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].
Latest publications
Engineered CD4 TCR T cells with conserved high-affinity TCRs targeting NY-ESO-1 for advanced cellular therapies in cancer.
Saillard M, Cenerenti M, Reichenbach P, (...), Coukos G, Romero P, Jandus C
Science advances – 2025 Jun 27
Phage display enables machine learning discovery of cancer antigen-specific TCRs.
Croce G, Lani R, Tardivon D, (...), Hebeisen M, Dunn SM, Gfeller D
Science advances – 2025 Jun 11
Predicting MHC-I ligands across alleles and species: how far can we go?
Tadros DM, Racle J, Gfeller D
Genome medicine – 2025 Mar 20
Donor HLA-DQ genetic and functional divergence affect the control of BK polyoma virus infection after kidney transplantation.
Chevalier MF, Allain V, Gras J, (...), Gfeller D, Feray C, Caillat-Zucman S
Science advances – 2025 Mar 5
SuperSpot: coarse graining spatial transcriptomics data into metaspots.
Teleman M, Gabriel AAG, Hérault L, Gfeller D
Bioinformatics (Oxford, England) – 2024 Dec 26
Team

David Gfeller
PhD Computational cancer biology, Associate Professor, Department of Oncology UNIL & CHUV, Ludwig adjunct scientist, Ludwig Institute for Cancer Research Lausanne
GfellerLabOther members
Selected Publications
Predicting MHC-I ligands across alleles and species: how far can we go?
Tadros DM, Racle J, Gfeller D
Genome medicine – 2025 Mar 20
SuperSpot: coarse graining spatial transcriptomics data into metaspots.
Teleman M, Gabriel AAG, Hérault L, Gfeller D
Bioinformatics (Oxford, England) – 2024 Dec 26
Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.
Croce G, Bobisse S, Moreno DL, (...), Guillame P, Harari A, Gfeller D
Nature communications – 2024 Apr 13
Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.
Racle J, Guillaume P, Schmidt J, (...), Bassani-Sternberg M, Harari A, Gfeller D
Immunity – 2023 Mar 30
Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8(+) T-cell epitopes.
Gfeller D, Schmidt J, Croce G, (...), Cesbron J, Racle J, Harari A
Cell systems – 2023 Jan 4
The MHC Motif Atlas: a database of MHC binding specificities and ligands.
Tadros DM, Eggenschwiler S, Racle J, Gfeller D
Nucleic acids research – 2022 Nov 1
Metacells untangle large and complex single-cell transcriptome networks.
Bilous M, Tran L, Cianciaruso C, (...), Carmona SJ, Pittet MJ, Gfeller D
BMC bioinformatics – 2022 Aug 13
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AGORA PRS Seminar | June 10th
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AGORA PRS Seminar | June 3rd
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🌱 Second AGORA Sustainability Day | May 21st
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PhD Thesis Defense | April 4th
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LabLinks: Tumor immunology and immunotherapy | November 11&12th
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AGORA PRS | October 8th
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13th Faculty & Staff Annual Retreat, Swiss Cancer Center Leman | September 26 & 27
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AGORA PRS | June 18th
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Les coulisses de la recherche | June 6th
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AGORA PRS | December 5th
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Ludwig Distinguished Lecture, Prof. Sohrab Shah | September 14th
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AGORA PRS | May 23rd
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