Assessing Lung Cancer Absolute Risk Trajectory Based on a Polygenic Risk Model

Cancer Res. 2021 Mar 15;81(6):1607-1615. doi: 10.1158/0008-5472.CAN-20-1237. Epub 2021 Jan 20.


Lung cancer is the leading cause of cancer-related death globally. An improved risk stratification strategy can increase efficiency of low-dose CT (LDCT) screening. Here we assessed whether individual's genetic background has clinical utility for risk stratification in the context of LDCT screening. On the basis of 13,119 patients with lung cancer and 10,008 controls with European ancestry in the International Lung Cancer Consortium, we constructed a polygenic risk score (PRS) via 10-fold cross-validation with regularized penalized regression. The performance of risk model integrating PRS, including calibration and ability to discriminate, was assessed using UK Biobank data (N = 335,931). Absolute risk was estimated on the basis of age-specific lung cancer incidence and all-cause mortality as competing risk. To evaluate its potential clinical utility, the PRS distribution was simulated in the National Lung Screening Trial (N = 50,772 participants). The lung cancer ORs for individuals at the top decile of the PRS distribution versus those at bottom 10% was 2.39 [95% confidence interval (CI) = 1.92-3.00; P = 1.80 × 10-14] in the validation set (P trend = 5.26 × 10-20). The OR per SD of PRS increase was 1.26 (95% CI = 1.20-1.32; P = 9.69 × 10-23) for overall lung cancer risk in the validation set. When considering absolute risks, individuals at different PRS deciles showed differential trajectories of 5-year and cumulative absolute risk. The age reaching the LDCT screening recommendation threshold can vary by 4 to 8 years, depending on the individual's genetic background, smoking status, and family history. Collectively, these results suggest that individual's genetic background may inform the optimal lung cancer LDCT screening strategy. SIGNIFICANCE: Three large-scale datasets reveal that, after accounting for risk factors, an individual's genetics can affect their lung cancer risk trajectory, thus may inform the optimal timing for LDCT screening.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Biomarkers, Tumor / genetics*
  • Case-Control Studies
  • Early Detection of Cancer / standards
  • Early Detection of Cancer / statistics & numerical data
  • Female
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study
  • Humans
  • Incidence
  • Lung / diagnostic imaging
  • Lung Neoplasms / diagnosis
  • Lung Neoplasms / epidemiology*
  • Lung Neoplasms / genetics
  • Lung Neoplasms / prevention & control
  • Machine Learning
  • Male
  • Mass Screening / standards
  • Mass Screening / statistics & numerical data
  • Medical History Taking
  • Middle Aged
  • Models, Genetic*
  • Multifactorial Inheritance*
  • Oligonucleotide Array Sequence Analysis
  • Practice Guidelines as Topic
  • Pulmonary Disease, Chronic Obstructive / epidemiology
  • Risk Assessment / methods
  • Risk Assessment / statistics & numerical data
  • Risk Factors
  • Smoking / epidemiology
  • Tomography, X-Ray Computed / standards
  • Tomography, X-Ray Computed / statistics & numerical data
  • United Kingdom / epidemiology


  • Biomarkers, Tumor