Performance of Three Inherited Risk Measures for Predicting Prostate Cancer Incidence and Mortality: A...

Single nucleotide polymorphism-based genetic risk score (GRS) has been developed and validated for prostate cancer (PCa) risk assessment. As GRS is population standardized, its value can be interpreted as a relative risk to the general population.

To compare the performance of GRS with two guideline-recommended inherited risk measures, family history (FH) and rare pathogenic mutations (RPMs), for predicting PCa incidence and mortality.

A prospective cohort was derived from the UK Biobank where 208 685 PCa diagnosis-free participants at recruitment were followed via the UK cancer and death registries.

Rate ratios (RRs) of PCa incidence and mortality for FH (positive vs negative), RPMs (carriers vs noncarriers), and GRS (top vs bottom quartile) were measured.

After a median follow-up of 9.67 yr, 6890 incident PCa cases (419 died of PCa) were identified. Each of the three measures was significantly associated with PCa incidence in univariate analyses; RR (95 % confidence interval [CI]) values were 1.88 (1.75-2.01) for FH, 2.89 (1.89-4.25) for RPMs, and 1.97(1.87-2.07) for GRS (all p < 0.001). The associations were independent in multivariable analyses. While FH and RPMs identified 11 % of men at higher PCa risk, addition of GRS identified an additional 22 % of men at higher PCa risk, and increases in C-statistic from 0.58 to 0.67 for differentiating incidence (p < 0.001) and from 0.65 to 0.71 for differentiating mortality (p = 0.002). Limitations were a small number of minority patients and short mortality follow-up.

This population-based prospective study suggests that GRS complements two guideline-recommended inherited risk measures (FH and RPMs) for stratifying the risk of PCa incidence and mortality.

In a large population-based prostate cancer (PCa) prospective study derived from UK Biobank, genetic risk score (GRS) complements two guideline-recommended inherited risk measures (family history and rare pathogenic mutations) in predicting PCa incidence and mortality. These results provide critical data for including GRS in PCa risk assessment.