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How Well do Polygenic Risk Scores Identify Men at High Risk for Prostate Cancer? Systematic Review and Meta-Analysis

Siltari, A. ; Lönnerbro, R. ; Evans-Axelsson, S. LU orcid ; Bjartell, A. LU and Kotik, D. (2023) In Clinical Genitourinary Cancer 21(2). p.1-316
Abstract
Objectives: Genome-wide association studies have revealed over 200 genetic susceptibility loci for prostate cancer (PCa). By combining them, polygenic risk scores (PRS) can be generated to predict risk of PCa. We summarize the published evidence and conduct meta-analyses of PRS as a predictor of PCa risk in Caucasian men. Patients and methods: Data were extracted from 59 studies, with 16 studies including 17 separate analyses used in the main meta-analysis with a total of 20,786 cases and 69,106 controls identified through a systematic search of ten databases. Random effects meta-analysis was used to obtain pooled estimates of area under the receiver-operating characteristic curve (AUC). Meta-regression was used to assess the impact of... (More)
Objectives: Genome-wide association studies have revealed over 200 genetic susceptibility loci for prostate cancer (PCa). By combining them, polygenic risk scores (PRS) can be generated to predict risk of PCa. We summarize the published evidence and conduct meta-analyses of PRS as a predictor of PCa risk in Caucasian men. Patients and methods: Data were extracted from 59 studies, with 16 studies including 17 separate analyses used in the main meta-analysis with a total of 20,786 cases and 69,106 controls identified through a systematic search of ten databases. Random effects meta-analysis was used to obtain pooled estimates of area under the receiver-operating characteristic curve (AUC). Meta-regression was used to assess the impact of number of single-nucleotide polymorphisms (SNPs) incorporated in PRS on AUC. Heterogeneity is expressed as I2 scores. Publication bias was evaluated using funnel plots and Egger tests. Results: The ability of PRS to identify men with PCa was modest (pooled AUC 0.63, 95% CI 0.62-0.64) with moderate consistency (I2 64%). Combining PRS with clinical variables increased the pooled AUC to 0.74 (0.68-0.81). Meta-regression showed only negligible increase in AUC for adding incremental SNPs. Despite moderate heterogeneity, publication bias was not evident. Conclusion: Typically, PRS accuracy is comparable to PSA or family history with a pooled AUC value 0.63 indicating mediocre performance for PRS alone. © 2022 (Less)
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author
; ; ; and
author collaboration
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Genetic variance, Polygenic risk score, Prostate cancer risk, Single-nucleotide polymorphism, genetic predisposition, genetics, genome-wide association study, human, male, meta analysis, prostate tumor, risk factor, single nucleotide polymorphism, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Risk Factors
in
Clinical Genitourinary Cancer
volume
21
issue
2
pages
1 - 316
publisher
Elsevier
external identifiers
  • scopus:85151045035
  • pmid:36243664
ISSN
1558-7673
DOI
10.1016/j.clgc.2022.09.006
language
English
LU publication?
yes
id
ede97ef6-db57-491f-920d-c890b5793886
date added to LUP
2023-10-31 10:30:36
date last changed
2023-11-01 03:04:04
@article{ede97ef6-db57-491f-920d-c890b5793886,
  abstract     = {{Objectives: Genome-wide association studies have revealed over 200 genetic susceptibility loci for prostate cancer (PCa). By combining them, polygenic risk scores (PRS) can be generated to predict risk of PCa. We summarize the published evidence and conduct meta-analyses of PRS as a predictor of PCa risk in Caucasian men. Patients and methods: Data were extracted from 59 studies, with 16 studies including 17 separate analyses used in the main meta-analysis with a total of 20,786 cases and 69,106 controls identified through a systematic search of ten databases. Random effects meta-analysis was used to obtain pooled estimates of area under the receiver-operating characteristic curve (AUC). Meta-regression was used to assess the impact of number of single-nucleotide polymorphisms (SNPs) incorporated in PRS on AUC. Heterogeneity is expressed as I2 scores. Publication bias was evaluated using funnel plots and Egger tests. Results: The ability of PRS to identify men with PCa was modest (pooled AUC 0.63, 95% CI 0.62-0.64) with moderate consistency (I2 64%). Combining PRS with clinical variables increased the pooled AUC to 0.74 (0.68-0.81). Meta-regression showed only negligible increase in AUC for adding incremental SNPs. Despite moderate heterogeneity, publication bias was not evident. Conclusion: Typically, PRS accuracy is comparable to PSA or family history with a pooled AUC value 0.63 indicating mediocre performance for PRS alone. © 2022}},
  author       = {{Siltari, A. and Lönnerbro, R. and Evans-Axelsson, S. and Bjartell, A. and Kotik, D.}},
  issn         = {{1558-7673}},
  keywords     = {{Genetic variance; Polygenic risk score; Prostate cancer risk; Single-nucleotide polymorphism; genetic predisposition; genetics; genome-wide association study; human; male; meta analysis; prostate tumor; risk factor; single nucleotide polymorphism; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Male; Polymorphism, Single Nucleotide; Prostatic Neoplasms; Risk Factors}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{1--316}},
  publisher    = {{Elsevier}},
  series       = {{Clinical Genitourinary Cancer}},
  title        = {{How Well do Polygenic Risk Scores Identify Men at High Risk for Prostate Cancer? Systematic Review and Meta-Analysis}},
  url          = {{http://dx.doi.org/10.1016/j.clgc.2022.09.006}},
  doi          = {{10.1016/j.clgc.2022.09.006}},
  volume       = {{21}},
  year         = {{2023}},
}