Dataset Information

ATM protein is deficient in over 40% of lung adenocarcinomas.

ABSTRACT: Lung cancer is the leading cause of cancer-related mortality in the USA and worldwide, and of the estimated 1.2 million new cases of lung cancer diagnosed every year, over 30% are lung adenocarcinomas. The backbone of 1st-line systemic therapy in the metastatic setting, in the absence of an actionable oncogenic driver, is platinum-based chemotherapy. ATM and ATR are DNA damage signaling kinases activated at DNA double-strand breaks (DSBs) and stalled and collapsed replication forks, respectively. ATM protein is lost in a number of cancer cell lines and ATR kinase inhibitors synergize with cisplatin to resolve xenograft models of ATM-deficient lung cancer. We therefore sought to determine the frequency of ATM loss in a tissue microarray (TMA) of lung adenocarcinoma. Here we report the validation of a commercial antibody (ab32420) for the identification of ATM by immunohistochemistry and estimate that 61 of 147 (41%, 95% CI 34%-50%) cases of lung adenocarcinoma are negative for ATM protein expression. As a positive control for ATM staining, nuclear ATM protein was identified in stroma and immune infiltrate in all evaluable cases. ATM loss in lung adenocarcinoma was not associated with overall survival. However, our preclinical findings in ATM-deficient cell lines suggest that ATM could be a predictive biomarker for synergy of an ATR kinase inhibitor with standard-of-care cisplatin. This could improve clinical outcome in 100,000's of patients with ATM-deficient lung adenocarcinoma every year.

SUBMITTER: Villaruz LC

PROVIDER: S-EPMC5295384 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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ATM protein is deficient in over 40% of lung adenocarcinomas.

Villaruz Liza C LC Jones Helen H Dacic Sanja S Abberbock Shira S Kurland Brenda F BF Stabile Laura P LP Siegfried Jill M JM Conrads Thomas P TP Smith Neil R NR O'Connor Mark J MJ Pierce Andrew J AJ Bakkenist Christopher J CJ

Oncotarget 20160901 36

Lung cancer is the leading cause of cancer-related mortality in the USA and worldwide, and of the estimated 1.2 million new cases of lung cancer diagnosed every year, over 30% are lung adenocarcinomas. The backbone of 1st-line systemic therapy in the metastatic setting, in the absence of an actionable oncogenic driver, is platinum-based chemotherapy. ATM and ATR are DNA damage signaling kinases activated at DNA double-strand breaks (DSBs) and stalled and collapsed replication forks, respectively ...[more]

PMID: 27259260

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Project description:Studies that only assess differentially-expressed (DE) genes do not contain the information required to investigate the mechanisms of diseases. A complete knowledge of all the direct and indirect interactions between proteins may act as a significant benchmark in the process of forming a comprehensive description of cellular mechanisms and functions. The results of protein interaction network studies are often inconsistent and are based on various methods. In the present study, a combined network was constructed using selected gene pairs, following the conversion and combination of the scores of gene pairs that were obtained across multiple approaches by a novel algorithm. Samples from patients with and without lung adenocarcinoma were compared, and the RankProd package was used to identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search tool for the retrieval of interacting genes/proteins database (STRING), the weighted gene coexpression network analysis (WGCNA) package and the differentially-coexpressed genes and links package (DCGL) were used for network construction. A combined network was also constructed with a novel rank-based algorithm using a combined score. The topological features of the 5 networks were analyzed and compared. A total of 941 DE genes were screened. The topological analysis indicated that the gene interaction network constructed using the WGCNA method was more likely to produce a small-world property, which has a small average shortest path length and a large clustering coefficient, whereas the combined network was confirmed to be a scale-free network. Gene pairs that were identified using the novel combined method were mostly enriched in the cell cycle and p53 signaling pathway. The present study provided a novel perspective to the network-based analysis. Each method has advantages and disadvantages. Compared with single methods, the combined algorithm used in the present study may provide a novel method to analyze gene interactions, with increased credibility.

Project description:Background Lung cancer is one of the most common cancers and the primary cause of cancer-related deaths in the world. The 5-year survival of lung cancer patients is lower than 15%. As a common subtype of lung cancer, lung adenocarcinoma still has a high morbidity and mortality, although many strategies have been made, such as surgical operation, chemotherapy, targeted therapy. The use of gene expression microarray has provided a feasible and effective approach for the study on lung cancer. However, the biomarkers and potential therapeutic targets of lung adenocarcinomas are still not completely identified. Our study is aimed to find biomarkers and therapeutic targets of lung adenocarcinomas by identifying the key protein-coding gene in lung adenocarcinomas by bioinformatical approaches. Methods We selected and obtained messenger RNA microarray datasets from Gene Expression Omnibus database to identify differentially expressed genes between lung adenocarcinomas and normal lung tissue. The differentially expressed genes were clarified by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the protein-protein interaction (PPI) network and statistical analyses. Subsequently, quantitative real-time PCR was used to verify the results of bioinformatic analysis. Results We obtained 1,264, 896 and 408 differentially expressed genes from GSE32863, GSE43458 and GSE63459, respectively. The 242 common differentially expressed genes in three datasets were related to cell adhesion molecules, ECM-receptor interaction, leukocyte transendothelial migration according to KEGG analysis. GO analysis showed that these common differentially expressed genes were enriched in tumor-related functions. ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T and KIAA0101 have the strongest protein-protein interaction relationships based on protein-protein interaction networks. Survival analysis showed that these nine genes were closely related to the survival of lung adenocarcinomas. The further qRT-PCR assays indicated that seven key genes (ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T) display differential profile between clinical lung adenocarcinoma specimens and their matched normal tissues. Conclusions ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T may be key protein coding genes in lung adenocarcinoma, and deserve further study to verify their feasibility and effectiveness as biomarkers and therapeutic targets for lung adenocarcinomas.

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ATM protein is deficient in over 40% of lung adenocarcinomas.

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ATM protein is deficient in over 40% of lung adenocarcinomas.

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