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Identification and regulation of circulating tumor-TCR-matched cytotoxic CD4+ lymphocytes by KLRG1 in bladder cancer
Serena S. Kwek, … , Lawrence Fong, David Y. Oh
Serena S. Kwek, … , Lawrence Fong, David Y. Oh
Published April 29, 2025
Citation Information: JCI Insight. 2025;10(11):e177373. https://doi.org/10.1172/jci.insight.177373.
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Research Article Clinical trials Immunology Oncology

Identification and regulation of circulating tumor-TCR-matched cytotoxic CD4+ lymphocytes by KLRG1 in bladder cancer

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Abstract

While cytotoxic CD4+ tumor-infiltrating lymphocytes have anticancer activity in patients, whether these can be noninvasively monitored and how these are regulated remains obscure. By matching single cells with T cell receptors (TCRs) in tumor and blood of patients with bladder cancer, we identified distinct pools of tumor-matching cytotoxic CD4+ T cells in the periphery directly reflecting the predominant antigenic specificities of intratumoral CD4+ tumor-infiltrating lymphocytes. On one hand, the granzyme B–expressing (GZMB-expressing) cytotoxic CD4+ subset proliferated in blood in response to PD-1 blockade but was separately regulated by the killer cell lectin-like receptor G1 (KLRG1), which inhibited their killing by interacting with E-cadherin. Conversely, a clonally related, GZMK-expressing circulating CD4+ population demonstrated basal proliferation and a memory phenotype that may result from activation of GZMB+ cells, but was not directly mobilized by PD-1 blockade. As KLRG1 marked the majority of circulating tumor-TCR-matched cytotoxic CD4+ T cells, this work nominates KLRG1 as a means to isolate them from blood and provide a window into intratumoral CD4+ recognition, as well as a putative regulatory receptor to mobilize the cytolytic GZMB+ subset for therapeutic benefit. Our findings also underscore ontogenic relationships of GZMB- and GZMK-expressing populations and the distinct cues that regulate their activity.

Authors

Serena S. Kwek, Hai Yang, Tony Li, Arielle Ilano, Eric D. Chow, Li Zhang, Hewitt Chang, Diamond Luong, Averey Lea, Matthew Clark, Alec Starzinski, Yimin Shi, Elizabeth McCarthy, Sima Porten, Maxwell V. Meng, Chun Jimmie Ye, Lawrence Fong, David Y. Oh

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Figure 3

Phenotypic heterogeneity and plasticity of individual TCR clusters.

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Phenotypic heterogeneity and plasticity of individual TCR clusters.
(A) ...
(A) A representative CD4+ T cell network cluster with matching TCR clonotype from blood and tumor, and a separate CD8+ T cell network cluster with a different matching TCR clonotype. Each dot is 1 cell and the color represents a different cellular phenotype. Tables show the number of cells with the indicated phenotype in the cluster. (B) Pseudotime trajectories of GZMB+, GZMK+, CXCL13+, Tregs, Prolif, and MAIT cells with blood- and tumor-matched TCRs in CD4+ and CD8+ T cells from 2 untreated standard-of-care (SOC) patients and 3 patients treated with anti–PD-L1 (atezolizumab, Atezo) on trial. Black circles are branch nodes. Major branches A, B, and C are labeled. (C) Plots showing percentage of Prolif cell phenotype in each TCR cluster from blood- and tumor-TCR-matched clusters in blood before and after anti–PD-L1 treatment. The number (n) of clusters is listed below the plots. *P < 0.05 by nonparametric Mann-Whitney U test. (D) Radar plots of mean percentage of selected cell phenotype in each TCR cluster in matched TCR clusters of pretreatment PBMCs (white circles), post-Atezo-treatment PBMCs (blue circles), and post-Atezo-treatment tumors (red circles) for CD4+ and CD8+ T cells.

Copyright © 2025 American Society for Clinical Investigation
ISSN 2379-3708

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