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PILRα on tumor cells interacts with the T cell surface protein CD99 to suppress antitumor immunity

Nature Cancer volume 6, pages 1184–1201 (2025)Cite this article

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Abstract

Immune checkpoint blockade using anti-programmed cell death protein 1/programmed cell death 1 ligand 1 antibody effectively targets the tumor-T cell interaction in cancer treatment, yet the overall response rate of less than 30% necessitates the identification of additional immune checkpoints modulating T cell function. Here, we identified the tumor cell-expressed paired immunoglobulin-like type 2 receptor alpha (PILRα) as an immune suppressor targeting T cells using high-throughput screening. PILRα inhibits T cell activation, proliferation and effector function by targeting CD99, a T cell surface antigen, suppressing ZAP70/NFAT/IL-2/JAK/STAT signaling. A cluster of O-glycosylated serine and threonine residues within the stalk region is critical for PILRα–CD99 interactions. Blocking these interactions with a stalk-targeting anti-PILRα antibody enhances T cell antitumor immunity and suppresses tumor growth. When combined with programmed cell death protein 1 antibody, anti-PILRα antibody shows synergistic tumor suppression. Notably, PILRα is highly expressed in several human cancers and predicts poor prognosis. These findings unveil PILRα as an immune checkpoint with therapeutic potential for clinical cancer immunotherapy.

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Fig. 1: High-throughput screening identifies PILRα as a T cell activity suppressor.
Fig. 2: The mucin stalk of PILRα is essential for its immunosuppressive function.
Fig. 3: PILRα binds CD99 to suppress T cell activity.
Fig. 4: PILRα inhibits CD99-activated ZAP70/NFAT/IL-2/JAK/STAT signaling in T cells.
Fig. 5: PILRα inhibits antitumor T cell immunity in mouse models.
Fig. 6: Anti-PILRα mAb suppresses tumor growth in humanized xenograft models.
Fig. 7: Anti-PILRα mAb enhances the antitumor effects of anti-PD-1 immunotherapy in a humanized xenograft model.
Fig. 8: PILRα is highly expressed in multiple cancer types and is predictive of poor prognosis in patients with cancer.

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Data availability

The RNA-seq data generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession no. GSE254013. The RNA-seq data used in this study are available in the GEO under accession no. GSE174617. All mass spectrometry raw files have been deposited in ProteomeXchange via the PRIDE database under accession no. PXD048903. The source data for Figs. 1–8 and Extended Data Figs. 1–10 have been provided as source data files. The remaining data are available in the article, its supplementary information, as source data files or from the corresponding author W.L. (w2liu@xmu.edu.cn) upon reasonable request. Source data are provided with this paper.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (no. 82472769), Guangdong Basic and Applied Basic Research Foundation (no. 2024A1515030245) and the Scientific Research Foundation of State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory (no. 2023XAKJ0102059) to L.X. This work was also supported by the National Natural Science Foundation of China (nos. 82125028, U22A20320, 31871319 and 91953114), the National Key Research and Development Program of China (nos. 2020YFA0803600 and 2020YFA0112300), the Shenzhen Science and Technology Program (no. JCYJ20230807091159001) and the Fundamental Research Funds for the Central University (nos. 20720190145 and 20720220003) to W.L. We thank K. Mao at Xiamen University for providing the OT-I mice. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Lin Xia, Jun-yi Liu, Chao Yu.

Authors and Affiliations

  1. State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China

    Lin Xia, Ya-hong Hu, Guo-sheng Hu, Yao-hui He & Wen Liu

  2. Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China

    Lin Xia, Ya-hong Hu, Guo-sheng Hu, Yao-hui He & Wen Liu

  3. ShenZhen Research Institute, Xiamen University, Shenzhen, China

    Lin Xia & Wen Liu

  4. National Institute of Diagnostics and Vaccine Development in Infectious Diseases, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China

    Jun-yi Liu, Chao Yu, Hong-wei Lin, Yun-yao Chen, Wen-xin Luo & Ning-shao Xia

  5. State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen, China

    Wen-xin Luo & Ning-shao Xia

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Contributions

W.L. and L.X. conceived and designed the study, and wrote the manuscript with input from W.-X.L. and N.-S.X. J.-Y.L. performed the main experiments. C.Y. performed the Ab screening. H.-W.L. and Ya-hong Hu performed the protein–protein interaction experiment. G.-S.H. performed the bioinformatics analysis. Yao-hui He performed the mass spectrometry analysis. Y.-Y.C. performed the CAR T preparation.

Corresponding authors

Correspondence to Lin Xia, Wen-xin Luo, Ning-shao Xia or Wen Liu.

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Extended data

Extended Data Fig. 1 Glycosylation and functional analysis of PILRα.

a, The identity and similarity of IgV domain among inhibitory or stimulatory molecules on T cell activity are shown. The identity refers to the percentage of identical residues between the IgV domain of PILRα and those of other inhibitory or stimulatory molecules, while the similarity refers to the protein sequence similarity between the IgV domain of PILRα and those of other inhibitory or stimulatory molecules. b, Primary T cells labeled with CFSE in αCD3-coated plates (1 μg ml−1) were treated with or without PILRα (ECD)-hIg (20 μg ml−1) for 72 h, followed by flow cytometry to measure T cell divisions (n = 5 biologically independent experiments). The discrete peaks represent individual generations of cells indicated by numbers, where generation 0 represents undivided cells. c,d,e,i,k, Quantification of CD25+, CD44+, and CD69+ staining in Fig. 2e (c), Fig. 2f (d and e), Fig. 2l (i), and Fig. 2p (k) is shown (n = 5 biologically independent experiments). f, PILRα (ECD)-hIg, PILRβ (ECD)-hIg, PILRα-iso1-hIg, and PILRα-iso2-hIg proteins were subjected to IB analysis using an anti-IgG1 Fc antibody. The IB image is representative of three individual experiments (n = 3 biologically independent experiments). g, PILRα (ECD)-hIg and PILRα (IgV)-hIg proteins were subjected to IB analysis using an anti-IgG1 Fc antibody. The IB image is representative of three individual experiments (n = 3 biologically independent experiments). h, The O-glycosylation sites in PILRα (ECD) predicted using NetOGlyc 3.1 algorithm (www.cbs.dtu.dk/services/NetOGlyc/). j, The O-glycosylation sites in PILRα (ECD) identified by LC-MS/MS analysis. Data are presented as mean ± s.e.m. c,d,e,i,k, P values were calculated using a one-way ANOVA with Tukey’s multiple comparisons test.

Source data

Extended Data Fig. 2 Anti-PILRα monoclonal antibody C21 specifically targets the stalk region of PILRα.

a,j, CD25+ quantification in Fig. 3d (a) and 3n (j) (n = 5 biologically independent experiments). b, Expression kinetics of CD99, PD-1, and TIM-3 in T cells with or without αCD3-stimulation for 48 h (n = 3 biologically independent experiments). c, Representative flow cytometry of CD99-Fc (10 μg ml−1) binding to WT or PILRAOE-MDA-MB-231 cells using Dylight 650-conjugated anti-human IgG Fc antibody (n = 3 biologically independent experiments). d, ELISA of hybridoma supernatants binding to PILRα-coated plates (OD450/OD630) (n = 3 biologically independent experiments). e,f, PILRα-expressing U87 cells incubated with hybridoma supernatants were stained with Alexa Fluor 488-conjugated anti-mouse IgG (H + L) antibody for high-content imaging (e), and immunofluorescence (IF) quantification (f) (n = 3 biologically independent experiments). g, Primary T cells were co-cultured with aAPC-293T cells expressing PILRα, with or without hybridoma supernatants for 72 h, followed by ELISA for IFNγ (n = 3 biologically independent experiments). Red clones were further purified. h,i, αCD3-stimulated (1 μg ml−1) NFAT-Lucia reporter Jurkat T cells were treated with or without PILRα (ECD)-hIg (20 μg ml−1) and anti-PILRα antibodies (50 μg ml−1), for luciferase activity (h) (n = 6 biologically independent experiments) and IFNγ detection (i) (n = 3 biologically independent experiments). k, PILRα (ECD)-hIg (2 μg ml−1) was pre-incubated with His-CD8-coated plates, followed by blocking ELISA using isotype, C21, or C126 (n = 3 biologically independent experiments). l, ELISA of PILRα (IgV)-hIg binding to C126-coated plate (n = 3 biologically independent experiments). m,n, αCD3-stimuated (1 μg ml−1) T cells were treated with or without PILRα (ECD)-hIg (20 μg ml−1) and isotype, C21, or C126 (50 μg ml−1) for 72 h, followed by flow cytometry for CD25 expression (m), and ELISA for IFNγ, granzyme B, and IL-2 (n) (n = 5 biologically independent experiments). Data are presented as mean ± s.e.m. a, P values were calculated using a two-way ANOVA with Tukey’s multiple comparisons test. j,n, P values were calculated using a one-way ANOVA with Tukey’s multiple comparisons test.

Source data

Extended Data Fig. 3 PILRα inhibits CD99-induced signaling in T cells.

a,c, The intensity of the protein bands in Fig. 4d (a) and Fig. 4i (c) was quantified using Image J. The phosphorylated protein levels, normalized to the total protein levels, are shown (n = 3 biologically independent experiments). b, Quantification of CD25+ staining in Fig. 4h (n = 5 biologically independent experiments). Data are presented as mean ± s.e.m. a, P values were calculated using an unpaired two-tailed Student’s t-test. b,c, P values were calculated using a one-way ANOVA with Tukey’s multiple comparisons test.

Source data

Extended Data Fig. 4 Flow cytometry analysis of spleen and tumor tissues from PILRα-engineered cell xenografts.

a, Representative flow cytometry of U87 and MDA-MB-231 cells stained with C21 antibody (n = 3 biologically independent experiments). b,c, Representative flow cytometry plots depicting the percentage of CD25+, IFNγ+, granzyme B+, and IL-2+ cells within CD8+ T cells in spleen or tumor tissues, as summarized in Fig. 5m (b) and Fig. 5s (c) (n = 5 independent mice).

Extended Data Fig. 5 PILRα suppresses antitumor T cell immunity in the MC38 allograft model.

a, Control MC38 cells (control) and MC38 cells with Pilra knockdown (PilraKD) or Pilra overexpression (PilraOE) were subjected to IB analysis. The IB image is representative of three individual experiments (n = 3 biologically independent experiments). b-d, C57BL/6 mice were inoculated subcutaneously with control, PilraKD, or PilraOE MC38 cells (1.0 × 106 cells per mouse). Tumor growth curve (b) (n = 7 independent mice), tumor weight (c) (n = 7 independent mice), and mice survival (d) (n = 5 independent mice) are shown. e, Spleen and tumor tissues from mice as described in (b) were subjected to flow cytometry analysis. The percentage of CD3+ T cells, CD3+CD8+ T cells, CD3+CD4+ T cells, CD19+ B cells, NK1.1+ NK cells, CD11b+ myeloid cells, and CD11b+F4/80+ macrophages in CD45+ leukocytes were counted (n = 5 independent mice). The box plots display the minima and maxima as the whiskers, the median as the central line, the bounds of the box as the first and third quartiles. f, Spleen and tumor tissues from mice as described in (b) were subjected to flow cytometry analysis, and the percentage of CD25+, IFNγ+, granzyme B+, and IL-2+ cells within CD8+ T cells is shown (n = 3 independent mice). Data are presented as mean ± s.e.m. b, P values were calculated using a two-way ANOVA with Geisser-Greenhouse’s correlation. c, left panel of e,f, P values were calculated using a one-way ANOVA with Holm-Šídák’s multiple comparisons test. Right panel of e,f, P values were determined using an unpaired two-tailed Student’s t-test. d, P values were determined using a log-rank (Mantel-Cox) test for survival, which was independently repeated twice with similar results.

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Extended Data Fig. 6 PILRα suppresses antigen-specific T cell responses in vitro and in vivo.

a,c,e OT-I T cells were co-cultured with or without control, PilraKD, or PilraOE MC38-OVA cells for 48 h, followed by flow cytometry for CD25 (a), IFNγ (c), and IL-2 (e) expression in OT-I cells (n = 3 biologically independent experiments). b,d,f, Quantification of CD25+ (b), IFNγ+ (d), and IL-2+ staining (f) in OT-I T cells from panels (a), (c), and (e), respectively (n = 3 biologically independent experiments). g,h, C57BL/6 mice were inoculated subcutaneously with control, PilraKD, or PilraOE MC38-OVA cells (5.0 × 106 cells per mouse, n = 3). Purified OT-I T cells were adoptively transferred intravenously into mice on day 7 (5.0 × 106 cells per mouse, n = 3). Representative flow cytometry plots depicting the percentage of TCR Vα2+ CD8+ OT-I T cells in spleen (g) and tumor (h) on day 12 are shown (n = 3 independent mice). i, The percentage of TCR Vα2+ CD8+ OT-I T cells in the spleen (g) and tumor (h) (n = 3 independent mice). j,k, Representative flow cytometry plots depicting the percentage of CD44+, CD25+, IFNγ+, and IL-2+ cells within CD8+ OT-I T cells in the spleen (j) and tumor (k) (n = 3 independent mice). l, The percentage of CD44+, CD25+, IFNγ+, and IL-2+ staining in CD8+ OT-I T cells in the spleen (j) and tumor (k) (n = 3 independent mice). Data are presented as mean ± s.e.m. b,d,f, left panel of i,l, P values were calculated using a one-way ANOVA with Holm-Šídák’s multiple comparisons test. Right panel of i,l, P values were calculated using an unpaired two-tailed Student’s t-test.

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Extended Data Fig. 7 Anti-PILRα monoclonal antibody C21 potently inhibits tumor growth and is relatively safe in humanized mouse xenograft models.

a,b, Tumor tissues from humanized MDA-MB-231 xenograft models described in Fig. 6a were collected on day 20 for flow cytometry analysis. CD8+ (CD45+CD3+CD8+), CD8+ TEM (CD45+CD3+CD8+CD62L-CD45RO+), and CD8+ TEFF (CD45+CD3+CD8+CD62L-CD45RO-) TILs gated by anti-CD45 antibody conjugated with APC (APC-CD45), anti-CD3 antibody conjugated with PerCP/Cy5.5 (PerCP/Cy5.5-CD3), anti-CD8 antibody conjugated with PE/Cy7 (PE/Cy7-CD8), anti-CD62L antibody conjugated with Alexa Fluor 700 (AF700-CD62L), and anti-CD45RO conjugated with FITC (FITC-CD45RO) were further analyzed by staining of anti-Granzyme B antibody conjugated with BV421 (BV421-Granzyme B) and anti-IFNγ antibody conjugated with PE (PE-IFNγ), or anti-CD25 antibody conjugated with BV421 (BV421-CD25). Representative flow cytometry plots depicting the percentage of IFNγ+ and granzyme B+ (a) and CD25+ (b) cells are shown (n = 5 independent mice). c, The percentage of CD25+, IFNγ+, and granzyme B+ cells within CD8+ (CD45+CD3+CD8+), CD8+ TEM (CD45+CD3+CD8+CD62L-CD45RO+), and CD8+ TEFF (CD45+CD3+CD8+CD62L-CD45RO-) subsets as described in (a) and (b) (n = 5 independent mice). d,g, Body weight of mice from the treatment as described in Fig. 6a (d) and Fig. 6h (g) (n = 5 independent mice). e,h, Representative gross morphology of major organs including heart, liver, spleen, lung, and kidney collected from mice in Fig. 6b (e) and Fig. 6i (h) (n = 5 independent mice). f,i, Representative H&E staining images of tissue sections from organs in panel e (f) and h (i) (n = 5 independent mice). Data are presented as mean ± s.e.m. c, P values were calculated using a one-way ANOVA with Tukey’s multiple comparisons test. f,i, Scale bar, 100 μm.

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Extended Data Fig. 8 Anti-PILRα monoclonal antibody C21 enhances EGFR CAR T cell efficacy in a MDA-MB-231 xenograft model.

a, NCG mice were subcutaneously (s.c.) inoculated with MDA-MB-231 cells (5.0 × 106 cells per mouse, n = 5). After 15 days, EGFR CAR T cells (5.0 × 106 cells per mouse) were administered intravenously (i.v.), followed by intraperitoneally (i.p.) injection of either C21 or C21mut antibodies (Abs) (10 mg kg−1, n = 5) 1 h post CAR T infusion. A second dose of either CAR T or antibody was administered one week later. b-d, Tumor growth curve (b), tumor weight (c), and tumors at day 27 (d) are shown (n = 5 independent mice). e,f Tumors at day 20 were collected for flow cytometry analysis. CD8+ (CD45+CD3+CD8+), CD8+ TEM (CD45+CD3+CD8+CD62L-CD45RO+), and CD8+ TEFF (CD45+CD3+CD8+CD62L-CD45RO-) TILs gated by anti-CD45 antibody conjugated with APC (APC-CD45), anti-CD3 antibody conjugated with PerCP/Cy5.5 (PerCP/Cy5.5-CD3), anti-CD8 antibody conjugated with PE/Cy7 (PE/Cy7-CD8), anti-CD62L antibody conjugated with Alexa Fluor 700 (AF700-CD62L), and anti-human CD45RO-FITC (FITC-CD45RO) were further analyzed by staining of anti-CD25 antibody conjugated with BV421 (BV421-CD25), or anti-Granzyme B antibody conjugated with BV421 (BV421-Granzyme B), and anti-IFNγ antibody conjugated with PE (PE-IFNγ). Representative flow cytometry plots depicting the percentage of CD25+ (e) and IFNγ+/granzyme B+ (f) cells are shown (n = 5 independent mice). g, The percentage of CD25+, IFNγ+, and granzyme B+ cells within CD8+ (CD45+CD3+CD8+), CD8+ TEM (CD45+CD3+CD8+CD62L-CD45RO+), and CD8+ TEFF (CD45+CD3+CD8+CD62L-CD45RO-) subsets as described in (e) and (f) (n = 5 independent mice). Data are presented as mean ± s.e.m. b, P values were calculated using a two-way ANOVA with Geisser-Greenhouse’s correlation. c, P values were calculated using a one-way ANOVA with Tukey’s multiple comparisons test. g, P values were calculated using a one-way ANOVA with Holm-Šídák’s multiple comparisons test.

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Extended Data Fig. 9 Combination therapy with anti-PILRα antibody C21 and anti-PD-1 antibody is relatively safe in a humanized U87 xenograft model.

a, Representative gross morphology of major organs including heart, liver, spleen, lungs, and kidneys collected from the mice in Fig. 7b are shown (n = 5 independent mice). b, Representative H&E staining images of tissue sections from organs in panel a (n = 5 independent mice). Scale bar, 100 μm.

Extended Data Fig. 10 PILRα expression correlates with CD99, immune checkpoints, and patient survival across multiple cancer types.

a, Positive correlation between PILRA and CD99 mRNA expression in tumor tissues form patients with TNBC (n = 1100), GBM (n = 516), HNSC (n = 522), KIRP (n = 290), THCA (n = 509), and STAD (n = 415), as analyzed by TIMER. Spearman’s correlation coefficient and P-value are given (two-sided t-test). False discovery rate-adjusted p < 0.05. Regression line with standard error interval is shown. b, Kaplan–Meier analysis of overall survival stratified by median baseline of CD99 levels in TNBC (low = 82, high = 85), KIRC (low = 350, high = 180), KIRP (low = 209, high = 78), and STAD patients (low = 152, high = 219). Kaplan–Meier Plotter curves are typically analyzed using the log-rank (Mantel-Cox) test to compare survival distributions between groups. c,d, Positive correlation between the expression of PILRA mRNA and mRNA encoding immune checkpoints PD-1 (PDCD1), CTLA-4 (CTLA4), and TIM-3 (HAVCR2) in TNBC (n = 191) (c) and GBM (n = 153) (d) patients, as determined by TIMER analysis. Spearman’s correlation coefficient and P-value are given (two-sided t-test). False discovery rate-adjusted p < 0.05. Regression line with standard error interval is shown.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Tables 1–5

Supplementary Table 1: List of genes induced by EGFR CAR T treatment in U87 GBM cells (n = 600). Supplementary Table 2: List of genes from sheet 1 that encode membrane proteins (n = 148). Supplementary Table 3: List of genes from sheet 2 that are overexpressed in GBM tumors compared to normal samples based on the TCGA database (n = 63). Supplementary Table 4: List of genes from sheet 2 that are overexpressed in GBM tumors compared to normal samples based on the TCGA database and are predictive of poor prognosis (n = 32). Supplementary Table 5: List of antibodies, chemicals, protein, mouse and cell lines, key commercial kits, instrument, software, algorithm and tools.

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Xia, L., Liu, Jy., Yu, C. et al. PILRα on tumor cells interacts with the T cell surface protein CD99 to suppress antitumor immunity. Nat Cancer 6, 1184–1201 (2025). https://doi.org/10.1038/s43018-025-00958-7

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