Unified genetic risk score for prostate cancer enables improved risk stratification for clinical decision-making.

Background

Current clinical approaches to inherited prostate cancer (PCa) risk rely on binary classification of pathogenic variant (PV) carrier status without accounting for gene-specific heterogeneity or polygenic risk. We developed an integrated genetic risk model reflecting a continuum of inherited susceptibility.

Methods

In the UK Biobank (UKB; n=218 484), we evaluated associations of PVs in 11 clinically recommended genes and a polygenic risk score (PRS) with incident PCa using Cox models. A continuum model (GenProb-PCa) incorporating gene-specific PVs and PRS was developed and compared with binary PV-based models. Model performance was assessed using discrimination, calibration and continuous net reclassification index (cNRI). External validation was performed in a health system cohort, the Genomic Health Initiative (GHI; n=6590).

Results

Five genes (ATM, BRCA2, CHEK2, HOXB13, MSH2) and the PRS were independently associated with PCa risk (all p<0.001) in UKB. Compared with binary models, GenProb-PCa demonstrated superior discrimination (C-index 0.69 vs 0.52; p<0.001), with significant improvement in reclassification (cNRI 0.58; p<0.001). Findings were validated in GHI using the UKB-derived coefficients (C-index 0.64 vs 0.54, p<0.001; cNRI 0.31, p<0.001). Compared with binary PV-based models, 20% of non-PV carriers were reclassified to higher risk groups and 60% of carriers to lower risk groups. The model identified individuals at markedly elevated lifetime risk, with cumulative incidence exceeding 20% by age 75 among the top 8% of the distribution.

Conclusions

An integrated continuum genetic risk model, GenProb-PCa, improves PCa risk stratification beyond binary approaches by capturing heterogeneity in inherited risk. This framework may enable more precise risk-based screening strategies.