Common Genetic Variants in Prostate Cancer Risk Prediction—Results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3)

Purpose BRCA1/2 mutations increase the risk of breast and prostate cancer in men. Common genetic variants modify cancer risks for female carriers of BRCA1/2 mutations. We investigated—for the first time to our knowledge—associations of common genetic variants with breast and prostate cancer risks for male carriers of BRCA1/ 2 mutations and implications for cancer risk prediction. Materials and Methods We genotyped 1,802 male carriers of BRCA1/2 mutations from the Consortium of Investigators of Modifiers of BRCA1/2 by using the custom Illumina OncoArray. We investigated the combined effects of established breast and prostate cancer susceptibility variants on cancer risks for male carriers of BRCA1/2 mutations by constructing weighted polygenic risk scores (PRSs) using published effect estimates as weights. Results In male carriers of BRCA1/2 mutations, PRS that was based on 88 female breast cancer susceptibility variants was associated with breast cancer risk (odds ratio per standard deviation of PRS, 1.36; 95% CI, 1.19 to 1.56; P = 8.6 × 10−6). Similarly, PRS that was based on 103 prostate cancer susceptibility variants was associated with prostate cancer risk (odds ratio per SD of PRS, 1.56; 95% CI, 1.35 to 1.81; P = 3.2 × 10−9). Large differences in absolute cancer risks were observed at the extremes of the PRS distribution. For example, prostate cancer risk by age 80 years at the 5th and 95th percentiles of the PRS varies from 7% to 26% for carriers of BRCA1 mutations and from 19% to 61% for carriers of BRCA2 mutations, respectively. Conclusion PRSs may provide informative cancer risk stratification for male carriers of BRCA1/2 mutations that might enable these men and their physicians to make informed decisions on the type and timing of breast and prostate cancer risk management.

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Interactions Between Genome-wide Significant Genetic Variants and Circulating Concentrations of Insulin-like Growth Factor 1, Sex Hormones, and Binding Proteins in Relation to Prostate Cancer Risk in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium

American Journal of Epidemiology ◽

10.1093/aje/kwr423 ◽

2012 ◽

Vol 175 (9) ◽

pp. 926-935 ◽

Cited By ~ 12

Author(s):

Konstantinos K. Tsilidis ◽

Ruth C. Travis ◽

Paul N. Appleby ◽

Naomi E. Allen ◽

Sara Lindstrom ◽

...

Keyword(s):

Prostate Cancer ◽

Growth Factor ◽

Cancer Risk ◽

Sex Hormones ◽

National Cancer Institute ◽

Genetic Variants ◽

Binding Proteins ◽

Prostate Cancer Risk ◽

Genome Wide ◽

Breast And Prostate Cancer

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The Association Between Three Genetic Variants in MicroRNAs (Rs11614913, Rs2910164, Rs3746444) and Prostate Cancer Risk

Cellular Physiology and Biochemistry ◽

10.1159/000491671 ◽

2018 ◽

Vol 48 (1) ◽

pp. 149-157 ◽

Cited By ~ 12

Author(s):

Yuanyuan Mi ◽

Kewei Ren ◽

Jiangang Zou ◽

Yu Bai ◽

Lifeng Zhang ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Risk ◽

Genetic Variants ◽

Genetic Model ◽

Meta Analysis ◽

Prostate Cancer Risk ◽

Asian Population ◽

Published Data ◽

Common Genetic Variants ◽

The Impact

Background/Aims: MicroRNAs (miRNAs) are a class of small non-coding RNA molecules which play a significant role in transcriptional and translational regulation. Published data on the association between the miRNA SNPs and prostate cancer (PCa) risk are somewhat inconclusive. Methods: We performed a meta-analysis of all available studies including 2,227 patients and 2,331 control subjects to evaluate the impact of three common genetic variants of microRNAs in prostate cancer risk. Odds ratios (ORs) with 95% confidence intervals (CIs) were utilized to investigate the strength of the association. Results: For miR-499 polymorphism, a significant association was observed between the rs3746444 A>G polymorphism and PCa risk in heterozygote comparison and dominant genetic model, in particular in Asian population subgroup. For miR-146a polymorphism, the rs2910164 CC genotype was associated with decreased PCa risk in Asian population in homozygote comparison. In addition, rs2910164 CC genotype had a weekly higher percentage value in subgroup of Gleason score < 7. Similar results were also indicated in localized prostate cancer in subgroup analysis by tumor stage. For miR-196a2 polymorphism, no association was observed between this variant and PCa risk in the overall group. However, in stratified analysis by ethnicity, we found that rs11614913 T allele was a risk factor for Asian PCa patients. Conclusions: Polymorphisms of miR-196a2 rs11614913, miR-146a rs2910164, and miR-499 rs3746444 may contribute to the risk for developing prostate cancer in Asian descendants. Moreover, miR-146a rs2910164 polymorphism was related to PCa prognosis.

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Evaluating the Association between Artificial Light-at-Night Exposure and Breast and Prostate Cancer Risk in Spain (MCC-Spain Study)

Environmental Health Perspectives ◽

10.1289/ehp1837 ◽

2018 ◽

Vol 126 (4) ◽

pp. 047011 ◽

Cited By ~ 43

Author(s):

Ariadna Garcia-Saenz ◽

Alejandro Sánchez de Miguel ◽

Ana Espinosa ◽

Antonia Valentin ◽

Núria Aragonés ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Risk ◽

Prostate Cancer Risk ◽

Artificial Light ◽

Light At Night ◽

Breast And Prostate Cancer

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Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools

BMC Medical Research Methodology ◽

10.1186/s12874-019-0839-0 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Johanna Tolksdorf ◽

Michael W. Kattan ◽

Stephen A. Boorjian ◽

Stephen J. Freedland ◽

Karim Saba ◽

...

Keyword(s):

Prostate Cancer ◽

Risk Factors ◽

Family History ◽

Cancer Risk ◽

Risk Prediction ◽

Prostate Biopsy ◽

Prostate Cancer Risk ◽

High Grade ◽

Prediction Tools ◽

History Of

Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). Conclusions We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

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