The association between short-term response and long-term survival for cervical cancer patients undergoing neoadjuvant chemotherapy: a system review and meta-analysis

Controversy exists regarding whether a short-term response has an impact on the long-term survival of cervical cancer patients undergoing neoadjuvant chemotherapy (NACT). This study was designed to identify the predictive role of an early response by pooling the results of previous studies. The PubMed and Embase databases were searched through July 2016, and the associations between an early response and disease-free survival (DFS) were pooled by hazard ratio (HR) using random effects models. Six studies involving 490 cervical cancer patients, with 336 responders and 154 non-responders, were finally included in the meta-analysis. The HR for 1-year DFS between early responders and non-responders was 0.25 (95% CI 0.10–0.58, P = 0.001). The HRs for 2-, 3-, 4-, and 5-year DFS were 0.28 (95% CI 0.15–0.56), 0.27 (95% CI 0.16–0.45), 0.29 (95% CI 0.17–0.50) and 0.33 (95% CI 0.20–0.54), respectively. No obvious heterogeneity was found among the studies, with I2 = 0, and a sensitivity analysis showed that all pooled results were robust with logHR confidence limits < 0. An early response was associated with DFS, and responders achieved a significantly higher survival rate than non-responders. This finding should be validated in future prospective studies.


Relationship between the clinical response and disease-free survival (DFS).
Characteristics of the studies. The details of the included studies are listed in Table 1. The table reveals the association between the clinical response and DFS, either with adjustment of parameters or alone. The 6 included studies consisted of 490 patients, which included 336 clinical responders and 154 clinical non-responders. All 6 studies were conducted in East Asian areas.
1-year HR. A forest plot was employed to illustrate the association between a short-term response and overall survival. The HR of each study was determined and is listed in Fig. 2. The dots in the middle of the bar indicate the HR, and the spread of the bars indicates the 95% CI of the HR. The diamond in each bar indicates the corresponding weight of the included study. The pooled result after the combination of the studies is shown at the bottom of the forest plot. The analysis showed a combined result with an HR = 0.25 and a 95% CI of 0.10-0.58. A Cochrane Q test produced a P value of 0.882 and an I 2 equal to 0%. A funnel plot was constructed to visually demonstrate the probability of publication bias (Fig. 3A). Non-parametric and parametric tests were also employed to detect   Figure S1). A sensitivity analysis was used to determine whether heterogeneity existed in the combined analysis, which is shown in Supplementary Figure S2.
2-year HR. The combined results for the second year are also shown by forest plot in Fig. 4. The plot shows an HR of 0.28 with a 95% CI of 0.15-0.56. A Cochrane Q test was also performed to test the possible heterogeneity in the analysis (P = 0.818) with I 2 = 0. A funnel plot was constructed to visually reveal the bias (Fig. 3B). Begg's and Egger's tests were also used to calculate the actual P value with non-parametric and parametric methods (Supplementary Figure S3). A sensitivity analysis was used to detect the heterogeneity in the combined analysis (Supplementary Figure S4). Each study was excluded individually, and the results of the remaining studies were pooled. Each combined HR was calculated individually. 5-year HR. The combined HR for the fifth year was also determined and shown by a forest plot (HR = 0.33 and 95% CI 0.20-0.54). A Cochrane Q test revealed P = 0.545 ( Fig. 7) while I 2 test showed a value of 0. A funnel plot was constructed (Fig. 3E), and Begg's and Egger's tests were conducted to investigate the publication bias (Supplementary Figure S9). A Sensitivity analysis was also conducted to test the robustness of the combined results (Supplementary Figure S10).

Discussion
By combining previous study results, the present study found that a short-term response was significantly associated with the long-term survival of cervical cancer patients who underwent NACT. Additionally, overall survival may be partly predicted by the short-term response when it is evaluated by the RECIST criteria. Our findings validated several previous studies in which the predictive role of the short-term response was also evaluated among cervical cancer patients. Chen and colleagues performed a randomized controlled trial (RCT) on 142 cervical cancer patients who underwent NACT from 1999 to 2003. They found that the response to NACT was an independent prognostic factor of long-term survival after adjustment for age, International Federation of Gynecology and Obstetrics (FIGO) stage, pathological grade, histological type, tumor size, lymph node metastasis, and parametrial infiltration. Cai and colleagues also performed a prospective RCT of 106 patients from 1999 to 2005, and they found that responders achieved a better survival rate than non-responders 11 . Li and colleagues performed a study on 304 patients in 2012, and they similarly found that responders achieved higher survival rates than non-responders 12 . Other studies that used the WHO criteria have also shown similar results indicating that a clinical response was associated with better long-term survival. In 2011, Xiong and colleagues conducted a retrospective study and demonstrated that the response to NACT was associated with long-term survival 13 . Our results were slightly different from those of Liu and colleagues, who found that a short-term response did not lead to a significantly higher survival rate 14 . We speculate that the statistical power of that study may not be sufficient to provide a definite conclusion, considering the number of subjects enrolled in the study 15 . Thus, we hypothesize that if the study population was larger, a significant difference would have been observed.
The predictive effect of the short-term response on long-term survival has always been a focus of research of solid tumors, as it may highlight a method for personalized treatment. The short-term response can be observed a very short time after chemotherapy, and the role of chemotherapy drugs may be quickly determined by doctors and patients. Accordingly, patients can be administered the most effective treatment regimens. A proper treatment regimen may help patients to achieve longer survival, and it may also help to decrease the cost of medical treatment.
Our study has some limitations. First, we did not pool individual data, which could have provided a more accurate result. Second, the difference in the survival rate between responders and non-responders was not investigated using WHO criteria in this study. Therefore, in future studies, we plan to collect individual data to obtain a more accurate result. We also plan to determine new methods to calculate the HR according to the WHO criteria.
In conclusion, we performed a combined analysis of the predictive role of the short-term response on long-term survival for cervical cancer patients who underwent NACT. We found that clinical responders achieved

Methods
Literature search. In August 2016, a search for literature in this field was performed in the PubMed and Embase databases by two doctors independently. The key words used for the search included the following: "NACT" or "neoadjuvant chemotherapy" or "preoperative chemotherapy", plus "cervical carcinoma" or "cervical cancer" or "uterine cervical neoplasms", plus "responding" or "response" or "clinical response" or "responder" or "remission" or "responsiveness". To include as many eligible articles as possible, we also reviewed the reference lists of the retrieved articles.

Study identification. Inclusion criteria.
To determine their eligibility, two reviewers independently reviewed the titles and abstracts of the articles (S.Y.K. and K.C.H.). The selected articles were required to be original research articles. The articles were written in English and published in a peer-reviewed journal in a relevant discipline. All cases in the articles were cervical carcinoma patients with a definite diagnosis. Using the  Newcastle-Ottawa Scale (NOS), our team performed a quality assessment of the included studies, as described in a previous study 16 .
Exclusion criteria. In the primary search, a total of 583 papers were retrieved. After reading the titles and abstracts, we excluded 428 articles from further analysis due to irrelevance to the present research. Then, we excluded articles that did not adopt the RECIST criteria; articles that were only concerned with the pathological response and not the clinical response were also excluded from further analysis; studies with only descriptive results but without statistical data were also excluded. These studies were carefully reviewed to exclude duplicated information. Finally, 6 articles were included in the present study, and these 6 studies were used for the final analysis 10,12,14,17-19 . Statistical analyses. According to the RECIST criteria, clinical responders included individuals with a complete response (CR) or partial response (PR), while clinical non-responders included those with stable disease (SD) or progressive disease (PD). The RECIST criteria are a widely used standard for evaluating the short-term response of solid tumors 20 .
The hazard ratio (HR) and 95% CI were the most common statistics used across the studies to measure the association between the short-term response and survival 10 . When this information could not be obtained from the articles, Engauge Digitizer software was used to determine the survival curve of the included studies 21 based on the calculus theory and integral theory 22,23 . The pooling process of the HR and its corresponding 95% CI was  visually illustrated by forest plots. During pooling, a Cochrane Q test was employed to test the heterogeneity; the significance level was set at P < 0.10, according to a previous study 24 . The I 2 statistic was also used to test the heterogeneity across the studies, and a value of I 2 > 50% was considered to indicate significant heterogeneity 25 . A random effects model was used to calculate the combined HR according to the DerSimonian and Laird method 26 . The possibility of publication bias was evaluated by visual screening of a funnel plot, and both Begg's test and Egger's test were used to evaluate the publication bias 27,28 . A sensitivity analysis was conducted to evaluate the robustness of the combined results 24 . During our research, one study was omitted at a time to test the robustness of the combined results. Stata version 11 (Stata Corp, College Station, TX) was used for the statistical analysis. Differences with a two-sided value of P < 0.05 were considered statistically significant.