Research Article
Tagging Single Nucleotide Polymorphisms in Cell Cycle Control
Genes and Susceptibility to Invasive Epithelial Ovarian Cancer
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Simon A. Gayther, Honglin Song, Susan J. Ramus, Susan Krüger Kjaer, Alice S. Whittemore,
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Lydia Quaye, Jonathan Tyrer, Danielle Shadforth, Estrid Hogdall, Claus Hogdall, Jan Blaeker,
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Richard DiCioccio, Valerie McGuire, Penelope M. Webb, Jonathan Beesley, Adele C. Green,
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David C. Whiteman, The Australian Ovarian Cancer Study Group, The Australian
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Cancer Study (Ovarian Cancer), Marc T. Goodman, Galina Lurie, Michael E. Carney,
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Francesmary Modugno, Roberta B. Ness, Robert P. Edwards, Kirsten B. Moysich,
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Ellen L. Goode, Fergus J. Couch, Julie M. Cunningham, Thomas A. Sellers, Anna H. Wu,
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Malcolm C. Pike, Edwin S. Iversen, Jeffrey R. Marks, Montserrat Garcia-Closas,
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Louise Brinton, Jolanta Lissowska, Beata Peplonska, Douglas F. Easton, Ian Jacobs,
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Bruce A.J. Ponder, Joellen Schildkraut, C. Leigh Pearce, Georgia Chenevix-Trench,
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Andrew Berchuck, Paul D.P. Pharoah, and on behalf of the Ovarian Cancer
Association Consortium
1
Translational Research Laboratories, University College London, London, United Kingdom; 2Cancer Research United Kingdom
Department of Oncology and 3Cancer Research United Kingdom Genetic Epidemiology Unit, University of Cambridge, Strangeways
Research Laboratory, Cambridge, United Kingdom; 4Danish Cancer Society, Copenhagen, Denmark; 5Stanford University School of
Medicine, Stanford, California; 6University of Copenhagen, Copenhagen, Denmark; 7Aarhus University Hospital, Skejby, Aarhus,
Denmark; 8Roswell Park Cancer Institute, Buffalo, New York; 9The Queensland Institute of Medical Research, Post Office Royal
Brisbane Hospital, Australia; 10Cancer Research Center, University of Hawaii, Honolulu, Hawaii; 11Department of Epidemiology and
University of Pittsburgh Cancer Institute, 12Department of Obstetrics, Gynecology and Reproductive Health, University of Pittsburgh
Cancer Institute, Pittsburgh, Pennsylvania; 13Mayo Clinic College of Medicine, Rochester, Minnesota; 14H. Lee Moffitt Cancer Center
and Research Institute, Tampa, Florida; 15University of Southern California, Keck School of Medicine, Los Angeles, California;
16
Duke University Medical Center, Durham, North Carolina; 17Division of Cancer Epidemiology and Genetics, National Cancer
Institute, Rockville, Maryland; 18Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Institute of
Oncology and Cancer Center, Warsaw, Poland; 19Nofer Institute of Occupational Medicine, Lodz, Poland; and
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Peter MacCallum Cancer Institute, Melbourne, Australia
Abstract
High-risk susceptibility genes explain <40% of the excess risk
of familial ovarian cancer. Therefore, other ovarian cancer
susceptibility genes are likely to exist. We have used a single
nucleotide polymorphism (SNP)–tagging approach to evaluate
common variants in 13 genes involved in cell cycle control—
CCND1, CCND2, CCND3, CCNE1, CDK2, CDK4, CDK6, CDKN1A,
CDKN1B, CDKN2A, CDKN2B, CDKN2C, and CDKN2D—and risk
of invasive epithelial ovarian cancer. We used a two-stage,
multicenter, case-control study. In stage 1, 88 SNPs that tag
common variation in these genes were genotyped in three
studies from the United Kingdom, United States, and Denmark
(f1,500 cases and 2,500 controls). Genotype frequencies in
cases and controls were compared using logistic regression.
In stage 2, eight other studies from Australia, Poland, and the
United States (f2,000 cases and f3,200 controls) were
genotyped for the five most significant SNPs from stage 1.
No SNP was significant in the stage 2 data alone. Using the
combined stages 1 and 2 data set, CDKN2A rs3731257 and
CDKN1B rs2066827 were associated with disease risk (unadjusted P trend = 0.008 and 0.036, respectively), but these were
not significant after adjusting for multiple testing. Carrying
Note: Supplementary data for this article are available at Cancer Research Online
(http://cancerres.aacrjournals.org/).
S.A. Gayther and H. Song contributed equally to this work.
Requests for reprints: Simon Gayther, Translational Research Laboratories, Windeyer
Institute, University College London, 46 Cleveland Street, London W1T 4JF, United
Kingdom. Phone: 44-20-7679-9204; Fax: 44-20-7679-9687; E-mail: [email protected].
I2007 American Association for Cancer Research.
doi:10.1158/0008-5472.CAN-06-3261
www.aacrjournals.org
the minor allele of these SNPs was found to be associated
with reduced risk [OR, 0.91 (0.85–0.98) for rs3731257; and OR,
0.93 (0.87–0.995) for rs2066827]. In conclusion, we have
found evidence that a single tagged SNP in both the CDKN2A
and CDKN1B genes may be associated with reduced ovarian
cancer risk. This study highlights the need for multicenter
collaborations for genetic association studies. [Cancer Res
2007;67(7):3027–35]
Introduction
Several genes have been identified that confer high-penetrance
susceptibility to epithelial ovarian cancer. The main protagonists
are BRCA1 and BRCA2. These genes are responsible for about half
of all families containing two or more ovarian cancer cases in firstdegree relatives and most families containing several ovarian
cancer cases (z3) or in which ovarian and breast cancers occur
together (1, 2).
However, the known susceptibility genes explain <40% of the
excess familial ovarian cancer risk (3). If other highly penetrant
ovarian cancer susceptibility genes exist, they are likely to be rare.
One possible explanation for the residual risks is the existence of
several common but only moderately or low-penetrance susceptibility alleles in the population (4).
There have been several studies that have attempted to identify
common variants in genes that may be associated with increased
risk of ovarian cancer. Candidate genes have usually been selected
based on biological plausibility. These include genes in pathways
controlling steroid hormone metabolism (e.g., progesterone receptor, androgen receptor, CYP17, prohibitin; refs. 5–14), DNA repair
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Cancer Res 2007; 67: (7). April 1, 2007
Cancer Research
Table 1A. Characteristics of the 11 ovarian cancer case control studies (listed alphabetically) included in the Ovarian Cancer
Association Consortium pooled study
Study name
Location
AOCS and ACS Multiregional,
(ovarian
Australia
cancer)
FROC (Family
Stanford, CA
Registry for
Ovarian
Cancer) Study
Hawaii Ovarian Hawaii
Cancer Study
HOPE
(hormones
and ovarian
cancer
prediction)
study
LAC-CCOC
Pittsburgh
Los Angeles, CA
Total
Total
Age
Age
number
number
range
range
of cases* of controls* (cases) (controls)
Reference
DNA source
Days from
diagnosis to
c
blood draw
Number
of SNPs
genotyped
9
Blood
lymphocytes
0–1,663 (103)
5
16, 17, 21, 23 Blood
lymphocytes;
mouthwash
55–1,477 (281)
88
Blood
lymphocytes;
mouthwash
Blood
lymphocytes;
mouthwash
16–2,026 (375)
5
0–280 (149)
5
Blood
lymphocytes
16, 17, 21, 23 Blood
lymphocytes
46–1,340 (252)
5
All preoperative
88
802 (71)
933 (42)
23–80
19–81
327 (39)
429 (59)
23–64
19–66
313 (241)
574 (426)
18–87
19–88
33
152 (8)
34–80
44–79
–
819 (198)
18–84
21–78
8
32–80
35–79
57 (1)
659 (211)
MALOVA
Copenhagen,
Denmark
(Malignant
Ovarian
Cancer study)
Minnesota
Mayo Clinic
Rochester,
ovarian
cancer casecontrol study
NCOCS
North Carolina
446
1221 (0)
317 (13)
462 (35)
28–91
23–90
32
610 (91)
843 (156)
22–74
20–75
9
POCS
Warsaw and
Lodz
264
625 (0)
24–74
24–74
SEARCH
ovarian
cancer
Cambridge,
United
Kingdom
731 (42)
855 (4)
21–74
39–77
Blood
lymphocytes
0–365 (31)
Blood
29–1,259 (187)
lymphocytes
–
Blood
0–525 (82)
lymphocytes;
mouthwash
16, 17, 21, 23 Blood
140–4,850 (1,231)
lymphocytes
5
5
5
88
*Numbers of subjects that are non-white are given in parentheses.
cThe mean is given in parentheses.
(e.g., BRCA1, BRCA2, RAD51, MSH2; refs. 15–17) and in putative
oncogenes and tumor suppressor genes (TP53, RB1, HRAS1, STK15;
refs. 18–23). Thus far, reported positive associations include
an increased ovarian cancer risk for two PGR haplotypes (8); a
protective effect for the PGR promoter +331A allele in endometrioid ovarian tumors (9); an increased risk of borderline ovarian
cancer associated with the Pro72Arg polymorphism in the TP53
gene (20); and an increased risk associated with the polymorphism
in the STK15 oncogene (23). However, none of these associations
are conclusive due to the small size of the initial studies, and there
are no reports indicating that they have been validated in
additional populations.
There are no published reports describing the association
between variants in cell-cycle control genes and ovarian cancer
susceptibility. In general, cancer is associated with a breakdown in
Cancer Res 2007; 67: (7). April 1, 2007
the mechanisms that regulate cell division. In mammalian cells, cell
division is controlled by the activity of cyclin-dependent kinases
(CDK) and their essential activating coenzymes, the cyclins and
CDK inhibitors (reviewed in refs. 24, 25). CDK activity is regulated
on several levels, including cyclin synthesis and degradation,
phosphorylation, and dephosphorylation. The interaction between
CDKs and cyclins is tightly controlled to ensure an ordered
progression through the cell cycle from G1 to S to G2. The events
occurring before DNA synthesis are probably the most thoroughly
understood. The key event in cell cycle regulation is transversion
of the restriction (R) point late in G1, which is crucial to the cell’s
destiny toward division, differentiation, senescence, or apoptosis. It
is believed that once the R point has been overcome, cell cycle
progression occurs almost automatically. In the event of cellular
stress, there are several proteins that can inhibit the cell cycle in G1;
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SNPs in Cell Cycle Control Genes and Ovarian Cancer Risk
Table 1B. Additional characteristics of the 11 ovarian cancer case-control studies included in the Ovarian Cancer Association
Consortium pooled study
Study
AOCS and ACS
Australia
FROC, USA
Hawaii, USA
HOPE, USA
LAC-CCOC, USA
MALOVA,
Denmark
Mayo, USA
NCOCS, USA
POCS, Poland
SEARCH, UK
Case ascertainment
Control ascertainment*
Diagnosed from 2002 onward; recruited
through surgical treatment centers
throughout Australia and cancer registries
of Queensland South Australia and West
Australia (AOCS) and cancer registries
of New South Wales and Victoria (ACS)
Consecutive cases diagnosed from 1997–2002
in Greater Bay Area Cancer Registry
San Francisco
Rapid case ascertainment through Hawaii
Tumor Registry from 1993 onward
Variable source including physician offices
cancer registries and pathology databases
from counties of western Pennsylvania,
eastern Ohio, and western New York
Rapid case ascertainment through Los
Angeles Cancer Surveillance program
from 1993 onward
Incident cases (35–79 yrs) diagnosed
1994–1999 from municipalities of
Copenhagen and Frederiksberg and
surrounding counties
Cases attending Mayo Clinic diagnosed
from 2000 onward identified in a
six-state surrounding region
Cases from 1999 onward identified from
48 counties within the region by
rapid-case ascertainment
Cases collected from cities of Warsaw
and Lodz 2001–2003 by rapid
ascertainment at participating hospitals
Cases <70 yrs from East Anglian West
Midlands and Trent regions of England;
prevalent cases diagnosed 1991–1998;
incident cases diagnosed 1998 onward
c
Participation rates
Randomly selected from Commonwealth
electoral roll. Frequency matched to
cases for geographic region
Cases: 84%;
controls: 47%
Random-digit dial identification from
study area
Cases: 75%;
controls: 75%
Randomly selected from Hawaii Department
of Health Annual Survey of the
representative households
Identified in same regions. All participants
undergo home interviews
Cases: 58.1%;
controls: 62.8%
Cases: 69%;
controls: 81%
Neighborhood-recruited controls
Cases: 73%;
controls: 73%
Random sample of general female
population (35–79 yrs) in study
area selected using computerized
Central Population Register
Identified through Mayo Clinic; healthy
women seeking general medical
examination
Controls identified from the same
region as cases
Cases: 79%;
controls: 67%
Identified at random through the Polish
electronic system; stratified by city
and 5-yr age categories
Selected from the EPIC-Norfolk cohort of 25,000
individuals aged 45–74 based in the same
geographic regions as the cases
Cases: 71%;
controls: 67%
Cases: 84%;
controls: 65%
Cases: 70%;
controls: 63%
Cases: 67%;
controls: 84%
*All controls were frequency matched to cases for age and ethnicity.
cParticipation rates are the percentage of those taking part as proportion of those invited to take part.
for example, in response to DNA damage, P53 accumulates in
the cell and induces P21/CDKN1A–mediated inhibition of cyclin
D/CDK.
Somatic alterations of genes involved in the G1 phase of the
cell cycle, including the cyclins, CDKs, and CDK inhibitors, are
common events in neoplastic development for multiple tumor
types; but different cell cycle proteins seem to be targets for
different cancers. A reason for this could be that although the
basic mechanisms of proliferative control are identical in
different tissues, the maintenance of normal cell-cycle control
is also partly regulated in a tissue-specific manner. For ovarian
cancer, overexpression of cyclin D1 (CCND1) has been reported
in most borderline and invasive ovarian cancers (26, 27); cyclin
D2 was found to be overexpressed in about 40% of ovarian
tumors compared with normal ovarian tissues (28); P16INK4a
(CDKN2A) is relatively frequently lost due to deletion, loss of
www.aacrjournals.org
expression, and hypermethylation in ovarian cancer cell lines
and primary tumors (29, 30); and in some primary ovarian
cancers, homozygous deletions of P16INK4a involving the neighboring P15INK4b (CDKN2B) gene are associated with a poor prognosis for ovarian cancer cases (31). Together, these data suggest
an important role for CDKs, cyclins, and CDK inhibitors in ovarian
cancer development.
The aim of this study was to evaluate the association between
common variants in 13 genes coding for cyclins, CDKs, and CDK
inhibitors and susceptibility to epithelial ovarian cancer. We used a
two-stage case-control study design in which single nucleotide
polymorphisms (SNP) that tag all known common variants in these
genes were first genotyped in three studies. The most significant
hits were then genotyped in eight additional ovarian cancer casecontrol studies from the international Ovarian Cancer Association
Consortium (OCAC).
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Cancer Research
Table 2. Details of tSNP selection for cell cycle control genes
Gene
Size
(kb)
Data
CCND1
CCND2
CCND3
CCNE1
CDK2
CDK4
CDK6
CDKN1A
CDKN1B
CDKN2A
CDKN2B
CDKN2C
CDKN2D
16.0
34.0
9.0
13.5
7.6
6.7
220.8
10.9
5.7
28.8
13.3
8.9
4.1
EGP
HapMap
EGP
EGP
EGP
EGP
HapMap
EGP
HapMap
EGP
EGP
EGP/HapMap
EGP
% of
No.
No. tSNPs
Additional Mean r 2 Number
gene
common
SNPs
with
resequenced variants Successfully
genotyped*
r 2 > 0.8
Additional
by EGP
genotyped assays failed
100
93
88
84
98
96
18
85
100
81
94
87
63
c
12
24
13
26
5
5
41
27
7
12
20
3
4
5
14
7
4
2
2
13
b
10
6
6
8
2
2
2
3
1
11
3
1
0
7
1
2
0
0
0
2
0
0
0
0
0
0
1
1
3
0
0
0
0.82
0.89
0.92
0.57
0.54
0.86
0.96
0.85
0.90
0.97
0.96
1.00
1.00
10
21
12
6
2
4
41
20
6
12
20
3
4
r 2 for
poorly
tagged
SNPs
LD
blocks
0.08–0.24
0.18–0.74
0.09
0.10–0.73
0.19–0.30
0.29
N/A
0.07–0.69
0.23
N/A
N/A
N/A
N/A
1
2
1
1
1
1
3
3
1
1
1
1
1
Abbreviation: N/A, not applicable.
*Additional SNPs that were genotyped that were not common in the data set used for tagging. These SNPs do not contribute to the estimates of tagging
efficiency.
cOne common SNP in EGP (environmental genome project; rs3212867) was monomorphic in the CEU HapMap population.
bAssay for rs3176359 was too rare in our populations (MAF = 0.002).
Materials and Methods
Study subjects. In total, 11 different ovarian cancer case-control studies
(summarized in Table 1A and 1B) contributed data to this study, three for
stage 1 and eight for stage 2 (see also refs. 8–10, 16, 17, 21, 23, 32, 33 for
further details). Most subjects were white Europeans in origin. Ten studies
used population-based ascertainment for cases and controls, and one study
was clinic based. All studies have received ethical committee approval, and
all study subjects provided informed consent.
Tag SNP selection. A set of tagging SNPs (tSNP) was identified for 13
genes comprising four cyclins (CCND1, CCND2, CCND3, CCNE1); three CDKs
(CDK2, CDK4, CDK6); six CDK inhibitors (CDKN1A, CDKN1B, CDKN2A,
CDKN2B, CDKN2C, CDKN2D). The principal hypothesis underlying the study
was that one or more common SNPs in these genes were associated with an
altered risk of ovarian cancer. We therefore identified a set of tagging SNPs
that efficiently tagged all the known common variants [minor allele
frequency (MAF) z0.05] and were likely to tag most of the unknown
common variants.
The selection of tSNPs is most reliable where the gene has been
resequenced in a sample of individuals sufficiently large to identify all
common variants. The National Institute of Environmental Health
Sciences (NIEHS) Environmental Genome Project (EGP) is currently
resequencing candidate genes for cancer across a panel of 90/95
individuals representative of U.S. ethnicities. The original panel (P1PDR90) of 90 individuals consists of 24 European Americans, 24 African
Americans, 12 Mexican Americans, 6 Native Americans, and 24 Asian
Americans, but the ethnic group identifiers are not available. Because it is
known that there is greater genetic and haplotype diversity in individuals
of African origin, we have identified and excluded 28 of the samples with
the greatest African ancestry in this population by comparing the
genotypes of the resequenced sample with genotypes for the same SNPs
from the National Heart, Lung, and Blood Institute Variation Discovery
Resource Project African American panel (http://pga.gs.washington.edu/
finished_genes.html). Data from the remaining 62 individuals were used to
identify tSNPs. Ideally, samples from Native American, Hispanic American,
and Asian American individuals should also be removed; but there is less
Cancer Res 2007; 67: (7). April 1, 2007
genetic diversity between these groups, and they cannot be excluded with
any certainty.
When resequencing data were not available, we used data from the
International HapMap Project (release 34/5 21-06-2005: last public release
used in this study) to select tSNPs. The HapMap Project has genotyped a
large number of SNPs in several populations, including 30 parent-offspring
trios collected in 1980 from U.S. residents with northern and western
European ancestry by the Centre d’Etude du Polymorphisme Humain
(CEPH).
The best measure of the extent to which one SNP tags another SNP is
the pairwise correlation coefficient (r 2p) because the loss in power incurred
by using a marker SNP in place of a true causal SNP is directly related to
this measure. We aimed to define a set of tSNPs such that all known
common SNPs had an estimated r 2p > 0.8 with at least one tSNP. However,
some SNPs are poorly correlated with other single SNPs but may be
efficiently tagged by a haplotype defined by multiple SNPs, so-called
‘‘aggressive tagging,’’ thus reducing the number of tSNPs needed. As an
alternative, therefore, we aimed for the correlation between each SNP and a
haplotype of tSNPs (r2s) to be at >0.8. The program Tagger21 uses a strategy
that combines the simplicity of pairwise methods with the potential
efficiency of multimarker approaches (3). It begins by selecting a minimal
set of markers such that all alleles to be captured are correlated at an
r 2p z 0.8 with a marker in that set. Certain markers can be forced into the
tag list or explicitly prohibited from being chosen as tags. After this, it tries
to ‘‘peel back’’ the tag list by replacing certain tags with multimarker tests.
Tagger avoids overfitting by only constructing multimarker tests from SNPs,
which are in strong linkage disequilibrium with each other, as measured by
a pairwise LD score. If an assay for one of the chosen tSNPs could not be
designed, the SNP tagging process was repeated with that SNP excluded
from the possible set of tSNPs. Table 2 shows details of the tSNP selection
for each of the genes analyzed in this study.
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21
http://www.broad.mit.edu/mpg/tagger/
www.aacrjournals.org
SNPs in Cell Cycle Control Genes and Ovarian Cancer Risk
Genotyping. Genotyping was carried out for 9 of the 11 studies using
TaqMan (Applied Biosystems, Foster City, CA) according to the manufacturer’s instructions. Primers and probes were Assays-by-Design (Applied
Biosystems). Following PCR amplification, end reactions were read on the
ABI Prism 7900 using Sequence Detection Software (Applied Biosystems).
Two studies [Australian Ovarian Cancer Study (AOCS) and Australian
Cancer Study (ACS); Table 1A and B] used iPlex technology (Sequenom) to
perform genotyping. Failed genotypes were not repeated. Plates with
genotype calling <95% were discarded. If there was genotyping discordance
between duplicates within an experiment, the data were discarded and
either repeated or the assay replaced with a different assay with similar
tagging properties.
Each group carried out genotyping on their own samples. Therefore, we
tested the quality of genotyping between laboratories for the five tSNPs
analyzed by all groups using the HAPMAPPT01 panel of CEPH-Utah triosstandard plate supplied by Coriell.22 This panel includes 90 different DNA
samples, five duplicate samples, and a negative template control in a 96-well
plate format. We compared genotype call rates and concordance between
studies. Call rates on these plates ranged from 96% to 99%. Overall
concordance on these plates was >99%.
Statistical methods. Deviations of genotype frequencies in the controls
from those expected under Hardy-Weinberg equilibrium (HWE) were
assessed by m2 tests [1 degree of freedom (d.f.)]. We treated the design as
a single, two-stage multicenter study rather than hypothesis-generating
and replication studies because joint analysis has been shown to be more
efficient than replication analysis in a staged design (34). The primary tests
of association were the univariate analyses between each tSNP and ovarian
cancer. Genotype frequencies in cases and controls were compared for each
study separately using m2 tests for heterogeneity (2 d.f.). The data were then
pooled, and a genetic model free test was carried out by comparing genotype
frequencies in cases and controls using unconditional logistic regression
with terms for genotype and study and an appropriate likelihood ratio test
(P het, 2 d.f.). We tested for heterogeneity between studies by comparing
logistic regression models with and without a genotype-study interaction
term using likelihood ratio tests. Genotypic specific risks with the common
homozygote as the baseline comparator were estimated as odds ratios
with associated 95% confidence limits by unconditional logistic regression.
We also tested for rare allele dose effect (assuming a multiplicative genetic
model) using m2 tests for each study separately and unconditional logistic
regression for the pooled data (P het, 1 d.f.). Primary analyses were restricted
to study subjects of white European origin. Secondary analyses included all
study subjects with additional adjustment for ethnic group.
In addition to the univariate analyses, we carried out specific haplotype
tests for those combinations of alleles that tagged specific SNPs. We also
Table 3. Genotype-specific risks for tSNPs with P < 0.2: combined analysis of 88 SNPs using SEARCH-MALOVA-Stanford
studies
Gene (no. tSNPs)*
SNP ID
dbSNP
CCND1 (7)
ccnd107
ccnd103
ccnd108
ccnd110
ccnd101
ccnd202
ccnd215
ccnd216
ccnd207
ccnd303
ccnd306
ccnd307
ccne102
ccne112
cdk202
cdk204
cdk612
cdk611
cdk613
cdk610
cdk605
cdkn1a06
cdkn1a01
cdkn1b05
cdkn1b02
cdkn2a04
cdkn2a10
cdkn2a13
rs602652
rs603965
rs3212879
rs3212891
rs7178
rs3217805
rs3217916
rs3217925
rs3217936
rs1051130
rs9529
rs3218110
rs997669
rs3218036
rs2069408
rs1045435
rs2285332
rs3731348
rs2237570
rs2282991
rs8
rs2395655
rs1801270
rs3759217
rs2066827
rs2811712
rs11515
rs3731257
CCND2 (14)
CCND3 (7)
CCNE1 (4)
CDK2 (2)
CDK6 (13)
CDKN1A (11)
CDKN1B (7)
CDKN2A/2B (17)
MAF
c
0.46
0.44
0.49
0.46
0.07
0.40
0.28
0.26
0.33
0.48
0.29
0.24
0.38
0.31
0.34
0.09
0.25
0.06
0.12
0.10
0.21
0.29
0.08
0.12
0.26
0.10
0.14
0.26
b
HetOR (95% CI)
1.14 (0.98–1.33)
1.06 (0.91–1.24)
0.85 (0.73–0.99)
0.86 (0.74–1.00)
1.24 (1.03–1.49)
1.07 (0.93–1.23)
1.00 (0.88–1.15)
0.98 (0.85–1.12)
1.01 (0.88–1.16)
1.13 (0.97–1.32)
1.00 (0.87–1.15)
1.11 (0.97–1.28)
1.10 (0.95–1.27)
1.07 (0.93–1.23)
0.90 (0.78–1.03)
1.10 (0.93–1.31)
0.92 (0.80–1.06)
1.07 (0.86–1.34)
0.89 (0.76–1.05)
0.84 (0.71–1.00)
1.18 (1.02–1.35)
1.12 (0.97–1.29)
0.91 (0.76–1.11)
1.13 (0.97–1.33)
0.88 (0.77–1.01)
0.98 (0.83–1.16)
1.0 (0.86–1.16)
0.90 (0.78–1.03)
HomOR (95% CI)x
1.24
1.29
0.82
0.83
1.22
0.86
0.76
0.74
0.77
0.88
0.79
1.07
1.14
1.26
0.90
1.97
0.86
0.28
0.70
0.85
1.41
1.16
0.52
1.03
0.68
1.91
1.55
0.80
(1.03–1.50)
(1.07–1.55)
(0.68–0.98)
(0.69–0.997)
(0.52–2.88)
(0.70–1.05)
(0.59–0.99)
(0.55–0.99)
(0.61–0.98)
(0.73–1.06)
(0.62–1.02)
(0.80–1.42)
(0.94–1.40)
(1.00–1.57)
(0.72–1.12)
(0.89–4.34)
(0.65–1.14)
(0.07–1.21)
(0.37–1.32)
(0.43–1.67)
(1.01–1.96)
(0.96–1.41)
(0.18–1.50)
(0.61–1.74)
(0.51–0.90)
(1.07–3.42)
(0.97–2.47)
(0.59–1.07)
P 2df
P trend
0.057
0.021
0.057
0.076
0.065
0.078
0.11
0.13
0.069
0.0090
0.14
0.32
0.31
0.12
0.27
0.14
0.38
0.19
0.23
0.14
0.017
0.19
0.32
0.30
0.0098
0.086
0.19
0.14
0.018
0.010
0.028
0.034
0.021
0.33
0.17
0.13
0.15
0.23
0.18
0.19
0.13
0.052
0.16
0.093
0.16
0.93
0.094
0.054
0.0042
0.082
0.20
0.17
0.0035
0.42
0.38
0.046
*We also analyzed tSNPs in the genes CDK4 (two SNPs); CDKN2C (two SNPs); CDKN2D (two SNPs), but none of these reached the required level of
statistical significance. Data from the combined analysis of all SNPs are given in Supplementary Tables S1 and S2. Confidence intervals that do not reach
or cross 1.00 and P values <0.05 are in bold type.
cMinor allele frequency.
bHeterozygous odds ratios (95% confidence intervals).
x Homozygous odds ratios (95% confidence intervals).
www.aacrjournals.org
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Cancer Research
Table 4. Age-adjusted ovarian cancer risks associated with five tSNPs in cell cycle control genes for white cases and white
controls only
Gene
dbSNP
MAF
A. Combined analysis of all stage 2 studies
CCND1
rs7178
0.08
CCND1
rs603965
0.45
CDK6
rs8
0.21
CDKN1B
rs2066827
0.23
CDKN2A/2B
rs3731257
0.27
No. cases
No. controls
2,194
1,874
2,188
2,202
2,190
3,294
2,833
3,298
3,297
3,290
1.01
1.00
1.02
1.05
0.92
(0.86–1.18)
(0.87–1.15)
(0.91–1.15)
(0.93–1.18)
(0.82–1.04)
1.01
1.01
0.91
0.88
0.89
5,725
5,236
5,720
5,719
5,705
1.09
1.02
1.09
0.98
0.89
(0.96–1.22)
(0.92–1.13)
(1.00–1.19)
(0.89–1.07)
(0.81–0.97)
1.12
1.13
1.06
0.79
0.87
B. Combined analysis for all studies, white subjects only
CCND1
rs7178
0.08
3,607
CCND1
rs603965
0.45
3,285
CDK6
rs8
0.21
3,597
CDKN1B
rs2066827
0.25
3,618
CDKN2A/2B
rs3731257
0.27
3,601
carried out a general comparison of common haplotype frequencies in each
gene haplotype block using the data from all the tSNPs in that block.
Haplotype blocks were defined such that the common haplotypes (>5%
frequency) accounted for at least 80% of the haplotype diversity. We
considered haplotypes with >2% frequency in at least one study to be
common. Rare haplotypes were pooled. For both specific haplotype marker
tests and the general comparison of haplotype frequencies by haplotype
block, haplotype frequencies and subject-specific expected haplotype
indicators were calculated separately for each study using the program
TagSNPs. This implements an expectation substitution approach to account
for haplotype uncertainty given unphased genotype data (35). Subjects
missing >50% genotype data in each block were excluded from haplotype
analysis. We used unconditional logistic regression to test the null
hypothesis of no association between specific tSNP haplotype frequency
and ovarian cancer stratified by study by comparing a model with terms for
subject-specific haplotype indicator and study with a model including study
term only. The global null hypothesis of no association between haplotype
frequency (by haplotype block) and ovarian cancer was tested by comparing
a model with multiplicative effects for each common haplotype (treating
the most common haplotype as the reference) to the intercept-only model.
Haplotype-specific odds ratios were also estimated with their associated
confidence intervals.
Results
Identifying Tagging SNPs in Cell Cycle Control Genes
Using a combination of NIEHS resequencing data and data from
the international HapMap project, we identified a total of 199
common SNPs (MAF z0.05) in the 13 genes. One-hundred and
sixty-seven SNPs were tagged with r 2 > 0.8, of which 10 were tagged
by 5 different multimarker haplotypes. The remaining SNPs were
tagged with r 2 between 0.07 and 0.74. These could not be tagged
more efficiently because assays could not be designed for them.
Eighty-one tSNPs were selected for this series. We also selected a
further seven SNPs that were not found in the EGP or HapMap data
sets used for tSNP selection. These SNPs do not contribute to the
estimates of tagging efficiency. Thus, we identified 88 SNPs from 13
genes for genotyping. Eleven genes were tagged with an average
22
http://locus.umdnj.edu/nigms/nigms_cgi/panel.cgi?id=2&query=HAPMAP01
Cancer Res 2007; 67: (7). April 1, 2007
HetOR (95% CI)
P 2df
P trend
(0.50–2.06)
(0.86–1.20)
(0.69–1.19)
(0.69–1.12)
(0.72–1.10)
0.99
0.98
0.69
0.36
0.29
0.91
0.90
0.83
0.82
0.13
(0.64–1.97)
(0.99–1.28)
(0.86–1.31)
(0.65–0.95)
(0.73–1.03)
0.37
0.15
0.17
0.044
0.021
0.12
0.084
0.082
0.036
0.0080
HomOR (95% CI)
r p2 > 0.8. For two genes (CDK2 and CCNE1), the best tagging we
were able to achieve was r p2 0.54 and 0.57, respectively. These data
are summarized in Table 2.
Genotyping in Ovarian Cancer Cases and Controls
Genotyping was done in two stages. In stage 1, the 88 tSNPs
described above were genotyped in three case-control populations
from the United Kingdom (SEARCH), Denmark [Malignant Ovarian
Cancer (MALOVA)], and the United States (Stanford). Combined,
these studies comprise f1,500 invasive ovarian cancer cases and
2,500 controls. In stage 2, we genotyped five SNPs from four genes
that showed the strongest evidence of association in stage 1, in
f3,000 additional cases and 4,400 controls from another eight
case-control studies.
Stage 1. Nineteen out of 264 genotype distributions in controls
deviated from HWE (P < 0.05). In the majority of cases, deviations
from HWE occurred in only one of the studies. The discrimination
of genotypes for these assays was good. For rs3176319 (CDKN1A),
the deviations from HWE were substantial in all three studies
(P < 107), and so this SNP was excluded from further analyses.
rs3176359 in CDKN1A was also excluded from further analyses
because it was found to be very rare in all three populations
(MAF = 0.002).
We identified 28 SNPs with a P V 0.2, of which 9 SNPs had a
P V 0.05 in tests for association. These data are summarized in
Table 3. The complete data for all SNPs are given in Supplementary
Tables S1 and S2. There was no evidence for association of genotype with age in controls, and as expected, age-adjusted risks were
similar to unadjusted risks (data not shown). The haplotype frequencies and associated risks for the five multimarker haplotype
tags are presented in Supplementary Table S3. There was no evidence for association at a significance level P V 0.05 for SNPs in 6 of
the 13 genes (CDK2, CDK4, CCND2, CDKN1A, CDKN2C, CDKN2D).
Evidence of association at the P = 0.05 level was found for the
following: five of seven SNPs in CCND1 (strongest association
P trend, 0.01); one of seven SNPs in CCND3 (P 2df = 0.0 09); 1 of 13
SNPs in CDK6 (P trend = 0.004); one of eight SNPs in CDKN1B
(P 2df = 0.01); and 1 of 17 SNPs in CDKN2A/2B (P 2df = 0.046). Further
details are provided in Table 3.
The global test of association with common haplotypes was not
significant for any of the 13 genes (data not shown). One haplotype
3032
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SNPs in Cell Cycle Control Genes and Ovarian Cancer Risk
that showed a significant frequency difference between cases and
controls was observed in CDK6 block 3 (haplotype frequency, 0.2 in
controls). This haplotype, comprising the rare allele of rs8 and
common alleles of rs479049, rs445, and rs992519, showed increased
risk for ovarian cancer compared with the most common
haplotype in this block (OR, 1.19 [1.06–1.35]; P = 0.004).
Stage 2. Five SNPs were genotyped in the stage 2 studies. These
were rs7178 and rs603965 (both in CCND1), rs8 (CDK6), rs2066827
(CDKN1B), and rs3731257 (CDKN2A/2B). The evidence for association for these SNPs from the stage 1 analyses (adjusted for age and
ethnicity) ranged from P trend 0.003 to 0.046. There was some
evidence for between-study heterogeneity for rs8 (P = 0.036). None
of the SNPs were significantly associated with ovarian cancer when
considering the stage 2 studies as a replication set (Table 4A);
rs3731257 was the most significant (P trend = 0.13).
Combined analysis. As joint analysis with appropriate adjustment for multiple testing has been shown to be more powerful
than replication analysis (34), we also carried out a combined
analysis of all the data. Figure 1 shows the genotype-specific risks
(estimated as odds ratios) for each study and for the combined data
for the analyses restricted to white subjects. Only two of the five
SNPs were significant at the 0.05 level (Table 4B). These were
rs2066827 (CDKN1B) and rs3731257 (CDKN2A-2B; P trend = 0.036 and
0.008, respectively). The rare alleles for both SNPs were associated
with reduced ovarian cancer risks [rs2066827 HetOR, 0.98 (0.89–
1.07); HomOR, 0.79 (0.65–0.95); and rs3731257 HetOR, 0.89 (0.81–
0.97); HomOR, 0.87 (0.73–1.03)]. There was little change in the
association of rs2066827 when the data for all ethnic groups were
included (P trend = 0.006); but for rs3731257, the association was
almost completely attenuated (P = 0.16). Standard methods for
correcting for multiple testing, such as the Bonferroni correction,
are too conservative when hypotheses are correlated, as is the case
here. We have therefore used a permutation procedure modified to
account for the staged design (36). A P value smaller than the most
significant result was observed in 368 out of 1,000 permutations,
which is equivalent to an adjusted P trend of 0.37.
Statistical power to identify subgroup effects from these studies
is limited; but there were sufficient data to carry out an analysis
with cases restricted to serous and nonserous histopathologic
subtypes. In total, there were f2,000 cases of serous histology in
the 11 studies combined and 1,600 nonserous cases. The analysis
based on this histologic subgroup stratification suggested marginal
associations for rs3731257 with both serous (P trend = 0.030) and
nonserous cancers (P trend = 0.042) and for rs7178 (P trend = 0.034),
rs8 (P 2df = 0.046), and rs2066827 (P trend = 0.023) with nonserous
cancers only (Table 5).
Consortium, but none was significant in the second stage. However,
the power to detect an effect in the replication studies was just 70%
to detect an allele with frequency and effect size similar to CDKN1B
rs2066827 (the most significant association from stage 1) at a type I
Discussion
We have assessed the effect of tSNPs in 13 cell cycle control
genes on the risks of invasive epithelial ovarian cancer. We chose to
analyze genes involved in the control of cell division (cyclins, CDK,
and CDK inhibitors) because several of these genes have previously
been implicated in the somatic development of multiple tumor
types. To our knowledge, this is the first report describing the
association between these genes and ovarian cancer susceptibility.
A two-stage design was used for this study. In the first stage, 88
tSNPs were genotyped in three ovarian cancer case-control studies.
The best five SNPs that were marginally associated with epithelial
ovarian cancer were then genotyped in a further eight case-control
studies from the international Ovarian Cancer Association
www.aacrjournals.org
Figure 1. Genotype-specific risks by study. Heterozygous and homozygous
genotype-specific odds ratios are illustrated for White subjects only for all five
SNPs analyzed in all studies. Lines, 95% confidence intervals; size of the box,
proportionate size of the study (number of white subjects genotyped) compared
with other studies. There were no homozygous odds ratios for some studies
because the minor allele was too rare in the population. Key to studies in
Table 1A and B: ACS, ACS Australia; AOC, AOCS Australia; HAW, Hawaii
Ovarian Cancer Study Hawaii; HOP, HOPE study, Pittsburgh, PA; MAY, Mayo
Clinic, Minnesota; MAL, MALOVA study, Denmark; NCO, North Carolina Ovarian
Cancer Study (NCOCS), North Carolina; POL, Polish Ovarian Cancer Study
(POCS), Poland; SEA, SEARCH (Studies of Epidemiology and Risk Factors
in Cancer Heredity) study, United Kingdom; USC, Los Angeles County
Case-Control Studies of Ovarian Cancer (LAC-COCC), southern California.
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Cancer Research
Table 5. The effects of histologic subtype on ovarian cancer risks associated with five tSNPs in cell cycle control genes
Histologic type
Serous
Nonserous
Gene
dbSNP
MAF
Number
of cases
Number
of controls
HetOR (95% CI)
HomOR (95% CI)
P 2df
P trend
CCND1
CCND1
CDK6
CDKN1B
CDKN2A/2B
CCND1
CCND1
CDK6
CDKN1B
CDKN2A/2B
rs7178
rs603965
rs8
rs2066827
rs3731257
rs7178
rs603965
rs8
rs2066827
rs3731257
0.08
0.45
0.21
0.25
0.27
0.08
0.45
0.21
0.25
0.27
2,014
1,807
2,009
2,022
2,012
1,593
1,478
1,588
1,596
1,589
5,725
5,236
5,720
5,719
5,705
5,725
5,236
5,720
5,719
5,705
0.99 (0.87–1.12)
0.99 (0.87–1.12)
1.04 (0.93–1.16)
1.02 (0.92–1.14)
0.90 (0.81–1.01)
1.09 (0.93–1.28)
1.07 (0.93–1.22)
1.16 (1.03–1.31)
0.93 (0.82–1.05)
0.87 (0.78–0.99)
1.11 (0.95–1.29)
1.11 (0.95–1.29)
1.14 (0.88–1.47)
0.82 (0.65–1.03)
0.86 (0.69–1.06)
2.16 (1.18–3.95)
1.15 (0.97–1.36)
1.00 (0.75–1.34)
0.75 (0.58–0.97)
0.88 (0.69–1.11)
0.06
0.26
0.53
0.18
0.11
0.034
0.26
0.046
0.058
0.073
0.74
0.27
0.28
0.30
0.03
0.034
0.12
0.063
0.023
0.042
NOTE: White invasive cases (serous/non serous) are compared with white controls.
error rate of 0.01. Because a combined analysis with adjustment for
multiple testing has been shown to have greater power than a
replication analysis, we also carried out a joint analysis of the
stages 1 and 2 data. Two SNPs, CDKN1B-rs2066827 and CDKN2A/
2B-rs3731257, showed weak evidence for association, but neither
was highly statistically significant (naı̈ve P trend = 0.036 and 0.008,
respectively), but these were not significant after adjustment for
multiple testing using a permutation procedure that allows for the
correlation between the SNPs. As an alternative to correcting for
multiple testing, some authors have suggested that simple but
stringent criteria should be applied to statistical tests for genetic
association; e.g., P < 104 for candidate gene studies or even
P < 107 for genomewide significance (37). Using these criteria, it
can be estimated that 26,448 cases and an equal number of
controls would be needed to confirm the effect for rs2066827 to
detect a codominant allele with a minor allele frequency of
0.25 that confers a risk of 1.08 with 90% power at a type I error rate
of 104.
Because the selection of SNPs in this study was based on a
tagging approach rather than on putative function, we are unable
to comment in great detail on the possible functional significance
of these findings. The rs2066827 variant in CDKN1B is potentially
functional. It encodes a nonsynonymous amino acid change
Val109Gly in CDKN1B and is situated in the interaction surface of
CDKN1B and its negative regulator p38jab1, in the region spanning
amino acid residues 97 to 151. This SNP could therefore alter the
interaction between CDKN1B and p38jab1. The Val109Gly variant has
previously been reported in association with advanced prostate
cancer risk (38).
The rs3731257 SNP in CDKN2A/2B is of no obvious functional
significance, but it could be in linkage disequilibrium with another
functional SNP within the gene. CDKN2A is a G1 CDK inhibitor that
binds to CDK4 and CDK6 and prevents their association with
D-type cyclins, thereby facilitating CDK4/6–cyclin D–mediated
phosphorylation and inactivation of retinoblastoma protein and
entry into S-phase. CDKN2B inhibits MDM2-mediated degradation
of P53; thus, its loss would lead to a reduction in levels of the P53
protein (39, 40). The known function of both proteins suggests a
plausible role in tumor development.
An alternative explanation for a false positive association is
hidden population stratification. This occurs when allele frequen-
Cancer Res 2007; 67: (7). April 1, 2007
cies differ between population subgroups, and cases and controls
are drawn differentially from those subgroups. However, this study
comprised 11 different case-control populations from England,
Denmark, Poland, Australia, and several states throughout the
United States. It is unlikely that population stratification is
important because any population stratification will be study
specific. Furthermore, the combined study was of sufficient size to
allow the analyses to be restricted to white-only cases and controls,
which represented the vast majority of all subjects.
Disease heterogeneity could also lead to false-positive reporting
or mask the presence of true associations. In this study, we
stratified cases according to histologic subtype and did the analysis
for the combined data after dividing cases into serous and
nonserous subgroups. Inevitably, this leads to a loss in statistical
power, and although some marginal associations were found, these
data should be treated with caution. Cases were collected from
multiples of different centers, and there are likely to be variations
between studies in the completeness of these data and in the
reporting of different histopathologic subtypes.
There was no evidence of association with disease risk for
polymorphisms in CCND2, CCNE1, CDK2, CDK4, CDKN1A,
CDKN2C, and CDKN2D at the 0.05 level. The SNPs tested in this
study were selected to tag all the common variants in each gene
and not because of their putative functional effects. However, we
were unable to design assays for 31 of the selected tSNPs, and 35 of
the 199 common SNPs were tagged with r p2 < 0.8. Furthermore,
although tSNPs based on HapMap and EGP data are likely to tag
most of the common SNPs, there is a possibility that other
unknown common variants were poorly tagged.
There have been many candidate SNP/gene association studies
for ovarian cancer published over the past few years. Few of the
published studies report results that are statistically significant; but
most of these studies have had insufficient statistical power to
detect moderate risks even for common genetic variants.
Furthermore, very few studies have used comprehensive tagging
approaches to capture all the common variation in a gene. Where
associations have been found, the existence of a susceptibility allele
remains unproven because results await confirmation, or there are
conflicting results in follow-up studies. The current study illustrates
the value of large consortia to follow-up putative positive
associations for common polymorphisms in complex diseases to
3034
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SNPs in Cell Cycle Control Genes and Ovarian Cancer Risk
clarify their risks. In the future, a consortium approach to genetic
epidemiology studies might also enable the analysis of rarer genetic
variants and gene-gene/gene-environment interactions where
individual studies have inadequate power.
Acknowledgments
Received 9/1/2006; revised 10/27/2006; accepted 2/1/2007.
Grant support: Funded by a grant from WellBeing (H. Song); B.A.J. Ponder is a
Gibb Fellow; P.D.P. Pharoah is a Senior Clinical Research Fellow; S.A. Gayther is a
Higher Education Funding Council for England–funded senior lecturer; the Mermaid
component of the Eve Appeal (S.J. Ramus); D.F. Easton is a Principal Research Fellow
of Cancer Research United Kingdom. E.L. Goode is a Fraternal Order of Eagles Fellow.
G. Chenevix-Trench and D. Whiteman are supported by Fellowships from the National
Health and Medical Research Council; P. Webb is supported by a Fellowship from the
Queensland Cancer Fund.
Stage 1 genotyping was funded by a grant from Cancer Research United Kingdom.
Stage 2 genotyping and the Ovarian Cancer Association Consortium are funded by the
Ovarian Cancer Research Fund, thanks to generous donations from the family and
friends of Kathryn Sladek Smith.
Additional support was provided by the Roswell Park Alliance and the National
Cancer Institute (CA71766 and core grant CA16056; Stanford study); the Mayo
Foundation (Mayo study); Intramural Research Funds of the National Cancer Institute,
Department of Health and Human Services, United States (Polish Ovarian Cancer
Study); U.S. Army Medical Research and Materiel Command under DAMD17-01-1-
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www.aacrjournals.org
0729, the Cancer Council Tasmania and Cancer Foundation of Western Australia
(AOCS study); The National Health and Medical Research Council of Australia (199600;
ACS study); National Cancer Institute grants K07-CA80668 and R01CA095023; and
Department of Defence grant DAMD17-02-1-0669 [the Hormones and Ovarian Cancer
Prediction (HOPE) project]; the California Cancer Research Program grants 0001389V-20170 and 2110200; U.S. Public Health Service grants CA14089, CA17054,
CA61132, CA63464, N01-PC-67010, and R03-CA113148, and the California Department
of Health Services subcontract 050-E8709 as part of its statewide cancer reporting
program (University of Southern California).
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.
We thank Joan MacIntosh, Hannah Munday, Barbara Perkins, Clare Jordan, Kristy
Driver, Mitul Shah, the local general practices and nurses and the East Anglian Cancer
Registry for recruitment of the United Kingdom cases: the EPIC-Norfolk investigators
for recruitment of the United Kingdom controls; Craig Luccarini and Don Conroy
for expert technical assistance. Dr. Stephen Chancok of the Core Genotyping Facility,
Division of Cancer Epidemiology and Genetics of the National Cancer Institute
(United States), Dr. Neonila Szeszenia-Dabrowska of the Nofer Institute of
Occupational Medicine (Lodz, Poland), and Dr. Witold Zatonski of the M.
Sklodowska-Curie Institute of Oncology and Cancer Center (Warsaw, Poland)
for their contribution to the Polish Breast Cancer Study; Debby Bass, Shari Hutchison,
Carlynn Jackson, Jessica Kopsic, and Mary Hartley for the HOPE project; Karin
Goodman and Renee Weatherly for subject recruitment; and Robert Vierkant, Zachary
Fredericksen, and Ludi Fan for data management in the Mayo Clinic study.
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Cancer Res 2007; 67: (7). April 1, 2007