T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies

Abstract
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Abstract

T cells are potent at eliminating pathogens and playing a crucial role in the adaptive immune response. T cell receptor (TCR) convergence describes T cells that share identical TCRs with the same amino acid sequences but have different DNA sequences due to codon degeneracy. We conducted a systematic investigation of TCR convergence using single-cell immune profiling and bulk TCRβ-sequence (TCR-seq) data obtained from both mouse and human samples, and uncovered a strong link between antigen-specificity and convergence. This association was stronger than T cell expansion, a putative indicator of antigen-specific T cells. By using flow sorted tetramer⁺ single T cell data, we discovered that convergent T cells were enriched for a neoantigen-specific CD8⁺ effector phenotype in the tumor microenvironment. Moreover, TCR convergence demonstrated better prediction accuracy for immunotherapy response than the existing TCR repertoire indexes. In conclusion, convergent T cells are likely to be antigen-specific and might be a novel prognostic biomarker for anti-cancer immunotherapy.

Data availability

All data used in this work are publicly available. The Python code related to TCR convergence calculation are availableat:https://github.com/Mia-yao/TCR-convergence/tree/main. The convergent TCR sequences of each cohort are uploaded to Zenodo, with DOI: 10.5281/zenodo.6603757.

The following previously published data sets were used

1. Nolan et al.
(2020) A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2
immuneAccess: https://doi.org/10.21417/ADPT2020COVID.

https://clients.adaptivebiotech.com/pub/covid-2020
1. Liu et al.
(2022) Concurrent delivery of immune checkpoint blockade improves tumor microenvironment for vaccine-generated immunity
GEO: GSE178881.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE178881

Article and author information

Author details

Mingyao Pan

Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-2912-9599
Bo Li

Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, United States

For correspondence
bo.li@utsouthwestern.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-8617-900X

Funding

National Cancer Institute (1R01CA245318)

Bo Li

National Cancer Institute (1R01CA258524)

Bo Li

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.