Delineation of an immunosuppressive gradient in hepatocellular carcinoma using high-dimensional proteomic and transcriptomic analyses

Abstract

The recent development of immunotherapy as a cancer treatment has proved effective over recent years, but the precise dynamics between the tumor microenvironment (TME), nontumor microenvironment (NTME), and the systemic immune system remain elusive. Here, we interrogated these compartments in hepatocellular carcinoma (HCC) using high-dimensional proteomic and transcriptomic analyses. By time-of-flight mass cytometry, we found that the TME was enriched in regulatory T cells (Tregs), tissue resident memory CD8⁺ T cells (T_RMs), resident natural killer cells (NK_Rs), and tumor-associated macrophages (TAMs). This finding was also validated with immunofluorescence staining on Foxp3⁺CD4⁺ and PD-1⁺CD8⁺ T cells. Interestingly, Tregs and T_RMs isolated from the TME expressed multiple markers for T-cell exhaustion, including PD-1, Lag-3, and Tim-3 compared with Tregs and T_RMs isolated from the NTME. We found PD-1⁺ T_RMs were the predominant T-cell subset responsive to anti-PD-1 treatment and significantly reduced in number with increasing HCC tumor progression. Furthermore, T-bet was identified as a key transcription factor, negatively correlated with PD-1 expression on memory CD8⁺ T cells, and the PD-1:T-bet ratio increased upon exposure to tumor antigens. Finally, transcriptomic analysis of tumor and adjacent nontumor tissues identified a chemotactic gradient for recruitment of TAMs and NK_Rs via CXCR3/CXCL10 and CCR6/CCL20 pathways, respectively. Taken together, these data confirm the existence of an immunosuppressive gradient across the TME, NTME, and peripheral blood in primary HCC that manipulates the activation status of tumor-infiltrating leukocytes and renders them immunocompromised against tumor cells. By understanding the immunologic composition of this gradient, more effective immunotherapeutics for HCC may be designed.

Keywords: CyTOF; hepatocellular carcinoma; regulatory T cells; resident memory T cells; tumor microenvironment.

Fig. 1.

High-dimensional analysis with Barnes–Hut SNE identified differentially enriched immune subsets in TILs, NILs, and PBMCs. (A) Time-of-flight mass cytometry (CyTOF) pipeline from data acquisition, dimension reduction, and clustering to node generation. The resulting nodes, clustered by similarity in their immune phenotypes, were subjected to statistical testing to identify significantly enriched nodes from a given group (TIL, NIL, or PBMC). (B) The 2D cellular t-SNE plots of CyTOF data from PBMCs, NILs, and TILs as gated on: CD8, CD4, CD56, CD14, PD-L1, PD-1, CTLA-4, Tim-3, and Lag-3. Each dot represents one single cell. Arrows showed distinct differences in TILs. (C) A 3D illustration of node percentages in either PBMC, NIL, or TIL compartments as grouped into node ID and major immune subsets. n = 7 from each compartment.

Fig. 2.

High-dimensional Barnes–Hut SNE analysis defines the distribution of specific immune subsets in TILs, NILs, and PBMCs. (A) A 2D heat map showing differential expression of 35 immune markers by TIL-enriched (red bar), NIL-enriched (blue bar), PBMC-enriched (green bar), TIL/NIL-enriched (purple bar), or NIL/PBMC-enriched (orange bar) nodes. Immune subsets were categorized based on their marker expression. (B) Percentage of Tregs, T_RMs, NK_Rs, and TAMs in PBMCs (n = 12), TILs (n = 12), or NILs (n = 7). Data represent the means ± SD and were analyzed by paired Student’s t test, **P < 0.01 and *P < 0.05. (C) Representative images from multiplex immunofluorescence tissue staining for CD8 (green), PD-1 (red), CD4 (magenta), Foxp3 (white), and DAPI (blue) on tumor and nontumor FFPE tissues. (Scale bar, 20 μm.) (D) Quantification of the number of Foxp3⁺CD4⁺ Treg and PD-1⁺CD8⁺ T cells per square millimeter in tumor versus nontumor tissues from n = 26 paired HCC samples. Paired Student’s t test. **P < 0.01 and ***P < 0.001.

Fig. 3.

The TME is enriched with more exhausted Tregs compared with the NTME. (A) Percentage of PD-1–, Lag-3–, or Tim-3–expressing Treg cells from TILs versus NILs or PBMCs. ***P < 0.001, **P < 0.01; *P < 0.05, paired Student’s t test. (B) Percentage of IL10⁺ Tregs from TILs, NILs, or PBMCs with or without 6-h PMA and ionomycin stimulation. **P < 0.01, paired Student’s t test. (C) Representative plots showing expression of IL-10 pregated on CD4⁺Foxp3⁺CTLA-4⁺ Tregs in TILs versus NILs and PBMCs from one HCC patient HEP178. (A–C) TILs (n = 12) versus NILs (n = 7–8) and PBMCs (n = 12). Data represent the means ± SD. (D) Correlation between expression of exhaustion markers: PD-1, Lag-3, and Tim-3 with IL-10 on Tregs from TILs and NILs. n = 21. P values and correlation coefficients (r) were calculated with Spearman’s correlation test. ***P < 0.001 and **P < 0.01.

Fig. 4.

Exhaustion marker and cytokine expression by memory CD8⁺ from TILs, NILs, and PBMCs. (A) Percentage of PD-1–, CTLA-4–, Lag-3–, or Tim-3–expressing CD8⁺CD45RO⁺CD103⁻ T_EMs and CD8⁺CD45RO⁺CD103⁺ T_RMs in TILs (n = 12) versus NILs (n = 7) or PBMCs (n = 12). Data represent the means ± SD and were analyzed by paired Student’s t test, ***P < 0.001, **P < 0.01, and *P < 0.05. (B) A 2D heat map representing the expression of exhaustion markers by T_EM (red line) or T_RM (blue line) nodes in TILs or NILs. (C) Percentage of TNFα and IFNγ-expressing T_EMs and T_RMs in TILs (n = 12) versus NILs (n = 5–7) or PBMCs (n = 12) with or without 6-h PMA and ionomycin stimulation. Data represent the means ± SD and were analyzed by paired Student’s t test, *P < 0.05. (D) Correlation between percentage of PD-1⁺, CTLA-4⁺, Lag-3⁺, or Tim-3⁺ T_EMs and T_RMs versus TNFα⁺IFNγ⁺ T_EMs and T_RMs in TILs and NILs upon 6-h PMA and ionomycin stimulation. n = 19 each. P values and correlation coefficients (r) were calculated with Spearman’s correlation test. ***P < 0.001, **P < 0.01, and *P < 0.05.

Fig. 5.

T-bet is the critical transcription factor that correlates with down-regulation of PD-1 upon tumor antigen exposure and tumor progression. (A) Percentage of T-bet–expressing T_EMs and in TILs (n = 12) versus NILs (n = 7). Data represent the means ± SD and were analyzed by paired Student’s t test. *P < 0.05. (B) Correlation between percentage of PD-1⁺ T_EMs and T_RMs versus T-bet⁺ T_EMs and T_RMs in TILs and NILs upon 6-h PMA and ionomycin stimulation. n = 19 each, P values and correlation coefficients (r) were calculated with Spearman’s correlation test. **P < 0.01. (C) Ratios of percentages of PD-1⁺ versus T-bet⁺ on CD8⁺CD45RO⁺ T_EM cells from PBMCs upon coculture with irradiated autologous tumor (n = 8) or PBMC (n = 6) and feeder cells. Data represent the means ± SD and were analyzed by paired Student’s t test with reference to day 0. **P < 0.01 and *P < 0.05. (D) Percentage of PD-1⁺ or T-bet⁺ T_EMs and T_RMs in stage 1 versus stages 2–4. Data represent the means ± SD and were analyzed by unpaired Student’s t test. n = 10 each. ***P < 0.001 and **P < 0.01. (E) Percentage of TNFα-expressing T_EMs and T_RMs from TILs after 18 h cocultured with autologous irradiated tumor cells with anti–PD-1 antibody (α–PD-1) or with isotype control mouse IgG1,κ-antibody (Ctl). n = 8. Data represent the means ± SD and were analyzed by paired Student’s t test with reference to control. **P < 0.01 and *P < 0.05. (F) Percentage of PD-1⁺ or T-bet⁺ T_EMs and T_RMs in HBV-infected (n = 12) versus nonvirally infected (n = 10) HCC patients. Data represent the means ± SD. P values were calculated using unpaired Student’s t test. *P < 0.05.

Fig. 6.

The tumor microenvironment shapes and dictates TIL infiltration in HCC. (A) Heat map showing NanoString analysis data from unsorted tumor versus adjacent nontumor tissues. n = 20 paired tissues. Box shows enriched genes in TME versus NTME with the magnified image on the Right. (B) Expression of CCR6 and CXCR3 on Tregs (CD4⁺Foxp3⁺), T_EMs (CD8⁺CD45RO⁺), NKs (CD56⁺), or monocytes (CD14⁺HLA-DR⁺) from PBMCs of HCC patients. n = 6–10. Data represent the means ± SD. (C) Correlation of RNA expression of CCL20 to percentage of TAMs, and CXCL10 to percentage of NK_Rs in TILs. n = 12. P values and correlation coefficients (r) were calculated with Spearman’s correlation test. *P < 0.05 and **P < 0.01. (D) Percentage of IL-10⁺ TAMs and granzyme B (GB)⁺ NKs from TILs (n = 12) versus NILs (n = 5–7) or PBMCs (n = 12) with or without 6-h PMA and ionomycin stimulation. Data represent the means ± SD and were analyzed by paired Student’s t test. *P < 0.05 and **P < 0.01. (E) Working hypothesis model showing tumor-enriched chemokines: CCL20 attracting CCR6⁺ TAMs, which produce high levels of IL-10; whereas CXCL10 attracts CXCR3⁺ NK_Rs that express low levels of granzyme B (GB) within the tumor site. Lower expression level of T-bet, and a reciprocal higher expression level of PD-1, and lower production of TNFα and IFNγ was observed on T_EMs and T_RMs infiltrating the tumor.