Cancers are composed of populations of cells with distinct molecular and phenotypic features, a phenomenon termed intra-tumor heterogeneity (ITH). ITH in lung cancers has not been well studied. We applied multi-region whole exome sequencing (WES) on 11 localized lung adenocarcinomas. All tumors showed clear evidence of ITH. On average, 76% of all mutations and 20/21 known cancer gene mutations were identified in all regions of individual tumors suggesting single-region sequencing may be adequate to identify the majority of known cancer gene mutations in localized lung adenocarcinomas. With a median follow-up of 21 months post-surgery, 3 patients have relapsed and all 3 patients had significantly larger fractions of subclonal mutations in their primary tumors than patients without relapse. These data indicate larger subclonal mutation fraction may be associated with increased likelihood of postsurgical relapse in patients with localized lung adenocarcinomas.

PURPOSE The ETS2 transcription factor is an evolutionarily conserved gene that is deregulated in cancer. We analyzed the transcriptome of lung adenocarcinomas and normal lung tissue by expression profiling and found that ETS2 was significantly down-regulated in adenocarcinomas. In this study, we probed the yet unknown functional role of ETS2 in lung cancer pathogenesis. EXPERIMENTAL DESIGN Lung adenocarcinomas (n=80) and normal lung tissues (n=30) were profiled using the Affymetrix Human Gene 1.0 ST platform. Immunohistochemical (IHC) analysis was performed to determine ETS2 protein expression in NSCLC histological tissue specimens (n=201). Patient clinical outcome, based on ETS2 IHC expression, was statistically assessed using the log-rank and Kaplan-Meier tests. RNA interference and over-expression strategies were employed to assess effects of ETS2 expression on the transcriptome and on various malignant phenotypes. RESULTS ETS2 expression was significantly reduced in lung adenocarcinomas compared to normal lung (p<0.001). Low ETS2 IHC expression was a significant predictor of shorter time to recurrence in NSCLC (p=0.009, HR=1.89) and adenocarcinoma (p=0.03, HR=1.86). Moreover, ETS2 was found to significantly inhibit lung cancer cell growth, migration and invasion (p<0.05), and microarray and pathways analysis revealed significant (p<0.001) activation of the HGF pathway following ETS2 knockdown. In addition, ETS2 was found to suppress MET phosphorylation and knockdown of MET expression significantly attenuated (p<0.05) cell invasion mediated by ETS2-specific siRNA. Furthermore, knockdown of ETS2 augmented HGF-induced MET phosphorylation, cell migration and invasion. CONCLUSION(S) Our findings point to a tumor suppressor role for ETS2 in human NSCLC pathogenesis through inhibition of the MET proto-oncogene.

Purpose Prospectively identifying who will benefit from adjuvant chemotherapy (ACT) would improve clinical decisions for non-small-cell lung cancer (NSCLC) patients. In this study, we aim to develop and validate a functional gene set that predicts the clinical benefits of ACT in NSCLC. Experimental Design An 18-hub-gene prognosis signature was developed through a systems biology approach, and its prognostic value was evaluated in six independent cohorts. The 18-hub-gene set was then integrated with genome-wide functional (RNAi) data and genetic aberration data to derive a 12-gene predictive signature for ACT benefits in NSCLC. Results Using a cohort of 442 Stage I–III NSCLC patients who underwent surgical resection, we identified an 18-hub-gene set which robustly predicted the prognosis of patients with adenocarcinoma in all validation datasets across four microarray platforms. The hub genes, identified through a purely data-driven approach, have significant biological implications in tumor pathogenesis, including NKX2-1, Aurora Kinase A, PRC1, CDKN3, MBIP, RRM2. The 12-gene predictive signature was successfully validated in two independent datasets (N=90 and N=176). The predicted benefit group showed significant improvement in survival after ACT (UT Lung SPORE data: hazard ratio=0.34, p=0.017; JBR.10 clinical trial data: hazard ratio=0.36, p=0.038), while the predicted non-benefit group showed no survival benefit for two datasets (hazard ratio=0.80, p=0.70; hazard ratio= 0.91, p=0.82). Conclusions This is the first study to integrate genetic aberration, genome-wide RNAi data, and mRNA expression data to identify a functional gene set that predicts which resectable patients with non-small-cell lung cancer will have a survival benefit with ACT.