Androgen receptor binding sites enabling genetic prediction of mortality due to prostate cancer in cancer-free subjects

Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry m...

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Published inNature communications Vol. 14; no. 1; pp. 4863 - 10
Main Authors Ito, Shuji, Liu, Xiaoxi, Ishikawa, Yuki, Conti, David D., Otomo, Nao, Kote-Jarai, Zsofia, Suetsugu, Hiroyuki, Eeles, Rosalind A., Koike, Yoshinao, Hikino, Keiko, Yoshino, Soichiro, Tomizuka, Kohei, Horikoshi, Momoko, Ito, Kaoru, Uchio, Yuji, Momozawa, Yukihide, Kubo, Michiaki, Kamatani, Yoichiro, Matsuda, Koichi, Haiman, Christopher A., Ikegawa, Shiro, Nakagawa, Hidewaki, Terao, Chikashi
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 23.08.2023
Nature Publishing Group
Nature Portfolio
Subjects
45/43
631/208/69
631/67/68
692/4028/67/589/466
Androgen receptors
Androgens
Binding sites
Data analysis
Genetic analysis
Health risks
Humanities and Social Sciences
Meta-analysis
Mortality
Mortality risk
multidisciplinary
Predictions
Prostate cancer
Receptors
Risk analysis
Science
Science (multidisciplinary)
Tumor suppressor genes
Online AccessGet full text

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Abstract Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3 , a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P  = 4.2 × 10 −10 ). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa. The prediction of mortality due to prostate cancer remains challenging. Here, the authors perform trans-ancestry metaanalysis with a focus on binding sites of the androgen receptor and develop a polygenic risk score.
AbstractList Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3, a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P = 4.2 × 10-10). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa.Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3, a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P = 4.2 × 10-10). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa.
Abstract Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3, a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P = 4.2 × 10−10). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa.
Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3, a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P = 4.2 × 10−10). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa.The prediction of mortality due to prostate cancer remains challenging. Here, the authors perform trans-ancestry metaanalysis with a focus on binding sites of the androgen receptor and develop a polygenic risk score.
Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3 , a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P  = 4.2 × 10 −10 ). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa. The prediction of mortality due to prostate cancer remains challenging. Here, the authors perform trans-ancestry metaanalysis with a focus on binding sites of the androgen receptor and develop a polygenic risk score.
Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3 , a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P  = 4.2 × 10 −10 ). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa.
ArticleNumber 4863
Author Yoshino, Soichiro
Tomizuka, Kohei
Ito, Shuji
Ishikawa, Yuki
Otomo, Nao
Nakagawa, Hidewaki
Kote-Jarai, Zsofia
Terao, Chikashi
Hikino, Keiko
Koike, Yoshinao
Kamatani, Yoichiro
Matsuda, Koichi
Ito, Kaoru
Kubo, Michiaki
Ikegawa, Shiro
Liu, Xiaoxi
Conti, David D.
Suetsugu, Hiroyuki
Uchio, Yuji
Momozawa, Yukihide
Haiman, Christopher A.
Eeles, Rosalind A.
Horikoshi, Momoko
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Snippet Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially...
Abstract Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa,...
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SubjectTerms 45/43
631/208/69
631/67/68
692/4028/67/589/466
Androgen receptors
Androgens
Binding sites
Data analysis
Genetic analysis
Health risks
Humanities and Social Sciences
Meta-analysis
Mortality
Mortality risk
multidisciplinary
Predictions
Prostate cancer
Receptors
Risk analysis
Science
Science (multidisciplinary)
Tumor suppressor genes
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Title Androgen receptor binding sites enabling genetic prediction of mortality due to prostate cancer in cancer-free subjects
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