Reproducibility, Performance, and Clinical Utility of a Genetic Risk Prediction Model for Prostate Cancer in Japanese

Shusuke Akamatsu, Atsushi Takahashi, Ryo Takata, Michiaki Kubo, Takahiro Inoue, Takashi Morizono, Tatsuhiko Tsunoda, Naoyuki Kamatani, Christopher A. Haiman, Peggy Wan, Gary K. Chen, Loic Le Marchand, Laurence N. Kolonel, Brian E. Henderson, Tomoaki Fujioka, Tomonori Habuchi, Yusuke Nakamura, Osamu Ogawa, Hidewaki Nakagawa

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26 Citations (Scopus)


Prostate specific antigen (PSA) is widely used as a diagnostic biomarker for prostate cancer (PC). However, due to its low predictive performance, many patients without PC suffer from the harms of unnecessary prostate needle biopsies. The present study aims to evaluate the reproducibility and performance of a genetic risk prediction model in Japanese and estimate its utility as a diagnostic biomarker in a clinical scenario. We created a logistic regression model incorporating 16 SNPs that were significantly associated with PC in a genome-wide association study of Japanese population using 689 cases and 749 male controls. The model was validated by two independent sets of Japanese samples comprising 3,294 cases and 6,281 male controls. The areas under curve (AUC) of the model were 0.679, 0.655, and 0.661 for the samples used to create the model and those used for validation. The AUCs were not significantly altered in samples with PSA 1-10 ng/ml. 24.2% and 9.7% of the patients had odds ratio <0.5 (low risk) or >2 (high risk) in the model. Assuming the overall positive rate of prostate needle biopsies to be 20%, the positive biopsy rates were 10.7% and 42.4% for the low and high genetic risk groups respectively. Our genetic risk prediction model for PC was highly reproducible, and its predictive performance was not influenced by PSA. The model could have a potential to affect clinical decision when it is applied to patients with gray-zone PSA, which should be confirmed in future clinical studies.

Original languageEnglish
Article numbere46454
JournalPloS one
Issue number10
Publication statusPublished - 10-10-2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General


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