s. fuessel 1, s. unversucht 1, r. koch 2, g. baretton 3, a. lohse 1, s. tomasetti 1, m. haase 3, m....
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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
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