A gene-based risk score for lung cancer susceptibility in smokers and ex-smokers. Issue 1008 (29th September 2009)

• Record Type: Journal Article
• Authors: Young, R P; Hopkins, R J; Hay, B A; Epton, M J; Mills, G D; Black, P N; Gardner, H D; Sullivan, R; Gamble, G D
• Is Part Of: Postgraduate medical journal. Volume 85:Issue 1008(2009)
• Page Start: 515
• Page End: 524
• Publication Date: 2009-09-29
• Subjects: lung cancer -- susceptibility -- risk -- genetics -- single nucleotide polymorphism; Medicine -- Periodicals; 610
• DOI: 10.1136/pgmj.2008.077107
• Languages: English
• ISSNs: 0032-5473

Abstract

Abstract : Background: Epidemiological and family studies suggest that lung cancer results from the combined effects of age, smoking and genetic factors. Chronic obstructive pulmonary disease (COPD) is also an independent risk factor for lung cancer and coexists in 40–60% of lung cancer cases. Methods: In a two-stage case–control association study, genetic markers associated with either susceptibility or protection against lung cancer were identified. In a test cohort of 439 Caucasian smokers or ex-smokers, consisting of healthy smokers and lung cancer cases, 157 candidate single nucleotide polymorphisms (SNPs) were screened. From this, 30 SNPs were identified, the genotypes (codominant or recessive model) of which were associated with either the healthy smokers (protective) or lung cancer (susceptibility) phenotype. After genotyping of this 30-SNP panel in a second validation cohort of 491 subjects and using the same protective and susceptibility genotypes from our test cohort, a 20-SNP panel was selected on the basis of independent univariate analyses. Results: Using multivariate logistic regression, including the 20 SNPs, it was also found that age, history of COPD, family history of lung cancer and gender were significantly and independently associated with lung cancer. Conclusions: When numeric scores were assigned to both the SNP and demographic data, and sequentially combined by a simple algorithm in a risk model, the composite score was found to be linearly related to lung cancer risk with a bimodal distribution. Genetic data may therefore be combined with other risk variables from smokers or ex-smokers to identify individuals who are most susceptible to developing lung cancer.