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Objectives

Extension of quantitative multi-gene expression studies on paired radical prostatectomy (RPE)–prostate tissue samples [supported by a grant from the DFG].

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Objectives

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  1. Extension of quantitative multi-gene expression studies on paired radical prostatectomy (RPE)–prostate tissue samples [supported by a grant from the DFG] S. Fuessel1, S. Unversucht1, R. Koch2, G. Baretton3, A. Lohse1, S. Tomasetti1, M. Haase3, M. Toma3, M. Froehner1, A. Meye1, M.P. Wirth1 1 Department of Urology, 2 Institute of Medical Informatics and Biometry, 3 Institute of Pathology, Technical University of Dresden, Germany

  2. Objectives • biomolecular PCa detection in prostate tissue samples (e.g. biopsies) as additional tool to standard diagnostics • quantification of PCa-related transcript markers promising • identification of expression patterns useful for diagnostic and prognostic purposes

  3. Material & methods I • model system: matched pairs of Tu & Tf prostate specimens from 169 RPE explants • quantitative PCR-assays for • 12 PCa-related genes: • AMACR, AR, DGPCR, EZH2, hepsin, PCA3 (DD3), • PDEF, prostein, PSA, PSGR, PSMA, TRPM8 • 1 reference gene: TATA box binding protein (TBP)

  4. Material & methods II • use of relative expression levels for statistics • ROC analyses (AUC values = diagnostic power) • evaluation of single & combined markers • mathematical logit models for : • prediction of PCa-presence • prediction of tumor extension in a given prostate tissue specimen

  5. n=169 Evaluation of single markers overexpression in PCa (paired Tu:Tf ratios): PCA3 (=DD3), AMACR, PSGR, hepsin, TRPM8 & PSMA  most promising PCa transcript markers

  6. Prediction of PCa presence EZH2 + hepsin + PCA3 + prostein + TRPM8 probability (p) of PCa presence for Tu tissues: median p = 87% for Tf tissues: median p = 10% • ROC-analyses: • former 4-gene-model •  AUC = 0.893 • (95% CI 0.76 ... 1.00) • new 5-gene-model • AUC = 0.914 • (95% CI 0.77 ... 1.00) predicted probability of tumor 1- Specificity • combinationof 5 transcript markers better diagnostic power than single markers and the former 4-gene-model • calculation of probability(p) of PCa presence in the analyzed tissue samples (169 pairs)

  7. TRPM8 PCA3 EZH2 lg (EZH2 / TBP) lg (PCA3 / TBP) lg (TRPM8 / TBP) Tf Tu (OCD) Tu (NOCD) Tf Tu (OCD) Tu (NOCD) Tf Tu (OCD) Tu (NOCD) Dependence on tumor stage • Discriminationbetween • organ-confined disease (OCD) and • non-organ-confined disease (NOCD)? for log-transformed relative expression levels of: Tf: n=169 OCD: n=78 NOCD: n=91  mathematical model for OCD-prediction

  8. probability (p) of OCD median p for NOCD 9% median p for OCD 49% ROC-analysis: 3-gene-model AUC = 0.830 (95% CI 0.72 ... 0.94) 1- Specificity predicted probability of OCD NOCD (n=91) OCD (n=78) OCD-prediction: EZH2 + PCA3 + TRPM8 goal: estimation of tumor stage on biopsies possibly useful for therapeutic decisions 

  9. Conclusion • biomolecular PCa detection on a given prostate tissue specimen by quantification of transcript patterns: • feasibility shown in a model system (RPE specimens) •  marker combinations  increased diagnostic power • 5 PCa-markers & 1 reference gene •  sufficient for different diagnostic purposes

  10. Outlook • transfer of the techniques to prostate biopsies •  to evaluate their applicability in PCa diagnostics? •  improvement of PCa detection? • Future aims: • correlation of transcript signatures with outcome •  follow-up needed for prognostic purposes • correct prediction of tumor behavior •  decision for an adapted treatment

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