Polygenic risk score improves prostate cancer risk prediction: Results from the Stockholm-1 cohort study

Markus Aly, Fredrik Wiklund, Jianfeng Xu, William B. Isaacs, Martin Eklund, Mauro D'Amato, Jan Adolfsson, Henrik Grönberg

Research output: Contribution to journalArticleResearchpeer-review

83 Citations (Scopus)

Abstract

Background: More than 1 million prostate biopsies are conducted yearly in the United States. The low specificity of prostate-specific antigen (PSA) results in diagnostic biopsies in men without prostate cancer (PCa). Additional information, such as genetic markers, could be used to avoid unnecessary biopsies. Objective: To determine whether single nucleotide polymorphisms (SNPs) associated with PCa can be used to determine whether biopsy of the prostate is necessary. Design, settings, and participants: The Stockholm-1 cohort (n = 5241) consisted of men who underwent a prostate biopsy during 2005 to 2007. PSA levels were retrieved from databases and family histories were obtained using a questionnaire. Thirty-five validated SNPs were analysed and converted into a genetic risk score that was implemented in a risk-prediction model. Results and limitations: When comparing the nongenetic model (based on age, PSA, free-to-total PSA, and family history) with the genetic model and using a fixed number of detected PCa cases, it was found that the genetic model required significantly fewer biopsies than the nongenetic model, with 480 biopsies (22.7%) avoided, at a cost of missing a PCa diagnosis in 3% of patients characterised as having an aggressive disease. However, the overall genetic model does not discriminate between aggressive and nonaggressive cases. Conclusion: Although the genetic model reduced the number of biopsies more than the nongenetic model, the clinical significance of this finding requires further evaluation.

Original languageEnglish
Pages (from-to)21-28
Number of pages8
JournalEuropean Urology
Volume60
Issue number1
DOIs
Publication statusPublished - 1 Jul 2011
Externally publishedYes

Keywords

  • Diagnosis
  • Polygenic
  • Prediction model
  • Prostate cancer
  • SNP

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