Dataset Information

Individual and joint contributions of genetic and methylation risk scores for enhancing lung cancer risk stratification: data from a population-based cohort in Germany.

ABSTRACT:

Background

Risk stratification for lung cancer (LC) screening is so far mostly based on smoking history. This study aimed to assess if and to what extent such risk stratification could be enhanced by additional consideration of genetic risk scores (GRSs) and epigenetic risk scores defined by DNA methylation.

Methods

We conducted a nested case-control study of 143 incident LC cases and 1460 LC-free controls within a prospective cohort of 9949 participants aged 50-75?years with 14-year follow-up. Lifetime smoking history was obtained in detail at recruitment. We built a GRS based on 31 previously identified LC-associated single-nucleotide polymorphisms (SNPs) and a DNA methylation score (MRS) based on methylation of 151 previously identified smoking-associated cytosine-phosphate-guanine (CpG) loci. We evaluated associations of GRS and MRS with LC incidence by logistic regression models, controlling for age, sex, smoking status, and pack-years. We compared the predictive performance of models based on pack-years alone with models additionally including GRS and/or MRS using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results

GRS and MRS showed moderate and strong associations with LC risk even after comprehensive adjustment for smoking history (adjusted odds ratio [95% CI] comparing highest with lowest quartile 1.93 [1.05-3.71] and 5.64 [2.13-17.03], respectively). Similar associations were also observed within the risk groups of ever and heavy smokers. Addition of GRS and MRS furthermore strongly enhanced LC prediction beyond prediction by pack-years (increase of optimism-corrected AUC among heavy smokers from 0.605 to 0.654, NRI 26.7%, p = 0.0106, IDI 3.35%, p = 0.0036), the increase being mostly attributable to the inclusion of MRS.

Conclusions

Consideration of MRS, by itself or in combination with GRS, may strongly enhance LC risk stratification.

SUBMITTER: Yu H

PROVIDER: S-EPMC7301507 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Individual and joint contributions of genetic and methylation risk scores for enhancing lung cancer risk stratification: data from a population-based cohort in Germany.

Yu Haixin H Raut Janhavi R JR Schöttker Ben B Holleczek Bernd B Zhang Yan Y Brenner Hermann H

Clinical epigenetics 20200618 1

<h4>Background</h4>Risk stratification for lung cancer (LC) screening is so far mostly based on smoking history. This study aimed to assess if and to what extent such risk stratification could be enhanced by additional consideration of genetic risk scores (GRSs) and epigenetic risk scores defined by DNA methylation.<h4>Methods</h4>We conducted a nested case-control study of 143 incident LC cases and 1460 LC-free controls within a prospective cohort of 9949 participants aged 50-75 years with 14-y ...[more]

PMID: 32552915

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Project description:OBJECTIVES:This study assessed the utility of the pooled cohort equation (PCE) and/or coronary artery calcium (CAC) for atherosclerotic cardiovascular disease (ASCVD) risk assessment in smokers, especially those who were lung cancer screening eligible (LCSE). BACKGROUND:The U.S. Preventive Services Task Force recommended and the Centers for Medicare & Medicaid Services currently pays for annual screening for lung cancer with low-dose computed tomography scans in a specified group of cigarette smokers. CAC can be obtained from these low-dose scans. The incremental utility of CAC for ASCVD risk stratification remains unclear in this high-risk group. METHODS:Of 6,814 MESA (Multi-Ethnic Study of Atherosclerosis) participants, 3,356 (49.2% of total cohort) were smokers (2,476 former and 880 current), and 14.3% were LCSE. Kaplan-Meier, Cox proportional hazards, area under the curve, and net reclassification improvement (NRI) analyses were used to assess the association between PCE and/or CAC and incident ASCVD. Incident ASCVD was defined as coronary death, nonfatal myocardial infarction, or fatal or nonfatal stroke. RESULTS:Smokers had a mean age of 62.1 years, 43.5% were female, and all had a mean of 23.0 pack-years of smoking. The LCSE sample had a mean age of 65.3 years, 39.1% were female, and all had a mean of 56.7 pack-years of smoking. After a mean of 11.1 years of follow-up 13.4% of all smokers and 20.8% of LCSE smokers had ASCVD events; 6.7% of all smokers and 14.2% of LCSE smokers with CAC = 0 had an ASCVD event during the follow-up. One SD increase in the PCE 10-year risk was associated with a 68% increase risk for ASCVD events in all smokers (hazard ratio: 1.68; 95% confidence interval: 1.57 to 1.80) and a 22% increase in risk for ASCVD events in the LCSE smokers (hazard ratio: 1.22; 95% confidence interval: 1.00 to 1.47). CAC was associated with increased ASCVD risk in all smokers and in LCSE smokers in all the Cox models. The C-statistic of the PCE for ASCVD was higher in all smokers compared with LCSE smokers (0.693 vs. 0.545). CAC significantly improved the C-statistics of the PCE in all smokers but not in LCSE smokers. The event and nonevent net reclassification improvements for all smokers and LCSE smokers were 0.018 and -0.126 versus 0.16 and -0.196, respectively. CONCLUSIONS:In this well-characterized, multiethnic U.S. cohort, CAC was predictive of ASCVD in all smokers and in LCSE smokers but modestly improved discrimination over and beyond the PCE. However, 6.7% of all smokers and 14.2% of LCSE smokers with CAC = 0 had an ASCVD event during follow-up.

Dataset Information

Individual and joint contributions of genetic and methylation risk scores for enhancing lung cancer risk stratification: data from a population-based cohort in Germany.

Background

Methods

Results

Conclusions

Publications

Individual and joint contributions of genetic and methylation risk scores for enhancing lung cancer risk stratification: data from a population-based cohort in Germany.

Similar Datasets

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