Prediction of individual genetic risk to prostate cancer using a polygenic score (original) (raw)
Related papers
Cancer Epidemiology Biomarkers & Prevention, 2012
Background: One of the goals of personalized medicine is to generate individual risk profiles that could identify individuals in the population that exhibit high risk. The discovery of more than two-dozen independent single-nucleotide polymorphism markers in prostate cancer has raised the possibility for such risk stratification. In this study, we evaluated the discriminative and predictive ability for prostate cancer risk models incorporating 25 common prostate cancer genetic markers, family history of prostate cancer, and age. Methods: We fit a series of risk models and estimated their performance in 7,509 prostate cancer cases and 7,652 controls within the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3). We also calculated absolute risks based on SEER incidence data. Results: The best risk model (C-statistic ¼ 0.642) included individual genetic markers and family history of prostate cancer. We observed a decreasing trend in discriminative ability with advancing age (P ¼ 0.009), with highest accuracy in men younger than 60 years (C-statistic ¼ 0.679). The absolute ten-year risk for 50-year-old men with a family history ranged from 1.6% (10th percentile of genetic risk) to 6.7% (90th percentile of genetic risk). For men without family history, the risk ranged from 0.8% (10th percentile) to 3.4% (90th percentile). Conclusions: Our results indicate that incorporating genetic information and family history in prostate cancer risk models can be particularly useful for identifying younger men that might benefit from prostatespecific antigen screening. Impact: Although adding genetic risk markers improves model performance, the clinical utility of these genetic risk models is limited. Cancer Epidemiol Biomarkers Prev; 21(3); 437-44. Ó2012 AACR.
Additional SNPs improve the performance of a polygenic hazard score for prostate cancer
2020
Background: Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). Materials and Method: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of indivi...
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 ...
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 ...
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.
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2015
Genome-wide association studies have identified multiple genetic variants associated with prostate cancer (PrCa) risk which explain a substantial proportion of familial relative risk. These variants can be used to stratify individuals by their risk of PrCa. We genotyped 25 PrCa susceptibility loci in 40,414 individuals and derived a polygenic risk score (PRS). We estimated empirical Odds Ratios for PrCa associated with different risk strata defined by PRS and derived age-specific absolute risks of developing PrCa by PRS stratum and family history. The PrCa risk for men in the top 1% of the PRS distribution was 30.6 (95% CI 16.4-57.3) fold compared with men in the bottom 1%, and 4.2 (95% CI 3.2-5.5) fold compared with the median risk. The absolute risk of PrCa by age 85 was 65.8% for a man with family history in the top 1% of the PRS distribution, compared with 3.7% for a man in the bottom 1%. The PRS was only weakly correlated with serum PSA level (correlation=0.09). Risk profiling ...
The Journal of Urology, 2009
INTRODUCTION-Five genetic variants along chromosomes 8q24 and 17q were previously shown to have a cumulative association with prostate cancer (CaP) risk. Our research group has previously demonstrated an association between these variants and clincopathologic characteristics. More recently, 4 additional CaP susceptibility variants were identified on chromosomes 2p15, 10q11, 11q13 and Xp11. Our objectives were to examine a cumulative risk assessment incorporating all 9 genetic variants, and to determine the relationship of the new variants with clincopathologic tumor features.
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. .
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
Prostate, 2010
BACKGROUND-Genome-wide association studies (GWAS) have led to the discovery of multiple SNPs that are associated with prostate cancer (PCa) risk. These SNPs may potentially be used for risk prediction. To date, there is not a stable estimate of their effect on PCa risk and their contribution to the genetic variation both of which are important for future risk prediction. METHODS-A literature review was conducted to identify SNPs associated with PCa risk with the following criteria: (1) GWAS in the Caucasian population; (2) SNPs with p-value < 1.0×10 −6 ; and (3) one SNP from each independent LD block. A meta-analysis was performed to estimate combined odds ratio (OR) and its 95% confidence interval (CI) for the identified SNPs. The proportion of total genetic variance that is attributable by each of these SNPs was also estimated. RESULTS-Thirty PCa risk-associated SNPs were identified. These SNPs had OR estimates between 1.12-1.47 except for marker rs16901979 (OR = 1.80). Significant heterogeneity in OR estimates was found among different studies for 13 SNPs. The proportion of total genetic variance attributed by each SNP ranged between 0.2%-0.9%. These 30 SNPs explained ~13 .5% of the total genetic variance of PCa risk in the Caucasian population. CONCLUSION-This study provides more stable OR estimates for PCa risk-associated SNPs, which is an important baseline for the effect of these SNPs in risk prediction. These SNPs explain a considerable proportion of genetic variance, however, the majority of genetic variance has yet to be explained.