A tissue biomarker panel predicting systemic progression after PSA recurrence post-definitive prostate cancer therapy

Area of Science:

• Oncology
• Molecular Biology
• Biomarker Discovery

Background:

• Rising prostate-specific antigen (PSA) levels after initial prostate cancer therapy can indicate recurrence.
• Distinguishing between true disease progression and non-progressive PSA recurrence is crucial for treatment planning.
• Current methods lack precision in identifying patients who will benefit from further therapy.

Purpose of the Study:

• To develop and validate an expression biomarker panel to predict systemic progression in men with rising PSA after prostatectomy.
• To identify specific gene expression patterns associated with clinical outcomes in prostate cancer patients.

Main Methods:

• A case-control study design was employed, comparing men with systemic progression (SYS) to those with PSA recurrence without clinical progression.
• Gene expression profiling was performed on paraffin-embedded tissue RNA, analyzing 1021 cancer-related genes.
• A 17-gene systemic progression model was developed and its predictive performance evaluated using Area Under the Curve (AUC).

Main Results:

• The developed 17-gene model achieved an AUC of 0.88 (95% CI: 0.84-0.92) for predicting systemic progression within 5 years.
• The model also predicted prostate cancer death (HR 2.5) and progression beyond 5 years (HR 4.7) in secondary analyses.
• Genes mapping to the 8q24 chromosomal region were significantly enriched in the predictive model.

Conclusions:

• Specific gene expression patterns are strongly associated with systemic progression following PSA recurrence in prostate cancer.
• This gene expression biomarker panel shows potential utility in guiding therapeutic decisions for men with rising PSA levels.
• Accurate prediction of disease progression can help personalize treatment strategies and improve patient outcomes.