Purpose: The EML4-ALK fusion gene has been detected in f7% of Japanese non-small cell lung cancers (NSCLC). We determined the frequency of EML4-ALK in Caucasian NSCLC and in NSCLC cell lines. We also determined whether TAE684, a specific ALK kinase inhibitor, would inhibit the growth of EML4-ALK-containing cell lines in vitro and in vivo. Experimental Design: We screened 305 primary NSCLC [both U.S. (n = 138) and Korean (n = 167) patients] and 83 NSCLC cell lines using reverse transcription-PCR and by exon array analyses.We evaluated the efficacy of TAE684 against NSCLC cell lines in vitro and in vivo. Results: We detected four different variants, including two novel variants, of EML4-ALK using reverse transcription-PCR in 8 of 305 tumors (3%) and 3 of 83 (3.6%) NSCLC cell lines. All EML4-ALK-containing tumors and cell lines were adenocarcinomas. EML4-ALK was detected more frequently in NSCLC patients who were never or light (<10 pack-years) cigarette smokers compared with current/former smokers (6% versus 1%; P = 0.049). TAE684 inhibited the growth of one of three (H3122) EML4-ALK-containing cell lines in vitro and in vivo, inhibited Akt phosphorylation, and caused apoptosis. In another EML4-ALK cell line, DFCI032, TAE684 was ineffective due to coactivation of epidermal growth factor receptor and ERBB2. The combination of TAE684 and CL-387,785 (epidermal growth factor receptor/ERBB2 kinase inhibitor) inhibited growth and Akt phosphorylation and led to apoptosis in the DFCI032 cell line. Conclusions: EML4-ALK is found in the minority of NSCLC. ALK kinase inhibitors alone or in combination may nevertheless be clinically effective treatments for NSCLC patients whose tumors contain EML4-ALK.

Purpose: One of the main challenges of lung cancer research is identifying patients at high risk for recurrence after surgical resection. Simple, accurate, and reproducible methods of evaluating individual risks of recurrence are needed. Experimental Design: Based on a combined analysis of time-to-recurrence data, censoring information, and microarray data from a set of 138 patients, we selected statistically significant genes thought to be predictive of disease recurrence. The number of genes was further reduced by eliminating those whose expression levels were not reproducible by real-time quantitative PCR. Within these variables, a recurrence prediction model was constructed using Cox proportional hazard regression and validated via two independent cohorts (n = 56 and n = 59). Results: After performing a log-rank test of the microarray data and successively selecting genes based on real-time quantitative PCR analysis, the most significant 18 genes had P values of <0.05.After subsequent stepwise variable selection based on gene expression information and clinical variables, the recurrence prediction model consisted of six genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, and IFI44). Two pathologic variables, pStage and cellular differentiation, were developed. Validation by two independent cohorts confirmed that the proposed model is significantly accurate (P = 0.0314 and 0.0305, respectively). The predicted median recurrence-free survival times for each patient correlated well with the actual data. Conclusions: We have developed an accurate, technically simple, and reproducible method for predicting individual recurrence risks. This model would potentially be useful in developing customized strategies for managing lung cancer.