Empirical Evaluation of the Use of Computational HLA Binding as an Early Filter to the Mass Spectrometry-Based Epitope Discovery Workflow

Cancers

MarcoJ. Bruno¹  SonjaI. Buschow¹  MaikelP. Peppelenbosch¹  AmyL. Kessler¹  MoniqueT. A. de Beijer¹  Rachid Bouzid¹  RobbieJ. Luijten¹  Karel Bezstarosti²  JeroenA. A. Demmers² 

[1] Erasmus MC, Department of Gastroenterology and Hepatology, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands;Proteomics Center, Erasmus MC, Department of Biochemistry, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands;

关键词: cancer;    immunopeptidomics;    antigen presentation;    HLA-peptide;   

DOI  :  10.3390/cancers13102307

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown