Through the years, clinical and laboratory studies have suggested the possibility that modifiable conditions during anesthesia and surgery affect cancer recurrence. One of these factors is opioid administration. Recently, several retrospective studies reported that patients who received general anesthesia with large amounts of opioids showed more cancer progression or recurrence than patients who received regional anesthesia or a lower amount of opioids.1–3 These studies caused significant alarm on the use of opioids in cancer patients, especially in breast and prostate cancers, but the retrospective nature of the studies with numerous confounding factors led to limited implications. Notably, a subsequent prospective study on abdominal cancer surgery failed to validate any influence of opioids on cancer outcome.4 However, it is increasingly recognized that opioids may have a significant impact on long-term prognosis of cancer patients. As a result, several large-scale, prospective, randomized clinical trials are underway to elucidate the underlying mechanisms (NCT00418457, NCT00684229).5
Opioid receptors exist mostly in central and peripheral neuronal tissues, and even though it is not well established, they are also expressed in immune cells and cancer tissues.6,7 Thus, it is possible that opioids regulate cancer growth through these extraneural opioid receptors by eliciting an immunologic or direct effect on cancer cells.
Until now, the effect of morphine on cancer progression has been studied mostly in relation to μ-opioid receptors (MOR).8–10 However, in the current study, we hypothesized that cancer cell proliferation can also be mediated by the opioid growth factor receptor (OGFR), previously known as the ζ-opioid receptor.
OGFR is distinguished from classic opioid receptors (μ, δ, and κ) and does not have any function in analgesia; it is a negative regulator of cell proliferation. Endogenous opioid peptide, [Met5]-enkephalin, also known as opioid growth factor (OGF), is a ligand of OGFR.11–13 OGFR exists in various tissues and regulates tissue growth, embryonic development, wound repair, and cancer progression.
We postulated that exogenous opioids such as morphine would also bind to OGFR. The supporting data for this assumption include the reversal of the effects of both morphine and OGF by the opioid antagonist, methylnaltrexone,14 displacement of OGF by morphine at high concentrations, and subsequent binding of morphine to OGFR in a radioisotope experiment.15
Therefore, we hypothesized that opioids interact with OGFR and suppress lung cancer cell growth. We investigated the expression of OGFR in human lung cancer tissues and cell lines and evaluated whether morphine suppresses the growth of lung cancer cell lines and evaluated the role of OGFR in morphine-induced cancer growth suppression.
In this study, we chose a lung cancer model because large doses of opioids are frequently administered to alleviate severe thoracotomy pain and because there are alternatives for analgesia, such as thoracic epidural, or paravertebral block, for lung cancer surgery.
This study was approved by the IRB at Samsung Medical Center (IRB number: 2013-01-041). In this study, previously collected human biological specimens were used after receiving written consent from the patients.
Sixteen patients who had undergone lung resection surgery between December 2009 and May 2011 were included in this study. Patients were diagnosed with lung cancer by pathology. All lung tissue specimens were paraffin-embedded, formalin-fixed tissues. Malignant tissues and normal tissues of the tumor margin were collected from each patient.
Cell Culture and Reagent
Human nonsmall cell lung cancer cell lines, H1703 (squamous cell carcinoma), A549 (adenocarcinoma), H1975 (adenocarcinoma), H358 (bronchioloalveolar carcinoma), H23 (adenocarcinoma), H1993 (adenocarcinoma), and HCC1588 (squamous cell carcinoma) were tested.
Cells were cultured in Roswell Park Memorial Institute complete medium at 37°C in a humidified atmosphere of 5% carbon dioxide and 95% air. All media were supplemented with 10% fetal calf serum and antibiotics (5000 units mL−1 penicillin, 5 µg mL−1 streptomycin). Morphine sulfate hydrate (BC World Pharm. Co., Ltd., Seoul, Korea) and OGF (Met/Lew-enkephalin; Santa Cruz Biotechnology, Dallas, TX, #sc-47705) were diluted with sterile water.
The antibodies for morphine, MOR, and OGFR were mouse morphine monoclonal antibody (Mybiosource, San Diego, CA, #MBS533090), rabbit MOR1 polyclonal antibody (Santa Cruz biotechnology, #sc-15310), rabbit OGFR polyclonal antibody, (Proteintech, Chicago, IL, #11177-1-AP, for confocal), and goat OGFR polyclonal antibody (Santa Cruz biotechnology, #sc-85798, for immunohistochemistry, Western blot), respectively.
The cells were incubated for 48 hours with various concentrations of morphine (control, 10–9, 10–7, and 10–5 M; n = 6 for each concentration) to determine the dose-proliferation effect. Cell proliferation was suppressed at all tested morphine concentrations compared with the control; however, dose dependency was not observed (cell count: median [interquartile] × 105/mL, control: 6.7 [6.0–6.9], 10–9 M: 5.1 [3.9–5.4], 10–7 M: 6.0 [5.8–6.5], 10–5 M: 4.8 [3.7–5.2], P = 0.0157 between control and 10–5 M, P = 0.0156 between control and 10–9 M; Bonferroni correction was performed with denominator = 4).
In our experiment, the morphine concentration of 10−7 M was chosen based on clinically effective analgesic concentration in plasma16 and based on the protocols of previous in vitro studies.6,17 The OGF concentration of 10−6 M was used based on the previous studies.18
Human Lung Immunohistochemistry
Malignant and adjacent normal sections were processed for immunohistochemistry. In brief, frozen sections were heated in high buffer (pH = 8.0) for 20 minutes and incubated overnight at room temperature with rabbit MOR1 polyclonal antibody (Santa Cruz Biotechnology, #sc-15310, 1:50) or goat OGFR polyclonal antibody (Santa Cruz Biotechnology, #sc-85798, 1:20). This was followed by a 50-minute incubation with a secondary antibody (polyclonal goat anti-rabbit horseradish peroxidase [HRP]-conjugated immunoglobulin G [DAKO, Glostrup, Denmark, #P044801] or polyclonal rabbit anti-goat HRP-conjugated immunoglobulin G [DAKO, #P044901], respectively) and a 30-minute incubation with an EnVision™ detection kit (Peroxidase/DAB K5007; DAKO). Immunoreactivity was visualized using an Olympus BX50 microscope (Tokyo, Japan).
Quantification of MOR and OGFR Expression in Human Lung Samples and Cell Lines
Sixteen paired tissue samples were analyzed using real-time reverse transcription polymerase chain reaction (RT-PCR) using TaqMan Gene Expression Assays. PCR amplification and detection were carried out in a StepOnePlus™ real-time PCR System (Applied Biosystems, Waltham, MA). Total RNA was isolated by Ambion® RNA isolation kit (Applied Biosystems). RT-PCR data were normalized to glyceraldehyde 3-phosphate dehydrogenase, and final relative quantitation (RQ) values were provided. To validate the expression of MOR and OGFR in human lung cancer cell lines, samples of A549, H1703, H358, H23, H1975, H1993, and HCC1588 were also analyzed.
Cells were harvested on drug treatment days 1, 2, and 3 by trypsinization, stained with trypan blue, and counted with a Countess™ automated cell counter (Invitrogen/Life Technologies, Carlsbad, CA) by a researcher who was blinded to the study design. At least 2 aliquots per well and 3 wells per treatment per time point were sampled.
Knockdown of OGFR
The OGFR small interfering RNA (siRNA) for human (#sc-75991) and control siRNA (#sc-37007) were purchased from Santa Cruz Biotechnology. Cells were incubated in 35-mm well plates containing 1 mL serum-containing media without antibiotics for 1 day. At 60% confluence, cells were transfected with either 20 nM OGFR-siRNA or scrambled siRNA solutions in serum and antibiotic-free media and incubated for 4 hours at 37°C. After drug treatment, cultures were incubated for an additional 48 hours in fresh complete media. Afterward, cells were collected and either counted or harvested for flow cytometry cell cycle analysis. OGFR knockdown was confirmed by Western blot analysis (goat OGFR polyclonal antibody [Santa Cruz biotechnology, #sc-85798]).
Cells were lysed in radioimmunoprecipitation assay buffer, then the samples in sodium dodecyl sulfate gel-loading buffer with 200 mM dithiothreitol were subjected to 15% sodium dodecyl sulfate polyacrylamide gel electrophoresis, followed by nitrocellulose transfer and antibody probing (primary: goat OGFR polyclonal antibody, #sc-85798, 1:200; Santa Cruz biotechnology; secondary: polyclonal rabbit anti-goat HRP-conjugated immunoglobulin G, #P044901, 1:5000; DAKO). Immunoreactive bands were visualized using enhanced chemiluminescence (Thermo Scientific, Carlsbad, CA, SuperSignal Chemiluminescent HRP Substrates). Optical densities were normalized to β-actin (Sigma-Aldrich, St. Louis, MO, 1:5000).
H1975 cells were grown on a 4-well glass slide (Lab-Tek2 Chamber Slide™ System, Sigma-Aldrich, #1545264) in regular growth media to 50% to 60% confluence. Cells were treated with drugs for 24 hours. Then, the cells were washed in phosphate buffered saline (PBS) and fixed in 4% paraformaldehyde for 15 minutes at room temperature. The cells were permeabilized in 0.2% Triton X-100 for 10 minutes and washed in PBS 3 times. This was followed by blocking using superblock (Thermo, #37515) for 1 hour at room temperature. The cells were incubated with primary antibodies (morphine: mouse morphine monoclonal, Mybiosource #MBS533090, 1:50; OGFR: rabbit OGFR polyclonal, Proteintech #11177-1-AP, 1:50) overnight at 4°C. Secondary antibodies were Alexa (Thermo Scientific) 594-conjugated goat anti-mouse immunoglobulin G for morphine antibody (orange, 1:200) and Alexa Fluor 488-conjugated goat anti-rabbit immunoglobulin G for OGFR antibody (green, 1:200). Secondary antibodies were treated for 1 hour at room temperature in a dark chamber. Finally, slides were mounted using vectashield mounting medium with 4,6-diamidino-2-phenylindole (Vector Laboratories, Burlingame, CA). Slides were observed using a LSM750 confocal microscope (Zeiss, Jena, Germany).
Cell Cycle Analysis by Flow Cytometry
For cell cycle analysis, cells were washed with ice-cold PBS, harvested by trypsinization, and fixed with 90% ethanol. After removing all fixative from cells, 0.5 mL of FxCycle™ PI/RNase Staining Solution (Molecular Probes by Life Technologies, Eugene, OR, #F10797) was added to the cell pellet and vortexed gently. Samples were incubated for 15 minutes at room temperature and protected from light. DNA content was determined using flow cytometry (BD Bioscience, Franklin Lakes, NJ, FACSVerse™, excitation: 488-nm, 532-nm; collection: 585/42 bandpass filter). The cell cycle distribution was assessed with FACSuite™ (San Diego, CA) program.
Data were expressed as the median (interquartile range). Because there are no previous study on OGFR in lung cancer, we referenced a previous finding on ovarian cancer.19 In this study, OGFR was 48% less expressed in the cancer tissues relative to normal ovarian tissues with approximate SDs of 20% for normal tissues and 10% for cancer tissues.18 Therefore, we regarded 48% as the meaningful difference in OGFR expression between lung cancer tissues and normal tissues with approximate SDs of 20% for normal tissues and 10% for cancer tissues. A sample size of 3 in each cancer and normal tissue group was required with an α level of 0.05 and statistical power of 80%. Considering an experimental failure, we chose a sample size of 8 (8 cancer tissues and 8 adjacent normal tissues) for each cancer type. The Mann-Whitney test was used for the comparison between 2 independent groups. Comparisons among multiple groups were performed with the Kruskal-Wallis test. P values were corrected by Bonferroni method in case of multiple testing (denominator = 3, if not specified). The relationship between cancer cell growth rate and OGFR expression was tested by Spearman correlation analysis. P value <0.05 was considered significant. Statistical analysis was executed using SAS version 9.3 (SAS Institute, Cary, NC).
OGFR Expression in Tissue and Cell Lines
Lung tissues obtained from 16 patients comprised malignant and adjacent normal lung tissues. Of the lung cancer specimens analyzed, 8 were squamous cell carcinoma and 8 were adenocarcinoma. The mean patient age was 62 years (range, 38–77 years), and 63% of the patients were men.
Immunohistochemical preparations revealed that both OGFR and MOR were expressed in normal and cancer tissues. Immunoreactivity was observed in the nucleus and throughout the cytoplasm (Fig. 1). In RT-PCR, OGFR expression was higher in normal lung tissues than in lung cancer tissues. MOR expression was also detected in the tissue samples, but it was much lower than that of OGFR (Table 1). OGFR expression was higher in adenocarcinoma than in squamous cell carcinoma (RT-PCR RQ value: median [interquartile range]: 13.1 [9.3–20.0] vs 4.3 [2.2–6.6]; P = 0.0046) (Table 1 and Fig. 2).
OGFR expression was also detected at various levels in all human lung cancer cell lines. In contrast, MOR expression was considerably lower than OGFR expression in most of the cell lines and even undetectable in H1703, H1975, and H1993. Conversely, H358, a bronchioloalveolar carcinoma, markedly expressed MOR (Table 2).
Inverse Correlation of OGFR Expression and Cell Growth
We chose 3 lung cancer cell lines (H1975, A549, and H1703) with different OGFR expressions and studied their growth potential in relation to OGFR expression. Growth rate was defined as growth magnification over 48 hours. H1975 (adenocarcinoma), which had the highest OGFR expression level, showed the slowest growth rate (median [interquartile range], 0.87 [0.83–0.88]). On the contrary, H1703 (squamous cell carcinoma), which had the lowest OGFR expression level, showed rapid growth (median [interquartile range]: 6.5 [6.0–7.5]). A549 (adenocarcinoma), which showed a medium level of OGFR expression, showed a medium growth rate (median [interquartile range], 3.8 [3.8–4.3]) (P = 0.0008 among groups). When samples were plotted by their OGFR expression and cell proliferation, an inverse relationship was observed (Spearman correlation analysis, r = −0.92, P = 0.0001; Fig. 3).
Morphine-Induced Cell Growth Suppression
We selected H1975, which markedly expressed OGFR but not MOR, to determine whether exogenous opioids modulate the growth of lung cancer cells through OGFR.
Morphine and OGF treatment reduced the median H1975 cell number by approximately 23% and 19%, respectively, compared with the control (median cell number [interquartile range] × 105/mL: control: 11.3 [9.6–12.0], morphine 8.7 [7.6–9.7], OGF 9.1[8.1–9.6], P = 0.01 between control and morphine, P = 0.002 between control and OGF, P values after Bonferroni correction) (Fig. 4). Furthermore, a significant decrease in H1975 cell growth was observed over 96 hours after morphine treatment (P < 0.001; Fig. 5).
Interaction Between Morphine and OGFR
Knockdown (kd) of OGFR resulted in an approximate 53% increase of H1975 cell growth compared to the control. The inhibitory effect of exogenous morphine on cancer cell lines was abolished when OGFR was knocked down (median cell number [interquartile range] × 105/mL: control 3.60 [3.58–3.70], OGFR kd 5.8 [4.7–6.1], morphine + OGFR kd 5.1 [5.0–5.3], P = 0.001 between control and OGFR kd, P = 0.0009 between control and morphine + OGFR kd, P values after Bonferroni correction; Fig. 6).
Confocal microscopy showed colocalization of morphine and OGFR. Addition of OGF displaced morphine from OGFR, which indicates that morphine and OGF bind to the same receptor, OGFR (Fig. 7).
Flow cytometry was performed to determine whether morphine affects the cell cycle. Morphine decreased the S phase cell population compared with the control (Table 3).
There have been inconsistent reports on whether opioids promote or inhibit lung cancer growth. We suggested that OGFR, a negative regulator of cell proliferation, is a binding site of morphine and showed that morphine interacts with OGFR to suppress the growth of lung cancer cell lines.
OGFR is a protein with 677 amino acids and is encoded by the OGFR gene at chromosome 20. 14,15,20 OGFR is localized in both the nucleus and the cytoplasm and functions as a receptor for OGF. OGF, also known as [Met5]-enkephalin, is an endogenous opioid peptide and is reported to inhibit the proliferation of neoplastic and normal cells.15
In the current study, OGFR was expressed in both human lung cancer tissues and cell lines. Similarly, Zagon et al.14 evaluated 31 human cancer cell lines, which represent >90% of neoplasia occurring in humans, and demonstrated that OGFR was expressed in all of these cancer cell lines, including lung cancers. The addition of OGF to cultures decreased cell numbers up to 41%, whereas naltrexone, an opioid receptor antagonist, increased cell proliferation by up to 44%.20–22
In the current study, the level of OGFR expression in lung cancer tissues was about one-fourth of that in the normal lung tissues. The decreased level of OGFR in the lung cancer tissues is analogous to other types of cancers. For example, in head and neck cancer, OGFR protein levels decreased in tumor tissues relative to normal epithelium.21 In human ovarian cancer, OGF and OGFR protein levels decreased by 58% and 48%, respectively, compared with that in normal ovarian cells.19 Therefore, it may be speculated that the decreased levels of OGFR in cancer cells are partly responsible for cancer progression.
Our experiment demonstrated that OGFR expression differed according to human lung cancer cell types and according to cancer cell lines (higher expression in adenocarcinoma than squamous carcinoma). The expression of OGFR and the growth of cancer cells showed an inverse correlation.
In this study, exogenous morphine suppressed the growth of the adenocarcinoma cell line H1975. Previous studies on lung cancer and morphine reported cancer cell proliferation8,10 and increased primary tumor growth and lung metastasis9 after morphine treatment. However, they used different cell lines8,10 or very low doses of morphine compared with other studies (1 nM vs 10 μM−1 mM).9 On the contrary, an in vivo study using a metastasized tumor from a colon cancer cell line showed that morphine inhibited cancer cell growth and metastasis to the lungs.23 All of these studies focused on MOR and did not measure OGFR.
In the current study, the suppressive effect of opioids on lung cancer was explained by the interaction between opioids and OGFR. We postulated that exogenous opioids such as morphine would bind to OGFR, and this was corroborated by confocal microscopy demonstrating colocalization of both morphine and OGFR. Furthermore, morphine was displaced from OGFR in the presence of OGF. In addition, the cancer-suppressive effect of morphine is attenuated upon knockdown of OGFR. These findings indicate that OGF and morphine share stereospecific, pharmacological characteristics.
Previous reports suggested cell cycle arrest as a mechanism of OGF-OGFR–related growth suppression. OGFR upregulates cyclin-dependent kinase inhibitory pathways (cyclin dependent kinase inhibitors p16 and/or p21) in the cell cycle, leading to a decrease in DNA synthesis and subsequent cell proliferation.24–26 In our study, morphine decreased cell population by approximately 46% in the S phase. Morphine induced G2/M phase cell cycle arrest in gastric cancer cell line.27 Therefore, the specific site of arrest may differ according to treatment or cell type.
This study has several limitations. First, the expression of opioid receptors may differ according to cancer cell type. Lung cancer cell lines tested in this study did not express MOR significantly but markedly expressed OGFR. However, large-cell carcinoma28 and bronchioloalveolar cells10 were reported to express a high level of MOR, and accordingly, H358, a bronchioloalveolar carcinoma, was found to markedly express MOR in our study. In general, cells expressing high levels of MOR may proliferate in response to morphine. Therefore, it would be interesting to determine the cell lines or human cancers that express MOR or OGFR and verify whether cancer cells respond differently to exogenous opioids according to their specific opioid receptors. Second, H1975 did not express MOR but might express other types of opioid receptors, such as δ and κ, in addition to OGFR. Morphine may have also bound these receptors and suppressed cell growth. Third, morphine is known to influence immune cells and the inflammatory reaction; therefore, the effect of opioids may be different in an in vivo model. The comprehensive effect of opioids should be evaluated further in in vivo experiments. However, the results of our experiment have shed light on the possibility of an opioid-OGFR interaction and a positive effect of opioids on cancer recurrence.
In conclusion, lung cancer expresses OGFR, which is a downregulator of cell growth. A clinical concentration of morphine suppresses the proliferation of H1975 (adenocarcinoma), which expresses high OGFR, and the underlying mechanism may be explained by morphine-OGFR binding. For clinical implications, the perioperative use of opioids may be safe with regard to cancer progression in high-OGFR lung cancers. In addition, perhaps both the reversal of morphine analgesia with naloxone and the treatment of morphine side effects with methylnaltrexone might have a negative impact on the outcomes in these cancers.
Name: Ji Yeon Kim, MD, PhD.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Name: Hyun Joo Ahn, MD, PhD.
Contribution: This author helped design the study, conduct the study, analyze the data, and prepare the manuscript.
Name: Jin Kyoung Kim, MD, PhD.
Contribution: This author helped conduct the study and analyze the data.
Name: Jhingook Kim, MD, PhD.
Contribution: This author helped analyze the data and prepare the submitted manuscript.
Name: Sang Hyun Lee, MD.
Contribution: This author helped analyze the data and prepare the manuscript.
Name: Hyun Byung Chae, MD.
Contribution: This author helped analyze the data and prepare the manuscript.
This manuscript was handled by: Markus W. Hollmann, MD, PhD, DEAA.
The authors thank Sun Ryu, PhD, for her RT PCR; statisticians: Sun Woo Kim, PhD in Statistics, Biostatistics and Clinical Epidemiology Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea; and English revision: Nikesh Gosalia, Vice president, Author Service, Editiage, Cactus Communications; Ko Justin Sangwook, MD, PhD, Department of Anesthesiology and Pain Medicine, Department of Thoracic and Cardiovascular surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
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© 2016 International Anesthesia Research Society
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