1 / 10

Objectives

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

kelli
Download Presentation

Objectives

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related