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      Overall Survival Benefits for Combining Targeted Therapy as Second-Line Treatment for Advanced Non-Small-Cell-Lung Cancer: A Meta-Analysis of Published Data

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          Abstract

          Background

          Combining targeted therapy has been extensively investigated in previously treated advanced non-small-cell lung cancer (NSCLC), but it is still unclear whether combining targeted therapy might offer any benefits against standard monotherapy with erlotinib. We thus performed a meta-analysis of randomized controlled trials to compare the efficacy and safety of combining targeted therapy versus erlotinib alone as second-line treatment for advanced NSCLC.

          Methods

          Several databases were searched, including Pubmed, Embase and Cochrane databases. The endpoints were overall survival (OS), progression-free survival (PFS), overall response rate (ORR) and grade 3 or 4 adverse event (AEs). The pooled hazard ratio (HR) or odds ratio (OR), and 95% confidence intervals (CI) were calculated employing fixed- or random-effects models depending on the heterogeneity of the included trials.

          Results

          Eight eligible trials involved 2417 patients were ultimately identified. The intention to treatment (ITT) analysis demonstrated that combining targeted therapy significantly improved OS (HR 0.90, 95%CI: 0.82–0.99, p = 0.024), PFS (HR 0.83, 95%CI: 0.72–0.97, p = 0.018), and ORR (OR 1.35, 95%CI 1.01–1.80, P = 0.04). Sub-group analysis based on phases of trials, EGFR-status and KRAS status also showed that there was a tendency to improve PFS and OS in combining targeted therapy, except that PFS for patients with EGFR-mutation or wild type KRAS favored erlotinib monotherapy. Additionally, more incidence of grade 3 or 4 rash, fatigue and hypertension were observed in combining targeted therapy.

          Conclusions

          With the available evidence, combining targeted therapy seems superior over erlotinib monotherapy as second-line treatment for advanced NSCLC. More studies are still needed to identify patients who will most likely benefit from the appropriate combining targeted therapy.

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          Most cited references28

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          Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints.

          M Parmar, V. Torri, L. Stewart (1998)
          Meta-analyses aim to provide a full and comprehensive summary of related studies which have addressed a similar question. When the studies involve time to event (survival-type) data the most appropriate statistics to use are the log hazard ratio and its variance. However, these are not always explicitly presented for each study. In this paper a number of methods of extracting estimates of these statistics in a variety of situations are presented. Use of these methods should improve the efficiency and reliability of meta-analyses of the published literature with survival-type endpoints.
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            Heterogeneity testing in meta-analysis of genome searches.

            Elias Zintzaras, John Ioannidis (2005)
            Genome searches for identifying susceptibility loci for the same complex disease often give inconclusive or inconsistent results. Genome Search Meta-analysis (GSMA) is an established non-parametric method to identify genetic regions that rank high on average in terms of linkage statistics (e.g., lod scores) across studies. Meta-analysis typically aims not only to obtain average estimates, but also to quantify heterogeneity. However, heterogeneity testing between studies included in GSMA has not been developed yet. Heterogeneity may be produced by differences in study designs, study populations, and chance, and the extent of heterogeneity might influence the conclusions of a meta-analysis. Here, we propose and explore metrics that indicate the extent of heterogeneity for specific loci in GSMA based on Monte Carlo permutation tests. We have also developed software that performs both the GSMA and the heterogeneity testing. To illustrate the concept, the proposed methodology was applied to published data from meta-analyses of rheumatoid arthritis (4 scans) and schizophrenia (20 scans). In the first meta-analysis, we identified 11 bins with statistically low heterogeneity and 8 with statistically high heterogeneity. The respective numbers were 9 and 6 for the schizophrenia meta-analysis. For rheumatoid arthritis, bins 6.2 (the HLA region that is a well-documented susceptibility locus for the disease) and 16.3 (16q12.2-q23.1) had both high average ranks and low between-study heterogeneity. For schizophrenia, this was seen for bin 3.2 (3p25.3-p22.1) and heterogeneity was still significantly low after adjusting for its high average rank. Concordance was high between the proposed metrics and between weighted and unweighted analyses. Data from genome searches should be synthesized and interpreted considering both average ranks and heterogeneity between studies. 2004 Wiley-Liss, Inc.
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              Power and sample size calculations for studies involving linear regression.

              W. D. Dupont, W. D. Plummer (1998)
              This article presents methods for sample size and power calculations for studies involving linear regression. These approaches are applicable to clinical trials designed to detect a regression slope of a given magnitude or to studies that test whether the slopes or intercepts of two independent regression lines differ by a given amount. The investigator may either specify the values of the independent (x) variable(s) of the regression line(s) or determine them observationally when the study is performed. In the latter case, the investigator must estimate the standard deviation(s) of the independent variable(s). This study gives examples using this method for both experimental and observational study designs. Cohen's method of power calculations for multiple linear regression models is also discussed and contrasted with the methods of this study. We have posted a computer program to perform these and other sample size calculations on the Internet (see http://www.mc.vanderbilt.edu/prevmed/psintro+ ++.htm). This program can determine the sample size needed to detect a specified alternative hypothesis with the required power, the power with which a specific alternative hypothesis can be detected with a given sample size, or the specific alternative hypotheses that can be detected with a given power and sample size. Context-specific help messages available on request make the use of this software largely self-explanatory.
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                Author and article information

                Contributors
                Apar Kishor Ganti: 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, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2013
                Publication date (Electronic): 8 February 2013
                Volume: 8
                Issue: 2
                Electronic Location Identifier: e55637
                Affiliations
                [1 ]Department of Oncology, the Sixth People’s Hospital, Shanghai Jiao Tong University, Shanghai, China
                [2 ]Department of Oncology, The Kunming Medical University, Kunming, Yunnan, China
                University of Nebraska Medical Center, United States of America
                Author notes

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

                Conceived and designed the experiments: YY WXQ ZS. Performed the experiments: YLJ ZS FL DLM LNT ANH QW. Analyzed the data: YJS WXQ ZS QW. Contributed reagents/materials/analysis tools: DLM LNT ANH WXQ YLJ QW. Wrote the paper: WXQ YY ZS.

                Article
                Publisher ID: PONE-D-12-23486
                DOI: 10.1371/journal.pone.0055637
                PMC ID: 3568141
                PubMed ID: 23409011
                SO-VID: 98637b8e-2626-48c6-b2ee-93101ffe45eb
                Copyright statement: Copyright @ 2013
                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 : 6 August 2012
                Date accepted : 27 December 2012
                Page count
                Pages: 9
                Funding
                The study was supported by grants from the National Natural Science Foundation of China (81001191) and Science and Technology Commission of Shanghai (10PJ1408300). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology
                Subject: Molecular Cell Biology
                Subject: Signal Transduction
                Subject: Signaling Cascades
                Subject: Tyrosine Kinase Signaling Cascade
                Subject: Computer Science
                Subject: Information Technology
                Subject: Databases
                Subject: Medicine
                Subject: Drugs and Devices
                Subject: Oncology
                Subject: Cancer Treatment
                Subject: Chemotherapy and Drug Treatment
                Subject: Cancers and Neoplasms
                Subject: Lung and Intrathoracic Tumors
                Subject: Non-Small Cell Lung Cancer

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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