Purpose: We have previously identified solute-linked carrier family A1 member 5 (SLC1A5) as an overexpressed protein in a shotgun proteomic analysis of stage I non-small cell lung cancer (NSCLC) when compared with matched controls. We hypothesized that overexpression of SLC1A5 occurs to meet the metabolic demand for lung cancer cell growth and survival.Experimental Design: To test our hypothesis, we first analyzed the protein expression of SLC1A5 in archival lung cancer tissues by immunohistochemistry and immunoblotting (N ¼ 98) and in cell lines (N ¼ 36). To examine SLC1A5 involvement in amino acid transportation, we conducted kinetic analysis of L-glutamine (Gln) uptake in lung cancer cell lines in the presence and absence of a pharmacologic inhibitor of SLC1A5, gamma-L-Glutamyl-p-Nitroanilide (GPNA). Finally, we examined the effect of Gln deprivation and uptake inhibition on cell growth, cell-cycle progression, and growth signaling pathways of five lung cancer cell lines.Results: Our results show that (i) SLC1A5 protein is expressed in 95% of squamous cell carcinomas (SCC), 74% of adenocarcinomas (ADC), and 50% of neuroendocrine tumors; (ii) SLC1A5 is located at the cytoplasmic membrane and is significantly associated with SCC histology and male gender; (iii) 68% of Gln is transported in a Na þ -dependent manner, 50% of which is attributed to SLC1A5 activity; and (iv) pharmacologic and genetic targeting of SLC1A5 decreased cell growth and viability in lung cancer cells, an effect mediated in part by mTOR signaling.Conclusions: These results suggest that SLC1A5 plays a key role in Gln transport controlling lung cancer cells' metabolism, growth, and survival.

Many tumors increase uptake and dependence on glucose, cystine or glutamine. These basic observations on cancer cell metabolism have opened multiple new diagnostic and therapeutic avenues in cancer research. Recent studies demonstrated that smoking could induce the expression of xCT (SLC7A11) in oral cancer cells, suggesting that overexpression of xCT may support lung tumor progression. We hypothesized that overexpression of xCT occurs in lung cancer cells to satisfy the metabolic requirements for growth and survival. Our results demonstrated that 1) xCT was highly expressed at the cytoplasmic membrane in non-small cell lung cancer (NSCLC), 2) the expression of xCT was correlated with advanced stage and predicted a worse 5-year survival, 3) targeting xCT transport activity in xCT overexpressing NSCLC cells with sulfasalazine decreased cell proliferation and invasion in vitro and in vivo and 4) increased dependence on glutamine was observed in xCT overexpressed normal airway epithelial cells. These results suggested that xCT regulate metabolic requirements during lung cancer progression and be a potential therapeutic target in NSCLC.

Small cell lung cancer (SCLC) is a devastating disease because of its tendency to early invasion and refractory relapse after initial treatment response. These aggressive traits have been associated with phenotypic heterogeneity, which however remains incompletely understood. To fill this knowledge gap, we inferred a set of 33 transcription factors (TFs) associated with gene signatures of the known neuroendocrine/epithelial (NE) and non-neuroendocrine/mesenchymal-like (ML) SCLC phenotypes. The topology of this SCLC TF network was derived from prior knowledge and simulated using Boolean modeling. These simulations predicted that the network settles into attractors (TF expression patterns) correlated with NE or ML phenotypes, suggesting that TF network dynamics underlie emergence of heterogeneous SCLC phenotypes in an epigenetic landscape. However, several cell lines and patient samples did not correlate with either the NE or ML attractors. Flow cytometry indicated that single cells within these cell lines simultaneously express surface markers of both NE and ML differentiation, revealing existence of a “hybrid” phenotype. Upon exposure to standard-of-care cytotoxic drugs or epigenetic modifiers, NE and ML cell populations converged toward the hybrid state, suggesting a possible escape route from treatment. Our findings indicate that SCLC phenotypic heterogeneity can be specified dynamically by attractor states of a master regulatory TF network. Thus, SCLC heterogeneity may be best understood as states within an epigenetic landscape. Understanding phenotypic transitions within this landscape could provide insights to clinical applications.

Aberrant expression of RNA-binding proteins has profound implications for cellular physiology and the pathogenesis of human diseases such as cancer. We previously identified the Fragile X-Related 1 gene (FXR1) as one amplified candidate driver gene at 3q26-29 in lung squamous cell carcinoma (SCC). FXR1 is an autosomal paralog of Fragile X mental retardation 1 and has not been directly linked to human cancers. Here we demonstrate that FXR1 is a key regulator of tumor progression and its overexpression is critical for nonsmall cell lung cancer (NSCLC) cell growth in vitro and in vivo. We identified the mechanisms by which FXR1 executes its regulatory function by forming a novel complex with two other oncogenes, protein kinase C, iota and epithelial cell transforming 2, located in the same amplicon via distinct binding mechanisms. FXR1 expression is a candidate biomarker predictive of poor survival in multiple solid tumors including NSCLCs. Because FXR1 is overexpressed and associated with poor clinical outcomes in multiple cancers, these results have implications for other solid malignancies.

We previously elucidated the pleotropic role of solute carrier family A1 member 5 (SLC1A5) as the primary transporter of glutamine (Gln), a modulator of cell growth and oxidative stress in non-small cell lung cancer (NSCLC). The aim of our study was to evaluate SLC1A5 as a potential new therapeutic target and candidate biomarker predictive of survival and response to therapy. SLC1A5 targeting was examined in a panel of NSCLC and human bronchial cell lines by RNA interference and by a small molecular inhibitor, gamma-L-glutamyl-p-nitroanilide (GPNA). The effects of targeting SLC1A5 on cell growth, Gln uptake, ATP level, autophagy and cell death were examined. Inactivation of SLC1A5 genetically or pharmacologically decreased Gln consumption, inhibited cell growth, induced autophagy and apoptosis in a subgroup of NSCLC cell lines that overexpress SLC1A5. Targeting SLC1A5 function decreased tumor growth in NSCLC xenografts. A multivariate Cox proportional hazards analysis indicates that patients with increased SLC1A5 mRNA expression have significantly shorter overall survival (p =0.01, HR =1.24, 95% CI: 1.05–1.46), adjusted for age, gender, smoking history and disease stage. In an immunohistochemistry study on 207 NSCLC patients, SLC1A5 protein expression remained highly significant prognostic value in both univariate (p < 0.0001, HR =1.45, 95% CI: 1.15–1.50) and multivariate analyses (p =0.04, HR =1.22, 95% CI: 1.01–1.31). These results position SLC1A5 as a new candidate prognostic biomarker for selective targeting of Gln-dependent NSCLC.

Advances in proteomic analysis of human samples are driving critical aspects of biomarker discovery and the identification of molecular pathways involved in disease etiology. Toward that end, in this report we are the first to use a standardized shotgun proteomic analysis method for in-depth tissue protein profiling of the two major subtypes of nonsmall cell lung cancer and normal lung tissues. We identified 3621 proteins from the analysis of pooled human samples of squamous cell carcinoma, adenocarcinoma, and control specimens. In addition to proteins previously shown to be implicated in lung cancer, we have identified new pathways and multiple new differentially expressed proteins of potential interest as therapeutic targets or diagnostic biomarkers, including some that were not identified by transcriptome profiling. Up-regulation of these proteins was confirmed by multiple reaction monitoring mass spectrometry. A subset of these proteins was found to be detectable and differentially present in the peripheral blood of cases and matched controls. Label-free shotgun proteomic analysis allows definition of lung tumor proteomes, identification of biomarker candidates, and potential targets for therapy. Molecular & Cellular Proteomics 11: 10.1074/mcp.M111.015370, 916 -932, 2012.Lung cancer is one of the deadliest cancers, with ϳ200,000 newly diagnosed individuals and 160,000 deaths every year in the United States (1). Despite the most advanced treatments that modern medicine has to offer, the five-year survival rate remains less than 15%. Although a small subset of tumors have been found to be driven by single mutated oncogenes for which active, but still noncurative, therapies are available, the vast majority of patients have complex multifactorial disease with few effective therapeutic options. New early detection strategies and molecular therapeutic targets are urgently needed to improve patient survival.Genomic analysis has enabled us to measure the sequence, copy number, and expression changes of thousands of genes simultaneously, which can be used to discover transcripts specifically altered or expressed in tumor tissues (2-4). Although genomic studies have given important new insights into the mechanisms of carcinogenesis, therapeutic targets, and most practical biomarkers are their protein products, and the correlation between transcript sequence or level and protein function remains complex and poorly understood. Protein expression, in part, depends on transcript levels, but it is clear that significant translational and post-translational regulation of protein levels and function occurs, adding another level of complexity in the regulation of activity, especially in tumor cells (5, 6). It would be ideal to have a comprehensive understanding of the novel changes in protein expression levels and the modifications of proteins in cancer cells, but the technology to directly study proteomes has lagged behind that to assess genomes and transcriptomes. We and others have used matrix-assisted laser desorption and...

BackgroundOncogenic mechanisms in small-cell lung cancer remain poorly understood leaving this tumor with the worst prognosis among all lung cancers. Unlike other cancer types, sequencing genomic approaches have been of limited success in small-cell lung cancer, i.e., no mutated oncogenes with potential driver characteristics have emerged, as it is the case for activating mutations of epidermal growth factor receptor in non-small-cell lung cancer. Differential gene expression analysis has also produced SCLC signatures with limited application, since they are generally not robust across datasets. Nonetheless, additional genomic approaches are warranted, due to the increasing availability of suitable small-cell lung cancer datasets. Gene co-expression network approaches are a recent and promising avenue, since they have been successful in identifying gene modules that drive phenotypic traits in several biological systems, including other cancer types.ResultsWe derived an SCLC-specific classifier from weighted gene co-expression network analysis (WGCNA) of a lung cancer dataset. The classifier, termed SCLC-specific hub network (SSHN), robustly separates SCLC from other lung cancer types across multiple datasets and multiple platforms, including RNA-seq and shotgun proteomics. The classifier was also conserved in SCLC cell lines. SSHN is enriched for co-expressed signaling network hubs strongly associated with the SCLC phenotype. Twenty of these hubs are actionable kinases with oncogenic potential, among which spleen tyrosine kinase (SYK) exhibits one of the highest overall statistical associations to SCLC. In patient tissue microarrays and cell lines, SCLC can be separated into SYK-positive and -negative. SYK siRNA decreases proliferation rate and increases cell death of SYK-positive SCLC cell lines, suggesting a role for SYK as an oncogenic driver in a subset of SCLC.ConclusionsSCLC treatment has thus far been limited to chemotherapy and radiation. Our WGCNA analysis identifies SYK both as a candidate biomarker to stratify SCLC patients and as a potential therapeutic target. In summary, WGCNA represents an alternative strategy to large scale sequencing for the identification of potential oncogenic drivers, based on a systems view of signaling networks. This strategy is especially useful in cancer types where no actionable mutations have emerged.

Purpose Chromosome 3q26-29 is a critical region of genomic amplification in lung squamous cell carcinomas (SCCs). Identification of candidate drivers in this region could help uncover new mechanisms in the pathogenesis and potentially new targets in SCC of the lung. Experimental Design We performed a meta-analysis of seven independent data sets containing a total of 593 human primary SCC tumor samples to identify consensus candidate drivers in 3q26-29 amplicon. Through integrating protein-protein interaction network information, we further filtered for candidates that may function together in a network. Computationally predicted candidates were validated using RNAi knock down and cell viability assays. Clinical relevance of the experimentally supported drivers was evaluated in an independent cohort of 52 lung SCC tumors using survival analysis. Results The meta-analysis identified 20 consensus candidates, among which four (SENP2, DCUN1D1, DVL3 and UBXN7) were involved in a small protein-protein interaction network. Knocking down any of the four proteins led to cell growth inhibition of the 3q26-29 amplified SCC. Moreover, knocking down of SENP2 resulted in the most significant cell growth inhibition and downregulation of DCUN1D1 and DVL3. Importantly, a gene expression signature composed of SENP2, DCUN1D1 and DVL3 stratified patients into subgroups with different response to adjuvant chemotherapy. Conclusion Together, our findings show that SENP2, DCUN1D1 and DVL3 are candidate driver genes in the 3q26-29 amplicon of SCC, providing novel insights into the molecular mechanisms of disease progression and may have significant implication in the management of SCC of the lung.