Breast cancer is continually known as the most common cancer and the leading cause of cancer death among women . Approximately, 70–75% of breast cancer patients express estrogen receptor alpha (ERα) which has been demonstrated to play an important role in promoting tumor progression [2,3]. Endocrine therapy, as a means of systemic therapy, is a major treatment strategy for both premenopausal and postmenopausal estrogen receptor positive patients [4,5]. Tamoxifen, classified as selective estrogen receptor modulators, has been the first choice in adjuvant endocrine therapy . The introduction of tamoxifen has significantly reduced the risk of cancer recurrence and metastasis for estrogen receptor positive patients [7–9]. However, approximately half of the cases have intrinsic resistance or develop acquired resistance after receiving tamoxifen treatment, which makes the resistance become a serious clinical problem to be solved [10–12]. Therefore, it is important to seek predictive biomarkers for tamoxifen-treated patients and take a deeper insight into the underlying molecular mechanisms of tamoxifen resistance.
TRAF4 is the member of tumor necrosis factor receptor-associated factor (TRAF) family, which functions as a signal transducer for the tumor necrosis factor receptors and interleukin-1/Toll-like receptors [13,14]. TRAF4 was originally found to be amplified and overexpressed in breast cancer and then demonstrated overexpression in multiple other cancer types . Recent studies have shown that TRAF4 played an important role in carcinogenesis and TRAF4 overexpression led to a poor prognosis in breast cancer patients [16–19]. However, whether TRAF4 has prognostic significance in tamoxifen-treated breast cancer or is involved in tamoxifen resistance remains unknown.
In this study, we aimed to identify a novel potential predictive biomarker of tamoxifen resistance and explore the role of TRAF4 in tamoxifen resistance. Using multiple gene expression analyses, we identified that high expression of TRAF4 was associated with poor prognosis in tamoxifen-treated breast cancer and TRAF4 was upregulated in tamoxifen-resistant breast cancer cells. Moreover, the results of cell experiments confirmed that tamoxifen resistance was positively correlated with the expression level of TRAF4.
Materials and methods
Cell culture and establishment of tamoxifen-resistant sublines
Estrogen receptor positive breast cancer cells T47D were purchased from the American Type Culture Collection (ATCC, Manassas, Virginia, USA) and authenticated by STR profiling analysis. 4-Hydroxytamoxifen was purchased from Sigma-Aldrich (H7904, Sigma, St. Louis, Missouri, USA). To establish the tamoxifen-resistant subline T47DR, T47D cells were treated with 1 μM 4-Hydroxytamoxifen and cultured in RPMI-1640 medium (Gibco, Massachusetts, USA) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin/streptomycin (Beyotime, Jiangsu, China) in a humidified conditions containing 5% CO2 at 37°C for more than six months. After six months, the resistance of T47DR cells to tamoxifen was verified by colony formation experiments and cell count kit-8 (CCK-8) assay. Then, T47DR cells were cultured in the above-mentioned medium containing 0.1 μM tamoxifen to maintain the resistance.
High-throughput sequencing and bioinformatics analysis
High-throughput sequencing was performed using T47D and T47DR cell lines. Nearly 5 × 106 cells of each cell line were prepared and subjected to Sangon Biotech (Shanghai, China). The paired-end sequencing on an Illumina Hiseq platform was performed following the manufacturer’s recommended protocol. Transcripts per million (TPM) of each gene were calculated for relative quantitative analysis. |Fold change| > 2 and q value < 0.05 between two cell lines were selected to represent statistical significance. The raw sequence data have been submitted to Gene Expression Omnibus (GEO) database (accession code GSE129544).
Another two datasets (GSE9893 and GSE31831) related to tamoxifen treatment or resistance were obtained from GEO. Standardized gene expression levels in 155 primary tumor samples of tamoxifen-treated breast cancer patients were analyzed based on GSE9893. Differential gene expression analysis between tamoxifen-sensitive cells (MCF7) and tamoxifen-resistant cells (MCF7R) was based on GSE31831 at the same time.
Colony formation experiments
The proliferation ability of different cell lines under tamoxifen treatment was analyzed with colony formation experiments. Cells in logarithmic growth phase were collected and seeded into six-well plates in triplicates at a density of 1000 cells/well. All cells were cultured in RPMI-1640 medium containing 5 μM tamoxifen, and the medium was renewed every three days over the next two weeks. Cell clones were stained with 0.2% crystal violet (Beyotime) and then photographed.
Cell count kit-8 assay
To evaluate the response of different cell lines to tamoxifen treatment, a CCK-8 assay was also performed according to the manufacturer’s protocol. Different cells were seeded into 96-well plates at a density of 5000 cells/well with different concentrations (0, 0.1, 1, 5, and 10 μM) of tamoxifen and then cultured for 48 h. Subsequently, RPMI-1640 containing 10% CCK-8 solution (Dojindo, Tokyo, Japan) was supplemented into each well and incubated in a 37°C incubator for 1–3 h. The spectrometric absorbance of each well at 450 nm was measured on a microplate reader (Thermo Fisher, Massachusetts, USA) to evaluate the cytotoxicity of tamoxifen to cell lines.
The total protein of cells was isolated with protein extraction reagent RIPA buffer (Beyotime) and quantified by the BCA protein assay kit (Beyotime). After SDS-PAGE electrophoresis and PVDF membrane transfer, the target protein was detected with primary antibodies against TRAF4 (Proteintech, Wuhan, China) and GAPDH (Cell Signaling Technology, Massachusetts, USA). The protein signals were determined with the ChemiDoc XRS+ System (Bio-Rad, California, USA) using the ECL detection kit (Beyotime).
RNA extraction and real-time quantitative PCR
Total RNA was extracted from cultured cells using RNAiso Plus reagent (TaKaRa, Otsu, Japan) according to the manufacturer’s protocol. mRNAs were reverse transcribed to cDNAs with a PrimeScript RT Master Mix Kit (TaKaRa, Japan). Quantitative real-time PCR (qRT-PCR) was performed in triplicate using synthesized primers (Tsingke, Beijing, China) with an SYBR Premix Ex Taq II Kit (TaKaRa to detect the mRNA levels. The expression levels of the target genes were quantitated utilizing the method of 2−ΔΔCT normalized according to the mean levels of GAPDH.
Primers for TRAF4 and GAPDH were as follows:
TRAF4 forward: TATTGGGCCTGCCTATCCG.
TRAF4 reverse: CAAAACTCGCACTTGAGGCG.
GAPDH forward: GGAGCGAGATCCCTCCAAAAT.
GAPDH reverse: GGCTGTTGTCATACTTCTCATGG.
Short interference RNAs for TRAF4 and corresponding siRNA negative controls were purchased from RiboBio (Guangzhou, China). Overexpression of pcDNA-TRAF4 plasmid and negative control vector were provided by GeneChem (Shanghai, China). Transient transfection was performed using Lipofectamine 3000 (Thermo Fisher) according to the manufacturer’s protocol. The transfection efficiency was evaluated using western blot and qRT-PCR. The siRNA target sequences for TRAF4 were as follows:
SPSS 23.0, R 3.5.3 and Graphpad Prism 7.0 were used for statistical analysis. The identification of differentially expressed genes was conducted using the ‘limma’ package in R and the ‘survivalROC’ package was used to determine the optimal cutoff value of the TRAF4 expression level for survival analysis. The experiment data are presented as the means ± SDs. Pearson’s chi-square test was used to compare differences between TRAF4 low and TRAF4 high groups. Differences between groups in CCK-8 assay were analyzed by the Student’s t or two-way ANOVA test. Survival rates of patients were estimated using the Kaplan–Meier method, and differences between groups were assessed using the log-rank test. Cox proportional hazard regression model was used to analyze the prognostic significance of TRAF4. A P value less than 0.05 was considered statistically significant.
TRAF4 is upregulated in tamoxifen-resistant breast cancer cells
T47D cells were treated with 1 μM tamoxifen for more than six months to establish the tamoxifen-resistant subline T47DR. Morphological changes were observed and T47DR cells appeared as spindle shape which was different from T47D (Fig. 1a). To verify whether T47DR cells were resistant to tamoxifen, colony formation experiments and CCK-8 assay were conducted to compare the cell proliferation ability and viability of T47D and T47DR cells under tamoxifen treatment. The results of colony formation experiments showed that cell colonies of T47DR cells were more than that of T47D cells when treated with tamoxifen (Fig. 1b). The results of the CCK-8 assay showed that the cell viability of T47DR cells was higher than that of T47D cells when the concentrations of tamoxifen were 5 and 10 μM (Fig. 1c). In order to compare the gene expression profile of T47D and T47DR cells, the RNA sequencing was conducted, and all the data were shown in GSE129544. To obtain an accurate and reliable result, we also conduct bioinformatic analysis based on another two GEO datasets. Genes with prognostic significance from GSE9893 and genes with differences in expression between MCF7R and MCF7 from GSE31831 were also included in the study. Finally, we found that several genes (the full list is shown in Supplementary Table 1, Supplemental digital content 1, http://links.lww.com/ACD/A335), including TRAF4, were in the intersection of the three datasets. As shown in Fig. 1d, the mRNA levels of TRAF4 were higher in T47DR cells and MCF7R cells whether MCF7R cells were treated with tamoxifen or estrogen.
High TRAF4 expression correlated with poor prognosis in tamoxifen-treated breast cancer
To explore the prognosis implication of TRAF4, we analyzed the association between TRAF4 expression and clinical outcomes based on dataset GSE9893. We compared the expression levels of TRAF4 in different subgroups for whether local recurrence or distant metastasis, overall death, and breast cancer-specific death occurred, respectively. The subgroups with these worse outcomes had higher TRAF4 expression (Fig. 2a–c). We then divided the 155 patients into two groups according to the TRAF4 expression for further analysis. To identify the optimal cutoff value for subdivision, we conducted a time-dependent receiver operating characteristic (ROC) curve analyses in R to distinguish five-year survivors from deceased patients. In overall survival analysis, the cutoff value for TRAF4 (AUC = 0.636) was 0.7500217 (Fig. 2d). In cancer-specific survival analysis, the cutoff value for TRAF4 (AUC = 0.656) was 0.8154676 (Fig. 2e). For more precise analysis, we chose 0.8154676 as the optimal cutoff value and patients were divided into TRAF4 low and TRAF4 high groups. In the cohort of 155 tamoxifen-treated patients, patients with high TRAF4 run a higher risk of local recurrence or distant metastasis (P < 0.001), overall death (P = 0.003), and breast cancer-specific death (P = 0.007) (Table 1). Survival analyses showed that TRAF4 high group was significantly associated with worse overall survival (P = 0.0083, Fig. 2f) and cancer-specific survival (P = 0.013, Fig. 2g), respectively. The hazard ratios for overall mortality and cancer-specific mortality were listed in Table 2 to investigate the clinical significance. Unadjusted hazard ratios of TRAF4 for overall mortality and cancer-specific mortality were 2.608 (P = 0.011) and 2.940 (P = 0.018), respectively. Multivariate Cox analyses adjusted for age, T stage, Scarff–Bloom–Richardson grade, and adjuvant therapy confirmed the independent prognostic significance of TRAF4 for both overall mortality (hazard ratio = 2.538, P = 0.017) and cancer-specific mortality (hazard ratio = 2.713, P = 0.036).
Confirmation of overexpression of TRAF4
In order to verify the results of sequencing and bioinformatic analysis, we further conducted western blot to detect the protein levels of TRAF4 in T47D and T47DR cell lines and found that TRAF4 was significantly overexpressed in T47DR cells (Fig. 3a). Moreover, the mRNA levels of TRAF4 examined by qRT-PCR were also higher in T47DR cells than that in T47D cells, which was consistent with the results of bioinformatic analysis (Fig. 3b). Taken together, our results strongly suggested that TRAF4 was upregulated in tamoxifen-resistant T47DR cells and may play an important role in tamoxifen resistance.
Knockdown of TRAF4 expression inT47DR cells reversed tamoxifen resistance
Given the above findings that TRAF4 was upregulated in tamoxifen-resistant cells, we then explored the potential involvement of TRAF4 in tamoxifen resistance of T47DR cells. To this end, we knocked down the expressing levels of TRAF4 in T47DR cells with exogenous introduction of TRAF4 siRNAs (TRAF4-si1, TRAF4-si2, and TRAF4-si3). Western blot and qRT-PCR assays were conducted to detect the knockdown efficiency, and the results showed that TRAF4-si1 and TRAF4-si2 led to a significant reduction of TRAF4 expression in T47DR cells (Fig.4a and b). Then, T47DR cells transfected with TRAF4-si1 and TRAF4-si2 were selected to further investigate the effects of TRAF4 knockdown on the resistance to tamoxifen. The results of colony formation experiments indicated that the introduction of TRAF4 siRNAs caused a remarkable decline of cell colonies of T47DR cells treated with tamoxifen (Fig. 4c). Besides, CCK-8 assay confirmed that knockdown of TRAF4 reduced the resistance to tamoxifen in T47DR cells (Fig. 4d). These results indicated that TRAF4 knockdown led to T47DR cells resensitization to tamoxifen and TRAF4 is required for sustaining tamoxifen resistance in breast cancer cells.
TRAF4 overexpression in T47DR cells increased tamoxifen resistance
To further demonstrate the role of TRAF4 in tamoxifen resistance, we transfected the overexpression plasmid of TRAF4 in T47DR cells. The increased expression of TRAF4 after transfection was confirmed by the western blot and qRT-PCR assays at protein and mRNA levels (Fig. 5a and b). We then performed colony formation experiments and CCK-8 assay to detect alteration in tamoxifen resistance after the transfection of the TRAF4 overexpression plasmid. The results showed that the resistance to tamoxifen of T47DR cells was further enhanced on account of TRAF4 overexpression (Fig. 5c and d). These results demonstrated that TRAF4 overexpression promoted tamoxifen resistance in breast cancer cells.
Tamoxifen as an effective endocrine treatment in the management of estrogen receptor positive breast cancer has significantly improved prognosis in breast cancer patients . However, the resistance to tamoxifen has been one of the major causes of breast cancer recurrence and death [10,11]. Many studies have attempted to figure out the underlying mechanisms involved in the resistance to tamoxifen. So far, several mechanisms have been demonstrated to be involved in tamoxifen resistance. These include estrogen receptor mutations, loss of ERα expression, overexpression of estrogen receptor coactivators, activation of oncogenic signaling pathways, etc. [21–24].
The TRAF4 gene, which is located in an amplified region of chromosome 17, is amplified in breast cancer and correlated with ERBB2 amplification [25,26]. Follow-up studies showed that TRAF4 overexpression existed in multiple cancer types, which implied the oncogenic role of TRAF4 [15,27–29]. Some studies revealed that TRAF4 was mainly implicated in TGF-β-induced SMAD and non-SMAD oncogenic signal pathways and played an indispensable role in TGF-β-induced epithelial-to-mesenchymal transition, migration, invasion, and metastasis of breast cancer cells . It is interesting to note that TGF-β signaling pathway can interfere with estrogen receptor signaling pathways. The crosstalk of the two signaling pathways may influence the development of antihormone resistance [30,31]. Another study showed that TRAF4 overexpression in breast cancer patients participated in the destabilization of p53 and functioned in mediating breast cancer resistance to drug therapies and regulating cell resistance to cytotoxic stress . Given the important role of TRAF4 in the progression of breast cancer, we investigated whether TRAF4 might be associated with tamoxifen resistance.
In our study, the high-throughput sequencing was performed to identify the gene expression profile of tamoxifen-sensitive cell line T47D and tamoxifen-resistant cell line T47DR. Among the previously uncharacterized genes correlated with endocrine resistance, we focused on the TRAF4, which also exhibited differential expression levels in another two datasets (GSE9893 and GSE31831) from GEO database. Different from previous studies that used the median level of mRNA expression as the cutoff value to classify low or high levels of expression [32,33], we used time-dependent ROC curve analyses to find the appropriate cutoff value of TRAF4 expression levels, which made our results more reliable. Based on the clinical information of GSE9893 cohort, high expression of TRAF4 was found to be associated with poor prognosis in tamoxifen-treated breast cancer and a good prognostic indicator of these patients. Then, the following cell experiments showed that TRAF4 promoted tamoxifen resistance and might be a potential predictive biomarker of tamoxifen resistance.
There are also some limitations in this study. As with many other previous studies [34–36], we established only one tamoxifen-resistant cell line, which might make our conclusion less general. Another limitation is that we only performed cell experiments to explore the roles of TRAF4 in tamoxifen resistance and potential signaling pathways that TRAF4 might be involved were not explored, which deserves further validation in a larger cohort of in-vitro and in-vivo studies. As shown in Supplementary Table 1, Supplemental digital content 1, http://links.lww.com/ACD/A335, several genes might be involved in tamoxifen resistance, but they have not been thoroughly studied.
According to our study, we found that overexpression of TRAF4 was strongly related to a poor prognosis of tamoxifen-treated breast cancer. Besides, TRAF4 was significantly involved in tamoxifen resistance and might provide new insights into underlying mechanisms of tamoxifen resistance in breast cancer. In the future, the role of TRAF4 in a complicated network of tamoxifen resistance may develop our understanding of cancer and may provide novel target therapy opportunities.
We thank everyone in our department for discussion and suggestions.
Our research was supported by National Natural Science Foundation of China (Grants: 8167101523).
The high-throughput sequencing data (GSE129544) used to support the findings of this study have not been made available before it is released in GEO database. ‘Reviewer access’ to GSE129544: ityxssamvbqbvct. The other data used to support the findings of this study are included in the article or GEO database.
Conflicts of interest
There are no conflicts of interest.
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