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.

MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.

Novel canonical and non-canonical viral antigens extend current targets for immunotherapy of HPV-driven cervical cancer.

Current immunotherapeutic approaches for human papillomavirus (HPV)-driven cervical cancer target the viral oncogenes E6 and E7. We report viral canonical and alternative reading frame (ARF)-derived sequences presented on cervical tumor cells, including antigens encoded by the conserved viral gene E1. We confirm immunogenicity of the identified viral peptides in HPV-positive women, and women with cervical intraepithelial neoplasia. We observe consistent transcription of the E1, E6, and E7 genes in 10 primary cervical tumor resections from the four most common high-risk HPV subtypes (HPV16, 18, 31, and 45), suggesting the suitability of E1 as therapeutic target. We finally confirm HLA presentation of canonical peptides derived from E6 and E7, and ARF-derived viral peptides from a reverse-strand transcript spanning the HPV E1 and E2 genes in primary human cervical tumor tissue. Our results extend currently known viral immunotherapeutic targets in cervical cancer and highlight E1 as an important cervical cancer antigen.

Ionizing Radiation Drives Key Regulators of Antigen Presentation and a Global Expansion of the Immunopeptidome.

Little is known about the pathways regulating MHC antigen presentation and the identity of treatment-specific T cell antigens induced by ionizing radiation. For this reason, we investigated the radiation-specific changes in the colorectal tumor cell proteome. We found an increase in DDX58 and ZBP1 protein expression, two nucleic acid sensing molecules likely involved in induction of the dominant interferon response signature observed after genotoxic insult. We further observed treatment-induced changes in key regulators and effector proteins of the antigen processing and presentation machinery. Differential regulation of MHC allele expression was further driving the presentation of a significantly broader MHC-associated peptidome postirradiation, defining a radiation-specific peptide repertoire. Interestingly, treatment-induced peptides originated predominantly from proteins involved in catecholamine synthesis and metabolic pathways. A nuanced relationship between protein expression and antigen presentation was observed where radiation-induced changes in proteins do not correlate with increased presentation of associated peptides. Finally, we detected an increase in the presentation of a tumor-specific neoantigen derived from Mtch1. This study provides new insights into how radiation enhances antigen processing and presentation that could be suitable for the development of combinatorial therapies. Data are available via ProteomeXchange with identifier PXD032003.

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens.

T cell recognition of a cognate peptide-major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

Decitabine increases neoantigen and cancer testis antigen expression to enhance T-cell-mediated toxicity against glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumor in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionized management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically "cold" tumor. METHODS: U87MG and patient-derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole-exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells. RESULTS: We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumor cells, leading to increased neoantigen- and CTA-specific T-cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumor and blood, which show increased tumor-specific activation in the presence of DAC-treated cells. CONCLUSIONS: Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T-cell responses in vitro. Our results support a potential use of DAC as a sensitizing agent for immunotherapy.

HLA variants have different preferences to present proteins with specific molecular functions which are complemented in frequent haplotypes.

Human leukocyte antigen (HLA) genes are the most polymorphic loci in the human genome and code for proteins that play a key role in guiding adaptive immune responses by presenting foreign and self peptides (ligands) to T cells. Each person carries up to 6 HLA class I variants (maternal and paternal copies of HLA-A, HLA-B and HLA-C genes) and also multiple HLA class II variants, which cumulatively define the landscape of peptides presented to T cells. Each HLA variant has its own repertoire of presented peptides with a certain sequence motif which is mainly defined by peptide anchor residues (typically the second and the last positions for HLA class I ligands) forming key interactions with the peptide-binding groove of HLA. In this study, we aimed to characterize HLA binding preferences in terms of molecular functions of presented proteins. To focus on the ligand presentation bias introduced specifically by HLA-peptide interaction we performed large-scale in silico predictions of binding of all peptides from human proteome for a wide range of HLA variants and established which functions are characteristic for proteins that are more or less preferentially presented by different HLA variants using statistical calculations and gene ontology (GO) analysis. We demonstrated marked distinctions between HLA variants in molecular functions of preferentially presented proteins (e.g. some HLA variants preferentially present membrane and receptor proteins, while others - ribosomal and DNA-binding proteins) and reduced presentation of extracellular matrix and collagen proteins by the majority of HLA variants. To explain these observations we demonstrated that HLA preferentially presents proteins enriched in amino acids which are required as anchor residues for the particular HLA variant. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Taking into consideration that HLA alleles are inherited in haplotypes, we hypothesized that haplotypes composed of a combination of HLA variants with different presentation preferences should be more advantageous as they allow presenting a larger repertoire of peptides and avoiding holes in immunopeptidome. Indeed, we demonstrated that HLA-A/HLA-B and HLA-A/HLA-C haplotypes which have a high frequency in the human population are comprised of HLA variants that are more distinct in terms of functions of preferentially presented proteins than the control pairs.

Broad auto-reactive IgM responses are common in critically ill patients, including those with COVID-19.

The pathogenesis of severe coronavirus disease 2019 (COVID-19) remains poorly understood. While several studies suggest that immune dysregulation plays a central role, the key mediators of this process are yet to be defined. Here, we demonstrate that plasma from a high proportion (93%) of critically ill COVID-19 patients, but not healthy controls, contains broadly auto-reactive immunoglobulin M (IgM) and less frequently auto-reactive IgG or IgA. Importantly, these auto-IgMs preferentially recognize primary human lung cells in vitro, including pulmonary endothelial and epithelial cells. By using a combination of flow cytometry, analytical proteome microarray technology, and lactose dehydrogenase (LDH)-release cytotoxicity assays, we identify high-affinity, complement-fixing, auto-reactive IgM directed against 260 candidate autoantigens, including numerous molecules preferentially expressed on the cellular membranes of pulmonary, vascular, gastrointestinal, and renal tissues. These findings suggest that broad IgM-mediated autoimmune reactivity may be involved in the pathogenesis of severe COVID-19, thereby identifying a potential target for therapeutic interventions.

HLA binding of self-peptides is biased towards proteins with specific molecular functions.

Human leukocyte antigen (HLA) is highly polymorphic and plays a key role in guiding adaptive immune responses by presenting foreign and self peptides to T cells. Each HLA variant selects a minor fraction of peptides that match a certain motif required for optimal interaction with the peptide-binding groove. These restriction rules define the landscape of peptides presented to T cells. Given these limitations, one might suggest that the choice of peptides presented by HLA is non-random and there is preferential presentation of an array of peptides that is optimal for distinguishing self and foreign proteins. In this study we explore these preferences with a comparative analysis of self peptides enriched and depleted in HLA ligands. We show that HLAs exhibit preferences towards presenting peptides from certain proteins while disfavoring others with specific functions, and highlight differences between various HLA genes and alleles in those preferences. We link those differences to HLA anchor residue propensities and amino acid composition of preferentially presented proteins. The set of proteins that peptides presented by a given HLA are most likely to be derived from can be used to distinguish between class I and class II HLAs and HLA alleles. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Finally, we show that the reported self peptidome preferences of distinct HLA variants can be compensated by combinations of HLA-A/HLA-B and HLA-A/HLA-C alleles in frequent haplotypes.

Potential CD8+ T Cell Cross-Reactivity Against SARS-CoV-2 Conferred by Other Coronavirus Strains.

While individuals infected with coronavirus disease 2019 (COVID-19) manifested a broad range in susceptibility and severity to the disease, the pre-existing immune memory to related pathogens cross-reactive against SARS-CoV-2 can influence the disease outcome in COVID-19. Here, we investigated the potential extent of T cell cross-reactivity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be conferred by other coronaviruses and influenza virus, and generated an in silico map of public and private CD8+ T cell epitopes between coronaviruses. We observed 794 predicted SARS-CoV-2 epitopes of which 52% were private and 48% were public. Ninety-nine percent of the public epitopes were shared with SARS-CoV and 5.4% were shared with either one of four common coronaviruses, 229E, HKU1, NL63, and OC43. Moreover, to assess the potential risk of self-reactivity and/or diminished T cell response for peptides identical or highly similar to the host, we identified predicted epitopes with high sequence similarity with human proteome. Lastly, we compared predicted epitopes from coronaviruses with epitopes from influenza virus deposited in IEDB, and found only a small number of peptides with limited potential for cross-reactivity between the two virus families. We believe our comprehensive in silico profile of private and public epitopes across coronaviruses would facilitate design of vaccines, and provide insights into the presence of pre-existing coronavirus-specific memory CD8+ T cells that may influence immune responses against SARS-CoV-2.

Acute experimental infection of bats and ferrets with Hendra virus: Insights into the early host response of the reservoir host and susceptible model species.

Bats are the natural reservoir host for a number of zoonotic viruses, including Hendra virus (HeV) which causes severe clinical disease in humans and other susceptible hosts. Our understanding of the ability of bats to avoid clinical disease following infection with viruses such as HeV has come predominantly from in vitro studies focusing on innate immunity. Information on the early host response to infection in vivo is lacking and there is no comparative data on responses in bats compared with animals that succumb to disease. In this study, we examined the sites of HeV replication and the immune response of infected Australian black flying foxes and ferrets at 12, 36 and 60 hours post exposure (hpe). Viral antigen was detected at 60 hpe in bats and was confined to the lungs whereas in ferrets there was evidence of widespread viral RNA and antigen by 60 hpe. The mRNA expression of IFNs revealed antagonism of type I and III IFNs and a significant increase in the chemokine, CXCL10, in bat lung and spleen following infection. In ferrets, there was an increase in the transcription of IFN in the spleen following infection. Liquid chromatography tandem mass spectrometry (LC-MS/MS) on lung tissue from bats and ferrets was performed at 0 and 60 hpe to obtain a global overview of viral and host protein expression. Gene Ontology (GO) enrichment analysis of immune pathways revealed that six pathways, including a number involved in cell mediated immunity were more likely to be upregulated in bat lung compared to ferrets. GO analysis also revealed enrichment of the type I IFN signaling pathway in bats and ferrets. This study contributes important comparative data on differences in the dissemination of HeV and the first to provide comparative data on the activation of immune pathways in bats and ferrets in vivo following infection.

Broad Auto-Reactive IgM Responses Are Common In Critically Ill COVID-19 Patients.

The pathogenesis of severe COVID-19 remains poorly understood. While several studies suggest that immune dysregulation plays a central role, the key mediators of this process are yet to be defined. Here, we demonstrate that plasma from a high proportion (77%) of critically ill COVID-19 patients, but not healthy controls, contains broadly auto-reactive immunoglobulin M (IgM), and only infrequently auto-reactive IgG or IgA. Importantly, these auto-IgM preferentially recognize primary human lung cells in vitro, including pulmonary endothelial and epithelial cells. By using a combination of flow cytometry, LDH-release assays, and analytical proteome microarray technology, we identified high-affinity, complement-fixing, auto-reactive IgM directed against 263 candidate auto-antigens, including numerous molecules preferentially expressed on cellular membranes in pulmonary, vascular, gastrointestinal, and renal tissues. These findings suggest that broad IgM-mediated autoimmune reactivity may be involved in the pathogenesis of severe COVID-19, thereby identifying a potential target for novel therapeutic interventions.

CiiiDER: A tool for predicting and analysing transcription factor binding sites.

The availability of large amounts of high-throughput genomic, transcriptomic and epigenomic data has provided opportunity to understand regulation of the cellular transcriptome with an unprecedented level of detail. As a result, research has advanced from identifying gene expression patterns associated with particular conditions to elucidating signalling pathways that regulate expression. There are over 1,000 transcription factors (TFs) in vertebrates that play a role in this regulation. Determining which of these are likely to be controlling a set of genes can be assisted by computational prediction, utilising experimentally verified binding site motifs. Here we present CiiiDER, an integrated computational toolkit for transcription factor binding analysis, written in the Java programming language, to make it independent of computer operating system. It is operated through an intuitive graphical user interface with interactive, high-quality visual outputs, making it accessible to all researchers. CiiiDER predicts transcription factor binding sites (TFBSs) across regulatory regions of interest, such as promoters and enhancers derived from any species. It can perform an enrichment analysis to identify TFs that are significantly over- or under-represented in comparison to a bespoke background set and thereby elucidate pathways regulating sets of genes of pathophysiological importance.