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1.  A prediction model for lung cancer diagnosis that integrates genomic and clinical features 
Lung cancer is the leading cause of cancer death, in part due to lack of early diagnostic tools. Bronchoscopy represents a relatively noninvasive initial diagnostic test in smokers with suspect disease, but has low sensitivity. We have reported a gene expression profile in cytologically normal large airway epithelium obtained via bronchoscopic brushings that is a sensitive and specific biomarker for lung cancer. Here, we evaluate the independence of the biomarker from other clinical risk factors and determine the performance of a clinicogenomic model that combines clinical factors and gene expression.
Training (n = 76) and test sets (n = 62) consisted of smokers undergoing bronchoscopy for suspicion of lung cancer at five medical centers. Logistic regression models describing the likelihood of having lung cancer using the biomarker, clinical factors, and these data combined were tested using the independent set of patients with non-diagnostic bronchoscopies. The model predictions were also compared with physicians’ clinical assessment.
The gene expression biomarker is associated with cancer status in the combined clinicogenomic model (p < 0.005). There is a significant difference in performance of the clinicogenomic relative to the clinical model (p < 0.05). In the test set, the clinicogenomic model increases sensitivity and NPV to 100%, and results in higher specificity (91%) and PPV (81%) compared to other models. The clinicogenomic model has high accuracy where physician assessment is most uncertain.
The airway gene expression biomarker provides information about the likelihood of lung cancer not captured by clinical factors, and the clinicogenomic model has the highest prediction accuracy. These findings suggest that use of the clinicogenomic model may expedite more invasive testing and definitive therapy for smokers with lung cancer and reduce invasive diagnostic procedures for individuals without lung cancer.
PMCID: PMC4167688  PMID: 19138936
2.  SIRT1 pathway dysregulation in the smoke-exposed airway epithelium and lung tumor tissue 
Cancer research  2012;72(22):5702-5711.
Cigarette smoke produces a molecular “field of injury” in epithelial cells lining the respiratory tract. However, the specific signaling pathways that are altered in the airway of smokers and the signaling processes responsible for the transition from smoking-induced airway damage to lung cancer remain unknown. In this study, we use a genomic approach to study the signaling processes associated with tobacco smoke exposure and lung cancer. First, we developed and validated pathway-specific gene expression signatures in bronchial airway epithelium that reflect activation of signaling pathways relevant to tobacco-exposure including ATM, BCL2, GPX1, NOS2, IKBKB, and SIRT1. Using these profiles and four independent gene expression datasets, we found that SIRT1 activity is significantly up-regulated in cytologically normal bronchial airway epithelial cells from active smokers compared to non-smokers. In contrast, this activity is strikingly down-regulated in non-small cell lung cancer. This pattern of signaling modulation was unique to SIRT1, and down-regulation of SIRT1 activity is confined to tumors from smokers. Decreased activity of SIRT1 was validated using genomic analyses of mouse models of lung cancer and biochemical testing of SIRT1 activity in patient lung tumors. Together, our findings indicate a role of SIRT1 in response to smoke and a potential role in repressing lung cancer. Further, our findings suggest that the airway gene-expression signatures derived in this study can provide novel insights into signaling pathways altered in the “field of inury” induced by tobacco smoke and thus may impact strategies for prevention of tobacco-related lung cancer.
PMCID: PMC4053174  PMID: 22986747
3.  Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq 
Cigarette smoke creates a molecular field of injury in epithelial cells that line the respiratory tract. We hypothesized that transcriptome sequencing (RNA-Seq) will enhance our understanding of the field of molecular injury in response to tobacco smoke exposure and lung cancer pathogenesis by identifying gene expression differences not interrogated or accurately measured by microarrays. We sequenced the high-molecular-weight fraction of total RNA (>200 nt) from pooled bronchial airway epithelial cell brushings (n = 3 patients per pool) obtained during bronchoscopy from healthy never smoker (NS) and current smoker (S) volunteers and smokers with (C) and without (NC) lung cancer undergoing lung nodule resection surgery. RNA-Seq libraries were prepared using 2 distinct approaches, one capable of capturing non-polyadenylated RNA (the prototype NuGEN Ovation RNA-Seq protocol) and the other designed to measure only polyadenylated RNA (the standard Illumina mRNA-Seq protocol) followed by sequencing generating approximately 29 million 36 nt reads per pool and approximately 22 million 75 nt paired-end reads per pool, respectively. The NuGEN protocol captured additional transcripts not detected by the Illumina protocol at the expense of reduced coverage of polyadenylated transcripts, while longer read lengths and a paired-end sequencing strategy significantly improved the number of reads that could be aligned to the genome. The aligned reads derived from the two complementary protocols were used to define the compendium of genes expressed in the airway epithelium (n = 20,573 genes). Pathways related to the metabolism of xenobiotics by cytochrome P450, retinol metabolism, and oxidoreductase activity were enriched among genes differentially expressed in smokers, whereas chemokine signaling pathways, cytokine–cytokine receptor interactions, and cell adhesion molecules were enriched among genes differentially expressed in smokers with lung cancer. There was a significant correlation between the RNA-Seq gene expression data and Affymetrix microarray data generated from the same samples (P < 0.001); however, the RNA-Seq data detected additional smoking- and cancer-related transcripts whose expression was were either not interrogated by or was not found to be significantly altered when using microarrays, including smoking-related changes in the inflammatory genes S100A8 and S100A9 and cancer-related changes in MUC5AC and secretoglobin (SCGB3A1). Quantitative real-time PCR confirmed differential expression of select genes and non-coding RNAs within individual samples. These results demonstrate that transcriptome sequencing has the potential to provide new insights into the biology of the airway field of injury associated with smoking and lung cancer. The measurement of both coding and non-coding transcripts by RNA-Seq has the potential to help elucidate mechanisms of response to tobacco smoke and to identify additional biomarkers of lung cancer risk and novel targets for chemoprevention.
PMCID: PMC3694393  PMID: 21636547
7.  Clinical impact of high-throughput gene expression studies in lung cancer 
Lung cancer is the leading cause of cancer death in the US and the world. The high mortality rate results, in part, from the lack of effective tools for early detection and the inability to identify subsets of patients who would benefit from adjuvant chemotherapy or targeted therapies. The development of high-throughput genome-wide technologies for measuring gene expression, such as microarrays, have the potential to impact the mortality rate of lung cancer patients by improving diagnosis, prognosis, and treatment. This review will highlight recent studies using high-throughput gene expression technologies that have led to clinically relevant insights into lung cancer. The hope is that diagnostic and prognostic biomarkers that have been developed as part of this work will soon be ready for wide-spread clinical application and will have a dramatic impact on the evaluation of patients with suspect lung cancer, leading to effective personalized treatment regimens.
PMCID: PMC2731413  PMID: 19096318
8.  RNAi Targeting of West Nile Virus in Mosquito Midguts Promotes Virus Diversification 
PLoS Pathogens  2009;5(7):e1000502.
West Nile virus (WNV) exists in nature as a genetically diverse population of competing genomes. This high genetic diversity and concomitant adaptive plasticity has facilitated the rapid adaptation of WNV to North American transmission cycles and contributed to its explosive spread throughout the New World. WNV is maintained in nature in a transmission cycle between mosquitoes and birds, with intrahost genetic diversity highest in mosquitoes. The mechanistic basis for this increase in genetic diversity in mosquitoes is poorly understood. To determine whether the high mutational diversity of WNV in mosquitoes is driven by RNA interference (RNAi), we characterized the RNAi response to WNV in the midguts of orally exposed Culex pipiens quinquefasciatus using high-throughput, massively parallel sequencing and estimated viral genetic diversity. Our data demonstrate that WNV infection in orally exposed vector mosquitoes induces the RNAi pathway and that regions of the WNV genome that are more intensely targeted by RNAi are more likely to contain point mutations compared to weakly targeted regions. These results suggest that, under natural conditions, positive selection of WNV within mosquitoes is stronger in regions highly targeted by the host RNAi response. Further, they provide a mechanistic basis for the relative importance of mosquitoes in driving WNV diversification.
Author Summary
West Nile virus (WNV) was introduced into New York state in 1999 and has since spread across the Americas. It is transmitted in nature between adult female mosquitoes and birds and occasionally infects humans and horses. Within the host, WNV exists as a diverse assortment of closely related mutants. WNV populations within mosquitoes are more complex genetically than are those within birds. The reasons for this discrepancy are unknown, but may be related to the host's innate antivirus response. We demonstrate that WNV is targeted by RNA interference, a highly sequence-specific pathway in the mosquito. Further, we present data that correlates the intensity of this targeting with virus mutation under natural conditions. These results provide a mechanistic explanation for the increasead complexity of WNV populations in mosquitoes: the RNAi response creates an intracellular environment where rare genotypes are favored. In addition, our results suggest that genetically diverse WNV populations may have an advantage over less diverse populations because they present a more complex target for the RNAi response. Finally, these data suggest that WNV, and possibly other viruses with high mutation rates, may escape an engineered antivirus intervention that is highly sequence-specific.
PMCID: PMC2698148  PMID: 19578437
9.  Characterization of the mid-foregut transcriptome identifies genes regulated during lung bud induction 
Gene expression patterns : GEP  2007;8(2):124-139.
To identify genes expressed during initiation of lung organogenesis, we generated transcriptional profiles of the prospective lung region of the mouse foregut (mid-foregut) microdissected from embryos at three developmental stages between embryonic day 8.5 (E8.5) and E9.5. This period spans from lung specification of foregut cells to the emergence of the primary lung buds. We identified a number of known and novel genes that are temporally regulated as the lung bud forms. Genes that regulate transcription, including DNA binding factors, co-factors, and chromatin remodeling genes, are the main functional groups that change during lung bud formation. Members of key developmental transcription and growth factor families, not previously described to participate in lung organogenesis, are expressed in the mid-foregut during lung bud induction. These studies also show early expression in the mid-foregut of genes that participate in later stages of lung development. This characterization of the mid-foregut transcriptome provides new insights into molecular events leading to lung organogenesis.
PMCID: PMC2440337  PMID: 18023262
Lung; development; organogenesis; foregut; endoderm; embryo; mouse; microarray; RNA amplification; gene expression; real time PCR; laser capture microdissection; transcription factors; chromatin remodeling; Fox; Notch
10.  Reversible and permanent effects of tobacco smoke exposure on airway epithelial gene expression 
Genome Biology  2007;8(9):R201.
Oligonucleotide microarray analysis revealed 175 genes that are differentially expressed in large airway epithelial cells of people who currently smoke compared with those who never smoked, with 28 classified as irreversible, 6 as slowly reversible, and 139 as rapidly reversible.
Tobacco use remains the leading preventable cause of death in the US. The risk of dying from smoking-related diseases remains elevated for former smokers years after quitting. The identification of irreversible effects of tobacco smoke on airway gene expression may provide insights into the causes of this elevated risk.
Using oligonucleotide microarrays, we measured gene expression in large airway epithelial cells obtained via bronchoscopy from never, current, and former smokers (n = 104). Linear models identified 175 genes differentially expressed between current and never smokers, and classified these as irreversible (n = 28), slowly reversible (n = 6), or rapidly reversible (n = 139) based on their expression in former smokers. A greater percentage of irreversible and slowly reversible genes were down-regulated by smoking, suggesting possible mechanisms for persistent changes, such as allelic loss at 16q13. Similarities with airway epithelium gene expression changes caused by other environmental exposures suggest that common mechanisms are involved in the response to tobacco smoke. Finally, using irreversible genes, we built a biomarker of ever exposure to tobacco smoke capable of classifying an independent set of former and current smokers with 81% and 100% accuracy, respectively.
We have categorized smoking-related changes in airway gene expression by their degree of reversibility upon smoking cessation. Our findings provide insights into the mechanisms leading to reversible and persistent effects of tobacco smoke that may explain former smokers increased risk for developing tobacco-induced lung disease and provide novel targets for chemoprophylaxis. Airway gene expression may also serve as a sensitive biomarker to identify individuals with past exposure to tobacco smoke.
PMCID: PMC2375039  PMID: 17894889

Results 1-10 (10)