Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jul 18;114(29):E5900-E5909.
doi: 10.1073/pnas.1706559114. Epub 2017 Jul 3.

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

Valerie Chew  1 , Liyun Lai  2 , Lu Pan  2 , Chun Jye Lim  2 , Juntao Li  2 , Raymond Ong  2 , Camillus Chua  2 , Jing Yao Leong  2 , Kiat Hon Lim  3   4 , Han Chong Toh  4   5 , Ser Yee Lee  4   5   6 , Chung Yip Chan  4   5   6 , Brian K P Goh  4   5   6 , Alexander Chung  4   5   6 , Pierce K H Chow  4   5   6 , Salvatore Albani  2
Affiliations

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

Valerie Chew et al. Proc Natl Acad Sci U S A. .

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 (TRMs), resident natural killer cells (NKRs), 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 TRMs isolated from the TME expressed multiple markers for T-cell exhaustion, including PD-1, Lag-3, and Tim-3 compared with Tregs and TRMs isolated from the NTME. We found PD-1+ TRMs 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 NKRs 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.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
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.
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, TRMs, NKRs, 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.
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.
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− TEMs and CD8+CD45RO+CD103+ TRMs 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 TEM (red line) or TRM (blue line) nodes in TILs or NILs. (C) Percentage of TNFα and IFNγ-expressing TEMs and TRMs 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+ TEMs and TRMs versus TNFα+IFNγ+ TEMs and TRMs 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.
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 TEMs 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+ TEMs and TRMs versus T-bet+ TEMs and TRMs 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+ TEM 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+ TEMs and TRMs 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 TEMs and TRMs 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+ TEMs and TRMs 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.
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+), TEMs (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 NKRs 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+ NKRs 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 TEMs and TRMs infiltrating the tumor.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Fridman WH, Pagès F, Sautès-Fridman C, Galon J. The immune contexture in human tumours: Impact on clinical outcome. Nat Rev Cancer. 2012;12:298–306. - PubMed
    1. Makarova-Rusher OV, Medina-Echeverz J, Duffy AG, Greten TF. The yin and yang of evasion and immune activation in HCC. J Hepatol. 2015;62:1420–1429. - PubMed
    1. Couzin-Frankel J. Breakthrough of the year 2013. Cancer immunotherapy. Science. 2013;342:1432–1433. - PubMed
    1. Errico A. Immunotherapy: PD-1-PD-L1 axis: Efficient checkpoint blockade against cancer. Nat Rev Clin Oncol. 2015;12:63. - PubMed
    1. Greten TF, Wang XW, Korangy F. Current concepts of immune based treatments for patients with HCC: From basic science to novel treatment approaches. Gut. 2015;64:842–848. - PMC - PubMed

Publication types

MeSH terms

Substances