Identifying modifiable and non-modifiable risk factors of epithelial ovarian cancer—can we get it better?

Ovarian cancer is the fifth most common cancer in women worldwide, with an estimated lifetime risk of one in 54–75, and one in 100 of ovarian cancer-related mortality (1). The role of irreversible and reversible risk factors in development of epithelial ovarian cancer (EOC) has been researched extensively, with a heavy focus on genetic factors and family history of breast/ovarian cancer. Ovarian cancer is one of the most heritable cancers with a three-fold increase in risk of developing ovarian cancer in women with a first degree relative (FDR) diagnosed with ovarian cancer; the relative risk (RR) is twofold higher for FDRs diagnosed younger than 50 years of age compared with those diagnosed older (4.72 vs. 2.53, P=0.0052), and higher for serous EOC compared to non-serous (RR 3.64 vs. 2.25, P=0.023) (2). The study by Kim et al. (3) considers the interaction between genetic [28 common single nucleotide variants (SNVs)] and environmental [oral contraceptive pill (OCP) use, parity, tubal ligation, breastfeeding, hormone replacement therapy (HRT), body BMI and endometriosis] risk factors, in 9,971 cases and 15,566 controls from 17 case-control studies included in the international Ovarian Cancer Association Consortium (OCAC).

Of non-genetic factors, hormonal factors in particular are known to play a significant role. A greater number of menstrual cycles throughout life confers a higher risk of EOC, suggesting that ovulation is involved in ovarian carcinogenesis. Ovulation-reducing factors including OCP use, pregnancy and breastfeeding are protective and ovulation-increasing factors such as early age at menarche, nulliparity, and later menopause are associated with higher risk (4). These factors have varying and interdependent effects. Pregnancies and OCP use have been found to reduce ovarian cancer risk of by 8–10% for each avoided year of ovulation, and age at menarche and menopause reduce risk for each avoided year of ovulation by 2.5% (5). The protective effect of pregnancy differs by age at first pregnancy, risk decreasing by approximately 10% for each progressive 5-year age at first childbirth, and having more than one child has been shown to significantly reduce risk of EOC, with an odds ratio (OR) of 0.6 for having 3 children and 0.5 for 4 or more (6). The risk associated with nulliparity differs depending on menopausal status, with lifetime ovulatory years being significantly associated with premenopausal women (OR =2.49) but not postmenopausal women (OR =0.88) (7).

Taking the OCP for five or more years reduces risk of EOC, with an OR of 0.56 (95% CI, 0.40–0.78) in one study for ever users (8). This effect has been observed to persist over time, and women who have taken it for more than 15 years have a 0.5% reduced cumulative incidence up to 75 years of age and 0.3% reduced mortality (4). The use of HRT has been found to have a modest effect on EOC risk. A meta-analysis of 12,110 women found the RR with <5 years of HRT to be 1.43 (95% CI, 1.31–1.56) and 1.53 (95% CI, 1.40–1.66) for serous ovarian cancer (9). They found that risk reduced the longer HRT had been stopped, but there was still a small increased risk 10 years after cessation of HRT (RR 1.25, 95% CI, 1.07–1.46, P=0.005). Meta-analysis has found tubal ligation to reduce risk for endometrioid cancers (RR =0.4) and serous cancers (RR =0.72) (10). The findings by Kim et al. are comparable with these figures.

The exact relationship between ovarian cancer and increased weight is unclear, with some evidence for increased risk of EOC but mixed findings regarding prognosis for those with the disease. Meta-analyses have shown obesity to be associated with increased risk of EOC (pooled effect OR =1.3, 95% CI, 1.1–1.5) and smaller effects for being overweight (OR =1.2, 95% CI, 1.0–1.3) (11). Obesity is a complex issue relating to many other variables. For example there has been shown to be significant heterogeneity (P<0.001) in the risk relating to BMI between women who have ever used HRT and those who have not (RR for never-users per 5 kg/m² increase in BMI =1.10, 95% CI, 1.07–1.13; P<0.001; RR for ever-users =0.95, 95% CI, 0.92–0.99; P=0.02) (12).

The link between endometriosis and ovarian cancer has been known since 1925. Conferring an up to a 3-fold increased risk, 15–20% of clear cell and endometrioid ovarian cancers are associated with endometriosis (13). A significant association has also been found with diabetes mellitus, controlling for related factors age, BMI, smoking and alcohol intake (RR 1.55, 95% CI, 1.11–2.19) (14). While alcohol is not a significant risk factor in any EOC subtypes, tobacco smoking has been found to be associated with risk of mucinous ovarian cancer alone (15).

As expected for a cancer with significant heritability, several genes have been identified as associated with increased ovarian cancer risk; most notably BRCA1, BRCA2, RAD51C, RAD51D, BRIP1, PALB2, and BARD1 involved in homologous recombination and MSH2, MSH6, MLH1 and PMS2 involved in mismatch repair as summarised previously (16). Pathogenic variants in moderate to high risk genes have been calculated to explain ovarian cancer risk in approximately 20–25% of ovarian cancers. Following identification of a pathogenic variant it is essential affected women and their families can be offered risk-reduction advice and interventions if appropriate. The use of screening for women deemed high-risk has been investigated using serum Ca-125 and transvaginal ultrasound, however impact on survival is unknown (17).

The most significant potential intervention is risk-reduction salpingo-oophorectomy (RRSO). This has been found by meta-analysis to reduce risk of ovarian/fallopian tube cancer by 80%, with greater risk reduction for women carrying pathogenic variants in BRCA2 than those with BRCA1 (18). The National Comprehensive Cancer Network (NCCN) recommends offering RRSO to pre-menopausal women with pathogenic BRCA1/2 variants who have completed childbearing (19). The ages they recommend RRSO are 45–50 years for RAD51C, RAD51D, BRIP1 and BRCA2 carriers and 35–40 for BRCA1 carriers (19). While research in salpingectomy alone is continuing, currently risk-reducing salpingectomy alone is not recommended outside of a clinical trial in international guidelines (19).

However, the increased risk of ovarian cancer in significant proportion of women with ovarian cancer and a strong family history of breast and/or ovarian cancer, who do not carry a pathogenic variant in a ovarian cancer predisposition gene, is unexplained. Of the genetic variants known to carry risk of ovarian cancer, the related confidence intervals of that risk are wide. Attention has turned to the contribution SNVs can make to ovarian cancer risk. Genome-wide association studies (GWAS) are well established and effective in identifying genetic loci associated with disease. The majority of variants are assumed not to be causal but to tag an area of linkage disequilibrium containing functional variants. Several GWAS to date have looked at variants associated with EOC risk, and GWAS-identified variants were calculated by Phelan et al. to account for approximately 6.4% of ovarian cancer risk (20).

It has been suggested that the information gained from GWAS of complex traits or diseases is surprisingly small despite large numbers of SNVs found to be reproducibly associated with phenotypes and traits, possibly because the pathophysiology of certain conditions is already well understood. One question is whether it would be better to focus on sequencing rather than divert resources into further GWAS. Annotating SNPs with information on expression has been found to increase ability to distinguish significant associations, and improve understanding of genes and relevant mechanisms. Increasingly variants within intronic regions are thought to be significant in regulating expression of target genes and therefore affecting susceptibility to disease. Long noncoding RNA (lncRNA) genes have been found to be significantly enriched at EOC risk regions, suggesting lncRNAs may be mechanistically involved in EOC predisposition and therefore be potential candidates for integrative epidemiologic and functional studies (21).

While no results from multiplicative or additive models were significant with Bonferroni correction in Kim et al.’s study (3), the most notable finding the authors’ discussed was the association of SNV rs13255292 and OCP use (ever vs. never) (P=3.48×10^-4). Comparing OCP use (ever vs. never) in women with the TT genotype for this variant had an OR of 0.53 (95% CI, 0.46–0.60) vs. 0.71 (95% CI, 0.66–0.77) for women with the CC genotype. As the C genotype is the risk allele for ovarian cancer for this SNV longer use of OCP may help reduce risk for those with this genotype. As discussed, the cancer-related loci 8q24 has been described associated with ovarian cancer (20,22). The lncRNA PVT1, encoded by the human PVT1 gene, is located in 8q24 and three mechanisms linked to tumorigenesis have been attributed to PVT1: interactions with MYC, DNA rearrangement, and encoding microRNA (23). The SNV rs6983267 at this region have been reported associated with increased ovarian cancer risk in premenopausal Han Chinese women (additive model: adjusted OR 1.62, 95% CI, 1.18–2.23, P=0.003), however there was no impact from OCP use (24).

The effect size of any single SNV even when significant is small. A more likely practical use of this data in advising women individually of risk reduction strategies is within a polygenic risk score (PRS) algorithm, combining all known significant SNVs, ideally also with non-genetic risk factor data. In women with breast cancer PRSs are shown to increase accuracy of estimating an individual’s risk, and to improve accuracy estimation further when combined with other risk factors including mammographic density. A useful model to assess ovarian cancer cumulative risk in terms of risk factors is BOADICEA (https://pluto.srl.cam.ac.uk/cgi-bin/bd4/v4beta14/bd.cgi) (25). This assesses risk of ovarian and breast cancer from family history information and is being modified to include reproductive and hormonal data, more recently identified genes such as PALB2 in addition to BRCA1/2, and a SNP PRS (25). Jervis et al. in 2014 investigated application of a polygenic score from an 11-SNP panel to ovarian cancer and no statistically significant relationship likely due to small number of SNPs used, although familial RR did increase with increasing PRS (2).

As technology in genetic analysis, and the statistical methods in analysing the ‘big data’ produced develop and adapt, studies such as Kim et al. (3) investigating different models and how we can use this combination of genetic and environmental risk factor data are important in determining the usefulness of this information and whether adjustment needs to be made for factors that affect both overall ovarian cancer risk and other risk factors that are included in models. At present only minor adjustment for one SNP appears necessary when combining a SNV PRS with known risk modifiers. By focusing on modifiable risk factors, as well as being aware of methods of risk reduction such as RRSO for women with high unmodifiable risk of EOC, scientists and physicians can help improve advice for women concerned about their risk of this life-threatening disease.

Acknowledgments

Funding: N Flaum is supported by CRUK via the funding to Cancer Research UK Manchester Centre: [C147/A18083] and [C147/A25254]. DG Evans is supported by the NIHR Manchester Biomedical Research Centre (IS-BRC-1215-20007).

Footnote

Provenance and Peer Review: This article was commissioned and reviewed by the Section Editor Dr. Weijun Jiang (Nanjing Normal University, Department of Reproductive and Genetics, Institute of Laboratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China).

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://gpm.amegroups.org/article/view/10.21037/gpm.2019.09.05/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Reid BM, Permuth JB, Sellers TA. Epidemiology of ovarian cancer: a review. Cancer Biol Med 2017;14:9-32. [Crossref] [PubMed]
2. Jervis S, Song H, Lee A, et al. Ovarian cancer familial relative risks by tumour subtypes and by known ovarian cancer genetic susceptibility variants. J Med Genet 2014;51:108-13. [Crossref] [PubMed]
3. Kim S, Wang M, Tyrer JP, et al. A comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants. Int J Cancer 2019;144:2192-205. [Crossref] [PubMed]
4. Rooth C. Ovarian cancer: risk factors, treatment and management. Br J Nurs 2013;22:S23-30. [Crossref] [PubMed]
5. Pelucchi C, Galeone C, Talamini R, et al. Lifetime ovulatory cycles and ovarian cancer risk in 2 Italian case-control studies. Am J Obstet Gynecol 2007;196:83.e1-7. [Crossref] [PubMed]
6. Chiaffarino F, Pelucchi C, Parazzini F, et al. Reproductive and hormonal factors and ovarian cancer. Ann Oncol 2001;12:337-41. [Crossref] [PubMed]
7. Tung KH, Wilkens LR, Wu AH, et al. Effect of anovulation factors on pre- and postmenopausal ovarian cancer risk: revisiting the incessant ovulation hypothesis. Am J Epidemiol 2005;161:321-9. [Crossref] [PubMed]
8. Pasalich M, Su D, Binns CW, et al. Reproductive factors for ovarian cancer in southern Chinese women. J Gynecol Oncol 2013;24:135-40. [Crossref] [PubMed]
9. Collaborative Group On Epidemiological Studies Of Ovarian Cancer. Menopausal hormone use and ovarian cancer risk: individual participant meta-analysis of 52 epidemiological studies. Lancet 2015;385:1835-42. [Crossref] [PubMed]
10. Cibula D, Widschwendter M, Majek O, et al. Tubal ligation and the risk of ovarian cancer: review and meta-analysis. Hum Reprod Update 2011;17:55-67. [Crossref] [PubMed]
11. Olsen CM, Green AC, Whiteman DC, et al. Obesity and the risk of epithelial ovarian cancer: a systematic review and meta-analysis. Eur J Cancer 2007;43:690-709. [Crossref] [PubMed]
12. Collaborative Group on Epidemiological Studies of Ovarian Cancer. Ovarian cancer and body size: individual participant meta-analysis including 25,157 women with ovarian cancer from 47 epidemiological studies. PLoS Med 2012;9:e1001200 [Crossref] [PubMed]
13. Pavone ME, Lyttle BM. Endometriosis and ovarian cancer: links, risks, and challenges faced. Int J Womens Health 2015;7:663-72. [Crossref] [PubMed]
14. Lee JY, Jeon I, Kim JW, et al. Diabetes mellitus and ovarian cancer risk: a systematic review and meta-analysis of observational studies. Int J Gynecol Cancer 2013;23:402-12. [Crossref] [PubMed]
15. Collaborative Group on Epidemiological Studies of Ovarian Cancer. Ovarian cancer and smoking: individual participant meta-analysis including 28,114 women with ovarian cancer from 51 epidemiological studies. Lancet Oncol 2012;13:946-56. [Crossref] [PubMed]
16. Flaum N, Crosbie EJ, Edmondson RJ, et al. Epithelial ovarian cancer risk: A review of the current genetic landscape. Clin Genet 2019; [Epub ahead of print]. [Crossref] [PubMed]
17. Rosenthal AN, Fraser LSM, Philpott S, et al. Evidence of Stage Shift in Women Diagnosed With Ovarian Cancer During Phase II of the United Kingdom Familial Ovarian Cancer Screening Study. J Clin Oncol 2017;35:1411-20. [Crossref] [PubMed]
18. Rebbeck TR, Kauff ND, Domchek SM. Meta-analysis of risk reduction estimates associated with risk-reducing salpingo-oophorectomy in BRCA1 or BRCA2 mutation carriers. J Natl Cancer Inst 2009;101:80-7. [Crossref] [PubMed]
19. Network NCC. Genetic/Familial High-Risk Assessment: Breast and Ovarian. 2018. Accessed 24th August 2018. Available online: https://www.nccn.org/professionals/physician_gls/pdf/genetics_screening.pdf
20. Phelan CM, Kuchenbaecker KB, Tyrer JP, et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat Genet 2017;49:680-91. [Crossref] [PubMed]
21. Reid BM, Permuth JB, Chen YA, et al. Integration of Population-Level Genotype Data with Functional Annotation Reveals Over-Representation of Long Noncoding RNAs at Ovarian Cancer Susceptibility Loci. Cancer Epidemiol Biomarkers Prev 2017;26:116-25. [Crossref] [PubMed]
22. Goode EL, Chenevix-Trench G, Song H, et al. A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24. Nat Genet 2010;42:874-9. [Crossref] [PubMed]
23. Cui M, You L, Ren X, et al. Long non-coding RNA PVT1 and cancer. Biochem Biophys Res Commun 2016;471:10-4. [Crossref] [PubMed]
24. Han J, Zhou J, Yuan H, et al. Genetic variants within the cancer susceptibility region 8q24 and ovarian cancer risk in Han Chinese women. Oncotarget 2017;8:36462-8. [PubMed]
25. Epidemiology CfCG. BOADICEA. University of Cambridge. 2016. Available online: https://pluto.srl.cam.ac.uk/cgi-bin/bd4/v4beta14/bd.cgi