Antigen and checkpoint receptor engagement recalibrates T cell receptor signal strength

Thomas Elliot, Emma Jennings, David Lecky, Natasha Thawait, Adriana Flores-Langarica, Alastair Copland, Kendle Maslowski, David Wraith, David Bending

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How T cell receptor (TCR) signal strength modulates T cell function and to what extent this is modified by immune checkpoint blockade (ICB) are key questions in immunology. Using Nr4a3-Tocky mice, we characterized early quantitative and qualitative changes that occur in CD4 + T cells in relation to TCR signaling strength. We captured how dose- and time-dependent programming of distinct co-inhibitory receptors rapidly recalibrates T cell activation thresholds and visualized the immediate effects of ICB on T cell re-activation. Our findings reveal that anti-PD1 immunotherapy leads to an increased TCR signal strength. We defined a strong TCR signal metric of five genes upregulated by anti-PD1 in T cells (TCR.strong), which was superior to a canonical T cell activation gene signature in stratifying melanoma patient outcomes to anti-PD1 therapy. Our study therefore reveals how analysis of TCR signal strength—and its manipulation—can provide powerful metrics for monitoring outcomes to immunotherapy.

Original languageEnglish
Pages (from-to)2481-2496.e6
Issue number11
Early online date16 Sept 2021
Publication statusPublished - 9 Nov 2021

Bibliographical note

Funding Information:
Work funded by the University of Birmingham (D.B.), the Wellcome Trust (214018/Z/18/Z to D.B.), and the MRC (MR/V009052/1 to D.B.). D.A.L. is funded by a Wellcome Trust 4-year Basic Science PhD program. E.K.J. is supported by a studentship from the MRC Discovery Medicine North (DiMeN) Doctoral Training Partnership (MR/N103840/1). A.C. and K.M.M. are funded by a CRUK Career Establishment Award (C61638/A27112 to K.M.M.). D.C.W and A.F-L. are funded by the University of Birmingham (UoB). Diagrams in Figures 1, 4, and 5 were adapted from the Servier Medical Art templates, which are licensed under a CC BY 3.0 unported license, We thank Dr. Leila Khoja, Dr. Neil Steven, and Dr. Lalit Pallan (Medical Oncology, UoB) for helpful discussion around the potential clinical utility of the TCR.strong metric. We also thank Dr. Sarah Dimeloe, Dr. Rebecca Drummond, and Dr. Wei-Yu Lu for their continued support and feedback on data in the manuscript. The graphical abstract was created using Conceptualization, funding acquisition, supervision, formal analysis, methodology, data curation, project administration, and writing of original draft, D.B. T.A.E.E. E.K.J. N.T. D.A.J.L. A.C. and D.B. performed and analyzed experiments. D.B. performed and analyzed RNA-seq experiments and performed all bioinformatic analyses, including conceptualization (together with T.A.E.E.) and implementation of the TCR.strong metric. D.C.W. provided resources and advice on methodology for Tg4 model immunization. A.F.-L performed cell sorting experiments. K.M.M. funding acquisition. T.A.E.E. and D.B. wrote the paper and all authors were involved in reviewing the original draft manuscript. The authors declare no competing interests. We worked to incorporate sex balance in the selection of non-human subjects. One or more of the authors of this paper self-identifies as a member of the LGBTQ+ community.


  • ICOS
  • IRF8
  • Nr4a3
  • OX40
  • PD1
  • TCR signaling
  • TCR.strong
  • immunotherapy
  • melanoma
  • nivolumab

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology
  • Infectious Diseases


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