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Role of the N-Acetyltransferase 2 Detoxification System in
Thyroid Cancer Susceptibility
Ana C.T. Guilhen, Natassia E. Bufalo, Elaine C. Morari, et al.
Clin Cancer Res 2009;15:406-412.
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Cancer Prevention and Susceptibility
Role of the N-Acetyltransferase 2 Detoxification System in
Thyroid Cancer Susceptibility
Ana C.T. Guilhen, Natassia E. Bufalo, Elaine C. Morari, Janaina L. Leite, LigiaV.M. Assumpcao,
Alfio J.A. Tincani, and Laura S.Ward
Abstract
Purpose: Genetic polymorphisms in genes encoding for enzymes involved in the biotransformation of carcinogens have been shown to be relevant as risk for cancer and may be of considerable
importance from a public health point of view. Considering that N-acetyltransferase 2 (NAT2)
polymorphisms modulate the response to ionizing radiation, the strongest risk factor recognized
to cause differentiated thyroid cancer (DTC) thus far, we sought to determine the influence of
NAT2 detoxification system on thyroid cancer susceptibility.
Experimental Design:We conducted a prospective case-control study, comparing195 patients
presenting with DTC that were previously genotyped for GSTT1, GSTM1, GSTP1, and CYP1A1,
comprising 164 papillary carcinomas and 31 follicular carcinomas, with 196 control individuals
paired for gender, age, ethnicity, diet routine, lifetime occupational history, smoking history,
general health conditions, and previous diseases.We used PCR-RFLP assays and the combination
of 6 variant alleles to define 18 NAT2 haplotypes that characterized slow, intermediate, or rapid
phenotypes.
Results: A multivariate logistic regression analysis identified the presence of *12A and the
absence of *12B, *13, *14B, *14D, *6A, and *7A NAT2 haplotypes as risk factors for DTC. The
inheritance of a rapid acetylation phenotype doubled the risk for a papillary carcinoma (odds ratio,
2.024; 95% confidence interval, 1.252-3.272).We found no relationship between genotypes and
clinical, pathologic, or laboratory features of patients or between genotypes and outcome.
Conclusions: We showed that NAT2 genotypes and the NAT2 rapid acetylation phenotype are
important susceptibility factors for DTC, suggesting that NAT2 detoxification system is involved
in this tumor pathogenesis.
Thyroid cancer is the type of human cancer that has the highest
most carcinogens do not produce their biological effects per se;
they require metabolic activation before they can interact with
cellular macromolecules (4). Many compounds are converted
to reactive electrophilic metabolites by oxidative (phase I)
enzymes such as cytochrome P450 (CYP) enzymes, which
presumably mostly activate the majority of carcinogens.
Conversely, phase II enzymes usually act as inactivating
enzymes catalyzing the conjugations of carcinogenic substance.
Phase II group of enzymes includes glutathione S-transferase
(GST), UDP-glucuronosyltransferase, and N-acetyltransferase
(NAT) systems (5).
Arylamine NAT2 is predominantly expressed in the liver. The
enzyme is responsible for the N-acetylation of arylamine and
hydrazine xenobiotics and has been implicated in the
susceptibility to various cancers and other disorders (6 – 10).
The wild-type NAT2*4 allele (haplotype) and 35 other variants
of NAT2 encoding gene were identified and classified in human
populations depending on the haplotype determined by the
combination of up to 4 of the 17 different single nucleotide
polymorphisms (SNP) present throughout the NAT2 coding
region (11).3 Several nonsynonymous polymorphisms result in
increase in incidence rate in the United States (1).1 Brazilian
data confirm the increase of Brazilian number of cases as well,
especially among women (2).2 This greater incidence may be
due to better, high-technology diagnostic tests, which are
detecting very small tumors, most of which pose no long-term
threat (1). However, a real increase, perhaps caused by
environmental factors, cannot be ruled out.
Epidemiologic studies have shown that a significant part of
all cancers are related to environmental factors, considering
tobacco smoke and diet as the main attributable exposures in a
long and increasing list of carcinogenic elements (3). However,
Authors’Affiliation: Laboratory of Cancer Molecular Genetics, Faculty of Medical
Sciences School, State University of Campinas, Sa‹o Paulo, Brazil
Received 7/15/08; revised 8/26/08; accepted 9/12/08.
Grant support: State of Sa‹o Paulo Research Foundation grant 0601651-1 and
National Council for Scientific and Technological Development grant 470317/
2006-0.
The costs of publication of this article were defrayed in part by the payment of page
charges. This article must therefore be hereby marked advertisement in accordance
with 18 U.S.C. Section 1734 solely to indicate this fact.
Requests for reprints: Laura S. Ward, Laboratory of Cancer Molecular Genetics,
Faculty of Medical Sciences School, State University of Campinas, P.O. Box 6111,
Campinas, Sa‹o Paulo, Brazil. Phone: 55-19-3521-9081; Fax: 55-19-3521-9081;
E-mail: ward@ unicamp.br.
F 2009 American Association for Cancer Research.
doi:10.1158/1078-0432.CCR-08-1835
Clin Cancer Res 2009;15(1) January 1, 2009
1
http://www.cancer.org/docroot/PRO/content/PRO____1____1____Cancer____Statistics___
2
_ 008____Presentation.asp
2
http://www.inca.gov.br/
3
http://www.louisville.edu/medschool/pharmacology/NAT.html
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NAT2 and Thyroid Cancer Susceptibility
confirmed all diagnoses. There were 164 papillary carcinomas and
31 follicular carcinomas. All patients were submitted to total or neartotal thyroidectomy. Patients with preoperatively or intraoperatively
palpable neck node metastases underwent regional neck dissection.
Four to 6 weeks after the surgeries, total body 131I scans were done.
All patients received 100 mCi 131I. Long-term levothyroxine at
suppressive doses was administered following total body scan to
maintain serum thyrotropin at low normal levels.
Data regarding lifetime occupational history, dietary habits, alcohol,
coffee and drug consumption, medical history with an emphasis on
previous and/or current thyroid diseases, use of exogenous hormones
and concomitant medications, reproductive history, and family history
of cancer and other anamnestic data were obtained during interviews
using a structured questionnaire. Individuals with history of previous
thyroid disease, accidental or medical radiation exposure, and
antecedents of other malignancies were excluded. Ethnicity was
determined by the interviewer in accordance with the Brazilian Institute
of Geography and Statistics; however, due to the difficulty in classifying
our highly heterogeneous population, we further grouped individuals
into Whites and non-Whites.2
The habit of cigarette smoking was recorded; however, due to the
limited reliable data obtained as to the duration in years of smoking, at
what age smoking started, quantity smoked, and number of years since
smoking stopped, the patients were grouped into the categories neversmoked and ever-smoked. This last group included comprised
individuals who consumed at least 20 packages, 20 cigarettes per pack
during 1 year for the prior 5 years. All data, including nodule size,
tumor histologic features, and laboratory examinations, were confirmed
in the patients’ records.
Follow-up. Cancer patients were followed with periodic total body
scans and serum thyrotropin and thyroglobulin measurements according to a routine follow-up protocol that included X-ray, ultrasonography, computed tomography scan, and other eventual procedures
to detect distant metastasis for a period of 12 to 341 months (38 F
69 months). Patients with high serum thyroglobulin levels (>2 mg/dL)
and/or suspicious total body scans were submitted to a thorough image
search. We defined tumors as recurrent and/or presenting long distance
metastasis according to the above variables.
Controls. We used DNA and anamnestic data of 196 individuals
selected from the general population of our region, considered to have a
normal iodine intake, who had been previously genotyped for GSTT1,
GSTM1, GSTP1, NAT2 , and CYP1A1 in our laboratory. These
individuals were matched based on genotypes, age, diet routine
including coffee and consumption of well-done meat, lifetime
occupational history, smoking history, general health conditions, and
previous diseases. Individuals with a history of past thyroid disease,
radiation exposure, specific environment or risk occupational exposure
risks, and antecedents of malignancy were excluded.
Identification of genotypes, haplotypes, and phenotypes. Blood specimens were obtained from all patients and control individuals. Genomic
DNA was extracted from frozen specimens and leukocytes were
separated from whole blood using a standard phenol-chloroform
protocol. Genotyping was conducted with blinding to case/control
status. We used a PCR-RFLP assay to investigate 6 SNP sites in the NAT2
gene. Three of the polymorphisms result in an amino acid change
(SNPs 191 G>A, 590 G>A, and 857 G>A) and the other three (282 C>T,
481 C>T, and 803 A>T) are silent mutations. Two sets of primers were
used for PCR amplification: NAT2 forward1 5¶-CATGGAGTTGGGCTTAGAGGC-3¶ and reverse1 5¶-ACTGCTCTCTCCTGATTTGGTCC-3¶ and
NAT2 forward2 5¶-ATCAGCCTCAGGTGCCTTGC-3¶ and reverse2 5¶-GAGTTGGGTGATACATACACAAGGG 3¶, which produce a 364 and 491 bp
fragments, respectively. These fragments were amplified separately,
although under similar conditions, using 50 AL volumes of a mixture
containing 100 ng DNA, 10 AL of each primer, 10 mmol/L Tris-HCl
(pH 8.0), 0.1 mmol/L of each deoxynucleotriphosphates, 2.0 mmol/L
MgCl2, and 0.5 units Taq DNA polymerase. PCR conditions were
carried out for 35 cycles at 94jC for 50 s, annealing temperatures at
Translational Relevance
Thyroid nodules are very frequent among the general
population in contrast to thyroid cancer. Recognition of
those individuals who are at an increased risk for cancer
is important for planning and implementing proper prevention and management strategies. Although diagnostic tools
such as ultrasonography and fine-needle aspiration cytology are very useful, these procedures are not appropriate to
screen large populations for malignancy. Conversely, the
identification of molecular markers such as high-frequency
genetic polymorphisms could be applied to a great number
of individuals and conceivably identify persons at risk
for cancer among the vast majority presenting benign
nodules. In an effort to delineate polygenic models of
thyroid cancer susceptibility and prognosis, we showed
that NAT2 system is an independent factor of susceptibility.
We suggest that, together with other markers, NAT2 polymorphisms may help delineate a polygenic model of DTC
risk among the large population of individuals with thyroid
nodules.
poor expression, an unstable protein, or decreased catalytic
activity (12). According to functional studies, NAT2 polymorphisms generate slow, intermediate, and rapid acetylators
(11, 13). Association between slow NAT2 acetylator genotypes
and increased risk for urinary bladder cancer as well as between
rapid NAT2 acetylator genotypes and colorectal cancer have
been consistently reported (9, 14).
Genes encoding metabolizing enzyme variants important in
the metabolism and biotransformation of toxic xenobiotics and
endobiotics have been frequently associated with the risk for
cancer. An increase in the susceptibility to thyroid cancer was
reported in individuals with GSTT1 and GSTM1 null genes,
GSTP1, codon 72 of TP53 and CYP1A1 variants, among other
components of the detoxifying system inherited genetic profile
(15 – 17). NAT2 polymorphisms modulate the response to
ionizing radiation, which is the strongest risk factor known for
the development of differentiated thyroid cancer (DTC) thus far
(18, 19). However, reports on the role of the NAT2 detoxification system in thyroid cancer susceptibility are still scarce in the
literature (20, 21). Hence, the aim of this study was to determine
the influence of NAT2 inherited genetic and phenotypic profiles
on thyroid cancer susceptibility in the Brazilian population.
Materials and Methods
This prospective case-control study was approved by the Ethics
Research Committee of the Faculty of Medical Sciences School, State
University of Campinas, and all individuals signed informed written
consent forms.
Patients. One hundred ninety-five patients presenting with
differentiated thyroid carcinomas that were previously genotyped
for GSTT1, GSTM1, GSTP1, and CYP1A1 were selected from the
patients consecutively referred to our Teaching Hospital (Faculty of
Medical Sciences School, State University of Campinas) for thyroid
nodule evaluation from year 1999 to 2008. The tumor stage and
grade of differentiation were obtained from surgical and pathologic
records. Experienced pathologists from the Teaching Hospital
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Cancer Prevention and Susceptibility
Table 1. Clinical characteristics represented by the percentage distribution of 196 control individuals, 164
papillary carcinoma patients, and 31 follicular carcinoma patients according to their smoking habits comparing
clinical features that included age (years), gender, ethnicity, presence of lymph node involvement at
diagnosis, distant metastasis and the stage at the time of the diagnosis, and recurrence and/or distant
metastasis rate during the follow-up
Controls
Clinical characteristics
Age (mean F SD)
Sex
Female
Male
Ethnicity
White
Non-White
Diagnosis
Lymph node
Distant metastasis
Stage
I + II
III + IV
Follow-up
Recurrence and/or distant metastasis
Papillary carcinomas
Follicular carcinomas
Smokers
(27.69%)
Nonsmokers
(72.31%)
Smokers
(24.86%)
Nonsmokers
(75.14%)
46.11 F 16.22
41.77 F 13.20
41.44 F 17.23
43.20 F 19.79
44.52 F 20.22
67.14
32.86
76.67
23.33
89.91
10.09
79.90
20.10
95.65
4.35
79.56
20.44
89.47
10.53
89.72
10.28
81.40
18.60
78.16
21.84
—
—
—
—
69.23
13.05
86.82
13.18
40.77
17.85
80.56
19.44
49.35
38.55
81.82
18.18
51.00
41.5
72.35
27.65
—
11.58
10.78
23.14
21.89
program version 2.1.31. All tests were conducted at the significance
level P = 0.05.
Because we evaluated associations with multiple SNPs and haplotypes, some associations would appear by chance. To correct for
multiple comparisons, we calculated the false-positive report probability (FPRP) for all associations observed to be statistically significant in
the overall analysis or which seemed to differ within subgroups in the
stratified analyses (22, 23). The FPRP were computed by the Excel
spreadsheet provided by Wacholder et al. and represent the probability
that the observed association is a false-positive result. The FPRP
depends on the observed result, the prior probability of an association,
and the power of calculation of the study. The calculations further
assume that the power is to detect, under the dominant model, an odds
ratio (OR) of 2.0 for both SNPs and haplotypes, considering prior
probabilities for association with thyroid cancer according to the values
calculated by the software to be noteworthy for each SNP and
haplotype. We considered FPRP < 0.200 to indicate a noteworthy
association.4
60jC for 45 s for NAT2FR1 and at 61jC for NAT2FR2 followed by 72jC
for 1 min, with an initial denaturation step at 94jC for 5 min and a
final extension step at 72jC for 10 min, using a Thermocycler MJ PTC200 PCR System. After amplification, the fragments were digested with
KpnI (T481C), BamHI (G857A), DdeI (A803G; Invitrogen/Life Technologies), and HpaII (G191A) for 16 h at 37jC (Fermentas Life
Sciences, EU); TaqI (G590A; Invitrogen/Life Technologies) for 16 h at
65jC; and BseGI (C282T; Fermentas Life Sciences, EU) for 16 h at 55jC
separately in a 15 AL final volume. The six variant alleles investigated
resulted in 18 (*5D, *6A, *6B, *6C, *6E, *7A, *7B, *11A, *12A, *12B,
*12C, *13, *14A, *14B, *14C, *14D, *14E, and *14G) different
haplotypes according to the current nomenclature.3 We used the
scheme suggested by Vatsis et al. to classify new NAT identified alleles
(11). Individuals homozygous for rapid NAT2 acetylator alleles
(NAT2 *4, NAT2 *11A, NAT2*12A, NAT2 *12B, NAT2*12C, and
NAT2*13A) were classified as rapid acetylator phenotype; individuals
homozygous for slow acetylator alleles were classified as slow acetylator
phenotype and heterozygous individuals (one rapid and one slow
NAT2 allele) were classified as intermediate acetylator phenotype.
GSTT1, GSTM1, GSTP1, and CYP1A1(m2) genotyping methods have
been described previously in other publications (15 – 17).
Statistical analysis. The statistical analysis was carried out using SAS
statistical software (Statistical Analysis System, version 8.1, 1999-2000).
Associations were assessed using contingency table analysis, and the m2
or Fisher’s exact test was used to examine the homogeneity between
cases and controls regarding gender, ethnicity, previous thyroid disease,
thyroid nodule size, medication use, cigarette smoking, disease extent,
and genotypes. The Mann-Whitney or Wilcoxon tests were used to
compare the age among two or more than two different genotype
groups, respectively.
Logistic regression was used to evaluate the effect of all genotypes
after adjusting for other potential confounders such as CYP1A1(m1)
genotype, age, ethnicity, gender, and tobacco, alcohol, and medication
consumption. A multivariate logistic regression model was applied
using type of tumor (papillary or follicular carcinoma) as dependent
variables and all genotypes and clinical risk factors, including gender,
ethnicity, age, cigarette smoking, and coffee drinking as explicative
variables. Study power, the likelihood of detecting an association when
one exists, was calculated using PS power and sample size calculation
Clin Cancer Res 2009;15(1) January 1, 2009
Results
Table 1 summarizes clinical characteristics, variables of
aggressiveness at diagnosis and during follow-up of the thyroid
cancer patients divided into smokers and nonsmokers, and
some of the genotypes analyzed. There were no differences
between the control individuals and the thyroid disease
patients regarding age, ethnicity, and cigarette smoking habits.
The demographic and lifestyle characteristics of the subjects in
both thyroid cancer and control groups, including coffee and
alcohol consumption, red meat, vegetable, and fat intake,
education level, and exercise practice, were similar. As we aimed
to identify the effect of NAT2 genotypes in thyroid cancer
susceptibility, patients and controls were paired for GSTT1,
GSTM1, GSTP1, and CYP1A1(m2), and their profile was similar
4
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NAT2 and Thyroid Cancer Susceptibility
Table 2. Percentage distribution of GSTM1, GSTT1, GSTP1, and CYP1A1(m2) genotypes among 196 controls
and 195 thyroid DTC, including 164 papillary carcinomas and 31 follicular carcinomas
Variants
Controls DTC
(%)
(%)
GSTT1
Positive
Null
GSTM1
Positive
Null
GSTP1
A/A
77.50
80
22.50
20
51.67
60
48.33
40
60
54.46
A/G-G/G
40
CYP1A1(m2)
A/A
67.20
A/G-G/G
32.79
CYP1A1(m1)
T/T
60.75
T/C-C/C
39.25
OR
(95% CI)
1.48
(74-2.95)
P
Papillary
OR
carcinomas (95% CI)
(%)
0.269
1.45
(0.84-2.49)
0.181
0.159
1.28
(0.84-2.49)
0.377
60
1.58
(0.89-2.80)
0.120
58.82
1.05
(0.59-1.86)
0.876
27.34
0.672
62.07
0.97
(0.33-2.81)
0.950
31.25
3.37
(1.10-10.40)
0.034
2.567
(0.987-6.678)
0.0533
1.082
(0.423-2.771)
0.868
68.75
65.69
1.310
(0.733-2.342)
0.3613
34.31
0.573
0.0463
(0.331-0.991)
0.76
(0.22-2.66)
37.93
41.18
1.46
0.1652
(0.854-2.511)
80
P
20
40
37.51
72.66
1.72
(0.81-3.66)
20
45.54
62.5
80
OR
Follicular
carcinomas (95% CI)
(%)
P
50
50
75.49
0.493
(0.269-0.903)
0.0200
24.51
61.54
38.46
between the groups. Therefore, CYP1A1(m1) was considered
a confounder in the adjusted logistic regression analysis.
Table 3 represents our results regarding the six NAT2
allelic polymorphisms investigated and their corresponding
as depicted in Table 2. However, we were not able to organize a
group of patients and controls paired for all genetic and
environmental exposure risks large enough to guarantee the
power of calculation and avoid CYP1A1(m1) disparity
Table 3. Comparison of the distribution of NAT2 genotypes represented in percentage among the 196
controls, the 195 thyroid DTC patients, and the 164 papillary carcinoma and 31 follicular carcinoma types of
DTC subgroups
Genotypes Controls DTC
481 C>T
CC
32.40
33.81
CT, TT
590 G>A
GG
67.60
66.19
53.07
61.87
GA, AA
803 A>G
AA
46.93
38.13
68.16
53.24
AG, GG
857 G>A
GG
31.84
46.76
92.18
92.81
GA, AA
191 G>A
GG
70.82
7.19
74.30
94.96
GA, AA
282 C>T
CC
25.70
5.04
31.84
66.19
CT, TT
68.16
33.81
OR
(95% CI)
P
0.938
0.7905
(0.586-1.502)
Papillary
OR
carcinomas (95% CI)
32.14
P
1.012
0.9633
(0.611-1.677)
67.86
0.697
0.1166
(0.444-1.094)
63.39
53.57
0.653
0.0843
(0.403-1.059)
93.75
1.855
0.0128
(1.140-3.018)
95.54
0.6150
0.6150
(0.307-2.011)
0.001
66.07
33.93
55.56
0.905
0.8095
(0.401-2.042)
51.85
1.987
0.0997
(0.877-4.503)
88.89
1.473
0.5646
(0.394-5.506)
11.11
0.135
0.0001
(0.052-0.352)
4.46
0.239
(0.149-0.382)
0.3937
48.15
6.25
0.153
0.0001
(0.067-0.352)
0.697
(0.30-1.597)
44.44
46.43
0.914
0.8346
(0.393-2.125)
40.74
P
59.26
36.61
1.880
0.0069
(1.189-2.973)
Follicular
OR
carcinomas (95% CI)
92.59
0.231
0.0523
(0.053-1.015)
7.41
0.240
0.0001
(0.145-0.396)
66.67
0.234
0.0009
(0.099-0.552)
33.33
NOTE: Genotypes are considered wild-type or variants according to human NAT2 alleles (http://www.louisville.edu/medschool/pharmacology/
NAT.html).
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genotypes. We observed that NAT2C282T and NAT2G191A
variants were more frequent among controls than patients.
Indeed, the absence of these variants increased the risk of
CDT by 6.68 and 4.60 times, respectively. The power of
calculation for these variants was 99.99% (C282T) and 99.97%
(G191A) and the FPRP had values <0.2 across the prespecified
prior probability of 0.1 (Table 6). These two variant-cancer
associations remained noteworthy (FPRP < 0.2). On the
contrary, NAT2A803G variants were more frequent among the
patients increasing the risk of CDT 1.88 times. Unfortunately,
the power of calculation for this variant was only 29.92%.
The analysis of the 18 haplotypes resulting from the six allelic
variants investigated showed that *11A, *12B, *13, *14B, *14D,
*6A, and *7A haplotypes were more frequent among controls
Table 4. Comparison of NAT2 haplotype distribution represented in percentage among the 196 controls, the
195 patients with DTC, and the 164 papillary carcinoma and 31 follicular carcinoma types of DTC subgroups
Haplotypes
NAT2*11A
Wild-type
Variant
NAT2*12A
Wild-type
Variant
NAT2*12B
Wild-type
Variant
NAT2*12C
Wild-type
Variant
NAT2*13
Wild-type
Variant
NAT2*14A
Wild-type
Variant
NAT2*14B
Wild-type
Variant
NAT2*14D
Wild-type
Variant
NAT2*14E
Wild-type
Variant
NAT2*14G
Wild-type
Variant
NAT2*4
Wild-type
Variant
NAT2*5D
Wild-type
Variant
NAT2*6A
Wild-type
Variant
NAT2*6B
Wild-type
Variant
NAT2*6C
Wild-type
Variant
NAT2*6E
Wild-type
Variant
NAT2*7A
Wild-type
Variant
NAT2*7B
Wild-type
Variant
Controls, n (%)
92 (51.69)
86 (48.31)
Cancer, n (%)
91 (66.42)
46 (33.58)
P
OR (95% CI)
0.009
0.5408 (0.3411-0.8572)
5.453 (2.281-13.035)
171 (96.07)
7 (3.93)
112 (81.75)
25 (18.25)
<0.0001
167 (93.82)
11 (6.18)
135 (98.54)
2 (1.46)
0.0453
0.2249 (0.04900-1.032)
148 (83.15)
30 (16.85)
106 (77.37)
31 (22.63)
0.2498
1.443 (0.8236-2.527)
156 (87.64)
22 (12.36)
133 (97.08)
4 (2.92)
0.0031
0.2133 (0.07167-0.6346)
168 (94.38)
10 (5.62)
135 (98.54)
2 (1.46)
0.0747
0.2489 (0.05360-1.156)
167 (93.82)
11 (6.18)
135 (98.54)
2 (1.46)
0.0453
0.2249 (0.04900-1.032)
164 (92.13)
14 (7.87)
136 (99.27)
1 (0.73)
0.0026
0.08613 (0.01118-0.6637)
174 (97.75)
4 (2.25)
135 (98.54)
2 (1.46)
0.7005
0.6444 (0.1163-3.572)
176 (98.88)
2 (1.12)
137 (100)
0 (0)
0.5069
0.2567 (0.01222-5.395)
170 (95.51)
8 (4.49)
122 (89.05)
15 (10.95)
0.0472
177 (99.44)
1 (0.56)
137 (100)
0 (0)
1.0000
0.4303 (0.01738-10.653)
125 (70.22)
53 (29.78)
112 (81.75)
25 (18.25)
0.0248
0.5264 (0.3069-0.9031)
170 (95.51)
8 (4.49)
130 (94.89)
7 (5.11)
0.7966
1.144 (0.4044-3.237)
177 (99.44)
1 (0.56)
133 (97.08)
4 (2.92)
0.1710
5.323 (0.5879-48.205)
174 (97.75)
4 (2.25)
123 (89.78)
14 (10.22)
0.0030
4.951 (1.591-15.407)
166 (93.26)
12 (6.74)
135 (98.54)
2 (1.46)
0.0273
0.2049 (0.04507-0.9318)
172 (96.63)
6 (3.37)
130 (94.89)
7 (5.11)
0.5700
1.544 (0.5066-4.704)
2.613 (1.074-6.357)
NOTE: Haplotypes are considered wild-type or variants according to human NAT2 alleles (http://www.louisville.edu/medschool/pharmacology/
NAT.html).
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NAT2 and Thyroid Cancer Susceptibility
Table 5. Comparison of the acetylation phenotype distribution represented in percentage among the 196
controls, the 195 patients with DTC, and the 164 papillary carcinoma and 31 follicular carcinoma types of DTC
subgroups
Acetylation Controls DTC
phenotype (%)
(%)
Slow
18.99
12.23
Intermediate
44.69
33.81
Rapid
36.31
53.95
OR
(95% CI)
P
OR
Papillary
carcinomas, (95% CI)
n (%)
0.5943
0.1237
(0.316-1.116)
0.6322
0.0510
(0.399-1.00)
2.055
0.0021
(1.30-3.22)
13
(11.60)
39
(34.82)
60
(53.58)
P
0.5600
0.1040
(0.281-1.115)
0.6611
0.1114
(0.4059-1.077)
2.024
0.0050
(1.252-3.272)
Follicular
OR
carcinomas, (95% CI)
n (%)
5
(18.51)
8
(29.62)
14
(51.87)
P
0.9693
1.0000
(0.342-2.744)
0.5211
0.1511
(0.2167-1.253)
1.889
0.1398
(0.836-4.264)
thyroid cancer yielded a FPRP < 0.2. The presence of *12A and
the absence of *13 and *14B haplotypes are also unlike to
represent a false-positive result of factors associated to the
susceptibility for thyroid cancer.
We were unable to establish any relationship between the
profile of the studied genes and patients’ clinical characteristics,
including smoking, dietary, and coffee drinking habits. A
sample size calculation indicated that a much larger cohort of
patients would be needed to reach the statistical power to detect
main effects or interactions or the precision to quantify them.
In addition, we were unable to find any additive or
combined effects of the genes studied for thyroid cancer risk,
including NAT2 and the previously examined GSTT1, GSTM1,
GSTP1, and CYP1A1 genes.
As described in our previous publications on the same cases,
we were once more unable to establish any relationship
between the genotypes investigated and the pathologic features
of the tumors or the patients’ outcome.
than cancer patients, whereas *12A, *4, and *6E predominated
among cancer patients as depicted in Table 4. A multivariate
logistic regression analysis confirmed the presence of *12A
and the absence of *12B, *13, *14B, *14D, *6A, and *7A as
significant risk factors for thyroid cancer. The power of
calculation for these haplotypes was 99% (*12A), 64% (12B),
93% (*13), 64% (*14B), 81% (*14D), 62% (*6A), and 64%
(*7A). FPRP calculation showed that NAT2 haplotypes *11A,
*12A, *13, *6A, and *6E associations were noteworthy as shown
in Table 6.
Table 5 describes the acetylation pattern of both control and
case groups evidencing that a rapid acetylation is a risk factor
for thyroid cancer. This effect is due to the papillary type, as
follicular carcinomas do not present a significant association
between acetylation and risk for cancer. Indeed, the risk of an
individual presenting a rapid acetylation to develop a papillary
thyroid cancer is increased more than two times [OR, 2.024;
95% confidence interval (95% CI), 1.252-3.272]. The power of
calculation for this analysis is 58% for the rapid, 51% for the
intermediate, and 30% for the slow acetylation phenotypes.
Table 6 shows FPRP estimates for selected results. In the
analyses including all thyroid cancer cases and controls, the
protective effect of C282T and G191A genotypes appear to be a
true result, because the association of these genotypes with
Discussion
Tobacco products provide a clear example of cancer
causation due to a lifestyle factor involving carcinogen
exposure. Most tobacco carcinogens require metabolic
Table 6. FPRP calculations for selected association between genetic polymorphism and thyroid cancer based
on Dong et al. (23) and the spreadsheet program provided at http://jncicancerspectrum.oupjournals.org/jnci/
content/vol96/issue6
SNP or haplotype
C282T
G191A
A803G
NAT2*11A
NAT2*12A
# NAT2*12B
NAT2*13
#NAT2*14B
#NAT2*14D
#NAT2*4
NAT2*6A
NAT2*6E
#NAT2*7A
OR (95% CI)
0.239 (0.149-0.382)
0.153 (0.067-0.352)
1.880 (1.189-2.973)
0.5408 (0.341-0.857)
5.453 (2.281-13.035)
0.2249 (0.0490-1.032)
0.2133 (0.634-0.716)
0.2249 (0.049-1.032)
0.08613 (0.011-0.663)
2.613 (1.074-6.357)
0.5264 (0.306-0.903)
4.951 (1.591-15.407)
0.2049 (0.045-0.931)
P
0.001
0.0001
0.0069
0.009
0.0001
0.0453
0.0031
0.0453
0.0026
0.0472
0.0248
0.0030
0.0273
Power of calculation
Prior probability
0.347
0.012
0.604
0.633
0.010
0.133
0.044
0.133
0.009
0.324
0.608
0.038
0.095
0.1
0.1
0.1
0.1
0.1
<0.25
0.25
<0.25
<0.25
<0.25
0.25
0.25
<0.25
FPRP
0.025
0.070
0.093
0.114
0.084
>0.2
0.176
>0.2
>0.2
>0.2
0.109
0.190
>0.2
NOTE: The power of calculation was obtained by software and the calculations were assumed that power is to detect an OR of 2.0 for SNPs and
haplotypes in the overall study population. SNP or haplotype comparisons are for combined heterozygotes and homozygotes for variant allele of
SNP or specified haplotypes versus all others. ORs are for thyroid cancer adjusted for possible confounders. FPRPs < 0.200 suggest that the
association does not represent a false-positive result.
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Cancer Prevention and Susceptibility
increase the risk, especially for papillary carcinomas. These data
also correlate very well with our recent description of an inverse
association among CYP1A1 allelic variants, smoking, and
thyroid cancer susceptibility (17).
A recent study in the Saudi Arabian population found
CYP1AI and GSTT1 null genes to increase the risk of thyroid
cancer, whereas GSTM1 null showed a protective effect (20).
These authors included NAT2 among the 9 SNPs in 7 genes
they analyzed, but the risk to develop thyroid cancer was
initially assessed with only 50 samples and they selected only
the SNPs that showed statistical significance to further confirm
the finding with the total 223 cases they had. Hence, the power
of calculation and the possibility of their results being falsepositive augmented significantly.
We suggest that the NAT2 detoxification system exerts an
important role in the protection against environmental and/or
endogenous factors involved in differentiated thyroid carcinomas pathogenesis. NAT2 polymorphisms represent independent factors of susceptibility that, together with other molecular
markers, may help delineate a polygenic model of DTC risk
among the large population of individuals with thyroid
nodules. Future investigations are needed to test the capacity
of this model reduce the number of patients submitted to fineneedle aspiration cytology and, consequently, the burden of the
procedure and its cost.
activation to exert their carcinogenic effects; there are competing detoxification pathways and the balance between metabolic
activation and detoxification differs among individuals and
cancer risk (24).
Identifying a risk profile for thyroid cancer may help
delineate polygenic models of cancer susceptibility and
prognosis. Such models are particularly interesting, considering
the elevated prevalence of thyroid nodules in the population,
and may help select individuals for specific preventive
interventions and determine which patients would have a
greater chance to benefit from specific measures.
Taking advantage of the high admixture of the Brazilian
population that represents a unique model in which the types
and frequency of genetic polymorphisms of GST, CYP, and
NAT enzymes are less influenced by ethnicity, we showed that
NAT2 gene inheritance influences thyroid cancer susceptibility.
NAT2 polymorphisms have been shown to modulate the
response to ionizing radiation, which is the strongest environmental factor proven to cause thyroid cancer thus far (25, 26).
NAT2*7 allele and the presence of slow acetylator phenotype
were related to a lower increase of the micronuclei frequency
detected in peripheral blood lymphocytes of patients submitted
to radioiodine treatment for thyroid cancer, indicating that they
could represent a protection against radiation-induced cellular
damage (21). Our data corroborate the observations made by
Hernández et al., suggesting that a rapid acetylator phenotype
and some NAT2 haplotypes may represent risk factors for
thyroid cancer (21). The inheritance of some variants such as
NAT2*7A is protective, whereas others such as NAT2*12A
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
References
1. Davies L, Welch HG. Increasing incidence of thyroid
cancer in the United States, 1973-2002. JAMA
2006;295:2164 ^ 7.
2. Coeli CM, Brito AS, Barbosa FS, Ribeiro MG, et al.
Incidence and mortality from thyroid cancer in Brazil.
Arq Bras Endocrinol Metabol 2005;4:503 ^ 9.
3. Pru«ss-Ustu«n A, Corvala¤n C. How much disease burden can be prevented by environmental interventions?
Epidemiology 2007;18:167 ^ 78.
4. Thier R, Bru«ningT, Roos PH, et al. Markers of genetic
susceptibility in human environmental hygiene and
toxicology: the role of selected CYP, NAT and GST
genes. Int J Hyg Environ Health 2003;206:149 ^ 71.
5. Cascorbi I. Genetic basis of toxic reactions to drugs
and chemicals. Toxicol Lett 2006;162:16 ^ 28.
6. Hein DW. Molecular genetics and function of NAT1
and NAT2: role in aromatic amine metabolism and carcinogenesis. Mutat Res 2002;506 ^ 507:65 ^ 77.
7. Cascorbi I, Brockmoller J, Mrozikiewicz PM, et al.
Homozygous rapid arylamine N-acetyltransferase
(NAT2) genotype as a susceptibility factor for lung
cancer. Cancer Res 1996;56:3961 ^ 6.
8. Ohno M,Yamaguchi I,Yamamoto I, et al. Slow N-acetyltransferase 2 genotype affects the incidence of
isoniazid and rifampicin-induced hepatotoxicity. Int
J Tuberc Lung Dis 2000;4:256 ^ 61.
9. He LJ,YuYM, Qiao F, et al. Genetic polymorphisms of
N-acetyltransferase 2 and colorectal cancer risk.
World J Gastroenterol 2005;11:4268 ^ 71.
10. HuangYS, Chern HD, Su WJ, et al. Polymorphism of
the N-acetyltransferase 2 gene as a susceptibility risk
factor for antituberculosis drug-induced hepatitis.
Hepatology 2002;35:883 ^ 9.
11. Vatsis KP, Weber WW, Bell DA, et al. Nomenclature
for N-acetyltransferases. Pharmacogenetics 1995;5:
1 ^ 17.
12. Hein DW, Doll MA, Fretland AJ, et al. Molecular
genetics and epidemiology of the NAT1 and NAT2
acetylation polymorphisms. Cancer Epidemiol Biomarkers Prev 2000;9:29 ^ 42.
13. Grant DM, Hughes NC, Janezic SA, et al. Human
acetyltransferase polymorphisms. Mutat Res 1997;
376:61 ^ 70.
14. Hein DW. N-acetyltransferase 2 genetic polymorphism: effects of carcinogen and haplotype on urinary
bladder cancer risk. Oncogene 2006;25:1649 ^ 58.
15. Granja F, Morari J, Morari EC, et al. GST profiling
may be useful in the screening for thyroid nodule malignancy. Cancer Lett 2004;209:129 ^ 37.
16. Morari EC, Leite JL, Granja F, et al.The null genotype
of glutathione S-transferase M1and T1locus increases
the risk for thyroid cancer. Cancer Epidemiol Biomarkers Prev 2002;11:1485 ^ 8.
17. Bufalo NE, Leite JL, Guilhen AC, et al. Smoking and
susceptibility to thyroid cancer: an inverse association
with CYP1A1 allelic variants. Endocr Relat Cancer
2006;13:1185 ^ 93.
18. Ron E, Lubin JH, Shore RE, et al. Thyroid cancer
after exposure to external radiation: a pooled analysis
of seven studies. Radiat Res 1995;141:259 ^ 77.
Clin Cancer Res 2009;15(1) January 1, 2009
412
19. Ron E. Thyroid cancer incidence among people
living in areas contaminated by radiation from the
Chernobyl accident. Health Phys 2007;93:502 ^ 11.
20. Siraj AK, Ibrahim M, Al-Rasheed M, et al. Polymorphisms of selected xenobiotic genes contribute to the
development of papillary thyroid cancer susceptibility
in Middle Eastern population. BMC Med Genet 2008;
9:61.
21. Herna¤ndez A, Xamena N, Gutie¤rrez S, et al. Basal
and induced micronucleus frequencies in human lymphocytes with different GST and NAT2 genetic backgrounds. Mutat Res 2006;606:12 ^ 20.
22. Wacholder S, Chanock S, Garcia-Closas M, et al.
Assessing the probability that a positive report is
false: an approach for molecular epidemiology studies.
J Natl Cancer Inst 2004;96:434 ^ 42.
23. Dong LM, Potter JD,White E, et al. Genetic susceptibility to cancer: the role of polymorphisms in candidate genes. JAMA 2008;299:2423 ^ 36.
24. Cox LA, Jr., Sanders E. Estimating preventable
fractions of disease caused by a specified biological
mechanism: PAHs in smoking lung cancers as an example. Risk Anal 2006;26:881 ^ 92.
25. Walraven JM, ZangY,Trent JO, et al. Structure/function evaluations of single nucleotide polymorphisms in
human N-acetyltransferase 2. Curr Drug Metab 2008;
9:471 ^ 86.
26. Kassayova M, Ahlersova E, Ahlers I. Changes of
pineal N-acetyltransferase activity in g irradiated rats.
Physiol Res 1993;42:167 ^ 9.
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Downloaded from clincancerres.aacrjournals.org on September 26, 2014. © 2009 American Association for
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