Epidermal Growth Factor Receptor Gene and Protein and Gefitinib Sensitivity in Non–Small-Cell Lung Cancer
Top Cited Papers
Open Access
- 4 May 2005
- journal article
- clinical trial
- Published by Oxford University Press (OUP) in JNCI Journal of the National Cancer Institute
- Vol. 97 (9), 643-655
- https://doi.org/10.1093/jnci/dji112
Abstract
Background: Gefitinib is a selective inhibitor of the epidermal growth factor (EGFR) tyrosine kinase, which is overexpressed in many cancers, including non–small-cell lung cancer (NSCLC). We carried out a clinical study to compare the relationship between EGFR gene copy number, EGFR protein expression, EGFR mutations, and Akt activation status as predictive markers for gefitinib therapy in advanced NSCLC. Methods: Tumors from 102 NSCLC patients treated daily with 250 mg of gefitinib were evaluated for EGFR status by fluorescence in situ hybridization (FISH), DNA sequencing, and immunohistochemistry and for Akt activation status (phospho-Akt [P-Akt]) by immunohistochemistry. Time to progression, overall survival, and 95% confidence intervals (CIs) were calculated and evaluated by the Kaplan–Meier method; groups were compared using the log-rank test. Risk factors associated with survival were evaluated using Cox proportional hazards regression modeling and multivariable analysis. All statistical tests were two-sided. Results: Amplification or high polysomy of the EGFR gene (seen in 33 of 102 patients) and high protein expression (seen in 58 of 98 patients) were statistically significantly associated with better response (36% versus 3%, mean difference = 34%, 95% CI = 16.6 to 50.3; P <.001), disease control rate (67% versus 26%, mean difference = 40.6%, 95% CI = 21.5 to 59.7; P <.001), time to progression (9.0 versus 2.5 months, mean difference = 6.5 months, 95% CI = 2.8 to 10.3; P <.001), and survival (18.7 versus 7.0 months, mean difference = 11.7 months, 95% CI = 2.1 to 21.4; P = .03). EGFR mutations (seen in 15 of 89 patients) were also statistically significantly related to response and time to progression, but the association with survival was not statistically significant, and 40% of the patients with mutation had progressive disease. In multivariable analysis, only high EGFR gene copy number remained statistically significantly associated with better survival (hazard ratio = 0.44, 95% CI = 0.23 to 0.82). Independent of EGFR assessment method, EGFR + /P-Akt + patients had a statistically significantly better outcome than EGFR − , P-Akt − , or EGFR + /P-Akt − patients. Conclusions: High EGFR gene copy number identified by FISH may be an effective molecular predictor for gefitinib efficacy in advanced NSCLC.Keywords
This publication has 38 references indexed in Scilit:
- EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinibProceedings of the National Academy of Sciences, 2004
- Determinants of Tumor Response and Survival With Erlotinib in Patients With Non—Small-Cell Lung CancerJournal of Clinical Oncology, 2004
- EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib TherapyScience, 2004
- Activating Mutations in the Epidermal Growth Factor Receptor Underlying Responsiveness of Non–Small-Cell Lung Cancer to GefitinibNew England Journal of Medicine, 2004
- Bronchioloalveolar Pathologic Subtype and Smoking History Predict Sensitivity to Gefitinib in Advanced Non–Small-Cell Lung CancerJournal of Clinical Oncology, 2004
- O-242 Gefitinib (‘Iressa’, ZD1839) monotherapy for pretreated advanced non-small-cell lung cancer in IDEAL 1 and 2: tumor response is not clinically relevantly predictable from tumor EGFR membrane staining aloneLung Cancer, 2003
- Loss of PTEN/MMAC1/TEP in EGF receptor-expressing tumor cells counteracts the antitumor action of EGFR tyrosine kinase inhibitorsOncogene, 2003
- Global cancer statistics in the year 2000The Lancet Oncology, 2001
- New Guidelines to Evaluate the Response to Treatment in Solid TumorsJNCI Journal of the National Cancer Institute, 2000
- Nonparametric Estimation from Incomplete ObservationsJournal of the American Statistical Association, 1958