• Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Department of Environmental Genomics, School of Public Health, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
• Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA.
• Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
• Department of Biostatistics, School of Public Health, Center for Global Health, Nanjing Medical University, Nanjing, China.
• Department of Urology, The First Affiliated Hospital of Nanjing Medical University & Jiangsu Province People's Hospital, 300 Guangzhou Road, Nanjing, 210029, China. gcheng@njmu.edu.cn.
• Department of Biostatistics, School of Public Health, Center for Global Health, Nanjing Medical University, Nanjing, China. mulongdu@hsph.harvard.edu.

BACKGROUND: Early-onset prostate cancer (EOPC, ≤ 55 years) has a unique clinical entity harboring high genetic risk, but the majority of EOPC patients still substantial opportunity to be early-detected thus suffering an unfavorable prognosis. A refined understanding of age-based polygenic risk score (PRS) for prostate cancer (PCa) would be essential for personalized risk stratification.

METHODS: We included 167,517 male participants [4882 cases including 205 EOPC and 4677 late-onset PCa (LOPC)] from UK Biobank. A General-, an EOPC- and an LOPC-PRS were derived from age-specific genome-wide association studies. Weighted Cox proportional hazard models were applied to estimate the risk of PCa associated with PRSs. The discriminatory capability of PRSs were validated using time-dependent receiver operating characteristic (ROC) curves with additional 4238 males from PLCO and TCGA. Phenome-wide association studies underlying Mendelian Randomization were conducted to discover EOPC linking phenotypes.

RESULTS: The 269-PRS calculated via well-established risk variants was more strongly associated with risk of EOPC [hazard ratio (HR) = 2.35, 95% confidence interval (CI) 1.99-2.78] than LOPC (HR = 1.95, 95% CI 1.89-2.01; I² = 79%). EOPC-PRS was dramatically related to EOPC risk (HR = 4.70, 95% CI 3.98-5.54) but not to LOPC (HR = 0.98, 95% CI 0.96-1.01), while LOPC-PRS had similar risk estimates for EOPC and LOPC (I² = 0%). Particularly, EOPC-PRS performed optimal discriminatory capability for EOPC (area under the ROC = 0.613). Among the phenomic factors to PCa deposited in the platform of ProAP (Prostate cancer Age-based PheWAS; https://mulongdu.shinyapps.io/proap ), EOPC was preferentially associated with PCa family history while LOPC was prone to environmental and lifestyles exposures.

CONCLUSIONS: This study comprehensively profiled the distinct genetic and phenotypic architecture of EOPC. The EOPC-PRS may optimize risk estimate of PCa in young males, particularly those without family history, thus providing guidance for precision population stratification.