Enzymatic processing and MHC loading in cancer immunotherapyAdvanced immunopeptidomics based discovery engine for the development of personalized cancer immunotherapy. (October 2022)

• Record Type: Journal Article
• Authors: Bassani-Sternberg, Michal; Müller, Markus; Huber, Florian; Stevenson, Brian; Racle, Julien; Michaux, Justine; Chong, Chloe; Coukos, George
• Is Part Of: Molecular immunology. Volume 150(2022)
• Page Start: 4
• Publication Date: 2022-10
• Subjects: Immunochemistry -- Periodicals; Molecular biology -- Periodicals; Immunochemistry -- Periodicals; Allergy and Immunology -- Periodicals; Molecular Biology -- Periodicals; Immunochimie -- Périodiques; Biologie moléculaire -- Périodiques; Immunochemistry; Molecular biology; Periodicals; Electronic journals; 571.96
• DOI: 10.1016/j.molimm.2022.05.023
• Languages: English
• ISSNs: 0161-5890

Abstract

Abstract : The remarkable clinical efficacy of the immune checkpoint blockade therapies has motivated researchers to discover immunogenic epitopes and exploit them for personalized vaccines. Mutated human leukocyte antigen binding peptides (HLAp) are currently the leading targets. Most studies attempt to identify neoantigens based on predicted affinity to HLA molecules. We have shown that the direct identification of tissue-derived neoantigens by mass spectrometry is becoming feasible and we have recently designed a novel high-throughput, reproducible and sensitive method for sequential immuno-affinity purification of HLA-I and -II peptides that is suitable for both cell lines and tissues. The massive amount of HLAp data we acquire while hunting down the neo-antigens is highly valuable. We have compiled a large immunopeptidomics database across dozens of cell types and HLA allotypes. First, we have shown that by taking advantage of co-occurring HLA-I alleles across dozens of immunopeptidomics datasets we can rapidly and accurately identify HLA-I binding motifs. Consequently, training HLA-I ligand predictors on refined motifs significantly improves the identification of neoantigens. Recently, we have acquired the largest reported high-quality HLA-II peptidomics dataset. We introduced completely novel algorithmic tools to analyze such data and developed for the first time HLA-II epitope prediction tool trained on peptidomics data that results in major improvements in prediction accuracy. Second, our database captures the global nature of the in vivo peptidome averaged over many HLA alleles, and therefore, reflects the propensity of peptides to be presented on HLA complexes, which is complementary to the existing neoantigen prediction features. We have shown as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50%. Overall, immunopeptidomics facilitates direct identification of neoantigens and it can also improve the prediction of clinically relevant neoantigens, and we develop a novel clinical pipeline for target selection for personalized anti-cancer vaccines.