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Clinical applications of gene-based risk prediction for lung cancer and the central role of chronic obstructive pulmonary disease.

Young RP, Hopkins RJ, Gamble GD - Front Genet (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Medical and Health Sciences, and Biological Sciences, University of Auckland Auckland, New Zealand.

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.

No MeSH data available.


Related in: MedlinePlus

Multivariate risk model to identify current and former smokers at greatest risk of lung cancer for targeted, cost-effective CT screening.
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Figure 2: Multivariate risk model to identify current and former smokers at greatest risk of lung cancer for targeted, cost-effective CT screening.

Mentions: We have reported that genetic factors (SNP variants) significantly add to our lung cancer model (Young et al., 2009b,c). This is important as it defines risk in people who are often too young to have a positive family history or who have not yet been diagnosed with COPD. As will be discussed in the next section, this group is important because they are at high risk of getting lung cancer but may miss screening eligibility due to age restrictions (Young and Hopkins, 2012a). Our model includes genes identified by genome-wide studies to be implicated in lung cancer and COPD (Figure 2; Young et al., 2011d). This is analogous to including obesity genes in a risk model for diabetes (e.g., FTO – fat-free mass gene) or cholesterol genes in a risk model for heart attack. In a ROC analysis of our gene-based risk model, the c statistic ranges from 0.72 to 0.79 with the genetic risk score (from the combined SNP panel) contributing approximately 50% of the total predictive utility, three- to fourfold greater than from family history alone (Young et al., 2009b,c,2011d). Not only does the genetic risk score contribute to the overall score independently of the other variables, for younger smokers, the risk was entirely derived from their SNP genotypes. In contrast to other risk models, our model was developed by comparing heavy smokers with widely disparate outcomes (susceptible vs resistant). Using these cohorts of smokers, we have been the first to show that genes conferring susceptibility to lung cancer involved genes also conferring susceptibility to COPD and vice-versa (Young et al., 2008, 2010b,2011c,d). While this might appear intuitive, of greater importance is that we found some genes that confer a protective effect on COPD also confer a protective effect for lung cancer (Young et al., 2010b,2011c,d). The contribution of these “protective” genes is very important in our risk model as they help to distinguish smokers at least genetic risk and most genetic risk (Young et al., 2009b,c), rather than just those at the greater risk-based solely only susceptibility SNPs (Spitz et al., 2009). What is important in these risk models is that the combination of these variables is superior to the use of only a few variables (e.g., age and smoking) and that genetic factors must be used in combination with the relevant clinical variables to have any useful predictive utility (Young et al., 2009b,c). Of note, we have used susceptible and protective genotypes (rather than alleles) in our risk score, better reflecting the effects conferred by “smoking-sensitive” genes like the α1-anti-trypsin gene (Young et al., 2012a).


Clinical applications of gene-based risk prediction for lung cancer and the central role of chronic obstructive pulmonary disease.

Young RP, Hopkins RJ, Gamble GD - Front Genet (2012)

Multivariate risk model to identify current and former smokers at greatest risk of lung cancer for targeted, cost-effective CT screening.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3472507&req=5

Figure 2: Multivariate risk model to identify current and former smokers at greatest risk of lung cancer for targeted, cost-effective CT screening.
Mentions: We have reported that genetic factors (SNP variants) significantly add to our lung cancer model (Young et al., 2009b,c). This is important as it defines risk in people who are often too young to have a positive family history or who have not yet been diagnosed with COPD. As will be discussed in the next section, this group is important because they are at high risk of getting lung cancer but may miss screening eligibility due to age restrictions (Young and Hopkins, 2012a). Our model includes genes identified by genome-wide studies to be implicated in lung cancer and COPD (Figure 2; Young et al., 2011d). This is analogous to including obesity genes in a risk model for diabetes (e.g., FTO – fat-free mass gene) or cholesterol genes in a risk model for heart attack. In a ROC analysis of our gene-based risk model, the c statistic ranges from 0.72 to 0.79 with the genetic risk score (from the combined SNP panel) contributing approximately 50% of the total predictive utility, three- to fourfold greater than from family history alone (Young et al., 2009b,c,2011d). Not only does the genetic risk score contribute to the overall score independently of the other variables, for younger smokers, the risk was entirely derived from their SNP genotypes. In contrast to other risk models, our model was developed by comparing heavy smokers with widely disparate outcomes (susceptible vs resistant). Using these cohorts of smokers, we have been the first to show that genes conferring susceptibility to lung cancer involved genes also conferring susceptibility to COPD and vice-versa (Young et al., 2008, 2010b,2011c,d). While this might appear intuitive, of greater importance is that we found some genes that confer a protective effect on COPD also confer a protective effect for lung cancer (Young et al., 2010b,2011c,d). The contribution of these “protective” genes is very important in our risk model as they help to distinguish smokers at least genetic risk and most genetic risk (Young et al., 2009b,c), rather than just those at the greater risk-based solely only susceptibility SNPs (Spitz et al., 2009). What is important in these risk models is that the combination of these variables is superior to the use of only a few variables (e.g., age and smoking) and that genetic factors must be used in combination with the relevant clinical variables to have any useful predictive utility (Young et al., 2009b,c). Of note, we have used susceptible and protective genotypes (rather than alleles) in our risk score, better reflecting the effects conferred by “smoking-sensitive” genes like the α1-anti-trypsin gene (Young et al., 2012a).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Medical and Health Sciences, and Biological Sciences, University of Auckland Auckland, New Zealand.

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.

No MeSH data available.


Related in: MedlinePlus