cDNA Microarray analysis of an invasive brain tumor

1. cDNA Microarray analysis of an invasive brain tumor OR More answers than you can handle Dominique B Hoelzinger

2. Overview • Introduction • Generating data • Analyzing data • Interpreting data

3. The biological problem • Glioblastoma multiforme • the deadliest brain cancer • Current treatments: • Surgery • Chemotherapy • Radiotherapy • Stem cells • Gene therapy

4. SPREAD OF GLIOBLASTOMA MULTIFORME • 1) corpus callosum • 2) Fornix • 3) Optic radiation • 4) Association pathways • 5) Anterior commissure

5. Glioma motility • What make these cells move? • What switches them from dividing to motile?

6. The ones that got away • Highly invasive • Surgeon can’t reach them • Chemotherapy and radiotherapy can’t reach them • They are not dividing core rim rim core

7. Laser Capture Microdissection

8. 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.

9. 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

10. About RNA

11. Overview • Introduction • Generating data • Analyzing data • Interpreting data

13. Robotic Array Assembly

14. cDNA microarray technology http://research.nhgri.nih.gov/microarray/image_analysis.html

15. Really raw data

18. Overview • Introduction • Generating data • Analyzing data • Interpreting data

19. 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

20. Genes down-regulated in migrating cells 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 • 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 • 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

21. Overview • Introduction • Generating data • Analyzing data • Interpreting data

27. BioHavasu project

33. 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.

34. Understanding relationships

35. Sub-cellular localization

36. 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.

37. So that I don’t have to spend hours finding diagrams myself…. Mef 2C LPA GCR G proteins HB-EGF

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

39. The ones that got away • Highly invasive • Surgeon can’t reach them • Chemotherapy and radiotherapy can’t reach them • They are not dividing core rim rim core

40. Genetics again!

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

42. Transcription factors

43. Consensus binding sites • Position weighted matrices • Define variation in promoter consensus sequences

44. The sequenced human genome

45. Finding the Promoter

46. Genome Browser Human Genome Browser Gateway

47. TESS

48. TESS Job W0793006061 : Tabulated Results

49. Promoter structure 2 1 3 4

50. Promoter Alinement