Epidermal growth factor receptor (EGFR) mutations are the strongest response predictors to EGFR tyrosine kinase inhibitors (TKI) therapy, but knowledge of the EGFR mutation frequency on lung adenocarcinoma is still limited to retrospective studies. The PIONEER study (NCT01185314) is a prospective molecular epidemiology study in Asian patients with newly diagnosed advanced lung adenocarcinoma, aiming to prospectively analyze EGFR mutation status in IIIB/IV treatment-naïve lung adenocarcinomas in Asia. We report the mainland China subset results. Eligible patients (≥20 yrs old, IIIB/IV adenocarcinoma and treatment-naïve) were registered in 17 hospitals in mainland China. EGFR was tested for mutations by amplification refractory mutation system using biopsy samples. Demographic and clinical characteristics were collected for subgroup analyses. A total of 747 patients were registered. Successful EGFR mutation analysis was performed in 741, with an overall mutation rate of 50.2%. The EGFR active mutation rate is 48.0% (with 1.3% of combined active and resistance mutations). Tobacco use (>30 pack-year vs. 0–10 pack-year, OR 0.27, 95%CI: 0.17–0.42) and regional lymph nodes involvement (N3 vs. N0, OR 0.47, 95%CI: 0.29–0.76) were independent predictors of EGFR mutation in multivariate analysis. However, even in regular smokers, the EGFR mutation frequency was 35.3%. The EGFR mutation frequency was similar between diverse biopsy sites and techniques. The overall EGFR mutation frequency of the mainland China subset was 50.2%, independently associated with the intensity of tobacco use and regional lymph nodes involvement. The relatively high frequency of EGFR mutations in the mainland China subset suggest that any effort to obtain tissue sample for EGFR mutation testing should be encouraged.
Cancer is a major and still increasing cause of death in humans. Most cancer cells have a fundamentally different metabolic profile from that of normal tissue. This shift away from mitochondrial ATP synthesis via oxidative phosphorylation towards a high rate of glycolysis, termed Warburg effect, has long been recognized as a paradigmatic hallmark of cancer, supporting the increased biosynthetic demands of tumor cells. Here we show that deletion of apoptosis-inducing factor (AIF) in a KrasG12D-driven mouse lung cancer model resulted in a marked survival advantage, with delayed tumor onset and decreased malignant progression. Mechanistically, Aif deletion leads to oxidative phosphorylation (OXPHOS) deficiency and a switch in cellular metabolism towards glycolysis in non-transformed pneumocytes and at early stages of tumor development. Paradoxically, although Aif-deficient cells exhibited a metabolic Warburg profile, this bioenergetic change resulted in a growth disadvantage of KrasG12D-driven as well as Kras wild-type lung cancer cells. Cell-autonomous re-expression of both wild-type and mutant AIF (displaying an intact mitochondrial, but abrogated apoptotic function) in Aif-knockout KrasG12D mice restored OXPHOS and reduced animal survival to the same level as AIF wild-type mice. In patients with non-small cell lung cancer, high AIF expression was associated with poor prognosis. These data show that AIF-regulated mitochondrial respiration and OXPHOS drive the progression of lung cancer.
Resolving the spatial distribution of the transcriptome at a subcellular level can increase our understanding of biology and diseases. To facilitate studies of biological functions and molecular mechanisms in the transcriptome, we updated RNALocate, a resource for RNA subcellular localization analysis that is freely accessible at http://www.rnalocate.org/ or http://www.rna-society.org/rnalocate/. Compared to RNALocate v1.0, the new features in version 2.0 include (i) expansion of the data sources and the coverage of species; (ii) incorporation and integration of RNA-seq datasets containing information about subcellular localization; (iii) addition and reorganization of RNA information (RNA subcellular localization conditions and descriptive figures for method, RNA homology information, RNA interaction and ncRNA disease information) and (iv) three additional prediction tools: DM3Loc, iLoc-lncRNA and iLoc-mRNA. Overall, RNALocate v2.0 provides a comprehensive RNA subcellular localization resource for researchers to deconvolute the highly complex architecture of the cell.
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