Research ArticleHEALTH AND MEDICINE

LincRNA-immunity landscape analysis identifies EPIC1 as a regulator of tumor immune evasion and immunotherapy resistance

See allHide authors and affiliations

Science Advances  10 Feb 2021:
Vol. 7, no. 7, eabb3555
DOI: 10.1126/sciadv.abb3555
  • Fig. 1 The landscape of immunity-associated lincRNAs in cancer.

    (A) t-SNE embedding of the four identified cancer immunity–associated lincRNA clusters. (B) LincRNA expression distribution in immune-related organs (bottom left). (C) Number of tumor-specific lincRNAs in each cancer type (left) and number of immune-specific lincRNAs in each cluster (right). (D and F) Association between lincRNA-based immune response (LIMER) score and cytotoxic T cell infiltration in melanoma (SKCM) (D) and liver cancer (LIHC) (F). Left: The expression of each LIMER lincRNAs (columns) in tumor patients (rows). Middle: The correlation between LIMER score (y axis) and cytotoxic T cell infiltration (x axis). Right: The infiltration (x axis) of other immune cells. All three panels share the same order of patients, which were sorted by descending the LIMER score. (E and G) Kaplan-Meier plot shows the association between LIMER score and patient progression-free intervals. Patients are equally stratified into three groups based on LIMER scores in the same cohort as (D) and (F), respectively.

  • Fig. 2 DNA methylation analysis on tumor-specific lincRNAs revealed EPIC1 as a suppressor of tumor immune response.

    (A) Consensus regulation (CR) score of 11 EA tumor-specific lincRNAs (C2 cluster) and 6 epigenetically silenced (ES) tumor-specific lincRNAs (C4 cluster) that are correlated with tumor immune response (heatmap) and their average differential expression across 23 cancer types (bar plots). (B) Correlation between epigenetic activation fraction of epigenetically induced lincRNA 1 (EPIC1) and its association with CD8A expression. (C, D, F, and G) DNA methylation (z score normalized beta value) of tumor-specific lincRNAs (C and F) and the expression of immune signature genes (D and G) in TCGA-BRCA and TCGA-UCEC patients. BRCA, Breast Cancer; UCEC, Uterine Corpus. (E and H) Survival curves of the patients with top and bottom 20% epigenetic activity in (C) and (F). (I) Correlation between EPIC1 methylation and GZMA and PRF1 expression. (J to O) Tumor volume (J, L, and N) and overall survival (K, M, and O) of BALB/c mice, BALB/c nude mice, and C57BL/6 mice that are inoculated with 4T1.2 cells or MC38 cells stably expressing empty vector (control) or EPIC1 (n = 5 animals per group). EC, Endometrial Carcinoma; GZMA, Granzyme A; PRF1, Perforin 1. Data are means ±SD. *P <0.05; **P <0.01; ***P < 0.001.

  • Fig. 3 EPIC1 decreases cytotoxic T lymphocyte infiltration and activation.

    (A) Control and EPIC1-overexpressed 4T1.2 tumors were paraffin embedded and applied with hematoxylin and eosin staining. Black arrows indicate the immune cells. (B) Frozen sections from the empty vector (control) and EPIC1 overexpressed (EPIC1) 4T1.2 tumors were subjected to immunostaining analysis of CD3 (red) along with 4′,6-diamidino-2-phenylindole for nuclei (blue). (C to H) Quantification of indicated TILs from control and EPIC1-overexpressed 4T1.2 or MC38 tumors in BALB/c mice or C57BL/6 mice, respectively (n = 5 for control, n = 5 for EPIC1 overexpression). TILs were analyzed by flow cytometry on the 15th day after transplantation. The right panel of each figure indicates the representative flow cytometry profiles. (I) The production of granzyme B by CD4+ and CD8+ TILs in 4T1.2 tumors was analyzed by flow cytometry, respectively. The right panel shows the representative flow cytometry profiles. (J and K) The production of interferon-γ (IFN-γ) by CD4+ and CD8+ TILs in 4T1.2 and MC38 tumors was analyzed by flow cytometry, respectively. The right panel shows the representative flow cytometry profiles. Data are means ± SD. *P < 0.05 **P < 0.01; ***P < 0.001.

  • Fig. 4 EPIC1 suppresses antigen presentation of tumor cells.

    (A) Left: Heatmap shows the association between EPIC1 expression and MHC-I and cytokine signatures in TCGA patients. Right: Heatmap shows the changes of same signatures after EPIC1 knockdown in different cell lines. (B and C) The expression of antigen presentation and antigen processing genes in control and EPIC1-overexpressed MCF-7 cells (B) and MC38 cells (C). Cells were treated with IFN-γ (0, 1, and 5 ng/ml) for 24 hours. (D and E) Cell surface levels of MHC-I in control and EPIC1-overexpressed MC38 (D) and 4T1.2 (E) cells. Cells were treated with IFN-γ (5 ng/ml) for indicated time points. (F) SIINFEKL-H2Kb presentation by the empty vector (control) or EPIC1-overexpressed MC38 cells. The quantification of MFI (mean fluorescence intensity) of SIINFEKL-H2Kb is shown on the right panel. Cells were treated with IFN-γ (5 ng/ml) for 24 hours. (G and H) Killing effect of MC38 (OVA+) cells overexpressed with empty vector (vehicle) or EPIC1 after coculture with OT-1 T cells (G). The production of IFN-γ of OT-1 cells was determined by enzyme-linked immunosorbent assay (H). (I and J) Activation (I) and the production of IFN-γ (J) of gp100 TCR-transduced CD8+ T cells were cocultured with 888-MEL (gp100+) and 526-MEL (gp100+) cells transduced with empty vector (Control) or EPIC1 for 24 hours. Data are means ± SD. *P < 0.05; **P < 0.01; ***P < 0.001.

  • Fig. 5 EPIC1 inhibits IFNGR1 expression and type II interferon signaling.

    (A) Heatmap (left) shows the association between EPIC1 expression and interferon signatures in TCGA patients. Color in the heatmap indicates the effect size. Dots indicate the logarithmic false discovery rate. Heatmap (right) shows the pathway changed after EPIC1 knockdown in different cell lines. The color of the dots in the heatmap indicates the enrichment score. The size of the dots indicates the false discovery rate. (B) Correlation between EPIC1 epigenetic activation and IFNG response score in immune response signature. (C to E) Immunoblot of IFNGR1, p-STAT, and MHC-I in human cancer cell lines MCF-7 (C), NCI-H1299 (D), and HCT116 (E) cells stably expressing empty vector (Control) or EPIC1 further treated with the indicated concentration of IFN-γ for 24 hours. (F and G) Immunoblot of p-STAT in murine breast cancer cells 4T1.2 and colorectal cancer cells MC38 stably expressing empty vector (control) or EPIC1 further treated with the indicated concentration of IFN-γ for 24 hours. LS, long exposure; SE, short exposure. (H and I) The measurement of p-STAT1 and MHC-I protein expression by immunoblot in MCF-7 (H) and NCI-H1299 (I) cells transduced with EPIC1 siRNA and further treated by JAK1/2 inhibitor ruxolitinib (5 μm) with/without IFN-γ (5 ng/ml). LS, long exposure; SE, short exposure.

  • Fig. 6 EPIC1’s regulation of type II interferon signaling is mediated by its interaction with EZH2 protein.

    (A) Correlation between EPIC1 expression and PRC2 inhibitor response in breast cancer cell lines. (B) Gene set enrichment analysis of enhancer of zeste homolog 2 (EZH2) targets in siEPIC1-treated MCF-7 cell lines. (C) The enrichment of EPIC1 and U1 by EZH2 RNA immunoprecipitation assay analyzed by real-time quantitative polymerase chain reaction (qPCR). Immunoblot of EZH2 indicates the immunoprecipitation efficiency of EZH2 (right). LS, long exposure; SE, short exposure. (D) Immunoblot of EZH2 protein retrieved by in vitro transcribed EPIC1 from MCF-7 and MC38 cells’ nuclear extracts. (E and F) Immunoblot of EZH2 pulled down by indicated EPIC1 truncations (E) and deletions (F). The right panels are the schematic of indicated EPIC1 deletions and their binding with EZH2 (i.e., EZH2-inter). (G and H) Measurement of p-STAT and MHC-I protein levels by immunoblot in MCF-7 (left) and HCT116 (right) stable cell line overexpressing EPIC1 and rescued with EZH2 siRNA treatment (G) or EZH2 inhibitor DZNep treatment (H). (I and J) Tumor size of EZH2 wild-type (WT) or knocked out (KO) 4T1.2 cells stably expressed by empty vector (control) and EPIC1 (n = 5 animals per group). Tumor size was measured every other day. ***P < 0.001. (K) Survival curve for each group in (I).

  • Fig. 7 Activation of EPIC1-EZH2 axis leads to anti-PD1 resistance through epigenetically silencing IFNGR1 and antigen presentation genes.

    (A and B) ChIP-qPCR analysis of H3K27me3 (A) and EZH2 (B) occupancy on the promoters of indicated genes in MCF-7 cells stably overexpressed with empty vector (control) or EPIC1. (C and D) Tumor size of 4T1.2 cells stably expressed by the empty vector (control) and EPIC1 after anti–PD-1 and isotype control antibody (IgG). Mice were treated with a control IgG antibody (n = 5) or anti–PD-1 antibody (n = 5). Tumor size was measured every other day and plotted individually in (D). *P < 0.05; **P < 0.01; ***P <0.001. (E) Survival curve for each treated group in (C). (F) Proposed model depicting the regulation of antitumor immunity and resistance to checkpoint blockade therapy by lincRNA EPIC1 in tumor cells.

Supplementary Materials

  • Supplementary Materials

    LincRNA-immunity landscape analysis identifies EPIC1 as a regulator of tumor immune evasion and immunotherapy resistance

    Weiwei Guo, Yue Wang, Min Yang, Zehua Wang, Yifei Wang, Smriti Chaurasia, Zhiyuan Wu, Min Zhang, Ghanshyam Singh Yadav, Sanjay Rathod, Fernando Concha-Benavente, Christian Fernandez, Song Li, Wen Xie, Robert L. Ferris, Udai S. Kammula, Binfeng Lu, Da Yang

    Download Supplement

    This PDF file includes:

    • Figs. S1 to S10
    • Tables S1 to S3

    Files in this Data Supplement:

Stay Connected to Science Advances

Navigate This Article