Tumors form highly complex structures comprising many different cell types like cancer and immune cells. In our research, we develop novel computational biology and immuno-informatics tools to better understand the interactions between immune and cancer cells.
Research group projects
1) Computational analysis of HLA peptidomics data and predictions of T cell neo-epitopes
In-depth and unbiased identification of peptides presented on HLA molecules with Mass Spectrometry provides a unique opportunity to collect very large HLA ligand datasets that can inform us about the binding properties of HLA molecules and can be used for training HLA ligand predictors. Through the development of novel machine learning algorithms, our lab has been the first to show how to deconvolute pooled HLA peptidomics data (i.e., coming from samples expressing up to 6 HLA-I alleles) and how to use these data to improve predictions of neo-antigens . Currently we are expanding these analyses to different types of HLA-I ligands and to HLA-II molecules .
2) Computational analysis of immune infiltrations in tumors
Tumors are composed of various cell types. Unfortunately cancer genomics studies are often restricted to bulk tumors. Our lab has recently developed a novel computational approach 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 . This will expand the scope of prospective analyses of immune infiltrations using gene expression profiling and enable retrospective analyses of thousands of cancer genomics datasets from human patients. 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.
 Bassani-Sternberg,..., Gfeller, PLoS Comp Bio 2017
 Racle,..., Gfeller, Nature Biotech 2019
 Racle,..., Gfeller, eLife 2017
Our computational tools are made available on the github of the lab : https://github.com/GfellerLab