Zhuqing Shi1, Elizabeth A Platz2, Jun Wei1, Rong Na1, Richard J Fantus3, Chi-Hsiung Wang1, Scott E Eggener3, Peter J Hulick4, David Duggan5, S Lilly Zheng1, Kathleen A Cooney6, William B Isaacs7, Brian T Helfand1, Jianfeng Xu8. 1. Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA. 2. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 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. 3. Section of Urology, University of Chicago Medicine, Chicago, IL, USA. 4. Department of Medicine, NorthShore University HealthSystem, Evanston, IL, USA. 5. Translational Genomics Research Institute, Affiliate of City of Hope, Phoenix, AZ, USA. 6. Duke University School of Medicine and Duke Cancer Institute, Durham, NC, USA. 7. 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. 8. Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA. Electronic address: jxu@northshore.org.
Abstract
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. 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.
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. 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.
Authors: Jianfeng Xu; W Kyle Resurreccion; Zhuqing Shi; Jun Wei; Chi-Hsiung Wang; S Lilly Zheng; Peter J Hulick; Ashley E Ross; Christian P Pavlovich; Brian T Helfand; William B Isaacs Journal: Prostate Cancer Prostatic Dis Date: 2022-03-28 Impact factor: 5.455
Authors: Minh-Phuong Huynh-Le; Roshan Karunamuni; Chun Chieh Fan; Lui Asona; Wesley K Thompson; Maria Elena Martinez; Rosalind A Eeles; Zsofia Kote-Jarai; Kenneth R Muir; Artitaya Lophatananon; Johanna Schleutker; Nora Pashayan; Jyotsna Batra; Henrik Grönberg; David E Neal; Børge G Nordestgaard; Catherine M Tangen; Robert J MacInnis; Alicja Wolk; Demetrius Albanes; Christopher A Haiman; Ruth C Travis; William J Blot; Janet L Stanford; Lorelei A Mucci; Catharine M L West; Sune F Nielsen; Adam S Kibel; Olivier Cussenot; Sonja I Berndt; Stella Koutros; Karina Dalsgaard Sørensen; Cezary Cybulski; Eli Marie Grindedal; Florence Menegaux; Jong Y Park; Sue A Ingles; Christiane Maier; Robert J Hamilton; Barry S Rosenstein; Yong-Jie Lu; Stephen Watya; Ana Vega; Manolis Kogevinas; Fredrik Wiklund; Kathryn L Penney; Chad D Huff; Manuel R Teixeira; Luc Multigner; Robin J Leach; Hermann Brenner; Esther M John; Radka Kaneva; Christopher J Logothetis; Susan L Neuhausen; Kim De Ruyck; Piet Ost; Azad Razack; Lisa F Newcomb; Jay H Fowke; Marija Gamulin; Aswin Abraham; Frank Claessens; Jose Esteban Castelao; Paul A Townsend; Dana C Crawford; Gyorgy Petrovics; Ron H N van Schaik; Marie-Élise Parent; Jennifer J Hu; Wei Zheng; Ian G Mills; Ole A Andreassen; Anders M Dale; Tyler M Seibert Journal: Prostate Cancer Prostatic Dis Date: 2022-02-12 Impact factor: 5.455
Authors: Jianfeng Xu; William B Isaacs; Mufaddal Mamawala; Zhuqing Shi; Patricia Landis; Jacqueline Petkewicz; Jun Wei; Chi-Hsiung Wang; W Kyle Resurreccion; Rong Na; Yasin Bhanji; Kristian Novakovic; Patrick C Walsh; S Lilly Zheng; Brian T Helfand; Christian P Pavlovich Journal: Prostate Date: 2021-05-06 Impact factor: 4.012
Authors: Burcu F Darst; Xin Sheng; Rosalind A Eeles; Zsofia Kote-Jarai; David V Conti; Christopher A Haiman Journal: Eur Urol Date: 2021-05-01 Impact factor: 24.267