1. http://urologie.uniklinikum-dresden.de / susanne.fuessel@uniklinikum-dresden.de Extension of quantitative multi-gene expression studies on paired radical prostatectomy (RPE) – prostate tissue samples S. Fuessel 1, S. Unversucht 1, R. Koch 2, G. Baretton 3, A. Lohse 1, S. Tomasetti 1, M. Froehner 1, A. Meye 1, M.P. Wirth 1 1 Department of Urology, 2 Institute of Medical Informatics and Biometry, 3 Institute of Pathology, Medical Faculty, Technical University of Dresden, Germany Tab.1 Analyzed transcript markers: gene names and accession numbers [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):1521-34. Fig.2 Prediction of PCa presence in a given prostate specimen by a 4-gene model The published 4-gene model was transferred to 169 patients and showed similar results as in the former cohort of 106 PCa patients [1]. Probabilities p of PCa presence were calculated by a logit-model using classified transcript levels of the 4 markers EZH2, PCA3, prostein and TRPM8. By the use of a cut-off probability of 0.7 70% of the Tu specimens were correctly predicted as malignant tissues. 1- Specificity AUC = 0.893 (95% CI 0.76... 1.00) ROC-analysis of the 4-gene model for PCa detection using EZH2 + PCA3 + prostein + TRPM8 predicted probability of tumor presence probability (p) of PCa presence in the analyzed 169 tissue pairs: median p for Tu 81% median p for Tf 21% ROC-analysis of the 5-gene model for PCa detection using EZH2 + hepsin + PCA3 + prostein + TRPM8 probability (p) of PCa presence in the analyzed 169 tissue pairs: median p for Tu 87% median p for Tf 10% Fig.3 Prediction of PCa presence in a given prostate specimen by a new 5-gene model The new 5-gene model was optimized on 169 patients and showed a slightly enhanced diagnostic power compared with the published 4-gene model [1]. Probabilities p of PCa presence were calculated by a logit-model using continuous transcript levels of the 5 markers EZH2, hepsin, PCA3, prostein and TRPM8. 1- Specificity AUC = 0.914 (95% CI 0.77... 1.00) predicted probability of tumor Fig.1 Degree of upregulation of PCa-related transcript markers in Tu compared to Tf (paired analyses) Distribution of Tu:Tf ratios of paired malignant and non-malignant prostate specimens: solid lines within the boxplots represent the median overexpression of the single transcript markers. Diagnostic power of the single markers in PCa detection is calculated by ROC analyses resulting in AUC values shown below each transcript marker. Introduction & Objectives Prostate cancer (PCa)  most common cancer diagnosis & 2 nd leading cause of cancer-related deaths high cancer specific survival rates for radically prostatectomized patients, but up to 1/3 disease recurrence within few years after PSA relapse  metastatic disease  death within 2-5 years advanced, recurrent & metastatic PCa treated by androgen-deprivation convert to androgen-independent growth already a relatively high percentage of aggressive tumors at first diagnosis requiring adapted treatment different diagnostic and prognostic problems:  early detection of PCa by PSA-screening, however, also detection of insignificant PCa  overtreatment?  definite detection of PCa in prostate biopsies by histopathological examination (sampling errors, false-negative specimens, insecure diagnosis, tumor understaging, equivocal IHC)  ascertainment of tumor extension  facilitation of therapeutic decisions  early estimation of tumor aggressiveness and prognosis  choice of suitable therapies  Aim of this study: establishment of techniques & models for a biomolecular PCa prediction by evaluation of PCa-specific transcript patterns in paired malignant & non-malignant prostate specimens Materials & Methods Patients: 169 patients with primary PCa treated by radical prostatectomy (RPE) in Department of Urology, Dresden median age of 64 yrs.(47 – 78 yrs.), median pre-operative serum PSA 9.01ng/ml tumor stages: 91 pT2, 60 pT3, 18 pT4  91 OCD (organ-confined disease) & 78 NOCD (non organ-confined disease) Gleason Scores (GS): 43 low grade (GS 7), 2 unknown GS lymph node involvement: 145 pN0, 22 pN1, 2 pNx no distant metastases (all cM0) Tissue preservation and processing: instantaneous section of the surgically removed prostates in the Institute of Pathology excision of tissue specimens: one malignant (Tu) and one apparently non-malignant (Tf) distant from the tumor immediate cryo-preservation in liquid nitrogen fixation of adjacent tissue specimens in formalin and embedding in paraffin  histopathological examination and estimation of percentages of tumor cells  0% in Tf & >70% in Tu preparation of cryo-sections (10µm, at least 30-50 slices) isolation of total RNA by Spin Tissue RNA Mini Kit (Invitek, Berlin, Germany) cDNA-synthesis using Superscript II reverse transcriptase (Invitrogen, Karlsruhe, Germany) and random hexamer primers (Amersham GE Healthcare, Freiburg, Germany) Quantitative PCR (QPCR): selection of PCa-related transcript markers (Tab.1) and QPCR-assays from the literature and own studies use of intron-spanning primer pairs and gene-specific hybridization probes or Taqman probes (TIB Molbiol, Berlin, Germany) and LightCycler (LC) technology (Roche, Mannheim, Germany) use of the kits “LC FastStart DNA Master Hybridization Probes” or “LightCycler TaqMan Master” (Roche) 1:5-dilution of cDNA  2µl per measurement, all PCRs with the same cDNA dilution at least two independent PCR runs for each cDNA sample, a third measurement if differences >30%, use of means of all measurements 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 standard of LC capillaries storage-stable coated with amounts of 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) Statistics: classification of patients according to tumor stage (pT) and grade (GS) using SAS software (SAS Institute Inc., Cary, USA) and SPSS software (SPSS Inc., Chicago, USA) relative expression levels not distributed normally  log-transformation  Student´s t ‑ test degree of upregulation in Tu compared to corresponding Tf  generation of pairwise ratios of Tu:Tf receiver-operating characteristic (ROC) curves  to assess the diagnostic power of each separate variable univariately and for multivariate diagnostic rules by the area under curve (AUC) of the ROC curves multivariate diagnostic rules based on optimized logistic regression models comprising optimal sets of competing variables and optimal cut-off points for each variable  4-gene PCa-prediction model: use of variables divided into 2-4 classes by optimizing cut-off points  5-gene PCa-prediction model: use of continuous variables  3-gene OCD-prediction model: use of continuous variables calculation of a predicted absolute probability (p) for each individual case by simple addition of regression parameters depending on the original values of the variables and subsequent transformation from the logit model into probability: p = exp(logit)/[1+exp(logit)]  for the origin of the tissue specimen  for tumor prediction  for the tumor extension  for OCD prediction no extern validation of the logit model in the sense of prospective application on independent series of patients, but cross validation based on a one-step approximation by always elimination of one case from the sample, estimation of the model parameters from the remaining sample, use of the resulting model on the removed case, and finally averaging of all errors of prediction Conclusions biomolecular PCa detection on a given prostate specimen conceivable as additional tool to standard diagnostics use of marker combinations yields in increased diagnostic power possibly prediction of tumor stage for facilitation of therapeutic decisions (e.g. RPE in case of OCD) measurement of only 5 transcript markers (EZH2, hepsin, PCA3, prostein, TRPM8) & 1 references gene might be sufficient for different diagnostic purposes feasibility of this approach was shown in a model system using paired prostate specimens from RPE explants  transfer of the techniques to prostate biopsies to evaluate their applicability in PCa diagnostics (preliminary studies on artificial biopsies from RPE explants showed: sufficient RNA for up to 10 QPCR measurements available, application of PCa prediction models feasible  validation on diagnostic biopsies)  transfer of the techniques to urine samples as possible non-invasive assay for PCa diagnostics probability (p) of OCD in the analyzed 169 tissue pairs: median p for NOCD 9% median p for OCD 49% predicted probability of OCD Fig.5 Prediction of organ-confined disease (OCD) in a given prostate specimen by a 3-gene model The new 3-gene model should allow a prediction of tumor extension possibly to facilitate therapeutic decisions. Probabilities p of a organ-confined PCa were calculated by a logit-model using continuous transcript levels of the 3 markers EZH2, PCA3 and TRPM8. Tf samples (n=169) and Tu specimens originating from NOCD (n=78) were combined and compared together with Tu specimens originating from OCD (n=91) ROC-analysis of the 3-gene-model for OCD prediction using EZH2 + PCA3 + TRPM8 1- Specificity AUC = 0.830 (95% CI 0.72... 0.94) Fig.4 Expression of EZH2, PCA3 and TRPM8 in dependence on tumor stage Distribution of log-transformed relative expression levels of 3 selected markers is shown by boxplots for 169 Tf specimens in comparison to Tu specimens originating from OCD (n=78) or NOCD (n=91). lg (TRPM8 / TBP) for log-transformed relative expression levels of TRPM8 lg (PCA3 / TBP) for log-transformed relative expression levels of PCA3 for log-transformed relative expression levels of EZH2 lg (EZH2 / TBP) Fig.6 Expression of selected markers in dependence on GS Distribution of log-transformed relative expression levels of 5 selected markers is shown by boxplots for 169 Tf specimens in comparison to Tu specimens originating from low GS (n=43), intermediate GS (n=74) or high GS (n=50). Differences between different GS-groups were mostly not significant (t-test), often only trends of a dependence on GS were observed. PCA3 prostein PSA AMACR PSMA Results TBP is the best out of 4 tested reference genes (GAPDH, HPRT, PBGD, TBP) as shown previously [1] establishment of standardized, highly sensitive QPCR-assays with detection limits of 10 transcript molecules significant upregulation of all PCa-related markers in Tu except for AR (paired t-test) varying degree of overexpression (Fig.1) in pairwise comparisons (Tu:Tf ratios)  highest upregulation found for PCA3, AMACR, PSGR & hepsin followed by TRPM8 & PSMA ROC-analyses for single markers for PCa detection: highest AUC values for PCA3, AMACR, hepsin & TRPM8 increase diagnostic power for PCa detection by marker combination (Fig.2 & 3):  published 4-gene model validated in a larger patient cohort  confirmation of previous data for 106 patients [1]  new 5-gene model optimized in the larger patient cohort  increased diagnostic power in PCa detection in a given prostate specimen dependence of relative transcript levels on tumor stage (Fig.4)  EZH2, PCA3 & TRPM8 show a moderate upregulation in OCD (pT2) compared to Tf and to NOCD (pT3 + pT4)  possibly useful for OCD prediction?  new 3-gene model with the potential for prediction of tumor extension (Fig.5) in a given prostate specimen dependence of relative transcript levels on Gleason Score (Fig.6)  only trends were observed (rarely significant)  decrease of AMACR, PCA3, prostein & PSA and increase of PSMA with rising GS gene names and synonymsAcc.no. AMACR = alpha-methylacyl-CoA racemaseNM_014324 AR = androgen receptorNM_000044 D ‑ GPCR = Dresden-G protein-coupled receptor = OR51E1 = olfactory receptor, family 51, subfamily E, member 1 AY698056 EZH2 = enhancer of zeste homologNM_004456 hepsin = HPN = TMPRSS1 = transmembrane protease, serine 1NM_002151 PCA3 = prostate cancer antigen 3 = DD3 = prostate-specific gene DD3AF103907 PDEF = prostate epithelium-specific Ets transcription factorNM_012391 prostein = SLC45A3 = solute carrier family 45, member 3NM_033102 PSA = prostate specific antigen = KLK3 = kallikrein-related peptidase 3NM_001648 PSGR = prostate specific G protein-coupled receptor = OR51E2 = olfactory receptor, family 51, subfamily E, member 2NM_030774 PSMA = prostate specific membrane antigen = FOLH1 = folate hydrolaseM99487 TRPM8 = transient receptor protein M8 = trp ‑ p8 = transient receptor potential cation channel, subfamily M, member 8] NM_024080 TBP = TATA box binding protein (reference gene)NM_003194