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Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2013 Mar 19;22(5):987–992. doi: 10.1158/1055-9965.EPI-13-0028

Analysis of Over 10,000 Cases Finds No Association between Previously-Reported Candidate Polymorphisms and Ovarian Cancer Outcome

Kristin L White 1, Robert A Vierkant 2, Zachary C Fogarty 2, Bridget Charbonneau 3, Matthew S Block 4, Paul DP Pharoah 5,6, Georgia Chenevix-Trench 7, Mary Anne Rossing 8,9, Daniel W Cramer 10,11, C Leigh Pearce 12, Joellen M Schildkraut 13,14, Usha Menon 15, Susanne Kruger Kjaer 16,17, Douglas A Levine 18, Jacek Gronwald 19, Hoda Anton Culver 20, Alice S Whittemore 21, Beth Y Karlan 22, Diether Lambrechts 23,24, Nicolas Wentzensen 25, Jolanta Kupryjanczyk 26, Jenny Chang-Claude 27, Elisa V Bandera 28, Estrid Hogdall 17,29, Florian Heitz 30,31, Stanley B Kaye 32, Peter A Fasching 33,34, Ian Campbell 35,36, Marc T Goodman 37, Tanja Pejovic 38,39, Yukie Bean 38,39, Galina Lurie 40, Diana Eccles 41, Alexander Hein 33, Matthias W Beckmann 33, Arif B Ekici 42, James Paul 43, Robert Brown 44, James Flanagan 44, Philipp Harter 30,31, Andreas du Bois 30,31, Ira Schwaab 45, Claus K Hogdall 16, Lene Lundvall 16, Sara H Olson 46, Irene Orlow 46, Lisa E Paddock 47, Anja Rudolph 27, Ursula Eilber 27, Agnieszka Dansonka-Mieszkowska 26, Iwona K Rzepecka 26, Izabela Ziolkowska-Seta 48, Louise Brinton 25, Hannah Yang 25, Montserrat Garcia-Closas 49, Evelyn Despierre 50, Sandrina Lambrechts 50, Ignace Vergote 50, Christine Walsh 22, Jenny Lester 22, Weiva Sieh 21, Valerie McGuire 21, Joseph H Rothstein 21, Argyrios Ziogas 20, Jan Lubiński 19, Cezary Cybulski 19, Janusz Menkiszak 51, Allan Jensen 17, Simon A Gayther 12, Susan J Ramus 12, Aleksandra Gentry-Maharaj 15, Andrew Berchuck 52, Anna H Wu 12, Malcolm C Pike 12,46, David Van Den Berg 12, Kathryn L Terry 10,11, Allison F Vitonis 10, Jennifer A Doherty 53, Sharon Johnatty 7, Anna deFazio 54; AOCS/ACS, Honglin Song 5, Jonathan Tyrer 5, Thomas A Sellers 55, Catherine M Phelan 55, Kimberly R Kalli 4, Julie M Cunningham 56, Brooke L Fridley 57, Ellen L Goode 3
PMCID: PMC3650102  NIHMSID: NIHMS447746  PMID: 23513043

Abstract

Background

Ovarian cancer is a leading cause of cancer-related death among women. In an effort to understand contributors to disease outcome, we evaluated single-nucleotide polymorphisms (SNPs) previously associated with ovarian cancer recurrence or survival, specifically in angiogenesis, inflammation, mitosis, and drug disposition genes.

Methods

Twenty-seven SNPs in VHL, HGF, IL18, PRKACB, ABCB1, CYP2C8, ERCC2, and ERCC1 previously associated with ovarian cancer outcome were genotyped in 10,084 invasive cases from 28 studies from the Ovarian Cancer Association Consortium with over 37,000 observed person-years and 4,478 deaths. Cox proportional hazards models were used to examine the association between candidate SNPs and ovarian cancer recurrence or survival with and without adjustment for key covariates.

Results

We observed no association between genotype and ovarian cancer recurrence or survival for any of the SNPs examined.

Conclusions

These results refute prior associations between these SNPs and ovarian cancer outcome and underscore the importance of maximally powered genetic association studies.

Impact

These variants should not be used in prognostic models. Alternate approaches to uncovering inherited prognostic factors, if they exist, are needed.

INTRODUCTION

In 2012, ovarian cancer was estimated to be the seventh leading cause of female cancer-related deaths worldwide (1). Despite standardized treatment approaches, inter-individual variation in outcomes exists; understanding the source of this variation, including potential inherited factors, is of high importance (2). Our prior studies of over 400 candidate genes in biological pathways that are relevant to ovarian cancer suggested association between ovarian cancer outcome and inherited variation in certain genes (3–5). These include the angiogenesis genes VHL (3) and HGF (4), taxol efflux and metabolism genes ABCB1 and CYP2C8 (5), DNA repair genes ERCC2 and ERCC1 (5), the inflammation gene IL18 (3), and the mitosis gene PRKACB (4). Here, we sought to confirm prior associations (p<0.05) between ovarian cancer outcome and 27 single-nucleotide polymorphisms (SNPs) in these genes using a much larger sample size than the discovery studies.

MATERIALS AND METHODS

A total of 10,084 women with invasive epithelial ovarian cancer (over 37,000 person-years follow-up) including 5,248 high-grade serous cases were examined. Participants from 28 studies (Table 1) in the Ovarian Cancer Association Consortium (OCAC) were genotyped on a custom Illumina iSelect BeadArray using centralized genotype calling and quality control procedures, as previously described (6). In brief, we excluded SNPs and samples with call rate < 95%; we restricted to women with > 90% predicted European ancestry and estimated principal components (PCs) representing European substructure (6).

Table 1.

Participating Invasive Epithelial Ovarian Cancer Studies

Abbrev
iation		 Name Location Case
source		 All Histologies High-Grade Serous
N N Deaths (%) Person-
years		 N N Deaths (%) Person-
years
SEA Study of Epidemiology and Risk Factors in Cancer Heredity UK: East Anglia and West Midlands Population 1,341 426 (32%) 5,010 518 229 (44%) 1,595
AUS Australian Ovarian Cancer Study/Australian Cancer Study (Ovarian Cancer) Australia Population 848 432 (51%) 2,796 498 313 (63%) 1,547
DOV Diseases of the Ovary and their Evaluation USA: 13 counties in western Washington state Population 761 296 (39%) 2,280 435 193 (44%) 1,224
MAY Mayo Clinic Ovarian Cancer Case-Control Study USA: North Central (MN, SD, ND, IL, IA, WI) Clinic 695 364 (52%) 1,858 497 297 (60%) 1,233
NEC New England Case Control Study USA: New Hampshire and Eastern Massachusetts Population 671 334 (50%) 3,948 343 236 (69%) 1,573
USC Los Angeles County Case-control studies of Ovarian Cancer-1 Los Angeles County Population 638 273 (43%) 2,323 363 189 (52%) 1,263
NCO North Carolina Ovarian Cancer Study USA: Central and eastern North Carolina (48 counties) Population 636 327 (51%) 2,708 366 240 (66%) 1,382
UKO United Kingdom Ovarian Cancer Population Study United Kingdom (England, Wales and Northern Ireland) Population 620 200 (32%) 2,180 269 110 (41%) 941
MAL MALignant OVArian Cancer Denmark Population 440 322 (73%) 2,046 183 158 (86%) 666
MSK Memorial Sloan-Kettering Cancer Center USA: New York City Hospital 391 108 (28%) 1,070 302 84 (28%) 749
POC Polish Ovarian Cancer Study Poland: Szczecin, Poznan, Opole, Rzeszów Population 355 203 (57%) 1,288 0 0 0
UCI University of California Irvine Ovarian Study USA: Southern California (Orange and San-Diego, Imperial Counties) Population 275 90 (33%) 968 144 63 (44%) 456
STA Genetic Epidemiology of Ovarian Cancer USA: Six counties in the San Francisco Bay area Population 257 142 (55%) 1,300 135 95 (70%) 581
LAX Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute USA: Southern California Hospital 256 148 (58%) 1,050 201 127 (63%) 831
BEL Belgian Ovarian Cancer Study Belgium, University Hospital Leuven Hospital 245 53 (22%) 307 173 46 (27%) 220
POL Polish Ovarian Cancer Case Control Study Poland, Warszaw and Lodz Population 211 113 (54%) 746 60 40 (67%) 187
WOC Warsaw Ovarian Cancer Study Poland: Warsaw and central Poland Clinic 201 85 (42%) 724 143 74 (52%) 498
GER German Ovarian Cancer Study Germany: two geographical regions in the states of Baden-Württemberg and Rhineland-Palatinate Population 183 118 (65%) 907 75 59 (79%) 312
NJO New Jersey Ovarian Cancer Study USA: New Jersey (six counties) Population 169 33 (20%) 316 80 24 (30%) 135
PVD Danish Pelvic Mass Study Denmark Population 162 69 (43%) 438 121 58 (48%) 310
HSK Dr. Horst Schmidt Kliniken Germany Clinic 141 84 (60%) 634 109 71 (65%) 463
RMH Royal Marsden Hospital Ovarian Cancer Study UK: London Hospital 138 59 (43%) 1,000 0 0 0
SRO Scottish Randomised Trial in Ovarian Cancer Coordinated through clinical trials unit, Glasgow UK from patients recruited world-wide Clinical trial 132 67 (51%) 231 94 47 (50%) 169
BAV Bavarian Ovarian Cancer Cases and Controls Southeast Germany Population 85 34 (40%) 245 44 18 (41%) 119
SOC Southampton Ovarian Cancer Study United Kingdom, Wessex region Hospital 74 33 (45%) 157 25 10 (40%) 47
HAW Hawaii Ovarian Cancer Case-Control Study USA: Hawaii Population 60 27 (45%) 266 36 18 (50%) 145
ORE Oregon Ovarian Cancer Registry Portland, Oregon Clinic 52 9 (17%) 123 34 6 (18%) 71
UKR UK Familial Ovarian Cancer Registry
TOTAL 		UK: National Familial register 47 29 (62%) 254 0 0 0
10,084 4,478 (44%) 37,171 5,248 2,805 (53%) 16,717
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All cases are of European ancestry; grade is missing for UCI, RMH, and UKR, thus excluded from high grade serous analysis; person-years indicates person-years at risk accounting for left truncation and right censoring at 10 years.

Cox proportional hazards regression modeling accounting for left truncation and censoring at 10 years following diagnosis was used to estimate per-allele hazard ratios (HRs) and 95% confidence intervals (CIs) for associations with death from any cause for all cases and for high-grade serous cases. Two models were assessed: a minimally adjusted model including covariates for age at diagnosis, five population substructure PCs, and study site, and a fully adjusted model which additionally included histology (for analyses of all cases only), tumor stage, tumor grade, and oral contraceptive use as these covariates were associated with survival in these data (p<0.05). Analyses were also conducted with a recurrence endpoint defined by time to disease recurrence or death (377 additional events, including 273 among women with high-grade serous disease).

RESULTS

Overall, there were no associations between SNPs and ovarian cancer survival in either the minimally or fully adjusted models; Table 2 shows HRs, 95% CIs, and p-values for all cases and high-grade serous cases. No heterogeneity across studies was observed (p-values>0.05). SNP rs2214825 in HGF was significantly associated with survival in the minimally adjusted model (p=0.03), although not in the fully adjusted model (Table 2). After excluding 2,015 women who contributed to the original report (4), no association was seen at p<0.05 (minimally adjusted HR=1.04, 95% CI=0.98–1.10, p=0.19). Additionally, in ERCC2, SNP rs50872 conferred a slightly increased risk of death among women with high-grade serous disease (p=0.03) in the fully adjusted model, but this association was not statistically significant at α=0.05 in the minimally adjusted model, and, after excluding 497 women in the original report (5), no statistically significant association was seen (fully adjusted HR=1.06, 95% CI=1.00–1.14, p=0.06). Near identical results were seen for these SNPs in analysis of time to recurrence. On the whole, while these candidates showed promise with large effect sizes (i.e., HRs >1.23 or <0.82) in earlier reports (3–5), our very large scale study refutes association at these loci with 95% CIs excluding prior risk estimates.

Table 2.

Association between SNPs and Ovarian Cancer Survival

All Histologies (n=10,084) High-Grade Serous (n=5,248)
Minimally Adjusted Fully Adjusted Minimally Adjusted Fully Adjusted
Gene SNP Min/Maj MAF HR (95% CI) p HR (95% CI) p HR (95% CI) p HR (95% CI) p
PRKACB rs12031680 A/C 0.34 1.02 (0.97–1.07) 0.41 1.02 (0.97–1.06) 0.42 1.00 (0.94–1.06) 1.00 1.01 (0.96–1.07) 0.62
rs12405120 G/A 0.49 1.00 (0.96–1.05) 0.86 1.01 (0.96–1.05) 0.79 1.01 (0.96–1.06) 0.73 1.00 (0.95–1.06) 0.95
rs1402694 G/A 0.44 0.99 (0.95–1.03) 0.65 0.98 (0.94–1.02) 0.39 0.98 (0.93–1.03) 0.40 1.00 (0.94–1.05) 0.87
rs12129768 G/A 0.11 0.99 (0.93–1.06) 0.85 1.02 (0.95–1.09) 0.57 0.99 (0.91–1.08) 0.85 1.03 (0.94–1.12) 0.56
VHL rs265318 C/A 0.11 1.02 (0.96–1.09) 0.55 1.05 (0.99–1.12) 0.12 1.04 (0.96–1.13) 0.31 1.06 (0.98–1.15) 0.18
rs1678607 A/C 0.12 1.01 (0.95–1.07) 0.79 1.02 (0.96–1.08) 0.57 1.00 (0.92–1.08) 0.99 1.00 (0.92–1.08) 0.95
HGF rs1800793 A/G 0.20 1.05 (0.99–1.10) 0.09 1.03 (0.98–1.08) 0.26 1.05 (0.99–1.12) 0.10 1.05 (0.98–1.12) 0.17
rs2214825 A/G 0.23 1.06 (1.01–1.11) 0.03 1.04 (0.99–1.09) 0.10 1.06 (1.00–1.13) 0.05 1.06 (1.00–1.12) 0.07
ABCB1 rs2235023 A/G 0.07 0.98 (0.90–1.06) 0.54 0.98 (0.90–1.06) 0.59 0.98 (0.89–1.08) 0.72 1.00 (0.90–1.10) 0.96
rs13237132 G/C 0.32 1.01 (0.96–1.05) 0.78 1.01 (0.97–1.06) 0.54 1.02 (0.97–1.08) 0.44 1.01 (0.96–1.07) 0.61
rs12334183 G/A 0.20 0.99 (0.94–1.04) 0.58 0.98 (0.93–1.03) 0.37 0.96 (0.90–1.02) 0.21 0.95 (0.89–1.01) 0.12
rs10264990 G/A 0.34 1.00 (0.96–1.04) 0.90 1.00 (0.96–1.05) 0.84 1.02 (0.96–1.08) 0.54 1.01 (0.96–1.07) 0.72
rs4148732 G/A 0.14 1.01 (0.96–1.08) 0.63 1.01 (0.95–1.07) 0.73 1.03 (0.95–1.11) 0.47 0.99 (0.92–1.08) 0.89
CYP2C8 rs1934954 G/A 0.08 0.97 (0.90–1.05) 0.50 1.00 (0.93–1.08) 0.99 0.97 (0.88–1.07) 0.55 0.98 (0.89–1.08) 0.71
rs11188148 G/A 0.12 0.99 (0.93–1.06) 0.81 1.00 (0.94–1.07) 0.98 0.97 (0.89–1.06) 0.52 0.98 (0.90–1.07) 0.68
rs1934983 G/A 0.29 0.99 (0.95–1.04) 0.79 0.99 (0.95–1.04) 0.78 1.00 (0.94–1.06) 0.92 1.00 (0.94–1.06) 0.89
rs2185571 A/G 0.29 0.99 (0.95–1.04) 0.71 0.99 (0.95–1.04) 0.67 0.99 (0.94–1.06) 0.86 0.99 (0.94–1.05) 0.82
IL18 rs549908 C/A 0.31 1.02 (0.98–1.07) 0.38 1.03 (0.98–1.08) 0.20 0.99 (0.94–1.05) 0.84 1.00 (0.94–1.05) 0.88
rs5744247 C/G 0.10 0.95 (0.89–1.02) 0.17 0.96 (0.89–1.03) 0.23 0.98 (0.90–1.07) 0.68 0.99 (0.91–1.08) 0.88
rs11214108 A/G 0.12 0.95 (0.89–1.02) 0.15 0.96 (0.90–1.03) 0.23 0.98 (0.90–1.06) 0.62 0.99 (0.92–1.08) 0.90
ERCC2 rs13181 C/A 0.38 0.99 (0.95–1.03) 0.64 0.99 (0.95–1.04) 0.71 0.98 (0.93–1.04) 0.52 0.98 (0.93–1.03) 0.43
rs1799787 A/G 0.31 1.00 (0.95–1.04) 0.87 0.99 (0.95–1.04) 0.67 0.98 (0.92–1.04) 0.48 0.97 (0.92–1.03) 0.33
rs238417 C/G 0.42 1.00 (0.96–1.04) 0.99 1.01 (0.97–1.06) 0.49 1.02 (0.97–1.08) 0.43 1.02 (0.96–1.07) 0.54
rs238416 A/G 0.36 1.01 (0.97–1.05) 0.71 1.02 (0.97–1.06) 0.45 1.02 (0.97–1.08) 0.44 1.02 (0.96–1.07) 0.58
rs238415 C/G 0.40 1.00 (0.96–1.05) 0.86 1.02 (0.97–1.06) 0.46 1.02 (0.97–1.08) 0.43 1.02 (0.96–1.07) 0.56
rs50872 A/G 0.24 1.01 (0.97–1.07) 0.56 1.02 (0.97–1.07) 0.50 1.06 (1.00–1.13) 0.05 1.07 (1.01–1.14) 0.03
ERCC1 rs735482 C/A 0.14 0.97 (0.91–1.03) 0.32 1.00 (0.94–1.06) 0.91 0.97 (0.90–1.05) 0.42 0.98 (0.91–1.06) 0.56
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SNPs are listed by rsid in chromosomal order; Min/Maj, minor/major allele; MAF, minor allele frequency; HR, hazard ratio represent the relative risk of death per-allele (0, 1, 2 copies of the minor allele); 95% CI, 95% confidence interval; P, P-values < 0.05 are bold; fully adjusted Cox model adjusted for age at diagnosis, population substructure principal components, study site, histology (for analyses of all cases only), tumor stage (I, II, III, unknown), tumor grade (grade 1, grade 2, grade 3, grade 4, unknown), and oral contraceptive use (yes, no, unknown); minimally adjusted Cox model adjusted for age at diagnosis, population substructure principal components, and study site.

DISCUSSION

Here, we aimed to confirm the relationship between previously-associated SNPs and ovarian cancer outcome using a sample of over 10,000 women from 28 studies participating in the OCAC. Results of this analysis did not confirm the associations originally observed (3–5). While associations with other SNPs may exist, we report no consistent associations between these 27 SNPs and ovarian cancer outcome and believe it critical to disseminate results to reduce the possibility of publication bias. These null results highlight the necessity of large-scale replication of initial SNP associations, as the most likely explanation for non-replication is that initial false positive findings resulted from chance in smaller studies.

Studies of SNPs and cancer outcome have been less fruitful than cancer susceptibility studies (7). This may be due to several challenges: lack of a large collection of homogeneous cases due to missing baseline clinical data, inability to verify chemotherapeutic or surgical data to evaluate whether SNP effects arise only in certain clinical contexts, and inconsistent follow-up methods leading to variable completeness of endpoints (8). Although ovarian cancer has high mortality rate and a generally uniform treatment compared to other cancers, there is a trade-off in expected power between the larger sample sizes of observational studies and the detailed data available from clinical trials. We propose that utilization of both study designs, including detailed tumor characteristics and coordination with animal and mechanistic studies, is the best path forward for the identification of predictive and prognostic factors in ovarian cancer outcomes.

ACKNOWLEDGEMENTS

We thank all the individuals who took part in this study and all the researchers, clinicians and technical and administrative staff who have made possible the many studies contributing to this work. In particular, we thank: D. Bowtell, A. deFazio, D. Gertig, A. Green, P. Parsons, N. Hayward, P. Webb and D. Whiteman (AUS); G. Peuteman, T. Van Brussel and D. Smeets (BEL); the staff of the genotyping unit, S. LaBoissière and F. Robidoux (Genome Quebec); T. Koehler (GER); G.S. Keeney, S. Windebank, C. Hilker and J. Vollenweider (MAY); L. Rodriquez, M. King, U. Chandran, D. Gifkins, and T. Puvananayagam (NJO); M. Sherman, A. Hutchinson, N. Szeszenia- Dabrowska, B. Peplonska, W. Zatonski, A. Soni, P. Chao and M. Stagner (POL); C. Luccarini, P. Harrington, the SEARCH team, and ECRIC (SEA); the Scottish Gynaecological Clinical Trails group and SCOTROC1 investigators (SRO); I. Jacobs, M. Widschwendter, E. Wozniak, N. Balogun, A. Ryan and J. Ford (UKO); C. Pye (UKR); A. Amin Al Olama, K. Michilaidou, and K. Kuchenbäker (COGS). The Australian Ovarian Cancer Study (AOCS) Management Group (D Bowtell, G. Chenevix-Trench, A. deFazio, D. Gertig, A. Green, and P.M. Webb) gratefully acknowledges the contribution of all the clinical and scientific collaborators (see reference 9). The Australian Cancer Study (ACS) Management Group comprises A. Green, P. Parsons, N. Hayward, P.M. Webb, and D. Whiteman.

The COGS project is funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 - HEALTH-F2-2009-223175). The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the Genetic Associations and Mechanisms in Oncology (GAME-ON): a NCI Cancer Post-GWAS Initiative (U19-CA148112). G.C.-T. and P.M.W. are supported by the National Health and Medical Research Council. B.K. holds an American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN).

Funding of the constituent studies was provided by the American Cancer Society (CRTG-00-196-01-CCE); the California Cancer Research Program (00-01389V-20170, N01-CN25403, 2II0200); the Canadian Institutes for Health Research; Cancer Council Victoria; Cancer Council Queensland; Cancer Council New South Wales; Cancer Council South Australia; Cancer Council Tasmania; Cancer Foundation of Western Australia; the Cancer Institute of New Jersey; Cancer Research UK (C490/A6187, C490/A10119, C490/A10124, C536/A13086, C536/A6689); the Celma Mastry Ovarian Cancer Foundation; the Danish Cancer Society (94-222-52); the ELAN Program of the University of Erlangen-Nuremberg; the Eve Appeal; the Helsinki University Central Hospital Research Fund; Helse Vest; Imperial Experimental Cancer Research Centre (C1312/A15589); the Norwegian Cancer Society; the Norwegian Research Council; the Ovarian Cancer Research Fund; Nationaal Kankerplan of Belgium; the L & S Milken Foundation; the Polish Ministry of Science and Higher Education; the US National Cancer Institute (K07-CA095666, K07-CA143047, K22-CA138563, N01-CN55424, N01-PC067010, N01-PC035137, P01-CA017054, P01-CA087696, P30-CA072720, P30-CA15083, P50-CA105009, P50-CA136393, R01-CA014089, R01-CA016056, R01-CA017054, R01-CA049449, R01-CA050385, R01-CA054419, R01-CA058598, R01-CA058860, R01-CA061107, R01-CA061132, R01-CA063682, R01-CA064277, R01-CA067262, R01-CA071766, R01-CA074850, R01-CA076016, R01-CA080742, R01-CA080978, R01-CA083918, R01-CA087538, R01-CA092044, R01-095023, R01-CA106414, R01-CA122443, R01-CA61107, R01-CA112523, R01-CA114343, R01-CA126841, R01-CA136924, R01-CA149429, R03-CA113148, R03-CA115195, R37-CA070867, R37-CA70867, U01-CA069417, U01-CA071966 and Intramural research funds); the US Army Medical Research and Material Command (DAMD17-98-1-8659, DAMD17-01-1-0729, DAMD17-02-1-0666, DAMD17-02-1-0669, W81XWH-10-1-0280); the National Health and Medical Research Council of Australia (199600 and 400281); the German Federal Ministry of Education and Research of Germany Programme of Clinical Biomedical Research (01 GB 9401); the state of Baden-Württemberg through Medical Faculty of the University of Ulm (P.685); the Minnesota Ovarian Cancer Alliance; the Mayo Foundation; the Fred C. and Katherine B. Andersen Foundation; the Lon V. Smith Foundation (LVS-39420); the Polish Committee for Scientific Research (4P05C 028 14 and 2P05A 068 27); the Oak Foundation; the OHSU Foundation; the Mermaid I project; the Rudolf-Bartling Foundation; the UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge, Imperial College London, University College Hospital “Womens Health Theme” and the Royal Marsden Hospital; and WorkSafeBC.

Footnotes

The authors have no financial conflicts of interest.

REFERENCES

  • 1.Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA: A Cancer Journal for Clinicians. 2011;61:69–90. doi: 10.3322/caac.20107. [DOI] [PubMed] [Google Scholar]
  • 2.Bolton KL, Ganda C, Berchuck A, Pharaoh PD, Gayther SA. Role of common genetic variants in ovarian cancer susceptibility and outcome: progress to date from the Ovarian Cancer Association Consortium (OCAC) Journal of Internal Medicine. 2012;271:366–378. doi: 10.1111/j.1365-2796.2011.02509.x. [DOI] [PubMed] [Google Scholar]
  • 3.Goode EL, Maurer MJ, Sellers TA, Phelan CM, Kalli KR, Fridley BL, et al. Inherited determinants of ovarian cancer survival. Clin Cancer Res. 2010;16:995–1007. doi: 10.1158/1078-0432.CCR-09-2553. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Goode EL, Chenevix-Trench G, Hartmann LC, Fridley BL, Kalli KR, Vierkant RA, et al. Assessment of hepatocyte growth factor in ovarian cancer mortality. Cancer Epidemiology, Biomarkers & Prevention. 2011;20:1638–1648. doi: 10.1158/1055-9965.EPI-11-0455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Peethambaram P, Fridley BL, Vierkant RA, Larson MC, Kalli KR, Elliott EA, et al. Polymorphisms in ABCB1 and ERCC2 associated with ovarian cancer outcome. Int J Mol Epidemiol Genet. 2011;2:185–195. [PMC free article] [PubMed] [Google Scholar]
  • 6.Pharoah PDP, Tsai YY, Ramus SJ, Phelan CM, Goode EL, Lawrenson K, et al. GWAS meta-analysis and replication identifies three novel common susceptibility loci for ovarian cancer. Nat Genet. doi: 10.1038/ng.2564. in press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Chung CC, Chanock SJ. Current status of genome-wide association studies in cancer. Hum Genet. 2011;130:59–78. doi: 10.1007/s00439-011-1030-9. [DOI] [PubMed] [Google Scholar]
  • 8.Diaz-Padilla I, Amir E, Marsh S, Liu G, Mackay H. Genetic polymorphisms as predictive and prognostic biomarkers in gynecological cancers: a systematic review. Gynecol Oncol. 2012;124:354–365. doi: 10.1016/j.ygyno.2011.10.034. [DOI] [PubMed] [Google Scholar]
  • 9. http://www.aocstudy.org/ [Google Scholar]

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