Current staging methods are inadequate for predicting the outcome of treatment of non–small-cell lung cancer (NSCLC). We developed a five-gene signature that is closely associated with survival of patients with NSCLC. We used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both. We studied gene expression in frozen specimens of lung-cancer tissue from 125 randomly selected patients who had undergone surgical resection of NSCLC and evaluated the association between the level of expression and survival. We used risk scores and decision-tree analysis to develop a gene-expression model for the prediction of the outcome of treatment of NSCLC. For validation, we used randomly assigned specimens from 60 other patients. Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3, and LCK) for RT-PCR and decision-tree analysis. The five-gene signature was an independent predictor of relapse-free and overall survival. We validated the model with data from an independent cohort of 60 patients with NSCLC and with a set of published microarray data from 86 patients with NSCLC. Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.

Purpose: Patients with non–small-cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR)–activating mutations have excellent response to EGFR tyrosine kinase inhibitors (TKIs), but T790M mutation accounts for most TKI drug resistance. This study used highly sensitive methods to detect T790M before and after TKI therapy and investigated the association of T790M and its mutation frequencies with clinical outcome. Patients and Methods: Direct sequencing, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) and next-generation sequencing (NGS) were used to assess T790M in the following two cohorts of patients with NSCLC: TKI-naive patients (n = 107) and TKI-treated patients (n = 85). Results were correlated with TKI treatment response and survival. Results: MALDI-TOF MS was highly sensitive in detecting and quantifying the frequency of EGFR-activating mutations and T790M (detection limits, 0.4% to 2.2%). MALDI-TOF MS identified more T790M than direct sequencing in TKI-naive patients with NSCLC (27 of 107 patients, 25.2% v three of 107 patients, 2.8%, respectively; P < .001) and in TKI-treated patients (before TKI: 23 of 73 patients, 31.5% v two of 73 patients, 2.7%, respectively; P < .001; and after TKI: 10 of 12 patients, 83.3% v four of 12 patients, 33.3%, respectively; P = .0143). The EGFR mutations and their frequencies were confirmed by NGS. T790M was an independent predictor of decreased progression-free survival (PFS) in patients with NSCLC who received TKI treatment (P < .05, multivariate Cox regression). Conclusion: T790M may not be a rare event before or after TKI therapy in patients with NSCLC with EGFR-activating mutations. The pretreatment T790M mutation was associated with shorter PFS with EGFR TKI therapy in patients with NSCLC.

Lung cancer in East Asia is characterized by a high percentage of never-smokers, early onset and predominant EGFR mutations. To illuminate the molecular phenotype of this demographically distinct disease, we performed a deep comprehensive proteogenomic study on a prospectively collected cohort in Taiwan, representing early stage, predominantly female, non-smoking lung adenocarcinoma. Integrated genomic, proteomic, and phosphoproteomic analysis delineated the demographically distinct molecular attributes and hallmarks of tumor progression. Mutational signature analysis revealed age- and gender-related mutagenesis mechanisms, characterized by high prevalence of APOBEC mutational signature in younger females and over-representation of environmental carcinogen-like mutational signatures in older females. A proteomics-informed classification distinguished the clinical characteristics of early stage patients with EGFR mutations. Furthermore, integrated protein network analysis revealed the cellular remodeling underpinning clinical trajectories and nominated candidate biomarkers for patient stratification and therapeutic intervention. This multi-omic molecular architecture may help develop strategies for management of early stage never-smoker lung adenocarcinoma.

Cancer stem cells (CSCs) are a promising target for treating cancer, yet how CSC plasticity is maintained in vivo is unclear and is difficult to study in vitro. Here we establish a sustainable primary culture of Oct3/4(+)/Nanog(+) lung CSCs fed with CD90(+) cancer-associated fibroblasts (CAFs) to further advance our knowledge of preserving stem cells in the tumour microenvironment. Using transcriptomics we identify the paracrine network by which CAFs enrich CSCs through de-differentiation and reacquisition of stem cell-like properties. Specifically, we find that IGF1R signalling activation in cancer cells in the presence of CAFs expressing IGF-II can induce Nanog expression and promote stemness. Moreover, this paracrine signalling predicts overall and relapse-free survival in stage I non-small cell lung cancer (NSCLC) patients. IGF-II/IGF1R signalling blockade inhibits Nanog expression and attenuates cancer stem cell features. Our data demonstrate that CAFs constitute a supporting niche for cancer stemness, and targeting this paracrine signalling may present a new therapeutic strategy for NSCLC.

Macrophages in a tumor microenvironment have been characterized as M1- and M2-polarized subtypes. Here, we discovered the different macrophages’ impacts on lung cancer cell A549. The M2a/M2c subtypes promoted A549 invasion and xenograft tumor growth. The M1 subtype suppressed angiogenesis. M1 enhanced the sensitivity of A549 to cisplatin and decreased the tube formation activity and cell viability of A549 cells by inducing apoptosis and senescence. Different macrophage subtypes regulated genes involved in the immune response, cytoskeletal remodeling, coagulation, cell adhesion and apoptosis pathways in A549 cells, which was a pattern that correlated with the altered behaviors of the A549 cells. Furthermore, we found that the identified M1/M2 gene signatures were significantly correlated with the extended overall survival of lung cancer patients. These results suggest that M1/M2 gene expression signature may be used as a prognostic indicator for lung cancer patients and M1/M2 polarization may be a target of investigation of immune-modulating therapies for lung cancer in the future.