The efficacy of chimeric antigen receptor (CAR) immunotherapy in animal models for solid tumors: A systematic review and meta-analysis

PLoS One. 2017 Nov 15;12(11):e0187902. doi: 10.1371/journal.pone.0187902. eCollection 2017.

Abstract

Background: Most recently, an emerging theme in the field of tumor immunology predominates: chimeric antigen receptor (CAR) therapy in treating solid tumors. The number of related preclinical trials was surging. However, an evaluation of the effects of preclinical studies remained absent. Hence, a meta-analysis was conducted on the efficacy of CAR in animal models for solid tumors.

Methods: The authors searched PubMed/Medline, Embase, and Google scholar up to April 2017. HR for survival was extracted based on the survival curve. The authors used fixed effect models to combine the results of all the trials. Heterogeneity was assessed by I-square statistic. Quality assessment was conducted following the Stroke Therapy Academic Industry Roundtable standard. Publication bias was assessed using Egger's test.

Results: Eleven trials were included, including 54 experiments with a total of 362 animals involved. CAR immunotherapy significantly improved the survival of animals (HR: 0.25, 95% CI: 0.13-0.37, P < 0.001). The quality assessment revealed that no study reported whether allocation concealment and blinded outcome assessment were conducted, and only five studies implemented randomization.

Conclusions: This meta-analysis indicated that CAR therapy may be a potential clinical strategy in treating solid tumors.

Publication types

  • Meta-Analysis
  • Review
  • Systematic Review

MeSH terms

  • Animals
  • Disease Models, Animal*
  • Humans
  • Immunotherapy*
  • Neoplasms / therapy*
  • Receptors, Antigen / immunology*
  • Receptors, Antigen / therapeutic use
  • Recombinant Fusion Proteins / immunology
  • Recombinant Fusion Proteins / therapeutic use

Substances

  • Receptors, Antigen
  • Recombinant Fusion Proteins

Grants and funding

The authors received no specific funding for this work.