Abstract NG03: Multiethnic prostate cancer GWAS meta-analysis identifies novel variants, improves genetic risk prediction across populations, and informs biological mechanisms of prostate cancer

2021 ◽  
Author(s):  
Burcu F. Darst ◽  
Lilit Moss ◽  
Edward J. Saunders ◽  
Nicholas Mancuso ◽  
Xin Sheng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Meta Analysis ◽  
Biological Mechanisms ◽  
Genetic Risk Prediction ◽  
Novel Variants

scholarly journals Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction

2021 ◽  
Vol 53 (1) ◽  
pp. 65-75
Author(s):  
David V. Conti ◽  
Burcu F. Darst ◽  
Lilit C. Moss ◽  
Edward J. Saunders ◽  
Xin Sheng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Susceptibility Loci ◽  
Genetic Risk Prediction ◽  
Genome Wide

Reproducibility, performance, and clinical utility of a genetic risk prediction model for prostate cancer in Japanese patients.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10520-10520
Author(s):  
Shusuke Akamatsu ◽  
Ryo Takata ◽  
Atsushi Takahashi ◽  
Takahiro Inoue ◽  
Michiaki Kubo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Predictive Performance ◽  
Risk Prediction Model ◽  
Clinical Scenario ◽  
Gray Zone ◽  
Diagnostic Biomarker ◽  
Genetic Risk Prediction

10520 Background: Prostate specific antigen (PSA) is widely used as a diagnostic biomarker for prostate cancer (PC). However, due to its low predictive performance, many patients without PC suffer from the harms of unnecessary prostate needle biopsies. The present study aims to evaluate the reproducibility and performance of a genetic risk prediction model and estimate its utility as a diagnostic biomarker in a clinical scenario. Methods: We created a logistic regression model incorporating 16 SNPs that were significantly associated with PC in a genome-wide association study of the Japanese. The model was validated by two independent sets of samples comprising 3,294 cases and 6,281 controls. Various cut offs were evaluated to be used in a clinical scenario. Results: The area under a curve (AUC) of the model was 0.679, 0.655, and 0.661 for the samples used to create the model and those used for validation respectively. The AUC of the model was not significantly altered in samples with PSA 1-10 ng/ml. 24.2% and 9.7% of the patients had odds ratio <0.5 (low risk) or >2 (high risk) in the model, and assuming the overall positive rate of prostate needle biopsies to be 20% in PSA gray zone (PSA 2-10 ng/ml), the positive biopsy rates were 10.7% and 42.4% respectively for the two genetic risk groups. Conclusions: The genetic risk prediction model was highly reproducible, and its predictive performance was not influenced by PSA. The model could have a potential to affect clinical decision when it is applied to patients with gray-zone PSA, which should be confirmed in future clinical studies.

scholarly journals Reproducibility, Performance, and Clinical Utility of a Genetic Risk Prediction Model for Prostate Cancer in Japanese

PLoS ONE ◽  
2012 ◽  
Vol 7 (10) ◽  
pp. e46454 ◽  
Author(s):  
Shusuke Akamatsu ◽  
Atsushi Takahashi ◽  
Ryo Takata ◽  
Michiaki Kubo ◽  
Takahiro Inoue ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Clinical Utility ◽  
Risk Prediction Model ◽  
Genetic Risk Prediction

scholarly journals Strengthening the Reporting of Genetic Risk Prediction Studies: The GRIPS Statement

2011 ◽  
Vol 4 (2) ◽  
pp. 206-209 ◽  
Author(s):  
A. Cecile J.W. Janssens ◽  
John P.A. Ioannidis ◽  
Cornelia M. van Duijn ◽  
Julian Little ◽  
Muin J. Khoury
Keyword(s):  
Risk Prediction ◽  
Genetic Risk ◽  
Genetic Risk Prediction

scholarly journals Moving towards population-based genetic risk prediction for ovarian cancer

2017 ◽  
Vol 124 (6) ◽  
pp. 855-858 ◽  
Author(s):  
B Rahman ◽  
L Side ◽  
S Gibbon ◽  
SF Meisel ◽  
L Fraser ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Population Based ◽  
Genetic Risk Prediction
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