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[Preprint]. 2023 Nov 13:2023.11.09.566384.
doi: 10.1101/2023.11.09.566384.

Identification of Cellular Interactions in the Tumor Immune Microenvironment Underlying CD8 T Cell Exhaustion

Christopher Klocke  1 , Amy Moran  2   3 , Andrew Adey  3   4 , Shannon McWeeney  1   3   5 , Guanming Wu  1   3
Affiliations

Identification of Cellular Interactions in the Tumor Immune Microenvironment Underlying CD8 T Cell Exhaustion

Christopher Klocke et al. bioRxiv. .

Abstract

While immune checkpoint inhibitors show success in treating a subset of patients with certain late-stage cancers, these treatments fail in many other patients as a result of mechanisms that have yet to be fully characterized. The process of CD8 T cell exhaustion, by which T cells become dysfunctional in response to prolonged antigen exposure, has been implicated in immunotherapy resistance. Single-cell RNA sequencing (scRNA-seq) produces an abundance of data to analyze this process; however, due to the complexity of the process, contributions of other cell types to a process within a single cell type cannot be simply inferred. We constructed an analysis framework to first rank human skin tumor samples by degree of exhaustion in tumor-infiltrating CD8 T cells and then identify immune cell type-specific gene-regulatory network patterns significantly associated with T cell exhaustion. Using this framework, we further analyzed scRNA-seq data from human tumor and chronic viral infection samples to compare the T cell exhaustion process between these two contexts. In doing so, we identified transcription factor activity in the macrophages of both tissue types associated with this process. Our framework can be applied beyond the tumor immune microenvironment to any system involving cell-cell communication, facilitating insights into key biological processes that underpin the effective treatment of cancer and other complicated diseases.

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Figures

Figure 1:
Figure 1:
Analysis overview. A) input – scRNA-seq data from skin tumor microenvironment; B) select CD8 T cells using marker genes; C) infer pseudotime trajectory from progenitor (TCF7 high, TOX low) exhausted to terminally (TCF7 low, TOX high) exhausted CD8 T cells using Monocle3; D) calculate sample-level exhaustion score, quantifying the distribution of cells along the exhaustion trajectory by sample; E) select highest- and lowest-scoring sample groups; compare transcription factor activity in these samples for individual cell types; F) perform gene-regulatory network analysis of exhaustion-associated transcription factors.
Figure 2:
Figure 2:
CD8 T cell exhaustion in human skin tumor samples. A, B, D-F) imputed gene expression of progenitor exhausted marker TCF7, terminal exhaustion marker TOX, and immune checkpoints LAG3, TIGIT, and PDCD1; C) Monocle3 pseudotime, characterizing progression from progenitor exhausted to terminally exhausted CD8 T cells; G) exhaustion pseudotime of CD8 T cells, ordered by sample-level exhaustion score.
Figure 3:
Figure 3:
CD8 T cell exhaustion in human HIV samples A, B, D-F) imputed gene expression of progenitor exhausted marker TCF7, terminal exhaustion marker TOX, and immune checkpoints LAG3, TIGIT, and PDCD1 C) Monocle3 pseudotime, characterizing progression from progenitor exhausted to terminally exhausted CD8 T cells G) exhaustion pseudotime of CD8 T cells, ordered by sample-level exhaustion score.
Figure 4:
Figure 4:
Overlap in exhaustion-associated transcription factor activity between melanoma and basal cell carcinoma (BCC) datasets Overlap analysis was performed between melanoma and basal cell carcinoma datasets to identify the proportion of significantly exhaustion-associated TFs that were shared vs. non-shared and whether this overlap was significant. A-C, E-F) The two tumor datasets exhibit significant overlap in up- and down-regulation of CD8 T cell activity, up-regulation of macrophage activity, and up- and down-regulation of NK cell activity associated with CD8 T cell exhaustion. D) The overlap of TFs down-regulated in the most exhausted samples was not significant.
Figure 5:
Figure 5:
Exhaustion-related transcription factor activity in CD8 T cells Exhaustion-associated transcription factors (graph nodes) and their regulatory relationships, inferred with pyscenic (graph edges) A) transcription factors up-regulated in the CD8 T cells of the most CD8 T cell-exhausted immune microenvironments B) transcription factors down-regulated in the CD8 T cells of the most CD8 T cell-exhausted immune microenvironments.
Figure 6:
Figure 6:
Exhaustion-related transcription factor activity in macrophages Exhaustion-associated transcription factor activity in macrophages (graph nodes) and their regulatory relationships, inferred with pyscenic (graph edges) A) transcription factors up-regulated in the macrophages of the most CD8 T cell-exhausted immune microenvironments B) transcription factors down-regulated in the macrophages of the most CD8 T cell-exhausted immune microenvironments.
Figure 7:
Figure 7:
Pathway analysis results of exhaustion-associated DEGs in macrophages. Pathways enriched in genes over-expressed in macrophages of high-exhaustion samples relative to low-exhaustion samples; red line = adjusted p-value cutoff of 0.05 A) tumor-specific exhaustion-associated pathway activity B) viral-specific exhaustion-associated pathway activity C) exhaustion-associated pathway activity shared between tumor and viral contexts.
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