The Nicotine Metabolite Ratio (NMR, ratio of trans-3’-hydroxycotinine and cotinine), has previously been associated with CYP2A6 activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET) gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs) at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; CYP2A6 and two CYP2A6 SNPs attained experiment-wide significance adjusted for correlated SNPs (CYP2A6 PACT=4.1E-7, rs4803381 PACT=4.5E-5, rs1137115, PACT=1.2E-3). Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In post-hoc analyses of CYP2A6 SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. CYP2A6 minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that CYP2A6 plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.
Citation: Bergen AW, Michel M, Nishita D, Krasnow R, Javitz HS, Conneely KN, et al. (2015) Drug Metabolizing Enzyme and Transporter Gene Variation, Nicotine Metabolism, Prospective Abstinence, and Cigarette Consumption. PLoS ONE 10(7): e0126113. https://doi.org/10.1371/journal.pone.0126113
Editor: Ludmila Prokunina-Olsson, National Cancer Institute, National Institutes of Health, UNITED STATES
Received: July 5, 2014; Accepted: March 29, 2015; Published: July 1, 2015
Copyright: © 2015 Bergen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: The analyses performed in this ms utilized two datasets, as described (Stage I and Stage II). Stage I data can be found here: dbGaP Study ID phs000931.v1.p1 Stage II genotype and phenotype data cannot be provided to dbGaP from five of the eight RCTs. Two RCTs are from the 1990s and informed written consent was not obtained for individual level data sharing, and three RCTs do not have permission from their Institutional Review Board to share data other than the data already provided to dbGaP and to the Pharmacogenetics of Nicotine Addiction Treatment Consortium. Therefore I will not submit individual level data from Stage II of this analysis to dbGaP as the remaining data from Stage II (10 SNPs in three RCTs) would be an incomplete dataset (representing 0% of the RCT data with baseline NMR, and 32% of the RCT data with baseline nicotine dependence and prospective abstinence measures). Readers may contact Andrew W Bergen PhD to request data from the three remaining RCTs.
Funding: This work was supported, in part, by: a Research Agreement between Medco Health Solutions, Inc., Affymetrix, Inc., and SRI International; grants and a contract from the National Institute of Drug Abuse, and grants from the National Cancer Institute, of the United States Department of Health and Human Services [R21 DA33813 to AWB; NIH/NIDA Contract No. HHSN271201300004C to JWB; R01 DA03706 to HH; R01s DA16752, DA18691, DA15732 and DA9253 to SH; P50 CA84724, K05 CA139871 and P50 DA19706 to TBB; R01s CA71358 and DA11170 to GES; and PGRN U01 DA20830 to NLB (2005-2010), and to RFT and CL (2010-2015)]; a grant from the University of California Tobacco Related Diseases Research Program (7PT2004 to NLB); by the Centre for Addiction and Mental Health (RFT); and by an endowed Chair in Addictions (RFT). Clinical trials (NCT Trial IDs) 00301145, 01621009, 01621022 and 00332644 were funded by the National Cancer Institute, 00087880 and 00086385 were funded by the National Institute of Drug Abuse, and 00326781 and 00322205 were funded by the National Cancer Institute and by the National Institute of Drug Abuse. Varenicline and nominal support for recruiting clinical trial 00301145 participants was provided by Pfizer. GlaxoSmithKline provided medication for clinical trials 01621022 and 00332644. Medco Health Solutions, Inc. and Affymetrix, Inc. participated with SRI International in the selection of the two clinical laboratory metabolic studies, of the metabolic outcome, and of the DMET Plus Array for genotyping and Affymetrix performed the DMET Plus Array genotyping of the two clinical laboratory metabolic study DNA samples. No other funders or sponsors had any other role in the design of the laboratory studies, the randomized controlled trials or of this two-stage study, the collection, analysis, or interpretation of data, the writing of, or the decision to submit the ms. BioRealm, LLC provided support in the form of a salary for author JWB, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of the authors are articulated in the ‘author contributions’ section.
Competing interests: AWB reports employment at SRI International. This project was partly funded by Medco Health Solutions, Inc. (acquired by Express Scripts in 2012) and Affymetrix, Inc. JWB is an employee and a LLC member of BioRealm. Varenicline and nominal support for recruiting clinical trial 00301145 participants was provided by Pfizer. GlaxoSmithKline provided medication for clinical trials 01621022 and 00332644. GES reports serving as a Pfizer consultant for a one-day meeting in 2008. SMH has served as a consultant to Gilead, Inc., and Sleepio, Inc. TBB reports no competing interests in the last five years. He discloses that in the five years prior to the trials included in this analysis and that he conducted (NCT IDs 01621009, 01621022 and 00332644), he served as a PI or Co-PI on research sponsored by pharmaceutical companies, including GlaxoSmithKline and Pfizer. He also discloses that he is an author and collaborator on work done by Washington University School of Medicine colleagues LS Chen, AJ Bloom, & LJ Bierut on work examining associations of CYP2A6 with cessation. NLB reports consultation with pharmaceutical companies that market smoking cessation medications and service as a paid expert witness in ligation against tobacco companies. CL reports having received research funding from Pfizer. RFT has consulted with pharmaceutical companies, mostly on smoking cessation. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.
The predominant enzyme involved in nicotine and cotinine metabolism is CYP2A6 [1, 2], where nicotine is metabolized primarily to cotinine, and cotinine primarily to trans-3’-hydroxycotinine. The Nicotine Metabolite Ratio (NMR) is the ratio of the metabolites trans-3’-hydroxycotinine and cotinine. The ratio reflects the enzymatic activity of CYP2A6 and is a biomarker of the rate of nicotine clearance . The ratio is associated with smoking topography , responsiveness to smoking cessation treatments , and the number of cigarettes smoked per day (CPD) . Significant correlations between the NMR and a) oral clearance of nicotine, b) oral clearance and t1/2 of cotinine, and c) lack of production of trans-3’-hydroxycotinine in individuals homozygous for null CYP2A6 alleles, support the validity of the NMR as a marker of CYP2A6 activity . In addition to CYP2A6, genetic and biochemical studies have identified contributions of additional DMET loci to measures of nicotine metabolism in diverse samples and study designs. Gene variants associated with differences in nicotine metabolism  influence smoking behavior, tobacco exposures and attributable disease risks, and could serve as biomarkers for disease risk, and treatment prognosis .
To discover and develop novel biomarkers of nicotine metabolism and related phenotypes, we performed a planned analysis to: a) identify DMET SNPs associated with the laboratory study-based NMR [9, 10] using the DMET Plus Array ; b) validate nominally significant DMET SNP associations with baseline NMR in clinical trial participants from two trials ; and, c) further validate nominally significant DMET SNPs with end-of-treatment and six-month seven day point prevalence abstinence (abstinence) in clinical trial participants from eight trials [12–18]. We also conducted post-hoc analyses of validated DMET SNPs in clinical trial participants with abstinence by smoking cessation pharmacotherapy assignment, and with nicotine dependence measures among all participants. Finally, we interrogated a meta-GWAS lung cancer database  for association results of two validated DMET SNPs.
Institutional Review Board approval for each study, and informed written consent from each participant, was obtained by the Principal Investigators of each study. Institutional Review Board approval for these analyses was obtained from the Committee on Human Research at the University of California San Francisco and the Human Subjects Committee at SRI International, and for the TRICL study by the institutional ethics review committees at the involved institutions.
Stage I Participants
Stage I participants were derived from two distinct studies (Table 1). Healthy twin pairs and siblings were recruited from the Northern California Twin Registry, age ≥18 and <65 years, <130% of height-adjusted ideal weight, neither pregnant nor intending to become pregnant, and after exclusion of chronic medical and psychiatric conditions (PKTWIN study) . Participants underwent a 30 minute infusion of deuterium-labeled nicotine (3’,3’-dideuteronicotine) and cotinine (2’,4’,5’,6’-tetradeuterocotinine), monitoring, and blood and urine collection in a hospital setting . Probands and two first-degree relatives from 158 pedigrees with ≥three ever-smoking individuals per pedigree were recruited to assess the relations between genetic factors, environmental factors and tobacco use (SMOFAM study) . Individuals from 61 pedigrees completed a clinical study of nicotine metabolism; oral administration of a fixed dose of deuterium-labeled nicotine and cotinine at home, monitored by a nurse, was followed by collection, aliquoting and freezing of saliva samples at multiple time points . Participants completed a detailed questionnaire and provided a blood sample for DNA extraction and analysis . Levels of nicotine, cotinine trans-3’-hydroxycotinine and their glucuronides were estimated via liquid chromatography-mass spectrometry and pharmacokinetic phenotypes calculated, as described , from each study.
Stage I Analysis
Genomic DNA was extracted from whole blood , and quantified . DNA samples were genotyped using the Affymetrix DMET Plus Array  (S1 File). Genotype and phenotype data are accessible through application to a data access committee using dbGaP Study ID phs000931.v1.p1. We performed association analyses with common biallelic SNPs and indels [≥0.05 minor allele frequency (MAF) in each dataset] with the NMR derived from the six hour biospecimen collection. In the PKTWIN dataset, the NMR was square-root transformed and adjusted for age, age-squared, BMI, sex, smoking status (current versus other), and any hormone use (menopausal status and/or reproductive hormone use versus no hormone use). In the SMOFAM dataset, the NMR was log transformed and adjusted for age, aged-squared, BMI, sex, and smoking status. We constructed principal components of population genetic variation using 655 unlinked (r2<0.5) DMET Plus Array SNPs in 323 PKTWIN individuals and 212 SMOFAM individuals, and used the first ten principal components to further adjust the NMR in each dataset. To avoid heterogeneity due to potential differential linkage disequilibrium among different continental population samples, genotype-phenotype analyses presented were performed on individuals who self-identified as White. Additive models were evaluated for each common SNP via Hierarchical Linear Modeling (HLM)  as our primary analysis (S1 File). We performed a sample-size weighted fixed-effect meta-analysis followed by PACT adjustment using test statistics from the HLM analyses. The meta-PACT procedure  assesses meta-analysis significance at two levels: adjusted for all SNPs tested within a gene, and within the entire experiment, accounting for intragenic SNP correlation.
Stage II Participants
We used DNA samples and clinical data from self-identified White participants from eight clinical trials of smoking cessation therapies conducted in six US sites [12–18] to validate nominally significant DMET SNPs from Stage I with pretreatment NMR and prospective abstinence (Table 2). Treatment-seeking smokers (≥10 CPD, ≥18 years of age, recruited using local media and screened for eligibility), were randomized to placebo or active pharmacotherapy [12, 14–18], or were prescribed varenicline and randomized to three modes of behavioral therapy ; all participants were provided with multiple sessions of supportive therapy (individual, group, telephone, internet or combined modes). Trial participants provided a blood [12, 14–18] or a saliva  sample on study entry; blood was used for pretreatment NMR determination from two clinical trials  and blood or saliva for DNA extraction from eight trials [12–18].
Stage II Analysis
For validation, we chose one or more SNPs with uncorrected meta-analysis p-values<0.05 from each of the ten genes with meta-PACT p-values<0.05 and evaluated availability of TaqMan SNP Genotyping Assays (Life Technologies). Of 16 selected SNPs, 12 SNPs had predesigned assays, two were proxy SNPs (r2 = 1, Utah residents with ancestry from northern and western Europe HapMap sample, CEU) with predesigned assays, and two had custom assays designed (S1 File and S4 Table). We performed genotyping on the TaqMan OpenArray SNP Genotyping System or ViiA 7 using laboratory study, clinical trial, and HapMap (Coriell Cell Repositories, Camden, NJ) DNA samples. Stage II SNP genotyping assays were evaluated for clustering, concordance and completion rates prior to removing DNA samples with low completion rates, then for concordance, completion and Hardy Weinberg Equilibrium (HWE). Quality control procedures and summary results are described in S1 File, and completion rate and HWE significance by assay and by clinical trial are described in S5 and S6 Tables. Six of 16 selected DMET SNPs failed genotype clustering, completion rate and HWE quality thresholds. Thus, ten SNPs from seven of 10 genes passed quality thresholds and were included in subsequent analyses. In the course of evaluating the specificity of custom assays for one CYP2A6 SNP (rs4803381), we resolved an ambiguous genomic location, i.e., evidence for rs4803381’s location proximal to CYP2A6 rather than proximal to CYP2A7 (S1 File).
We used linear regression to estimate association of ten DMET SNPs on natural log transformed NMR in treatment-seeking smokers from two trials , adjusted for age, age-squared, BMI, sex, the first three principal components of population genetic variation obtained from analysis of 45 ancestry informative markers , and site. We used logistic regression to estimate association between ten DMET SNPs on end of treatment and on six month abstinence in treatment-seeking smokers from eight trials [12–18], adjusting for age, age-squared, BMI, sex, education (college degree or less than a college degree), marital status (married or other), principal components, and 26 treatment arms. For both analyses, we imputed missing values for BMI (N = 43), education (N = 10) and marital status (N = 7) 20 times, analyses were performed on each data set, and results combined with adjustment to reflect the variance attributable to the imputations .
For two CYP2A6 SNPs (rs4803381 and rs1137115) we estimated the proportions of variance and significance of association with: cigarettes per day (CPD), coded as a continuous variable and as in the Fagerström Test for Nicotine Dependence (FTND)  or Cigarette Dependence (FTCD) ; time to first cigarette after waking (TTFC); and total FTND/FTCD score. Model covariates in nicotine dependence regressions were age, age squared, BMI, sex, education, marital status, three principal components of population genetic variation, and site. We performed regression analyses to evaluate the influence of rs4803381 on prospective abstinence outcomes stratified by pharmacotherapy. Model covariates in abstinence regressions were the same as in the nicotine dependence regressions with the addition of clinical trial arm. Power analyses of SNP associations with NMR and six month prospective abstinence were performed. The TRICL meta-GWAS database, comprised of multiple case:control studies of lung cancer , was interrogated for existing association results at two CYP2A6 SNPs.
Analysis software, annotation, and model parameters
We used: SAS (Cary, NC) for data curation and HWE testing; plink  to evaluate Mendelian transmission; GCTA  to estimate principal components; STATA (StataCorp, College Station, TX) to perform HLM, imputation and other regression analyses; PACT  and meta-PACT  to adjust for multiple testing; Haploview to calculate linkage disequlibrium ; SNAP  to identify proxy SNPs; Quanto  for power analyses; and dbSNP  and Affymetrix resources for SNP annotation. Chromosome coordinates are from the NCBI36/hg18 assembly . SNP models were additive, tests were two-sided, and all alphas were 0.05.
Stage I participants, common DMET SNPs and the NMR
PKTWIN participants are significantly (P < .001) more likely to be female, less likely to be a current smoker, and, lower in adiposity than SMOFAM participants (Table 1). Five SMOFAM individuals were excluded from analysis, one with a NMR value more than five standard deviates above the mean, and four due to genotypes inconsistent with interview-based family relationships. We excluded 121 and 28 DMET SNPs in PKTWIN and SMOFAM, respectively, from analysis due to nominally significant deviation (p-values<0.05) from HWE. We tested 608 DMET SNPs common in PKTWIN and 531 SNPs common in SMOFAM, and adjusted for multiple tests in genes with more than one SNP tested using the PACT procedure. We meta-analyzed 507 SNPs within 173 genes in 449 individuals from the PKTWIN and SMOFAM datasets and adjusted for multiple SNPs within a gene, and for two datasets, using the meta-PACT procedure. S1–S3 Tables contain genotype counts and HLM analyses results, meta-analysis Z scores and p-values, and meta-PACT gene-wise p-values, respectively. Ten genes exhibited gene-wise meta-PACT p-values<0.05; CYP2A6, CYP2D6, and SPG7 were the top three ranked genes (Table 3). Forty-one SNPs had uncorrected meta-analysis p-values<0.05 with CYP2A6 and CYP2D6 SNPs in the top three ranked SNPs. rs4803381 was the top ranked SNP with an uncorrected meta-analysis P = 1.33E-07. After multiple test correction for 507 SNPs, two CYP2A6 SNPs attained experiment-wide significance: rs4803381 (PACT = 4.53E-05) and rs1137115 (PACT = .0012), located in the promoter (c.-1013) and first exon (c.51) of CYP2A6. These SNPs are in strong linkage disequilibrium with each other (r2 = 0.63 and 0.62 and D’ = 0.99 and 1.00 in PKFAM and SMOFAM), and in ex vivo hepatic tissue have been shown to be associated with significantly reduced protein and activity . rs4803381 and rs1137115 are assocated with many CYP2A6* star allele (*) haplotypes (*1B/*1B2, *1B5, *1B6, *1B8, *1B9, *1B10, *1B11, *1D, *1J, *9A, *9B, *18C, *24A, *24B, *31A, *31B, *35A and *1A, *1B14, *1B17, *2, *14, *18B, *20, *21, *28A, *28B, *41, *42, *44, *45, respectively) .
Stage II participants, NMR and prospective abstinence
DNA samples from 2,499 clinical trial participants (Table 2) were subject to genotyping at 16 DMET SNPs, and to quality control filtering (S1 File and S4–S6 Tables). Two CYP2A6 SNPs evaluated for association with pretreatment NMR were associated at genome-wide significance [β = -0.280, 95%CI (-0.336, -0.225), P = 1.25E-21, N = 633 individuals, and -0.240, 95%CI (-0.301, -0.178), P = 7.30E-14, N = 614 individuals]. The Stage II mean (standard deviation) completion rates for the two CYP2A6 SNPs over the eight randomized clinical trials were: rs4803381, 0.9946 (0.0045) and rs1137115, 0.9834 (0.0218). One of eight clinical trial HWE p-values for rs4803381 was < .05, while all other p-values were >.155; no clinical trial HWE p-value for rs1137115 was < .05. The two CYP2A6 SNPs are in strong linkage disequilibrium with each other (r2 = 0.58 and D’ = 0.98 in the two clinical trials). No other analyzed DMET SNPs were statistically significantly associated with pretreatment NMR, including the CYP2D6 SNP rs28371725 (Table 4). In joint analysis of rs4803381 and rs1137115 with baseline NMR (N = 605 individuals), rs4803381 was experiment-wide statistically significantly associated [β = -0.209, 95%CI (-0.293, -0.125), P = 1.31E-6], while rs1137115 was not [β = -0.075, 95%CI (-0.168, 0.017), P = 0.110]. Pretreatment NMR variance accounted for by covariates alone, with rs4803381, with rs1137115, and with both SNPs were 9.2%, 21.9%, 18.5%, and 22.2%. The mean (SD) and N of the residualized transformed NMR in individuals with SNP reference, heterozygote and minor allele homozygote genotypes were: -0.947 (0.153) 263, -0.950 (0.166) 296; and -0.953 (0.161) 74 for rs4803381; and -0.955 (0.156) 351, -0.951 (0.166) 228, -0.947 (0.143) 35 for rs1137115. Coefficient signs from Stage I and II analyses matched for both CYP2A6 SNPs. No DMET SNPs were associated with prospective abstinence over all participants, at either time point (S7 Table); power to detect ORs of 1.2 to 1.4-fold was good to excellent (S8 Table).
rs4803381 and rs1137115 were statistically significantly associated with cigarettes per day at different levels of significance (continuous, p-values of .0001 and .0017; categorical, p-values of .0020 and .0215), but not with TTFC or with total FTND (Table 5 and S9 Table). In joint analysis of rs4803381 and rs1137115, rs4803381 remained nominally associated with CPD (continuous, P = .0279; categorical, P = .0376).
In analyses of end-of-treatment and six month abstinence among individuals randomized to one of six (end-of-treatment) and to one of nine (six months) different pharmacotherapies, we observed two results of interest (S10 Table). In the first result of interest, we observed increased abstinence in individuals randomized to nicotine replacement therapy (NRT) (S10 Table). After analyses by mode of administration, we observed increased abstinence at end-of-treatment and at six months, with a trend towards significance (p-values < .10) in individuals randomized to NRT patch [in 339 individuals, ORend-of-treatment (95%CI) P = 1.348 (0.961–1.890) 0.084 and ORsix months (95%CI) P = 1.386 (0.973–1.975) 0.071]. These observations, while not statistically significant, are the most directly interpretable. We also observed a nominally statistically significant rs4803381 association with six month abstinence in one arm of 66 individuals first treated with combined NRT and bupropion from baseline to 12 weeks and then randomized to chronic bupropion (P = 0.023). This result has a more speculative interpretation.
rs1137115 was nominally significantly associated with lung cancer in a meta-analysis of four studies from the TRICL database [fixed effects odds ratio (95%CI) P 0.92 (0.87–0.97) 0.0022], and in two of the individual case:control studies (S11 Table). rs4803381 does not have results in the TRICL database. The direction of effect of the rs1137115 association with lung cancer is consistent with the reduced nicotine metabolism and cigarette consumption associated with the rs1137115 minor allele we observed in our Stage I and II analyses, and with the functional effects of rs1137115 evaluated in ex vivo hepatic tissue [35, 37].
There were five novel findings in this study, two in the a priori phase, and three in the post-hoc phase. CYP2A6 was associated with laboratory based NMR at experiment-wide significance in an analysis of 173 DMET genes. rs4803381 and rs1137115 were associated with the pretreatment NMR at genome-wide significance. In post-hoc analyses in clinical trial participants, these two CYP2A6 SNPs were shown to be associated at experiment-wide significance with baseline CPD in treatment-seeking smokers of eight clinical trials, and rs4803381 was associated with six month abstinence in individuals randomized to chronic bupropion treatment at nominal significance. In the post-hoc meta-analysis of four case:control studies, rs1137115 was associated with lung cancer at nominal significance. The minor alleles of these CYP2A6 SNPs were associated with reduced nicotine metabolism, smoking heaviness and lung cancer risk in a mechanistically interpretable fashion (reduced nicotine metabolism rate reduces cigarette consumption reduces lung cancer risk).
rs4803381, rs1137115, and correlates
Our multivariate analysis of two CYP2A6 SNPs, demographics, population genetic variation and clinical trial site accounted for up to 22.2% of the variance of the pretreatment NMR with the individual SNPs accounting for approximately half of this variance. Validation of the association of CYP2A6 SNPs with the NMR confirms the role CYP2A6 plays in nicotine metabolism [7, 38, 39]. Both SNPs are common polymorphisms in continental ancestry samples, where the rs4803381 European ancestry minor allele is the major allele in Asian and African HapMap populations. The minor alleles of rs4803381 and rs1137115 are associated with reduced CYP2A6 transcript levels [35, 37], CYP2A6 protein level , and CYP2A6 activity [35, 37] and are linked with reduced function CYP2A6 alleles  (S4 Table).
rs4803381 and rs1137155 were significantly associated with cigarette consumption coded continuously or as in the FTND/FTCD (Table 5). The influence of a single allele of rs4803381 and of rs1137115 in our multivariate regressions of treatment-seeking smokers were -0.11 and -0.098 standard deviates of continuously coded CPD, or a reduction of 0.99 and 0.88 cigarette per day (unadjusted means in S9 Table), which represent effect sizes larger than those associated with other CYP2A6 SNPs, e.g., rs1801272 (c.479T>A/p.L160H), with effect size of 0.68 CPD (N = 66,380, P = 1.1E-4) . rs1137115 was nominally significantly associated with lung cancer in a meta-GWAS database, adding to existing evidence that other CYP2A6 variants contribute to lung cancer risk (S2 File). The functional effects of rs4803381 and rs1137115, i.e., reduced transcription, protein level and activity [35, 37] suggest that CYP2A6 variation that influences regulation may be more important than CYP2A6 non-synonymous variants that directly influence enzyme activity, e.g., rs1801272, but with lower prevalence .
The validated CYP2A6 SNPs were not associated with the clinically relevant outcome of abstinence in all trial participants, adjusted for pharmacotherapy. However, when we performed analyses stratified by pharmacotherapy, we observed interesting effect sizes in several smaller strata, including trending associations with increased abstinence in individuals randomized to NRT patch, and nonminally significant decreased abstinence in individuals randomized to chronic bupropion. The former result is consistent with reported results in individuals from two trials [18, 42] analyzed together here, with the influence of CYP2A6 activity on abstinence previously evaluated using the biochemical NMR , or the CYP2A6 activity model . In our analysis of indivdiuals randomized to NRT patch from two trials [18, 42], we find that a single CYP2A6 SNP is a statistically trending predictor where reduced nicotine metabolism implies increased nicotine plasma levels and increased abstinence. The latter result in individuals randomized to chronic bupropion therapy reaches our a priori nominally statistically significant threshold but is of uncertain clinical significance due to the small size of the pharmacotherapy group and multiple pharmacotherapy groups examined. A speculative interpretation might suggest that linkage disequilibrium between rs4803381 and CYP2B6 SNPs that influence expression or activity might influence six month abstinence in indivduals undergoing chronic treatment with bupropion. Interaction between CYP2A6 and CYP2B6 variants in association with nicotine metabolism has been reported . CYP2B6 SNPs that influence bupropion metabolism also influence abstinence in individuals at six months in individuals treated with bupropion for 10 weeks , and linkage disequilbrium between relevant CYP2A6 and CYP2B6 alleles is low . CYP2B6 association with smoking heaviness has been reported at robust statistical significance (rs7260329, P = 6E-6) in a genome-wide scan . Mechanistic understanding of the potential association of rs4803381 and six month abstinence in individuals randomized to chronic bupropion treatment awaits replication and more data to permit testing of CYP2A6 and CYP2B6 metabolic and treatment outcome hypotheses.
In this study, we did not identify the monooxygenase gene FMO3 in our Stage I meta-analysis; FMO3 haplotypes were previously associated with the ratio of cotinine to the sum of nicotine and cotinine in a laboratory study , with CPD in dependent smokers , and with the NMR among slow metabolizers . Future work should include haplotype and diplotype analyses to evaluate the joint influence of multiple variants. Limitations of DMET Plus array gene coverage contributed to the inability to identify some DMET gene variants previously associated with nicotine metabolism. Finally, cytochrome P450 SNPs were over-represented among DMET SNPs that failed quality control thresholds in Stage II of our analysis, suggesting that methods that more effectively interrogate cytochrome P450 loci variation may enable further analyses of NMR variance and related phenotypes at these loci. Significant CYP2A6 SNP associations with CPD, with prospective abstinence by pharmacotherapy, and with lung cancer were post-hoc and were not replicated in this study. In addition, findings from analyses of treatment-seeking cigarette smokers may not generalize to all tobacco product smokers.
In this two-stage scan of DMET gene variation association with the NMR, we utilized two of the largest laboratory-based studies of nicotine metabolism to nominate DMET genes and SNPs, and eight randomized controlled trials of smoking cessation therapies and a lung cancer meta-GWAS database, to validate novel SNP associations with the NMR and to explore associations with related phenotypes. We ranked DMET candidate genes and SNPs by their association with the laboratory study NMR using a single model. We validated association of CYP2A6 SNPs with a similar phenotype, the clinical trial-based pretreatment NMR. This suggests that multiple CYP2A6 polymorphisms and non-genetic covariates will improve the predictive power of CYP2A6 activity models and support a continued focus on CYP2A6 activity models for use where phenotypic measures of nicotine metabolites are not available.
Enhanced knowledge of the genes that influence nicotine metabolism, smoking behaviors and clinical outcomes will help to characterize the risks from gene variants on smoking behaviors and smoking-attributable disease, identify novel biomarkers for therapeutic efficacy, and novel targets for the development of smoking cessation therapies. Based on this analysis and additional studies cited, CYP2A6 SNPs account for large fractions of the variance of the NMR, smaller fractions of the variance of cigarette consumption, and influence risk for lung cancer, but do not account for other nicotine dependence factors. This study does not provide guidance on the use of individual DMET SNPs to assign clinical treatment for tobacco dependence as the findings of interest in this study were unreplicated. Research must continue to improve our understanding of factors influencing tobacco product exposures and attributable disease, to develop improved interventions, and to protect the public health.
S1 Table. PKTWIN and SMOFAM DMET Plus SNP HLM Results.
S2 Table. PKFAM DMET Plus SNP Meta-analysis Results.
S4 Table. SNPs chosen for TaqMan SNP Genotyping Assay Genotyping.
S5 Table. TaqMan SNP Genotyping Assay completion rate, by RCT.
S6 Table. TaqMan SNP Genotyping Assay HWE Exact p-value, by RCT.
S7 Table. DMET SNPs and Prospective Abstinence in Eight RCTs.
S8 Table. Power to Detect SNP Odds Ratios, Six Month Abstinence.
S9 Table. rs4803381, rs1137115 and Measures of Nicotine Dependence.
S10 Table. rs4803381 and Abstinence by Pharmacotherapy Randomization.
S11 Table. TRICL rs1137115 Lung Cancer Results.
S1 File. Discovery (Stage I) and Validation (Stage II) Analyses.
We thank: the laboratory study and clinical trial participants for their contributions to research; Eric J. Stanek, Jay Kaufman and Richard Hockett for their contributions to Stage I genotyping; Kirsten Copren and Kathryn Thompson at the University of California San Francisco Helen Diller Family Comprehensive Cancer Center Genome Core for their contributions to Stage II genotyping; Gregory Kronmal, Pius Brzoska, and Elizabeth Pitts of ThermoFisher for their custom TaqMan SNP Genotype assay designs; and Heike Bickeböller of the University of Göttingen, Richard Houlston of the Institute of Cancer Research, Maria Teresa Landi and Neil Caporaso of the National Cancer Institute, Rayjean Hung of the Lunenfeld-Tanenbaum Research Institute, and William Wheeler of Information Management Services for TRICL meta-GWAS database results.
AWB, MM, RK, DN and HSJ had full access to all of the laboratory study and clinical trial data in the study and take responsibility for the integrity and the accuracy of the data analysis of the laboratory study and clinical trial data. The TRICL Research Team takes responsibility for the integrity and the accuracy of the data analysis of the lung cancer meta-GWAS. The content of this ms is solely the responsibility of the authors and does not represent the official views of Affymetrix, GlaxoSmithKline, Medco Health Solutions, the National Cancer Institute, the National Institute of Drug Abuse, Pfizer, SRI International, ThermoFisher or the University of California Tobacco Related Disease Research Program.
Conceived and designed the experiments: AWB MM HSJ GES. Performed the experiments: MM RK DN HSJ. Analyzed the data: AWB MM DN RK HSJ KNC. Contributed reagents/materials/analysis tools: HSJ DN RK KNC CNL-S HH JBM SMH TBB DVC NLB CL RFT GES TRICL. Wrote the paper: AWB MM DN RK HSJ KNC CNL-S AZXZ JWB JBM SMH TBB NLB CL RFT GES.
- 1. Nakajima M, Yamamoto T, Nunoya K, Yokoi T, Nagashima K, Inoue K, et al. (1996) Role of human cytochrome P4502A6 in C-oxidation of nicotine. Drug metabolism and disposition: the biological fate of chemicals 24: 1212–1217.
- 2. Nakajima M, Yamamoto T, Nunoya K, Yokoi T, Nagashima K, Inoue K, et al. (1996) Characterization of CYP2A6 involved in 3'-hydroxylation of cotinine in human liver microsomes. The Journal of pharmacology and experimental therapeutics 277: 1010–1015. pmid:8627511
- 3. Dempsey D, Tutka P, Jacob P 3rd, Allen F, Schoedel K, Tyndale RF, et al. (2004) Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity. Clinical pharmacology and therapeutics 76: 64–72. pmid:15229465
- 4. Strasser AA, Benowitz NL, Pinto AG, Tang KZ, Hecht SS, Carmella SG, et al. (2011) Nicotine metabolite ratio predicts smoking topography and carcinogen biomarker level. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20: 234–238.
- 5. Lerman C, Schnoll RA, Hawk LW, Cinciripinni P, George TP, Wileyto EP, et al. (2015) A Randomized Placebo-controlled Trial to Test a Genetically-informed Biomarker For Personalizing Treatment for Tobacco Dependence. Lancet Respir Med 3:131–138. pmid:25588294
- 6. Falcone M, Jepson C, Benowitz N, Bergen AW, Pinto A, Wileyto EP, et al. (2011) Association of the nicotine metabolite ratio and CHRNA5/CHRNA3 polymorphisms with smoking rate among treatment-seeking smokers. Nicotine & tobacco research: official journal of the Society for Research on Nicotine and Tobacco 13: 498–503.
- 7. Bloom J, Hinrichs AL, Wang JC, von Weymarn LB, Kharasch ED, Bierut LJ, et al. (2011) The contribution of common CYP2A6 alleles to variation in nicotine metabolism among European-Americans. Pharmacogenetics and genomics 21: 403–416. pmid:21597399
- 8. Bough KJ, Lerman C, Rose JE, McClernon FJ, Kenny PJ, Tyndale RF, et al. (2013) Biomarkers for smoking cessation. Clinical pharmacology and therapeutics 93: 526–538. pmid:23588313
- 9. Swan GE, Benowitz NL, Jacob P 3rd, Lessov CN, Tyndale RF, Wilhelmsen K, et al. (2004) Pharmacogenetics of nicotine metabolism in twins: methods and procedures. Twin Res 7: 435–448. pmid:15527659
- 10. Swan GE, Hudmon KS, Jack LM, Hemberger K, Carmelli D, Khroyan TV, et al. (2003) Environmental and genetic determinants of tobacco use: methodology for a multidisciplinary, longitudinal family-based investigation. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 12: 994–1005.
- 11. Deeken J (2009) The Affymetrix DMET platform and pharmacogenetics in drug development. Curr Opin Mol Ther 11: 260–268. pmid:19479659
- 12. Lerman C, Jepson C, Wileyto EP, Epstein LH, Rukstalis M, Patterson F, et al. (2006) Role of functional genetic variation in the dopamine D2 receptor (DRD2) in response to bupropion and nicotine replacement therapy for tobacco dependence: results of two randomized clinical trials. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 31: 231–242.
- 13. Swan GE, McClure JB, Jack LM, Zbikowski SM, Javitz HS, Catz SL, et al. (2010) Behavioral counseling and varenicline treatment for smoking cessation. American journal of preventive medicine 38: 482–490. pmid:20409497
- 14. Hall SM, Humfleet GL, Gorecki JA, Munoz RF, Reus VI and Prochaska JJ (2008) Older versus younger treatment-seeking smokers: differences in smoking behavior, drug and alcohol use, and psychosocial and physical functioning. Nicotine & tobacco research: official journal of the Society for Research on Nicotine and Tobacco 10: 463–470.
- 15. Hall SM, Humfleet GL, Munoz RF, Reus VI, Robbins JA and Prochaska JJ (2009) Extended treatment of older cigarette smokers. Addiction 104: 1043–1052. pmid:19392908
- 16. McCarthy DE, Piasecki TM, Lawrence DL, Jorenby DE, Shiffman S, Fiore MC, et al. (2008) A randomized controlled clinical trial of bupropion SR and individual smoking cessation counseling. Nicotine & tobacco research: official journal of the Society for Research on Nicotine and Tobacco 10: 717–729.
- 17. Piper ME, Federman EB, McCarthy DE, Bolt DM, Smith SS, Fiore MC, et al. (2007) Efficacy of bupropion alone and in combination with nicotine gum. Nicotine & tobacco research: official journal of the Society for Research on Nicotine and Tobacco 9: 947–954.
- 18. Piper ME, Smith SS, Schlam TR, Fiore MC, Jorenby DE, Fraser D, et al. (2009) A randomized placebo-controlled clinical trial of 5 smoking cessation pharmacotherapies. Archives of general psychiatry 66: 1253–1262. pmid:19884613
- 19. Wang Y, McKay JD, Rafnar T, Wang Z, Timofeeva MN, Broderick P, et al. (2014) Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer. Nature genetics 46: 736–741. pmid:24880342
- 20. Miller SA, Dykes DD and Polesky HF (1988) A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 16: 1215. pmid:3344216
- 21. Nishita DM, Jack LM, McElroy M, McClure JB, Richards J, Swan GE, et al. (2009) Clinical trial participant characteristics and saliva and DNA metrics. BMC medical research methodology 9: 71. pmid:19874586
- 22. Rabe-Hesketh S, Skrondal A and Gjessing HK (2008) Biometrical modeling of twin and family data using standard mixed model software. Biometrics 64: 280–288. pmid:17484777
- 23. Conneely KN and Boehnke M (2010) Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits. Genetic epidemiology 34: 739–746. pmid:20878715
- 24. Bergen AW, Javitz HS, Krasnow R, Nishita D, Michel M, Conti DV, et al. (2013) Nicotinic acetylcholine receptor variation and response to smoking cessation therapies. Pharmacogenetics and genomics 23: 94–103. pmid:23249876
- 25. Rubin DB (1987) Multiple Imputation for Nonresponse in Surveys. New York: J. Wiley & Sons.
- 26. Heatherton TF, Kozlowski LT, Frecker RC and Fagerstrom KO (1991) The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. British journal of addiction 86: 1119–1127. pmid:1932883
- 27. Fagerstrom K (2012) Determinants of tobacco use and renaming the FTND to the Fagerstrom Test for Cigarette Dependence. Nicotine & tobacco research: official journal of the Society for Research on Nicotine and Tobacco 14: 75–78.
- 28. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. American journal of human genetics 81: 559–575. pmid:17701901
- 29. Yang J, Lee SH, Goddard ME and Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. American journal of human genetics 88: 76–82. pmid:21167468
- 30. Conneely KN and Boehnke M (2007) So Many Correlated Tests, So Little Time! Rapid Adjustment of P Values for Multiple Correlated Tests. American journal of human genetics 81.
- 31. Barrett JC, Fry B, Maller J and Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics (Oxford, England) 21: 263–265.
- 32. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ and de Bakker PI (2008) SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics (Oxford, England) 24: 2938–2939.
- 33. Gauderman WJ and Morrison JM (2006) QUANTO: A computer program for power and sample size calculations for genetic-epidemiology studies.
- 34. Coordinators NR (2014) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 42: D7–17. pmid:24259429
- 35. Haberl M, Anwald B, Klein K, Weil R, Fuss C, Gepdiremen A, et al. (2005) Three haplotypes associated with CYP2A6 phenotypes in Caucasians. Pharmacogenetics and genomics 15: 609–624. pmid:16041240
- 36. Sim SC (2014) CYP2A6 allele nomenclature. In: Sim S. C., editor editors. The Human Cytochrome P450 (CYP) Allele Nomenclature Database. Karolinska Institutet.
- 37. Bloom AJ, Harari O, Martinez M, Zhang X, McDonald SA, Murphy SE, et al. (2013) A compensatory effect upon splicing results in normal function of the CYP2A6*14 allele. Pharmacogenetics and genomics 23: 107–116. pmid:23292114
- 38. Messina ES, Tyndale RF and Sellers EM (1997) A major role for CYP2A6 in nicotine C-oxidation by human liver microsomes. The Journal of pharmacology and experimental therapeutics 282: 1608–1614. pmid:9316878
- 39. Mwenifumbo JC, Al Koudsi N, Ho MK, Zhou Q, Hoffmann EB, Sellers EM, et al. (2008) Novel and established CYP2A6 alleles impair in vivo nicotine metabolism in a population of Black African descent. Human mutation 29: 679–688. pmid:18360915
- 40. Thorgeirsson TE, Gudbjartsson DF, Surakka I, Vink JM, Amin N, Geller F, et al. (2010) Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior. Nature genetics 42: 448–453. pmid:20418888
- 41. Chang MH, Lindegren ML, Butler MA, Chanock SJ, Dowling NF, Gallagher M, et al. (2009) Prevalence in the United States of selected candidate gene variants: Third National Health and Nutrition Examination Survey, 1991–1994. American journal of epidemiology 169: 54–66. pmid:18936436
- 42. Lerman C, Tyndale R, Patterson F, Wileyto EP, Shields PG, Pinto A, et al. (2006) Nicotine metabolite ratio predicts efficacy of transdermal nicotine for smoking cessation. Clinical pharmacology and therapeutics 79: 600–608. pmid:16765148
- 43. Chen LS, Bloom AJ, Baker TB, Smith SS, Piper ME, Martinez M, et al. (2014) Pharmacotherapy effects on smoking cessation vary with nicotine metabolism gene (CYP2A6). Addiction 109: 128–137. pmid:24033696
- 44. Ring HZ, Valdes AM, Nishita DM, Prasad S, Jacob P 3rd, Tyndale RF, et al. (2007) Gene-gene interactions between CYP2B6 and CYP2A6 in nicotine metabolism. Pharmacogenetics and genomics 17: 1007–1015. pmid:18004205
- 45. Lee AM, Jepson C, Hoffmann E, Epstein L, Hawk LW, Lerman C, et al. (2007) CYP2B6 genotype alters abstinence rates in a bupropion smoking cessation trial. Biol Psychiatry 62: 635–641. pmid:17223085
- 46. Lee AM, Jepson C, Shields PG, Benowitz N, Lerman C and Tyndale RF (2007) CYP2B6 genotype does not alter nicotine metabolism, plasma levels, or abstinence with nicotine replacement therapy. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 16: 1312–1314.
- 47. Bloom AJ, Murphy SE, Martinez M, von Weymarn LB, Bierut LJ and Goate A (2013) Effects upon in-vivo nicotine metabolism reveal functional variation in FMO3 associated with cigarette consumption. Pharmacogenetics and genomics 23: 62–68. pmid:23211429
- 48. Chenoweth MJ, Zhu AZ, Sanderson Cox L, Ahluwalia JS, Benowitz NL and Tyndale RF (2014) Variation in P450 oxidoreductase (POR) A503V and flavin-containing monooxygenase (FMO)-3 E158K is associated with minor alterations in nicotine metabolism, but does not alter cigarette consumption. Pharmacogenetics and genomics 24: 172–176. pmid:24448396