Reclassification of prostate cancer risk using sequentially identified SNPs: Results from the REDUCE trial.
Prostate
; 77(11): 1179-1186, 2017 Aug.
Article
em En
| MEDLINE
| ID: mdl-28670847
BACKGROUND: Although the clinical validity of risk-associated single nucleotide polymorphisms (SNPs) for assessment of disease susceptibility has been consistently established, risk reclassification from increasing numbers of implicated risk-associated SNPs raises concern that it is premature for clinical use. Our objective is to assess the degree and impact of risk reclassification with the increasing number of SNPs. METHODS: A total of 3239 patients from the Reduction by Dutasteride of Prostate Cancer Events (REDUCE) trial were included. Four genetic risk scores (GRSs) were calculated based on sets of sequentially discovered prostate cancer (PCa) risk-associated SNPs (17, 34, 51, and 68 SNPs). RESULTS: Pair-wise correlation coefficients between sets of GRSs increased as more SNPs were included in the GRS: 0.80, 0.86, and 0.95 for 17 versus 34 SNPs, 34 versus 51 SNPs, and 51 versus 68 SNPs, respectively. Using a GRS of 1.5 as a cutoff for higher versus lower risk, reclassification rates of PCa risk decreased: 14.11%, 12.04%, and 8.15% for 17 versus 34 SNPs, 34 versus 51 SNPs, and 51 versus 68 SNPs, respectively. Evolving GRSs, nevertheless, provide a tool for further refining risk assessment. When all four sequential GRSs were considered, the detection rates of PCa for men whose GRSs were consistently <1.5, reclassified, and consistently ≥1.5 were 20.8%, 29.67%, and 39.26%, respectively (Ptrend = 1.12 × 10-8 ). In comparison, the detection rates of PCa in men with negative or positive family history were 23.75% and 31.78%, respectively. CONCLUSIONS: Risk assessment using currently available SNPs is justified. Multiple GRS values from evolving sets of SNPs provide a valuable tool for better refining risk.
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Assunto principal:
Neoplasias da Próstata
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Polimorfismo de Nucleotídeo Único
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Inibidores de 5-alfa Redutase
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Dutasterida
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article