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

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. establishment of standardized quantitativeestablishment 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 specimens169 matched pairs of malignant and non-malignant prostate tissue specimens (Tu + Tf) from RPE specimens evaluation of 4 housekeeping genes as referenceevaluation of 4 housekeeping genes as reference for internal normalization:  different expression between Tu and Tf? Material & methods I

4. Material & methods II different expression of 3 reference genes: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. transcript marker name AMACR AR * D-GPCR (OR51E1) EZH2hepsin PCA3 (DD3) PDEF Prostein * PSA * 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 III 12 PCa-related genes known from literature were tested * prostate-specific genes, but not highly overexpressed in PCa

6. 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

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

8. Optimized 4-gene-model for PCa-prediction: EZH2 + PCA3 + prostein + TRPM8 1- Specificity AUC = 0.89 (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%

9. Dependence of marker expression on tumor stage: Discrimination between organ-confined disease (OCD) and non- organ-confined disease (NOCD) for therapeutic decision? comparison only of Tu-samples of OCD vs. NOCD orcomparison only of Tu-samples of OCD vs. NOCD or comparison of Tf- vs. Tu-samples of OCD vs. Tu-samples NOCDcomparison of Tf- vs. Tu-samples of OCD vs. Tu-samples NOCD  mathematical models for OCD-prediction in process  mathematical models for OCD-prediction in process Tf: n=169 OCD: n=90 NOCD: n=79

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

11. artificial biopsies: Tf & Tu from one RPE specimenartificial biopsies: Tf & Tu from one RPE specimen snap-frozen in planar direction on paper strip in liquid N 2snap-frozen in planar direction on paper strip in liquid N 2  cryo-cuttings for RNA-isolation & pathological survey  cryo-cuttings for RNA-isolation & pathological survey H&E-stained cuttings (PCa-patient: pT2a, pN0, pMx Gleason Score: 7 [3+4]) Tu-prostate tissue sample Tf-prostate tissue sample Tu-prostate tissue sample Tf-prostate tissue sample Artificial needle core biopsies from RPE explants

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

13. 11 patients with a primary PCa11 patients with a primary PCa age: 51 to 71 years (median 66 years)age: 51 to 71 years (median 66 years) serum PSA levels: 1.29 to 24.32 ng/ml (median 6.9 ng/ml)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) 8 patients with intermediate grade (GS 7) and 1 patient with high grade (GS 8) and 1 patient with high grade (GS 8) Patient cohort of the pilot study

14. relative expression levels [zmol gene/ zmol TBP] (n = 40 samples) transcript marker name malignant (Tu) n=26median non-malignant (Tf) n=14medianP-values(unpairedt-test)over-expression (Tu vs. Tf) (Tu vs. Tf)LNCaP(control) AMACRPCA3PSMA 2,104 (25.4 to 4,800) 36.45 (5.4 to 166.3) 36.45 (5.4 to 166.3) 25.87 (1.7 to 221.5) 25.87 (1.7 to 221.5) 91.65 (5.4 to 640.2) 1.67 (0.1 to 34.4) 1.67 (0.1 to 34.4) 2.49 (0 to 72.6) 2.49 (0 to 72.6)<0.001<0.001<0.00123.021.810.425.83 0.19 0.1924.83 PSGRTRPM8EZH2hepsinPDEFPSA 47.67 (2.2 to 222.9) 47.67 (2.2 to 222.9) 31.71 (6.8 to 218.1) 31.71 (6.8 to 218.1) 0.80 (0.1 to 1.807) 0.80 (0.1 to 1.807) 0.38 (0.2 to 1.080) 0.38 (0.2 to 1.080) 34.13 (1.8 to 136.1) 34.13 (1.8 to 136.1) 174.36 (26.8 to 1,395) 8.80 (0.2 to 313.4) 8.80 (0.2 to 313.4) 6.95 (0.1 to 58.7) 6.95 (0.1 to 58.7) 0.17 (0 to 1.222) 0.17 (0 to 1.222) 0.12 (0 to 0.80) 0.12 (0 to 0.80) 14.34 (0.2 to 63.2) 78.18 (0.2 to 737.0) 0.006<0.0010.001<0.0010.0760.0215.44.64.73.22.42.2 0.02 0.02 1.99 1.99 4.48 4.48 0.05 0.05 3.39 3.39 5.38 5.38 prosteinAR 8.74 (0.9 to 47.0) 8.74 (0.9 to 47.0) 14.52 (4.3 to 31.8) 14.52 (4.3 to 31.8) 6.99 (0 to 45.3) 6.99 (0 to 45.3) 11.77 (0.5 to 18.7) 0.4890.0301.31.2 1.54 1.5414.23 Marker expression in artificial biopsies

15. Validation of the multi-gene model on artificial biopsies 4-gene model (EZH2, TRPM8, PCA3, prostein) Tu-biopsies (n = 26) PCa-prediction: 77 % (20 biopsies) Tf-biopsies (n = 14) „false positive“: 43 % (6 biopsies)  „false-positive“: meaning?  verification in future studies with increased sample numbers

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

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

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