Epidemiological studies increasingly rely on biomarkers of exposure.1,2 However, most biomarkers tend to be short-lived, with half-lives of only days to months. This is an important limitation for the investigation of diseases, such as cancer, with long latency periods. A well-validated biomarker for tobacco smoking is cotinine, which has a half-life of only 16 hours.3,4 As a result, cotinine does not distinguish between former smokers and those who have never smoked and can validate only whether former smokers have actually quit smoking.5 The identification of a persistent biomarker of tobacco exposure would not only be useful for molecular epidemiology but also would suggest a paradigm for quantification of other exposures that are also difficult to measure.
Epigenetic modifications such as DNA methylation and histone modification are key determinants of chromatin structure and gene expression. These modifications are maintained during cell division and, when perturbed, play a key role in cancer development.6–8 Epigenetic changes may also represent a biological indicator of lifetime accumulation of environmental exposures related to aging,9 hormones,10 ionizing radiation,11 alcohol,12 smoking,1,13 and perhaps many others.
We have previously performed a DNA methylation study of white blood cell DNA within a large prospective cohort of current, former, and never smokers, based on the results of two epigenetic-wide association studies including 374 subjects (half who subsequently developed breast or colon cancer and half who were healthy controls) and a validation cohort of 180 subjects.14 Decreased methylation levels at eight genomic loci were associated with current smoking using a cutoff of P<10–7 (Bonferroni corrected) and several were also validated by bisulphite pyrosequencing in an independent sample set. Here, we assess the performance of DNA methylation measured by bisulphite pyrosequencing at four selected genomic loci, combined into a methylation index (MI), as a biomarker of former exposure to tobacco smoke.
All study participants were drawn from the Turin component of the European Prospective Investigation into Cancer and Nutrition (EPIC-Turin) cohort, a general population cohort that consists of approximately 10,000 persons with standardized lifestyle and personal history questionnaires, anthropometric data, and blood samples collected for DNA extraction.15,16 Smoking status was ascertained from questionnaire data. For the test sample set, 81 healthy persons were sampled from 1,805 who had been previously measured for serum cotinine,17 including 33 nonsmokers, 30 former smokers, and 18 current smokers. These included 66 men and 15 women with similar smoking status. Smoking duration was calculated as age at recruitment minus age at smoking initiation (for current smokers) and age at quitting minus age at smoking initiation (for former smokers). For the validation component of this study, 180 healthy women were randomly sampled from the EPIC-Turin cohort (n = 102 nonsmokers, n= 45 former smokers, n = 33 current smokers).
DNA samples were extracted from buffy coats using the QIAsymphony DNA Midi Kit (Qiagen, Crawley, UK). Genomic DNA (250 ng) from each subject was bisulphite converted and pyrosequenced as described previously.18 The loci included one in the aryl hydrocarbon receptor repressor (AHRR) gene (Chr5: 373,299), two intergenic loci at 2q37 (Chr2: 233,284,112 and Chr2:233,284,661), and one intergenic locus at 6p21.33 (Chr6: 30,720,080). Individuals (test, n = 11 and validation, n = 42) that were heterozygous for a previously identified G→A single nucleotide polymorphism at the first locus in AHRR (Chr5: 373,299) were removed from the analysis to avoid confounding of the AHRR methylation results. The pyrosequencing assay for this locus determines both the methylation assay and the genotype in the same assay for this cytosine-guanine dinucleotide (CG) site.14 Alternative CG sites that could be used in place of this AHRR site included cg21161138, for which a pyrosequencing assay was developed, or cg05575921, which requires the further development of a pyrosequencing assay. Illumina 450K DNA methylation microarray processing and analysis methods have been described elsewhere.14 The pyrosequencing data for the four loci in the validation cohort (n=180) were generated in a previous study.14 As these data represent actual methylation values, there was no normalization performed on the pyrosequencing data.
Wilcoxon ranked-sum tests were used to compare nonparametric data. Pearson tests were performed to assess any correlation between methylation values at various genomic loci. The methylation model was constructed using a stepwise iterative generalized linear regression model of the data for never smokers versus former smokers starting with pyrosequencing data from six loci with previously reported assays.14 We excluded one AHRR CG site (cg21161138) and F2RL3 (cg03636183) that were not independent of other loci (P > 0.05 in the linear regression model) and were highly correlated with the other four markers (R > 0.6). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated using receiver operative curve (ROC) analyses. Area under the curve (AUC) values were calculated for each genomic locus and the overall MI and compared with those for cotinine in differentiating between never versus former smokers. Binomial tests were used to assess PPVs. All statistical analyses were performed in R, v2.13.1.
The association between methylation levels in four of the genomic loci and smoking status that was observed previously14 was confirmed in the test set of 81 persons (Table 1 and Figure 1). Cotinine levels above the cutoff of 15 ng/mL5,19 were associated with current smoking status (P < 0.0001, Table 2) with a high predictive value for current smokers compared with never smokers using ROC analysis (AUC =0.97) (Table 1). Table 1 indicates the AUC values based on methylation values for each genomic locus in predicting former smoker status for the test set, in addition to that for cotinine: the AUC for AHRR_p1 (AUC = 0.71), 6p21 (AUC = 0.63), 2q37_p1 (AUC = 0.68), and 2q37_p3 (AUC = 0.66) individually had a greater ability to distinguish former from nonsmokers than cotinine levels (AUC = 0.47). We combined the methylation values of all four loci into a single MI using the MI model = (β1M1 × β2M2 × β3M3 × β4M4), where β represents the β-coefficient for the methylation locus in association with smoking status and M represents the methylation level of each locus as a percentage (equivalent to raw β values from 450K methylation array data). For the purpose of this analysis, we have not transformed methylation values to M values as this did not alter the performance of the model.
The AUC value for the combined MI of these four loci in differentiating never from former smokers was 0.82 (95% confidence interval [CI] = 0.64–0.99) in the test set and 0.83 (95% CI = 0.70–0.96) in the validation set (Figure 2) with a sensitivity of 69% and 71%, respectively. Using previously published 450K methylation array data,14 we show the MI defined in the present study correlates strongly with duration of smoking in former smokers (Pearson’s correlation R = 0.47, Figure 3). Furthermore, using the current pyrosequencing data in the validation set, the MI also correlated strongly with the time since quitting in former smokers (Pearson’s correlation R= −0.51, Figure 4), as well as duration of smoking (R = 0.63, data not shown).
Any biomarker of tobacco exposure should reflect the degree of exposure, including the intensity and duration of smoking.5 Our study gives strong evidence for long-standing methylation changes as a result of smoking. Methylation is a relatively stable DNA modification.20 However, methylation changes may be reversible after the cessation of an exposure, although in the case of tobacco the timing of reversion is unknown.
We and others have found, using the Illumina 450K methylation beadchip, that a particular region of DNA methylation in the AHRR gene is strongly associated with smoking and can be a marker of past smoking exposure.14,21–23AHRR is part of the aryl hydrocarbon pathway that metabolizes cigarette smoke components, including the carcinogenic dioxins and dioxin-like compounds.24,25 Monick and colleagues,22 using the 450K array, found that a single probe in the AHRR gene was associated with smoking status (false discovery rate P < 0.05) in cultured lymphoblast cell lines; Philibert et al23 also using the same 450K platform identified the same site in both men and women in peripheral blood lymphocyte DNA (false discovery rate P < 0.05). Additionally, in a much larger study (n = 1,062) on the 450K platform, Joubert et al extended this by identifying 26 CG sites associated with smoking in cord blood DNA (Bonferroni P < 0.05), which showed that maternal smoking could potentially affect methylation in the newborn baby.
In the majority of loci, we noted that smoking induces hypomethylation (loss of methylation). This has been observed in all studies to date. We validated this hypomethylation using an alternative method of bisulphite pyrosequencing for six loci.14 Specifically regarding one of the top hits in the AHRR gene (eg, cg05575921), Monick et al,22 Joubert et al,21 Philibert et al,23 and our own study have all shown a decrease in methylation, which suggests that the direction of association is consistent. A notable exception is CYP1A1, for which increased methylation in relation to maternal smoking in pregnancy was identified and replicated in an independent population in a study by Joubert et al.21 The contrasting effects of maternal smoking during pregnancy on methylation at CGs in AHRR and CYP1A1 are of interest because of the opposing function of these genes in the aryl-hydrocarbon receptor pathway.26 Numerous other loci have been associated with methylation changes in smokers, including two that we previously identified (the intergenic loci 2q37 and 6p21); the functions of these are currently unknown.
One of the limitations of this study is that it has been conducted in a single population enrolled in the EPIC cohort in Turin. Further validation in other populations is needed. In particular, investigators who have serum cotinine measurements along with smoking history and 450K methylation data can perform a similar analysis to generate a biomarker of past smoking specific for the 450K platform. Another limitation in this analysis is that there is a sex imbalance in the test and validation sample sets. An analysis of larger cohorts may identify sex-specific differences. Also, we had few persons who were self-described as former smokers with high cotinine levels (>15 ng/mL, n = 1) or current smokers with low cotinine levels (<15 ng/mL, n = 2). It would be useful to assess the performance of the MI versus self-reported smoking status in a larger study.
We have shown that the epigenetic changes associated with smoking are detected in blood DNA in former smokers many years after they have quit smoking14 (median 13 years [interquartile range 9–18] in the present study, Figures 3 and 4). Given that the majority of white blood cell types have lifespans of ∼30 days, this suggests that the exposure must also be affecting the hematopoietic stem and progenitor cells, which perpetuate the epigenetic alterations in the daughter differentiated cells. Further evidence of long-term perpetuation of methylation associated with exposures was observed in the Dutch famine study, with insulin-like growth factor 2 hypomethylation in exposed persons detected 60 years later.27 If exposures such as smoking can increase a person’s risk of cancer, even in former smokers, then we hypothesize that those exposures throughout life must also affect the tissue-specific stem and progenitor cells and potentially the cell of origin for the initial carcinogenic events. Although we have measured this biomarker in blood DNA, which proves a suitable DNA source, other sources of DNA (such as buccal swabs) may be equally useful for measuring this long-term biomarker of cigarette smoke exposure. Further investigation into DNA methylation of other cell types affected by tobacco smoking is warranted.
In sum, we have determined a set of differentially methylated genomic loci dependent on tobacco exposure that can predict former smoking status with high positive predictive and sensitivity values when combined into a single DNA MI. This provides a direct molecular measure of prior exposure to tobacco that can be performed using pyrosequencing. These data suggest that epigenetic patterns detected in blood may provide molecular biomarkers of other exposures that are also difficult to quantify in epidemiological studies.
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