[HTML][HTML] Combination of EGFR gene copy number and protein expression predicts outcome for advanced non-small-cell lung cancer patients treated with gefitinib

FR Hirsch, M Varella-Garcia, F Cappuzzo, J McCoy… - Annals of …, 2007 - Elsevier
FR Hirsch, M Varella-Garcia, F Cappuzzo, J McCoy, L Bemis, AC Xavier, R Dziadziuszko…
Annals of Oncology, 2007Elsevier
Background: Biological markers for optimal selection of patient to epidermal growth factor
receptor (EGFR)-targeted therapies are not established in advanced non-small-cell lung
cancer (NSCLC). Patients and methods: EGFR/HER2 gene copy number by FISH, EGFR
protein and pAKT expression by immunohistochemistry (IHC) and EGFR and KRAS
mutations were tested in 204 gefitinib-treated NSCLC patients. Results: Increased EGFR
and HER2 gene copy number (FISH+), EGFR protein overexpression (IHC+), EGFR …
Abstract
Background: Biological markers for optimal selection of patient to epidermal growth factor receptor (EGFR)-targeted therapies are not established in advanced non-small-cell lung cancer (NSCLC).
Patients and methods:EGFR/HER2 gene copy number by FISH, EGFR protein and pAKT expression by immunohistochemistry (IHC) and EGFR and KRAS mutations were tested in 204 gefitinib-treated NSCLC patients.
Results: Increased EGFR and HER2 gene copy number (FISH+), EGFR protein overexpression (IHC+), EGFR mutations and pAKT overexpression were all associated with significantly higher response rates (33%, 29%, 22%, 39% and 20% respectively). EGFR FISH+ (32%) and IHC+ (61%) correlated with improved survival, while EGFR mutations (27%), KRAS mutations (26%) and pAKT expression (69%) did not. In multivariate survival analysis EGFR FISH and IHC were independent predictive markers. EGFR FISH+/IHC+ patients (23%) had a median survival of 21 months versus 6 months for double-negative patients (30%).
Conclusion: Combination of EGFR FISH and IHC is effective predictor for benefit from gefitinib. Patients with double-negative results are unlikely to benefit in western NSCLC populations.
Elsevier