1. S. Fuessel 1, S. Unversucht 1, R. Koch 2, G. Baretton 3, A. Lohse 1, S. Tomasetti 1, M. Haase 3, M. Toma 3, M. Froehner 1, A. Meye 1, M.P. Wirth 1 1 Department of Urology, 2 Institute of Medical Informatics and Biometry, 3 Institute of Pathology, Technical University of Dresden, Germany Extension of quantitative multi-gene expression studies on paired radical prostatectomy (RPE)– prostate tissue samples [supported by a grant from the DFG]

2. main problem: early identification of significant PCa for therapeutic decisionsmain problem: early identification of significant PCa for therapeutic decisions need for new additional PCa-markers to improve diagnostic and prognostic powerneed for new additional PCa-markers to improve diagnostic and prognostic power quantification of transcript markers as promising toolquantification of transcript markers as promising tool expression signatures more reliable than single markersexpression signatures more reliable than single markers Objective

3. 169 matched pairs of malignant and non-malignant prostate tissue specimens (Tu + Tf) from RPE specimens169 matched pairs of malignant and non-malignant prostate tissue specimens (Tu + Tf) from RPE specimens establishment of standardized quantitativeestablishment of standardized quantitative PCR-assays (QPCR) evaluation of 4 housekeeping genes (GAPDH, HPRT, PBGD, TBP) as reference for internal normalization:evaluation of 4 housekeeping genes (GAPDH, HPRT, PBGD, TBP) as reference for internal normalization:  only TATA box binding protein (TBP) suitable (no different expression between Tu and Tf) Material & methods I

4. transcript marker name AMACRAR D-GPCR (OR51E1) EZH2hepsin PCA3 (DD3) PDEFprosteinPSA PSGR (OR51E2) PSMATRPM8  -methylacyl-CoA-racemase androgen receptor G protein-coupled receptor (olfactory receptor) enhancer of zeste homolog 2 membrane associated protease prostate cancer antigen 3 prostate-derived Ets factor prostate cancer-associated gene 6 prostate specific antigen prostate specific G protein-coupled receptor prostate specific membrane antigen transient receptor protein M8 Material & methods II 12 PCa-related genes known from literature were tested

5. evaluation of single & combined markersevaluation of single & combined markers (ROC-analyses) (ROC-analyses) mathematical models for PCa-specific transcript signaturesmathematical models for PCa-specific transcript signatures goals: - prediction of PCa-presencegoals: - prediction of PCa-presence - prediction of tumor extension - prediction of tumor extension - prediction of tumor aggressiveness - prediction of tumor aggressiveness  final aim: bioprofiling of PCa  final aim: bioprofiling of PCa Material & methods III

6. Evaluation of single markers: overexpression in PCa? PCA3 (=DD3), AMACR, PSGR, hepsin, TRPM8 & PSMA  most promising PCa transcript markers n=169

7. Optimized 4-gene-model for PCa-prediction: EZH2 + PCA3 + prostein + TRPM8 1- Specificity AUC = 0.893 (95% CI 0.76... 1.00) ROC-analysis of the 4-gene-combination predicted probability of tumor classification of relative expression levels of these 4 genes according optimized cut-offs  logit-value for each tissue sample (Tu and Tf)classification of relative expression levels of these 4 genes according optimized cut-offs  logit-value for each tissue sample (Tu and Tf) logit-model: p = exp(logit)/[1+exp(logit)]logit-model: p = exp(logit)/[1+exp(logit)] n=169 probability (p) of PCa presence in the analyzed tissue samples: median p for Tu 81% median p for Tu 81% median p for Tf 21% median p for Tf 21%

8. New 5-gene-model for PCa-prediction: EZH2 + hepsin + PCA3 + prostein + TRPM8 ROC-analysis of the 5-gene-combination using relative expression levels of these 5 genes as continuous values  logit-value for each tissue sample (Tu and Tf)using relative expression levels of these 5 genes as continuous values  logit-value for each tissue sample (Tu and Tf) logit-model: p = exp(logit)/[1+exp(logit)]logit-model: p = exp(logit)/[1+exp(logit)] probability (p) of PCa presence in the analyzed tissue samples: median p for Tu 87% median p for Tu 87% median p for Tf 10% median p for Tf 10% 1- Specificity AUC = 0.914 (95% CI 0.77... 1.00) predicted probability of tumor

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

10. probability (p) of OCD in the analyzed 169 Tu tissues: median p for NOCD 9% median p for NOCD 9% median p for OCD 49% median p for OCD 49% ROC-analysis of the 3-gene-model for OCD prediction 1- Specificity AUC = 0.830 (95% CI 0.72... 0.94) New 3-gene-model for OCD-prediction: EZH2 + PCA3 + TRPM8 predicted probability of OCD NOCD (n=91) OCD (n=78) using relative expression levels of these 3 genes as continuous values  logit-value for each tissue sample (Tu-NOCD and Tu-NOCD)using relative expression levels of these 3 genes as continuous values  logit-value for each tissue sample (Tu-NOCD and Tu-NOCD) logit-model: p = exp(logit)/[1+exp(logit)]logit-model: p = exp(logit)/[1+exp(logit)]

11. biomolecular PCa detection on a given prostate specimenbiomolecular PCa detection on a given prostate specimen  as additional tool to standard diagnostics? use of transcript marker combinationsuse of transcript marker combinations  increased diagnostic power measurement of only 5 transcript PCa-markers (EZH2, hepsin, PCA3, prostein, TRPM8) & 1 reference genemeasurement of only 5 transcript PCa-markers (EZH2, hepsin, PCA3, prostein, TRPM8) & 1 reference gene  might be sufficient for different diagnostic purposes feasibility of the approach shown in a model systemfeasibility of the approach shown in a model system  using paired prostate specimens from RPE explants  using paired prostate specimens from RPE explants Outlook I

12. transfer of the techniques to prostate biopsiestransfer of the techniques to prostate biopsies  to evaluate their applicability in PCa diagnostics?  improvement of PCa detection? possibly prediction of tumor stage using biopsiespossibly prediction of tumor stage using biopsies  therapeutic decisions? Future aims: correlation of transcript signatures with outcome?correlation of transcript signatures with outcome?  follow-up needed for prognostic purposes correct prediction of tumor aggressivenesscorrect prediction of tumor aggressiveness  active surveillance vs. curative treatment Outlook II