Association of polymorphisms in AKT1 and EGFR with clinical outcome and toxicity in non-small cell lung cancer patients treated with gefitinib.

EGFR mutations are strongly predictive of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor activity in non-small cell lung cancer (NSCLC), but resistance mechanisms are not completely understood. The interindividual variability in toxicity also points out to the need of novel pharmacogenetic markers to select patients before therapy. Therefore, we evaluated the associations between EGFR and AKT1 polymorphisms and outcome/toxicity in gefitinib-treated NSCLC patients. Polymorphic loci in EGFR, and AKT1, and EGFR and K-Ras mutations were assessed in DNA isolated from blood samples and/or paraffin-embedded tumor from 96 gefitinib-treated NSCLC patients. Univariate and multivariate analyses compared genetic variants with clinical efficacy and toxicity using Fisher's, log-rank test, and Cox's proportional hazards model. AKT1-SNP4 association with survival was also evaluated in 127 chemotherapy-treated/gefitinib-naive patients, whereas its relationship with AKT1 expression and gefitinib cytotoxicity was studied in 15 NSCLC cell lines. AKT1-SNP4 A/A genotype was associated with shorter time-to-progression (P = 0.04) and overall survival (P = 0.007). Multivariate analyses and comparison with the gefitinib-nontreated population underlined its predictive significance, whereas the in vitro studies showed the association of lower AKT1 mRNA levels with gefitinib resistance. In contrast, EGFR-activating mutations were significantly correlated with response, longer time-to-progression, and overall survival, whereas EGFR -191C/A (P < 0.001), -216 G/T (P < 0.01), and R497K (P = 0.02) polymorphisms were strongly associated with grade >1 diarrhea. AKT1-SNP4 A/A genotype seems to be a candidate biomarker of primary resistance, whereas EGFR -191C/A, -216G/T, and R497K polymorphisms are associated with diarrhea when using gefitinib in NSCLC patients, thus offering potential new tools for treatment optimization.