| Title: | Assessing Lung Cancer Absolute Risk Trajectory Based on a Polygenic Risk ModelPolygenic Model for Lung Cancer Absolute Risk Trajectory |
| Journal: | Cancer Research |
| Published: | 20 Jan 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33472890/ |
| DOI: | https://doi.org/10.1158/0008-5472.can-20-1237 |
| URL: | https://resolver.sub.uni-goettingen.de/purl?gro-2/82534 |
| Citations: | 62 (35 in last 2 years) as of 8 Aug 2024 |
Publication 9545
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
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.</p>
28 Keywords
- Adult
- Age Factors
- Aged
- Biomarkers, Tumor
- Case-Control Studies
- Early Detection of Cancer
- Female
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Incidence
- Lung
- Lung Neoplasms
- Machine Learning
- Male
- Mass Screening
- Medical History Taking
- Middle Aged
- Models, Genetic
- Multifactorial Inheritance
- Oligonucleotide Array Sequence Analysis
- Practice Guidelines as Topic
- Pulmonary Disease, Chronic Obstructive
- Risk Assessment
- Risk Factors
- Smoking
- Tomography, X-Ray Computed
- United Kingdom
29 Authors
- Rayjean J. Hung
- Matthew T. Warkentin
- Yonathan Brhane
- Nilanjan Chatterjee
- David C. Christiani
- Maria Teresa Landi
- Neil E. Caporaso
- Geoffrey Liu
- Mattias Johansson
- Demetrius Albanes
- Loic Le Marchand
- Adonina Tardon
- Gad Rennert
- Stig E. Bojesen
- Chu Chen
- John K. Field
- Lambertus A. Kiemeney
- Philip Lazarus
- Shanbeth Zienolddiny
- Stephen Lam
- Angeline S. Andrew
- Susanne M. Arnold
- Melinda C. Aldrich
- Heike Bickeböller
- Angela Risch
- Matthew B. Schabath
- James D. McKay
- Paul Brennan
- Christopher I. Amos
1 Application
| Application ID | Title |
|---|---|
| 23261 | Mediation and Mendelian Randomization analyses to investigate the causal relationship between genetic variants, lung function and lung cancer risk |
Enabling scientific discoveries that improve human health