EGFR-TKI therapy for patients with brain metastases from non-small-cell lung cancer: a pooled analysis of published data.

Abstract

INTRODUCTION:

Brain metastases are one of the leading causes of death from non-small-cell lung cancer (NSCLC). The use of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) to treat brain metastases remains controversial. Thus, we performed a pooled analysis of published data to evaluate the efficacy of EGFR-TKIs in NSCLC patients with brain metastases, particularly for tumors with activating EGFR mutations.

METHODS:

Several data sources were searched, including PubMed, Web of Science, and ASCO Annual Meetings databases. The end points were intracranial overall response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and adverse events. The pooled ORR, DCR, PFS, and OS with 95% confidence intervals (CIs) were calculated employing fixed- or random-effect models, depending on the heterogeneity of the included studies.

RESULTS:

Sixteen published studies were included in this analysis, with a total of 464 enrolled patients. The EGFR mutational status was unknown for 362 (unselected group), and 102 had activating EGFR mutations. The pooled intracranial ORR and DCR were 51.8% (95% CI: 45.8%-57.8%) and 75.7% (95% CI: 70.3%-80.5%), respectively. A higher ORR was observed in the EGFR mutation group than in the unselected group (85.0% vs 45.1%); a similar trend was observed for the DCR (94.6% vs 71.3%). The pooled median PFS and OS were 7.4 months (95% CI, 4.9-9.9) and 11.9 months (95% CI, 7.7-16.2), respectively, with longer PFS (12.3 months vs 5.9 months) and OS (16.2 months vs 10.3 months) in the EGFR mutation group than in the unselected group.

CONCLUSION:

This pooled analysis strongly suggests that EGFR-TKIs are an effective treatment for NSCLC patients with brain metastases, particularly in those patients harboring EGFR mutations. Larger prospective randomized clinical trials are warranted to confirm our conclusion and identify the most appropriate treatment model.

• Authors: Yun Fan, Xiaoling Xu, Conghua Xie
• Journal: OncoTargets and therapy (Onco Targets Ther) Vol. 7 Pg. 2075-84 ( 2014) ISSN: 1178-6930 [Print] New Zealand
• PMID: 25419145 (Publication Type: Journal Article)