Background: Androgen deprivation therapy (ADT) improves outcomes in men undergoing definitive radiotherapy for prostate cancer but carries significant toxicities. Clinical parameters alone are insufficient to accurately identify patients who will derive the most benefit, highlighting the need for improved patient selection tools to minimize unnecessary exposure to ADT's side effects while ensuring optimal oncological outcomes. The ArteraAI Prostate Test, incorporating a multimodal artificial intelligence (MMAI)-driven digital histopathology-based biomarker, offers prognostic and predictive information to aid in this selection. However, its clinical utility in real-world settings has yet to be measured prospectively.
Methods: This multicentre implementation trial aims to collect real-world data on the use of the previously validated Artera MMAI-driven prognostic and predictive biomarkers in men with intermediate-risk prostate cancer undergoing curative radiotherapy. The prognostic biomarker estimates the 10-year risk of metastasis, while the predictive biomarker determines the likely benefit from short-term ADT (ST-ADT). A total of 800 participants considering ST-ADT in conjunction with curative radiotherapy will be recruited from multiple Australian centers. Eligible patients with intermediate-risk prostate cancer, as defined by the National Comprehensive Cancer Network, will be asked to participate. The primary endpoint is the percentage of patients for whom testing led to a change in the shared ST-ADT recommendation, analyzed using descriptive statistics and McNemar's test comparing recommendations before and after biomarker testing. Secondary endpoints include the impact on quality of life and 5-year disease control, assessed through linkage with the Prostate Cancer Outcomes Registry. The sample size will be re-evaluated at an interim analysis after 200 patients.
Discussion: ASTuTE will determine the impact of a novel prognostic and predictive biomarker on shared decision-making in the short term, and both quality of life and disease control in the medium term. If the biomarker demonstrates a significant impact on treatment decisions, it could lead to more personalized treatment strategies for men with intermediate-risk prostate cancer, potentially reducing overtreatment and improving quality of life. A potential limitation is the variability in clinical practice across different centers inherent in real-world studies.
Trial Registration: Australian New Zealand Clinical Trials Registry, ACTRN12623000713695p. Registered 5 July 2023.
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http://dx.doi.org/10.1186/s12885-025-13622-1 | DOI Listing |
Eur Urol
March 2025
Division of Medical Oncology, Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA. Electronic address:
Owing to the "cold" tumor immune microenvironment of prostate cancer, immune-targeting agents have shown limited efficacy in patients with advanced prostate cancer, highlighting the need for new therapies with novel mechanisms of action. In this context, T-cell engagers (TCEs), which induce T-cell-mediated killing of cancer cells by binding the CD3 receptor on T cells and a specific tumor antigen expressed on malignant cells, represent a promising therapeutic option. Multiple studies have explored the use of TCEs in previously treated patients with metastatic castration-resistant prostate cancer, and several ongoing trials are currently assessing novel TCEs either as single agents or in combinatorial regimens with molecules with a distinct mechanism of action (eg, androgen receptor pathway inhibitors and other immune-targeting agents).
View Article and Find Full Text PDFKeio J Med
March 2025
Center for Hereditary Breast and Ovarian Cancer Syndrome, Keio University Hospital, Tokyo, Japan.
Hereditary breast and ovarian cancer syndrome (HBOC) is traditionally associated with mutations in the BRCA1 and BRCA2 genes, predominantly impacting breast, ovarian, pancreatic, and prostate cancers. However, recent research suggests that these mutations may also predispose carriers to a broader spectrum of malignancies, including biliary tract, cervical, colorectal, endometrial, esophageal, and gastric cancers. This review presents findings from extensive datasets, including a significant study from a nationwide Japanese biobank that examined cancer risks in 63,828 patients and 37,086 controls.
View Article and Find Full Text PDFJ Control Release
March 2025
Université Paris-Saclay, CNRS, Institut Galien Paris-Saclay, Bâtiment Henri Moissan, 17, Avenue des Sciences, 91400 Orsay, France. Electronic address:
Porphysomes are a class of liposome-like nanoparticles that have demonstrated efficacy in photothermal therapy (PTT) and photodynamic therapy (PDT) against cancer. These nanoparticles results from the self-assembly of amphiphilic phospholipid-porphyrin (PL-Por) conjugates. Despite their potential, porphysomes exhibit a high photothermal effect and a weak photodynamic activity as long as they remain intact within the body.
View Article and Find Full Text PDFThe patient was a 51-year-old man who was diagnosed as having prostate cancer(adenocarcinoma)in December Year X-3. He underwent total prostatectomy in June Year X-2. The lesions were confined to the right lobe of the prostate.
View Article and Find Full Text PDFAm J Hum Genet
March 2025
Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
Genome-wide association studies have been highly successful at identifying common variants associated with cancer; however, they do not explain all the inherited risks of cancer. Family-based studies, targeted sequencing, and, more recently, exome-wide association studies have identified rare coding variants in some genes associated with cancer risk, but the overall contribution of these variants to the heritability of cancer is less clear. Here, we describe a method to estimate the genome-wide contribution of rare coding variants to heritability that fits models to the burden effect sizes using an empirical Bayesian approach.
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