1. . Quantitative multi-gene expression profiling of primary prostate cancer* Meye A, Schmidt U 1, Füssel S, Koch R, Baretton GB, Lohse A, Tomasetti S, Froehner M, and Wirth MP Department of Urology, Institute of Biometry, Institute of Pathology, Technical University of Dresden, Germany * supported by a grant from the Deutsche Forschungsgemeinschaft (Me-1649, to A.M., M.F. & G.B.). Material Tumor patients and cell lines - matched tissue samples (Tu & Tf) from 106 pts. (hormone- naïve) with primary PCa (RPE cases, cM0) -patients’ median age 64 yrs (48 to 78), serum levels of PSA (day -1 pre-surgery) 1.3 to 57.2 ng/ml (median 8.3) - histopathological examination of according to the UICC sys- tem: 59 (56%) patients had organ-confined disease (OCD, pT2), 47 (44%) had non organ-confined disease (NOCD, pT3 and pT4); 92 (87%) patients pN0, 14 (13%) pN1; low grade PCa (GS 2 to 6; n=28 (26%), intermediate grade PCa (GS = 7; n=5) & high grade PCa (GS 8 to 10; n=27); - 77 pts. without PSA relapse after surgery (follow-up 32 m), 10 with PSA relapse (PSA ≥0.2ng/ml), 29 adjuvant treatedt - prostate cell lines DU 145, LNCaP, 22Rv1, PC-3 and BPH-1 RNA isolation, cDNA synthesis and quantitative PCR (QPCR) - 50-60 slices of kryo-preserved tissue samples for isolation of total RNA (Spin Tissue RNA Mini Kit; Invitek) - 2 portions of 500 ng RNA for RT (Superscript II), both cDNA samples were pooled & diluted 1:5 - QPCR assays with HP or TaqMan probes to quantify the mRNA of 4 housekeeping & 9 prostate-related transcripts (list, at the right) at least twice as independent PCRs for each cDNA sample, if differences of >30% a third QPCR round - quantity standard curves (LC capillaries coated with 10E1-E7 molecules of PCR fragments); transcript amounts calculation by LC-software 3.5; relative expression levels of prostate- related markers by normalization to the reference gene (zmol transcripts of marker per zmol transcripts of reference gene) Statistics and correlation of the QPCR results to clinical data - analyses by SAS software & SPSS software packages - log-transformed relative mRNA expression levels of markers (comparison Tu & Tf, paired t ‑ test); receiver-operating characteristic (ROC) curves (to assess the diagnostic power of each separate variable univariately) & for the multivariate diagnostic rule by the area under curve (AUC) of ROC curve Objective The aim of this study was to evaluate whether one of 9 prostate cancer (PCa)-related transcript markers or a combi- nation of them are predictors for PCa. After careful assessment of the potential of the different prostate-associated and/or PCa- relevant candidates for comparative analyses, standardized and validated measurements of mRNA levels were performed. In addition, the power of the single markers for predicting local- ized disease was assessed. In order to choose a suitable refer- ence gene for prostate tissue pairs, the mRNA expression levels of 4 housekeeping genes were determined in parallel. Results Standardization of the real-time QPCR & choice of a suitable reference gene for prostate tissues - run-to-run performance of assays (at least 23 PCR runs/marker): mean slopes of the regression curves 0.984 (GAPDH) to 1.149 (TBP); correlation for these curves at least 99.9 %, s.d. 4 to 18 %; median slope of the quantity standard curves –3.575 to –3.341 - log-transformed mRNA expression levels of 4 reference genes (GAPDH, HPRT, PBGD, TBP, normalized RNA amount) were compared (Tu vs. Tf); only TBP was not differntially expressed between Tu & Tf (Fig. 1  ), therefore TBP level for normalization. Differential expression of prostate-related genes (see list at the right)  - median relative expression levels of the prostate-related genes in Tu and Tf specimens: Tab1  - log-transformed relative mRNA levels in Tu > Tf for AibZIP, D-GPCR, EZH2, PCA3, PDEF, PSA, TRPM8 (all p<0.001) & prostein (p=0.018; paired t ‑ test); highest Tu:Tf ratios (Fig2  ) PCA3 (median 37.5-fold) & TRPM8 (median 3.7-fold) Univariate and multivariate analyses for the prediction of malignant prostate tissue ROC curves were generated & and AUC were calculated for every single parameter (PCA3 has the highest AUC value =0.85 (Tab2a , Fig3  ). As an example, choosing a sensitivity of 95%, this would result in a specificity of 46%, a positive predictive value of 64%, and a negative predictive value of 91% when using a cut off value of 0.4 zmol PCA3/zmol TBP. EZH2 and TRPM8 also had AUC values of more than 0.80 thus performing better than the other single markers (Tab2a  ). - a developed logit model (based on EZH2, PCA3, prostein & TRPM8) yielded an AUC of 0.90 (Tab2b  ); all variables were divided into 2 to 4 classes resulting in different cut points to distinguish between Tu & Tf (Tab2b  ); using a Wald test, the contrast between the univariate (PCA3 only) & logit model was significant (p=0.0015) indicating a better performance of the multivariate model Subgroup analysis and correlation with relevant clinico-pathological parameters - D ‑ GPCR with continuously rising relative expression levels from low GS to high GS (not significant) - in OCD vs. NOCD significantly higher expression levels for prostein, PSA & TRPM8 (Fig4  ), no differences for N status Correlation of gene expression with treatment failure - no significant differences between patients with undetectable PSA (n=77) & those with PSA relapse (n=10); in contrast, statistically significant differences for AibZIP (p=0.049), PDEF (p=0.01), prostein (p=0.006) and PSA (p=0.04) in the Tu tissues of patients without a PSA relapse and patients who had received adjuvant therapy since they had NOCD at the time of surgery (n=29) Conclusions - mRNA levels of AibZIP, D-GPCR, EZH2, PCA3, PDEF, PSA, TRPM8 (all Tf - PCA3 is a powerful predictor of primary PCa but the inclusion of EZH2, prostein & TRPM8 adds even more to the diagnostic power (ROC analyses) - the finding of a significantly higher mRNA expression of 3 genes (prostein, PSA, TRPM8) in organ-confined tumors compared to non-organ-confined tumors could be of clinical importance and should be reevaluated in prospective studies using specimens from diagnostic biopsies axel.meye@uniklinikum-dresden.de axel.meye@uniklinikum-dresden.de or www.tu-dresden.de\meduro\www.tu-dresden.de\meduro\ Fig1 (left): Boxplots of mRNA expression levels of different reference genes (distribution of log- transformed transcript levels, normalized to RNA amounts; differences of unpaired t ‑ tests) Fig2 (left below): Ratios of expression levels (Tu:Tf) of prostate-related genes Tab1 (below): Relative transcript levels of prostate-related genes in prostate tissues and cell lines (zmol gene/zmol TBP) Tab2a (right): Calculation of AUC values for the expression levels by ROC analyses Comparison of the univariate model for PCA3 with the logit model based on the 4-gene signature ROC curves for PCA3 (AUC=0.85) in comparison to the multivariate logit model comprising EZH2, PCA3, prostein and TRPM8 (AUC=0.90). Fig4 (above): Correlation of expression of prostein, PSA & TRPM8 with T stage [unpaired t-test, log-transformed relative expression levels for Tf (n=106), OCD (n=59) & NOCD (n=47) samples] Fig1 Fig2 Fig3 Fig4 Tab1 Tab2a Tab2b Tab2b (above): Diagnostic rule of a logit model for the prediction of PCa [calculation example for patient #1: the probability p for Tu tissue results from the transformation p=exp(logit)/[1+exp(logit)] = exp(2.298)/[1+exp(2.298)]=90.9% * Cut points used are given as expression levels (zmol gene/zmol TBP). Prostate-related genes