The diversity of T-cell epitopes in cancer is overwhelming due the heterogeneity of genetic alterations and the polymorphism of HLA genes. To narrow down the most promising candidates, our lab has developed state-of-the-art predictors of HLA-I [Gfeller et al. J Immunol 2018, Gfeller et al. Cell Systems 2023] and HLA-II ligands [Racle et al. Nature Biotech 2019, Racle et al. Immunity 2023], as well as predictors of neo-epitope TCR recognition [Schmidt et al. Cell Rep Med 2021, Gfeller et al. Cell Systems 2023]. These predictions are largely based on high-quality HLA peptidomics data and machine learning algorithms for motif deconvolution [Bassani-Sternberg and Gfeller J Immunol 2016 , Racle et al. Nature Biotech 2019 ]. Our findings also revealed alternative binding modes of HLA ligands [Guillaume et al. PNAS 2018, Racle et al. Immunity 2023],  and enabled us to better understand the properties of the antigen presentation pathways.