Circulating HPV cDNA in the blood as a reliable biomarker for cervical cancer: A meta-analysis.

The applications of liquid biopsy have attracted much attention in biomedical research in recent years. Circulating cell-free DNA (cfDNA) in the serum may serve as a unique tumor marker in various types of cancer. Circulating tumor DNA (ctDNA) is a type of serum cfDNA found in patients with cancer and contains abundant information regarding tumor characteristics, highlighting its potential diagnostic value in the clinical setting. However, the diagnostic value of cfDNA as a biomarker, especially circulating HPV DNA (HPV cDNA) in cervical cancer remains unclear. Here, we performed a meta-analysis to evaluate the applications of HPV cDNA as a biomarker in cervical cancer. A systematic literature search was performed using PubMed, Embase, and WANFANG MED ONLINE databases up to March 18, 2019. All literature was analyzed using Meta Disc 1.4 and STATA 14.0 software. Diagnostic measures of accuracy of HPV cDNA in cervical cancer were pooled and investigated. Fifteen studies comprising 684 patients with cervical cancer met our inclusion criteria and were subjected to analysis. The pooled sensitivity and specificity were 0.27 (95% confidence interval [CI], 0.24-0.30) and 0.94(95% CI, 0.92-0.96), respectively. The pooled positive likelihood ratio and negative likelihood ratio were 6.85 (95% CI, 3.09-15.21) and 0.60 (95% CI, 0.46-0.78), respectively. The diagnostic odds ratio was 15.25 (95% CI, 5.42-42.94), and the area under the summary receiver operating characteristic curve was 0.94 (95% CI, 0.89-0.99). There was no significant publication bias observed. In the included studies, HPV cDNA showed clear diagnostic value for diagnosing and monitoring cervical cancer. Our meta-analysis suggested that detection of HPV cDNA in patients with cervical cancer could be used as a noninvasive early dynamic biomarker of tumors, with high specificity and moderate sensitivity. Further large-scale prospective studies are required to validate the factors that may influence the accuracy of cervical cancer diagnosis and monitoring.

Introduction

Human papillomavirus (HPV) is a type of papillomavirus that infects human skin and mucosa squamous epithelial cells. HPVs are DNA double-stranded spherical small viruses with a diameter of about 55 nm. The HPV genome contains approximately 7900 bases and can be divided into three functional regions [1]. The proteins E6 and E7, encoded by the early genes of HPV, can inhibit the functions of p53 and pRh in normal cervical epithelial cells and cause abnormal proliferation of cancerous cells, resulting in the development of genital warts and atypical proliferation of epithelial cells [2]. The immune system of most patients can eliminate HPV within approximately 9–16 months after infection. However, persistent infection by some high-risk HPVs, particularly HPV16 and HPV18, may lead to cervical cancer [3-5].

Cervical cancer is the fourth most common cancer among women worldwide. However, 85% of cases occur in developing countries [6]. Cervical cancer is now relatively uncommon in high-income countries owing to the introduction of HPV screening programs and HPV vaccines, which have led to a 70% decrease in the incidence and mortality rates of cervical cancer over past several decades [7]. Despite major advances in detection and prevention, an estimated 530,000 cases were recorded, and nearly 90% of 270,000 deaths occurred in middle- and low-income developing countries in 2012 [8]. There is still a need for minimally invasive and specific tests for HPV-induced cancer.

Recent progress in the analysis of blood samples for circulating tumor cells or cell-free circulating tumor DNA (ctDNA) has shown that liquid biopsies may have potential applications in the detection and monitoring of cancer [9-11]. Similarly, in a study on cervical cancer, HPV cDNA has become a major focus, providing a strong basis for early diagnosis and prognosis in cervical cancer [10, 12, 13]. Cervical cancer is typically caused by high-risk HPVs (hrHPVs), primarily genotypes 16 and 18 [4]. hrHPVs linearize DNA for integration into the cervical host genome and induce the expression of E6 and E7 genes, which are involved in the oncogenesis of cervical cancer [14, 15]. Cervical cancer cells and HPV cDNA harbor genomic rearrangements that can be released into the patient’s peripheral blood. From a diagnostic monitoring viewpoint, the consistent presence of HPV cDNA in the blood of patients with cervical cancer can be used as a tumor marker. Although the mechanism mediating this phenomenon is unclear, the presence of such HPV cDNA in cervical cancer shows some diagnostic value. Interestingly, some studies have shown that circulating HPV cDNA acts as a tumor DNA marker in patients with primary tumors caused by HPV infection [10]. Many recent studies have focused on ctDNA in cervical cancer; however, the exact relationships are still unclear [12, 16–18].

Accordingly, in this study, we performed a comprehensive analysis of the precise value of HPV cDNA for the diagnosis of cervical cancer.

Materials and methods

Protocol

The complete protocol is available here: http://dx.doi.org/10.17504/protocols.io.8t4hwqw.

Search strategy

This meta-analysis was conducted following the criteria of Preferred Reporting Items for Systematic Review and Meta Analyses [19]. A literature search was systematically performed using PubMed, Embase, Cochrane Library, and WANFANG medicine online databases for all relevant articles without language or regional limitations. No limitations were set with regard to the start date for publication, and the search ended on March 18, 2019. The following search terms were used: “cervical cancer AND HPV cDNA”, “cervical cancer AND ctDNA”, “cervix cancer AND ctDNA”, “cervical carcinoma AND ctDNA” OR “circulating DNA AND cervical cancer”. Various alterations in spelling and abbreviations were also used as search terms. Titles and abstracts were carefully screened for relevance, and duplicates were removed. The full text of each report that met the preliminary criteria was retrieved and assessed for inclusion into this meta-analysis.

Inclusion and exclusion criteria

In this meta-analysis, eligible studies were selected according to these following inclusion criteria: (1) evaluated the diagnostic accuracy of quantitative analysis of HPV cDNA in cervical cancer; (2) the diagnostic value of HPV cDNA in cervical cancer was reported or could be calculated from the published data; (3) full text and all data could be retrieved and were available; (4) the techniques and target genes were clearly stated in the articles; (5) studies included at least 10 patients with cervical cancer and relevant negative controls. When the same patient population was used in several studies, only the most recent was included.

The exclusion criteria were as follows: (1) the diagnostic or prognostic value could not be deduced from incomplete data in the studies provided; (2) repeated studies from the same study group; (3) sample size less than 10; (4) data only from experiments based on cell lines; (5) studies published in languages other than English.

Quality assessment

Two reviewers (CD Wan and YL Gu) independently reviewed and evaluated all eligible studies according to the Newcastle-Ottawa scale [20]. In case of disagreement, the decision was made by a third researcher, and disagreement was settled through discussion. The data extracted from the basic feature table included authors’ names, country, sample type, detection method, numbers of experimental and control groups, and analysis indicators. The outcome indicators included positives, false positives, false negatives, true negatives, sensitivity, and specificity. To assess the methodological quality of each study and potential risk of bias, QUADAS-2 Guidelines were used to evaluate the quality of all articles that met the inclusion criteria [21].

Statistical analysis

We used standard methods recommended for meta-analysis of diagnostic test evaluations [19]. The meta-analysis was carried out with Meta-DiSc 1.4 and STATA 14.0 statistical software. The sensitivity was defined as the proportion of patients with HPV cDNA presence among all patients confirmed as having cervical cancer. The specificity was defined as the proportion of patients with negative HPV cDNA detection among all negative control volunteers without cervical cancer. The positive likelihood ratio (PLR) was calculated as sensitivity/(1 –specificity), whereas the negative likelihood ratio (NLR) was calculated as 1 –sensitivity/specificity. DOR was calculated as PLR / NLR and was used as an indication of how much greater the chance was of having cervical cancer for patients with HPV cDNA presence than for those without HPV cDNA. These indicators were summarized using a bivariate meta-analysis model, and the threshold effect was determined by receiver operative characteristic (ROC) curve and Spearman correlation analyses; P values of less than 0.05 indicated a significant threshold effect. Heterogeneity between studies was analyzed by chi-squared and I tests; a P value of less than 0.1 or an I higher than 50% indicated the existence of significant heterogeneity [22]. Meta-regression analysis was performed to explore the sources of heterogeneity. Deek’s funnel plot asymmetry test was used to test whether there was publication bias [23]. All statistical tests were two-sided, and results with P values of less than 0.05 were considered statistically significant.

Results

Study selection process

The initial search retrieved a total of 236 studies. As shown in Fig 1, 10 studies were eligible for review after carefully screening and rechecking. All relevant characteristics of these studies are summarized in Table 1. In total, 684 patients with cervical cancer were evaluated in these studies published between 2001 and 2018. Among these studies, 6 enrolled patients from Asian countries/areas (one from Hong Kong, one from Thailand, one from India, one from Iran, and one from Taiwan). Additionally, two studies were performed in France, and two were performed in America. Numerous review papers and duplicates between the literature databases were excluded.

Fig 1

Flow chart of the enrolled studies.

Table 1

Main characteristics of all the studies enrolled the meta-analysis.

No.	Study	year	region	method	TP	FP	FN	TN	Sample source	Sample time	sensitivity	specificity	scores
1	Pornthanakasem W[10]	2001	Thailand	qPCR	6	0	44	20	plasma	BT	36.00%	100.00%	7
2	Dong SM[24]	2002	America	qPCR	13	1	219	59	plasma	C	48.70%	98.33%	7
3	Hsu KF[25]	2003	Taiwan	qPCR	27	0	85	40	serum	BT	45.2%	88.60%	6
4	Sathish N[26]	2004	India	PCR+RFLP	8	0	50	40	plasma	BT	48.2%	100.00%	8
5	Yang HJ[27]	2004	HongKong	qPCR	34	17	34	94	plasma	BT	50%	84.68%	8
6	Wei YC[28]	2007	Taiwan	Nested qPCR	11	0	6	6	plasma	BT	64.70%	100.00%	5
7	Jaberipour M[29]	2011	Iran	qPCR	19	8	62	80	plasma	BT	23.5%	90.91%	8
8	Campitelli M[30]	2012	France	DIPS-PCR	13	0	3	20	serum	BT	81.25%	100.00%	7
9	Jeannot E[31]	2016	France	ddPCR	39	0	8	18	serum	BT	83.00%	100.00%	6
10	Kang Z[17]	2017	America	ddPCR	19	0	2	45	serum	C	90.48%	100.00%	7

Sample time:BT, before treatment; C, combined

Review of eligible studies

The 10 eligible studies with data regarding the diagnostic value of HPV cDNA in cervical cancer are shown in Table 1. From these studies, 263 patients with cervical cancer were evaluated before treatment, and 421 patients were evaluated when undergoing treatment or after treatment. Patients with primary or metastatic cervical cancer with a TNM stage of I–IV who received surgery, chemotherapy, radiotherapy, or targeted therapy were included. The types of cervical cancer were squamous and adenomatous (approximate ratio of 4:1), as shown in S1 Table.

The quality score of all studies was 6 to 8 points, with an average of 6.9 (Table 1). A quality assessment of the eligible studies was performed using QUADAS-2 (Fig 2). The included 10 studies were assessed using RevMan 5.3 software, and most of studies showed moderately low or unclear risk of bias. Two studies [10, 30] increased the risk of bias owing to a lack of patient selection. Two studies [24,31] did not mention the use of a blinding method or reference standard, which may have resulted in an unknown risk of bias in the meta-analysis.

Fig 2

Quality assessment of the included studies according to QUADAS-2.

Detection of HPV cDNA and probes

Polymerase chain reaction (PCR) was mainly applied to detect HPV cDNA in the studies included in this analysis. Two studies [10, 31] used Taqman PCR. Additionally, two studies [17, 28] used droplet digital PCR (ddPCR), and one study [30] used methylation-specific (MSP) PCR and one study [28] used nested PCR. Six of the studies extracted ctDNA from plasma, and the other four studies extracted DNA from serum (Table 1). For the HPV cDNA the probes used the different studies were not exactly the same, as showed in S2 Table.

HPV cDNA diagnostic accuracy in cervical cancer

All of 10 studies were pooled into meta-analysis of diagnostic accuracy. The Spearman correlation coefficient was 0.276 (P>0.05), suggesting that there was no threshold effect. Accordingly, heterogeneity owing to non-threshold effects was assessed with Q tests and I statistics. There was significant heterogeneity in the pooled sensitivity(I = 96.2%, P<0.001) and specificity(I = 77.3%, P<0.001); Thus a random effects model would be applied to analyze the diagnostic parameters. As presented in Fig 3, the overall pooled sensitivity and specificity were 0.27(95% CI 0.24–0.30) and 0.94(95%CI 0.92–0.96), respectively. The overall pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 6.85 (95%CI 3.09–15.21) and 0.60 (95%CI 0.46–0.78), respectively. The pooled diagnostic odd ratio (DOR) was 15.25 (95%CI 5.42–42.94). The summary receiver operator characteristic curve (SROC) was presented in Fig 4; the area under the SROC curve AUC was 0.94 (95%CI 0.89–0.99).

Fig 3

Diagnostic accuracy forest plots.

(A) Forest plots of pooled sensitivity. (B) Forest plots of pooled specificity. (C) Forest plots of PLR. (D) Forest plots of NLR. (F)Forest plots of pooled DOR.

Fig 4

Summary receiver operating characteristic plot for the pooled studies diagnosis.

Subgroup analysis and meta-regression

Subgroup analysis was performed to explain the source of the significant heterogeneity in the diagnostic analysis. These different parameters in all of included studies were conducted including sample source (serum versus plasma), sample time (before treatment versus others), race or region (Asian versus Caucasian), patient number(≥50 cases versus <50 cases), and detection method (qPCR vs MSP and ddPCR). These diagnostic parameters of subgroups were showed in Table 2. Meta-regression based on those five factors were applied to investigate the source of heterogeneity. As showed in Table 3, the source and race or region factors showed significantly influence on heterogeneity of universal diagnostic value (P<0.05).

Table 2

Results of subgroups analysis.

Subgroup	Sensitivity(95% CI)	Specificity(95% CI)	PLR(95% CI)	NLR(95% CI)	DOR(95% CI)
Source
Plasma	0.18(0.15–0.22)	0.92(0.89–0.95)	3.25(2.19–4.83)	0.80(0.66–0.96)	4.76(2.86–7.91)
Serum	0.50(0.43–0.57)	1.00(0.97–1.00)	36.12(9.10–143.24)	0.25(0.04–1.61)	139.15(31.72–610.40)
Method
qPCR	0.21(0.18–0.25)	0.94(0.91–0.96)	4.70(2.35–9.40)	0.74(0.60–0.91)	8.70(3.41–22.22)
MSP and ddPCR	0.83(0.71–0.91)	1.00(0.97–1.00)	32.29(4.64–224.73)	0.19(0.11–0.32)	165.21(20.21–1350.4)
Race or Region
Mongolian	0.27(0.23–0.32)	0.92(0.88–0.95)	3.37(2.26–5.03)	0.78(0.68–0.89)	5.14(3.07–8.60)
Caucasian	0.27(0.22–0.32)	0.99(0.96–1.00)	18.65(4.04–86.14)	0.26(0.01–7.97)	76.54(5.92–989.35)
Time
Before treatment	0.35(0.31–0.40)	0.93(0.89–0.95)	5.40(2.56–11.38)	0.63(0.50–0.79)	11.54(4.30–30.98)
Under- or after treatment	0.13(0.10–0.17)	0.99(0.95–1.00)	14.51(0.55–382.34)	0.34(0.00–175.98)	43.82(0.24–799.36)
Patient Number
<50 case	0.56(0.49–0.62)	0.92(0.88–0.95)	12.78(2.74–59.60)	0.33(0.15–0.75)	40.37(6.17–265.04)
≥50 case	0.14(0.11–0.17)	0.96(0.93–0.98)	3.78(1.55–9.23)	0.86(0.75–0.98)	4.30(1.83–10.10)
Overall	0.27(0.24–0.30)	0.94(0.92–0.96)	6.85(3.09–15.21)	0.60(0.46–0.78)	15.25(5.42–42.94)

Table 3

Meta regression of diagnostic value.

parameter	Coef	SE	RDOR(95%CI)	P
Source	-3.414	0.7992	0.03(0.00–0.22)	0.0037
Method	-2.620	1.4951	0.07(0.00–2.50)	0.1232
Race or region	-2.536	0.7686	0.08(0.01–0.49)	0.0131
Time	-1.483	1.6317	0.23(0.00–10.75)	0.3935
Patient number	1.569	1.6562	4.80(0.10–241.01)	0.3751

Sensitivity analysis

To further explore the heterogeneity of the included studies, a sensitivity analysis was conducted by removing individual studies. As shown in Fig 5, no outlier study was identified, and the results were considerable stable and reliable.

Fig 5

Sensitivity analysis of the overall pooled study.

Publication bias

We applied Deeks’ funnel plot asymmetry tests to estimate the publication bias of the included studies. As shown in Fig 6, the regression line was nearly vertical, confirming the lack of significant publication bias across the overall enrolled studies (P = 0.49).

Fig 6

Deek’s funnel plot to assess publication bias.

ESS, effective sample size.

Discussion

Although pathological examination is the gold standard of clinical tumor treatment, obtaining such specimen without interruption directly from tumors is a difficult procedure and cannot reflect tumor dynamic changes after treatment [32]. Cancers are known to shed tumor cell DNA into the blood stream [33], and examining the levels and mutations in ctDNA can provide almost real-time information regarding tumor status, which is called “liquid biopsy”. Liquid biopsy has the potential to improve post-treatment surveillance by following subtle changes in tumor cfDNA and has recently been extensively investigated as a potential new diagnostic technique [32, 34].

Despite major advances in early detection, including Pap smears and co-human papillomavirus testing, cervical cancer is the fourth leading cause of cancer-related death in women worldwide [35]. There is an urgent need for a minimally invasive and specific test for disease monitoring. Persistent infection and integration of HPV into the cell genome are the first causes of most cervical cancers. After integration, its HPV gene behaves the same as other functional genes of human chromosome [35]. The proliferation or apoptosis of cervical cancer cells will release ctDNA into the peripheral blood circulation system, including HPV DNA. As one of ctDNAs in cervical cancer, circulating HPV DNA (HPV cDNA) can be widely evaluated using liquid biopsies for detecting cancer and monitoring disease [36].

Many previous meta-analyses have reported that the diagnostic accuracy of quantitative analysis of ctDNA is superior to conventional biomarkers for the diagnosis of several cancers, including ovarian cancer [22], gastric cancer [32], lung cancer [37], and colon cancer [38]. To the best of our knowledge, this is the first meta-analysis exploring HPV cDNA in patients with cervical cancer. Meta-analysis can overcome the problem of small sample size and inadequate statistical power in genetic studies of complex traits and provide more reliable results than single case-control studies [39]. Because the relationship between HPV cDNA and cervical cancer is still unclear, we performed a comprehensive analysis of the clinical utility of HPV cDNA in the diagnosis of patients with cervical cancer.

This meta-analysis combined the outcomes of 684 patients with cervical cancer from 10 individual studies, investigating the diagnostic values of HPV cDNA. From the 10 studies, the pooled sensitivity and specificity were 0.27 (95% CI, 0.24–0.30) and 0.94 (95% CI, 0.92–0.96), respectively. LRs of greater than 10 or less than 0.1 indicate large and often conclusive shifts from pretest to post-test probability [34]. In this meta-analysis, the overall pooled PLR and NLR were 6.85 (95% CI, 3.09–15.21) and 0.60 (95% CI, 0.46–0.78), respectively. This result indicated that patients with cervical cancer had approximately 7 times greater chance of being HPV cDNA positive than normal controls, with an error rate of approximately 60% when the true negative was determined in the HPV cDNA negative test. The pooled DOR was 15.25 (95% CI, 5.42–42.94), which indicated a relatively high accuracy of HPV cDNA in cervical cancer. Summary ROC (SROC) can be applied to summarize overall test performance, and the area under the SROC curve (AUC) was 0.94 (95% CI, 0.89–0.99), suggesting that HPV cDNA in the plasma or serum of patients with cervical cancer had excellent accuracy for diagnosing cervical cancer. Because significant heterogeneity existed, if relatively accurate diagnostic parameters were achieved, subgroup analysis would be needed to analyze the source. Subgroup analyses revealed that the heterogeneity of sensitivity could be related to the source of the specimen (e.g., plasma versus serum), the region (e.g., Mongolian versus Caucasian), the time of specimen collection (e.g., before treatment versus after treatment), the number of patients (e.g., less than 50 versus greater than or equal to 50), and the method of analysis (e.g., quantitative PCR versus MSP-PCR and ddPCR). Most studies employed a qPCR method that demonstrated relatively high specificity but low sensitivity. With qPCR it is difficult to detect very small amounts of circulating nucleic acids in blood. Over time, more accurate diagnostic parameters were obtained by ddPCR. We found that MSP-PCR and ddPCR were more accurate for detecting HPV cDNA in patients than qPCR. With the application of new detection methods, higher sensitivity and specificity had been obtained. In particular, the application of ddPCR in liquid biopsy greatly improves the diagnostic value of HPV cDNA [36]. However, statistical regression data showed that all these differences between subgroups were not statistically significant (P > 0.05). Taken together, these results indicated that the study design did not substantially affect the diagnostic accuracy. Heterogeneity may have been caused by other factors, such as patient age, tumor type, tumor size, TNM stage, and differences in the experimental protocols, which could not be analyzed in the current study because of loss of data or unrecognizable details. Therefore, further studies with large sample sizes and more details, e.g., race, specimen features, and tumor properties, are needed to confirm these findings.

Cervical cancer differs from other cancers because HPV infection is a crucial step in tumorigenesis, accounting for 99.7% of cervical cancer cases. HPV16 and HPV18 are the two most important serotypes, identified in more than 70% of cervical carcinomas worldwide [40]. Specific changes in circulating nucleics with regard to oncogenes, tumor-suppressor mutations, microsatellite alterations, and hypermethylation can be similarly detected. Although the 10 studies included in this meta-analysis had very high specificity, there was uneven sensitivity. The pooled results indicated that there was significant heterogeneity in sensitivity that could impact diagnostic accuracy. The Spearman correlation coefficient was 0.276 (P > 0.05), suggesting that the threshold effect was not the source of heterogeneity. Because the size of HPV cDNA fragments is generally approximately 200 bp [41], PCR primer pairs that target shorter DNA fragments may identify more patients with detectable HPV DNA. With more primer pairs, further increases in detection rates may be possible. Other influencing factors, such as patient number, specimen extraction time, region, and specimen source, may also influence these parameters; however, these differences were not statistically significant. In addition, publication bias was also not significant, indicating that the results of this meta-analysis were reliable and credible.

There were several limitations to this meta-analysis. First, the sensitivities of the included studies varied widely. Different gene detection methods could have led to major differences. Therefore, significant heterogeneity between studies could not be avoided. The unique characteristics of HPV cDNA limit its sensitivity as a diagnostic indicator, and more sensitive and accurate detection techniques may need to be applied. Although subgroup and regression analyses were performed to explore the sources of heterogeneity, the results of these analyses explained few effectors. Second, some studies with limited patient numbers and controls were included in this meta-analysis, reducing the effectiveness of the combined statistical analysis. Relatively few papers on HPV cDNA in patients with cervical cancer have been published. Third, owing to the nature of our research, selected bias and incomplete searches could have occurred. Finally, different probes used for PCR of HPV cDNA may have result in a potential source of bias.

Conclusions

Despite some limitations, this meta-analysis clearly indicated that HPV cDNA detection may be a very specific, but relatively sensitive test in patients with cervical cancer. Our findings provided reliable evidence that HPV cDNA was a promising potential biomarker for the diagnosis of cervical cancer. Of course, to obtain a more accurate statistical data analysis, additional studies with larger sample sizes from patients of different ethnicities will be necessary in the future.

All information extracted from eligible studies.

(XLSX)

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The primers of the detected HPV cDNA in 10 enrolled studies.

(DOCX)

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PRISMA checklist.

(DOC)

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The full search strategy and search terms used for PubMed database.

(DOCX)

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24 Oct 2019

PONE-D-19-27050

Diagnostic value of circulating tumor DNA as an effective biomarker in cervical cancer: a meta-analysis

PLOS ONE

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Additional Editor Comments:

Before sending this paper to the reviewers I feel the authors should rewrite the present paper. It contains valuable information but I feel it is non-sense to perform a metanalysis combining HPV and non-HPV tests. The metaanalysis should mainly focus on HPV cDNA. The studies looking at non-HPV DNA (Table 1: studies 5,11,14,15) should be analyzed separately. For the HPV cDNA the probes used in the different studies shoudl be mentioned in a Table

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18 Nov 2019

We appreciate your suggestion on focusing on the data from HPV cDNA in our meta-analysis. Based on the findings from our original manuscript, we believe that, after integration into the human chromosome, HPV cDNA behaves like other functional genes. Subgroup analysis also showed that there were no significant differences between HPV cDNA and ctDNA of other genes. However, too many miscellaneous ctDNAs may potentially result in heterogeneity that is incompatible with the setup for meta-analysis. Therefore, we have excluded studies on non-HPV cDNA and solely focused on studies including HPV cDNA. The tables and figures have also been replaced in the revised manuscript.

Submitted filename: a_rebuttal_letter.docx

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27 Dec 2019

PONE-D-19-27050R1

Circulating HPV cDNA in the blood as a reliable biomarker for cervical cancer: a meta-analysis

PLOS ONE

Dear Mr. Wan,

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Reviewer #1: This manuscript reports meta-analysis results for HPV cDNA in the blood as a diagnostic biomarker for cervical cancer. I have below comments.

For Table 1, please add the number of cases, non-cases, TP, FP, FN, TN and cut-off values (if any) for each study. The sensitivities of No. 1, 2 and 4 studies are different from those reported in Table S1.

For quality assessment, please provide a table to report the QUADAS assessment scores for each included study individually.

Page 10, under Quality assessment, the 2nd line “The included 15 studies…” should be “The included 10 studies...”

In Figure 3, it is not clear if random effects model was used for pooled sensitivity or specificity.

In Figure 3, please add meta-analysis analysis for DOR.

In subgroup analysis, race is a factor, but in Table 2A, no race is included. The term of race or region and their contents should be consistent through the text and tables.

What are the estimates in Figure 5? For sensitivity analysis please include pooled results for each of major outcomes (sensitivity, specificity, and DOR).

Page 18, in Discussion, the statement, “Most studies employed a qPCR method that demonstrated relatively high sensitivity and specificity”, does not reflect the truth. From Tables 1 and S1 (even though the numbers are not consistent between these two tables), those studies with qPCR method show very low sensitivities.

From Table 1, it seems DIPS-PCR and ddPCR would give high sensitivity but not other method. But in Discussion, authors don’t think the test methods would contribute heterogeneity, which is not convincing.

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Reviewer #1: No

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9 Jan 2020

Thank you for your time and effort in reviewing our manuscript and providing us with comprehensive and valuable feedback.

Please find below the clarifications for the concerns raised:

Review Comment: For Table 1, please add the number of cases, non-cases, TP, FP, FN, TN and cut-off values (if any) for each study. The sensitivities of No. 1, 2 and 4 studies are different from those reported in Table S1.

Clarifications: The number of TP, FP, FN, TN for each study has been added in Table1. Meanwhile some unimportant data have been deleted from Table 1. The sensitivities of No.1, 2 and 4 studies in Table S1 also have been corrected.

Review Comment: For quality assessment, please provide a table to report the QUADAS assessment scores for each included study individually.

Clarifications: QUASAS assessment scores were added to Table 1.

Review Comment: Page 10, under Quality assessment, the 2nd line “The included 15 studies…” should be “The included 10 studies...”

Clarifications: In the last revision, the original 15 papers were included in this meta-analysis. According to the requirements, the theme was re-locked on HPV cDNA. The original 15 articles were reduced to 10. We forgot to revise them here. Now it has been corrected.

Review Comment: In Figure 3, it is not clear if random effects model was used for pooled sensitivity or specificity. In Figure 3, please add meta-analysis analysis for DOR.

Clarifications: In Figure 3, both sensitivity and specificity analysis are obtained under the random effects mode. DOR results have also been added to Table 3.

Review Comment: In subgroup analysis, race is a factor, but in Table 2A, no race is included. The term of race or region and their contents should be consistent through the text and tables.

Clarifications: The term of race or region and their contents have been checked consistently through the text and tables.

Review Comment: What are the estimates in Figure 5? For sensitivity analysis please include pooled results for each of major outcomes (sensitivity, specificity, and DOR).

Clarifications: Sensitivity, specificity, and DOR aren’t generally provided in sensitivity analysis of meta review literatures (including sensitivity analysis of meta literature in PLoS One and other journals). The sensitivity analysis here is to examine the effect of a single study on the total combined effect (estimates and their 95%CI). Figure 5 showed that the results were relatively stable and reliable.

Review Comment: Page 18, in Discussion, the statement, “Most studies employed a qPCR method that demonstrated relatively high sensitivity and specificity”, does not reflect the truth. From Tables 1 and S1 (even though the numbers are not consistent between these two tables), those studies with qPCR method show very low sensitivities.

Clarifications: The original sentence has been revised to: “Most studies employed a qPCR method that demonstrated relatively high specificity but low sensitivity. The sensitivity of qPCR is difficult to detect very small amounts of circulating nucleic acids in blood.” It is true that MSP-PCR and ddPCR were more accurate for detecting HPV cDNA in patients than qPCR. The reasons for these results are also discussed in text.

Review Comment: From Table 1, it seems DIPS-PCR and ddPCR would give high sensitivity but not other method. But in Discussion, authors don’t think the test methods would contribute heterogeneity, which is not convincing.

Clarifications: From the perspective of raw data, differences in methodology can indeed bring about greater heterogeneity, but meta-analysis shows that the test methods do not contribute statistically significant heterogeneity. In discussion, we suggested some possible causes of heterogeneity. Of cause, further studies with large sample sizes and more details, e.g., race, specimen features, and tumor properties, are needed to confirm these suggestions.

The revised parts of the manuscript have been highlighted in yellow. Thank you for your consideration. I look forward to your response.

Submitted filename: a_rebuttal_letter.docx

Click here for additional data file.

13 Jan 2020

Circulating HPV cDNA in the blood as a reliable biomarker for cervical cancer: a meta-analysis

PONE-D-19-27050R2

Dear Dr. Wan,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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Peter van Dam

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Just change: With qPCR it is difficult to detect .... (discussion)

Reviewers' comments:

23 Jan 2020

PONE-D-19-27050R2

Circulating HPV cDNA in the blood as a reliable biomarker for cervical cancer: a meta-analysis

Dear Dr. Wan:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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