The role of bevacizumab in solid tumours: A literature based meta-analysis of randomised trials

Background: Bevacizumab is a humanised monoclonal antibody which blocks the binding of circulating vascular endothelial growth factor to its receptors. To date, the Food and Drug Administration has approved bevacizumab for the treatment of several solid tumours. To assess the impact of bevacizumab-based regimens on outcome in these advanced solid tumour types, we performed a meta-analysis. We included all of the randomised trials (phase II or III) where bevacizumab was tested in the first line setting compared with a control arm, including chemotherapy, placebo or other anti-neoplastic agents. Methods: A literature-based meta-analysis of randomised controlled trials (RCTs) in accordance with the preferences for reported items in systematic reviews and meta-analyses guidelines were undertaken. The primary end-point considered was overall survival (OS). The secondary end-points were progression-free survival (PFS) time, response rate and safety. A subgroup analysis was performed to highlight any differences between studies in different tumour types for all end-points. Results: The pooled analysis from RCTs on bevacizumab-based regimens revealed significantly increased OS (hazard ratio [HR] for death 0.92, 95% confidence interval [CI]: 0.88 -0.95; P < 0.0001), PFS (HR: 0.72, 95% CI: 0.67-0.78; P < 0.00001) and response rate (risk ratio: 1.38, 95% CI: 1.27-1.50; P < 0.00001) compared to control arm in solid tumours overall and in colorectal, lung, ovarian and renal cancer as single indications. However, notably, no effect on survival was seen in breast cancer. Conclusion: This study confirmed that bevacizumab-based regimens result in a significant effect on survival and response in advanced colorectal, lung, ovarian and kidney cancer. In cancers where bevacizumab failed overall as in breast cancer, a dedicated biomarkers analysis is warranted to select the proper subgroup of patient that might have the adequate clinical benefit. (C) 2017 Elsevier Ltd. All rights reserved.