cDNA%20Microarray%20analysis%20of%20an%20invasive%20brain%20tumor

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cDNA Microarray analysis of an invasive brain
tumor

• OR
• More answers than you can handle
• Dominique B Hoelzinger

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Overview

1. Introduction
2. Generating data
3. Analyzing data
4. Interpreting data

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The biological problem

• Glioblastoma multiforme
• the deadliest brain cancer
• Current treatments
• Surgery
• Chemotherapy
• Radiotherapy
• Stem cells
• Gene therapy

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SPREAD OF GLIOBLASTOMA MULTIFORME

• 1) corpus callosum
• 2) Fornix
• 3) Optic radiation
• 4) Association pathways
• 5) Anterior commissure

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Glioma motility

• What make these cells move?
• What switches them from dividing to motile?

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The ones that got away

• Highly invasive
• Surgeon cant reach them
• Chemotherapy and radiotherapy cant reach them
• They are not dividing

core
rim
rim
core
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Laser Capture Microdissection
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1) Prepare
Follow routine protocols for preparing a tissue
on a plain, uncovered microscope slide
Visualize the sample through the video monitor or
the microscope. Position the CapSure film
carrier over the cell(s) of interest
2) Locate
Press the button to pulse the low power infrared
laser. The desired cell(s) adhere to the CapSure
film carrier.
3) Capture
4) Microdissect
Lift the CapSure film carrier, with the desired
cell(s) to the film surface. The surrounding
tissue remains intact.
5) Analyze
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.
 
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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

10mm
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About RNA
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Overview

1. Introduction
2. Generating data
3. Analyzing data
4. Interpreting data

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Robotic Array Assembly
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cDNA microarray technology
http//research.nhgri.nih.gov/microarray/image_ana
lysis.html
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Really raw data
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Overview

1. Introduction
2. Generating data
3. Analyzing data
4. Interpreting data

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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

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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 SERPINB2 serine (or cystein) proteinase
  inhibitor, 2 (P
• 4 SERPINH2 serine (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

Cytoskeleton 12 VIM vimentin 7
PLEK plekstrin 5 MSN moesin 4
CAPG Capping protein (actin filament),
gelsolin-like 3 KANK kidney ankyrin
repeat-containing protein Apoptosis 4
CASP4 caspase 4 4 PIG3 p53 induced gene
3 Transcription 14 FP36L1 zinc finger
protein 36, C3H type-like 1 (ERF-1) 7 ID4
inhibitor of DNA binding 4, dominant neg
helix-loop-helix protein 3 BTF3 basic
transcription factor 3 6 EYA2 eyes absent
(Drosophila) homolog 2 4 EGR1 Early growth
response 1 4 JUNB Jun B proto-oncogene 4
CEBPB CCAAT/enhancer binding protein
(C/EBP), beta 3 NFKBIA nuclear factor kappa-B
inhibitor alpha 3 FOXM1 forkhead box
1M Proliferation 3 CKS2 CDC28 protein
kinase regulatory subunit 2 3 CDC20 cell
division cycle 20 Unknown function 5
H47315 EST 7 MT1L metallothionein 1L 6
CLIC1 chloride intracellular channel 1 6
MT2A metallothionein 2A 4
HNRPH1 heterogeneous nuclear ribonucleoprotein
H1 4 R68464 EST 4 APOE apolipoprotein
E 3 KIAA0630 KIAA0630 protein 3
MSI2 Musashi homolog 2
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Overview

1. Introduction
2. Generating data
3. Analyzing data
4. Interpreting data

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BioHavasu project
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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.

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Understanding relationships
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Sub-cellular localization
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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.

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So that I dont have to spend hours finding
diagrams myself.
Mef 2C
LPA
GCR
G proteins
HB-EGF
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Promoter Analysis

• Find the promoter region
• Genome browser
• Find transcription binding site
• TESS
• Genomatix
• Biobase, etc
• Align several promoters to find common patterns

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The ones that got away

• Highly invasive
• Surgeon cant reach them
• Chemotherapy and radiotherapy cant reach them
• They are not dividing

core
rim
rim
core
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Genetics again!
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Transcription

• Core promoter
• Transcription factors
• Co-activators
• Enhancers

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Transcription factors
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Consensus binding sites

• Position weighted matrices
• Define variation in promoter consensus sequences

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The sequenced human genome
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Finding the Promoter
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Genome Browser
Human Genome Browser Gateway
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TESS
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TESS Job W0793006061 Tabulated Results
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Promoter structure
2
1
3
4
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Promoter Alinement
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Genomatix
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The next step, biological significance

• Proof of transcriptional regulation proof of
  protein
• Cellular specificity
• Subcellular localization
• Activity

Tissue micro-array
TissueInformatics
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Conclusion

• cDNA microarray technology has opened a flood
  gate of information
• Biologists need HELP
• Expedite the interpretation of data.
• ideas wanted