Article

Individual and Cumulative Effects of GWAS Susceptibility Loci in Lung Cancer: Associations after Sub-Phenotyping for COPD

Department of Medicine, Auckland Hospital, Auckland, New Zealand.
PLoS ONE (Impact Factor: 3.53). 02/2011; 6(2):e16476. DOI: 10.1371/journal.pone.0016476
Source: PubMed

ABSTRACT Epidemiological studies show that approximately 20-30% of chronic smokers develop chronic obstructive pulmonary disease (COPD) while 10-15% develop lung cancer. COPD pre-exists lung cancer in 50-90% of cases and has a heritability of 40-77%, much greater than for lung cancer with heritability of 15-25%. These data suggest that smokers susceptible to COPD may also be susceptible to lung cancer. This study examines the association of several overlapping chromosomal loci, recently implicated by GWA studies in COPD, lung function and lung cancer, in (n = 1400) subjects sub-phenotyped for the presence of COPD and matched for smoking exposure. Using this approach we show; the 15q25 locus confers susceptibility to lung cancer and COPD, the 4q31 and 4q22 loci both confer a reduced risk to both COPD and lung cancer, the 6p21 locus confers susceptibility to lung cancer in smokers with pre-existing COPD, the 5p15 and 1q23 loci both confer susceptibility to lung cancer in those with no pre-existing COPD. We also show the 5q33 locus, previously associated with reduced FEV(1), appears to confer susceptibility to both COPD and lung cancer. The 6p21 locus previously linked to reduced FEV(1) is associated with COPD only. Larger studies will be needed to distinguish whether these COPD-related effects may reflect, in part, associations specific to different lung cancer histology. We demonstrate that when the "risk genotypes" derived from the univariate analysis are incorporated into an algorithm with clinical variables, independently associated with lung cancer in multivariate analysis, modest discrimination is possible on receiver operator curve analysis (AUC = 0.70). We suggest that genetic susceptibility to lung cancer includes genes conferring susceptibility to COPD and that sub-phenotyping with spirometry is critical to identifying genes underlying the development of lung cancer.

Full-text

Available from: Robert P Young, Jun 16, 2015
0 Followers
 · 
110 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Lung cancer is the leading cause of cancer death worldwide in part due to our inability to identify which smokers are at highest risk and the lack of effective tools to detect the disease at its earliest and potentially curable stage. Recent results from the National Lung Screening Trial have shown that annual screening of high-risk smokers with low-dose helical computed tomography of the chest can reduce lung cancer mortality. However, molecular biomarkers are needed to identify which current and former smokers would benefit most from annual computed tomography scan screening in order to reduce the costs and morbidity associated with this procedure. Additionally, there is an urgent clinical need to develop biomarkers that can distinguish benign from malignant lesions found on computed tomography of the chest given its very high false positive rate. This review highlights recent genetic, transcriptomic and epigenomic biomarkers that are emerging as tools for the early detection of lung cancer both in the diagnostic and screening setting.
    BMC Medicine 07/2013; 11:168. DOI:10.1186/1741-7015-11-168 · 7.28 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Lung cancer is the leading cause of cancer death worldwide and nearly 90% of cases are attributable to smoking. Quitting smoking and early diagnosis of lung cancer, through computed tomographic screening, are the only ways to reduce mortality from lung cancer. Recent epidemiological studies show that risk prediction for lung cancer is optimized by using multivariate risk models that include age, smoking exposure, history of chronic obstructive pulmonary disease (COPD), family history of lung cancer, and body mass index. It has also been shown that COPD predates lung cancer in 65-70% of cases, conferring a four- to sixfold greater risk of lung cancer compared to smokers with normal lung function. Genome-wide association studies of smokers have identified a number of genetic variants associated with COPD or lung cancer. In a case-control study, where smokers with normal lungs were compared to smokers who had spirometry-defined COPD or histology confirmed lung cancer, several of these variants were shown to overlap, conferring the same susceptibility or protective effects on both COPD and lung cancer (independent of COPD status). In this perspective article, we show how combining clinical data with genetic variants can help identify heavy smokers at the greatest risk of lung cancer. Using this approach, we found that gene-based risk testing helped engage smokers in risk mitigating activities like quitting smoking and undertaking lung cancer screening. We suggest that such an approach could facilitate the targeted selection of smokers for cost-effective life-saving interventions.
    Frontiers in Genetics 10/2012; 3:210. DOI:10.3389/fgene.2012.00210
  • [Show abstract] [Hide abstract]
    ABSTRACT: Variants in FAM13A have been found in genome-wide association studies (GWAS) to associate with lung function in the general population as well as in several common chronic lung diseases (CLD) such as chronic obstructive pulmonary disease (COPD), asthma, as well as in idiopathic interstitial pneumonias (IIP). The gene was cloned in 2004, yet the encoded protein has not been characterised and its function is unknown. The redundancy of its genetic contribution in CLD suggests a major function of this gene both in lung physiology and CLD. This review provides a brief summary of the current knowledge of FAM13A, and demonstrates the necessity to resolve its biological function besides its well accepted genetic contribution. Further interpretations of FAM13A variants may help in the understanding of CLD mechanisms and reveal opportunity for intervention.
    Journal of Medical Genetics 08/2014; 51(10). DOI:10.1136/jmedgenet-2014-102525 · 5.64 Impact Factor