Objective: To assess the role of selected downstream Bcl-2 family members (Bad, Bax, Bcl-2 and Bcl-xL) in the development of androgen-independent prostate cancer (AIPC), as androgen-deprivation therapy is the treatment of choice in advanced prostate cancer, yet patients generally relapse and progress to an AI state within 18-24 months.
Patients, Materials And Methods: The patient cohort was established by retrospectively selecting patients with prostate cancer who had an initial response to androgen-deprivation therapy, but subsequently relapsed with AIPC. In all, 58 patients with prostate cancer were included with matched androgen-dependent (AD) and AI prostate tumours available for immunohistochemical analysis; two independent observers using a weighted-histoscore method scored the staining. Changes in Bad, Bax, Bcl-2 and Bcl-xL expression during transition to AIPC were evaluated and then correlated to known clinical variables.
Results: High Bad expression in AD tumours was associated with an increased time to biochemical relapse (P = 0.007) and a trend towards improved overall survival (P = 0.053). There were also trends towards a decrease in Bad (P = 0.068) and Bax (P = 0.055) expression with progression to AIPC. There were no significant results for Bcl-2 or Bcl-xL.
Conclusion: There is evidence to suggest that Bad expression levels at diagnosis influence time to biochemical relapse and overall survival, and that levels of pro-apoptotic proteins Bad and Bax fall during AIPC development. Bad might therefore represent a possible positive prognostic marker and potential therapeutic target for AIPC in the future.
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http://dx.doi.org/10.1111/j.1464-410X.2007.07001.x | DOI Listing |
J Med Internet Res
January 2025
Cancer Rehabilitation and Survivorship, Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Background: Virtual follow-up (VFU) has the potential to enhance cancer survivorship care. However, a greater understanding is needed of how VFU can be optimized.
Objective: This study aims to examine how, for whom, and in what contexts VFU works for cancer survivorship care.
PLoS One
January 2025
Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data.
View Article and Find Full Text PDFEndocr Relat Cancer
January 2025
S Dehm, Masonic Cancer Center, University of Minnesota, Minneapolis, United States.
Treatment for castration-resistant prostate cancer (CRPC) primarily involves the suppression of androgen receptor (AR) activity using androgen receptor signaling inhibitors (ARSIs). While ARSIs have extended patient survival, resistance inevitably develops. Mechanisms of resistance include genomic aberrations at the AR locus that reactivate AR signaling, or lineage plasticity that drives emergence of AR-independent phenotypes.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles.
Importance: The phase 3 randomized EMBARK trial evaluated enzalutamide with or without leuprolide in high-risk nonmetastatic hormone-sensitive prostate cancer. Eligibility relied on conventional imaging, which underdetects metastatic disease compared with prostate-specific membrane antigen-positron emission tomography (PSMA-PET).
Objective: To describe the staging information obtained by PSMA-PET/computed tomography (PSMA-PET/CT) in a patient cohort eligible for the EMBARK trial.
Int Urol Nephrol
January 2025
Faculty of Medical Sciences, Pharmacology and Toxicology Department, University of Kragujevac, Kragujevac, Serbia.
Purposes: Intermediate-risk prostate cancer (IR PCa) is the most common risk group for localized prostate cancer. This study aimed to develop a machine learning (ML) model that utilizes biopsy predictors to estimate the probability of IR PCa and assess its performance compared to the traditional clinical model.
Methods: Between January 2017 and December 2022, patients with prostate-specific antigen (PSA) values of ≤ 20 ng/mL underwent transrectal ultrasonography-guided prostate biopsies.
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