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

Lung cancer (LC) is the leading cause of cancer-related death worldwide, accounting for more than 1,761,000 deaths in 2018 [1]. Prognosis of LC is generally poor, with 5-year survival ranging from 10 to 20% in different countries [2]. Poor survival is due to the majority of tumors being detected at an advanced stage, at which options for curative treatment are limited [3–5]. Survival and prognosis may be much better when LC is detected at an early stage through the use of screening [6]. Randomized trials have demonstrated that the potential of reducing LC mortality by screening the high-risk group of heavy smokers with low-dose computed tomography (LDCT) [6, 7].

Risk stratification for LC screening is so far mostly based on smoking history [7]. Although smoking is a major risk factor for LC, less than 20% of lifelong smokers develop LC, and a non-negligible proportion of LCs also occur in people not meeting the definitions of heavy smoking [8]. Therefore, additional markers for pre-selecting those at highest risk for LC screening are highly desirable. In the past decade, dozens of single-nucleotide polymorphisms (SNPs) associated with LC risk have been identified through genome-wide association studies (GWAS) [9]. Genetic risk scores (GRS) based on SNPs identified from GWAS studies were found to enhance performance of risk prediction models for several common illnesses, such as cardiovascular disease [10], diabetes [11, 12], breast cancer [13, 14], colorectal cancer [15], and prostate cancer [16, 17]. However, few studies have evaluated the contributions of GRS to risk stratification of LC.

Studies have shown that changes in DNA methylation in blood prior to lung cancer diagnosis mainly occur at smoking-associated genes [18, 19]. In recent years, epigenome-wide association studies (EWAS) have identified a large number of CpG sites in whole blood DNA whose methylation levels were strongly related to smoking history and were also found to be related to LC risk [20, 21]. In this study, we aimed to assess the individual and joint potential of a GRS and a methylation risk score (MRS) based on smoking-related CpGs for enhancing LC risk stratification in a cohort of older adults who were followed for 14 years.

We have evaluated the individual and joint potential of a GRS based on 31 SNPs and a MRS based on 151 smoking-associated CpGs for enhancing risk stratification in LC prevention. We found moderate associations of the GRS and strong associations of the MRS with the risk of LC even after comprehensive adjustment for smoking history in a case-control study that was nested in a general population-based cohort study. Furthermore, we demonstrated that the addition of MRS and GRS strongly enhanced the risk prediction compared to the standard risk stratification by pack-years both within the entire study population as well as within the high-risk groups of ever smokers and heavy smokers, the improvement being mostly attributable to the inclusion of the MRS. Our results provide support for including MRS, potentially along with GRS, into LC risk assessment models to more accurately stratify individuals and select those at highest risk for inclusion in screening programs.

LDCT is an effective screening tool for early detection of LC and is recommended by the United States Preventive Services Task Force to screen for LC among high-risk heavy smokers (aged 55–80 years, with ≥ 30 pack-years of smoking who are either current smokers or have quit smoking ≤ 15 years ago) [7]. However, false positive results, high costs, and potential radiation exposure remain major concerns for LDCT-based screening [7, 22]. This underlines the importance of identifying individuals at highest risk to maximize the benefits and minimize the harms of screening. To facilitate this, multiple risk prediction models using traditional factors like age, sex, family history, smoking history, occupational exposure, etc. have been proposed [23–25]. Traditional smoking-based risk models have mostly used self-reported smoking exposure information, which may not accurately represent the actual exposure [26, 27]. Biomarkers, reflecting biologically relevant smoking exposure, such as smoking-associated DNA methylation markers, might therefore be most useful to improve current LC risk stratification based on self-reported smoking history. In addition, studies have demonstrated that methylation changes at smoking-associated genes rather than at the LC-related genes were involved in the initiation of LC [18, 19, 28]. Therefore, smoking-associated CpGs might be more suitable for LC screening than LC-related CpGs. Unlike DNA methylation, SNPs do not vary over time and are not affected by exposures and disease status [29]. The SNPs identified by GWAS may be associated with either the initiation or progression of LC (or both), regardless of their location at smoking-associated genes or elsewhere. By incorporating all LC-associated SNPs into GRS and smoking-associated CpGs into MRS, this study comprehensively evaluated the genetic and epigenetic effects in LC risk stratification.

During the last decade, large-scale GWAS [9, 30, 31] have identified numerous LC susceptibility loci. Prior studies have explored the value of genetic variants in LC risk prediction [32–34]. Cheng et al. [34] developed a risk model that included both a risk score based on 38 genetic variants (selected from 241 genetic variants identified in large-scale studies of ethnically diverse populations) and self-reported smoking information, which was evaluated in a training and testing set. In the testing set, a modest improvement in AUC for a model that included both a GRS and smoking information (AUC = 0.647), compared with a model that included smoking information only (AUC = 0.625), was reported. In a study by Qian et al. [33], inclusion of 301 GWAS detected SNPs barely improved prediction performance of a model that included epidemiologic factors (age, sex, and pack-years) only (AUC 0.617 vs. 0.607 in the test set). In our study, adding a GRS based on 31 GWAS-identified SNPs to a model based on pack-years likewise yielded at best only a very limited increase in AUCs. However, relevant increases were achieved by including the MRS, with models adding either MRS alone or both GRS and MRS yielding substantial discrimination improvement as indicated by NRI and IDI.

Over the past several years, a number of studies have highlighted the value of smoking-associated DNA methylation biomarkers assessed in blood for LC prediction [21, 35–38]. Few studies have investigated the degree of improvement of prediction models based on smoking-associated DNA methylation markers beyond self-reported smoking exposure. Baglietto et al. [38] estimated the gain in prediction accuracy of a model additionally including 6 smoking-associated CpGs to a model only including self-reported smoking information (smoking status, pack-years) in two cohorts and reported increases in AUCs of 0.026 and 0.055, respectively, in the discovery studies from which the MRS was derived. In previous analyses of the ESTHER study which had focused on one or three DNA methylation markers only, substantially lower improvements of prediction accuracy had been observed [21, 36]. The results of the current study suggest that using more comprehensive MRS may substantially improve risk prediction.

Our current study differs from prior studies by evaluating not only the predictive value of genetic or methylation information individually but also by comparing their predictive value and evaluating their combined predictive value for LC prediction beyond prediction by pack-years alone. While GRS showed modest predictive value for LC risk, the predictive value of MRS was much higher, and addition of GRS to models including MRS did not improve the predictive value in the entire study population. Nevertheless, among the risk groups of ever and heavy smokers, the combination of pack-years with both GRS and MRS resulted in the highest predictive performance.

Our results may have important clinical implications for LC screening and preventive strategies. Our results suggested that epigenetic signatures may have the potential to better select patients at highest risk for LC screening. Future research should explore possibilities to further enhance prediction by more refined MRS and possible combinations with other potentially promising epigenetic signatures, such as microRNA signatures [39, 40]. Future studies should also address the acceptance, feasibility, and cost-effectiveness of such risk stratification in LC screening programs.

To our knowledge, this is the first study to comprehensively evaluate the individual and joint performance of GRS and MRS for predicting LC risk in a prospective cohort with 14-year follow-up. Additionally, detailed information on smoking was available which enabled evaluation of the predictive value beyond the currently recommended, exclusively smoking-based criteria for selecting people for LC screening. However, our study also has some important limitations. In particular, despite the large size of the cohort, this study was based on the limited number of LC cases hindered more detailed analyses by important factors, such as age and sex of the study population, time between blood sampling and LC occurrence, or different LC subtypes. Potential variation of predictive performance by such factors should be evaluated in even larger studies. Furthermore, although our estimates of prediction performance of combinations of predefined MRS, GRS, and smoking history were internally corrected for potential over-optimism by bootstrapping, further validation in independent cohorts is warranted. Future, ideally much larger studies should also address the performance of GRS and MRS in predicting risk of specific genetic variants of lung cancer.

In summary, despite its limitations, this study provides evidence for the potential of GRS and particularly MRS, by themselves and in combination, for enhancing LC risk stratification. To our knowledge, this is the first prospective cohort study evaluating both types of scores in direct comparison and combination. We showed that, although both GRS and MRS predicted LC risk, predictive value, especially predictive value beyond smoking history was much stronger for MRS than for GRS. The predictive value of MRS based on a large number of established smoking-related CpGs investigated in this study also outperformed the previously demonstrated predictive value of a few single CpGs. Consideration of MRS, by itself or in combination with GRS, may therefore have the potential to enhance risk stratification for LC screening. Further research is warranted to replicate and expand our results in larger and ethnically diverse populations and include screening cohorts in order to more comprehensively evaluate the potential of the risk scores for identifying high-risk individuals for LC screening. Future studies should also aim for identification of additional genetic or epigenetic markers and integration of additional environmental or life-style factors into the risk-prediction models in order to further enhance risk stratification for LC and pave the way for better targeting LC screening offers to those at highest risk.

The authors gratefully acknowledge contributions of Katarina Cuk in processing of DNA samples and performing laboratory work, and of Hauke Thomsen in imputing genotyped data.

HY: statistical analysis and interpretation of data and drafting of the manuscript; JR: critical revision of the manuscript for important intellectual content; BS and BH: coordination of follow-up and work-up of follow-up data of the ESTHER study; YZ: study design and supervision; and HB: study design and supervision, critical revision of the manuscript for important intellectual content, and obtained funding. The authors read and approved the final manuscript.

Haixin Yu was partly supported by the China Scholarship Council (No. 201606170104). The ESTHER study was supported by the Baden-Württemberg State Ministry of Science; Research and Arts (Stuttgart, Germany); the Federal Ministry of Education and Research (Berlin, Germany); the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (Berlin, Germany); and the Saarland state Ministry for Social Affairs, Health, Women and Family Affairs (Saarbrücken, Germany). The sponsors had no role in the study design, in the collection, analysis and interpretation of data, and preparation, review, or approval of the manuscript.

Anonymized data used for this study are available from the corresponding author upon reasonable request.

All participants provided written informed consent. The study was approved by the ethics committees of the University of Heidelberg and of the state medical board of Saarland, Germany.

Not applicable.

The authors declare no conflict of interest.

Supplementary information accompanies this paper at 10.1186/s13148-020-00872-y.