What do we do?
At 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 analyze 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 characterize immune infiltrations and the different states of immune cells from gene expression profiles of tumours and immune cells.
During 2016, the group developed a novel bioinformatics tools to analyze large HLA peptidomics datasets, and gain a better understanding of the properties of HLA peptide ligands.
Main publications 2016
- 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).