Purpose: Single-nucleotide variations (SNVs) (formerly single-nucleotide polymorphism [SNV]) influence genetic predisposition to endometrial cancer. We hypothesized that a polygenic risk score (PRS) comprising multiple SNVs may improve endometrial cancer risk prediction for targeted screening and prevention.
Methods: We developed PRSs from SNVs identified from a systematic review of published studies and suggestive SNVs from the Endometrial Cancer Association Consortium. These were tested in an independent study of 555 surgically-confirmed endometrial cancer cases and 1202 geographically-matched controls from Manchester, United Kingdom and validated in 1676 cases and 116,960 controls from the UK Biobank (UKBB).
Results: Age and body mass index predicted endometrial cancer in both data sets (Manchester: area under the receiver operator curve [AUC] = 0.77, 95% CI = 0.74-0.80; UKBB: AUC = 0.74, 95% CI = 0.73-0.75). The AUC for PRS19, PRS24, and PRS72 were 0.58, 0.55, and 0.57 in the Manchester study and 0.56, 0.54, and 0.54 in UKBB, respectively. For PRS19, women in the third tertile had a 2.1-fold increased risk of endometrial cancer compared with those in the first tertile of the Manchester study (odds ratio = 2.08, 95% CI = 1.61-2.68, Ptrend = 5.75E-9). Combining PRS19 with age and body mass index improved discriminatory power (Manchester study: AUC = 0.79, 95% CI = 0.76-0.82; UKBB: AUC =0.75, 95% CI = 0.73-0.76).
Conclusion: An endometrial cancer risk prediction model incorporating a PRS derived from multiple SNVs may help stratify women for screening and prevention strategies.
Keywords: Endometrial cancer; Genetic predisposition; Polygenic risk score; Prevention; Single-nucleotide variations (SNVs).
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