Genetic variations predicting progression with docetaxel and novel androgen-receptor pathway inhibitors

Masaki Shiota, Shusuke Akamatsu, Yuya Sekine, Hiroko Kimura, Shintaro Narita, Naohiro Fujimoto, Naoki Terada, Leandro Blas, Tomonori Habuchi, Toshiyuki Kamoto, Yukihide Momozawa, Masatoshi Eto

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Genetic variations represented by single-nucleotide polymorphisms (SNPs) could be helpful for choosing an effective treatment for patients with prostate cancer. This study investigated the prognostic and predictive values of SNPs associated with the prognoses of pharmacotherapy for prostate cancer through their pharmacological mechanisms. Patients treated with docetaxel or androgen receptor pathway inhibitors (ARPIs), such as abiraterone and enzalutamide, for castration-resistant prostate cancer were included. The SNPs of interest were genotyped for target regions. The prognostic and predictive values of the SNPs for time to progression (TTP) were examined using the Cox hazard proportional model and interaction test, respectively. Rs1045642 in ABCB1, rs1047303 in HSD3B1, rs1856888 in HSD3B1, rs523349 in SRD5A2, and rs34550074 in SLCO2A1 were differentially associated with TTP between docetaxel chemotherapy and ARPI treatment. In addition to rs4775936 in CYP19A1, rs1128503 in ABCB1 and rs1077858 in SLCO2B1 might be differentially associated with TTP between abiraterone and enzalutamide treatments. Genetic predictive models using these SNPs showed a differential prognosis for treatments. This study identified SNPs that could predict progression as well as genetic models that could predict progression when patients were treated with docetaxel versus ARPI and abiraterone versus enzalutamide. The use of genetic predictive models is expected to be beneficial in selecting the appropriate treatment for the individual patient.

Original languageEnglish
Pages (from-to)1625-1634
Number of pages10
JournalCancer Science
Issue number4
Publication statusPublished - Apr 2023

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research


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