PD Dr. rer. nat. Holger Sültmann

dpt. of Molecular Genome Analysishead: Prof. Dr. Annemarie PoustkaDKFZ Heidelberg

cDNA microarrays for gene expression studies in complex disease

Hanahan and WeinbergCell 100, 57-70 (2000)

genome projects: change of paradigms

hypothesis-driven research

high throughput

gene-driven research

cDNA expression profiling - goals

! target genes for new therapeutic approaches! identification of pathways associated with

disease development and progression! classification of disease (sub)types! disease diagnosis, prognosis,

and treatment response

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Promotor

DNA(gene)

Exon 1 Exon 2 Exon n

hn-mRNA

poly A tailprotein coding region

translation

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protein

nucleus

cytoplasm

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5` 3`

PCR for cDNA clone amplification(A)n

cDNA clone

(A)n

vector PCR

DNA array

genespecific PCR

DNA array 31500 clones

QC: M13-primed PCR amplification

doublebands no product

low product amount

spotting robot forcDNA nylon filters

tumor/normal-tissue

33P-labelledcDNA

AAAAAATTTT

RNA

signal intensities(31500-cDNA-clone filter)

cDNA expression profilingusing 31500-clone nylon filters

DNA-DNA-hybridization

data analysisratio of RNA abundance in normal/tumor samples

T98-08850

N98-08850

kidney cancer (renal cell carcinoma, RCC)! types:• clear cell (75%)• chromophilic (10%)• chromophobe (5%)• oncocytoma (5%)• duct-Bellini (1%)

! epidemiology: • 95000 deaths p.a. worldwide• risk increased by 43% since 1973 (USA)• males have 2-3x higher risk than females• risk is associated with genetic (VHL gene),

metabolic (obesity, blood pressure), environmental (cadmium) factors, and age

! genetic markers:• 3p deletion or translocation (clear cell and chromophilic)• VHL, VEGF, EGFR, TGFA, VIM, GAPDH, LDHA

! medical treatment:• surgery, immunotherapy

clear cell carcinoma

! ratio 3.5, percentile threshold 30%

! sign (-1/0/1) statisticDKFZ HeidelbergMolecular Genome AnalysisDr. Holger Sültmann

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data analysis

gene expression changes in kidney cancer

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up: 1025 genes/ESTs; down: 717 genes/ESTs (37 samples)

Boer et al., Genome Research 11(11), 1861-1870, 2001

mathematics/informatics: data visualization (ββββ2m)

frequency

log ratio

37 patients

glucose 6-phosphatase

ATPADPphosphofructokinase

fructose 1,6-bisphosphatase

aldolase

Pi

ATPADP

Pi

triosephosphate isomerase

Glycolysisupregulated

Gluconeogenesisdownregulatedphosphoglycerate kinase

enolase

ADP

ATP

pyruvate kinase PEP carboxykinase

ATP

GDP

GTP

NAD+ +PiNADH + H+GAP-DH

ADP

ATPADP

Pi

pyruvate carboxylase

PEP

pyruvate

glucose

Glc-6-P

oxalo acetate

2-P-glycerate

DHAP

Frc-6-P

Frc-6-Bis-P

GA-3-P

Bis-P-glycerate

3-P-glycerate

pathway identification

comprehensive31500 (70000) elements

indication-specific3000-8000 elements

cDNA arrays at MGA (DKFZ)

kidney cancerbreast cancerbrain cancerGIS tumors

tumor tissue

RNA

normal tissue

cDNA-Chip

higher in tumor

lower in tumor

balanced expression

RNA

cDNA

cDNA

cDNA expression profiling on glass arrays

Omnigrid (Genemachines) Arrayer

slide spottingexperiences

robot stopped during spotting

some pins temporarilynon-functional

384-well spotting plate deformed

04/05/2001

27/04/2001

11/05/2001

slide surfaces before spotting

AS3 (300301) Lys5 (300301)

spotting pin quality decline

after delivery of 5x105 spots

after delivery of 3x105 spots

cDNA array experiments and analysis

PCR products: PCR product control on agarose gels purification (precipitation, chromatography)

slide spotting: spotting solutions (3xSSC, +/- betaine, commercial ...)slide surfaces (non-treated, AS, poly-L-Lys, AL, ...)pre- and post-spotting slide treatment

RNA QC: agarose gel electrophoresis, Agilent RNA chipsRNA labelling: amount of RNA required (10 µg/slide/channel)

amount of fluorescent dye requiredreaction conditions (enzyme, temperature, duration ...)

hybridization: commercial, DIG-easy with Cot/Denhardt`sunder coverslides, hyb-machinein closed humidified hyb-chambers submerged in water

post-hyb-treatment of slides: SSC/SDS, water, ethanol-dilutions, dryingimage analysis: Arrayvision, self-defined grids data: handling and storagedata analysis: QC, normalization, statistical evaluation

+ 5q

5q

prognosis of RCCC correlates with chr. 5q amplification(cytogenetic data; Gunawan et al., Cancer Research, in press)

50 marker genes/ESTs correlating with 5q amplif."""" molecular classification of clear cell renal cancer[gene expression data; 21 (10 vs. 11) patients]

diagnosis/prognosis

highest expressed genes

595652

EC vs. A DocER (+) vs. ER (-)PR (+) vs. PR (-)

2074pre vs. post chemotherapy

65response vs. no response276pre vs. post chemotherapy

differentially expressed genes(p-value < 0.001)

breast cancer samples on 31500 clones:neoadjuvant chemotherapy:- inn--vivo sensibility testvivo sensibility test-- visualization of therapy success visualization of therapy success -- better conditions for surgerybetter conditions for surgery-- early therapy for early therapy for micrometastasesmicrometastases

(12 patients)

biopsy

indication-specific arrays at DKFZ/MGA! kidney cancer: 2200 differentially expressed genes/ESTs

+ 1800 oncologically relevant genes ! brain cancer: 2950 highly expressed genes/ESTs

+ 520 differentially expressed genes/ESTs+ 580 genes from literature searches+ 1150 oncologically relevant genes

! breast cancer: 3300 highly expressed genes/ESTs+ 1900 genes from literature searches+ 550 differentially expressed genes/ESTs+ 1150 oncologically relevant genes

! GIS tumors: 1200 highly expressed genes/ESTs+ 600 differentially expressed genes/ESTs+ 1150 oncologically relevant genes

immunohistochemistry

CMN TIII

N TIII

N TI

N TI

N TIII

Northern blot hybridization

quantitive PCR

additional experimental evidence

the challenge: complex systems

Annemarie PoustkaHolger SültmannWolfgang HuberMarkus VogtFrank BergmannPatrick HerdeKlaus SteinerJörg SchneiderFlorian HallerKatharina FinisStephanie SüßYvonne KeßlerKai DobersteinAnne Dörsam

Judith BoerFriederike Wilmer

Martin VingronAnja von HeydebreckMPI for molecular genetics, Berlin

Günter SawitzkiStatlab, university ofHeidelberg

Laszlo FüzesiBastian Gunawandpt. of pathology, university of Göttingen

Molecular Genome AnalysisCollaborations

Bernhard KornMatthias SchickRZPD Heidelberg

Stefan WiemannUte ErnstSara Burmester

Daniel Göttel

http://www.dkfz.de/abt0840/