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Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes

ABSTRACT: Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines – i.e. Mascot, Mascot+Percolator and PEAKS DB – as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by mass spectrometry to benchmark methods’ performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated. Head of the research group Molecular Immunology at King’s College London and the Francis Crick Institute, London (UK). Email: michele.mishto@kcl.ac.uk,

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Michele Mishto

LAB HEAD: Juliane Liepe

PROVIDER: PXD031709 | Pride | 2022-04-06

REPOSITORIES: Pride

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Project description:Tumor antigens are central to antitumor immunity. Recent evidence suggests that peptides from non-canonical (nonC) aberrantly translated proteins can be presented on HLA-I by tumor cells. Here, we investigated the presentation and immunogenicity of nonC antigens across different cancer types to better understand their contribution to cancer immunosurveillance and to address whether they could be exploited therapeutically. To this end, we employed a proteogenomics pipeline to identify nonC HLA-I ligands derived from off-frame translation of coding sequences and non-coding regions (UTR, ncRNA, intronic and intergenic) in patient-derived tumor cell lines (TCL) of different histological types (4 gynecological cancer, 3 melanoma and 2 head and neck cancer patients). First, peptides bound to HLA-I were isolated and analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) using state-of-the-art procedures. Amino acid (Aa) sequences were identified through the previously described pipeline Peptide-PRISM, with some modifications. Briefly, for each MS spectrum, the top 10 candidates were first identified by de novo sequencing and later mapped to a database including the 3-frame transcriptome and 6-frame genome. Additionally, whole-exome sequencing (WES) information of each TCL was included to interrogate the presentation of mutated peptides derived cancer-specific NSM. The false-discovery rate (FDR) was calculated independently for each category considering the search space and peptide length in a stratified mixture model as previously described. Next, in order to select nonC peptides preferentially presented by tumor cells, immunopeptidomics data from several healthy tissues and samples available from the HLA ligand atlas was used to filter out peptides presented in healthy tissues. To overcome a potential bias toward frequent alleles, the peptides were excluded at the ORF level rather than the Aa sequence. As a result, we found that from a total of 839 unique nonC peptides detected in our tumor samples, 38.5% were predicted to derive from ORFs also present in healthy tissue (nonC-HL). Hence, 61.6% (n=517) were considered preferentially presented on tumor HLA-I, referred to as non-canonical tumor ligands (nonC-TL). Out results showed that, although nonC-TL constitued the most abundant source of candidate tumor antigens, as compared to neoantigens, cancer-germline or melanoma-associated antigens, pre-existing antitumor T cells in cancer patients preferentially recognized neoantigens rather than nonC-TL. Nonetehless, nonC-TL elicited de novo T-cell responses via in vitro sensitization of donor lymphocytes. We identified TCRs specific to three nonC-TL, two of which mapped to the 5’ UTR regions of HOXC13 and ZKSCAN1, and one mapping to a non-coding spliced variant of the C5orf22C. These immunogenic nonC-TL were expressed across tumor types but were barely or not detected in healthy cells. Our findings predict a limited contribution of nonC-TL to cancer immunosurveillance but demonstrate they are attractive novel targets for widely applicable immunotherapies.

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Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes

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