Immunopeptidomics

Our main goal is to identify clinically relevant cancer specific Human Leukocyte Antigen (HLA) ligands that will guide the development of personalized cancer immunotherapy using state of the art proteogenomics and mass-spectrometry (MS) approaches.

• We develop proteogenomics and MS-based immunopeptidomics analytical and computational approaches to identify HLA ligands derived from tumor-associated proteins, mutated neoantigens, non-canonical ORFs and post translationally modified peptides.
• We have initiated fundamental discovery work to elucidate how tumor cells present antigens and what are the bases of tumor immunogenicity in mouse and human.
• We are investigating the differences between T cell inflamed and non-inflamed tumors in terms of antigen presentation and the antigenic landscape and how drugs modulate the immunopeptidome.
• We have established a continuous bioinformatics pipeline called NeoDisc enabling direct and personalized identification and prediction of neoantigens by combining genomic information derived from exome-seq analysis and transcriptomics data with measured immunopeptidomics data.
• We apply our discovery pipeline in the context of phase I clinical vaccine and adoptive T cell therapy trials at the CHUV to identify personalized neoantigens from patient tumor samples.