Gfeller lab

Tumors form highly complex structures comprising many different cell types like cancer and immune cells. In our research, we use and develop computational biology and immuno-informatics to better understand and predict the interactions between immune and cancer cells. 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

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 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.

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. Together with several experimental collaborators, we are developing machine learning algorithms both for understanding and modelling the properties of the TCR repertoire and unraveling the determinants of TCR specificity for their cognate epitopes.

Bulk and single-cell genomics analyses of tumors

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 ]).


David Gfeller

PhD Computational cancer biology, Associate Professor, Department of Oncology UNIL & CHUV, Ludwig adjunct scientist, Ludwig Institute for Cancer Research Lausanne GfellerLab

Selected Publications

Predictions of immunogenicity reveal potent SARS-CoV-2 CD8+ T-cell epitopes

Gfeller D, Schmidt J, Croce G, Guillaume P, Bobisse S, Genolet R, Queiroz L, Cesbron J, Racle J, Harari A

BioRxiv – 2022 May 23

Deciphering the landscape of phosphorylated HLA-II ligands.

Solleder M, Racle J, Guillaume P, (...), Coukos G, Bassani-Sternberg M, Gfeller D

iScience – 2022 Apr 6

Metacells untangle large and complex single-cell transcriptome networks

Bilous M, Tran L, Cianciaruso C, Gabriel A, Michel H, Carmona SJ, Pittet MJ, Gfeller D

BioRxiv – 2021 June 8

EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data.

Racle J, Gfeller D

Methods in molecular biology (Clifton, N.J.) – 2020

Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-Seq.

Carmona SJ, Siddiqui I, Bilous M, Held W, Gfeller D

Oncoimmunology – 2020 Mar 12

Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands.

Solleder M, Guillaume P, Racle J, (...), Coukos G, Bassani-Sternberg M, Gfeller D

Molecular & cellular proteomics : MCP – 2019 Dec 17

Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.

Racle J, Michaux J, Rockinger GA, (...), Jandus C, Bassani-Sternberg M, Gfeller D

Nature biotechnology – 2019 Oct 14

The Length Distribution and Multiple Specificity of Naturally Presented HLA-I Ligands.

Gfeller D, Guillaume P, Michaux J, (...), Racle J, Coukos G, Bassani-Sternberg M

Journal of immunology (Baltimore, Md. : 1950) – 2018 Nov 14

Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Gfeller D, Bassani-Sternberg M

Frontiers in immunology – 2018 Jul 25

The C-terminal extension landscape of naturally presented HLA-I ligands.

Guillaume P, Picaud S, Baumgaertner P, (...), Bassani-Sternberg M, Filippakopoulos P, Gfeller D

Proceedings of the National Academy of Sciences of the United States of America – 2018 Apr 30

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.

Racle J, de Jonge K, Baumgaertner P, Speiser DE, Gfeller D

eLife – 2017 Nov 13

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.

Bassani-Sternberg M, Chong C, Guillaume P, (...), Kandalaft LE, Coukos G, Gfeller D

PLoS computational biology – 2017 Aug 23

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types.

Carmona SJ, Teichmann SA, Ferreira L, (...), Stubbington MJ, Cvejic A, Gfeller D

Genome research – 2017 Jan 13

Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions.

Bassani-Sternberg M, Gfeller D

Journal of immunology (Baltimore, Md. : 1950) – 2016 Aug 10

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