Large fractions of the human population do not express GSTM1 and GSTT1 (GSTM1/T1) enzymes because of deletions in these genes. These variations affect xenobiotic metabolism and have been evaluated in relation to lung cancer risk, mostly based on null/present gene models. We measured GSTM1/T1 heterozygous deletions, not tested in genome-wide association studies, in 2120 controls and 2100 cases from the Environment And Genetics in Lung cancer Etiology (EAGLE) study. We evaluated their effect on mRNA expression on lung tissue and peripheral blood samples and their association with lung cancer risk overall and by histology types. We tested the null/present, dominant and additive models using logistic regression. Cigarette smoking and gender were studied as possible modifiers. Gene expression from blood and lung tissue cells was strongly down-regulated in subjects carrying GSTM1/T1 deletions by both trend and dominant models (p<0.001). In contrast to the null/present model, analyses distinguishing subjects with 0, 1 or 2 GSTM1/T1 deletions revealed several associations. There was a decreased lung cancer risk in never-smokers (OR=0.44;95%CI=0.23–0.82; p=0.01) and women (OR=0.50;95%CI=0.28–0.90; p=0.02) carrying 1 or 2 GSTM1 deletions. Analogously, male smokers had an increased risk (OR=1.13;95%CI=1.0–1.28; p=0.05) and women a decreased risk (OR=0.78;95%CI=0.63–0.97; p=0.02) for increasing GSTT1 deletions. The corresponding gene-smoking and gene-gender interactions were significant (p<0.05). Our results suggest that decreased activity of GSTM1/T1 enzymes elevates lung cancer risk in male smokers, likely due to impaired carcinogens’ detoxification. A protective effect of the same mutations may be operative in never-smokers and women, possibly because of reduced activity of other genotoxic chemicals.
GST; copy numbers; gene expression; lung cancer; smoking and gender differences
The authors investigated the relation between alcohol consumption and lung cancer risk in the Environment and Genetics in Lung Cancer Etiology (EAGLE) Study, a population-based case-control study. Between 2002 and 2005, 2,100 patients with primary lung cancer were recruited from 13 hospitals within the Lombardy region of Italy and were frequency-matched on sex, area of residence, and age to 2,120 randomly selected controls. Alcohol consumption during adulthood was assessed in 1,855 cases and 2,065 controls. Data on lifetime tobacco smoking, diet, education, and anthropometric measures were collected. Adjusted odds ratios and 95% confidence intervals for categories of mean daily ethanol intake were calculated using unconditional logistic regression. Overall, both nondrinkers (odds ratio = 1.42, 95% confidence interval: 1.03, 2.01) and very heavy drinkers (≥60 g/day; odds ratio = 1.44, 95% confidence interval: 1.01, 2.07) were at significantly greater risk than very light drinkers (0.1–4.9 g/day). The alcohol effect was modified by smoking behavior, with no excess risk being observed in never smokers. In summary, heavy alcohol consumption was a risk factor for lung cancer among smokers in this study. Although residual confounding by tobacco smoking cannot be ruled out, this finding may reflect interplay between alcohol and smoking, emphasizing the need for preventive measures.
alcohol drinking; case-control studies; ethanol; lung neoplasms; risk factors; smoking
Background Exposure to occupational carcinogens is an important preventable cause of lung cancer. Most of the previous studies were in highly exposed industrial cohorts. Our aim was to quantify lung cancer burden attributable to occupational carcinogens in a general population.
Methods We applied a new job–exposure matrix (JEM) to translate lifetime work histories, collected by personal interview and coded into standard job titles, into never, low and high exposure levels for six known/suspected occupational lung carcinogens in the Environment and Genetics in Lung cancer Etiology (EAGLE) population-based case–control study, conducted in Lombardy region, Italy, in 2002–05. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated in men (1537 cases and 1617 controls), by logistic regression adjusted for potential confounders, including smoking and co-exposure to JEM carcinogens. The population attributable fraction (PAF) was estimated as impact measure.
Results Men showed an increased lung cancer risk even at low exposure to asbestos (OR: 1.76; 95% CI: 1.42–2.18), crystalline silica (OR: 1.31; 95% CI: 1.00–1.71) and nickel–chromium (OR: 1.18; 95% CI: 0.90–1.53); risk increased with exposure level. For polycyclic aromatic hydrocarbons, an increased risk (OR: 1.64; 95% CI: 0.99–2.70) was found only for high exposures. The PAFs for any exposure to asbestos, silica and nickel–chromium were 18.1, 5.7 and 7.0%, respectively, equivalent to an overall PAF of 22.5% (95% CI: 14.1–30.0). This corresponds to about 1016 (95% CI: 637–1355) male lung cancer cases/year in Lombardy.
Conclusions These findings support the substantial role of selected occupational carcinogens on lung cancer burden, even at low exposures, in a general population.
lung neoplasms; case–control study; carcinogens; occupational health
Germlines are the source of DNA in all cells. A mutation at the germline level is the first step to developing cancer, and the vast majority of cancer is genetic. Melanoma, the leading cause of skin cancer death, is known to be highly heritable and rare. Using a family model, high risk variants related to melanoma can be identified. The goal of the study is to integrate information from sequencing, epigenetics, and expression to identify functional and regulatory genes that are associated with melanoma. Families with two or more 1st degree relatives with melanoma were considered at high risk and were investigated in this study. Initially, sequencing data of families with 3 or more relatives with the disease were examined and shared DNA variants were selected for further examination. Genetic databases and annotation tools were used to identify genes based on their known gene function and regulation, pathways, and variant conservation. Gene browsers were also used to identify any histone markers, DNA methylation sites, and other epigenetic indicators. Based on our candidate genes, there is a possibility of genetic heterogeneity, in which multiple genes may be responsible for disease susceptibility. Selected candidate genes will undergo fine mapping to further investigate the region and replication in additional families and population studies of melanoma.
The detection of tumor suppressor gene promoter methylation in sputum-derived exfoliated cells predicts early lung cancer. Here we identified genetic determinants for this epigenetic process and examined their biological effects on gene regulation. A two-stage approach involving discovery and replication was employed to assess the association between promoter hypermethylation of a 12-gene panel and common variation in 40 genes involved in carcinogen metabolism, regulation of methylation, and DNA damage response in members of the Lovelace Smokers Cohort (n=1434). Molecular validation of three identified variants was conducted using primary bronchial epithelial cells. Association of study-wide significance (P<8.2×10−5) was identified for rs1641511, rs3730859, and rs1883264 in TP53, LIG1, and BIK, respectively. These SNPs were significantly associated with altered expression of the corresponding genes in primary bronchial epithelial cells. In addition, rs3730859 in LIG1 was also moderately associated with increased risk for lung cancer among Caucasian smokers. Together, our findings suggest that genetic variation in DNA replication and apoptosis pathways impacts the propensity for gene promoter hypermethylation in the aerodigestive tract of smokers. The incorporation of genetic biomarkers for gene promoter hypermethylation with clinical and somatic markers may improve risk assessment models for lung cancer.
DNA damage response; promoter hypermethylation; single nucleotide polymorphism; sputum; smoker
Tobacco-induced lung cancer is characterized by a deregulated inflammatory microenvironment. Variants in multiple genes in inflammation pathways may contribute to risk of lung cancer.
We therefore conducted a three-stage comprehensive pathway analysis (discovery, replication and meta-analysis) of inflammation gene variants in ever smoking lung cancer cases and controls. A discovery set (1096 cases; 727 controls) and an independent and non-overlapping internal replication set (1154 cases; 1137 controls) were derived from an ongoing case-control study. For discovery, we used an iSelect BeadChip to interrogate a comprehensive panel of 11737 inflammation pathway SNPs and selected nominally significant (p<0.05) SNPs for internal replication.
There were 6 SNPs that achieved statistical significance (p<0.05) in the internal replication dataset with concordant risk estimates for former smokers and 5 concordant and replicated SNPs in current smokers. Replicated hits were further tested in a subsequent meta-analysis using external data derived from two published GWAS and a case-control study. Two of these variants (a BCL2L14 SNP in former smokers and a SNP in IL2RB in current smokers) were further validated. In risk score analyses, there was a 26% increase in risk with each additional adverse allele when we combined the genotyped SNP and the most significant imputed SNP in IL2RB in current smokers and a 36% similar increase in risk for former smokers associated with genotyped and imputed BCL2L14 SNPs.
Before they can be applied for risk prediction efforts, these SNPs should be subject to further external replication and more extensive fine mapping studies.
Inflammation SNPS; lung cancer; smokers
We present a Bayesian variable selection method for the setting in which the number of independent variables or predictors in a particular dataset is much larger than the available sample size. While most existing methods allow some degree of correlations among predictors but do not consider these correlations for variable selection, our method accounts for correlations among the predictors in variable selection. Our correlation-based stochastic search (CBS) method, the hybrid-CBS algorithm, extends a popular search algorithm for high-dimensional data, the stochastic search variable selection (SSVS) method. Similar to SSVS, we search the space of all possible models using variable addition, deletion or swap moves. However, our moves through the model space are designed to accommodate correlations among the variables. We describe our approach for continuous, binary, ordinal, and count outcome data. The impact of choices of prior distributions and hyper-parameters is assessed in simulation studies. We also examined performance of variable selection and prediction as the correlation structure of the predictors varies. We found that the hybrid-CBS resulted in lower prediction errors and better identified the true outcome associated predictors than SSVS when predictors were moderately to highly correlated. We illustrate the method on data from a proteomic profiling study of melanoma, a skin cancer.
Correlated predictors; correlation-based search; proteomic data
Affordable early screening in subjects with high risk of lung cancer has great potential to improve survival from this deadly disease. We measured gene expression from lung tissue and peripheral whole blood (PWB) from adenocarcinoma cases and controls to identify dysregulated lung cancer genes that could be tested in blood to improve identification of at-risk patients in the future. Genome-wide mRNA expression analysis was conducted in 153 subjects (73 adenocarcinoma cases, 80 controls) from the Environment And Genetics in Lung cancer Etiology (EAGLE) study using PWB and paired snap-frozen tumor and non-involved lung tissue samples. Analyses were conducted using unpaired t-tests, linear mixed effects and ANOVA models. The area under the receiver operating characteristic curve (AUC) was computed to assess the predictive accuracy of the identified biomarkers. We identified 50 dysregulated genes in stage I adenocarcinoma versus control PWB samples (False Discovery Rate ≤0.1, fold change ≥1.5 or ≤0.66). Among them, eight (TGFBR3, RUNX3, TRGC2, TRGV9, TARP, ACP1, VCAN, and TSTA3) differentiated paired tumor versus non-involved lung tissue samples in stage I cases, suggesting a similar pattern of lung cancer-related changes in PWB and lung tissue. These results were confirmed in two independent gene expression analyses in a blood-based case-control study (n=212) and a tumor-non tumor paired tissue study (n=54). The eight genes discriminated patients with lung cancer from healthy controls with high accuracy (AUC=0.81, 95% CI=0.74–0.87). Our finding suggests the use of gene expression from PWB for the identification of early detection markers of lung cancer in the future.
microarray gene expression; peripheral blood; lung cancer; stage I
DNA repair genes are important for maintaining genomic stability and limiting carcinogenesis. We analyzed all single nucleotide polymorphisms (SNPs) of 125 DNA repair genes covered by the Illumina HumanHap300 (v1.1) BeadChips in a previously conducted genome-wide association study (GWAS) of 1,154 lung cancer cases and 1,137 controls and replicated the top-hits of XRCC4 SNPs in an independent set of 597 cases and 611 controls in Texas populations. We found that six of 20 XRCC4 SNPs were associated with a decreased risk of lung cancer with a P value of 0.01 or lower in the discovery dataset, of which the most significant SNP was rs10040363 (P for allelic test = 4.89 ×10−4). Moreover, the data in this region allowed us to impute a potentially functional SNP rs2075685 (imputed P for allelic test = 1.3 ×10−3). A luciferase reporter assay demonstrated that the rs2075685G>T change in the XRCC4 promoter increased expression of the gene. In the replication study of rs10040363, rs1478486, rs9293329, and rs2075685, however, only rs10040363 achieved a borderline association with a decreased risk of lung cancer in a dominant model (adjusted OR = 0.80, 95% CI = 0.62–1.03, P = 0.079). In the final combined analysis of both the Texas GWAS discovery and replication datasets, the strength of the association was increased for rs10040363 (adjusted OR = 0.77, 95% CI = 0.66–0.89, Pdominant = 5×10−4 and P for trend = 5×10−4) and rs1478486 (adjusted OR = 0.82, 95% CI = 0.71 −0.94, Pdominant = 6×10−3 and P for trend = 3.5×10−3). Finally, we conducted a meta-analysis of these XRCC4 SNPs with available data from published GWA studies of lung cancer with a total of 12,312 cases and 47,921 controls, in which none of these XRCC4 SNPs was associated with lung cancer risk. It appeared that rs2075685, although associated with increased expression of a reporter gene and lung cancer risk in the Texas populations, did not have an effect on lung cancer risk in other populations. This study underscores the importance of replication using published data in larger populations.
XRCC4; variant; Genetic susceptibility; genome-wide association study; replication study
Oncogenic BRAF mutations are more frequent in cutaneous melanoma from sites with little or moderate sun-induced damage than from sites with severe cumulative solar ultraviolet (UV) damage. We studied cutaneous melanomas from geographic regions with different levels of ambient UV radiation to delineate the relative effects of cumulative UV damage, age and anatomic site on the frequency of BRAF mutations.
We show that BRAF-mutated melanomas occur in a younger age group on skin without marked solar elastosis, and less frequently affect the head and neck area, compared to melanomas without BRAF mutations. The findings indicate that BRAF-mutated melanomas arise early in life at low cumulative UV doses, whereas melanomas without BRAF mutations require accumulation of high UV doses over time. The effect of anatomic site on the mutation spectrum further suggests regional differences among cutaneous melanocytes.
BRAF; genetics; melanoma; mutation; pathology; solar elastosis; ultraviolet exposure
An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene–environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene–environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene–environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene–environment interaction based on the single-marker approach is far from significant.
Many common diseases result from a complex interplay of genetic and environmental risk factors. It is important to study the potential genetic and environmental risk factors jointly in order to achieve a better understanding of the mechanisms underlying disease development. The standard single-marker approach that studies the environmental risk factor and one genetic marker at a time could misrepresent the gene–environment interaction, as the single genetic marker might not be an appropriate surrogate for the underlying genetic functioning polymorphisms. We propose a method to look at gene–environment interaction at the gene/region level by integrating information observed on multiple genetic markers within the selected gene/region with measures of environmental exposure. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the proposed model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region and find evidence for gene–environment interaction (P-value = 0.016), with the smoking effect varying according to a subject's genetic profile.
Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies. This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium (HWE) test p-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis (PCA) to SNP selection. The methods are illustrated with examples from the ‘Gene Environment Association Studies’ (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of genome-wide association studies.
GWAS; DNA sample quality; genotyping artifact; Hardy-Weinberg equilibrium; chromosome aberration
The molecular drivers that determine histology in lung cancer are largely unknown. We investigated whether microRNA (miR) expression profiles can differentiate histological subtypes and predict survival for non-small cell lung cancer.
We analyzed miR expression in 165 adenocarcinoma (AD) and 125 squamous cell carcinoma (SQ) tissue samples from the Environmental And Genetics in Lung cancer Etiology (EAGLE) study using a custom oligo array with 440 human mature antisense miRs. We compared miR expression profiles using t-tests and F-tests and accounted for multiple testing using global permutation tests. We assessed the association of miR expression with tobacco smoking using Spearman correlation coefficients and linear regression models, and with clinical outcome using log-rank tests, Cox proportional hazards and survival risk prediction models, accounting for demographic and tumor characteristics.
MiR expression profiles strongly differed between AD and SQ (global p<0.0001), particularly in the early stages, and included miRs located on chromosome loci most often altered in lung cancer (e.g., 3p21-22). Most miRs, including all members of the let-7 family, were down-regulated in SQ. Major findings were confirmed by QRT-PCR in EAGLE samples and in an independent set of lung cancer cases. In SQ, low expression of miRs down-regulated in the histology comparison was associated with 1.2 to 3.6-fold increased mortality risk. A 5-miR signature significantly predicted survival for SQ.
We identified a miR expression profile that strongly differentiated AD from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches.
Epidemiological and mechanistic evidence on the association of quercetin-rich food intake with lung cancer risk and carcinogenesis are inconclusive. We investigated the role of dietary quercetin and the interaction between quercetin and P450 and glutathione S-transferase (GST) polymorphisms on lung cancer risk in 1822 incident lung cancer cases and 1991 frequency-matched controls from the Environment And Genetics in Lung cancer Etiology study. In non-tumor lung tissue from 38 adenocarcinoma patients, we assessed the correlation between quercetin intake and messenger RNA expression of the same P450 and GST metabolic genes. Multivariate odds ratios (ORs) and 95% confidence intervals (CIs) for sex-specific quintiles of intake were calculated using unconditional logistic regression adjusting for putative risk factors. Frequent intake of quercetin-rich foods was inversely associated with lung cancer risk (OR = 0.49; 95% CI: 0.37–0.67; P-trend < 0.001) and did not differ by P450 or GST genotypes, gender or histological subtypes. The association was stronger in subjects who smoked >20 cigarettes per day (OR = 0.35; 95% CI: 0.19–0.66; P-trend = 0.003). Based on a two-sample t-test, we compared gene expression and high versus low consumption of quercetin-rich foods and observed an overall upregulation of GSTM1, GSTM2, GSTT2, and GSTP1 as well as a downregulation of specific P450 genes (P-values < 0.05, adjusted for age and smoking status). In conclusion, we observed an inverse association of quercetin-rich food with lung cancer risk and identified a possible mechanism of quercetin-related changes in the expression of genes involved in the metabolism of tobacco carcinogens in humans. Our findings suggest an interplay between quercetin intake, tobacco smoking, and lung cancer risk. Further research on this relationship is warranted.
MicroRNAs (miRs) are endogenous, non-coding RNAs involved in many cellular processes and have been associated with the development and progression of cancer. There are many different ways to evaluate miRs.
We described some of the most commonly used and promising miR detection methods.
Each miR detection method has benefits and limitations. Microarray profiling and quantitative real-time reverse transcription PCR (qRT-PCR) are the two most common methods to evaluate miR expression. However, the results from microarray and qRT-PCR do not always agree. High-throughput, high-resolution next generation sequencing of small RNAs may offer the opportunity to quickly and accurately discover new miRs and confirm the presence of known miRs in the near future.
All of the current and new technologies have benefits and limitations to consider when designing miR studies. Results can vary across platforms, requiring careful and critical evaluation when interpreting findings.
Although miR detection and expression analyses are rapidly improving, there are still many technical challenges to overcome. The old molecular epidemiology tenet of rigorous biomarker validation and confirmation in independent studies remains essential.
Although pneumonia has been suggested as a risk factor for lung cancer, previous studies have not evaluated the influence of number of pneumonia diagnoses in relation to lung cancer risk.
The Environment And Genetics in Lung cancer Etiology (EAGLE) population-based study of 2,100 cases and 2,120 controls collected information on pneumonia more than one year before enrollment from 1,890 cases and 2,078 controls.
After adjusting for study design variables, smoking, and chronic bronchitis, pneumonia was associated with decreased risk of lung cancer (odds ratio (OR), 0.79; 95% confidence interval (CI), 0.64–0.97), especially among individuals with ≥3 diagnoses versus none (OR, 0.35; 95% CI, 0.16–0.75). Adjustment for chronic bronchitis contributed to this inverse association. In comparison, pulmonary tuberculosis was not associated with lung cancer (OR, 0.96; 95% CI, 0.62–1.48).
The apparent protective effect of pneumonia among individuals with multiple pneumonia diagnoses may reflect an underlying difference in immune response and requires further investigation and confirmation.
Careful evaluation of number of pneumonia episodes may shed light on lung cancer etiology.
pneumonia; epidemiology; lung cancer; multiple infections; tuberculosis
The presence of recurrent high-risk mutations in CDKN2A and CDK4 among melanoma-prone kindreds suggests that a high-throughput, multiplex assay could serve as an effective initial screening tool. Moreover, with the emergence of new melanoma risk single nucleotide polymorphisms (SNPs) through genome-wide association studies, a flexible platform that can easily accommodate these new risk alleles is needed for more accurate genetic risk profiling. To this end, we have developed a novel melanoma-associated mutation detection method using a multiplex bead-based assay. This assay is suitable for high-throughput CDKN2A and CDK4 genotyping and can be eventually adapted to multiple loci across various constituent populations.
Genomic DNA from a 1603 subjects (1005 in training set, 598 in validation set) were amplified by multiplex PCR using five primer sets followed by multiplex allele-specific primer extension for 39 different known germline variants. The products were then sorted on an xMAP™ (formerly Tag-It™) array and detected by use of the Luminex xMAP™ system. Genotypes were compared to previously-determined sequence data.
In the Toronto training cohort, variants were detected in 145 samples, giving complete concordance between the bead assay and direct sequencing results. Analysis of the 598 samples from the GenoMEL validation set led to identification of 150/155 expected variants (96.77% concordance). Overall, the bead assay correctly genotyped 1540/1603 (96.07%) of all individuals in the study and 1540/1545 (99.68%) of individuals whose mutations were represented in the probe set. Out of a total of 62,512 SNP calls, 62,517 (99.99%) were correctly assigned.
In this initial evaluation, the multiplex bead-based assay for familial melanoma appears to be a highly accurate method for genotyping CDKN2A and CDK4 variants.
Melanoma; CDKN2A; CDK4; p14ARF; familial; high-throughput
Lung cancer kills more than 1 million people worldwide each year. Whereas several human papillomavirus (HPV)–associated cancers have been identified, the role of HPV in lung carcinogenesis remains controversial.
We selected 450 lung cancer patients from an Italian population–based case–control study, the Environment and Genetics in Lung Cancer Etiology. These patients were selected from those with an adequate number of unstained tissue sections and included all those who had never smoked and a random sample of the remaining patients. We used real-time polymerase chain reaction (PCR) to test specimens from these patients for HPV DNA, specifically for E6 gene sequences from HPV16 and E7 gene sequences from HPV18. We also tested a subset of 92 specimens from all never-smokers and a random selection of smokers for additional HPV types by a PCR-based test for at least 54 mucosal HPV genotypes. DNA was extracted from ethanol- or formalin-fixed paraffin-embedded tumor tissue under strict PCR clean conditions. The prevalence of HPV in tumor tissue was investigated.
Specimens from 399 of 450 patients had adequate DNA for analysis. Most patients were current (220 patients or 48.9%) smokers, and 92 patients (20.4%) were women. When HPV16 and HPV18 type–specific primers were used, two specimens were positive for HPV16 at low copy number but were negative on additional type-specific HPV16 testing. Neither these specimens nor the others examined for a broad range of HPV types were positive for any HPV type.
When DNA contamination was avoided and state-of-the-art highly sensitive HPV DNA detection assays were used, we found no evidence that HPV was associated with lung cancer in a representative Western population. Our results provide the strongest evidence to date to rule out a role for HPV in lung carcinogenesis in Western populations.
MiR arrays distinguish themselves from gene expression arrays by their more limited number of probes, and the shorter and less flexible sequence in probe design. Robust data processing and analysis methods tailored to the unique characteristics of miR arrays are greatly needed. Assumptions underlying commonly used normalization methods for gene expression microarrays containing tens of thousands or more probes may not hold for miR microarrays. Findings from previous studies have sometimes been inconclusive or contradictory. Further studies to determine optimal normalization methods for miR microarrays are needed.
We evaluated many different normalization methods for data generated with a custom-made two channel miR microarray using two data sets that have technical replicates from several different cell lines. The impact of each normalization method was examined on both within miR error variance (between replicate arrays) and between miR variance to determine which normalization methods minimized differences between replicate samples while preserving differences between biologically distinct miRs.
Lowess normalization generally did not perform as well as the other methods, and quantile normalization based on an invariant set showed the best performance in many cases unless restricted to a very small invariant set. Global median and global mean methods performed reasonably well in both data sets and have the advantage of computational simplicity.
Researchers need to consider carefully which assumptions underlying the different normalization methods appear most reasonable for their experimental setting and possibly consider more than one normalization approach to determine the sensitivity of their results to normalization method used.
Family history (FH) of lung cancer is an established risk factor for lung cancer, but the modifying effect of smoking in relatives has been rarely examined. Also, the role of FH of non-malignant lung diseases on lung cancer risk is not well known. We examined the role of FH of cancer and FH of non-malignant lung diseases in lung cancer risk, overall, and by personal smoking, FH of smoking, and histology in 1,946 cases and 2,116 population-based controls within the Environment And Genetics in Lung cancer Etiology (EAGLE) study. Odds ratios (ORs) and 95% CI from logistic regression were calculated adjusting for age, gender, residence, education, and cigarette smoking. FH of lung cancer in any family member was associated with increased lung cancer risk (OR = 1.57, 95% CI = 1.25–1.98). The odds associated with fathers’, mothers’ and siblings’ history of lung cancer were 1.41, 2.14, and 1.53, respectively. The associations were generally stronger in never smokers, younger subjects, and for the adenocarcinoma and squamous cell carcinoma subtypes. FH of chronic bronchitis and pneumonia were associated with increased (OR =1.49, 95% CI = 1.23–1.80) and decreased (OR = 0.73, 95% CI = 0.61–0.87) lung cancer risk, respectively. FH of lung cancer and FH of non-malignant lung diseases affected lung cancer risk independently, and did not appear to be modified by FH of smoking.
family history; lung cancer; smoking; chronic bronchitis; pneumonia
The major factors individually reported to be associated with an increased frequency of CDKN2A mutations are increased number of patients with melanoma in a family, early age at melanoma diagnosis, and family members with multiple primary melanomas (MPM) or pancreatic cancer.
These four features were examined in 385 families with ⩾3 patients with melanoma pooled by 17 GenoMEL groups, and these attributes were compared across continents.
Overall, 39% of families had CDKN2A mutations ranging from 20% (32/162) in Australia to 45% (29/65) in North America to 57% (89/157) in Europe. All four features in each group, except pancreatic cancer in Australia (p = 0.38), individually showed significant associations with CDKN2A mutations, but the effects varied widely across continents. Multivariate examination also showed different predictors of mutation risk across continents. In Australian families, ⩾2 patients with MPM, median age at melanoma diagnosis ⩽40 years and ⩾6 patients with melanoma in a family jointly predicted the mutation risk. In European families, all four factors concurrently predicted the risk, but with less stringent criteria than in Australia. In North American families, only ⩾1 patient with MPM and age at diagnosis ⩽40 years simultaneously predicted the mutation risk.
The variation in CDKN2A mutations for the four features across continents is consistent with the lower melanoma incidence rates in Europe and higher rates of sporadic melanoma in Australia. The lack of a pancreatic cancer–CDKN2A mutation relationship in Australia probably reflects the divergent spectrum of mutations in families from Australia versus those from North America and Europe. GenoMEL is exploring candidate host, genetic and/or environmental risk factors to better understand the variation observed.
; multiple primary melanomas; pancreatic cancer
Red and processed meat intake may increase lung cancer risk. However, the epidemiologic evidence is inconsistent and few studies have evaluated the role of meat-mutagens formed during high cooking temperatures. We investigated the association of red meat, processed meat, and meat-mutagen intake with lung cancer risk in Environment And Genetics in Lung cancer Etiology (EAGLE), a population-based case-control study. Primary lung cancer cases (n=2101) were recruited from 13 hospitals within the Lombardy region of Italy examining ~80% of the cases from the area. Non-cancer population controls (n=2120), matched to cases on gender, residence, and age, were randomly selected from the same catchment area. Diet was assessed in 1903 cases and 2073 controls, and used in conjunction with a meat-mutagen database to estimate intake of heterocyclic amines and benzo[a]pyrene. Multivariable odds ratios (ORs) and 95% confidence intervals (CIs) for sex-specific tertiles of intake were calculated using unconditional logistic regression. Red and processed meat were positively associated with lung cancer risk (highest-versus-lowest tertile: OR=1.8; 95% CI=1.5–2.2; p-trend<0.001 and OR=1.7; 95% CI=1.4–2.1; p-trend<0.001, respectively); the risks were strongest among never smokers (OR=2.4, 95% CI=1.4–4.0, p-trend=0.001 and OR=2.5, 95% CI=1.5–4.2, p-trend=0.001, respectively). Heterocyclic amines and benzo[a]pyrene were significantly associated with increased risk of lung cancer. When separated by histology, significant positive associations for both meat groups were restricted to adenocarcinoma and squamous cell carcinoma, but not small cell carcinoma of the lung. In summary, red meat, processed meat, and meat-mutagens were independently associated with increased risk of lung cancer.
red meat; processed meat; meat-mutagens; cooking methods; lung cancer
The authors examined the relation between occupation and lung cancer in the large, population-based Environment And Genetics in Lung cancer Etiology (EAGLE) case-control study. In 2002–2005 in the Lombardy region of northern Italy, 2,100 incident lung cancer cases and 2,120 randomly selected population controls were enrolled. Lifetime occupational histories (industry and job title) were coded by using standard international classifications and were translated into occupations known (list A) or suspected (list B) to be associated with lung cancer. Smoking-adjusted odds ratios and 95% confidence intervals were calculated with logistic regression. For men, an increased risk was found for list A (177 exposed cases and 100 controls; odds ratio = 1.74, 95% confidence interval: 1.27, 2.38) and most occupations therein. No overall excess was found for list B with the exception of filling station attendants and bus and truck drivers (men) and launderers and dry cleaners (women). The authors estimated that 4.9% (95% confidence interval: 2.0, 7.8) of lung cancers in men were attributable to occupation. Among those in other occupations, risk excesses were found for metal workers, barbers and hairdressers, and other motor vehicle drivers. These results indicate that past exposure to occupational carcinogens remains an important determinant of lung cancer occurrence.
carcinogens; case-control studies; industry; lung neoplasms; occupational health; occupations
Chronic obstructive pulmonary disease (COPD) has been consistently associated with increased risk of lung cancer. However, previous studies have had limited ability to determine whether the association is due to smoking.
The Environment And Genetics in Lung cancer Etiology (EAGLE) population-based case-control study recruited 2100 cases and 2120 controls, of whom 1934 cases and 2108 controls reported about diagnosis of chronic bronchitis, emphysema, COPD (chronic bronchitis and/or emphysema), or asthma more than 1 year before enrollment. We estimated odds ratios (OR) and 95% confidence intervals (CI) using logistic regression. After adjustment for smoking, other previous lung diseases, and study design variables, lung cancer risk was elevated among individuals with a history of chronic bronchitis (OR = 2.0, 95% CI = 1.5–2.5), emphysema (OR = 1.9, 95% CI = 1.4–2.8), or COPD (OR = 2.5, 95% CI = 2.0–3.1). Among current smokers, association between chronic bronchitis and lung cancer was strongest among lighter smokers. Asthma was associated with a decreased risk of lung cancer in males (OR = 0.48, 95% CI = 0.30–0.78).
These results suggest that the associations of personal history of chronic bronchitis, emphysema, and COPD with increased risk of lung cancer are not entirely due to smoking. Inflammatory processes may both contribute to COPD and be important for lung carcinogenesis.