Download Association Between Single-Nucleotide Polymorphisms

2557
Association Between Single-Nucleotide Polymorphisms
in Hormone Metabolism and DNA Repair Genes and
Epithelial Ovarian Cancer: Results from Two Australian
Studies and an Additional Validation Set
Jonathan Beesley,1 Susan J. Jordan,1,2 Amanda B. Spurdle,1 Honglin Song,5
Susan J. Ramus,6 Suzanne Kruger Kjaer,7,8 Estrid Hogdall,7 Richard A. DiCioccio,9
Valerie McGuire,10 Alice S. Whittemore,10 Simon A. Gayther,6 Paul D.P. Pharoah,5
Penelope M. Webb,1 Georgia Chenevix-Trench,1 Australian Ovarian Cancer
Study Group,1,3 Australian Cancer Study (Ovarian Cancer),1
and Australian Breast Cancer Family Study4
Queensland Institute of Medical Research; 2School of Population Health, University of Queensland, Brisbane, QLD, Australia;
Peter MacCallum Cancer Center, East Melbourne, Victoria, Australia; 4Molecular, Environmental and Analytic Epidemiology,
The University of Melbourne, Carlton, Victoria, Australia; 5CR-UK Department of Oncology, University of Cambridge,
Strangeways Research Laboratory, Cambridge, United Kingdom; 6Translational Research Laboratories, Institute for
Women’s Health, University College London, United Kingdom; 7Institute of Cancer Epidemiology, Danish Cancer
Society; 8The Julianne Marie Center, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
9
Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, New York; and 10Department
of Health Research and Policy, Stanford University School of Medicine, Palo Alto, California
1
3
Abstract
Although some high-risk ovarian cancer genes have
been identified, it is likely that common low penetrance alleles exist that confer some increase in ovarian
cancer risk. We have genotyped nine putative functional single-nucleotide polymorphisms (SNP) in
genes involved in steroid hormone synthesis (SRD5A2,
CYP19A1, HSB17B1, and HSD17B4) and DNA repair
(XRCC2, XRCC3, BRCA2, and RAD52) using two
Australian ovarian cancer case-control studies, comprising a total of 1,466 cases and 1,821 controls of
Caucasian origin. Genotype frequencies in cases and
controls were compared using logistic regression. The
only SNP we found to be associated with ovarian
cancer risk in both of these two studies was SRD5A2
V89L (rs523349), which showed a significant trend of
increasing risk per rare allele (P = 0.00002). We then
genotyped another SNP in this gene (rs632148; r 2 =
0.945 with V89L) in an attempt to validate this finding
in an independent set of 1,479 cases and 2,452 controls
from United Kingdom, United States, and Denmark.
There was no association between rs632148 and
ovarian cancer risk in the validation samples, and
overall, there was no significant heterogeneity between the results of the five studies. Further analyses
of SNPs in this gene are therefore warranted to
determine whether SRD5A2 plays a role in ovarian
cancer predisposition. (Cancer Epidemiol Biomarkers
Prev 2007;16(12):2557 – 65)
Introduction
Ovarian cancer is the leading cause of death from
gynaecologic malignancy. The vast majority of malignant
ovarian cancers are of epithelial origin and can be
classified into four major subtypes: serous, mucinous,
Received 6/14/07; revised 9/9/07; accepted 10/1/07.
Grant support: Cancer Research UK, Roswell Park Alliance, Danish Cancer Society,
and National Cancer Institute grants CA71766, CA16056, and RO1 CA61107. NHMRC
Senior Principal Research Fellowship (G. Chenevix-Trench), NHMRC Career
Development award (A.B. Spurdle), Queensland Cancer Fund Senior Research
Fellowship (P.M. Webb), Well-being of Women grant (H. Song), Cancer Research U.K.
Senior Clinical Research Fellowship (P.D.P. Pharoah), HEFCE Senior Lecturer fund
(S.A. Gayther), and Mermaid Component of the Eve Appeal (S.J. Ramus). The AOCS
was supported by U.S. Army Medical Research and Materiel Command grant
DAMD17-01-1-0729, National Health and Medical Research Council of Australia grant
199600, Cancer Council Tasmania, and Cancer Foundation of Western Australia.
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: G. Chenevix-Trench, Division of Cancer and Cell Biology,
Queensland Institute of Medical Research, c/o Royal Brisbane Hospital Post Office,
Herston, Queensland 4029, Australia. Phone: 617-3362-0390; Fax: 61-7-3362-0105.
E-mail: georgiaT@qimr.edu.au
Copyright D 2007 American Association for Cancer Research.
doi:10.1158/1055-9965.EPI-07-0542
endometrioid, and clear cell (1). Mutations in the highrisk breast cancer susceptibility genes, BRCA1 and
BRCA2, as well as the mismatch repair genes, MSH2
and MLH1, underlie most ‘‘hereditary’’ ovarian cancers
(2). The known ovarian cancer susceptibility genes have
been estimated to explain f40% of the excess familial
risk of ovarian cancer (3). Thus, it is likely that other
ovarian cancer susceptibility genes exist. Several genetic
models may explain residual familial clustering, but
other highly penetrant genes are likely to be rare because
mutations in BRCA1/2 are responsible for most families
containing three or more ovarian cancer cases. A more
plausible alternative is that the remaining familial
clustering is driven by variants at multiple loci, each
conferring a more moderate risk of the disease. Such
variants will also confer risks of nonfamilial ovarian
cancer.
The ovarian surface epithelium or epithelial-lined
inclusion cysts within the ovarian cortex have traditionally been considered to be the most likely site of origin
of epithelial ovarian cancers. The most widely cited
Cancer Epidemiol Biomarkers Prev 2007;16(12). December 2007
2558
Hormone Metabolism and DNA Repair Genes SNPs
hypothesis for the etiology of these cancers is that
proposed by Fathalla (4), which states that repeated
ovulatory cycles increase the risk of ovarian cancer
because of the resulting proliferation of the surface
epithelium. If the incessant ovulation hypothesis of
ovarian cancer is correct, one might expect that errors
arising during DNA synthesis might confer elevated
risks of ovarian cancer. Genetic variants in double-strand
DNA repair genes, including those involved in homologous recombination, such as XRCC2, XRCC3, BRCA2,
and RAD52, may also influence cancer risk.
However, the incessant ovulation model does not take
into account all of the known epidemiologic risk factors
for ovarian cancer, and these other factors suggest a role
for hormones. In particular, ovarian cancer risk is
decreased with increasing duration of contraceptive pill
use and parity (5, 6). It has been specifically suggested
that exposure of the ovarian surface epithelium to androgens may increase cancer risk and exposure to progesterones confer a protective effect (7, 8). Reproductive
hormones control normal ovarian function by regulating
processes, such as cell proliferation, differentiation, and
apoptosis. At ovulation, ovarian surface epithelial cells
are exposed to very high levels of estrogens contained
within follicular fluid (8), and Syed et al. (9) have shown
that an increased concentration of estrogen stimulates
ovarian surface epithelial cell proliferation in vitro (9).
Exposure to exogenous estrogens has been shown to be
related to increased ovarian cancer risk in most studies
(10-12).
In this study, we examined the role of specific,
putative functional single-nucleotide polymorphisms
(SNP) in the SRD5A2, CYP19A1, HSB17B1, and
HSD17B4 genes, encoding components of the hormone
synthetic pathways. The product of the SRD5A2 gene,
5-a-reductase, catalyzes the conversion of testosterone
to the more biologically active dihydrotestosterone. A
common nonsynonymous coding SNP (V89L; rs523349)
seems to affect the rate of this conversion. Recombinant
protein carrying the leucine residue has been shown to
result in 30% less testosterone production (13-15). CYP19
encodes the enzyme cytochrome P450c19a aromatase,
which catalyses the conversion of androgens to estrogens
(16). An SNP in the 3¶ untranslated region (rs10046) has
been described previously, and the T allele has been
linked to ‘‘high enzyme activity’’ and increased CYP19
mRNA levels (17). An association has also been shown
between the C allele and decreased circulating estradiol
levels (18). The final step of estradiol synthesis (conversion of estrone to the more biologically active estradiol) is
catalyzed by type I 17h-hydroxysteroid dehydrogenase,
which is encoded by the HSD17b1 gene (16). This
enzyme also catalyses the conversion of the weak
androgen, androstenedione, to testosterone (19). A nonsynonymous change in the protein sequence (S313G;
rs605059) has been studied previously (20), although sitedirected mutagenesis shows little effect on catalytic or
immunologic activity of the protein (21). In this study, we
evaluated the role of HSD17b1 S313G and another
nonsynonymous change (A238V) on ovarian cancer risk.
Further regulation of estradiol levels occurs by unidirectional oxidation of estrone by type 4 peroxisomal
17h-hydroxysteroid dehydrogenase, encoded by the
HSD17b4 gene, and so, we also genotyped a previously
unstudied SNP in the 3¶ end of this gene (W511R;
rs17145454). To our knowledge, this is the first investigation of these SNPs in SRD5A2, CYP19A1, HSB17B1,
and HSD17B4 in ovarian cancer.
We have examined SNPs in some of these genes in
a previous case-control comparison (22-24) but have now
extended this analysis to the Australian Ovarian Cancer
Study (AOCS) to provide more statistical power, particularly to evaluate rare variants and different histologic
subtypes of ovarian cancer. We present here our analysis
of nine SNPs in a subset of genes involved in steroid
hormone synthesis and DNA repair using two Australian
case-control studies, comprising a total of 1,466 cases and
1,821 controls, as well as the attempted validation of the
most convincing finding from this analysis in an
independent set of 1,479 cases and 2,452 controls from
United Kingdom, United States, and Denmark.
Materials and Methods
Subjects. Clinical and epidemiologic data and DNA
samples for genotyping were obtained from women who
participated in two separate Australian studies of
epithelial ovarian cancer and, for the SRD5A2 validation
set, from a consortium of three ovarian cancer studies
from United Kingdom, United States, and Denmark
(25, 26). Approval for all studies was obtained from the
relevant Human Research Ethics Committees, and all
participants provided informed consent.
Study 1. This study has been described in detail
elsewhere (23, 24, 27-29) but, in brief, it included a total of
510 Caucasian women newly diagnosed before the age of
82 with epithelial ovarian cancer (including borderline
tumors) who were recruited as part of a populationbased case-control study conducted in New South Wales,
Victoria, and Queensland between 1992 and 1995
(n = 341) or treated at the Royal Brisbane Hospital,
Queensland, between 1985 and 1996 (n = 169). The
comparison group came from two sources: 294 unrelated
adult monozygotic twins (one from each pair) recruited
into a multicenter study between 1992 and 1993 (30) and
686 control women recruited into a population-based
case-control study of breast cancer between 1992 and
2000 (31). As the monozygotic twin control set was not a
population-based sample, we compared the distributions
of genotypes in the two groups. There were no significant
differences, so the two comparison groups were pooled.
Epidemiologic data relating to potential confounders,
such as parity, oral contraceptive use, hormone replacement therapy, and smoking status were only available
for the case and control women recruited through the
population-based studies.
Study 2. The second study was a national populationbased case-control study of ovarian cancer, the AOCS.
Between January 2002 and June 2006, eligible case
women ages between 18 and 79 years and newly
diagnosed with epithelial ovarian cancer (including
borderline tumors) were recruited primarily in specialized gynecologic oncology units by research nurses.
Additional case women missed at the major treatment
centers were identified through cancer registries and,
with the treating doctor’s permission, invited to participate (recruitment through the New South Wales and
Victorian Cancer Registries was conducted in parallel
Cancer Epidemiol Biomarkers Prev 2007;16(12). December 2007
Cancer Epidemiology, Biomarkers & Prevention
under a separately funded study: the Australian Cancer
Study). Women who were unable to give informed
consent due to language difficulties, mental incapacity,
or illness were excluded, as were those whose diagnosis
was not histopathologically confirmed. Of the 4,005
women identified as potentially eligible for the study,
806 (20%) were excluded due to death (n = 304), illness
(n = 201), or inability to give informed consent (n = 301).
Of those invited to participate, 2,714 (85%) agreed to take
part (68% of those identified). Of these, a further 730
women (27%) were excluded after pathology review
because they were not confirmed as having an eligible
cancer. Two researchers independently abstracted information on site of tumor, histologic subtype, invasiveness,
and grade from histology reports, and discrepancies
were resolved by consensus. A formal review of a full set
of diagnostic slides was undertaken for a sample of 87
women by one of a group of gynaecologic pathologists.
There was agreement of 97% on tumor site, 98% on
tumor behavior, and 99% on tumor subtype between the
results of the formal review and the abstracted data.
Control women, frequency matched to the entire case
series, were randomly selected from the Australian
Electoral Roll (enrollment to vote is compulsory in
Australia) after stratifying for age (in 5-year groups)
and state of residence. Selected women were mailed
study information and subsequently contacted by telephone by research nurses. At least five attempts were
made to telephone each woman, and those not contacted
were sent a second letter. Women were excluded if they
reported a previous history of ovarian cancer or a
previous bilateral oophorectomy, as were those women
who were unable to give informed consent due to illness,
mental incapacity, or language difficulties. Of the
potential control women contacted and invited to
participate, two percentage were excluded on the basis
of illness or language difficulties and of the remaining
women, 1,614 (47%) agreed to take part. Of these, 104
women were excluded from analyses because they
reported a prior ovarian cancer (n = 7) or bilateral
oophorectomy (n = 97).
All participants were asked to complete a detailed
health and life-style questionnaire, and 87% of eligible
cases (n = 1717) and 85% of controls (n = 1287) provided
a sample of blood. The questionnaire covered demographic and physical characteristics, family history,
medical and surgical history, life-style habits (including
smoking and alcohol consumption), and reproductive
and contraceptive histories. Missing information and/or
inconsistencies were clarified during a subsequent
telephone interview. For this study, we genotyped 956
AOCS cases and 841 AOCS controls for whom DNA was
available at the time.
SRD5A2 Validation Set. Three case-control studies
from United Kingdom (SEARCH), Denmark (MALOVA),
and United States (GEOC), comprising 1,479 cases and
2,452 controls, were used for a validation set. These
studies have been described in detail previously (25, 26).
Analysis included only 1,400 cases and 2,393 controls of
self-reported Caucasian ancestry for whom genotype
information was available. Tumor histologic subtype
breakdown for cases was as follows: serous 47%,
mucinous 11%, endometrioid 16%, clear cell carcinoma
8%, other 19%.
Laboratory Methods. DNA extraction methods for
study 1 have been described earlier (27, 28). For study 2,
DNA was extracted from peripheral blood using a salt
extraction method modified from (ref. 32) or QiAMP
blood kit (Qiagen, Inc.). All DNA samples were
quantitated using a NanoDrop ND-1000 spectrophotometer, and then 5 ng of each gDNA was aliquoted into
384-well plates and dried down at room temperature.
Case and control DNAs were randomly assigned to well
positions. Each plate included 36 randomly chosen,
repeat samples to confirm assay reproducibility and four
negative, template-free controls.
SNPs were genotyped using MALDI-TOF mass spectrophotometric mass determination of allele-specific
primer extension products using Sequenom’s MassARRAY system. The design of oligonucleotides was carried
out according to the guidelines of Sequenom, Inc., and
was done using MassARRAY assay design software
(Version 1.0). Primer sequences are available on request.
Multiplex PCR amplification of amplicons containing
SNPs of interest was done using Qiagen HotStart Taq
Polymerase and Perkin-Elmer GeneAmp 2400 thermal
cycler. Primer extension reactions were carried out
according to manufacturer’s instructions for either
homogenous MassEXTEND or iPLEX chemistries. Assay
data were analyzed using Sequenom TYPER software
(Version 3.0). Seven of the nine SNPs were genotyped
with both the homogenous MassEXTEND and iPLEX
chemistries to obtain confidence with the new iPLEX
chemistry and to confirm deviations from Hardy-Weinberg equilibrium with a different chemistry. The concordance was 99.88% to 100% for each SNP. Whenever the
two chemistries provided different results (0.12% genotypes), we omitted the discrepant sample.
The V89L SNP was not amenable to TaqMan (Applied
Biosystems) genotyping technology, so for the SRD5A2
validation study, we genotyped rs632148, which was
reported to tag the V89L (rs523349) SNP with a pair-wise
correlation coefficient (r 2) of 0.945. To estimate r 2 in our
own population, we genotyped 30 individuals (33 cases
and 57 controls) with each of the three rs523349
genotypes for the rs632148 SNP.
Data Analysis. The two Australian data sets were
initially analyzed separately to provide a test and
replication set and were then combined to increase the
power, particularly to examine the different histologic
subtypes. Analyses were restricted to Caucasian women
(510 cases and 980 controls for study 1 and 956 and 841
for study 2) who made up >95% of each study
population. The Hardy-Weinberg equilibrium assumption was first assessed in case and control groups for each
SNP using a standard m2 test. Univariate tests of
association for each SNP were carried out using the
likelihood ratio test with 2 df. Odds ratios (OR) and 95%
confidence intervals (95% CI) were calculated using
unconditional logistic regression. All models were
adjusted for age (at diagnosis for cases and at first
contact for controls) as a continuous term, and when data
from more than one study were combined, a term for
study was also included. Other factors, such as parity
(0, 1-2, z3), pregnancies (z6 months of duration), and
use of oral contraceptives (never, <5 years, z5 years of
duration) were not included in final models as they
changed point estimates by <10%. Stratification was used
Cancer Epidemiol Biomarkers Prev 2007;16(12). December 2007
2559
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Hormone Metabolism and DNA Repair Genes SNPs
to investigate gene-environment interactions, and if this
suggested interaction, the statistical significance of the
relevant multiplicative term was assessed. Heterogeneity
between the results from different studies was assessed
using an interaction term for study by each gene. A
nominal P value of <0.05 was considered statistically
significant. Analysis of the validation set was as above.
Results
There was no evidence of deviation from the HardyWeinberg equilibrium in controls from study 1 for eight
of the nine SNPs investigated, although there was
borderline significant deviation from Hardy-Weinberg
equilibrium for the HSD17b1 Gly313Ser SNP (rs605059)
gene (P = 0.05). In study 2, there was also little evidence
of deviation from the Hardy-Weinberg equilibrium but
there was a statistically significant deviation from HardyWeinberg equilibrium for BRCA2 (rs144848; P = 0.04)
with the results from both homogenous MassEXTEND
and iPLEX chemistry (33). Cases and controls from study
2 (mean age, 58 and 57 years, respectively) and cases
from study 1 (mean age, 57 years) were on average of
very similar in age, whereas the controls from study 1
(mean age, 44 years) were significantly younger (P <
0.0001); all results were thus adjusted for age. The
distribution of histologic subtypes of tumors was similar
for both studies (Table 1).
The risk of epithelial ovarian cancer associated with
the various SNPs was determined separately for study 1
and study 2 (Table 2). For study 1, the only statistically
significant association was seen for SRD5A2 (rs523349).
The rare allele was associated with an increased risk in a
codominant manner (P trend = 0.02) with the GC genotype
showing a 30% increase in risk compared with the GG
genotype (1.30; 95% CI, 1.01-1.68) and the CC genotype
having a slightly higher risk (OR, 1.44; 95% CI, 0.93-2.24),
albeit not significant at the 5% level. Having a C allele for
HSD17b4 (rs17145454) was associated with a nonsignificant decreased risk of ovarian cancer (OR, 0.79; 95% CI,
0.56-1.12 for CT versus TT; OR, 0.31; 95% CI, 0.07-1.53 for
CC versus TT; P trend = 0.07), but no associations of note
were seen between the other investigated SNPs and risk
of ovarian cancer.
Table 1. Histologic subtypes of ovarian tumors of case
women in Australian studies 1 and 2
Histologic
subtype of cancer
Invasive
Serous
Mucinous
Endometrioid
Clear cell
Other*
Borderline
Serous
Mucinous
Other
Study 1
Study 2
Cases
(n = 510), n (%)
Cases
(n = 956), n (%)
257
27
59
26
55
(50)
(5)
(12)
(5)
(11)
45 (9)
36 (7)
5 (1)
517
21
78
44
92
(54)
(3)
(8)
(5)
(9)
95 (10)
98 (10)
9 (1)
*Includes mixed epithelial cancers, transitional cell cancers, and
malignant mixed müllerian tumors.
A statistically significant association between SRD5A2
genotype and ovarian cancer risk was also seen in study
2 (P trend = 0.0001). As for study 1, there was a small
increase in risk associated with the GC genotype
compared with the GG genotype (OR, 1.14; 95% CI,
0.93-1.40) and a larger statistically significant OR
associated with the CC genotype (OR, 1.83; 95% CI
1.37-2.44; P = 0.00004). There was also a significant trend
of increasing risk per C allele (30% increase in risk per C
allele, P = 0.0001). In study 2, the trend of decreasing risk
with one or more C alleles was not as marked for
HSD17b4 as that seen in study 1, but the CC homozygotes again had a nonsignificant decreased risk of
ovarian cancer (OR, 0.59; 95% CI, 0.17-2.11; P = 0.4). In
study 2, the AC genotype of BRCA2 (rs144848) was
associated with a 36% increase in risk (OR, 1.36; 95% CI,
1.12-1.66) compared with the AA genotype, but those
with CC genotypes did not seem to be at increased risk. It
is of note that there was significant deviation from
Hardy-Weinberg equilibrium for BRCA2 in this group of
control women with a deficiency of heterozygotes, but, as
described above, this could not be attributed to genotyping errors.
When the data from study 1 and study 2 were
combined, there was no evidence of heterogeneity
between the studies for any of the genotypes (P interaction
> 0.05). In the combined data set, the association for
SRD5A2 rs523349 became highly significant (P trend =
0.00002). Both heterozygotes (OR, 1.16; 95% CI, 1.00-1.36;
P = 0.06) and CC homozygotes (OR, 1.70; 95% CI, 1.352.16, P = 0.000009) had increased risk, and there was a
significant trend of increasing risk per extra C allele (26%
per C allele, P = 0.00002). In the combined analysis, no
statistically significant associations were found for
HSD17b4, but a small significant risk persisted for those
heterozygous for the BRCA2 SNP (OR, 1.27; 95% CI, 1.091.49; P < 0.005). In addition, a small, but statistically
significant, increase in risk was associated with the TC
(OR, 1.20; 95% CI, 1.01-1.43; P = 0.04) but not the CC (OR,
1.15; 95% CI, 0.93-1.41) genotype of CYP19A1 (rs10046).
We used the combined dataset to investigate whether
risk associated with the various SNPs might be modified
by exposures known to influence ovarian cancer risk.
Stratifying analyses by use of oral contraceptives (ever
versus never), use of hormone replacement therapy (ever
versus never), smoking status (ever versus never), and
age (<50 years compared with z50 years) did not provide
any evidence that the genotype effects varied according
to these characteristics. A borderline significant interaction (P = 0.045) associated with parity was found for the
RAD52 SNP (rs4987208). Women who were nulliparous
and were GT heterozygotes for this SNP had a significant
75% decrease in risk of ovarian cancer compared with TT
homozygotes (OR, 0.25; 95% CI, 0.06-0.98), whereas OR
for parous women with the GT genotype was 1.06 (95%
CI, 0.64-1.75). No other significant interactions were seen
for parity.
Variations in risk between histologic subtypes of
epithelial ovarian cancer were investigated using the
combined data from study 1 and study 2 (Table 3). The
positive association between the C allele for SRD5A2 and
risk persisted for borderline and invasive tumors of the
serous and mucinous subtypes (but did not reach
statistical significance for borderline serous tumors)
and seemed to be somewhat stronger for invasive
Cancer Epidemiol Biomarkers Prev 2007;16(12). December 2007
Cancer Epidemiology, Biomarkers & Prevention
Table 2. ORs and 95% CIs for the associations between genotype and risk of epithelial ovarian cancer in
Australian studies 1 and 2
Gene
Cases/controls (n)
Cases/controls (n)
Study 1
Study 2
Study 1
Study 2
Combined
392/402
378/336
169/94
P = 0.0002
1.00
1.30 (1.01-1.68)*
1.44 (0.93-2.24)
1.00
1.14 (0.93-1.40)
c
1.83 (1.37-2.44)
1.00
1.16 (1.00-1.36)
c
1.70 (1.35-2.16)
767/679
141/114
4/6
P = 0.6
1.00
0.79 (0.56-1.12)
0.31 (0.07-1.53)
1.00
1.10 (0.84-1.44)
0.59 (0.17-2.11)
1.00
0.99 (0.81-1.22)
0.47 (0.18-1.23)
282/247
442/404
189/147
P = 0.5
1.00
1.20 (0.91-1.59)
0.93 (0.66-1.32)
1.00
0.96 (0.77-1.20)
1.13 (0.86-1.49)
1.00
1.07 (0.90-1.27)
1.06 (0.86-1.31)
262/247
469/387
208/193
P = 0.4
1.00
1.28 (0.95-1.72)
1.33 (0.94-1.88)
1.00
1.15 (0.92-1.43)
1.02 (0.78-1.32)
1.00
1.20 (1.01-1.43)*
1.15 (0.93-1.41)
725/739
15/21
0/0
P = 0.4
1.00
1.13 (0.34-3.73)
1.00
0.73 (0.37-1.43)
1.00
0.77 (0.43-1.39)
460/461
401/296
69/68
P = 0.008
1.00
1.19 (0.92-1.54)
1.13 (0.70-1.84)
1.00
b
1.36 (1.12-1.66)
1.02 (0.71-1.46)
1.00
b
1.27 (1.09-1.49)
1.11 (0.83-1.47)
799/696
117/115
7/7
P = 0.7
1.00
0.84 (0.59-1.20)
1.23 (0.37-4.14)
1.00
0.88 (0.67-1.16)
0.92 (0.32-2.63)
1.00
0.86 (0.70-1.07)
1.19 (0.54-2.61)
291/288
339/351
101/108
P = 0.9
1.00
0.83 (0.64-1.08)
1.03 (0.71-1.49)
1.00
0.96 (0.77-1.20)
0.92 (0.67-1.26)
1.00
0.91 (0.77-1.07)
0.95 (0.75-1.20)
909/806
28/26
P = 0.9
1.00
0.69 (0.31-1.55)
1.00
0.95 (0.55-1.63)
1.00
0.88 (0.55-1.35)
SRD5A2 (rs523349)
GG
234/510
GC
217/378
CC
52/71
P = 0.03
HSD17B4 (rs17145454)
TT
438/819
CT
66/145
CC
2/11
P = 0.2
HSD17B1 gly313ser (rs605059)
AA
125/291
GA
229/446
GG
87/215
P = 0.2
CYP19A1 (rs10046)
TT
115/269
TC
254/479
CC
130/221
P = 0.1
HSD17B1 ala238val
CC
497/356
TC
6/5
TT
1/0
P = 0.6
BRCA2 (rs144848) [1]
AA
249/502
AC
203/383
CC
40/63
P = 0.5
XRCC2 (rs3218536) [2]
GG
414/819
AG
67/142
AA
5/8
P = 0.8
XRCC3 (rs861539) [2]
CC
207/370
TC
223/471
TT
74/131
P = 0.3
RAD52 (rs4987208) [3]
TT
458/348
GT
13/13
P = 0.5
Age-adjusted ORs (95% CIs)
NOTE: [1], data from study 1 have been previously reported in Auranen et al. (29); [2], data from study 1 have been previously reported in Webb et al. (24);
[3], data from study 1 have been previously reported in Kelemen et al. (23).
*P < 0.05.
cP < 0.0001.
bP < 0.005.
mucinous cancers (OR, 2.81; 95% CI, 1.23-6.38 for CC
versus GG genotype). We also considered site of tumor
as the case groups included women with primary
peritoneal and fallopian tube cancers. Although these
cancers are clinically and histologically similar to their
ovarian counterparts, our previous analyses suggest that
there are etiologic differences between ovarian/fallopian
tube and primary peritoneal tumors.11 Although the OR
11
Susan J Jordan, Adèle C. Green, David C. Whiteman, Christopher J.
Bain, Dorota M. Gertig M., Penelope M. Webb for the Australian Cancer
Study Group (ovarian cancer) and the Australian Ovarian Cancer Study
Group. Serous ovarian, fallopian tube and primary peritoneal cancers —
one disease or three? In press. International Journal of Cancer.
for CC homozygotes of SRD5A2 compared with GG
homozygotes was somewhat higher for serous peritoneal
cancers (OR, 2.64; 95% CI, 1.44-4.83) than for serous
ovarian cancers (OR, 1.81; 95% CI, 1.33-2.48), there were
no statistically significant differences by tumor site.
Two associations were statistically significant for
invasive clear cell cancers. The CT genotype of HSD17b4
was associated with a 65% decrease in risk of clear cell
cancer (OR, 0.35; 95% CI, 0.13-0.96; P = 0.04). There were
no women with clear cell tumors who were homozygous
CC for HSD17b4. Risk of clear cell cancers was also
associated with the HSD17b1 Gly313Ser (rs605059) SNP.
Having the GG genotype was associated with a more
than 2-fold increase in risk of clear cell cancer compared
with having the AA genotype (OR, 2.33; 95% CI,
Cancer Epidemiol Biomarkers Prev 2007;16(12). December 2007
2561
2562
Hormone Metabolism and DNA Repair Genes SNPs
1.14-4.79; P = 0.02), and there was a significant trend of
risk increase associated with each additional G allele
(52% per G allele; P = 0.02).
The only nominally statistically significant finding for
the DNA repair SNPs related to serous borderline
tumors. The AG genotype for XRCC2 (rs3218536) was
associated with a 51% decreased risk compared with the
GG genotype (OR, 0.49; 95% CI, 0.25-0.95; P = 0.03),
although AA homozygotes had a nonsignificant 2-fold
increase in risk (OR, 2.12; 95% CI, 0.59-7.58).
Given our similar findings for SRD5A2 (rs523349) in
both the Australian case-control studies, we sought to
validate this result in a large consortium of three casecontrol studies from United Kingdom, United States, and
Denmark (25, 26). The rs523349 was not amenable to
genotyping by TaqMan, so instead we genotyped a 3¶
untranslated region tagging SNP in SRD5A2 (rs632148),
which tags rs523349 with an r 2 of 0.945 based on the
HapMap genotype data for the CEU samples (30 trios of
European origin). To determine the r 2 in our own
population, we genotyped 90 individuals from the AOCS
with both the rs523349 and rs632148 SNPs and also
showed that, in this sample, the r 2 between the two SNPs
was 0.9.
There was marginal evidence of deviation from the
Hardy-Weinberg equilibrium in cases for the validation
set overall (P = 0.06), which seemed to be driven by a
deficiency of heterozygotes in the SEARCH sample.
Although there was no evidence for Hardy-Weinberg
disequilibrium in controls overall, there was a similar
deficiency of heterozygotes in controls in the MALOVA
sample (P = 0.03).
We found no association between this SRD5A2 SNP
and ovarian cancer risk in any of these validation casecontrol sets individually nor when the three studies
were pooled (P trend = 0.9; Table 4). There was no
evidence for heterogeneity between the three sample
sets, and the combined OR for the validation set was
not significantly different from 1 for the heterozygote
genotype (OR, 0.93; 95% CI 0.80-1.07) or the homozygote genotype (OR, 1.12; 95% CI 0.89-1.40). Analysis of
subgroups defined by histologic subtype revealed no
evidence for an increased risk within any particular
subtype (data not shown).
Table 3. ORs for genotype by ovarian tumor behavior and histologic subtype (combined results from Australian
studies 1 and 2)
Gene
Cases/controls
n
ORs*
Serous
borderline
Mucinous
Borderline
Serous
invasive
Mucinous
invasive
Endometrioid
invasive
Clear cell
invasive
(n = 140)
(n = 134)
(n = 772)
(n = 48)
(n = 137)
(n = 70)
1.00
1.61 (1.09-2.38)
c
1.79 (1.01-3.18)
1.00
1.12 (0.92-1.36)
b
1.80 (1.36-2.38)
1.00
1.27 (0.66-2.46)
c
2.81 (1.23-6.38)
1.00
1.17 (0.81-1.70)
1.06 (0.57-1.99)
1.00
0.71 (0.42-1.20)
0.90 (0.39-2.07)
1.00
1.13 (0.69-1.85)
0.93 (0.12-7.24)
1.00
1.01 (0.78-1.30)
0.56 (0.18-1.73)
1.00
0.50 (0.18-1.41)
—
1.00
0.96 (0.59-1.58)
—
1.00
c
0.35 (0.13-0.96)
—
1.00
0.87 (0.58-1.30)
0.79 (0.46-1.35)
1.00
1.02 (0.83-1.26)
0.93 (0.71-1.21)
1.00
1.43 (0.72-2.86)
0.85 (0.33-2.18)
1.00
1.09 (0.71-1.65)
1.20 (0.73-1.98)
1.00
1.67 (0.87-3.20)
c
2.33 (1.14-4.79)
1.00
1.15 (0.74-1.77)
1.23 (0.75-2.03)
1.00
1.23 (0.99-1.53)
1.11 (0.86-1.44)
1.00
0.56 (0.28-1.11)
0.94 (0.45-1.95)
1.00
1.35 (0.86-2.12)
1.46 (0.88-2.43)
1.00
1.19 (0.67-2.14)
1.03 (0.51-2.08)
1.00
0.76 (0.18-3.26)
1.00
1.03 (0.53-2.00)
1.00
0.39 (0.05-2.92)
1.00
0.80 (0.11-6.04)
1.00
1.19 (0.82-1.73)
0.73 (0.33-1.64)
1.00
1.39 (1.14-1.68)
1.19 (0.84-1.68)
1.00
0.71 (0.36-1.40)
1.95 (0.83-4.60)
1.00
1.12 (0.77-1.63)
1.01 (0.50-2.02)
1.00
1.00 (0.59-1.68)
0.56 (0.17-1.84)
1.00
0.73 (0.41-1.30)
—
1.00
0.88 (0.68-1.15)
1.13 (0.42-3.04)
1.00
1.86 (0.93-3.73)
—
1.00
0.83 (0.49-1.41)
2.01 (0.44-9.08)
1.00
0.78 (0.37-1.68)
2.25 (0.29-17.76)
1.00
1.05 (0.68-1.62)
1.35 (0.76-2.38)
1.00
0.88 (0.72-1.09)
1.00 (0.75-1.34)
1.00
1.18 (0.60-2.34)
1.16 (0.44-3.07)
1.00
0.83 (0.56-1.23)
0.72 (0.39-1.33)
1.00
0.90 (0.51-1.58)
0.88 (0.38-1.99)
1.00
0.49 (0.12-2.06)
1.00
0.99 (0.58-1.68)
1.00
0.66 (0.09 4.99)
1.00
1.12 (0.43-2.92)
1.00
0.88 (0.21-3.74)
SRD5A2 (rs523349)
GG
626/912
1.00
GC
595/714
1.03 (0.71-1.50)
CC
221/165
1.54 (0.90-2.62)
HSD17B4 (rs17145454)
TT
1205/1498
1.00
CT
207/259
1.32 (0.84-2.09)
CC
6/17
0.99 (0.13-7.64)
HSD17B1 gly313ser (rs605059)
AA
422/547
1.00
GA
702/857
1.10 (0.72-1.67)
GG
291/368
1.41 (0.86-2.30)
CYP19A1 (rs10046)
TT
377/516
1.00
TC
723/866
1.16 (0.77-1.77)
CC
338/414
1.11 (0.68-1.82)
HSD17B1 ala238val
CC
1222/1095
1.00
TC
21/26
0.32 (0.04-2.43)
BRCA2 (rs144848)
AA
709/963
1.00
AC
604/679
1.38 (0.97-1.98)
CC
109/131
0.63 (0.27-1.49)
XRCC2 (rs3218536)
GG
1213/1515
1.00
AG
184/257
0.49 (0.25-0.95)
AA
12/15
2.12 (0.59-7.58)
XRCC3 (rs861539)
CC
498/658
1.00
TC
562/822
0.98 (0.66-1.44)
TT
175/239
0.59 (0.30-1.16)
RAD52 (rs4987208)
TT
1367/1154
1.00
GT
41/39
0.45 (0.11-1.91)
1.00
—
*Adjusted for age and study.
cP V 0.05.
bP < 0.0001.
Cancer Epidemiol Biomarkers Prev 2007;16(12). December 2007
Cancer Epidemiology, Biomarkers & Prevention
Table 4. ORs and 95% CIs for the associations between SRD5A2 (rs632148) genotype and risk of epithelial ovarian
cancer in the validation set
Gene
Cases/
controls (n)
Cases/
controls (n)
Cases/
controls (n)
SEARCH
MALOVA
STANFORD
SEARCH
MALOVA
STANFORD
Combined
196/553
184/494
58/146
P trend = 0.8
138/167
121/168
24/29
P trend = 0.7
1.00
0.87 (0.70-1.08)
1.18 (0.82-1.69)
1.00
1.03 (0.81-1.31)
1.11 (0.78-1.57)
1.00
0.88 (0.64-1.23)
1.01 (0.56-1.83)
1.00
0.93 (0.80-1.07)
1.12 (0.89-1.40)
SRD5A2 (rs632148)
GG
335/397
GC
270/368
CC
74/71
P trend = 0.13
Age-adjusted ORs (95% CIs)
When the data from all five studies were combined,
there was no evidence of heterogeneity between the
studies (P = 0.15), and the pooled OR suggested a
statistically significant 40% increase in risk of ovarian
cancer among rare homozygotes (P trend = 0.002).
Discussion
We used two large Australian case-control populations to
test for associations between nine SNPs in genes
involved in steroid hormone synthesis and DNA repair
and ovarian cancer risk. All analyses were restricted to
Caucasians to reduce the potential problems of population stratification. For many of the SNPs we genotyped,
we and others had previously reported no or weak
associations with ovarian cancer risk (22-24, 29). The
main rationale for this study was therefore to have
additional power to look for associations with different
histologic subtypes of ovarian cancer. In study 1, the only
significant finding was seen for SRD5A2 (rs523349), and
this increased risk associated with the GC and CC
genotypes was also noted in study 2 with a 30%
increased risk per C allele (P = 0.0001). This association
was seen for both invasive and borderline tumors of the
serous and mucinous subtypes of ovarian cancer, but
there was no association for endometrioid and clear cell
tumors. The exclusion of women who died before
recruitment or were very sick means that very aggressive
disease may be underrepresented in the case group.
However, this is unlikely to have introduced any bias
unless the etiology of aggressive cancers differs markedly from that of less aggressive cancers. The borderline
significant findings with HSD17B4 (rs17145454) in study
1 and with BRCA2 (rs144848) and CYP19A1 (rs10046) in
study 2 were not replicated in the other study. In
addition, the modest associations seen for clear cell
cancers in the combined analysis are likely to be due to
chance as a result of the relatively large number of tests
done. No associations were seen for any of the SNPs we
genotyped in DNA repair genes in either study 1 or
study 2.
SRD5A2 catalyses the conversion of testosterone to
dihydrotestosterone, a more potent androgen than
testosterone. Functional studies of SRD5A2 variants
using recombinant enzyme constructs have shown
differential enzyme activities (15). In particular, the valine
variant of V89L has an increased turnover rate compared
with the leucine variant. Little is known of the role of
testosterone in the ovary, but high levels of androgens
have been linked to polycystic ovary syndrome (reviewed
in ref. 34), and there is some evidence that women with
polycystic ovary syndrome are at increased risk of
developing ovarian cancer (35). The SRD5A2 V89L SNP
has been examined in several prostate cancer studies with
inconsistent results (36-43). However, a comprehensive
metaanalysis of SRD5A2 V89L frequencies in prostate
cancer association studies clearly excluded any increased
risk conferred by this allele (44).
We sought to validate the consistent association that
we found in studies 1 and 2 for SRD5A2 V89L (rs523349)
and ovarian cancer risk in the SEARCH/MALOVA/
Stanford consortium (25, 26) using a tagging SNP
(rs632148) that correlated with rs523349 (r 2 = 0.9). The
combined sample size of the three validation studies had
>95% power to detect an OR of 1.7 for the homozygote
genotype; thus, it should have been large enough to see
an association even allowing for the less than perfect
correlation between the SNPs. Despite this, we did not
find any evidence of an association between SRD5A2
rs632148 and ovarian cancer risk in any of these three
case-control studies or in combined analyses, although a
40% increase in risk for the rare homozygote could not be
excluded (combined OR, 1.1; 95% CI, 0.9-1.4). Despite the
strong association seen in the initial studies and the lack
of association in the three validation studies, overall,
there was no significant heterogeneity between the
results of the five studies. Given this lack of heterogeneity, the best estimate of the association is obtained from
the combined analysis of all five studies which suggested
a significant 40% increase in risk of ovarian cancer
among women with the rare homozygote genotype of
SRD5A2 V89L or rs632148. Given the key role for
SRD5A2 in androgen metabolism, further analysis of
SNPs in the SRD5A2 gene is warranted in even larger
studies, as would be a comprehensive analysis of SNPs in
CYP19A1, HSB17B1, HSD17B4, XRCC2, XRCC3, BRCA2,
and RAD52.
Appendix A. The AOCS Group
Management Group: D. Bowtell (Peter MacCallum
Cancer Center, PMCC), G. Chenevix-Trench, A. Green,
P. Webb (Queensland Institute of Medical Research,
QIMR), A. deFazio (Westmead Hospital), D. Gertig
(University of Melbourne).
Project Managers: N. Traficante (PMCC), S. Moore
(QIMR), J. Hung (Westmead Hospital).
Data Managers: S. Fereday (PMCC), K. Harrap, T.
Sadkowsky (QIMR).
Research Nurses: NSW — A. Mellon, R. Robertson
(John Hunter Hospital), T. Vanden Bergh (Royal Hospital
for Women), J. Maidens (Royal North Shore Hospital),
Cancer Epidemiol Biomarkers Prev 2007;16(12). December 2007
2563
2564
Hormone Metabolism and DNA Repair Genes SNPs
K. Nattress (Royal Prince Alfred Hospital), Y.E. Chiew,
A. Stenlake, H. Sullivan (Westmead Hospital); QLD — B.
Alexander, P. Ashover, S. Brown, T. Corrish, L. Green, L.
Jackman, K. Martin, B. Ranieri (QIMR); SA — J. White
(QIMR); TAS — V. Jayde (Royal Hobart Hospital); VIC —
L. Bowes (PMCC), P. Mamers (Monash Medical Center);
WA — T. Schmidt, H. Shirley, S. Viduka, Hoa Tran,
Sanela Bilic, Lydia Glavinas (Western Australia Research
Tissue Network).
Clinical Collaborators: NSW — A. Proietto, S. Braye, G.
Otton (John Hunter Hospital), T. Bonaventura, J. Stewart
(Newcastle Mater Misericordiae), M. Friedlander (Prince
of Wales Hospital), D. Bell, S. Baron-Hay, A. Ferrier, G.
Gard, D. Nevell, B. Young (until mid 2003; Royal North
Shore Hospital), C. Camaris, R. Crouch, L. Edwards, N.
Hacker, D. Marsden, G. Robertson (Royal Hospital for
Women), P. Beale, J. Beith, J. Carter, C. Dalrymple, A.
Hamilton, R. Houghton, P. Russell (Royal Prince Alfred
Hospital), A. Brand, R. Jaworski, P. Harnett, G. Wain
(Westmead Hospital); QLD — A. Crandon, M. Cummings, K. Horwood, A. Obermair, D. Wyld (Royal
Brisbane and Women’s Hospital, RBWH), J. Nicklin
(RBWH and Wesley Hospital), L. Perrin (RBWH and
Mater Misericordiae Hospitals), B. Ward (Mater Misericordiae Hospitals); SA — M. Davy, C. Hall, T. Dodd, T.
Healy, K. Pittman (Royal Adelaide Hospital, Burnside
Memorial Hospital), D. Henderson, S. Hyde (Flinders
Medical Center), J. Miller, J. Pierdes (Queen Elizabeth
Hospital); TAS — P. Blomfield, D. Challis, R. McIntosh, A.
Parker (Royal Hobart Hospital); VIC — B. Brown, R.
Rome (Freemasons Hospital), D. Allen, P. Grant, S. Hyde,
R. Laurie, M. Robbie (Mercy Hospital for Women),
D. Healy, T. Jobling, T. Maniolitas, J. McNealage, P.
Rogers, B. Susil, A. Veitch, J. Constable, S. Ping Tong,
I. Robinson, I. Simpson (Monash Medical Center), K.
Phillips, D. Rischin, P. Waring, M. Loughrey, N. O’Callaghan, Bill Murray (PMCC), V. Billson, S. Galloway, J.
Pyman, M. Quinn (Royal Women’s Hospital); WA — I.
Hammond, A. McCartney, Y. Leung (King Edward
Memorial Hospital, St. John of God).
Scientific Collaborators: I. Haviv (PMCC), D. Purdie,
D. Whiteman (QIMR), N. Zeps (WARTN).
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
Acknowledgments
We thank Adele Green and David Purdie for DNA and data
from the Survey of Women’s Health; Nick Martin for the DNA
and data from SSAGA (study 1); New South Wales, Queensland,
South Australian, Victorian, and Western Australian Cancer
Registries, as well as all the collaborating institutions represented within the AOCS Study Group and the women who
participated in the study, for their cooperation; Hannah
Munday, Barbara Perkins, Clare Jordan, Mitul Shah, the local
general practices and nurses, and Eastern Cancer Intelligence
Unit for recruitment of U.K. cases; Lydia Quaye for genotyping
the validation set; EPIC-Norfolk investigators for recruitment
of U.K. controls; and all the study participants who contributed
to this research and all the members of the AOCS Study Group
who are listed on http://www.aocstudy.org.
21.
22.
23.
24.
25.
26.
27.
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