Prediction of prostate cancer from prostate biopsy in Chinese men using a genetic score derived from 24 prostate cancer risk-associated SNPs

Jiang, Haowen; Liu, Fang; Zhong, Wang; Na, Rong; Zhang, Limin; Wu, Yishuo; Zheng, Jie; Lin, Xiaoling; Jiang, Deke; Sun, Jielin; Zheng, S. Lilly; Ding, Qiang; Xu, Jianfeng

doi:10.1002/pros.22661

Cited by 36 publications

(45 citation statements)

References 20 publications

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“…About 70 PCa risk‐associated SNPs have been discovered throughout the genome since 2007, using GWAS in European, African American, Japanese, and Chinese populations . Several studies have consistently demonstrated that genetic score calculated based on these risk‐associated SNPs outperforms family history in measuring inherited risk for PCa and is an independent predictor of biopsy outcome . It is also expected that additional PCa risk‐associated variants, including common SNPs and rare variants, will be identified with the use of larger sample sizes and sequencing and genotyping technologies that have a better genome coverage.…”

Section: Discussionmentioning

confidence: 99%

Plateau effect of prostate cancer risk‐associated SNPs in discriminating prostate biopsy outcomes

Ren

Zhou

et al. 2013

The Prostate

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BACKGROUND Additional prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) continue to be identified. It is unclear whether addition of newly identified SNPs improves the discriminative performance of biopsy outcomes over previously established SNPs. METHODS A total of 667 consecutive patients that underwent prostate biopsy for detection of PCa at Huashan Hospital and Changhai Hospital, Shanghai, China were recruited. Genetic scores were calculated for each patient using various combinations of 29 PCa risk-associated SNPs. Performance of these genetic scores for discriminating prostate biopsy outcomes were compared using the area under a receiver operating characteristic curve (AUC). RESULTS The discriminative performance of genetic score derived from a panel of all 29 SNPs (24 previous and 5 new) was similar to that derived from the 24 previously established SNPs, the AUC of which were 0.60 and 0.61, respectively (P = 0.72). When SNPs with the strongest effect on PCa risk (ranked based on contribution to the total genetic variance from an external study) were sequentially added to the models for calculating genetic score, the AUC gradually increased and peaked at 0.62 with the top 13 strongest SNPs. Under the 13-SNP model, the PCa detection rate was 21.52%, 36.74%, and 51.98%, respectively for men with low (<0.5), intermediate (0.5–1.5), and high (>1.5) genetic score, P-trend = 9.91 × 10−6. CONCLUSION Genetic score based on PCa risk-associated SNPs implicated to date is a significant predictor of biopsy outcome. Additional small-effect PCa risk-associated SNPs to be discovered in the future are unlikely to further improve predictive performance.

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Section: Discussionmentioning

confidence: 99%

Plateau effect of prostate cancer risk‐associated SNPs in discriminating prostate biopsy outcomes

Ren

Zhou

et al. 2013

The Prostate

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“…The association of GRS with PCa risk has been demonstrated consistently, including a large case‐control study with 43,303 PCa cases and 43,737 controls , prospective biopsy cohorts , existing clinical trial populations , and others . However, its association with PCa risk among men with a negative FH has not been previously evaluated.…”

Section: Discussionmentioning

confidence: 99%

Adding genetic risk score to family history identifies twice as many high-risk men for prostate cancer: Results from the prostate cancer prevention trial

et al. 2016

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Background 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. Methods 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 a 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. Results 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 significantly higher (29.02%) than FH− men (23.43%, P=0.001). When both FH+ or GRS>1.4 are considered, more than twice as many men (36%) can be classified as higher risk, as evidenced by a significantly higher PCa detection rate (30.98%) than in the remaining men (20.61%, P=5.30×10−15). If targeting only FH+ men, four of five PCa cases would go undetected, as would a similarly large fraction (~80%) of high-grade PCa cases. In comparison, if targeting FH+ or GRS>1.4 men, almost half of all PCa cases would be detected, including 45% of high-grade PCa cases. Conclusions GRS can supplement family history to better identify higher risk men for targeted intervention.

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“…In prostate cancer (PCa), PRS based on well‐established risk‐associated SNPs has been consistently demonstrated to be an effective tool for PCa risk stratification. Higher percentiles of PRS scores and higher observed PCa risk have been reported in many study populations . In this study, we investigated the validity performance of PRS based on the GPS methods (P + T and LDpred) and compared it with GRS in two independent populations from an existing clinical trial cohort.…”

Section: Introductionmentioning

confidence: 99%

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

Shi

Lin

et al. 2019

The Prostate

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Background Several polygenic risk score (PRS) methods are available for measuring the cumulative effect of multiple risk‐associated single nucleotide polymorphisms (SNPs). Their performance in predicting risk at the individual level has not been well studied. Methods We compared the performance of three PRS methods for prostate cancer risk assessment in a clinical trial cohort, including genetic risk score (GRS), pruning and thresholding (P + T), and linkage disequilibrium prediction (LDpred). Performance was evaluated for score deciles (broad‐sense validity) and score values (narrow‐sense validity). Results A training process was required to identify the best P + T model (397 SNPs) and LDpred model (3 011 362 SNPs). In contrast, GRS was directly calculated based on 110 established risk‐associated SNPs. For broad‐sense validity in the testing population, higher deciles were significantly associated with higher observed risk; Ptrend was 7.40 × 10−11, 7.64 × 10−13, and 7.51 × 10−10 for GRS, P + T, and LDpred, respectively. For narrow‐sense validity, the calibration slope (1 is best) was 1.03, 0.77, and 0.87, and mean bias score (0 is best) was 0.09, 0.21, and 0.10 for GRS, P + T, and LDpred, respectively. Conclusions The performance of GRS was better than P + T and LDpred. Fewer and well‐established SNPs of GRS also make it more feasible and interpretable for genetic testing at the individual level.

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Prediction of prostate cancer from prostate biopsy in Chinese men using a genetic score derived from 24 prostate cancer risk-associated SNPs

Cited by 36 publications

References 20 publications

Plateau effect of prostate cancer risk‐associated SNPs in discriminating prostate biopsy outcomes

Plateau effect of prostate cancer risk‐associated SNPs in discriminating prostate biopsy outcomes

Adding genetic risk score to family history identifies twice as many high-risk men for prostate cancer: Results from the prostate cancer prevention trial

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

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