ORCID Profile
0000-0002-0529-2765
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: American Society for Microbiology
Date: 09-2019
DOI: 10.1128/JVI.00634-19
Abstract: Mass spectrometry (MS)-based approaches are increasingly being employed for large-scale identification of HLA-bound peptides derived from pathogens, but only very limited profiling of the HIV-1 immunopeptidome has been conducted to date. Notably, a growing body of evidence has recently begun to indicate a protective role for HLA-C in HIV-1 infection, which may suggest that despite the fact that levels of HLA-C expression on both uninfected and HIV-1-infected cells are lower than those of HLA-A/B, HLA-C still presents epitopes to CD8 + T cells effectively. To explore this, we analyzed HLA-C*12:02-restricted HIV-1 peptides presented on HIV-1-infected cells expressing only HLA-C*12:02 (a protective allele) using liquid chromatography-tandem MS (LC-MS/MS). We identified a number of novel HLA-C*12:02-bound HIV-1 peptides and showed that although the majority of them did not elicit T cell responses during natural infection in a Japanese cohort, they included three immunodominant epitopes, emphasizing the contribution of HLA-C to epitope presentation on HIV-infected cells.
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: Public Library of Science (PLoS)
Date: 18-06-2018
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Wayne Paes.