Incorporation of a genetic factor into an epidemiologic model for prediction of individual risk of lung cancer: the Liverpool Lung Project

• Published in: Cancer prevention research (Philadelphia, Pa.) Pa.), 2010-05, Vol.3 (5), p.664-669
• Main Authors: Raji, Olaide Y, Agbaje, Olorunsola F, Duffy, Stephen W, Cassidy, Adrian, Field, John K
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Area Under Curve; Case-Control Studies; Genetic Predisposition to Disease; Genotype; Humans; Lung Neoplasms - epidemiology; Lung Neoplasms - genetics; Membrane Proteins - genetics; Polymorphism, Single Nucleotide; Risk Factors; ROC Curve; Sensitivity and Specificity; United Kingdom - epidemiology; Young Adult
• ISSN: 1940-6207; 1940-6215
• DOI: 10.1158/1940-6207.CAPR-09-0141

The Liverpool Lung Project (LLP) has previously developed a risk model for prediction of 5-year absolute risk of lung cancer based on five epidemiologic risk factors. SEZ6L, a Met430IIe polymorphic variant found on 22q12.2 region, has been previously linked with an increased risk of lung cancer in a case-control population. In this article, we quantify the improvement in risk prediction with addition of SEZ6L to the LLP risk model. Data from 388 LLP subjects genotyped for SEZ6L single-nucleotide polymorphism (SNP) were combined with epidemiologic risk factors. Multivariable conditional logistic regression was used to predict 5-year absolute risk of lung cancer with and without this SNP. The improvement in the model associated with the SEZ6L SNP was assessed through pairwise comparison of the area under the receiver operating characteristic curve and the net reclassification improvements (NRI). The extended model showed better calibration compared with the baseline model. There was a statistically significant modest increase in the area under the receiver operating characteristic curve when SEZ6L was added into the baseline model. The NRI also revealed a statistically significant improvement of around 12% for the extended model; this improvement was better for subjects classified into the two intermediate-risk categories by the baseline model (NRI, 27%). Our results suggest that the addition of SEZ6L improved the performance of the LLP risk model, particularly for subjects whose initial absolute risks were unable to discriminate into "low-risk" or "high-risk" group. This work shows an approach to incorporate genetic biomarkers in risk models for predicting an individual's lung cancer risk.