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Active surveillance characterizes human intratumoral T cell exhaustion
Ran You, … , Bushra Samad, Matthew F. Krummel
Ran You, … , Bushra Samad, Matthew F. Krummel
Published July 22, 2021
Citation Information: J Clin Invest. 2021;131(18):e144353. https://doi.org/10.1172/JCI144353.
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Concise Communication Immunology Oncology

Active surveillance characterizes human intratumoral T cell exhaustion

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Abstract

Intratumoral T cells that might otherwise control tumors are often identified in an “exhausted” state, defined by specific epigenetic modifications and upregulation of genes such as CD38, cytotoxic T-lymphocyte–associated protein 4 (CTLA4), and programmed cell death 1 (PD1). Although the term might imply inactivity, there has been little study of this state at the phenotypic level in tumors to understand the extent of their incapacitation. Starting with the observation that T cells move more quickly through mouse tumors the longer they reside there and progress toward exhaustion, we developed a nonstimulatory, live-biopsy method for the real-time study of T cell behavior within individual patient tumors. Using 2-photon microscopy, we studied native CD8+ T cell interaction with antigen-presenting cells (APCs) and cancer cells in different microniches of human tumors and found that T cell speed was variable by region and by patient and was inversely correlated with local tumor density. Across a range of tumor types, we found a strong relationship between CD8+ T cell motility and the exhausted T cell state that corresponded with our observations made in mouse models in which exhausted T cells moved faster. Our study demonstrates T cell dynamic states in individual human tumors and supports the existence of an active program in “exhausted” T cells that extends beyond incapacitating them.

Authors

Ran You, Jordan Artichoker, Adam Fries, Austin W. Edwards, Alexis J. Combes, Gabriella C. Reeder, Bushra Samad, Matthew F. Krummel

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Figure 4

Exhausted T cells exhibit motile features.

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Exhausted T cells exhibit motile features.
(A) Volcano plots of differen...
(A) Volcano plots of differentially expressed genes between CD38hi (top 33rd percentile of all samples) and CD38lo (bottom 33rd percentile) CRC samples. Red dots are above the cutoff for P = 0.05. (B) Gene ontology analysis of the upregulated genes in the CD38hi versus CD38lo samples. (C) Fold-change of promotility genes in exhausted versus nonexhausted T cells from RNA-Seq of melanoma or lung cancer samples as reported in refs. 14–17. (D) Schematic diagram of the experimental design for E–G. (E) Representative images of PD-1 staining for RFP (d14) and GFP in OT-I cells (d4) that resided in the same field. Scale bars: 30 μm. (F) Speed-based, color-coded track displacement of GFP and RFP OT-I cells. (G) PD-1 intensity on OT-I cells was plotted against the mean speed of each cell. Data were pooled from 3 different ROIs and are representative of 2 independent experiments. P and r2 values were obtained by linear regression analysis.

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