Tab.2: Diagnostic rule for the 4-gene logit model for the prediction of PCa Transcript levels of the 4 transcript markers of one patient are given as calculation example. The probability P results from transformation: P = exp(2.232) / [1+exp(2.232)] = = 90.3% Results validation of standardized, highly sensitive QPCR-assays with detection limits of 10 transcript molecules without microdissection relative expression levels of tested transcript markers in the diagnostic biopsies (for Tf biopsies shown in Tab.1) were similar to those obtained from larger prostate tissue samples [1] highest expression levels found for AMACR and PSA, lowest for hepsin and EZH2 (Tab.1) significant differences for PCA3, AMACR and PDEF as single markers between Tf-PCa und Tf-BPH biopsies, 7-fold increased PCA3 expression in Tf biopsies from PCa patients (Tab.1)  potential discrimination between PCa and BPH even if only Tf samples available use of 2 optimized gene models for PCa prediction in a given biopsy sample (Fig.1):  4-gene model (EZH2, PCA3, prostein, TRPM8) & 5-gene model (including hepsin)  no significant discrimination between Tf and Tu biopsies from PCa patients due to the low number of Tu-PCa biopsies (n=11) so far  significant discrimination between Tf-PCa vs. Tf-BPH and between Tu-PCa vs. Tf-BPH  predicitve power for PCa detection in Tf biopsies from PCa patients compared to Tf-biopsies from BPH patients with both models (with cut-off for P = 0.25, Fig.1): Tab.1:Relative expression levels of PCa-related marker genes in tumor- free (Tf) biopsies from PCa patients and from BPH patients [1] Schmidt U, Fuessel S, Koch R, Baretton GB, Lohse A, Tomasetti S, Unversucht S, Froehner M, Wirth MP, Meye A. Quantitative multi-gene expression profiling of primary prostate cancer. Prostate. 2006;66(14): Objectives Prostate cancer (PCa): most common cancer diagnosis and 2 nd leading cause of cancer-related deaths several diagnostic and prognostic problems:  early detection of PCa by PSA-screening, however, also detection of insignificant PCa  overtreatment?  limitations in discrimination from benign prostate hyperplasia (BPH) in the PSA gray zone  definite detection of PCa in prostate biopsies by histopathological examination may be impaired by sampling errors, false-negative specimens, insecure diagnosis, understaging or equivocal immunohisto.  ascertainment of tumor extension  facilitation of therapeutic decisions  early estimation of tumor aggressiveness and prognosis  choice of suitable therapies  already a relatively high percentage of aggressive tumors at first diagnosis requiring adapted treatment Aim of this study: validation of techniques & models for a biomolecular PCa prediction by evaluation of PCa-specific transcript patterns using quantitative PCR (QPCR) in prostate biopsies from patients with suspected PCa Materials & Methods Patients and sample processing: patients with suspected PCa (elevated PSA levels, suspect digital rectal examination) undergoing standard 12 core prostate biopsies gave their written informed consent for the sampling of 4 additional biopsies immediate cryo-preservation in liquid nitrogen on a small paper strip  serial cryo-sections (4µm) 1.) 6 representative H&E-stained cryo-sections equally spaced distributed over the profile of each biopsy for histopathological examination in addition to the 12 routine biopsies and for estimation of tumor cell content 2.) 6 consecutive cryo-sections for immunohistochemistry in case of dubious histopathological results 3.) 3-4 staples of 50 cryo-sections for isolation of RNA (or DNA) 63 patients: 21 pts with PCa (detected in the routine biospies) & 42 pts with BPH  234 research biopsies classification of the additional biopsies:a) Tu-PCa: 11 tumor cell-containing biopsies from 11 patients with PCa b) Tf-PCa: 75 tumor-free biopsies from 20 patients with PCa c) Tf-BPH: 148 tumor-free biopsies from 42 patients with BPH isolation of total RNA from 50 cryo-sections by Spin Tissue RNA Mini Kit (Invitek, Berlin, Germany) and subsequent cDNA-synthesis using Superscript II reverse transcriptase (Invitrogen, Karlsruhe, Germany) and random hexamer primers (Amersham GE Healthcare, Freiburg, Germany) Evaluation of transcript patterns by QPCR: QPCR-assays from the literature and own studies for 11 PCa-related transcript markers (Tab.1) using intron- spanning primer pairs and gene-specific hybridization or Taqman probes (TIB Molbiol, Berlin, Germany), LightCycler technology (Roche, Mannheim, Germany) and the kits “LC FastStart DNA Master Hybridization Probes” or “LightCycler TaqMan Master” (Roche) 1:5-dilution of cDNA  2µl per measurement, all PCRs performed with the same cDNA dilution at first: quantification of reference gene TATA box binding protein (TBP); only cDNA samples with at least 10 molecules TBP were included in the following quantification of selected PCa-related transcript markers at least two independent PCR runs for each cDNA sample, a third measurement if differences >30%, use of means for further calculations positive control (cDNA from the PCa cell line LNCaP) and negative control (without template) generation of quantity standard curves by the use of reaction tubes storage-stable coated with 10 1 to 10 7 molecules of HPLC-calibrated PCR fragments (AJ Roboscreen, Leipzig, Germany) calculation of transcript amounts by the automated analysis mode of the LC-software 3.5 relative expression levels of prostate-related markers  normalization to the reference gene TBP (zmol transcripts of the marker per zmol TBP transcripts) only 5 selected transcript markers (EZH2, hepsin, PCA3, prostein, TRPM8) were measured in all biopsies the remaining 6 PCa-associated transcript markers (Tab.1) were analyzed in only 32 tumor-free biopsies from 8 patients with PCa (Tf-PCa) and 73 tumor-free biopsies from 20 patients with BPH (Tf-BPH) Use of transcript marker models for PCa detection: calculation of n-fold over-expression ratios for each marker from median relative expression levels in tumor- free biopsies from patients with PCa (Tf-PCa) vs. tumor-free biopsies from patients with BPH (Tf-BPH) (Tab.1) calculation of p-values using the Mann-Whitney-U-test (p ≤ 0.05 regarded as significant) comparison of the relative expression levels for 5 selected transcript markers between the three biopsy groups multivariate diagnostic rules for PCa prediction models based on optimized logistic regression models comprising optimal sets of competing variables and optimal cut-off points for each variable [1]: - 4-gene model (EZH2, PCA3, prostein, TRPM8): use of variables classified by optimized cut-offs (Tab.2) - 5-gene model (EZH2, hepsin, PCA3, prostein, TRPM8): use of continuous variables  calculation of a predicted absolute probability (P) for each individual biopsy by simple addition of regression parameters depending on the original values of the variables and subsequent transformation from the logit model into probability [1]: P = exp(logit)/[1+exp(logit)] Conclusions biomolecular PCa detection in a given prostate specimen is conceivable as additional tool to standard PCa diagnostics measurement of only 4-5 transcript markers (EZH2, hepsin, PCA3, prostein, TRPM8) and 1 references gene sufficient for diagnostic purposes use of marker combinations yields in increased diagnostic power feasibility of this approach was shown in different model systems using larger prostate tissue samples, artificial prostate biopsies from RPE explants (previous studies) and diagnostic biopsies (this study) without microdissection validation on more biopsy samples (particularly Tu biopsies) necessary identification of transcript patterns useful for prognosis at the time of biopsy validation of the gene models on urine as alternative sample system / Biomolecular prostate cancer detection in minimal prostate tissue samples – a feasible additional diagnostic tool? S. Schneider 1, J. Seifert 1, S. Propping 1, S. Voigt 1, A. Lohse-Fischer 1, S. Tomasetti 1, S. Fuessel 1, M. Haase 2, R. Koch 3, G. B. Baretton 4, M.-O. Grimm 1, M.P. Wirth 1 1 Department of Urology, 2 OncoRay, Center for “Radiation Research in Oncology”, 3 Institute of Medical Informatics and Biometry, 4 Institute of Pathology, Medical Faculty, Technical University of Dresden, Germany 4-gene model5-gene model sensitivity61 %58 % specificity75 %88 % positive predictive value58 %74 % negative predictive value77 %79 % transcript marker relative expression levels (zmol / zmol TBP) n-fold over- expression ratio (Tf-PCa vs. Tf-BPH) p values (Mann- Whitney- U-test) Tf-PCa n=32 (from 8 pts.) median [range] Tf-BPH n=73 (from 20 pts.) median [range] PCA3 PSGR PSA PSMA AMACR PDEF EZH2 TRPM8 AR Prostein Hepsin 0.91 [0-50] 3.73 [0-44] [ ] 4.83 [0-39] [17-466] [2-51] 0.18 [0-1] [5-66] 6.05 [2-11] [2-41] 0.03 [0-1] 013 [0-9] 1.97 [0-48] [0-2514] 2.74 [0-35] [5-183] [0-79] 0.13 [0-1] [0-81] 5.16 [0-12] [0-56] 0.00 [0-1] n.d. < 0,01 0,44 0,08 0,10 < 0,01 0,01 0,57 0,11 0,20 0,48 0,09 Fig.1: Comparison of the 4-gene and 5-gene model for the prediction of PCa Boxplots: The boxes within the plots present the 25-75th percentiles. The medians are depicted as solid lines. White circles indicate outlier values with distances from boxes between 1.5-fold to 3 ‑ fold box-levels in two directions, whereas increased distances >3-fold box levels are labeled with asterisks. cut-off 0.25 predictive probability of PCa presence Tf-BPH n=148 (42 pts) Tf-PCa n=75 (20 pts) Tu-PCa n=11 (11 pts) 4-gene model Tf-BPH n=148 (42 pts) Tf-PCa n=75 (20 pts) Tu-PCa n=11 (11 pts) 5-gene model transcript marker relative expression level regression coefficient value for calculation sample logit for calculation sample Intercept EZH2< 1.2 ≥ PCA3< to < to < 47 ≥ Prostein< to < 20 ≥ TRPM8< 6 6 to < to < 42 ≥ sum (logit):2.232