Journal
NATURE MACHINE INTELLIGENCE
Volume 3, Issue 10, Pages 864-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s42256-021-00383-2
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Funding
- Common Fund of the Office of the Director of the National Institutes of Health
- NCI
- NHGRI
- NHLBI
- NIDA
- NIMH
- NINDS
- National Institutes of Health (NIH) [CCSG 5P30CA142543/TW, R01CA258584/TW]
- Cancer Prevention Research Institute of Texas [CPRIT RP190208/TW]
- University of Texas MD Anderson Cancer Center
- University Cancer Foundation at the University of Texas MD Anderson Cancer Center
- Exon 20 Group
- Rexanna's Foundation for Fighting Lung Cancer
- Waun Ki Hong Lung Cancer Research Fund
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The study introduces a model called pMTnet for predicting T-cell receptor binding specificity to neoantigens and T cell antigens, revealing that neoantigens are generally more immunogenic than self-antigens, and patient responses to specific neoantigens in cancer treatment are associated with T-cell expansion and affinity.
Neoantigens play a key role in the recognition of tumour cells by T cells; however, only a small proportion of neoantigens truly elicit T-cell responses, and few clues exist as to which neoantigens are recognized by which T-cell receptors (TCRs). We built a transfer learning-based model named the pMHC-TCR binding prediction network (pMTnet) to predict TCR binding specificities of the neoantigens-and T cell antigens in general-presented by class I major histocompatibility complexes. pMTnet was comprehensively validated by a series of analyses and exhibited great advances over previous works. By applying pMTnet to human tumour genomics data, we discovered that neoantigens were generally more immunogenic than self-antigens, but human endogenous retrovirus E (a special type of self-antigen that is reactivated in kidney cancer) is more immunogenic than neoantigens. We further discovered that patients with more clonally expanded T cells that exhibit better affinity against truncal rather than subclonal neoantigens had more favourable prognosis and treatment response to immunotherapy in melanoma and lung cancer but not in kidney cancer. Predicting TCR-neoantigen/antigen pairing is one of the most daunting challenges in modern immunology; however, we achieved an accurate prediction of the pairing using only the TCR sequence (CDR3 beta), antigen sequence and class I major histocompatibility complex allele, and our work revealed unique insights into the interactions between TCRs and major histocompatibility complexes in human tumours, using pMTnet as a discovery tool. T-cell immunity is driven by the interaction between peptides presented by major histocompatibility complexes (pMHCs) and T-cell receptors (TCRs). Only a small proportion of neoantigens elicit T-cell responses, and it is not clear which neoantigens are recognized by which TCRs. The authors develop a transfer learning model to predict TCR binding specificity to class-I pMHCs.
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