Abstract 1898: SNAF: Accurate and compatible computational framework for identifying splicing derived neoantigens

Abstract Patient outcome with immune checkpoint blockade (ICB) therapy relies largely on the presence and detection of tumor neoantigens. Neoantigens can be produced from a broad spectrum of genetic (SNV, fusion) and non-genetic (splicing, alternative promoter) sources. While alternative splicing is a broad source of neoantigens, concerns regarding their tumor specificity have hindered their evaluation as therapeutic targets relative to SNVs. To systematically define tumor-specific and likely immunogenic neoantigens, we developed SNAF (Splicing Neo Antigen Finder), an easy-to-use and open-source computational workflow. SNAF automates the prediction of high-confidence patient-specific and shared splicing derived neoantigens from RNA-Seq data. This workflow leveraged deep learning and hierarchical Bayesian models to prioritize potential splicing-neoantigen candidates by their tumor specificity (inferred off-target effects) and immunogenicity (T cell reactivity). SNAF is compatible with all major splicing identification algorithms and can be easily integrated into any existing splicing analysis workflows. SNAF predicted splicing neoantigens were validated with an improved accuracy over alternative approaches in ovarian cancer HLA targeted MS profiles. Applied to 825 breast cancer patients in TCGA, we find that splicing neoantigens are frequently shared among patients and overall burden is highly associated with overall survival and specific clinical subtypes. These data suggest alternative genomic monitoring of splice-derived neoantigen burden can be used to prioritize selection of patients for ICB. Citation Format: Guangyuan Li, Nathan Salomonis. SNAF: Accurate and compatible computational framework for identifying splicing derived neoantigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1898.