Using Cg05575921 methylation to predict lung cancer risk: a potentially bias-free precision epigenetics approach

References

• Centers for Disease Control and Prevention, Centres for Disease Control. Annual Smoking-Attributable Mortality, Years of Potential Life Lost, and Productivity Losses— United States, 1997-2001. Morb Mortal Wkly. 1997–2001;54625–628:2005.
   Google Scholar
• Husmann L, Stolzmann P. Staging, restaging and response evaluation of non-small-cell lung cancer. In: Hodler J, von Schulthess GK, Kubik-Huch RA, editors. Diseases of the chest and heart 2015–2018: diagnostic imaging and interventional techniques. Milano: Springer Milan; 2015. p. 183–188.
   Google Scholar
• Alberg AJ, Brock MV, Ford JG. Epidemiology of lung cancer: diagnosis and management of lung cancer: American college of chest physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e1S–e29S.
   Google Scholar
• The National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395–409.
   Google Scholar
• The National Lung Screening Trial Research Team. Results of initial low-dose computed tomographic screening for lung cancer. N Engl J Med. 2013;368(21):1980–1991.
   Google Scholar
• de Koning HJ, van der Aalst CM, de Jong PA. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020;382(503–513):503–513.
   Google Scholar
• Krist AH. Screening for lung cancer: US preventive services task force recommendation statement. JAMA. 2021;325(10):962–970.
   Google Scholar
• Moyer VA. Screening for lung cancer: U.S. preventive services task force recommendation statement. Ann Intern Med. 2014;160(5):330–338.
   Google Scholar
• Aberle DR, Adams AM, Berg CD, National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365:395–409.
   Google Scholar
• Rampinelli C, De Marco P, Origgi D. Exposure to low dose computed tomography for lung cancer screening and risk of cancer: secondary analysis of trial data and risk-benefit analysis. BMJ. 2017;356. DOI:10.1136/bmj.j347.
   Google Scholar
• Pinsky PF, Gierada DS, Hocking W. National Lung Screening Trial findings by age: medicare-eligible versus under-65 population. Ann Intern Med. 2014;161(627–633):627.
   Google Scholar
• Huo J, Xu Y, and Sheu T. Complication rates and downstream medical costs associated with invasive diagnostic procedures for lung abnormalities in the community setting complications and medical costs associated with diagnostic procedures for lung abnormalities complications and medical costs associated with diagnostic procedures for lung abnormalities. JAMA Internal Medicine. 2019;179(3): 324–332.
   Google Scholar
• Rahouma M, Kamel M, Abouarab A. Lung cancer patients have the highest malignancy-associated suicide rate in USA: a population-based analysis. Ecancermedicalscience. 2018;12. DOI:10.3332/ecancer.2018.859.
   Google Scholar
• Monick MM, Beach SRH, Plume J. Coordinated changes in AHRR methylation in lymphoblasts and pulmonary macrophages from smokers. Am J Med Genet B Neuropsychiatr Genet. 2012;159B(141–151):141–151.
   Google Scholar
• Gao X, Jia M, Zhang Y. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. Clin Epigenetics. 2015;7(113). DOI:10.1186/s13148-015-0148-3
   Google Scholar
• Takeuchi F, Takano, K, and Yamamoto, M, et al. Clinical implication of smoking-related aryl-hydrocarbon receptor repressor (AHRR) hypomethylation in Japanese adults. Circ J. 2022;86(6):986–992.
   Google Scholar
• Philibert R, Mills JA, Long JD. The reversion of cg05575921 methylation in smoking cessation: a potential tool for incentivizing healthy ageing. Genes (Basel). 2020;11(1415):1415.
   Google Scholar
• Dawes K, Andersen A, Reimer R. The relationship of smoking to cg05575921 methylation in blood and saliva DNA samples from several studies. Sci Rep. 2021;11(21627). DOI:10.1038/s41598-021-01088-7.
   Google Scholar
• Zhang Y, Elgizouli M, Schöttker B. Smoking-associated DNA methylation markers predict lung cancer incidence. Clin Epigenetics. 2016;8(127). DOI:10.1186/s13148-016-0292-4.
   Google Scholar
• Bojesen SE, Timpson N, Relton C. AHRR (cg05575921) hypomethylation marks smoking behaviour, morbidity and mortality. Thorax. 2017;72(7):646–653.
   Google Scholar
• Jacobsen KK, Schnohr P, Jensen GB. AHRR (cg05575921) Methylation Safely Improves Specificity of Lung Cancer Screening Eligibility Criteria: A Cohort Study. Cancer Epidemiol Prev Biomarkers. 2022;31(4):758–765.
   Google Scholar
• Hillman BJ. Economic, legal, and ethical rationales for the ACRIN national lung screening trial of CT screening for lung cancer. Acad Radiol. 2003;10(3):349–350.
   Google Scholar
• National Lung Screening Trial Research Team. The national lung screening trial: overview and study design. Radiology. 2011;258(1):243–253.
   Google Scholar
• Philibert R, Dogan M, Noel A. Dose response and prediction characteristics of a methylation sensitive digital PCR assay for cigarette consumption in adults. Front Genet. 2018;9. DOI:10.3389/fgene.2018.00137.
   Google Scholar
• Dawes K, Andersen A, Papworth E. Refinement of cg05575921 demethylation response in nascent smoking. Clin Epigenetics. 2020;12:1–11.
   Google Scholar
• Royston P, Parmar MK. Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med. 2002;21(15):2175–2197.
   Google Scholar
• Pencina MJ, Larson MG, D’Agostino RB. Choice of time scale and its effect on significance of predictors in longitudinal studies. Stat Med. 2007;26(1343–1359):1343–1359.
   Google Scholar
• Anderson DR. Model based inference in the life sciences: a primer on evidence. New York: Springer; 2008.
   Google Scholar
• Jackson CH. flexsurv: a Platform for Parametric Survival Modelling in R. J Stat Softw. 2016;70(8). DOI:10.18637/jss.v070.i08
   Google Scholar
• Lazic SE. Medical risk prediction models: with ties to machine learning. J R Stat Soc Ser A. 2022;185(1):425–425.
   Google Scholar
• Wood SN. Generalized additive models: an introduction with R. New York: Chapman and Hall/CRC; 2006.
   Google Scholar
• Fleiss JL. Statistical methods for rates and proportions. New York NY: John Wiley & Sons Inc; 1981.
   Google Scholar
• Tammemägi MC, Katki HA, Hocking WG. Selection criteria for lung-cancer screening. N Engl J Med. 2013;368(728–736):728–736.
   Google Scholar
• Wheaton AG, Liu Y, Croft JB. Chronic obstructive pulmonary disease and smoking status—United States, 2017. Morbidity Mortality Weekly Rep. 2019;68(24):533.
   Google Scholar
• Vyas DA, Eisenstein LG, and Jones DS. Hidden in plain sight — reconsidering the use of race correction in clinical algorithms. N Engl J Med. 2020;383(9):874–882.
   Google Scholar
• Caraballo RS, Asman K. Epidemiology of menthol cigarette use in the United States. Tob Induc Dis. 2011;9(S1):S1
   Google Scholar
• Gardiner PS. The African Americanization of menthol cigarette use in the United States. Nicotine Tob Res. 2004;6(1):S55–S65.
   Google Scholar
• Miyazawa M, Marumoto S, and Takahashi T. Metabolism of (+)- and (-)-menthols by CYP2A6 in human liver microsomes. J Oleo Sci. 2011;60(3):127–132.
   Google Scholar
• Jabba SV, and Jordt S-E. Risk analysis for the carcinogen pulegone in mint- and menthol-flavoured e-cigarettes and smokeless tobacco products. JAMA Intern Med. 2019;179(12):1721.
   Google Scholar
• Taylor SC, Nadeau K, Abbasi M. The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends Biotechnol. 2019;37(7):761–774.
   Google Scholar
• Zhang Y, Florath I, Saum K-U. Self-reported smoking, serum cotinine, and blood DNA methylation. Environ Res. 2016;146:395–403.
   Google Scholar
• Zeilinger S, Kühnel B, Klopp N. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS One. 2013;8(e63812):e63812.
   Google Scholar
• Shenker NS, Polidoro, S, and van Veldhoven, K. Epigenome-wide association study in the European prospective investigation into cancer and nutrition (EPIC-turin) identifies novel genetic loci associated with smoking. Hum Mol Genet. 2012;22(5): 843–851.
   Google Scholar
• Stueve TR, Li W-Q, Shi J. Epigenome-wide analysis of DNA methylation in lung tissue shows concordance with blood studies and identifies tobacco smoke-inducible enhancers. Hum Mol Genet. 2017;26(3014–3027):3014–3027.
   Google Scholar
• Philibert RA, Beach S, and Brody GH. The DNA methylation signature of smoking: an archetype for the identification of biomarkers for behavioural illness. Genes motiv use subst. 2014;61:109–127.
   Google Scholar
• Sakurai S, Shimizu T, Ohto U. The crystal structure of the AhRR/ARNT heterodimer reveals the structural basis of the repression of AhR-mediated transcription. J Biol Chem. 2017;292(17609–17,616):17609–17,616.
   Google Scholar
• Chen A, Krebs, N, and Zhu, J. Sex/gender differences in cotinine levels among daily smokers in the Pennsylvania adult smoking study. J Women’s Health. 2017;26(11):1222–1230.
   Google Scholar
• Melikian AA, Djordjevic M, Hosey J. Gender Differences Relative to Smoking Behaviour and Emissions of Toxins From Mainstream Cigarette Smoke. Nicotine Tob Res. 2007;9(3):377–387.
   Google Scholar
• Halpern SD, French B, and Small DS. Randomized trial of four financial-incentive programs for smoking cessation. NEJM. 2015;372(22):2108–2117.
   Google Scholar
• Uthoff J, Stephens MJ, and Newell JD. Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. Med Phys. 2019;46(7):3207–3216.
   Google Scholar
• Gillies RJ, and Schabath MB. Radiomics improves cancer screening and early detection. Cancer Epidemiol Biomarkers Prev. 2020;29(12):2556–2567.
   Google Scholar
• Khawaja A, Bartholmai BJ, and Rajagopalan S. Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification. J Thorac Dis. 2020;12(6):3303–3316.
   Google Scholar
• Li J, Stults C, and Liang S-Y. Adherence to provider referrals for lung cancer screening with low dose computed tomography before and during the COVID-19 pandemic. Soc Res Nicotine Tob Res. S2–104 (Baltimore, MD). 2022;22.
   Google Scholar