Genetic susceptibility to sporadic ovarian cancer: A systematic review

Biochimica et Biophysica Acta 1816 (2011) 132–146

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Biochimica et Biophysica Acta

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Review

Genetic susceptibility to sporadic ovarian cancer: A systematic review

M.G.M. Braem a, L.J. Schouten b, P.H.M. Peeters a, P.A. van den Brandt b, N.C. Onland-Moret a,⁎a Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Stratenum 6.131, P.O. Box 85500, 3508 GA Utrecht, The Netherlandsb Maastricht University, GROW, School for Oncology and Developmental Biology, Department Epidemiology, P.O. Box 616, 6200 MD Maastricht, The Netherlands

⁎ Corresponding author at: Julius Center for HealthNetherlands. Tel.: +31 88 75 59367; fax: +31 88 75 55

E-mail addresses: [email protected] ([email protected] (P.A. den Brandt), N

0304-419X/$ – see front matter © 2011 Elsevier B.V. Adoi:10.1016/j.bbcan.2011.05.002

a b s t r a c t
a r t i c l e i n f o
Article history:Received 26 February 2011Received in revised form 18 May 2011Accepted 18 May 2011Available online 25 May 2011

Keywords:Ovarian cancerGenetic susceptibilityGenetic variantsRisk assessmentReviewPolymorphism

Ovarian cancer is a highly lethal disease. Many researchers have, therefore, attempted to identify high riskpopulations. In this perspective, numerous genetic association studies have been performed to discovercommon ovarian cancer susceptibility variants. Accordingly, there is an increasing need to synthesize theevidence in order to identify true associations.A comprehensive and systematic assessment of all available data on genetic susceptibility to sporadic ovariancancer was carried out. The evidence of statistically significant findings was evaluated based on the number ofpositive replications, the ratio of positive and negative studies, and the false-positive report probability (FPRP).The authors reviewed three genome-wide association studies (GWAS) and 147 candidate gene studies,published from 1990 to October 2010, including around 1100 genetic variants in more than 200 candidategenes and 20 intergenic regions. Genetic variants with the strongest evidence for an association with ovariancancer include the rs2854344 in the RB1 gene and SNPs on chromosomes 9p22.2, 8q24, 2q31, and 19p13.Promising genetic pathways for ovarian cancer include the cell cycle, DNA repair, sex steroid hormone andoncogenic pathway.Concluding, this review shows that many genetic association studies have been performed, but only a fewgenetic variants show strong evidence for an association with ovarian cancer. More research is needed toelucidate causal genetic variants, taking into consideration gene–gene and gene–environment interactions,combined effects of common and rare variants, and differences between histological subtypes of this cancer.

Sciences and Primary Care, University Medical Center U485..M. Braem), [email protected] (L.J. [email protected] (N.C. Onland-Moret).

ll rights reserved.

© 2011 Elsevier B.V. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1332. Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

2.1. Search strategy and selection criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1332.2. Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1332.3. Data items. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1332.4. Quality assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1332.5. Data interpretation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1353.1. Quality assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1353.2. Genome-wide association studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1353.3. Candidate gene studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

3.3.1. Sex steroid hormone genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1403.3.2. Cell cycle genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1403.3.3. DNA repair genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1413.3.4. Oncogenes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1413.3.5. Miscellaneous candidate genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1413.3.6. Genomic regions initially identified by GWAS for other cancers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1413.3.7. Lack of overlap with results from GWAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

3.4. Stratification based on quality measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

trecht, Mailbox Str 6.131, P.O. Box 85500, 3508 GA Utrecht, The

), [email protected] (P.H.M. Peeters),

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133M.G.M. Braem et al. / Biochimica et Biophysica Acta 1816 (2011) 132–146

4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1436. Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1437. Authors’ contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Table 1Credibility criteria for statistically significant findings.

Criteria Number of replications Negative/positive FPRP

1. Introduction

Ovarian cancer is the fifth most common cancer among women inEurope and the United States [1,2]. This aggressive type of cancer ismainly diagnosed at a progressed stage and has the highest mortalityrate among gynecologic malignancies [1]. It is, therefore, important toidentify causal factors that influence a woman's risk to this type ofcancer. Several risk-reducing factors have already been identified,including increasing number of children, oral contraceptive use, earlyage at menopause, shorter menstrual lifespan, and hysterectomy [3–5]. There is also growing evidence that genetic factors influencesusceptibility to ovarian cancer. Family based linkage studies havediscovered ovarian cancer genes with strong penetrance, such asBRCA1/2 and DNA mismatch repair genes (MMR; MLH1, MSH2, MSH6and PMS2) [6–12]. These family-based linkage studies have proven tobe successful in the past decades, particularly for monogeneticdiseases. In the late nineties the “common disease-common variant”hypothesis was formulated, stating that several common diseasealleles together play a role in the etiology of common complexdiseases, such as sporadic ovarian cancer. For studying geneticvariants under this “common disease-common variant hypothesis,”candidate gene association studies seemed more suitable [13]. Thecandidate gene approach uses knowledge of the functional role ofgenes in disease etiology to identify causal variants, genotyping thesevariants in a population and comparing its frequencies between casesand controls [14,15]. With the completion of the human genomesequence [16], technological advances, the initiation of the Interna-tional HapMap Project [17], and other initiatives such as theSNP500Cancer project [18], a transition has recently been observedfrom the hypothesis-driven candidate gene approach to an agnosticapproach, using large-scale association testing. These genome-wideassociation studies (GWAS) compare genotype frequencies of hun-dreds of thousands of single nucleotide polymorphisms (SNPs)distributed throughout the genome between large numbers of casesand unaffected controls [19]. Three such GWAS scans for ovariancancer have been published so far [20–22].

Numerous genetic variants have been studied in association withovarian cancer, showing inconsistent results. Therefore, there is anincreasing need to summarize the evidence to identify true geneticassociations. Some reviews on the genetic epidemiology of ovariancancer have already been published, but these were very descriptive[23–26]. No search strategies and arguments for inclusion or exclusionof genes and genetic pathways were described. Moreover, thesereviews lacked systematic data and quality assessment, as well assystematic evaluation and interpretation of findings. The aim of thisreview is to systematically present and evaluate all available datafrom genome-wide association and candidate gene studies of ovariancancer.

study ratio

Very likely ≥2 positive replications b1 b 0.5Likely ≥2 positive replications 0 N0.5

≥1 positive replication b1 b0.50 replications – b0.5

Possibly 1 positive replication 0 N0.5≥2 positive replications 0≤1 N0.5

Uncertain ≥1 positive replication N1 N0.50 replications – N0.5

Abbreviations: FPRP, false positive report probability.

2. Materials and methods

2.1. Search strategy and selection criteria

We searchedMedline (PubMed) and Embase for publications from1990 onwards by using following Medical Subject Headings (MeSH)terms: “Ovarian Neoplasms” AND (“Polymorphism, Genetic” OR

“Repetitive Sequences, Nucleic Acid”). We subsequently searchedfor additional publications through HuGE Navigator's Phenopedia,querying for “Ovarian Neoplasms.” Additional publications werefound by hand searching reference lists of original articles andreviews. Results were manually assessed by reviewing titles andabstracts, and those reporting genetic factors related to sporadicovarian cancer susceptibility involving human participants of any agegroup, and published in English were included. Studies in whichtumor DNA from cases was compared with genomic DNA fromcontrols were excluded. We selected all papers that were publisheduntil October 2010. In case of overlapping reports, only one study wasretained.

2.2. Data processing

A single reviewer (M.G.M.B.) identified, selected and processed thestudies to ensure consistency in selection and processing of thepublications. Data were extracted using structured forms. Thereproducibility of the data extraction form was examined byextracting 10 randomly chosen papers by an additional reviewerindependently (NCOM).

2.3. Data items

All publications were sought for following information: authors,publication year, gene or loci, SNPid, genotype, additional genotypeinformation, measure of association, 95% confidence interval (CI), Pvalue or Bayes factor (BF), study design, ethnicity or country, samplesize (number of cases and number of controls), and genotypingmethods. We used the HuGE Navigator's Variant Name Mapper [27],dbSNP [28], and ALFRED [29] to uncover lacking genetic referencenumbers.

2.4. Quality assessment

Quality assessment was performed independently by two re-viewers (M.G.M.B. and N.C.O.M.). A 16-item score list was developedbased on published criteria for genetic association studies (i.e.,STREGA and NCI-NHGRI Working Group criteria) [30,31]. A finalquality score was obtained by adding up scores from all criteria andsubsequently transforming these to a 0- to 10-point scale. The qualityassessment included items regarding study design, sample size,

1065

865 200

95 105

66 2

38

19

Investigated genetic variants

Statistically significant variants

Not attempted to replicate

Never positively replicated

Positively replicated by at least one study

Exclusively positively replicated

Included in replication studies

FPRP < 0.5

Fig. 1. Flowchart summarizing the number of investigated genetic variants among candidate gene studies.

134 M.G.M. Braem et al. / Biochimica et Biophysica Acta 1816 (2011) 132–146

power and actions to prevent bias in general. Regarding thegenotyping process, we assessed whether quality control measureswere carried out, as genotyping errors may lead to incorrect alleleidentification or incorrect allele frequencies, causing misclassification.We also assessed whether any methods were used to assess oraddress population stratification, as this might confound the associ-ation between genetic variant and ovarian cancer. When multiplegenetic variants were investigated in one study, we checked whetherany methods were described to adjust for multiple testing.

2.5. Data interpretation

A common problem with genetic association studies is thepublication of false-positive findings, as a consequence of chancefindings (type I error) [14,32,33]. Statistically significant findingsfrom candidate gene studies were, therefore, evaluated based on thenumber of positive replications, the number of positive and negativestudies and the false-positive report probability (FPRP; “theprobability of no association given a statistically significantfinding”). For two-stage studies, the second stage was considereda separate replication study of the first stage (the initial study).When different SNPs in one gene were significantly associated withovarian cancer, we tested whether these SNPs were in LD with

Fig. 2. Chromosomal location of identified ov

other significant SNPs in this gene. To straightforwardly examinethe number of positive and negative results, we calculated the ratioof negative on positive studies. Thus, a ratio larger than 1 meansthat more negative than positive studies were published, a ratiosmaller than 1 represents more positive than negative studies, and aratio of 0 stands for only positive studies. The FPRP is determinedby the magnitude of the P value, the statistical power and thefraction of tested hypotheses that is true. In other words, the higherthe FPRP value, the higher the chance of a false-positive report. Weestimated this FPRP for each statistically significant finding, using aBayesian method proposed by Wacholder et al.[34]. As suggested bythe authors, we preset the FPRP value at 0.5. We calculated theFPRP for a low prior probability of association (0.001) and for a verylow prior probability of association (0.000001), as was proposed byDong et al.[35]. For each genetic variant, the FPRP value wascalculated using this estimated prior probability range, andstatistical power to detect an odds ratio of both 1.5 and 1.2 (orits reciprocals, 0.67 and 0.83). The FPRP was calculated using theExcel spreadsheet, provided by Wacholder et al.[34]. We graded thecredibility of a statistically significant association between a geneticvariant and the risk of ovarian cancer, “very likely,” if the initialfinding was positively replicated by two studies or more, thenegative/positive study ratio was smaller than one and the FPRP

arian cancer susceptibility SNPs and loci.


value was lower than 0.5; “likely,” if the initial finding waspositively replicated by two studies or more, the negative/positivestudy ratio was equal to zero and the FPRP value was larger than0.5, or if the initial finding was positively replicated by at least onestudy, the negative/positive study ratio was smaller than 1, and theFPRP value was lower than 0.5, or if the initial finding was neverattempted to replicate, but the FPRP value was lower than 0.5;“possibly,” if the initial finding was positively replicated by onesubsequent study, the negative/positive study ratio was equal tozero and the FPRP value was larger than 0.5, or if the initial findingwas positively replicated by two or more studies, but alsonegatively replicated by other studies; “uncertain,” if the numberof negative studies was larger than the number of positive studies,or if the initial finding was never attempted to replicate and theFPRP value was larger than 0.5 (Table 1). In order to giveresearchers the opportunity to make their own judgment on theassociations reported, we presented all statistically significantassociations, with their estimated FPRP values, in SupplementaryTable 2. We performed an additional stratification of the studies,based on the overall quality score, as well as on the year ofpublication and the number of cases included in the studies.

3. Results

We identified a total of 151 genetic association studies, comprisingthree GWAS and 148 candidate gene studies [20–22,36–177]. Ofthese, one article was excluded from this review because of duplicatedreporting of results [175]. Characteristics of the 150 remaining studiesare presented in Supplementary Table 1. All studies included werecase–control designed. Most populations studied were from Europeanancestry. Data were limited for Asian and African populations. Thenumber of publications on genetic association studies of ovariancancer increased from 2000 onwards, with a first peak in 2005. Moststudies were published in 2008 and 2009. In total, around 1100polymorphisms in about 230 genes and 20 intergenic regions wereinvestigated. Fig. 2 shows the chromosomal location of loci identifiedthrough GWAS, and SNPs from candidate gene studies that werepositively replicated by at least one study. The credibility of theassociations is indicated by different colors.

3.1. Quality assessment

The overall median quality score was 4.7, with a minimum of1.1 and a maximum of 8.8. Overall, studies with the lowest qualitywere published before the year 2000 and studies with the highestquality were published from 2005 onwards. In 65.1% of the studies,genetic reference numbers were lacking. Most of the studies clearlydescribed the design of the study (90%); however, only half ofthem described the study participants in detail (53%). Powercalculations were performed a priori in 12 studies, and post-hoc in17 studies. The median number of cases that were included was313, with a minimum of 39 and a maximum of 9943. A strongincrease in the number of cases included was observed from 2007onwards. Eighty percent of the studies had a control group thatwas at least equally large in size. Only 9% addressed underlyingpopulation stratification. Moreover, 20% did not adjust for multipletesting when necessary. Quality control for genotyping methodswas poor. Error or call rates were described in only 32.2% of thestudies, and only 20.8% described both. Moreover, the validity ofthe genotyping method was assessed in only 34% of the studies.Only half of the studies (49%) assessed the degree of reproduc-ibility between quality control replicates and only a smallproportion of studies reported the laboratory or centre wheregenotyping was performed (18.1%), and whether genotypes wereassigned using all of the data simultaneously or in smaller batches(16.8%). Twenty-four percent failed to assess Hardy–Weinberg

equilibrium (HWE) and only 58% described the method used toassess HWE.

3.2. Genome-wide association studies

Three GWAS for ovarian cancer have been performed, however,with overlapping sets of study participants (Table 3) [20–22]. Thefirst GWAS published in 2009, by Song et al., identified an ovariancancer susceptibility locus on chromosome 9p22.2 containing 12SNPs (rs3814113, rs4445329, rs10810666, rs10962656, rs12379183,rs2153271, rs7861573, rs10756819, rs1416742, rs12379687,rs4961501, rs1339552) [20]. The SNP with the smallest P value(rs3814113; P: 2.5×10−17) was additionally genotyped in 2670ovarian cancer cases and 4668 controls in phase 3, confirming itsassociation with ovarian cancer (OR: 0.82, 95% CI: 0.79–0.86, Ptrend:5.1×10−19). This association was stronger for serous ovariancancers (OR: 0.77, 95% CI: 0.73–0.81, Ptrend: 4.1×10−21). Thesecond GWAS in ovarian cancer, published in September 2010, byGoode et al., performed a re-analysis of phase 1 and 2 of the firstGWAS and an additional analysis restricted to cases of the seroussubtype [22]. This revealed 5 new loci in all subtypes (at 1p31,1p36, 2q31, 11p14 and 17q21) and 4 in the serous subtype only(at 2p22, 3q25, 7p21 and 8q24). In phase 3, 30 SNPs from theseloci were genotyped in an additional 4353 ovarian cancer cases (allsubtypes) and 6021 controls. One SNP at the 2q31 loci (rs2072590;OR: 1.19, 95% CI: 1.12–1.26) and three SNPs at the 8q24 locus(rs10088218, rs1516982, and rs100098821; OR: 0.79, 95% CI: 0.73–0.87, OR: 0.85, 95% CI: 0.79–0.92, OR: 0.79, 95% CI: 0.71–0.86respectively) were found to be associated with ovarian cancer. OneSNP at the 3q25 locus showed genome-wide significance when allstudy phases were combined only (OR: 1.19, 95% CI: 1.11–1.27).Associations were generally stronger for serous subtypes. Inparallel with the second GWAS, a GWAS on ovarian cancer survivalwas published by Bolton et al., also studied the association withovarian cancer risk [21]. One of the SNPs most strongly associatedwith survival in phase 1 and 2 showed genome-wide significancefor risk of ovarian cancer in phase 3 of this GWAS (rs2363956 at19p13; OR: 1.13, 95% CI: 1.06–1.20, Ptrend 9.4×10−6). Another SNPshowed genome-wide significance for risk of serous ovarian canceronly when all study phases were combined (rs1870 at 19p13;Ptrend 3×10−9).

The FPRP values of all SNPs identified through these GWAS werebelow 0.5. This is, however, not surprising given the very stringentcut-off used for declaring statistical significance in GWAS.

3.3. Candidate gene studies

Of the 1065 genetic variants studied in candidate gene studies,only 200 variants were declared to be statistically significantlyassociated with ovarian cancer risk in the original report, of which32 were only associated with serous ovarian cancer (Fig. 1;Supplementary Table 2). Almost half of the 200 associations werenever attempted to replicate (Fig. 1; Supplementary Table 3.). Only38 of the 105 findings that were included in replication studieswere at least once positively replicated. Of these, only 19 findingswere exclusively positively replicated (Fig. 1; Table 2). Theremaining associations were both positively and negatively repli-cated, and even at times more often negatively than positively. Forthese 38 ever-positively replicated findings, the maximum numberof replication studies was five, but most findings (76%) were onlyattempted to replicate once or twice.

Candidate genes that were statistically significantly associatedwith ovarian cancer and were positively replicated by at least onestudy can be broadly classified into 4 pathways: the sex steroidhormone pathway, the cell cycle pathway, the DNA repair pathway,

Table 2Genetic variants associated with the risk of ovarian cancer.

Gene SNPid Credibilityscore

First author,year

Ethnicity/country Sample sizecases/controls

Genotype OR(95% CI)

P/BF Power FPRP

OR:1.2

OR:1.5

OR: 1.2 OR: 1.5

0.001 0.000001 0.001 0.000001

Cell cycle genesABL1 rs2855192 Possible Cunningham 2009

Discovery SetNon-Hispanic whites 829/941 Log-additive 1.26 (1.03–1.55) 0.02 0.322 0.951 0.989 1.000 0.968 1.000

Rare homozygote 2.81 (1.29–6.09) 0.016 0.056 0.998 1.000 0.994 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 1.76 (1.07–2.88) 0.064 0.262 0.997 1.000 0.989 1.000

CCND1 rs3212879 Likely Gayther 2007Stage 1

Mixed (DK, UK, US) 1500/2500 Heterozygote 0.85 (0.73–0.99) 0.028 0.620 1.000 0.983 1.000 0.973 1.000

Rare homozygote 0.82 (0.68–0.98) 0.447 0.997 0.985 1.000 0.967 1.000Goode 2009 Mixed (DK, UK, US) 2120/3382 Log-additive 0.93 (0.86–1.01) 0.999 1.000 0.98 1.000 0.980 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 0.85 (0.73–0.99) 0.0717 0.620 1.000 0.983 1.000 0.973 1.000

Rare homozygote 0.82 (0.68–0.99) 0.450 0.995 0.989 1.000 0.975 1.000rs3212891 Possible Gayther 2007

Stage 1Mixed (DK, UK, US) 1500/2500 Heterozygote 0.86 (0.74–1.00) 0.034 0.678 1.000 0.987 1.000 0.980 1.000

Rare homozygote 0.83 (0.69–1.00) 0.500 0.997 0.989 1.000 0.979 1.000Goode 2009 Mixed (DK, UK, US) 2120/3382 Log-additive 0.93 (0.86–1.00) 0.999 1.000 0.98 1.000 0.980 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 0.86 (0.74–1.00) 0.092 0.678 1.000 0.987 1.000 0.980 1.000

Rare homozygote 0.83 (0.69–1.00) 0.500 0.997 0.990 1.000 0.980 1.000rs602652 Likely Gayther 2007

Stage 1Mixed (DK, UK, US) 1500/2500 Rare homozygote 1.24 (1.03–1.50) 0.368 0.975 0.986 1.000 0.965 1.000

Goode 2009 Mixed (DK, UK, US) 2120/3382 Log-additive 1.08 (1.00–1.17) 0.995 1.000 0.984 1.000 0.983 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 1.24 (1.03–1.49) 0.363 0.979 0.984 1.000 0.957 1.000

rs603965 Likely Gayther 2007Stage 1

Mixed (DK, UK, US) 1500/2500 Rare homozygote 1.29 (1.07–1.55) 0.22 0.946 0.968 1.000 0.874 1.000

Goode 2009 Mixed (DK, UK, US) 2120/3382 Log-additive 1.08 (1.00–1.17) 0.985 1.000 0.984 1.000 0.983 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 1.28 (1.06–1.55) 0.254 0.948 0.978 1.000 0.924 1.000

rs7178 Uncertain Gayther 2007Stage 1

Mixed (DK, UK, US) 1500/2500 Heterozygote 1.24 (1.03–1.49) 0.021 0.363 0.979 0.984 1.000 0.957 1.000

Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 1.24 (1.04–1.49) 0.0716 0.363 0.979 0.984 1.000 0.957 1.000CCND2 rs3217916 b Uncertain Gayther 2007


Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 0.75 (0.58–0.97) 0.220 0.887 0.992 1.000 0.970 1.000rs3217925 b Uncertain Gayther 2007


Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 0.72 (0.54–0.96) 0.166 0.789 0.993 1.000 0.970 1.000rs3217936 Uncertain Gayther 2007


Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 0.77 (0.61–0.97) 0.262 0.942 0.990 1.000 0.966 1.000CCND3 rs3218086 Possible Cunningham 2009


Rare homozygote 1.75 (1.01–3.03) 0.089 0.291 0.998 1.000 0.994 1.000Goode 2009 Mixed (DK, UK, US) 2120/3382 Log-additive 1.31 (1.09–1.57) 0.171 0.929 0.953 1.000 0.788 1.000

CCNE1 rs3218036 Uncertain Gayther 2007 Stage 1 Mixed (DK, UK, US) 1500/2500 Rare homozygote 1.26 (1.00–1.57) 0.332 0.940 0.992 1.000 0.977 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 1.27 (1.01–1.59) 0.310 0.927 0.992 1.000 0.976 1.000

CDK2 rs1045435 Uncertain Cunningham 2009Discovery Set

Non-Hispanic whites 829/941 Heterozygote 1.31 (1.01–1.71) 0.11 0.259 0.840 0.955 1.000 0.982 1.000

Goode 2009 Mixed (DK, UK, US) 2120/3382 Log-additive 1.14 (1.00–1.31) 0.765 1.000 0.988 1.000 0.985 1.000rs2069414 Possible Cunningham 2009


Heterozygote 1.36 (1.02–1.81) 0.03 0.195 0.749 0.994 1.000 0.979 1.000Goode 2009 Mixed (DK, UK, US) 2120/3382 Log-additive 1.58 (1.20–2.09) 0.027 0.358 0.980 1.000 0.790 1.000

CDK6 rs8 Possible Gayther 2007 Stage 1 Mixed (DK, UK, US) 1500/2500 Heterozygote 1.18 (1.02–1.35) 0.0042 0.597 1.000 0.964 1.000 0.941 1.000Rare homozygote 1.41 (1.01–1.96) 0.169 0.644 0.996 1.000 0.984 1.000

Gayther 2007Stage 2

Mixed (AUS, EUR, US) 3000/4400 Heterozygote 1.09 (1.00–1.19) 0.17 0.984 1.000 0.982 1.000 0.982 1.000

Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 1.17 (1.02–1.35) 0.0185 0.636 1.000 0.980 1.000 0.969 1.000Rare homozygote 1.44 (1.04–1.99) 0.135 0.598 0.995 1.000 0.978 1.000

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CDKN1B rs2066827 Likely Gayther 2007Stage 1

Mixed (DK, UK, US) 1500/2500 Rare homozygote 0.68 (0.51–0.90) 0.082 0.664 0.988 1.000 0.913 1.000

Gayther 2007 Stage 2 Mixed (AUS, EUR, US) 3000/4400 Homozygote 0.79 (0.65–0.95) 0.300 0.987 0.976 1.000 0.925 1.000Goode 2009 Mixed (DK, UK, US) 2120/3382 Log-additive 0.84 (0.76–0.94) 0.583 1.000 0.803 1.000 0.704 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 0.68 (0.51–0.90) 0.082 0.664 0.988 1.000 0.913 1.000

CDKN2A/2B rs3731257 Likely Gayther 2007Stage 2

Mixed (AUS,EUR, US)

3000/4400 Heterozygote 0.89 (0.81–0.97) 0.021 0.944 1.000 0.894 1.000 0.888 1.000

Goode 2009 Mixed (DK, UK, US) 2120/3382 Log-additive 0.89 (0.80–1.00) 0.880 1.000 0.983 1.000 0.980 1.000E2F2 rs760607 Uncertain Cunningham 2009


Heterozygote 0.78 (0.63–0.97) 0.288 0.962 0.989 1.000 0.964 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 2.94 (1.38–6.23) 0.947 0.010 0.04 0.998 1.000 0.992 1.000

Rare homozygote 2.91 (1.37–6.15) 0.010 0.041 0.998 1.000 0.992 1.000RB1 rs2854344 Very Likely Song 2006_1 Mixed (DK, UK, US) N1500/4800 Heterozygote 0.71 (0.58–0.86) 0.006 0.055 0.856 0.893 1.000 0.350 1.000

Ramus 2008 Non-Hispanic whites 4624/8113 Log-additive 0.88 (0.79–1.00) 0.041 0.815 1.000 0.984 1.000 0.980 1.000Heterozygote 0.86 (0.75–0.98) 0.703 1.000 0.971 1.000 0.959 1.000

Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 0.73 (0.59–0.91) 0.0076 0.127 0.879 0.976 1.000 0.854 1.000rs4151620 Uncertain Song 2006_1 Mixed (DK, UK, US) N1500/4800 Rare homozygote 0.20 (0.07–0.55) 0.003 0.012 0.998 1.000 0.993 1.000

Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 0.22 (0.08–0.62) 0.006 0.022 0.999 1.000 0.995 1.000

DNA repair genesBRCA1 rs799917 Uncertain Auranen 2005 Mixed (DK, UK, US) 1600/4241 Heterozygote 1.1 (1.0– 1.3) 0.25 0.850 1.000 0.997 1.000 0.996 1.000

Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 1.16 (1.01–1.34) 0.13 0.667 1.000 0.985 1.000 0.978 1.000BRCA2 rs144848 Uncertain Auranen 2003 Anglo-Saxon and

Northern Europeandescent (UK and AUS)

1121/2643 HH 1.36 (1.04– 1.77) 0.03 0.176 0.767 0.992 1.000 0.967 1.000

Beesley 2007 Caucasian (AUS) 1466/1821 AC 1.27 (1.09– 1.49) 0.243 0.979 0.932 1.000 0.774 1.000RAD52 rs11226 Possible Auranen 2005 Mixed (DK, UK, US) 1600/4241 Heterozygote 1.1 (1.0– 1.3) 0.09 0.846 1.000 0.997 1.000 0.996 1.000

Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 1.17 (1.00–1.37) 0.11 0.623 0.999 0.988 1.000 0.981 1.000XRCC2 rs3218536 Uncertain Auranen 2005 Mixed (DK, UK, US) 1600/4241 Heterozygote 0.8 (0.7– 1.0) 0.003 0.373 0.940 0.993 1.000 0.982 1.000

Rare homozygote 0.3 (0.1– 0.9) 0.035 0.076 0.999 1.000 0.998 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 0.23 (0.07–0.79) 0.021 0.045 0.999 1.000 0.998 1.000

MSH2 rs4952887 Possible Song 2006_2 Mixed (DK, UK, US) 1531/2570 Heterozygote 0.82 (0.67–0.99) 0.16 0.452 0.977 0.991 1.000 0.981 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 0.82 (0.67–1.00) 0.12 0.450 0.982 0.989 1.000 0.975 1.000

MSH3 rs6151662 Possible Song 2006_2 Mixed (DK, UK, US) 1531/2570 Rare homozygote 0.28 (0.08–0.91) 0.037 0.075 0.999 1.000 0.998 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 0.27 (0.08–0.92) 0.036 0.073 0.999 1.000 0.998 1.000

MSH6 rs3136245 Possible Song 2006_2 Mixed (DK, UK, US) 1531/2570 Rare homozygote 0.67 (0.46–0.97) 0.128 0.500 0.996 1.000 0.985 1.000Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Rare homozygote 0.68 (0.47–0.99) 0.149 0.531 0.997 1.000 0.988 1.000

OncogenesKRAS rs61764370 Possible Ratner 2010_

ExplorativeStudy

US 320/228 G/G and G/T 1.7 (1.11–2.63) 0.016 0.059 0.287 0.997 1.000 0.984 1.000

Ratner 2010_Validation Study

US 100/101 G/G and G/T 2.46 (1.14–5.29) 0.02 0.033 0.103 0.998 1.000 0.995 1.000

NMI rs11683487 Uncertain Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 0.78 (0.66–0.93) 0.0198 0.244 0.986 0.958 1.000 0.851 1.000Quaye 2009_2(Stage 1)

Mixed (DK, UK, US) 1800/3000 0.80 (0.69–0.93) 0.038 0.316 0.998 0.921 1.000 0.786 1.000

Sex steroid hormone genesAR CAG Very Likely Schildkraut 2007 African-American 99/141 CAG_S repeat

b16 vs. ≥162.5 (1.1–5.5) 0.034 0.102 0.999 1.000 0.996 1.000

Terry 2005_1 US 987/1034 ≥22 CAG_S vs. b19 1.35 (1.07–1.71) 0.250 0.851 0.993 1.000 0.978 1.000Terry 2005_1 US 987/1034 1 CAG increase in

shorter allele length1.08 (1.04–1.12) 1.000 1.000 0.032 0.971 0.032 0.971

Schildkraut 2007 African-American 99/141 CAG_L repeat b19vs. ≥19

2.7 (1.3–5.8) 0.019 0.066 0.998 1.000 0.994 1.000

Ludwig 2009 Caucasian (Polish) 215/352 1 CAG increase inlonger allele length

0.89 (0.83–0.97) 0.006 0.944 1.000 0.894 1.000 0.888 1.000

Terry 2005_1 US 987/1034 ≥22 CAG repeats vs. b22 1.31 (1.01–1.69) 0.250 0.851 0.993 1.000 0.978 1.000Santarosa 2002 Italian 121/100 ≥26 CAG repeats vs. ≤21 3.57 (1.24–10.26) 0.021 0.054 0.999 1.000 0.997 1.000Terry 2005_1 US 987/1034 1 CAG increase in

average allele length1.05 (1.01–1.10) 1.000 1.000 0.975 1.000 0.975 1.000

Ludwig 2009 Caucasian (Polish) 215/352 0.89 (0.81–0.99) 0.025 0.901 1.000 0.973 1.000 0.970 1.000

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Table 2 (continued)

Gene SNPid Credibilityscore

First author,year


Genotype OR(95% CI)

P/BF Power FPRP

OR:1.2

OR:1.5

OR: 1.2 OR: 1.5

0.001 0.000001 0.001 0.000001

ESR1 rs2295190 Possible Doherty 2010_Explorative study

US 1128/1866 1.24 (1.06–1.44) 0.006 0.996 1.000 0.945 1.000 0.945 1.000

Doherty 2010_Replication study

Mixed (AUS, EUR, US) 5729/7450 1.09 (1.02–1.17) 0.017 0.334 0.994 0.935 1.000 0.829 1.000

PGR PROGINS(rs1042838)

Possible Pearce 2006 Non-Latinawhites (US)

267/397 AA 3.23 (1.19–8.75) 0.022 0.026 0.066 0.999 1.000 0.997 1.000

Quaye 2009_1 Mixed (DK, UK, US) 1491/3145 Heterozygote 1.25 (1.07–1.46) 0.0232 0.303 0.989 0.941 1.000 0.831 1.000Agoulnik 2004 German 84/440 3.02 (1.86–4.91) b0.01 0.000 0.002 0.988 1.000 0.777 1.000Leite 2008 Brazilian 80/282 T2 2.2 (1.80–3.54) 0.006 0.057 0.995 1.000 0.953 1.000

CYP1B1 rs1056836 Uncertain Goodman 2001 Hawaii 129/144 Val/Val 3.8 (1.2–11.4) 0.005 0.020 0.049 0.999 1.000 0.997 1.000Val/Leu 1.8 (1.0–3.3) 0.095 0.278 0.998 1.000 0.995 1.000

Holt 2007 Mixed (African-American &Caucasian; US)

310/585 CC 1.9 (1.2–3.0) 0.024 0.155 0.996 1.000 0.974 1.000

CG/CC 1.5 (1.1–2.3) 0.153 0.500 0.998 1.000 0.992 1.000CYP19 rs749292 Possible Goodman 2008 Mixed (Japanese,

Caucasian, Hawaiian,Filipino, other; Hawaii)

367/602 AG 1.48 (1.07–2.04) 0.002 0.100 0.533 0.994 1.000 0.969 1.000

AA 1.87 (1.24–2.82) 0.017 0.146 0.994 1.000 0.951 1.000AG OR AA 1.59 (1.17–2.16) 0.036 0.355 0.988 1.000 0.895 1.000

Kostopoulos 2009 US 1354/1851 Rare homozygote 1.30 (1.04–1.62) 0.238 0.899 0.988 1.000 0.956 1.000Any variant 1.18 (1.00–1.39) 0.580 0.998 0.988 1.000 0.979 1.000

SRD5A2 ⁎ rs523349 Likely Beesley 2007 Caucasian (AUS) 1466/1821 Rare homozygote 1.70 (1.35–2.16) 0.002 0.153 0.866 1.000 0.084 0.989

Other candidate genesFGF2 ⁎ rs308447 Likely Johnatty 2009 Mixed (non-Hispanic

whites; AUS and US)1457/3137 Heterozygote 0.72 (0.59−0.87) a 0.001 0.070 0.889 0.905 1.000 0.429 0.999

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VDR rs2228570(rs10735810)

Possible Lurie 2007 Caucasian 313/574 Ff 2.5 (1.3–4.8) 0.014 0.062 0.998 1.000 0.990 1.000

Tworoger 2009 US 1473/2006 ff 1.26 (1.01–1.57) 0.332 0.940 0.992 1.000 0.977 1.000Ff+ff 1.16 (1.00–1.35) 0.669 1.000 0.988 1.000 0.982 1.000

Lurie 2010 Non-Hispanic whites 1820/3479 Per allele 1.09 (1.00–1.19) 0.06 0.984 1.000 0.982 1.000 0.982 1.000rs11568820 Possible Lurie 2007 Japanese 313/574 0.5

(0.3–0.9)0.046 0.205 0.998 1.000 0.990 1.000

Tworoger 2009 US 1473/2006 GA 1.19 (1.02–1.39) 0.13 0.542 0.998 0.981 1.000 0.966 1.000GA+AA 1.16 (1.00–1.36) 0.662 0.999 0.990 1.000 0.985 1.000

PON1 rs662 Possible Lurie 2008 Mixed (Caucasian,Japanese; NativeHawaiian)

274/452 AG 0.67 (0.45–0.99) 0.03 0.141 0.591 0.997 1.000 0.987 1.000

AA 0.60 (0.37–0.97) 0.093 0.396 0.998 1.000 0.989 1.000Arpaci 2009 Turkish 50/52 AA 2.80 (1.65–4.76) 0.000 0.001 0.011 0.994 1.000 0.931 1.000

TP53 rs2078486 Possible Schildkraut 2009 Mixed (non-Hispanicwhites; AUS,EUR & US)

5206/8790 1.49 (1.04–2.15) 55.8 0.124 0.514 0.996 1.000 0.985 1.000

Schildkraut 2010 US 364/761 Log-additive 1.65 (1.21–2.25) a 19604 0.022 0.273 0.986 1.000 0.850 1.000

Genomic regions initially identified by GWASs of other cancers11p15.5(MRPL23)

rs2107425 Likely Quaye 2009_1 Mixed(DK, UK, US)

1491/3145 0.71(0.62–0.82)

0.0000217 0.017 0.921 0.158 0.995 0.003 0.774

Song 2009Stage 1

Non-Hispanic whites(AUS, EUR, US)

2927/4143 0.91 (0.84–0.98) 0.015 0.993 1.000 0.927 1.000 0.927 1.000

Abbreviations: AUS, Australia; BF, Bayes factor; CI, confidence interval; DK, Denmark; EUR, Europe; FPRP, false positive report probability; GWAS, genome-wide association study; OR, odds ratio; SNPid, single nucleotide polymorphism ID; UK, UnitedKingdom; US, United States.

a Serous subtype only.b The SNPs rs3217916 and rs3217925 are in LD (r2=0.81).⁎ These findings were never attempted to replicate, but are considered true positives based on the FPRP value.

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Table 3Results from genome-wide association studies of ovarian cancer.

Gene SNPid First author,year


Genotype OR (95% CI) P/BF Power FPRP

OR:1.2

OR:1.5

OR: 1.2 OR: 1.5

0.001 0.000001 0.001 0.000001

GWAS 1locus 9p22.2 rs3814113 Song 2009 Mixed (AUS, EUR, US) 9943/13072 T/C 0.82 (0.79–0.86) 2.5×10−19 0.057 1.000 0.000 0.000 0.000 0.000

rs4445329 Song 2009 European ancestry 4964/5379 G/A 0.79 (0.75–0.84) 2.7×10−17 0.058 1.000 0.000 0.000 0.000 0.000rs10810666 Song 2009 European ancestry 4964/5380 C/T 0.80 (0.75–0.85) 1.2×10−12 0.117 1.000 0.000 0.000 0.000 0.000rs10962656 Song 2009 European ancestry 4964/5381 G/A 0.81 (0.76–0.87) 5.9×10−9 0.252 1.000 0.000 0.029 0.000 0.007rs12379183 Song 2009 European ancestry 4964/5382 A/G 0.82 (0.78–0.87) 1.4×10−10 0.344 1.000 0.000 0.000 0.000 0.000rs2153271 Song 2009 European ancestry 4964/5383 A/G 0.85 (0.81–0.90) 4.7×10−10 0.793 1.000 0.000 0.031 0.000 0.024rs7861573 Song 2009 European Ancestry 4964/5384 G/A 0.83 (0.78–0.88) 3.6×10−10 0.500 1.000 0.000 0.001 0.000 0.000rs10756819 Song 2009 European ancestry 4964/5385 A/G 0.83 (0.79–0.88) 4.9×10−12 0.500 1.000 0.000 0.001 0.000 0.000rs1416742 Song 2009 European ancestry 4964/5386 T/C 0.86 (0.82–0.90) 1.7×10−9 0.937 1.000 0.000 0.000 0.000 0.000rs12379687 Song 2009 European ancestry 4964/5387 G/T 0.82 (0.77–0.88) 1.3×10−8 0.368 1.000 0.000 0.090 0.000 0.035rs4961501 Song 2009 European ancestry 4964/5388 G/T 0.82 (0.77–0.87) 8.5×10−12 0.344 1.000 0.000 0.000 0.000 0.000rs1339552 Song 2009 European ancestry 4964/5389 G/A 0.85 (0.81–0.89) 1.3×10−10 0.845 1.000 0.000 0.000 0.000 0.000

GWAS 22q31 rs2072590 Goode 2010 European ancestry 4342/6001 Per allele 1.19 (1.12–1.26) 1.9×10–8 0.942 1.000 0.000 0.000 0.000 0.0003q25 rs2665390* Goode 2010 European ancestry 4285/5953 Per allele 1.19 (1.07–1.34) 1.8×10−3 0.600 1.000 0.000 0.211 0.000 0.1388q24.21 rs10088218 Goode 2010 European ancestry 4339/6007 Per allele 0.79 (0.73–0.87) 2.8×10–7 0.158 1.000 0.010 0.914 0.002 0.626

rs1516982 Goode 2010 European ancestry 4353/6021 Per allele 0.85 (0.79–0.92) 9.4×10−5 0.722 1.000 0.073 0.987 0.054 0.983rs10098821 Goode 2010 European ancestry 4353/6021 Per allele 0.79 (0.71–0.86) 6.0×10−7 0.127 1.000 0.000 0.293 0.000 0.050

GWAS 319p13 rs2363956 Bolton 2010 European ancestry 4476/6013 Per allele 1.13 (1.06–1.20) 9.4×10−6 0.975 1.000 0.065 0.986 0.063 0.985

Abbreviations: AUS, Australia; BF, Bayes factor; CI, confidence interval; DK, Denmark; EUR, Europe; FPRP, false positive report probability; GWAS, genome-wide association study;OR, odds ratio; SNPid, single nucleotide polymorphism ID; US, United States.⁎ Reached genome-wide significance when all phases combined.


and the oncogenic pathway (Table 2) [178]. It should be stated,however, that the identification of these pathways exclusivelydepends on the pathways that are investigated by candidate genestudies, i.e., based on an a priori hypothesis, and, therefore, do notrepresent all possible underlying pathways. We have additionallyinvestigated the pathways of these genetic variants using Panther DBand KEGG. This resulted in the cell cycle, P53 signaling, steroidhormone and mismatch repair pathways.

3.3.1. Sex steroid hormone genesVarious studies have been performed investigating variants in

genes involved in hormone biosynthesis and metabolism in associ-ation with ovarian cancer susceptibility, as experimental andepidemiological evidence support a role for sex steroid hormones inovarian cancer pathogenesis [179]. Associations between variants inthe AR, ESR1, PGR, CYP1B1 and CYP19A1 genes and ovarian cancer riskwere replicated by at least one study, of which only differences in CAGrepeat lengths in the AR gene were considered to be very likelyassociated with ovarian cancer. However, results remain inconsistentregarding the direction of the effect and sample sizes of all studiesinvestigating the AR gene were small. Racial heterogeneity couldpartly explain this inconsistency as previous studies have observedsignificant shorter CAG repeat lengths in the androgen receptor gene(AR) in blacks compared to whites [180,181]. Among African-Americans, shorter AR repeat lengths were associated with anincreased ovarian cancer risk [134], while among Caucasians theopposite was found in all but one study, namely, an increased ovariancancer risk among women carrying longer AR repeat lengths[130,155,182]. The positive association between 1 CAG increase inthe length of the shorter allele and ovarian cancer among Caucasiansshowed an FPRP value lower than 0.5 based on a prior probability of0.001 and statistical power to detect an OR of both 1.2 and 1.5.Therefore, this association is very likely to represent a trueassociation.

Rs523349 in the SRD5A2 gene was associated with ovarian cancerin one study, but was never attempted to replicate [71]. The FPRPvalue, however, was lower than 0.5 at a prior probability of 0.001 and

statistical power to detect an OR of 1.5. This SNP is, therefore,considered to be likely associated with ovarian cancer.

PROGINS alleles in the PGR gene [60,107,119,125], SNPs in the ESR1(rs2295190) [87], and CYP19A1 (rs749292) [39,102] genes wereconsidered possibly associated with ovarian cancer. The associationbetween CYP1B1 (rs1056836) [41,52] and ovarian cancer remainsuncertain.

3.3.2. Cell cycle genesCell cycle genes control cell proliferation and mutations of cell

cycle regulators have been frequently found in human neoplasms[183]. These genes have, therefore, been widely investigated fortheir association with cancer. A total of 41 cell cycle genes havebeen studied in association with ovarian cancer and of the 43statistically significant genetic variants reported, 19 SNPs in 11 cellcycle genes were replicated by at least one study (ABL1[83,125],CCND1[36,83,94,125], CCND2[94,125], CCND3[36,83], CCNE1[94,125],CDK2[36,83], CDK6[94,125], CDKN1B[36,94,125], CDKN2A/2B [36,94],E2F2[83,125], and RB1[125,126,143]). Of these, only rs2854344 inthe RB1 gene was considered to be very likely associated withovarian cancer [143]. For this SNP, the FPRP value was lower than0.5 at a prior probability of 0.001 and statistical power to detect anOR of 0.67. Based on the positive associations in candidate genestudies, the first GWAS on ovarian cancer also checked this SNP andfound a similar effect size, however, without reaching genome-widesignificance (genome-wide P value: 0.15) [20]. rs3212879, rs602652and rs603965 in CCND1[36,83,94,125], rs2066827 in CDKN1B[36,94,125], and rs3731257 in CDKN2A/2B[36,94] are likely to beassociated with ovarian cancer, while the association betweenrs2855192 in ABL1[83,125], rs3218086 in CCND3[36,83], rs2069414in CDK2[36,83], and rs8 in CDK6[94,125] and ovarian cancer isconsidered possible and the associations between the remaininggenetic variants in cell cycle genes remain uncertain. The SNPs inCDKN1B (rs2066827) and CDKN2A/2B (rs3731257) have also beeninvestigated in the first GWAS; however, no association wasobserved (GWAS HR: 1.02 and 0.97; genome-wide P value: 0.89and 0.76, respectively) [20].


It is important to note that, in general, the replication studies of thecell cycle genes used partially overlapping sets of study participantswith the original study.

3.3.3. DNA repair genesThe DNA repair pathway is known to be critical in the

development of ovarian cancer, as has been shown through themarkedly increased ovarian risk among carriers of germline muta-tions in the BRCA1/2 and MMR genes [6–12]. Polymorphisms in 19DNA repair genes showed a statistically significant association withsporadic ovarian cancer risk, of which associations for 7 genes werereplicated by at least one study (BRCA1, BRCA2, MSH2, MSH3, MSH6,RAD52 and XRCC2) [65,66,71,125,142]. The FPRP values for none ofthese observed associations were lower than 0.5 at any of the twoprior probabilities. The association between SNPs in the BRCA1(rs799917) [65,125], RAD52 (rs11226) [65,125] and MMR genesMSH2 (rs4952887) [125,142],MSH3 (rs6151662) [125,142] andMSH6(rs3136245) [125,142], and ovarian cancer risk were consideredpossible, while the association for SNPs in the BRCA2 (rs144848)[66,71] and XRCC2 gene (rs3218536) [65,125] remained uncertain.

Again, some of the replication studies of the DNA repair genes alsohad partially overlapping sets of study participants with the originalstudy.

3.3.4. OncogenesCancer develops through a complex process of sequential

alterations in several oncogenes and tumor-suppressor genes.Oncogenes encode proteins that control cell proliferation andapoptosis. This knowledge has led to the investigation of poly-morphisms in oncogenes in 12 ovarian cancer association studies,including variants in a total of 7 oncogenes. The association betweenrs61764370 in KRAS[177] and ovarian cancer risk was consideredpossible, while the association for rs11693487 in NMI [124,125] wasconsidered uncertain. For rs11693487, the first GWAS found a similarrisk estimate, but without reaching genome-wide significance (OR:0.86, GWAS P value: 0.027) [20].

3.3.5. Miscellaneous candidate genesSome associated candidate genes cannot be classified in the

aforementioned pathways and are, therefore, discussed in thisparagraph. The association between rs308447 in the growth factorgene FGF2 and ovarian cancer risk is considered likely, as it was neverattempted to replicate, but was considered a true positive, based on itsFPRP value lower than 0.5, at a prior probability of 0.001 and statisticalpower to detect an OR of 0.67 [55]. Two SNPs in the vitamin D receptor(VDR) gene (rs10735810 and rs11568820) [111,161,184], one SNP inthe paraoxonase 1 (PON1) gene (rs662) [64,112] and one in the TP53gene (rs2078486) [132,133] are possibly associated with ovariancancer risk. Results for rs11568820 in the VDR gene were inconsistentacross ethnic groups. Heterozygous or homozygous Japanese womenwere at decreased risk of ovarian cancer, while Caucasian womenwere at increased risk [111,161,184]. Furthermore, results for PON1are inconsistent. One study observed an inverse association betweenrs662 and ovarian cancer risk among Caucasian, Japanese and NativeHawaiian women, while a subsequent study observed a positiveassociation between this SNP and ovarian cancer among Turkishwomen [64,112].

3.3.6. Genomic regions initially identified by GWAS for other cancersSeveral recent GWAS discovered susceptibility loci for breast,

prostate, colorectal and other cancers, which map to non-codingregions of the genome. Subsequent candidate gene studies investi-gated these loci for their association with other cancers, includingovarian cancer. These variants are considered initial findings in thisreview. rs2107425 at 11p15.5 was considered to be likely associatedwith ovarian cancer risk, as it was positively replicated by one study

and considered noteworthy based on an FPRP value lower than 0.5 ata prior probability of 0.001 and statistical power to detect an OR ofboth 1.2 and 1.5 [125,140].

3.3.7. Lack of overlap with results from GWASThere was no overlap between genetic variants identified through

candidate gene studies and those identified through GWAS. Nor, wereany of the variants from candidate gene studies in LD with variantsidentified through GWAS.

3.4. Stratification based on quality measures

After excluding studies with low quality (quality scoreb5.5),studies with less than 1000 cases and studies published before 2007,only the association with rs2854344 in RB1 remained ‘very likely.’Moreover, the associations with rs602652, rs3212879 and rs603965in CCND1, rs2066827 in CDKN1B, rs523349 in SRD5A2, rs308447 inFGF2, and rs2107425 in 11p15.5 remained ‘likely.’

4. Discussion

This systematic review aimed at including all available geneticassociation studies of sporadic ovarian cancer and revealed 150different studies, which examined around 1100 different variants inmore than 200 genes. To summarize, genetic variants that show thestrongest evidence for an association with ovarian cancer risk includers2854344 in the RB1 gene and SNPs on chromosomes 9p22.2, 2q31,8q24, and 3q25. Overall, promising genetic pathways for ovariancancer include the sex steroid hormone pathway, the cell cyclepathway, the DNA repair pathway, and the oncogenic pathway.

Strengths of this systematic review include the comprehensivesearch for evidence, the inclusion of all genetic variants related toovarian cancer risk, the independent quality assessment, and thesystematic evaluation of the evidence based on 3 grading parameters.We provided an objective summary of all reported associationsbetween genetic variants and ovarian cancer susceptibly. Even thoughwe used an extensive search strategy, there may still be publicationbias, which arises when nonsignificant findings remain unpublished,thereby artificially inflating the apparent magnitude of an effect [185].By using the quality criteria we attempted to quantitatively assess thelevel of evidence of reported associations. This scale was neverevaluated or calibrated against other data. Therefore, it is impossibleto know how our quality criteria would perform compared to othercriteria used. On the other hand, the criteria are entirely reasonableand based on the STREGA and NCI-NHGRI Working Group criteria,which are consensus criteria developed specifically for geneticassociation studies [30,31]. Moreover, we can never be sure, thatwhen a certain quality issuewas not stated in the publication, this wasnot carried out.

Methodological quality of the reported studies on the geneticepidemiology of ovarian cancer was generally low, and sample sizeswere small, leading to insufficient power. However, this was mainlythe case for older studies, as during that time, there were no generalguidelines on the reporting of results and there was no comprehen-sive and systematic nomenclature system for the description ofgenetic variants. As such, we observed a clear trend, from 2007onwards, of increasing number of cases by increasing publication year,as well as an improved quality over time. Not surprisingly then, theidentification of genetic variants remarkably increased from 2007onwards. For that reason, we performed an additional stratificationbased on the overall quality, the number of cases and publication year.Based on these criteria, the evidence for an association is highest forrs2854344 in RB1 (‘very likely’) and among the cell cycle genesCCND1 (rs602652, rs3212879 and rs603965), CDKN1B (rs2066827),sex steroid hormone gene SRD5A2 (rs523349), fibroblast growthfactor gene FGF2 (rs308447), and loci 11p15.5 (rs2107425) (‘likely’).


Replication of statistically significant associations has usually beenthe gold standard to validate initial significant findings [31,186,187].However, for ovarian cancer, only half of the statistically significantassociations have ever been attempted to replicate, and of these themedian number of replication studies was only two, with a maximumof five replications. Moreover, more than half of the associations thatwere actually attempted to replicate never showed a positivereplication. Also, the results from the GWAS have not yet beenreproduced by independent studies. Thus, it is difficult to evaluate thecredibility of these findings when considering the number ofreplications only.

The fact that so very few polymorphisms in genetic associationstudies can be positively replicated can be explained by thepublication of false-positive findings, which might be due to 1)the inflation of the type 1 error rate due to multiple testing, 2)systematic genotyping errors and 3) population stratification orconfounding by other variables [14,32,33]. Twenty percent of thestudies included in this review lacked adjustment for multipletesting when necessary. Misclassification due to systematic geno-typing error could also have occurred, since quality control forgenotyping methods was poor. Finally, false-positive associationscould be a consequence of residual population stratification, as only9% of the studies included in this review addressed populationstratification [188]. However, most study populations were fromEuropean Ancestry and bias due to population stratification inassociation studies published so far, with relatively low samplesizes, is expected to be low [14,34,189–191]. Lack of statisticalpower might also explain part of the non-replication. The initialstudy often suggests a stronger effect size than is found bysubsequent studies (“winner's curse phenomenon”) [192–194]. Onthe other hand, non-replication might also be a consequence offalse-negative findings (type II error), due to lack of power of thereplication study [14,32]. False-negative findings might also haveoccurred through the underestimation of genetic effects due togene–gene and gene–environment interactions [14,32,195]. Finally,disease heterogeneity can be a cause of the non-replicationproblem. For breast cancer, for example, there is clear evidencethat certain loci are subtype-specific [196]. Ovarian cancer is also aheterogeneous disease and increasing evidence suggests thatdevelopment of various subtypes of ovarian cancer is attributableto different etiologies [197,198]. Different genes might therefore beassociated with different ovarian cancer subtypes [199]. Very fewstudies had enough power to stratify according to subtype.However, some associations were found to be exclusively associatedwith serous ovarian cancer [22,55,122,133,176].

None of the ‘very likely,’ ‘likely’ and ‘possibly’ associated variantsfrom candidate gene studies reached genome-wide significance inthe any of the GWAS. Only one GWAS reported the P values forthree of these SNPs [20]. None of these SNPs reached genome-widesignificance. However, for SNP rs2854344 (RB1) effect-sizes werevery similar to the candidate gene studies, suggesting a false-negative result in the GWAS. As raw data from GWAS were notavailable, we were unable to verify the performance of othernoteworthy SNPs from candidate gene studies in these GWAS. Only2 of the noteworthy SNPs (i.e., rs3212879 (CCND1) and rs2228570(VDR)) were not covered by the GWAS. All other SNPs were eitheravailable on the GWAS array, had a proxy SNP (SNP in LD; r2N0.8)on the GWAS array, were present in HapMap phase 2 (used forimputation in the GWAS), or had a proxy SNP (r2N0.8) in HapMapphase 2.

Furthermore, the most strongly associated GWAS-loci have notbeen previously identified through candidate gene studies. Also, itshould be noted that even in the ovarian cancer GWAS performed sofar, with overlapping study populations, overlap in identified loci waslacking. A number of factors could explain these discrepancies. On theone hand, candidate gene studies generate many false-positive

findings, while on the other hand, GWAS apply relatively stringent Pvalues (usually Pb0.5×10−8), resulting in a considerable amount offalse-negative findings. Therefore, the genetic variants identified bycandidate gene studies, with relatively low allele-frequencies, will notbe picked up by GWAS. Also, we do not know whether the identifiedvariants are the real causal variants. It is well established thatidentified risk variants in non-coding regions of the genome are ableto regulate a gene at considerable genomic distance.

The ideogram that takes into account all hits from GWAS andcandidate gene studies for ovarian cancer suggests locus heterogene-ity. We also noticed that some loci contain multiple SNPs withdifferent eligibility scores. This is, for example, clearly seen onchromosome 11 and 12, where both ‘likely,’ ‘possibly,’ and ‘uncertain’SNPs share the same chromosomal location. This enhances thecredibility of these ‘possibly’ and ‘uncertain’ SNPs.

Numerous genetic association studies have been performed forovarian cancer, but only few variants showed strong evidence for anassociation with ovarian cancer. This is, however, the case for everycommon trait, i.e., only few variants have been associated with anygiven trait. This is not surprising, since in general single SNPs areinvestigated, while under the common disease–common varianthypothesis, common diseases are caused by the effects of severalcommon variants combined. Also, complex diseases are likelycaused by the joint action of multiple loci within a gene, the jointaction of multiple genes within a pathway, and the joint action ofgenes with environmental risk factors. For that reason, gene-basedand pathway-based approaches have been proposed, which jointlyconsider multiple contributing variants within a gene or pathway[203,204]. Furthermore, there are also other types of geneticvariation in the human genome that might contribute to geneticrisk and need more investigation, such as tandem repeats, insertionsand deletions (indels), copy number variations (CNV), copy neutralvariations, and copy neutral loss of heterozygosity (LOG) orhomozygosity [205]. These genetic variants have not yet beenintensively and completely studied or even discovered in thehuman genome. After the relatively disappointing results of GWASand candidate gene studies in relation to cancer occurrence, thenext generation of genetic studies will likely focus on these othertypes of variation in the human genome.

Besides the small number of true genetic associations found forovarian cancer, only a small fraction of the heritable component of thiscancer is explained by those real common variants. In fact, geneticvariants only explain a small proportion of the heritability of anygiven trait. It is now speculated that the contribution of rare variants(minor allele frequencyb0.5%) might be more important; however,these rare variants of strong effect cannot be captured by currentgenotyping arrays [200,201]. Dickson et al. demonstrated in asimulation study that those signals that have been detected forcommon variants could come from the effect of rare variants. Theypropose a theory in which rare variants are presumed to create“synthetic genome-wide associations” by occurring, stochastically,more often in association with one of the alleles at the common siteversus the other allele [202].

Candidate gene and GWAS studies remain powerful tools, but theyshould be performed by carefully considering study quality andpower, and jointly consider genetic variants using gene-based andpathway-based approaches. Many discovered cancer susceptibilityloci map to non-coding regions of the genome, highlighting the needof more in depth evaluation of the already discovered loci, of whichfunctions still remain unclear. Also, focus should point to the study ofrare genetic variants, possibly in combination with common geneticvariants. Finally, future studies should put more emphasis on gene–gene and gene–environment interactions, as well as differencesbetween different ovarian cancer subtypes. To be able to performthese kinds of studies, the constitution of large prospective consortiais necessary.


5. Conclusion

In conclusion, there is strong evidence to believe that rs2854344 inthe RB1 gene and SNPs on chromosomes 9p22.2, 2q31, 8q24, and 3q25are associated with ovarian cancer. Moreover, genes in the sex steroidhormone pathway, the cell cycle pathway, the DNA repair pathway,and the oncogenic pathway likely contribute to ovarian cancersusceptibility.

6. Conflict of interest

None declared.

7. Authors’ contributions

M.G.M. Braem contributed to the conception and design of thearticle, literature search, data assessment and interpretation, qualityassessment, figures, manuscript writing and editing, and finalapproval of the manuscript.

N.C. Onland-Moret contributed to the conception and design of thearticle, data interpretation, quality assessment, manuscript writingand editing, and final approval of the manuscript.

All other authors contributed to the editing, and final approval ofthe manuscript.

Supplementarymaterials related to this article can be found onlineat doi:10.1016/j.bbcan.2011.05.002.

Acknowledgements

This study was financially supported by the Dutch Cancer Society(UU2008-4267). This funding source had no involvement in studydesign; in the collection, analysis, and interpretation of data; in thewriting of the report; and in the decision to submit the paper forpublication.

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