Manfred P. Wirth Department of Urology Technical University of Dresden

1. Diagnostic potential of transcript signatures in minimal prostate tissue specimens Manfred P. Wirth Department of Urology Technical University of Dresden [supported by a grant from the DFG]

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

3. Material & methods I • establishment of standardized quantitative • PCR-assays (using gene-specific fluorescent probes) • 169 matched pairs of malignant and non-malignant prostate tissue specimens (Tu + Tf) from RPE specimens • evaluation of 4 housekeeping genes as reference • for internal normalization: •  different expression between Tu and Tf?

4. Material & methods II • different expression of 3 reference genes: • GAPDH = glyceraldehyde-3-phosphate dehydrogenase • HPRT = hypoxanthine phosphoribosyltransferase • PBGD = porphobilinogen deaminase • only TATA box binding protein (TBP) suitable (no different expression)

5. Material & methods III 12 PCa-related genes known from literature were tested * prostate-specific genes, but not highly overexpressed in PCa

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

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

8. predicted probability of tumor AUC = 0.89 (95% CI 0.76 ... 1.00) 1- Specificity Optimized 4-gene-model for PCa-prediction: EZH2 + PCA3 + prostein + TRPM8 • 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)]  probability (p) of PCa presence in the analyzed tissue samples: median p for Tu 81% median p for Tf 21% ROC-analysis of the 4-gene-combination n=169

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=90 NOCD: n=79 • comparison only of Tu-samples of OCD vs. NOCD or • comparison of Tf- vs. Tu-samples of OCD vs. Tu-samples NOCD •  mathematical models for OCD-prediction in process

10. Transfer to artificial biopsies • translation of the techniques to prostate biopsies •  additional diagnostic tool on minimal prostate • tissue samples for better PCa-detection •  11 selected PCa-related genes and TBP •  first results of application and validation of two • multi-gene-models for PCa prediction

11. H&E-stained cuttings (PCa-patient: pT2a, pN0, pMx Gleason Score: 7 [3+4]) Tu-prostate tissue sample Tf-prostate tissue sample Artificial needle core biopsies from RPE explants • artificial biopsies: Tf & Tu from one RPE specimen • snap-frozen in planar direction on paper strip in liquid N2 •  cryo-cuttings for RNA-isolation & pathological survey

12. artificial biopsies (cryo conservation): prostate tissue sample between glass plates biopsy on paper strip liquid nitrogen Cryo-cuttings: --for RNA isolation and 6 representaive cryo-slices for histopthaological examination biopsy profile cryo-slices Handling and processing of artificial biopsies

13. Patient cohort of the pilot study • 11 patients with a primary PCa • age: 51 to 71 years (median 66 years) • serum PSA levels: 1.29 to 24.32 ng/ml (median 6.9 ng/ml) • Histopathological examination: according to the UICC system • 7 patients (64%) with organ-confined disease (OCD; pT2) • 4 patients (36%) with non organ-confined disease (NOCD; pT3/T4) • Tumor grading: 2 patients with low grade (GS 6) • 8 patients with intermediate grade (GS 7) • and 1 patient with high grade (GS 8)

14. Marker expression in artificial biopsies

15. Validation of the multi-gene model on artificial biopsies • „false-positive“: meaning? • verification in future studies with increased sample numbers

16. Outlook I • translation of the techniques to diagnostic biopsies •  improvement of PCa detection • (Are false-positives really false-positives?) • correct prediction of tumor aggressiveness •  active surveillance vs. curative treatment • correlation of transcript signatures with outcome? •  follow-up needed for prognostic purposes

17. Outlook II • detection of PCa-specific transcripts in urine samples •  non-invasive tumor detection? •  PCA3 (DD3) detection in urine samples in • validation (APTIMA PCA3; Gen-probe incorp.) • (PCA3 is a non-coding RNA  only at transcript level measurable) •  transcript quantification in urine samples as a promising tool

18. Acknowledgment • Dept. of Urology, Technical University of Dresden: •  Laboratory: Axel Meye, Susanne Füssel, Susanne Unversucht, • Andrea Lohse, Silke Tomasetti, Uta Schmidt •  Clinic: Michael Fröhner, Stefan Zastrow, Marc-Oliver Grimm • Inst. of Pathology, Technical University of Dresden: •  Gustavo Baretton, Michael Haase, Marietta Toma • Inst. of Medical Informatics and Biometry •  Rainer Koch