Schmidt and Smith et al. developed PRIME, a tool to predict the immunogenicity of CD8+ T cell epitopes that captured both antigen presentation on HLA molecules and TCR recognition. PRIME improved prediction accuracy over tools focusing only on HLA-I binding or antigen presentation, performed faster than other predictors, and displayed improved accuracy in predicting epitopes of high structural avidity to CD8+ T cells. PRIME revealed higher immunogenicity of aromatic residues, which was validated in vivo, and provided evidence of immunoediting in humans, as highly immunogenic recurrent mutations were less frequent in patients.
Contributed by Shishir Pant
ABSTRACT: CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors. However, the rules that govern which peptides are truly recognized by existing T cell receptors (TCRs) remain poorly understood, precluding accurate predictions of neo-epitopes for cancer immunotherapy. Here, we capitalize on recent (neo-)epitope data to train a predictor of immunogenic epitopes (PRIME), which captures molecular properties of both antigen presentation and TCR recognition. PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition that we experimentally validate. Analysis of cancer genomics data reveals that recurrent mutations tend to be less frequent in patients where they are predicted to be immunogenic, providing further evidence for immunoediting in human cancer. PRIME will facilitate identification of pathogen epitopes in infectious diseases and neo-epitopes in cancer immunotherapy.
Author Info: (1) Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland; (2) Department of Chemistry and Biochemistry and
Author Info: (1) Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland; (2) Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA; (3) Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland; (4) Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland (5) Nexthink, Prilly, Switzerland ; (6) Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; (7) Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland; (8) These authors contributed equally; (9) Lead contact *Correspondence: brian-baker@nd.edu (B.M.B.), alexandre.harari@chuv.ch (A.H.), david.gfeller@unil.ch (D.G.)
