Periodic Reporting for period 1 - MIMIC (Molecular mimicry as a key parameter shaping T cell immunity)
Berichtszeitraum: 2022-09-01 bis 2025-02-28
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.
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.
Our current knowledge of T cell cross-recognition is derived from only few examples, and it has previously not been feasible to examine this at large-scale, to determine a set of general rules and facilitate prediction in the strong heterogenetic setting of cancer immunotherapy. Recently, major progress has been made in three specific areas – all in which my research group has played an important role:
1) Novel tools allow us to understand the recognition signature of a given TCR, we term this the ‘TCR fingerprint’
2) We finally have large data sets assessing the T cell recognition of neoantigens in patients undergoing immune therapy
3) A new single-cell based technology provides the ability to link pMHC to TCR sequence in a high-throughput assay
Additionally, novel insight into SARS-CoV2 T cell recognition has provided us with an ideal model system.
In MIMIC, I will combine these technologies with our knowledge of T cell recognition of neoepitopes, to address the questions of how molecular mimicry and previous infections shape the T cell repertoire and affect the ability to raise a strong T cell response towards an emerging tumor.
1) Novel tools allow us to understand the recognition signature of a given TCR, we term this the ‘TCR fingerprint’
2) We finally have large data sets assessing the T cell recognition of neoantigens in patients undergoing immune therapy
3) A new single-cell based technology provides the ability to link pMHC to TCR sequence in a high-throughput assay
Additionally, novel insight into SARS-CoV2 T cell recognition has provided us with an ideal model system.
In MIMIC, I will combine these technologies with our knowledge of T cell recognition of neoepitopes, to address the questions of how molecular mimicry and previous infections shape the T cell repertoire and affect the ability to raise a strong T cell response towards an emerging tumor.
MIMIC will generate fundamental new knowledge to increase our understanding of T cell cross-recognition and molecular mimicry. The outcomes will have broad impact in the field of immunology and enhance our ability to predict T cell interactions in the context of disease. This will facilitate improved design of vaccines and immunotherapeutics, where preexisting T cell recognition can be taken into account, either by actively targeting and improving this, or by actively avoiding it to raise novel T cell populations. The desired strategy will depend of the clinical context, but with MIMIC we will take an important step towards obtaining the tools and knowledge needed to address such interactions.
MIMIC is paradigm changing, as all current strategies for prediction of T cell recognition rely solely on the prediction of epitope presentation to MHC class I and II. With the approach I will take we will start to interrogate T cell recognition based on its structural interaction with pMHC and its ability to cross-recognize several peptide sequences. Better prediction of T cell recognition for cancer antigens is critical for the development of effective and precise cancer immunotherapy treatments. Such improvements will benefit millions of cancer patients worldwide.
MIMIC is paradigm changing, as all current strategies for prediction of T cell recognition rely solely on the prediction of epitope presentation to MHC class I and II. With the approach I will take we will start to interrogate T cell recognition based on its structural interaction with pMHC and its ability to cross-recognize several peptide sequences. Better prediction of T cell recognition for cancer antigens is critical for the development of effective and precise cancer immunotherapy treatments. Such improvements will benefit millions of cancer patients worldwide.