Background
Chemotherapy resistance is a challenging problem in the treatment of epithelial ovarian cancer (EOC) patients. While a majority (80 %) of women initially responds to first-line platinum/taxane chemotherapy, recurrent disease presents in 60–85 % of patients, and is fundamentally incurable [
1,
2]. However, patients with recurrent disease that is platinum-sensitive have a better response rate and improved progression free survival (PFS) and overall survival (OS) when treated with combination therapy [
2]. In patients with platinum-resistant disease, single agent paclitaxel was shown to produce an objective response rate of only 22–30 % [
2]. In the case of patients with disease that is resistant to both platinum and paclitaxel, other options are available, such as pegylated liposomal doxorubicin (PLD), topotecan, and gemcitabine [
2]; however, it is not always clear which patients may benefit most from each therapy. Ultimately, although multidrug regimens are associated with higher toxicity, they are more effective than single agent therapies. Gaining a better understanding of factors contributing to resistance to platinum and taxane based therapies will be valuable in guiding treatment decisions for ovarian cancer patients.
Human epididymis protein-4 (HE4/WFDC2) is a small secretory protein that belongs to the family of whey acidic protein (WAP) domain-containing anti-proteases [
3], and has been shown to possess cross-class anti-protease activity itself [
4]. It is overexpressed in ovarian cancer tissues compared to normal ovaries, and has been identified as a novel biomarker for EOC [
5,
6]. Serum levels predict ovarian cancer with equivalent sensitivity to CA125, but with the advantage that HE4 is less frequently elevated in patients with benign gynecological conditions [
5]. A multicenter study led by our institution established a new algorithm for the diagnosis of women with an ovarian mass [
6]. The FDA-approved Risk of Ovarian Malignancy Algorithm (ROMA) uses HE4 along with CA125 and menopausal status to predict a woman’s risk of ovarian cancer and monitor disease with improved sensitivity and specificity over the Risk of Malignancy Index (RMI) that used CA125, pelvic sonography, and menopausal status [
6].
Recently, HE4 has been associated with the development of chemoresistance clinically. We have previously determined that HE4 and ROMA scores are more sensitive predictors of platinum response than CA125 alone [
7]. Angioli et al. reported that serum HE4 levels predict platinum-resistant versus sensitive disease at the third chemotherapy cycle with 100 % sensitivity and 85 % specificity [
8]. Similarly, another study found that serum HE4 concentration is higher in patients resistant to first-line chemotherapy [
9]. Among high-grade serous ovarian cancer (HGSC) patients, those whose HE4 levels displayed greater reduction during neoadjuvant chemotherapy had improved OS [
10]. Collectively, these studies point to HE4 as a predictor of chemotherapy response/resistance, but do not address the question of whether HE4 has a causative role in the development of resistance.
We have also shown that SKOV3 ovarian cancer cells overexpressing HE4 are more resistant to cisplatin and paclitaxel, while HE4-overexpressing OVCAR8 ovarian cancer cells exhibit greater cisplatin resistance than their null vector-transfected counterparts [
7]. In mice, SKOV3 xenografts overexpressing HE4 also grew larger than control SKOV3 xenografts [
7]. In support of these data, Wang et al. found that recombinant HE4-treated SKOV3 cells display reduced carboplatin-induced apoptosis, a decreased ratio of
BAX/
BCL2, and an overall downregulation of genes involved in DNA damage response and apoptosis [
11]. However, much remains to be elucidated regarding how HE4 promotes resistance to platinum or taxane therapies. Therefore, we sought first to confirm our preliminary studies suggesting a causative role for HE4 in cisplatin and paclitaxel resistance. Our second goal was to examine the gene expression profile of SKOV3 cells overexpressing HE4, as well as determine differences in regulation of gene expression in response to cisplatin treatment. Herein, we also begin to explore a few of the multifold processes that may contribute to HE4-mediated chemoresistance.
Methods
Cell culture
SKOV3 and OVCAR8 ovarian cancer cells were obtained from American Type Culture Collection (Manassas, VA). Cells were cultured in Dulbecco Modified Eagle Medium (DMEM, Gibco, 11965-065) with 10 % fetal bovine serum (FBS) and 1 % penicillin/streptomycin and kept in a 37 °C humidified incubator with 5 % CO2.
Stable cell lines
All null vector (NV) and HE4-overexpressing stable cell lines were previously established [
7]. To generate HE4-CRISPR Double Nickase stable cell lines, SKOV3-C1 cells were transfected in 6-well plates with 1 μg HE4 Double Nickase Plasmid (Santa Cruz, sc-402876-NIC) or Control Double Nickase Plasmid (Santa Cruz, sc-437281), using 5 μl Lipofectamine 2000 (Invitrogen). After 48 h, media was changed to 1 μg/ml puromycin containing media for five days, then split into larger plates and selected for an additional five days. RNA and tissue culture supernatant was collected to confirm downregulation of HE4 levels by quantitative RT-PCR (qRT-PCR) and ELISA. Cells were maintained in DMEM supplemented with 10 % FBS, 1 % penicillin/streptomycin, and 1 μg/ml puromycin.
Cell treatments
Cells were treated with the described doses of cis-diamminedichloroplatinum(II) (cisplatin, Sigma Aldrich, 1134357) or paclitaxel (Sigma Aldrich, T7402) dissolved in dimethyl sulfoxide (DMSO, Sigma Aldrich, D8418), or DMSO alone as a control. Cells were collected directly into either Trizol (Ambion, 15596018) or Cell Lysis Buffer (Cell Signaling, 9803) at the indicated time points for analysis. Cells were treated with recombinant human HE4 (rHE4, MyBioSource, MBS355616) added directly to the media to a final concentration of 20 nM. Cells were treated with recombinant human epidermal growth factor (rEGF, Calbiochem, 324831) at a concentration of 10 ng/ml.
Cell viability assays
All cells were seeded at 2 000 cells/well in 96-well plates. Cells were treated with increasing doses of cisplatin and paclitaxel as described. After 48 h, cell viability assays were performed by adding 10 μl/well of CellTiter 96® Aqueous One Solution Cell Proliferation MTS Assay (Promega, G3580), incubating at 37 °C/5 % CO2 for 2 h, and reading absorbance at 492 nm. Results are displayed as percent survival of vehicle treated cells.
Microarray
SKOV3-NV and SKOV3-C1 cells were treated in triplicate at 80 % confluency with 25 μM cisplatin or DMSO vehicle. Total RNA was collected 24 h later using an RNeasy Mini Kit (Qiagen, 74104) and checked for purity by NanoDrop 2000 (Thermo Scientific). The RNA samples were randomly assigned numbers and submitted to the Brown Genomics Core Facility for Bioanalyzer (Agilent 2100) RNA quality analysis. Affymetrix HTA 2.0 Arrays were performed according to the manufacturer’s instructions at the Core Facility using 150 ng total input RNA.
DAVID analysis
Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.7 [
12,
13] was used to identify the top ten enriched annotation terms among 180 genes differentially expressed (1.5-fold in either direction,
p < .05) between SKOV3-NV and SKOV3-C1. Default DAVID parameters were employed as follows:
Kappa Similarity: Similarity Term Overlap – 3; Similarity Threshold – 0.5
Classification: Initial Group Membership – 3; Final Group Membership – 3; Multiple Linkage Threshold – 0.5
Enrichment Threshold: EASE – 1.0
Stringency: Medium
Quantitative PCR
RNA was collected using an RNeasy Mini Kit (Qiagen, 74104) or Trizol extraction/LiCl precipication. Total RNA (500 ng) was reverse transcribed into cDNA using the iScript cDNA Synthesis Kit (Bio-Rad, 1708890) according the manufacturer’s protocol. To validate differentially expressed genes between SKOV3-NV and SKOV3-C1 cells identified by microarray, the same RNA samples used for the microarray were employed. To validate the differential cisplatin-induced upregulation of EGR1 between SKOV3-NV and SKOV3-C1/C7, microarray RNA samples were used, as well as RNA isolated from SKOV3-C7 cells that were treated in the same manner as the cells used in the microarray. Quantitative PCR was performed in triplicate by loading 1 μl cDNA reaction, 2 μl each of 5 μM custom forward and reverse primers (Invitrogen) or 1 μM forward and reverse validated primers (realtimeprimers.com), 10 μl SYBR Green (Applied Biosciences [ABI], 4367659) and 5 μl RNAse-free water to each well. Samples were run on an ABI 7500 Fast Real-Time PCR System, and data was analyzed using the ΔΔCt method. Relative expression levels were normalized to 18 s rRNA to correct for equivalent total RNA levels. Validated MAPT, CYP1B1, and EGR1 primers were purchased from realtimeprimers.com. Custom primer sequences (Invitrogen) are as follows:
AKT3 F – AAG GGA AGA ATG GAC AGA
AKT3 R – ATG GGT TGT AGA GGC ATC
NMUR2 F – CCG TTC CAC ATT GAC CGA CT
NMUR2 R – CAC CAC ATG GAC GAG GTT GA
SEPT3 F – TTG CCC TGC TTC GAG ACT TT
SEPT3 R – CTT TCC TCT GTG TCC ACG CT
18 s rRNA F – CCG CGG TTC TAT TTT GTT GG
18 s rRNA R – GGC GCT CCC TCT TAA TCA TG
Western blot
Protein was extracted from cell pellets in Cell Lysis Buffer (Cell Signaling, 9803) with 1 mM PMSF, according to the manufacturer’s protocol. Protein concentrations were determined by DC Protein Assay (Bio-Rad Laboratories, 5000116). Western blot analysis was performed by loading equal amounts of protein boiled with Novex Sample Reducing Agent (Life Technologies, NP009) and NuPAGE LDS sample buffer (ThermoFisher Scientific, NP0007) into a 4–12 % gradient NuPAGE Novex Bis-Tris gel [Life Technologies, NP0321BOX (mini), WG1402BX10 (midi)]. Protein was transferred by semi-dry transfer to methanol-activated 0.2 μm PVDF membranes (Bio-Rad, 162-0177) at 0.12-0.2 A for 1 h 15 m. Membranes were blocked in 5 % milk in phosphate-buffered saline with 0.05 % Tween 20 (PBS-T) for 30 m at room temperature, incubated in primary antibody in 5 % milk in PBS-T overnight at 4 °C, and then in secondary antibody in 5 % milk in PBS-T for 1 h at room temperature, with PBS-T washes in between. Amersham ECL Prime Western Blot Detection System (GE Healthcare, RPN2232) was used for detection of HRP-tagged secondary antibodies. Blots were developed using x-ray film in a Kodac film developer or imaged directly in a Biorad Chemidoc MP Imaging System. GAPDH was used as a loading control. Antibodies and dilutions used are as follows:
PARP (Cell Signaling, 9532, 1:1000)
phospho-p44/42 MAPK (ERK1/2) (Cell Signaling, 4370, 1:2000)
p44/42 (ERK1/2) (Cell Signaling, 9102, 1:2000)
EGR1 (Santa Cruz, sc-110, 1:200)
p38 (Cell Signaling, 9212, 1:1000)
phospho-p38 (Cell Signaling, 9215, 1:1000)
GAPDH (Cell Signaling, 2118, 1:2000)
β-tubulin (Cell Signaling, 2146, 1:2000)
α-tubulin (Cell Signaling, 2144, 1:1000)
Densitometry
Image J was used to perform densitometry analysis of western blots. Images of blots were analyzed in 8-bit TIFF format, using the “analyze gel” function. Where no band was detected, a value of “1” was assigned. Relative band densities were normalized to a loading control, or the appropriate total protein for phospho-proteins, and then the lowest value was set to 1.
Statistics
In all instances where statistics are shown, they represent n ≥ 3 independent experiments, and p-values were determined by unpaired 2-tailed Student t-test. For the microarray, Affymetrix Transcriptome Analysis Console (TAC) software was used to generate fold-changes and statistical significance, and ANOVA p-values generated by TAC were used for p-value cutoffs.
Discussion
The ways in which HE4 affects chemoresistance are likely multi-factorial and cell type-specific. In cells that already display a high degree of chemoresistance due to other mechanisms, such as OVCAR8 cells, the effect of increasing levels of HE4 may be minimal. In contrast, in cells with low levels of HE4 that are not as chemoresistant, increasing the level of HE4 may produce dramatic changes in apoptotic response to drug treatment, as we have observed in SKOV3 cells.
Human HE4 purified from seminal fluid has been described to possess cross-class protease activity [
4]; however, that study reports seminal fluid HE4 to exist as a trimer migrating at 42 kDa under non-reducing conditions and 14 kDa under SDS-PAGE reducing conditions. It is possible that HE4 from different tissues may preferentially exist in different forms, thus possessing different functions. Indeed, in our lab, we observe HE4 migrating on SDS-PAGE under reducing conditions as a 25 kDa protein, which is in agreement with a study by Drapkin et al. showing glycosylated HE4 migrating at 25 kDa in CaOV3 and OVCAR5 cells [
29]. This diversity in size may result in different enzymatic activities of HE4. If HE4 does modulate protease activity, it logically will affect a wide variety of cellular functions, since proteases are essential for many biological processes including growth factor signaling. For example, hepatocyte growth factor (HGF) [
30], transforming growth factor beta (TGFβ) [
31], and certain members of the platelet-derived growth factor (PDGF) family [
32] are activated by proteases. Our data would suggest that anti-proteases may also serve a function in activating growth factor cascades in some cases.
In our current study, we have observed a variety of differences between SKOV3-NV and HE4-overexpressing cells in how they respond to cisplatin and paclitaxel. EGR1 is a transcription factor that is induced by a variety of stimuli or stresses, including growth factors, hormones, ionizing radiation, and chemotherapeutic drugs [
16,
19,
33‐
37]. It has been shown to regulate differentiation, proliferation, and apoptosis in cell type-specific manners by promoting expression of several genes, including
TP53 (p53),
BCL2,
PTEN,
IGF2,
PDGF,
VEGF,
TGFB1, and
TNF [
18]. In our study, at 24 h after cisplatin treatment,
EGR1 is upregulated in SKOV3-NV cells, but not in SKOV3-C1 or SKOV3-C7 cells. EGR1 is a transcriptional regulator of growth differentiation factor 15 (
GDF15) [
38], which was also upregulated in SKOV3-NV by cisplatin but not in SKOV3-C1 cells. While
GDF15 has been linked to platinum resistance in pancreatic cancer [
39] and ovarian cancer [
40], it has also been shown to be a common platinum-responsive gene [
41,
42], and was identified as a potential serum marker for cisplatin-response of ovarian cancer cells [
43]. Thus, the fact that it is not upregulated by cisplatin in SKOV3-C1 cells is also indicative of their dampened cisplatin resistance. Another gene, DNA-damage-inducible transcript 3 (
DDIT3), was also cisplatin-induced in SKOV3-NV cells, but not SKOV3-C1 cells. Suppression of
DDIT3 mRNA upregulation has been shown to be involved in chemoresistance of malignant pleural mesothelioma cells [
44], further suggesting that multiple mechanisms may play a role in HE4-mediated chemoresistance. Lastly, tumor necrosis factor (TNF), whose gene is also EGR1-regulated [
18], activates MAPKs and induces apoptosis [
45]; thus, its lack of cisplatin-mediated upregulation in SKOV3-C1 cells could also contribute to suppression of pro-apoptotic signaling and cisplatin response in these cells.
EGR1 gene expression is known to be regulated by activated MAPKs, including phospho-ERK [
19,
21] and phospho-p38 [
20]. Although we observe comparable increases in phospho-ERK levels in SKOV3-NV and SKOV3-C1 cells treated with 25 μM cisplatin, it is possible that HE4 affects the activity of phospho-ERK, leading to suppression of EGR1. Indeed, our results showing subtle suppression of ERK activation in HE4-overexpressing clones treated with high dose cisplatin (80 μM), as well as an effect of rHE4 on ERK activation in SKOV3-WT and OVCAR8-WT cells, support this hypothesis. HE4-mediated suppression of p38 appears to be more straightforward. We did not observe any effect of rHE4 on phospho-p38 levels (data not shown), but we did see more robust activation of p38 in SKOV3-NV cells than in SKOV3-C1 or SKOV3-C7 cells in response to two different doses of cisplatin (25 μM and 80 μM), confirming that some elements of MAPK signaling are deregulated in HE4-overexpressing cells. This suppression of p38 activation could play a role in the suppression of cisplatin-induced upregulation of
EGR1.
Another significant effect that we report here is the increase in α-tubulin and β-tubulin levels in SKOV3-WT and OVCAR8-WT cells treated with rHE4. Interestingly, tubulins have been reported to be the target of the serine protease HtrA1 [
46]; therefore, if HE4 inhibits serine protease activity, tubulins may accumulate. We postulate that the increase in tubulin levels we observe is not due to a transcriptional effect, since we did not detect an increase in any tubulin gene in OVCAR8-WT cells treated with 20 nM rHE4 for 6 h (data not shown). However, as we show here, we do see an increase in gene expression levels of
MAPT in SKOV3-C1 cells compared to SKOV3-NV, which together with a putative stabilization of tubulin protein could influence paclitaxel resistance. Several lines of evidence connect β-tubulin and
MAPT to taxane resistance. In addition to the correlation between
MAPT and β-tubulin III levels and paclitaxel resistance in gastric cancer [
23], downregulation of
MAPT was also shown to improve taxane response in breast cancer cell lines [
24] and in ovarian cancer three-dimensional collagen I matrix culture [
22]. Clinically, expression of the tau protein is associated with worse survival of taxane-treated breast cancer patients [
28]. Since tau proteins are responsible for stabilizing microtubules [
47], higher levels of MAPT could affect the polymerization of microtubules by paclitaxel.
In addition to differences in how HE4-overexpressing cells respond to cisplatin, intrinsic differences between SKOV3-NV and SKOV3-C1 cells could also contribute to resistance, or could indicate other biological functions of HE4. For example,
AKT3, which is more highly expressed in SKOV3-C1 cells, has been shown to promote cisplatin resistance in SKOV3 and OVCAR3 cells [
48]. Another gene of interest that is upregulated in SKOV3-C1 cells is
SEPT3. The septin family of GTP-binding proteins polymerize to form cytoskeletal filaments [
49], further implicating HE4’s putative involvement in cytoskeleton organization. Several genes that appear upregulated in HE4-overexpressing cells are in agreement with the pro-proliferative role described for HE4 [
7,
11,
50], including
AKT3, Ras-related GTP binding D (
RRAGD), and glypican 4 (
GPC4).
Several other future directions remain to clarify how HE4 promotes collateral resistance to platinum and taxane therapies. Further clarification of precisely how HE4 affects cell signaling, including MAPK signaling, in diverse cell lines is necessary. Confirmation of HE4’s anti-protease function in ovarian cancer cells would be useful in this regard. Moreover, additional information is needed on how HE4 affects tubulin organization and dynamics. Lastly, although we do not have any preliminary data to suggest that there are differences in drug uptake or clearance between SKOV3-NV and SKOV3-C1 cells, it would be necessary to measure intracellular drug levels at various time points post-treatment before ruling this out as another contributing mechanism to chemoresistance. Future studies should also address whether HE4 promotes resistance to other commonly used ovarian cancer treatments such as doxorubicin.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
JRR, RGM, RKS, CS and KK designed experiments. JRR executed all experiments with assistance from NR. NY developed null vector and HE4-overexpressing stable clones. CS performed the microarrays. JRR prepared this manuscript in its entirety. All authors reviewed, edited, and approved this manuscript.