Cell
Volume 184, Issue 17, 19 August 2021, Pages 4512-4530.e22
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Article
CXCR6 positions cytotoxic T cells to receive critical survival signals in the tumor microenvironment

https://doi.org/10.1016/j.cell.2021.07.015Get rights and content
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Highlights

  • CXCR6 is critical for sustained tumor control mediatedby CD8+ cytotoxic T cells (CTLs)

  • CXCR6 optimizes CTL interactions with the CCR7+ DC3 state of conventional DCs

  • DC3s trans-present IL-15 to TCF-1neg effector-like CTLs to sustain their survival in the TME

  • DC3s densely cluster in T cell-rich perivascular niches of the tumor stroma

Summary

Cytotoxic T lymphocyte (CTL) responses against tumors are maintained by stem-like memory cells that self-renew but also give rise to effector-like cells. The latter gradually lose their anti-tumor activity and acquire an epigenetically fixed, hypofunctional state, leading to tumor tolerance. Here, we show that the conversion of stem-like into effector-like CTLs involves a major chemotactic reprogramming that includes the upregulation of chemokine receptor CXCR6. This receptor positions effector-like CTLs in a discrete perivascular niche of the tumor stroma that is densely occupied by CCR7+ dendritic cells (DCs) expressing the CXCR6 ligand CXCL16. CCR7+ DCs also express and trans-present the survival cytokine interleukin-15 (IL-15). CXCR6 expression and IL-15 trans-presentation are critical for the survival and local expansion of effector-like CTLs in the tumor microenvironment to maximize their anti-tumor activity before progressing to irreversible dysfunction. These observations reveal a cellular and molecular checkpoint that determines the magnitude and outcome of anti-tumor immune responses.

Keywords

TCF-1
CTL
CCR7+ dendritic cells
CXCR6
CXCL16
IL-15
tumor microenvironment
scRNA-seq
TCGA
multiphoton intravital microscopy

Data and code availability

scRNA-seq data generated during this study, including includes gene counts pre- and post-normalization, per-cell meta data, as well we the raw FASTQ files, is publicly available on GEO (GSE179111).

The code generated during this study is available at https://github.com/pittetmi/paper-code-data/tree/main/Di_Pilato_et_al_2021

The UMAP visualization of the single-cell transcriptome data is available for interactive exploration at https://kleintools.hms.harvard.edu/tools/springViewer_1_6_dev.html?cgi-bin/client_datasets/dipilato2020/d4m.3a-pova

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These authors contributed equally

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