• Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA. Electronic address: ishui@hsph.harvard.edu.
• Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
• Department of Surgery, Division of Urology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
• Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
• Genomic Epidemiology Group, German Cancer Research Center (Deutsches Krebsforschungszentrum), Heidelberg, Germany.
• Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
• Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, MD, USA.
• National Institute for Public Health and the Environment, Bilthoven, The Netherlands; Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, The Netherlands; School of Public Health, Imperial College London, London, United Kingdom.
• Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA; Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Gaithersburg, MD, USA.
• Urologic Oncology, University of Colorado, Denver, CO, USA.

BACKGROUND: Screening and diagnosis of prostate cancer (PCa) is hampered by an inability to predict who has the potential to develop fatal disease and who has indolent cancer. Studies have identified multiple genetic risk loci for PCa incidence, but it is unknown whether they could be used as biomarkers for PCa-specific mortality (PCSM).

OBJECTIVE: To examine the association of 47 established PCa risk single-nucleotide polymorphisms (SNPs) with PCSM.

DESIGN, SETTING, AND PARTICIPANTS: We included 10 487 men who had PCa and 11 024 controls, with a median follow-up of 8.3 yr, during which 1053 PCa deaths occurred.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The main outcome was PCSM. The risk allele was defined as the allele associated with an increased risk for PCa in the literature. We used Cox proportional hazards regression to calculate the hazard ratios of each SNP with time to progression to PCSM after diagnosis. We also used logistic regression to calculate odds ratios for each risk SNP, comparing fatal PCa cases to controls.

RESULTS AND LIMITATIONS: Among the cases, we found that 8 of the 47 SNPs were significantly associated (p<0.05) with time to PCSM. The risk allele of rs11672691 (intergenic) was associated with an increased risk for PCSM, while 7 SNPs had risk alleles inversely associated (rs13385191 [C2orf43], rs17021918 [PDLIM5], rs10486567 [JAZF1], rs6465657 [LMTK2], rs7127900 (intergenic), rs2735839 [KLK3], rs10993994 [MSMB], rs13385191 [C2orf43]). In the case-control analysis, 22 SNPs were associated (p<0.05) with the risk of fatal PCa, but most did not differentiate between fatal and nonfatal PCa. Rs11672691 and rs10993994 were associated with both fatal and nonfatal PCa, while rs6465657, rs7127900, rs2735839, and rs13385191 were associated with nonfatal PCa only.

CONCLUSIONS: Eight established risk loci were associated with progression to PCSM after diagnosis. Twenty-two SNPs were associated with fatal PCa incidence, but most did not differentiate between fatal and nonfatal PCa. The relatively small magnitudes of the associations do not translate well into risk prediction, but these findings merit further follow-up, because they may yield important clues about the complex biology of fatal PCa.

PATIENT SUMMARY: In this report, we assessed whether established PCa risk variants could predict PCSM. We found eight risk variants associated with PCSM: One predicted an increased risk of PCSM, while seven were associated with decreased risk. Larger studies that focus on fatal PCa are needed to identify more markers that could aid prediction.