What do we do?
In the Computational Cancer Biology Group, our aim is to study the interactions between cancer and immune cells. To this end, we develop machine learning algorithms to analyse large-scale genomics and proteomics data. In particular, we are focusing on molecular and cellular aspects of cancer immune cell interactions. At the molecular level, we develop tools to predict (neo-)antigen presentation by integrating large HLA peptidomics datasets. At the cellular level, we are developing novel approaches to characterizing immune infiltrations and the different states of immune cells from gene expression profiles of tumours and immune cells.
All our tools are available at https://github.com/GfellerLab
- Novel bioinformatics tools to analyze large HLA peptidomics datasets (MixMHCp) and improved HLA-I ligand predictor (MixMHCpred).
- First identification of NK cells in non-mammalian species.
- Estimation of the Proportion of Immune and Cancer cells (EPIC) from bulk tumor gene expression data.
- Racle J, de Jonge K, Baumgaertner P, Speiser DE, Gfeller D, Simultaneous Enumeration Of Cancer And Immune Cell Types From Bulk Tumor Gene Expression Data, eLife, (2017).
- Bassani-Sternberg M, Chong C, Guillaume P, Solleder M, Pak HS, Gannon PO, Kandalft LE, Coukos G, Gfeller D, Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity, PLoS Computational Biology, 13(8):e1005725 (2017).
- Carmona SJ, Teichman SA, Ferreira L, Macaulay IC, Stubbington M, Cvejic A, Gfeller D, Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types, Genome Research, 27, 451-461 (2017).
- Bassani-Sternberg M, Gfeller D, Unsupervised HLA peptidome deconvolution improves ligand prediction accuracy and predicts cooperative effects in peptide-HLA interactions, Journal of Immunology, 197, 2492 (2016).
- Gfeller D, Bassani-Sternberg M, Schmidt J, Luescher IF, Current tools for predicting cancer-specific T cell immunity, OncoImmunology, e1177691 (2016).