Adrian Cassidy

A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25.

Lung cancer is the most common cause of cancer death worldwide, with over one million cases annually. To identify genetic factors that modify disease risk, we conducted a genome-wide association study by analysing 317,139 single-nucleotide polymorphisms in 1,989 lung cancer cases and 2,625 controls from six central European countries. We identified a locus in chromosome region 15q25 that was strongly associated with lung cancer (P = 9 × 10-10). This locus was replicated in five separate lung cancer studies comprising an additional 2,513 lung cancer cases and 4,752 controls (P = 5 × 10-20 overall), and it was found to account for 14% (attributable risk) of lung cancer cases. Statistically similar risks were observed irrespective of smoking status or propensity to smoke tobacco. The association region contains several genes, including three that encode nicotinic acetylcholine receptor subunits (CHRNA5, CHRNA3 and CHRNB4). Such subunits are expressed in neurons and other tissues, in particular alveolar epithelial cells, pulmonary neuroendocrine cells and lung cancer cell lines, and they bind to N′-nitrosonornicotine and potential lung carcinogens. A non-synonymous variant of CHRNA5 that induces an amino acid substitution (D398N) at a highly conserved site in the second intracellular loop of the protein is among the markers with the strongest disease associations. Our results provide compelling evidence of a locus at 15q25 predisposing to lung cancer, and reinforce interest in nicotinic acetylcholine receptors as potential disease candidates and chemopreventative targets.

Lung cancer susceptibility locus at 5p15.33

We carried out a genome-wide association study of lung cancer (3,259 cases and 4,159 controls), followed by replication in 2,899 cases and 5,573 controls. Two uncorrelated disease markers at 5p15.33, rs402710 and rs2736100 were detected by the genome-wide data (P = 2 × 10−7 and P = 4 × 10−6) and replicated by the independent study series (P = 7 × 10−5 and P = 0.016). The susceptibility region contains two genes, TERT and CLPTM1L, suggesting that one or both may have a role in lung cancer etiology.

The LLP risk model: an individual risk prediction model for lung cancer.

Using a model-based approach, we estimated the probability that an individual, with a specified combination of risk factors, would develop lung cancer within a 5-year period.Data from 579 lung cancer cases and 1157 age- and sex-matched population-based controls were available for this analysis. Significant risk factors were fitted into multivariate conditional logistic regression models. The final multivariate model was combined with age-standardised lung cancer incidence data to calculate absolute risk estimates.Combinations of lifestyle risk factors were modelled to create risk profiles. For example, a 77-year-old male non-smoker, with a family history of lung cancer (early onset) and occupational exposure to asbestos has an absolute risk of 3.17% (95% CI, 1.67–5.95). Choosing a 2.5% cutoff to trigger increased surveillance, gave a sensitivity of 0.62 and specificity of 0.70, while a 6.0% cutoff gave a sensitivity of 0.34 and specificity of 0.90. A 10-fold cross validation produced an AUC statistic of 0.70, indicating good discrimination. If independent validation studies confirm these results, the LLP risk models’ application as the first stage in an early detection strategy is a logical evolution in patient care.

Cigarette smoking and lung cancer – relative risk estimates for the major histological types from a pooled analysis of case-control studies

Lung cancer is mainly caused by smoking, but the quantitative relations between smoking and histologic subtypes of lung cancer remain inconclusive. By using one of the largest lung cancer datasets ever assembled, we explored the impact of smoking on risks of the major cell types of lung cancer. This pooled analysis included 13,169 cases and 16,010 controls from Europe and Canada. Studies with population controls comprised 66.5% of the subjects. Adenocarcinoma (AdCa) was the most prevalent subtype in never smokers and in women. Squamous cell carcinoma (SqCC) predominated in male smokers. Age‐adjusted odds ratios (ORs) were estimated with logistic regression. ORs were elevated for all metrics of exposure to cigarette smoke and were higher for SqCC and small cell lung cancer (SCLC) than for AdCa. Current male smokers with an average daily dose of >30 cigarettes had ORs of 103.5 (95% confidence interval (CI): 74.8–143.2) for SqCC, 111.3 (95% CI: 69.8–177.5) for SCLC and 21.9 (95% CI: 16.6–29.0) for AdCa. In women, the corresponding ORs were 62.7 (95% CI: 31.5–124.6), 108.6 (95% CI: 50.7–232.8) and 16.8 (95% CI: 9.2–30.6), respectively. Although ORs started to decline soon after quitting, they did not fully return to the baseline risk of never smokers even 35 years after cessation. The major result that smoking exerted a steeper risk gradient on SqCC and SCLC than on AdCa is in line with previous population data and biological understanding of lung cancer development.

Hypomethylation of retrotransposable elements correlates with genomic instability in non-small cell lung cancer

LINE‐1 and Alu elements are non‐LTR retrotransposons, constituting together over 30% of the human genome and they are frequently hypomethylated in human tumors. A relationship between global hypomethylation and genomic instability has been shown, however, there is little evidence to suggest active role for hypomethylation‐mediated reactivation of retroelements in human cancer. In our study, we examined by Pyrosequencing the methylation levels of LINE‐1 and Alu sequences in 48 primary nonsmall cell carcinomas and their paired adjacent tissues. We demonstrate a significant reduction of the methylation levels of both elements (p = 7.7 × 10−14 and 9.6 × 10−7, respectively). The methylation indices of the 2 elements correlated (p = 0.006), suggesting a possible common mechanism for their methylation maintenance. Genomic instability was measured utilizing 11 fluorescent microsatellite markers located on lung cancer hot‐spot regions such as 3p, 5q 9p, 13q and 17p. Hypomethylation of both transposable elements was associated with increased genomic instability (LINE, p = 7.1 × 10−5; Alu, p = 0.008). The reduction of the methylation index of LINE‐1 and Alu following treatment of 3 lung cell lines with 5‐aza‐2′‐deoxycitidine, consistently resulted in increased expression of both elements. Our study demonstrates the strong link between hypomethylation of transposable elements with genomic instability in non‐small cell lung cancer and provides early evidence for a potential active role of these elements in lung neoplasia. As demethylating agents are now entering lung cancer trials, it is imperative to gain a greater insight into the potential reactivation of silent retrotransposons in order to advance for the clinical utilization of epigenetics in cancer therapy.

Lung cancer risk prediction: a tool for early detection.

Although 45% of men and 39% of women will be diagnosed with cancer in their lifetime, it is difficult to predict which individuals will be affected. For some cancers, substantial progress in individual risk estimation has already been made. However, relatively few models have been developed to predict lung cancer risk beyond effects of age and smoking. This paper reviews published models for lung cancer risk prediction, discusses their potential contribution to clinical and research settings and suggests improvements to the risk modeling strategy for lung cancer. The sensitivity and specificity of existing cancer risk models is less than optimal. Improvement in individual risk prediction is important for selection of individuals for prevention or early detection interventions. In addition to smoking, factors related to occupational exposure, personal medical history and family history of cancer can add to the predictive power. A good risk prediction model is one that can identify a small fraction of the population in which a large proportion of the disease cases will occur. In the future, genetic and other biological markers are likely to be useful, although they will require rigorous evaluation. Validation is essential to establish the predictive effect and for ongoing monitoring of the models continued relevance.

Exposure to Diesel Motor Exhaust and Lung Cancer Risk in a Pooled Analysis from Case-Control Studies in Europe and Canada

RATIONALE Diesel motor exhaust is classified by the International Agency for Research on Cancer as probably carcinogenic to humans. The epidemiologic evidence is evaluated as limited because most studies lack adequate control for potential confounders and only a few studies have reported on exposure-response relationships. OBJECTIVES Investigate lung cancer risk associated with occupational exposure to diesel motor exhaust, while controlling for potential confounders. METHODS The SYNERGY project pooled information on lifetime work histories and tobacco smoking from 13,304 cases and 16,282 controls from 11 case-control studies conducted in Europe and Canada. A general population job exposure matrix based on ISCO-68 occupational codes, assigning no, low, or high exposure to diesel motor exhaust, was applied to determine level of exposure. MEASUREMENTS AND MAIN RESULTS Odds ratios of lung cancer and 95% confidence intervals were estimated by unconditional logistic regression, adjusted for age, sex, study, ever-employment in an occupation with established lung cancer risk, cigarette pack-years, and time-since-quitting smoking. Cumulative diesel exposure was associated with an increased lung cancer risk highest quartile versus unexposed (odds ratio 1.31; 95% confidence interval, 1.19-1.43), and a significant exposure-response relationship (P value < 0.01). Corresponding effect estimates were similar in workers never employed in occupations with established lung cancer risk, and in women and never-smokers, although not statistically significant. CONCLUSIONS Our results show a consistent association between occupational exposure to diesel motor exhaust and increased risk of lung cancer. This association is unlikely explained by bias or confounding, which we addressed by adjusted models and subgroup analyses.

Predictive accuracy of the Liverpool Lung Project risk model for stratifying patients for computed tomography screening for lung cancer: a case-control and cohort validation study.

BACKGROUND External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit. OBJECTIVE To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America. DESIGN Case-control and prospective cohort study. SETTING Europe and North America. PATIENTS Participants in the European Early Lung Cancer (EUELC) and Harvard case-control studies and the LLP population-based prospective cohort (LLPC) study. MEASUREMENTS 5-year absolute risks for lung cancer predicted by the LLP model. RESULTS The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening. LIMITATIONS The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model. CONCLUSION Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population risk thresholds for initiating lung cancer screening.

Occupational exposure to crystalline silica and risk of lung cancer: a multicenter case-control study in Europe.

Background: The role of crystalline silica dust as a possible cause of lung cancer has been controversial. Relatively few large community-based studies have been conducted to investigate the lung cancer risk from exposure to silica at low levels, taking into account potential confounding factors. Methods: Detailed lifestyle and occupational information were collected from 2852 newly diagnosed cases of lung cancer and 3104 controls between 1998 and 2002 in 7 European countries. For each job held, local experts assessed the probability, intensity, and duration of silica exposure. Results: Occupational exposure to crystalline silica was associated with an increased risk of lung cancer (odds ratio = 1.37; 95% confidence interval = 1.14–1.65). This risk was most apparent for the upper tertile of cumulative exposure (OR = 2.08; 95% CI = 1.49–2.90; P for trend <0.0001), duration of exposure (1.73; 1.26–2.39; P for trend = 0.0001) and weighted duration of exposure (1.88; 1.35–2.61; P for trend <0.0001). We did not observe any interaction beyond a multiplicative model between tobacco smoking and silica exposure. Conclusions: Our results support the hypothesis that silica is an important risk factor for lung cancer. This risk could not be explained by exposure to other occupational carcinogens or smoking, and it was present for the main histologic types of lung cancer, different sources of silica exposure, and different industrial settings.

Assessing exposure misclassification by expert assessment in multicenter occupational studies

Background: In a multicenter case-control study of lung cancer in central and eastern Europe and in Liverpool, exposure to occupational agents was assessed by teams of local experts. We performed an interteam agreement study to estimate the levels of exposure misclassification and the expected attenuation of the risk estimate. Methods: Eight teams of experts and a reference rater assessed exposure to 70 putative lung carcinogens for 19 jobs. Agreement among teams was calculated through Cohen’s kappa, sensitivity, and specificity. Results: Each team showed an overall fair to good agreement with the reference (kappa between 0.53 and 0.64). The agreement among teams in the presence of exposure was excellent for 9 agents, fair to good for 16, and poor for 29. For all agents the specificity was high (average 0.94), although sensitivity varied considerably. Conclusions: This study of expert exposure assessment showed a small range in reliability among teams of experts, but large differences among agents. This paper presents the range in levels of misclassification that can be expected using experts for assessing occupational exposure to different agents, and the attenuation of the odds ratio that can be expected to result from this misclassification.