Epidermal growth factor receptor expression analysis in chemotherapy-naive patients with advanced non-small-cell lung cancer treated with gefitinib or placebo in combination with platinum-based chemotherapy.
Academic Article
Overview
abstract
PURPOSE: Two large, randomized, placebo-controlled trials (IRESSA NSCLC Trial Assessing Combination Therapy; INTACT 1 and 2) in non-small-cell lung cancer (NSCLC) failed to show survival benefit for gefitinib (IRESSA) in combination with first-line platinum-based chemotherapy. Epidermal growth factor receptor (EGFR) staining was assessed retrospectively in relation to survival response to gefitinib in combination with chemotherapy. METHODS: Tumor biopsies obtained prior to start of therapy were assessed by immunohistochemistry for EGFR using the Dako EGFR pharmDx assay (Dako, Denmark). Analyses were stratified by trial and performed independently for patients randomized to placebo and gefitinib as well as for both treatment groups combined. A restricted backwards elimination Cox regression analysis was conducted to identify independent EGFR factors that were statistically significant (P < 0.10), and these were also tested for treatment interaction to assess if they served as predictive factors. RESULTS: Analyses found two statistically significant EGFR-based prognostic factors representing growth pattern and percent membrane staining in patients treated with gefitinib (P = 0.0023), placebo (P = 0.0128), and both combined (P < 0.0001). The prognostic effect was independent of other known prognostic factors. There was no predictive effect of either the growth pattern or membrane staining variable. CONCLUSIONS: While some previous studies indicate that higher EGFR expression correlates with poor survival, our analyses provide statistically significant evidence that the combination of EGFR expression and growth pattern is a strong prognostic indicator for improved survival within this setting. The effects of membrane staining and growth pattern are still significant when adjusting for mutation.
