe a practical framework to prospectively detect clinically prohibitive cross-reactivity of therapeutic TCR candidates. Cross-reactivity screening consisted of multifaceted series of assays including assessment of p-MHC tetramer binding, cell line recognition, and reactivity against candidate peptide libraries. Peptide libraries were generated using conventional contact residue motif-guided search, amino acid substitution matrix-based search unguided by motif information, and combinatorial peptide library scan-guided search. We demonstrate the additive nature of a layered approach, which efficiently identifies unsafe cross-reactivity including one undetected by conventional motif-guided search. These findings have important implications for the safe development of TCR-based therapies.

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

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

Optimization of a high-throughput shotgun immunoproteomics pipeline for antigen identification

Identification of proteins which initiate and/or perpetuate adaptive immune responses has potential to greatly impact pre-clinical and clinical work across numerous fields. To date, however, the methodologies available to identify antigens responsible for driving adaptive immune responses have been plagued by numerous issues which have drastically limited their widespread adoption. Therefore, in this study, we sought to optimize a shotgun immunoproteomics approach to alleviate these persistent issues and create a high-throughput, quantitative methodology for antigen identification. Three individual components of a previously published approach, namely the protein extraction, antigen elution, and LC-MS/MS analysis steps, were optimized in a systematic manner. These studies determined that preparation of protein extracts using a one-step tissue disruption method in immunoprecipitation (IP) buffer, eluting antigens from affinity chromatography columns with 1% trifluoroacetic acid (TFA), and TMT-labeling & multiplexing equal volumes of eluted samples for LC-MS/MS analysis, resulted in quantitative longitudinal antigen identification, with reduced variability between replicates and increased total number of antigens identified. This optimized pipeline provides a multiplexed, highly reproducible, and fully quantitative approach to antigen identification which is broadly applicable to determine the role of antigenic proteins in inciting (i.e., primary antigens) and perpetuating (i.e., secondary antigens) a wide range of diseases. SIGNIFICANCE: Using a systematic, hypothesis-driven approach, we identified potential improvements for three individual steps of a previously published approach for antigen-identification. Optimization of each step created a methodology which resolved many of the persistent issues associated with previous antigen identification approaches. The optimized high-throughput shotgun immunoproteomics approach described herein identifies more than five times as many unique antigens as the previously published method, greatly reduces protocol cost and mass spectrometry time per experiment, minimizes both inter- and intra-experimental variability, and ensures each experiment is fully quantitative. Ultimately, this optimized antigen identification approach has the potential to facilitate novel antigen identification studies, allowing evaluation of the adaptive immune response in a longitudinal manner and encourage innovations in a wide array of fields.

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.

Tracking tumor-specific CD8+ T cell responses

In a recent article, Puig-Saus et al. computationally predict and experimentally validate neoantigen-specific T cell responses in patients with melanoma. They identify a restricted set of neoantigens recognized by polyclonal CD8+ T cells as a unique feature of anti-PD-1 responders and engineer autologous tumor-responsive T cells expressing neoantigen-specific TCRs, providing proof-of-concept for future cellular therapies.

Cell surface marker-based capture of neoantigen-reactive CD8+ T-cell receptors from metastatic tumor digests

BACKGROUND

Cellular immunotherapies using autologous tumor-infiltrating lymphocytes (TIL) can induce durable regression of epithelial cancers in selected patients with treatment-refractory metastatic disease. As the genetic engineering of T cells with tumor-reactive T-cell receptors (TCRs) comes to the forefront of clinical investigation, the rapid, scalable, and cost-effective detection of patient-specific neoantigen-reactive TIL remains a top priority.

METHODS

We analyzed the single-cell transcriptomic states of 31 neoantigen-specific T-cell clonotypes to identify cell surface dysfunction markers that best identified the metastatic transcriptional states enriched with antitumor TIL. We developed an efficient method to capture neoantigen-reactive TCRs directly from resected human tumors based on cell surface co-expression of CD39, programmed cell death protein-1, and TIGIT dysfunction markers (CD8⁺ TIL^TP).

RESULTS

TIL^TP TCR isolation achieved a high degree of correlation with single-cell transcriptomic signatures that identify neoantigen-reactive TCRs, making it a cost-effective strategy using widely available resources. Reconstruction of additional TIL^TP TCRs from tumors identified known and novel antitumor TCRs, showing that at least 39.5% of TIL^TP TCRs are neoantigen-reactive or tumor-reactive. Despite their substantial enrichment for neoantigen-reactive TCR clonotypes, clonal dynamics of 24 unique antitumor TIL^TP clonotypes from four patients indicated that most in vitro expanded TIL^TP populations failed to demonstrate neoantigen reactivity, either by loss of neoantigen-reactive clones during TIL expansion, or through functional impairment during cognate neoantigen recognition.

CONCLUSIONS

While direct usage of in vitro-expanded CD8⁺ TIL^TP as a source for cellular therapy might be precluded by profound TIL dysfunction, isolating TIL^TP represents a streamlined effective approach to rapidly identify neoantigen-reactive TCRs to design engineered cellular immunotherapies against cancer.

Dynamics and specificities of T cells in cancer immunotherapy

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

Yeast display platform with expression of linear peptide epitopes for high-throughput assessment of peptide-MHC-II binding

Yeast display can serve as a powerful tool to assess the binding of peptides to the major histocompatibility complex (pMHC) and pMHC-T-cell receptor binding. However, this approach is often limited by the need to optimize MHC proteins for yeast surface expression, which can be laborious and may not yield productive results. Here we present a second-generation yeast display platform for class II MHC molecules (MHC-II), which decouples MHC-II expression from yeast-expressed peptides, referred to as "peptide display." Peptide display obviates the need for yeast-specific MHC optimizations and increases the scale of MHC-II alleles available for use in yeast display screens. Because MHC identity is separated from the peptide library, a further benefit of this platform is the ability to assess a single library of peptides against any MHC-II. We demonstrate the utility of the peptide display platform across MHC-II proteins, screening HLA-DR, HLA-DP, and HLA-DQ alleles. We further explore parameters of selections, including reagent dependencies, MHC avidity, and use of competitor peptides. In summary, this approach presents an advance in the throughput and accessibility of screening peptide-MHC-II binding.

Cellular Cancer Immunotherapy Development and Manufacturing in the Clinic

The transfusion of naturally derived or modified cellular therapies, referred to as adoptive cell therapy (ACT), has demonstrated clinical efficacy in the treatment of hematologic malignancies and metastatic melanoma. In addition, cellular vaccination, such as dendritic cell-based cancer vaccines, continues to be actively explored. The manufacturing of these therapies presents a considerable challenge to expanding the use of ACT as a viable treatment modality, particularly at academic production facilities. Furthermore, the expanding commercial interest in ACT presents new opportunities as well as strategic challenges for the future vision of cellular manufacturing in academic centers. Current trends in the production of ACT at tertiary care centers and prospects for improved manufacturing practices that will foster further clinical benefit are reviewed herein.

Neoantigen-targeted CD8+ T cell responses with PD-1 blockade therapy

Neoantigens are peptides derived from non-synonymous mutations presented by human leukocyte antigens (HLAs), which are recognized by antitumour T cells1-14. The large HLA allele diversity and limiting clinical samples have restricted the study of the landscape of neoantigen-targeted T cell responses in patients over their treatment course. Here we applied recently developed technologies15-17 to capture neoantigen-specific T cells from blood and tumours from patients with metastatic melanoma with or without response to anti-programmed death receptor 1 (PD-1) immunotherapy. We generated personalized libraries of neoantigen-HLA capture reagents to single-cell isolate the T cells and clone their T cell receptors (neoTCRs). Multiple T cells with different neoTCR sequences (T cell clonotypes) recognized a limited number of mutations in samples from seven patients with long-lasting clinical responses. These neoTCR clonotypes were recurrently detected over time in the blood and tumour. Samples from four patients with no response to anti-PD-1 also demonstrated neoantigen-specific T cell responses in the blood and tumour to a restricted number of mutations with lower TCR polyclonality and were not recurrently detected in sequential samples. Reconstitution of the neoTCRs in donor T cells using non-viral CRISPR-Cas9 gene editing demonstrated specific recognition and cytotoxicity to patient-matched melanoma cell lines. Thus, effective anti-PD-1 immunotherapy is associated with the presence of polyclonal CD8+ T cells in the tumour and blood specific for a limited number of immunodominant mutations, which are recurrently recognized over time.

Multiple sclerosis: Neuroimmune crosstalk and therapeutic targeting

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

The use of non-functional clonotypes as a natural calibrator for quantitative bias correction in adaptive immune receptor repertoire profiling

High-throughput sequencing of adaptive immune receptor repertoires is a valuable tool for receiving insights in adaptive immunity studies. Several powerful TCR/BCR repertoire reconstruction and analysis methods have been developed in the past decade. However, detecting and correcting the discrepancy between real and experimentally observed lymphocyte clone frequencies are still challenging. Here, we discovered a hallmark anomaly in the ratio between read count and clone count-based frequencies of non-functional clonotypes in multiplex PCR-based immune repertoires. Calculating this anomaly, we formulated a quantitative measure of V- and J-genes frequency bias driven by multiplex PCR during library preparation called Over Amplification Rate (OAR). Based on the OAR concept, we developed an original software for multiplex PCR-specific bias evaluation and correction named iROAR: immune Repertoire Over Amplification Removal (https://github.com/smiranast/iROAR). The iROAR algorithm was successfully tested on previously published TCR repertoires obtained using both 5' RACE (Rapid Amplification of cDNA Ends)-based and multiplex PCR-based approaches and compared with a biological spike-in-based method for PCR bias evaluation. The developed approach can increase the accuracy and consistency of repertoires reconstructed by different methods making them more applicable for comparative analysis.

Physical-Chemical Regulation of Membrane Receptors Dynamics in Viral Invasion and Immune Defense

Mechanical cues dynamically regulate membrane receptors functions to trigger various physiological and pathological processes from viral invasion to immune defense. These cues mainly include various types of dynamic mechanical forces and the spatial confinement of plasma membrane. However, the molecular mechanisms of how they couple with biochemical cues in regulating membrane receptors functions still remain mysterious. Here, we review recent advances in methodologies of single-molecule biomechanical techniques and in novel biomechanical regulatory mechanisms of critical ligand recognition of viral and immune receptors including SARS-CoV-2 spike protein, T cell receptor (TCR) and other co-stimulatory immune receptors. Furthermore, we provide our perspectives of the general principle of how force-dependent kinetics determine the dynamic functions of membrane receptors and of biomechanical-mechanism-driven SARS-CoV-2 neutralizing antibody design and TCR engineering for T-cell-based therapies.

Neoantigens: promising targets for cancer therapy

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

Facile repurposing of peptide-MHC-restricted antibodies for cancer immunotherapy

Monoclonal antibodies (Abs) that recognize major histocompatability complex (MHC)-presented tumor antigens in a manner similar to T cell receptors (TCRs) have great potential as cancer immunotherapeutics. However, isolation of 'TCR-mimic' (TCRm) Abs is laborious because Abs have not evolved the structurally nuanced peptide-MHC restriction of αβ-TCRs. Here, we present a strategy for rapid isolation of highly peptide-specific and 'MHC-restricted' Abs by re-engineering preselected Abs that engage peptide-MHC in a manner structurally similar to that of conventional αβ-TCRs. We created structure-based libraries focused on the peptide-interacting residues of TCRm Ab complementarity-determining region (CDR) loops, and rapidly generated MHC-restricted Abs to both mouse and human tumor antigens that specifically killed target cells when formatted as IgG, bispecific T cell engager (BiTE) and chimeric antigen receptor-T (CAR-T). Crystallographic analysis of one selected pMHC-restricted Ab revealed highly peptide-specific recognition, validating the engineering strategy. This approach can yield tumor antigen-specific antibodies in several weeks, potentially enabling rapid clinical translation.

Epitope identification for p53R273C mutant

BACKGROUND

With the rise of immunotherapy based on cancer neoantigen, identification of neoepitopes has become an urgent problem to be solved. The TP53 R273C mutation is one of the hotspot mutations of TP53, however, the immunogenicity of this mutation is not yet clear. The aim of this study is to identify potential epitopes for p53R273C mutant.

METHODS

In this study, bioinformatic methods, peptide exchange assay, and peptide-immunized human leukocyte antigen (HLA) transgenic mouse model were used to explore the immunogenicity of this mutation.

RESULTS

Peptides with higher affinity to common HLA-A alleles (A*11:01, A*02:01) were discovered by computational prediction. All the 8-11 mer peptides contain the mutation site were synthesized and soluble peptides were used in the peptide exchange assay. However, the exchange efficiencies of these predicted peptides to HLAs were lower. Fortunately, other peptides with higher exchange efficiency were discovered. Then, the immunogenicity of these peptides was validated with the HLA-A2 transgenic mice model.

CONCLUSION

We identified three potential neoepitopes of p53R273C for HLA-A*02:01, one potential neoepitope for HLA-A*11:01 and no neoepitope for HLA-A*24:02.

NKG2D discriminates diverse ligands through selectively mechano-regulated ligand conformational changes

Stimulatory immune receptor NKG2D binds diverse ligands to elicit differential anti-tumor and anti-virus immune responses. Two conflicting degeneracy recognition models based on static crystal structures and in-solution binding affinities have been considered for almost two decades. Whether and how NKG2D recognizes and discriminates diverse ligands still remain unclear. Using live-cell-based single-molecule biomechanical assay, we characterized the in situ binding kinetics of NKG2D interacting with different ligands in the absence or presence of mechanical force. We found that mechanical force application selectively prolonged NKG2D interaction lifetimes with the ligands MICA and MICB, but not with ULBPs, and that force-strengthened binding is much more pronounced for MICA than for other ligands. We also integrated steered molecular dynamics simulations and mutagenesis to reveal force-induced rotational conformational changes of MICA, involving formation of additional hydrogen bonds on its binding interface with NKG2D, impeding MICA dissociation under force. We further provided a kinetic triggering model to reveal that force-dependent affinity determines NKG2D ligand discrimination and its downstream NK cell activation. Together, our results demonstrate that NKG2D has a discrimination power to recognize different ligands, which depends on selective mechanical force-induced ligand conformational changes.

Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences

Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.

Tertiary lymphoid structures as unique constructions associated with the organization, education, and function of tumor-infiltrating immunocytes

Tertiary lymphoid structures (TLSs) are formations at sites with persistent inflammatory stimulation, including tumors. These ectopic lymphoid organs mainly consist of chemo-attracting B cells, T cells, and supporting dendritic cells (DCs). Mature TLSs exhibit functional organization for the optimal development and collaboration of adaptive immune response, delivering an augmented effect on the tumor microenvironment (TME). The description of the positive correlation between TLSs and tumor prognosis is reliable only under a certain condition involving the localization and maturation of TLSs. Emerging evidence suggests that underlying mechanisms of the anti-tumor effect of TLSs pave the way for novel immunotherapies. Several approaches have been developed to take advantage of intratumoral TLSs, either by combining it with therapeutic agents or by inducing the neogenesis of TLSs.

T cell Repertoire Profiling and the Mechanism by which HLA-B27 Causes Ankylosing Spondylitis

Purpose of Review

Ankylosing spondylitis (AS) is strongly associated with the HLA-B27 gene. The canonical function of HLA-B27 is to present antigenic peptides to CD8 lymphocytes, leading to adaptive immune responses. The ‘arthritogenic peptide’ theory as to the mechanism by which HLA-B27 induces ankylosing spondylitis proposes that HLA-B27 presents peptides derived from exogenous sources such as bacteria to CD8 lymphocytes, which subsequently cross-react with antigens at the site of inflammation of the disease, causing inflammation. This review describes findings of studies in AS involving profiling of T cell expansions and discusses future research opportunities based on these findings.

Recent Findings

Consistent with this theory, there is an expanding body of data showing that expansion of a restricted pool of CD8 lymphocytes is found in most AS patients yet only in a small proportion of healthy HLA-B27 carriers.

Summary

These exciting findings strongly support the theory that AS is driven by presentation of antigenic peptides to the adaptive immune system by HLA-B27. They point to new potential approaches to identify the exogenous and endogenous antigens involved and to potential therapies for the disease.

Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning

Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called KD-seq, it determines the absolute affinity of protein-ligand interactions. We also apply ProBound to profile the kinetics of kinase-substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein-ligand interactions.