Assessing the Clinical Utility of Published Prostate Cancer Polygenic Risk Scores in a Large Biobank Data Set

Randy A Vince Jr; Helen Sun; Udit Singhal; Fredrick R Schumacher; Erika Trapl; Johnie Rose; Jennifer Cullen; Nicholas Zaorsky; Johnathan Shoag; Holly Hartman; Angela Y Jia; Daniel E Spratt; Lars G Fritsche; Todd M Morgan

doi:10.1016/j.euo.2024.04.017

Assessing the Clinical Utility of Published Prostate Cancer Polygenic Risk Scores in a Large Biobank Data Set

Eur Urol Oncol. 2024 May 10:S2588-9311(24)00111-1. doi: 10.1016/j.euo.2024.04.017. Online ahead of print.

Authors

Affiliations

¹ Department of Urology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA. Electronic address: randy.vince@uhhospitals.org.
² Department of Urology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
³ Department of Urology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
⁴ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
⁵ Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
⁶ Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.

PMID: 38734542
DOI: 10.1016/j.euo.2024.04.017

Abstract

Background and objective: Polygenic risk scores (PRSs) have been developed to identify men with the highest risk of prostate cancer. Our aim was to compare the performance of 16 PRSs in identifying men at risk of developing prostate cancer and then to evaluate the performance of the top-performing PRSs in differentiating individuals at risk of aggressive prostate cancer.

Methods: For this case-control study we downloaded 16 published PRSs from the Polygenic Score Catalog on May 28, 2021 and applied them to Michigan Genomics Initiative (MGI) patients. Cases were matched to the Michigan Urological Surgery Improvement Collaborative (MUSIC) registry to obtain granular clinical and pathological data. MGI prospectively enrolls patients undergoing surgery at the University of Michigan, and MUSIC is a multi-institutional registry that prospectively tracks demographic, treatment, and clinical variables. The predictive performance of each PRS was evaluated using the area under the covariate-adjusted receiver operating characteristic curve (aAUC), and the association between PRS and disease aggressiveness according to prostate biopsy data was measured using logistic regression.

Key findings and limitations: We included 18 050 patients in the analysis, of whom 15 310 were control subjects and 2740 were prostate cancer cases. The median age was 66.1 yr (interquartile range 59.9-71.6) for cases and 56.6 yr (interquartile range 42.6-66.7) for control subjects. The PRS performance in predicting the risk of developing prostate cancer according to aAUC ranged from 0.51 (95% confidence interval 0.51-0.53) to 0.67 (95% confidence interval 0.66-0.68). By contrast, there was no association between PRS and disease aggressiveness.

Conclusions and clinical implications: Prostate cancer PRSs have modest real-world performance in identifying patients at higher risk of developing prostate cancer; however, they are limited in distinguishing patients with indolent versus aggressive disease.

Patient summary: Risk scores using data for multiple genes (called polygenic risk scores) can identify men at higher risk of developing prostate cancer. However, these scores need to be refined to be able to identify men with the highest risk for clinically significant prostate cancer.

Keywords: Polygenic risk score; Prostate cancer; Risk stratification; Screening.