Targeting the human urine RNAome for tumor diagnostics by qPCR Files/Informationen/urine RNA qPCRVienna 2010.pdf · Assumption III: Mathematical Models Mimic Complex Biological Tumor

Targeting the human urine RNAome

for tumor diagnostics by qPCR

Alfred Schöller phD Clinical Molecular Biologist

qPCR 2010 Vienna international symposium 7th – 9th April 2010

DIAGNOSTICS CENTER WEINVIERTEL

exterior lacks MIQE conformity interior suffices already Austrian

gene laboratory regulations

Body Fluids Contain Nucleic Acids

„Non-invasive“:

- whole blood (mononucleated cell fraction, serum/plasma)

- lymph fluid

- synovial fluid

- saliva

- tear fluid

- sweat

- urine (urine sediment)

- amniotic fluid

- breast milk

- malignant ascites

„Invasive“: tissue from a variety of needle core biopsies, liquor

and spinal fluid punctation

Accessibility of Clinical Markers

.

.

Diagnostic Groups of Nucleic Acids

Four different groups of nucleic acid fractions in body fluids should be distinguished,

because they can differentially associate with diseases:

1. intact cell-associated nucleic acid fraction

2. cell-free nucleic acids (circulating nucleic acids sensu lato)

3. exosome-vesicle-bound nucleic acids (subgroup of cell-free nucleic acids)

4. nucleic acid fractions enriched by clinical-routine manipulations

(bronchoalveolar lavage fluid, bladder washings, urine and plasma

analysis after a digital rectal prostate examination, urine analysis after an

ultrasound-guided biopsy, expressed prostatic secretions)

van der Vaart M, Pretorius PJ Is the role of circulating DNA as a biomarker of cancer being prematurely overrated? Clin Biochem 43:26-36.

Urine „INFORMATION EXTRACTION“

COLLECTION CONTENT ANALYSIS

MALDI/TOF

biochips

real time qPCR

proteins/peptides

DNA

(cellular/ transrenal

fDNA)

RNA

(cellular/fRNA)

(metabolites)

http://metlin.scripps.edu/ http://mosaiques-diagnostics.com

Urinomics

Metabonomics Urine Proteomics

Urine Methylomics is a promising novel field for tumor diagnostics

Dehan P et al (2009) DNA methylation and cancer diagnosis: new methods and applications. Expert Rev Mol

Diagnostics 9:651-657.

Urine RNAomics

Organe Diagnostic Urine Real-Time PCR Assays

- kidney diseases

- kidney allograft transplant rejections

- bladder cancer

- prostate cancer

Although several international groups are working in the field, a complete catalogue

(database) of urine RNA molecule content during various disease stages of the

urogenital system has not been established yet. State of the art papers describe

hundreds of RNA species:

Hanke M et al (2009) A robust methodology to study urine microRNA as tumor marker: microRNA-126 and microRNA-182 are related to urinary

bladder cancer. Urol Oncol. 2009 Apr 16. [Epub ahead of print]

not present

hidden

distorted

fragmented

Preanalytics: urine presents a major obstacle to a standardized

clinical diagnostics!

IN

A B C

INFORMATION

F

O

RMA TIO

N

urine

Modes of RNA-based Diagnostics

qualitative: cancer stage specific mRNAs or

mRNA splice variants

micro RNAs, non-coding RNAs

quantitative: mRNA gene expression profiling

Draw Backs of Urine RNA-based Diagnostics

Information Content - fluctuating urine volumina

- salt concentration

- elevated temperature

- aggressive chemical milieu

- presence of nucleases

996,7

785

625,3

471,7

208,3

944,7

321,7 539,7 263,7

Source: Statistic Austria

total: 5.087

incidence rate 2002

mortality rate 2002

total: 1.138

Epidemiology of Prostate Cancer

Prostate Cancer

ZONAL AREA %GLANDULAR TISSUE %CANCER OCCURRENCE

Transition zone 5% 20%

Central zone 15-20% 5-10%

Peripheral zone 70% 70%

Periurethral zone <5% 0%

Fibromuscular stroma 0% 0% from DeVita et al. eds., Cancer, 1997, Lippincott Raven Publishers

http://www.pedb.org/

http://cgap.nci.nih.gov/

Human Prostate Gene

DataBase

http://www.ucsf.edu/pgdb/

http://microarray-pubs.stanford.edu/prostateCA/

Prostate Cancer Molecular Subtypes Home Page

Prostate Organ Gene Expression Data Bases

Calcium-dependent phospholipid-binding proteins: ANX3

Enzymes: FAS, GalNAC-T3, PAP, hTERT, PTOV1, PLK1, PIN1, PIM-1, hepsin, AMACR

Membrane proteins: KAI-1, PSCA, PSMA, PCTA-1, STEAP, HCA, STAMP1, TMPRSS2,

TRPM8, MAGE, BCL-2, E-Cadherin, GOLM1

Ribosomal proteins: RPS2, PTI-1

Structural proteins: EPCA, d-catenin, thymosin b15, caveolin-1, D-PCa-2,

NMP-48

Secretory proteins: PSP94, CRISP-3, PSAP

Transcription factors: p53, E2F3, c-Myc, AlbZIP, MTA-1, EZH2

Vesicle trafficking: TPD52 (gene amplification)

Peptide hormones: TGF-b, IGF-1, FGF-1/2, MIC-1

Somatic fusion genes: TMPRSS2-ERG/ETV, SLC45A3-ERG

Non-coding RNAs: DD3/PCA3, PCGEM1

Unknown function: PCa-24

Genes Over/Under-Expressed In Prostate Cancer

Tricoli et al (2004) Detection of prostate cancer and predicting progression: current and future diagnostic markers. Clin

Cancer Res 10: 3943-3953.

Quinn et al (2005) Molecular markers of prostate cancer outcome. Eur. J. Cancer 41:858-887.

total urine filtration

centrifugation

Supernatant

proteins, peptides, fDNA, fRNA

Cell Pellet/Filtrate

proteins, RNA/gDNA/mDNA Downes MR et al (2007) Urinary markers for prostate cancer. BJU Int. 99:263-268.

Müller H, Brenner H (2006) Urine markers as possible tools for prostate cancer screening: review of performance

characteristics and practicality. Clin Chem 52:562-573.

Tomlins SA et al (2009) ETS gene fusions in prostate cancer: from discovery to daily clinical practice. Eur Urol 56:275-

286.

Hessels D, Schalken JA (2009) The use of PCA3 in the diagnosis of prostate cancer. Nat Rev Urol 6:255-261.

Jamaspishvili T et al (2010) Urine markers in monitoring for prostate cancer. Prostate Cancer Prostatic Dis 13:12-19.

Urine RNA-Markers For

Prostate Cancer

Current State of the Art RNA-Based Urine Tumor

Diagnostics by qPCR

The Beginning of the Golden Age of Urine RNA

Diagnostics or What the Clinician Wants to Know?

Prostate Cancer Risk Assessment (hereditary risk, environmental susceptibility, chemoprevention)

Prostate Cancer Adult Screening And Early Detection

(tumor specific markers, early onset of expression, high sensitivity)

Prostate Tumor Assessment Markers (differentiation latent/clinical tumor, tumor aggressiveness/malignancy,

preoperative staging and tumor subtyping, survival)

Prostate Cancer Monitoring Markers - chronical prostate inflammation

- environmental exposure to toxicants

- neuroendocrine differentiation

- progression

- proliferation

- androgen independence (hormone refractory prostate cancer)

- angiogenesis

- metastasis (lymphe node, bone)

- relapse after RPE

- therapy monitoring (minimal residual disease, efficacy of therapy)

Personalized Prostate Cancer Care Markers - patient specific drug therapy design (pharmacogenomics)

- patient-specific immunotherapy (target identification and monitoring)

- patient-specific gene therapy (vector construct monitoring)

Dhanasekaran et al (2001) Nature 412:822

Assumption I: Tumor Stage Specific Gene

Expression

Sboner A et al (2010) Molecular sampling of prostate cancer: a dilemma for predicting disease progression. BMC Med Genomics. 3:8.

[Epub ahead of print]

Assumption II: Urine RNA Biomarker Levels Mirror

Individual Tumor Expression Profiles

mRNA miRNA

PRINCIPAL COMPONENT ANALYSIS OF REAL TIME PCR RNA EXPRESSION DATA

Assumption III: Mathematical Models Mimic

Complex Biological Tumor Behaviours

metastasis

latent versus

clinical CaP

relapse

malignancy

Development of SYBR Green I Assays For Prostate

Biomarker mRNA/miRNAs

Total RNA was purified from urine of histologically confirmed prostate cancer

patients prior and after a DRE (n = 83), BPH patients (n = 24) and males aged

below 35 years (n = 36) as controls using a cell filtration kit from ZYMOResearch.

fRNA was purified from 2 ml centrifuged urine with a ZR Viral RNA Kit and miRNA

with a miRNEASY kit (Qiagen) from the cell pellet after a centrifugation. Reverse

transcription was performed with a Transcriptor First Strand cDNA Synthesis kit

(Roche) using anchored primer.

18s RNA amplification

RNA Quality Control

linearity of the reverse transcription reaction

SYBR Green I Assays For Prostate Biomarker

mRNAs

34 genes

> 50 primer pairs

Cell-Free Urine RNA (fRNA) Is Not an Artifact of

Contaminating Genomic DNA

Dilution LinReg

Determination of Primer Efficiences

Amplification Curves In Different Urine Fractions

Characterization of Melting Curves (Reference

Genes)

Reference gene selection by geNORM and Normfinder analysis. 10 patients/group (normal urine

from young males < 35 years of age, pre-DRE BPH, post-DRE-BPH, pre-DRE CaP, post-DRE

CaP) were analysed using 14 housekeeping genes by Lightcycler II and Lightcycler 480 real

time PCR. Top panel: geNORM, middle panel: Normfinder, bottom panel: best number of

reference genes for the calculation of a normalization factor. .

Reference Gene Selection

cell-free RNA (ufRNA) miRNA from cell pellets pellets

Reference Gene Selection

Gene Expression Analysis in Urine Cell Filtrates

ufRNA Content is Significantly Increased in Post-

DRE-Urine

ufRNA Is a Significant Novel Ressource Allowing

Multiple Gene Expression Analysis

qPCR analysis of genes A, C, D and RPS2. Data are processed with GenEx Professional, normalized to the 3 best

reference genes and plotted as fold-changes (log2).

Quantification of Novel Markers in Cell-Free Urine

miRNA Can Be Quantified in the Cell-Free and the

Cellular Urine Compartment

1. relative real time qPCR combined with mathematical data modelling

(principal component analysis) is a proper means for urine

biomarker characterization

2. cell free urine ufRNA found enriched in post-DRE urine is a potential

novel RNA resource for prostate cancer diagnostics

3. the presence of cellular as well as cell free miRNA in patient urine

opens the path to a whole range of new clinical diagnostic assays

4. clinical gene expression profiling requires prostate cancer cell

enrichment

Conclusions

Urine mRNA quantification

kidney

bladder

prostate

leukocyte

Some Technical Solutions

relative quantification with evaluated reference genes

(assuming > after a DER 80% are prostate cancer cells)

cell percentage calculations by cell target identification

complex subtractive RNA hybridizations

prostate cell enrichment by immunomagnetics or cell chips

single cell qPCR by high-throughput methods

urine cell pellet/filtrate

The Main Obstacle to a Meaningful Urine Gene

Expression Profiling is the RNA Background

Acknowledgements

Primar Dr. Karl Grubmüller Michaela Mayer

Simona Cionca

Nicole Novak

Judith Ott

Austrian Institute Technology (AIT)

Dr. Peter Ertl

Verena Charwat

Drexel University, Philadelphia, USA

Dr. Mark Stearns

Dr. Min Wang

Dr. Youji Hu

Supported by

AUSTRIAN NATIONAL BANK P11491

DR. PRÖLL MILESTONE AWARD 2006

RIZ Genius Prize 2009

biomed austria

FH Wr. Neustadt

Wednesday 5th Mai 1545 Stadtsaal Mistelbach: Departure Gregor Mendel Museum

(Brno, Czech Republic)

Friday 7th Mai 1800 Stadtsaal Mistelbach: Lecture Dr. Kary Mullis „The unusual

origin of PCR“ (afterwards Art Meets Science at the Museum Center Mistelbach)