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available at www.sciencedirect.com journal homepage: www.europeanurology.com

Prostate Cancer

Performance of Three Inherited Risk Measures for Predicting Prostate Cancer Incidence and Mortality: A Population-based Prospective Analysis Zhuqing Shi a, Elizabeth A. Platz b,c, Jun Wei a, Rong Na a, Richard J. Fantus d, Chi-Hsiung Wang a, Scott E. Eggener d, Peter J. Hulick e, David Duggan f, S. Lilly Zheng a, Kathleen A. Cooney g, William B. Isaacs c, Brian T. Helfand a, Jianfeng Xu a,* a

Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA; b Department of Epidemiology, Johns Hopkins Bloomberg

School of Public Health, Baltimore, MD, USA; c Department of Urology and the James Buchanan Brady Urologic Institute, Johns Hopkins University School of Medicine, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA; d Section of Urology, University of Chicago Medicine, Chicago, IL, USA; e Department of Medicine, NorthShore University HealthSystem, Evanston, IL, USA; f Translational Genomics Research Institute, Affiliate of City of Hope, Phoenix, AZ, USA; g Duke University School of Medicine and Duke Cancer Institute, Durham, NC, USA

Article info

Abstract

Article history: Accepted November 11, 2020

Background: Single nucleotide polymorphism–based genetic risk score (GRS) has been developed and validated for prostate cancer (PCa) risk assessment. As GRS is population standardized, its value can be interpreted as a relative risk to the general population. Objective: To compare the performance of GRS with two guideline-recommended inherited risk measures, family history (FH) and rare pathogenic mutations (RPMs), for predicting PCa incidence and mortality. Design, setting, and participants: A prospective cohort was derived from the UK Biobank where 208 685 PCa diagnosis-free participants at recruitment were followed via the UK cancer and death registries. Outcome measurements and statistical analysis: Rate ratios (RRs) of PCa incidence and mortality for FH (positive vs negative), RPMs (carriers vs noncarriers), and GRS (top vs bottom quartile) were measured. Results and limitations: After a median follow-up of 9.67 yr, 6890 incident PCa cases (419 died of PCa) were identified. Each of the three measures was significantly associated with PCa incidence in univariate analyses; RR (95 % confidence interval [CI]) values were 1.88 (1.75–2.01) for FH, 2.89 (1.89–4.25) for RPMs, and 1.97(1.87–2.07) for GRS (all p < 0.001). The associations were independent in multivariable analyses. While FH and RPMs identified 11 % of men at higher PCa risk, addition of GRS identified an additional 22 % of men at higher PCa risk, and increases in C-statistic from 0.58 to 0.67 for differentiating incidence (p < 0.001) and from 0.65 to 0.71 for differentiating mortality (p = 0.002). Limitations were a small number of minority patients and short mortality follow-up. Conclusions: This population-based prospective study suggests that GRS complements two guideline-recommended inherited risk measures (FH and RPMs) for stratifying the risk of PCa incidence and mortality.

Associate Editor: Matthew Cooperberg Statistical Editor :Andrew Vickers Keywords: Family history Genetic risk score High-penetrance genes Prostate cancer Risk assessment

* Corresponding author. 1001 University Place, Evanston, IL 60201, USA. Tel. +1 (224) 264-7501; Fax: +1 (224) 364-7675. E-mail address: [email protected] (J. Xu).

https://doi.org/10.1016/j.eururo.2020.11.014 0302-2838/© 2020 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Please cite this article in press as: Shi Z, et al. Performance of Three Inherited Risk Measures for Predicting Prostate Cancer Incidence and Mortality: A Population-based Prospective Analysis. Eur Urol (2020), https://doi.org/10.1016/j.eururo.2020.11.014

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Patient summary: In a large population-based prostate cancer (PCa) prospective study derived from UK Biobank, genetic risk score (GRS) complements two guideline-recommended inherited risk measures (family history and rare pathogenic mutations) in predicting PCa incidence and mortality. These results provide critical data for including GRS in PCa risk assessment. © 2020 European Association of Urology. Published by Elsevier B.V. All rights reserved.

1.

Introduction

Prostate cancer (PCa) is the second most common cancer and fifth leading cause of death in men worldwide [1]. Inherited PCa risk is well established, with heritability estimated at 57 %0 [2]. Three inherited risk factors have consistently been associated with PCa—family history (FH), rare pathogenic mutations (RPMs) in several candidate genes, and single nucleotide polymorphism (SNP)-based polygenic risk scores such as genetic risk score (GRS) [3]. FH is an indirect measurement of both genetic risk and shared environmental factors, and is typically associated with approximately 1.5–2.0-fold risk in prospective studies [3,4]. With advances in DNA sequencing and genotyping technologies, two direct measurements of inherited risk are now feasible. One is sequencing for germline mutations in candidate genes, including HOXB13 and BRCA2 [5–7]. Pathogenic mutations in these genes are rare in the general population (95 % call rate at the patient and SNP levels). These SNPs were used for calculating GRS, an odds ratio (OR)-weighted and population-standardized polygenic risk score [14]. GRS was calculated by multiplying the per-allele OR for each SNP and normalizing the risk by the average risk expected in the population of specific races. The performance of GRS is similar to that of other OR-weighted polygenic risk score methods. However, because GRS is population standardized, its value can be considered as a relative risk to the general population regardless of the number of SNPs used for generating the score [14]. We grouped men into three risk groups: low (bottom quartile), average (middle two quartiles), and high (top quartile) risks. Detailed information of these SNPs used for GRS calculation is available in Supplementary Table 1. DNA sequencing information on 18 PCa candidate genes (ATM, ATR, BRCA1, BRCA2, BRIP1, CHEK2, FANCA, GEN1, HOXB13, MLH1, MSH2, MSH6, NBN, PALB2, PMS2, RAD51C, RAD51D, and TP53) was available for 20 361 men with WES data. Variant Call Format (VCF) based on functionally equivalent pipelines was used, and none of these genes have alternative contigs [15]. Selection of participants for WES was not based on the assessment of familial PCa risk [12]. Sequence variants (depth >20 and alternative allele 30 %) were annotated using a five-tier variant classification protocol based on published recommendations [16]. Only mutations assigned pathogenic or likely pathogenic status and not flagged as those of low complexity by the genome Aggregation Database

Please cite this article in press as: Shi Z, et al. Performance of Three Inherited Risk Measures for Predicting Prostate Cancer Incidence and Mortality: A Population-based Prospective Analysis. Eur Urol (2020), https://doi.org/10.1016/j.eururo.2020.11.014

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Table 1 – Characteristics of study participants for the prostate cancer incidence cohort derived from UK Biobank Clinical values

Subjects with SNP data

Participants with all 3 genetic risk measures

p value

All participants, no. White individuals, no. (%) Black individuals, no. (%) Age at study recruitment (yr), median (IQR) Follow-up person-time for PCa (yr), median (IQR)a Diagnosed with PCa, no. (%) PCa incidence rate (per 100 000 person-years) Follow-up person-time for PCa death (yr), median (IQR) Died of PCa, no. (%) PCa mortality rate (per 100 000 person-years) Positive family history, no. (%)

208 685 206 053 (99.7) 2632 (1.3) 58.50 (50.50–64.50) 9.67 (8.91–10.39) 6890 (3.3) 354 9.7(8.98–10.41) 419 (0.20) 21 16 534 (8.2)

20 361 20 083 (99.6) 278 (1.4) 59.50 (51.50–64.50) 8.61 (8.42–8.81) 656 (3.2) 375 8.62(8.44–8.82) 32 (0.16) 18 2011 (9.9)

0.2 0.2