Cancer - Lung & Other Cancers:

A Five-Gene Signature and Clinical Outcome in Non-Small-Cell Lung Cancer.

Chen, H-Y et al., 2007. N. Engl. J. Med. 356(1):11-20.

Background 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. Methods 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. Results 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. Conclusions Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.

Journal Link  |  PMID

Comments:

This paper illustrates how 'ribotyping' (mRNA profiling) can be designed to not only diagnose but also to stage NSCL cancer relative to the design of an effective therapeutic regimen.  Although quite sophisticated in its experimental goal, the procedure described is well illustrated and documented for the moderately sophisticated student or physician.  In addition, it contains a glossary of critical terms.

Predicting lung cancer by detecting aberrant promoter methylation in sputum.

Palmisano, WA et al., 2000.  Cancer Res. 60(21):5954-5958.

Abstract

Despite the promise of using DNA markers for the early detection of cancer, none has proven universally applicable to the most common and lethal forms of human malignancy. Lung carcinoma, the leading cause of tumor-related death, is a key example of a cancer for which mortality could be greatly reduced through the development of sensitive molecular markers detectable at the earliest stages of disease. By increasing the sensitivity of a PCR approach to detect methylated DNA sequences, we now demonstrate that aberrant methylation of the p16 and/or O6-methylguanine-DNA methyltransferase promoters can be detected in DNA from sputum in 100% of patients with squamous cell lung carcinoma up to 3 years before clinical diagnosis. Moreover, the prevalence of these markers in sputum from cancer-free, high-risk subjects approximates lifetime risk for lung cancer. The use of aberrant gene methylation as a molecular marker system seems to offer a potentially powerful approach to population-based screening for the detection of lung cancer, and possibly the other common forms of human cancer.

Journal Link  |  PMID

SIEGE: Smoking Induced Epithelial Gene Expression Database.

Shah, V et al., 2005. Nucleic Acids Res. 33:D573-D579

Abstract

The SIEGE (Smoking Induced Epithelial Gene Expression) database is a clinical resource for compiling and analyzing gene expression data from epithelial cells of the human intra-thoracic airway. This database supports a translational research study whose goal is to profile the changes in airway gene expression that are induced by cigarette smoke. RNA is isolated from airway epithelium obtained at bronchoscopy from current-, former- and never-smoker subjects, and hybridized to Affymetrix HG-U133A Genechips, which measure the level of expression of approximately 22,500 human transcripts. The microarray data generated along with relevant patient information is uploaded to SIEGE by study administrators using the database's web interface, found at http://pulm.bumc.bu.edu/siegeDB. PERL-coded scripts integrated with SIEGE perform various quality control functions including the processing, filtering and formatting of stored data. The R statistical package is used to import database expression values and execute a number of statistical analyses including t-tests, correlation coefficients and hierarchical clustering. Values from all statistical analyses can be queried through CGI-based tools and web forms found on the 'Search' section of the database website. Query results are embedded with graphical capabilities as well as with links to other databases containing valuable gene resources, including Entrez Gene, GO, Biocarta, GeneCards, dbSNP and the NCBI Map Viewer.

Journal Link | PMID

Comments

The SIEGE database is described as both an experimental tool to study the relationship between smoking lung cancer, and as a reference for clinically understanding the disease process of this cancer.  This article describes how multiple Internet resources can be combined for these purposes.

Gene expression profiling of human lung tissue from smokers with severe emphysema

Spira, A et al., 2004. Am. J. Respir. Cell Mol. Biol. 31(6):601-610

Abstract

The mechanism by which inhaled smoke causes the anatomic lesions and physiologic impairment of chronic obstructive pulmonary disease remains unknown. We used high-density microarrays to measure gene expression in severely emphysematous lung tissue removed from smokers at lung volume reduction surgery (LVRS) and normal or mildly emphysematous lung tissue from smokers undergoing resection of pulmonary nodules. Class prediction algorithms identified 102 genes that accurately distinguished severe emphysema from non-/mildly emphysematous lung tissue. We also defined a number of genes whose expression levels correlated strongly with lung diffusion capacity for carbon monoxide and/or forced expiratory volume at 1 s. Genes related to oxidative stress, extracellular matrix synthesis, and inflammation were increased in severe emphysema, whereas expression of endothelium-related genes was decreased. To identify candidate genes that might be causally involved in the pathogenesis of emphysema, we linked gene expression profiles to chromosomal regions previously associated with chronic obstructive pulmonary disease in genome-wide linkage analyses. Unsupervised hierarchical clustering of the LVRS samples revealed distinct molecular subclasses of severe emphysema, with body mass index as the only clinical variable that differed between the groups. Class prediction models established a set of genes that predicted functional outcome at 6 mo after LVRS. Our findings suggest that the gene expression profiles from human emphysematous lung tissue may provide insight into pathogenesis, uncover novel molecular subclasses of disease, predict response to LVRS, and identify targets for therapeutic intervention.

Journal Link | PMID

Comments

An illustrative example of how transcriptomics can be used to understand the normal and abnormal lung physiology.  In addition, its diagnostic value is shown for defining the progression of emphysema and its disease pathogenesis.

Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer

Spira, A et al., 2007. Nat. Med. 13(3):361-366

Abstract

Lung cancer is the leading cause of death from cancer in the US and the world. The high mortality rate (80-85% within 5 years) results, in part, from a lack of effective tools to diagnose the disease at an early stage. Given that cigarette smoke creates a field of injury throughout the airway, we sought to determine if gene expression in histologically normal large-airway epithelial cells obtained at bronchoscopy from smokers with suspicion of lung cancer could be used as a lung cancer biomarker. Using a training set (n = 77) and gene-expression profiles from Affymetrix HG-U133A microarrays, we identified an 80-gene biomarker that distinguishes smokers with and without lung cancer. We tested the biomarker on an independent test set (n = 52), with an accuracy of 83% (80% sensitive, 84% specific), and on an additional validation set independently obtained from five medical centers (n = 35). Our biomarker had approximately 90% sensitivity for stage 1 cancer across all subjects. Combining cytopathology of lower airway cells obtained at bronchoscopy with the biomarker yielded 95% sensitivity and a 95% negative predictive value. These findings indicate that gene expression in cytologically normal large-airway epithelial cells can serve as a lung cancer biomarker, potentially owing to a cancer-specific airway-wide response to cigarette smoke.

Journal Link | PMID

Comment

A good example of how standard diagnostic pathology can be combined with the new transcriptomic approaches in the diagnosis of lung cancer and its disease progression.  It clearly illustrates how smoking negatively affects the molecular parameters of 'normal' looking cells and can be used for earlier diagnostic tests.