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

• Published in: Nature 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
• Language: English
• 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
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-39858-8

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.