Abstract LB-002: Optimizing immunogenicity prediction of neoepitopes

Epitopes that arise from a somatic mutation, so called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. It has however become evident that the vast majority of these neoepitope candidates do not induce a T cell response when tested in vivo or in vitro, i.e. they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. Several studies have used different combinations of immunoinformatic tools such as MHC binding predictions to prioritize the initial set of neoepitopes candidates. The tools to use and thresholds to apply for this prioritization has so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. To establish the appropriate tools and thresholds in the cancer setting, we here curated a set of immunogenic neoepitopes from the published literature and performed detailed analyses to detect what features discriminate immunogenic neoepitopes from a background set of mutated peptides. We experimentally measured the HLA binding affinity of all curated immunogenic neoepitopes. In doing so, we aimed to identify the optimal affinity threshold to effectively identify immunogenic neoepitopes. As a next step, we sought to assess the added value of different immunoinformatics tools, including various HLA binding prediction algorithms, processing prediction, stability prediction, and immunogenicity prediction, to most effectively detect immunogenic neoepitopes. The obtained results are now going to be used to facilitate the development of more accurate prediction algorithms. Citation Format: Zeynep Kosaloglu-Yalcin, Manasa Lanka, John Sidney, Kerrie Vaughan, Jason Greenbaum, Alessandro Sette, Bjoern Peters. Optimizing immunogenicity prediction of neoepitopes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-002. doi:10.1158/1538-7445.AM2017-LB-002