Common Genetic Variants in Prostate Cancer Risk Prediction--Results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3) (original) (raw)
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A genetic hazard score to personalize prostate cancer screening, applied to population data
2019
Background: Genetic risk stratification may inform decisions of whether, and when, a man should undergo prostate cancer (PCa) screening. We previously validated a polygenic hazard score (PHS), a weighted sum of 54 single-nucleotide polymorphism genotypes, for accurate prediction of age of onset of aggressive PCa and improved screening performance. We now assess the potential impact of PHS-informed screening. Methods: United Kingdom population data were fit to a continuous model of age-specific PCa incidence. Using hazard ratios estimated from ProtecT trial data, age-specific incidence rates were calculated for percentiles of genetic risk. Incidence of higher-grade PCa (Gleason≥7) was estimated from age-specific data from the linked CAP trial. PHS and incidence data were combined to give a risk-equivalent age, when a man with a given PHS percentile will have risk of higher-grade PCa equivalent to that of a typical man at age 50 (50-years standard). Positive predictive value (PPV) of ...
The Prostate, 2016
While family history (FH) has been widely used to provide risk information, it captures only a small proportion of subjects with higher genetic susceptibility. Our objective is to assess whether a genetic risk score (GRS) calculated from prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) can supplement FH for more effective risk stratification for PCa screening decision-making. A GRS was calculated based on 29 PCa risk-associated SNPs for 4,528 men of European descent in the placebo arm of the Prostate Cancer Prevention Trial (PCPT). At study entry, participants were free of PCa diagnosis. Performance of FH and GRS were measured by observed detection rate of PCa and high-grade PCa (Gleason score ≥7) during the 7-year study. GRS was a significant predictor of PCa in men with or without a positive FH (P = 1.18 × 10(-4) and P = 4.50 × 10(-16) , respectively). Using FH alone, as expected, the 17% of men who were FH+ had a PCa detection rate that was significant...
Clinical validity and utility of genetic risk scores in prostate cancer
Asian journal of andrology
Current issues related to prostate cancer (PCa) clinical care (e.g., over-screening, over-diagnosis, and over-treatment of nonaggressive PCa) call for risk assessment tools that can be combined with family history (FH) to stratify disease risk among men in the general population. Since 2007, genome-wide association studies (GWASs) have identified more than 100 SNPs associated with PCa susceptibility. In this review, we discuss (1) the validity of these PCa risk-associated SNPs, individually and collectively; (2) the various methods used for measuring the cumulative effect of multiple SNPs, including genetic risk score (GRS); (3) the adequate number of SNPs needed for risk assessment; (4) reclassification of risk based on evolving numbers of SNPs used to calculate genetic risk, (5) risk assessment for men from various racial groups, and (6) the clinical utility of genetic risk assessment. In conclusion, data available to date support the clinical validity of PCa risk-associated SNPs ...
Prediction of individual genetic risk to prostate cancer using a polygenic score
The Prostate, 2015
Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Add...
The Prostate, 2014
BACKGROUND. Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with higher risk of prostate cancer (PCa). This study aimed to evaluate whether published SNPs improve the performance of a clinical risk-calculator in predicting prostate biopsy result. METHODS. Three hundred forty-six patients with a previous prostate biopsy (191 positive, 155 negative) were enrolled. After literature search, nine SNPs were selected for their statistically significant association with increased PCa risk. Allelic odds ratios were computed and a new logistic regression model was built integrating the clinical risk score (i.e., prior biopsy results, PSA level, prostate volume, transrectal ultrasound, and digital rectal examination) and a multilocus genetic risk score (MGRS). Areas under the receiver operating characteristic (ROC) curves (AUC) of the clinical score alone versus the integrated clinicgenetic model were compared. The added value of the MGRS was assessed using the Integrated Discrimination Improvement (IDI) and Net Reclassification Improvement (NRI) statistics. RESULTS. Predictive performance of the integrated clinico-genetic model (AUC ¼ 0.781) was slightly higher than predictive performance of the clinical score alone (AUC ¼ 0.770). The prediction of PCa was significantly improved with an IDI of 0.015 (P-value ¼ 0.035) and a continuous NRI of 0.403 (P-value < 0.001). CONCLUSIONS. The predictive performance of the clinical model was only slightly improved by adding MGRS questioning the real clinical added value with regards to the cost of genetic testing and performance of current inexpensive clinical risk-calculators. Prostate
Clinical utility of five genetic variants for predicting prostate cancer risk and mortality
Protein Science, 2009
BACKGROUNDA recent report suggests that the combination of five single-nucleotide polymorphisms (SNPs) at 8q24, 17q12, 17q24.3 and a family history of the disease may predict risk of prostate cancer. The present study tests the performance of these factors in prediction models for prostate cancer risk and prostate cancer-specific mortality.A recent report suggests that the combination of five single-nucleotide polymorphisms (SNPs) at 8q24, 17q12, 17q24.3 and a family history of the disease may predict risk of prostate cancer. The present study tests the performance of these factors in prediction models for prostate cancer risk and prostate cancer-specific mortality.METHODSSNPs were genotyped in population-based samples from Caucasians in King County, Washington. Incident cases (n = 1,308), aged 35–74, were compared to age-matched controls (n = 1,266) using logistic regression to estimate odds ratios (OR) associated with genotypes and family history. Cox proportional hazards models estimated hazard ratios for prostate cancer-specific mortality according to genotypes.SNPs were genotyped in population-based samples from Caucasians in King County, Washington. Incident cases (n = 1,308), aged 35–74, were compared to age-matched controls (n = 1,266) using logistic regression to estimate odds ratios (OR) associated with genotypes and family history. Cox proportional hazards models estimated hazard ratios for prostate cancer-specific mortality according to genotypes.RESULTSThe combination of SNP genotypes and family history was significantly associated with prostate cancer risk (ptrend = 1.5 × 10−20). Men with ≥5 risk factors had an OR of 4.9 (95% CI 1.6–18.5) compared to men with none. However, this combination of factors did not improve the ROC curve after accounting for known risk predictors (i.e., age, serum PSA, family history). Neither the individual nor combined risk factors was associated with prostate cancer-specific mortality.The combination of SNP genotypes and family history was significantly associated with prostate cancer risk (ptrend = 1.5 × 10−20). Men with ≥5 risk factors had an OR of 4.9 (95% CI 1.6–18.5) compared to men with none. However, this combination of factors did not improve the ROC curve after accounting for known risk predictors (i.e., age, serum PSA, family history). Neither the individual nor combined risk factors was associated with prostate cancer-specific mortality.CONCLUSIONGenotypes for five SNPs plus family history are associated with a significant elevation in risk for prostate cancer and may explain up to 45% of prostate cancer in our population. However, they do not improve prediction models for assessing who is at risk of getting or dying from the disease, once known risk or prognostic factors are taken into account. Thus, this SNP panel may have limited clinical utility. Prostate 69:363–372, 2009. © 2008 Wiley-Liss, Inc.Genotypes for five SNPs plus family history are associated with a significant elevation in risk for prostate cancer and may explain up to 45% of prostate cancer in our population. However, they do not improve prediction models for assessing who is at risk of getting or dying from the disease, once known risk or prognostic factors are taken into account. Thus, this SNP panel may have limited clinical utility. Prostate 69:363–372, 2009. © 2008 Wiley-Liss, Inc.
The Prostate, 2010
BACKGROUND-Prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) are continuously being discovered. Their ability to identify men at high risk and the impact of increasing numbers of SNPs on predictive performance are not well understood. METHODS-Absolute risk for PCa was estimated in a population-based case-control study in Sweden (2,899 cases and 1,722 controls) using family history and three sets of sequentially discovered PCa risk-associated SNPs. Their performance in predicting PCa was assessed by positive predictive values (PPV) and sensitivity.
Cancer Epidemiology, Biomarkers & Prevention, 2010
Disease risk–associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies have the potential to be used for disease risk prediction. An important feature of these risk-associated SNPs is their weak individual effect but stronger cumulative effect on disease risk. Several approaches are commonly used to model the combined effect in risk prediction, but their performance is unclear. We compared two methods to model the combined effect of 14 prostate cancer risk–associated SNPs and family history for the estimation of absolute risk for prostate cancer in a population-based case-control study in Sweden (2,899 cases and 1,722 controls). Method 1 weighs each risk allele equally using a simple method of counting the number of risk alleles, whereas method 2 weighs each risk SNP differently based on its odds ratio. We found considerable differences between the two methods. Absolute risk estimates from method 1 were generally higher than those of method 2, ...
Precision Prostate Cancer Screening with a Polygenic Risk Score
medRxiv (Cold Spring Harbor Laboratory), 2020
Prostate cancer (PC) is the second-most common type of cancer and the fifth-leading cause of cancerrelated death in men worldwide. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with PC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of men according to PRS could be applied in secondary prevention. We aimed to construct a PRS model and to develop a pipeline for personalized prostate cancer screening. Previously published PRS models for predicting the risk of prostate cancer were collected from the literature. These were validated on the Estonian Biobank (EGC) consisting of a total of 16,390 qualitycontrolled genotypes with 262 prevalent and 428 incident PC cases and on 209 634 samples in the UK Biobank with 3254 prevalent cases and 6959 incident cases. The best performing model was selected based on the AUC in prevalent data and independently validated in both incident datasets. Using Estonian PC background information, we performed absolute risk simulations and developed individual risk-based clinical follow-up recommendations. The best-performing PRS included 121 SNPs. The C-index of the Cox regression model associating PC status with PRS was 0.641 (SE = 0.015) with a hazard ratio of 1.65 (95% confidence interval 1.51-1.81) on the incident EGC dataset. The model is able to identify individuals with more than a 3-fold risk increase. The risk of an average 45-year old could be attained by individuals between the ages of 41 and 52. A 41year old male on the 95th percentile has the same risk as an average 45-year old but by age 55, he has attained the same genetic risk as an average 68-year-old. PRS is a powerful predictor of prostate cancer risk that can be combined with current non-invasive practices of PC screening. We have developed PRS-based recommendations for personalized PSA testing. Our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations. .