Proteasome Inhibitors Block DNA Repair and Radiosensitize Non-Small Cell Lung Cancer

Despite optimal radiation therapy (RT), chemotherapy and/or surgery, a majority of patients with locally advanced non-small cell lung cancer (NSCLC) fail treatment. To identify novel gene targets for improved tumor control, we performed whole genome RNAi screens to identify knockdowns that most reproducibly increase NSCLC cytotoxicity. These screens identified several proteasome subunits among top hits, including the topmost hit PSMA1, a component of the core 20 S proteasome. Radiation and proteasome inhibition showed synergistic effects. Proteasome inhibition resulted in an 80–90% decrease in homologous recombination (HR), a 50% decrease in expression of NF-κB-inducible HR genes BRCA1 and FANCD2, and a reduction of BRCA1, FANCD2 and RAD51 ionizing radiation-induced foci. IκBα RNAi knockdown rescued NSCLC radioresistance. Irradiation of mice with NCI-H460 xenografts after inducible PSMA1 shRNA knockdown markedly increased murine survival compared to either treatment alone. Proteasome inhibition is a promising strategy for NSCLC radiosensitization via inhibition of NF-κB-mediated expression of Fanconi Anemia/HR DNA repair genes.American Society for Radiation Oncology (Junior Faculty Career Research Training Award)Harvard University. Joint Center for Radiation Therapy (Foundation Grant)Dana-Farber/Harvard Cancer Center (SPORE Developmental Research Project Award in Lung Cancer Research)National Cancer Institute (U.S.) (Award K08CA172354

Metagenes Associated with Survival in Non-Small Cell Lung Cancer

NSCLC (non-small cell lung cancer) comprises about 80% of all lung cancer cases worldwide. Surgery is most effective treatment for patients with early-stage disease. However, 30%–55% of these patients develop recurrence within 5 years. Therefore, markers that can be used to accurately classify early-stage NSCLC patients into different prognostic groups may be helpful in selecting patients who should receive specific therapies

RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization

Background RT-qPCR is a sensitive and increasingly used method for gene expression quantification. To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. There is increasing evidence, however, that the expression of commonly used reference genes can vary significantly in certain contexts. Results Using the Genevestigator database of normalized and well-annotated microarray experiments, we describe the expression stability characteristics of the transciptomes of several organisms. The results show that a) no genes are universally stable, b) most commonly used reference genes yield very high transcript abundances as compared to the entire transcriptome, and c) for each biological context a subset of stable genes exists that has smaller variance than commonly used reference genes or genes that were selected for their stability across all conditions. Conclusion We therefore propose the normalization of RT-qPCR data using reference genes that are specifically chosen for the conditions under study. RefGenes is a community tool developed for that purpose. Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes. RefGenes is available within Genevestigator at http://www.genevestigator.com

Identification of Reference Genes across Physiological States for qRT-PCR through Microarray Meta-Analysis

The accuracy of quantitative real-time PCR (qRT-PCR) is highly dependent on reliable reference gene(s). Some housekeeping genes which are commonly used for normalization are widely recognized as inappropriate in many experimental conditions. This study aimed to identify reference genes for clinical studies through microarray meta-analysis of human clinical samples.After uniform data preprocessing and data quality control, 4,804 Affymetrix HU-133A arrays performed by clinical samples were classified into four physiological states with 13 organ/tissue types. We identified a list of reference genes for each organ/tissue types which exhibited stable expression across physiological states. Furthermore, 102 genes identified as reference gene candidates in multiple organ/tissue types were selected for further analysis. These genes have been frequently identified as housekeeping genes in previous studies, and approximately 71% of them fall into Gene Expression (GO:0010467) category in Gene Ontology.Based on microarray meta-analysis of human clinical sample arrays, we identified sets of reference gene candidates for various organ/tissue types and then examined the functions of these genes. Additionally, we found that many of the reference genes are functionally related to transcription, RNA processing and translation. According to our results, researchers could select single or multiple reference gene(s) for normalization of qRT-PCR in clinical studies