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Transcript Markers from Urinary Extracellular Vesicles for Predicting Risk Reclassification of Prostate Cancer
Kati Erdmann1,2,3, Florian Distler4, Sebastian Gräfe1,2
1Department of Urology, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany.
Cancers
|July 13, 2024
Summary
Urinary extracellular vesicle (uEV) transcripts combined with PSA density and MRI show promise for predicting prostate cancer (PCa) risk reclassification in active surveillance (AS) patients. This novel approach improves monitoring accuracy compared to individual markers.
Area of Science:
- Urology
- Oncology
- Molecular Diagnostics
Background:
- Current methods like PSA and MRI lack sufficient accuracy for predicting prostate cancer (PCa) risk reclassification in patients on active surveillance (AS).
- Accurate risk stratification is crucial for managing PCa patients on AS, balancing the need for timely intervention with avoiding unnecessary procedures.
Purpose of the Study:
- To investigate the potential of specific transcripts in urinary extracellular vesicles (uEVs) to predict PCa risk reclassification in patients undergoing AS.
- To compare the predictive performance of uEV transcripts, alone and in combination with clinical parameters, against established markers.
Main Methods:
- Prospective collection of urine samples from 72 PCa patients on AS before control biopsy.
- Quantification of 29 PCa-associated transcripts from uEVs using quantitative PCR.
- Assessment of predictive ability using ROC curve analysis (AUC) and multivariate regression, comparing transcript panels with PSA, PSA derivatives, and MRI.
Main Results:
- Individual transcripts (AMACR, HPN, MALAT1, PCA3, PCAT29) showed some predictive potential (AUC 0.614-0.655).
- Established markers (PSA, PSA density, PSA velocity, MRI maxPI-RADS) demonstrated moderate predictive accuracy (AUC 0.681-0.747, 64-68% accuracy).
- A combined model incorporating AMACR, MALAT1, PCAT29, PSA density, and MRI maxPI-RADS achieved significantly higher predictive performance (AUC 0.867, 87% sensitivity, 83% specificity, 85% accuracy).
Conclusions:
- Urinary extracellular vesicle (uEV) transcripts hold significant potential as non-invasive biomarkers for monitoring PCa patients on active surveillance.
- Combining uEV transcripts with clinical parameters like PSA density and MRI offers a superior strategy for predicting PCa risk reclassification.
- This multi-marker approach enhances monitoring accuracy, potentially improving clinical decision-making for active surveillance management.
