Scientific Challenge: Immunotherapy has revolutionized cancer treatment, yet only a minor fraction of patients respond to frequently used immunotherapeutic treatments. T cell recognition of peptide-major histocompatibility (pMHC) class I complexes is essential to maintain immune surveillance and eliminate cancerous cells. Numerous products of genetic and epigenetic alterations can serve as targets for T cell recognition of cancer, yet our capacity to predict what MHC-embedded targets T cells can recognize on the surface of cancer cells is still poor, with a less than 5% hit rate. While we have robust tools for prediction of antigen presentation, we still have very limited understanding of the factors driving immunogenicity – i.e. which of the presented targets will give rise to a T cell recognition.
A fundamental mechanism influencing T cell recognition is molecular mimicry. It has long been proposed that the ability of a given T-cell receptor (TCR) to recognize multiple different pMHC complexes is essential to provide immunological coverage of all potential pathogens that we may encounter. T cell epitopes, that at first glance appear very different, may have structural similarities once embedded in the MHC I binding groove, and hence appear similar to the given TCR (molecular mimicry).
Objective: In MIMIC, I will determine the role of molecular mimicry in T cell recognition and demonstrate how pre-existing immunity may shape the T cell recognition of cancer antigens. I will use the SARS-CoV2 infection as a model system to understand molecular mimicry, and apply the learnings from this to cancer immunogenicity.
Expected outcome: I predict that by understanding the influence of molecular mimicry, the rules governing the immunogenicity of T cell epitopes can be determined and the selection of antigens optimized - this will be essential to develop precision T cell therapies targeting tumor antigens of relevance for the individual patient.
