scholarly journals Polygenic Risk Score Improves Prostate Cancer Risk Prediction: Results from the Stockholm-1 Cohort Study

2011 ◽  
Vol 60 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Markus Aly ◽  
Fredrik Wiklund ◽  
Jianfeng Xu ◽  
William B. Isaacs ◽  
Martin Eklund ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cohort Study ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Prostate Cancer Risk ◽  
Polygenic Risk Score ◽  
Polygenic Risk

scholarly journals A Polygenic Risk Score Predicts Incident Prostate Cancer Risk in Older Men but Does Not Select for Clinically Significant Disease

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5815
Author(s):  
Andrew Bakshi ◽  
Moeen Riaz ◽  
Suzanne G. Orchard ◽  
Prudence R. Carr ◽  
Amit D. Joshi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Score ◽  
Prostate Cancer Risk ◽  
Older Men ◽  
Polygenic Risk Score ◽  
Gleason Grade ◽  
Polygenic Risk ◽  
Clinically Significant ◽  
Significant Disease

Despite the high prevalence of prostate cancer in older men, the predictive value of a polygenic risk score (PRS) remains uncertain in men aged ≥70 years. We used a 6.6 million-variant PRS to predict the risk of incident prostate cancer in a prospective study of 5701 men of European descent aged ≥70 years (mean age 75 years) enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) clinical trial. The study endpoint was prostate cancer, including metastatic or non-metastatic disease, confirmed by an expert panel. After excluding participants with a history of prostate cancer at enrolment, we used a multivariable Cox proportional hazards model to assess the association between the PRS and incident prostate cancer risk, adjusting for covariates. Additionally, we examined the distribution of Gleason grade groups by PRS group to determine if a higher PRS was associated with higher grade disease. We tested for interaction between the PRS and aspirin treatment. Logistic regression was used to independently assess the association of the PRS with prevalent (pre-trial) prostate cancer, reported in medical histories. During a median follow-up time of 4.6 years, 218 of the 5701 participants (3.8%) were diagnosed with prostate cancer. The PRS predicted incident risk with a hazard ratio (HR) of 1.52 per standard deviation (SD) (95% confidence interval (CI) 1.33–1.74, p < 0.001). Men in the top quintile of the PRS distribution had an almost three times higher risk of prostate cancer than men in the lowest quintile (HR = 2.99 (95% CI 1.90–4.27), p < 0.001). However, a higher PRS was not associated with a higher Gleason grade groups. We found no interaction between aspirin treatment and the PRS for prostate cancer risk. The PRS was also associated with prevalent prostate cancer (odds ratio = 1.80 per SD (95% CI 1.65–1.96), p < 0.001).While a PRS for prostate cancer is strongly associated with incident risk in men aged ≥70 years, the clinical utility of the PRS as a biomarker is currently limited by its inability to select for clinically significant disease.

Broad‐ and narrow‐sense validity performance of three polygenic risk score methods for prostate cancer risk assessment

The Prostate ◽  
2019 ◽  
Vol 80 (1) ◽  
pp. 83-87 ◽  
Author(s):  
Hongjie Yu ◽  
Zhuqing Shi ◽  
Xiaoling Lin ◽  
Quanwa Bao ◽  
Haifei Jia ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Assessment ◽  
Cancer Risk ◽  
Risk Score ◽  
Prostate Cancer Risk ◽  
Cancer Risk Assessment ◽  
Polygenic Risk Score ◽  
Narrow Sense ◽  
Polygenic Risk

scholarly journals Combined Effect of a Polygenic Risk Score and Rare Genetic Variants on Prostate Cancer Risk

2021 ◽  
Author(s):  
Burcu F. Darst ◽  
Xin Sheng ◽  
Rosalind A. Eeles ◽  
Zsofia Kote-Jarai ◽  
David V. Conti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Score ◽  
Genetic Variants ◽  
Prostate Cancer Risk ◽  
Combined Effect ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Rare Genetic Variants

Abstract 881: Benchmarking genome-wide polygenic risk score development techniques in colorectal cancer risk prediction

2021 ◽  
Author(s):  
Minta Thomas ◽  
Lori C Sakoda ◽  
Jeffrey K Lee ◽  
Mark A Jenkins ◽  
Andrea Burnett-Hartman ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Colorectal Cancer Risk ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genome Wide

scholarly journals The Discovery of Breast Cancer Risk Gene and Establishment of Prediction Model Based on Estrogen Metabolism Regulation

2020 ◽  
Author(s):  
Feng Zhao ◽  
Zhixiang Hao ◽  
Yanan Zhong ◽  
Yinxue Xu ◽  
Meng Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Healthy Subjects ◽  
Estrogen Metabolism ◽  
Polygenic Risk Score ◽  
Polygenic Risk

Abstract Background In this study, we aim to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes with breast cancer (BC) and establish a risk prediction model based on polygenic risk score. Methods Unrelated BC patients and healthy subjects were recruited for analysis of the estrogen levels and the single nucleotide polymorphisms (SNPs) of estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes which was calculated using a Bayesian approach. The independent sample t test was used to evaluate the difference between PRS scores of BC and healthy subjects. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (ROC). Results The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulated in the serum of BC patients. Then,we established PRS model to evaluate the role of multiple genes SNPs. The PRS model 1 (M1) was established from 6 GWAS-identified high risk genes SNPs. On the basis of M1, we added 7 estrogen metabolism enzyme genes SNPs to establish PRS model 2 (M2). The independent sample t test results show that there is no difference between BC and healthy subjects in M1 (P = 0.17), however, there is significant difference between BC and healthy subjects in M2 (P = 4.9*10− 5). The ROC curve results also show that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than M1 (AUC = 54.56%). Conclusion Estrogens and the related metabolic enzymes gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme genes SNPs has a good ability in breast cancer risk prediction, and the accuracy is greatly improved comparing PRS model only includes GWAS-identified genes SNPs.

scholarly journals Prostate cancer risk prediction using a polygenic risk score

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Csilla Sipeky ◽  
Kirsi M. Talala ◽  
Teuvo L. J. Tammela ◽  
Kimmo Taari ◽  
Anssi Auvinen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Score ◽  
Prostate Cancer Risk ◽  
Population Based ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Randomised Study ◽  
Additional Information ◽  
Increased Risk ◽  
Hereditary Factors

Abstract Hereditary factors have a strong influence on prostate cancer (PC) risk and poorer outcomes, thus stratification by genetic factors addresses a critical need for targeted PC screening and risk-adapted follow-up. In this Finnish population-based retrospective study 2283 clinically diagnosed and 455 screen-detected patients from the Finnish Randomised Study of Screening for Prostate Cancer (FinRSPC), 2400 healthy individuals have been involved. Individual genetic risk through establishment of a polygenic risk score based on 55 PC risk SNPs identified through the Finnish subset of the Collaborative Oncological Gene-Environment Study was assessed. Men with PC had significantly higher median polygenic risk score compared to the controls (6.59 vs. 3.83, P < 0.0001). The polygenic risk score above the control median was a significant predictor of PC (OR 2.13, 95% CI 1.90–2.39). The polygenic risk score predicted the risk of PC with an AUC of 0.618 (95% CI 0.60–0.63). Men in the highest polygenic risk score quartile were 2.8—fold (95% CI 2.4–3.30) more likely to develop PC compared with men in the lowest quartile. In the FinRSPC cohort, a significantly higher percentage of men had a PSA level of ≥ 4 ng/mL in polygenic risk score quartile four compared to quartile one (18.7% vs 8.3%, P < 0.00001). Adding the PRS to a PSA-only model contributed additional information in predicting PC in the FinRSPC model. Results strongly suggest that use of the polygenic risk score would facilitate the identification of men at increased risk for PC.

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