Inhibition of the Wnt/β-Catenin Pathway Overcomes Resistance to Enzalutamide in Castration-Resistant Prostate Cancer.
Zhuangzhuang ZhangLijun ChengJie LiElia FarahNadia Atallah LanmanPete E PascuzziSanjay GuptaXiaoqi LiuPublished in: Cancer research (2018)
Enzalutamide is a second-generation nonsteroidal antiandrogen clinically approved for the treatment of castration-resistant prostate cancer (CRPC), yet resistance to endocrine therapy has limited its success in this setting. Although the androgen receptor (AR) has been associated with therapy failure, the mechanisms underlying this failure have not been elucidated. Bioinformatics analysis predicted that activation of the Wnt/β-catenin pathway and its interaction with AR play a major role in acquisition of enzalutamide resistance. To validate the finding, we show upregulation of β-catenin and AR in enzalutamide-resistant cells, partially due to reduction of β-TrCP-mediated ubiquitination. Although activation of the Wnt/β-catenin pathway in enzalutamide-sensitive cells led to drug resistance, combination of β-catenin inhibitor ICG001 with enzalutamide inhibited expression of stem-like markers, cell proliferation, and tumor growth synergistically in various models. Analysis of clinical datasets revealed a molecule pattern shift in different stages of prostate cancer, where we detected a significant correlation between AR and β-catenin expression. These data identify activation of the Wnt/β-catenin pathway as a major mechanism contributing to enzalutamide resistance and demonstrate the potential to stratify patients with high risk of said resistance.Significance: Wnt/β-catenin inhibition resensitizes prostate cancer cells to enzalutamide. Cancer Res; 78(12); 3147-62. ©2018 AACR.
Keyphrases
- prostate cancer
- cell proliferation
- radical prostatectomy
- cell cycle
- poor prognosis
- stem cells
- induced apoptosis
- pi k akt
- cell cycle arrest
- epithelial mesenchymal transition
- signaling pathway
- risk assessment
- bone marrow
- long non coding rna
- binding protein
- cell death
- machine learning
- squamous cell carcinoma
- oxidative stress
- bioinformatics analysis
- big data
- deep learning
- fluorescence imaging
- childhood cancer
- squamous cell