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A reactive oxygen species scoring system predicts cisplatin sensitivity and prognosis in ovarian cancer patients

A reactive oxygen species scoring system predicts cisplatin sensitivity and prognosis in ovarian... Background: 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. Methods: 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). Results: 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 2 2 2 datasets (TCGA dataset r = 0.574, P = 0.032; Thothill dataset r = 0.266, P = 0.049; TJ dataset r = 0.632, P = 0.001) and with cisplatin sensitivity in ovarian cancer cell lines (r = 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). Conclusions: ROS status is associated with chemotherapy resistance. ROS score system might be a prognostic biomarker in predicting the survival benefit from ovarian cancer patients. Keywords: Serous ovarian Cancer, ROS, Scoring system, Prognosis Background treatment, a significant proportion of patients relapse Ovarian cancer is the second common diagnosed and and develop platinum resistance [1, 2]. the most lethal of the various gynecologic malignancies Reactive oxygen species (ROS) are oxygen-containing re- [1]. Although ovarian cancer patients are sensitive to active chemical molecules generated during metabolic pro- platinum- and taxane-based chemotherapy during initial cesses. ROS play an essential role in signal transduction pathways [3], cell cycle progression [3–5], gene transcription [3, 6], cell differentiation [7, 8], and cell death [6]. Elevated oxidative stress and delicate redox balance were detected in * Correspondence: gumpc@126.com † cancer cells due to activation of oncogene, high metabolic ac- Chaoyang Sun and Ensong Guo contributed equally to this work. Cancer Biology Research Center (Key laboratory of Chinese Ministry of tivity, and mitochondrial malfunction [6, 9, 10]. Deprivation Education), Tongji Hospital, Tongji Medical College, Huazhong University of of the redox balance through an increase in ROS levels or a Science and Technology, Wuhan, People’s Republic of China 2 decrease in the cellular antioxidant capacity to induce cellular Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s ROS burst have shown therapeutic benefits in cancer cells Republic of China [11–13]. Most chemotherapeutics, including platinum and Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This 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. Sun et al. BMC Cancer (2019) 19:1061 Page 2 of 12 taxanes, exert anti-cancer effects by inducing ROS-mediated the experiment was ranged from 3 to 8 generations. All cell damage in cancer cells [14–17]. Since new therapeutic cell lines were cultured in a 37 °C humidified atmos- approaches combining chemotherapeutics with ROS- phere containing 5% CO . elevating drugs have exhibited improvement of the cytotox- icity and reduction of the resistance [18–20]. Moreover, Assessment of cell viability some studies have shown that part of patients with drug re- Viability of cells were assessed by Cell Counting Kit-8 sistance attribute to the lower level of tumor cell oxidative 3 reagent (CCK8, Dojindo, Tokyo, Japan). 5 × 10 cancer stress and stronger antioxidant ability [21, 22]. cells were seed in 96-well plate and treated with Therefore, it is of increasing interest to develop a cDDP at different concentrations for 48 h with or prognostic method to predict patients who will benefit without ROS-elevating (PLX4032 (1 μM), Piperlongu- from chemotherapy or additive ROS inducer based on mine (PIPER, 10 μM) and β-phenylethyl isothiocyanate the quantifiable criteria of ROS status. In this paper, we (PEITC, 10 μM)) or ROS-scavenging drugs (glutathi- reflected that ROS is involved in the drug resistance and one (GSH, 2 mM), N-acetyl cysteine (NAC, 1 mM) chemo-sensitivity in vitro and in vivo. We then estab- and Vitamin C (VitC, 1 mM)). Supernatants were re- lished a comprehensive scoring system through analyz- moved and 100 μlof CCK8solution(1:10 dilution) ing the relationship between the expressions of ROS were added to the cancer cells. After 2 h incubation pathway genes, including genes involved in oxidative at 37 °C in dark, optical density (OD) at 450 nm was stress, oxidation reaction, antioxidant response, and measured by a microplate reader. The IC50 value for prognosis of patients in TCGA database. We validated each cell line was determined by nonlinear regression the effect of this system in Tothill database and ovarian analysis using GraphPad Prism (GraphPad Software cancer patients from our hospital. The result showed Inc., San Diego, CA). The results were tested by three that this scoring system might be clinically applied to independent experiments. predict the outcome of chemotherapy in ovarian cancer patients. ROS measurements ROS measurement was assayed using dichloro-dihydro- Methods fluorescein diacetate (DCFDA; Beyotime, Shanghai, Cell culture China), according to the manufacturers’ instructions. SKOV3(HTB-77), Caov3(HTB-75),OVCAR3(HTB- Briefly, cells were loaded with DCFH-DA, washed with 161), and OV-90 (CRL-11732) ovarian cancer cell lines ice-cold HBSS. Then, the fluorescence intensity of the were purchased from the American Type Culture Col- cells was measured at 488 nm by flow cytometry. lection (ATCC, Manassas, VA, US) and cultured as rec- ommended. Caov3 was cultured in DMEM with 10% FBS (Invitrogen) and OVCAR3 was cultured in RPMI Tumor Xenograft studies 1640 with 10% FBS (Invitrogen) and 10μg/ml insulin The study was approved by the Ethical Committee of the (Bovine). SKOV3 was cultured in McCoy 5A with 10% Medical Faculty of Tongji Medical College (Wuhan, China), FBS (Invitrogen). OV-90 was grown in a 1:1 mixture of and performed in accordance with the relevant guidelines MCDB 105 medium containing a final concentration of and regulations. Six-week-old athymic female homozygous 1.5 g/L sodium bicarbonate and Medium 199 contain- BALB/c nude mice (SPF) wereboughtfromBeijing Hua ing a final concentration of 2.2 g/L sodium bicarbonate Fukang biological Polytron Technologies Inc., and reared in with 15% FBS (Thermo Scientific). Cisplatin-sensitive accordance with the relevant guidelines and regulations. ovarian cancers cell line (OV2008) and its resistant C13* cells (10 cells/0.1 ml PBS/mice) intraperitoneally variant (C13*) were gifts from Prof. Benjamin K. Tsang injected into the right flank of BALB/C nude mice, under in the Ottawa Health Research Institute, Ottawa, isoflurane-induced anesthesia.Treatment beganwhenthe Canada [23] and cultured in RPMI 1640 medium with tumor volume reached between 70 and 100mm .The mice 10% FBS (Invitrogen). All cells were free from myco- were randomly divided into 4 groups (n = 6) including PBS plasma and were used between 3 and 5 passages after group, cDDP treatment group (cDDP 2.5 mg/kg, i.p., every thawing. All cells were authenticated by china center 4 days for 28 days), PIPER treatment group (PIPER 2 mg/ for type culture collection (CTCC, Wuhan, China) kg, i.p., daily for 28 consecutive days), and cDDP combin- using short tandem repeat (STR) DNA profiling. Pri- ation with PIPER treatment group (same dose as used in mary cell lines were isolated from ovarian cancer tissue the single-agent groups). Following the initial treatment, specimens of patients undergoing surgical resection as the tumor sizes were measured every 2 days. Tumor vol- previously described [24] and cultured in DMEM/F12 umes (V) were calculated by the following formula: V medium (Invitrogen) with 20% FBS (Invitrogen). The (mm ) = length × (square of width)/2. The mice were eu- passage number of primary ovarian cancer cells during thanized by cervical dislocation. Tumors were excised, Sun et al. BMC Cancer (2019) 19:1061 Page 3 of 12 weighed, paraformaldehyde-fixed paraffin embedding and and median OS was measured by pearson correlation used for ex vivo immunohistochemical staining. coefficient. Area under the curve (AUC) values were cal- culated from the ROC curves. All tests were two-sided, Immunohistochemistry (IHC) and scoring and P-values < 0.05 were considered to indicate a statisti- IHC staining was performed as described previously cally significant difference. All calculations were per- [25]. Briefly, tissue sections were incubated with anti- formed with SPSS (Version 25.0). body γ-H2AX (Abcam, Cat: ab2893, dilution 1:200), Ki67 (Abcam, Cat: ab15580, dilution 1:200), CD34 Results Mouse monoclonal (Abcam, Cat: ab198395, 1:1000), and ROS levels are associated with cDDP sensitivity of ovarian cleaved Caspase-3 (Cell signaling Technology, Cat: 9661, cancer both in vitro and in vivo 1:200) overnight at 4 °C and stained by 3,3′-diaminoben- To determine whether the ROS levels of ovarian can- zidine (DAB). Tumor-cell staining was assigned a score cer cells play a role in cisplatin (cDDP) resistance, 6 as described previously [25]. All specimens were evalu- cell lines, including SKOV3, Caov3, OVCAR3, OV-90, ated by two independent experts simultaneously. OV2008, and C13* were exposed to different concen- trations of cDDP (Fig. 1aand Additional file 2:Figure Study design, patients, and sample processing S1A). The IC of CDDP-treated cell lines are dis- This study was designed using a discovery stage and valid- played in Table S2. We also measured the intrinsic ation phase. In the discovery stage, 511 SOC patients with ROS levels in each of the cell lines using flow cytome- level 3 mRNA data were obtained from the TCGA data- try (Additional file 2: Figure S1B). However, intrinsic base [26] to establish a scoring system. In the validation ROS levels had no statistically significant impact on phase, the scores were validated using the largest outside cDDP sensitivity. Then, the cancer cells were treated independent dataset-Tothill dataset (GSE9899, n =285). with different cDDP concentrations in addition to Patients lacking the serous pathologic type (n = 45) were fixed concentrations of ROS-elevating (PLX4032 excluded from the Tothill dataset. To further validate the (1 μM), Piperlongumine (PIPER, 10 μM) and β- scoring system, 105 blocks of formalin-fixed, paraffin- phenylethyl isothiocyanate (PEITC, 10 μM)) or ROS- embedded (FFPE) tissues from primary epithelial ovarian scavenging drugs (glutathione (GSH, 2 mM), N-acetyl cancer were obtained. The study was approved by the Eth- cysteine (NAC, 1 mM) and Vitamin C (VitC, 1 mM)). ical Committee of the Medical Faculty of Tongji Medical The doses of ROS-elevating or ROS-scavenging were College. All patients written informed consents. The surgi- preselected while mono-therapy has no effect on can- cal staging was assessed in accordance with the Inter- cer cell proliferation or apoptosis (Additional file 2: national Federation of Gynecology and Obstetrics (FIGO) Figure S1C). Interestingly,mostROS-elevating drugs classification. Optimal debulking was defined as ≤1cm re- increased cDDP cytotoxicity in all ovarian cancer cell sidual disease. All clinicopathological characteristics are lines, especially in C13*, OV2008 and SKOV3 (Fig. 1a, reported in Additional file 1: Table S1. Additional file 2: Figure S1A, and Additional file 3: TableS2).Bycontrast, the cytotoxicityofcDDPincell Quantitative real-time PCR (qRT-PCR) linessuch asOV90and OVCAR3 wasreduced when RNAs from 105 FFPE cases were extracted from four 10- combined with ROS-scavenging drugs (Additional file μm-thick FFPE sections using the miRNeasy FFPE kit 2: Figure S1A and Additional file 3:Table S2). While (Qiagen, Valencia, CA, USA). The cDNA was synthesized Caov3 exhibited a moderate response in the presence by the SuperScript® IV First-Strand Synthesis System of both ROS-scavenging and ROS-elevating drugs (Thermo Fisher Scientific, China). Real-time PCR amplifi- (Additional file 2: Figure S1A and Additional file 3: cation was performed on an CFX Connect™ Real-Time Table S2). Furthermore, the sensitizing effects of ROS- PCR Detection System with SYBR reagent (Bio-Rad, elevating drugs were confirmed in 6 primary cells de- China). GAPDH was used as an internal control. rived from ovarian cancer patients (Fig. 1b, Additional file 2: Figure S1D and Additional file 3:Table S2), sup- Statistical analysis porting its clinical relevance. PIPER showed a better Student’s t-test was performed to compare the statistical sensitizing effect of cDDP cytotoxicity than other difference between two groups. Multiple comparisons ROS-scavenging drugs, we further verified its sensitiz- were accessed using the one-way analysis of variance ing effect in another 5 primary cells. Five primary cell (ANOVA). Survival was analyzed by the Kaplan–Meier lines were derived from 3 patients with recurrent ovar- method with the log-rank test. Univariate and multivari- ian cancer and 2 patients with primary ovarian cancer. able Cox regression analyses were used to test for statis- As expected, no matter what kind of ovarian cancer tical independence between the score, pathological, and patients, PIPER increased cDDP cytotoxicity (Fig. 1c clinical variables. The relationship between the score and Additional file 2:FigureS1E). Sun et al. BMC Cancer (2019) 19:1061 Page 4 of 12 Fig. 1 ROS levels are associated with cDDP sensitivity of ovarian cancer. a cDDP IC50 curves for ovarian cancer cell lines C13*, OV2008 and SKOV3 with or without ROS-elevating drugs (PLX4032, 1 μM, Piperlongumine (PIPER, 10 μM) and β-phenylethyl isothiocyanate (PEITC, 10 μM)). b Cell viability of 3 strains of primary cancer cells was assayed after treatment with increasing concentrations of cDDP with or without ROS-elevating drugs for 48 h by CCK-8. c Cell viability of primary cancer cells derived from patients with recurrent ovarian cancer or primary ovarian cancer was assayed after treatment with increasing concentrations of cDDP with or without PIPER for 48 h by CCK-8. a-c The two-tailed P-values < 0.05 were considered to indicate statistically significant differences. The results were tested by three independent experiments. d Growth curves of C13* subcutaneous xenograft tumors treated with vehicle, cDDP (2 mg/kg, intraperitoneally every 4 days), PIPER (2 mg/kg, intraperitoneally daily for 28 consecutive days), and cDDP plus PIPER (same dose as used in the single-agent groups) are shown. Tumor volumes were calculated as length × (square of width)/2. n = 8 per group. (*P < .05, **P < .001, two-sided Student t-test). e Tumor weights in nude mice were measured on day 35 after tumor cell injection. n = 8 per group. (*P < .05, **P < .001, two-sided Student t-test). (F) The immunohistochemistry analyses for caspase 3, Ki67, γ- H2AXand CD34 staining were carried out on C13* xenograft tumor sections collected from mice treated with the indicated treatments. Representative staining is shown. Scale bars = 50 μm. Data in (a–e) are the mean values ±95% confidence intervals. On the basis of the sensitizing effects of ROS-elevating combination therapy diminished blood vessels (CD34), sup- drugs on cDDP in cell lines and primary ovarian cancer pressed proliferation (Ki67) and increased DNA damage cells, we explored cDDP and PIPER combinations in C13* (H2ax) and apoptosis (cleaved caspase-3) compared to (cDDP resistant) xenograft tumors. As expected, C13* tu- cDDP or PIPER mono-therapy (Fig. 1f). mors are highly resistant to cDDP mono-therapy. Combin- These results revealed that baseline ROS levels in ation of PIPER and cDDP markedly delay tumor growth, ovarian cancer cells measured by DCFDA do not ac- while PIPER mono-therapy showed a minimal effect on curately predict their sensitivity to cDDP. ROS- tumor growth (Fig. 1d and e). IHC analysis showed that elevating drugs increased ovarian cancer cell Sun et al. BMC Cancer (2019) 19:1061 Page 5 of 12 sensitivity to cDDP in varying degrees. So, it is neces- patients in TCGA (n = 511) were divided into two sary to build a scoring system to assess ovarian cancer groups according to the median expression values of the patients who may benefit from the combination of 179 ROS related genes individually. 25 of 179 ROS re- cDDP and ROS-elevating drugs. lated genes were selected whose expression levels were associated (P < 0.15) with the OS of ovarian cancer pa- Establishment of the ROS scoring system in ovarian tients in TCGA dataset (n = 511) (Table 1 and Add- cancer patients itional file 4: Figure S2). For each patient, if high gene ROS-elevating drugs sensitized ovarian cancer cells to expression was associated with a good prognosis, all pa- cDDP, which to some extent depended on intrinsic ROS tients with a higher expression value than the median levels in the cells. However, there is no reliable and con- expression obtained one point, and vice versa. The venient methods to quantified ROS status in tumors. So, scores of all candidate genes of each patient were added we developed a ROS scoring system based on expres- to obtain a total score, which was called the ROS score. sions of ROS related genes. To identify ROS pathway The score divided patients into two categories (“low” genes, we selected 179 ROS related genes according to or “high”) with equal ranges. Kaplan-Meier and univari- our knowledge and works of literature. Ovarian cancer ate Cox proportional hazard regression analyses revealed Table 1 ROS-related genes were used to construct the score Gene Symbol P Survival Name AKT2 0 low V-akt murine thymoma viral oncogene homolog 2 FOSB 0.005 low FBJ murine osteosarcoma viral oncogene homolog B CITED4 0.009 high Cbp/p300-interacting transactivator CYBA 0.012 high Cytochrome b-245, alpha polypeptide JUNB 0.013 low Jun B proto-oncogene CYP27B1 0.014 high Cytochrome P450, family 27, subfamily B, polypeptide 1 FOS 0.014 low FBJ murine osteosarcoma viral oncogene homolog NFIX 0.027 low Nuclear factor I/X TXNRD1 0.041 low Thioredoxin reductase 1 USP14 0.054 high Ubiquitin specific peptidase 14 RIT1 0.058 low Ras-like without CAAX 1 KEAP1 0.0581 high Kelch-like ECH-associated protein 1 CYP3A7 0.061 high Cytochrome P450, family 3, subfamily A, polypeptide 7 TXN 0.07 low Thioredoxin GCLC 0.07 high Glutamate-cysteine ligase, catalytic subunit AKR7A3 0.072 low Aldo-keto reductase family 7, member A3 JUN 0.074 low Jun proto-oncogene CUL3 0.076 low Cullin 3 GSTA3 0.076 high Glutathione S-transferase alpha 3 PMF1 0.078 high Polyamine-modulated factor 1 PPARG 0.106 low Peroxisome proliferator-activated receptor gamma SOD1 0.122 high Superoxide dismutase 1, soluble ABCC4 0.123 high ATP-binding cassette, sub-family C, member 4 GSTM3 0.14 low Glutathione S-transferase mu 3 (brain) NOX4 0.142 high NADPH oxidase 4 NOTE: “High” indicates that gene expression above the median gene expression was associated with better overall survival; “low” indicates that gene expression above the median gene expression was associated with poor overall survival Sun et al. BMC Cancer (2019) 19:1061 Page 6 of 12 Fig. 2 Prognostic value of the ROS scoring system in TCGA dataset. a A Kaplan-Meier analysis of overall survival (OS) for ovarian cancer patients in TCGA dataset with the ROS scoring system (high [scores 13–25], the green line v.s. low [scores 0–12], the blue line) is shown (P < 0.001, respectively, log-rank test). b A Kaplan-Meier analysis of overall survival (OS) for advanced stage (stage III/ IV) ovarian cancer patients in TCGA dataset with the ROS scoring system is shown (P < 0.001, respectively, log-rank test). c A Kaplan-Meier analysis of overall survival (OS) for advanced stage (stage III/ IV) ovarian cancer patients received a platinum and taxane regimen as first-line chemotherapy in TCGA dataset with the ROS scoring system is shown (P < 0.001, respectively, log-rank test). All statistical tests were two-sided. patients with high scores (scores 13–25) had better OS compared to patients with low scores (scores 0–12) (high v.s. low scores, median OS = 4.66 years v.s. 2.92 years, HR = 0.43; 95% CI = 0.34 to 0.55, P < 0.001) (Fig. 2a). Moreover, the ROS scoring system performed well independent of tumor stages, treatments, and differ- ent molecular subtypes. Patients with high ROS scores are associated with better prognosis in stages III/IV pa- tients (HR = 0.44; 95% CI = 0.34 to 0.56, P < 0.001) (Fig. 2b), in stage III/IV patients receiving first-line chemo- therapy with platinum and taxane regimens (HR = 0.38; 95% CI = 0.27 to 0.53, P < 0.001) (Fig. 2c), or in different ovarian cancer molecular subtypes (immunoreactive, dif- ferentiated, proliferative, mesenchymal) (Additional file 5: Figure S3). Prognosis prediction value of the ROS scoring system in ovarian cancer patients The association between ROS score and OS was con- firmed in an independent outside dataset (Tothill, GSE9899), the largest ovarian cancer datasets available (high v.s. low scores, median OS = 4.77 years v.s. 3.04 years; HR = 0.65; 95% CI = 0.44 to 0.94, P = 0.022) (Fig. 3a). To further validate the prognosis prediction values of this ROS scoring system and broaden the scope of applications. We quantified these 25 survival-related ROS genes using a widely-applicable and convenient method, qRT-PCR analysis from FFPE tissues in 105 ovarian tumors. Consistent with afore-mentioned associ- ations based on microarray data, patients with high ROS scores have longer survival times (high v.s. low, median OS = 3.76 years v.s. 2.36 years; HR = 0.40; 95% CI = 0.22 to 0.74, P = 0.002) (Fig. 3b). Univariate and multivariate Cox proportional hazard regression analyses were ap- plied to estimate the relationship between OS and the score (high v.s. low) compared with other clinical fac- tors, including age (≤59 v.s. ≥60 years), FIGO stage (IV v.s. III), histological grade (3 v.s. 1–2), and extent of sur- gical debulking (0–10 v.s. ≥11 mm residual tumor). Only the score is an independent prognostic factor in TCGA, Tothill dataset and our in house patients datasets (Fig. 3c, Additional file 5: Figure S4). Sun et al. BMC Cancer (2019) 19:1061 Page 7 of 12 Fig. 3 Validation of prognostic value of the ROS scoring system in Tothill and TJ datasets. a A Kaplan-Meier analysis of overall survival (OS) for ovarian cancer patients in Tothill (GSE9899) dataset with the ROS scoring system is shown (P = 0.022, respectively, log-rank test). b A Kaplan-Meier analysis of overall survival (OS) for ovarian cancer patients in TJ dataset with the ROS scoring system is shown (P = 0.003, respectively, log-rank test). c Univariate and multivariable Cox proportional hazards regression analyses incorporating the score and known prognostic clinical factors, including age at diagnosis (≤59 v.s. ≥60 years), stage (III v.s. IV), grade (1–2 v.s. 3), and extent of surgical debulking (0–10 v.s. ≥11 mm residual tumor); each as categorical variables. Solid squares represent the hazard ratio (HR) of death and open-ended horizontal lines represent the 95% confidence intervals (CIs). All P values were calculated using Cox proportional hazards analysis. The contribution of the ROS scoring system as a Univariate and multivariate analyses were repeated continuous variable toward prediction of OS in all when the ROS score was assessed as a continuous vari- datasets and cDDP sensitivity in 6 ovarian cancer cell able. Again, the ROS score outperformed other clinical lines covariates as an independent prognostic factor in TCGA To further verify the associations between scores and dataset, Tothill dataset, and in TJ dataset (Table 2). patients’ survival, we performed correlation analysis between each score and the median survival times in patients with the same scores. Interestingly, there was Predictive accuracy of the ROS scoring system in ovarian a positive correlation between scores and the median Cancer patients survival times (r = 0.758, P = 0.032 in TCGA dataset; To further evaluate the contribution of the score to OS r = 0.516, P = 0.049 in Tothill dataset; and r = 0.795, prediction, ROC curve analysis was performed using the P = 0.001in TJ dataset) (Fig. 4). following variables: clinical covariates (age, grade, stage, Sun et al. BMC Cancer (2019) 19:1061 Page 8 of 12 Fig. 4 Prognostic value of the ROS scoring system in all datasets. a-c Correlation of score as a continuous variable with OS in TCGA (a), Tothill (b) and TJ (c) datasets. For each patient’s tumor, a point was given for each ROS related gene for which higher than median expression was associated with longer survival, and vice versa. The sum of these points constituted our score. and residual tumor (AGSR)); ROS score (Score); and result in different outcomes and prognoses in ovarian clinical covariates plus ROS score (AGSR + Score). cancer patients. For these analyses, patients were divided into two groups In this study, we established a quantifiable ROS scor- with a survival time higher or lower than the median OS ing system able to predict ovarian cancer patient prog- (TCGA dataset median OS =3.79 years, Tothill dataset me- nosis based on the expression levels of ROS related dian OS = 3.62 years, TJ dataset median OS = 2.81 years), genes in the TCGA dataset (n = 511). Moreover, we vali- and those with a survival time shorter than the median OS dated this system in another published dataset (Tothill at last follow-up were excluded. Interestedly, the score dataset, GSE9899, n = 241). We further validated the alone have higher predictive values than clinical covariates scoring system in our in-house patient dataset (TJ data- in all datasets (TCGA dataset AUC = 0.71 v.s. 0.65, Tothill set, n = 105). We indicated that the scoring system ac- dataset AUC = 0.73 v.s. 0.67, TJ dataset AUC = 0.74 v.s. curately determined the prognosis of ovarian cancer 0.66) (Fig. 5a). Moreover, combined with clinical covariates patients. The use of FFPE sections and qPCR also ex- and ROS score could further improve predictive perform- tended the use of the scoring system. Both datasets dem- ance in all dataset (TCGA dataset AUC = 0.73, Tothill data- onstrated that the system is prognostic for survival. set AUC = 0.81, TJ dataset AUC = 0.78) (Fig. 5band c). A number of gene profile-based prognosis techniques, used in combination with microarrays or PCR, have been Discussion previously developed to predict survival in patients with Compared with other types of cancers, one unique ovarian cancer [34–36], but the results have not been satis- feature of ovarian cancer is that over 50% of ovarian factory. We demonstrated that our scoring system is super- cancers contain p53 mutation [27]. Specifically, p53 ior to other known clinical factors in predicting OS, not mutation was identified in 96% of all serous ovarian only in the TCGA dataset but also in our dataset and an- tumors [28]. Suppression of p53 led to significant de- other online validation set. TCGA divided ovarian cancer creases in the expression of SESN1, SESN2, and into four molecular subtypes (immunoreactive, differenti- GPX1, suggesting that p53 is involved in cellular me- ated, proliferative, mesenchymal) based on gene clustering, tabolism and antioxidant response [29–31]. So, p53 but these clusters did not associate with OS. However, stat- mutation could increase ROS levels and oxidative istical significance was observed when the score was applied damage of DNA in ovarian cancer cells. Thus, alter- to all subtypes except the proliferative subtype. Our score ations in the expression of ROS genes that affect ROS extends application of the TCGA classification model. Our production or scavenging may be closely linked to the system was also able to predict outcomes to first-line plat- resistance of ovarian cancer cells to chemotherapy. inum and taxane chemotherapy in ovarian cancer. This fea- An increasing number of studies have identified rela- ture has profound clinical significance because there are no tionships between ROS related genes (such as SOD, other good clinical factors to predict the response to CAT, GLS2 and so on) and drug resistant [32, 33]. We platinum-based standard chemotherapy. Most patients with found that ROS pathway function or activity plays a cru- advanced serous ovarian cancer will relapse after a few cial role in chemotherapy responses in ovarian cancer years even after standard therapies like thorough operation cells and transplanted mouse models. Our results sug- and chemotherapy are used. In addition, about 30% of pa- gest that ROS related gene expression changes are im- tients with primary platinum resistance undergo multiple portant mechanisms by which ovarian cancer cells cycles of useless and potentially toxic treatment before acquire resistance to anticancer drugs, and these changes second-line drug treatments are used. Moreover, agents Sun et al. BMC Cancer (2019) 19:1061 Page 9 of 12 Table 2 Univariate and multivariable analysis using prognostic factors in all of datasets Cohort Characteristics Univariate Cox Regression Multivariate Cox Regression HR 95% CI P HR 95% CI P −14 −10 TCGA Score 0.878 (0.849,0.908) 2.23*10 0.889 (0.857,0.922) 3.27*10 Age 1.019 (1.008,1.030) 0.001 1.018 (1.007,1.030) 0.002 Grade 1 1 0.173 1 0.654 2 vs 1 1.254 (0.302,5.21) 0.756 0.814 (0.191,3.459) 0.78 3 vs 1 1.773 (0.44,7.138) 0.421 1.319 (0.325,5.358) 0.699 Others 2.304 (0.489,10.866) 0.291 1.256 (0.252,6.25) 0.781 Stage I-II 1 0.002 1 0.047 III 2.374 (1.258,4.479) 0.008 2.226 (1.032,4.802) 0.041 IV 3.219 (1.633,6.345) 0.001 2.805 (1.241,6.336) 0.013 Surgical debulking 1.293 (0.989,1.691) 0.061 1.138 (0.861,1.504) 0.362 Tothill Score 0.929 (0.877,0.985) 0.013 0.924 (0.866,0.985) 0.015 Age 1.021 (1.000,1.042) 0.047 1.025 (1.003,1.047) 0.027 Grade 1 1 0.467 1 0.804 2 vs 1 1.964 (0.549,6.499) 0.269 1.269 (0.374, 4.309) 0.703 3 vs 1 2.116 (0.660,6.791) 0.208 1.246 (0.369,4.200) 0.723 Others 0.751 (0.077,7.287) 0.805 0.47 (0.045,4.868) 0.527 Stage I-II 1 0.01 1 0.046 III 4.012 (1.269,12.685) 0.018 3.319 (1.006,10.950) 0.049 IV 6.657 (1.906,23.245) 0.003 5.118 (1.377,19.024) 0.015 Surgical debulking 0.608 (0.397,0.932) 0.022 0.688 (0.439,1.079) 0.104 TJ Score 0.841 (0.746,0.949) 0.005 0.862 (0.750,0.990) 0.036 Age 1.048 (1.011,1.087) 0.011 1.050 (1.013,1.089) 0.008 Grade 1 1 0.943 1 0.693 2 vs 1 1.146 (0.363,3.622) 0.816 1.551 (0.470,5.126) 0.471 3 vs 1 1.274 (0.496,3.272) 0.615 1.799 (0.638,5.075) 0.267 Others 1.538 (0.296,7.981) 0.608 2.400 (0.408,14.115) 0.333 Stage I-II 1 0.087 1 0.038 III 3.101 (1.063,9.046) 0.038 3.986 (1.282,12.392) 0.017 IV 3.661 (1.098,12.210) 0.035 4.956 (1.378,17.829) 0.014 Surgical debulking 0.443 (0.225,0.874) 0.019 0.617 (0.298,1.276) 0.193 that increase the ROS levels of cancer cells could be used patient with poor prognosis predicted by the ROS scor- as a standard treatment to improve chemotherapeutic re- ing system, if possible, we can combine ROS-inducing sponses for patients with ovarian cancers with low ROS drugs with platinum- and taxane-based chemotherapies levels. to improve outcomes. We know there are lots of prob- In this study, we just demonstrate the prognostic value lems, such as the in vivo stability of ROS-elevating of the ROS scoring system, which leads to the possibility drugs, targeted property, and safety need to be resolved of clinical application. For individual ovarian cancer before ROS becoming a therapeutic target. However, Sun et al. BMC Cancer (2019) 19:1061 Page 10 of 12 Fig. 5 Predictive accuracy of the ROS scoring system compared with prognostic clinical factors. Receiver operating characteristic (ROC) analysis of the score and clinical covariates in predicting overall survival in TCGA (a), Tothill (b) and TJ (c) datasets. Using statistical models constructed based on multivariable Cox proportional hazards, ROC curves were calculated incorporating clinical variables of age, grade, and stage (left); age, grade, stage, and score (middle); and score alone (right). The area under the curve (AUC) was calculated for ROC curves, and sensitivity and specificity was calculated to assess the score performance. further study based on patient-derived tumor xenograft mechanisms of gene regulation, including microRNAs, (PDX) animal models for intraperitoneal administration DNA methylation, and CNV region changes were not of ROS-elevating drugs may lead to the possibility of considered. We are looking forward to future studies of clinical transformation. this type and the development of more comprehensive This study has several limitations. First, although we prediction models. 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 pro- Conclusions spectively in a clinical trial, and we believe that the score We established a ROS scoring system that could predict is ready for such testing. Second, our gene expression pro- the outcomes of ovarian cancer patients. This system of- filing and analysis is only limited to ROS related pathways. fers considerable improvement over existing methods However, other gene expression pathways that may be im- for prognostic classification and has the potential to pro- portant in survival predictions were neglected. Third, this vide clinicians with useful, readily available information study is limited in its gene expression profiling. Other for personalized chemotherapy in the future. Sun et al. BMC Cancer (2019) 19:1061 Page 11 of 12 Supplementary information Plan Projects (2015BAI13B05), the Chinese National Key Plan of Precision Supplementary information accompanies this paper at https://doi.org/10. Medicine Research (2016YFC0902901), and Nature and Science Foundation 1186/s12885-019-6288-7. 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 Additional file 1: Table S1. Clinicopathologic characteristics of ovarian Commission of Hubei Province (WJ2015MA001). The funding bodies had no cancer patients in 3 datasets. (docx 16.8 kb) (DOCX 16 kb) influence on the design of the study and collection, analysis, and interpret- Additional file 2: Figure S1. ROS level is related to the survival of ation of data and in writing the manuscript. ovarian cancer cells treated with cDDP. (jpg 2.83mb) (A) Cell viability of ovarian cancer cell lines Caov3, OV90 and OVCAR3 was measured after Availability of data and materials treatment with gradient concentrations of cDDP with or without ROS- The datasets used and/or analyzed during the current study are available elevating (PLX4032, 1 μM, Piperlongumine (PIPER, 10 μM) and β- from the corresponding author on reasonable request. phenylethyl isothiocyanate (PEITC, 10 μM)) or scavenging drugs (glutathi- one (GSH, 2 mM), N-acetyl cysteine (NAC, 1 mM) and Vitamin C (VitC, 1 mM)) for 48 h by CCK-8. (B) Intracellular ROS concentrations of SKOV3, Ethics approval and consent to participate Caov3, OVCAR3, OV-90, OV2008, and C13* were measured by DCF-DA Primary cell lines and animal studies were approved by the Ethical staining. (C) CCK8 detected cell viability of ovarian cancer cell lines after Committee of the Medical Faculty of Tongji Medical College (Wuhan, China), treatment with ROS-elevating. (D) cDDP IC curves for 3 strains of pri- and were performed according to the relevant guidelines and regulations. mary cancer cells with or without ROS-elevating or scavenging drugs for All procedures performed in studies were in accordance with the ethical 48 h by CCK-8. (E) Cell viability of primary cancer cells derived from pa- standards of the institutional and/or national research committee and with tients with recurrent or primary ovarian cancer was measured after treat- the 1964 Helsinki declaration and its later amendments or comparable ment with gradient concentrations of cDDP with or without PIPER for 48 ethical standards. Written informed consent was obtained from all individual h by CCK-8. The two-tailed P-values < 0.05 were considered to indicate participants included in the study. statistically significant differences. The results were tested by three inde- pendent experiments. (JPG 2907 kb) Consent for publication Additional file 3: Table S2. The IC50 of ovarian cancer cell lines and Not applicable. primary cancer cells treated with different combinations of drugs. (docx 18.8 kb) (DOCX 18 kb) Competing interests Additional file 4: Figure S2. 84 of 179 ROS related genes with Kaplan- The authors declare that they have no competing interests. Meier log-rank P values < 0.5. (tif 328 kb) Genes with P < 0.15 are highlighted in red. (TIF 328 kb) Author details Additional file 5: Figure S3. Prognostic Value of the ROS Scoring 1 Cancer Biology Research Center (Key laboratory of Chinese Ministry of System in four ovarian cancer molecular subtypes of TCGA. (tif 495 kb) A Education), Tongji Hospital, Tongji Medical College, Huazhong University of Kaplan-Meier analysis of overall survival (OS) were performed in the differ- 2 Science and Technology, Wuhan, People’s Republic of China. Department of entiated, immunoreactive, mesenchymal, and proliferative for ovarian Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, cancer patients in TCGA dataset with the ROS scoring system (high Huazhong University of Science and Technology, Wuhan, People’s Republic [scores 13–25], the green line v.s. low [scores 0–12], the blue line) is 3 of China. Department of Surgery, Tongji Hospital, Tongji Medical College, shown (P < 0.001, log-rank test). All statistical tests were two-sided. (TIF Huazhong University of Science and Technology, Wuhan, People’s Republic 495 kb) of China. Additional file 6: Figure S4. Univariate analyses were performed in TCGA and Tothill dataset. (tif 1.21mb) Univariate analyses incorporating Received: 16 April 2019 Accepted: 24 October 2019 the score and known prognostic clinical factors, including age at diagnosis (≤59 v.s. ≥60 years), stage (III v.s. IV), grade (1–2 v.s. 3), and surgical debulking (0–10 v.s. ≥11 mm residual tumor). 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PLoS One. 2018;13(11):e0207319. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Cancer Springer Journals

A reactive oxygen species scoring system predicts cisplatin sensitivity and prognosis in ovarian cancer patients

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Springer Journals
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Copyright © 2019 by The Author(s).
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Biomedicine; Cancer Research; Oncology; Surgical Oncology; Health Promotion and Disease Prevention; Biomedicine, general; Medicine/Public Health, general
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10.1186/s12885-019-6288-7
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Abstract

Background: 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. Methods: 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). Results: 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 2 2 2 datasets (TCGA dataset r = 0.574, P = 0.032; Thothill dataset r = 0.266, P = 0.049; TJ dataset r = 0.632, P = 0.001) and with cisplatin sensitivity in ovarian cancer cell lines (r = 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). Conclusions: ROS status is associated with chemotherapy resistance. ROS score system might be a prognostic biomarker in predicting the survival benefit from ovarian cancer patients. Keywords: Serous ovarian Cancer, ROS, Scoring system, Prognosis Background treatment, a significant proportion of patients relapse Ovarian cancer is the second common diagnosed and and develop platinum resistance [1, 2]. the most lethal of the various gynecologic malignancies Reactive oxygen species (ROS) are oxygen-containing re- [1]. Although ovarian cancer patients are sensitive to active chemical molecules generated during metabolic pro- platinum- and taxane-based chemotherapy during initial cesses. ROS play an essential role in signal transduction pathways [3], cell cycle progression [3–5], gene transcription [3, 6], cell differentiation [7, 8], and cell death [6]. Elevated oxidative stress and delicate redox balance were detected in * Correspondence: gumpc@126.com † cancer cells due to activation of oncogene, high metabolic ac- Chaoyang Sun and Ensong Guo contributed equally to this work. Cancer Biology Research Center (Key laboratory of Chinese Ministry of tivity, and mitochondrial malfunction [6, 9, 10]. Deprivation Education), Tongji Hospital, Tongji Medical College, Huazhong University of of the redox balance through an increase in ROS levels or a Science and Technology, Wuhan, People’s Republic of China 2 decrease in the cellular antioxidant capacity to induce cellular Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s ROS burst have shown therapeutic benefits in cancer cells Republic of China [11–13]. Most chemotherapeutics, including platinum and Full list of author information is available at the end of the article © The Author(s). 2019 Open Access This 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. Sun et al. BMC Cancer (2019) 19:1061 Page 2 of 12 taxanes, exert anti-cancer effects by inducing ROS-mediated the experiment was ranged from 3 to 8 generations. All cell damage in cancer cells [14–17]. Since new therapeutic cell lines were cultured in a 37 °C humidified atmos- approaches combining chemotherapeutics with ROS- phere containing 5% CO . elevating drugs have exhibited improvement of the cytotox- icity and reduction of the resistance [18–20]. Moreover, Assessment of cell viability some studies have shown that part of patients with drug re- Viability of cells were assessed by Cell Counting Kit-8 sistance attribute to the lower level of tumor cell oxidative 3 reagent (CCK8, Dojindo, Tokyo, Japan). 5 × 10 cancer stress and stronger antioxidant ability [21, 22]. cells were seed in 96-well plate and treated with Therefore, it is of increasing interest to develop a cDDP at different concentrations for 48 h with or prognostic method to predict patients who will benefit without ROS-elevating (PLX4032 (1 μM), Piperlongu- from chemotherapy or additive ROS inducer based on mine (PIPER, 10 μM) and β-phenylethyl isothiocyanate the quantifiable criteria of ROS status. In this paper, we (PEITC, 10 μM)) or ROS-scavenging drugs (glutathi- reflected that ROS is involved in the drug resistance and one (GSH, 2 mM), N-acetyl cysteine (NAC, 1 mM) chemo-sensitivity in vitro and in vivo. We then estab- and Vitamin C (VitC, 1 mM)). Supernatants were re- lished a comprehensive scoring system through analyz- moved and 100 μlof CCK8solution(1:10 dilution) ing the relationship between the expressions of ROS were added to the cancer cells. After 2 h incubation pathway genes, including genes involved in oxidative at 37 °C in dark, optical density (OD) at 450 nm was stress, oxidation reaction, antioxidant response, and measured by a microplate reader. The IC50 value for prognosis of patients in TCGA database. We validated each cell line was determined by nonlinear regression the effect of this system in Tothill database and ovarian analysis using GraphPad Prism (GraphPad Software cancer patients from our hospital. The result showed Inc., San Diego, CA). The results were tested by three that this scoring system might be clinically applied to independent experiments. predict the outcome of chemotherapy in ovarian cancer patients. ROS measurements ROS measurement was assayed using dichloro-dihydro- Methods fluorescein diacetate (DCFDA; Beyotime, Shanghai, Cell culture China), according to the manufacturers’ instructions. SKOV3(HTB-77), Caov3(HTB-75),OVCAR3(HTB- Briefly, cells were loaded with DCFH-DA, washed with 161), and OV-90 (CRL-11732) ovarian cancer cell lines ice-cold HBSS. Then, the fluorescence intensity of the were purchased from the American Type Culture Col- cells was measured at 488 nm by flow cytometry. lection (ATCC, Manassas, VA, US) and cultured as rec- ommended. Caov3 was cultured in DMEM with 10% FBS (Invitrogen) and OVCAR3 was cultured in RPMI Tumor Xenograft studies 1640 with 10% FBS (Invitrogen) and 10μg/ml insulin The study was approved by the Ethical Committee of the (Bovine). SKOV3 was cultured in McCoy 5A with 10% Medical Faculty of Tongji Medical College (Wuhan, China), FBS (Invitrogen). OV-90 was grown in a 1:1 mixture of and performed in accordance with the relevant guidelines MCDB 105 medium containing a final concentration of and regulations. Six-week-old athymic female homozygous 1.5 g/L sodium bicarbonate and Medium 199 contain- BALB/c nude mice (SPF) wereboughtfromBeijing Hua ing a final concentration of 2.2 g/L sodium bicarbonate Fukang biological Polytron Technologies Inc., and reared in with 15% FBS (Thermo Scientific). Cisplatin-sensitive accordance with the relevant guidelines and regulations. ovarian cancers cell line (OV2008) and its resistant C13* cells (10 cells/0.1 ml PBS/mice) intraperitoneally variant (C13*) were gifts from Prof. Benjamin K. Tsang injected into the right flank of BALB/C nude mice, under in the Ottawa Health Research Institute, Ottawa, isoflurane-induced anesthesia.Treatment beganwhenthe Canada [23] and cultured in RPMI 1640 medium with tumor volume reached between 70 and 100mm .The mice 10% FBS (Invitrogen). All cells were free from myco- were randomly divided into 4 groups (n = 6) including PBS plasma and were used between 3 and 5 passages after group, cDDP treatment group (cDDP 2.5 mg/kg, i.p., every thawing. All cells were authenticated by china center 4 days for 28 days), PIPER treatment group (PIPER 2 mg/ for type culture collection (CTCC, Wuhan, China) kg, i.p., daily for 28 consecutive days), and cDDP combin- using short tandem repeat (STR) DNA profiling. Pri- ation with PIPER treatment group (same dose as used in mary cell lines were isolated from ovarian cancer tissue the single-agent groups). Following the initial treatment, specimens of patients undergoing surgical resection as the tumor sizes were measured every 2 days. Tumor vol- previously described [24] and cultured in DMEM/F12 umes (V) were calculated by the following formula: V medium (Invitrogen) with 20% FBS (Invitrogen). The (mm ) = length × (square of width)/2. The mice were eu- passage number of primary ovarian cancer cells during thanized by cervical dislocation. Tumors were excised, Sun et al. BMC Cancer (2019) 19:1061 Page 3 of 12 weighed, paraformaldehyde-fixed paraffin embedding and and median OS was measured by pearson correlation used for ex vivo immunohistochemical staining. coefficient. Area under the curve (AUC) values were cal- culated from the ROC curves. All tests were two-sided, Immunohistochemistry (IHC) and scoring and P-values < 0.05 were considered to indicate a statisti- IHC staining was performed as described previously cally significant difference. All calculations were per- [25]. Briefly, tissue sections were incubated with anti- formed with SPSS (Version 25.0). body γ-H2AX (Abcam, Cat: ab2893, dilution 1:200), Ki67 (Abcam, Cat: ab15580, dilution 1:200), CD34 Results Mouse monoclonal (Abcam, Cat: ab198395, 1:1000), and ROS levels are associated with cDDP sensitivity of ovarian cleaved Caspase-3 (Cell signaling Technology, Cat: 9661, cancer both in vitro and in vivo 1:200) overnight at 4 °C and stained by 3,3′-diaminoben- To determine whether the ROS levels of ovarian can- zidine (DAB). Tumor-cell staining was assigned a score cer cells play a role in cisplatin (cDDP) resistance, 6 as described previously [25]. All specimens were evalu- cell lines, including SKOV3, Caov3, OVCAR3, OV-90, ated by two independent experts simultaneously. OV2008, and C13* were exposed to different concen- trations of cDDP (Fig. 1aand Additional file 2:Figure Study design, patients, and sample processing S1A). The IC of CDDP-treated cell lines are dis- This study was designed using a discovery stage and valid- played in Table S2. We also measured the intrinsic ation phase. In the discovery stage, 511 SOC patients with ROS levels in each of the cell lines using flow cytome- level 3 mRNA data were obtained from the TCGA data- try (Additional file 2: Figure S1B). However, intrinsic base [26] to establish a scoring system. In the validation ROS levels had no statistically significant impact on phase, the scores were validated using the largest outside cDDP sensitivity. Then, the cancer cells were treated independent dataset-Tothill dataset (GSE9899, n =285). with different cDDP concentrations in addition to Patients lacking the serous pathologic type (n = 45) were fixed concentrations of ROS-elevating (PLX4032 excluded from the Tothill dataset. To further validate the (1 μM), Piperlongumine (PIPER, 10 μM) and β- scoring system, 105 blocks of formalin-fixed, paraffin- phenylethyl isothiocyanate (PEITC, 10 μM)) or ROS- embedded (FFPE) tissues from primary epithelial ovarian scavenging drugs (glutathione (GSH, 2 mM), N-acetyl cancer were obtained. The study was approved by the Eth- cysteine (NAC, 1 mM) and Vitamin C (VitC, 1 mM)). ical Committee of the Medical Faculty of Tongji Medical The doses of ROS-elevating or ROS-scavenging were College. All patients written informed consents. The surgi- preselected while mono-therapy has no effect on can- cal staging was assessed in accordance with the Inter- cer cell proliferation or apoptosis (Additional file 2: national Federation of Gynecology and Obstetrics (FIGO) Figure S1C). Interestingly,mostROS-elevating drugs classification. Optimal debulking was defined as ≤1cm re- increased cDDP cytotoxicity in all ovarian cancer cell sidual disease. All clinicopathological characteristics are lines, especially in C13*, OV2008 and SKOV3 (Fig. 1a, reported in Additional file 1: Table S1. Additional file 2: Figure S1A, and Additional file 3: TableS2).Bycontrast, the cytotoxicityofcDDPincell Quantitative real-time PCR (qRT-PCR) linessuch asOV90and OVCAR3 wasreduced when RNAs from 105 FFPE cases were extracted from four 10- combined with ROS-scavenging drugs (Additional file μm-thick FFPE sections using the miRNeasy FFPE kit 2: Figure S1A and Additional file 3:Table S2). While (Qiagen, Valencia, CA, USA). The cDNA was synthesized Caov3 exhibited a moderate response in the presence by the SuperScript® IV First-Strand Synthesis System of both ROS-scavenging and ROS-elevating drugs (Thermo Fisher Scientific, China). Real-time PCR amplifi- (Additional file 2: Figure S1A and Additional file 3: cation was performed on an CFX Connect™ Real-Time Table S2). Furthermore, the sensitizing effects of ROS- PCR Detection System with SYBR reagent (Bio-Rad, elevating drugs were confirmed in 6 primary cells de- China). GAPDH was used as an internal control. rived from ovarian cancer patients (Fig. 1b, Additional file 2: Figure S1D and Additional file 3:Table S2), sup- Statistical analysis porting its clinical relevance. PIPER showed a better Student’s t-test was performed to compare the statistical sensitizing effect of cDDP cytotoxicity than other difference between two groups. Multiple comparisons ROS-scavenging drugs, we further verified its sensitiz- were accessed using the one-way analysis of variance ing effect in another 5 primary cells. Five primary cell (ANOVA). Survival was analyzed by the Kaplan–Meier lines were derived from 3 patients with recurrent ovar- method with the log-rank test. Univariate and multivari- ian cancer and 2 patients with primary ovarian cancer. able Cox regression analyses were used to test for statis- As expected, no matter what kind of ovarian cancer tical independence between the score, pathological, and patients, PIPER increased cDDP cytotoxicity (Fig. 1c clinical variables. The relationship between the score and Additional file 2:FigureS1E). Sun et al. BMC Cancer (2019) 19:1061 Page 4 of 12 Fig. 1 ROS levels are associated with cDDP sensitivity of ovarian cancer. a cDDP IC50 curves for ovarian cancer cell lines C13*, OV2008 and SKOV3 with or without ROS-elevating drugs (PLX4032, 1 μM, Piperlongumine (PIPER, 10 μM) and β-phenylethyl isothiocyanate (PEITC, 10 μM)). b Cell viability of 3 strains of primary cancer cells was assayed after treatment with increasing concentrations of cDDP with or without ROS-elevating drugs for 48 h by CCK-8. c Cell viability of primary cancer cells derived from patients with recurrent ovarian cancer or primary ovarian cancer was assayed after treatment with increasing concentrations of cDDP with or without PIPER for 48 h by CCK-8. a-c The two-tailed P-values < 0.05 were considered to indicate statistically significant differences. The results were tested by three independent experiments. d Growth curves of C13* subcutaneous xenograft tumors treated with vehicle, cDDP (2 mg/kg, intraperitoneally every 4 days), PIPER (2 mg/kg, intraperitoneally daily for 28 consecutive days), and cDDP plus PIPER (same dose as used in the single-agent groups) are shown. Tumor volumes were calculated as length × (square of width)/2. n = 8 per group. (*P < .05, **P < .001, two-sided Student t-test). e Tumor weights in nude mice were measured on day 35 after tumor cell injection. n = 8 per group. (*P < .05, **P < .001, two-sided Student t-test). (F) The immunohistochemistry analyses for caspase 3, Ki67, γ- H2AXand CD34 staining were carried out on C13* xenograft tumor sections collected from mice treated with the indicated treatments. Representative staining is shown. Scale bars = 50 μm. Data in (a–e) are the mean values ±95% confidence intervals. On the basis of the sensitizing effects of ROS-elevating combination therapy diminished blood vessels (CD34), sup- drugs on cDDP in cell lines and primary ovarian cancer pressed proliferation (Ki67) and increased DNA damage cells, we explored cDDP and PIPER combinations in C13* (H2ax) and apoptosis (cleaved caspase-3) compared to (cDDP resistant) xenograft tumors. As expected, C13* tu- cDDP or PIPER mono-therapy (Fig. 1f). mors are highly resistant to cDDP mono-therapy. Combin- These results revealed that baseline ROS levels in ation of PIPER and cDDP markedly delay tumor growth, ovarian cancer cells measured by DCFDA do not ac- while PIPER mono-therapy showed a minimal effect on curately predict their sensitivity to cDDP. ROS- tumor growth (Fig. 1d and e). IHC analysis showed that elevating drugs increased ovarian cancer cell Sun et al. BMC Cancer (2019) 19:1061 Page 5 of 12 sensitivity to cDDP in varying degrees. So, it is neces- patients in TCGA (n = 511) were divided into two sary to build a scoring system to assess ovarian cancer groups according to the median expression values of the patients who may benefit from the combination of 179 ROS related genes individually. 25 of 179 ROS re- cDDP and ROS-elevating drugs. lated genes were selected whose expression levels were associated (P < 0.15) with the OS of ovarian cancer pa- Establishment of the ROS scoring system in ovarian tients in TCGA dataset (n = 511) (Table 1 and Add- cancer patients itional file 4: Figure S2). For each patient, if high gene ROS-elevating drugs sensitized ovarian cancer cells to expression was associated with a good prognosis, all pa- cDDP, which to some extent depended on intrinsic ROS tients with a higher expression value than the median levels in the cells. However, there is no reliable and con- expression obtained one point, and vice versa. The venient methods to quantified ROS status in tumors. So, scores of all candidate genes of each patient were added we developed a ROS scoring system based on expres- to obtain a total score, which was called the ROS score. sions of ROS related genes. To identify ROS pathway The score divided patients into two categories (“low” genes, we selected 179 ROS related genes according to or “high”) with equal ranges. Kaplan-Meier and univari- our knowledge and works of literature. Ovarian cancer ate Cox proportional hazard regression analyses revealed Table 1 ROS-related genes were used to construct the score Gene Symbol P Survival Name AKT2 0 low V-akt murine thymoma viral oncogene homolog 2 FOSB 0.005 low FBJ murine osteosarcoma viral oncogene homolog B CITED4 0.009 high Cbp/p300-interacting transactivator CYBA 0.012 high Cytochrome b-245, alpha polypeptide JUNB 0.013 low Jun B proto-oncogene CYP27B1 0.014 high Cytochrome P450, family 27, subfamily B, polypeptide 1 FOS 0.014 low FBJ murine osteosarcoma viral oncogene homolog NFIX 0.027 low Nuclear factor I/X TXNRD1 0.041 low Thioredoxin reductase 1 USP14 0.054 high Ubiquitin specific peptidase 14 RIT1 0.058 low Ras-like without CAAX 1 KEAP1 0.0581 high Kelch-like ECH-associated protein 1 CYP3A7 0.061 high Cytochrome P450, family 3, subfamily A, polypeptide 7 TXN 0.07 low Thioredoxin GCLC 0.07 high Glutamate-cysteine ligase, catalytic subunit AKR7A3 0.072 low Aldo-keto reductase family 7, member A3 JUN 0.074 low Jun proto-oncogene CUL3 0.076 low Cullin 3 GSTA3 0.076 high Glutathione S-transferase alpha 3 PMF1 0.078 high Polyamine-modulated factor 1 PPARG 0.106 low Peroxisome proliferator-activated receptor gamma SOD1 0.122 high Superoxide dismutase 1, soluble ABCC4 0.123 high ATP-binding cassette, sub-family C, member 4 GSTM3 0.14 low Glutathione S-transferase mu 3 (brain) NOX4 0.142 high NADPH oxidase 4 NOTE: “High” indicates that gene expression above the median gene expression was associated with better overall survival; “low” indicates that gene expression above the median gene expression was associated with poor overall survival Sun et al. BMC Cancer (2019) 19:1061 Page 6 of 12 Fig. 2 Prognostic value of the ROS scoring system in TCGA dataset. a A Kaplan-Meier analysis of overall survival (OS) for ovarian cancer patients in TCGA dataset with the ROS scoring system (high [scores 13–25], the green line v.s. low [scores 0–12], the blue line) is shown (P < 0.001, respectively, log-rank test). b A Kaplan-Meier analysis of overall survival (OS) for advanced stage (stage III/ IV) ovarian cancer patients in TCGA dataset with the ROS scoring system is shown (P < 0.001, respectively, log-rank test). c A Kaplan-Meier analysis of overall survival (OS) for advanced stage (stage III/ IV) ovarian cancer patients received a platinum and taxane regimen as first-line chemotherapy in TCGA dataset with the ROS scoring system is shown (P < 0.001, respectively, log-rank test). All statistical tests were two-sided. patients with high scores (scores 13–25) had better OS compared to patients with low scores (scores 0–12) (high v.s. low scores, median OS = 4.66 years v.s. 2.92 years, HR = 0.43; 95% CI = 0.34 to 0.55, P < 0.001) (Fig. 2a). Moreover, the ROS scoring system performed well independent of tumor stages, treatments, and differ- ent molecular subtypes. Patients with high ROS scores are associated with better prognosis in stages III/IV pa- tients (HR = 0.44; 95% CI = 0.34 to 0.56, P < 0.001) (Fig. 2b), in stage III/IV patients receiving first-line chemo- therapy with platinum and taxane regimens (HR = 0.38; 95% CI = 0.27 to 0.53, P < 0.001) (Fig. 2c), or in different ovarian cancer molecular subtypes (immunoreactive, dif- ferentiated, proliferative, mesenchymal) (Additional file 5: Figure S3). Prognosis prediction value of the ROS scoring system in ovarian cancer patients The association between ROS score and OS was con- firmed in an independent outside dataset (Tothill, GSE9899), the largest ovarian cancer datasets available (high v.s. low scores, median OS = 4.77 years v.s. 3.04 years; HR = 0.65; 95% CI = 0.44 to 0.94, P = 0.022) (Fig. 3a). To further validate the prognosis prediction values of this ROS scoring system and broaden the scope of applications. We quantified these 25 survival-related ROS genes using a widely-applicable and convenient method, qRT-PCR analysis from FFPE tissues in 105 ovarian tumors. Consistent with afore-mentioned associ- ations based on microarray data, patients with high ROS scores have longer survival times (high v.s. low, median OS = 3.76 years v.s. 2.36 years; HR = 0.40; 95% CI = 0.22 to 0.74, P = 0.002) (Fig. 3b). Univariate and multivariate Cox proportional hazard regression analyses were ap- plied to estimate the relationship between OS and the score (high v.s. low) compared with other clinical fac- tors, including age (≤59 v.s. ≥60 years), FIGO stage (IV v.s. III), histological grade (3 v.s. 1–2), and extent of sur- gical debulking (0–10 v.s. ≥11 mm residual tumor). Only the score is an independent prognostic factor in TCGA, Tothill dataset and our in house patients datasets (Fig. 3c, Additional file 5: Figure S4). Sun et al. BMC Cancer (2019) 19:1061 Page 7 of 12 Fig. 3 Validation of prognostic value of the ROS scoring system in Tothill and TJ datasets. a A Kaplan-Meier analysis of overall survival (OS) for ovarian cancer patients in Tothill (GSE9899) dataset with the ROS scoring system is shown (P = 0.022, respectively, log-rank test). b A Kaplan-Meier analysis of overall survival (OS) for ovarian cancer patients in TJ dataset with the ROS scoring system is shown (P = 0.003, respectively, log-rank test). c Univariate and multivariable Cox proportional hazards regression analyses incorporating the score and known prognostic clinical factors, including age at diagnosis (≤59 v.s. ≥60 years), stage (III v.s. IV), grade (1–2 v.s. 3), and extent of surgical debulking (0–10 v.s. ≥11 mm residual tumor); each as categorical variables. Solid squares represent the hazard ratio (HR) of death and open-ended horizontal lines represent the 95% confidence intervals (CIs). All P values were calculated using Cox proportional hazards analysis. The contribution of the ROS scoring system as a Univariate and multivariate analyses were repeated continuous variable toward prediction of OS in all when the ROS score was assessed as a continuous vari- datasets and cDDP sensitivity in 6 ovarian cancer cell able. Again, the ROS score outperformed other clinical lines covariates as an independent prognostic factor in TCGA To further verify the associations between scores and dataset, Tothill dataset, and in TJ dataset (Table 2). patients’ survival, we performed correlation analysis between each score and the median survival times in patients with the same scores. Interestingly, there was Predictive accuracy of the ROS scoring system in ovarian a positive correlation between scores and the median Cancer patients survival times (r = 0.758, P = 0.032 in TCGA dataset; To further evaluate the contribution of the score to OS r = 0.516, P = 0.049 in Tothill dataset; and r = 0.795, prediction, ROC curve analysis was performed using the P = 0.001in TJ dataset) (Fig. 4). following variables: clinical covariates (age, grade, stage, Sun et al. BMC Cancer (2019) 19:1061 Page 8 of 12 Fig. 4 Prognostic value of the ROS scoring system in all datasets. a-c Correlation of score as a continuous variable with OS in TCGA (a), Tothill (b) and TJ (c) datasets. For each patient’s tumor, a point was given for each ROS related gene for which higher than median expression was associated with longer survival, and vice versa. The sum of these points constituted our score. and residual tumor (AGSR)); ROS score (Score); and result in different outcomes and prognoses in ovarian clinical covariates plus ROS score (AGSR + Score). cancer patients. For these analyses, patients were divided into two groups In this study, we established a quantifiable ROS scor- with a survival time higher or lower than the median OS ing system able to predict ovarian cancer patient prog- (TCGA dataset median OS =3.79 years, Tothill dataset me- nosis based on the expression levels of ROS related dian OS = 3.62 years, TJ dataset median OS = 2.81 years), genes in the TCGA dataset (n = 511). Moreover, we vali- and those with a survival time shorter than the median OS dated this system in another published dataset (Tothill at last follow-up were excluded. Interestedly, the score dataset, GSE9899, n = 241). We further validated the alone have higher predictive values than clinical covariates scoring system in our in-house patient dataset (TJ data- in all datasets (TCGA dataset AUC = 0.71 v.s. 0.65, Tothill set, n = 105). We indicated that the scoring system ac- dataset AUC = 0.73 v.s. 0.67, TJ dataset AUC = 0.74 v.s. curately determined the prognosis of ovarian cancer 0.66) (Fig. 5a). Moreover, combined with clinical covariates patients. The use of FFPE sections and qPCR also ex- and ROS score could further improve predictive perform- tended the use of the scoring system. Both datasets dem- ance in all dataset (TCGA dataset AUC = 0.73, Tothill data- onstrated that the system is prognostic for survival. set AUC = 0.81, TJ dataset AUC = 0.78) (Fig. 5band c). A number of gene profile-based prognosis techniques, used in combination with microarrays or PCR, have been Discussion previously developed to predict survival in patients with Compared with other types of cancers, one unique ovarian cancer [34–36], but the results have not been satis- feature of ovarian cancer is that over 50% of ovarian factory. We demonstrated that our scoring system is super- cancers contain p53 mutation [27]. Specifically, p53 ior to other known clinical factors in predicting OS, not mutation was identified in 96% of all serous ovarian only in the TCGA dataset but also in our dataset and an- tumors [28]. Suppression of p53 led to significant de- other online validation set. TCGA divided ovarian cancer creases in the expression of SESN1, SESN2, and into four molecular subtypes (immunoreactive, differenti- GPX1, suggesting that p53 is involved in cellular me- ated, proliferative, mesenchymal) based on gene clustering, tabolism and antioxidant response [29–31]. So, p53 but these clusters did not associate with OS. However, stat- mutation could increase ROS levels and oxidative istical significance was observed when the score was applied damage of DNA in ovarian cancer cells. Thus, alter- to all subtypes except the proliferative subtype. Our score ations in the expression of ROS genes that affect ROS extends application of the TCGA classification model. Our production or scavenging may be closely linked to the system was also able to predict outcomes to first-line plat- resistance of ovarian cancer cells to chemotherapy. inum and taxane chemotherapy in ovarian cancer. This fea- An increasing number of studies have identified rela- ture has profound clinical significance because there are no tionships between ROS related genes (such as SOD, other good clinical factors to predict the response to CAT, GLS2 and so on) and drug resistant [32, 33]. We platinum-based standard chemotherapy. Most patients with found that ROS pathway function or activity plays a cru- advanced serous ovarian cancer will relapse after a few cial role in chemotherapy responses in ovarian cancer years even after standard therapies like thorough operation cells and transplanted mouse models. Our results sug- and chemotherapy are used. In addition, about 30% of pa- gest that ROS related gene expression changes are im- tients with primary platinum resistance undergo multiple portant mechanisms by which ovarian cancer cells cycles of useless and potentially toxic treatment before acquire resistance to anticancer drugs, and these changes second-line drug treatments are used. Moreover, agents Sun et al. BMC Cancer (2019) 19:1061 Page 9 of 12 Table 2 Univariate and multivariable analysis using prognostic factors in all of datasets Cohort Characteristics Univariate Cox Regression Multivariate Cox Regression HR 95% CI P HR 95% CI P −14 −10 TCGA Score 0.878 (0.849,0.908) 2.23*10 0.889 (0.857,0.922) 3.27*10 Age 1.019 (1.008,1.030) 0.001 1.018 (1.007,1.030) 0.002 Grade 1 1 0.173 1 0.654 2 vs 1 1.254 (0.302,5.21) 0.756 0.814 (0.191,3.459) 0.78 3 vs 1 1.773 (0.44,7.138) 0.421 1.319 (0.325,5.358) 0.699 Others 2.304 (0.489,10.866) 0.291 1.256 (0.252,6.25) 0.781 Stage I-II 1 0.002 1 0.047 III 2.374 (1.258,4.479) 0.008 2.226 (1.032,4.802) 0.041 IV 3.219 (1.633,6.345) 0.001 2.805 (1.241,6.336) 0.013 Surgical debulking 1.293 (0.989,1.691) 0.061 1.138 (0.861,1.504) 0.362 Tothill Score 0.929 (0.877,0.985) 0.013 0.924 (0.866,0.985) 0.015 Age 1.021 (1.000,1.042) 0.047 1.025 (1.003,1.047) 0.027 Grade 1 1 0.467 1 0.804 2 vs 1 1.964 (0.549,6.499) 0.269 1.269 (0.374, 4.309) 0.703 3 vs 1 2.116 (0.660,6.791) 0.208 1.246 (0.369,4.200) 0.723 Others 0.751 (0.077,7.287) 0.805 0.47 (0.045,4.868) 0.527 Stage I-II 1 0.01 1 0.046 III 4.012 (1.269,12.685) 0.018 3.319 (1.006,10.950) 0.049 IV 6.657 (1.906,23.245) 0.003 5.118 (1.377,19.024) 0.015 Surgical debulking 0.608 (0.397,0.932) 0.022 0.688 (0.439,1.079) 0.104 TJ Score 0.841 (0.746,0.949) 0.005 0.862 (0.750,0.990) 0.036 Age 1.048 (1.011,1.087) 0.011 1.050 (1.013,1.089) 0.008 Grade 1 1 0.943 1 0.693 2 vs 1 1.146 (0.363,3.622) 0.816 1.551 (0.470,5.126) 0.471 3 vs 1 1.274 (0.496,3.272) 0.615 1.799 (0.638,5.075) 0.267 Others 1.538 (0.296,7.981) 0.608 2.400 (0.408,14.115) 0.333 Stage I-II 1 0.087 1 0.038 III 3.101 (1.063,9.046) 0.038 3.986 (1.282,12.392) 0.017 IV 3.661 (1.098,12.210) 0.035 4.956 (1.378,17.829) 0.014 Surgical debulking 0.443 (0.225,0.874) 0.019 0.617 (0.298,1.276) 0.193 that increase the ROS levels of cancer cells could be used patient with poor prognosis predicted by the ROS scor- as a standard treatment to improve chemotherapeutic re- ing system, if possible, we can combine ROS-inducing sponses for patients with ovarian cancers with low ROS drugs with platinum- and taxane-based chemotherapies levels. to improve outcomes. We know there are lots of prob- In this study, we just demonstrate the prognostic value lems, such as the in vivo stability of ROS-elevating of the ROS scoring system, which leads to the possibility drugs, targeted property, and safety need to be resolved of clinical application. For individual ovarian cancer before ROS becoming a therapeutic target. However, Sun et al. BMC Cancer (2019) 19:1061 Page 10 of 12 Fig. 5 Predictive accuracy of the ROS scoring system compared with prognostic clinical factors. Receiver operating characteristic (ROC) analysis of the score and clinical covariates in predicting overall survival in TCGA (a), Tothill (b) and TJ (c) datasets. Using statistical models constructed based on multivariable Cox proportional hazards, ROC curves were calculated incorporating clinical variables of age, grade, and stage (left); age, grade, stage, and score (middle); and score alone (right). The area under the curve (AUC) was calculated for ROC curves, and sensitivity and specificity was calculated to assess the score performance. further study based on patient-derived tumor xenograft mechanisms of gene regulation, including microRNAs, (PDX) animal models for intraperitoneal administration DNA methylation, and CNV region changes were not of ROS-elevating drugs may lead to the possibility of considered. We are looking forward to future studies of clinical transformation. this type and the development of more comprehensive This study has several limitations. First, although we prediction models. 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 pro- Conclusions spectively in a clinical trial, and we believe that the score We established a ROS scoring system that could predict is ready for such testing. Second, our gene expression pro- the outcomes of ovarian cancer patients. This system of- filing and analysis is only limited to ROS related pathways. fers considerable improvement over existing methods However, other gene expression pathways that may be im- for prognostic classification and has the potential to pro- portant in survival predictions were neglected. Third, this vide clinicians with useful, readily available information study is limited in its gene expression profiling. Other for personalized chemotherapy in the future. Sun et al. BMC Cancer (2019) 19:1061 Page 11 of 12 Supplementary information Plan Projects (2015BAI13B05), the Chinese National Key Plan of Precision Supplementary information accompanies this paper at https://doi.org/10. Medicine Research (2016YFC0902901), and Nature and Science Foundation 1186/s12885-019-6288-7. 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 Additional file 1: Table S1. Clinicopathologic characteristics of ovarian Commission of Hubei Province (WJ2015MA001). The funding bodies had no cancer patients in 3 datasets. (docx 16.8 kb) (DOCX 16 kb) influence on the design of the study and collection, analysis, and interpret- Additional file 2: Figure S1. ROS level is related to the survival of ation of data and in writing the manuscript. ovarian cancer cells treated with cDDP. (jpg 2.83mb) (A) Cell viability of ovarian cancer cell lines Caov3, OV90 and OVCAR3 was measured after Availability of data and materials treatment with gradient concentrations of cDDP with or without ROS- The datasets used and/or analyzed during the current study are available elevating (PLX4032, 1 μM, Piperlongumine (PIPER, 10 μM) and β- from the corresponding author on reasonable request. phenylethyl isothiocyanate (PEITC, 10 μM)) or scavenging drugs (glutathi- one (GSH, 2 mM), N-acetyl cysteine (NAC, 1 mM) and Vitamin C (VitC, 1 mM)) for 48 h by CCK-8. (B) Intracellular ROS concentrations of SKOV3, Ethics approval and consent to participate Caov3, OVCAR3, OV-90, OV2008, and C13* were measured by DCF-DA Primary cell lines and animal studies were approved by the Ethical staining. (C) CCK8 detected cell viability of ovarian cancer cell lines after Committee of the Medical Faculty of Tongji Medical College (Wuhan, China), treatment with ROS-elevating. (D) cDDP IC curves for 3 strains of pri- and were performed according to the relevant guidelines and regulations. mary cancer cells with or without ROS-elevating or scavenging drugs for All procedures performed in studies were in accordance with the ethical 48 h by CCK-8. (E) Cell viability of primary cancer cells derived from pa- standards of the institutional and/or national research committee and with tients with recurrent or primary ovarian cancer was measured after treat- the 1964 Helsinki declaration and its later amendments or comparable ment with gradient concentrations of cDDP with or without PIPER for 48 ethical standards. Written informed consent was obtained from all individual h by CCK-8. The two-tailed P-values < 0.05 were considered to indicate participants included in the study. statistically significant differences. The results were tested by three inde- pendent experiments. (JPG 2907 kb) Consent for publication Additional file 3: Table S2. The IC50 of ovarian cancer cell lines and Not applicable. primary cancer cells treated with different combinations of drugs. (docx 18.8 kb) (DOCX 18 kb) Competing interests Additional file 4: Figure S2. 84 of 179 ROS related genes with Kaplan- The authors declare that they have no competing interests. Meier log-rank P values < 0.5. (tif 328 kb) Genes with P < 0.15 are highlighted in red. (TIF 328 kb) Author details Additional file 5: Figure S3. Prognostic Value of the ROS Scoring 1 Cancer Biology Research Center (Key laboratory of Chinese Ministry of System in four ovarian cancer molecular subtypes of TCGA. (tif 495 kb) A Education), Tongji Hospital, Tongji Medical College, Huazhong University of Kaplan-Meier analysis of overall survival (OS) were performed in the differ- 2 Science and Technology, Wuhan, People’s Republic of China. Department of entiated, immunoreactive, mesenchymal, and proliferative for ovarian Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, cancer patients in TCGA dataset with the ROS scoring system (high Huazhong University of Science and Technology, Wuhan, People’s Republic [scores 13–25], the green line v.s. low [scores 0–12], the blue line) is 3 of China. Department of Surgery, Tongji Hospital, Tongji Medical College, shown (P < 0.001, log-rank test). All statistical tests were two-sided. (TIF Huazhong University of Science and Technology, Wuhan, People’s Republic 495 kb) of China. Additional file 6: Figure S4. Univariate analyses were performed in TCGA and Tothill dataset. (tif 1.21mb) Univariate analyses incorporating Received: 16 April 2019 Accepted: 24 October 2019 the score and known prognostic clinical factors, including age at diagnosis (≤59 v.s. ≥60 years), stage (III v.s. IV), grade (1–2 v.s. 3), and surgical debulking (0–10 v.s. ≥11 mm residual tumor). Solid squares References represent the hazard ratio (HR) of death and open-ended horizontal lines 1. Torre LA, Trabert B, DeSantis CE, Miller KD, Samimi G, Runowicz CD, Gaudet represent the 95% confidence intervals (CIs). All P values were calculated MM, Jemal A, Siegel RL. Ovarian cancer statistics, 2018. CA Cancer J Clin. using Cox proportional hazards analysis. (TIF 1241 kb) 2018;68(4):284–96. Additional file 7. ARRIVE checklist. (PDF 397 kb) 2. Cortez AJ, Tudrej P, Kujawa KA, Lisowska KM. Advances in ovarian cancer therapy. Cancer Chemother Pharmacol. 2018;81(1):17–38. 3. Zhang J, Wang X, Vikash V, Ye Q, Wu D, Liu Y, Dong W. ROS and ROS- Abbreviations mediated cellular signaling. Oxidative Med Cell Longev. 2016;2016:4350965. cDDP: Cisplatin; FFPE: Formalin-fixed, paraffin-embedded; GSH: Glutathione; 4. Boonstra J, Post JA. Molecular events associated with reactive oxygen NAC: N-acetyl cysteine; OS: Overall survival; PEITC: β-phenylethyl species and cell cycle progression in mammalian cells. Gene. 2004;337:1–13. isothiocyanate; PIPER: Piperlongumine; ROS: Reactive oxygen species; 5. Patterson JC, Joughin BA, van de Kooij B, Lim DC, Lauffenburger DA, Yaffe TCGA: The Cancer Genome Atlas; VitC: Vitamin C; FIGO: International MB. ROS and Oxidative stress are elevated in mitosis during asynchronous Federation of Gynecology and Obstetrics; AUC: Area under the curve. cell cycle progression and are exacerbated by mitotic arrest. Cell Syst. 2019; 8(2):163–7 e162. Acknowledgements 6. Kumari S, Badana AK, G MM, G S, Malla R: Reactive Oxygen Species: A Key Not applicable. Constituent in Cancer Survival Biomark Insights 2018, 13:1177271918755391. 7. Zhou D, Shao L, Spitz DR. Reactive oxygen species in normal and tumor Authors’ contributions stem cells. Adv Cancer Res. 2014;122:1–67. 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PLoS One. 2018;13(11):e0207319. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Published: Nov 8, 2019

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