Technology meets TILs: Deciphering T cell function in the -omics era

Abstract

T cells are at the center of cancer immunology because of their ability to recognize mutations in tumor cells and directly mediate cancer cell killing. Immunotherapies to rejuvenate exhausted T cell responses have transformed the clinical management of several malignancies. In parallel, the development of novel multidimensional analysis platforms, such as single-cell RNA sequencing and high-dimensional flow cytometry, has yielded unprecedented insights into immune cell biology. This convergence has revealed substantial heterogeneity of tumor-infiltrating immune cells in single tumors, across tumor types, and among individuals with cancer. Here we discuss the opportunities and challenges of studying the complex tumor microenvironment with -omics technologies that generate vast amounts of data, highlighting the opportunities and limitations of these technologies with a particular focus on interpreting high-dimensional studies of CD8⁺ T cells in the tumor microenvironment.

Keywords: CD4; CD8; PD-1; T cell; bystander; cancer immunology; flow cytometry; immunotherapy; scRNA-seq; techniques; tumor-infiltrating lymphocytes.

Figure 1:

Human CD8⁺ T cell differentiation and associated phenotypic markers. Tumor-specific phenotypes are shown in red. Upon antigenic encounter, naïve CD8⁺ T cells differentiate into effector cells and further differentiate into different states depending on antigen clearance and chronic/recurrent antigen loads. Memory T cells specific for yellow fever virus (YFV) and the common human pathogens Influenza virus (Flu), Epstein-Barr virus (EBV) and cytomegalovirus (CMV), as well as tumor-specific cells (stem-like/progenitor exhausted and terminally differentiated) are shown along a schematic continuum of antigen load/recurrence. Of note, CMV- and EBV-specific memory CD8⁺ T cells can adopt various phenotypes and even resemble the phenotype of tumor-specific stem-like CD8⁺ T cells. Figure created with BioRender.

Figure 2:

Inference of a reverse differentiation trajectory from scRNA-seq data. A: TCR-transgenic CD8⁺ T cells were transferred to mice which were subsequently infected with LCMV Armstrong. At illustrated timepoints, cells were sorted and subjected to scRNA-seq. B: Expression of T cell differentiation markers at indicated time points. C: UMAP projection of sequenced cells, with an illustrated arrow showing actual cell differentiation trajectory. D: Identical UMAP projection, with inferred trajectories. E: Heatmap of T cell differentiation marker expression, with cells in real-time order. F: Heatmap of T cell differentiation marker expression, with cells in pseudotime order. Raw data are from (Kurd et al., 2020). Code and data to reproduce the scRNA-seq analysis are available on Mendeley Data, doi: 10.17632/3dvt79c7yt.1.

Figure 3:

Expression of an exhaustion-associated gene set in non-exhausted CD8⁺ T cells. A: Relative expression of the GSE41867_MEMORY_VS_EXHAUSTED_CD8_TCELL_DAY30_LCMV_DN gene set (Godec et al., 2016) in CD8⁺ T cells following acute infection. B: Exhaustion-associated genes are significantly enriched in day 6 vs day 90 cells, despite neither subset being biologically exhausted.

Figure 4:

Quantification of TIM-3 on human T cells. A: CCR7 and TIM-3 staining on circulating T cells of a healthy donor. With this tissue only, it is unclear if TIM-3 staining was successful, and if so, where TIM-3⁺ cells should be gated. B: CCR7 and TIM-3 expression on tumor-infiltrating T cells, stained at the same time as PBMCs in (A). C: Overlay of CCR7 and TIM-3 staining in healthy donor PBMCs (red) and TILs (black). D: Estimates of TIM-3 expression on circulating T cells of healthy donors from various studies. Flow data in panels A-C were previously reported (Sudmeier et al., 2022).

Figure 5:

Perils of data visualization and analysis in high-parameter flow cytometry. A: As more parameters are collected in a flow cytometry experiment, the potential biological insight increases. Unfortunately, both the difficulty of analysis and sensitivity of the experiment to such error also increase. An example is shown in panels B-H, where tumor-infiltrating lymphocytes from a brain metastasis patient were analyzed with a T cell-focused, 22-color flow cytometry panel. B-D: UMAP projection of CD8⁺ T cells, with cells colored by expression of PD-1 (B), CTLA-4 (C), and FOXP3 (D). Data shown are properly compensated and scaled. E: UMAP projection of CD8⁺ T cells, with cells colored by expression of FOXP3. The color intensity scale has been modified compared to panel (D); data are properly compensated. F: UMAP projection of CD8⁺ T cells, with cells colored by expression of FOXP3 (same scale as panel E). Fluorescence spillover of CTLA-4 PE/Dazzle into FOXP3 PE/Cy5 has been undercompensated by 10%. (G) An alternate display method, such as a histogram, using an internal positive control for FOXP3 expression would reveal improper scaling of FOXP3 fluorescence intensities (circled population). H: The true percentage of FOXP3⁺ CD8⁺ T cells in this sample is <0.5%. Data to reproduce the flow cytometry analysis are available on Mendeley Data, doi: 10.17632/3dvt79c7yt.1.