Therapeutic Targeting of Sunitinib-Induced AR Phosphorylation in Renal Cell Carcinoma

Androgen receptor (AR) plays a crucial role in the development and progression of prostate cancer (1). AR expression has also been reported in other solid tumors, including renal cell carcinoma (RCC; refs. 2–6). AR signaling has been reported to promote progression in RCC via the HIF-2α/VEGF signaling pathway, by recruiting vascular endothelial cells (5), and by altering the AKT/NFκB signaling axis (7). However, AR has also been reported to potentially be a good outcome prognosticator in a retrospective analysis of patients with RCC (8), suggesting that the biological role played by AR in RCC remains unclear.

Although AR is activated by ligand binding in prostate cancer, recent reports have identified several posttranslational modifications, including AR phosphorylation, that alter AR activity (9). Most of the identified phosphosites in full-length AR lay in the serine and tyrosine residues, and the phosphorylation of these sites has been implicated in several different cellular responses, including AR expression and transcriptional activity (10, 11). Members of the Src family may facilitate AR activation through direct phosphorylation of pY534/267, and inhibition of these kinases abrogated AR phosphorylation and induced tumor cell regression in prostate cancer models (12, 13). Similarly, phosphorylation and stabilization of AR by cyclin-dependent kinase 1 (CDK1) has been reported in prostate cancer (14). Phosphorylation of pS81, by CDK1, leads to the activation of AR and inhibition of this kinase decreased AR pS81 and AR activity (14). Other reports have shown that AR can be either activated, or inhibited, via AKT-mediated pS213/791 phosphorylation (15–17). Interestingly, it is known that most of AR phosphorylation occurs in tumors with low androgen levels (18), and renal cell carcinoma has been reported to have very low levels of androgens (19).

Enzalutamide is a second-generation AR antagonist that inhibits AR–ligand interaction and AR transcriptional activity and has been approved for the treatment of castration-resistant prostate cancer (20, 21). Receptor tyrosine kinase inhibitors (RTKI) such as sunitinib, pazopanib, and axitinib are effective treatments for patients with clear cell renal cell carcinoma (ccRCC; ref. 22). New RTKIs, such as cabozantinib (23, 24) and lenvatinib (25), have been recently approved in the first- and second-line setting, respectively. However, resistance to RTKIs represents a major hurdle in the clinical management of advanced ccRCC. Despite the clinical benefit, generally acquired resistance to RTKIs occurs within 12 months in the first-line setting. Several potential mechanisms have been identified to play a role in drug resistance, including upregulation of alternative pathways (22, 26, 27). Our group has recently reported that epigenetic tumor cell adaptation to RTKis may lead to kinome reprogramming, as well as increased global serine and tyrosine phosphorylation (28). Thus, the identification of specific, “druggable” targets may delay/overcome the occurrence of drug resistance and prolong the clinical benefit of RTKis in RCC.

Here, we report the role of AR and its association with resistance to RTKis in RCC models. Furthermore, we show that sunitinib promotes AR activation via increased phosphorylation, and the AR antagonist, enzalutamide, restores sensitivity to sunitinib by inducing SPOP-mediated AR degradation.

In vitro assays were performed using commercially available RCC cell lines 786-0, UMRC2, ACHN, Caki2 (ATCC), and lab-generated 786-0R (acquired sunitinib resistant). Cells were maintained and cultured in the appropriate media, supplemented with 10% FBS and 1% penicillin and streptomycin. All cells are routinely tested and checked for the absence of Mycoplasma. Authentication was conducted by Multiplex 10 STRs loci detection method (ATCC). For transient transfection, cells were transfected with 50 nmol/L siSPOP (Ambion) or siControl (Ambion) using Lipofectamine 2000 transfection reagent, according to the manufacturer's protocol (cat. #11668027, Thermo Fisher Scientific). For treatment, cells were plated in 24-well plated and 24 hours post seeding cells were treated with either sunitinib (5 μmol/L, LC Laboratories), enzalutamide (500 nmol/L, Selleckchem), axitinib (5 μmol/L, LC Laboratories), or MG-132 (10 μmol/L, Selleckchem) or combinations, for either 24, 48, or 72 hours. Crystal violet assay (Sigma) was used to evaluate cells growth after different time point treatment, and absorbance was read using a spectrometer (xMarks Spectrometer, Bio-Rad). For in vivo studies, 786-0 (sunitinib sensitive) and RP-R-02LM (sunitinib resistant) models were used. All in vivo experiments were approved and performed in strict accordance with the guidelines of the Institutional Animal Care and Use Committee at Indiana University, Indianapolis, IN. The 786-0 and RP-R-02LM viable tumors were selected, dissected into ∼1 mm² tumor pieces, and implanted subcutaneously into 6-week-old homozygous ICR, severe combined immune-deficient (SCID) female mice. All mice were operated under sedation with oxygen, isoflurane, and buprenorphine. When tumors were established, and reached 50 mm², mice were randomly grouped and placed in either control group or treatment groups (n = 5–10). Mice received sunitinib treatment (40 mg/kg 5 days on, 2 days off, orally), enzalutamide (MDV300) treatment (10 mg/kg, orally), or a combination of both. Tumor burden was assessed once a week by caliper measurement of two diameters of the tumor (L × W = mm²) and reported as tumor volume ((L × W2)/2 = mm³). Body weights and endpoint tumor weights were assessed using a weighing scale and recorded in grams. Tissue and blood were collected under aseptic conditions. One milliliter of blood was collected by cardiac bleeds (terminal) at the end of the experiment. Serum and plasma were separated, and aliquots were stored at −80°C for further analysis. Tumor tissues were excised, cut into sections, and were snap-frozen and stored in −80°C, fixed in 10% buffered formalin, or zinc for histopathology and saved in TRIzol for RNA analysis.

Steroid standards, dihydrotestosterone, progesterone, testosterone, and epitestosterone were purchased from Steraloids. Steroid ¹³C₃ internal standards were purchased from IsoSciences. Hydroxylamine hydrochloride, ultrapure methanol, and water (Chromasolv) were purchased from Sigma-Aldrich. Steroids were extracted from sample homogenates after addition of internal-standard (0.5 ng each) using tert-butyl methyl ether, and the separated organic layer was evaporated. The extracts were subsequently derivatized using hydroxylamine hydrochloride in water/methanol (29). An Agilent 6495 triple quadrupole mass spectrometer, equipped with a Jet Stream electrospray ion source, a 1290 Infinity II ultrahigh-performance liquid chromatography system, and MassHunter Workstation software was used to quantify steroids. Chromatographic separation of testosterone, epitestosterone, dihydrotestosterone, and progesterone oximes result in elution at 3.4, 3.6, 3.9, and 4.3 minutes, respectively. Molecular ion transitions monitored for progesterone (m/z 345.2 to 124.2), DHT (m/z 306.2 to 255.2), testosterone (m/z 304.2 to 124.1), and epi-testosterone (304.2 to 124.1). The same ion transitions plus 3 mass units were monitored for ¹³C₃ internal standards. The lower limit of quantification was 2.0 femtograms for testosterone, epitestosterone, and progesterone and 25 femtograms for dihydrotestosterone.

Tissue specimens were fixed for 24 hours, paraffin embedded and sectioned (4 μm). IHC and immunofluorescence were performed using standard protocols. In brief, sections were deparaffinized and rehydrated through graded alcohol washes. Antigen unmasking was achieved by boiling slides in either sodium citrate buffer (pH = 6.0) or EDTA. To examine the expressions of our proteins of interests, tissue section was blocked with 2.5% horse serum (Vector Laboratories) and incubated overnight in primary antibodies against AR (1:1,000; cat. #5153; Cell Signaling Technology). For IHC, following primary incubation, tissue sections were incubated in horseradish-conjugated anti-rabbit, according to the manufacturer's protocol (Vector Laboratories), followed by enzymatic development in diaminobenzidine (DAB) and counter stained in hematoxylin. Sections were dehydrated and mounted with cytoseal 60 (Thermo Fisher Scientific). For immunofluorescence staining, sections were blocked with 5% BSA (Sigma), stained with either phosphotyrosine (1:50; sc-508, Santa Cruz Biotechnology), phosphoserine (1:50, 600-401-26), or AR (1:400; 5153; Cell Signaling Technology), AR-C19 (1:10; sc-815, Santa Cruz Biotechnology), Ki67 (1:10; MA5-14520, Thermo Fisher Scientific), Tunel (cat. #G3250, Promega), phospho-AR (pS81; 1:50, 04-078, Millipore), and incubated overnight at 4°C. Following primary incubation, sections were incubated with either Alexa Fluor or FITC fluorophores conjugated anti-rabbit (Thermo Fisher Scientific) or anti-mouse (1:400; Thermo Fisher) antibody, at room temperature in a humid light-tight box. Afterward, slides were stained with actin green (1:10; cat #R37110, Thermo Fisher), counter stained with Hoechst (cat #23491-45.4; Sigma), and mounted with VectorShield mounting medium (Vector Laboratories). Stained sections were analyzed either under bright field (IHC), or under appropriate fluorescence wavelength (immunofluorescence) using the EVOS FL cell imaging microscope (Life Technology) and Leica Confocal microscope (Leica). The number of positive cells was determined in a blinded fashion, by analyzing four random 20× fields per tissue and quantified using ImageJ software.

RNA was extracted in accordance with manufacturer's protocol (miRNeasy; Qiagen), and RNA sequencing was performed as previously described (25). In brief, RNA illumine sequencing reads were demultiplex, aligned against human genome (hg19), and results aligned to BAM formatted sequence alignment map via cufflinks program. Differential expressed transcripts were identified between 786-0 and 786-0R samples, and ranked based on the square root of the sum of squares for the log2 fold change. For qRT-PCR analysis, gene expression assessment on AR target genes was performed using the Prime PCR array (Bio-Rad) according to the manufacturer's protocol. AR primer used is forward primer; 5-GGTGAGCAGAGTGCCCTATC-3 and reverser primer; 5-TCGGGTATTTCGCATGTCCC-3. In brief, the denaturation step was carried out at 95°C for 10 seconds; the annealing step was carried out at 58°C for 30 seconds, and extension step at 72°C for 1 minute using the 7900HT fast real-time PCR system (Applied Biosystems). Sequence Detection Systems Software v2.3 was used to identify cycle threshold (Ct) values and generate gene expression curves. All data were normalized to either GAPDH expression.

UMR-C2 cells were transfected with either of two different siRNAs (Silencer Select siRNAs, Sigma #4392420) targeting either exon 9-10 (id: s15954) or exon 10 (id: s15956) of SPOP. UMR-C2 cells were cultured in 6-well plates until 50% to 60% confluence in antibiotic free RPMI-1641, transfected with SPOP-siRNA with final concentration 100 nmol/L using Lipofectamine RNAiMAX Transfection Reagent (Invitrogen #13778075) according to the manufacturer's instructions. At 72 hours after transfection, cells were harvested for either immunoblotting or immunofluorescence analyses.

Whole-cell protein extracts from tissue and cell were denatured, separated on SDS–PAGE gels, and transferred to nitrocellulose membranes. After blocking in 5% enhanced blocking agent (GE) in Tris-buffered saline–Tween, membranes were probed overnight at 4°C with either, AR (1;1,000; cat. #5153, Cell Signaling Technology), SPOP (1:1,000; ab168619, Abcam), or phospho-AR (S81; 1:1000; Cell Signaling Technology). After incubation with the appropriate secondary antibody, results were detected using Western Lightning Chemiluminescence Reagent Plus, according to the manufacturer's instructions (Thermo Fisher Scientific) and captured on film. Quantitative measurements of Western blot analysis were performed using ImageJ and GraphPad software (Prism 7).

Data analyses are expressed as the mean + SEM. Statistical significance where appropriate was evaluated using a two-tailed Student t test when comparing two groups, or by one-way analysis of variance (ANOVA), using the Student–Newman–Keuls posttest for multiple comparison. A P < 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001 was considered significant; ns, not significant. Statistical analyses were done by GraphPad software.

To identify potential markers associated with drug resistance in RCC, we recently performed a reverse phase protein array (RPPA) analysis in a patient-derived xenograft (PDX) model (RP-R-01), where in vivo transient acquired resistance to sunitinib was observed following chronic drug exposure (30). As expected, we detected dynamic changes in several proteins as tumors progressed from RTKi sensitivity to acquired resistance. To our surprise, among the protein changes, there was a significant increase (P > 0.05) in AR expression in the sunitinib-resistant tumors (Fig. 1A). We confirmed this finding by immunohistochemistry and qPCR in both RP-R-01 and RP-R-02 tumor models at the time of resistance to sunitinib (30), and in a derived metastatic model (RP-R-02LM) that is intrinsically resistant to sunitinib (Fig. 1B and C; ref. 28). Thus, we analyzed gene and protein AR expression in the human RCC cell line 786-0 and its sunitinib-resistant derivative (786-0R), and we detected a significant increase following chronic drug exposure (Fig. 1D and E). We also assessed AR expression in other RCC cell lines (Fig. 1E). We observed that AR levels correlated with sensitivity to sunitinib, showing higher IC50 in AR⁺ RCC cell lines (786-0R, UMRC2, and Caki2) as compared with AR⁻ RCC cell lines (786-0 and ACHN; Fig. 1F). These data suggest that higher AR expression is associated with resistance to the direct antitumor effect of sunitinib. Thus, we tested whether inhibition of AR activity with the AR antagonist, enzalutamide, has biological activity in our RCC models. We treated AR⁻ 786-0 and ACHN, AR⁺ 786-0R, UMRC2, and Caki2 cells with sunitinib, enzalutamide, or combination for 48 hours, and then performed a crystal violet assay. Quantitative analysis indicated a synergistic effect of enzalutamide and sunitinib in sunitinib-resistant (AR⁺), but not in sunitinib-sensitive (AR⁻), RCC cell lines (Fig. 1G; Supplementary Fig. S1A and S1B). Interestingly, single-agent enzalutamide did not have a significant effect on AR⁺ 786-0R RCC cell viability, but the combination with sunitinib inhibited Ki67 expression (Fig. 1H). Similar results were obtained with another AR antagonist, bicalutamide, and also with axitinib, another RTKi approved for the treatment of RCC (Supplementary Fig. S1C). To further explore the contribution of AR in modulating resistance to sunitinib, we overexpressed AR^wt in 786-0 cells (AR⁻) using a pEGFP-C1-AR–expressing plasmid. Following 3 weeks of selection, we performed qRT-PCR to confirm successful transfection (Supplementary Fig. S1D), and conducted a sunitinib dose–response assay to determine whether AR overexpression decreased sensitivity to sunitinib. Interestingly, dose–response curves indicated a shift in the IC₅₀ from 5.2 μmol/L in the 786-0 (parental) to 12.3 μmol/L in 786-0AR (Fig. 1I). In a separate experiment, the concomitant treatment with enzalutamide reverted in part the sunitinib sensitivity (Supplementary Fig. S1E). Taken together, these data suggest that AR expression modifies, in part, sunitinib resistance in RCC.

To determine whether AR expression in our RCC models was associated with functional activity, we analyzed RNA-seq data and ran a gene array on AR signaling and AR targeted genes, comparing the AR⁻ sunitinib-sensitive 786-0 cell line and the derived AR⁺ sunitinib-resistant 786-0R cell line. Indeed, the generated heat map indicated an increase in mRNA expression levels of AR targeted genes (i.e., APPBP2, ZBTB16, KLK4, KLK2, and TMPRSS2), suggesting that sunitinib-induced AR is transcriptionally active (Fig. 2A and B; Supplementary Fig. S2A–S2C). To determine whether sunitinib has a direct effect on AR expression, we treated 786-0, 786-0R (after sunitinib washout for one week), and UMRC2 cells with sunitinib. Then, we examined gene expression of AR-targeted genes in the presence or absence of sunitinib treatment. Indeed, quantitative gene expression data showed increased gene expression of AR-driven KLK2, KLK4, ZBTB16, and MYC (2–100-fold) in both 786-0R and UMRC2 cells (Fig. 2C). We confirmed that the several AR-driven proteins were overexpressed in sunitinib-treated 786-0R and enzalutamide has an inhibitory effect (Supplementary Fig. S3).

AR activation in prostate cancer is generally driven by dihydrotestosterone binding, nuclear translocation, and dimerization leading to DNA binding. To determine whether AR activation required the presence of androgens, we cultured the RCC cell lines in charcoal stripped media. To our surprise, the absence of androgens did not influence the cell growth of either AR⁻ or AR⁺ cell lines and neither modulated AR gene expression (Supplementary Fig. S4A and S4B). To further determine the contribution of androgens to AR activity in RCC, we performed mass-spectrometry analysis on PDX (RP-R-01, RP-R-02 with acquired sunitinib resistance, and RP-R-02LM with intrinsic sunitinib resistance), and tumor cell lines (786-0, 786-0R, UMRC2, and UMRC2R), and did not detect any significant presence of either testosterone, epitestosterone, or progesterone (Supplementary Fig. S4C). Taken together, these data suggest that AR activity in RCC following sunitinib is likely due to ligand-independent mechanisms.

To address the potential mechanism(s) of sunitinib-induced AR activation, we assessed whether there was an increase in pS81, the most commonly phosphorylated residue in AR, following sunitinib resistance. Immunofluorescence analysis revealed a significant increase in both total nuclear AR expression and pS81 AR, with sunitinib treatment in AR⁺ UMRC2 and 786-0R (after sunitinib wash out) cell lines (Fig. 2D–F). Increased total and pS81 AR was confirmed by Western blot analysis (Fig. 2G). AR phosphorylation has been implicated in nuclear translocation in prostate cancer. Indeed, in 786-0, following a transient GFP-labeled AR transfection, a short treatment with sunitinib indicated a strong nuclear localization of AR (Fig. 2H). To determine whether the increase in CDK1 was associated with sunitinib resistance and induced AR phosphorylation, we analyzed the RP-RP-02LM tumors treated in vivo with sunitinib (30). Indeed, in this intrinsically sunitinib-resistant tumor, drug treatment led to increased CDK1 protein expression (Fig. 2I). Interestingly, we observed a similar increase of CDK1 in sunitinib-resistant 786-0R cells as compared with 786-0 cells in vitro (Fig. 2J). Taken together, these data suggest that sunitinib induces ligand-independent AR activation, and consequent nuclear translocation, likely via S81 phosphorylation.

The mechanism of action of enzalutamide on AR activity in prostate cancer is primarily due to the inhibition of ligand binding to AR, which leads to the impediment of full-length AR transcriptional activity. To determine the effect of enzalutamide on AR activity/expression in our system, we treated RCC cells with sunitinib, enzalutamide, or combination and measured AR expression by immunofluorescence. As expected, baseline AR expression was high in 786-0R and UMRC2 cells, and was increased by sunitinib treatment (Fig. 3A). However, concomitant treatment with enzalutamide abrogated sunitinib-induced AR expression. Surprisingly, we did not notice a significant decrease in AR expression in cells treated with enzalutamide alone. To determine whether enzalutamide-induced AR inhibition was due to induced protein degradation, we ran a separate experiment where 786-0R cells were exposed to either enzalutamide, sunitinib, or combination in the presence or the absence of the proteasome inhibitor MG132. Visual and quantitative data showed no significant decrease in AR expression with enzalutamide, or combination treatment, in the presence of MG132, suggesting that enzalutamide induced AR degradation in the presence of sunitinib (Fig. 3B and C). AR modulation was confirmed by Western blotting (Supplementary Fig. S5). AR rescued degradation by MG132 in the presence of sunitinib and enzalutamide was associated with restoration of cell proliferation, as suggested by Ki67 staining (Supplementary Fig. S6). Next, we investigated the potential mechanism by which enzalutamide induces AR degradation in sunitinib-treated RCC cells. Cullin-RING ligases (CRL) complexes, specifically the CRL3 complex, have been identified as bona fide ubiquitination ligases of AR (31, 32). Thus, we performed Western blot analysis in our RCC lines to determine the level of SPOP expression (Supplementary Fig. S7A). We then performed a transient SPOP knockdown in UMRC2 cells, which expressed the highest levels of SPOP compared with other RCC cell lines. Upon confirmation of successful knockdown (Supplementary Fig. S7B), we analyzed the effect of SPOP siRNA on AR modulation. In the presence of sunitinib, there was a significant increased AR expression in UMRC2siSPOP cells as compared with UMRC2, while enzalutamide failed to abrogate this surge in UMRC2siSPOP cells (Fig. 3D and E). We confirmed the effect of SPOP by using a different siRNA (Supplementary Fig. S7C and S7D). The lack of AR degradation induced by enzalutamide in UMRC2siSPOP cells in the presence of sunitinib was associated with loss of the antiproliferative effect of this combination (Fig. 3F and G). Interestingly, rescued enzalutamide-induced AR degradation by siSPOP was not associated with restored global serine and tyrosine phosphorylation in the presence of sunitinib, suggesting that AR phosphorylation and activation may be part of an epigenetic reprogramming but it does not affect global protein phosphorylation (Supplementary Fig. S8A and S8B; ref. 28). Overall, these data suggest that SPOP may mediate enzalutamide-induced AR degradation, primarily in the presence of sunitinib in RCC. To further investigate the mechanisms responsible for enzalutamide-induced AR degradation, we assessed the interaction of AR and ubiquitin in 786-0R cells treated with sunitinib, following drug washout. Indeed, immunoprecipitation studies showed that ubiquitin associated with AR following sunitinib treatment (Fig. 3H), suggesting the involvement of AR ubiquitination in its proteasome-dependent degradation.

To determine whether the effect of enzalutamide was specifically due to binding to AR, we performed a competitive assay using dihydrotestosterone (DHT). Following drug washout in charcoal stripped media, 786-0R cells were exposed to DHT, and AR nuclear localization was observed (Supplementary Fig. S9). Sunitinib alone also induced strong AR nuclear localization, which was abrogated with concomitant enzalutamide treatment. However, concomitant DHT treatment completely rescued the effect of enzalutamide on AR. Taken together, our results suggest that sunitinib induces AR phosphorylation in RCC, and upon AR binding, enzalutamide likely inhibits AR nuclear translocation that prones to SPOP-mediated AR degradation via the ubiquitin–proteasome pathway (Fig. 3I; Supplementary Videos S1 and S2).

To test the effectiveness of sunitinib in combination with enzalutamide in vivo, we performed independent experiments using the 786-0 (sunitinib sensitive) RCC model. In the first experiment, we wanted to determine whether the combination of sunitinib and enzalutamide delayed sunitinib resistance. We implanted 786-0 tumor pieces subcutaneously into male mice, and, once tumors reached an average size of 100 mm³, we started treatment with either sunitinib, enzalutamide, or combination. We observed a significant delay in acquired resistance to sunitinib in the combination treatment group, without over toxicities (Fig. 4A; Supplementary Fig. S10A and S10B). Endpoint tumor weights indicated no significant changes in tumor burden within single-agent groups, but a significant decrease in the combination group (P > 0.001). Next, we wanted to investigate the effect of enzalutamide and sunitinib in combination, after tumors acquired sunitinib resistance. We implanted 786-0 (sunitinib sensitive) tumors, and when tumors were established, and reached an average size of 150 mm³, we randomly grouped the mice into two initial groups, control (n = 10) and sunitinib treatment (n = 20). We began sunitinib treatment and observed tumor growth until day 45, when tumors became resistant to sunitinib (≥50% increase volume from nadir; Fig. 4B). Then, mice in the sunitinib group were further subgrouped into either sunitinib plus enzalutamide treatment arm (n = 10) or enzalutamide treatment arm (n = 10). Tumor growth curves and endpoint tumor weights indicate that tumors in mice treated with sunitinib plus enzalutamide regressed in size, compared with single-agent enzalutamide (Fig. 4B). Furthermore, assessment of AR pSer81 expression across treatment groups showed an increase with sunitinib resistance, as compared with the control and the combination treatment group (Fig. 4C). Decreased AR pSer81 expression in the combination group was associated with increased apoptosis (TUNEL; Fig. 4D). Despite inhibition of angiogenesis (CD31 staining), sunitinib-resistant tumor cells continued to proliferate in vivo (Ki67 staining), though the combination group showed the lowest proliferation rate (Fig. 4E and F).

In our original screening of AR target genes, we observed that human kallikrein 2 (hK2 or KLK2) and human kallikrein4 (hK4 or KLK4) were increased in 786-R cells. Thus, we decided to measure KLK2 in our models. To determine whether we were effectively inhibiting AR activity with enzalutamide treatment, we measured KLK2 in conditioned media from our in vitro studies, and in serum from our in vivo studies, by using a human KLK2 ELISA kit. We were able to detect circulating KLK2 in the 786-0 model, and the levels were decreased in the enzalutamide-treated mice, and more significantly in the combination group (Fig. 5A). In tissue culture supernatants, the amount of KLK2 was associated with AR status in RCC cell lines (Fig. 5B), was in vitro modulated by enzalutamide (Fig. 5C), and was increased in the serum in our sunitinib acquired (RP-R-R02) and intrinsic (RP-R-02LM) resistance models (Fig. 5D). Interestingly, in a small number of patients enrolled in our phase I clinical trial with sunitinib and deltaparin (33), patients with ccRCC who had disease progression, presented increased serum levels KLK2, as compared with nonprogressors (defined as patients with either stable disease or objective response as best response) following 3 months of treatment (Fig. 5E and F).

Our study identified AR expression to be significantly increased across sunitinib-resistant RCC models. We report that sunitinib induces AR activity via AR S81 phosphorylation and consequent nuclear translocation. Furthermore, enzalutamide-induced degradation of phosphorylated AR leads to restoration of sunitinib sensitivity in RCC models, both in vitro and in vivo. These findings provide a rationale for the clinical testing of combination strategies with AR and RTK inhibitors in RCC.

We have recently reported that sunitinib resistance induces epigenetic-driven kinome reprogramming in RCC models, leading to increased global serine and tyrosine phosphorylation (28). In view of these findings, we hypothesized that the increased AR activation/expression, initially observed in our sunitinib-resistant PDX model by RPPA, was due to the phosphorylation of AR from sunitinib-induced kinase activation. Posttranslational modifications have been reported to regulate AR activity (34). Indeed, our in vitro and in vivo data indicate that RCC cells exposed to sunitinib show increased serine 81 phosphorylated AR. There is evidence that Src activation is one of the alternative pathways induced by RTKi resistance (28, 35), and this kinase is involved in AR phosphorylation (13). Our data suggest that CDK1 may also be induced upon sunitinib resistance. Thus, following chronic exposure to RTKi and consequent acquired resistance, there may be converging pathways leading to AR phosphorylation via kinome reprogramming in RCC (28).

The well-established mode of AR inhibition in prostate cancer is through the blockage of DHT-AR binding and/or androgen synthesis (36–38). Enzalutamide, a well-established AR antagonist, has been reported to inhibit AR activity in prostate cancer primarily via competition with ligand binding, inhibition of AR nuclear translocation, and chromatin binding. Based on the results suggesting increased AR activity in resistant cells with sunitinib exposure, we assessed the combination effect of enzalutamide and sunitinib in resistant RCC models. Thus, we observed a significant inhibitory effect on cell viability in the combination group, as compared with cells treated with single agents. Most interestingly, our in vivo data showed a regression of tumor growth with the combination treatment. Cullin-RING ligases (CRL) complexes, specifically the CRL3 complex, have been identified as ubiquitination ligases of AR (31, 32). The complex includes the SPOP adapter, a BTB (Bric-a-brac/Tramtrack/broad complex) domain protein that recognizes the SPOP binding motif harbored in wild-type AR and promotes AR degradation, and consequently, inhibition of AR transcriptional activity in prostate cancer. SPOP-mediated degradation is further enhanced in the presence of enzalutamide (39). Thus, SPOP has been reported to be a tumor suppressor gene in prostate cancer, and its mutations have been involved in enzalutamide resistance (40, 41). In contrast, SPOP has also been implicated in RCC progression, but its role, with respect to AR signaling, has not been assessed (42, 43). Our data in RCC models suggest that enzalutamide-induced AR degradation, primarily in the presence of sunitinib, occurs via the anchoring of SPOP on AR. Immunoprecipitation studies also suggest that ubiquitin is associated with AR, following sunitinib treatment. Thus, we hypothesize that posttranslational modifications induced by chronic exposure to sunitinib and binding to enzalutamide, induce AR conformational changes that facilitate AR cytoplasmic retention and SPOP-ubiquitin-proteasome-dependent degradation, with subsequent inhibition of previously activated AR-driven survival pathways. Further mechanistic studies will be necessary to identify whether AR phosphorylation occurs at additional critical residues, and which survival signalings are involved.

The treatment of RCC is rapidly evolving, but RTKis remain the standard of care. However, acquired drug resistance represents a major hurdle. Effective therapeutic strategies to overcome/delay resistance have not been developed yet. AR has been previously reported to be expressed in RCC and has been identified as a potential therapeutic target (4, 5). Interestingly, under our experimental conditions, single-agent enzalutamide did not elicit a significant antitumor effect in AR-expressing RCC models. However, the concomitant presence of sunitinib and consequent phosphorylation of AR induced inhibition of cell proliferation and apoptosis. This observation would suggest that AR signaling may act primarily as a survival pathway in the context of kinome reprogramming in RCC. Thus, we believe that the clinical development of AR antagonists as single agent modality in patients with RCC may fail to achieve a meaningful clinical outcome, unless paired with RTKis. The clinical testing of this combination strategy will need to also take into account the potential drug–drug interaction between these agents, because sunitinib, for example, is metabolized predominantly by the hepatic cytochrome P450 enzyme CYP3A4 (44). Enzalutamide is a strong CYP3A4 inducer and could decrease patient exposure to sunitinib, thus requiring a dose adjustment (45).

In our analysis of AR target genes, we observed that KLK2 and KLK4 were among the top 10 genes increased in 786-R cells. Interestingly, KLK3 (or PSA) was not expressed, likely due to epigenetic silencing. Thus, we decided to measure KLK2 in our models, because KLK2 is a secreted kallikrein and is measurable in patients with prostate cancer (4K score test). Our preliminary data are intriguing because they suggest a correlation between levels of KLK2 and the presence of an activated AR pathway. More interestingly, in a small cohort of patients with RCC who were treated with sunitinib, KLK2 was induced in patients whose disease had progressed, but not in those with an objective response. If confirmed in future studies, we hypothesize that measuring circulating levels of KLK2 may not only predict whether a patient will respond to RTKis, but will also help to determine when the tumor is becoming resistant. This potential biomarker will also be helpful to monitor disease response to AR and RTK inhibitors.

Taken together, our data suggest that posttranslational modifications of AR may modulate sunitinib resistance in RCC and may be targeted by enzalutamide treatment via SPOP-mediated degradation. In conclusion, our work provides important molecular insights in RTKi resistance RCC and identifies AR protein and a circulating AR-modulated kallikrein as potential therapeutic target and predictive marker, respectively, for this disease, and potentially other solid tumors where AR has been reported to be biologically active, such as lung and breast cancer (3, 46).

No potential conflicts of interest were disclosed.

Conception and design: R. Adelaiye-Ogala, S. Chintala, C. Kao, R. Pili

Development of methodology: R. Adelaiye-Ogala, N.P. Damayanti, S. Chintala, R. Pili

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): R. Adelaiye-Ogala, N.P. Damayanti, S. Arisa, S. Chintala, M.A. Titus, R. Pili

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): R. Adelaiye-Ogala, N.P. Damayanti, A.R. Orillion, M.A. Titus, C. Kao, R. Pili

Writing, review, and/or revision of the manuscript: R. Adelaiye-Ogala, N.P. Damayanti, S. Chintala, R. Pili

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): R. Adelaiye-Ogala, A.R. Orillion, S. Arisa, R. Pili

Study supervision: R. Adelaiye-Ogala, S. Chintala, R. Pili

This research was in part funded by the Roswell Park Cancer Institute Cancer Center Support Grant (P30CA016056; R. Pili), from the National Cancer Institute, and a generous donation by Richard and Deidre Turner (R. Pili). This investigation was conducted, in part, in a facility constructed with support from Research Facilities Improvement Program Grant Number C06 RR020128-01 from the National Center for Research Resources, National Institutes of Health.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.