Abstract
Sunitinib is considered a first-line therapeutic option for patients with advanced clear cell renal cell carcinoma (ccRCC). Despite sunitinib's clinical efficacy, patients eventually develop drug resistance and disease progression. Herein, we tested the hypothesis whether initial sunitinib resistance may be transient and could be overcome by dose increase. In selected patients initially treated with 50 mg sunitinib and presenting with minimal toxicities, sunitinib dose was escalated to 62.5 mg and/or 75 mg at the time of tumor progression. Mice bearing two different patient-derived ccRCC xenografts (PDX) were treated 5 days per week with a dose-escalation schema (40–60–80 mg/kg sunitinib). Tumor tissues were collected before dose increments for immunohistochemistry analyses and drug levels. Selected intrapatient sunitinib dose escalation was safe and several patients had added progression-free survival. In parallel, our preclinical results showed that PDXs, although initially responsive to sunitinib at 40 mg/kg, eventually developed resistance. When the dose was incrementally increased, again we observed tumor response to sunitinib. A resistant phenotype was associated with transient increase of tumor vasculature despite intratumor sunitinib accumulation at higher dose. In addition, we observed associated changes in the expression of the methyltransferase EZH2 and histone marks at the time of resistance. Furthermore, specific EZH2 inhibition resulted in increased in vitro antitumor effect of sunitinib. Overall, our results suggest that initial sunitinib-induced resistance may be overcome, in part, by increasing the dose, and highlight the potential role of epigenetic changes associated with sunitinib resistance that can represent new targets for therapeutic intervention. Mol Cancer Ther; 14(2); 513–22. ©2014 AACR.
Introduction
Renal cell carcinomas (RCC) are responsible for approximately 85% of all primary renal neoplasms (1). It is ranked among the top 10 most common cancers in the United States with an estimated incidence of 65,150 and 13,680 deaths expected to have occurred in 2013 (1, 2). Among the histologic types of RCC, clear cell renal cell carcinoma (ccRCC) is the most common type arising from the proximal tubules, and accounting for 75% to 85% of RCC tumors. This histologic type of RCC often presents with the deletion of chromosome 3p that harbors the Von Hippel–Landau (VHL) gene (2, 3).
Recent advances in the management of recurrent RCC have established agents targeting vascular endothelial growth factor (VEGF), such as sunitinib, as the standard initial therapeutic option (4). Sunitinib, a multi-targeted tyrosine kinase inhibitor (TKI), is a FDA-approved antiangiogenic agent that primarily targets VEGF receptors (VEGFR) and platelet-derived growth factor receptors (PDGFR; ref. 5). In a randomized phase III clinical study, sunitinib was shown to prolong progression-free survival (PFS) in patients with metastatic RCC (4). Despite its clinical efficacy, the majority of patients, who initially responded to sunitinib, eventually develop resistance and progress. Several potential mechanisms responsible for acquired resistance to anti-VEGF drugs have been proposed such as the continuous activation of the VEGF axis via upstream or downstream effectors (6–9). Other factors that may contribute to sunitinib resistance include activation of VEGF-independent pathways such as bFGF, c-met, IL8, or other angiogenic cytokines (10), altered pharmacokinetics, drug sequestration (11), or epithelial-to-mesenchymal transition (12).
Epigenetic modifications of histone protein in the chromatin have been shown to play an important role regulating the fidelity of gene transcription patterns in cells by the catalytic activity of histone deacetylases and methyltransferase (13). The enhancer of zeste homologue 2 (EZH2), a catalytic subunit of the polycomb repressive complex 2 (PRC2), is a histone methyltransferase that catalyzes the methylation of lysine 27 on histone 3 and trimethylation of lysine 27 on histone 3 (H3K27me3) leads to the repression of gene expression (14). Overexpression of EZH2 has been implicated in a variety of cancer types. In addition, EZH2 has been shown to contribute to tumor angiogenesis by inactivating antiangiogenic factors via methylation at their promoter region (15). Posttranslational modifications on the tri- and dimethylated H3K4 have been associated with the regulation of angiogenesis and migration via the CCL14 chemokine pathway (16).
Herein, we report the preclinical and clinical effect of introducing a sunitinib dose escalation regimen as a therapeutic strategy to overcome initial drug-induced resistance in ccRCC. We also show that drug resistance may be associated with epigenetic changes such as the overexpression of methyltransferase EZH2 and modulation of histone marks.
Materials and Methods
Cell lines and establishment of sunitinib-resistant cell line
The 786-0 renal cell carcinoma cell lines were obtained from American type culture collection (ATCC). Cells are routinely (every 6 months) tested in the laboratory for mycoplasma contamination using the Mycoplasma Detection Kit in accordance to the manufacturer's instructions (Life Technologies). No authentication of human genotype was done by the authors. Cells were maintained in 5% CO2 at 37°C in RPMI media supplemented with 10% fetal bovine serum (FBS) and 0.1% penicillin–streptomycin. Sunitinib-resistant cell lines, 786-0R, were established by exposing 786-0 cells to an initial dose of sunitinib (2 μmol/L) and gradually increasing concentrations up to 5 μmol/L. Resistant cell lines, 786-0R, were then continuously exposed to 5 μmol/L of sunitinib.
EZH2 short hairpin RNA stable transfection
We used four unique 29mer short hairpin RNA (shRNA) constructs, as well as a scrambled negative control noneffective shRNA packaged in a lentiviral green fluorescent protein (GFP) vector, which were purchased from OriGene Technologies, Inc. 786-0 cells, which have considerably higher expression of EZH2 and less responsive to sunitinib (IC50 = 5 μmol/L) were plated for 24 hours. At approximately 60% confluence, cells were transfected using polybrene (Sigma-Aldrich) according to the manufacturer's instructions. Stable clones were selected with puromycin (5 μg/mL) starting at 48 hours after transfection. All infected cells were assayed by Western blot analysis and quantitative real-time PCR to determine the efficiency of shEZH2 knockdown. Stable transfected cells were propagated and maintained in media containing puromycin (5 μg/mL).
Xenograft models
RP-R-01 and RP-R-02 are patient-derived ccRCC models. RP-R-01 was established from a skin metastasis in a patient with sporadic ccRCC who initially responded to sunitinib treatment but developed drug resistance. RP-R-01 is characterized by the deletion of the VHL gene (12). RP-R-02 was developed from a skin metastasis in a patient with hereditary ccRCC (VHL syndrome) who was treatment naïve. RP-R-01 and RP-R-02 ccRCC models had undergone several passages in vivo and still maintain the clear cell morphology (Fig. 1A). All in vivo experiments were approved and performed in strict accordance with the guidelines of the Institutional Animal care and use committee (IACUC) at Roswell Park Cancer Institute. Six-week-old homozygous Icr Severe Combined Immune-deficient (SCID) female mice were housed in a sterile, pathogen-free facility and maintained in a temperature-controlled room under a 12-hour light/dark schedule with water and food ad libitum. RP-R-01 and RP-R-02 viable tumors were selected and dissected into approximately 1-mm2 tumor pieces and implanted subcutaneously into mice. All mice were operated under sedation with oxygen, isoflurane, and buprenorphine. When tumors were established, mice were randomly grouped and placed in either control group or treatment group (n = 20).
Tumor growth curve in preclinical ccRCC models in response to sunitinib dose escalation. A, H&E staining of tumor samples confirms the original clear cell morphology. B, RNA and DNA were prepared from tumor tissues. Alu expression to determine human origin was assessed by PCR. C and D, mice bearing RP-R-01 or RP-R-02 tumors were treated with either sunitinib or vehicle. The starting dose of sunitinib was 40 mg/kg daily (5 days/week). Dose was increased to 60 and 80 mg/kg at the time of overt resistance (>50% from nadir). E and F, in a separate experiment, mice bearing RP-R-01 or RP-R-02 tumors were treated with sunitinib (80 mg/kg). Tumor growth was assessed once a week by caliper measurements and body weights by weigh scale. Results are expressed as the mean ± SE.
Sunitinib treatment and dose escalation schedule
Sunitinib was provided by Pfizer pharmaceuticals. Mice bearing either RP-R-01 or RP-R-02 tumors were randomly grouped into control and treatment groups. Mice in treatment groups were treated with a starting dose of 40 mg/kg (sunitinib free base) 5 days on, 2 days off by oral gavage. Treatment dose was increased to 60 mg/kg when tumors were observed to be resistant to the initial dose and then subsequently to 80 mg/kg. Tumors were defined to be sensitive when either stable growth or regression was observed following the start of treatment. Tumors were defined resistant when a ≥50% increase in tumor growth from baseline was observed on treatment. Tumor volumes were measured once a week by caliper measurement according to the formula: tumor volume (mm3) = longest length × shortest length2 × 0.5. Body weights were assessed once a week using a weighing scale and recorded in grams. Endpoint tumor weights were assessed using a weigh scale and recorded in grams.
Blood and tissue collection
Tissue and blood were collected under aseptic conditions. Blood (200 μL) was collected by submandibular bleeds before treatment or when tumors were responsive to treatment and 1 mL of blood by cardiac bleeds (terminal) when tumors were non responsive to treatment, before dose escalation. Serum and plasma were separated and aliquots were stored at −80°C for further analysis. Tumor tissues were excised, weighed, and cut into sections. Sections were snap-frozen and stored in −80°C, fixed in 10% buffered formalin or zinc for histopathology.
Clinical ccRCC patients
Sequential ccRCC patients seen at either the Sunnybrook Odette Cancer Centre or Roswell Park Cancer Institute who presented with progression at the standard sunitinib dose and schedule (daily 50 mg, 4 weeks on/2 weeks off) on imaging and with < grade 2 toxicity were offered sunitinib dose escalation (62.5 mg and 75 mg, 2 weeks on, 1 week off). The number presented is the total of patients that were offered before an ongoing prospective study started, and represents approximately 10% of patients treated with sunitinib. Grade 2 toxicities such mucositis, diarrhea, hematologic, and hand-foot syndrome prevented patients to be considered for sunitinib dose escalation. Hypertension (controlled), hypothyroidism, and skin discoloration were not considered significant toxicities. Patients with no overt toxicities were treated to some type of grade 2 toxicity (mucositis, diarrhea, hematologic, hand foot syndrome). Patients were routinely monitored for side effects and underwent imaging for tumor response assessment at standard time intervals. Every patient was informed about the rationale behind the dose escalation and the discussion was documented in the physician note. Patients understood that they were individually dose reduced based on toxicity and that we were individually dose escalating based on toxicity. Ultimately, all the patients that progressed on the escalated dose of sunitinib received second-line therapies as well.
In vitro assays
786-0 and 786-0R cells were seeded in 24-well plates (Santa Cruz biotechnology) and allowed to attach for 24 hours. Afterward, cells were treated with increased concentrations of sunitinib (LC laboratories), the EZH2 inhibitor GSK126 (Cayman chemical company), or combination of both sunitinib and GSK126 using combination index values (Calcusyn software). Cell were fixed and stained with crystal violet (Sigma-Aldrich) after 48 hours of treatment. Afterward, stained cells were washed with ddH2O to remove excess dye, air-dried, and dissolved in methanol. Cell viability was quantitated by absorbance using a spectrometer at 570 nm (xMarks Spectrometer; Bio-Rad). 786-0shRNA (scramble) and 786-0shEZH2 were seeded in 24-well plates and 24 hours later, cells were treated with 2, 4, 6, and 8μmol/L of sunitinib. After 48 hours of treatment, cells were fixed, stained, and read using spectrometer.
Measurement of sunitinib concentrations by LC/MS-MS methods
For the sunitinib pharmacokinetic studies, at the time of sensitivity to 40 mg/kg dose, blood and tumor samples were collected 24 hours after treatment (7 days after the treatment start date) in the RP-R-01 model. At the time of resistance to 40 mg/kg dose (day 74 on treatment), the second tumor and blood samples via terminal bleed from the same group of mice were collected. The last collection occurred on day 92 and day 153 at the time of response to 60 mg/kg dose and resistance, respectively. An LC/MS-MS method was used to determine sunitinib and its metabolite Su-12662 plasma concentrations. Calibrators and quality control (QC) samples were prepared in normal human plasma and extracted in duplicate along with patient samples. Calibrators ranged from 1 to 1,000 ng/mL for sunitinib and 0.5 to 1,000 ng/mL for Su-12662. QC samples were prepared at 3, 75, and 750 ng/mL. Samples were extracted by protein precipitation using a TomTec Quarda 4 robotic system. Briefly, 50 μL of sample, standard, or QC was added to a 96-well plate, 150 μL of ACN containing the internal standard D, L, propranolol was added to each well, and the plate vortexed and centrifuged, 100 μL of supernatant was then added to a clean 1 mL plate containing 100 μL of 10 mmol/L ammonium formate pH 5.0, plate was then vortexed and centrifuged briefly and 10 μL was injected. Analysis was carried out using a Thermo Scientific TSQ quantum Ultra triple quad mass spectrometer in positive APCI mode. The mass transitions monitored are as follows: sunitinib, 399.17/282.91; Su-12662 371.17/282.913; and D, L, Propranolol (IS), 260.17/116.06. Chromatographic separation was carried out using a Luna CN 5 μm 50 × 2.0 mm column with a mobile phase consisting of 50:50 10 mmol/L ammonium formate pH 5.0: acetonitrile at a flow rate of 950 μL/min. Column temperature was maintained at 30°C, whereas samples were maintained at 4°C. Unknown concentrations were calculated on the basis of the weighted 1/x2 linear regression of the calibrators. Tissue extraction consisted of adding 280 μL of 20% MeOH diluent to tissue sample with four 3-mm stainless steel beads and processed using a bullet blender.
Histologic and immunohistochemistry analysis
Tissue specimens were fixed for 24 hours, paraffin-embedded, and sectioned (5 μm). 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. H&E staining was performed by the standard methods. For immunohistochemistry staining (IHC), sections were further incubated in hydrogen peroxide to reduce endogenous activity. To examine the expressions of our proteins of interests, tissue section were blocked with 2.5% horse serum (Vector Laboratories), and incubated overnight in primary antibodies against Ki67 (1:500, Thermo Fisher) and CD31 (1:100, Dianova), EZH2 (1:100, Cell Signaling Technology), H3K27me3 (1:200, Cell Signaling Technology), H3K4me2 (1:1500, Cell Signaling Technology), H3K4me3 (1:800, Cell Signaling Technology), and H3K9me2 (1:200, Cell Signaling Technology). Following primary incubation, tissue sections were incubated in horseradish peroxidase (HRP)-conjugated anti-rabbit or anti-rat antibody according to the manufacturer's protocol (Vector Laboratories) followed by enzymatic development in diaminobenzidine (DAB) and counter stained in hematoxyline. Section were dehydrated and mounted with cytoseal 60 (Thermo Scientific). Respective isotype negative controls were used for evaluation of specific staining. Stained sections were analyzed either under bright field (IHC) using the Zeiss Axio microscope. The number of positive cells was determined in a blinded fashion by analyzing four random ×20 fields per tissue and quantified using the ImageJ software.
Western blot analysis
Control and treated 786-0 cells were lysed in RIPA buffer (Sigma-Aldrich) supplemented with protease and phosphatase inhibitor cocktails (Pierce). Protein concentrations were determined by the Bradford assay (Bio-Rad). Samples containing 50 μg of protein underwent electrophoresis on 12% SDS-polyacrylamide gels and transferred onto nitrocellulose membranes. Proteins of interests were detected using the following primary antibodies; EZH2 (1:1,000, Cell Signaling Technology), E-cadherin (1:1,000, Cell signaling Technology), E2F-1 (1:1,000, Cell Signaling Technology), and β-actin (Santa Cruz Biotechnology). After incubation with HRP-conjugated secondary antibodies (Bio-Rad), membranes were exposed to chemiluminescence 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 6).
RNA isolation and quantitative RT-PCR
Total RNA was isolated using the TRIzol reagent (Life Technologies) according to the manufacturer's instructions and measured using the nano-drop (purity ≥ 1.89). Quantitative RT-PCR was performed utilizing EZH2, E-cadherin, and GAPDH human-specific primers (IDT Technologies). 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 CFX connect real-time system (Bio-Rad). CFX software was used to identify cycle threshold (Ct) values and generate gene expression curves, all data were normalized to GAPDH expression.
Exome sequencing
Sample preparation.
DNA libraries for the samples were prepared using the TruSeq DNA Sample v2 preparation kit (Illumina, Inc.) as per the manufacturer's instructions. One microgram of each DNA sample was fragmented using Covaris shearing to a size range of 300 to 400 bp. Following end repair and 3′ adenylation, indexing adapters were ligated to the fragment ends. Following purification, the fragmented DNA was PCR amplified for five cycles, purified, and validated for appropriate size on a 2100 Bioanalyzer High Sensitivity DNA chip (Agilent Technologies, Inc.). Each DNA library was quantitated using quantitative PCR (KAPA Biosystems) before exome capture. TruSeq exome capture: 500 ng of each TruSeq DNA library was pooled (6-plex) and enriched for exome sequences using the TruSeq Exome Enrichment Kit (Illumina, Inc.) as per the manufacturer's instructions. Following two rounds of hybridization to the capture baits, the enriched library was PCR amplified for 10 cycles, AMPure XP bead purified, and validated for appropriate size on a 2100 Bioanalyzer High Sensitivity DNA chip (Agilent Technologies, Inc.). The enriched library was quantitated using quantitative PCR (KAPA Biosystems) and normalized to 8 pmol/L.
Statistical analysis
Data analyses are expressed as the mean ± standard error of 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 post-test for multiple comparison. A P value < 0.05 was considered to be significant; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns = not significant. Statistical analyses were performed by using the GraphPad Prism Software.
Results
Sunitinib dose escalation overcomes drug-induced resistance in RP-R-01 and RP-R-02 ccRCC PDX models
To evaluate the effect a sunitinib dose escalation regimen and overcome transient drug resistance in vivo, we used the RP-R-01 and RP-R-02 PDX models. These VHL-negative models maintain the original clear cell morphology, human Alu sequence and contain common ccRCC gene mutations including PBRM1, SETD2, and KDM6A (Fig. 1A and B and Supplementary Fig. S1). In a first set of experiments, we implanted RP-R-01 tumors under the skin of mice (Supplementary Fig. S2A). When tumors reached a palpable size, we separated mice into two groups: control group and sunitinib treatment group. Then, we started sunitinib at the dose of 30 mg/kg and we monitored tumor growth. When the tumors became resistant, we excised them and reimplanted in mice. As they reached a palpable size, we increased the sunitinib dose to 45 mg/kg. We repeated the same schema with 60 mg/kg dose. Our results showed a dose-dependent effect of sunitinib and a transient drug-acquired resistance. In view of these preliminary data, we decided to conduct an intramouse dose-escalation experiment in both RP-R-R01 and RP-R-02 models. In this new set of experiments, as tumors became resistant they were not excised and continued to be treated at higher doses. Our data show that at the starting dose of 40 mg/kg, tumors were responsive to sunitinib in both RP-R-01 and RP-R-02 models as suggested by the regression in tumor size (Fig. 1C and D). However, after 4 weeks of treatment, RP-R-01 tumors began to grow despite sunitinib treatment. Although RP-R-02 showed significant response to sunitinib treatment, these PDX tumors eventually became resistant to treatment at day 84. Then, we increased the dose from 40 mg/kg to 60 mg/kg and observed again a decrease in tumor growth. However, following a period of stabilization tumors began to grow. Finally, as we increased the dose to 80 mg/kg and, we noticed again inhibition in tumor growth. Mice under sunitinib dose escalation regimen showed no signs of drug toxicity such as loss of body weight (Supplementary Fig. S3A), lethargy, abnormal behavior or loose stool, although we noticed yellowing of paws and furs. Histopathology assessment of the liver tissues from mice on dose-escalation studies indicated no vascular changes, hematoma formation, or necrosis (Supplementary Fig. S3B). In a separate set of experiments, we started treating both RP-R-R01 and RP-R-R02 directly at 80 mg/kg sunitinib dose. As shown in Fig. 1E and F, RP-R-01 tumors initially regressed but eventually became resistant to sunitinib within 4 months. In contrast, high dose of sunitinib induced a prolonged tumor response in the RP-R-02 model, suggesting again the dose-dependent effect of sunitinib on tumor growth.
Microvessel density changes, proliferation status with initial response, and subsequent resistance to sunitinib
To determine whether the transient response and resistance to sunitinib in the PDX model RP-R-01 was associated with reversible biologic effects, we assessed microvessel density and proliferation activity by immunohistochemistry analyses of CD31 and Ki67 staining, respectively. We observed a decrease in tumor vasculature with response to sunitinib treatment, which was expected. However, sunitinib-resistant tumors became hypervascularized again (Fig. 2A). Ki67 staining also showed increased proliferation with resistance to sunitinib treatment. Similar patterns were observed in the RP-R-02 model (Fig. 2B).
Immunohistochemical staining for CD31 and Ki67 in RP-R-01 and RP-R-02 tumor. Tissue samples from RP-R-01 (A) and RP-R-02 (B) were fixed for 24 hours, paraffin embedded, and cut onto slides. Slides were stained with CD31 and Ki67 antibody. Representative pictures of the immunostaining are depicted (top). Quantitative analyses were performed (bottom). Results are based on four randomly selected fields per tissue and are expressed as the mean ± SE. Statistical significance was determined by the Student t test. *, P < 0.05; **, P < 0.01; ****, P < 0.0001.
Intratumor and plasma concentrations of sunitinib
To determine whether the transient sunitinib resistance was due to reduction of drug levels, we assessed the concentration of sunitinib in the tumor and in the circulation at the time of response and when tumors became resistant. We used a state-of-the-art, quantum ultra-triple mass spectrometer in APCI positive mode. RP-R-01 tumor tissues and plasma samples were measured for sunitinib concentration and its metabolite Su-12662. Supplementary Fig. S4A showed that there was a reduction in the plasma concentrations of sunitinib and its metabolite in the tumors when they became resistant at the 40 mg/kg dose. Similar reduction, although not statistically significant, was observed in the tumors resistant at 60 mg/kg dose. However, in contrast with the plasma concentrations, we observed increased intratumoral concentrations of sunitinib at higher doses, although not statistically significant (Supplementary Fig. S4B). As previously reported, intratumor sunitinib concentrations were >10-fold higher than those in the plasma (11, 12).
Resistance to sunitinib is associated with epigenetic changes in ccRCC PDX models and a ccRCC patient tissue sample
Previous studies have shown that epigenetic changes, including differential expression of the histone methyltransferase EZH2 may be associated with drug resistance (17–18). A survey of more than 400 ccRCC tumors from the TCGA data portal identified several changes in the methylation status of the lysine tails of histone 3 and the associated mutated genes with progression of disease to advance staged (19). On the basis of these evidences, we were interested in assessing the expression levels of EZH2 and the methylation status of histone marks (H3K27me3, H3K4me2, H3K4me3, and H3K9me2) in tumors that were either sensitive or resistant to sunitinib. We performed IHC staining on cut sections of paraffin-embedded specimens from the original nephrectomy, the skin metastasis developed on sunitinib, and the derived PDX (RP-R-01), that was again sensitive to sunitinib (12). These tumor samples were obtained from a patent with ccRCC who initially responded to sunitinib but then progressed on treatment. Our results showed a significant increase in the expression levels of EZH2 in the skin metastasis as compared with the primary nephrectomy and the derived PDX (Fig. 3A and B). We also observed a parallel increase in the associated histone mark H3K27me3, although not statistically significant. In addition, our result showed differential expression levels of dimethylated H3K4 and H3K9me3 (Supplementary Fig. S5). We also detected increased expression levels of di- and trimethylated H3K4 and H3K9me3 at the time of resistance to 60 mg/kg sunitinib in the RP-R-01 model (Supplementary Fig. S6). Thus, we wanted to determine whether similar changes occurred in the PDX models RP-R-01 and RP-R-02 following resistance to sunitinib. Interestingly, we observed a transient and reversible increase in the expression levels of EZH2 when tumors became resistant to sunitinib treatment as compared with the sensitive phase, in both PDX models with a return to lower levels with dose escalation to 60 and 80 mg (Fig. 3C). No clear changes in the expression levels of the histone mark H3K27me3 were observed (data not shown). Modulation of EZH2 expression with response or transient resistance to sunitinib was associated with changes in the expression of E-cadherin, a downstream gene product of EZH2 (Fig. 3D). This dynamic inverse correlation of EZH2 and E-cadherin expression levels was also observed in the original set of experiments at the time of sunitinib resistance (Supplementary Fig. S2D).
Epigenetic modification associated with resistance to sunitinib resistance and disease progression. Sections from the original nephrectomy, the skin metastasis developed on sunitinib from the same patient, and the derived xenograft (RP-R-01) sensitive again to sunitinib were stained for EZH2 and H3K27me3. B, quantitative analyses were performed. IHC quantitative results are based on four randomly selected fields per tissue and are expressed as the mean percentage of positive cells/total cells ± SE. C, as shown in Fig. 1A and B, tissue samples from control and tumors sensitive and resistant to sunitinib at each dose were fixed for 24 hours and paraffin embedded. Quantitative analyses were performed. D, expression levels of E-cadherin were assessed by immunoblotting assay of tumor lysates. Total β-actin was also assessed as loading control and relative expression levels were determined by densitometry. Statistical significance was determined by the Student t test. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, not significant.
Pharmacologic and biochemical inhibition of EZH2 in ccRCC cell lines enhances response to sunitinib treatment
To determine to role of EZH2 in modulating the antitumor effect of sunitinib, we induced either pharmacologic or biochemical inhibition of this histone methyltransferase. Mechanistic studies showed that stable transfected cells, 786-0shEZH2_D, have a significant decrease of EZH2 gene/protein expression and an increase in E-cadherin gene expression (Fig. 4A and Supplementary Fig. S7A and S7B). Similar effects were observed by using the EZH2 inhibitor GSK126 (Supplementary Fig. S7A and S7B). 786-0shEZH2_D cells were more sensitive to sunitinib treatment, as indicated by the significant decrease in cell viability as compared with the scramble control, 786-0shRNA (Fig. 4B). Similarly, we observed a significant decrease in cell viability in 786-0 and 786-0R cells following treatment with the EZH2 inhibitor GSK126 and sunitinib in combination as compared with single agents, with a combination index suggesting synergism (Fig. 4C and D). Interestingly, pharmacologic induced reduction in cell viability was associated with reduced expression levels of E2F-1, an upstream target gene of EZH2 (Fig. 4E). The potential role of EZH2 in sunitinib resistance was also suggested by the increased baseline levels of this HMT in the sunitinib-resistant cells 786-0R (Fig. 4F).
Increased sensitivity to sunitinib following biochemical and pharmacologic inhibition of EZH2 in ccRCC cell lines. A, Western blot analysis showing the efficiency of EZH2 knockdown in 786-0 cell line. B, specific knockdown of EZH2 in 786-0 cells are more sensitive to sunitinib as compared with the scrambled template control, 786-0_shRNA. C, 786-0 and 786-0R cell lines were treated with either sunitinib, GSK126, or both for 48 hours. Bar chart, significant decrease in cell viability in combination treatment arm as compared with the single agents alone. D, combination index values for sunitinib and GSK126 in 786-0 (CI, 0.224) and 786–0R (CI, 0.224). E, Western blot analysis shows a decrease in E2F-1 with single treatments or combination in 786-0 cells; however, in the resistant cell lines, expression levels are significantly low in combination treatment. F, Western blot analysis showing increase in EZH2 expression in sunitinib-resistant cell lines, 786-0R compared with the parental 786-0. Statistical significance was determined by the Student t test. **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, not significant.
Response to sunitinib dose escalation in ccRCC patients
In parallel to the preclinical studies, a subset of patients with ccRCC being treated at either the Odette Cancer Centre or Roswell Park Cancer Institute, who initially received sunitinib at the standard dose and schedule (daily 50 mg, 4 weeks on/2 weeks off) and did not present significant side effects (see Materials and Methods), were offered dose escalation (62.5 mg and 75 mg) at the time of early disease progression (daily dose, 2 weeks on/1 week off). In Fig. 5A and B are depicted the computed tomography (CT) scans of 2 patients showing the initial progression on 50 mg sunitinib dose and response to 75 mg dose. Table 1 reports the added PFS following the dose escalation.
Response to sunitinib dose escalation in patients with ccRCC. Two patients with ccRCC who were initially responsive to 50 mg of sunitinib eventually became resistant. Increased dose of 75 mg restored response to sunitinib. A and B, representative CT images from two patients; arrows, tumor lesions.
Clinical benefit in patients with sunitinib dose escalation
Discussion
Acquired resistance to anti-VEGF therapies remains a challenge in the clinical management of patients with ccRCC. Several potential mechanisms that are driving the tumor/host adaptation to this targeted therapeutic strategy have been proposed. In our study, we assessed the effect of sunitinib dose escalation using two TKI-sensitive ccRCC PDX models, and showed that the initial drug resistance was transient and reversible. At the time of resistance, as we incrementally increased the sunitinib dose, we observed restored tumor sensitivity to sunitinib in both PDX models. These preclinical observations were mirrored by our clinical experience in a selected group of patients with ccRCC who were initially responsive to sunitinib and then became drug resistance. In the absence of significant side effects, we were able to increase sunitinib dose from 50 to 62.5 and 75 mg and observed clinical benefit in the majority of the patients. Dose escalation was made feasible by a modified sunitinib schedule of 14 days on and 7 days off that may work better than the standard schedule based on our DCE-US data (20). Our retrospective data suggest that an individualized sunitinib regimen based on this schedule is well tolerated and may induce greater clinical benefit than the traditional schedule (4 weeks on/2 weeks off) likely due to optimal drug exposure for each patient (20). A prospective trial is ongoing in Canadian centers where dose and schedule are individualized based on toxicity and dose escalation is allowed in patients with minimal toxicity on the standard 50 mg dose.
The concept of individualized therapy has multiple examples from the old literature (21). The relationship between exposure to TKIs and efficacy has been previously reported (22, 23) supporting a rationale for sunitinib dose escalation. A large meta-analysis indicated that increased exposure to sunitinib is associated with improved clinical outcomes, although also with some increased risk of adverse effects (23). Despite the clinical use of a universal “flat” dose, there are both preclinical and clinical evidences of a dose-dependent effect of TKI in RCC. Our tumor growth data in the two PDXs clearly show greater and prolonged effect of high-dose sunitinib (80 mg/kg vs. 40 mg/kg) with sustained tumor regression in the sunitinib-naïve RP-R-02 (Fig. 1A and B). The PK data did not suggest that the transient resistance to sunitinib was due to decreased drug levels. As shown in supplementary Fig. S4, there was a slight increase in plasma concentration of sunitinib in the animal treated with 60 mg/kg as compared with 40 mg/kg, although the difference was not statistically significance. Interestingly, there was a trend for decreased plasma sunitinib concentration following prolonged exposure of the drug, although these differences were not statistically significant. However, we did not observed a significant drop of sunitinib concentrations in the tumors, but rather an increase in the group resistant to the 60 mg/kg dose. However, under our experimental conditions, we cannot rule out the possibility that PK might have contributed to the reversible tumor sensitivity upon dose escalation of sunitinib, but this looks unlikely, in view also of the observed intratumor drug accumulation. We can speculate that tumors/host may “adapt” to the pharmacologic effects of sunitinib and increased intratumor concentrations, potentially as a consequence of intracellular lysosomal sequestration, may be needed to maintain the same inhibitory effects (11). Previous work has shown that sunitinib may have both antiangiogenic and anticancer effect with the latter perhaps becoming more important at higher doses (11). The assumption that sunitinib may also have a direct antitumor effect led us to examine the effect of in vitro chronic drug exposure of RCC cells. We used low micromolar sunitinib concentrations that appear to be achievable in vivo based on the intratumor accumulation reported with this drug (supplementary Fig. S4; ref. 12). We recognize that developing of “resistance” in vitro may not necessarily mirror what is happening in patients. However, in our case, in vitro chronic exposure of sunitinib induced less sensitivity to sunitinib and it was associated with increased EZH2 expression similarly to what we observed in tumor-bearing animals that were exposed to chronic drug administration. Further studies will be necessary to confirm that the in vitro biologic and molecular changes induced by chronic exposure to sunitinib are predictive of the changes occurring in vivo.
The role of TKI dose escalation has been evaluated in a limited set of clinical studies but it remains an unanswered question (24). Intrapatient dose escalation based on drug tolerability has been reported with sorafenib showing clinical benefit but rising also concerns for feasibility due to increased toxicity (25, 26). In a randomized double-blind phase II trial, the greater proportion of patients in the axitinib titration group achieved an objective response supporting the rationale for individual drug dose titration with this TKI in selected patients with RCC even though this was not associated with a better PFS (27). The potential benefit of escalating the dose of sorafenib at the time of progression has been reported suggesting that in progressive patients, treatment with a higher dose could be a valid option if tolerated (28). In our limited experience, we observed that in 10 out of 16 cases sunitinib dose escalation implemented at the time of disease progression resulted in clinical benefit as shown by a ≥5 month added PFS. These preliminary data suggest that intrapatient sunitinib dose escalation at the time of disease progression is feasible in a selected patient population, and may overcome, although transiently in the majority of patients, the initial acquired drug resistance. A prospective clinical study led by Dr. Bjarnason is currently testing this hypothesis in patients with ccRCC treated with first-line sunitinib.
Epigenetic modifications have been implicated in cancer progression. Chromatin remodeling gene, have been reported to be altered in RCC (19, 29). Interestingly, overexpression of EZH2 has been associated with poorer outcome in patients with RCC (30). In our studies, we observed that resistant tumors to sunitinib had an increased level of EZH2 expression. However, this increase was reversible upon dose escalation, suggesting that tumor adaptation to TKI is dynamic, as likely driven by epigenetic alterations. Under our experimental conditions, we observed a correlation between EZH2 expression and H3K27 trimethylation in the original patient tumors but not in the PDX, perhaps due to the already high basal expression of H3K27me3 in the xenografts. Similarly, mechanistic studies suggest that inhibition of EZH2 expression or activity sensitized ccRCC cells to sunitinib in vitro and provide a rationale for a potential role of EZH2 in modulating tumor response and resistance to sunitinib treatment. A downstream target of EZH2 such as E-cadherin was increased with decrease in EZH2, which was also accompanied by a decrease in E2F-1 (31). E2F-1 has been reported in previous studies to play a role in multidrug resistance and negatively regulating cell proliferation when overexpressed (32). In our studies, we also observe increased expression of other histone marks associated with “active” gene expression regulations, such as H3K4me2 and H3K9me2. Further studies will identify the potential genes involved in these epigenetic changes. The current development of EZH2 inhibitors opens the possibility of considering rationale combination strategies with TKI to overcome/delay the initial occurrence of drug resistance. Combining TKIs with other epigenetic therapies, such as histone deacetylase inhibitors and drugs that target hypoxia inducible factors, should also be considered in future clinical trials for patients with ccRCC.
In conclusion, our data suggest that sunitinib dose escalation is a potential strategy to reverse initial acquired drug resistance. The molecular mechanism(s) responsible for the tumor resensitization to sunitinib remains to be identified but it seems to be associated with modulation of EZH2 and downstream target genes. Future studies are warranted to define the role of EZH2 and other epigenetic “drivers” involved in the tumor adaptation and acquired resistance to TKIs.
Disclosure of Potential Conflicts of Interest
G.A. Bjarnason received a commercial research grant from, has speakers' bureau honoraria from, and is a consultant/advisory board member for Pfizer. R. Pili received a commercial research grant from and is a consultant/advisory board member for Pfizer. No potential conflicts of interest were disclosed by the other authors.
Authors' Contributions
Conception and design: R. Adelaiye, P. Sotomayor, G.A. Bjarnason, R. Pili
Development of methodology: R. Adelaiye, E. Ciamporcero, P. Sotomayor, S.-Y. Ku, S. Chintala, R. Pili
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): R. Adelaiye, E. Ciamporcero, K.M. Miles, M. Tsompana, D. Conroy, L. Shen, A. Orillion, J. Prey, G. Fetterly, M. Buck, S. Chintala, G.A. Bjarnason, R. Pili
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): R. Adelaiye, P. Sotomayor, J. Bard, D. Conroy, J. Prey, G. Fetterly, M. Buck, G.A. Bjarnason, R. Pili
Writing, review, and/or revision of the manuscript: R. Adelaiye, S. Chintala, G.A. Bjarnason, R. Pili
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): R. Adelaiye, S. Ramakrishnan, S. Chintala, R. Pili
Study supervision: P. Sotomayor, R. Pili
Grant Support
This research was supported in part by the National Cancer Institute, NIH (P30CA016056/1R01CA135321; to R. Pili), a research grant from Pfizer (R. Pili), and a donation from Dr. Richard Turner and Mrs. Deidre Turner (R. Pili).
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.
Acknowledgments
The authors acknowledge the MTMR and Pathology Core Facilities at Roswell Park Cancer Institute for animal handling and processing the tissue samples, respectively. The authors also thank Jeffery Conroy and Sean Glean at the Genomics Consortium and Genomics Shared Resource at Roswell Park Cancer Institute for helping with the genomic analysis.
Footnotes
Note: Supplementary data for this article are available at Molecular Cancer Therapeutics Online (http://mct.aacrjournals.org/).
- Received March 10, 2014.
- Revision received November 26, 2014.
- Accepted November 29, 2014.
- ©2014 American Association for Cancer Research.
References
Volume 14, Issue 2
