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 ] and HLA-II ligands [Racle et al. Nature Biotech 2019 ], as well as predictors of neo-epitope TCR recognition [Schmidt et al. Cell Rep Med 2021 ]. 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 novel properties of HLA molecules [Guillaume et al. PNAS 2018 ] and enabled us to better understand the properties of the antigen presentation pathways.

Tumors are composed of heterogeneous cell types, comprising both cancer cells and non-malignant cells. The presence and phenotype of these different cell types plays an important role in tumor progression and response to therapy. Our lab has developed computational tools to simultaneously Estimate the Proportion of Immune and Cancer cells (EPIC) from bulk tumor gene expression data that can quantitatively predict the fraction of all major immune cell types, as well as cancer cells [Racle et al. 2017 ]. In parallel, we are actively working on single-cell RNA-Seq data analysis for cancer and immunology, to explore cell type heterogeneity in a fully unbiased and marker-free approach [Carmona et al. 2020 ]. Recently, we have developed a powerful approach to facilitate the analysis of large single-cell genomics data based on the concept of ‘metacells’ (read more about this in [Bilous et al. 2021 ]).