Gfeller Lab

Department of Oncology UNIL CHUV
Ludwig Institute for Cancer Research Lausanne

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© M. Solleder

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© Fondation ISREC

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© Fondation ISREC

WELCOME

Our lab is using both computational and experimental approaches to better understand and predict cancer immune cell interactions. The 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). Our offices are located in the beautiful AGORA building (have a flight around), next to the hospital (CHUV) and dedicated to translational cancer research.

NEWS

  • 10.01.2024 – Happy to share the first extensive review/tutorial on how to build metacells and use them for single-cell RNA-Seq data analysis – great work by Mariia Bilous, Léonard Hérault and Aurélie Gabriel.
  • 18.12.2023 – Ever wondered how to perdict MHC-II for alleles without ligands – here is a detailed tutorial on how to use MixMHC2pred for this task.
  • 10.11.2023 – Pre-print alert for EPIC-ATAC – awesome work by Aurelie Gabriel, extending the EPIC method to bulk ATAC-Seq
  • 16.09.2023 – Pre-print alert for MixTCRpred – great work by Giancarlo Croce
  • 20.04.2023 – Final version of our paper on MHC-II ligands published in Immunity
  • 06.01.2023 – Check out our latest review about predictions of naturally presented MHC ligand
  • 04.01.2023 – Latest update on MixMHCpred (2.2) and PRIME (2.0) published in Cell Systems.
  • 02.11.2022 – New paper published in NAR describing our MHC Motif Atlas.
  • 01.10.2022 – Warm welcome to Dana Moreno and a good start into your PhD.
  • 01.09.2022 – Welcome to Matei Telemann. We wish you lots of success in your PhD.
  • 04.07.2022 – Welcome to Animesh Awasthi who is joining us from India for the summer.
  • 01.07.2022 – Huge effort by Julien Racle to understand and predict MHC-II binding motifs and CD4 T-cell epitope binding modes. All the details are available here.
  • 28.05.2022 – New manuscript on CD8 T-cell epitope predictions.
  • 01.04.2022 – Welcome to Yan Liu. We wish her lots of success and joy in her PhD.
  • 19.01.2022 – Exciting PhD positionS in the lab. Come and join us to work on modeling/predicting T-cell recognition of cancer cells or single-cell genomics in computational oncoimmunology.
  • 03.01.2022 – Welcome Léonard Hérault. Thrilled to have you in the lab to explore new horizons of the SuperCell method.
  • 01.11.2021 – A warm welcome to Hugo Michel. Hugo, we wish you the best of success for your 4-months internship.
  • 05.10.2021 – Large recruitment of PhD students in the Quantitative Biology doctoral school – great panel of labs, consider applying there!
  • 15.09.2021 – We have open positions, check out the details below.
  • 29.06.2021 – New manuscript about phosphorylated HLA-II ligands.
  • 08.06.2021 – Great work from Mariia Bilous: read about the metacell concept and the SuperCell method and why/how it can be used for single-cell RNA-Seq data analysis.

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Research Projects

Modelling TCR repertoire and specificity

T cells have the ability to generate billions of T-Cell Receptors 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. To address this question we are combining machine learning algorithms with multiple experimental strategies to screen TCRs recognizing different epitopes and train predictors of TCR-epitope interactions. In [Croce et al. 2023], we describe the first version of our tools (MixTCRpred) and how it can be used to analyse both bulk and epitope-specific TCR repertoires.

Related tools

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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 MHC genes. To narrow down the most promising candidates, our lab has developed state-of-the-art predictors of MHC-I [Gfeller et al. Cell Systems 2023] and MHC-II ligands [Racle et al. Nature Biotech 2019; 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 MHC peptidomics data and machine learning algorithms for motif deconvolution [Bassani-Sternberg and Gfeller J Immunol 2016, Racle et al. Nature Biotech 2019]. Our findings provide detailed MHC motif [Tadros et al. NAR 2023] and revealed novel structural properties of MHC molecules [Guillaume et al. PNAS 2018; Racle et al. Immunity 2023].

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 RNA-seq and ATAC-seq data that can quantitatively predict the fraction of all major immune cell types, as well as cancer cells [Racle et al. 2017; Racle et al. 2020; Gabriel et al. 2023]. In parallel, 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. 2022], and our recent review/tutorial [Bilous et al 2024]).

Computational Tools

Estimating the Proportion of Immune and Cancer cells from bulk gene expression data

Predictions for HLA-I ligand interactions

Predictions for HLA-II ligand interactions

Coarse-graining of large single-cell RNA-seq data into metacells

Selected Publications

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, Perez MAS, Croce G, Genolet R, Coukos G, Zoete V, Pojer F, Bassani-Sternberg M, Harari A, Gfeller D. (2023) Immunity, 2023, 56, 1-17.

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, Guillaume P, Bobisse S, Genolet R, Queiroz L, Cesbron J, Racle J, Harari A. (2023), Cell Systems, 14, 72.

Metacells untangle large and complex single-cell transcriptome networks. Bilous M, Tran L, Cianciaruso C, Gabriel A, Michel H, Carmona, S, Pittet MJ, Gfeller D., 2022. BMC Bioinformatics, 23, 336.

Group Members

Principle Investigator

David Gfeller

Senior Scientists

Julien Racle

Postdoctoral Researchers

Giancarlo Croce
Aurélie Gabriel
Léonard Hérault

PhD Students

Daniel Tadros
Yan Liu

Matei Telemann

Dana Moreno

Alumni

Open Positions

Currently, we do not have open positions. But if you are still interested in working in my group, contact me directly and we will see if there is a match. We also strongly encourage and provide support for candidates interested in applying to their own PhD/post-doc fellowships.

Funding

  • Sinergia
  • Swiss National Science Foundation
  • Swiss Cancer League

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David Gfeller
Associate Professor
Department of Oncology UNIL CHUV
Ludwig Institute for Cancer Research Lausanne

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Bâtiment AGORA
Rue du Bugnon 25A
1005 Lausanne

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