cDNA Microarray analysis of an

invasive brain tumor

ORMore answers than you

can handleDominique B Hoelzinger

Overview

I. IntroductionII. Generating dataIII. Analyzing dataIV. Interpreting data

The biological problem

• Glioblastoma multiforme– the deadliest brain cancer

• Current treatments:– Surgery– Chemotherapy– Radiotherapy– Stem cells– Gene therapy

SPREAD OF SPREAD OF GLIOBLASTOMA GLIOBLASTOMA

MULTIFORMEMULTIFORME 1) corpus 1) corpus

callosumcallosum 2) Fornix2) Fornix 3) Optic radiation 3) Optic radiation 4) Association 4) Association

pathwayspathways 5) Anterior 5) Anterior

commissure commissure

Glioma motility

• What make these cells move?

• What switches them from dividing to motile?

The ones that got away• Highly invasive

– Surgeon can’t reach them– Chemotherapy and radiotherapy can’t reach

them– They are not dividing

core

corerim

rim

Laser Capture Microdissection

1) Prepare Follow routine protocols for preparinga tissue on a plain, uncovered microscope slide

2) Locate

3) Capture

4) Microdissect

5) Analyze

Visualize the sample through the video monitor or the microscope. Position the CapSure™ film carrier over the cell(s) of interest

Press the button to pulse the low power infrared laser. The desired cell(s) adhere to the CapSure ™ film carrier.

Lift the CapSure ™ film carrier, with the desired cell(s)to the film surface. The surrounding tissue remains intact.

Place the CapSure ™ film carrier directly onto a standard microcentrifuge tube (Eppendorf) containing the extraction buffer. The cell contents, DNA, RNA or are ready for subsequent molecular analysis.

Microdissection of single cells

• Identify invading glioma cells on cryostat sections

• Using 20x magnification, laser-capture tumor cells

• Retrieve captured cells on LCM Cap

• Verify cell capture by inspection of Cap

10m

About RNA

Overview

I. IntroductionII. Generating dataIII. Analyzing dataIV. Interpreting data

Robotic Array Assembly

cDNA microarray technology

http://research.nhgri.nih.gov/microarray/image_analysis.html

Really raw data

Overview

I. IntroductionII. Generating dataIII. Analyzing dataIV. Interpreting data

GeneSpring

• Normalizes the calculated data

• Selects genes more than two-fold over or under the ratio of 1 (equally expressed in both populations)

• Custer analysis

• Principal Components Analysis

Genes down-regulated in migrating cells

• C/R Name Description• Extracellular• 33 IGFBP5 insulin-like growth factor binding protein 5• 12 IGFBP2 insulin-like growth factor binding protein 2• 11 DEPP decidual protein induced by progesterone• 11 ABCC3 ATP-binding cassette, C (CFTR/MRP) 3• 10 TNC tenascin C (hexabrachion)• 7 SRPX sushi-repeat-containing protein, X chrom• 5 SFRP4 secreted frizzled-related protein 4• 4 SERPINB2serine (or cystein) proteinase inhibitor, 2 (P• 4 SERPINH2serine (or cystein) proteinase inhibit• 3 MUC1 mucin 1• 3 EGFR-RS Likely ortholog of mouse EGF

• Vascular Involvement/Angiogenesis• 43 FCGR3A Fc fragment of IgG, low affinity IIIa,• 42 PTGER4 prostaglandin E receptor 4 (subtype• 17 HLA-DRA major histocompatibility complex, class II, 6 CD163

CD 163 antigen• 5 VEGF vascular endothelial growth factor• 5 VCAM1 vascular cell adhesion molecule 1• 4 LMO2 LIM domain only 2 (rhombotin-like1)• 4 CD68 CD68 antigen• Signal Transduction• 6 IQGAP IQ motiv containing GTPase activating• 8 RDC1 G protein-coupled receptor• 4 RGS16 Regulator of G-protein signaling 16• 3 NFKBIA NFKB inhibitor, alpha• 3 PLD2 phospholipase D 2• 3 TK2 thymidine kinase 2, mitochondrial• 3 ABL1 abelson murine leukemia viral oncogene homolog 1

Cytoskeleton12 VIM vimentin7 PLEK plekstrin5 MSN moesin4 CAPG Capping protein (actin filament), gelsolin-like3 KANK kidney ankyrin repeat-containing proteinApoptosis4 CASP4 caspase 44 PIG3 p53 induced gene 3Transcription14 FP36L1 zinc finger protein 36, C3H type-like 1 (ERF-1)7 ID4 inhibitor of DNA binding 4, dominant neg helix-loop-helix protein3 BTF3 basic transcription factor 36 EYA2 eyes absent (Drosophila) homolog 24 EGR1 Early growth response 14 JUNB Jun B proto-oncogene4 CEBPB CCAAT/enhancer binding protein (C/EBP), beta3 NFKBIA nuclear factor kappa-B inhibitor alpha3 FOXM1 forkhead box 1MProliferation3 CKS2 CDC28 protein kinase regulatory subunit 23 CDC20 cell division cycle 20Unknown function5 H47315 EST7 MT1L metallothionein 1L6 CLIC1 chloride intracellular channel 16 MT2A metallothionein 2A4 HNRPH1 heterogeneous nuclear ribonucleoprotein H14 R68464 EST4 APOE apolipoprotein E3 KIAA0630 KIAA0630 protein

3 MSI2 Musashi homolog 2

Overview

I. IntroductionII. Generating dataIII. Analyzing dataIV. Interpreting data

BioHavasu project

Unusual Suspects: Cataloging Cancer

Related Proteins, Genes using Biomedical

Literature• Pathway involvement (activity of protein): Determine the cellular pathway(s) during which the protein is involved : apoptosis, proliferation, or migration

• Interaction (protein/protein , protein/nucleic acids or protein /fatty acids): Determine protein binding. Swissprot, Entrez protein or Expasy

• Disease (protein/disease, protein/tissue type): Determine the types of cancer that the protein is related to.

• Protein Action (protein/function): Determine the diverse activation and inhibition relationships between proteins as well as sub-cellular localization.

Understanding relationships

GRIA1

LPA

FAK

Rac

PTPRN2EGFR

FGF 9

WASPPak

Cofilin

tenascinC

integrins

Nucleation of actin atmembrane

Actin depolymerization

Rho

ROCK

2

Eph B6

Elastin

Cdc42

STX11

paxillin

DTR

Actin

GPCRs

Rho

Laminin 5 HGF/SF Collagen IXDKK3

Guanine exchange factors

Ephrin-B2KLK6

PKC

myosin

Retrograde flow of actin

filaments

MLC phosphatase

MLCK

LIM kinase

TNC

LPA

FAK

Rac

EGFR-RS

VEGF

Pak

Cofilin

tenascinC

Nucleation of actin atmembrane

Actin depolymerization

ARHGAP8

ROCK

profilin

Actinpolymerization

stress fibers

OPCML EFNB3ENPP2

SFRP4

Cdc42

G proteinspaxillin

Actin

AP3M2

GPCRs

Ras

Rho

RGS16

Up-regulated during invasion

SERPIN B2 IGFBP2VCAM IGFBP5

SPOCK

Guanine exchange factors

SERPINH2

PKCB

Retrograde flow of actin

filaments

MLC phosphatase

MLCK

LIM kinase

RGS7

Down-regulated during invasion

CAPG

IQGAP

ZFP36L1, ID4, BTF3,EYA2, EGR1, JUNB

TRABID, MEF2C,ETS2, BACH2

CASP4, PIG3

DAP3,BCL2L2

ApoptosisTranscription factors

Sub-cellular localization

Proposed Ontology-Directed Extraction Methodology

• Model Medical Terminology: Identify existing medical ontologies such as UMLS for modeling the domain knowledge.

• Text Classifier Module: Build a classifier for identifying “interesting” sentences in MEDLINE abstracts.

• Natural Language Processing: Identify pre-processing steps for structuring free-text. Such steps involve part of speech tagging, noun and verb phrase chunking and shallow parsing.

• Relationship Extractor Module: Build an extractor system using machine-learning techniques, such as ILP, for learning rules that combine the medical ontologies with learned patterns on sentences to extract relationships among proteins.

• Usability, Performance and Scalability: Determine if the system is usable by biologists, if it can be easily trained to extract new types of relationships and its recall and precision is at acceptable levels.

So that I don’t have to spend hours finding diagrams

myself….

Mef 2C

HB-EGF

LPAGCR

G proteins

Promoter Analysis

• Find the promoter region– Genome browser

• Find transcription binding site– TESS– Genomatix– Biobase, etc

• Align several promoters to find common patterns

The ones that got away• Highly invasive

– Surgeon can’t reach them– Chemotherapy and radiotherapy can’t reach

them– They are not dividing

core

corerim

rim

Genetics again!

Transcription

• Core promoter

• Transcription factors

• Co-activators

• Enhancers

Transcription factors

Consensus binding sites

• Position weighted matrices– Define variation in

promoter consensus sequences

The sequenced human genome

Finding the

Promoter

Genome Browser

Human Genome Browser Gateway

TESS

TESS Job W0793006061 : Tabulated Results

Promoter structure

1 2

3 4

Promoter Alinement

Genomatix

The next step, biological significance

• Proof of transcriptional regulation = proof of protein– Cellular specificity– Subcellular localization– Activity Tissue micro-array

TissueInformatics

Conclusion

• cDNA microarray technology has opened a flood gate of information

• Biologists need HELP• Expedite the interpretation of data.• ideas wanted