Trends in Immunology
Volume 38, Issue 8, August 2017, Pages 567-576
Journal home page for Trends in Immunology

Review
Molecular Dissection of CD8+ T-Cell Dysfunction

https://doi.org/10.1016/j.it.2017.05.008Get rights and content

Trends

Recent advances have identified novel CD8+ T-cell functional states in chronic inflammatory conditions associated with distinct transcriptional programs.

Single-cell analysis has revealed extensive transcriptional heterogeneity in the CD8+ T-cell response in cancer.

CRISPR/Cas9 genome editing in mature CD8+ T cells has enabled testing of candidate regulators in vivo.

Analysis of the chromatin landscape in CD8+ T cells has revealed distinct epigenetic changes associated with distinct functional states.

Chronic viral infections and cancer often lead to the emergence of dysfunctional or ‘exhausted’ CD8+ T cells, and the restoration of their functions is currently the focus of therapeutic interventions. In this review, we detail recent advances in the annotation of the gene modules and the epigenetic landscape associated with T-cell dysfunction. Together with analysis of single-cell transcriptomes, these findings have enabled a deeper and more precise understanding of the transcriptional mechanisms that induce and maintain the dysfunctional state and highlight the heterogeneity of CD8+ T-cell phenotypes present in chronically inflamed tissue. We discuss the relevance of these findings for understanding the transcriptional and spatial regulation of dysfunctional T cells and for the design of therapeutics.

Section snippets

T-Cell Dysfunction

In chronic disease settings, such as in chronic viral infections and cancer, effector CD8+ T cells progressively acquire an ‘exhausted’ or dysfunctional T-cell state characterized by variable deficits in their effector functions, including cytotoxicity and the production of proinflammatory cytokines [interleukin-2 (IL-2), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ)] [1] (Figure 1A). Importantly, dysfunctional CD8+ T cells are not completely inert as it has been shown that they can

Molecular Signatures of T-Cell Dysfunction

Several gene signatures based on analyses of populations of dysfunctional CD8+ T cells from cancer and chronic viral infections have been published 9, 21, 22, 23, 24, 25. These signatures have shown that there is great similarity between virus- and cancer-associated CD8+ T-cell dysfunction and have greatly advanced our understanding of factors that contribute to dysfunctional phenotype. Comparisons of the gene expression profiles of functional versus dysfunctional virus-specific CD8+ T cells

Gene Modules for T-Cell Dysfunction and Activation

While the dysfunction signatures discussed earlier have been useful in identifying factors that contribute to T-cell dysfunction, it was noted early on that these signatures highly overlap with signatures of healthy T-cell activation (reviewed in [29]). For example, co-inhibitory receptors (e.g., PD-1, Tim-3, CTLA-4) and effector molecules (e.g., GZMC, IFN-γ) present in the dysfunction signatures have been shown to be associated with healthy T-cell activation. This overlap between the

Heterogeneity of CD8+ T-Cell States within Tumor

The discovery of refined gene modules for T-cell dysfunction, activation/dysfunction, and activation raises the question of how these modules are expressed in individual CD8+ T cells and how this relates to their functional phenotype (Figure 1B). For example, do the dysfunctional CD8+ T cells previously defined based on surface expression of co-inhibitory receptors uniformly express the activation/dysfunction and dysfunction modules or are there subpopulations that express these modules to

Transcription Factors

In addition to Gata3 [24], several other transcription factors have been shown to play a role in promoting CD8+ T-cell dysfunction. The leucine zipper-containing transcription factor Maf was reported to be upregulated in dysfunctional CD8+ TILs in murine melanoma tumors and in TILs from melanoma patients when compared with naïve and effector CD8+ T cells [9]. Loss of Maf resulted in better tumor control while overexpression resulted in dampened TIL accumulation and antitumor activity. Martinez

Implications for Therapy

Immunotherapies, such as co-inhibitory receptor blockade and adoptive T-cell transfer, are transforming the treatment for advanced cancer [50]. Understanding how these therapies work and finding ways to improve them is an active area of research 51, 52, 53, 54, 55. Technological advances such as in-depth genomic analyses will be instrumental in this regard. Indeed, such analyses have shown that CTLA-4 and PD-1 blockade therapies achieve efficacy through distinct mechanisms [56]. Moreover,

Concluding Remarks and Future Perspectives

Single-cell analysis has the potential to identify new and more refined gene modules in CD8+ T cells. It will be important to discern transient from more stable states, potentially with epigenetic analysis coupled with perturbations (see Outstanding Questions). Testing whether the different transcriptional populations identified by single-cell transcriptomics are distinct in cytotoxicity, cytokine secretion, proliferative capacity, and T-cell receptor expression will resolve their functional

Acknowledgments

A.C.A. acknowledges support from the National Institutes of Health (R01CA187975 and P01AI073748).

Glossary

Gene module
a set of genes that have similar expression profiles across conditions tested. Gene modules may include hundreds or even thousands of genes. Unlike a gene signature, a module does not necessarily define a specific cell state. For example, a dysfunction/activation module for CD8+ T cells consists of genes that are upregulated in both the dysfunction and activation states.
Gene signature
a set of genes that defines a specific cell state. Typically, gene signatures include tens to

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