Skip to main content
Passa alla visualizzazione normale.

CIRINO BOTTA

Systematic review and meta-analysis on targeted therapy in advanced pancreatic cancer

  • Authors: Ciliberto D.; Staropoli N.; Chiellino S.; Botta C.; Tassone P.; Tagliaferri P.
  • Publication year: 2016
  • Type: Articolo in rivista
  • OA Link: http://hdl.handle.net/10447/513531

Abstract

Aim A systematic review and meta-analysis from literature has been performed to assess the impact of targeted therapy in advanced pancreatic cancer. Methods By searching different literature databases and major cancer meetings proceedings, data from all randomized clinical trials designed to investigate molecular targeted agents in the treatment of advanced pancreatic cancer were collected. The time-frame between January 2007 and March 2015 was selected. Data on predefined end-points, including overall survival, progression-free survival in terms of Hazard Ratio and response-rate were extracted and analyzed by a random effects model. Pooled data analysis was performed according to the DerSimonian and Laird test. The occurrence of publication bias was investigated through Begg's test by visual inspection of funnel plots. Results Twenty-seven randomized clinical trials for a total of 8205 patients were selected and included in the final analysis. A significant benefit was demonstrated for anti-EGFR agents on overall survival (HR = 0.880; 95% confidence interval (CI) 0.797-0.972; p = 0.011). In the pooled analysis no benefit on overall survival (OS: pooled HR = 0.957; 95%CI 0.900-1.017; p = 0.153), or progression-free survival (PFS: pooled HR = 0.908; 95%CI 0.817-1.010; p = 0.075) for targeted-based therapies as compared to conventional treatments could be demonstrated. No advantage was reported in response-rate (OR for RR = 1.210; 95%CI 0.990-1.478; p = 0.063). Begg's funnel plot showed no evidence of publication bias. Conclusion The use of molecular targeted agents does not translate into clinical benefit. Therefore, our work highlights the need to identify predictive factors for patient selection and rationally designed clinical trials.