ABSTRACT
Introduction: Prostate cancer (PCa) is one of the most common adult malignancies worldwide, and a major leading cause of cancer-related death in men in Western societies. In the last years, the prognosis of advanced PCa patients has been impressively improved thanks to the development of different therapeutic agents, including taxanes (docetaxel and cabazitaxel), second-generation anti-hormonal agents (abiraterone and enzalutamide), and the radiopharmaceutical Radium-223. However, great efforts are still needed to properly select the most appropriate treatment for each single patient.
Areas covered: Several prognostic or predictive biomarkers have been studied, none of which has an established validated role in daily clinical practice. This paper analyzed the major biomarkers (including PSA, androgen receptor (AR) splice variants, βIII-tubulin, ALP, circulating tumor cells, and DNA repair genes) with a potential prognostic and/or predictive role in advanced PCa patients.
Expert commentary: Surrogate biomarkers – measurable, reproducible, closely associated with tumor behavior and linked to relevant clinical outcomes – are urgently needed to improve PCa patient management.
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Article Highlights
Biomarkers of response to the different treatment options for mCRPC are urgently needed to improve patients’ management.
High levels of βIII-tubulin have been associated to taxane-resistance.
The role of PSA as a predictor of response to taxanes is still controversial.
CTCs are one of the most promising translational biomarker. A declining in CTCs count could act as a predictive factor for chemotherapy response.
AR-V7 is considered a key factor of resistance to AR targeting treatments (abiraterone and enzalutamide).
ALP decline during Radium-223 therapy has been investigated as predictor of response.
DRGs defects have been identified as potential biomarkers predictive of response to PARP inhibitors.
In the future, the identification of driver mutations present at a definite stage of PCa disease represents the foundations for an increasingly more accurate personalized medicine.
Declaration of interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Reviewers Disclosure
Peer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.