. 2018 Aug;55(8):546-554.

doi: 10.1136/jmedgenet-2018-105313. Epub 2018 May 5.

Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study

Xin Yang¹, Goska Leslie¹, Aleksandra Gentry-Maharaj², Andy Ryan², Maria Intermaggio³, Andrew Lee¹, Jatinderpal K Kalsi², Jonathan Tyrer⁴, Faiza Gaba⁵, Ranjit Manchanda^{2

5

6}, Paul D P Pharoah^{1

4}, Simon A Gayther^{7

8}, Susan J Ramus^{3

9}, Ian Jacobs^{2

10

11}, Usha Menon², Antonis C Antoniou¹

Affiliations

¹ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
² Department of Women's Cancer, Institute for Women's Health, University College London, London, UK.
³ School of Women's and Children's Health, University of New South Wales, Sydney, New South Wales, Australia.
⁴ Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
⁵ Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University of London, London, UK.
⁶ Department of Gynaecological Oncology, Barts Health NHS Trust, Royal London Hospital, London, UK.
⁷ Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
⁸ Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
⁹ The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
¹⁰ University of New South Wales, Sydney, New South Wales, Australia.
¹¹ University of Manchester, Manchester, UK.

PMID: 29730597
PMCID: PMC6073911
DOI: 10.1136/jmedgenet-2018-105313

Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study

Xin Yang et al. J Med Genet. 2018 Aug.

. 2018 Aug;55(8):546-554.

doi: 10.1136/jmedgenet-2018-105313. Epub 2018 May 5.

Authors

Affiliations

¹ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
² Department of Women's Cancer, Institute for Women's Health, University College London, London, UK.
³ School of Women's and Children's Health, University of New South Wales, Sydney, New South Wales, Australia.
⁴ Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
⁵ Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University of London, London, UK.
⁶ Department of Gynaecological Oncology, Barts Health NHS Trust, Royal London Hospital, London, UK.
⁷ Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
⁸ Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
⁹ The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
¹⁰ University of New South Wales, Sydney, New South Wales, Australia.
¹¹ University of Manchester, Manchester, UK.

PMID: 29730597
PMCID: PMC6073911
DOI: 10.1136/jmedgenet-2018-105313

Abstract

Background: Genome-wide association studies have identified >30 common SNPs associated with epithelial ovarian cancer (EOC). We evaluated the combined effects of EOC susceptibility SNPs on predicting EOC risk in an independent prospective cohort study.

Methods: We genotyped ovarian cancer susceptibility single nucleotide polymorphisms (SNPs) in a nested case-control study (750 cases and 1428 controls) from the UK Collaborative Trial of Ovarian Cancer Screening trial. Polygenic risk scores (PRSs) were constructed and their associations with EOC risk were evaluated using logistic regression. The absolute risk of developing ovarian cancer by PRS percentiles was calculated.

Results: The association between serous PRS and serous EOC (OR 1.43, 95% CI 1.29 to 1.58, p=1.3×10^-11) was stronger than the association between overall PRS and overall EOC risk (OR 1.32, 95% CI 1.21 to 1.45, p=5.4×10^-10). Women in the top fifth percentile of the PRS had a 3.4-fold increased EOC risk compared with women in the bottom 5% of the PRS, with the absolute EOC risk by age 80 being 2.9% and 0.9%, respectively, for the two groups of women in the population.

Conclusion: PRSs can be used to predict future risk of developing ovarian cancer for women in the general population. Incorporation of PRSs into risk prediction models for EOC could inform clinical decision-making and health management.

Keywords: evaluation; ovarian cancer; polygenic risk scores; prospective cohort study; risk prediction.

PubMed Disclaimer

Conflict of interest statement

Competing interests: UM and IJ have a financial interest through Abcodia Ltd in the third party exploitation of the trial biobank.

Figures

**Figure 1**
Quantile–quantile plot shows the observed against expected −log₁₀p values of pairwise SNP*SNP interaction tests under the null hypothesis of multiplicative model. The dashed line shows the 95% concentration band.

**Figure 2**
(A) Distribution of the standardised overall and serous polygenic risk scores (PRSs) in overall and serous ovarian cancer cases and controls. The dashed vertical lines show the PRS means. (B) OR estimates between overall/serous PRS percentiles and overall/serous ovarian cancer risk relative to the middle PRS quintile (40%–60%). The solid line shows the estimated ORs with 95% CI, and the dashed line represents the theoretical OR values assuming multiplicative model.

**Figure 3**
Absolute risk of developing overall ovarian cancers by overall polygenic risk score (PRS) percentiles.

See this image and copyright information in PMC

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Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study

Affiliations

Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical