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      Circulating Cell Free DNA as the Diagnostic Marker for Ovarian Cancer: A Systematic Review and Meta-Analysis

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          Abstract

          Background

          Quantitative analyses of circulating cell-free DNA (cfDNA) are potential methods for the detection of ovarian cancer. Many studies have evaluated these approaches, but the results were too inconsistent to be conclusive. This study is the first to systematically evaluate the accuracy of circulating cfDNA for the diagnosis of ovarian cancer by conducting meta-analysis.

          Methods

          We searched PubMed, Embase, Cochrane Library and the Chinese National Knowledge Infrastructure (CNKI) databases systematically for relevant literatures up to December 10, 2015. All analyses were conducted using Meta-DiSc1.4 and Stata 12.0 software. Sensitivity, specificity and other measures of accuracy of circulating cfDNA for the diagnosis of ovarian cancer were pooled. Meta-regression was performed to identify the sources of heterogeneity.

          Results

          This meta-analysis included a total of 9 studies, including 462 ovarian cancer patients and 407 controls. The summary estimates for quantitative analysis of circulating cfDNA in ovarian cancer screen were as follows: sensitivity, 0.70 (95% confidence interval (CI), 0.65–0.74); specificity, 0.90 (95% CI, 0.87–0.93); positive likelihood ratio, 6.60 (95% CI, 3.90–11.17); negative likelihood ratio, 0.34 (95% CI, 0.25–0.47); diagnostic odds ratio, 26.05 (95% CI, 14.67–46.26); and area under the curve, 0.89 (95% CI, 0.83–0.95), respectively. There was no statistical significance for the evaluation of publication bias.

          Conclusions

          Current evidence suggests that quantitative analysis of cfDNA has unsatisfactory sensitivity but acceptable specificity for the diagnosis of ovarian cancer. Further large-scale prospective studies are required to validate the potential applicability of using circulating cfDNA alone or in combination with conventional markers as diagnostic biomarker for ovarian cancer and explore potential factors that may influence the accuracy of ovarian cancer diagnosis.

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          • Record: found
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          Meta-DiSc: a software for meta-analysis of test accuracy data

          Javier Zamora, Víctor Abraira, Alfonso Muriel … (2006)
          Background Systematic reviews and meta-analyses of test accuracy studies are increasingly being recognised as central in guiding clinical practice. However, there is currently no dedicated and comprehensive software for meta-analysis of diagnostic data. In this article, we present Meta-DiSc, a Windows-based, user-friendly, freely available (for academic use) software that we have developed, piloted, and validated to perform diagnostic meta-analysis. Results Meta-DiSc a) allows exploration of heterogeneity, with a variety of statistics including chi-square, I-squared and Spearman correlation tests, b) implements meta-regression techniques to explore the relationships between study characteristics and accuracy estimates, c) performs statistical pooling of sensitivities, specificities, likelihood ratios and diagnostic odds ratios using fixed and random effects models, both overall and in subgroups and d) produces high quality figures, including forest plots and summary receiver operating characteristic curves that can be exported for use in manuscripts for publication. All computational algorithms have been validated through comparison with different statistical tools and published meta-analyses. Meta-DiSc has a Graphical User Interface with roll-down menus, dialog boxes, and online help facilities. Conclusion Meta-DiSc is a comprehensive and dedicated test accuracy meta-analysis software. It has already been used and cited in several meta-analyses published in high-ranking journals. The software is publicly available at .
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            Diagnostic tests 4: likelihood ratios.

            Jonathan Deeks, Douglas G. Altman (2004)
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              Recent progress in the diagnosis and treatment of ovarian cancer.

              Danijela Jelovac, Deborah Armstrong (2011)
              Epithelial ovarian cancer is the most lethal of the gynecologic malignancies, largely due to the advanced stage at diagnosis in most patients. Screening strategies using ultrasound and the cancer antigen (CA) 125 tumor marker are currently under study and may lower stage at diagnosis but have not yet been shown to improve survival. Women who have inherited a deleterious mutation in the BRCA1 or BRCA2 gene and those with the Lynch syndrome (hereditary nonpolyposis colorectal cancer) have the highest risk of developing ovarian cancer but account for only approximately 10% of those with the disease. Other less common and less well-defined genetic syndromes may increase the risk of ovarian cancer, but their contribution to genetic risk is small. A clear etiology for sporadic ovarian cancer has not been identified, but risk is affected by reproductive and hormonal factors. Surgery has a unique role in ovarian cancer, as it is used not only for diagnosis and staging but also therapeutically, even in patients with widely disseminated, advanced disease. Ovarian cancer is highly sensitive to chemotherapy drugs, particularly the platinum agents, and most patients will attain a remission with initial treatment. Recent advances in the delivery of chemotherapy using the intraperitoneal route have further improved survival after initial therapy. Although the majority of ovarian cancer patients will respond to initial chemotherapy, most will ultimately develop disease recurrence. Chemotherapy for recurrent disease includes platinum-based, multiagent regimens for women whose disease recurs more than 6 to 12 months after the completion of initial therapy and sequential single agents for those whose disease recurs earlier. New targeted biologic agents, particularly those involved with the vascular endothelial growth factor pathway and those targeting the poly (ADP-ribose) polymerase (PARP) enzyme, hold great promise for improving the outcome of ovarian cancer.
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                Author and article information

                Contributors
                Jeffrey Chalmers: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 2 June 2016
                Publication date Collection: 2016
                Volume: 11
                Issue: 6
                Electronic Location Identifier: e0155495
                Affiliations
                [001]Department of Gynecology and Obstetrics, the People’s Hospital of Three Gorges University/the First People’s Hospital of Yichang, Yichang 443000, China
                The Ohio State University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: QZ WL MZ. Performed the experiments: QZ BL WZ ZH. Analyzed the data: QZ BL AC. Contributed reagents/materials/analysis tools: BL WZ ZH. Wrote the paper: QZ MZ AC. Preliminary image editing and retouching: ZH MZ.

                Article
                Publisher ID: PONE-D-16-00033
                DOI: 10.1371/journal.pone.0155495
                PMC ID: 4890757
                PubMed ID: 27253331
                SO-VID: 200e8699-143c-4938-a77e-5b3dfdaeb6e4
                Copyright © © 2016 Zhou et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 10 January 2016
                Date accepted : 30 April 2016
                Page count
                Figures: 8, Tables: 4, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81401187
                Award Recipient :
                This study was supported by National Natural Science Foundation of China (No. 81401187)(Quan Zhou).
                Categories
                Subject: Research Article
                Subject: Medicine and Health Sciences
                Subject: Oncology
                Subject: Cancer Detection and Diagnosis
                Subject: Medicine and Health Sciences
                Subject: Oncology
                Subject: Cancers and Neoplasms
                Subject: Gynecological Tumors
                Subject: Ovarian Cancer
                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Statistical Methods
                Subject: Meta-Analysis
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics (Mathematics)
                Subject: Statistical Methods
                Subject: Meta-Analysis
                Subject: Research and Analysis Methods
                Subject: Research Design
                Subject: Quantitative Analysis
                Subject: Biology and Life Sciences
                Subject: Biochemistry
                Subject: Biomarkers
                Subject: Research and Analysis Methods
                Subject: Database and Informatics Methods
                Subject: Database Searching
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Biology and Life Sciences
                Subject: Molecular Biology
                Subject: Molecular Biology Techniques
                Subject: Artificial Gene Amplification and Extension
                Subject: Polymerase Chain Reaction
                Subject: Reverse Transcriptase-Polymerase Chain Reaction
                Subject: Research and Analysis Methods
                Subject: Molecular Biology Techniques
                Subject: Artificial Gene Amplification and Extension
                Subject: Polymerase Chain Reaction
                Subject: Reverse Transcriptase-Polymerase Chain Reaction
                Custom metadata
                Data Availability All relevant data are within the paper and its Supporting Information files.

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