Microarrays, SNPs and Applications

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Microarrays, SNPs and Applications

• Eleftherios P.
  Diamandis MD,Ph.D
• (ediamandis_at_mtsinai.on.ca)
  
• Websitewww.acdclab.org

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Microarrays

• What 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.

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Microscope slide
DNA microarray
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Microarray TechnologyManufacture or Purchase
MicroarrayHybridizeDetectData Analysis

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Advantages of Microarrays

• Small volume deposition (nL)
• Minimal wasted reagents
• Access many genes / proteins simultaneously
• Can be automated
• Potentially quantitative

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Limitations of Microarrays

• Relatively new technology (10 years old)
• Still has technical problems (background)
• Poor reproducibility between investigators
• Still mostly manual procedure
• Relatively expensive

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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

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If Microarrays Are So Good Why Didnt We Use Them
Before??

• Not all genes were available
• No SNPs known
• No suitable bioinformatics
• New proteins now becoming available

Microarrays and associated technologies should be
regarded as by-products of the Human Genome
Initiative,Nanotechnology and Bioinformatics
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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

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History

• 1991 - Photolithographic printing (Affymetrix)
• 1994 - First cDNA collections are developed at
  Stanford.
• 1995 - Quantitative monitoring of gene expression
  patterns
• with a complementary DNA microarray
• 1996 - Commercialization of arrays (Affymetrix)
• 1997- Genome-wide expression monitoring in S.
  cerevisiae (yeast)
• 2000 Portraits/Signatures of cancer
• 2003 - Introduction to clinical practice
• 2004-Whole human genome on one microarray

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Microarray FabricationTwo Major
Methodsa 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



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Principles of DNA Microarrays
(printing oligos by photolithography)
(Fodor et al. Science 1991251767-773)
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Microarrays, such as Affymetrixs GeneChip, now
include all 50,000 known human genes.
Science, 302211, 10 October, 2003
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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

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Preparation of Labeled mRNAfor 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
• 
  

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Microarray Applications
Differential Gene Expression
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RNA extraction and labeling to determine
expression level
cRNA
cRNA
Cy3-UTP green fluorescence
Cy5-UTP red fluorescence
sample of interest compared to standard reference
reverse transcriptase, T7 RNA polymerase
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(No Transcript)
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Differential Gene Expression(Budding vs
Non-Budding Yeast)
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Normal vs. Normal
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Normal vs. Tumor
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Lung Tumor Up-Regulated
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Lung Tumor Down-Regulated
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Lung Tumor Up-Regulated
Signal transduction
Cytoskeleton
Proteases/Inhibitors
Kinases
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Lung Tumor Up-Regulated
Cyclin B1
Signal transduction
Cytoskeleton
Cyclin-dependent kinase
Tumor expression- related protein
Proteases/Inhibitors
Kinases
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Lung Tumor Down-Regulated
Cytoskeleton
Signal transduction
Proteases/Inhibitors
Kinases
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Lung Tumor Down-Regulated
Cytoskeleton
Signal transduction
Tumor necrosis factor-related protein
Proteases/Inhibitors
Kinases
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Genes Common to Many Tumors(e.g.Kidney Liver
Lung)
Up-regulated
Down-regulated
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Microarray Applications
Whole Organism Biology
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Whole Genome Biology With Microarrays
Cell cycle in yeast Study of all yeast
genes simultaneously! Red High expression Blue
Low expression
Lockhart and Winzeler Nature 2000405827-836
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Microarray Applications
Single Nucleotide Polymorphism (SNP) Analysis
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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

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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?
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Why Are SNPs Useful?

• Diagnostic ApplicationDetermine somebodys
  haplotype (sets of SNPs) and assess disease
  risk.
• Be careful These disease-related haplotypes are
  not as yet known!

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Nature 2003 426 789-796
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(No Transcript)
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Genotyping SNP Microarray

• Immobilized allele-specific oligo probes
• Hybridize with labeled PCR product
• Assay multiple SNPs on a single array

Many other methods For SNP analysis have been
developed
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SNP Analysis by Microarray
GeneChip HuSNPTM Mapping Assay
(Affymetrix) More than 10,000 single nucleotide
polymorphisms (SNPs) covering all 22 autosomes
and the X chromosome in a single experiment (soon
to move to 100,000 SNPs per experiment). Coverage
1 SNP per 210 kb of DNA Needs250 ng of genomic
DNA-1 PCR reaction
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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 21959-60
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The US government has blocked the sale of a new
kind of DNA diagnostic test, putting up an
unexpected barrier to the marketing of technology
to distinguish genetic differences in how
patients metabolize certain drugs.
Science 2003 302 1134
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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

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Microarray Applications
Sequencing by Hybridization
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Sequencing By Hybridization

• Address the need for high-speed, low-cost
  sequencing of large sequences in parallel.
• ExampleConsider examining 50Kb of sequence for
  1,000 individuals.

Conventional Method
Microarray
50Kb x 1,000 50 Mb of sequence. At a rate of
500 bases per lane and 30 sequencing lanes, you
can produce 15 Kb of sequence per day. You need
10 years for the project.
With one microarray of 1.25 x 1.25 cm dimension,
you can scan 50 Kb of sequence at once. You need
1,000 microarrays to complete task. This may be
completed in a few days.
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Sequencing by Microarray Technology
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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

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GeneChip p53 Assay Performance Characteristics

• Bases of genomic DNA analyzed 1262
  bpBase calling accuracy for missense gt
  99.9mutationsTime from purified DNA to data
  4.5 hrsMaximum 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.

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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

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Microarray Applications

• Food Expert-ID (available by Bio-Merieux2004)
• 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

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Microarray Applications
Protein Microarrays
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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

(AptamersRNAs that bind proteins with high
specificity and affinity)
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Applications

• Screening for
• Small molecule targets
• Post-translational modifications
• Protein-protein interactions
• Protein-DNA interactions
• Enzyme assays
• Epitope mapping

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High-throughput proteomic analysis
Haab et al. Genome Biology 200011-22
Protein array now commercially available by BD
Biosciences(2002)
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Cytokine Specific Microarray (Microarray
version of ELISA)
Detection system
BIOTINYLATED MAb
ANTIGEN
CAPTURE MAb
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Competing High Throughput Protein Technologies

• Bead-Based Technologies
• Luminex-flow cytometry
• Illumina-bead chips
• Microfluidics
• Zyomyx
• Mass spectrometry
• Ciphergen-protein chips

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Microarray Clinical Applications
Cancer Diagnostics
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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
cancers
Perou et al. Nature 2000406747-752
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Molecular Portraits of Cancer
Breast Cancer Perou et al. Nature 2000406747-752
Green Underexpression Black Equal
expression Red Overexpression
Left Panel Cell Lines Right Panel Breast Tumors
Figure Represents 1753 Genes
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Differential Diagnosis of Childhood Malignancies
Ewing Sarcoma Yellow Rhabdomyosarcoma
Red Burkitt Lymphoma Blue Neuroblastoma Green
Khan et al. Nature Medicine 20017673-679
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Differential Diagnosis of Childhood
Malignancies(small round blue-cell tumors, SRBCT)
EWS Ewing Sarcoma NB Neuroblastoma RMS
Rhabdomyosarcoma BL Burkitts Lymphoma
Note the relatively small number of genes
necessary for complete discrimination
Khan et al. Nature Medicine 20017673-679
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Microarray Milestone
June 2003
Question Can microarray profiling be used in
clinical practice? Prognosis/Prediction of
therapy/Selection of patients who should be
treated aggressively?

• Nature 2002 415 530-536
• NEJM 2002 347 1999-2009Vant 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

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Treatment Tailoring by Profiling
premenopausal, lymph node negative
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295 patients
Kaplan-Meier Survival Curves
survival
metastases-free
time (years)
time (years)
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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)

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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

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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 gt 500 million!)
• No validation of test No FDA approval
• Test has no value for predicting response to
  treatment

Science 20043031754-5
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Commercial Clashes

• Mammaprint marketed by Agendia, Amsterdam, The
  Netherlands
• Based on L.Vant 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 20043031754-5
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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)

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Gene Expression Analysis of Tumors
cDNA Microarray
Lakhani and Ashworth Nature Reviews Cancer
20011151-157
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Tissue Microarray
Alizadeh et al. J Pathol 200119541-52
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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)