Epidermal growth factor receptor mutation analysis in cytological specimens and responsiveness to gefitinib in advanced non-small cell lung cancer patients.

Abstract

BACKGROUND:

Epidermal growth factor receptor (EGFR) mutation is the key predictor of EGFR tyrosine kinase inhibitors (TKIs) efficacy in non-small cell lung cancer (NSCLC). We conducted this study to verify the feasibility of EGFR mutation analysis in cytological specimens and investigate the responsiveness to gefitinib treatment in patients carrying EGFR mutations.

METHODS:

A total of 210 cytological specimens were collected for EGFR mutation detection by both direct sequencing and amplification refractory mutation system (ARMS). We analyzed EGFR mutation status by both methods and evaluated the responsiveness to gefitinib treatment in patients harboring EGFR mutations by overall response rate (ORR), disease control rate (DCR) and progression free survival (PFS).

RESULTS:

Of all patients, EGFR mutation rate was 28.6% (60/210) by direct sequencing and 45.2% (95/210) by ARMS (P<0.001) respectively. Among the EGFR wild type patients tested by direct sequencing, 26.7% of them were positive by ARMS. For the 72 EGFR mutation positive patients treated with gefitinib, the ORR, DCR and median PFS were 69.4%, 90.2% and 9.3 months respectively. The patients whose EGFR mutation status was negative by direct sequencing but positive by ARMS had lower ORR (48.0% vs. 80.9%, P=0.004) and shorter median PFS (7.4 vs. 10.5 months, P=0.009) as compared with that of EGFR mutation positive patients by both detection methods.

CONCLUSIONS:

Our study verified the feasibility of EGFR analysis in cytological specimens in advanced NSCLC. ARMS is more sensitive than direct sequencing in EGFR mutation detection. EGFR Mutation status tested on cytological samples is applicable for predicting the response to gefitinib. Abundance of EGFR mutations might have an influence on TKIs efficacy.

• Authors: Lin Li, Zijin Zhang, Zhixin Bie, Zheng Wang, Ping Zhang, Xin Nie, Yuanming Li, Hui Wang, Bin Ai, Gang Cheng
• Journal: Chinese journal of cancer research = Chung-kuo yen cheng yen chiu (Chin J Cancer Res) Vol. 27 Issue 3 Pg. 294-300 (Jun 2015) ISSN: 1000-9604 [Print] China
• PMID: 26157326 (Publication Type: Journal Article)