Objective: Ovarian cancer is the second most common malignancy in women, but it is a fatal gynecological tumor. Although it has a standard treatment regimen, resistance to chemotherapy makes patients more prone to early recurrence, leading to poor survival rates. Therefore, this study investigated factors related to platinum resistance through a complete analysis of clinical data. Design: Clinical data of patients with ovarian cancer were collected, and the patients were categorized into platinum-sensitive and platinum-resistant groups. By comparing the differences in clinical data between the groups, the key factors affecting platinum resistance were analyzed. Participants/Materials, Setting, Methods: We collected the clinical data of patients with epithelial ovarian cancer (EOC) who were admitted to the Department of Oncology of the General Hospital of Ningxia Medical University between January 1, 2019, and December 31, 2020. We conducted univariate and multivariate analyses and evaluated overall survival and progression-free survival using the Kaplan-Meier method. Results: We enrolled 161 patients with EOC, of whom 124 demonstrated platinum sensitivity and 37 demonstrated platinum resistance after the initial platinum-based chemotherapy. Univariate analyses revealed that the International Federation of Gynecology and Obstetrics (FIGO) stage, neoadjuvant chemotherapy, and Fagotti score were associated with an increased risk of platinum resistance for the first recurrence. In multivariate logistic regression analysis, only Fagotti score and neoadjuvant chemotherapy were associated with an increased risk of platinum resistance (odds ratio: 0.372 and 0.328, 95% confidence interval: 0.160–0.863 and 0.141–0.762, p = 0.021 and 0.010, respectively). Limitations: The sample size of this study was relatively small because of nonstandard treatment of some patients, the absence of clinical data, and failure of follow-up. Conclusions: Patients with EOC exhibiting platinum resistance had a very poor prognosis. The Fagotti score and neoadjuvant chemotherapy appeared to increase the risk of platinum resistance at first recurrence.

In 2020, approximately 22,530 new cases of epithelial ovarian cancer (EOC) were recorded globally, making ovarian cancer the most fatal gynecological cancer and the seventh most common cancer-related death in women [1]. Over the past decades, surgical methods and techniques for ovarian cancer have greatly improved, along with the emergence of new drugs such as antiangiogenic drugs (bevacizumab) [2] and ADP-ribose polymerase inhibitors (olaparib and niraparib) [3, 4]. Although the overall survival (OS) rate of patients with ovarian cancer has increased, novel therapeutic interventions are required to improve the survival of patients because the fatality and relapse rates remain high.

The recommended treatment for ovarian cancer is primary debulking surgery (PDS) followed by chemotherapy, the first-line chemotherapy regimen consisting of paclitaxel and platinum-based chemotherapy; 6–8 times of chemotherapy are usually required after surgery. For patients with larger tumors or extensive metastases that cannot be resected directly or patients with poor general condition and serious comorbidities who are not candidates for PDS as the first choice, neoadjuvant chemotherapy (NACT) is an alternative treatment. For the above patients who cannot be operated on first, we will choose chemotherapy 3–4 times before surgery, and this chemotherapy is defined as NACT. Chemotherapy drugs are also platinum-based chemotherapy combined with paclitaxel, and the main purpose is to shrink the lesion to win the chance of surgery. The surgery after NACT is called interval debulking surgery (IDS) [5]. However, approximately 70% patients who underwent platinum-based chemotherapy develop recurrence and eventually platinum resistance [6]. Chemoresistance is one reason for the high relapse and mortality rates. However, the risk factors for platinum-based chemotherapy resistance remain unclear. Thus, in this study, we collected the related clinical data of patients with EOC from the General Hospital of Ningxia Medical University to analyze the possible relationship between platinum resistance and EOC.

Patients

We enrolled patients with EOC who were admitted to the Department of Oncology at the General Hospital of Ningxia Medical University between January 1, 2019, and December 31, 2020. All patients provided complete clinical data and were followed up by an outpatient service or via telephone call until death or more than 6 months after their initial treatment. The inclusion criteria were as follows: (1) received the standard treatment (PDS followed by platinum-based chemotherapy or NACT followed by IDS) and (2) followed up regularly after initial treatment. Conversely, we excluded those with other malignant tumors and serious complications of the heart, brain, and lungs.

Procedure

In line with the National Comprehensive Cancer Network (NCCN) guidelines established in 2020, we grouped the included patients into two groups: group A (patients with platinum-resistant tumors) and group B (patients with platinum-sensitive tumors). Group A comprised patients whose diseases responded to primary platinum therapy and then progressed less than 6 months after the last dose of the therapy, progressed during platinum therapy, or were stable or persistent during the therapy. Group B included patients with platinum-sensitive tumors whose disease relapsed 6 months or more after the initial treatment.

We collected clinical data regarding ovarian cancer in terms of primary treatments and first recurrence, as well as patients’ age, nationality, histological grade and subtype, the Federation of Gynecology and Obstetrics stage, primary tumor’s initial maximum diameter, volume of ascites, pleural effusion, cancer antigen 125 (CA125), human epididymis protein 4 (HE4), NACT, optimal debulking surgery, Fagotti score, lymph node metastasis presence/absence, P53, ER, PR, OS, and progression-free survival (PFS).

Statistical Analysis

Statistical data were analyzed using SPSS software version 23.0. We employed Student’s t test for continuous variables and the χ2 test or Fisher’s exact test for categorical variables. The risk factors for platinum resistance were determined by logistic regression analyses and expressed as odds ratios (ORs) and 95% confidence intervals (CIs). The Kaplan-Meier method was used to analyze survival curves. A p value <0.05 indicates a statistically significant difference.

Patients’ Clinical Data

After excluding 15 patients who underwent surgeries in a different hospital, 9 patients who were lost to follow-up, and 10 patients who lacked clinical data, we ultimately included 161 patients. All 161 eligible patients received the standard treatment of PDS (patients at stage IV were included only in case of positive pleural effusion or any resectable disease) or NACT-IDS according to the NCCN guidelines by gynecologic oncologists and accepted the initial treatment at the General Hospital of Ningxia Medical University, with complete clinical data. Group A and B comprised 37 and 124 patients, respectively.

Patients’ General Characteristics

As shown in Table 1, most patients had Han nationality (139, 86.3%), followed by 21 patients with Hui nationality and 1 patient with Man nationality. FIGO stages I, II, III, and IV accounted for 26, 4, 103, and 28 patients, respectively; thus, stage III had the highest proportion (64%). Majority of the patients (102, 63.4%) had histological grade 3, followed by 12 patients with grade 1 and 4 patients with grade 2; meanwhile, the histological grade of 43 patients was unknown. Regarding the histological type, most patients had serous ovarian cancer (126, 78.3%); endometrioid, mucinous, clear cell, and others were found in 5, 7, 12, and 11 patients, respectively.

Table 1.

Patient characteristics

Variablen (%)
All cases 161 
Median age (range), years 55.412 (30–77) 
Nationality 
 Han 139 (86.3) 
 Others 22 
FIGO stage 
 I 26 
 II 
 III 103 (64) 
 IV 28 
Histological grade 
 1 12 
 2 
 3 102 (63.4) 
 Unknown 43 
Histological type 
 Serous 126 (78.3) 
 Endometrioid 
 Mucinous 
 Clear cell 12 
 Others 11 
Variablen (%)
All cases 161 
Median age (range), years 55.412 (30–77) 
Nationality 
 Han 139 (86.3) 
 Others 22 
FIGO stage 
 I 26 
 II 
 III 103 (64) 
 IV 28 
Histological grade 
 1 12 
 2 
 3 102 (63.4) 
 Unknown 43 
Histological type 
 Serous 126 (78.3) 
 Endometrioid 
 Mucinous 
 Clear cell 12 
 Others 11 

Univariate Analysis for Platinum Resistance

We found platinum resistance in 37 patients (23.0%) and platinum sensitivity in 124 patients (77.0%). Univariate analysis revealed that FIGO stage, NACT, and Fagotti score were associated with an increased risk of platinum resistance at first recurrence (shown in Tables 2, 3). At FIGO stages III–IV, 36 (97.3%) patients had platinum resistance, whereas 95 (76.6%) had platinum sensitivity. Thus, the platinum-resistant group had a higher rate of FIGO stages III–IV than the platinum-sensitive group. In NACT-IDS, 22 (59.5%) patients had platinum resistance and 43 (34.7%) had platinum sensitivity; therefore, the platinum resistance group had a higher rate of NACT-IDS. Furthermore, 16 (43.2%) patients with platinum resistance had a Fagotti score >4, and 24 (19.4%) patients with platinum sensitivity had a Fagotti score >4; hence, the platinum-resistant group had a high rate of Fagotti score >4.

Table 2.

Univariate analysis for platinum resistance

Platinum-resistant group (n = 37)Platinum-sensitive group (n = 124)χ2p value
Age 56.568±7.816 55.774±8.688 −0.498 0.619 
   0.003 0.954 
 >55 18 61   
 ≤55 19 63   
Nationality   1.124 0.289 
 Han 30 109   
 Others 15   
FIGO stage   6.735 0.009 
 I-II 29   
 III-IV 36 95   
Histological grade   3.142 0.076 
 High 28 74   
 Others 50   
Histological type   0.225 0.636 
 Serous 30 96   
 Others 28   
CA125 861.700 767.700 0.192 0.661 
HE4 517.150 306.250 3.843 0.05 
Maximum diameter of primary tumor P25 = 5.5 P75 = 12.7 0.015 0.904 
Ascites, mL   1.025 0.311 
 >1,000 19 52   
 ≤1,000 18 72   
Pleural effusion   2.807 0.094 
 Yes 12 24   
 No 25 100   
Ascites tumor cells   1.746 0.186 
 Yes 22 50   
 No 18   
Lymphatic metastasis   0.845 0.358 
 Yes 10 21   
 No 10 34   
P53   1.969 0.161 
 Yes 18 45   
 No 35   
ER 
 + 17 56 0.383  
 − 22 p= 0.536  
PR 
 + 23 χ2 = 0.746  
 − 16 45 p= 0.388  
Platinum-resistant group (n = 37)Platinum-sensitive group (n = 124)χ2p value
Age 56.568±7.816 55.774±8.688 −0.498 0.619 
   0.003 0.954 
 >55 18 61   
 ≤55 19 63   
Nationality   1.124 0.289 
 Han 30 109   
 Others 15   
FIGO stage   6.735 0.009 
 I-II 29   
 III-IV 36 95   
Histological grade   3.142 0.076 
 High 28 74   
 Others 50   
Histological type   0.225 0.636 
 Serous 30 96   
 Others 28   
CA125 861.700 767.700 0.192 0.661 
HE4 517.150 306.250 3.843 0.05 
Maximum diameter of primary tumor P25 = 5.5 P75 = 12.7 0.015 0.904 
Ascites, mL   1.025 0.311 
 >1,000 19 52   
 ≤1,000 18 72   
Pleural effusion   2.807 0.094 
 Yes 12 24   
 No 25 100   
Ascites tumor cells   1.746 0.186 
 Yes 22 50   
 No 18   
Lymphatic metastasis   0.845 0.358 
 Yes 10 21   
 No 10 34   
P53   1.969 0.161 
 Yes 18 45   
 No 35   
ER 
 + 17 56 0.383  
 − 22 p= 0.536  
PR 
 + 23 χ2 = 0.746  
 − 16 45 p= 0.388  
Table 3.

Univariate analysis of treatment-related factors for platinum resistance

Platinum-resistant group (n = 37)Platinum-sensitive group (n = 124)χ2p value
Treatment   7.270 0.007 
 NACT-IDS 22 43   
 PDS 15 81   
Total cycles of chemotherapy   0.091 0.763 
 >6 12 37   
 ≤6 25 87   
Cycles of NACT   0.025 0.873 
 >4   
 ≤4 20 41   
Optimal cytoreduction   1.257 0.262 
 Yes 27 101   
 No 10 23   
Fagotti score   8.709 0.003 
 >4 16 24   
 ≤4 21 100   
Platinum-resistant group (n = 37)Platinum-sensitive group (n = 124)χ2p value
Treatment   7.270 0.007 
 NACT-IDS 22 43   
 PDS 15 81   
Total cycles of chemotherapy   0.091 0.763 
 >6 12 37   
 ≤6 25 87   
Cycles of NACT   0.025 0.873 
 >4   
 ≤4 20 41   
Optimal cytoreduction   1.257 0.262 
 Yes 27 101   
 No 10 23   
Fagotti score   8.709 0.003 
 >4 16 24   
 ≤4 21 100   

However, no significant difference was noted between the two groups in terms of age, nationality, histological grade, histological subtype, primary tumor’s initial maximum diameter, ascites volume, pleural effusion, CA125, HE4, total number of chemotherapy cycles, NACT cycles, optimal debulking surgery, lymph node metastasis presence/absence, P53, ER, and PR.

Multivariate Analysis for Platinum Resistance

The variables that showed significance in the univariate analysis (FIGO stage, NACT, and Fagotti score) were further analyzed using multivariate logistic regression. As shown in Table 4, only the Fagotti score and NACT remained associated with an increased risk of platinum resistance (OR: 0.372 and 0.328, 95% CI: 0.160–0.863 and 0.141–0.762, p = 0.021 and 0.010, respectively).

Table 4.

Multivariate analysis for platinum resistance

Factorsp valueOR95% CI
FIGO stage (I–II vs. III–IV) 0.195 0.243 0.029–2.067 
Fagotti score (≤4 vs. >4) 0.021 0.372 0.160–0.863 
PDS versus NACT-IDS 0.010 0.328 0.141–0.762 
Factorsp valueOR95% CI
FIGO stage (I–II vs. III–IV) 0.195 0.243 0.029–2.067 
Fagotti score (≤4 vs. >4) 0.021 0.372 0.160–0.863 
PDS versus NACT-IDS 0.010 0.328 0.141–0.762 

Prognosis Analysis of the Groups

As shown in Table 5, the total median OS was 79 months (95% CI: 45.948–112.052) and the total median PFS was 14 months (95% CI: 11.324–16.676). The median OS and PFS were significantly worse in the platinum-resistant group at the first recurrence (both p = 0.000, shown in Fig. 1, 2) with OS: 22 months (95% CI: 20.207–23.793) versus 95 months (95% CI: 77.240–113.364); PFS: 4 months (95% CI: 3.793–4.207) versus 18 months (95% CI: 14.595–21.405). The five-year survival rate was 59.4%; in the subgroup analysis, the rate for the platinum-sensitive group was 70.7%, whereas it was 0% for the platinum-resistant group.

Table 5.

Prognosis analysis

GroupsNumberDeathMedian OS, monthsMedian PFS, months5-year survival rate, %
Platinum-sensitive group 124 17 95 (77.240–113.364) 18 (14.595–21.405) 70.7 
Platinum-resistant group 37 20 22 (20.207–23.793) 4 (3.793–4.207) 0.0 
Sum 161 37 79 (45.948–112.052) 14 (11.324–16.676) 59.4 
GroupsNumberDeathMedian OS, monthsMedian PFS, months5-year survival rate, %
Platinum-sensitive group 124 17 95 (77.240–113.364) 18 (14.595–21.405) 70.7 
Platinum-resistant group 37 20 22 (20.207–23.793) 4 (3.793–4.207) 0.0 
Sum 161 37 79 (45.948–112.052) 14 (11.324–16.676) 59.4 
Fig. 1.

Kaplan-Meier analyses of patients’ OS: all patients (a); platinum-sensitive group versus platinum-resistant group (b).

Fig. 1.

Kaplan-Meier analyses of patients’ OS: all patients (a); platinum-sensitive group versus platinum-resistant group (b).

Close modal
Fig. 2.

Kaplan-Meier analyses of the patients’ progression-free survival (PFS): all patients (a); platinum-sensitive group versus platinum-resistant group (b).

Fig. 2.

Kaplan-Meier analyses of the patients’ progression-free survival (PFS): all patients (a); platinum-sensitive group versus platinum-resistant group (b).

Close modal

Carboplatin or cisplatin combined with paclitaxel is the first-line chemotherapy regimen for ovarian cancer [7], and this is also the treatment option for NACT. However, approximately 70% of patients treated with platinum-based chemotherapy develop recurrence; of these patients, only 10% respond well to secondary platinum-based chemotherapy, whereas the majority of them develop platinum resistance [8]. Patients with platinum resistance have limited treatment options, resulting in high fatality rates. Currently, the mechanism of resistance remains poorly understood, with no retrospective and prospective studies analyzing the association between platinum resistance and complete clinical data of all patients with ovarian cancer.

Therefore, we collected complete clinical data of all patients with ovarian cancer at all stages and conducted a retrospective study. Consequently, the total platinum resistance rate was 23%, and the proportions of FIGO stage (III–IV), NACT, and Fagotti score (>4) were 97.3%, 59.5%, and 43.2%, respectively. FIGO stage was associated with platinum resistance only in univariate analysis. Conversely, Fagotti score (>4) and NACT were associated with platinum resistance in univariate and multivariate analyses and had a higher incidence of platinum resistance (OR: 0.372 and 0.328, 95% CI: 0.160–0.863 and 0.141–0.762, p = 0.021 and 0.010, respectively). Considering that NACT was closely associated with platinum resistance, we further analyzed the relationship between chemotherapy-related factors, including the total number of chemotherapy cycles and NACT cycles, and platinum resistance, but no correlation was found.

In some retrospective studies, NACT was associated with platinum resistance in univariate analysis, but in multivariate analysis, it was no longer a risk factor for platinum resistance at the first relapse; however, at the second relapse, NACT was associated with platinum resistance (OR: 4.06 and 1.92, p = 0.001 and 0.009, respectively) [9, 10]. In another study, NACT was a risk factor for platinum resistance at first recurrence, as observed in univariate and multivariate analyses (OR: 2.950, p = 0.001) [11]. In contrast, one study showed that NACT was associated with platinum resistance only in univariate analysis (OR: 1.3, p = 0.01) [12]. Moreover, a high number of NACT cycles induced platinum resistance [20, 21]; however, another study did not find increased risk of platinum resistance or poor survival in patients with more NACT cycles [22].

Preoperative tumor burden can be evaluated by the Fagotti score, which has also been used to analyze the relationship between intra-abdominal tumor burden and the chance of altering the natural history of disease by PDS and adjuvant chemotherapy [13]. This scoring method has also been used to predict whether optimal cytoreduction can be performed [14] and whether intraperitoneal tumor spread correlates with patients’ prognosis in other tumors [15]. In the present study, we used the Fagotti score for the first time to evaluate the intra-abdominal ovarian tumor burden and analyze the relationship between the ovarian tumor burden and platinum resistance.

In a previous study, the Fagotti score or laparoscopic predictive index score (PI score) was calculated according to parameters such as the presence of omental cake, extensive peritoneal and diaphragmatic carcinomatosis, mesenteric retraction, bowel and stomach infiltration, spleen and/or liver superficial metastasis [14]. Both our univariate and multivariate analyses showed that the Fagotti score was associated with platinum resistance, indicating that high ovarian tumor burden is related to platinum resistance and that the Fagotti score can be a good index for evaluating ovarian tumor burden. However, other clinical data, such as primary tumor’s maximum diameter, CA125, and HE4, which may not fully assess tumor burden or may be influenced by other factors, were not closely associated with platinum resistance.

According to our results, both NACT and high Fagotti scores can induce platinum resistance. Hence, we speculate that a higher tumor burden makes NACT more easily induce chemotherapy, considering that the larger the tumor load, the more likely it is to lead to an increased risk for platinum resistance. However, the mechanism underlying it remains largely unknown. Several studies have shown that NACT can enhance the stemness of ovarian cancer [16] and induce gene mutation [17] toward platinum. Moreover, owing to NACT, residual cancer cells after NACT can be easily overlooked in IDS and become the source of future platinum resistance [18].

Our study emphasized that patients with platinum resistance have an extremely poor prognosis, given that their OS and PFS were worse than those of the platinum-sensitive group; the 5-year survival rate of the platinum-resistant group was even 0%. The longest OS was 42 months. One patient with an OS rate of 40 months, although alive, had systemic metastases during the last follow-up. The platinum-free interval is the time from the last dose of platinum-based chemotherapy to the first occurrence of evidence of cancer progression or recurrence. Platinum-free interval is an effective and simple algorithm for predicting platinum resistance and prognosis in patients with ovarian cancer [19]. Platinum-resistant patients currently have limited treatment options; thereafter, after predicting platinum resistance in patients with ovarian cancer, we need to explore new treatment options to prolong their survival.

However, this study is retrospective, and there may be some selection bias and review bias. In addition, only ovarian cancer patients in the General Hospital of Ningxia Medical University were selected, which lacked multicenter data. Finally, for various reasons, the sample size is still relatively small. In the later stage, the data of ovarian cancer patients from multiple hospitals in multiple centers should be combined for statistical research, and the sample size should be increased to further prove the conclusion of our study.

NACT and high Fagotti scores or tumor burden may increase the risk of platinum resistance. However, conducting joint multicenter studies with a larger sample size is required to further verify our results. We also need to explore the possible mechanism of platinum resistance to improve the treatment strategy and survival rate.

The authors thank everyone in their team for their hard work and thank the Ningxia Science and Technology Department for the financial support of this research.

This study protocol was approved by the Medical Research Ethics Committee of the General Hospital of Ningxia Medical University, Approval No. KYLL-2023-0088. Informed consent was signed with all the patients, and a retrospective study was conducted after obtaining the consent of the patients.

No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all the authors for publication.

The research was supported by the Key Research and Development Project of Ningxia Hui Autonomous Region.

Ha Chunfang contributed to the conception of the study; Xiong Zhuo mainly contributed to the writing of papers and data collection; Wu Mingyong and Wei Meng contributed to the collection of clinical data and patient follow-up; Li Ruyue performed the data analyses and constructive discussions.

We confirm that the data supporting the findings of this study are available within the article. Further inquiries can be directed to the corresponding author Chunfang Ha or co-author Zhuo Xiong.

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