T cells have the ability to generate billions of T-Cell Receptors (TCRs) that can recognize various epitopes displayed on HLA molecules. While information about the presence of specific T cells in a tumor can be obtained with TCR-sequencing technologies, a major challenge remains to know which T cells recognize which epitopes. We are combining experimental screening technologies with machine learning algorithms for understanding and modelling the properties of the TCR repertoire and unraveling the determinants of TCR specificity [Croce et al., Nature Com 2024, Croce et al., Science Advances 2025].

Tumors are composed of heterogeneous cell types, comprising both cancer cells and non-maligant 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., Elife 2017, Gabriel et al., Elife 2024 ]. Recently, we have developed a powerful approach to facilitate the analysis of large single-cell genomics data based on the concept of ‘metacells’ [Bilous et al., BMC Bioinformatics 2022, Bilous et al., MSB 2024, Teleman et al., Bioinformatics 2024].