Research ArticleCancer

PRMT5 control of cGAS/STING and NLRC5 pathways defines melanoma response to antitumor immunity

See allHide authors and affiliations

Science Translational Medicine  08 Jul 2020:
Vol. 12, Issue 551, eaaz5683
DOI: 10.1126/scitranslmed.aaz5683

A helping hand for checkpoint blockade

Although immunotherapy using immune checkpoint blockade can be very effective against tumors such as melanoma, it does not work for all patients, and additional interventions continue to be necessary. Kim et al. found that the epigenetic modifier protein arginine methyltransferase 5 (PRMT5), which has multiple protumorigenic functions, promotes immunosuppression in melanoma by two different mechanisms. The authors delineated these immune mechanisms and demonstrated that inhibition of PRMT5 enhances the efficacy of immune checkpoint inhibition in multiple mouse models of melanoma, suggesting the combination’s potential for clinical translation.

Abstract

Protein arginine methyltransferase 5 (PRMT5) controls diverse cellular processes and is implicated in cancer development and progression. Here, we report an inverse correlation between PRMT5 function and antitumor immunity. PRMT5 expression was associated with an antitumor immune gene signature in human melanoma tissue. Reducing PRMT5 activity antagonized melanoma growth in immunocompetent but not immunocompromised mice. PRMT5 methylation of IFI16 [interferon-γ (IFN-γ)–inducible protein 16] or its murine homolog IFI204, which are components of the cGAS/STING (stimulator of IFN genes) pathway, attenuated cytosolic DNA–induced IFN and chemokine expression in melanoma cells. PRMT5 also inhibited transcription of the gene encoding NLRC5 (nucleotide-binding oligomerization domain-like receptor family caspase recruitment domain containing 5), a protein that promotes the expression of genes implicated in major histocompatibility complex class I (MHCI) antigen presentation. PRMT5 knockdown augmented IFN and chemokine production and increased MHCI abundance in melanoma. Increased expression of IFI204 and NLRC5 was associated with decreased melanoma growth in murine models, and increased expression of IFI16 and NLRC5 correlated with prolonged survival of patients with melanoma. Combination of pharmacological (GSK3326595) or genetic (shRNA) inhibition of PRMT5 with immune checkpoint therapy limited growth of murine melanoma tumors (B16F10 and YUMM1.7) and enhanced therapeutic efficacy, compared with the effect of either treatment alone. Overall, our findings provide a rationale to test PRMT5 inhibitors in immunotherapy-based clinical trials as a means to enhance an antitumor immune response.

INTRODUCTION

Unleashing an antitumor immune response using immune checkpoint inhibitors has revolutionized cancer therapy. Better understanding of immune checkpoint regulatory pathways is expected to increase the rate of success and efficacy of immune checkpoint therapy (ICT). At present, only a subset of tumor types benefits from ICT, and a notable percentage of patients either fails to respond or acquires resistance to ICT (for example, 10 to 44% objective response rate after ipilimumab, nivolumab, or pembrolizumab treatment in advanced melanoma) (14). Thus, understanding tumor-intrinsic mechanisms that underlie either the response to or the evasion of ICT should provide tools to overcome intrinsic or adaptive resistance to therapy.

Among issues relevant to responses to ICT are the extent of tumor infiltration and activation of immune cells, especially CD8 T cells. Activation and tumor infiltration of CD8 T cells are subject to tight regulation. Activation of oncogenic Wnt/β-catenin signaling or loss of tumor suppressor phosphatase and tensin homolog (PTEN) hampers CD8 T cell infiltration of tumors and confers resistance to ICT (5, 6). Impaired recruitment of CD8 T cells to tumors can also be mediated by epigenetic factors, namely, EZH2 (histone-lysine N-methyltransferase)–mediated repression of chemokines (CXCL9 and CXCL10) or LSD1 (lysine-specific histone demethylase)–mediated repression of type I interferon (IFN) response (7, 8). Loss of antigen presentation, a mechanism underlying tumor-intrinsic immune evasion, is associated with resistance to ICT. Homozygous deletion of B2M, encoding the β subunit (β2-microglobulin) for all human leukocyte antigen (HLA) class I complexes, impairs antigen processing and presentation by tumor cells, contributing to resistance to ICT in melanoma and lung cancer (9, 10). These findings support the idea that control of tumor-intrinsic immunosuppression, for example, by altering the IFN response, chemokine production, and antigen presentation, may overcome resistance to ICT.

The epigenetic modifier PRMT5 (protein arginine methyltransferase 5) catalyzes monomethylation and symmetric dimethylation of arginine (Arg or R) residues on histones and nonhistone proteins, thereby regulating diverse processes including those related to oncogenesis, such as transcription, RNA splicing, translation, and the DNA damage response (11, 12). In lymphomagenesis, the MYC-PRMT5 axis is implicated in maintaining fidelity of pre-mRNA splicing (13), whereas in lung cancer PRMT5 transcriptionally controls transforming growth factor–β–driven invasion (14). In glioblastoma, PRMT5 is also thought to function in removal of introns retained in proliferation genes (15). PRMT5 activity decreases in 20 to 40% of tumors that harbor CDKN2A deletion, due to codeletion of its proximal MTAP (encoding methylthioadenosine phosphorylase) gene. The accumulation of MTAP substrate 5′-O-methylthioadenosine (MTA) inhibits PRMT5 and renders MTAP-deleted tumors more sensitive to PRMT5 inhibition (1619).

Several adaptor proteins are important for PRMT5 activity and substrate selectivity. Among them is WD repeat domain 77 (WDR77), which mediates PRMT5 methylation of histones and a concomitant transcriptional repression (20). The adaptor pICln participates in PRMT5-dependent methylation of Sm proteins, which enables the assembly of small nuclear ribonucleoproteins and subsequent mRNA splicing (21). The adaptor SHANK associated RH domain interactor (SHARPIN) contributes to PRMT5-dependent methylation of SKI proto-oncogene, resulting in SOX10 transcription (19). Thus, the presence of PRMT5 adaptor proteins is a critical determinant of substrate selectivity.

Given that PRMT5, or factors required for its activity, is promising therapeutic targets, especially in MTAP-deleted tumors, small-molecule PRMT5 inhibitors (PRMT5i) have been developed. A SAM (S-adenosylmethionine) noncompetitive PRMT5i (GSK3326595) reportedly activates the p53 pathway by controlling alternative splicing of MDM4 (22, 23). In addition, two SAM-competitive PRMT5is (JNJ-64619178 and PF-06939999) are being evaluated in diverse tumor types (NCT02783300, NCT03573310, NCT03614728, and NCT03854227). Given that PRMT5 is also implicated in embryonic development and hematopoiesis, it is likely to exert cell-specific effects by methylation of diverse cellular targets (24, 25).

Here, we identified and characterized a tumor-intrinsic function of PRMT5 in promoting immunosuppression in melanoma. We showed that PRMT5 limits both the cyclic guanosine monophosphate–adenosine monophosphate synthase (cGAS)/stimulator of interferon genes (STING) cytosolic DNA sensing pathway and major histocompatibility complex class I (MHCI) antigen presentation, complementary modules that contribute to tumor immune evasion and play important roles in the response to ICT.

RESULTS

PRMT5 expression is inversely correlated with an antitumor immune signature

The finding that SHARPIN interaction with PRMT5 is important for PRMT5 methylation of specific substrates (19) led us to assess SHARPIN expression in cohorts of melanoma tumor specimens. Analysis of melanoma patient datasets revealed that low SHARPIN expression in MTAP-low tumors is associated with better survival (fig. S1, A and B). To identify pathways that may be regulated by SHARPIN, we assessed differentially expressed genes (DEGs) in cohorts of metastatic melanoma patient specimens. Relative to MTAP-low tumors with high SHARPIN expression, gene set enrichment analysis (GSEA) revealed that those with low SHARPIN expression exhibited enrichment of genes associated with immune-related pathways [T helper cell 1 (TH1)/ TH2, interleukin-2 (IL2)/signal transducers and activators of transcription 5 (Stat5), and tumor necrosis factor–α (TNFα)] (fig. S1, C and D, and table S1), suggesting that SHARPIN may be involved in the control of immune phenotypes in MTAP-low melanoma.

Because both MTAP and SHARPIN augment PRMT5 activity (19), we asked whether high or low PRMT5 expression in melanoma (Fig. 1A) was associated with particular gene sets. GSEA showed that melanomas with low PRMT5 expression exhibited enriched expression of immune-associated genes (Fig. 1, B and C; fig. S1, E and F; and tables S2 and S3), similar to the MTAP-low melanomas with low SHARPIN expression. Analysis of an independent cohort of melanomas (GSE78220, n = 27) (26) confirmed enrichment of an immune gene signature (allograft rejection and IFN-γ response) in tumors with low PRMT5 expression (fig. S2).

Fig. 1 Melanoma specimens with low PRMT5 expression show an enriched immune gene signature.

(A) PRMT5 expression in metastatic melanoma specimens (n = 368) based on TCGA datasets. Inset shows comparison between low (blue box) and high (red box) PRMT5 expression cohorts. RSEM (RNA-Seq by Expectation Maximization), NOM (nominal). (B) Top-ranked pathways predicted using the ingenuity pathway analysis (IPA) based on differentially expressed genes (DEGs) in specimens exhibiting either low or high PRMT5 expression. Blue bars indicate pathways likely inhibited in the PRMT5 high-expression group. iCOS (inducible T cell costimulator), iCOSL (inducible T cell costimulator ligand). (C) Representative immune gene sets enriched on the basis of GSEA of DEGs from melanoma specimens with low or high PRMT5 expression. The top genes for the two representative gene sets are labeled in respective heatmaps. Columns represent PRMT5 low expression (n = 100, gray bar) and high expression (n = 100, yellow bar) TCGA-SKCM samples, and rows represent genes. Normalized expression for the genes in each gene sets were converted to log2 (FPKM + 0.1) and subsequently transformed to z scores. The gene sets were k-means clustered (K = 5), choosing the K by plotting the within sum of squares (a metric denoting dissimilarity among the members of a cluster) versus different values of K. Genes associated with immune responses “Interferon gamma response” or “Interferon gamma production” are indicated on the heatmaps.

Among the nine PRMT family members, PRMT5, PRMT1, PRMT2, CARM1, and PRMT7 are expressed at relatively high levels in human melanoma specimens (fig. S3A), and, among these, PRMT5, PRMT1, and CARM1 are coexpressed (fig. S3B). High PRMT1, PRMT5, or CARM1 expression correlated with lower survival (fig. S3C). Low PRMT5 expression exhibited the strongest correlation with expression of immune response genes (fig. S3D). Correspondingly, enrichment of an immune pathway signature was also seen in melanomas harboring low MTAP expression and low PRMT5 activity (fig. S3E), supporting a potential role for PRMT5 in tumor immunity.

PRMT5 inhibition attenuates tumor growth in an immunocompetent murine melanoma model

Inspired by the melanoma tumor results, we assessed PRMT5 function in antitumor immunity in B16F10 (B16) metastatic murine melanoma cells expressing either scrambled (Scr) or PRMT5-specific short hairpin RNA (shRNA). PRMT5 knockdown (KD) in B16 cells reduced PRMT5 abundance and decreased PRMT5 activity (Fig. 2A). PRMT5 KD did not affect the growth of B16 cells in culture (Fig. 2B). However, inoculation of these cells into immunocompetent syngeneic C57BL/6 or into immunocompromised nonobese diabetic–severe combined immunodeficient gamma (NSG) mice revealed important differences. In C57BL/6 mice, PRMT5 KD markedly inhibited growth (37 to 62% reduction in tumor volume and 28 to 54% reduction in tumor weight at day 17; Fig. 2C and fig. S4A), phenotypes not seen in B16 cells that were inoculated in the NSG mice (Fig. 2D and fig. S4B). Despite variation in KD efficiency, most tumors from the PRMT5 KD cells exhibited reduced PRMT activity (fig. S4C). The ability of PRMT5 KD B16 cells to develop tumors in immunocompromised but not immunocompetent mice suggested that PRMT5 inhibition of melanoma growth requires an intact immune system.

Fig. 2 Attenuation of melanoma growth after PRMT5 inhibition requires intact host immunity.

(A) Western blot analysis showing PRMT5 abundance (top) and activity (middle; Arg-methyl) in protein extracts prepared from B16 murine melanoma cells transduced with scrambled (Scr) or PRMT5-specific hairpin shRNAs (shPRMT5-1 or shPRMT5-2) and probed with indicated antibodies. β-Actin served as a loading control (bottom). “S. exp” and “L. exp” represent short and long exposure, respectively. “SDME-RG” is an abbreviation for the antibody recognizing symmetric dimethyl arginine. (B) Growth in culture of B16 cells stably expressing shPRMT5 or Scr control (n = 5 for each group) using ATPlite assay. Statistical significance was assessed by one-way ANOVA with Dunnett’s test. (C) Volume of control (Scr, n = 8) and PRMT5 KD B16 tumors (shPRMT5-1, n = 8; shPRMT5-2, n = 7) grafted (0.2 million cells, s.c.) into immunocompetent C57BL/6 mice and measured at indicated time points. Statistical significance was measured by two-way ANOVA with Dunnett’s test. (D) Volume of control (Scr, n = 5) and PRMT5 KD B16 tumors (shPRMT5, n = 6) grafted (0.2 million cells, s.c.) into immunocompromised NSG mice and measured at indicated time points. Statistical significance was measured by two-way ANOVA with Tukey’s test. (E) Western blot analysis of PRMT5 abundance (top) and activity (middle) in extracts of tumor cells cultured from indicated tumor pools (Scr-pool, cells from five scrambled-KD tumors; shPRMT5 pools 1 and 2, cells from three shPRMT5 KD tumors each). Tumors were isolated from NSG mice [shown in (D)]. β-Actin served as a loading control (bottom). (F) Control or shPRMT5 KD tumor cells isolated and pooled from tumors grown in NSG mice [as shown in (E)] were regrafted into syngeneic immunocompetent mice (Scr, n = 5; shPRMT5, n = 6) and assessed at indicated time points. Statistical significance was measured at days 13 and 17 by two-way ANOVA with Tukey’s test. (G) Western blot analysis of PRMT5 abundance (top) and activity (bottom) in tumors generated as in (F). GAPDH (glyceraldehyde-3-phosphate dehydrogenase) served as a loading control (middle). (H) Western blot analysis of PRMT5 abundance and activity in YUMMER1.7 cells transfected with the indicated expression vectors. GAPDH served as a loading control. EV, empty vector. (I) Growth of YUMMER1.7 cells in culture after transfection with control (EV + EV, n = 5) or PRMT5 + WDR77 constructs (n = 5) [protein analysis is shown in (H)]. (J and K) Volume of control and PRMT5 + WDR77–overexpressing YUMMER1.7 cell tumors (Scr, n = 8; PRMT5 + WDR77, n = 8) grafted (0.4 million cells, s.c.) into C57BL6 (J) or NSG (K) mice and measured at indicated time points. Statistical significance was measured at days 16 and 20 by two-way ANOVA with Tukey’s test. (L) Western blot analysis of PRMT5 abundance and activity and WDR77 abundance in tumors generated as in (K). GAPDH served as a loading control. Data are presented as means ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. ns, not significant.

We transferred B16 tumors from NSG into C57BL/6 mice and observed growth inhibition of PRMT5 KD but not control (Scr) tumors (Fig. 2F and fig. S4D), indicating that the tumors from the NSG mice could be controlled in an immunocompetent environment. Both the abundance and activity of PRMT5 were reduced in PRMT5 KD cells (shPRMT5 pools 1 and 2) and tumors (shPRMT5) (Fig. 2, E and G). Consistent with the observations in B16 cells, inducible PRMT5 KD in YUMM1.7 cells, which are derived from a genetically engineered murine melanoma model (BrafV600E/Pten−/−/Cdkn2a−/−), inhibited tumor growth (52% by day 24; fig. S4E), and had a limited effect on growth of these cells in culture (fig. S4F). Effectiveness of KD was confirmed at the levels of PRMT5 abundance and activity (fig. S4G).

To substantiate the phenotypes seen upon KD of PRMT5, we performed gain-of-function studies using YUMMER1.7 cells, which are derived from ultraviolet type B (UVB)–irradiated YUMM1.7 cells and exhibit increased mutation burden and antigenicity (27). Growth of tumors derived from YUMMER1.7 cells is inhibited in C57BL/6 but not Rag−/− mice (27). We hypothesized that gain of PRMT5 function in YUMMER1.7 cells reduced immunogenicity and increased tumor growth in immunocompetent mice. To explore this hypothesis, we generated YUMMER1.7 cells overexpressing PRMT5 or its adaptor WDR77, which is essential for PRMT5 methyltransferase activity (28), or a combination of both (Fig. 2H). YUMMER1.7 cells expressing both PRMT5 and WDR77 had increased PRMT5 protein and corresponding activity (Fig. 2H). Those cells also showed a moderate, albeit statistically significant (P = 0.0025), growth advantage in culture (relative to cells with empty vector) (Fig. 2I). The impact of PRMT5 and WDR77 coexpression on tumor growth in immunocompetent mice was variable (Fig. 2J), likely due to variability in the abundance of the proteins and PRMT5 activity (fig. S4H). Nevertheless, coexpression of both PRMT5 and WDR77 in YUMMER1.7 cells increased tumor growth in immunocompetent mice, relative to mice with control YUMMER1.7 tumors (Fig. 2J). Such an increase in tumor growth was restricted to immune-competent animals and was not seen in immunocompromised (NSG) mice (Fig. 2, K and L), consistent with PRMT5 activity supporting tumor growth by limiting antitumor immune responses through a tumor-intrinsic mechanism.

PRMT5 controls melanoma infiltration of immune cells

Immunocompetent mice harboring tumors derived from PRMT5 KD B16 cells exhibited reduced tumor growth (70 to 75% lower tumor weight at day 17) relative to mice with tumors from Scr cells (fig. S4, I and J). To assess whether the difference in tumor growth related to differences in the infiltrating immune cells, we profiled the immune cells in tumors isolated at day 17 by flow cytometry (fig. S4K). In two independent experiments (Fig. 3A, left and right), we observed significantly (P < 0.05) higher numbers of tumor-infiltrating immune cells in PRMT5 KD tumors, relative to Scr tumors. In the first experiment (Fig. 3A, left), we evaluated T lymphocytes [CD45+, CD3+, CD4+, and CD8+ cells, natural killer (NK) cells (NK1.1+), dendritic cells (DCs) (CD11c+MHCII+), and macrophages (CD11b+F4/ 80+)] (Fig. 3A, left graph). In the second experiment (Fig. 3A, right), we evaluated CD45+ T cells and two immune suppressor cell types, myeloid-derived suppressor cells (MDSCs; CD11b+GR1+) and regulatory T cells (Tregs; CD4+FOXP3+). To ascertain whether the PRMT5 KD promoted an antitumor immunity phenotype, we calculated the relative abundance of active CD8+ T cells (CD44hiCD8+) to that of MDSC or Tregs. In PRMT5 KD tumors, the ratio of activated CD8+ T (CD44hi CD8+) to MDSCs (CD11b+GR1+) or Tregs (CD4+FOXP3+) was significantly higher (P = 0.0246 for MDSC and P = 0.0383 for Tregs) (Fig. 3B). Consistent with these observations, immunohistochemical analysis of tumors collected at day 12 confirmed increased infiltration of CD4+ and CD8+ lymphocytes in PRMT5 KD tumors (Fig. 3C).

Fig. 3 PRMT5 decreases the abundance of tumor-infiltrating leukocytes, enhancing antitumor immunity and survival of tumor-bearing mice.

(A) Immune cell phenotyping performed by flow cytometry using the indicated cell surface markers on Scr- or shPRMT5-transduced B16 tumors grafted into C57BL/6 mice, collected at day 17. Two independent experiments are presented with values for each tumor indicated. Statistical significance was assessed by multiple t test with Holm-Sidak method (α = 0.05). (B) Ratio of abundance was calculated by dividing number of activated CD8 T cells (CD44hiCD8+) by that of MDSC (CD11b+GR1+) or regulatory T cells (CD4+ FOXP3+). Statistical significance was assessed by Student’s t test (two-tailed, unpaired). (C) Infiltration of CD4+ and CD8+ immune cells into Scr- or shPRMT5-B16 tumors 12 days after grafting into C57BL/6 mice was evaluated by immunohistochemistry (left) and quantified with ImageJ. Data for each tumor (Scr, n = 5; shPRMT5, n = 3) are shown with means and SEM indicated. DAPI, 4′,6-diamidino-2-phenylindole. (D) Immune cell phenotyping performed by flow cytometry using the indicated cell surface markers in YUMMER1.7 tumors expressing control (EV + EV, n = 7) or PRMT5 + WDR77 constructs (n = 4) from C57BL/6 mice. Tumors were collected at day 12. (E to H) B16 cells stably expressing control (Scr) or PRMT5 shRNA (shPRMT5) were grafted into C57BL/6 mice administered control (IgG) or neutralizing antibodies against either NK1.1 (200 μg per mouse; E and F) or CD8 (200 μg per mouse; G and H) every 3 days starting 1 day before tumor inoculation. Eight mice were used for each condition. Shown are tumor volumes (E and G) and percent survival (F and H). Data for each tumor are shown with means and SD indicated, unless specified. Statistical significance in (A) and (D) was assessed by multiple t test with Holm-Sidak method (α = 0.05), that in (B) and (C) was assessed by Student’s t test (two-tailed, unpaired), that in (E) and (G) was assessed by two-way ANOVA with Tukey’s test, and that in (F) and (H) was assessed by log-rank test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Conversely, PRMT5- and WDR77-overexpressing YUMMER1.7 tumors from immunocompetent mice collected at day 12 showed decreased immune cell infiltration. The number of T cells (CD45+), activated T cells (CD44hiCD4+ and CD44hiCD8+), NK cells, DCs (CD11c+MHCII+), and macrophages (CD11b+F4/80+) were all significantly (P < 0.05) decreased in PRMT5- and WDR77-overexpressing YUMMER1.7 tumors, compared to tumors from cells with empty vectors (Fig. 3D).

To substantiate the possible contribution of key immune infiltrating cell types on the degree of tumor growth, we monitored the size of PRMT5 KD B16 tumors after depletion of either NK cells or CD8+ T cells (fig. S4, L and M). PRMT5 KD tumors were bigger in mice injected with neutralizing antibodies to NK (anti-NK1.1) compared with PRMT5 KD tumors grown in mice injected with immunoglobulin G (IgG) (Fig. 3E). Depletion of NK cells also abolished the survival benefit gained by PRMT5 KD (Fig. 3F). Likewise, growth of PRMT5 KD tumors was higher in mice injected with neutralizing antibodies to CD8+ T cells (anti-CD8), compared with tumors from these cells in mice injected with IgG (Fig. 3G). Depletion of CD8+ T cells also reduced the survival of mice with PRMT5 KD tumors (Fig. 3H). These observations indicated that PRMT5 has a tumor-intrinsic effect that limits immune cell infiltration and that impairing PRMT5 activity confers a survival benefit that depends on immune cell infiltration into the melanoma tumor.

PRMT5 methylates IFI16, an intracellular DNA-sensing protein that interacts with SHARPIN

We explored the expression of a particular PRMT5 adaptor–encoding genes and antitumor immunity. We found that metastatic melanoma specimens with low variation in PRMT5 expression (fig. S5A) could be divided into distinct groups based on high or low expression of the adaptor genes for SHARPIN, WDR77, coordinator of PRMT5 and differentiation stimulator (COPRS), chloride nucleotide-sensitive channel 1A (CLNS1A), RIO kinase 1 (RIOK1), and multiple endocrine neoplasia I (MEN1) (fig. S5B). With GSEA and excluding PRMT5 expression as a variable, we evaluated the association between these adaptor-encoding genes and immune-associated genes (Fig. 4, A and B). Low SHARPIN expression correlated with an enriched immune gene signature (Fig. 4B).

Fig. 4 PRMT5 methylates the cGAS complex component IFI16.

(A) Melanoma patient specimens with comparable PRMT5 expression were grouped on the basis of low or high levels of expression of genes encoding PRMT5 adaptor proteins (SHARPIN, WDR77, RIOK1, COPRS, CLNS1A, and MEN1; see fig. S5). DEGs were analyzed using GSEA. (B) Heatmap depicting normalized enrichment score (NES) and q value of false discovery rate (FDR-q) for expression of genes encoding PRMT5 adaptor proteins in immune-associated Hallmark gene sets. JAK, Janus kinase; NF-κB, nuclear factor κB. (C) Immunoprecipitation (IP) followed by immunoblotting (IB) of WM115 cell lysates (1.2 mg) with indicated antibodies or antibodies against the indicated proteins. (D) B16 cells were treated with vehicle [dimethyl sulfoxide (DMSO)] or PRMT5 inhibitor (PRMT5i; EPZ015666, 10 μM) for 48 hours. IP followed by IB of B16 cells lysates (1.5 mg) was performed with the indicated antibodies or antibodies against the indicated proteins or protein modifications. (E and F) A375 (E) or B16 (F) cells treated with vehicle or a PRMT5 inhibitor (EPZ015666, 10 μM) as in (D) before lysates (A375, 1.0 mg; B16, 2.5 mg) were prepared and subjected to IP followed by IB as indicated. (G) B16 cells stably expressing indicated constructs were treated 24 hours with DMSO or PRMT5i (EPZ015666, 10 μM), and then lysates were subjected to IP with V5 antibody and IB as indicated. WT, wild-type IFI204; Mt1, IFI204 R12A; Mt2, IFI204 R538A; Mt1/2, IFI204 R12A/R538A. (H) In vitro methylation of wild-type IFI204 or mutant IFI204 proteins (200 ng) purified from HEK293T cell lysates with or without recombinant active PRMT5 plus WDR77 complex (500 ng). Proteins were visualized using PonceauS and InstantBlue staining (bottom), and methylation was detected by autoradiography (top). Histone 4 served as a positive control. Data in (C) to (G) are representative of three independent experiments, and (H) is representative of two independent experiments.

Because SHARPIN serves as an adaptor for PRMT5 and its substrates, we used liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis to identify SHARPIN-interacting proteins in the human melanoma WM115 cell line, which has homozygous MTAP deletion and is sensitive to SHARPIN KD (19). In addition to identifying known SHARPIN-interacting proteins, PRMT5 and components of the linear ubiquitin assembly complex [RNF31 (ring finger protein 31) and RBCK1 (RANBP2-type and C3H4-type zinc finger containing 1)], we identified IFN-γ–inducible protein 16 (IFI16) (table S4). IFI16 contains a DNA binding hematopoietic IFN-inducible nuclear protein (HIN) domain (29, 30) and is implicated in controlling p53 transcriptional activity (31), in cell cycle regulation by binding to the retinoblastoma protein (32), in antimicrobial immunity by sensing cytosolic DNA (29), and in inflammasome assembly through its interaction with cGAS and STING (23, 33). Interaction of IFI16, or its murine homolog IFI204, with SHARPIN was confirmed by immunoprecipitation in human WM115 melanoma cells, human A375 melanoma cells, mouse B16 melanoma cells, and transfected human embryonic kidney (HEK) 293T cells (Fig. 4, C and D, and fig. S6, A to C). WM115 are MTAP-deleted and have lower basal PRMT5 activity than A375 cells, which express MTAP (19). Inhibition of PRMT5 with EPZ015666 appeared to facilitate the interaction between IFI16 and SHARPIN in A375 melanoma cells (fig. S6C). In both A375 cells and WM115 melanoma cells, inhibition of PRMT5 reduced IFI16 methylation (Fig. 4E and fig. S6D). Likewise, IFI204 exhibited reduced methylation in EPZ015666-treated B16 cells (Fig. 4F).

A search for consensus RG motifs with arginine residues that could be methylated (19, 34, 35) identified Arg12 in the N-terminal PYRIN (protein-protein interaction) domain and Arg538 in the C-terminal HIN (DNA binding) domain in the murine IFI204 protein (fig. S6E). When expressed in B16 cells or immunoprecipitated from HEK293T cells and tested in vitro, the IFI204 mutants were less methylated than those with wild-type IFI204 (Fig. 4, G and H, and fig. S6F).

IFI16 methylation attenuates dsDNA–induced TBK1–IRF3 activation and IFN and chemokine production

IFI16 binding to intracellular double-stranded DNA (dsDNA) induces the expression of IFNB1 and the chemokines CCL5 and CXCL10 (23, 29, 33). Because we detected PRMT5-mediated methylation of IFI16 and IFI204, we explored the effect of this modification on IFI204-dependent IFN-β and chemokine induction. Attenuating PRMT5 activity (by KD or EPZ015666) in B16 cells increased the expression of Ifnb1, Ccl5, and Cxcl10 after stimulation of the cells with 70–base pair dsDNA [referred to as V70mer (29)] (Fig. 5, A and B). Conversely, PRMT5 overexpression decreased dsDNA-stimulated expression of all three genes in both B16 and YUMMER1.7 cells (Fig. 5C and fig. S7A). These observations suggested that PRMT5-dependent methylation of IFI204 limits dsDNA stimulation of IFN-β and chemokines.

Fig. 5 PRMT5 methylation of IFI204 determines the degree of cGAS/STING pathway activation.

(A to C) Effect of manipulation of PRMT5 activity on expression of immune genes in B16 cells stimulated with dsDNA. Effect of KD by either scrambled (Scr) or PRMT5-specific shRNAs (shPRMT5-1 and shPRMT5-2), 24-hour exposure to PRMT5i (EPZ015666, 10 μM), or ectopic expression of empty vectors (EV) or PRMT5 + WDR77 (pLX304-WDR77/pLenti-PRMT5) is shown. After respective treatments, cells were transfected with V70mer (500 ng/ml). Six hours after transfection, cell lysates were prepared and assayed using quantitative polymerase chain reaction (qPCR) for expression of indicated transcripts. Data are presented as fold change relative to each experiment’s control. (D to F) Effect of PRMT5 KD on cGAS/STING complex components in B16 cells stimulated with dsDNA (V70mer, 1.5 μg/ml). PRMT5 KD was achieved with transduction of scrambled (Scr) or PRMT5-specific hairpin shRNA, shPRMT5-1. In (D), results from two experiments are separated with a solid line. In (E), seminative PAGE results are shown with “d” and “m” indicating “dimer” and “monomer” forms of STING. In (F), blue native PAGE results are shown. (G and H) Analysis of cGAS/STING complex components with indicated antibodies using Western blot analysis of lysates prepared from B16 cells either treated with PRMT5i (EPZ015666, 10 μM) or stably expressing PRMT5 + WDR77 after stimulation with dsDNA (transfected V70mer, 1.5 μg/ml) for indicated times. (I) B16 cells stably expressing pLX304 (EV), IFI204WT (WT), IFI204R12A (Mt1), or IFI204R538A mutant (Mt2) were transfected with V70mer (500 ng/ml) for 6 hours and then assessed for expression of indicated transcripts by qPCR. (J) B16 cells stably expressing the indicated IFI204 were transfected with V70mer (1.5 μg/ml) for indicated times, followed by analysis of cell lysates by seminative PAGE blotting with antibodies against the indicated proteins with STING dimer (d) and monomer (m) forms labeled. (K) B16 cells transduced with Scr or PRMT5-specific shRNAs were transfected with scrambled control (siCont) or STING-specific (siSting) siRNAs for 48 hours. Cells were then stimulated 6 hours with dsDNA (transfected V70mer, 500 ng/ml) before lysates were prepared for qPCR analysis of indicated transcripts. Western blot inset depicts STING abundance. Quantified data are presented as means ± SD. Other data are representative of three independent experiments. Statistical significance was assessed by two-way ANOVA with Dunnett’s test in (A) and (I) or Sidak’s test in (B), (C), and (K). *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Sensing of dsDNA involves an interaction between IFI16/IFI204 and the cGAS-STING pathway to activate TANK Binding Kinase 1 (TBK1)–IFN regulatory factor 3 (IRF3) signaling and IFN production (23, 33). Therefore, we assessed whether PRMT5 methylation of IFI204 affected cGAS-STING signaling. Overexpression of SHARPIN in B16 cells attenuated intracellular dsDNA-mediated activation of TBK1-IRF3 signaling and subsequent Ifnb1, Ccl5, and Cxcl10 expression (fig. S7, B and C). Conversely, PRMT5 KD in B16 cells augmented dsDNA-induced activation of TBK1-IRF3 as reflected by increased TBK1, IRF3, and STING phosphorylation (Fig. 5D), increased STING dimerization, detected with seminative polyacrylamide gel electrophoresis (PAGE) (Fig. 5E), and greater STING polymerization, detected with blue native PAGE (Fig. 5F) (33, 36). Similarly, pharmacological inhibition of PRMT5 in B16 cells appeared to enhance TBK1-IRF3 signaling (Fig. 5G). Consistent with these findings, ectopic expression of PRMT5 + WDR77 in B16 or YUMMER1.7 cells appeared to decrease TBK1-IRF3 activation (Fig. 5H and fig. S7D).

To support the importance of IFI204 methylation, we monitored changes in TBK1-IRF3 signaling after dsDNA stimulation in cells expressing wild-type IFI204 or methylation-defective mutants. Compared to cells that expressed empty vector, ectopically expressed IFI204 in B16 cells enhanced TBK1-IRF3 signaling (fig. S7E) and increased the expression of Ifnb1 and Ccl5 (fig. S7F) after dsDNA treatment. Compared to cells overexpressing wild-type IFI204, cells expressing methylation-defective R12A IFI204 (IFI204Mt1), but not R538A (IFI204Mt2), had greater TBK1-IRF3–mediated expression of downstream genes (Fig. 5I). Consistent with these observations, IFI204Mt1 expression, but not that of IFI204Mt2, increased STING dimerization and polymerization after dsDNA stimuli (Fig. 5J and fig. S7G), suggesting a critical role of IFI204 methylation on Arg12 in regulating the activity of the dsDNA-stimulated STING pathway. In agreement, small interfering RNA (siRNA)–mediated STING KD reduced activation of Ifnb1, Ccl5, and Cxcl10 seen after PRMT5 KD (Fig. 5K and fig. S7H). Changes in PRMT5 abundance did not alter cGAS or STING abundance (fig. S7I), suggesting that PRMT5 limits the activation but not the amounts of cGAS/STING pathway components.

After dsRNA stimuli, Toll-like receptor 3 (TLR3) and retinoic acid-inducible gene-I (RIG-I) also stimulate a type I IFN response (37). To determine whether PRMT5 regulated this nucleic acid–sensing pathway, we treated B16 melanoma cells with the TLR3/RIG-I agonist polyinocinic and polycytidylic acid [poly(I:C)] and monitored Ifnb1 and chemokine expression. PRMT5 KD significantly (P < 0.05) enhanced the expression of Ifnb1, Ccl5, and Cxcl10 in response to either high–molecular weight (HMW) or low–molecular weight (LMW) poly(I:C) (fig. S7J). Consistent with cGAS/STING pathway observations, B16 melanoma cells overexpressing IFI204Mt1, but not IFI204Mt2, exhibited increased induction of Ifnb1 and chemokine expression by poly(I:C), relative to cells overexpressing wild-type IFI204 (fig. S7K). Together, these observations revealed an unexpected role for PRMT5/IFI204 in dsDNA-induced STING-dependent and dsRNA-induced activation of the type I IFN response.

PRMT5 regulates antigen presentation by controlling NLRC5 expression

To search for PRMT5-regulated genes that may affect tumor immune responses, we surveyed both The Cancer Genome Atlas (TCGA) metastatic melanoma dataset (n = 368) and the Cancer Cell Line Encyclopedia (CCLE) (n = 58) (38). Of coregulated genes (155 genes from TCGA and 135 from CCLE) (fig. S8A), 9 were common to both datasets. One of these nine, only the one encoding transcriptional activator NLRC5 (nucleotide-binding oligomerization domain-like receptor family caspase recruitment domain containing 5), had been implicated in regulating MHCI gene expression (39, 40). NLRC5, along with B2M, HLA-A, HLA-B, HLA-C, and proteasome 20S subunit beta 9 (PSMB9), are implicated in antigen presentation, as predicted by ingenuity pathway analysis (IPA) [log (P value) = −13.9; Fig. 1B and fig. S8A]. NLRC5 expression was inversely correlated with PRMT5 expression in both CCLE [correlation coefficient (r) = −0.516, P < 0.0001] and TCGA (r = −0.3158, P < 0.0001) datasets (Fig. 6, A to C, and fig. S8B). Increased NLRC5 expression is also reported in lung cancer cells subjected to PRMT5 KD (14), consistent with our observations (fig. S8C).

Fig. 6 PRMT5 negatively regulates NLRC5 to modulate MHCI antigen presentation.

(A) Correlation of PRMT5 expression with that of genes implicated in antigen presentation in melanoma cell lines (CCLE, Cancer Cell Line Encyclopedia datasets, n = 58) was evaluated using Pearson’s correlation coefficient (plotted on x axis) and –log (P value) (plotted on y axis). Blue line indicates cutoff level for P < 0.05. (B) Pearson’s correlation of PRMT5 and NLRC5 mRNA expression in melanoma cell lines (CCLE, n = 58). (C) Pearson’s correlation of PRMT5 and NLRC5 mRNA expression in melanoma patient specimens (TCGA, n = 368). (D to F) Effect of manipulating PRMT5 activity on expression of genes implicated in antigen presentation. Transcript abundance was assessed by quantitative reverse transcriptase PCR (qRT-PCR) in samples from B16 cells either transduced with Scr or PRMT5-specific shRNAs (shPRMT5-1 and shPRMT5-2) (D), treated with PRMT5i (MTA, 100 μM for 24 hours) (E), or stably expressing empty vector (EV) or PRMT5 + WDR77 (F). (G) Immunoblotting of lysates of B16 cells transduced with scrambled (Scr) or shPRMT5 and treated 24 hours with indicated concentrations of interferon-γ (IFN-γ). β-Actin served as a loading control. Data are representative of three independent experiments. (H) Cell surface MHCI abundance (H-2Kb) in B16 cells subjected to indicated treatments, as assessed by flow cytometry (left). Quantification of mean fluorescence intensity (MFI) (right). FITC, fluorescein isothiocyanate. Data are presented as means ± SD. Statistical significance was assessed by two-way ANOVA with Dunnett’s in (D) or Sidak’s test in (E), (F), and (H). *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Down-regulation of proteins functioning in MHCI-mediated antigen presentation can underlie immune evasion by cancer cells. Thus, we asked whether PRMT5 activity inhibited NLRC5 expression, the regulation of NLRC5 target genes implicated in immune evasion (39), or both. Reducing PRMT5 (either by KD or pharmacological inhibition with MTA) in B16 cells increased basal Nlrc5 expression and that of Nlrc5 target genes implicated in antigen processing and presentation (Fig. 6, D and E). In contrast, ectopic expression of PRMT5 + WDR77 in B16 or YUMMER1.7 melanoma cells decreased the expression of NLRC5 and its target genes (Fig. 6F and fig. S8D). In agreement with earlier reports (38, 40), overexpression of NLRC5 or stimulation with IFN-γ, which induces NLRC5 expression, increased the abundance of PSMB9 and of the amount of MHCI (H-2Kb) at the surface of B16 cells (fig. S8, E to G). Likewise, PRMT5 KD in B16 cells increased NLRC5 and PSMB9 abundance in cells stimulated with IFN-γ (Fig. 6G) and increased MHCI abundance at the cell surface (Fig. 6H). PRMT5 KD did not alter the abundance of the IFN-γ receptor (fig. S8H), suggesting that PRMT5 was not affecting this receptor (39). Overall, these findings suggest that PRMT5 controls MHCI abundance and antigen presentation through an effect on NLRC5 transcription.

IFI204 and NLRC5 expression inhibits in vivo growth of mouse melanoma tumors

Because our data indicated that PRMT5 impaired the function of IFI204 in stimulating type I IFN activity and reduced NLRC5 expression, we asked whether the expression of a methylation mutant form of IFI204 or the overexpression of NLRC5 would phenocopy PRMT5 KD. We established B16 cells expressing methylation defective IFI204 (IFI204Mt1), murine NLRC5, or both (Fig. 7A). Ectopic expression of NLRC5 alone, but not of IFI204Mt1 alone, significantly (P = 0.009) inhibited tumor growth in mice (Fig. 7B). Tumor growth suppression was enhanced in melanoma expressing both NLRC5 and IFI204Mt1, indicating that both pathways mediate antitumor immunity and could contribute to the effects of PRMT5 inhibition. When cells were grown in culture, however, we did not observe growth suppression of lines ectopically expressing IFI204Mt1 and/or NLRC5, consistent with lack of changes observed for PRMT5 inhibition and consistent with the notion that in vivo these factors function in immune recognition of the tumor (Fig. 7C).

Fig. 7 Coexpression of mutant IFI204 and NLRC5 inhibits melanoma growth.

(A) B16 cells were transduced with empty vector (EV) or expression vectors with IFI204Mt1, NLRC5, or both and then analyzed by Western blotting for indicated proteins. (B) Tumor growth was assessed in mice (n = 8) grafted with B16 cells with the indicated constructs. (C) Growth of B16 cells with the indicated constructs in culture. Data are presented as means ± SD. Statistical significance of changes in tumor growth and cell growth was assessed using two-way ANOVA with Tukey’s correction and one-way ANOVA with Dunnett’s test. (D and E) Correlation of IFI16 and NLRC5 expression with melanoma patient survival. Left: Classification of specimens based on low or high expression of IFI16 or NLRC5 (based on TCGA, metastatic population of melanoma, n = 368). Right: Overall survival of patients with melanoma based on relative expression of IFI16 or NLRC5.

As we observed for tumors from PRMT5 KD cells in immune competent mice, immunohistochemical analysis of tumors (collected at day 12) showed increased infiltration of CD4+ and CD8+ T cells in tumors expressing both NLRC5 and IFI204Mt1 (fig. S9A). Moreover, the expression of PRMT1, PRMT5, and PRMT7 decreased in cells expressing both IFI204Mt1 and NLRC5 (fig. S9B), suggesting a possible feed-forward mechanism limiting expression and, concomitantly, activity of other PRMTs.

Expression of IFI16 and NLRC5 is associated with prolonged patient survival

To determine whether our findings were relevant for human melanoma, we analyzed melanoma specimens in the TCGA dataset (n = 368 metastatic melanomas). We performed survival analysis on 100 samples, each with high or low IFI16 or NLRC5 expression (Fig. 7, D and E). Tumors with higher expression of either IFI16 (P = 0.0257) or NLRC5 (P < 0.0001) were associated with a significantly (P = 0.0064 for IFI16 and P < 0.0001 for NLRC5) prolonged survival of patients with melanoma (Fig. 7, D and E). Correspondingly, higher expression of IFI16 or NLRC5 correlated with enrichment of an immune gene signature (fig. S9, C and D), supporting the notion that IFI16 and NLRC5 are important for the antitumor immune response.

PRMT5 inhibition enhances ICT in a murine melanoma model

On the basis of our results, we propose a model for PRMT5 as a suppressor of the antitumor immune response, which is achieved by limiting infiltration or activation (or both) of immune cells and tumor cell recognition by immune cells (Fig. 8A). Consistent with this model, PRMT5 KD tumors had higher expression of Ifnb1, Ccl5, and Cxcl10 (Fig. 8B) and of Pd-l1(Cd274), an immune checkpoint ligand (Fig. 8C). Because enhancing the immune response to so-called “cold” tumors could augment ICT effectiveness (4143) and considering our finding that PRMT5 KD increased immune cell infiltration into melanoma tumors in immunocompetent mice, we asked whether PRMT5 inhibition enhanced ICT effectiveness. PRMT5 KD (shPRMT5 + IgG) significantly (P = 0.0406, Fisher’s exact test) attenuated B16 tumor growth in six of eight mice compared with one of eight in the control group (Scr + IgG) (Fig. 8D, top).

Fig. 8 PRMT5 inhibition synergizes with anti–PD-1 immune checkpoint therapy.

(A) Proposed model for PRMT5 control of expression of IFN and chemokine genes and antigen presentation. (B and C) Expression of transcripts encoding indicated IFN- and chemokine-encoding genes (B) and immune checkpoint components (C) from tumors transduced with control (Scr, n = 7) or PRMT5 KD (shPRMT5, n = 7), 17 days after tumor cell inoculation. (D) B16 cells transduced with scrambled (Scr) or shPRMT5 were grafted into C57BL/6 mice (n = 8). The mice were treated with control IgG or anti–PD-1 antibody (200 μg per mouse at days 8, 11, 14, 17, and 20). Tumor volume (top) and percent survival (bottom) were assessed at indicated time points. (E) B16 cells were grafted into C57BL/6 mice (n = 6 to 8) subsequently treated with PRMT5i (GSK3326595, 40 mg/kg from day 10), anti–PD-1 antibody (200 μg per mouse at days 11, 14, and 17), or both on the same schedule as used for monotherapy. Tumor volume (top) and percent survival (bottom) were assessed at indicated time points. (F) YUMM1.7 cells were grafted into C57BL/6 mice (n = 7 to 8). The mice were treated with PRMT5i (GSK3326595, 40 mg/kg from day 7), anti–PD-1 antibody (at days 8, 11, 14, and 17), or both on the same schedule as used for monotherapy. Tumor volume (top) and percent survival (bottom) were monitored at indicated time points. (G) YUMM1.7 cells were grafted into C57BL/6 mice (n = 7). The mice were administered anti-CD8 antibody (200 μg per mouse) every 3 days, starting 1 day before tumor inoculation. As indicated, mice were also administered PRMT5i (GSK3326595, 40 mg/kg from day 8), anti–PD-1 antibody (at days 9, 12, 15, and 18), or both on the same schedule as used for monotherapy. Tumor volume (top) and percent survival (bottom) were monitored at indicated time points. For statistical analyses, tumor response was calculated on the basis of tumor volume and percent survival, using Fisher’s exact test and a log-rank test, respectively. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

When combined with anti–PD-1 (programmed cell death protein 1) therapy, PRMT5 KD (shPRMT5 + anti–PD-1) significantly (P = 0.0002, Fisher’s exact test) suppressed tumor growth (eight responders of eight), compared with anti–PD-1 treatment alone (Scr + anti–PD-1) (zero responders of eight) (Fig. 8D, top). Although the difference in tumor growth suppression at early time points was not significantly (P = 0.4667, Fisher’s exact test) different between the combination of anti–PD-1 therapy plus PRMT5 KD and PRMT5 KD alone (Fig. 8D, top, and fig. S10A), the long-term survival of mice was significantly better (P = 0.0001) when treated with combination of shPRMT5 (PRMT5 KD) + anti–PD-1 therapy (median survival, 27 days) relative to mice treated with anti–PD-1 alone (median survival, 17.5 days) or PRMT5 KD alone (median survival, 20 days) (Fig. 8D, bottom). These findings indicated improved therapeutic efficacy by the combination of PRMT5 KD with anti–PD-1 therapy.

We next subjected B16 and YUMM1.7 melanoma models to combined therapy, consisting of anti–PD-1 antibodies and the PRMT5i GSK3326595. The dose of the PRMT5i (40 mg/kg) was based on an earlier report (22) and confirmed in the B16 tumor model (fig. S10B). Compared to anti–PD-1 therapy alone, treatment with GSK3326595 plus anti–PD-1 antibodies augmented the antitumor response, reflected in reduced tumor size and survival in both the B16 (Fig. 8E and fig. S10, C and D) and YUMM1.7 (Fig. 8F) tumor models. Note that tumor growth inhibition seen in both B16 or YUMM1.7 models after treatment with anti–PD-1 antibody or PRMT5i alone was limited (one to two responders; Fig. 8, E and F), consistent with the expected response of cold tumors. Changes in tumor burden, which was monitored at different time points in the course of tumor development, revealed a greater response rate (number of mice responding with a <50% reduction in tumor burden, compared to the control group) to the combination therapy (57.1 to 87.5%), than that seen in mice undergoing either monotherapy [anti–PD-1 antibody (12.5 to 33.3%) or PRMT5i (14.3 to 66.7%)] (Fig. 8, E and F, fig. S10C, and table S5). Consistent with the limited response observed after PRMT5i monotherapy, notable changes were not observed in immune cell infiltration or activation (fig. S10, E to G). The effective inhibition in tumor growth observed after combination of PRMT5i with anti–PD-1 antibody was abolished upon the administration of neutralizing antibodies to deplete CD8+ cells (Fig. 8G and fig. S10H). These observations confirmed that CD8+ cells mediate antitumor immunity elicited by the combination therapy (PRMT5 inhibition with anti–PD-1 therapy). Unlike anti–PD-1 antibody therapy, administration of anti–cytotoxic T lymphocyte associated protein 4 (CTLA-4) antibody did not augment the effect of PRMT5i, compared to control or either treatment alone (fig. S10, I and J), indicating that a subset of immune checkpoint components are regulated by PRMT5.

DISCUSSION

Understanding mechanisms that underlie tumor cell recognition by the immune system is expected to greatly increase the effectiveness of antitumor immune therapies. Here, we report that tumor-intrinsic PRMT5 activity antagonizes the immune response by regulating antigen presentation and processing and production of IFN and chemokines. We showed that PRMT5-catalyzed methylation of IFI16 (IFI204) represses activation of the intracellular dsDNA-induced cGAS/STING pathway and inhibits TBK1/IRF3 signaling and IFN and chemokine production. With IFI204, we determined that only one of the two putative methylation sites (R12) affected STING/cGAS signaling, implying that the second (R538) site has an independent, as yet unknown, activity. Because this Arg methylation site is located within the N-terminal PYRIN domain of IFI16 (IFI204), which is implicated in STING-mediated activation of the IFNB1 gene promoter (23, 33), we cannot exclude the possibility that such methylation may alter STING complex formation and activity. We observed that mutating Arg12, but not Arg538 in the HIN (DNA binding) domain, is sufficient to enhance dsDNA stimulation of STING activation. Accordingly, STING dimerization and subsequent activation of IRF3 and p65 are impaired in Staphylococcus-infected lung tissues in IFI204 knockout mice (44). Thus, further understanding of mechanisms underlying the effects of arginine methylation of IFI16 (IFI204) on STING is needed. Our results also revealed the importance of IFI204 methylation in IFN and chemokine production by dsRNA stimuli, consistent with an earlier report of the role of IFI16 in dsRNA-induced signaling (45). In all, our findings established that IFI16 (IFI204) methylation by PRMT5 suppresses dsDNA or dsRNA activation of STING or RIG-I/TLR pathways, respectively, with a concomitant effect on type I IFN and chemokine expression.

We also show that PRMT5 negatively regulates NLRC5 transcription, which then limits antigen processing and presentation, an independent hallmark of tumor evasion of immune surveillance (46). Single-cell sequencing of malignant cells from patients with melanoma identified MHCI antigen presentation genes as factors down-regulated and associated with T cell exclusion and, possibly, resistance to ICT (47). Correspondingly, elevated NLRC5 expression enhanced cell surface abundance of MHCI and inhibited tumor growth (48). As an epigenetic regulator, PRMT5 may control NLRC5 expression by changing its access to the transcriptional machinery or through methylation of DNA binding proteins, both mechanisms that control NLRC5 transcription (49). Likewise, PRMT5 control of histone 4 arginine methylation could facilitate DNA methyltransferase DNMT3A recruitment and subsequent DNA methylation (50). Given the importance of the antigen processing and presentation pathway to ICT resistance, further understanding of mechanisms underlying NLRC5 expression in cancer will help establish more durable therapies. We found that higher expression of IFI16 and NLRC5 coincides with prolonged survival of human patients with melanoma.

The finding that tumors lacking PRMT5 exhibit the increased expression of genes for both type I IFN and the immune checkpoint ligand, Pd-l1 (Cd274), provided a rationale for evaluating the effect of anti–PD-1 therapy in tumors subjected to PRMT5 inhibition. Our data showed enhanced efficiency of anti–PD-1 therapy on cold nonresponsive tumors when combined with either genetic or pharmacological inhibition of PRMT5 in mouse models. These findings address an unmet clinical need, namely, the ability to apply combination of PRMT5 inhibition and anti–PD-1 therapy to cold tumors, which are not responsive to PD-1 therapy.

Recent studies report suppression of Tregs and CD8+ T cells after treatment with PRMT5i (EPZ015666 and DST-437) (51, 52). We found that administration of the PRMT5i (GSK3326595; 40 mg/kg, daily) alone was not sufficient to attenuate tumor growth, nor was it capable of increasing immune cell infiltration, consistent with the limited overall changes we observed in protein methylation. This contrasts with the clear changes seen in each of these parameters upon genetic KD of PRMT5 in tumors. These observations suggested that optimization of doses and formulation is necessary to improve therapeutic effects of pharmacological PRMT5i. The effect of PRMT5i on immune system components, as on stromal and other microenvironmental niches, should thus be monitored after different formulation and various dosing and frequency schedules. Despite the lack of benefit as monotherapy, combination of either genetic or pharmacological PRMT5i with anti–PD-1 therapy led—in both cases—to effective inhibition of melanoma growth, which was CD8+ cell dependent. Although our studies focus on PRMT5 inhibition, the possible inclusion of inhibitors that target multiple PRMT family members may prove effective (53, 54).

Overall, our findings establish that PRMT5 controls important tumor intrinsic regulatory axes for antigen presentation and cGAS/STING activation, which underlie tumor immune evasion. PRMT5 inhibition may thus enhance antitumor immune responses and provide an opportunity to mitigate a cold tumor’s resistance to ICT.

MATERIALS AND METHODS

Study design

The objectives of the present study were to (i) determine the effect of PRMT5 on control of the antitumor immune response, (ii) define relevant underlying molecular mechanisms, and (iii) evaluate therapeutic efficacy of PRMT5 inhibition alone or in combination with immune therapy in vivo. Our study relied on analyses of human melanoma databases, in vitro analyses of signal transduction and gene expression pathways for the type I IFN proinflammatory response and antigen processing and presentation, and in vivo animal studies monitoring melanoma growth and response to therapies. We evaluated PRMT5 immune suppressive function in syngeneic murine models of melanoma, using less-immunogenic B16 and YUMM1.7 cells for loss-of-function studies and immunogenic YUMMER1.7 cells for gain-of-function studies. Genetic inactivation of PRMT5 was restricted to use of lentiviral shRNAs (multiple) in studies of both cultured cells and in vivo, because total ablation of PRMT5 using CRISPR-Cas9 approaches resulted in complete lethality (fig. S11). We thus complemented our studies using a first-in-class pharmacological inhibitor for PRMT5, EPZ015666, which provided independent confirmation to the genetic-based inhibition studies.

Phenotypes seen in melanoma cells subjected to PRMT5 KD were confirmed using pharmacological PRMT5i and through the analysis of PRMT gain of function in overexpression assays. Animal care and related procedures followed institutional guidelines and was conducted with approval of the Institutional Animal Care and Use Committee of Sanford Burnham Prebys Medical Discovery Institute. Animal cohort sizes were designed to detect differences in treatment effects at 80% power (α error rate, 0.05), with the exception of studies conducted to assess immune phenotypes. Mice exhibiting scurfy skin (atopic dermatitis) before study initiation were excluded. All experiments were conducted two to three times, except for tumor studies in which specific immune cells were depleted; in those analyses, cohort size was sufficient to support the statistical power stated above. Each experiment consisted of three to four technical replicates. Sample identity for tumor studies were blinded for the investigator who grafted them into mice.

Cell culture and treatment

Human and murine melanoma cells [B16, purchased from American Type Culture Collection (ATCC); YUMM1.7 and YUMMER1.7, obtained from Yale University (27); A375 and WM115, obtained from the Wistar Institute (55); and HEK293T cells, from ATCC] were maintained in Dulbecco’s modified Eagle’s medium (HyClone) containing 10% fetal bovine serum (Omega Scientific and PEAK serum) plus penicillin/streptomycin (10,000 U/ml; Thermo Fisher Scientific) in 5% CO2 at 37°C. Stably transduced cells were maintained with appropriate antibiotics, including puromycin (InvivoGen; 1 μg/ml) and blasticidin (InvivoGen; 10 μg/ml). Cells were maintained in growth phase and did not exceed 80% confluency. Cells were stimulated by treatment with (i) IFN-γ (R&D Systems), (ii) by transfection with LMW/HMW poly(I:C) (InvivoGen; 250 ng/ml), or by (iii) transfection of vaccinia virus dsDNA V70mer (500 ng/ml for detecting Ifnb1/chemokine expression and 1.5 μg/ml for detecting TBK1/IRF3 activation). V70mer was prepared by annealing the complementing oligonucleotides 5′-CCATCAGAAAGAGGTTTAATATTTTTGTGAGACCATCGGGGCCGCGCCTCCCCCGCGAGGCCGCCGGCG-3′ (29).

Animal experiments

All animal experiments were conducted with approval of the Institutional Animal Care and Use Committee of Sanford Burnham Prebys Medical Discovery Institute (animal use form no. 18-044). The murine melanoma lines B16, YUMM1.7, and YUMMER1.7 were injected subcutaneously (s.c.) (2.0 × 105 cells of B16 or YUMM1.7 and 4.0 × 105 cells of YUMMER1.7) into the lower right flank of 6- to 8-week-old male C57BL/6 (B16, YUMM1.7, and YUMMER1.7) or NSG (B16 and YUMMER1.7) mice. To induce transduced inducible shPRMT5, doxycycline (10 mg/ml; Fisher Bioreagents) was prepared in methylcellulose solution (0.5% hydroxylmethylcellulose and 0.2% Tween 80) and administered to mice (0.2 ml, oral gavage, daily) (56). Tumor sizes were monitored using calipers. At indicated time points, tumors were collected, weighed, and assessed for immune phenotypes using flow cytometry or immunofluorescence. To assess efficacy of immune checkpoint antibodies, mice were grafted with B16 or YUMM1.7 (2.0 × 105 cells, s.c.) cells and treated with 200 μg of control IgG [rat IgG2a (BE0089, Bio X Cell)], anti-CD152 (CTLA-4) [9H10 (BE0131, Bio X Cell)], or anti-CD279 (PD-1) [RMP1-14 (BE0146, Bio X Cell)]. Antibodies were injected (intraperitoneally) three to five times (every 3 days starting from the indicated date). The PRMT5i GSK3326595 (Chemitek) was prepared in methylcellulose solution and administered to mice (40 mg/kg, oral gavage, daily). To deplete NK or CD8+ cells, mice were treated with anti-NK1.1 antibody [PK136 (BE0036, Bio X Cell)] or anti-CD8 antibody [2.43 (BE0061, Bio X Cell), respectively; controls were treated with 200 μg IgG [rat IgG2b (BE0090, Bio X Cell)]. Antibodies were injected (intraperitoneally) every 3 days starting 1 day before tumor cell inoculation. The efficiency of depletion was assessed using flow cytometry of blood samples collected at day 8 after tumor inoculation. To assess percent survival of animals, mice bearing tumors exceeding 2000 mm3 were defined as “dead.”

GSEA and IPA

GSEA was performed using a GSEA Desktop Application downloaded from software.broadinstitute.org/gsea/. Gene expression [RNA sequencing (RNA-seq)] data from specimens from human patients with melanoma obtained from the TCGA or Gene Expression Omnibus databases were used to identify genes differentially expressed between patient groups with characteristics of interest (low/high expression of PRMT5 or MTAP). Curated sets of Hallmark (50 gene sets) (49) and C5 gene ontology (GO) (5917 gene sets) genes from the Molecular Signature Database (MSigDB v6.1) served as input. High-ranked gene sets from the analysis were presented along with the enrichment plot, normalized enrichment score (NES), nominal P value, q value of false discovery rate (FDR), and a heatmap of the corresponding gene set. For heatmap plots, fragments per kilobase of transcript per million mapped reads (FPKM) values for PRMT5 low (n = 100) and high (n = 100) were obtained from TCGA–skin cutaneous melanoma (SKCM) samples for each gene set (Hallmark and GO) were log2-transformed (FPKM + 0.1) and subsequently converted to row (gene) z scores using scale() function in R. Appropriate numbers of clusters (K) were determined by plotting the “within sum of squares” (a metric denoting dissimilarity among the members of a cluster) versus different values of K. Heatmaps of gene z scores were plotted using ComplexHeatmap version 2.3.2 (57) by applying k-means clustering (K = 5 in both cases) and 100 k-means runs to get a consensus clustering. Genes associated with immune signature are indicated on the heatmaps. All of the above analyses were performed in R version 3.6.1. For IPA (QIAGEN Inc.), DEGs with an unpaired t test, P < 0.05, and a fold difference of >2.0 between low and high groups were analyzed using core analysis. High-ranked canonical pathways were presented along with P values (right-tailed Fisher’s exact test), ratio (coverage of pathway), and z score with pathway directionality (filled blue bars).

Statistical analysis

Statistical analyses were performed using Prism software (version 7.00, GraphPad). For comparison of means of two groups with normal (or about normal) distributions, an unpaired t test was applied. In multiple t tests between two groups, adjusted P values were computed with Holm-Sidak method. To compare means between >2 groups, we used one-way analysis of variance (ANOVA) with multiple comparison corrections (Dunnett’s test). For animal experiments, we used two-way ANOVA (time and treatment) with Dunnett’s, Tukey’s, or Sidak’s multiple comparison test. For Kaplan-Meier plots to compare overall survival, we used a log-rank test to determine significance of differences between groups. To evaluate response to therapy in a mouse model, we used Fisher’s exact test (version 7.00, GraphPad). For that analysis, we defined a tumor with a volume of <50% of control tumors as “responding to treatment.” For all analyses, a difference with P < 0.05 was considered significant, unless specified.

SUPPLEMENTARY MATERIALS

stm.sciencemag.org/cgi/content/full/12/551/eaaz5683/DC1

Materials and Methods

Fig. S1. SHARPIN expression is associated with immune genes and pathways.

Fig. S2. Immune-associated genes are enriched in melanomas with low PRMT5 expression.

Fig. S3. PRMT5 gene expression or activity linked with immune-associated gene sets.

Fig. S4. PRMT5 controls tumor growth and immune cell infiltration.

Fig. S5. Expression of PRMT5 adaptors in low PRMT5 melanoma specimens.

Fig. S6. Methylation of SHARPIN-interacting proteins IFI16 and IFI204.

Fig. S7. PRMT5-SHARPIN attenuates STING activity, whereas IFI204 augments it.

Fig. S8. NLRC5 expression inversely correlates with PRMT5.

Fig. S9. Expression of methylation mutant IFI204 and NLRC5 in B16 tumors increases T cell infiltration and correlates with enriched immune-associated gene sets.

Fig. S10. Genetic or pharmacologic inhibition of PRMT5 augments therapeutic effect of PD-1 blockade.

Fig. S11. CRISPR gene editing of Prmt5.

Table S1. Top-enriched Hallmark gene sets from GSEA of DEGs from patients with melanoma with low MTAP and low SHARPIN expression versus low MTAP and high SHARPIN expression.

Table S2. Top-enriched Hallmark gene sets from patients with melanoma with high versus low PRMT5 expression.

Table S3. Top-enriched GO gene sets from patients with melanoma with high versus low PRMT5 expression.

Table S4. SHARPIN-interacting proteins.

Table S5. Response of melanoma mouse models to monotherapy or combination therapy.

Data file S1. Primary data.

Data file S2. Raw images of Western blots provided for clarity.

References (5860)

REFERENCES AND NOTES

Acknowledgments: We thank G. Garcia and M. Sevilla at the Histology Shared Resource Facility, H. Clarke and J. Wade at the animal Shared Resource Facility, Y. Altman at the fluorescence-activated cell sorting (FACS) Shared Resource Facility, and J. Yin and R. Murad at the Bioinformatic Shared Resource Facility for help in respective experiments. We thank M. Tam (BioLegend) for providing the initial batch of anti–PD-1 antibodies, M. Bosenberg (Yale University) for providing YUMM1.7 and YUMMER1.7 lines, and M. Herlyn (Wistar Institute) for providing WM115 line. The results shown here are in whole or part based on data generated by the TCGA Research Network: www.cancer.gov/tcga. Editorial services were provided by N. R. Gough (BioSerendipity LLC, Elkridge, MD). Funding: This work was supported by National Cancer Institute (NCI) grant R35CA197465, DOD grant CA1810216, and MRA grant 509524 to Z.A.R. and R21CA198468 to Hyungsoo Kim. Support through grant P30 CA030199 to Shared Resource Facilities at the Sanford Burnham Prebys NCI Cancer Center is acknowledged. Author contributions: Conception and design: Hyungsoo Kim and Z.A.R. Development of methodology: Hyungsoo Kim, Heejung Kim, and Z.A.R. Acquisition of data (providing animals, acquiring and managing patients, and providing facilities): Heejung Kim, Hyungsoo Kim, Y.F., H.T., S.T., and Y.L. Data analysis and interpretation (statistical analysis, biostatistics, and computational analysis): Hyungsoo Kim, Heejung Kim, and Z.A.R. Writing the manuscript: Hyungsoo Kim, Heejung Kim, and Z.A.R. Administrative, technical, or material support (reporting or organizing data and constructing databases): Hyungsoo Kim and Z.A.R. Study supervision: Hyungsoo Kim and Z.A.R. Competing interests: Z.A.R. is a cofounder and serves as scientific advisor to Pangea Therapeutics. All other authors declare that they have no competing interests. Data and materials availability: Original data for all figures are provided as an Excel file with data for all histograms (data file S1). A pdf file for original raw Western blots (data file S2) is provided for clarity. Original LC-MS/MS data have been uploaded to MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) and are available under PXD017368 accession number.

Stay Connected to Science Translational Medicine

Navigate This Article