Prediction of individual genetic risk to prostate cancer using a polygenic score

Szulkin, Robert; Whitington, Thomas; Eklund, Martin; Aly, Markus; Eeles, Rosalind; Easton, Douglas F.; Kóte-Jarai, Zsofia; Olama, Ali Amin Al; Benlloch, Sara; Muir, Ken; Giles, Graham G.; Southey, Melissa C.; FitzGerald, Liesel M.; Henderson, Brian E.; Schumacher, Fredrick; Haiman, Christopher A.; Schleutker, Johanna; Wahlfors, Tiina; Tlj., Tammela; Nordestgaard, Børge G.; Key, Timothy J.; Travis, Ruth C.; Neal, David E.; Donovan, Jenny; Hamdy, Freddie C.; Pharoah, Paul D.P.; Pashayan, Nora; Khaw, K-T; Stanford, Janet L.; Thibodeau, Stephen N.; McDonnell, Shannon K.; Schaid, Dan; Maier, Christiane; Vogel, Walther; Luedeke, Manuel; Herkommer, Kathleen; Kibel, Adam S.; Cybulski, Cezary; Lubiński, Jan; Kluźniak, Wojciech; Cannon‐Albright, Lisa A.; Brenner, Hermann; Butterbach, Katja; Stegmaier, Christa; Park, J Y; Sellers, Thomas A.; Hy, Lin; Slavov, Chavdar; Kaneva, Radka; Mitev, Vanio; Batra, Jyotsna; Clements, Judith A.; Spurdle, Amanda B.; Teixeira, Manuel R.; Paulo, Paula; Maia, Sofia; Pandha, Hardev; Michael, Agnieszka; Kierzek, Andrzej M.; Grönberg, Henrik; Wiklund, Fredrik

doi:10.1002/pros.23037

Cited by 54 publications

(42 citation statements)

References 25 publications

(28 reference statements)

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“…Several methods are commonly used to measure the cumulative effect of multiple risk-associated SNPs, including sRAC, PRS, and GRS. 18,[24][25][26] Although the same conclusion of the study can be made from any of these methods, there are important differences between Therefore, GRS is a better choice for risk assessment at an individual level risk assessment.…”

Section: Resultsmentioning

confidence: 99%

Reclassification of prostate cancer risk using sequentially identified SNPs: Results from the REDUCE trial

Chen

Packiam

et al. 2017

Prostate

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Section: Resultsmentioning

confidence: 99%

Reclassification of prostate cancer risk using sequentially identified SNPs: Results from the REDUCE trial

Chen

Packiam

et al. 2017

Prostate

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“…The Prolaris score (Myriad Genomic Inc., Salt Lake City, UT, USA) is a genomic assay that provides a Cell Cycle Progression (CCP) score based on level of expression of mRNA of 31 cell cycle progression genes relative to the level of 15 housekeeping genes 32 . CCP scores have shown a strong correlation on univariate analysis with PCa death in a conservatively managed cohort after biopsy (hazard ratio (HR) 2.02, 95% CI 1.62, 2.53) 33 .…”

Section: Prostatic Tissue Biomarkersmentioning

confidence: 99%

Prostate cancer biomarkers: Are we hitting the mark?

McGrath

Christidis

Perera

et al. 2016

Prostate International

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PurposeLocalised prostate cancer diagnosis and management is increasingly complex due to its heterogeneous progression and prognostic subgroups. Pitfalls in current screening and diagnosis have prompted the search for accurate and invasive molecular and genetic biomarkers for prostate cancer. Such tools may be able to distinguish clinically significant cancers from less aggressive variants to assist with prostate cancer risk stratification and guide decisions and healthcare algorithms. We aimed to provide a comprehensive review of the current prostate cancer biomarkers available and in development.MethodsMEDLINE and EMBASE databases searches were conducted to identify articles pertaining to the use of novel biomarkers for prostate cancer.ResultsA growing number of novel biomarkers are currently under investigation. Such markers include urinary biomarkers, serology-based markers or pathological tissue assessments of molecular and genetic markers. While limited clinical data is present for analysis, early results appear promising. Specifically, a combination of serum and urinary biomarkers (Serum PSA + Urinary PCA3 + Urinary TMPRSS2-ERG fusion) appears to provide superior sensitivity and specificity profiles compared to traditional diagnostic approaches (AUC 0.88).ConclusionThe accurate diagnosis and risk stratification of prostate cancer is critical to ensure appropriate intervention. The development of non-invasive biomarkers can add to the information provided by current screening practices and allows for individualised risk stratification of patients. The use of these biomarkers appears to increase the sensitivity and specificity of diagnosis of prostate cancer. Further studies are necessary to define the appropriate use and time points of each biomarker and their effect on the management algorithm of prostate cancer.

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“…Genome‐wide association studies (GWAS) have identified at least 100 single‐nucleotide polymorphism loci or SNPs that are associated with increased risk of PCa, at least in men of European descent . Although the impact of each individual risk SNP is small, men who carry the top percentiles of inherited risk alleles are 4‐6‐fold more likely to be diagnosed with PCa . Recent studies suggest PCa risk SNPs account for ∼33% of familial PCa risk in men of European ancestry .…”

Section: Introductionmentioning

confidence: 99%

“…3 Although the impact of each individual risk SNP is small, men who carry the top percentiles of inherited risk alleles are 4-6-fold more likely to be diagnosed with PCa. 4,5 Recent studies suggest PCa risk SNPs account for ∼33% of familial PCa risk in men of European ancestry. 3 An analysis of PCa risk SNPs in the Norwegian population has not been previously reported to our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Genetic factors influencing prostate cancer risk in Norwegian men

et al. 2017

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Norway has one of the highest rates of death due to prostate cancer (PCa) in the world. To assess the contribution of both common and rare single nucleotide variants (SNPs) to the prostate cancer burden in Norway, we assessed the frequency of the established prostate cancer susceptibility allele, HOXB13 G84E, as well as a series of validated, common PCa risk SNPs in a Norwegian PCa population of 779 patients. The G84E allele was observed in 2.3% of patients compared to 0.7% of control individuals, OR = 3.8, P = 1 × 10-4. While there was a trend toward an earlier age at diagnosis, overall the clinicopathologic features of PCa were not significantly different in G84E carriers and non-carriers. Evaluation of 32 established common risk alleles revealed significant associations of risk alleles at 13 loci, including SNPs at 8q24, and near TET2, SLC22A3, NKX3-1, CASC8, MYC, DAP2IP, MSMB, HNF1B, PPP1R14A, and KLK2/3. When the data for each SNP are combined into a genetic risk score (GRS), Norwegian men within the top decile of GRS have over 5-fold greater risk to be diagnosed with PCa than men with GRS in the lowest decile. These results indicate that risk alleles of HOXB13 and common variant SNPs are important components of inherited PCa risk in the Norwegian population, although these factors appear to contribute little to the malignancy's aggressiveness.

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Prediction of individual genetic risk to prostate cancer using a polygenic score

Abstract: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.

Cited by 54 publications

References 25 publications

Reclassification of prostate cancer risk using sequentially identified SNPs: Results from the REDUCE trial

Reclassification of prostate cancer risk using sequentially identified SNPs: Results from the REDUCE trial

Prostate cancer biomarkers: Are we hitting the mark?

Genetic factors influencing prostate cancer risk in Norwegian men

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