Imiquimod-induced pruritus in female wild-type and knockin Wistar rats: underscoring behavioral scratching in a rat model for antipruritic treatments.

OBJECTIVES: Animal models of skin disease are used to evaluate therapeutics to alleviate disease. One common clinical dermatological complaint is pruritus (itch), but there is a lack of standardization in the characterization of pre-clinical models and scratching behavior, a key itch endpoint, is often neglected. One such model is the widely used imiquimod (IMQ) mouse model of psoriasis. However, it lacks characterized behavioral attributes like scratching, nor has widely expanded to other species like rats. Given these important attributes, this study was designed to broaden the characterization beyond the expected IMQ-induced psoriasis-like skin inflammatory skin changes and to validate the role of a potential therapeutic agent for pruritus in our genetic rat model. The study included female Wistar rats and genetically modified knockin (humanized proteinase-activated receptor 2 (F2RL1) female rats, with the widely used C57BL/6 J mice as a methodology control for typical IMQ dosing. RESULTS: We demonstrate that the IMQ model can be reproduced in rats, including their genetically modified derivatives, and how scratching can be used as a key behavioral endpoint. We systemically delivered an anti-PAR2 antibody (P24E1102) which reversed scratching bouts-validating this behavioral methodology and have shown its feasibility and value in identifying effective antipruritic drugs.

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.

TCR sequencing and cloning methods for repertoire analysis and isolation of tumor-reactive TCRs.

T cell receptor (TCR) technologies, including repertoire analyses and T cell engineering, are increasingly important in the clinical management of cellular immunity in cancer, transplantation, and other immune diseases. However, sensitive and reliable methods for repertoire analyses and TCR cloning are still lacking. Here, we report on SEQTR, a high-throughput approach to analyze human and mouse repertoires that is more sensitive, reproducible, and accurate as compared with commonly used assays, and thus more reliably captures the complexity of blood and tumor TCR repertoires. We also present a TCR cloning strategy to specifically amplify TCRs from T cell populations. Positioned downstream of single-cell or bulk TCR sequencing, it allows time- and cost-effective discovery, cloning, screening, and engineering of tumor-specific TCRs. Together, these methods will accelerate TCR repertoire analyses in discovery, translational, and clinical settings and permit fast TCR engineering for cellular therapies.

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.

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.

CD8 T cell function and cross-reactivity explored by stepwise increased peptide-HLA versus TCR affinity.

Recruitment and activation of CD8 T cells occur through specific triggering of T cell receptor (TCR) by peptide-bound human leucocyte antigen (HLA) ligands. Within the generated trimeric TCR-peptide:HLA complex, the molecular binding affinities between peptide and HLA, and between TCR and peptide:HLA both impact T cell functional outcomes. However, how their individual and combined effects modulate immunogenicity and overall T cell responsiveness has not been investigated systematically. Here, we established two panels of human tumor peptide variants differing in their affinity to HLA. For precise characterization, we developed the "blue peptide assay", an upgraded cell-based approach to measure the peptide:HLA affinity. These peptide variants were then used to investigate the cross-reactivity of tumor antigen-specific CD8 T cell clonotypes derived from blood of cancer patients after vaccination with either the native or an affinity-optimized Melan-A/MART-1 epitope, or isolated from tumor infiltrated lymph nodes (TILNs). Vaccines containing the native tumor epitope generated T cells with better functionality, and superior cross-reactivity against potential low affinity escape epitopes, as compared to T cells induced by vaccines containing an HLA affinity-optimized epitope. Comparatively, Melan-A/MART-1-specific TILN cells displayed functional and cross-reactive profiles that were heterogeneous and clonotype-dependent. Finally, we took advantage of a collection of T cells expressing affinity-optimized NY-ESO-1-specific TCRs to interrogate the individual and combined impact of peptide:HLA and TCR-pHLA affinities on overall CD8 T cell responses. We found profound and distinct effects of both biophysical parameters, with additive contributions and absence of hierarchical dominance. Altogether, the biological impact of peptide:HLA and TCR-pHLA affinities on T cell responses was carefully dissected in two antigenic systems, frequently targeted in human cancer immunotherapy. Our technology and stepwise comparison open new insights into the rational design and selection of vaccine-associated tumor-specific epitopes and highlight the functional and cross-reactivity profiles that endow T cells with best tumor control capacity.

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.

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.

Optimized combinatorial pMHC class II multimer labeling for precision immune monitoring of tumor-specific CD4 T cells in patients.

BACKGROUND: With immunotherapy gaining increasing approval for treatment of different tumor types, scientists rely on cutting edge methods for the monitoring of immune responses and biomarker development in patients. Due to the lack of tools to efficiently detect rare circulating human tumor-specific CD4 T cells, their characterization in patients still remains very limited. METHODS: We have used combinatorial staining strategies with peptide major histocompatibility complex class II (pMHCII) multimer constructs of different alleles to establish an optimized staining procedure for in vitro and direct ex-vivo visualization of tumor-specific CD4 T cells, in patient samples. Furthermore, we have generated reversible multimers to achieve optimal cell staining and yet disassemble prior to in vitro cell expansion, thus preventing activation induced cell death. RESULTS: We observed a vastly improved detection of tumor-specific, viral-specific and bacterial-specific cells with our optimization methods compared with the non-optimized staining procedure. By increasing the variety of fluorochromes used to label the pMHCII multimers, we were also able to increase the parallel detection of different specificities within one sample, including antigen-specific CD8 T cells. A decrease in cell viability was observed when using the full optimization method, but this was mitigated by the removal of neuraminidase and the use of reversible multimers. CONCLUSION: This new optimized staining procedure represents an advance toward better detection and analysis of antigen-specific CD4 T cells. It should facilitate state-of-the art precision monitoring of tumor-specific CD4 T cells and contribute to accelerate the use and the targeting of these cells in cancer immunotherapy.

High-throughput identification of human antigen-specific CD8+ and CD4+ T cells using soluble pMHC multimers.

Peptide major histocompatibility complex (pMHC) multimers have been used since decades to identify, isolate and analyze antigen-specific T cells by flow (and more recently mass) cytometry. Yet well established as a standard technology, improvements are still required to face the growing needs of personalized immune monitoring. Here we review the latest developments about (i) the quality of pMHC class I and II monomers, (ii) the importance of the multimeric scaffold, (iii) the staining conditions and (iv) the high-throughput synthesis of pMHC monomers. Finally, innovative multiplexed, combinatorial strategies for parallel detection of antigen-specific T cells in a single sample are discussed.

Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands.

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

CD8 Binding of MHC-Peptide Complexes in cis or trans Regulates CD8+ T-cell Responses.

The coreceptor CD8αß can greatly promote activation of T cells by strengthening T-cell receptor (TCR) binding to cognate peptide-MHC complexes (pMHC) on antigen presenting cells and by bringing p56Lck to TCR/CD3. Here, we demonstrate that CD8 can also bind to pMHC on the T cell (in cis) and that this inhibits their activation. Using molecular modeling, fluorescence resonance energy transfer experiments on living cells, biochemical and mutational analysis, we show that CD8 binding to pMHC in cis involves a different docking mode and is regulated by posttranslational modifications including a membrane-distal interchain disulfide bond and negatively charged O-linked glycans near positively charged sequences on the CD8ß stalk. These modifications distort the stalk, thus favoring CD8 binding to pMHC in cis. Differential binding of CD8 to pMHC in cis or trans is a means to regulate CD8+ T-cell responses and provides new translational opportunities.

Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.

Predictions of epitopes presented by class II human leukocyte antigen molecules (HLA-II) have limited accuracy, restricting vaccine and therapy design. Here we combined unbiased mass spectrometry with a motif deconvolution algorithm to profile and analyze a total of 99,265 unique peptides eluted from HLA-II molecules. We then trained an epitope prediction algorithm with these data and improved prediction of pathogen and tumor-associated class II neoepitopes.

The Length Distribution and Multiple Specificity of Naturally Presented HLA-I Ligands.

HLA-I molecules bind short peptides and present them for recognition by CD8+ T cells. The length of HLA-I ligands typically ranges from 8 to 12 aa, but variability is observed across different HLA-I alleles. In this study we collected recent in-depth HLA peptidomics data, including 12 newly generated HLA peptidomes (31,896 unique peptides) from human meningioma samples, to analyze the peptide length distribution and multiple specificity across 84 different HLA-I alleles. We observed a clear clustering of HLA-I alleles with distinct peptide length distributions, which enabled us to study the structural basis of peptide length distributions and predict peptide length distributions from HLA-I sequences. We further identified multiple specificity in several HLA-I molecules and validated these observations with binding assays. Explicitly modeling peptide length distribution and multiple specificity improved predictions of naturally presented HLA-I ligands, as demonstrated in an independent benchmarking based on the new human meningioma samples.

The C-terminal extension landscape of naturally presented HLA-I ligands.

HLA-I molecules play a central role in antigen presentation. They typically bind 9- to 12-mer peptides, and their canonical binding mode involves anchor residues at the second and last positions of their ligands. To investigate potential noncanonical binding modes, we collected in-depth and accurate HLA peptidomics datasets covering 54 HLA-I alleles and developed algorithms to analyze these data. Our results reveal frequent (442 unique peptides) and statistically significant C-terminal extensions for at least eight alleles, including the common HLA-A03:01, HLA-A31:01, and HLA-A68:01. High resolution crystal structure of HLA-A68:01 with such a ligand uncovers structural changes taking place to accommodate C-terminal extensions and helps unraveling sequence and structural properties predictive of the presence of these extensions. Scanning viral proteomes with the C-terminal extension motifs identifies many putative epitopes and we demonstrate direct recognition by human CD8+ T cells of a 10-mer epitope from cytomegalovirus predicted to follow the C-terminal extension binding mode.

OBE022, an Oral and Selective Prostaglandin F2α Receptor Antagonist as an Effective and Safe Modality for the Treatment of Preterm Labor.

Preterm birth is the major challenge in obstetrics, affecting ∼10% of pregnancies. Pan-prostaglandin synthesis inhibitors [nonsteroidal anti-inflammatory drugs (NSAIDs)] prevent preterm labor and prolong pregnancy but raise concerns about fetal renal and cardiovascular safety. We conducted preclinical studies examining the tocolytic effect and fetal safety of the oral prodrug candidate OBE022 [(S)-2-amino-3-methyl-butyric acid (S)-3-{[(S)-3-(biphenyl-4-sulfonyl)-thiazolidine-2-carbonyl]-amino}-3-(4-fluoro-phenyl)-propyl ester] and its parent OBE002 [(S)-3-(biphenyl-4-sulfonyl)-thiazolidine-2-carboxylic acid [(S)-1-(4-fluoro-phenyl)-3-hydroxy-propyl]-amide], both potent and highly selective antagonist of the contractile prostaglandin F2α (PGF2α ) receptor (FP). Efficacy of OBE022 and OBE002, alone and in combination with other tocolytics, was assessed in human tissues and pregnant animal models for inhibition of uterine contraction and delay of parturition. Selective safety of OBE022 and/or OBE002, compared with NSAID indomethacin, was assessed on renal function, closure of the ductus arteriosus, and inhibition of platelet aggregation. In in vitro studies, OBE002 inhibited spontaneous, oxytocin- and PGF2α -induced human myometrial contractions alone and was more effective in combination with atosiban or nifedipine. In in vivo studies, OBE022 and OBE002 reduced spontaneous contractions in near-term pregnant rats. In pregnant mice, OBE022 delayed RU486 [(8S,11R,13S,14S,17S)-11-[4-(dimethylamino)phenyl]-17-hydroxy-13-methyl-17-prop-1-ynyl-1,2,6,7,8,11,12,14,15,16-decahydrocyclopenta[a]phenanthren-3-one] -induced parturition and exerted synergistic effects in combination with nifedipine. OBE022 and/or OBE002 did not show the fetal side effects of ductus arteriosus constriction, impairment of kidney function, or inhibition of platelet aggregation observed with indomethacin. Orally active OBE022 and OBE002 exhibits potent tocolytic effects on human tissues ex vivo and animal models in vivo without causing the adverse fetal side effects seen with indomethacin. Selectively targeting the FP receptor in combination with existing tocolytics may be an effective strategy for preventing or delaying preterm delivery.

Sensitive and frequent identification of high avidity neo-epitope specific CD8 + T cells in immunotherapy-naive ovarian cancer.

Immunotherapy directed against private tumor neo-antigens derived from non-synonymous somatic mutations is a promising strategy of personalized cancer immunotherapy. However, feasibility in low mutational load tumor types remains unknown. Comprehensive and deep analysis of circulating and tumor-infiltrating lymphocytes (TILs) for neo-epitope specific CD8+ T cells has allowed prompt identification of oligoclonal and polyfunctional such cells from most immunotherapy-naive patients with advanced epithelial ovarian cancer studied. Neo-epitope recognition is discordant between circulating T cells and TILs, and is more likely to be found among TILs, which display higher functional avidity and unique TCRs with higher predicted affinity than their blood counterparts. Our results imply that identification of neo-epitope specific CD8+ T cells is achievable even in tumors with relatively low number of somatic mutations, and neo-epitope validation in TILs extends opportunities for mutanome-based personalized immunotherapies to such tumors.

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.

The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays.