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Imaging, Diagnosis, Prognosis |
Authors' Affiliations: 1 CR-UK Department of Oncology and 2 CR-UK Genetic Epidemiology Unit, University of Cambridge, Strangeways Research Laboratory; 3 Eastern Cancer Registration and Information Centre (ECRIC), Cambridge, United Kingdom; 4 Department of Viruses, Hormones, and Cancer, Institute of Cancer Epidemiology, Danish Cancer Society; 5 University of Copenhagen, Copenhagen, Denmark; 6 Translational Research Laboratory, Department of Gynaecological Oncology, University College London, London, United Kingdom; 7 Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, New York; and 8 Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California
Requests for reprints: Honglin Song, CR-UK Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, United Kingdom. Phone: 44-1223-740161; Fax: 44-1223-740147; E-mail: honglin{at}srl.cam.ac.uk.
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Abstract |
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Experimental Design: We examined associations between common germ-line genetic variation in 14 genes involved in cell cycle pathway (CCND1, CCND2, CCND3, CCNE1, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CDKN2D, CDK2, CDK4, CDK6, and RB1) and survival among women with invasive ovarian cancer participating in a multicenter case-control study from United Kingdom, Denmark, and United States. DNAs from up to 1,499 women were genotyped for 97 single-nucleotide polymorphisms that tagged the known common variants (minor allele frequency 
0.05) in these genes. The genotypes of each polymorphism were tested for association with survival by Cox regression analysis.
Results: A nominally statistically significant association between genotype and ovarian cancer survival was observed for polymorphisms in CCND2 and CCNE1. The per-allele hazard ratios (95% confidence intervals) were 1.16 (1.03-1.31; P = 0.02) for rs3217933, 1.14 (1.02-1.27; P = 0.024) for rs3217901, and 0.85 (0.73-1.00; P = 0.043) for rs3217862 in CCND2 and 1.39 (1.04-1.85; P = 0.033) for rs3218038 in CCNE1. However, these were not significant after adjusting for multiple hypothesis tests.
Conclusion: It is unlikely that common variants in cell cycle pathways examined above associated with moderate effect in survival after diagnosis of ovarian cancer. Much larger studies will be needed to exclude common variants with small effects.
Somatic alterations and rare germ-line mutations in high-penetrance susceptibility genes have been shown to correlate with ovarian cancer prognosis and survival (2, 3), but the effects of common inherited genetic variation are less well understood. Single-nucleotide polymorphisms (SNP) may have clinic pathologic importance as prognostic markers. If such germ-line genetic markers of prognosis can be reliably identified, they have the potential to improve on the prediction of outcome for ovarian cancer as well as offer insights into the biological determinants of response to treatment and prognosis.
Several studies investigating common genetic variation and prognosis in ovarian cancer have been published (4–17), some of which have reported significant associations at a nominal P < 0.05 (7–14, 16). However, most of these studies were small (<220 cases), and none reached the level of statistical significance that has been suggested as appropriate for genetic association studies in candidate genes (P < 10–4; ref. 18).
Cell cycle checkpoints are part of the regulatory pathways that control the order and timing of cell cycle transitions to ensure the fidelity of critical events such as DNA replication and chromosome segregation (19). Disruption of cell cycle control pathways has previously been related to the prognosis of human cancers (20). For example, CCND1 variants were associated with prognosis in non–small-cell lung cancer, breast cancer, and squamous cell carcinoma of the head and neck, but were not associated with overall prognosis of epithelial ovarian cancer (21–24). In addition, CDKN2A and RB1 expression levels have been associated with prognosis for advanced-stage ovarian cancer (25).
We have previously investigated the association of 97 tag SNPs that capture the common variation in 14 genes involved in G1-S phase progression (CCND1, CCND2, CCND3, CCNE1, CDK2, CDK4, CDK6, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CDKN2D, and RB1) with susceptibility to invasive epithelial ovarian cancer (26, 27). We found four borderline significant associations with ovarian cancer susceptibility: RB1 rs2854344 (P = 0.0015), RB1 rs4151620 (P = 0.0001), CDKN2A rs3731257 (Ptrend = 0.008), and CDKN1B rs2066827 (Ptrend = 0.036). The purpose of the current study was to test the hypothesis that germ-line genetic variation in cell cycle control genes affects survival after diagnosis of ovarian cancer. To do this, we have linked the genetic data from the analysis of the 14 cell cycle control genes described above to outcome data from regional cancer registries in 
1,500 invasive ovarian cancer cases.
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Materials and Methods |
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The MALOVA study is a population-based, Danish case-control study of ovarian cancer. Eligible cases were women ages 30 to 80 years, who were diagnosed with an ovarian tumor from December 1994 to May 1999. The study included 18 different hospitals from the municipalities of Copenhagen and Frederiksberg as well as the counties of Copenhagen, Frederiksborg, Roskilde, Western Sealand, Storstrøm, Funen, Southern Jutland, and Northern Jutland. By the end of the study period, a total of 959 ovarian cancer patients were identified in this study area. Of these, 53 patients were considered too ill to participate; 45 women died before being contacted. Thus, 861 were invited to take part, of whom 652 (76%) provided a blood sample. Samples were collected at the time of diagnosis. Samples from 446 invasive cases were available for this study. DNA was extracted from blood samples by Whatman International. This study has been approved by the scientific ethical committee in the study area (KF01-384/95) and all subjects provided written consent.
The U.S. subjects were ascertained from six counties in Northern California as part of the FROC study. Patients with epithelial ovarian cancer diagnosed between March 1, 1997 and July 31, 2001 were identified through the Greater Bay Area Cancer Registry operated by the Northern California Cancer Center as part of the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute. Rapid case ascertainment was used to identify cases within 1 month of diagnosis. Eligible patients were those diagnosed with invasive or low malignant potential epithelial ovarian cancer, ages 20 to 64 years, who resided in any of six Bay Area counties (Alameda, Contra Costa, Marin, San Francisco, San Mateo, or Santa Clara). Of the 915 eligible patients, 24 were not contacted because their physicians refused permission; 209 refused to participate, had died, or could not be located; and 682 (75%) were interviewed. Of these, 579 (85% of those interviewed and 63% of total) genomic DNAs were isolated from leucocytes of peripheral blood using the Puregene Kit (Gentra Systems). Genomic DNA was also isolated from exfoliated cells in buccal mouthwash rinses as previously described (28). Research was conducted with protocols approved by the Institutional Review Boards of Stanford University School of Medicine and Roswell Park Cancer Institute. This analysis is restricted to the 327 White cases with invasive epithelial ovarian cancer, for whom DNA was available.
Follow-up. Active follow-up was carried out for SEARCH participants by the Eastern and West Midland cancer registries at 3 and 5 years after diagnosis, and then at 5-year intervals. This is done by searching hospital information systems for recent visits. When it is observed that a patient has had no recent visit, the general practitioner of the patient is contacted to ascertain the patient's vital status. In addition, the registries obtain notification of deaths through death certificate flagging with the Office for National Statistics. There is a lag time with this process of a few weeks for cancer deaths and 2 months to a year for noncancer deaths. At the time of analysis, 189 patients had died within 10 years of diagnosis.
FROC did active follow-up of subjects until 2002. Updates of the vital status from the Greater Bay Area Cancer Registry, which is operated by the Northern California Cancer Center as part of the National Cancer Institute Surveillance, Epidemiology, and End Results Program, were carried out for patients once or twice during the study and most recently in 2004. In the state of California, cancer is a state-mandated reportable condition, and the cancer registry for the greater Bay Area has been in existence since 1973. It is from this group that all investigators who want to do population-based cancer studies in this area receive their data. Staff periodically review computerized hospital tumor registry data or medical records for updated information. They also receive updates on the person's vital status from the state's death index (which is usually 18 months behind for reporting).
MALOVA did active follow-up until 2003. In Denmark, all inhabitants have a unique personal (10-digit) identification number (CPR number), which is used universally in the Danish society. These identification numbers, which comprise information on date of birth and sex, are registered in the computerized Danish National Central Population Register. The register contains information on, for example, dates of death and emigration. All cases in this study were traced in the register and "date of death," "emigration," or "May 2003," whichever came first, was registered. Hospital files were collected from all patients in the study. Women who died during follow-up were linked to a Danish Hospital Reference System and information about exact cause of death was assessed in each case by matching information in this register with the clinical records.
Selection of genes and SNPs. The 14 genes were selected as candidate genes for ovarian cancer susceptibility. We used a comprehensive SNP tagging approach in which tag SNPs were chosen to capture all the known common genetic variation in each gene with a minimum correlation coefficient (r2) of 0.8. Data from the International Hap Map project9 and resequencing data from the Environmental Genome Project10 were used to select tag SNPs. In total, 97 SNPs were chosen to tag 224 common variants [see Song et al. (26) and Gayther et al. (27) for details].
Genotyping. A detailed description of the genotyping has already been published for these genes (26, 27). Briefly, all of samples were genotyped using the TaqMan 7900HT Sequence Detection System according to manufacturer's instructions. Primer and probe sequences and assay conditions used for each polymorphism analyzed are available on request. All assays were carried out in 384-well plates and included 12 duplicate samples in each plate for quality control. Genotypes were determined using Allelic Discrimination Sequence Detection Software (Applied Biosystems). Genotyping of SEARCH and FROC samples was carried out at the Department of Oncology, University of Cambridge, and MALOVA samples were genotyped at the Translational Research Laboratory, University of College London. Call rates ranged from 91% to 99.8% for all the individual studies for all the SNPs (except rs4151620, rs2854344, and rs3092904, which failed in partial plates of MALOVA study) and overall concordance between duplicate samples was >98%. Individual samples with failed calls were not repeated. Hence, there are variations in the number of samples successfully genotyped for each polymorphism.
Statistical methods. The effect of each SNP on survival was assessed using Cox regression analysis stratified by study. Because samples were taken at a variable time after diagnosis for SEARCH and FROC studies, analyses were conducted allowing for left-truncated data. Time at risk began at the date of blood sample receipt and ended at the date of death from any cause, or, if death did not occur, on the last time known to be alive. Follow-up for all cases was censored at 10 years after diagnosis. The primary tests were the likelihood ratio test for trend (1 degree of freedom) based on the number of rare alleles carried. The hazard ratio (HR) per rare allele carried was also estimated from the Cox regression. We also investigated possible effects of combination of alleles that tagged specific SNPs (multimarker haplotypes). For specific haplotype marker tests, haplotype frequencies and subject-specific expected haplotype indicators were calculated separately for each study using the program TagSNPs (29). This implements an expectation substitution approach to account for haplotype uncertainty given unphased genotype data. Subjects missing >50% genotype data were excluded from haplotype analysis. We used unconditional logistic regression to test the null hypothesis of no association between specific multimarker tagging haplotype and survival by comparing a model with terms for subject-specific haplotype indicator with an intercept-only model. The assumption of proportional hazards was assessed graphically by log-log survival curves.
Data on histopathologic subtype, tumor stage, grade, and age at diagnosis were available for 79%, 99.6%, 75%, and 100% of the cases, respectively (Table 1). This enabled us to evaluate the significance of each polymorphism after adjusting for known prognostic factors. Factors were grouped as follows: age at diagnosis (<40, 40-49, 50-59, and >60 years); tumor stage (localized and advanced); grade (well differentiated, moderately differentiated, and poorly/undifferentiated); and histologic subtype [serous, clear cell, endometrioid, mucinous, papillary NOS (not otherwise specified), and other]. The SNPs significantly associated with survival at the 5% level were retested in multivariate analysis models to adjust for "true" stratification for prognostic factors (as opposed to adjustment by inclusion of dummy covariates), so that baseline hazard could vary by strata. All analyses were done with Stata version 8.0.
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Results |
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We identified 19 SNPs with a P 
0.2, of which 4 SNPs had a P 0.05 in test for association. The results of the univariate Cox regression analyses are summarized in Table 2
. The complete data for all SNPs are given in Supplementary Table S1. None of the SNPs in RB1, CDK2, CDK4, CDK6, CDKN2A, CDKN2B, CDKN2C, CDKN2D, CCND1, CCND3, or CDKN1A was significantly associated with survival (P > 0.05). The trend tests were nominally significant for CCNE1-05, CCND2-10, CCND2-18, and CCND2-17 (P = 0.033, P = 0.043, P = 0.024, and P = 0.020, respectively; Table 2). The hazard associated with CCNE1-05 increased by 39% [per rare allele HR, 1.39; 95% confidence interval (95% CI), 1.04-1.85; Table 2]. CCND2-10 and CCND2-18 were correlated with CCND2-17 (r2 = 0.14 and 0.52, respectively). CCND2-10 was associated with reduced hazard (HR, 0.85; 95% CI, 0.73-1.00), whereas CCND2-18 and CCND2-17 were associated with increased hazard [HR, 1.14 (95% CI, 1.02-1.27) for CCND2-18; HR, 1.16 (95% CI 1.03-1.31) for CCND2-17; Table 2]. Figure 1
shows the cumulative hazard by genotype for the most significant association (CCND2-17). A model including all three significant CCND2 SNPs was significant (likelihood ratio test, P = 0.019). However, when using a forward stepwise regression procedure only, CCND2-18 was retained in the final model (P = 0.02). The haplotype analysis including these three CCND2 SNPs showed that the haplotype comprising the common allele of CCND2-10, the rare allele of CCND2-18, and the rare allele of CCND2-17 was associated with ovarian cancer survival [haplotype frequency, 0.25; P = 0.013; HR, 1.21 (95% CI, 1.04-1.41)] using the most common haplotype (comprising the common alleles of the three SNPs; frequency = 0.38) as baseline.
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Discussion |
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The differences between the component studies are a potential weakness, but these differences are unlikely to bias the results or influence their interpretation. The time between diagnosis and recruitment was longer for SEARCH than for the other studies, and so the cancers tended to have better prognostic features (nonserous subtype, lower grade, and earlier stage) and the average annual mortality rate was lower. However, we allowed for this in the analysis, and the estimates of the genotype effects will not be biased, provided that the Cox proportional hazards assumption is not violated. There are also likely to be differences in the way the patients were treated by study center, but again this will not result in any bias as treatment is unlikely to be correlated with individual genotype.
We have found no evidence that common variations in RB1, CDK2, CDK4, CDK6, CDKN2A, CDKN2B, CDKN2C, CDKN2D, CCND1, CCND3, or CDKN1A are associated with outcome after a diagnosis of ovarian cancer. We observed marginally significant evidence for an association between survival and SNPs in CCND2, CDKN1B, and CCNE1, but these results were not significant after adjusting for multiple hypothesis testing. The SNPs under study were selected to adequately tag all the common variants in each gene, and not because of their predicted effects on structure and function. Nevertheless, it is possible that important, unidentified variants were not efficiently tagged. Furthermore, some known common variants were poorly tagged because of tag SNP assay failure. Again, power to detect association with these SNPs is limited. It is also possible that rare variants in these genes are important predictors of outcome. For these genes, it is likely that EGP resequencing and Hapmap data sets identified most of the common variants, and we are confident that the set of tag SNPs we selected has adequately tagged the known and unknown common variants within each gene. We may also have failed to detect any association with survival because of lack of statistical power to detect modest effects. Despite our large sample size, there were just 637 deaths in our cohort. Assuming a type I error rate of 0.05, we had only 51% power to detect a codominant allele of frequency of 0.1 that confers a relative hazard of 1.3 and 86% power to detect a similar allele with frequency of 0.3. Power to detect recessive alleles with similar effects was poor. Power may also be reduced by the use of all-cause mortality rather than ovarian cancer–specific mortality as an end point because some women will have died from other causes that might not be related to genotype. However, in the age group of women included in these studies, the proportion of women dying from causes unrelated to ovarian cancer is likely to be small and any reduction in power will be limited.
In conclusion, common variation in the 14 genes of cell cycle pathways does not seem to be associated with moderate variation in prognosis after a diagnosis of ovarian cancer. Much larger studies will be needed to exclude common variants with small effects.
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Acknowledgments |
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Footnotes |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Note: Supplementary data for this are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).
10 http://pga.gs.washington.edu/finished_genes.html 
Received 5/15/07; revised 9/26/07; accepted 11/12/07.
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-reductase type II TA repeat polymorphism is not associated with risk of breast or ovarian cancer in Australian women. Cancer Epidemiol Biomarkers Prev 2001;10:1287–93.This article has been cited by other articles:
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  L. Quaye, D. Dafou, S. J. Ramus, H. Song, A. G. Maharaj, M. Notaridou, E. Hogdall, S. K. Kjaer, L. Christensen, C. Hogdall, et al. Functional complementation studies identify candidate genes and common genetic variants associated with ovarian cancer survival Hum. Mol. Genet., May 15, 2009; 18(10): 1869 - 1878. [Abstract] [Full Text] [PDF]
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