Journal article
A DNA Methylation-based algorithm Improves Lung Cancer risk prediction in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial
Lung cancer (Amsterdam, Netherlands), Vol.217, 109462
07/2026
DOI: 10.1016/j.lungcan.2026.109462
PMID: 42167026
Abstract
Measuring DNA methylation levels at cg05575921 can improve prediction of lung cancer (LC) risk in a screening eligible population. However, these findings were based on a limited number of largely White study participants, with a history of heavy smoking (>30 pack years [PY]) and the cg05575921 based-metric was not directly compared to existing standards for LC prediction.] METHOD: We determined cg05575921 methylation levels in a nested case and control cohort featuring 1156 LC cases and 3039 controls, matched for age, sex, race and self-reported smoking status (current and former), who participated in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. We then constructed survival algorithms that tested whether adding cg05575921 methylation levels to a model consisting of PLCO
and PY improved the prediction of time to lung cancer diagnosis as compared to the PLCO
algorithm.
Models adding cg05575921 methylation levels to PLCO
and PY significantly improved prediction of LC occurrence over 20-year follow up for subjects who report current or former smoking. In this set of subjects matched for age, sex and smoking status, a simple algorithm using cg05575921 and PY outperformed the PLCO
in all smokers (20-yr area under the curve [AUC] 0.725 vs 0.689), ≥ 20 PY smokers (20-yr AUC 0.662 vs 0.634) and < 20 PY smokers (20-yr AUC 0.666 vs 0.549). Among participants with < 20 PY smoking histories, those with cg05575921 methylation levels < 80% were at 3.3-fold greater risk for LC than those with similar PY history but with cg05575921 methylation levels > 80%.
The use of cg05575921 methylation levels can improve the accuracy of LC risk prediction and may be particularly useful identifying persons with a < 20 PY history who are at elevated risk for LC.These findings require validation in an external screening population.
Details
- Title: Subtitle
- A DNA Methylation-based algorithm Improves Lung Cancer risk prediction in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial
- Creators
- Kelsey Dawes - Behavioral Diagnostics (United States)James A Mills - University of IowaRichard M Hoffman - University of IowaEllyse M Froehlich - Behavioral Diagnostics (United States)Kaitlyn deBlois - Behavioral Diagnostics (United States)Jessica C Sieren - University of IowaCraig Williams - Information Management ServicesShannon Merkle - Information Management ServicesJeffrey D Long - University of IowaSteven Rh Beach - University of GeorgiaRobert A Philibert - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Lung cancer (Amsterdam, Netherlands), Vol.217, 109462
- DOI
- 10.1016/j.lungcan.2026.109462
- PMID
- 42167026
- NLM abbreviation
- Lung Cancer
- ISSN
- 0169-5002
- eISSN
- 1872-8332
- Publisher
- Elsevier; CLARE
- Grant note
- National Cancer Institute: 1R44CA285136
This article is dedicated to the memory of Dr. Illeana Arias, former Principal Deputy Director of the United States Centers for Disease Control, for her passionate advocacy of prevention science. The authors thank the National Cancer Institute for providing the human material collected by the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. The authors also thank Dr. Martin Tammemagi, who generously provided the PLCOm2012 formulas to us, and Dr. Paul Pinsky, who helped obtain and interpret PLCO data. This work was supported by National Cancer Institute grant 1R44CA285136 (Philibert and Dawes, Multiple Principal Investigators) .
- Language
- English
- Electronic publication date
- 05/18/2026
- Date published
- 07/2026
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Psychiatry; Iowa Neuroscience Institute; Biostatistics; General Internal Medicine; Internal Medicine
- Record Identifier
- 9985164727302771
Metrics
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