Empirical evaluation of the use of computational hla binding as an early filter to the mass spectrometry-based epitope discovery workflow

Simple Summary Many different human leukocyte antigen (HLA)-types exist across the population that each binds a specific motif of amino acids. HLA-peptide complexes are the driving force behind recognition of cancers and infected cells by cytotoxic T cells. HLA-immunopeptidomics aims to identify peptides derived from (cancer) antigens in the HLA-binding cleft with mass spectrometry (MS). Peptides eluted from HLA are analyzed by MS and translated to a protein derived amino acid sequence by specialized software. These software packages use statistical thresholds to limit false discoveries and return only the most confidently identified peptides. However, we believe, as do others, that many useful peptides can still be found in the excluded pool of peptides. This idea drove the development of specialized algorithms that utilize HLA specific motifs to retrieve additional relevant peptides. It is unknown, however, how many peptides could potentially be found in this pool. By adjusting the statistical threshold, we empirically demonstrate the vastness of valuable data beyond the traditional thresholds that await to be discovered. Abstract Immunopeptidomics is used to identify novel epitopes for (therapeutic) vaccination strategies in cancer and infectious disease. Various false discovery rates (FDRs) are applied in the field when converting liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectra to peptides. Subsequently, large efforts have recently been made to rescue peptides of lower confidence. However, it remains unclear what the overall relation is between the FDR threshold and the percentage of obtained HLA-binders. We here directly evaluated the effect of varying FDR thresholds on the resulting immunopeptidomes of HLA-eluates from human cancer cell lines and primary hepatocyte isolates using HLA-binding algorithms. Additional peptides obtained using less stringent FDR-thresholds, although generally derived from poorer spectra, still contained a high amount of HLA-binders and confirmed recently developed tools that tap into this pool of otherwise ignored peptides. Most of these peptides were identified with improved confidence when cell input was increased, supporting the validity and potential of these identifications. Altogether, our data suggest that increasing the FDR threshold for peptide identification in conjunction with data filtering by HLA-binding prediction, is a valid and highly potent method to more efficient exhaustion of immunopeptidome datasets for epitope discovery and reveals the extent of peptides to be rescued by recently developed algorithms.