Epidermal Growth Factor Receptor-Related Tumor Markers and Clinical Outcomes with Erlotinib in Non-small Cell Lung Cancer: An Analysis of Patients from German Centers in the TRUST Study

Relationships between clinical outcomes and epidermal growth factor receptor (EGFR)-related tumor markers were investigated in patients with advanced non-small cell lung cancer. Patients with stage IIIB/IV non-small cell lung cancer (0–2 prior regimens) received erlotinib (150 mg PO per day). Response and survival were evaluated, and tumor samples were assessed by immunohistochemistry (EGFR, phosphorylated mitogen-activated protein kinase, and phosphorylated AKT protein expression), fluorescence in situ hybridization (FISH; EGFR gene copy number), and DNA sequencing (EGFR, KRAS gene mutations). Among 311 patients, 8% had a complete/partial response; the disease control rate was 66%. Median Overall survival (OS) was 6.1 months; 1-year survival rate was 27.2%. Two of 4 patients with EGFR mutations had tumor responses, versus 2/68 with wild-type EGFR (p = 0.014). Progression-free survival (PFS) (HR = 0.31) and OS (HR = 0.33) were significantly prolonged in patients with EGFR mutations. Response rate was significantly higher in patients with EGFR FISH-positive (17%) than FISH-negative tumors (6%), and both PFS (HR = 0.58) and OS (HR = 0.63) significantly favored patients with EGFR FISH-positive tumors; median OS was 8.6 months in the EGFR FISH-positive group. None of 17 patients with a KRAS mutation had a tumor response, but the impact of KRAS mutation status on survival outcomes was of borderline statistical significance. Neither phosphorylated mitogen-activated protein kinase nor phosphorylated AKT immunohistochemistry status had a significant effect on PFS and OS with erlotinib. The presence of EGFR mutations and EGFR FISH-positive tumors may predispose patients to achieving better outcomes on erlotinib, but may have a beneficial impact on prognosis (irrespective of treatment). Prospective, placebo-controlled studies are needed to determine the predictive value of the putative biomarkers.