Expression of estrogen receptor beta predicts a clinical response and longer progression-free survival after treatment with EGFR–TKI for adenocarcinoma of the lung

Abstract

PurposeEpidermal growth factor receptor (EGFR)–tyrosine kinase inhibitor (EGFR–TKI) demonstrates a dramatic clinical response for the lung adenocarcinoma patients harboring a somatic mutation of EGFR. Such EGFR mutations are frequently found in adenocarcinoma with a strong expression of estrogen receptor (ER) beta, which has been shown to correlate with a favorable prognosis for the patients with EGFR mutations. The aim of this study is to elucidate the correlation between expression of ER beta and the therapeutic effect of EGFR–TKI in adenocarcinoma of the lung. Patients and methodsForty-three patients who were treated with EGFR–TKI for adenocarcinoma of the lung were evaluated. The expression of ER beta and the EGFR mutation were evaluated by immunohistochemistry and the polymerase chain reaction, respectively. Patients divided into two groups by the nuclear expression of ER beta. The clinical response and survival data were compared between the two groups. ResultStrong (S) and weak (W) expression of ER beta was observed in 21 and 22 patients, respectively. EGFR mutations were detected in 30 (69.8%) cases. The S group had more frequent EGFR mutations than the W group (85.7%, 54.5%, p=0.045). The S group had better response rate (p=0.006) and longer progression-free survival (PFS; p=0.001) than the W group. Even in a limited analysis in the patients with EGFR mutations, the S group had tended to have a better response rate (77.8%, 41.7%, p=0.063), and significant longer PFS (p=0.012) than the W group. ConclusionA strong expression of ER beta predicts a good clinical outcome for patients with adenocarcinoma of the lung after treatment with EGFR–TKI. This suggests that the expression status of ER beta can be a candidate surrogate marker for EGFR–TKI treatment of patients with adenocarcinoma of the lung. Further investigation will be necessary to identify biomarkers using a larger cohort of patients in a prospective study.