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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 1

Combined blood and tumor TCR repertoire network analysis.

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Combined blood and tumor TCR repertoire network analysis.
(A) Blood and ...
(A) Blood and tumor samples were obtained from standard-of-care (SOC) and anti–PD-L1–treated patients with bladder cancer (on trial NCT02451423) for scRNA-seq/scTCR-seq, using matching TCRs on single cells as a barcode for matched antigenic specificity between compartments. (B) UMAP plot of 157,054 single cells clustered together, where each phenotypic population is identified with a distinct color and density plots showing distribution of cells for the subset of samples sorted for CD8+ or CD4+ T cells, or obtained from normal adjacent tissue (NAT), tumor, and blood. n = 7 tumors, 6 NAT, and 7 matched PBMC samples (before and after treatment for immunotherapy-treated patients). (C) Dot plot showing fractions of cells and mean expression of selected genes (bottom labels) in each cell cluster (side labels). (D) Feature plots showing expression of transcripts GZMB, GZMK, or KI67 (red) in the cells from samples sorted for CD4+ cells (gray) from blood or tumor superimposed on the UMAP plot. CD4+ cells expressing cytotoxic transcripts are known to express CD4 protein based on sorting, but are coclustered with a predominance of CD8+ T cells due to their cytotoxic gene expression. (E) Phenotypic analysis of all CD4+ T cells using gene signatures for Th1, Th2, and Trm (pairwise comparison statistics in Supplemental Dataset 5). (F) Percentage of TCRs that are unique, expanded (cluster size >1), and expanded and blood-tumor matched for CD4+ and CD8+ T cells from blood and tumor. (G) Network TCR plots of a representative patient. Each dot represents 1 cell, and the dots within in each cluster in red have identical paired TCRαβ.

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