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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
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
