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  • What is a microarray? It is a glass microscopic slide. It is like a 1 square cm chessboard, but instead of 64 squares it now has 25000 squares, each contains DNA from a specific gene. They are all ordered , so that later on you can identify which gene was on which spot. As you can see here column 17 row 7 has CyclinD1 DNA, row 8 has E2F1 DNA and so on. Using these arrays you can measure the activity of all these genes in two cell populations.
  • When a cell is active it makes transcripts or RNA. We extract the RNA from the cells and label them with a red fluorescent dye or a green fluorescent dye. In this example the tumor of interest is labeled red, and the reference sample is labeled green.
  • The DNA microarray glass slides are then bathed in a mixture of red and green transcripts. When a gene has same amount of activity in the tumor cell as the reference there will be an equal amount of transcripts labeled red and green and this will give rise to yellow. However when in a tumor cell a gene has a higher activity than the reference, and thus will have more transcripts with a red fluorescent dye attached to it, the DNA on the slide will be colored red. And visa versa is true as well, no expression in the tumor, it will turn green.
  • So how do we tailor treatment by profiling, Premenopausal lymphnode megative woman can be separated intio 2 groups by gene expression profiling. 60% will be classified in the poor prognosis signature group, 40% of the woman will be classified in the good prognosis signature group. Woman with the poor signature will receive chemo and hormanal therapy, and woman with the good signature will not receive adjuvant therapy, or only hormaonal therapy.
  • In the graph on the leftthe probability that patients would remain free of distant metastases is shown in the blue curve, and the red curve indicates the poor profile. The Kaplan Meier analysis of the survival is shown in the graph next to it. Metastases free survival is highly correlated to the probability of overal survival.
  • Microarrays;application

    1. 1. Microarrays, SNPs and ApplicationsDNA mRNA Protein
    2. 2. MicroarraysWhat is a microarray? A microarray is a compact device that contains a large number of well-defined immobilized capture molecules (e.g. synthetic oligos, PCR products, proteins, antibodies) assembled in an addressable format. You can expose an unknown (test) substance on it and then examine where the molecule was captured. You can then derive information on identity and amount of captured molecule.
    3. 3. Microscope slide DNA microarray 16 17 18 Actin CyclinD DHFR 7 DNA DNA DNA RB E2F1 tubulin 8 DNA DNA DNA control Myc Src1 9 DNA DNA DNA
    4. 4. Microarray Technology Manufacture or Purchase Microarray Hybridize DetectData Analysis
    5. 5. Advantages of Microarrays Small volume deposition (nL) Minimal wasted reagents Access many genes / proteins simultaneously Can be automated Potentially quantitative
    6. 6. Limitations of Microarrays Relatively new technology (10 years old) Still has technical problems (background) Poor reproducibility between investigators Still mostly manual procedure Relatively expensive
    7. 7. Applications of Microarrays Gene expression patterns Single nucleotide polymorphism (SNP) detection Sequence by hybridization / genotyping / mutation detection Study protein expression (multianalyte assay) Protein-protein interactions Provides: Massive parallel information
    8. 8. If Microarrays Are So Good WhyDidn’t We Use Them Before??  Not all genes were available  No SNPs known  No suitable bioinformatics  New proteins now becoming availableMicroarrays and associated technologies should beregarded as by-products of the Human GenomeInitiative,Nanotechnology and Bioinformatics
    9. 9. Reviews on Microarrays A whole issue on Microarray Technology has been published by Nature Genetics, Dec. 2002 (Vol. 32) Books:  Bowtell D. Sambrook J. DNA Microarrays. Cold Spring Harbor Laboratory Press, 2003  Schena M. Microarray Analysis. Wiley Liss, 2003
    10. 10. History1991 - Photolithographic printing (Affymetrix)1994 - First cDNA collections are developed at Stanford.1995 - Quantitative monitoring of gene expression patterns with a complementary DNA microarray1996 - Commercialization of arrays (Affymetrix)1997- Genome-wide expression monitoring in S. cerevisiae (yeast)2000 – Portraits/Signatures of cancer2003 - Introduction to clinical practice2004-Whole human genome on one microarray
    11. 11. Microarray FabricationTwo Major Methods:[a] Affymetrix → Photolithography (400,000 spots in 1.25 x 1.25 cm area!)[b] Everybody else → Mechanical deposition (printing) [0.5 - 2nL] on glass slides, membranes,etc
    12. 12. Principles of DNA Microarrays (printing oligos by photolithography) (Fodor et al. Science 1991;251:767-773)
    13. 13. Microarrays, such as Affymetrix’sGeneChip, now include all 50,000known human genes. Science, 302:211, 10 October, 2003
    14. 14. Affymetrix Expression Arrays They immobilize oligonucleotides (de novo synthesis; 25 mers) For specificity and sensitivity, they array 22 oligos per gene Latest version covers 50,000 genes (whole human genome) in one array (Agilent Technologies has the same density array; G4112A) They label-test RNA with biotin and detect with streptavidin- fluor conjugates
    15. 15. Preparation of Labeled mRNA for Hybridization Use oligo-dT with a T7 RNA polymerase promoter for reverse transcription of extracted mRNA (procedure makes cDNA) Use T7 RNA polymerase and biotin-labeled ribonucleotides for in vitro transcription (produces biotinylated, single-stranded cRNA) Alternatively: You can directly label cRNA with Cy-3 and Cy-5 fluors using T7 RNA polymerase
    16. 16. Microarray Applications Differential Gene Expression
    17. 17. RNA extraction and labeling to determine expression levelsample 1 RNA RNA sample 2 (tumor cDNA cDNA (reference) tissue) cRNA cRNA Cy3-UTP Cy5-UTP green fluorescencered fluorescence sample of interestreverse transcriptase, compared toT7 RNA polymerase standard reference
    18. 18. Tumor tissue Reference tissuecRNA (red) cRNA (green) 1 2 3 4 5 6 10 7 1 2 3 4 5 6 7 8 9 8 9 10 1 2 3 4 5 6 7 8 9 10 10 1 2 3 4 5 6 7 8 9 Human genes on a microarray slide 10 1 2 3 4 5 6 7 8 9
    19. 19. Differential Gene Expression(Budding vs Non-Budding Yeast)
    20. 20. Normal vs. Normal
    21. 21. Normal vs. Tumor
    22. 22. Lung Tumor: Up-Regulated
    23. 23. Lung Tumor: Down-Regulated
    24. 24. Lung Tumor: Up-RegulatedSignal transduction CytoskeletonProteases/Inhibitors Kinases
    25. 25. Lung Tumor: Up-RegulatedSignal transduction Cyclin B1 Cytoskeleton Cyclin-dependent kinase Tumor expression- related proteinProteases/Inhibitors Kinases
    26. 26. Lung Tumor: Down-Regulated Signal transduction Cytoskeleton Proteases/Inhibitors Kinases
    27. 27. Lung Tumor: Down-Regulated Signal transduction Cytoskeleton Tumor necrosis factor-related protein Proteases/Inhibitors Kinases
    28. 28. Genes Common to Many Tumors (e.g.Kidney; Liver; Lung) Up-regulatedDown-regulated
    29. 29. Microarray Applications Whole Organism Biology
    30. 30. Whole Genome Biology With Microarrays Cell cycle in yeast Study of all yeast genes simultaneously! Red: High expression Red Blue: Low expression Blue Lockhart and Winzeler Nature 2000;405:827-836
    31. 31. Microarray ApplicationsSingle Nucleotide Polymorphism (SNP) Analysis
    32. 32. Single Nucleotide Polymorphisms (SNP)  DNA variation at one base pair level; found at a frequency of 1 SNP per 1,000 - 2,000 bases  A map of 9 x 106 SNPs has been described in humans (by the International SNP map working group (HapMap)  60,000 SNPs fall within exons; the rest are in introns
    33. 33. Why Are SNPs Useful? Human genetic diversity depends on SNPs between individuals (these are our major genetic differences, plus micro/minisatellites) Specific combinations of alleles (called “Haplotypes”) seem to play a major role in our genetic diversity How does this genotype affect the phenotype Disease predisposition?
    34. 34. Why Are SNPs Useful? Diagnostic Application Determine somebody’s haplotype (sets of SNPs) and assess disease risk. Be careful: These disease-related haplotypes are not as yet known!
    35. 35. Nature 2003 426: 789-796
    36. 36. Genotyping: SNP Microarray Immobilized allele-specific oligo probes Hybridize with labeled PCR product Assay multiple SNPs on a single array TTAGCTAGTCTGGACATTAGCCATGCGGAT GACCTGTAATCG TTAGCTAGTCTGGACATTAGCCATGCGGAT Many other methods GACCTATAATCG For SNP analysis have been developed
    37. 37. SNP Analysis by MicroarrayGeneChip® HuSNPTM Mapping Assay (Affymetrix)More than 10,000 single nucleotide polymorphisms(SNPs) covering all 22 autosomes and the Xchromosome in a single experiment (soon to move to100,000 SNPs per experiment).Coverage:1 SNP per 210 kb of DNANeeds:250 ng of genomic DNA-1 PCR reaction
    38. 38. Commercial Microarray for Clinical Use (Pharmacogenomics) Roche Product CYP 450 Genotyping (drug metabolizing system) FDA Confusion Class 1 medical device? (no PMA) Class 2 or 3 medical device? (requires pre-market approval) From: Nature Biotechnology 2003 21:959-60
    39. 39. “The US government has blocked the sale of a new kind of DNA diagnostic test, putting up anunexpected barrier to the marketing of technology to distinguish genetic differences in how patients metabolize certain drugs.” Science 2003 302: 1134
    40. 40. SNP Detection by Mass Spectrometry High throughput detection of SNPs can be achieved by mass spectrometry SNP Center in Toronto (PMH) runs a Sequenom Mass Spectrometry system
    41. 41. Microarray Applications Sequencing by Hybridization
    42. 42. Sequencing By Hybridization Address the need for high-speed, low-cost sequencing of large sequences in parallel. Example: Consider examining 50Kb of sequence for 1,000 individuals.Conventional Method Microarray50Kb x 1,000 = 50 Mb of With one microarray of 1.25 x 1.25sequence. At a rate of 500 cm dimension, you can scan 50 Kbbases per lane and 30 of sequence at once. You needsequencing lanes, you can 1,000 microarrays to complete task. This may be completed in a fewproduce 15 Kb of sequence per days.day. You need 10 years for theproject.
    43. 43. Sequencing by Microarray Technology
    44. 44. GeneChip p53 Assay Reagents p53 Primer Set: PCR primer pairs of exons 2-11 optimized for a single-tube multiplex reaction Fragment Reagent: DNase 1 for DNA fragmentation Control Oligonucleotide F1: Positive hybridization control p53 Reference DNA: Human placental DNA
    45. 45. GeneChip p53 Assay Performance Characteristics Bases of genomic DNA analyzed 1262 bp Base calling accuracy for missense > 99.9% mutations Time from purified DNA to data 4.5 hrs Maximum steady state throughout equivalent to 6310 bp/hr As validated on a set of 60 human p53 genomic DNA samples. “Maximum steady state through-put based on one GeneChip analysis system.
    46. 46. Microarray Applications-Non Human - Chips Avaliable Now (2004) Pathogens (detection of Bird-Flu Virus strains) Smallpox (bioterrorism) Malaria (Plasmodium anopheles) Zebrafish/Xenopus laevis (model organisms) SARS Virus sequencing
    47. 47. Microarray Applications Food Expert-ID (available by Bio-Merieux;2004) DNA chip can verify quickly the animal species composition and the authenticity of raw or processed food and animal feed By providing multi-species identification, FoodExpert-ID will help to improve safety of food for human and animal consumption, thereby contributing to consumer health protection
    48. 48. Microarray Applications Protein Microarrays
    49. 49. Protein Microarrays  Protein microarrays are lagging behind DNA microarrays  Same idea but immobilized elements are proteins instead of nucleic acids  Number of elements (proteins) on current protein microarrays are limited (approx. 500)  Antibodies for high density microarrays have limitations (cross- reactivities)  Aptamers or engineered antibodies/proteins may be viable alternatives(Aptamers:RNAs that bind proteins with high specificity and affinity)
    50. 50. ApplicationsScreening for: Small molecule targets Post-translational modifications Protein-protein interactions Protein-DNA interactions Enzyme assays Epitope mapping
    51. 51. High-throughput proteomic analysis Label all Proteins in Mixture Haab et al. Genome Biology 2000;1:1-22Protein array now commerciallyavailable by BD Biosciences(2002)
    52. 52. Cytokine Specific Microarray (Microarray version of ELISA) IL-1 β IL-6 IL-10 VEGF MIX marker protein cytokine Detection system BIOTINYLATED MAb ANTIGEN CAPTURE MAb
    53. 53. Competing High Throughput Protein Technologies Bead-Based Technologies  Luminex-flow cytometry  Illumina-bead chips Microfluidics  Zyomyx Mass spectrometry  Ciphergen-protein chips
    54. 54. Microarray Clinical Applications Cancer Diagnostics
    55. 55. Molecular Portraits of Cancer Rationale: The phenotypic diversity of breast and other tumors might be accompanied by a corresponding diversity in gene expression patterns that can be captured by using cDNA microarrays Then Systematic investigation of gene expression patterns in human tumors might provide the basis of an improved taxonomy of breast cancersPerou et al. Nature 2000;406:747-752
    56. 56. Molecular Portraits of Cancer Breast Cancer Perou et al. Nature 2000;406:747-752 Green: Underexpression Green Black: Equal expression Black Red: Overexpression Red Left Panel: Cell Lines Right Panel: Breast Tumors Figure Represents 1753 Genes
    57. 57. Differential Diagnosis ofChildhood Malignancies Ewing Sarcoma: Yellow Rhabdomyosarcoma: Red Burkitt Lymphoma: Blue Neuroblastoma: Green Khan et al. Nature Medicine 2001;7:673-679
    58. 58. Differential Diagnosis of Childhood Malignancies (small round blue-cell tumors, SRBCT) EWS = Ewing Sarcoma NB = Neuroblastoma RMS = Rhabdomyosarcoma BL = Burkitt’s Lymphoma Note the relatively small number of genes necessary for complete discrimination Khan et al. Nature Medicine 2001;7:673-679
    59. 59. Microarray Milestone: June 2003Question:Can microarray profiling be used in clinical practice?Prognosis/Prediction of therapy/Selection of patients whoshould be treated aggressively?Nature 2002; 415: 530-536NEJM 2002; 347: 1999-2009 Van’t Veer and colleagues are using microarray profiling as a routine tool for breast cancer management (administration of adjuvant chemotherapy after surgery).Their profile is based on expression of 70 genes
    60. 60. Treatment Tailoring by Profiling premenopausal, lymph node negative Gene Expression profiling 60% 40% Poor signature Good signature~ 56 % metastases at 10 yrs ~ 13 % metastases at 10 yrs ~ 50 % death at 10 yrs ~ 4 % death at 10 yrs Adjuvant chemo- and No adjuvant therapy hormonal therapy or hormonal therapy only
    61. 61. 295 patients Kaplan-Meier Survival Curvesmetastases-free survival time (years) time (years)
    62. 62. Profiling in Clinical Practice Metastatic potential is an early and inherent ability rather than late and acquired Predictive power of prognostic signature confirmed in validation series Prognostic profile outperforms clinical parameters ~30-40% reduction of unnecessary treatment and avoidance of undertreatment (LN0 and LN+)
    63. 63. Therapeutic Implications Who to treat:  Prognostic profile as diagnostic tool  improvement of accurate selection for adjuvant therapy (less under- and over-treatment)  Prognostic profile implemented in clinical trials  reduction in number of patients & costs (select only patients that are at metastatic risk) How to treat:  Predictive profile for drug response  selection of patients who benefit
    64. 64. Commercial Clashes  Oncotype DX by “Genomic Health Inc”, Redwood City, CA  A prognostic test for breast cancer metastasis based on profiling 250 genes; 16 genes as a group have predictive value; $3,400 per test  215,000 breast cancer cases per year (potential market value > $500 million!)  No validation of test; No FDA approval  Test has no value for predicting response to treatmentScience 2004;303:1754-5
    65. 65. Commercial Clashes Mammaprint marketed by Agendia, Amsterdam, The Netherlands Based on L.Van’t Veer publications Test costs Euro 1650; based on 70 gene signature Prospective trials underway Celera and Arcturus developing similar tests (prognosis/prediction of therapy)Science 2004;303:1754-5
    66. 66. Tissue Microarrays Printing on a slide tiny amounts of tissue Array many patients in one slide (e.g. 500) Process all at once (e.g. immunohistochemistry) Works with archival tissue (paraffin blocks)
    67. 67. Gene Expression Analysis of Tumors cDNA Microarray Lakhani and Ashworth Nature Reviews Cancer 2001;1:151-157
    68. 68. Tissue Microarray Alizadeh et al. J Pathol 2001;195:41-52
    69. 69. Microarray Future: Conclusions Differential gene experssion studies will continue(robusness) Inexpensive, high-throughput, genome-wide scans for clinical applications Protein microarrays are now being deployed (may take over) Focus on biology and improved technology SNP analysis-Disease predisposition Pharmacogenomics Diagnostics-Multiparametric analysis Replacement of single-gene experiments(paradigm shift)