Effect of antitumor treatments on triple-negative breast cancer patients: A PRISMA-compliant network meta-analysis of randomized controlled trials

Abstract

Background: Triple-negative breast cancer (TNBC) lacks the expression of the estrogen receptor, progesterone receptor, and receptor tyrosine-protein kinase erbB-2 (HER2/neu), which renders hormone-related endocrine and targeted therapy essentially futile.

Objective: We performed a meta-analysis to assess the effects of antitumor regimens in the treatment of TNBC patients.

Methods: We searched electronic databases, including PubMed, Embase, and the Cochrane Library, through January 2017 using the following keywords: "triple negative breast cancer," "TNBC," and "random*" without language restrictions. The major outcome in the present analysis was the overall response rate (ORR), and the secondary outcomes were progression-free survival (PFS) and overall survival (OS). A network meta-analysis and multilevel mixed-effects logistic regression were used to compare antitumor regimens.

Results: We included 35 articles assessing a total of 8476 TNBC patients in our systematic review. The regimen of Bevacizumab, Carboplatin, and Paclitaxel (78.2%) was the most likely to improve the ORR in TNBC patients, followed by EndoTAG-1 and Paclitaxel (69.7%), Carboplatin and Paclitaxel (65.0%), and Bevacizumab and Paclitaxel (61.8%). In the patients without metastasis, the regimen of Bevacizumab, Carboplatin, and Paclitaxel (74.9%) remained the most likely to improve the ORR. We could not analyze the results for patients with metastasis or outcomes of PFS and OS because no >4 regimens formed a network. In the regression analysis, Bevacizumab (odds ratio [OR], 1.71; 95% confidence interval [CI], 1.43-2.05; P < .001) and Carboplatin (OR, 2.07; 95% CI, 1.62-2.64; P < .001) correlated with superior ORR outcome, and Iniparib (OR, 1.51; 95% CI, 1.11-2.07; P = .009) correlated with superior OS outcome.

Conclusion: The regimen including Bevacizumab, Carboplatin, and Paclitaxel was the most likely to improve the ORR in TNBC patients and in advanced metastatic TNBC patients. The administration of Bevacizumab and Carboplatin provided greater benefit toward improved patient ORR.

Figure 1

PRISMA flowchart illustrating the selection of studies included in the present analysis. The illustration shows the number of documents obtained from the database, the simple screening process, and the final number of studies included in the analysis.

Figure 2

Traditional meta-analysis of overall response rate (ORR) among regimens. The forest plot shows a traditional meta-analysis for ORR results. The results were not pooled because of the various types of intervention and control regimens.

Figure 3

Network of comparisons for overall response rate included in the analyses. (A) All TNBC patients; (B) TNBC patients without metastasis. In the network plot, the connection of two interventions indicates a direct comparison. The nodes are weighted according to the number of studies and the edges according to the precision of the direct estimate for each pairwise comparison. B1 = Bevacizumab, C1 = Capecitabine, C2 = Carboplatin, E1 = EndoTAG-1, E2 = Epirubicin, I1 = Iniparib, O = Onartuzumab, P = Paclitaxel, T2 = Tigatuzumab, TNBC = triple-negative breast cancer.

Figure 4

Forest plot of antitumor agents for overall response rate (ORR) by multilevel mixed-effects logistic regression. The components of different therapeutic strategies were analyzed to assess the relationship to the ORR of patients by logistic regression.

Figure 5

Forest plot of antitumor agents for progression-free survival (PFS) by multilevel mixed-effects logistic regression. The components of different therapeutic strategies were analyzed to assess the relationship to the PFS of patients by logistic regression.

Figure 6

Forest plot of anti-tumor agents for overall survival (OS) by multilevel mixed-effects logistic regression. The components of different therapeutic strategies were analyzed to assess the relationship to the OS of patients by logistic regression.