Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and ß chains. Here, we collect and curate a dataset of 17,715 αßTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.

Semi-supervised integration of single-cell transcriptomics data.

Batch effects in single-cell RNA-seq data pose a significant challenge for comparative analyses across samples, individuals, and conditions. Although batch effect correction methods are routinely applied, data integration often leads to overcorrection and can result in the loss of biological variability. In this work we present STACAS, a batch correction method for scRNA-seq that leverages prior knowledge on cell types to preserve biological variability upon integration. Through an open-source benchmark, we show that semi-supervised STACAS outperforms state-of-the-art unsupervised methods, as well as supervised methods such as scANVI and scGen. STACAS scales well to large datasets and is robust to incomplete and imprecise input cell type labels, which are commonly encountered in real-life integration tasks. We argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction.

A phase 1 trial of adoptive transfer of vaccine-primed autologous circulating T cells in ovarian cancer.

We have previously shown that vaccination with tumor-pulsed dendritic cells amplifies neoantigen recognition in ovarian cancer. Here, in a phase 1 clinical study ( NCT01312376 /UPCC26810) including 19 patients, we show that such responses are further reinvigorated by subsequent adoptive transfer of vaccine-primed, ex vivo-expanded autologous peripheral blood T cells. The treatment is safe, and epitope spreading with novel neopeptide reactivities was observed after cell infusion in patients who experienced clinical benefit, suggesting reinvigoration of tumor-sculpting immunity.

Neoantigen-specific CD8 T cells with high structural avidity preferentially reside in and eliminate tumors.

The success of cancer immunotherapy depends in part on the strength of antigen recognition by T cells. Here, we characterize the T cell receptor (TCR) functional (antigen sensitivity) and structural (monomeric pMHC-TCR off-rates) avidities of 371 CD8 T cell clones specific for neoantigens, tumor-associated antigens (TAAs) or viral antigens isolated from tumors or blood of patients and healthy donors. T cells from tumors exhibit stronger functional and structural avidity than their blood counterparts. Relative to TAA, neoantigen-specific T cells are of higher structural avidity and, consistently, are preferentially detected in tumors. Effective tumor infiltration in mice models is associated with high structural avidity and CXCR3 expression. Based on TCR biophysicochemical properties, we derive and apply an in silico model predicting TCR structural avidity and validate the enrichment in high avidity T cells in patients' tumors. These observations indicate a direct relationship between neoantigen recognition, T cell functionality and tumor infiltration. These results delineate a rational approach to identify potent T cells for personalized cancer immunotherapy.

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.

CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4+ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.

Cancer vaccines based on whole-tumor lysate or neoepitopes with validated HLA binding outperform those with predicted HLA-binding affinity.

Antigen selection and prioritization represent crucial determinants of vaccines' efficacy. Here, we compare two personalized dendritic cell-based vaccination strategies using whole-tumor lysate or neoantigens. Data in mouse and in cancer patients demonstrate that peptide vaccines using neoantigens predicted on the sole basis of in silico peptide-major histocompatibility complex (MHC) binding affinity underperform relative to whole-tumor-lysate vaccines. In contrast, effective in vitro peptide-MHC binding affinity and peptide immunogenicity significantly improve the prioritization of tumor-rejecting neoepitopes and result in more efficacious vaccines.

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.

Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes.

The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.

The MHC Motif Atlas: a database of MHC binding specificities and ligands.

The highly polymorphic Major Histocompatibility Complex (MHC) genes are responsible for the binding and cell surface presentation of pathogen or cancer specific T-cell epitopes. This process is fundamental for eliciting T-cell recognition of infected or malignant cells. Epitopes displayed on MHC molecules further provide therapeutic targets for personalized cancer vaccines or adoptive T-cell therapy. To help visualizing, analyzing and comparing the different binding specificities of MHC molecules, we developed the MHC Motif Atlas (http://mhcmotifatlas.org/). This database contains information about thousands of class I and class II MHC molecules, including binding motifs, peptide length distributions, motifs of phosphorylated ligands, multiple specificities or links to X-ray crystallography structures. The database further enables users to download curated datasets of MHC ligands. By combining intuitive visualization of the main binding properties of MHC molecules together with access to more than a million ligands, the MHC Motif Atlas provides a central resource to analyze and interpret the binding specificities of MHC molecules.

Both APRIL and antibody-fragment-based CAR T cells for myeloma induce BCMA downmodulation by trogocytosis and internalization.

BACKGROUND: Chimeric antigen receptor (CAR) T cell therapy targeting B cell maturation antigen (BCMA) on multiple myeloma (MM) produces fast but not long-lasting responses. Reasons for treatment failure are poorly understood. CARs simultaneously targeting two antigens may represent an alternative. Here, we (1) designed and characterized novel A proliferation inducing ligand (APRIL) based dual-antigen targeting CARs, and (2) investigated mechanisms of resistance to CAR T cells with three different BCMA-binding moieties (APRIL, single-chain-variable-fragment, heavy-chain-only). METHODS: Three new APRIL-CARs were designed and characterized. Human APRIL-CAR T cells were evaluated for their cytotoxic function in vitro and in vivo, for their polyfunctionality, immune synapse formation, memory, exhaustion phenotype and tonic signaling activity. To investigate resistance mechanisms, we analyzed BCMA levels and cellular localization and quantified CAR T cell-target cell interactions by live microscopy. Impact on pathway activation and tumor cell proliferation was assessed in vitro and in vivo. RESULTS: APRIL-CAR T cells in a trimeric ligand binding conformation conferred fast but not sustained antitumor responses in vivo in mouse xenograft models. In vitro trimer-BBζ CAR T cells were more polyfunctional and formed stronger immune synapses than monomer-BBζ CAR T cells. After CAR T cell-myeloma cell contact, BCMA was rapidly downmodulated on target cells with all evaluated binding moieties. CAR T cells acquired BCMA by trogocytosis, and BCMA on MM cells was rapidly internalized. Since BCMA can be re-expressed during progression and persisting CAR T cells may not protect patients from relapse, we investigated whether non-functional CAR T cells play a role in tumor progression. While CAR T cell-MM cell interactions activated BCMA pathway, we did not find enhanced tumor growth in vitro or in vivo. CONCLUSION: Antitumor responses with APRIL-CAR T cells were fast but not sustained. Rapid BCMA downmodulation occurred independently of whether an APRIL or antibody-based binding moiety was used. BCMA internalization mostly contributed to this effect, but trogocytosis by CAR T cells was also observed. Our study sheds light on the mechanisms underlying CAR T cell failure in MM when targeting BCMA and can inform the development of improved treatment strategies.

Metacells untangle large and complex single-cell transcriptome networks.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. RESULTS: We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop. CONCLUSIONS: SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.

Deciphering the landscape of phosphorylated HLA-II ligands.

CD4+ T cell activation in infectious diseases and cancer is governed by the recognition of peptides presented on class II human leukocyte antigen (HLA-II) molecules. Therefore, HLA-II ligands represent promising targets for vaccine design and personalized cancer immunotherapy. Much work has been done to identify and predict unmodified peptides presented on HLA-II molecules. However, little is known about the presentation of phosphorylated HLA-II ligands. Here, we analyzed Mass Spectrometry HLA-II peptidomics data and identified 1,943 unique phosphorylated HLA-II ligands. This enabled us to precisely define phosphorylated binding motifs for more than 30 common HLA-II alleles and to explore various molecular properties of phosphorylated peptides. Our data were further used to develop the first predictor of phosphorylated peptide presentation on HLA-II molecules.

Sensitive identification of neoantigens and cognate TCRs in human solid tumors.

The identification of patient-specific tumor antigens is complicated by the low frequency of T cells specific for each tumor antigen. Here we describe NeoScreen, a method that enables the sensitive identification of rare tumor (neo)antigens and of cognate T cell receptors (TCRs) expressed by tumor-infiltrating lymphocytes. T cells transduced with tumor antigen-specific TCRs identified by NeoScreen mediate regression of established tumors in patient-derived xenograft mice.

Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting.

CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors. However, the rules that govern which peptides are truly recognized by existing T cell receptors (TCRs) remain poorly understood, precluding accurate predictions of neo-epitopes for cancer immunotherapy. Here, we capitalize on recent (neo-)epitope data to train a predictor of immunogenic epitopes (PRIME), which captures molecular properties of both antigen presentation and TCR recognition. PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition that we experimentally validate. Analysis of cancer genomics data reveals that recurrent mutations tend to be less frequent in patients where they are predicted to be immunogenic, providing further evidence for immunoediting in human cancer. PRIME will facilitate identification of pathogen epitopes in infectious diseases and neo-epitopes in cancer immunotherapy.

Inflammatory B cells correlate with failure to checkpoint blockade in melanoma patients.

The understanding of the role of B cells in patients with solid tumors remains insufficient. We found that circulating B cells produced TNFα and/or IL-6, associated with unresponsiveness and poor overall survival of melanoma patients treated with anti-CTLA4 antibody. Transcriptome analysis of B cells from melanoma metastases showed enriched expression of inflammatory response genes. Publicly available single B cell data from the tumor microenvironment revealed a negative correlation between TNFα expression and response to immune checkpoint blockade. These findings suggest that B cells contribute to tumor growth via the production of inflammatory cytokines. Possibly, these B cells are different from tertiary lymphoid structure-associated B cells, which have been described to correlate with favorable clinical outcome of cancer patients. Further studies are required to identify and characterize B cell subsets and their functions promoting or counteracting tumor growth, with the aim to identify biomarkers and novel treatment targets.

Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer.

CD4 T cells have been implicated in cancer immunity for their helper functions. Moreover, their direct cytotoxic potential has been shown in some patients with cancer. Here, by mining single-cell RNA-seq datasets, we identified CD4 T cell clusters displaying cytotoxic phenotypes in different human cancers, resembling CD8 T cell profiles. Using the peptide-MHCII-multimer technology, we confirmed ex vivo the presence of cytolytic tumor-specific CD4 T cells. We performed an integrated phenotypic and functional characterization of these cells, down to the single-cell level, through a high-throughput nanobiochip consisting of massive arrays of picowells and machine learning. We demonstrated a direct, contact-, and granzyme-dependent cytotoxic activity against tumors, with delayed kinetics compared to classical cytotoxic lymphocytes. Last, we found that this cytotoxic activity was in part dependent on SLAMF7. Agonistic engagement of SLAMF7 enhanced cytotoxicity of tumor-specific CD4 T cells, suggesting that targeting these cells might prove synergistic with other cancer immunotherapies.

Nongenetic Evolution Drives Lung Adenocarcinoma Spatial Heterogeneity and Progression.

Cancer evolution determines molecular and morphologic intratumor heterogeneity and challenges the design of effective treatments. In lung adenocarcinoma, disease progression and prognosis are associated with the appearance of morphologically diverse tumor regions, termed histologic patterns. However, the link between molecular and histologic features remains elusive. Here, we generated multiomics and spatially resolved molecular profiles of histologic patterns from primary lung adenocarcinoma, which we integrated with molecular data from >2,000 patients. The transition from indolent to aggressive patterns was not driven by genetic alterations but by epigenetic and transcriptional reprogramming reshaping cancer cell identity. A signature quantifying this transition was an independent predictor of patient prognosis in multiple human cohorts. Within individual tumors, highly multiplexed protein spatial profiling revealed coexistence of immune desert, inflamed, and excluded regions, which matched histologic pattern composition. Our results provide a detailed molecular map of lung adenocarcinoma intratumor spatial heterogeneity, tracing nongenetic routes of cancer evolution. SIGNIFICANCE: Lung adenocarcinomas are classified based on histologic pattern prevalence. However, individual tumors exhibit multiple patterns with unknown molecular features. We characterized nongenetic mechanisms underlying intratumor patterns and molecular markers predicting patient prognosis. Intratumor patterns determined diverse immune microenvironments, warranting their study in the context of current immunotherapies.This article is highlighted in the In This Issue feature, p. 1307.

Retrospective Analysis of Dento-alveolar Injuries at a Swiss University Clinic (2011-2015).

The aim of this study was to evaluate all dento-alveolar injuries occurring within a period of 5 years that were examined at the Department of Oral Surgery and Stomatology at the University of Bern. The case histories of 852 patients (522 males and 330 females) were assessed retrospectively. The mean age was 17 years and 9 months. The youngest patient was 10 months old, the oldest was 91 years old. Most accidents were recorded in the months of March and July. Friday was observed as the weekday when accidents were at their peak. 54% of trauma patients were first examined on the day of the accident. The most frequent causes of accident were falls, cycling or sports accidents. Injury types included concussion (72.4%), subluxation (14%) and luxation (7.7%). Crown fractures without pulp exposure were recorded in 522 teeth (10.7%). Rare types of trauma were crown fractures with pulp exposure (3.7%), and avulsion injuries (3.2%). The most frequently affected teeth were central incisors (40%), followed by lateral incisors (35.6%) and canines (19.5%). 71.5% of the patients with dento-alveolar trauma also had soft-tissue injuries (from abrasions to lacerations), mostly lip (51.6%), gingival (19%), and chin injuries (10.9%). The multivariate analysis showed that age had a significant impact on concussions, subluxations, intrusions, crown fractures, root fractures and non-oral injuries. For concussions and subluxations, the probability of an injury was the highest for the mixed dentition group. For intrusions, the probability of an injury decreased with age, but it was vice versa for crown fractures, root fractures and non-oral injuries. It was found that bicycle injuries had the highest injury probability for avulsions, luxations, subluxations, soft tissue - and non-oral injuries.

Single-cell transcriptomics identifies multiple pathways underlying antitumor function of TCR- and CD8αß-engineered human CD4+ T cells.

Transgenic coexpression of a class I-restricted tumor antigen-specific T cell receptor (TCR) and CD8αß (TCR8) redirects antigen specificity of CD4+ T cells. Reinforcement of biophysical properties and early TCR signaling explain how redirected CD4+ T cells recognize target cells, but the transcriptional basis for their acquired antitumor function remains elusive. We, therefore, interrogated redirected human CD4+ and CD8+ T cells by single-cell RNA sequencing and characterized them experimentally in bulk and single-cell assays and a mouse xenograft model. TCR8 expression enhanced CD8+ T cell function and preserved less differentiated CD4+ and CD8+ T cells after tumor challenge. TCR8+CD4+ T cells were most potent by activating multiple transcriptional programs associated with enhanced antitumor function. We found sustained activation of cytotoxicity, costimulation, oxidative phosphorylation- and proliferation-related genes, and simultaneously reduced differentiation and exhaustion. Our study identifies molecular features of TCR8 expression that can guide the development of enhanced immunotherapies.

Structural dissimilarity from self drives neoepitope escape from immune tolerance.

T-cell recognition of peptides incorporating nonsynonymous mutations, or neoepitopes, is a cornerstone of tumor immunity and forms the basis of new immunotherapy approaches including personalized cancer vaccines. Yet as they are derived from self-peptides, the means through which immunogenic neoepitopes overcome immune self-tolerance are often unclear. Here we show that a point mutation in a non-major histocompatibility complex anchor position induces structural and dynamic changes in an immunologically active ovarian cancer neoepitope. The changes pre-organize the peptide into a conformation optimal for recognition by a neoepitope-specific T-cell receptor, allowing the receptor to bind the neoepitope with high affinity and deliver potent T-cell signals. Our results emphasize the importance of structural and physical changes relative to self in neoepitope immunogenicity. Considered broadly, these findings can help explain some of the difficulties in identifying immunogenic neoepitopes from sequence alone and provide guidance for developing novel, neoepitope-based personalized therapies.