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. 2023 Jul 13;15(7):mjad047. doi: 10.1093/jmcb/mjad047

Estrogen receptor α-mediated signaling inhibits type I interferon response to promote breast carcinogenesis

Li-Bo Cao 1,2,3, Zi-Lun Ruan 4,5,6, Yu-Lin Yang 7,8,9, Nian-Chao Zhang 10,11,12, Chuan Gao 13,14,15, Cheguo Cai 16,17,18, Jing Zhang 19,20,21, Ming-Ming Hu 22,23,24,✉, Hong-Bing Shu 25,26,27,✉
Editor: Zhiyuan Shen
PMCID: PMC11066933  PMID: 37442610

Abstract

Estrogen receptor α (ERα) is an important driver and therapeutic target in ∼70% of breast cancers. How ERα drives breast carcinogenesis is not fully understood. In this study, we show that ERα is a negative regulator of type I interferon (IFN) response. Activation of ERα by its natural ligand estradiol inhibits IFN-β-induced transcription of downstream IFN-stimulated genes (ISGs), whereas ERα deficiency or the stimulation with its antagonist fulvestrant has opposite effects. Mechanistically, ERα induces the expression of the histone 2A variant H2A.Z to restrict the engagement of the IFN-stimulated gene factor 3 (ISGF3) complex to the promoters of ISGs and also interacts with STAT2 to disrupt the assembly of the ISGF3 complex. These two events mutually lead to the inhibition of ISG transcription induced by type I IFNs. In a xenograft mouse model, fulvestrant enhances the ability of IFN-β to suppress ERα+ breast tumor growth. Consistently, clinical data analysis reveals that ERα+ breast cancer patients with higher levels of ISGs exhibit higher long-term survival rates. Taken together, our findings suggest that ERα inhibits type I IFN response via two distinct mechanisms to promote breast carcinogenesis.

Keywords: estrogen receptor, type I interferon, breast cancer

Introduction

Breast cancer has become one of the most common and fatal cancer types in the world (Loibl et al., 2021). Estrogen receptor α (ERα), a nuclear hormone receptor (HR), is expressed in ∼70% of breast cancers and is an important therapeutic target for ERα+ breast cancer (Harvey et al., 1999; Sledge et al., 2014). Previous studies suggested that ERα promotes breast carcinogenesis by up-regulating proliferation regulators, including survivin, growth factors, and cell cycle-related genes. For example, estradiol (E2), a natural ligand of ERα, activates cyclin-dependent kinase 4 by up-regulating cyclin D expression, thereby promoting cell cycle progression (Frasor et al., 2003). A recent study demonstrated that ERα acts as an RNA-binding protein to sustain tumor cell survival and drug resistance by regulating RNA metabolism (Xu et al., 2021). In addition, several chemical inhibitors that target ERα have been used to treat ERα+ breast cancer patients. For example, tamoxifen acts as a selective antagonist to suppress ERα transcriptional activity, while fulvestrant (FUL) is a more potent ERα antagonist that also causes ERα protein degradation (Guan et al., 2019).

Type I interferons (IFNs), including IFN-α and IFN-β, are secreted by various immune and non-immune cells and exhibit potent antiviral and antitumor activities (Ivashkiv and Donlin, 2014; Hu and Shu, 2018). Type I IFNs share a common and ubiquitously expressed receptor, which includes the IFNAR1 and IFNAR2 subunits. Upon stimulation with type I IFNs, IFNAR1 and IFNAR2 hetero-dimerize and recruit the tyrosine kinases JAK1 and TYK2. Activated JAK1 and TYK2 phosphorylate STAT1 at Y701 and STAT2 at Y690, leading to their hetero-dimerization and interaction with IRF9 to form the interferon-stimulated gene factor 3 (ISGF3) complex. The ISGF3 complex is translocated into the nucleus, where it drives the transcription of interferon-stimulated genes (ISGs) by binding to the interferon-stimulated response element (ISRE) at ISG promoters (Ivashkiv and Donlin, 2014). Type I IFNs display antitumor activities in a variety of ways. For instance, type I IFNs can regulate major histocompatibility complex (MHC) expression in tumor cells and promote tumor immunogenicity, which is important for the initiation of antitumor immunity (de Charette et al., 2016). Type I IFNs can also promote the maturation of dendritic cells and the activation of adaptive immunity (Parker et al., 2016). Additionally, type I IFNs can trigger an intrinsic antitumor response by inducing downstream genes to inhibit proliferation and promote apoptosis of tumor cells (Parker et al., 2016). Consistently, compared with wild-type mice, STAT1-deficient mice spontaneously develop breast cancer, and IFNAR−/− mice exhibit augmented tumorigenesis (McCarty et al., 2002; Chan et al., 2012). Therefore, type I IFNs have been approved by the FDA for the treatment of a variety of solid tumors and hematological tumors (Borden, 2019). However, certain cancer cells are resistant to the treatment with type I IFNs via unknown molecular mechanisms (Matin et al., 2001; Wagner et al., 2004). Furthermore, ERα+ breast cancer is recognized as an immunologically ‘cold’ tumor and is resistant to immune checkpoint blockade (ICB) therapy probably due to impaired type I IFN response (Wang et al., 2017; Liang et al., 2018; Cao et al., 2021; Noguchi et al., 2021). Thus, exploring whether and how type I IFN response is impaired in ERα+ breast cancer cells would lead to a better understanding of breast carcinogenesis and breast cancer resistance to ICB therapy.

Here, we show that ERα is an important suppressor of type I IFN response in ERα+ breast cancer. Our findings in breast cancer cells and the xenograft mouse model suggest that ERα promotes breast carcinogenesis by inhibiting type I IFN response via two distinct mechanisms.

Results

ERα levels are negatively correlated with ISG levels in breast cancers

Based on the expression levels of HR, including ERα and progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), breast cancers are categorized into four types, i.e. HR+/HER2−, HR+/HER2+, HR−/HER2+, and HR−/HER2− (Kim et al., 2018). To investigate whether these molecular features are correlated with ISG levels (Supplementary Table S1) that represent the intensity of type I IFN response in breast cancers, we analyzed RNA sequencing (RNA-seq) data of clinical breast cancer samples from The Cancer Genome Atlas (TCGA) database. These samples were divided into three groups with high, medium, and low levels of ISGs and subjected to binary classifications of the four molecular features, i.e. ERα+ vs. ERα−, PR+ vs. PR−, HER2+ vs. HER2−, and triple-negative (TN) vs. non-TN (Figure 1A; Supplementary Figure S1). Further analysis indicated that the percentages of ERα+ and PR+ samples were higher in the ISG low group than in the ISG high group, whereas the percentages of HER2+ and TN samples were higher in the ISG high group than in the ISG low group (Figure 1B). We then quantified ISG levels in each clinical sample (Bhandari et al., 2019). Statistical analysis indicated that the average ISG scores were significantly lower in ERα+, PR+, and non-TN samples than in their respective negative samples but there was no significant difference between HER2+ and HER2− samples (Figure 1C). These results suggest that ERα and PR levels are reversely correlated with ISG levels in clinical breast cancer samples.

Figure 1.

Figure 1

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ERα levels are negatively correlated with ISG levels in breast cancers. (A) A total of 711 breast cancer samples from TCGA database were clustered according to ISG levels (blue: low; red: high). The upper colored bars represent breast cancer samples classified according to different molecular features. (B) Percentages of samples with different molecular features in ISG high, medium, and low groups, respectively. (C) Statistical analysis of ISG scores in samples with different molecular features.

ERα inhibits type I IFN response

Since PR has been reported to inhibit the IFN response (Goodman et al., 2019; Walter et al., 2020), we investigated whether ERα regulates ISG transcription. The reporter assays demonstrated that overexpression of ERα inhibited IFN-β-induced STAT1/2 activation in a dose-dependent manner in 293T cells but did not affect IFN-γ-induced IRF1 promoter activation (Figure 2A). Quantitative polymerase chain reaction (qPCR) analyses showed that overexpression of ERα inhibited IFN-β-induced transcription of the downstream IFIT1 and IFI44 genes but did not markedly affect IFN-γ-induced transcription of the IRF1 gene (Figure 2B). Next, endogenous ERα was knocked down in MCF7 and T-47D breast cancer cells by siRNAs (Figure 2C). ERα deficiency promoted IFN-β-induced transcription of downstream genes, including IFIT1, IFI44, and RSAD2, but did not affect IFN-γ-induced transcription of the IRF1 gene (Figure 2C), consistent with a previous report that ERα does not affect IFN-γ-induced IRF1 expression (Mostafa et al., 2014). Furthermore, it was reported that type I IFNs can induce the expression of MHC I/II-related B2M, HLA-A, HLA-B, HLA-C, and HLA-DOB genes and apoptosis-related TRAIL gene (Kayagaki et al., 1999; Pusztai et al., 2016; Chen et al., 2019), which were commonly down-regulated in tumors to reduce tumor immunogenicity (Cornel et al., 2020) and favor tumor cell growth and survival (Parker et al., 2016). Consistently, the transcription of these genes and the expression of MHC molecules induced by IFN-β significantly increased in ERα-deficient MCF7 cells (Figure 2D and E). These results suggest that ERα inhibits IFN-β-induced transcription of downstream genes in various ERα+ breast cancer cells. Notably, IFN-β-induced transcription of the CD247 gene (encoding PD-L1) significantly increased in ERα-deficient MCF7 cells (Figure 2D), suggesting that ERα may affect anti-PD1/PD-L1 therapy by modulating PD-L1 expression. Interestingly, ERα deficiency promoted the transcription of a subset of IFN-γ-induced downstream genes, such as GBP1 and MHC (Supplementary Figure S2). It has been reported that IFN-γ not only mediates the canonical STAT1-dependent activation of the gamma interferon-activated sequence (GAS) element to induce a subset of genes (e.g. IRF1) (Majoros et al., 2017), but also activates non-canonical signaling pathways, including the JAK1–SNX8–IKKβ–NF-κB axis (Sizemore et al., 2004; Wei et al., 2017) and the ISGF3 complex, to regulate gene expression (Matsumoto et al., 1999; Zimmermann et al., 2005; Majoros et al., 2017). Our results suggest that ERα regulates IFN-γ-triggered non-canonical, but not the canonical, signaling pathways.

Figure 2.

Figure 2

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ERα inhibits IFN-β-induced transcription of ISGs. (A and B) Effects of ERα on IFN-β- and IFN-γ-triggered signaling and ISG transcription. (A) 293T cells (1 × 105) were transfected with STAT1/2- or IRF1 promoter-luciferase reporter plasmid (10 ng) and increasing amounts of ERα plasmid (25 and 50 ng) for 24 h. Then, the cells were left untreated or treated with IFN-β (100 ng/ml) or IFN-γ (100 ng/ml) for 10 h before luciferase assays. (B) 293T cells were transfected with FLAG-tagged ERα plasmid for 24 h and then left untreated or treated with IFN-β (100 ng/ml) or IFN-γ (100 ng/ml) for the indicated periods. qPCR analysis was performed to detect the mRNA levels of the indicated genes. (C–E) Effects of ERα deficiency on IFN-β-induced downstream gene transcription and MHC expression. (C) MCF7 or T-47D cells (4 × 105) were transfected with two independent siRNAs targeting ERα or a control siRNA for 48 h. Then, the cells were left untreated or treated with IFN-β (100 ng/ml) or IFN-γ (100 ng/ml) for the indicated periods. qPCR analysis was performed to detect the mRNA levels of the indicated genes. (D and E) MCF7 cells (4 × 105) were transfected with two independent siRNAs targeting ERα or a control siRNA for 48 h. Then, the cells were left untreated or treated with IFN-β (100 ng/ml) for 4 h, followed by qPCR analysis to detect the mRNA levels of the indicated genes (D), or for 48 h, followed by flow cytometry analysis to detect MHC expression with the indicated antibodies (E). All the experiments were repeated at least twice with similar results. The data are presented as mean ± standard deviation (SD) (n = 3 replicates) from one representative experiment. *P < 0.05; **P < 0.01; ns, not significant; unpaired t-test.

We next determined the effects of ERα ligands on the transcription of type I IFN-induced downstream genes. E2, the natural agonistic ligand of ERα, inhibited IFN-β-induced transcription of downstream genes, including IFIT1, IFI44, and RSAD2, in MCF7 and T-47D cells (Figure 3A). Propyl pyrazole triol (PPT), a selective ERα agonist, also markedly inhibited IFN-β-triggered expression of downstream genes in MCF7 and T-47D cells (Figure 3B). In contrast, FUL, an antiestrogen and potent ERα antagonist, promoted IFN-β-induced transcription of downstream genes in MCF7 and T-47D cells (Figure 3C). Notably, both ERα deficiency and FUL stimulation activated basal type I IFN response (Figures 2C and 3C). As expected, ERα deficiency markedly promoted IFN-β-triggered ISG transcription, thus abolishing both inhibitory effects of PPT and promoting effects of FUL (Figure 3D). Considering that E2 also activates GPER1, which was reported to inhibit type I IFN response in reproductive and fetal tissues (Harding et al., 2021), we investigated the effects of GPER1 deficiency on type I IFN response in ERα+ breast cancer cells. As shown in Supplementary Figure S3A–C, GPER1 knockout by CRISPR–Cas9 did not affect IFN-β-induced ISG transcription or E2-mediated inhibition of ISG levels in MCF7 cells, whereas ERα knockdown not only augmented IFN-β-triggered ISG transcription but also markedly reversed the inhibitory effects of E2. We also found that ESR1 (encoding ERα) was highly expressed, while GPER1 mRNA level was very low in MCF7 and T-47D cells (Supplementary Figure S3D). These results suggest that ERα is the primary E2 receptor for the regulation of ISG expression in ERα+ breast cancer cells.

Figure 3.

Figure 3

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ERα ligands regulate IFN-β-induced transcription of ISGs. (A–C) Effects of ERα ligands on IFN-β-induced transcription of ISGs. MCF7 or T-47D cells were treated with E2, PPT, or FUL at increasing concentrations for 16 h and then left untreated or treated with IFN-β (100 ng/ml) for the indicated periods. qPCR analysis was performed to detect the mRNA levels of the indicated genes. (D) Effects of ERα deficiency on the functions of ERα ligands. MCF7 cells were transfected with a siRNA targeting ERα or a control siRNA for 48 h. The cells were then treated with PPT (10 nM) or FUL (50 nM) for 16 h and IFN-β (100 ng/ml) for 4 h, followed by qPCR analysis to detect the mRNA levels of the indicated genes. All the experiments were repeated at least twice with similar results. The data are presented as mean ± SD (n = 3 replicates) from one representative experiment. *P < 0.05; **P < 0.01; ns, not significant; unpaired t-test.

Induction of the H2AZ1 gene contributes to ERα-mediated inhibition of type I IFN response

To investigate molecular mechanisms by which ERα inhibits type I IFN-induced signaling, we first identified genes induced by ERα signaling. We identified 3316 genes induced by PPT in MCF7 cells by RNA-seq analysis (Supplementary Figure S4A and Table S2). By cross-examining 3873 genes that were shown to negatively regulate type I IFN response (Supplementary Figure S4B; Harding et al., 2021), we identified 418 candidate genes that were induced by PPT and negatively regulated type I IFN response (Supplementary Figure S4C and Table S3). Then, we screened 121 corresponding cDNA clones available in our laboratory for their abilities to regulate IFN-β-induced transcription of the IFI44 gene (Supplementary Table S4) and identified H2A.Z (encoded by the H2AZ1 gene) as a candidate protein. H2A.Z is a variant of histone H2A that replaces conventional H2A in a subset of nucleosomes and thereby plays an important role in the transcriptional regulation of certain genes (Farris et al., 2005; Valdes-Mora et al., 2012). As shown in Supplementary Figure S4D, overexpression of H2A.Z inhibited IFN-β-induced transcription of the IFI44 gene. qPCR and immunoblotting confirmed that H2A.Z expression was induced by PPT and inhibited by FUL in a dose-dependent manner in MCF7 cells (Figure 4A and B). Furthermore, we reconstituted ERα-deficient MCF7 cells with wild-type ERα or mutant ERαL39P/Y43P with impaired transcriptional activity (Metivier et al., 2000). qPCR and immunoblotting demonstrated that basal or PPT-induced expression of H2A.Z was inhibited in ERα-deficient cells, which was fully rescued by reconstitution with wild-type ERα but not ERαL39P/Y43P (Figure 4C and D). These results suggest that ERα promotes H2A.Z expression dependent on its transcriptional activity.

Figure 4.

Figure 4

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ERα signaling induces the transcription of H2AZ1 gene to inhibit type I IFN response. (A–D) Effects of ERα signaling on the expression of H2A.Z. (A and B) MCF7 cells were treated with PPT or FUL at increasing concentrations for 16 h. (C and D) ERα-deficient MCF7 cells were reconstituted with wild-type ERα or mutant ERαL39P/Y43P and then treated with PPT for 16 h. qPCR and immunoblotting analyses were performed to detect the H2A.Z mRNA level (A and C) and H2A.Z protein level (B and D), respectively. WT, wild-type; Mut, mutant. (E–H) Effects of H2A.Z deficiency on IFN-β-induced transcription of ISGs and functions of ERα signaling. H2A.Z-deficient 293T (E) and MCF7 (F–H) cells were generated using the CRISPR–Cas9 method. H2A.Z-deficient (gH2AZ1) and control (gC) cells without pre-treatment (E and F), transfected with a siRNA targeting ERα or a control siRNA for 48 h (G), or pre-treated with PPT or FUL at increasing concentrations for 16 h (H) were left untreated or treated with IFN-β (100 ng/ml) for 4 h. qPCR analysis was performed to detect the mRNA levels of the indicated ISGs. All the experiments were repeated at least twice with similar results. The data are presented as mean ± SD (n = 3 replicates) from one representative experiment. **P < 0.01; ns, not significant; unpaired t-test.

To determine the role of H2A.Z in the regulation of type I IFN response, we generated H2A.Z-deficient 293T and MCF7 cells using the CRISPR–Cas9 method. In these cells, IFN-β-induced transcription of the IFIT1, IFI44, and RSAD2 genes was significantly augmented (Figure 4E and F), consistent with a previous study showing that H2A.Z restricts optimal ISGF3 engagement to the ISRE and inhibits the transcription of downstream effector genes (Au-Yeung and Horvath, 2018). In addition, ERα knockdown or FUL significantly promoted whereas PPT significantly inhibited IFN-β-induced transcription of the IFIT1, IFI44, and RSAD2 genes in H2A.Z-deficient MCF7 cells (Figure 4H). These results suggest that H2A.Z is only responsible for part of ERα signaling-triggered inhibition of the IFN-β response.

ERα interacts with STAT2 to disrupt the ISGF3 complex

We next investigated H2A.Z-independent mechanisms. Immunoblotting results showed that IFN-β-induced phosphorylation of STAT1Y701 and STAT2Y690, indicative of STAT1/2 activation, was not affected by ERα knockdown (Figure 5A), PPT or FUL stimulation (Figure 5B), or H2A.Z knockout (Figure 5C), and IFN-γ-induced phosphorylation of STAT1Y701 and STAT1S727 was also not affected (Supplementary Figure S5A), suggesting that ERα and H2A.Z do not affect IFN-induced phosphorylation of STAT1/2.

Figure 5.

Figure 5

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ERα interacts with STAT2 to disrupt the ISGF3 complex. (A–C) Effects of ERα signaling and H2A.Z on IFN-β-induced phosphorylation of STAT1 and STAT2. MCF7 cells transfected with two independent siRNAs targeting ERα or a control siRNA for 48 h (A), MCF7 cells pre-treated with PPT or FUL at increasing concentrations for 16 h (B), or H2A.Z-deficient (gH2AZ1) and control (gC) MCF7 cells (C) were left untreated or treated with IFN-β (100 ng/ml) for the indicated periods (for 1 h in B). Immunoblotting analysis was performed with the indicated antibodies. (D–H) ERα interacts with STAT2 (D and E) and disrupts STAT2 association with STAT1 or IRF9 (F–H). (D and F) In the mammalian overexpression system, 293T cells (4 × 106) were transfected with the indicated Flag- and HA-tagged plasmids for 20 h. Cell lysates were immunoprecipitated with an anti-HA antibody or control mouse IgG. The immunoprecipitates and lysates were analyzed by immunoblotting with anti-Flag and anti-HA antibodies. (E, G, H) For endogenous co-IP assays, MCF7 cells pre-treated with DMSO or PPT (5 nM) for 16 h (E and G) or transfected with a siRNA targeting ERα or a control siRNA for 48 h (H) were left untreated or treated with IFN-β (100 ng/ml) for 1 h. Co-IP and immunoblotting were performed with the indicated antibodies. The blots shown are representative of at least two repeated experiments with similar results.

We then examined whether ERα physically associates with components of the ISGF3 complex. In the mammalian overexpression system, ERα interacted with STAT2 but not with STAT1 or IRF9 (Figure 5D), and overexpression of ERα inhibited the interaction between STAT2 and STAT1 or IRF9 (Figure 5F). Endogenous co-immunoprecipitation (co-IP) assays demonstrated that ERα associated with STAT2 upon IFN-β stimulation, which was further increased by PPT treatment in MCF7 cells (Figure 5E). Furthermore, PPT treatment inhibited (Figure 5G) but ERα knockdown promoted IFN-β-induced association between STAT2 and STAT1 or IRF9 in MCF7 cells (Figure 5H). Additionally, overexpression of ERα did not affect the self-interaction of STAT1, which is required for STAT1-mediated activation of GAS elements at the promoters of downstream genes induced by IFN-γ (Supplementary Figure S5B; Majoros et al., 2017).

STAT2 contains six domains, i.e. the N-terminal (NT), coiled-coil (CC), DNA binding (DB), linker (LK), SH2, and C-terminal (CT) domains (Supplementary Figure S6A; Duncan and Hambleton, 2021). Domain mapping experiments indicated that ERα interacted with either the NT–CC or DB–LK domain of STAT2, STAT1 interacted with the NT–CC–DB–LK domain of STAT2, and IRF9 interacted with the NT–CC domain of STAT2 (Supplementary Figure S6B–D). Further in vitro pulldown assays demonstrated that His-tagged ERα interacted with GST-tagged full-length STAT2 or truncated STAT2 (amino acids 1–571) in vitro (Supplementary Figure S7A and B), consistent with the results of co-IP assays. These results suggest that ERα competes with STAT1 and IRF9 to bind to STAT2, thereby disrupting the assembly of the ISGF3 complex.

The selective ERα agonist PPT inhibits IFN-β-induced chromatin occupancy of STAT2

To examine whether ERα affects the occupancy of the ISGF3 complex at ISG promoters, we detected STAT2 occupancy at the promoters of ISGs, including IFIT1, IFI44, and RSAD2, by chromatin immunoprecipitation (ChIP) assays. As expected, IFN-β induced STAT2 occupancy at ISG promoters, which was inhibited by PPT treatment. ERα deficiency augmented IFN-β-induced STAT2 occupancy and abolished the inhibitory effects of PPT (Figure 6A). In addition, ERα deficiency further promoted IFN-β-induced STAT2 occupancy at ISG promoters in both control and H2A.Z-deficient MCF7 cells (Figure 6B). These results suggest that ERα signaling inhibits the chromatin occupancy of the ISGF3 complex and consequent IFN-β response via two distinct mechanisms (Figure 6C).

Figure 6.

Figure 6

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Effects of ERα signaling on IFN-β-induced STAT2 occupancy at ISG promoters. (A and B) MCF7 cells (A) or H2A.Z-deficient (gH2AZ1) and control (gC) MCF7 cells (B) were transfected with a siRNA targeting ERα or a control siRNA for 48 h. Then, the cells were left untreated or treated with IFN-β (100 ng/ml) for 4 h with (A) or without (B) a 16-h pre-treatment of PPT (10 nM). ChIP–qPCR analysis was performed to detect STAT2 occupancy at ISG promoters. (C) ERα signaling inhibits type I IFN response via two distinct mechanisms. All the experiments were repeated at least twice with similar results. The data are presented as mean ± SD (n = 3 replicates) from one representative experiment. *P < 0.05; **P < 0.01; ns, not significant; unpaired t-test.

The ERα antagonist FUL enhances the ability of IFN-β to suppress breast tumor growth

Type I IFNs have been shown to directly inhibit proliferation and induce apoptosis in tumor cells (Parker et al., 2016). As shown in Figure 3C, FUL promoted type I IFN response in a dose-dependent manner in MCF7 and T-47D cells. Next, we examined whether FUL enhances the ability of type I IFNs to suppress breast tumor growth. Both IFN-β and FUL inhibited MCF7 cell growth in vitro (Figure 7A) and breast tumor growth in a xenograft model (Figure 7B), while their combination were more effective than either one alone. In addition, FUL markedly potentiated IFN-β-induced transcription of ISGs in xenograft breast tumors (Figure 7C). These results suggest that FUL enhances the ability of IFN-β to suppress breast tumor growth.

Figure 7.

Figure 7

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IFN-β and FUL synergistically inhibit the growth of breast tumors. (A) MCF7 cells were treated with IFN-β (200 UI/ml) and/or FUL (20 nM) for 72 h before the CCK8 assay. (B and C) BALB/c nude mice with MCF7-derived xenograft tumors were treated with IFN-β (2 × 105 UI) every two days and/or FUL (175 mg/kg) weekly. (B) Xenograft tumors were isolated and measured. (C) The ISG levels in tumors were analyzed by qPCR. (D and E) BALB/c nude mice with control or IFNAR1-deficient MCF7-derived xenograft tumors were treated with FUL (250 mg/kg) weekly. (D) Xenograft tumors were isolated and measured. (E) The ISG levels in tumors were analyzed by qPCR. All the experiments were repeated at least twice with similar results. The data are presented as mean ± SD (n = 3 replicates in A or n = 4 replicates in B–E) from one representative experiment. *P < 0.05; **P < 0.01; ns, not significant; unpaired t-test.

To determine whether FUL-mediated inhibition of breast tumor growth depends on type I IFN response, IFNAR1-deficient MCF7 cells were generated using the CRISPR–Cas9 method. In the xenograft model, IFNAR1 deficiency promoted breast tumor growth and reduced the ability of FUL to suppress breast tumor growth (Figure 7D). Meanwhile, FUL promoted the transcription of ISGs in control but not IFNAR1-deficient breast tumors (Figure 7E). These results suggest that FUL suppresses breast tumor growth in part depending on type I IFN response.

Overall survival of breast cancer patients is positively correlated with ISG levels

To determine the clinical relevance of ISG levels that represent the intensity of type I IFN response in breast cancers, we analyzed 992 clinical samples from TCGA database and found that patients with high ISG levels had higher overall survival rates than patients with low ISG levels (Figure 8A). For patients with ERα+ breast cancer, hormone therapy, which inhibits ERα activity, is considered the first-line therapy (Yuan et al., 2021). By analyzing clinical samples from the METABRIC database, we found that the levels of ISGs, including MHC molecules, in breast cancer patients receiving hormone therapy were higher than that in patients not receiving hormone therapy (Figure 8B and C). This might be caused by the enhanced type I IFN response in cancer cells and increased infiltrating immune cells after hormone therapy.

Figure 8.

Figure 8

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Correlation between overall survival and ISG levels of breast cancer patients. (A) Overall survival of breast cancer patients with high or low ISG levels. Clinical samples were from TCGA database. (B and C) Statistical analysis of ISG levels (B) and ISG scores (C) in clinical samples with or without ERα-antagonistic hormone therapy. HT, hormone therapy. Clinical samples were from the METABRIC database. (D) Schematic diagram showing how ERα drives breast carcinogenesis.

Discussion

In this study, we analyzed the correlation between molecular features of breast cancers and ISG levels using TCGA database and found that ERα levels were negatively correlated with ISG levels in breast cancers. We then investigated effects of ERα signaling on type I IFN response in breast cancer cells. E2 and PPT, the agonistic ligands of ERα, markedly inhibited IFN-β-induced transcription of ISGs in a dose-dependent manner, whereas FUL, a pure antiestrogen and potent ERα antagonist, had the opposite effect. Furthermore, ERα deficiency abolished the inhibitory effects of PPT and the promoting effects of FUL on IFN-β-triggered transcription of ISGs. These findings establish an inhibitory role of ERα in type I IFN response.

Mechanistically, ERα signaling inhibits type I IFN response through two distinct ways. Activation of ERα induces the expression of H2A.Z, which restricts the engagement of the ISGF3 complex to the ISRE at ISG promoters. Alternatively, ERα interacts with the N-terminus of STAT2, which inhibits the interaction between STAT2 and STAT1 or IRF9 and thus disrupts the ISGF3 complex. These dual actions of ERα ultimately result in the inhibition of ISG transcription by reducing the occupancy of the ISGF3 complex at ISG promoters.

In a xenograft mouse model, FUL potentiated type I IFN response in breast tumors. Consistently, FUL potentiated IFN-β-triggered inhibition of breast tumor growth. Additionally, ERα+ breast cancer patients with high ISG levels had higher overall survival rates than those with low ISG levels. These results suggest that the benefit of high ISGs for breast cancer patients may be associated with the stronger type I IFN response, supporting the conclusion that hormone therapy targeting ERα promotes type I IFN response, which contributes to the better overall survival of ERα+ breast cancer patients. In addition to inhibiting ERα signaling, hormone therapy may suppress tumor growth by promoting type I IFN response.

It has been well established that type I IFNs regulate MHC expression in tumor cells and promote tumor immunogenicity, which is important for the initiation of antitumor immunity (de Charette et al., 2016; Goel et al., 2017). We noticed that ERα signaling inhibited IFN-β-induced expression of MHCI/II and PD-L1, supporting the conclusion that ERα signaling regulates antitumor immunity. These results are also consistent with the observation of low PD-L1 expression levels in ERα+ breast cancer cells (Ali et al., 2015) and provide a possible explanation for the resistance to anti-PD1/PD-L1 therapy in ERα+ breast cancer patients. Unfortunately, the lack of a suitable ERα+ mouse breast cancer model limits our further exploration of the effects of ERα on antitumor immunity. Nevertheless, our findings suggest that ERα-mediated signaling inhibits type I IFN response via two distinct mechanisms to promote breast tumor growth.

Materials and methods

Constructs

Expression plasmids for HA- or Flag-tagged STAT1 and IRF9, Flag-tagged ERα, and HA-tagged STAT2, were constructed by standard molecular biology techniques.

Cell culture

293T cells were provided by Dr Gary Johnson (National Jewish Health). MCF7 cells were provided by Dr Cheguo Cai (Wuhan University). T-47D cells were provided by Dr Jing Zhang (Wuhan University). These cell lines were cultured in Dulbecco's modified Eagle's medium (Gibco) supplemented with 10% fetal bovine serum (FBS; CellMax) and 1% penicillin–streptomycin (HyClone) at 37°C with 5% CO2. For hormone-related experiments, MCF7 cells and T-47D cells were cultured in phenol red-free medium supplemented with 10% carbon-absorbed FBS and 1% penicillin–streptomycin at 37°C with 5% CO2. All the cells were negative for mycoplasma.

Transfection and reporter assays

293T cells were seeded in 48-well plates and transfected on the following day by standard calcium phosphate precipitation. To ensure that each transfection received the same amount of total DNA, the empty control plasmid was added to each transfection. To normalize transfection efficiency, the pRL-TK (Renilla luciferase) reporter plasmid (0.02 μg) was added to each transfection. Luciferase assays were performed using a dual-specific luciferase assay kit (Promega). Firefly luciferase activities were normalized on the basis of Renilla luciferase activities.

RNAi experiments

The following sequences were used to target human ESR1 (encoding ERα) mRNA: #1, 5′-ACATCATCTCGGTTCCGCA-3′; #2, 5′-GCTACTGTGCAGTGTGCAA-3′.

Gene knockout by CRISPR–Cas9

Double-stranded oligonucleotides corresponding to the target sequences were cloned into the lenti-CRISPR-V2 vector, which was co-transfected with packaging plasmids into 293T cells. After 48 h, the viruses were harvested for infecting target cells. The infected cells were selected with puromycin (2 μg/ml) for 6 days to establish stable cell lines. The following sequences were used to target the indicated genes: Control-gRNA: 5′-GTAGTCGGTACGTGACTCGT-3′; H2AZ1-gRNA: 5′-CGACCAGTCAT GGACGTGT-3′; IFNAR1-gRNA: 5′-GCGGCTGCGGACAACACCCA-3′; GPER1-gRNA#1: 5′-GCACCTGCAGCACACCGACG-3′; GPER1-gRNA#2: 5′-GGTTGATGAAGTACAGGTCG-3′.

qPCR

Total RNA was isolated for qPCR analysis to measure the mRNA levels of the indicated genes. Data shown were the relative abundance of the indicated mRNA normalized to that of ACTB. Gene-specific primer sequences are listed in Supplementary Materials and methods.

Flow cytometry

MCF7 cells (1 × 106) were stained with the appropriate antibodies diluted in phosphate-buffered saline (PBS) plus 2% FBS for 30 min on ice.

Recombinant protein purification

The cDNA encoding full-length or truncated STAT2 was cloned into the pGEX-6p-1-GST plasmid. The cDNA encoding ERα was cloned into the pET30c plasmid. The plasmids were transformed into the BL21 Escherichia Coli strain. The expression of the recombinant proteins was induced with 0.1 mM IPTG at 16°C for 24 h. The recombinant proteins were purified with Glutathione Sepharose 4B or HisPur Ni-NTA beads.

In vitro pulldown assay

Glutathione Sepharose 4B or HisPur Ni-NTA beads containing 1 μg of His-tagged ERα were incubated with 1 μg of GST-tagged full-length STAT2 or truncated STAT2 (amino acids 1–571) in pulldown buffer (PBS, pH 7.4, and 0.1 mM PMSF). The mixture was rotated at 4°C for 1 h, and then the beads were washed six times with washing buffer (PBS, pH 7.4, and 0.5 M NaCl). The proteins were eluted from the beads by boiling in 100 μl of 2× sodium dodecyl sulfate (SDS) buffer and separated by SDS–polyacrylamide gel electrophoresis (PAGE), followed by immunoblotting analysis with the indicated antibodies.

Co-IP and immunoblotting analysis

Cells were lysed with lysis buffer (20 mM Tris–HCl, pH 7.5, 1% nonidet P-40, 10 mM NaCl, 3 mM EDTA, and 3 mM EGTA) containing complete protease inhibitors and sonicated for 1 min. The lysates were centrifugated at 13000 rpm for 10 min at 4°C. The supernatants were immunoprecipitated with the indicated antibodies. Then, the beads were washed with cold lysis buffer three times. The bound proteins were separated by SDS–PAGE, followed by immunoblotting analysis with the indicated antibodies. Quantification of the bands was performed using the ImageJ software.

RNA-seq analysis

MCF7 cells were cultured in phenol red-free medium supplemented with 10% carbon-absorbed FBS for 4 days. Total RNA was extracted from cells treated or untreated with PPT (5 nM) for 16 h. Raw RNA-seq reads were aligned to Homo sapiens GRCh38 by using Tophat2 (Kim et al., 2013) with the default parameters. Gene counts were quantified using HTSeq (Anders et al., 2015) with REFSEQ annotation. Differentially expressed genes were identified using DEseq2 (Love et al., 2014) with a cutoff of P-value <0.01 and fold change >1.5, ranked by the statistics.

ChIP–qPCR assays

For ChIP experiments, cells were cross-linked for 15 min at room temperature with 1% formaldehyde. The cross-linked chromatin was sonicated, diluted, and immunoprecipitated with Protein G-agarose prebound with the indicated antibody at 4°C overnight. Precipitated protein–DNA complexes were eluted, and cross-linking was reversed at 65°C for 12 h and isolated with phenol:chloroform:isoamyl. The sample was precipitated with ethanol at −20°C overnight, washed with 75% ethanol, and resuspended in H2O. ChIP DNA was used for qPCR assays. Gene-specific primer sequences are listed in Supplementary Materials and methods.

Reagents and antibodies

Reagents and antibodies are listed in Supplementary Materials and methods.

Mice

BALB/c nude mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd and maintained in specific-pathogen-free rooms. E2 cypionate (0.1 mg/kg) was injected subcutaneously between the shoulder blades once every 7 days to activate ERα signaling and promote tumor growth for the duration of the study. Twenty-one days after subcutaneous injection of MCF7 cells (1 × 107), the mice were treated with intratumoral injection of IFN-β (2 × 105 UI) and/or subcutaneous injection of FUL. Tumor volume was determined as length (mm) × width (mm2) × 0.5. All mouse studies were approved by the Animal Care Committee of Wuhan University Medical Research Institute.

Clinical sample analysis

Normalized expression array data for 114 ISGs and clinical data for breast cancer patients were downloaded from cBioPortal (www.cbioportal.org; Cerami et al., 2012). Patients with definitive clinical data of IHC-detected ER, PR, and HER2 were selected for further analysis. Statistical analyses were performed using R (v4.1.2; www.r-project.org) with its customized routines and existing packages. R-package Pheatmap (v1.0.12) was used to implement consensus hierarchical clustering and generate expression heatmaps using default settings, including the Ward.2D linkage algorithm method based on the Euclidean distance. We then annotated and analyzed the clinical HR states of breast cancer samples in different clusters. The clusters (high, medium, and low) were classified based on ISG expression levels.

For each ISG, clinical samples with mRNA abundance values higher than the mean value were scored +1, and clinical samples with mRNA abundance values lower than the mean value were scored −1. ISG score for each clinical sample was generated by repeating this procedure for every ISG. High scores indicate tumors with high ISG levels, while low scores indicate tumors with low ISG levels. Breast cancer patients were divided into ISG low group (ISG score <0) and ISG high group (ISG score >0). Considering that the clinical implications of driver factors in breast cancer could be underestimated due to the lack of long-term clinical follow-up, and the median overall survival event time for breast cancer is 41.8 months (Liu et al., 2018), we analyzed prognostic conditions of patients with clear clinical annotations for the first 120 months. Due to the lack of clinical information related to patients’ hormone therapy in TCGA, we obtained the related gene expression data of patients with or without hormone therapy from METABRIC, a dataset of breast cancer gene expression profiles (Curtis et al., 2012).

Data availability

The RNA-seq data in this study are available at the Gene Expression Omnibus under accession code GSE203058.

Statistics

Unpaired Student's t-test calculated by the GraphPad Prism Software was used for statistical analysis. Statistical significance was set at *P < 0.05 or **P < 0.01.

Supplementary Material

mjad047_Supplemental_Files
mjad047_supplemental_files.zip (4.8MB, zip)

Contributor Information

Li-Bo Cao, Department of Infectious Diseases, Medical Research Institute, Zhongnan Hospital of Wuhan University, College of Life Sciences, Wuhan University, Wuhan 430072, China; Frontier Science Center for Immunology and Metabolism, Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China; Research Unit of Innate Immune and Inflammatory Diseases, Chinese Academy of Medical Sciences, Wuhan 430072, China.

Zi-Lun Ruan, Department of Infectious Diseases, Medical Research Institute, Zhongnan Hospital of Wuhan University, College of Life Sciences, Wuhan University, Wuhan 430072, China; Frontier Science Center for Immunology and Metabolism, Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China; Research Unit of Innate Immune and Inflammatory Diseases, Chinese Academy of Medical Sciences, Wuhan 430072, China.

Yu-Lin Yang, Department of Infectious Diseases, Medical Research Institute, Zhongnan Hospital of Wuhan University, College of Life Sciences, Wuhan University, Wuhan 430072, China; Frontier Science Center for Immunology and Metabolism, Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China; Research Unit of Innate Immune and Inflammatory Diseases, Chinese Academy of Medical Sciences, Wuhan 430072, China.

Nian-Chao Zhang, Department of Infectious Diseases, Medical Research Institute, Zhongnan Hospital of Wuhan University, College of Life Sciences, Wuhan University, Wuhan 430072, China; Frontier Science Center for Immunology and Metabolism, Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China; Research Unit of Innate Immune and Inflammatory Diseases, Chinese Academy of Medical Sciences, Wuhan 430072, China.

Chuan Gao, Department of Infectious Diseases, Medical Research Institute, Zhongnan Hospital of Wuhan University, College of Life Sciences, Wuhan University, Wuhan 430072, China; Frontier Science Center for Immunology and Metabolism, Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China; Research Unit of Innate Immune and Inflammatory Diseases, Chinese Academy of Medical Sciences, Wuhan 430072, China.

Cheguo Cai, Department of Infectious Diseases, Medical Research Institute, Zhongnan Hospital of Wuhan University, College of Life Sciences, Wuhan University, Wuhan 430072, China; Frontier Science Center for Immunology and Metabolism, Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China; Research Unit of Innate Immune and Inflammatory Diseases, Chinese Academy of Medical Sciences, Wuhan 430072, China.

Jing Zhang, Department of Infectious Diseases, Medical Research Institute, Zhongnan Hospital of Wuhan University, College of Life Sciences, Wuhan University, Wuhan 430072, China; Frontier Science Center for Immunology and Metabolism, Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China; Research Unit of Innate Immune and Inflammatory Diseases, Chinese Academy of Medical Sciences, Wuhan 430072, China.

Ming-Ming Hu, Department of Infectious Diseases, Medical Research Institute, Zhongnan Hospital of Wuhan University, College of Life Sciences, Wuhan University, Wuhan 430072, China; Frontier Science Center for Immunology and Metabolism, Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China; Research Unit of Innate Immune and Inflammatory Diseases, Chinese Academy of Medical Sciences, Wuhan 430072, China.

Hong-Bing Shu, Department of Infectious Diseases, Medical Research Institute, Zhongnan Hospital of Wuhan University, College of Life Sciences, Wuhan University, Wuhan 430072, China; Frontier Science Center for Immunology and Metabolism, Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China; Research Unit of Innate Immune and Inflammatory Diseases, Chinese Academy of Medical Sciences, Wuhan 430072, China.

Funding

This work was supported by grants from the State Key R&D Program of China (2022YFA1304900), the National Natural Science Foundation of China (32188101, 31830024, 31922021, and 32170713), the Fundamental Research Funds for the Central Universities (2042022dx0003), and Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2019-I2M-5-071).

Conflict of interest: none declared.

Author contributions: H.-B.S., M.-M.H., and L.-B.C. conceived and designed the study; L.-B.C., Z.-L.R., Y.-L.Y., N.-C.Z., and C.G. performed the experiments; H.-B.S., M.-M.H., L.-B.C., C.C., and J.Z. analyzed all the data. H.-B.S., M.-M.H., and L.-B.C. wrote the manuscript. All the authors have read and approved the article.

References

  1. Ali  H.R., Glont  S.E., Blows  F.M.  et al. (2015). PD-L1 protein expression in breast cancer is rare, enriched in basal-like tumours and associated with infiltrating lymphocytes. Ann. Oncol.  26, 1488–1493. [DOI] [PubMed] [Google Scholar]
  2. Anders  S., Pyl  P.T., Huber  W. (2015). HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics  31, 166–169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Au-Yeung  N., Horvath  C.M. (2018). Histone H2A.Z suppression of interferon-stimulated transcription and antiviral immunity is modulated by GCN5 and BRD2. iScience  6, 68–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bhandari  V., Hoey  C., Liu  L.Y.  et al. (2019). Molecular landmarks of tumor hypoxia across cancer types. Nat. Genet.  51, 308–318. [DOI] [PubMed] [Google Scholar]
  5. Borden  E.C. (2019). Interferons α and β in cancer: therapeutic opportunities from new insights. Nat. Rev. Drug Discov.  18, 219–234. [DOI] [PubMed] [Google Scholar]
  6. Cao  X., Liang  Y., Hu  Z.  et al. (2021). Next generation of tumor-activating type I IFN enhances anti-tumor immune responses to overcome therapy resistance. Nat. Commun.  12, 5866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cerami  E., Gao  J., Dogrusoz  U.  et al. (2012). The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer discovery  2, 401–404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chan  S.R., Vermi  W., Luo  J.Q.  et al. (2012). STAT1-deficient mice spontaneously develop estrogen receptor α-positive luminal mammary carcinomas. Breast Cancer Res.  14, R16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chen  S., Crabill  G.A., Pritchard  T.S.  et al. (2019). Mechanisms regulating PD-L1 expression on tumor and immune cells. J. Immunother. Cancer  7, 305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cornel  A.M., Mimpen  I.L., Nierkens  S. (2020). MHC class I downregulation in cancer: underlying mechanisms and potential targets for cancer immunotherapy. Cancers  12, 1760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Curtis  C., Shah  S.P., Chin  S.F.  et al. (2012). The genomic and transcriptomic architecture of 2000 breast tumours reveals novel subgroups. Nature  486, 346–352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. de Charette  M., Marabelle  A., Houot  R. (2016). Turning tumour cells into antigen presenting cells: the next step to improve cancer immunotherapy?  Eur. J. Cancer  68, 134–147. [DOI] [PubMed] [Google Scholar]
  13. Duncan  C.J.A., Hambleton  S. (2021). Human disease phenotypes associated with loss and gain of function mutations in STAT2: viral susceptibility and type I interferonopathy. J. Clin. Immunol.  41, 1446–1456. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Farris  S.D., Rubio  E.D., Moon  J.J.  et al. (2005). Transcription-induced chromatin remodeling at the c-myc gene involves the local exchange of histone H2A.Z. J. Biol. Chem.  280, 25298–25303. [DOI] [PubMed] [Google Scholar]
  15. Frasor  J., Danes  J.M., Komm  B.  et al. (2003). Profiling of estrogen up- and down-regulated gene expression in human breast cancer cells: insights into gene networks and pathways underlying estrogenic control of proliferation and cell phenotype. Endocrinology  144, 4562–4574. [DOI] [PubMed] [Google Scholar]
  16. Goel  S., DeCristo  M.J., Watt  A.C.  et al. (2017). CDK4/6 inhibition triggers anti-tumour immunity. Nature  548, 471–475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Goodman  M., Trinca  G., Walter  K.  et al. (2019). Progesterone receptor attenuates STAT1-mediated IFN signaling in breast cancer. J. Immunol.  202, 3076–3086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Guan  J., Zhou  W., Hafner  M.  et al. (2019). Therapeutic ligands antagonize estrogen receptor function by impairing its mobility. Cell  178, 949–963.e18. [DOI] [PubMed] [Google Scholar]
  19. Harding  A.T., Goff  M.A., Froggatt  H.M.  et al. (2021). GPER1 is required to protect fetal health from maternal inflammation. Science  371, 271–276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Harvey  J.M., Clark  G.M., Osborne  C.K.  et al. (1999). Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J. Clin. Oncol.  17, 1474–1481. [DOI] [PubMed] [Google Scholar]
  21. Hu  M.M., Shu  H.B. (2018). Cytoplasmic mechanisms of recognition and defense of microbial nucleic acids. Annu. Rev. Cell. Dev. Biol.  34, 357–379. [DOI] [PubMed] [Google Scholar]
  22. Ivashkiv  L.B., Donlin  L.T. (2014). Regulation of type I interferon responses. Nat. Rev. Immunol.  14, 36–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kayagaki  N., Yamaguchi  N., Nakayama  M.  et al. (1999). Type I interferons (IFNs) regulate tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) expression on human T cells: a novel mechanism for the antitumor effects of type I IFNs. J. Exp. Med.  189, 1451–1460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kim  D., Pertea  G., Trapnell  C.  et al. (2013). TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol.  14, R36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kim  Y.J., Kim  J.S., Kim  I.A. (2018). Molecular subtype predicts incidence and prognosis of brain metastasis from breast cancer in SEER database. J. Cancer Res. Clin. Oncol.  144, 1803–1816. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Liang  Y., Tang  H., Guo  J.  et al. (2018). Targeting IFNα to tumor by anti-PD-L1 creates feedforward antitumor responses to overcome checkpoint blockade resistance. Nat. Commun.  9, 4586. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Liu  J., Lichtenberg  T., Hoadley  K.A.  et al. (2018). An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell  173, 400–416.e11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Loibl  S., Poortmans  P., Morrow  M.  et al. (2021). Breast cancer. Lancet  397, 1750–1769. [DOI] [PubMed] [Google Scholar]
  29. Love  M.I., Huber  W., Anders  S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.  15, 550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Majoros  A., Platanitis  E., Kernbauer-Holzl  E.  et al. (2017). Canonical and non-canonical aspects of JAK–STAT signaling: lessons from interferons for cytokine responses. Front. Immunol.  8, 29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Matin  S.F., Rackley  R.R., Sadhukhan  P.C.  et al. (2001). Impaired α-interferon signaling in transitional cell carcinoma: lack of p48 expression in 5637 cells. Cancer Res. 61, 2261–2266. [PubMed] [Google Scholar]
  32. Matsumoto  M., Tanaka  N., Harada  H.  et al. (1999). Activation of the transcription factor ISGF3 by interferon-γ. Biol. Chem.  380, 699–703. [DOI] [PubMed] [Google Scholar]
  33. McCarty  M.F., Bielenberg  D., Donawho  C.  et al. (2002). Evidence for the causal role of endogenous interferon-α/β in the regulation of angiogenesis, tumorigenicity, and metastasis of cutaneous neoplasms. Clin. Exp. Metastasis  19, 609–615. [DOI] [PubMed] [Google Scholar]
  34. Metivier  R., Petit  F.G., Valotaire  Y.  et al. (2000). Function of N-terminal transactivation domain of the estrogen receptor requires a potential α-helical structure and is negatively regulated by the A domain. Mol. Endocrinol.  14, 1849–1871. [DOI] [PubMed] [Google Scholar]
  35. Mostafa  A.A., Codner  D., Hirasawa  K.  et al. (2014). Activation of ERα signaling differentially modulates IFN-γ induced HLA-class II expression in breast cancer cells. PLoS One  9, e87377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Noguchi  E., Shien  T., Iwata  H. (2021). Current status of PD-1/PD-L1 blockade immunotherapy in breast cancer. Jpn. J. Clin. Oncol.  51, 321–332. [DOI] [PubMed] [Google Scholar]
  37. Parker  B.S., Rautela  J., Hertzog  P.J. (2016). Antitumour actions of interferons: implications for cancer therapy. Nat. Rev. Cancer  16, 131–144. [DOI] [PubMed] [Google Scholar]
  38. Pusztai  L., Karn  T., Safonov  A.  et al. (2016). New strategies in breast cancer: immunotherapy. Clin. Cancer Res.  22, 2105–2110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Sizemore  N., Agarwal  A., Das  K.  et al. (2004). Inhibitor of κB kinase is required to activate a subset of interferon γ-stimulated genes. Proc. Natl Acad. Sci. USA  101, 7994–7998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Sledge  G.W., Mamounas  E.P., Hortobagyi  G.N.  et al. (2014). Past, present, and future challenges in breast cancer treatment. J. Clin. Oncol.  32, 1979–1986. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Valdes-Mora  F., Song  J.Z., Statham  A.L.  et al. (2012). Acetylation of H2A.Z is a key epigenetic modification associated with gene deregulation and epigenetic remodeling in cancer. Genome Res.  22, 307–321. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wagner  T.C., Velichko  S., Chesney  S.K.  et al. (2004). Interferon receptor expression regulates the antiproliferative effects of interferons on cancer cells and solid tumors. Int. J. Cancer  111, 32–42. [DOI] [PubMed] [Google Scholar]
  43. Walter  K.R., Balko  J.M., Hagan  C.R. (2020). Progesterone receptor promotes degradation of STAT2 to inhibit the interferon response in breast cancer. Oncoimmunology  9, 1758547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Wang  X., Schoenhals  J.E., Li  A.  et al. (2017). Suppression of type I IFN signaling in tumors mediates resistance to anti-PD-1 treatment that can be overcome by radiotherapy. Cancer Res.  77, 839–850. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Wei  J., Guo  W., Lian  H.  et al. (2017). SNX8 mediates IFNγ-triggered noncanonical signaling pathway and host defense against Listeria monocytogenes. Proc. Natl Acad. Sci. USA  114, 13000–13005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Xu  Y., Huangyang  P., Wang  Y.  et al. (2021). ERα is an RNA-binding protein sustaining tumor cell survival and drug resistance. Cell  184, 5215–5229.e17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Yuan  Y., Zhang  S., Yan  M.  et al. (2021). Chemotherapy or endocrine therapy, first-line treatment for patients with hormone receptor-positive HER2-negative metastatic breast cancer in China: a real-world study. Ann. Transl. Med.  9, 831. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Zimmermann  A., Trilling  M., Wagner  M.  et al. (2005). A cytomegaloviral protein reveals a dual role for STAT2 in IFN-γ signaling and antiviral responses. J. Exp. Med.  201, 1543–1553. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mjad047_Supplemental_Files
mjad047_supplemental_files.zip (4.8MB, zip)

Data Availability Statement

The RNA-seq data in this study are available at the Gene Expression Omnibus under accession code GSE203058.

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