Article

Genetic variability in the metabolism of the tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) to 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL)

Department of Environmental Health, Environmental and Occupational Medicine and Epidemiology (EOME) Program, Harvard School of Public Health, Boston, MA 02115, USA.
International Journal of Cancer (Impact Factor: 5.09). 03/2012; 130(6):1338-46. DOI: 10.1002/ijc.26162
Source: PubMed

ABSTRACT

Urinary metabolites of the tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and its glucuronides, termed total NNAL, have recently been shown to be good predictors of lung cancer risk, years before diagnosis. We sought to determine the contribution of several genetic polymorphisms to total NNAL output and inter-individual variability. The study subjects were derived from the Harvard/Massachusetts General Hospital Lung cancer case-control study. We analyzed 87 self-described smokers (35 lung cancer cases and 52 controls), with urine samples collected at time of diagnosis (1992-1996). We tested 82 tagging SNPs in 16 genes related to the metabolism of NNK to total NNAL. Using weighted case status least squares regression, we tested for the association of each SNP with square-root (sqrt) transformed total NNAL (pmol per mg creatinine), controlling for age, sex, sqrt packyears and sqrt nicotine (ng per mg creatinine). After a sqrt transformation, nicotine significantly predicted a 0.018 (0.014, 0.023) pmol/mg creatinine unit increase in total NNAL for every ng/mg creatinine increase in nicotine at p < 10E-16. Three HSD11B1 SNPs and AKR1C4 rs7083869 were significantly associated with decreasing total NNAL levels: HSD11B1 rs2235543 (p = 4.84E-08) and rs3753519 (p = 0.0017) passed multiple testing adjustment at FDR q = 1.13E-05 and 0.07 respectively, AKR1C4 rs7083869 (p = 0.019) did not, FDR q = 0.51. HSD11B1 and AKR1C4 enzymes are carbonyl reductases directly involved in the single step reduction of NNK to NNAL. The HSD11B1 SNPs may be correlated with the functional variant rs13306401 and the AKR1C4 SNP is correlated with the enzyme activity reducing variant rs17134592, L311V.

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Available from: Yang Zhao, Aug 24, 2015
Genetic variability in the metabolism of the tobacco-specific
nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone
(NNK) to 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol
(NNAL)
Monica Ter-Minassian
1
, Kofi Asomaning
1
, Yang Zhao
1
, Feng Chen
1
,LiSu
1
, Steven G. Carmella
2
, Xihong Lin
3
,
Stephen S. Hecht
2
and David C. Christiani
1,4,5
1
Department of Environmental Health, Environmental and Occupational Medicine and Epidemiology (EOME) Program, Boston, MA
2
Masonic Cancer Center, University of Minnesota, Minneapolis, MN
3
Department of Biostatistics, Harvard School of Public Health, Boston, MA
4
Department of Epidemiology, Harvard School of Public Health, Boston, MA
5
Department of Medicine, Pulmonary and Critical Care Unit, Massachusetts General Hospital, Boston, MA
Urinary metabolites of the tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK),
4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and its glucuronides, termed total NNAL, have recently been shown to be
good predictors of lung cancer risk, years before diagnosis. We sought to determine the contribution of several genetic
polymorphisms to total NNAL output and inter-individual variability. The study subjects were derived from the Harvard/
Massachusetts General Hospital Lung cancer case-control study. We analyzed 87 self-described smokers (35 lung cancer
cases and 52 controls), with urine samples collected at time of diagnosis (1992–1996). We tested 82 tagging SNPs in 16
genes related to the metabolism of NNK to total NNAL. Using weighted case status least squares regression, we tested for the
association of each SNP with square-root (sqrt) transformed total NNAL (pmol per mg creatinine), controlling for age, sex, sqrt
packyears and sqrt nicotine (ng per mg creatinine). After a sqrt transformation, nicotine significantly predicted a 0.018 (0.014,
0.023) pmol/mg creatinine unit increase in total NNAL for every ng/mg creatinine increase in nicotine at p < 10E-16. Three
HSD11B1 SNPs and AKR1C4 rs7083869 were significantly associated with decreasing total NNAL levels: HSD11B1 rs2235543
(p 5 4.84E-08) and rs3753519 (p 5 0.0017) passed multiple testing adjustment at FDR q 5 1.13E-05 and 0.07 respectively,
AKR1C4 rs7083869 (p 5 0.019) did not, FDR q 5 0.51. HSD11B1 and AKR1C4 enzymes are carbonyl reductases directly
involved in the single step reduction of NNK to NNAL. The HSD11B1 SNPs may be correlated with the functional variant
rs13306401 and the AKR1C4 SNP is correlated with the enzyme activity reducing variant rs17134592, L311V.
Lung cancer is the leading cause of cancer-related mortality
and has the third leading incidence rate of cancer in the United
States.
1
Long-term inhalation of tobacco smoke is the main
cause of lung cancer with a relative risk of approximately 20
for smokers compared to non-smokers and a 30% increased
risk for those exposed to second-hand smoke.
2
Yet not all
smokers develop lung cancer; the cumulative risk by age 75 for
smokers is an estimated 10 to 20%,
3,4
which may indicate that
individual susceptibility to tobacco smoke carcinogens is modi-
fied by other factors such as genetics. Tobacco-specific
Key words: NNK, NNAL, tobacco specific nitrosamine, genetic polymorphism, HSD11B1.
Abbreviations: AKR: aldo-keto reductases; CEU: Centre d’Etude du Polymorphisme Humain (CEPH) Utah residents with ancestry from
northern and western Europe; CPD: cigarettes smoked per day; CYP: cytochrome p450; Gluc: glucuronide; FDR: false discovery rate; GWAS:
genome-wide association study; HSD11B1: 11-hydroxy-steroid deydogenase type 1; LOD: limit of detection; LOX: lysyl oxidase; MAF: minor
allele frequency; NNK: 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone; NNAL: 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol; PTGS:
prostaglandin endoperoxide synthase; TSNA: tobacco-specific nitrosamines; sqrt: square-root; UGT: UDP-glucuronosyl transferases.
Additional Supporting Information may be found in the online version of this article.
Grant sponsor: Harvard National Institute for Environmental Health Sciences (NIEHS) Center for Environmental Health; Grant number:
ES00002; Grant sponsors: National Institutes of Health Office of Extramural Research (NIH-OER), National Cancer Institute (NCI), NIH-
OER Ruth L. Kirschstein-National Research Service Award; Grant number: T32 ES 007069; Grant sponsor: NCI; Grant number: CA074386
DOI: 10.1002/ijc.26162
History: Received 14 Dec 2010; Accepted 20 Apr 2011; Online 4 May 2011
Correspondence to: Monica Ter-Minassian, Department of Environmental Health, Harvard School of Public Health, 677 Huntington Ave.,
Boston, MA 02115, USA, Fax: þ617-432-3323, E-mail: mtermina@hsph.harvard.edu or mtermina@post.harvard.edu
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nitrosamines (TSNA) such as NNK (4-(methylnitrosamino)-1-
(3-pyridyl)-1-butanone) and polycyclic aromatic hydrocarbons
are considered to be important carcinogens for lung cancer de-
velopment. Formed from nicotine in the tobacco curing pro-
cess, NNK is one of the most carcinogenic TSNAs in unburned
tobacco and its smoke.
5
The increase in nitrate levels in ciga-
rettes to enhance more complete combustion of tobacco has
been postulated as one factor leading to the increase in lung
adenocarcinoma due to the increased formation of TSNAs.
6
NNK induces lung adenocarcinomas in various laboratory
rodents, regardless of the route of administration.
7
After
humans smoke or otherwise consume tobacco, NNK is rapidly
metabolized to NNAL (4-(methylnitrosamino)-1-(3-pyridyl)-
1-butanol) and NNAL-Gluc.
8,9
NNK and NNAL form DNA
adducts after a-hydroxylation and are among the mo st potent
carcinogenic TNSAs for lung tumorigenesis studied in mice.
7
NNK is involved in multiple mechanisms in carcinogenesis.
NNK induces signaling pathways that promote cell survival
and proliferation
10
and anti-inflammation in part through its
role as a high affinity agonist of b-adrenergic and a7-nicotinic
acetylcholine receptors.
11,12
This altered signaling not only
affects neoplastic cells in various organs (lung, nose, colon, kid-
ney, pancreas, liver) but also appears to affect pulmonary neu-
roendocrine cells leading to pulmonary disorders such as
asthma and sudden infant death syndrome in infants of smok-
ing mothers.
13
Urinary levels of total NNAL (NNAL plus NNAL-Gluc)
have recently been shown to be associated with lung cancer in
a dose-dependent manner in cigarette smokers in two separate
nested case–control studies.
14
The highest tertile of total
NNAL conferred an odds ratio of 2.11 (1.25–3.54) after con-
trolling for me asures of cigarette smoking and cotinine levels.
Similarly, total NNAL levels have been associated with oral leu-
koplakia in smokeless tobacco users.
15
Levels of total NNAL
increase with an increase in the number of cigarettes smoked
per day (CPD), but the rate decreases over 15 cigarettes per
day and there is considerable variability.
16,17
Inter-individual
variability in NNK metabolism was directly demonstrated by
incubating freshly isolated human lung non-tumor cells with
NNK and measuring metabolites of NNK bioactivation and
transformation to NNAL, with wide metabolite ranges .
18
Genetic polymorphisms in the genes affecting NNK metabo-
lism may contribute to this variability. SNPs in UGT2B10 and
CYP2A13 have been shown to alter NNAL-Gluc form ation and
NNK a-hydroxylation activity respectively in vitro.
19,20
There
have been no prior epidemiological studies, however, examin-
ing the associations of these genes with total NNAL levels.
Carbonyl reductases and UDP-glucuronosyl transferases
are involved in conversion of NNK to NNAL and detoxifica-
tion to NNAL-Gluc. HSD11B1 codes for 11-beta-hydroxyste-
roid dehydrogenase, Type I, located in lung and liver, which
converts NNK to (R)-NNAL and shows a range of variability
in mRNA expression and activity.
21
AKR1C1, AKR1C2 and
AKR1C4 code for aldo-keto reduct ases that convert NNK to
(S)-NNAL.
22
AKR1C1 has been sho wn to be highly over-
expressed in non-small cell lung carcinoma (NSCLC) and
small cell lung carcinoma, bronchial epithelial cells of
NSCLC, and oral cancer cells by Affymetrix microarray.
23
AKR1C4*5 (L311V rs17134592) is found on the C-terminal
loop and affects steroid subst rate specificity and K
cat
.
23
UGT2B10 codes for the UDP-glucuronosyl transferases that
converts NNAL to NNAL-Gluc. In microsomes from human
embryonic kidney (HEK)-293 cells overexpressing the
UGT2B10 variant of SNP rs4657958 Asp67Tyr exhibited
minimal glucuronide formation activity from NNAL or other
TSNAs tested in vitro.
19
Other pathways reduce the availabil-
ity of NNK to be converted to NNAL. Cytochrome P450s are
involved in toxification, converting NNK to intermediates
that produce DNA add ucts. Relevant genes are CYP2A13,
CYP2A6, CYP3A4/5, CYP2B6 and CYP2E1. CYP2A13 is
mainly expressed in nasal epithelium, trachea and lung and
is primarily responsible for a-hydroxylation of NNK.
24
CYP2A13 R257C (rs8192789), D158E and V323L
(rs3885816), had two to threefold decreased catalytic effi-
ciency for NNK a-hydroxylation
20
although allele frequencies
in Caucasians are less than 3% for these SNPs. The CYP2A13
R257C variant carrier was associated with substantially
reduced risk for lung adenocarcinoma [odds ratio ¼ 0.41
(0.23–0.71)].
25
The enzyme CYP2A6 is also invo lved in NNK
metabolism, primarily in the liver, but also metabolizes nico-
tine to cotinine. Pyridine-N-oxidation detoxifies both NNK
and NNAL. Other genes relevant for NNK metabolism are
CYP2C8, LOX (lysyl oxidase), PTGS (prostaglandin endoper-
oxide synthase).
26
In summary, candidate genes in this study
include HSD11B1, AKR1C1/2/34, UGT2B10, CYP2A13,
CYP2A6, CYP3A4/5, CYP2B6, CYP2E1, CYP2C8, LOX,
PTGS1/2 and UGT2A3 (Fig. 1).
The total N NAL biomarker endpoint is narrowly defined
compared to the broader phenotype of lung cancer although
urinary measurements are representative of a relatively short
time window of exposure before the time of collection. We
hypothesized inter-individual variability in total NNAL levels
is due to in part to genetic variation in specific metabolic genes
of NNK metabolism which could help explain genetic suscepti-
bility of smokers to lung cancer. We aimed to examine the
roles of polymorphisms in 17 genes involved in NNK metabo-
lism in the large Harvard/ Massachusetts General Hospital
(MGH) Lung Cancer Study from which study participants had
been genotyped in a genome-wide association study (GWAS)
and for whom there were also frozen urine samples.
Material and Methods
Study population
Samples were drawn from the Harvard/ MGH Lung Cancer
Study conducted from 1992 to the present. Interviewer-
administered health questionna ires adapted from American
Thoracic Society questionnaire
27
provided information on
demographics (such as age, sex) and detailed smoking histor-
ies from each subject. Current smokers were classified as
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those who reported smoking more than 100 cigarettes during
their lifetime and who were smoking less than 1 year before
diagnosis or enrollment. Figure 2 outlines our sampling pro-
cedure. One thousand cases and 1,000 controls, all self-
reported Caucasian, were genotyped using the Illumina
Human 610-Quad BeadChip for a GWAS on lung cancer
risk and survival. Nine hundred eighty-four cases and 970
controls remained after applying quality control on ambigu-
ous gender (genotype did not agree with the questionnaire),
possible relatives and popula tion outliers identified by a prin-
ciple components analysis.
Urine samples had been collected early in the study
(1992–1996) on 699 subjects at the time of diagnosis for the
cases. The collected samples were frozen immediately and
stored at 20
C for 5 years and 80
C for 10 years. Cases
were requested to cease smoking before tumor resection and
sample collection. Three hundred thirty genotyped subjects
also had urine samples (5 ml) which could be used for this
study of which 92 were current smokers. We limited our
analysis of total NNAL, total nicotine and total cotinine in
urine samples to the current smokers only, to maximize the
probability of NNK exposure from mainstream tobacco
smoking. There were 39 lung cancer cases and 53 controls.
Analysis of biomarkers
The outcome, total NNAL excretion (pmol per mg creatinine
of NNAL plus NNAL-Gluc) in human urine was measured
by gas chromatography with nitrosamine selective detection
(GC-TEA).
28
Total NNAL limit of detection (LOD) was set
at <0.15 pmol/ml urine. Total NNAL LOD values were
replaced with 0.07 pmol/ml urine. Total nicotine was meas-
ured as an alternative measure of exposure to NNK, which is
undetectable in urine by GC-TEA. Total cotinine was meas-
ured as a potential confounder. Total nicotine per mg creati-
nine and total cotinine per mg creatinine (free nicotine or
cotinine plus their N-glucuronides) were analyzed by gas
chromatography-mass spectrometry.
29
Urinary creatinine was
assayed with VITROS chemistry products CREA slides from
Ortho Clinical Diagnostics (Raritan, NJ).
Figure 2. Sampling flowchart from study participants genotyped for
a Genome Wide Association Study (GWAS) and from those that
also had urine samples.
Figure 1. Selected genes of NNK to NNAL and of nicotine to cotinine metabolism. Selected genes or measured compounds are in bold;
total NNAL, nicotine or cotinine includes their glucuronides.
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Selection of candidate genes and genotyping methods
Genotyping had been completed with the Illumina Human
610-Quad BeadChip from which tag SNPs could be extracted
on selected genes. Seventeen candidate genes for metabolic
enzymes with good evidence that they use NNK as a
substrate (Table 1) were selected from the litera-
ture
9,19,20,22,23,25,26,30
and the bioinformatic databases Gene-
Cards (available at: http://www.genecards.org/index.shtml)
and PharmGKB (available at: http://www.pharmgkb.org/).
One hundred forty-one tag SNPs from Illumina Human 610-
Quad BeadChip within 2 kb of the gene were chosen where
gene start and stop endpoints were used as defined by
National Center for Biotechnology Information (NCBI) build
36, the same build that the Illumina 610 BeadChip was based
on. Call rates of genotyping were all greater than 95%. One
hundred twenty-six SNPs passed the Hardy-Weinberg test of
equilibrium at p 0.05 in the original GWAS 970 controls.
One per pair of SNPs in perfect LD (r
2
¼ 1) were removed.
The final 82 SNPs analyzed (Supporting Information Table
1) had sufficiently large minor allele frequencies (MAFs) and
representative genotype frequencies in our subset of subjects
with urine samples, using the following criteria. Recall that
there were a total of 984 cases and 970 controls GWAS geno-
typed and passed quality control; of these there were 92 cur-
rent smokers with urine samples and 552 current smokers
without urine samples (referred to as the just GWAS sample)
(Fig. 2). The SNPs had an MAF 0.05 in all 254 current
smoker controls. We compared genotype frequencies in all
92 subjects analyzed in this study to those in the 552 just
GWAS current smokers. We also compared the genotype fre-
quencies in 53 controls analyzed in this study to the 201 just
GWAS current smoker controls. To ensure our sample is
similar to the GWAS sample, we retained SNPs where the
genotype frequencies did not differ by the Fisher exact test (a
¼ 0.05) in the two comparisons.
Statistical analysis
We used SAS/Genetics software (ver. 9.1.3; SAS Institute,
Cary, NC) and PLINK (ver. 1.06)
31
available at: http://
pngu.mgh.harvard.edu/purcell/plink/ to perform the analyses.
We determined allele frequencies in cases and controls sepa-
rately. To check for genotyping error, we examined departure
from Hardy Weinberg Equilibrium in controls, using a v
2
test. All statistical testing was done at the two-sided 0.05 level
for the p value and 20% for the Benjamini-Hochberg false
discovery rate (FDR) q value.
32
We examined the role of each genetic polymorphism in
predicting urinary total NNAL levels using modified linear
regression models. Since we performed linear regression on a
secondary outcome from data derived from a case–control
study we had to take into account that cases are oversampled
in case–control designs. We used a weighted least squares
regression, weighting cases by (the prevalence of lung cancer
in the Massachusetts general population (¼0.000745))/(the
proportion of cases in the dataset) and weighting controls by
(1 the lung cancer prevalence)/(the proportion of controls
in the dataset).
33
We also tested associations in the control
group alone. Biomarker results and packyears
34
were square-
root transformed before analysis to normalize. Variables in
the model included age, sex, square-root packyears and the
concentration of urinary total nicotine (ng/mg creatinine) as
potential confounding variables. The associations between the
polymorphisms and total NNAL levels are reported as the
change in total NNAL levels for each additional increase in
the number of variant alleles (additive genetic model), and
variant carriers versus non-carriers (dominant model) and
their corresponding 95% confidence intervals (95% CI) and
p values. We used the SAS macro, Happy, available at:
http://www.hsph.harvard.edu/faculty/kraft/soft.htm,
35
to ana-
lyze haplotype associations for significant genes using the
additive model. Haplotypes with frequencies less than 5%
were combined into a single group. We also tested for inter-
actions among significant SNPs in different genes with a
cross product term.
We tested whether cotinine could be a confounder of the
SNP-NNAL association, (since some of the same enzymes
(CYP2A6, UGT2B10 and CYP2A13) also metabolize nicotine
to cotinine). We tested the associations of cotinine with each
of the SNPs controlling for nicotine and packyears, and
tested the association of cotinine with NNAL, using all sub-
jects and in controls alone. Biomarkers were log-transformed
to meet the normality assumption with cotinine as the out-
come in this analysis alone. For the SNPs significantly associ-
ated with cotinine, we included sqrt-cotinine in the model
(with sqrt-nicotine and other variables previously mentioned)
as a confounder of total NNAL outcome.
Table 1. Candidate genes in NNK metabolism
Chromosome Gene No. SNPs
10 AKR1C1 2
10 AKR1C2 3
10 AKR1C3 3
10 AKR1C4 9
19 CYP2A13 3
19 CYP2A6 0
19 CYP2B6 12
10 CYP2C8 11
10 CYP2E1 9
7 CYP3A4 2
7 CYP3A5 6
1 HSD11B1 5
5 LOX 2
9 PTGS1 7
1 PTGS2 2
4 UGT2A3 4
4 UGT2B10 2
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Results
Ninety-two Caucasian samples were analyzed for total
NNAL, total nicotine, total cotinine and for creatinine. Creat-
inine was determined for all samples. For total NNAL, four
samples (all cases) were not determined due to co-eluting
peaks and 14 samples (12 cases) were at the LOD and so
were replaced with 0.7 pmol/ml. For total nicotine only one
sample, a control, was not determined due to co-eluting
peaks. The same sample was not determined for cotinine , in
addition to another case. Since the undetermined samples for
nicotine and cotinine did not overlap for NNAL, our effective
sample size controlling for nicotine was 87 (35 lung cancer
cases and 52 controls) and for our cotinine analysis was 86.
Sqrt-total NNAL was correlated with sqrt-cotinine (Spearman
q ¼ 0.82 in controls, 0.37 in cases) and sqrt-nicotine (spear-
man q ¼ 0.74 in controls, 0.14 in cases).
For the 87 samples analyzed with complete total NNAL and
nicotine (Table 2), there were 48 females and 39 males with
ages ranging from 33 to 77 with a median of 62 years. Partici-
pants smoked 4 to 60 cigarettes per day (CPD) with a median
of 20. There were no missing values for age, sex or CPD. Age,
gender and sqrt-packyears were not significant variables pre-
dicting urinary sqrt-total NNAL when sqrt-nicotine was
included. Sqrt-nicotine was highly significant predicting a
0.018 (0.014–0.023) pmol/mg creatinine unit increase in sqrt
total NNAL for every ng/mg creatinine increase in sqrt-nico-
tine at p < 10E-16. Removing the two outliers, one in cases
and controls each (the highest value of total nicotine), did not
significantly change the results of the association with sqrt-nic-
otine and sqrt-total NNAL (b ¼ 0.0188 (0.014–0.023),
p ¼ 3.11E-15), therefore outliers remained in the analysis. For
controls only, only sqrt-nicotine was significant at b ¼ 0.0185
(0.013–0.024), p ¼ 5.52E-09. Four SNPs in HSD11B1 and
AKR1C4 were associated with sqrt NNAL under the additive
model (Table 3; Supporting Information Table 2 shows all SNP
results under the additive model). One SNP in CYP2E1 was
also significan t under the dominant model.
Three HSD11B1 SNPs, located in a 14-kb region flanked
by rs2235543 and rs3753519 in intron 1 with strong linkage
disequilibrium, were significantly associated with decreased
total NNAL levels (Table 3). Using the whole data set, two of
these SNPs passed multiple testing adjustment: rs2235543
C!T(p
additive
¼ 1.37E-07, FDR q
additive
¼ 1.13E-05; p
dominant
¼ 7.61E-05, FDR q
dominant
¼0.0062) and rs3753519 G!A
(p
additive/dominant
¼ 0.0017, FDR q
additive/dominant
¼ 0.07).
HSD11B1 rs10863782 was also significant at p ¼ 0.035 under
both models, but did not pass multiple testing adjustment.
Similar although slightly attenuated results were seen in con-
trols alone. The same three HSD11B1 SNPs were significant
and rs2235543 still passed a multiple testing adjustment with
p
additive
¼ 0.0013, FDR q
additive
¼ 0.10; p
dominant
¼ 0.004 FDR
q
dominant
¼ 0.31. A haplot ype analysis of all five SNPs
included for HSD11B1 showed that one haplotype that con-
tained a minor allele of the three SNPs rs2235543, rs10863782
and rs37535 19 and major allele of the two others had a fre-
quency of 5% in our population (3.6 in controls and 7% in
cases) and was associated with decreasing NNAL levels, b ¼
0.59 (0.87 to 0.31) with a p value of 3.41E-05 using all
subjects and 0.0098 in controls only. None of the other haplo-
types were significantly associated.
Table 2. Descriptive characteristics of the study population
1
Characteristics Total (n 5 87) Lung cancer cases (n 5 35) Controls (n 5 52)
Age
2
62 (33 to 77) 65 (35 to 77) 59 (33 to 76)
Gender
Males 39 (44.8%) 20 (57.1%) 19 (36.5%)
Females 48 (55.2%) 15 (42.9%) 33 (63.5%)
Packyears
2
45.5 (8.3 to 172.5) 58 (8.3 to 172.5) 36.1 (8.7 to 89.9)
Sqrt (packyrs) 6.67 (2.88 to 13.13) 7.62 (2.88 to 13.13)
3
6.0 (2.94 to 9.48)
3
Cigarettes/day
2
20 (4 to 60) 20 (4 to 60) 20 (4 to 50)
Time smoking (yrs)
2
42.8 (17.3 to 61.7) 49 (20 to 58) 41.5 (17.3 to 61.7)
Total NNAL (pmol/mg creatinine)
4
1.41 (0.04 to 9.83) 0.44 (0.04 to 7.62) 2.42 (0.09 to 9.83)
Sqrt(NNAL) 1.19 (0.2 to 3.1) 0.66 (0.2 to 2.76) 1.55 (0.2 to 3.13)
3
LOD
3
14 12 2
Nicotine (ng/mg creatinine) 740.57 (2.66 to 10,933.8) 31.1 (2.66 to 2,139.9) 2,034.1(12.74 to 10,933.8)
Sqrt(nicotine) 27.2 (1.63 to 104.56) 5.58 (1.63 to 46.26) 45.10 (3.57 to 104.56)
3
Cotinine (ng/mg creatinine) 1,714.96 (4.53 to 10,939.1)
5
129 (4.53 to 5,314.4)
4
3,259.9 (22.2 to 10,939.1)
Sqrt(cotinine) 41.4 (2.13 to 104.59) 11.3 (2.13 to 72.89) 57.09 (4.72 to 104.59)
3
Creatinine (mg/ml) 0.97 (0.15 to 5.54) 0.80 (0.15 to 5.54) 1.10 (0.35 to 3.32)
1
All Caucasian smokers, restricted to complete measurements on total NNAL and nicotine.
2
Median (range).
3
Normally distributed.
4
Total NNAL
pmol/ml LOD ¼ <0.15 pmol/ml urine (LOD values replaced with 0.07 pmol/ml urine).
5
Total N ¼ 86, cases N ¼ 34.
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AKR1C4 rs7083869 G!A, located in the second intron,
was also associated with sqrt-total NNAL at p ¼ 0.019 under
both models, using all subjects, but did not pass multiple
testing adjustment, FDR q ¼ 0.51. In controls alone, this
SNP was borderline not significant at p ¼ 0.06, FDR q ¼
0.99 under both models. A haplotype analysis of all nine
SNPs selected in AKR1C4 showed that the minor allele of
this SNP was located only on a single common haplotype,
along with minor alleles of rs11253042 and rs6601927. This
haplotype had a frequency of 14.4% in our population (15.4%
in controls and 12.9% in cases). The haplotype was associated
with decreasing NNAL levels, b ¼0.17(0.44 to 0.09) with a
p value of 0.21 using all subjects and 0.27 in controls only. The
other haplotypes were also not significantly associated. There
were no significant interactions of AKR1C4 rs7083869 with
each of the three significant HSD11B1 SNPs.
One SNP in CYP2E1 rs915907 C!A had a borderline
significant result under the dominant genetic model, regression
coefficient ¼0.19 (0.37 to 0.0009), p ¼ 0.048, FDR q ¼
0.75, but not under the additive model, p ¼ 0.12, FDR q ¼ 0.73,
using all subjects. This SNP was also not significant for controls
only under either genetic model, p ¼ 0.19, FDR q ¼ 0.99.
Cotinine was a statistically significant confounder of the
association with total NNAL for SNPs in genes not known to
be involved in nicotine to cotinine metabolism except for
UGT2B10. Cotinine was significantly associated with total
NNAL after controlling for packyears and nicotine (with a
log transformation of all variables, b ¼ 1.23 (0.84–1.62) p <
5E-05). Cotinine was significantly associated (although not
after multiple testing adjustment), with similar results under
both genetic models, with the following SNPs after control-
ling for age, sex, log-pack-years and log-nicotine using all
subjects: under the additive model, HSD11B1 rs3753519 with
p ¼ 0.004 FDR q ¼ 0.24, CYP3A4 rs4646437 at p ¼ 0.006
FDR q ¼ 0.24, and HSD11B1 rs10863782 at p ¼ 0.026, and
HSD11B1 rs2235543 at p ¼ 0.028, both FDR q ¼ 0.57. In
controls alone, the same SNPs and UGT2B10 rs861340 at p
¼ 0.03 and UGT2B10 rs835316 at p ¼ 0.04, both FDR q ¼
0.55, were nominally significant under both genetic models.
For SNPs that appeared to be confounded with cotinine
levels, we reanalyzed the NNAL associations including sqrt-
cotinine in the model. In all subjects, the SNPs HSD11B1
rs2235543 and rs3753519 remained significant at p ¼ 4.86E-
06 and p ¼ 0.014 respectively, although the regression coeffi-
cients were decreased by about half to 0.27 (0.39 to
0.16) and to 0.23 (0.42 to 0.047), respectively.
HSD11B1 rs10863782 was no longer significant at p ¼ 0.18
and the UGTB10 SNP remained unassociated with total
NNAL levels after adjusting for cotinine.
Discussion
After adjusting for multiple testing, genetic polymorphisms
HSD11B1 rs2235543 and rs3753519 appear to be associated
with total NNAL metabolite levels with the minor alleles
associated with decreasing levels. Variants HSD11B1
Table 3. Association of significant SNPs with square-root total NNAL
All subjects Controls only
Case Control
Case
minor
Control
minor
Gene SNP
Estimate
(95% CI)
1
p Value
FDR
q value
Estimate
(95% CI)
1
p Value
FDR
q value
genotype
counts
2
genotype
counts
2
allele
frequency
allele
frequency
HSD11B1 rs2235543 0.38 (0.52 to 0.27) 1.37E-07 1.07E-05 0.38 (0.59 to 0.16) 0.0013 0.10 2/5/28 1/8/43 0.13 0.10
HSD11B1 rs3753519 0.42 (0.68 to 0.16) 0.0017 0.07 0.42 (0.73 to 0.12) 0.0076 0.31 2/5/28 0/7/45 0.13 0.07
AKR1C4 rs7083869 0.21 (0.38 to 0.04) 0.019 0.51 0.21 (0.43 to 0.013) 0.06 0.99 2/14/19 0/19/33 0.26 0.18
HSD11B1 rs10863782 0.26 (0.51 to 0.02) 0.035 0.72 0.26 (0.52 to 0.01) 0.039 0.99 2/7/26 0/12/40 0.16 0.12
The significant p-values and q-values are in bold. Mentioned in Methods, p 0.05 and q < 0.2 are considered significant.
1
Additive genetic model, controlling for age, sex, square-root packyears, square-root nicotine.
2
Homozygous variant/ heterozygous/ homozygous wild type.
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Int. J. Cancer: 130, 1338–1346 (2012)
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rs10863782, AKR1C4 rs7083869 and CY P2E1 rs915907 may
also be associated with decreasing total NNAL metabolite lev-
els but these SNPs did not pass a multiple testing adjustment,
possibly due to sample size. Both enzymes HSD11B1 and
AKR1C4 are carbonyl reductases directly involved in the sin-
gle step reduction of NNK to NNAL.
The enzyme HSD11B1 is primarily known for its ability
to reversibly oxidize glucocorticoids at carbon 11 such as cor-
tisol to cortisone, but its ability to catalyze the carbonyl
reduction of NNK and other non steroidal carbonyl com-
pounds has recently been discovered.
36
The enzyme 11b-
hydroxysteroid dehydrogenase shows high inter-individual
variation both in the mRNA expression and activity of
NNAL formation.
21
HSD11B1 has two mRNA transcripts,
48.7 kb and 30.1 kb. One potentially functional genetic vari-
ant G!A, in HSD11B1 has been reported, rs13306421,
located two nucleotides 5
0
to the translation initiation site of
the smaller transcript. Compared with the common G allele,
the A allele in vitro was translated at higher levels and
resulted in higher enzyme expression and activity.
37
However,
this variant has a very low frequency in the Caucasian popu-
lation. Another study reported a 20% reduction in luciferase
activity in a reporter-gene assay with a rare HSD11B1 haplo-
type including rs846911 and rs860185 which indicated altered
transcription.
38
SNPs rs846911 and rs860185 are only 2 kb 5
0
to rs1330621 and only 0.73 kb downstream of the significant
SNP rs3753519. The SNPs we analyzed encompass this 2 kb
area around the second transcription start site. Similar to
HapMap CEU (CEPH (Centre d’Etude du Polymorphisme
Humain) Utah residents with ancestry from northern and
western Europe) data (release 24),
39
all the selected SNPs in
HSD11B1 had a pairwise r
2
< 0.80 but strong LD as meas-
ured by D’. The single haplotype containing the minor alleles
of the three significant HSD11B1 SNPs was associated with a
stronger regression coefficient than the single SNPs alone. It
is possible that these three SNPs, which are 5
0
to these
reported variants close to the initiation site for the smaller
HSD11B1 transcript, serve as a marker for the causal SNP or
haplotype producing reduced enzyme activity.
AKR1C4 is an aldo-keto reductase, along with AKR1C1
and AKR1C2, involved in formation of (S)-NNAL, the
NNAL enantiomer that is less readily glucuronidated in
rats.
22,23,40
(S)-NNAL can be oxidized back to NNK. In con-
trast, 11b-HSD catalyzes formation of (R )-NNAL which, in
rats, is further glucuronidated and excreted.
23,40
This enzy-
matic stereospecificity of AKR1C4 to a less excretable NNAL
form may explain the lower point estimate of the association
of the AKR1C4 SNP compared to the HSD11B1 SNPs. A
number of studies have reported that AKR1C4 L311V
(encoded by rs17134592) lowers enzyme activity by as much
as three to fivefold.
41
The SNP rs17134592 has a 15% MAF
in Caucasians (dbSNP) and is in strong LD (D of 1 with
HapMap CEU data) with our significant SNP rs7083869, but
is also correlated with the other non-significant SNPs. There
were no significant haplotypes associated with NNAL values
although the regression coefficient of the single haplotype
containing the minor allele rs7083869 was very similar to the
single SNP alone. It is possible that the common minor allele
of rs11253042, located in this haplotype and also other hap-
lotypes, is attenuating the signal.
We observed that different SNPs were associated with total
NNAL levels and with cotinine levels although there was some
overlap with HSD11B1 rs3753519 G!A. Neither HSD11B1 nor
CYP3A4 are known to be directly involved in nicotine to coti-
nine metabolism, although nicotine has been shown to induce
CYP3A4 and other CYP enzyme expression through the nuclear
receptor pregnane X receptor.
42,43
While both SNPs in both
HSD11B1 and CYP3A4 did not pass a multiple testing adjust-
ment for an association with cotinine levels, the raw p value
could also be the result of unknown uncontrolled confounders
since both enzymes have broad substrate specificity.
Some inhibitors of HSD11B1 and AKRC enzymes could
explain some of the inte r-individual variability seen in NNAL
output. They could be potential uncontrolled confounders if
related to the polymorphisms and if a significant proportion
of the study population is exposed. Some known HSD11B1
enzyme inhibitors are endogenous steroids (glucocorticoids,
estrogen, progesterone, cholesterol, bile acids), exogenous ste-
roids (glucocorticoids, dexamethasone, oral contraceptives),
drugs (carbenoxolone, furosemide, ethacrynic acid, gossypol,
ketoconazole, metyrapone) and dietary compounds (narin-
genin (found in grapefruit), glycyrrhetinic acid (licorice), and
flavonoids).
44,45
Alcohol is also an inhibito r of HSD11B1 and
AKR1C1, 1C2, 1C4 enzymes. We had insufficient informa-
tion on these compounds in our study. On the other hand,
the SNP(s) may modify the inhibitors effects, in other words
an inhibitor may have different effects in the presence of dif-
ferent forms of the HSD11B1 enzyme due to the polymor-
phism. Additional study on genetic interactions with these
inhibitors could further highlight the importance of
HSD11B1 in contributing to NNAL variability and by exten-
sion to lung cancer development in inhibitor exposed versus
unexposed populations.
There are few limitations to this study. Coverage of some
relevant genes may be insufficient in part due to coverage of the
Illumina 610 chip (e.g. CYP2A6), and due to our own stringent
criteria for SNPs that would sufficiently capture the genotype
frequencies in the larger sample of smokers genotyped for the
GWAS. The total sample size of 87 subjects may be insufficient
to detect mo derate associations or associations with low fre-
quency SNPs, although two highly associated SNPs we found
had MAFs less than 10% in controls. Additionally, our SNP
selection process tried to ensure that our small sample was
comparable to the much larger sample of genotyped individuals
that we did not have urine samples for. The controls are fairly
representative of smokers in eastern Massachusetts in terms of
self reported smoking habi ts,
46
and the lung cancer cases’ self
reported smoking habits are significantly higher as expected.
However, cases were requested to cease smoking before tumor
resection and before urine samp le collection which resulted in
Cancer Genetics
1344 Genetic polymorphisms associated with NNK metabolism
Int. J. Cancer: 130, 1338–1346 (2012)
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lower median nicotine and NNAL levels in the cases compared
to controls that smoked, although higher levels than non-smok-
ers or individuals exposed to second-hand smoke.
47
Given the
small sample size of cases, the request to stop smoking and
possibly the motivation of a recent diagnosis, it is difficult to
determine if the lung cancer disease condition itself had any
impact on the inter-individual variability of NNAL.
We were able to detect NNAL in long term frozen urine
samples with little apparen t loss, as has been demonstrated
in other studies.
48
Time since smoking last cigarette to urine
sample collection may have affected NNAL levels since the
initial half-life of NNAL is 3 days.
49
However, the nicotine,
cotinine and NNAL ranges in our controls are representative
of other smokers.
16,28
NNAL levels in the cases appear to be
similar to people who stopped smoking 2 to 3 weeks before
sample collection.
49
The lower than expected NNAL levels in
cases did not appear to have had much of an effect on our
results since they were similar in all subjects combined com-
pared to controls only.
To our knowledge, this study is the first to examine read-
ily available, high quality genotyped data on a number of
polymorphisms in NNK metabolism with total NNAL levels
as a biomarker. The results show that the most significant
SNPs that are associated with inter-individual variability of
NNAL in a sample of smokers are also correlated with poten-
tially functional variants in HSD11B1 and possibly AKR1C4
that have been associated with altered mRNA expression and
enzyme activity in vitro. While confirmation in a larger sam-
ple of smokers is warranted , HSD11B1 and possi bly AKR1C4
polymorphisms may provide part of the mechanism by which
varying NNAL levels have been shown to be a useful prog-
nostic factor for lung cancer.
14
Acknowledgements
The authors thank Salvatore Mucci for valuable contributions in data collec-
tion and management and Benjamin Church for sample management. M.T.
was awarded the Harvard-NIEHS Center (ES00002) for Environmental
Health pilot grant to conduct this work.
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  • [Show abstract] [Hide abstract] ABSTRACT: 11β-Hydroxysteroid dehydrogenase isoform 1 (11β-HSD1) is a member of the alcohol short-chain family enzyme, and catalyzes the interconversion between active glucocorticoid cortisol (in human) and inactive cortisone. The redox ratio of NADP+/NADPH determines its direction with NADPH favoring its reductase activity and NADP+ favoring its oxidase activity. In many tissues such as the liver and lung, 11β-HSD1 behaves primarily as a reductase because of the intracellular enzyme coupling with hexose-6-phosphate dehydrogenase, which uses the NADP+ as cofactor to supply NADPH driving 11β-HSD1 to function as a reductase. 11β-HSD1 catalyzes the metabolism of 7- or 11-keto steroids as well as many toxins and drugs. Some steroids, drugs and toxins are metabolically activated. The present review discusses the steroid, drug and toxin metabolism by 11β-HSD1.
    No preview · Article · Nov 2011 · Toxicology
  • [Show abstract] [Hide abstract] ABSTRACT: Paranoia is commonly a mood-incongruent psychotic symptom of mania which may be related to dopamine dysregulation. Progesterone and its metabolite allopregnanolone (ALLO) have been found in animals to antagonize the effects of dopamine. We therefore examined serum progesterone, its endogenous antagonist DHEAS and polymorphisms of the genes coding for certain steroidogenetic enzymes (AKR1C4, HSD3B2, and SRD5A1) in 64 males and 96 females with bipolar 1 or 2 disorder with or without paranoid ideation during mood elevation. Euthymic morning serum progesterone, DHEAS and cortisol concentrations were measured in males and in premenopausal women who were in follicular phase and not taking oral contraceptives. In women only, SNPs in AKR1C4 reduced the likelihood of having exhibited paranoid ideation by circa 60%. The haplotype of all 4 SNPs in the AKR1C4 gene reduced the risk of exhibiting paranoia by 80% (OR 0.19, 95% CI 0.06-0.61, p=0.05). A history of paranoid ideation was not, however, related to progesterone or DHEAS concentration. Serum DHEAS and progesterone concentrations were lower in men who had shown paranoid ideation during mania/hypomania compared with those who had not (F=7.30, p=0.006) however this was not coupled to polymorphisms in the selected genes. The ancestral G in rs4659174 in HSD3B2 was in men associated with a lower risk of paranoid ideation (likelihood ratio χ(2) 3.97, p=0.046, OR 0.31 (95% CI 0.10-0.96)) but did not correlate with hormone concentrations. Hence, gene variants in the steroidogenetic pathway and steroids concentration differences may be involved in the susceptibility to paranoia during mood elevation.
    No preview · Article · Feb 2012 · European neuropsychopharmacology: the journal of the European College of Neuropsychopharmacology
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