Purpose: In patients with non^small cell lung cancer (NSCLC), mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain have been associated with sensitivity to erlotinib and gefitinib.We undertook this study to explore the relationship between EGFR mutation type and clinical variables, including treatment with gefitinib and erlotinib. Experimental Design: In patients with NSCLC, EGFR exon 19 deletion mutations and EGFR L858R point mutations were analyzed by nonsequencing PCR-based methods from paraffin blocks of tissue obtained before treatment. The results were correlated with clinical information (sex, pathologic subtype, race/ethnicity, treatment, and overall survival).Results: The two most common EGFR mutations were identified in 24% (70 of 291; 95% confidence interval, 26%-38%) of tumors from patients with NSCLC. EGFR mutation was associated with Asian ethnicity (P = 0.0023) and being a''never smoker'' (P = 0.0001). Among patients with EGFR mutations, 39% (27 of 70) had EGFR L858R, whereas 61% (43 of 70) had an EGFR exon 19 deletion. After treatment with erlotinib (n = 12) or gefitinib (n = 22), patients with EGFR mutations had a median overall survival of 20 months. After treatment with erlotinib or gefitinib, patients with EGFR exon 19 deletions had significantly longer median survival than patients with EGFR L858R (34 versus 8 months; log-rank P = 0.01). Conclusions: EGFR mutations in exons 19 or 21are correlated with clinical factors predictive of response to gefitinib and erlotinib. Those with EGFR exon 19 deletion mutations had a longer median survival than patients with EGFR L858R point mutation. These observations warrant confirmation in a prospective study and exploration of the biological mechanisms of the differences between the two major EGFR mutations.

The likelihood of EGFR mutations in exons 19 and 21 decreases as the number of pack-years increases. Mutations were less common in people who smoked for more than 15 pack-years or who stopped smoking cigarettes less than 25 years ago. These data can assist clinicians in assessing the likelihood of exon 19 and 21 EGFR mutations in patients with lung adenocarcinoma when mutational analysis is not feasible.

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Lung neoplasms commonly develop in patients previously treated for head and neck carcinomas. The derivation of these tumors, either as new primary lung cancers or as metastatic head and neck cancers, is difficult to establish based on clinical or histopathologic criteria since both are squamous cell carcinomas and have identical features under light microscopy. However, this distinction has significant treatment and prognostic implications. Gene expression profiling was performed on a panel of 52 sequentially collected patients with either primary lung (n = 21) or primary head and neck (n = 31) carcinomas using the Affymetrix HG_U95Av2 high-density oligonucleotide microarray. Unsupervised hierarchical clustering with Ward linkage and the Pearson correlation metric was performed. To assess robustness, bootstrap resampling was performed with 1,000 iterations. A t test of the normalized values for each gene was used to determine the genes responsible for segregating head and neck from lung primary carcinomas, and those with the most differential expression were used for later analyses. In the absence of a large "test" set of tumors, we used a supervised leave-one-out cross-validation to test how well we could predict the tumor origin. Once a gene expression profile was established, 12 lung lesions taken from patients with previously treated head and neck cancers were similarly analyzed by gene expression profiling to determine their sites of origin. Unsupervised clustering analysis separated the study cohort into two distinct groups which reliably remained segregated with bootstrap resampling. Group 1 consisted of 30 tongue carcinomas. Group 2 consisted of 21 lung cancers and 1 tongue carcinoma. The clustering was not changed even when normal lung or tongue profiles were subtracted from the corresponding carcinomatous lesions, and a leave-one-out cross-validation showed a 98% correct prediction (see Supplementary Data 1). A minimum set of 500 genes required to distinguish these groups was established. Given the ability to segregate these lesions using molecular profiling, we analyzed the lung tumors of undetermined origin. All cases clearly clustered with either lung or tongue tumor subsets, strongly supporting our hypothesis that this technique could elucidate the tissue of origin of metastatic lesions. Although histologically similar, squamous cell carcinomas have distinct gene expression profiles based on their anatomic sites of origin. Accordingly, the application of gene expression profiling may be useful in identifying the derivation of lung nodules and consequently enhances treatment planning.