32
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Prognostic impact of tumor-infiltrating lymphocytes in high grade serous ovarian cancer: a systematic review and meta-analysis

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background:

          Tumor-infiltrating lymphocytes (TILs) are involved in the antitumor immune response. The association between prognosis in patients with TILs and high-grade serous ovarian cancer (HGSOC) remains obscure, with some studies reporting conflicting results.

          Methods:

          We conducted an extensive literature search of electronic databases and retrieved prognostic data of each selected subtype of TILs, including CD3+, CD4+, CD8+, CD103+, and PD-1+ TILs. The fixed-effects model was applied to derive the pooled hazard ratio (HR) and 95% confidence interval (CI) of these markers.

          Results:

          The systematic review process yielded 19 eligible studies comprising 6004 patients with HGSOC. We compared TIL-positive and TIL-negative patients, and the pooled HRs from the multivariate analysis revealed that intraepithelial CD8+ TILs were positively correlated with progression-free survival (PFS, HR 0.46, 95% CI 0.25–0.67) and overall survival (OS, HR 0.90, 95% CI 0.86–0.9); stromal CD8+ TILs were positively correlated with OS (HR 0.61, 95% CI 0.36–0.87). Furthermore, the pooled HRs from univariate analysis demonstrated that intraepithelial CD3+, CD4+, CD8+, and CD103+ TILs were positively associated with OS (HR 0.58, 95% CI 0.44–0.72; HR 0.37, 95% CI 0.16–0.59; HR 0.51, 95% CI 0.42–0.60, and HR 0.59, 95% CI 0.44–0.74, respectively); stromal CD4+ and CD8+ TILs were significantly associated with OS (HR 0.63, 95% CI 0.32–0.94 and HR 0.78, 95% CI 0.58–0.97, respectively). However, the pooled HR from the multivariate analysis revealed that PD-1+ TILs were not associated with the OS of patients with HGSOC (HR 0.97, 95% CI 0.90–1.04).

          Conclusion:

          This meta-analysis provided evidence of the association of CD3+, CD4+, CD8+, and CD103+ TILs with the survival benefits (OS and PFS) of patients with HGSOC.

          Related collections

          Most cited references48

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement

          Systematic reviews should build on a protocol that describes the rationale, hypothesis, and planned methods of the review; few reviews report whether a protocol exists. Detailed, well-described protocols can facilitate the understanding and appraisal of the review methods, as well as the detection of modifications to methods and selective reporting in completed reviews. We describe the development of a reporting guideline, the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Protocols 2015 (PRISMA-P 2015). PRISMA-P consists of a 17-item checklist intended to facilitate the preparation and reporting of a robust protocol for the systematic review. Funders and those commissioning reviews might consider mandating the use of the checklist to facilitate the submission of relevant protocol information in funding applications. Similarly, peer reviewers and editors can use the guidance to gauge the completeness and transparency of a systematic review protocol submitted for publication in a journal or other medium.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Quantifying heterogeneity in a meta-analysis.

            The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A basic introduction to fixed-effect and random-effects models for meta-analysis.

              There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd. Copyright © 2010 John Wiley & Sons, Ltd.
                Bookmark

                Author and article information

                Contributors
                Journal
                Ther Adv Med Oncol
                Ther Adv Med Oncol
                TAM
                sptam
                Therapeutic Advances in Medical Oncology
                SAGE Publications (Sage UK: London, England )
                1758-8340
                1758-8359
                31 October 2020
                2020
                : 12
                : 1758835920967241
                Affiliations
                [1-1758835920967241]Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
                [2-1758835920967241]Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
                [3-1758835920967241]Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
                [4-1758835920967241]Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an, Shaanxi Province, 710061, China
                [5-1758835920967241]Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an, Shaanxi Province, 710061, China
                Author notes
                [†]

                These authors have contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-6061-1706
                Article
                10.1177_1758835920967241
                10.1177/1758835920967241
                7607723
                33193829
                dfa2e267-0d67-40f4-9dce-82f698314a01
                © The Author(s), 2020

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 18 May 2020
                : 23 September 2020
                Funding
                Funded by: The First Affiliated Hospital of Xi’an Jiaotong University, ;
                Categories
                Systematic Review
                Custom metadata
                January-December 2020
                ts1

                high-grade serous ovarian cancer,meta-analysis,prognosis,systematic review,tumor-infiltrating lymphocytes

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content381

                Cited by28

                Most referenced authors2,182