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. 2020 Sep;29(9):1731-1738.
doi: 10.1158/1055-9965.EPI-19-1527. Epub 2020 Jun 24.

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data

Minh-Phuong Huynh-Le  1   2 , Chun Chieh Fan  2 , Roshan Karunamuni  1   2 , Eleanor I Walsh  3 , Emma L Turner  3 , J Athene Lane  3   4 , Richard M Martin  3   4   5 , David E Neal  6   7   8 , Jenny L Donovan  9 , Freddie C Hamdy  6   10 , J Kellogg Parsons  11 , Rosalind A Eeles  12   13 , Douglas F Easton  14 , Zsofia Kote-Jarai  12 , Ali Amin Al Olama  14   15 , Sara Benlloch Garcia  14 , Kenneth Muir  16   17 , Henrik Grönberg  18 , Fredrik Wiklund  18 , Markus Aly  18   19   20 , Johanna Schleutker  21   22 , Csilla Sipeky  21 , Teuvo Lj Tammela  23   24 , Børge Grønne Nordestgaard  25   26 , Timothy J Key  27 , Ruth C Travis  27 , Paul D P Pharoah  28 , Nora Pashayan  28   29 , Kay-Tee Khaw  30 , Stephen N Thibodeau  31 , Shannon K McDonnell  32 , Daniel J Schaid  32 , Christiane Maier  33 , Walther Vogel  34 , Manuel Luedeke  33 , Kathleen Herkommer  35 , Adam S Kibel  36 , Cezary Cybulski  37 , Dominika Wokolorczyk  37 , Wojciech Kluzniak  37 , Lisa A Cannon-Albright  38   39 , Hermann Brenner  40   41   42 , Ben Schöttker  40   43 , Bernd Holleczek  40   44 , Jong Y Park  45 , Thomas A Sellers  45 , Hui-Yi Lin  46 , Chavdar Kroumov Slavov  47 , Radka P Kaneva  48 , Vanio I Mitev  48 , Jyotsna Batra  49   50 , Judith A Clements  50   51 , Amanda B Spurdle, Manuel R Teixeira  52   53 , Paula Paulo  52   54 , Sofia Maia  52   54 , Hardev Pandha  55 , Agnieszka Michael  55 , Ian G Mills  6 , Ole A Andreassen  56 , Anders M Dale  2   57 , Tyler M Seibert; Australian Prostate Cancer BioResource (APCB); PRACTICAL Consortium
Affiliations

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data

Minh-Phuong Huynh-Le et al. Cancer Epidemiol Biomarkers Prev. 2020 Sep.

Abstract

Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.

Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.

Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.

Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.

Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.

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Conflict of interest statement

Conflicts of Interest

All authors declare no support from any organization for the submitted work except as follows:

A.M. Dale and T.M. Seibert report a research grant from the US Department of Defense. O.A. Andreassen reports research grants from KG Jebsen Stiftelsen, Research Council of Norway, and South East Norway Health Authority.

Authors declare no financial relationships with any organizations that might have an interest in the submitted work in the previous three years except as follows, with all of these relationships outside the present study:

T.M. Seibert reports honoraria from Multimodal Imaging Services Corporation for imaging segmentation, honoraria from WebMD, Inc. for educational content, as well as a past research grant from Varian Medical Systems. A.S. Kibel reports advisory board memberships for Sanofi-Aventis, Dendreon, and Profound. O.A. Andreassen reports speaker honoraria from Lundbeck.

Authors declare no other relationships or activities that could appear to have influenced the submitted work except as follows:

O.A. Andreassen has a patent application # U.S. 20150356243 pending; A.M. Dale also applied for this patent application and assigned it to UC San Diego. A.M. Dale has additional disclosures outside the present work: founder, equity holder, and advisory board member for CorTechs Labs, Inc.; founder and equity holder in HealthLytix, Inc., advisory board member of Human Longevity, Inc.; recipient of nonfinancial research support from General Electric Healthcare. O.A. Andreassen is a consultant for HealthLytix, Inc.

Additional acknowledgments for the PRACTICAL consortium and contributing studies are described in the Supplemental Material.

Figures

Figure 1.
Figure 1.
Annual incidence of prostate cancer in the United Kingdom, 2013–2015. Dots represent the raw, age-specific incidence rates of each age range, per 100,000 males. The black line represents the results of linear regression for an exponential curve to give a continuous model of age-specific incidence in the United Kingdom, R2=0.96, p=0.001.
Figure 2.
Figure 2.
Incidence of clinically significant prostate cancer, as derived from application of polygenic hazard score (PHS) hazard ratios and population data from the United Kingdom. The overall population incidence is taken as the median risk (50th percentile); this accounts for age-specific proportions of prostate cancer that were clinically significant in the CAP trial. Hazard ratios were calculated within ProtecT data for various levels of genetic risk ranges (0–2, 2–10, 10–30, 30–70, 70–90, 90–98, and 98–100) to correspond to percentiles of interest (1, 5, 20, 50, 80, 95, and 99, respectively), and used to adjust the median incidence curve. Blue lines represent genetic risk lower than the median while red lines represent genetic risk higher than the median.
Figure 3.
Figure 3.
Application of prostate cancer risk-equivalent age to the clinical scenario of whether to screen a 60-year-old man (median age from ProtecT). The risk-equivalent age is the patient’s true age adjusted by PHS level. This plot shows results for all men from ProtecT aged approximately 60 years old (range: 55–64), grouped by their calculated prostate cancer risk-equivalent age: <55, 55–64, or ≥65. The positive predictive value (PPV) of PSA testing for clinically significant prostate cancer and the corresponding standard errors of the mean of PSA testing are shown for each of these 3 groups.
See this image and copyright information in PMC

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