A reactive oxygen species scoring system predicts cisplatin sensitivity and prognosis in ovarian cancer patients
To reveal roles of reactive oxygen species (ROS) status in chemotherapy resistance and to develop a ROS scoring system for prognosis prediction in ovarian cancer.
We tested the sensitizing effects of ROS elevating drugs to cisplatin (cDDP) in ovarian cancer both in vitro and in vivo. A ROS scoring system was developed using The Cancer Genome Atlas (TCGA) database of ovarian cancer. The associations between ROS scores and overall survival (OS) were analyzed in TCGA, Tothill dataset, and our in-house dataset (TJ dataset).
ROS-inducing drugs increased cisplatin-induced ovarian cancer cell injury in vitro and in vivo. ROS scoring system was established using 25 ROS-related genes. Patients were divided into low (scores 0–12) and high (scores 13–25) score groups. Improved patient survival was associated with higher scores (TCGA dataset hazard ratio (HR) = 0.43, P < 0.001; Tothill dataset HR = 0.65, P = 0.022; TJ dataset HR = 0.40, P = 0.003). The score was also significantly associated with OS in multiple datasets (TCGA dataset r2 = 0.574, P = 0.032; Thothill dataset r2 = 0.266, P = 0.049; TJ dataset r2 = 0.632, P = 0.001) and with cisplatin sensitivity in ovarian cancer cell lines (r2 = 0.799, P = 0.016) when used as a continuous variable. The scoring system showed better prognostic performance than other clinical factors by receiver operating characteristic (ROC) curves (TCGA dataset area under the curve (AUC) = 0.71 v.s. 0.65, Tothill dataset AUC = 0.73 v.s. 0.67, TJ dataset AUC = 0.74 v.s. 0.66).
ROS status is associated with chemotherapy resistance. ROS score system might be a prognostic biomarker in predicting the survival benefit from ovarian cancer patients.
KeywordsSerous ovarian Cancer ROS Scoring system Prognosis
Reactive oxygen species
The Cancer Genome Atlas
International Federation of Gynecology and Obstetrics
Area under the curve.
Ovarian cancer is the second common diagnosed and the most lethal of the various gynecologic malignancies . Although ovarian cancer patients are sensitive to platinum- and taxane-based chemotherapy during initial treatment, a significant proportion of patients relapse and develop platinum resistance [1, 2].
Reactive oxygen species (ROS) are oxygen-containing reactive chemical molecules generated during metabolic processes. ROS play an essential role in signal transduction pathways , cell cycle progression [3, 4, 5], gene transcription [3, 6], cell differentiation [7, 8], and cell death . Elevated oxidative stress and delicate redox balance were detected in cancer cells due to activation of oncogene, high metabolic activity, and mitochondrial malfunction [6, 9, 10]. Deprivation of the redox balance through an increase in ROS levels or a decrease in the cellular antioxidant capacity to induce cellular ROS burst have shown therapeutic benefits in cancer cells [11, 12, 13]. Most chemotherapeutics, including platinum and taxanes, exert anti-cancer effects by inducing ROS-mediated cell damage in cancer cells [14, 15, 16, 17]. Since new therapeutic approaches combining chemotherapeutics with ROS-elevating drugs have exhibited improvement of the cytotoxicity and reduction of the resistance [18, 19, 20]. Moreover, some studies have shown that part of patients with drug resistance attribute to the lower level of tumor cell oxidative stress and stronger antioxidant ability [21, 22].
Therefore, it is of increasing interest to develop a prognostic method to predict patients who will benefit from chemotherapy or additive ROS inducer based on the quantifiable criteria of ROS status. In this paper, we reflected that ROS is involved in the drug resistance and chemo-sensitivity in vitro and in vivo. We then established a comprehensive scoring system through analyzing the relationship between the expressions of ROS pathway genes, including genes involved in oxidative stress, oxidation reaction, antioxidant response, and prognosis of patients in TCGA database. We validated the effect of this system in Tothill database and ovarian cancer patients from our hospital. The result showed that this scoring system might be clinically applied to predict the outcome of chemotherapy in ovarian cancer patients.
SKOV3 (HTB-77), Caov3 (HTB-75), OVCAR3 (HTB-161), and OV-90 (CRL-11732) ovarian cancer cell lines were purchased from the American Type Culture Collection (ATCC, Manassas, VA, US) and cultured as recommended. Caov3 was cultured in DMEM with 10% FBS (Invitrogen) and OVCAR3 was cultured in RPMI 1640 with 10% FBS (Invitrogen) and 10μg/ml insulin (Bovine). SKOV3 was cultured in McCoy 5A with 10% FBS (Invitrogen). OV-90 was grown in a 1:1 mixture of MCDB 105 medium containing a final concentration of 1.5 g/L sodium bicarbonate and Medium 199 containing a final concentration of 2.2 g/L sodium bicarbonate with 15% FBS (Thermo Scientific). Cisplatin-sensitive ovarian cancers cell line (OV2008) and its resistant variant (C13*) were gifts from Prof. Benjamin K. Tsang in the Ottawa Health Research Institute, Ottawa, Canada  and cultured in RPMI 1640 medium with 10% FBS (Invitrogen). All cells were free from mycoplasma and were used between 3 and 5 passages after thawing. All cells were authenticated by china center for type culture collection (CTCC, Wuhan, China) using short tandem repeat (STR) DNA profiling. Primary cell lines were isolated from ovarian cancer tissue specimens of patients undergoing surgical resection as previously described  and cultured in DMEM/F12 medium (Invitrogen) with 20% FBS (Invitrogen). The passage number of primary ovarian cancer cells during the experiment was ranged from 3 to 8 generations. All cell lines were cultured in a 37 °C humidified atmosphere containing 5% CO2.
Assessment of cell viability
Viability of cells were assessed by Cell Counting Kit-8 reagent (CCK8, Dojindo, Tokyo, Japan). 5 × 103 cancer cells were seed in 96-well plate and treated with cDDP at different concentrations for 48 h with or without ROS-elevating (PLX4032 (1 μM), Piperlongumine (PIPER, 10 μM) and β-phenylethyl isothiocyanate (PEITC, 10 μM)) or ROS-scavenging drugs (glutathione (GSH, 2 mM), N-acetyl cysteine (NAC, 1 mM) and Vitamin C (VitC, 1 mM)). Supernatants were removed and 100 μl of CCK8 solution (1:10 dilution) were added to the cancer cells. After 2 h incubation at 37 °C in dark, optical density (OD) at 450 nm was measured by a microplate reader. The IC50 value for each cell line was determined by nonlinear regression analysis using GraphPad Prism (GraphPad Software Inc., San Diego, CA). The results were tested by three independent experiments.
ROS measurement was assayed using dichloro-dihydro-fluorescein diacetate (DCFDA; Beyotime, Shanghai, China), according to the manufacturers’ instructions. Briefly, cells were loaded with DCFH-DA, washed with ice-cold HBSS. Then, the fluorescence intensity of the cells was measured at 488 nm by flow cytometry.
Tumor Xenograft studies
The study was approved by the Ethical Committee of the Medical Faculty of Tongji Medical College (Wuhan, China), and performed in accordance with the relevant guidelines and regulations. Six-week-old athymic female homozygous BALB/c nude mice (SPF) were bought from Beijing Hua Fukang biological Polytron Technologies Inc., and reared in accordance with the relevant guidelines and regulations. C13* cells (107 cells/0.1 ml PBS/mice) intraperitoneally injected into the right flank of BALB/C nude mice, under isoflurane-induced anesthesia. Treatment began when the tumor volume reached between 70 and 100mm3. The mice were randomly divided into 4 groups (n = 6) including PBS group, cDDP treatment group (cDDP 2.5 mg/kg, i.p., every 4 days for 28 days), PIPER treatment group (PIPER 2 mg/kg, i.p., daily for 28 consecutive days), and cDDP combination with PIPER treatment group (same dose as used in the single-agent groups). Following the initial treatment, the tumor sizes were measured every 2 days. Tumor volumes (V) were calculated by the following formula: V (mm3) = length × (square of width)/2. The mice were euthanized by cervical dislocation. Tumors were excised, weighed, paraformaldehyde-fixed paraffin embedding and used for ex vivo immunohistochemical staining.
Immunohistochemistry (IHC) and scoring
IHC staining was performed as described previously . Briefly, tissue sections were incubated with antibody γ-H2AX (Abcam, Cat: ab2893, dilution 1:200), Ki67 (Abcam, Cat: ab15580, dilution 1:200), CD34 Mouse monoclonal (Abcam, Cat: ab198395, 1:1000), and cleaved Caspase-3 (Cell signaling Technology, Cat: 9661,1:200) overnight at 4 °C and stained by 3,3′-diaminobenzidine (DAB). Tumor-cell staining was assigned a score as described previously . All specimens were evaluated by two independent experts simultaneously.
Study design, patients, and sample processing
This study was designed using a discovery stage and validation phase. In the discovery stage, 511 SOC patients with level 3 mRNA data were obtained from the TCGA database  to establish a scoring system. In the validation phase, the scores were validated using the largest outside independent dataset-Tothill dataset (GSE9899, n = 285). Patients lacking the serous pathologic type (n = 45) were excluded from the Tothill dataset. To further validate the scoring system, 105 blocks of formalin-fixed, paraffin-embedded (FFPE) tissues from primary epithelial ovarian cancer were obtained. The study was approved by the Ethical Committee of the Medical Faculty of Tongji Medical College. All patients written informed consents. The surgical staging was assessed in accordance with the International Federation of Gynecology and Obstetrics (FIGO) classification. Optimal debulking was defined as ≤1 cm residual disease. All clinicopathological characteristics are reported in Additional file 1: Table S1.
Quantitative real-time PCR (qRT-PCR)
RNAs from 105 FFPE cases were extracted from four 10-μm-thick FFPE sections using the miRNeasy FFPE kit (Qiagen, Valencia, CA, USA). The cDNA was synthesized by the SuperScript® IV First-Strand Synthesis System (Thermo Fisher Scientific, China). Real-time PCR amplification was performed on an CFX Connect™ Real-Time PCR Detection System with SYBR reagent (Bio-Rad, China). GAPDH was used as an internal control.
Student’s t-test was performed to compare the statistical difference between two groups. Multiple comparisons were accessed using the one-way analysis of variance (ANOVA). Survival was analyzed by the Kaplan–Meier method with the log-rank test. Univariate and multivariable Cox regression analyses were used to test for statistical independence between the score, pathological, and clinical variables. The relationship between the score and median OS was measured by pearson correlation coefficient. Area under the curve (AUC) values were calculated from the ROC curves. All tests were two-sided, and P-values < 0.05 were considered to indicate a statistically significant difference. All calculations were performed with SPSS (Version 25.0).
ROS levels are associated with cDDP sensitivity of ovarian cancer both in vitro and in vivo
On the basis of the sensitizing effects of ROS-elevating drugs on cDDP in cell lines and primary ovarian cancer cells, we explored cDDP and PIPER combinations in C13* (cDDP resistant) xenograft tumors. As expected, C13* tumors are highly resistant to cDDP mono-therapy. Combination of PIPER and cDDP markedly delay tumor growth, while PIPER mono-therapy showed a minimal effect on tumor growth (Fig. 1d and e). IHC analysis showed that combination therapy diminished blood vessels (CD34), suppressed proliferation (Ki67) and increased DNA damage (H2ax) and apoptosis (cleaved caspase-3) compared to cDDP or PIPER mono-therapy (Fig. 1f).
These results revealed that baseline ROS levels in ovarian cancer cells measured by DCFDA do not accurately predict their sensitivity to cDDP. ROS-elevating drugs increased ovarian cancer cell sensitivity to cDDP in varying degrees. So, it is necessary to build a scoring system to assess ovarian cancer patients who may benefit from the combination of cDDP and ROS-elevating drugs.
Establishment of the ROS scoring system in ovarian cancer patients
ROS-related genes were used to construct the score
V-akt murine thymoma viral oncogene homolog 2
FBJ murine osteosarcoma viral oncogene homolog B
Cytochrome b-245, alpha polypeptide
Jun B proto-oncogene
Cytochrome P450, family 27, subfamily B, polypeptide 1
FBJ murine osteosarcoma viral oncogene homolog
Nuclear factor I/X
Thioredoxin reductase 1
Ubiquitin specific peptidase 14
Ras-like without CAAX 1
Kelch-like ECH-associated protein 1
Cytochrome P450, family 3, subfamily A, polypeptide 7
Glutamate-cysteine ligase, catalytic subunit
Aldo-keto reductase family 7, member A3
Glutathione S-transferase alpha 3
Polyamine-modulated factor 1
Peroxisome proliferator-activated receptor gamma
Superoxide dismutase 1, soluble
ATP-binding cassette, sub-family C, member 4
Glutathione S-transferase mu 3 (brain)
NADPH oxidase 4
Prognosis prediction value of the ROS scoring system in ovarian cancer patients
The contribution of the ROS scoring system as a continuous variable toward prediction of OS in all datasets and cDDP sensitivity in 6 ovarian cancer cell lines
Univariate and multivariable analysis using prognostic factors in all of datasets
Univariate Cox Regression
Multivariate Cox Regression
2 vs 1
3 vs 1
2 vs 1
3 vs 1
2 vs 1
3 vs 1
Predictive accuracy of the ROS scoring system in ovarian Cancer patients
To further evaluate the contribution of the score to OS prediction, ROC curve analysis was performed using the following variables: clinical covariates (age, grade, stage, and residual tumor (AGSR)); ROS score (Score); and clinical covariates plus ROS score (AGSR + Score).
Compared with other types of cancers, one unique feature of ovarian cancer is that over 50% of ovarian cancers contain p53 mutation . Specifically, p53 mutation was identified in 96% of all serous ovarian tumors . Suppression of p53 led to significant decreases in the expression of SESN1, SESN2, and GPX1, suggesting that p53 is involved in cellular metabolism and antioxidant response [29, 30, 31]. So, p53 mutation could increase ROS levels and oxidative damage of DNA in ovarian cancer cells. Thus, alterations in the expression of ROS genes that affect ROS production or scavenging may be closely linked to the resistance of ovarian cancer cells to chemotherapy.
An increasing number of studies have identified relationships between ROS related genes (such as SOD, CAT, GLS2 and so on) and drug resistant [32, 33]. We found that ROS pathway function or activity plays a crucial role in chemotherapy responses in ovarian cancer cells and transplanted mouse models. Our results suggest that ROS related gene expression changes are important mechanisms by which ovarian cancer cells acquire resistance to anticancer drugs, and these changes result in different outcomes and prognoses in ovarian cancer patients.
In this study, we established a quantifiable ROS scoring system able to predict ovarian cancer patient prognosis based on the expression levels of ROS related genes in the TCGA dataset (n = 511). Moreover, we validated this system in another published dataset (Tothill dataset, GSE9899, n = 241). We further validated the scoring system in our in-house patient dataset (TJ dataset, n = 105). We indicated that the scoring system accurately determined the prognosis of ovarian cancer patients. The use of FFPE sections and qPCR also extended the use of the scoring system. Both datasets demonstrated that the system is prognostic for survival.
A number of gene profile-based prognosis techniques, used in combination with microarrays or PCR, have been previously developed to predict survival in patients with ovarian cancer [34, 35, 36], but the results have not been satisfactory. We demonstrated that our scoring system is superior to other known clinical factors in predicting OS, not only in the TCGA dataset but also in our dataset and another online validation set. TCGA divided ovarian cancer into four molecular subtypes (immunoreactive, differentiated, proliferative, mesenchymal) based on gene clustering, but these clusters did not associate with OS. However, statistical significance was observed when the score was applied to all subtypes except the proliferative subtype. Our score extends application of the TCGA classification model. Our system was also able to predict outcomes to first-line platinum and taxane chemotherapy in ovarian cancer. This feature has profound clinical significance because there are no other good clinical factors to predict the response to platinum-based standard chemotherapy. Most patients with advanced serous ovarian cancer will relapse after a few years even after standard therapies like thorough operation and chemotherapy are used. In addition, about 30% of patients with primary platinum resistance undergo multiple cycles of useless and potentially toxic treatment before second-line drug treatments are used. Moreover, agents that increase the ROS levels of cancer cells could be used as a standard treatment to improve chemotherapeutic responses for patients with ovarian cancers with low ROS levels.
In this study, we just demonstrate the prognostic value of the ROS scoring system, which leads to the possibility of clinical application. For individual ovarian cancer patient with poor prognosis predicted by the ROS scoring system, if possible, we can combine ROS-inducing drugs with platinum- and taxane-based chemotherapies to improve outcomes. We know there are lots of problems, such as the in vivo stability of ROS-elevating drugs, targeted property, and safety need to be resolved before ROS becoming a therapeutic target. However, further study based on patient-derived tumor xenograft (PDX) animal models for intraperitoneal administration of ROS-elevating drugs may lead to the possibility of clinical transformation.
This study has several limitations. First, although we reproduced our findings in two other datasets, this study is a retrospective analysis, and sample selection bias may exist. Of course, we hope that this score will be tested prospectively in a clinical trial, and we believe that the score is ready for such testing. Second, our gene expression profiling and analysis is only limited to ROS related pathways. However, other gene expression pathways that may be important in survival predictions were neglected. Third, this study is limited in its gene expression profiling. Other mechanisms of gene regulation, including microRNAs, DNA methylation, and CNV region changes were not considered. We are looking forward to future studies of this type and the development of more comprehensive prediction models.
We established a ROS scoring system that could predict the outcomes of ovarian cancer patients. This system offers considerable improvement over existing methods for prognostic classification and has the potential to provide clinicians with useful, readily available information for personalized chemotherapy in the future.
D.M., and G.C. designing the study, C.S., E.G., B.Z., W.S., and J.H1. (corresponding to Jia Huang) conducting experiments, D.W., P.W., C.W., S.W, W.Z., Q.G., X.X., B.W., and J.H2.(corresponding to Junbo Hu) analyzing data. All authors read and approved the final version of the manuscript.
This study is supported by the National Key Research and Development Program (2016YFC1303012), the National Basic Research Program of China (973 Program, 2015CB553903), the National Science-technology Supporting Plan Projects (2015BAI13B05), the Chinese National Key Plan of Precision Medicine Research (2016YFC0902901), and Nature and Science Foundation of China (81402163, 81402164, 81472783, 81572569, 81501530, 81671394, 81370469), the International S&T Cooperation Program of China (No. 2013DFA31400), and the Research Project of Health and Family Planning Commission of Hubei Province (WJ2015MA001). The funding bodies had no influence on the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Ethics approval and consent to participate
Primary cell lines and animal studies were approved by the Ethical Committee of the Medical Faculty of Tongji Medical College (Wuhan, China), and were performed according to the relevant guidelines and regulations. All procedures performed in studies were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study.
Consent for publication
The authors declare that they have no competing interests.
- 3.Zhang J, Wang X, Vikash V, Ye Q, Wu D, Liu Y, Dong W. ROS and ROS-mediated cellular signaling. Oxidative Med Cell Longev. 2016;2016:4350965.Google Scholar
- 6.Kumari S, Badana AK, G MM, G S, Malla R: Reactive Oxygen Species: A Key Constituent in Cancer Survival Biomark Insights 2018, 13:1177271918755391.Google Scholar
- 11.Liu J, Wang Z. Increased oxidative stress as a selective anticancer therapy. Oxidative Med Cell Longev. 2015;2015:294303.Google Scholar
- 23.Asselin E, Mills GB, Tsang BK. XIAP regulates Akt activity and caspase-3-dependent cleavage during cisplatin-induced apoptosis in human ovarian epithelial cancer cells. Cancer Res. 2001;61(5):1862–8.Google Scholar
- 26.Goldman M, Craft B, Hastie M, Repečka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN et al: The UCSC Xena platform for public and private cancer genomics data visualization and interpretation 2019:326470.Google Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.