S. FuesselS. Fuessel11, S. Unversucht, S. Unversucht11, R. Koch, R. Koch22, G. , G. BarettonBaretton33, A. Lohse, A. Lohse11, S. Tomasetti, S. Tomasetti11, M. , M.

HaaseHaase33, M. Toma, M. Toma33, M. Froehner, M. Froehner11, A. Meye, A. Meye11, , M.P. WirthM.P. Wirth11

11 Department of Urology, Department of Urology, 22 Institute of Medical Institute of Medical Informatics and Biometry, Informatics and Biometry, 33 Institute of Pathology, Institute of Pathology,

Technical University of Dresden, GermanyTechnical University of Dresden, Germany

Extension of quantitative multi-gene Extension of quantitative multi-gene

expression studies on paired radical expression studies on paired radical

prostatectomy (RPE)–prostate tissue prostatectomy (RPE)–prostate tissue

samplessamples

[supported by a grant from the DFG][supported by a grant from the DFG]

•main problem: early identification of main problem: early identification of significant PCa for therapeutic decisionssignificant PCa for therapeutic decisions

•need for new additional PCa-markers to need for new additional PCa-markers to improve diagnostic and prognostic powerimprove diagnostic and prognostic power

•quantification of transcript markers as quantification of transcript markers as promising toolpromising tool

•expression signatures more reliable than expression signatures more reliable than single markerssingle markers

ObjectiveObjective

• 169 matched pairs of malignant and non-169 matched pairs of malignant and non-malignant prostate tissue specimens (Tu + Tf) malignant prostate tissue specimens (Tu + Tf) from RPE specimensfrom RPE specimens

• establishment of standardized quantitativeestablishment of standardized quantitativePCR-assays (QPCR)PCR-assays (QPCR)

• evaluation of 4 housekeeping genes (GAPDH, evaluation of 4 housekeeping genes (GAPDH, HPRT, PBGD, TBP) as reference for internal HPRT, PBGD, TBP) as reference for internal normalization:normalization: only TATA box binding protein (TBP) suitableonly TATA box binding protein (TBP) suitable

(no different expression between Tu and Tf)(no different expression between Tu and Tf)

Material & methods IMaterial & methods I

transcript marker nametranscript marker name

AMACRAMACR

ARAR

D-GPCR D-GPCR (OR51E1) (OR51E1)

EZH2EZH2

hepsinhepsin

PCA3 (DD3)PCA3 (DD3)

PDEFPDEF

prosteinprostein

PSAPSA

PSGR (OR51E2)PSGR (OR51E2)

PSMAPSMA

TRPM8TRPM8

-methylacyl-CoA-racemase-methylacyl-CoA-racemase

androgen receptorandrogen receptor

G protein-coupled receptor (olfactory G protein-coupled receptor (olfactory receptor) receptor)

enhancer of zeste homolog 2enhancer of zeste homolog 2

membrane associated protease membrane associated protease

prostate cancer antigen 3prostate cancer antigen 3

prostate-derived Ets factorprostate-derived Ets factor

prostate cancer-associated gene 6prostate cancer-associated gene 6

prostate specific antigenprostate specific antigen

prostate specific G protein-coupled receptor prostate specific G protein-coupled receptor

prostate specific membrane antigenprostate specific membrane antigen

transient receptor protein M8transient receptor protein M8

Material & methods IIMaterial & methods II12 PCa-related genes known from literature were tested12 PCa-related genes known from literature were tested

• evaluation of evaluation of single & combined markerssingle & combined markers

(ROC-analyses) (ROC-analyses)

• mathematical modelsmathematical models for PCa-specific for PCa-specific

transcript signaturestranscript signatures

• goals: goals: -- prediction of PCa-presence prediction of PCa-presence

- prediction of - prediction of tumor extension tumor extension

- prediction of tumor aggressiveness- prediction of tumor aggressiveness

final aim: bioprofiling of PCafinal aim: bioprofiling of PCa

Material & methods IIIMaterial & methods III

Evaluation of single markers: Evaluation of single markers: overexpression in PCa?overexpression in PCa?

PCA3 AMACR PSGR hepsin TRPM8 PSMA D-GPCR EZH2 PDEF PSA prostein AR

Tu

:Tf

rati

os

(pai

red

anal

ysis

)

10-2

10-1

100

101

102

103

104

0.866 0.843 0.775 0.842 0.814 0.751 0.652 0.792 0.763 0.655 0.569 0.565

univariate ROC analyses: AUC values of single markers

11.9 x

43.0 x

6.6 x 6.5 x3.7 x3.9 x

2.1 x 2.0 x2.0 x1.1 x1.6 x

1.1 x

median overexpression (paired analysis)

PCA3 (=DD3), AMACR, PSGR, hepsin, TRPM8 & PSMAPCA3 (=DD3), AMACR, PSGR, hepsin, TRPM8 & PSMA most promising PCa transcript markersmost promising PCa transcript markers

n=169

Optimized 4-gene-model for PCa-prediction:Optimized 4-gene-model for PCa-prediction:

EZH2 + PCA3 + prostein + TRPM8EZH2 + PCA3 + prostein + TRPM8

ROC Prädiktor aus Publikation alte+neue Daten

Sens

itivi

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 Specifity0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

1- Specificity

AUC = 0.893(95% CI 0.76 ... 1.00)

ROC-analysis of theROC-analysis of the4-gene-combination4-gene-combination

tumorfrei Tumor0

0.25

0.50

0.75

1.00predictedprobability

for tumor

pre

dic

ted

pro

bab

ility

of

tum

or

• classification of relative expression levels of these 4 genes classification of relative expression levels of these 4 genes according optimized cut-offs according optimized cut-offs logit-value for each tissue sample logit-value for each tissue sample (Tu and Tf)(Tu and Tf)

• logit-modellogit-model: p = exp(logit)/[1+exp(logit)] : p = exp(logit)/[1+exp(logit)] 

n=169

probability (p) of PCa probability (p) of PCa presence in the analyzed presence in the analyzed

tissue samples:tissue samples:

median p for Tu 81%median p for Tu 81% median p for Tf 21%median p for Tf 21%

New 5-gene-model for PCa-prediction:New 5-gene-model for PCa-prediction:

EZH2 + hepsin + PCA3 + prostein + TRPM8EZH2 + hepsin + PCA3 + prostein + TRPM8

ROC-analysis of theROC-analysis of the5-gene-combination5-gene-combination

• using relative expression levels of these 5 genes as continuous using relative expression levels of these 5 genes as continuous values values logit-value for each tissue sample (Tu and Tf) logit-value for each tissue sample (Tu and Tf)

• logit-modellogit-model: p = exp(logit)/[1+exp(logit)] : p = exp(logit)/[1+exp(logit)] 

probability (p) of PCa probability (p) of PCa presence in the analyzed presence in the analyzed

tissue samples:tissue samples:

median p for Tu 87%median p for Tu 87% median p for Tf 10%median p for Tf 10%

Sens

itivi

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 Specifity0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

1- Specificity

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

Tumor tumorfreitumorfrei Tumor

0

0.25

0.50

0.75

1.00predictedprobability

for tumor

pre

dic

ted

pro

bab

ility

of

tum

or

Dependence of marker expression on tumor Dependence of marker expression on tumor

stage:stage:Discrimination between organ-confined disease (OCD) Discrimination between organ-confined disease (OCD)

andand

non- organ-confined disease (NOCD) for therapeutic non- organ-confined disease (NOCD) for therapeutic

decision?decision?

TfTf: n=169 : n=169 OCDOCD: n=78 : n=78 NOCDNOCD: n=91: n=91

for log-transformed relative expression levels of:for log-transformed relative expression levels of:

EZH2

tf tu NOCD tu OCDtf tu OCD tu NOCD 4

6

4

2

0

2

4predictedprobability

for tumor

lg (

EZ

H2

/ T

BP

)

Tf Tu (OCD) Tu (NOCD)

PCA3

tf tu NOCD tu OCDtf tu OCD tu NOCD 4

7.5

5.0

2.5

0

2.5

5.0

7.5predictedprobability

for tumor

lg (

PC

A3

/ T

BP

)

Tf Tu (OCD) Tu (NOCD)

TRPM8

tf tu NOCD tu OCDtf tu OCD tu NOCD 4

5.0

2.5

0

2.5

5.0

7.5predictedprobability

for tumor

lg (

TR

PM

8 /

TB

P)

Tf Tu (OCD) Tu (NOCD)

mathematical model for OCD-predictionmathematical model for OCD-prediction

probability (p) of OCD in the probability (p) of OCD in the analyzed 169 Tu tissues:analyzed 169 Tu tissues:

median p for NOCD 9%median p for NOCD 9% median p for OCD 49%median p for OCD 49%

ROC-analysis of the 3-gene-modelROC-analysis of the 3-gene-modelfor OCD predictionfor OCD prediction

Sens

itivi

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 Specifity0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

1- Specificity

AUC = 0.830(95% CI 0.72 ... 0.94)

New 3-gene-model for OCD-prediction:New 3-gene-model for OCD-prediction:

EZH2 + PCA3 + TRPM8EZH2 + PCA3 + TRPM8

NOCD OCDNOCD OCD

0

0.2

0.4

0.6

0.8

1.0predictedprobability

for tumor

pre

dic

ted

pro

bab

ility

of

OC

D

NOCD (n=91) OCD (n=78)

• using relative expression levels of these 3 genes as continuous using relative expression levels of these 3 genes as continuous values values logit-value for each tissue sample (Tu-NOCD and Tu- logit-value for each tissue sample (Tu-NOCD and Tu-NOCD)NOCD)

• logit-modellogit-model: p = exp(logit)/[1+exp(logit)] : p = exp(logit)/[1+exp(logit)] 

• biomolecular PCa detection on a given prostate biomolecular PCa detection on a given prostate

specimenspecimen

as additional tool to standard diagnostics?as additional tool to standard diagnostics?

• use of transcript marker combinationsuse of transcript marker combinations

increased diagnostic powerincreased diagnostic power

• measurement of only 5 transcript PCa-markers (EZH2, measurement of only 5 transcript PCa-markers (EZH2,

hepsin, PCA3, prostein, TRPM8) & 1 reference genehepsin, PCA3, prostein, TRPM8) & 1 reference gene

might be sufficient for different diagnostic purposesmight be sufficient for different diagnostic purposes

• feasibility of the approach shown in a model systemfeasibility of the approach shown in a model system

using paired prostate specimens from RPE explantsusing paired prostate specimens from RPE explants

Outlook IOutlook I

• transfer of the techniques to prostate biopsiestransfer of the techniques to prostate biopsies to evaluate their applicability in PCa diagnostics?to evaluate their applicability in PCa diagnostics? improvement of PCa detection?improvement of PCa detection?

• possibly prediction of tumor stage using biopsiespossibly prediction of tumor stage using biopsies therapeutic decisions?therapeutic decisions?

Future aims:Future aims:• correlation of transcript signatures with outcome?correlation of transcript signatures with outcome?

follow-up needed for prognostic purposesfollow-up needed for prognostic purposes

• correct prediction of tumor aggressivenesscorrect prediction of tumor aggressiveness active surveillance active surveillance vs. vs. curative treatmentcurative treatment

Outlook IIOutlook II