ORCID Profile
0000-0002-8221-7697
Current Organisation
University of Oxford
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Publisher: Research Square Platform LLC
Date: 03-08-2022
DOI: 10.21203/RS.3.RS-1890352/V1
Abstract: Cryptic Human Leukocyte Antigen (HLA)-presented peptide identification from unannotated genome sources is a priority for target antigen discovery for development of next generation immunotherapies in cancer. Current immunopeptidomic approaches utilize the integration of transcriptomics data to inform spectral interpretation, however, recent observations that tumour-associated antigen-encoding RNA levels are often low highlights limitations of such proteogenomic approaches 1 . We here employ a de novo sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical HLA-associated peptide sequences (HLAp) in cancer without integration of transcript sequence information. Our strategy integrates HLA binding prediction, peptide retention time prediction, and average local confidence scores culminating in the machine learning model MARS (MHC binding prediction, Average Local Confidence Score, and Retention time integration for improved de novo candidate Selection). We demonstrate increased HLA-I peptide identification sensitivity by benchmarking our model against de novo sequencing alone with a large synthetic HLA-I peptide library dataset. We further define the sensitivity of MARS by reanalysis of a published dataset of high-quality non-canonical HLAp identifications in human cancer cell line and tissue datasets and achieve almost 2-fold improvement of the full sequence recall (FSR) for high quality spectral assignments in comparison to de novo sequencing alone 2 . We minimize the false discovery rate (FDR) through a step-wise peptide sequence mapping strategy and are able to expand the reported non-canonical peptide space with an assignment accuracy above 85.7%. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, non-canonical peptide sequences in primary tumour tissue at reduced FDR, in the absence of transcriptomic sequencing data.
Publisher: Public Library of Science (PLoS)
Date: 30-03-2020
Publisher: Elsevier BV
Date: 11-2022
Publisher: Research Square Platform LLC
Date: 31-12-2020
DOI: 10.21203/RS.3.RS-128348/V1
Abstract: 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.
Publisher: Cold Spring Harbor Laboratory
Date: 25-12-2020
DOI: 10.1101/2020.12.25.424183
Abstract: T cell recognition of a cognate peptide-MHC complex (pMHC) presented on the surface of infected or malignant cells, is of utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T Cell Receptors (TCR) would greatly facilitate identification of vaccine targets for both pathogenic diseases as well as personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the centre 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 SARS-CoV-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 training data of the models seem 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 vs. cancer peptides. Overall, we demonstrate that accurate and reliable prediction of immunogenic CD8+ T cell targets remains unsolved, thus we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.
Publisher: Research Square Platform LLC
Date: 20-07-2022
DOI: 10.21203/RS.3.RS-1828302/V1
Abstract: Selective binding of TCR-based therapies that target a single tumour-specific peptide epitope 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 computational modeling. We developed the first experimental platform to de novo identify interactomes of TCR-like molecules directly in human tissues using mass spectrometry. As proof of concept, we confirm the target epitope of a novel MAGE-A4-specific TCR-like antibody. We further determine 16 cross-reactive sequences for ESK1, a TCR-like bispecific antibody recognizing WT-1 with known off-target activity, in healthy liver tissue. We observe strong, off-target-induced T cell activation for 8/16 sequences, demonstrating the high specificity of the approach. Off-target sequences define a previously missed amino acid compensation motif that structurally mimics the target peptide groove coordination and allows for peptide interaction with the engager molecule. We establish the importance of the identified off-target activity by demonstrating 3D liver spheroid killing in the presence of ESK1 and healthy donor PBMC. Finally, we utilize our approach to de-risk our novel MAGE-A4 targeting TCR-like bispecific antibody prior to entering now ongoing clinical trials. We conclude that our strategy offers an accurate, scalable route for de-risking TCR-based therapeutics prior to first-in-human clinical application.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Cold Spring Harbor Laboratory
Date: 17-02-2021
DOI: 10.1101/2021.02.16.431395
Abstract: 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.
Publisher: Frontiers Media SA
Date: 05-11-2020
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Isaac Woodhouse.