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Lung Cancer Risk Prediction Using Common SNPs Located in GWAS-Identified Susceptibility Regions.

Joel L Weissfeld1, Yan Lin, Hui-Min Lin

  • 1Departments of *Epidemiology and †Biostatistics, Graduate School of Public Health, Pittsburgh, Pennsylvania; Departments of ‡Medicine, §Radiology, and ‖Cardiothoracic Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; and ¶Department of Pharmacology, School of Medicine, University of Minnesota, Minneapolis, Minnesota.

Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer
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Summary

Genetic markers in three lung cancer susceptibility regions identified by genome-wide association studies (GWAS) improved lung cancer risk prediction. However, the predictive improvement was likely too small to impact clinical practice for lung cancer screening.

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Area of Science:

  • Genetics
  • Oncology
  • Epidemiology

Background:

  • Genome-wide association studies (GWAS) have identified specific regions associated with lung cancer susceptibility.
  • Evaluating the predictive performance of single-nucleotide polymorphisms (SNPs) within these regions is crucial for risk assessment.

Purpose of the Study:

  • To assess the predictive value of SNPs in GWAS-identified lung cancer susceptibility regions.
  • To determine if incorporating these genetic markers improves lung cancer risk prediction models.

Main Methods:

  • Genotyped 77 SNPs in 778 lung cancer cases and 1166 controls from GWAS regions.
  • Utilized stepwise logistic regression and decision-tree analyses for variable selection and model development.
  • Assessed prediction improvement using receiver operating characteristic curves and net reclassification in a lung cancer screening cohort.

Main Results:

  • Identified two significant SNPs in each of three GWAS regions (5p15.33, 6p21.33, 15q25.1).
  • High-risk genotypes in these regions were associated with a threefold increased odds of lung cancer (OR, 3.14).
  • Adding genetic risk scores to age and smoking models yielded a modest improvement in lung cancer prediction (AUC 0.725 vs. 0.717, p=0.056).

Conclusions:

  • Genotyping SNPs in three GWAS-identified lung cancer susceptibility regions enhanced predictive models.
  • The observed improvement in prediction was marginal, likely insufficient to alter current clinical disease control practices.