Evaluation of Candidate Stromal Epithelial Cross-Talk Genes Identifies Association between Risk of Serous Ovarian Cancer and TERT, a Cancer Susceptibility “Hot-Spot”
a Cancer Susceptibility ''Hot-Spot''. PLoS Genet 6(7): e1001016. doi:10.1371/journal.pgen.1001016
Evaluation of Candidate Stromal Epithelial Cross-Talk Genes Identifies Association between Risk of Serous Ovarian Cancer and TERT , a Cancer Susceptibility ''Hot- Spot''
Sharon E. Johnatty 0
Jonathan Beesley 0
Xiaoqing Chen 0
Stuart Macgregor 0
David L. Duffy 0
Amanda B. Spurdle 0
Anna deFazio 0
Natalie Gava 0
Penelope M. Webb 0
Australian Ovarian Cancer 0
Study Group 0
Australian Cancer Study (Ovarian Cancer) 0
Mary Anne Rossing 0
Jennifer Anne Doherty 0
Marc T. Goodman 0
Galina Lurie 0
Pamela J. Thompson 0
Lynne R. Wilkens 0
Roberta B. Ness 0
Kirsten B. Moysich 0
Jenny Chang-Claude 0
Shan Wang-Gohrke 0
Daniel W. Cramer 0
Kathryn L. Terry 0
Susan E. Hankinson 0
Shelley S. Tworoger 0
Montserrat Garcia-Closas 0
Hannah Yang 0
Stephen J. Chanock 0
Paul D. Pharoah 0
Honglin Song 0
Alice S. Whitemore 0
Celeste L. 0
Daniel O. Stram 0
Anna H. Wu 0
Malcolm C. Pike 0
Simon A. Gayther 0
Susan J. Ramus 0
Usha Menon 0
Aleksandra Gentry-Maharaj 0
Hoda Anton-Culver 0
Argyrios Ziogas 0
Estrid Hogdall 0
Susanne K. Kjaer 0
Claus Hogdall 0
Andrew Berchuck 0
Joellen M. Schildkraut 0
Edwin S. Iversen 0
Patricia G. Moorman 0
Catherine M. Phelan 0
Thomas A. Sellers 0
Julie M. Cunningham 0
Robert A. 0
David N. Rider 0
Ellen L. Goode 0
Izhak Haviv 0
Georgia Chenevix-Trench 0
Cancer Association Consortium" 0
0 Editor: Emmanouil T. Dermitzakis, University of Geneva Medical School , Switzerland
We hypothesized that variants in genes expressed as a consequence of interactions between ovarian cancer cells and the host micro-environment could contribute to cancer susceptibility. We therefore used a two-stage approach to evaluate common single nucleotide polymorphisms (SNPs) in 173 genes involved in stromal epithelial interactions in the Ovarian Cancer Association Consortium (OCAC). In the discovery stage, cases with epithelial ovarian cancer (n = 675) and controls (n = 1,162) were genotyped at 1,536 SNPs using an Illumina GoldenGate assay. Based on Positive Predictive Value estimates, three SNPs-PODXL rs1013368, ITGA6 rs13027811, and MMP3 rs522616-were selected for replication using TaqMan genotyping in up to 3,059 serous invasive cases and 8,905 controls from 16 OCAC case-control studies. An additional 18 SNPs with Pper-allele,0.05 in the discovery stage were selected for replication in a subset of five OCAC studies (n = 1,233 serous invasive cases; n = 3,364 controls). The discovery stage associations in PODXL, ITGA6, and MMP3 were attenuated in the larger replication set (adj. Pper-allele$0.5). However genotypes at TERT rs7726159 were associated with ovarian cancer risk in the smaller, five-study replication study (Pper-allele = 0.03). Combined analysis of the discovery and replication sets for this TERT SNP showed an increased risk of serous ovarian cancer among non-Hispanic whites [adj. ORper-allele 1.14 (1.04-1.24) p = 0.003]. Our study adds to the growing evidence that, like the 8q24 locus, the telomerase reverse transcriptase locus at 5p15.33, is a general cancer susceptibility locus.
Copyright: 2010 Johnatty et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: ACS/AOCS - National Health and Medical Research Council of Australia (#199600, ACS study; GC-T and PMW); U.S. Army Medical Research and Materiel
Command under DAMD17-01-1-0729, Award no. W81XWH-06-1-0220; the Cancer Council Tasmania and Cancer Foundation of Western Australia; Westmead
Millennium Foundation and the Westmead Gynaecological Oncology Research Fund, Westmead Hospital, Westmead, NSW, Australia (NG). DOVE - NIH
R01CA112523 and RO1 CA87538. HOPE - National Cancer Institute, Award number R01CA095023. The GER (German Ovarian Cancer Study or GOCS) was
supported by the German Federal Ministry of Education and Research of Germany, Programme of Clinical Biomedical Research grant 01 GB 9401, the genotyping
in part by the state of Baden-Wu rttemberg through Medical Faculty of the University of Ulm (P.685) and data management by the German Cancer Research
Center. UCI - National Cancer Institute grants CA-58860, CA-92044 and the Lon V. Smith Foundation grant LVS-39420. NECC - National Cancer Institute
R01CA54419 and P50CA105009. MAY - National Institutes of Health R01-CA122443. PBCS (POL) - Intramural Research Funds of the National Cancer Institute,
Department of Health and Human Services, USA. HAWAII - US Public Health Service grant R01-CA-58598, and contracts N01-CN-55424 and N01-PC-67001 from the
National Cancer Institute, NIH, Department of Health and Human Services. SEARCH - programme grant from Cancer Research UK. UKOPS - The work of SAG, SJR,
AG-M, and UM was undertaken at UCLH/UCL who received a proportion of funding from the Department of Healths NIHR Biomedical Research Centres funding
scheme. The UKOPS study was funded by the Oak Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
. These authors contributed equally to this work.
" Membership of the Ovarian Cancer Association Consortium is provided in the Acknowledgments.
Ovarian cancer is the seventh leading cause of cancer mortality
among women globally, accounting for 4.2% of cancer deaths ,
due in part to the lack of practical screening methods and
detectable symptoms in the early stages of tumor progression .
Although the aetiology of ovarian cancer has not been fully
elucidated, it is generally agreed that family history of ovarian or
breast cancer is the most important risk factor for epithelial
ovarian cancer . Hereditary ovarian cancer occurring in breast/
ovarian cancer families has been linked to mutations in the BRCA1
and BRCA2 genes, while cases occurring in association with Lynch
syndrome have been linked to mutations in MSH2 and MLH1
[4,5]. Given that only 3% to 5% of ovarian cancer cases present
from high-risk families and residual family history associations ,
it is likely that several low-penetrance genes with relatively
common alleles that confer slightly increased risk may account
for a portion of the risk of non-familial ovarian cancer. The
Ovarian Cancer Association Consortium (OCAC) was established
in 2005 to provide a forum for the identification and validation of
common low-penetrance ovarian cancer susceptibility
polymorphisms with increased power . OCAC recently conducted a
genome-wide association study (GWAS) and identified the first
susceptibility locus associated with invasive ovarian cancer risk .
A number of hypotheses have been put forward to explain the
pathogenesis of ovarian cancer [8,9], including that of incessant
ovulation which causes repeated minor trauma to the surface of
the ovary, leading to proliferation of ovarian epithelium and repair
of the ovulatory wound . However, it has also been
hypothesized that fallopian tube epithelial cells migrating to the
ovulatory wound could serve as precursors to ovarian cancer .
Research in the past two decades compellingly suggests that the
neighbors of cancer cells, collectively termed stroma, are not
uninvolved bystanders  and studies involving
three-dimensional cell culture models underscore the involvement of the
extracellular matrix surrounding cancer cells in the signalling
pathways that promote cell survival . Fibroblasts with a
carcinoma-promoting phenotype [carcinoma-associated
fibroblasts (CAFs)] residing in the breast cancer microenvironment
lack the ability of normal fibroblasts to attenuate the growth of
neighbouring transformed epithelial cells . In addition,
xenograft models have shown that CAFs accelerate cancer
progression through their ability to secrete stromal cell-derived
factor 1 . Furthermore, expression profiling of ovarian tumor
samples has identified a group of high-grade invasive cancers
characterized by a reactive stromal gene expression signature and
extensive desmoplasia, which confer an inherently poor prognosis
. If this CAF-dependent model of tumorigenesis is correct, it
assigns a key role to the neighboring stroma in cancer initiation.
We therefore hypothesized that subtle variation in the
expression or function of genes expressed as a consequence of
interactions between ovarian cancer cells and the host
microenvironment could contribute to ovarian cancer susceptibility. We
used a two-stage approach to comprehensively evaluate common
variation in 173 genes selected for their putative role in
stromalepithelial interactions using a tagging-SNP approach and data
from sixteen case-control studies participating in the Ovarian
Cancer Association Consortium (OCAC).
Candidate gene selection and justification are provided in Text
S1 and Table S1. Characteristics of all case-control studies that
contributed data to discovery and replication analyses are
provided in Table S2. Comparison of the mean age at diagnosis
for cases and age at interview for controls showed that cases were
significantly older compared to controls (p,0.05). Figure S1
provides an overview of SNP and cases-controls numbers analysed
in the discovery and replication stages of this study. Discovery
samples consisted of serous invasive cases from the AUS (550 cases
and 1,101 controls) and MAY (125 cases and 61 controls; all
nonHispanic Whites) studies. AUS participants were not selected for
ethnicity, but comprised of predominantly non-Hispanic White
women. Of the 1,837 women with genotype data, three were
excluded by PLINK default thresholds because .10% of SNPs
failed genotyping for these individuals. Of the 1,536 single
nucleotide polymorphisms (SNPs) genotyped, 1,309 SNPs passed
our initial quality control (QC) criteria, and of these, seven were
excluded by PLINK default thresholds. The remaining 1,302
SNPs were subject to further pruning as follows: 37 SNPs with
significantly different frequencies of missing genotype data
between cases and controls (PMiss,0.05); 296 SNPs with duplicate
In this article, we report the findings from a large-scale
analysis of common variation in genes that are expressed
as a consequence of interactions between ovarian cancer
cells and their host micro-environment that could
influence serous ovarian cancer risk. We evaluated 1,302
common variants within or near 173 genes in two large
case-control studies from the Ovarian Cancer Association
Consortium (OCAC) and selected three variants for further
evaluation in sixteen OCAC studies and an additional 18
for evaluation in five OCAC studies. We observed a
significantly increased risk of serous ovarian cancer
associated with a variant in the telomerase reverse
transcriptase (TERT) gene. Although TERT variants have
not been previously shown to contribute to ovarian cancer
risk, several studies have recently reported associations
between TERT variants and other forms of cancer,
including gliomas, lung cancer, adenocarcinoma, basal cell
carcinoma, prostate cancer, and multiple other cancers.
TERT encodes a protein that is essential for the replication
and maintenance of chromosomal integrity during cell
division. In cancer cells, TERT has been linked to genomic
instability and tumour cell proliferation. Further studies are
necessary to confirm our findings and to investigate the
mechanisms for the observed association.
discordance and/or failure to meet Hardy-Weinberg equilibrium
(HWE) criteria (0.001,PHWE,0.05). Of the remaining 969 SNPs
analysed in the discovery stage, 59 SNPs with PTrend,0.05 were
considered for the replication study (see Table S3).
Based on positive predictive value (PPV) estimates, the three
SNPs selected for replication using TaqMan genotyping by the 16
OCAC studies were PODXL (podocalyxin-like) rs1013368 (PPV
33.1%), ITGA6 (integrin, alpha 6) rs13027811 (PPV 4.5%) and
MMP3 (matrix metallopeptidase 3) rs522616 (PPV 4.4%) (Table 1).
These 16 OCAC studies included all histologic subtypes, and
ethnicities. An additional 18 SNPs with PTrend,0.05 which fitted
into the iPLEX design were selected for replication by a subset of
five of the 16 OCAC studies [AUS (additional samples not in the
discovery set), MAL, SEA, UKO, and USC]. FGF2 rs17473132
included among the 18 selected SNPs (PTrend = 0.008) has been
previously reported elsewhere  and is therefore excluded from
this report. Replication sample sizes varied by SNP depending on
which participating OCAC study met QC criteria; MAY, NCO,
NEC and NHS failed QC for PODXL rs1013368, and GER and
STA failed QC for ITGA6 rs13027811. Table 2 provides the risk
estimates adjusted for age and study site for SNPs included in the
replication analysis. There was no evidence of between-study
heterogeneity for any replication SNP with the exception of TERT
rs7726159 (p = 0.005) (Table S4). Further examination of the
sitespecific Odds Ratios (ORs) showed that this was driven in part by
the smaller USC study, the exclusion of which resulted in a p-value
for between-study heterogeneity of 0.09. The associations
observed in the discovery set for the three SNPs selected based
on PPV values (PODXL rs1013368, ITGA6 rs13027811, and
MMP3 rs522616), were completely attenuated in the larger
replication analysis of 16 case control studies (adj. Pper-allele$0.5)
However, adjusted log additive estimates for TERT (telomerase
reverse transcriptase) rs7726159 retained a statistically significant
p-value in the replication study of non-Hispanic White serous
invasive cases and controls (Pper-allele = 0.03), and showed evidence
of log additive effects across genotypes. We re-analysed this SNP
combining discovery and replication data and observed some
evidence of between-study heterogeneity (p = 0.027) which again
improved with the exclusion of the smaller studies (USC and
MAY; p = 0.16). Risk estimates for serous invasive ovarian cancer
adjusted for age and study site remained statistically significant in
the combined dataset [adj. ORper-allele 1.14 (1.041.24) p = 0.003;
Table 3]. Likewise, in exploratory analyses of genotype data on all
ethnicities stratified by histological subtype, a increased risk
associated with this SNP was observed for serous invasive cases
in models adjusted for age, site and ethnicity [adj. ORper-allele 1.17
(1.081.27) p = 7.2161025]. TERT rs7726159 was also associated
with serous borderline tumors, but not with any other invasive or
borderline subtypes (Table 4, and Figure 1). For MMP7
rs17098236, the combined age- and site-adjusted estimate from
the log additive model suggested an association with serous
ovarian cancer but the point estimates were not in the same
direction as those obtained in discovery analysis (0.84 vs.1.19; see
Table S3 and Table 2). All other SNPs in the smaller replication
study failed to replicate the significant associations observed in the
Herein we report a large-scale analysis of 1,309 SNPs in 173
genes selected for their putative role in stromal epithelial cross talk,
using a two-stage design for assessment of ovarian cancer risk. In
the discovery stage we used data from two OCAC case-control
studies (AUS and MAY) of predominantly non-Hispanic White
women, and observed that SNPs in several genes were associated
with risk of serous tumours in unadjusted log-additive models
(Table S3). The most significant associations observed (PODXL
rs1013368, ITGA6 rs13027811, and MMP3 rs522616;
Table 3. Combined discovery and replication analysis: site-specific and combined risk estimates for serous ovarian cancer for TERT
rs7726159 among non-Hispanic whites.
Combined (all studies)
aEstimates are adjusted for age (at interview in controls, at diagnosis in cases) and additionally for study site in combined (all studies) estimates.
PTrend#0.001; Table 1) were then genotyped in a total of sixteen
OCAC studies including additional samples from discovery studies
(AUS and MAY), and also from non-serous histologies and all
ethnicities. None of these three SNPs were significantly associated
with ovarian cancer risk (Pper-allele$0.5). The power of the
replication sample to detect the odds ratios observed in the
discovery set at a type 1 error rate of 0.05 assuming log additive
effects was .99.9% for all three SNPs. Combining discovery and
replication data would have provided greater power to detect a
significant effect , but this was not considered for these SNPs
because estimates were unequivocally null in replication analysis
and/or in the opposite direction compared to the smaller
We analysed an additional 18 SNPs, including one in FGF2
reported elsewhere  in a second smaller replication study using
five case-control studies from OCAC, and found evidence of an
allelic association between TERT rs7726159 and serous tumors
(Table 2). Although the PPV for TERT rs7726159 was 1.4%, it
was not selected for the larger replication stage in all sixteen
OCAC case-control studies because of limited resources. Our
estimate from the replication study, adjusted for age and study site,
showed an overall 12% increased risk of serous ovarian cancer
associated with each minor allele among non-Hispanic Whites.
Site-specific estimates were also statistically significant in
casecontrol studies with the largest samples sizes (SEA, AUS and
MAL) (Table 3). We detected significant study heterogeneity in
this combined sample of all studies (p = 0.027), and this effect was
attenuated when the smallest sample sizes (USC and MAY) were
removed from the dataset; p = 0.16). Inclusion of data on all
ethnicities additionally adjusted for race resulted in a significance
level (adj. Pper-allele = 7.2161025) that met the conservative
Bonferroni adjustment for multiple testing (0.05/21 = adj.
Pper-allele#0.0024). In addition, the estimates from log-additive
models for TERT rs7726159 in the combined discovery and
replication non-Hispanic White samples would almost meet
Bonferroni adjustment (adj. Pper-allele = 0.003).
TERT encodes the catalytic subunit of telomerase and
activation of telomerase has been implicated in human cell
immortalization and cancer cell pathogenesis. TERT was selected
as a candidate gene because it serves as an epithelial stem cell
marker  and we hypothesized that cross-talk modifies critical
aspects of epithelial transformation. TERT is a ribonucleoprotein
enzyme that maintains telomere ends, and is essential for the
replication of chromosomes and suppression of cell senescence.
Telomere dysfunction is associated with genomic instability and
consequently increased risk of tumor formation . The
rs7726159 variant resides in intron 3 of TERT and has no obvious
functional significance, but it could be in linkage disequilibrium
Table 4. Combined discovery and replication analysis: risk estimates for TERT rs7726159 for all races according to tumor behaviour
and histological subtypes.
Tumor Behavior Subtype
aControls aCases bOR (95% CI)
1.30 (1.161.45) 5.761026 1.25 (1.051.49) 0.011 1.17
1.63 (1.212.18) 0.001
2.04 (1.383.02) 0.0004 1.46
aCases and controls derived from AUS, MAL, MAY, SEA, UKO and USC studies.
bEstimates are adjusted for age (at interview in controls, at diagnosis in cases), race and study site.
Figure 1. Histology-specific adjusted per allele risk estimates for rs7726159 for all ethnicities. Lines indicate 95% confidence intervals;
bolded ORs and 95% CIs indicate statistically significant estimates (P,0.05); size of the solid box is the proportionate sample size for each histology
sub-group with genotype data.
with another functional or causal SNP within the gene. An
alternative explanation for the observed association is population
stratification, which occurs when allele frequencies differ with
population subgroups, or when cases and controls are drawn from
different subgroups. We suggest that this is not a likely explanation
because cases and controls were drawn from the same source
populations within each study, and replication analyses were
restricted to non-Hispanic White women or adjusted for ethnicity
where applicable. However, it is possible that the association with
serous ovarian cancer may vary across populations because of
interaction with other genes or environmental factors, and
additional studies would be required to confirm these findings.
Although TERT variants have not been previously reported to
be associated with ovarian cancer, a recent meta-analysis of two
GWAS identified another SNP in TERT, rs2736100, as
significantly associated with gliomas (OR = 1.27; P = 1.506
10217) . GWAS have found that rs2736100 is also associated
with lung cancer (OR = 1.14; P = 461026)  and more
specifically, with the adenocarcinoma subtype (OR = 1.23;
P = 3.0261027)  (Figure 2A). Associations have also been
reported between the TERT- CLPTM1L (cleft lip and palate
transmembrane 1-like gene - cisplatin resistance-related protein 9-)
locus and lung cancer (rs402710; OR = 1.17; P = 261027) ,
basal cell carcinoma (rs401681; OR = 1.20; P = 4.861029) ,
pancreatic cancer (rs401681; OR 1.19; (P = 3.6661027) , and
multiple cancer types that are known to originate in the
epithelium, including bladder, prostate and cervical cancer .
We genotyped rs2736100 in the discovery samples and found a
borderline, but inverse, association with serous ovarian cancer
[OR = 0.88 (0.771.01) PTrend = 0.06]. We also found a borderline
association with rs11133719 and serous ovarian cancer risk
[OR = 0.81 (0.670.98) PTrend = 0.025] in discovery samples.
Linkage disequilibrium (LD) estimation between the 11 TERT
SNPs that we genotyped in stage 1 in 1,047 non-Hispanic White
controls showed a moderate pairwise correlation between
rs2736100 and rs7726159 (r2 = 0.43; Figure 2B) but rs7726159,
which we selected from NIEHS, is not in HapMap and so has not
been genotyped in GWAS of ovarian or other cancers. Further
analysis of this locus is necessary in order to definitively identify the
causal SNP(s) at this locus.
To our knowledge, this is the first comprehensive evaluation of
genes involved in stromal epithelial cross-talk and serous ovarian
cancer. Candidate gene and SNP selection for discovery stage
analysis was aimed at optimizing the likelihood of detecting a
signal by including tagging and putatively functional SNPs with
minor allele frequency (MAF).5%. Although a tagSNP approach
has been shown to improve the power of the study for common
variants , modest effects from SNPs with low MAFs may
remain undetected. This was illustrated in a recent re-analysis of
two SNPs in the DCN gene that failed to achieve the minimal
PTrend#0.05 in stage 1 analysis, but conferred a small but
significantly decreased risk of serous ovarian cancer in a combined
analysis of data from two additional studies . We therefore
suggest caution in interpreting null findings, and the need for large
discovery and replication studies. Our discovery study was
reasonably well powered, so the failure to find any associations
with SNPs in genes involved in stromal epithelial cross-talk, except
in DCN and TERT, suggests that genetic variation in this pathway
is not a major determinant of serous ovarian cancer risk.
In summary, we have identified an association between TERT
rs7726159 and serous ovarian cancer in a large sample of
nonHispanic White women participating in five OCAC case-control
studies. We plan to further our investigation of this SNP and others
in linkage disequilibrium with it, to determine whether TERT,
CLPTM1L or another gene in the region is the functional target of
this association. Our study adds to the growing evidence that, as well
as the 8q24 locus [21,29,3032], the TERT-CLPTM1L locus at
5p15.33, is a general cancer susceptibility locus. This is particularly
interesting given the key roles of c-MYC (the nearest gene to the
8q24 locus) and TERT in tumorigenesis. TERT and MYC are both
expressed in normal and transformed proliferating cells, and can
induce immortalization when constitutively expressed . The
TERT promoter contains numerous MYC binding sites that
mediate TERT transcriptional activation , suggesting that
TERT is a target of MYC activity. Although TERT variants have
not been previously reported to be associated with ovarian cancer,
multiple genome-wide association studies have reported associations
with this locus and risk of other cancers. Further analyses of this
locus, including fine mapping, resequencing and functional assays,
will be necessary to definitively identify the causal SNP(s).
Materials and Methods
Approval from respective human research ethics committees
was obtained, and all participants provided written informed
consent. Sixteen OCAC case-control studies (summarized in
Table S2) contributed data to this two-stage risk analysis. Samples
in the discovery stage were derived from two case-control studies,
AUS (550 cases and 1,101 controls) and MAY (125 cases and 61
controls). Cases in the discovery set were all diagnosed with serous
carcinoma of the ovary, fallopian tube or peritoneum, and most of
the participants were non-Hispanic white women. Cases and
controls from an additional 14 OCAC studies, as well as an
additional 284 AUS and 477 MAY samples, including cases with
other histologies, were included in a stage 2 analysis designed to
replicate the most promising SNPs from the discovery stage.
Fifteen studies used population-based case and control
ascertainment, and one (MAY) was clinic-based. All studies have been
previously described [7,35,36]. The final combined dataset of all
discovery and replication samples consisted of a total of 10,067
controls (9,953 were self-classified as non-Hispanic White) and
5,976 ovarian cancer cases of all histologies and morphologies,
including 3,734 serous invasive cases (3,710 were self-classified as
non-Hispanic Whites) (Table S2).
Candidate gene and SNP selection
Our approach and our choice of candidate genes was based on
extensive preliminary data we have accumulated from gene
expression profiles of co-cultured of theca fibroblast and epithelial
ovarian cells (I. Haviv, personal communication), and expression
profiles of murine ovarian epithelial cells identifying candidates
that are regulated through the estrus cycle [37,38] (see Text S1). A
compiled list of candidates was uploaded on the Ingenuity
Pathway Analysis web interface and GeneSpring GX in order to
obtain further candidates inferred from the literature.
Prioritisation based on literature evidence for a plausible role in oncogenesis
resulted in a list of 255 candidate genes of interest including
CXCL9, CTGF, LCN2, DCN, and VIL2. CXCL9 is associated with
ovarian cancer survival and acts by recruiting T-cells and inducing
immune surveillance , and is expressed in epithelial cells
cocultured with fibroblasts. CTGF is likely to be the driver of the
CAF phenotype. CTGF (TGFb-stimulated) expression is
associated with desmoplastic stroma  and elevated angiogenesis .
LCN2, DCN and VIL2 were regulated through the murine estrus
cycle, and appear to be hormone responsive (either directly or
indirectly) . Furthermore, comparison with expression profiles
of human ovarian carcinomas [42,43] showed that all three are
differentially expressed in tumors compared with normal epithelial
cells. Further details for candidate gene selection and justification
are provided in Text S1 and Table S1.
We identified SNPs within 5 kb of these 255 genes (58,114
SNPs in total from dbSNP, Ensembl, the International HapMap
Consortium , Perlegen Sciences , SeattleSNPs [pga.mbt.
washington.edu/], NIEHS SNPs [http://egp.gs.washington.edu],
and the Innate Immunity PGA [http://www.nhlbi.nih.gov/
resources/pga/]. We used the binning algorithm of ldSelect 
to identify 4,567 tagSNPs among these (r2.0.8) and minor allele
frequencies (MAFs).0.05 based on the most informative available
source (84% of genes used HapMap, 10% used SeattleSNPs, 3%
used Perlegen Sciences, 2% used NIEHS SNPs, and 1% used
Innate Immunity PGA). We prioritized the list to 166 genes based
on known function and the number of bins in each gene (excluding
genes with a large number of bins), in an attempt to identify
,1,500 key SNPs. Based on Illumina design scores, we picked the
best tagSNP in each bin (or two tagSNPs, if there were .10
tagSNPs in a bin but none of them had an optimal design score).
We also used PATROCLES (www.patrocles.org,) to identify
supplemental SNPs with MAFs.0.05 in microRNA binding sites
or non-synonymous SNPs from public databases to the potential
SNP list. This identified an additional 170 miRNA binding site
SNPs and nsSNPs with Illumina design scores.0.6. In total this
gave 1,410 tagSNPs, miRNA binding site SNPs and nsSNPs. In
order to reach the final total of 1,536 SNPs for the Illumina
GoldenGate assay, we added tagSNPs in another 12 candidate
genes with MAF$0.01. The final list of 1,536 SNPs included 106
supplemental SNPs and 1,430 tagSNPs in 173 genes (see Table
Genotyping and quality control
The discovery samples were predominantly non-Hispanic
White women with serous ovarian cancer and controls derived
from two studies, the AUS and MAY studies, and were genotyped
using the Illumina GoldenGate assay and Illumina BeadStudio
software [47,48]. Plates were prepared containing randomly
mixed cases and controls, with two duplicated samples and one
blank per plate (n = 20). The Illumina GoldenGate assay was
performed according to the manufacturers instructions. Following
completion of the assay, all plates were analysed using Illumina
BeadStudio software version 126.96.36.199. The original raw genotype
dataset contained genotype information for 1,920 samples
(including blanks and duplicates) and 1,536 SNPs. Following
automatic clustering, SNPs were ranked using their GenTrain
score (ranging from 0 to 1) and those with GenTrain scores,0.5
were manually checked and adjusted according to Illumina
guidelines. Samples with call rates below 95% and SNPs with
call rates below 98% were excluded. A total of 1,292 SNPs passed
this initial quality control (QC). Genotyping quality was also
assessed using tests for Hardy-Weinberg equilibrium (HWE). Plots
were examined for SNPs with significant deviations from HWE in
controls (0.001,P,0.05) and the genotype data was excluded
if the clustering was found to be suboptimal. SNPs with
PHWE,0.001 were excluded from analysis. In addition, we
genotyped 17 SNPs in CXCL9, CTGF, LCN2, DCN, and VIL2,
that had not been amenable to the Illumina GoldenGate assay or
failed QC criteria, at the Queensland Institute of Medical
Research using MALDI-TOF mass spectrophotometric mass
determination of allele-specific primer extension products with
Sequenoms MassARRAY platform and iPLEX Gold technology.
The final discovery dataset for analysis consisted of 675 cases and
1,162 controls with genotype data on 1,309 SNPs.
The three SNPs in PODXL, ITGA6 and MMP3 selected for
replication by all participating OCAC sites (with the exception of
MMP3 at the MAY site) were genotyped with the TaqMan allele
discrimination assay (Taqman Applied Biosystems, Foster City,
CA), using primers designed by Assays-by-Design (Applied
Biosystems). MAY genotyping of MMP3 rs522616 was performed
as part of a 1,536 Illumina Golden Gate Assay at the Mayo Clinic
with cases and controls randomly mixed within each plate.
Additional genotyping details are provided elsewhere .
Samples from five OCAC case-control studies (MAL, SEA,
UKO, USC and additional samples from AUS) were genotyped
for these and other replication SNPs, at the Queensland Institute
of Medical Research using Sequenom iPLEX Gold technology.
Primer design was carried out according Sequenoms guidelines
using MassARRAY Assay Design software (version 1.0). Multiplex
PCR amplification of fragments containing target SNPs was
performed using Qiagen HotStart Taq Polymerase and a Perkin
Elmer GeneAmp 2400 thermal cycler with 10 ng genomic DNA
in 384 well plates. Shrimp Alkaline Phosphatase and allele-specific
primer extension reactions were carried out according to
manufacturers instructions for iPLEX GOLD chemistry. Assay
data were analysed using Sequenom TYPER software (Version
Only replication SNPs that met OCACs QC criteria (including
.95% call rate, and .98% concordance between duplicates) were
included in the analysis .
The primary test for association in stage 1 was univariate
analyses of the relationship between SNP genotypes and risk of
serous ovarian cancer using the PLINK v0.99 Whole Genome
Association Analysis toolset (http://pngu.mgh.harvard.edu/
purcell/plink/) . Single-marker basic allelic association (x2
1df) tests (assoc option) analyses were performed on each of the
1,309 post-QC SNPs in a total of 1,837 women. PLINK default
thresholds were utilized, resulting in further exclusions: maximum
missing genotypes per person#0.10 (mind option), maximum
failed genotypes per SNP#0.10 (geno option), MAF$0.01 (maf
option). Summary statistics were obtained for each SNP on the
frequency of missing genotype data among cases and controls as
well as a comparison of missingness between cases and controls
using the Fishers exact test (test-missing option). Deviations from
expected HWE proportions were analysed using the Fishers exact
test and the MAFs were also estimated for all SNPs. The Cochran
Armitage Trend test (x2 1df) assuming the log additive model
(model option) was performed to test the association between the
minor allele of each SNP and serous ovarian tumors.
Selection of stage 1 SNPs for replication analyses in stage 2 was
prioritized as follows: first, SNPs with at least one failed duplicate,
SNPs with a significantly different proportion of missing genotype
data between cases and controls (PMiss,0.05), SNPs not
conforming to HWE criteria (see Genotyping and quality control)
for either cases, controls or both, and SNPs with no significant
trend in allelic dose response (PTrend.0.05) were excluded;
secondly, we estimated from the remaining SNPs which were
likely to be the best predictors of serous ovarian cancer risk by
calculating the positive predictive value (PPV) using the PTrend
values, the power of the study to detect this association, and the
prior probability of 0.0001 . Cases and controls from up to 14
additional studies participating in OCAC were included in
replication analyses. We selected the three SNPs with the highest
PPV for the larger replication analysis by all studies. Some
additional individuals from AUS and MAY (not in the discovery
set) were included in the replication analysis. Replication samples
were examined to determine the distribution of race/ethnicity
across studies, and analyses were restricted to White non-Hispanic
women with serous invasive ovarian tumors. Significant
differences by study site between age at interview for controls and age and
diagnosis for cases were assessed using the Students t-test for
comparison of means. The MAF for each SNP was estimated from
the control population for each study. The combined odds ratios
(OR) and their 95% confidence intervals (95% CIs) were obtained
from unconditional logistic regression models for each SNP
genotype. Assuming a log additive model of inheritance, the
perallele ORs and their 95% CIs associated with serous invasive
ovarian cancer in non-Hispanic Whites for each SNP selected for
replication were estimated by fitting the number of rare alleles
carried as a continuous covariate. Separate comparisons for
women with one copy (heterozygotes) and women with two copies
(rare homozygotes) of the minor allele vs. those with no copies
(reference homozygotes) were conducted for all replication SNPs.
Between-study heterogeneity was assessed using the likelihood
ratio test to compare logistic regression models with and without a
genotype-by-study interaction term. Risk estimates from all
replication analyses were adjusted for age at diagnosis for cases
or age at interview for controls and study site. Exploratory analyses
combining all ethnicities were additionally adjusted for ethnicity.
Forest plots generated in exploratory analyses according to
histological subtype were obtained using the rmeta library (v2.15)
implemented in the R project for Statistical Computing (http://
www.r-project.org/), and LD plots were generated using
Haploview v4.1 . All tests for association were two-tailed, and unless
otherwise specified, statistical significance was assessed at p,0.05
and tests for association in stage 2 were performed in STATA v.
9.0 (StataCorp, USA).
Figure S1 Study design for two-stage analysis of selected SNPs
in genes involved in stromal-epithelial interactions in the Ovarian
Cancer Association Consortium (OCAC).
Found at: doi:10.1371/journal.pgen.1001016.s001 (0.08 MB TIF)
Table S1 Candidate genes, putative role/special justification for
selection and reference list.
Found at: doi:10.1371/journal.pgen.1001016.s002 (0.05 MB
Table S2 Characteristics of serous ovarian cancer cases and
controls used in discovery and replication analyses according to
contributing OCAC study.
Found at: doi:10.1371/journal.pgen.1001016.s003 (0.05 MB
Table S3 SNPs successfully genotyped (Illumina & Sequenom)
in the discovery stage with PTrend#0.05 for serous ovarian cancer
Found at: doi:10.1371/journal.pgen.1001016.s004 (0.12 MB
Text S1 Candidate gene selection and justification.
Found at: doi:10.1371/journal.pgen.1001016.s006 (0.06
We are grateful to the family and friends of Kathryn Sladek Smith for their
generous support of OCAC through their donations to the Ovarian Cancer
Research Fund. The PBCS thanks Dr. Louise Brinton and Mark Sherman
from the Division of Cancer Epidemiology and Genetics of the National
Cancer Institute, USA, Drs. Neonila Szeszenia-Dabrowska and Beata
Peplonska of the Nofer Institute of Occupational Medicine (Lodz, Poland),
Witold Zatonski of the Department of Cancer Epidemiology and
Prevention, The M. Sklodowska-Curie Cancer Center and Institute of
Oncology (Warsaw, Poland), and Pei Chao and Michael Stagner from
Information Management Services (Sliver Spring MD, USA), for their
valuable contributions to the study. The GER study acknowledges Ursula
Eilber and Tanja Koehler for competent technical assistance for German
Ovarian Cancer study. The AOCS Management Group (D. Bowtell, G.
Chenevix-Trench, A. deFazio, D. Gertig, A. Green, P. Webb) gratefully
acknowledges the contribution of all the clinical and scientific collaborators
(see http://www.aocstudy.org/). The AOCS and ACS Management
Group (A. Green, P. Parsons, N. Hayward, P. Webb, D. Whiteman)
thank all of the project staff and collaborating institutions. We also thank all
the participants in all the participating studies.
The Ovarian Cancer Association Consortium
Georgia Chenevix-Trench, Sharon E. Johnatty, Jonathan Beesley,
Xiaoqing Chen, Penelope M. Webb, The Australian Cancer Study
(Ovarian Cancer), The Australian Ovarian Cancer Study Group, The
Queensland Institute of Medical Research, Queensland; Peter MacCallum
Cancer Centre, Melbourne Victoria (AUSTRALIA); Anna H. Wu,
Malcolm C. Pike, Celeste Leigh Pearce, Christopher K. Edlund, David
J. Van Den Berg, University of Southern California, Keck School of
Medicine, Los Angeles, CA; Montserrat Garcia-Closas, Hannah P. Yang,
Stephen Chanock, Nicolas Wentzensen, Louise A. Brinton, National
Cancer Institute, National Institutes of Health, Rockville, MD; Hoda
Anton-Culver , Argyrios Ziogas, Wendy Brewster, School of Medicine,
University of California, Irvine, CA; Ellen L. Goode, Brooke L. Fridley,
Robert A. Vierkant, Julie M. Cunningham, Mayo Clinic College of
Medicine, Rochester, MN; Andrew Berchuck, Joellen M. Schildkraut,
Edwin S. Iversen, Jr, Patricia G. Moorman, Duke University Medical
Center, Durham, NC; Marc T. Goodman, Michael E. Carney, Pamela J.
Thompson, Galina Lurie, Cancer Research Center of Hawaii, University
of Hawaii, Honolulu, HI; Daniel W. Cramer, Margaret A. Gates,
Immaculata DeVivo, Susan E. Hankinson, Shelley S. Tworoger, Kathryn
L. Terry, Brigham and Womens Hospital, Harvard School of Public
Health, Boston, MA; Jennifer A. Doherty, Kara L. Cushing-Haugen, Chu
Chen, Mary Anne Rossing, Fred Hutchinson Cancer Research Center,
Seattle,WA; Linda S. Cook, Department of Internal Medicine, University
of New Mexico. Albuquerque, NM; Kirsten Moysich, Richard DiCioccio,
Matthew T. Grasela, Roswell Park Cancer Institute, Buffalo, NY; Roberta
B. Ness, University of Texas School of Public Health, Houston, TX; Alice
S. Whittemore, Valerie McGuire, Weiva Sieh, Stanford University School
of Medicine, Stanford, CA; Johnathan M. Lancaster, H. Lee Moffitt
Cancer Center & Research Institute, Tampa, FL; Rachel T.
Palmieri,University of North Carolina at Chapel Hill, NC; Harvey A. Risch, Yale
University School of Public Health, New Haven, CT (UNITED STATES);
Claus Hogdall, Estrid Hogdall, Susanne Kruger Kjaer, Danish Cancer
Society/The Juliane Marie Centre, Copenhagen (DENMARK); Ralf
Butzow, University of Helsinki, Haartman Insitute, Helsinki (FINLAND);
Simon A. Gayther , Aleksandra Gentry-Maharaj, Usha Menon, Susan J.
Ramus, University College London, London, Paul D.P. Pharoah, Barbara
Perkins, Mitul Shah, Honglin Song, University of Cambridge, Strangeways
Research Laboratory, Cambridge (UNITED KINGDOM), Linda E
Kelemen, Alberta Health Services, Calgary (CANADA), Jacek Gronwald,
Jan Lubinski, Pomeranian Medical University, Szczecin; Jolanta Lissowska,
Cancer Center and M Sklodowska-Curie Institute of Oncology, Warszawa
(POLAND); Jenny Chang-Claude, Deutsches Krebsforschungszentrum,
Heidelberg; Shan Wang-Gohrke, University of Ulm, Ulm (GERMANY).
Conceived and designed the experiments: ABS AdF PMW MTG GL JCC
SEH MGC SJC PDP ASW CLP SAG SJR UM EH AB JMS TAS ELG GCT. Performed the experiments: JB XC NG IH. Analyzed the data: SEJ JB XC SM DLD NG CLP DNR. Contributed reagents/materials/analysis tools: SEJ SM DLD ABS AdF PMW MAR JAD MTG GL PJT LRW
RBN KBM JCC SWG DWC KLT SEH SST MGC HY JL SJC PDP HS
ASW CLP DOS AMW MCP SAG SJR UM AGM HAC AZ EH SKK CH AB JMS ESI PGM CMP TAS JMC RAV DNR ELG IH GCT. Wrote the paper: SEJ JB GCT.
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