Dataset Information

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.

ABSTRACT:

Background

A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.

Methods

United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ?7, stage T3-T4, PSA ?10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.

Results

The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.

Conclusions

PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.

Impact

Personalized genetic risk assessments could inform prostate cancer screening decisions.

SUBMITTER: Huynh-Le MP

PROVIDER: S-EPMC7483627 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.

Huynh-Le Minh-Phuong MP Fan Chun Chieh CC Karunamuni Roshan R Walsh Eleanor I EI Turner Emma L EL Lane J Athene JA Lane J Athene JA Martin Richard M RM Neal David E DE Donovan Jenny L JL Hamdy Freddie C FC Parsons J Kellogg JK Eeles Rosalind A RA Easton Douglas F DF Kote-Jarai Zsofia Z Amin Al Olama Ali A Benlloch Garcia Sara S Muir Kenneth K Grönberg Henrik H Wiklund Fredrik F Aly Markus M Schleutker Johanna J Sipeky Csilla C Tammela Teuvo Lj TL Nordestgaard Børge Grønne BG Key Timothy J TJ Travis Ruth C RC Pharoah Paul D P PDP Pashayan Nora N Khaw Kay-Tee KT Thibodeau Stephen N SN McDonnell Shannon K SK Schaid Daniel J DJ Maier Christiane C Vogel Walther W Luedeke Manuel M Herkommer Kathleen K Kibel Adam S AS Cybulski Cezary C Wokolorczyk Dominika D Kluzniak Wojciech W Cannon-Albright Lisa A LA Brenner Hermann H Brenner Hermann H Schöttker Ben B Holleczek Bernd B Park Jong Y JY Sellers Thomas A TA Lin Hui-Yi HY Slavov Chavdar Kroumov CK Kaneva Radka P RP Mitev Vanio I VI Batra Jyotsna J Clements Judith A JA Spurdle Amanda B AB Teixeira Manuel R MR Paulo Paula P Maia Sofia S Pandha Hardev H Michael Agnieszka A Mills Ian G IG Andreassen Ole A OA Dale Anders M AM Seibert Tyler M TM

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20200624 9

<h4>Background</h4>A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.<h4>Methods</h4>United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific cli ...[more]

PMID: 32581112

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Project description:Several different approaches are available to clinicians for determining prostate cancer (PCa) risk. The clinical validity of various PCa risk assessment methods utilizing single nucleotide polymorphisms (SNPs) has been established; however, these SNP-based methods have not been compared. The objective of this study was to compare the three most commonly used SNP-based methods for PCa risk assessment. Participants were men (n = 1654) enrolled in a prospective study of PCa development. Genotypes of 59 PCa risk-associated SNPs were available in this cohort. Three methods of calculating SNP-based genetic risk scores (GRSs) were used for the evaluation of individual disease risk such as risk allele count (GRS-RAC), weighted risk allele count (GRS-wRAC), and population-standardized genetic risk score (GRS-PS). Mean GRSs were calculated, and performances were compared using area under the receiver operating characteristic curve (AUC) and positive predictive value (PPV). All SNP-based methods were found to be independently associated with PCa (all P < 0.05; hence their clinical validity). The mean GRSs in men with or without PCa using GRS-RAC were 55.15 and 53.46, respectively, using GRS-wRAC were 7.42 and 6.97, respectively, and using GRS-PS were 1.12 and 0.84, respectively (all P < 0.05 for differences between patients with or without PCa). All three SNP-based methods performed similarly in discriminating PCa from non-PCa based on AUC and in predicting PCa risk based on PPV (all P > 0.05 for comparisons between the three methods), and all three SNP-based methods had a significantly higher AUC than family history (all P < 0.05). Results from this study suggest that while the three most commonly used SNP-based methods performed similarly in discriminating PCa from non-PCa at the population level, GRS-PS is the method of choice for risk assessment at the individual level because its value (where 1.0 represents average population risk) can be easily interpreted regardless of the number of risk-associated SNPs used in the calculation.

Project description:Background & aimsThe presence of specific single nucleotide polymorphisms (SNPs) can be used to calculate an individual's risk for colorectal cancer (CRC), called a genetic risk score (GRS). We investigated whether GRS can identify individuals with clinically relevant neoplasms in a screening colonoscopy population.MethodsWe derived a GRS based on 48 SNPs associated with CRC, identified in a comprehensive literature search. We obtained genetic data from 1043 participants (50-79 years old) in a screening colonoscopy study in Germany, recruited from 2005 through 2013 (294 with advanced neoplasms, 249 with non-advanced adenoma (NAAs), and 500 without neoplasms). Each participant was assigned a GRS by aggregating their risk alleles (0, 1, or 2). Risk of advanced neoplasms and NAA according to GRS was calculated by multiple logistic regression. Risk advancement periods were calculated. We replicated our findings using data from a subset of the Tennessee Colorectal Polyp Study.ResultsAn increased GRS was associated with higher prevalence of advanced neoplasms, but not NAAs. Participants in the middle and upper tertiles of GRS had a 2.2-fold and 2.7-fold increase in risk, respectively, of advanced neoplasms compared to those in the lower tertile. Adjusted odds ratios (ORs) were 1.09 (95% confidence interval [CI], 0.76-1.57) for NAA in the middle tertile and 1.05 (95% CI, 0.70-1.55) for NAA in the upper tertile. The ORs were largest for proximal advanced neoplasms for participants in the middle tertile (OR, 3.55; 95% CI 1.85-6.82) and the upper tertile (OR, 3.61; 95% CI 1.84-7.10). The risk advancement period for medium vs low GRS was 13.4 years (95% CI 4.8-22.0) and for high vs low GRS was 17.5 years (95% CI, 7.8-27.3).ConclusionsIn a genetic analysis of participants in a CRC screening study in Germany, an increased GRS (based on CRC-associated SNPs) was associated with increased prevalence of advanced neoplasms. These findings might be used in defining risk-adapted screening ages.

Dataset Information

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.

Background

Methods

Results

Conclusions

Impact

Publications

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.

Similar Datasets

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