Tab.1 Analyzed transcript markers: gene names and accession numbers

gene names and synonyms Acc.no.

AMACR = alpha-methylacyl-CoA racemase NM_014324

AR = androgen receptor NM_000044

D‑GPCR = Dresden-G protein-coupled receptor = OR51E1 = olfactory receptor, family 51, subfamily E, member 1 AY698056

EZH2 = enhancer of zeste homolog NM_004456

hepsin = HPN = TMPRSS1 = transmembrane protease, serine 1 NM_002151

PCA3 = prostate cancer antigen 3 = DD3 = prostate-specific gene DD3 AF103907

PDEF = prostate epithelium-specific Ets transcription factor NM_012391

prostein = SLC45A3 = solute carrier family 45, member 3 NM_033102

PSA = prostate specific antigen = KLK3 = kallikrein-related peptidase 3 NM_001648

PSGR = prostate specific G protein-coupled receptor = OR51E2 = olfactory receptor, family 51, subfamily E, member 2 NM_030774

PSMA = prostate specific membrane antigen = FOLH1 = folate hydrolase M99487

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

http://urologie.uniklinikum-dresden.de / susanne.fuessel@uniklinikum-dresden.dehttp://urologie.uniklinikum-dresden.de / susanne.fuessel@uniklinikum-dresden.de

Introduction & Objectives• downregulation of genes, which are associated with tumor growth, by nucleic acid based inhibitors such as

antisense-oligodeoxynucleotides (AS-ODNs) and small interfering RNAs (siRNAs) promising strategy for anti-

tumor therapies• bladder cancer (BCa) cell lines as model system: EJ28 & 5637 (both high grade & invasively growing)• targeting of different genes involved in multiple pathways contributing to tumor genesis and progression

vascular endothelial growth factor (VEGF) angiogenesis human telomerase reverse transcriptase (hTERT) cell imortalization (stabilization of telomeres) survivin (SVV) inhibitor of apoptosis protein (IAP) & essential for cell cycle progression

• all over-expressed in BCa and associated with tumor progression and poor outcome• former own studies: separate downregulation of the three genes by AS-ODNs and siRNA leading to impairment

of BCa cell proliferation• aims of the study:

enhancement tumor growth inhibition by multi-target gene silencing by AS-ODNs or siRNAs application of the inhibitors in a EJ28 xenograft model to prove their in vivo efficacy

Materials & MethodsPatients:• 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), 74 intermediate (GS=7), 50 high (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 101 to 107 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

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)

Fig.4 Expression of EZH2, PCA3 and TRPM8 in dependence on tumor stageDistribution 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).

Fig.6 Expression of selected markers in dependence on GSDistribution 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.

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

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.

AUC = 0.914(95% CI 0.77 ... 1.00)

[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.

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.

Mono- and Multi-target Inhibition Mono- and Multi-target Inhibition in vitroin vitro and and in vivoin vivoby Synthetic Nucleic Acids in Bladder Cancer Cellsby Synthetic Nucleic Acids in Bladder Cancer Cells

S. FuesselS. Fuessel11, D. Kunze, D. Kunze11, D. Wuttig, D. Wuttig11, Y. Burmeister, Y. Burmeister11, K. Krämer, K. Krämer11, I. Kausch, I. Kausch22, C. Blietz, C. Blietz22, A. Meye, A. Meye11, D. Jocham, D. Jocham22, M.P. Wirth, M.P. Wirth11

11 Department of Urology, Technical University of Dresden, Germany; Department of Urology, Technical University of Dresden, Germany; 22 Department of Urology, Medical School, University of Luebeck Department of Urology, Medical School, University of Luebeck

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