Talk title: The Human Immunoepeptidome Project – accelerating the development of personalized cancer immunotherapy
Position: Head of Immunopeptidomics Unit, Department of Oncology, University Hospital of Lausanne (CHUV), Switzerland and the Ludwig Institute for Cancer Research, Lausanne Branch.
Michal Bassani-Sternberg received her PhD degree in Biology (2010) from the Technion – Israel Institute of Technology. From 2011 to 2015 she was a post-doctoral researcher at the Proteomics and Signal Transduction Department, headed by Prof. Matthias Mann at the Max Planck Institute of Biochemistry. She was awarded the Minerva and the Alexander von Humboldt fellowships. She developed mass spectrometry-based workflows for direct identification of HLA binding peptides eluted form tumor specimens and uncovered dozens of new cancer specific antigens including patient’s specific neo-antigens. Currently she is heading the Immunopeptidomics Unit affiliated to the Center of Experimental Therapeutics at the Oncology Department at the CHUV. The Immunopeptidomics Unit implements advanced experimental and computational mass-spectrometry based antigen discovery workflows to support development of personalized cancer immunotherapy and early phase experimental cancer vaccines.
In the first part of my talk I will present the recently launched HUPO Human Immuno-Peptidome Project (HUPO-HIPP). The long-term goal of this project is to map the entire repertoire of peptides presented by human leukocyte antigen (HLA) molecules using mass spectrometry technologies. I will summarize the discussions held during the 1st HUPO-HIPP workshop (May 2017) and the current ongoing activities of this initiative.
In the second part of my talk I will present how my lab develops and applies advanced in-depth high-throughput mass-spectrometry based immunopeptidomics for the development of personalized cancer vaccines. We have shown that MS analysis of HLA-I binding peptides (HLAp) eluted from tissue samples is a promising approach to discover the actual in-vivo presented neo-antigens; yet, it is applicable to a small fraction of samples due to the large amount of biological sample that is required. Still, the massive amount of HLAp data acquired while hunting down the neo-antigens is highly valuable. We show that by taking advantage of co-occurring HLA-I alleles across dozens of immunopeptidomics datasets we can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Consequently, training HLA-I ligand predictors on refined motifs significantly improves the identification of neoantigens.