1. Introduction & Objectives: This study describes the evaluation of the expression pattern of prostate-specific transcripts in 106 matched prostate tissues as molecular predictors for prostate cancer (PCa). Methods: Total RNA was prepared from cryo-preserved paired malignant (Tu) and non-malignant prostate tissue (Tf) specimens which had been removed during radical prostatectomy and examined by a trained pathologist. Quantitative PCR (QPCR) assays with site-specific hybridization or TaqMan probes were performed for four housekeeping genes (GAPDH, HPRT, PBGD, TBP) and nine prostate-associated genes (AibZIP, DD3/PCA3, D-GPCR, EZH2, PDEF, prostein, PSA, PSCA, TRPM8), and expression was measured using the LightCycler. Results: In the analyzed patient cohort, statistical differences for the commonly used housekeeping genes GAPDH (p=0.038), HPRT (p=0.036) and PBGD (p=0.00003) were observed. The only housekeeping gene being not differentially expressed between malignant and non-malignant prostate tissues was TBP (p=0.531). Hence, all expression values were normalized to TBP (“relative mRNA expression”). Since the relative expression levels were not distributed normally, these values were logarithmized, and tested for Gaussian distribution again. The logarithmized relative expression levels were sufficiently normally distributed and therefore used for comparisons employing Student´s t-test. The logarithmized relative mRNA expression of AibZIP, DD3/PCA3, EZH2, PDEF, TRPM8 (all p<0.000001) D-GPCR (p=0.00034), prostein (p=0.019) & PSA (p=0.00002) were significantly higher in Tu vs. Tf tissues. In order to assess the diagnostic power of single transcripts, ROC curves were generated, and their AUCs were calculated for all single parameters. DD3/PCA3 is the marker with the highest AUC (0.85) indicating the best performance as a single tumor marker. Furthermore, the data were analyzed with regard to a multivariate model. After analysis of the contribution of each marker, a logit model was developed. This model employs the logarithmized relative expression levels of DD3/PCA3, EZH2, prostein & TRPM8, and yields an AUC of 0.90. Conclusions: The combination of different PCa-associated markers allows the molecular genetic prediction of PCa based on a multivariate model. The identified panel of attractive transcript markers should be evaluated in future prospective studies using diagnostic biopsies from PCa patients. Materials & Methods Tumor patients matched pairs of malignant (or tumor, Tu) & non-malignant (tumor-free, Tf) tissue samples from 106 patients with primary PCa (all treated by RPE), median age 64 yrs. (48-78), all cM0, no hormonal pre-treatment, serum PSA levels 1.3-57.2ng/ml (median 8.3ng/ml) histopathological examination according to the UICC classification. 59 (56%) patients had organ- confined (pT2, OCT) & 47 (44%) non organ-confined disease (≥pT3, NOCT); 92 (87%) pN0 patients & 14 (13%) pN1; 28 (26%) patients with GS ( 7); 29 patients received adjuvant treatment (hormonal, radiatio), these were considered as treatment failure at the time of surgery (no follow-up) median time of follow-up for the 77 other patients was 32 months (2-84); 10 patients with PSA relapse (≥0.2ng), 67 without relapse (median time of follow-up 35 months, 10-84) Cell lines: prostate cell lines DU 145, LNCaP, 22Rv1, PC-3 & BPH-1 from ATCC & DSMZ RNA isolation & cDNA synthesis inclusion criteria: Tu tissue samples with ≥ 70 % tumor cells & Tf ones with 30 slices (usually 50-60) of 10µm, extraction of total RNA by Spin Tissue RNA Mini Kit & Invisorb Spin Cell RNA Mini kit (Invitek, Berlin) Two portions of 500ng of total RNA in RT reaction (Superscript II RT, Invitrogen), both reactions were pooled & diluted 1:5 in water; if 1µg RNA was not available, less total RNA was employed all raw expression data were normalized to the amount of total RNA Amplification of prostate-specific and reference genes quantitative PCR (QPCR, LC) for 4 reference genes & 9 prostate-specific transcripts (Tab 1); quantification using HP (for all genes but GAPDH & DD3) or TaqMan probes ( for GAPDH & DD3) all QPCR assays at least twice as independent PCR runs for each sample, if relative differences of >30% a third time; means of all measurements for further calculations mRNA copy number of a single marker in relation to the external DNA standards quantity standard curves employing LC capillaries storage-stable coated with 10E1 to 10E7 molecules of HPLC calibrated cDNA fragments, calculations with relative expression ratios Statistics and correlation of the quantification results to the clinical data patients were subdivided into groups according to their clinically relevant parameters: pT2 vs. pT3/4, pN0 vs. pN1, low grade PCa (l ‑ GS=GS 2 to 6) vs. intermediate grade PCa (GS=7) vs. high grade PCa (h ‑ GS=GS 8 to 10), analyses with the SAS & SPSS software since the relative expression levels were not distributed normally, these values were logarithmized & used for comparisons employing Student´s t-test logarithmized relative mRNA levels of different markers: comparison between Tu & Tf tissue pairs unpaired homoscedastic t ‑ test to evaluate the suitability of the mRNA expression levels of single markers to differentiate between the specific clinical parameter groups in PCa tissues receiver-operating characteristic (ROC) curves to assess the diagnostic power of each separate variable univariately & for the multivariate diagnostic rules by the area under curve (AUC) of the ROC, differences between ROC curves using a Wald test. validity & quality of the resulting logit models by Hosmer & Lemeshow goodness-of-fit tests, by Akaike information criterion), by p-values of each regression parameter, & by AUC #12: Quantitative multigene expression profiling of primary prostate cancer Schmidt U 1 *, Füssel S 1 *, Koch R 2, Baretton G 3, Lohse A 1, Tomasetti S 1, Froehner M 1, Wirth MP 1, Meye A 1# 1 Department of Urology, 2 Institutes of Medical Informatics & Biometry, 3 Institute of Pathology, Technical University Dresden, Germany *U.S. & S.F. contributed equally to this work. # axel.meye@uniklinikum-dresden.de Results Standardization of the real-time PCR assays run-to-run performance for 23 PCR runs per gene: mean slope of the calibration curves (measured vs. coated molecule numbers) of reference & prostate-associated genes was 0.984 (GAPDH) to 1.149 (TBP), correlation for these curves >99.9 %, SD 4-18% (medians 8-14%), median slope of the standard curves –3.575 to –3.341  good PCR performance for all tests. Choice of a reference gene comparison of logarithmized mRNA expression levels of the 4 housekeeping genes (Tu vs. Tf): statistical differences for GAPDH, HPRT & PBGD but not for TBP (fig.1)  TBP for normalization Relative expression data of matched pairs of prostate tissue specimens and cell lines median relative expression levels of the 9 prostate-specific genes  table II: for both Tu & Tf specimens, the genes with the lowest & the highest median relative mRNA expression levels were EZH2 and PSA, respectively; all genes with expression range over 2-5 orders of magnitude for all PCa-associated transcript markers but EZH2 the relative expression values in 5 prostate- derived cell lines were over 1-3 orders of magnitude lower than in Tu tissue Differential expression of prostate-associated genes in matched Tu and Tf tissues logarithmized relative mRNA expression of AibZIP (p<0.0001), DD3/PCA3 (p<0.0001), D-GPCR (p=0.00034), EZH2 (p<0.0001), PDEF (p<0.0001), prostein (p=0.019), PSA (p=0.00002) & TRPM8 (p<0.0001), higher in Tu than in Tf; most evident for DD3/PCA3, EZH2, PDEF & TRPM8 (fig. 2) highest Tu:Tf ratios for DD3/PCA3 & TRPM8 (fig. 2). Comparing AibZIP, D-GPCR, EZH2 and PDEF, these prostate-associated genes showed less pronounced yet modest ratios of expression between Tu & Tf reflecting differences of expression by factor 2 (fig. 2, ratios). Univariate & multivariate analyses + definition of cut-off values for the prediction of Tu tissue ROC curves & AUC for all single parameters: DD3/PCA3 is the marker with the highest AUC (0.85) indicating the best performance as a single tumor marker (table IIIa), for example: choosing a sensitivity of 95%, this would result in a specificity of 46%, a PPV of 64%, & a NPV of 91% using a cut-off value of 0.4 zmol DD3/PCA3/zmol TBP mRNA AUCs of EZH2 & TRPM8 >0.80  better than the other single markers (table IIIa) multivariate logit model: AUC=0.90 (DD3/PCA3, EZH2, prostein & TRPM8 expression, table IIIb) different cutpoints to distinguish between Tu & Tf tissue (table IIIb), Hosmer & Lemeshow goodness-of-fit test: p=0.47 indicating a good performance of this model, Bootstrap cross validated estimation of the AUC: p=0.87; at the example sensitivity of 95%, the model´s specificity 61%, PPV 71%, NPV 93%; by Wald test: contrast between univariate (DD3/PCA3 relative expression) & the logit model was significant (p=0.0015 with Bonferroni adjustment). Subgroup analysis and correlation with relevant clinicopathological parameters significant decreases in low-GS vs. high-GS tumors for prostein (p=0.032) & PSA (p=0.026) for both prostein (p=0.016) & PSA (p=0.001) different expression in intermediate vs. high-grade EZH2: rise slightly from low-grade to intermediate-grade tumors (p=0.061) T stage:  mRNA of prostein (p=0.002), PSA (p=0.003) & TRPM8 (p=0.009) in OCTvs.NOCT (fig. 3) only for TRPM8, significant differences between NOCT & TF (p Tf  predictability of organ-confined disease? differences for AibZIP (p<0.001), DD3/PCA3 (p<0.001), D-GPCR (p=0.001), EZH2 (p<0.001) & PDEF (p<0.001) between NOCT & Tf but not between OCT & NOCT differences in expression Tf vs. Tu from pN1 patients for AibZIP (p<0.001), DD3/PCA3 (p<0.001), EZH2 (p<0.001), PDEF (p<0.001) & TRPM8 (p=0.002) Correlation of gene expression with treatment failure differences for AibZIP (p=0.049), PDEF (p=0.01), prostein (p=0.006) & PSA (p=0.04) expression between the Tu of patients without PSA relapse & patients who had to be considered as treatment failure since they had non organ-confined disease at the time of surgery and who received adjuvant treatment (fig. 4); logarithmized relative expression in patients with adjuvant treatment was lower; patients with vs. without adjuvant treatment: different for prostein (p=0.045, down-regulation) & PSCA (p=0.016, up-regulation) TABLE II – Relative mRNA expression levels (zmol prostate-specific gene/zmol TBP) TABLE IIIa (above) – Calculation of AUC for ROC curves of the prostate-associated genes All numbers are given for the prostate-specific gene normalized to TBP [s.e. – standard error, 95%CI – confidence interval] TABLE IIIb (right) – Diagnostic rule of a logit model for the prediction of a prostate tumor The calculation example is for patient 1. In this case, the probability p for malignant tissue results from the transformation p = exp(logit)/[1+exp(logit)] = exp(2.298)/[1+exp(2.298)] = 0.909 Considering the measured values of the non-malignant tissue (prostein/TBP = 2.01, EZH2/TBP = 1.17, TRPM8/TBP = 1.86, DD3/PCA3/TBP = 0.396), a predicted probability of being malignant is calculated with 6.9% and thus a predicted probability of 93.1% (=100%-6.9%) for being non-malignant. TABLE I (above) – QPCR assays Fig 2 (right) – Expression ratios (Tu:Tf) for the 9 prostate- associated transcripts Tf Tu Tf Tu Tf Tu Tf Tu p=0.038p=0.036p=0.00003p=0.531 p=0.002p=0.003 p=0.102 p=0.150 p=0.113 p=0.099 p=0.009 A) B) C) D) E) F) G) Fig. 1: Boxplots of logarithmized mRNA expression levels of 4 reference genes Fig. 3: Correlation of logarithmized relative mRNA levels with clinico-pathological features Tf Tu Tf Tu Tf Tu Tf Tu