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Table of Contents
Year : 2019  |  Volume : 30  |  Issue : 3  |  Page : 107-113

Label-free quantitative phosphoproteomics reveals the role of beta-estradiol in sunitinib-resistant renal cell carcinoma growth via perturbing transforming growing factor-beta pathway

1 School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
2 School of Medicine, College of Medicine, Fu Jen Catholic University; Department of Surgery, Sijhih Cathay General Hospital, New Taipei City, Taiwan
3 Division of Urology, Department of Surgery, Cathay General Hospital, Taipei, Taiwan
4 School of Medicine, College of Medicine, Fu Jen Catholic University; Division of Urology, Department of Surgery, Cathay General Hospital, Taipei, Taiwan

Date of Submission22-Oct-2018
Date of Decision09-Dec-2018
Date of Acceptance04-Jan-2019
Date of Web Publication20-Jun-2019

Correspondence Address:
Yen-Chieh Wang
Division of Urology, Department of Surgery, Cathay General Hospital, Taipei 10630
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/UROS.UROS_129_18

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Context: Sunitinib is the first-line targeted therapy for metastatic renal cell carcinoma (RCC). However, resistance to sunitinib often occurred in patients receiving sunitinib treatment. On the other hand, 17-beta-estradiol (estrogen or E2) has been demonstrated to repress RCC growth in vitro, whether E2 can also affect the growth of sunitinib-resistant RCC remains unknown. Aims: In this study, the role of E2 in inhibiting sunitinib-resistant RCC growth and the underlining acting mechanisms was explored. Settings and Design: Sunitinib resistance was first induced in vitro in ACHN cells. The effect of E2 on cellular growth was then assayed. Label-free phosphoproteomics was also conducted. Subjects and Methods: ACHN cells were first challenged with 10-μM sunitinib up to 4 months to induce drug resistance. Then, E2 at different concentrations were tested in both parental and sunitinib-resistant ACHN cells. To conduct phosphoproteomics study, the total cell lysates from E2-treated ACHN cells were harvested, trypsin digested, and the phosphopeptides were enriched by Fe-IMAC. Statistical Analysis Used: For comparing the E2-induced cell growth inhibition, Student's t-test was used, and P < 0.05 was considered statistically significant. As for label-free phosphoproteomics, false discovery rate <0.01 and phosphosite possibility >0.75 were considered as positive identifications. Results: E2 at the physiological concentration, that is, 10 nM, can repress the cell growth in both parental and sunitinib-resistant ACHN cells. Further, label-free phosphoproteomics revealed that transforming growth factor beta (TGF-β) pathway, cell cycle, and cytoskeleton bindings were enhanced in sunitinib-resistant cells but can be reduced by E2 treatment. On the other hand, programmed cell death and apoptosis were repressed in sunitinib-resistant cells, and E2 at 10 nM did not reverse the effect. We further validated the expression of SMAD3, an important molecule in TGF-β pathway, and found that SMAD3 decreased in sunitinib-resistant cells but can be upregulated by E2 treatment. Conclusions: Our study demonstrated that E2 can inhibit the cell growth in sunitinib-resistant RCC cells at physiological concentration by upregulating SMAD3 in the TGF-β pathway, which may lead to growth inhibition in RCC.

Keywords: Estrogen, phosphoproteomics, renal cell carcinoma, sunitinib resistance

How to cite this article:
Ku WC, Chen SK, Lin CM, Tang C, Wang YC. Label-free quantitative phosphoproteomics reveals the role of beta-estradiol in sunitinib-resistant renal cell carcinoma growth via perturbing transforming growing factor-beta pathway. Urol Sci 2019;30:107-13

How to cite this URL:
Ku WC, Chen SK, Lin CM, Tang C, Wang YC. Label-free quantitative phosphoproteomics reveals the role of beta-estradiol in sunitinib-resistant renal cell carcinoma growth via perturbing transforming growing factor-beta pathway. Urol Sci [serial online] 2019 [cited 2020 Aug 6];30:107-13. Available from: http://www.e-urol-sci.com/text.asp?2019/30/3/107/260778

  Introduction Top

In upper urinary tract diseases, renal cell carcinoma (RCC) is the most important death-causing cancer.[1] At present, nephrectomy, either partial or radical, is considered the best management for early-stage, primary RCC. For those who develop metastatic RCC, cytoreductive nephrectomy in combination with targeted therapies is helpful in some selected cases, but most patients still required more aggressive interventions, for example, a sequential combination of immunotherapy and antiangiogenic therapy.[2],[3]

Currently, sunitinib is the commonly used first-line antiangiogenic therapy for managing metastatic RCC.[3],[4] Sunitinib is a tyrosine kinase inhibitor that primarily represses the signaling transduction of vascular endothelial growth factor (VEGF) receptors as well as the platelet-derived growth factor receptors.[5] Chronic administration of sunitinib, however, can develop drug resistance by inducing counterregulatory alterations in tumor cells. Factors that are contributing to sunitinib resistance include lysosomal sequestering, tumor microenvironment changes, upregulation of proangiogenic pathway such as phosphoinositide 3-kinase signaling pathway, and activation of alternative signaling pathways.[5],[6] Recent studies have shown that VEGF receptor, AXL and MET, are activated in chronic sunitinib treatment, resulting in increased cell migration and invasion.[7],[8] Therefore, there is still urgent need to find other therapeutic strategy for managing sunitinib resistance in RCC.

Given the facts that men and women who received hysterectomy have a higher risk of RCC as compared to women who had no such history,[1],[9] it has been proposed that that 17-β-estradiol, or E2, may play a role in RCC development. In supporting this argument, E2 was found to repress RCC growth through estrogen receptor alpha and beta.[10],[11] In addition, E2 can regulate several kinases (e.g., AKT, ERK, and JAK) related to EGFR signaling and protein targets (i. e. SQSTM1 and LC-3B) related to autophagy in RCC cells through estrogen receptors.[11],[12] Therefore, it is reasonable to postulate that E2 may also regulate cell physiology in sunitinib-resistant RCC cells through affecting phosphorylation dynamics.

With instrumental and technological advances in mass spectrometry, phosphoproteomics has been a promising approach for deciphering cellular signaling dynamics in response to environmental stimuli.[13] Mass spectrometry-based phosphoproteomics also allow site-specific phosphorylation identification and quantification. It is also possible to perform deep phosphoproteomic profiling by combining advanced liquid chromatography with meter-long monolithic column.[14] We also demonstrated that E2 regulates RCC phosphorylation signaling using quantitative phosphoproteomics.[15]

In this study, we evaluated the effect of E2 on sunitinib-resistant RCC cell growth. We also took advantage of label-free quantitative phosphoproteomics, in combination with meter-long monolithic column chromatography, to gain insight into the role of E2 on phosphorylation signaling in sunitinib-resistant cells. The result of this study may provide a potential alternative approach for managing drug resistance in RCC.

  Subjects and Methods Top


All chemicals, unless specified otherwise, were purchased from Sigma-Aldrich (St. Louis, MO, USA).

Cell culture, E2 treatment, and cell viability

The RCC cell lines ACHN (ATCC CRL-1611) were maintained in Eagle's Minimum Essential Medium (without phenol red) supplemented with 10% fetal bovine serum (Life Technologies, Grand Island, NY, USA). Sunitinib-resistant ACHN cell line at was in-house generated by gradually increasing sunitinib exposure to 15 μM in vitro.[16] The success of sunitinib resistance was confirmed by cell growth and sunitinib accumulation in lysosomes.[5]

For the E2 treatment, the ACHN cells were cultured in 96-well plates (4 × 103 cells/well in 100-μL culture medium) or 10-cm culture dish (5 × 105 cells/dish in 10-mL culture medium) for 24 h. Freshly prepared E2 in dimethyl sulfoxide was added, and the cells were further incubated for 48 h. The cell viability and number were measured by colorimetric MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium) assay.

Protein digestion and phosphopeptide enrichment

Proteins from the total cell lysate harvested in a buffer containing 4% sodium dodecyl sulfate (SDS), 100-mM triethylammonium bicarbonate, and protease and phosphatase inhibitor cocktails. Proteins were acetone precipitated, resuspend in a digestion buffer, and digested by sequencing grade modified trypsin (1:100 w/w) (Thermo Fisher Scientific, Bremen, Germany), and desalted. The desalted peptides were dried by speed-vac, resuspended in 6% acetic acid, and subjected to phosphopeptide enrichment. Phosphopeptides were enriched by iron-immobilized metal affinity chromatography (Fe-IMAC) as previously described.[17] The resulting phosphopeptides were desalted for downstream nano LC-MS/MS analyses.

Nano LC-MS/MS and raw data analyses

The phosphopeptides were analyzed using nano LC-MS/MS on a Dionex Ultimate 3000 RSLCnano system (Thermo Fisher Scientific) online coupled to LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific). Phosphopeptides were first separated by MonoCap ® C18 HighResolution Ultra 2000 monolithic column (GL Sciences Inc.) at a flow rate of 500 nL/min. The mobile phases used for nanoLC were 0.5% acetic acid in water (Buffer A) and a mixture of 0.5% acetic acid and 80% ACN (Buffer B). The LC gradient conditions were 5% to 10% Buffer B in 5 min, 10% to 40% Buffer B in 480 min, 40% to 100% Buffer B in 5 min, and 100% Buffer B in 10 min. The LTQ Orbitrap XL system was operated at data-dependent mode using the setting as previously described.[15] In this study, two independent biological batches were analyzed. For each biological batch, duplicate nanoLC-MS/MS analyses were performed.

Protein identification and label-free quantitation were analyzed using the MaxQuant Software (v, Martinsried, Bavaria, Germany)[18] against the SWISS-PROT sequence database (version 2016_03 with 20,226 human sequence entries). False discovery rate at the peptide and protein levels were fixed at 1%. The confidence of phosphorylation site was determined by posttranslational modification scoring algorithm, and only Class I (localization probability >0.75) sites were selected for bioinformatics analyses.[19] All nanoLC-MS/MS raw files and MaxQuant-generated result data were deposited onto the ProteomeXchange [20] Consortium through the PRIDE partner repository with the dataset identifier PXD011020.


The phosphosite quantitation information were log2-transformed, median-centered, and further analyzed by GproX (v1.16)[21] using default settings. All enriched features, including motif analyses and gene ontology (GO) biological process enrichment, in clustering analyses were chosen for P < 0.05. For protein–protein interaction analyses, web-based tool from STRING database was used.[22] A high confidence (>0.7) for protein–protein interaction was considered, and all disconnected proteins in the network were discarded. Functional enrichment (i.e., GO biological processes and molecular functions) in the network was analyzed with a minimum enrichment P = 0.05.[22]

Western blotting

Preparation of cell lysate and Western blotting were performed as previously described.[11] The primary antibodies used in this study and their dilution factors were as follows: anti-SMAD3 [EP568Y] antibody (AB40854, Abcam, Cambridge, UKA) at 1:5000, anti-SMAD2/3 (phospho T8) antibody (AB63399, Abcam) at 1:500, and anti-GAPDH antibody (AM4300, Ambion, Austin, TX) antibody at 1:6000.

  Results Top

We first tested whether the cell growth of sunitinib-resistant RCC cells can be regulated by E2, which has been shown to repress RCC growth in vitro.[10],[15] With increasing E2 concentrations (10 nM to 25 μM), cell growths were repressed (P < 0.01) in both parental and sunitinib-resistant RCC cells [Figure 1]. Furthermore, sunitinib-resistant RCC cells were more sensitive to E2 treatment when compared to parental RCC cells (P < 0.01 for 10 nM E2 and P < 0.05 for higher E2 concentrations) [Figure 1]. Collectively, the data suggested that E2 can be a potential cell growth inhibitor against sunitinib-resistant RCC cells.
Figure 1: E2 repressed the growth of sunitinib-resistant renal cell carcinoma cells. ACHN renal cell carcinoma cells were treated with indicated E2 concentrations for 48 h before viable cells counted by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium assay. Asterisk represents P < 0.05 for different E2 concentrations versus control (0 μM)

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It is well known that sunitinib induces drug resistance in RCC cells by altering phosphorylation signaling,[5],[7] and E2 can also change cellular singling pathway to repress RCC growth.[15] We, therefore, explored possible phosphorylation dynamics in E2-treated sunitinib-resistant RCC cells using quantitative phosphoproteomics. To mimic the physiological condition, we used 10-nM E2 to treat both parental and sunitinib-resistant ACHN cells and performed label-free quantitative phosphoproteomics. After phosphopeptide purification with Fe-IMAC [17] and follow-up nano LC-MS/MS analyses, we have identified 5897 Class I (site probability >0.75)[19] phosphosites. After retrieving the quantitation information of each phosphosite by MaxLFQ algorithm,[18] log2-transformed, median-centered,[23] and filtering for at least one quantitative information in each treatment group, we finally quantitated 2280 phosphosites. The dataset, to the best of our knowledge, is the first and most comprehensive phosphoproteomics study in E2-treated sunitinib-resistant RCC cells.

We next performed unsupervised clustering by fuzzy-c means of the phosphoproteomic data using GproX [21] and revealed eight distinct clusters with minimal membership of 0.3 [Figure 2]. As shown in [Figure 2], E2 repressed protein phosphorylation in both parental and sunitinib-resistant ACHN cells in Cluster 1 and 2. In Cluster 8, on the other hand, E2 enhanced protein phosphorylation in E2-treated parental and sunitinib-resistant cells. Cluster 7 showed a similar pattern with Cluster 8 but smaller changes in E2-treated parental ACHN cells. Cluster 3, 4, and 6 demonstrated opposite phosphorylation patterns between E2-treated parental and sunitinib-resistant ACHN cells. Finally, Cluster 5 showed downregulated phosphorylation changes in sunitinib-resistant cells, and E2 treatment did not alter the phosphorylation pattern in both ACHN cells.
Figure 2: Clustering analysis of label-free quantitative phosphoproteomic data by GproX. Phosphosites were clustered into eight patterns of expression in different treatments by unsupervised clustering with fuzzy-c means in GproX

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To assess whether particular kinase phosphorylation motifs and/or GO terms are enriched in a given cluster, we further performed feature enrichment analysis using GproX.[21] The cutoff values of enriched features in each clusters were set at P < 0.05. As shown in [Figure 3]a, Cluster 4 showed highly enriched motifs in calmodulin-dependent protein kinase IV and casein kinase II. In addition, various kinase motifs, such as Akt, Chk1, 14-3-3, PKA, PKC, calmodulin-dependent kinase II, and phosphorylase kinase, were also enriched in Cluster 5. On the other hand, several GO biological processes were enriched in Cluster 1, 4, 5, and 6 [Figure 3]b. For example, proteins in Cluster 1 were related to negative regulation of transforming growth factor beta (TGF-β) and ubiquitin-dependent SMAD protein catabolic process. Cluster 4 and 6 were related to RNA or protein transport. Finally, Cluster 5 was related to cellular component disassembly involved in execution phase of apoptosis and programmed cell death.
Figure 3: Phosphorylation motif and gene ontology feature enrichment of phosphosite expression clusters using GproX. Phosphosite expression clusters from Figure 2 were further analyzed for feature enrichment by (a) potential kinase motifs and (b) gene ontology biological processes

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During GproX analyses, Cluster 2 and 8, both of which showed either upregulation or downregulation by E2 treatment, did not contain any enriched features. To retrieve the possible protein–protein interaction network of Cluster 2 and 8, we further performed network analysis by STRING database.[22] Although no enriched network was found in Cluster 8, a protein–protein interaction network centered by CDK1 was observed in Cluster 2 [Figure 4]a. Several enriched GO pathways were also observed in Cluster 2, including poly (A) RNA binding, protein binding, cytoskeleton-related binding, and cell cycle [Table 1].
Figure 4: Protein–protein interaction analysis and Western blotting validation of SMAD3. (a) Protein–protein interaction network centered on CDK1 generated by STRING with high confidence (>0.7) using proteins in Cluster 2. (b) Validation of SMAD3 expression by Western blotting in parental and sunitinib-resistant ACHN cells. (c) Possible mechanism of SMAD3 regulation by sunitinib-resistance and E2

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Table 1: Enriched gene ontology pathways in Cluster 2 by STING database

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Taken all bioinformatics-based analyses together, the label-free quantitative phosphoproteomics revealed that TGF-β pathway, cell cycle, and cytoskeleton bindings were enhanced in sunitinib-resistant cells but can be reduced by E2 treatment (Cluster 1 and 2). On the other hand, programmed cell death and apoptosis were likely repressed in sunitinib-resistant cells, and E2 at 10 nM did not reverse the effects (Cluster 5).

Finally, we analyzed the TGF-β pathway-related proteins quantified in our phosphoproteomics studies. Although many phosphosites were identified in the TGF-β pathway-related proteins, only E2-regulated phosphosites and associated phosphopeptides in sunitinib-resistant cells (P < 0.05) were selected, including NEDD4L and TGFB1I1 in Cluster 1 as well as SMAD3 in Cluster 8 [Table 2]. Among them, SMAD3, a key regulator of TGF-β pathway, was selected for Western blotting validation. As shown in [Figure 4]b, the expression of SMAD3 as well as phospho-SMAD3 decreased in response to increasing sunitinib concentrations (lane 1-3), and E2 can upregulate the repression of SMAD3 and phospho-SMAD3 (lane 4). The Western blotting validation result was consistent to the phosphoproteomics data [Table 2].
Table 2: Identified proteins involving in the regulation of transforming growth factor beta/SMAD3 pathway

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  Discussion Top

Sunitinib is the first-line targeted therapy for metastatic RCC which interacted with VEGFR as well as platelet-derived growth factor receptors, two key players in tumor angiogenesis. However, clinical data have shown that 30% patients are intrinsically resistant to sunitinib treatment; for the remaining 70% of patients respond to initial sunitinib therapy, acquired resistance to sunitinib is usually developed within 6–15 months.[24] Based on the several preclinical studies, the acting mechanisms for sunitinib resistance are multifactorial, including increased tumor invasiveness/metastasis and activation of alternative signaling pathways.[5] In concordance with other observations, our phosphoproteomics data showed the perturbation of Akt kinase activity in sunitinib-resistant RCC cells [Figure 3], indicating probable restoration of the angiogenic signaling pathway.[25]

The male-to-female ratio of RCC ranges is roughly 2:1 in young age patients, whereas in patients older than 70 years old, the ratio was approximately 1:1.[26] E2, of which the serum concentration decreased dramatically after menopause, has been long considered a protective effect in RCC patients. Our group along with others have shown that estrogen can repress RCC growth by inhibiting growth-related signaling pathways.[10],[15] In this study, we further extend the probable role of estrogen in preventing the growth of sunitinib-resistant RCC cells. From our phosphoproteomics study, estrogen at physiological concentration, for example, 10 nM, reduced cell cycle and cytoskeleton-related signaling in sunitinib-resistant RCC cells. To the best of our knowledge, this is the first evidence to consolidate the repressive role of estrogen in sunitinib resistance.

Our phosphoproteomic study also reveals the potential association between E2 and TGF-β pathway in RCC cells. In an earlier study, the reduction of TGF-β receptor and downstream SMAD proteins in RCC correlated to tumor development, suggesting that the downregulation of TGF-β/SMAD signaling pathway is important to kidney neoplasia.[27] In supporting of this argument, our data also demonstrated that sunitinib resistance downregulates SMAD3 in the TGF-β pathway, but E2 treatment reversed the effect [Table 2] and [Figure 4]b. In addition, NEDD4L and TGFB1I1 act as SMAD3 inhibitors by inducing proteasomal degradation [28] or preventing phosphorylation [29] [Table 2]. Based on our data and other literature, it is likely that sunitinib resistance induces the upregulation of NEDD4L and TGFB1I1 and subsequently downregulate SMAD3 as well as the associated TGF-β pathway [Figure 4]c. E2 treatment can reverse the effect, therefore, upregulate the tumor-suppressive TGF-β pathway [Figure 4]c. It is noted that in other studies, in contrast, the migration and invasiveness in RCC cell lines can be repressed by inhibiting TGF-β pathway.[30],[31] Therefore, more studies will be required to decipher the exact role of TGF-β pathway in the development of sunitinib resistance in RCC cells.

An interesting observation in the current study is that E2 did not increase cellular apoptosis [Figure 2], Cluster 5], as we observed in previous studies.[11],[12] The possible reason is that lower E2 concentration, that is, 10 nM versus 28 μM, was used in this study compared to the previous one. Based on the current study, E2 may activate TGF-β pathway at physiological concentration, while higher E2 concentration further affects cellular apoptosis and autophagy.[11],[12] In either condition, E2 can inhibit cellular growth in both parental and sunitinib resistance RCC cells. Taken together, our study demonstrates the clinical potential of E2 for managing metastatic and sunitinib-resistant RCC.

One limitation of the current study is the lack of clinical incidence for sunitinib resistance between men and women. In this study, we used ACHN cell line, which was from 22-year-old Caucasian and induced sunitinib resistance in this cell line. We have also tried to raise sunitinib-resistant cell line in a female-originated A498 RCC cell line. However, we cannot get any sunitinib-resistant A498 cells (data not shown). Based on our data in the current study, we are not able to address the association of gender (or sex hormone) and sunitinib resistance, especially the acquired resistance. Further investigations are required to clarify this issue.

  Conclusion Top

In conclusion, we demonstrated that E2 can inhibit the cell growth in sunitinib-resistant RCC cells at physiological concentration, and our phosphoproteomics study also revealed that the upregulation of SMAD3 in the TGF-beta pathway by E2 may lead to RCC growth inhibition.

Financial support and sponsorship

This study was financially supported by the grants from the Ministry of Science and Technology (NSC105-2314-B-030-011) and Cathay General Hospital (103-CGH-FJU-15 and 104-CGH-FJU-07), Taipei, Taiwan.

Conflicts of interest

There are no conflicts of interest.

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  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1], [Table 2]


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