Clinical significance of stratifying prostate cancer patients through specific circulating genes.

Patient stratification remains a challenge for optimal treatment of prostate cancer (PCa). This clinical heterogeneity implies intra-tumoural heterogeneity, with different prostate epithelial cell subtypes not all targeted by current treatments. We reported that such cell subtypes are traceable in liquid biopsies through representative transcripts.

Cost-Effectiveness of PARP Inhibitors for Patients with BRCA1/2-Positive Metastatic Castration-Resistant Prostate Cancer-The Canadian Perspective.

Background/objectives: Through phase III clinical trials, PARP inhibitors have demonstrated outcome improvements in mCRPC patients with alterations in BRCA1/2 genes who have progressed on a second-generation androgen receptor pathway inhibitor (ARPI). While improving outcomes, PARP inhibitors contribute to the ever-growing economic burden of PCa. The objective of this project is to evaluate the cost-effectiveness of PARP inhibitors (olaparib, rucaparib, or talazoparib) versus the SOC (docetaxel or androgen receptor pathway inhibitors (ARPI)) for previously progressed mCRPC patients with BRCA1/2 mutations from the Canadian healthcare system perspective.

Navigating Focal Therapy for Prostate Cancer: Contemporary Perspectives and Future Trajectories in the Canadian Context.

This article equips Canadian urologists with the latest advancements in focal therapy (FT) principles and outcomes while providing an overview of its current landscape in Canada, including challenges and future directions. We conducted a nonsystematic review of the literature on FT in urology and prostate cancer (PCa), focusing on Canadian-led studies. Articles were identified using PubMed, MEDLINE, and Google Scholar and selected based on relevance and originality.

Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform.

The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa.