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

Shuji Ito, Xiaoxi Liu, Yuki Ishikawa, David D. Conti, Nao Otomo, Zsofia Kote-Jarai, Hiroyuki Suetsugu, Rosalind A. Eeles, Yoshinao Koike, Keiko Hikino, Soichiro Yoshino, Kohei Tomizuka, Momoko Horikoshi, Kaoru Ito, Yuji Uchio, Yukihide Momozawa, Michiaki Kubo, Yoichiro Kamatani, Koichi Matsuda, Christopher A. Haiman, Shiro Ikegawa, Hidewaki Nakagawa and Chikashi Terao ()
Additional contact information
Shuji Ito: The Laboratory for Statistical and Translational Genetics
Xiaoxi Liu: The Laboratory for Statistical and Translational Genetics
Yuki Ishikawa: The Laboratory for Statistical and Translational Genetics
David D. Conti: University of Southern California
Nao Otomo: The Laboratory for Statistical and Translational Genetics
Zsofia Kote-Jarai: The Institute of Cancer Research
Hiroyuki Suetsugu: The Laboratory for Statistical and Translational Genetics
Rosalind A. Eeles: The Institute of Cancer Research
Yoshinao Koike: The Laboratory for Statistical and Translational Genetics
Keiko Hikino: The Laboratory for Pharmacogenomics
Soichiro Yoshino: The Laboratory for Statistical and Translational Genetics
Kohei Tomizuka: The Laboratory for Statistical and Translational Genetics
Momoko Horikoshi: The Laboratory for Genomics of Diabetes and Metabolism
Kaoru Ito: The Cardiovascular Genomics and Informatics
Yuji Uchio: Shimane University
Yukihide Momozawa: The Laboratory for Genotyping Development
Michiaki Kubo: Haradoi Hospital
Yoichiro Kamatani: The University of Tokyo
Koichi Matsuda: Human Genome Center
Christopher A. Haiman: University of Southern California
Shiro Ikegawa: The Laboratory for Bone and Joint Diseases
Hidewaki Nakagawa: Laboratory for Cancer Genomics
Chikashi Terao: The Laboratory for Statistical and Translational Genetics

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39858-8

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DOI: 10.1038/s41467-023-39858-8

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