Challenge: Decipher the T-cell receptor cancer-recognition code
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.