Gfeller lab

Tumors form highly complex structures comprising many different cell types, like cancer and immune cells, which are all interacting with each other. In our research, we combine computational and experimental approaches to unravel the determinants of specificity in immune cancer cell interactions. Our lab is affiliated to the Department of Oncology at the University of Lausanne (UNIL), the Ludwig Institute for Cancer Research (LICR) and the Swiss Institute of Bioinformatics (SIB). ...

Research projects

Modelling TCR repertoire and specificity

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].

Analysis and predictions of antigen presentation and TCR recognition

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 MHC-I [Gfeller et al., J Immunol 2018, Gfeller et al., Cell Systems 2023, Tadros et al., Genome Med 2025] and MC-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.

Bulk and single-cell genomics analyses of tumors

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].

Team

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AGORA PRS Seminar | June 10th

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PhD Thesis Defense | April 4th

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