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The phosphatase PAC1 acts as a T cell suppressor and attenuates host antitumor immunity

Nature Immunology volume 21, pages 287–297 (2020)Cite this article

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Abstract

Cancer cells subvert immune surveillance through inhibition of T cell effector function. Elucidation of the mechanism of T cell dysfunction is therefore central to cancer immunotherapy. Here, we report that dual specificity phosphatase 2 (DUSP2; also known as phosphatase of activated cells 1, PAC1) acts as an immune checkpoint in T cell antitumor immunity. PAC1 is selectively upregulated in exhausted tumor-infiltrating lymphocytes and is associated with poor prognosis of patients with cancer. PAC1hi effector T cells lose their proliferative and effector capacities and convert into exhausted T cells. Deletion of PAC1 enhances immune responses and reduces cancer susceptibility in mice. Through activation of EGR1, excessive reactive oxygen species in the tumor microenvironment induce expression of PAC1, which recruits the Mi-2β nucleosome-remodeling and histone-deacetylase complex, eventually leading to chromatin remodeling of effector T cells. Our study demonstrates that PAC1 is an epigenetic immune regulator and highlights the importance of targeting PAC1 in cancer immunotherapy.

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Fig. 1: PAC1 is selectively upregulated in exhausted TILs and restrains T cell response.
Fig. 2: PAC1 facilitates tumor development by suppressing immune response.
Fig. 3: Oxidative stress maintains PAC1 expression via EGR1 induction.
Fig. 4: PAC1 is critical for ROS-mediated T cell dysfunction.
Fig. 5: PAC1 accumulates on chromatin dependent on its N terminus.
Fig. 6: PAC1 recruits the NuRD complex to reshape chromatin accessibility during T cell activation.

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Data availability

The data and materials that support the findings of this study are available from the corresponding author upon reasonable request. The RNA-seq data in this study have been deposited in the SRA database with the accession code PRJNA512164. The ATAC-seq data have been deposited in the SRA database with the accession code PRJNA548971. The mass spectrometry proteome data in the FLAG pull-down assay have been deposited in the ProteomeXchange Consortium via the PRIDE (ref. 50) partner repository with the dataset identifier PXD012201 (username: reviewer61582@ebi.ac.uk, password: Dch8UNoq). The ChIP–seq data have been deposited in the GEO database with the accession code GSE141261. The CUT-Tag data have been deposited in the SRA database with the accession code PRJNA579783 Source data for Figs. 1–6 and Extended Data Figs. 1–6 are provided with the paper.

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Acknowledgements

We thank H. Tang (Institute Pasteur of Shanghai Chinese Academy of Sciences) for providing OT-I mice; J. Zhang (Peking University Health Science Center) for providing LCMV virus; Z. Zhu for help with mouse experiments; M. Chen, J. Gong and Z. Hou for help with tumor models; and X. Li. and Y. Zhu for help with bioinformatics analysis. This work was supported by grants from the National Key Research and Development Program of China (grant no. 2016YFA0500302 to Y.Y.), the National Natural Science Foundation of China (key grant nos 81430056, 31420103905 and 81874235 to Y.Y.; grant no. 81501360 to D.L.; and grant no. 31800749 to L.L.), the Beijing Natural Science Foundation (key grant no. 7161007 to Y.Y.), the Lam Chung Nin Foundation for Systems Biomedicine, the Fund for Fostering Young Scholars of Peking University Health Science Center (grant no. BMU2018YJ003 to D.L.) and the China Postdoctoral Science Foundation (grant no. 2018M630045 to L.L.).

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  1. These authors contributed equally: Dan Lu, Liang Liu.

Authors and Affiliations

  1. Institute of Systems Biomedicine, Peking University Health Science Center, Beijing, China

    Dan Lu, Liang Liu, Yizhe Sun, Jia Song, Qi Yin, Guangze Zhang, Fang Qi, Zixi Hu, Zeliang Yang, Zhe Zhou, Xuyang Zhao, Yan Jin, Michael A. McNutt & Yuxin Yin

  2. Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China

    Yizhe Sun, Jia Song, Guangze Zhang, Fang Qi, Zeliang Yang, Zhe Zhou & Yuxin Yin

  3. Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, China

    Qi Yin & Yuxin Yin

  4. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China

    Ying Hu, Lianhai Zhang & Jiafu Ji

  5. Beijing Key Laboratory of Tumor Systems Biology, Peking University Health Science Center, Beijing, China

    Yuxin Yin

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Contributions

D.L., L.L. and Y.Y. conceived and designed the experiments. D.L. and L.L. performed most of the experiments and analyzed the data. Y.S., J.S., F.Q., Z.H. and Z.Y. assisted in experiments. Q.Y., Z.Z. and Y.J. provided technical assistance. Y.H., L.Z. and J.J. provided human samples. X.Z. did mass spectrometry analysis. G.Z. performed bioinformatics analysis. M.A.M. revised the manuscript. Y.Y. supervised this research. D.L., L.L., Y.S. and Y.Y. wrote the paper.

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Correspondence to Yuxin Yin.

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Extended data

Extended Data Fig. 1 PAC1 is correlated with inhibitory receptors in cancers.

(a) PAC1 (DUSP2) expression levels in various human tissues. Data are from the website (http://gemini.cancer-pku.cn/). (b) The relationship of disease-free survival of patients with hepatocellular carcinoma and inhibitory receptors (PDCD1, CTLA4, LAG3, HAVCR2, TIGIT, and CD160) expression. Data are from the TCGA database. (n = 325 samples, P = 0.0346). (c) The relationship of PAC1 expression with overall survival of lung cancer (n = 293 samples, P = 0.043), ovarian cancer (n = 55 samples, P = 0.03) or gastric cancer (n = 43 samples, P = 0.00044). Data are from the indicated GSE datasets. (d) The relationship of PAC1 expression with overall survival of colorectal carcinoma (n = 541 samples, P = 0.0047), esophageal carcinoma (n = 183 samples, P = 0.0046) or glioblastoma multiforme (n = 112 samples, P = 0.0175). Data are from the TCGA database. Statistical significance was assessed by Log-rank (Mantel-Cox) test (b-d).

Source data

Extended Data Fig. 2 PAC1 mitigates T cell response.

(a) Flow cytometry analysis of cell cycle of Jurkat cells stably transfected with Mock or PAC1. (n = 3 cell cultures, mean ± s.e.m. * P = 0.0226, ** P = 0.0015 and **** P = 0.0000012). (b) qRT-PCR analysis of mRNA levels of GZMB, IFNG, TNF and IL2 in Jurkat cells stably expressing Mock, PAC1WT or PAC1C/S left untreated (UT) or stimulated with PMA plus ionomycin (PMA + iono) for 6 h (n = 2 cell cultures). (c) Flow cytometry analysis of the frequency of CD8+ and CD4+ T cells from wild-type or Pac1-/- LN stimulated with anti-CD3 (2 μg/ml) plus anti-CD28 (1 μg/ml) for the indicated lengths of time. The CD8+/CD4+ T cell ratio was calculated (n = 4 mice, mean ± s.e.m. ** P = 0.0075 and **** P < 0.0001). (d) Wild-type and Pac1-/- CD4+ or CD8+ T cells were treated with anti-CD3 plus anti-CD28 for 48 h, and proliferation was determined by CFSE dilution assay (n = 4 mice, mean ± s.e.m. NS = 0.1172, * P < 0.05, ** P < 0.01 and **** P = 0.000093). (e) Naive CD8+ T cells from wild-type or Pac1-/- mice were treated with anti-CD3 plus anti-CD28 for 24 h, followed by quantification of IL-2 production by ELISA assay (pooled mice, n = 6 cell cultures, mean ± s.e.m. **** P = 0.0000003) or Il2 expression by qRT-PCR analysis (pooled mice, n = 2 cell cultures, mean ± s.e.m. NS = 0.6353 and *** P = 0.0004). Statistical significance was assessed by two-tailed unpaired Student’s t test (a,c,d,e). Data are representative of three (b-d) or two (a,e) independent experiments.

Source data

Extended Data Fig. 3 PAC1 impedes host anti-tumor immunity.

(a) Body weight changes of mice followed with AOM-DSS treatment (n = 5 mice, mean ± s.e.m. * P = 0.015, ** P < 0.01 and *** P < 0.001). (b) Hematoxylin-and-eosin (H&E) staining of tumors in indicated mice (n = 7 mice). Scale bars, 50 μm. (c) Tumor growth of mice subcutaneously injected with 1 × 106 B16-F10 cells (WT, n = 7 mice; Pac1-/-, n = 6 mice, mean ± s.e.m. * P < 0.05, ** P = 0.0092 and *** P = 0.00062). (d) Survival comparison of indicated mice (c) (WT, n = 7 mice; Pac1-/-, n = 6 mice, ** P = 0.0022). (e) qRT-PCR analysis of Pac1 mRNA levels in naive CD8+ T cells left untreated or activated with anti-CD3 plus anti-CD28 for 2 or 48 hours, T cells purified from MLN and melanoma and the B16 cell line (pooled mice, n = 2 technical replicates, mean ± s.e.m. *** P < 0.001 and **** P = 0.000014). (f, g) Indicated mononuclear cells stimulated with PMA plus ionomycin for 5 h (f) or left untreated (g). IFN-γ and TNF (f) or PD-1 (g) expression in CD8+ cells was evaluated with flow cytometry (WT, n = 5 mice; Pac1-/-, n = 6 mice, mean ± s.e.m. NS = 0.2568, * P = 0.018, ** P < 0.01, *** P = 0.00014 and **** P = 0.0000077). (h) Gross appearance of lungs and quantification of lung metastases from indicated mice 20 days after intravenous injection with 1 × 106 B16-F10 cells (n = 5 mice, mean ± s.e.m. * P = 0.0398). Statistical significance was assessed by the Log-rank (Mantel-Cox) test (d) or two-tailed unpaired Student’s t test (a,c,e-h). Data are representative of three (c,d,h) or two (a,b,e-g) independent experiments.

Source data

Extended Data Fig. 4 PAC1 is essential for ROS-mediated T cell dysfunction.

(a) Morphological examination of lungs from indicated mice on day 7 post-infection with LCMV Cl13 (n = 4 mice). (b) H&E staining of the lung from indicated mice untreated (UT) or stimulated LCMV Cl13 for 7 days (n = 4 mice). Scale bars, 100 μm. (c) The frequencies of CD8+ GP33-41+ cells isolated from the lung of indicated mice on day 7 post-infection with LCMV Cl13 (n = 5 mice, mean ± s.e.m. ** P = 0.0059). (d) Flow cytometry analysis of the expression of IFN-γ and TNF in CD8+ GP33-41+ cells stimulated with GP33-41 (2 μg/ml) peptides (n = 5 mice, mean ± s.e.m. *** P < 0.001). (e) The splenocytes from indicated mice immunized by LCMV Arm were incubated with LLC cells infected with LCMV Arm (left) or Cl13 (right). The cell viability was measured by CCK8 (n = 3 cell cultures, mean ± s.e.m. * P < 0.05, ** P < 0.01, *** P < 0.001 and **** P = 0.000044). (f) GSEA of genes expressed in indicated CD8+ T cells on day 7 post-infection with LCMV Cl13 (n = 2 mice). KO, Pac1-/-; ES, enrichment score; NES, normalized enrichment score. (g, h) Wild-type and Pac1-/- lymphocytes were treated with anti-CD3 (2 μg/ml) and anti-CD28 (1 μg/ml) in the presence of H2O2 (50 μM) for 6 (g) or 24 h (h), followed by flow cytometric analysis of TNF expression (g) or CD25 expression (h) in CD8+ T cells respectively (n = 3 mice, mean ± s.e.m. NS = 0.1609, ** P = 0.0024, *** P = 0.0002 and **** P = 0.000067). Statistical significance was assessed by two-tailed unpaired Student’s t test (c,d,e-h). Data are representative of three (a-d) or two (e-h) independent experiments.

Source data

Extended Data Fig. 5 PAC1 interacts with NuRD complex and modulates chromatin accessibility.

(a) DAVID enrichment analysis for PAC1 interactome from Jurkat cells treated with PMA plus ionomycin for 4 h. (b) Co-immunoprecipitation analysis of PAC1-GFP with indicated components of NuRD complex. (c) Co-immunoprecipitation analysis of PAC1WT-GFP or PAC1C/S-GFP together with FLAG-tagged HDAC1 (left panel) or HDAC2 (right panel). (d) Co-immunoprecipitation analysis of HDAC1-HA together with FLAG-tagged PAC1 and its truncations. (e) Flow cytometry analysis of GZMB expression in Jurkat cells stably expressing indicated vectors with PMA plus ionomycin in the presence or absence of HDAC inhibitor Trichostatin A (TSA) or Sodium Butyrate (SB) for 6 h (n = 6 cell cultures, mean ± s.e.m.). (f) qRT-PCR analysis of mRNA levels of TNF, IL2, GZMB or IFNG in Jurkat cells stably expressing Mock, PAC1WT or PAC1C/S left untreated or stimulated with PMA plus ionomycin in the presence or absence of TSA or SB for 6 h (n = 2 cell cultures). (g) Secondary spectra of acetylated lysine 5 and 8 residues of Histone H4 (top panel) as well as acetylated lysine 18 and 23 residues of Histone H3 (bottom panel) affected by PAC1WT or PAC1C/S during T cell activation. (h) Immunoblot analysis of acetyl-H3K27 in Jurkat cells stably expressing indicated vectors left treated with PMA plus ionomycin or not. (i-k) Distribution of chromatin regions or peaks in genomic regions of the ATAC-seq (i), ChIP-seq (j) and CUT&Tag (k) as in Fig. 6f. Statistical significance was assessed by two-tailed unpaired Student’s t test (a,e). Data are representative of two independent experiments (a-k).

Source data

Extended Data Fig. 6 Model for the role of PAC1 in ROS-mediated tumor-infiltrating lymphocyte dysfunction.

Prolonged antigenic stimulation induces excessive ROS production, which in turn upregulates PAC1 expression via EGR1 signaling in effector T cells. Instead of utilizing phosphatase activity, PAC1 dampens T cell cytotoxic function via recruitment of the NuRD complex and remodeling the epigenetic program of effector T cells, eventually leading to T cell exhaustion and cancer immune escape.

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Dan Lu, Liu, L., Sun, Y. et al. The phosphatase PAC1 acts as a T cell suppressor and attenuates host antitumor immunity. Nat Immunol 21, 287–297 (2020). https://doi.org/10.1038/s41590-019-0577-9

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