Abstract PL02-02: Immunopeptidomics: Accelerating the development of personalized cancer immunotherapy

Cancer immunotherapy amplifies the inherent capacity of cytotoxic T cells to eliminate tumor cells by recognizing human leukocyte antigen-binding peptides (HLAp) presented specifically on tumors but not on normal cells. Recent data show that recognition of mutated neoantigens plays a key role. Currently, mass spectrometry (MS) is the only unbiased methodology to comprehensively uncover the repertoire of HLAp presented in vivo. Recently, we have developed an in-depth MS-based immunopeptidomics approach combined with exome sequencing analysis to directly identify neoantigens from human melanoma tumors (Bassani-Sternberg et al., Nat Commun 2016). This approach is not feasible for substantial number of patients for whom not enough tissue material is available. So far, the discovery of neoantigens relies mainly on prediction-based interrogation of the "mutanome." In a proof-of-concept study we showed that incorporation of deconvoluted HLAp data in ligand prediction algorithms can improve their accuracy (Bassani-Sternberg and Gfeller, J Immunol 2016). In addition, we assembled a comprehensive HLAp database in term of number of peptides and diversity of HLA-I molecules. We showed that by taking advantage of co-occurring HLA-I alleles, we can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our novel and scalable approach uncovers new motifs for several alleles that up to now had no known ligands. HLA-ligand predictors trained on such data substantially improve neoantigen predictions in four melanoma and two lung cancer patients, indicating that unbiased HLAp data are ideal for in silico identification of neoantigens (Bassani-Sternberg et al., PLOS Comp Biol 2017). Furthermore, our immunopeptidomics database revealed hotspots that are sub-sequences of proteins frequently presented. We introduce two immunopeptidomics MS-based features to guide prioritization of neoantigens: the number of peptides matching a protein in our database and the overlap of the predicted wild-type peptide with other peptides in our database. We show as a proof of concept that our immunopeptidomics MS-based features improved neoantigen prioritization by up to 50% (Mueller et al., submitted). Overall, our work shows that, in addition to providing huge training data to improve the HLA binding prediction, immunopeptidomics also captures other aspects of the natural in vivo presentation that significantly improve prediction of clinically relevant neoantigens. Citation Format: Michal Bassani-Sternberg, Chloe Chong, Fabio Marino, HuiSong Pak, David Gfeller, Markus Muller, George Coukos. Immunopeptidomics: Accelerating the development of personalized cancer immunotherapy [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr PL02-02.