T-cell receptors
Challenge: Decipher the T-cell receptor cancer-recognition code
ACTIVE TEAM: MATCHMAKERS
This was a challenge in an earlier round, we are not currently accepting applications for this challenge.
Context
Many cancers harbour tumour-infiltrating T cells that are potentially reactive to cancer (neo)antigens. While it is possible to sequence the T-cell receptors (TCR) present on these immune cells, it is presently not possible to use this information to comprehensively and at scale infer the antigen that is recognised by the receptor. Deciphering the T-cell receptor code will allow accurate prediction of the nature of the cancer antigens that T cells detect based on their T-cell receptor sequence. A better understanding of the interaction between the major histocompatibility complex (MHC)-bound antigens and the T-cell receptors has the potential to greatly improve future cancer immunotherapies as well as understanding and treatment of autoimmune and infectious diseases.
Barriers and opportunities
The recent success of Tumour Infiltrating Lymphocyte (TIL) cell therapy highlights the notion that TILs can be tumour-reactive. Unfortunately, in most cases the nature of the antigens recognised by the TCR present on these TILs is unknown. Knowledge of the antigens that are recognised by TILs will provide opportunities to potentiate the immune responses against cancer cells and to develop advanced cellular therapies.
Overcoming this barrier calls for new approaches to predict the nature of the antigen(s) recognised by TIL TCRs.
Such approaches could include, but are not limited to:
- High throughput identification of TCR-sequences/ antigen complexes and prediction/ elucidation of their structures and specificity
- Development and use of AI and/ or machine learning tools to identify relationships between TCR sequence and antigen structure
- Development and validation of a tool to predict the (neo)antigens recognised based on a TCR sequence
Vision and Impact
This challenge seeks to develop a comprehensive understanding of the TCR-peptide-MHC interaction and to improve our knowledge of the nature of the cancer antigens that are recognised by T cells. Addressing this challenge will require an inter-disciplinary team that could include structural biologists, immunologists, computational biologists with knowledge of artificial intelligence/ machine learning tools and high throughput screening specialists.
The ultimate goal of this challenge is to improve and broaden the efficacy of cancer immunotherapies and therefore proposals could include initial (pre-clinical) proof-of-concept studies to validate the tools developed by the challenge team.
Plain language summary: Why T-cell receptors?
T cells – a type of white blood cell – are an important part of the immune response to infection and cancer. T cells have receptors on their surfaces that recognise ‘antigen’ molecules that come from pathogens or cancer cells. After a T cell attaches to a cancer antigen, the cancer cell is ‘marked’ for destruction by the immune system.
Although T-cell receptors can be studied in detail, the information can’t be used to work out which antigens are recognised by the receptors on a large scale. This Cancer Grand Challenge seeks to improve our understanding of how T cells interact with cancer antigens. If we can predict what antigens a T cell ‘sees’ based on its receptor, we can find ways to boost the effectiveness of therapies that harness the power of the immune system (immunotherapies) to fight cancer.
You can find the plain language summaries for each of our nine new challenges here.