Transcript The Impact of the Human Genome Project on Clinical

Microarrays, SNPs and Applications
DNA
mRNA Protein
Eleftherios P. Diamandis MD,Ph.D
([email protected])
Website:www.acdclab.org
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.
Microscope slide
DNA
microarray
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17
18
7
Actin
DNA
CyclinD
DNA
DHFR
DNA
8
RB
DNA
E2F1
DNA
tubulin
DNA
9
control
DNA
Myc
DNA
Src1
DNA
Microarray Technology
Manufacture or Purchase Microarray
Hybridize
Detect
Data Analysis
Advantages of Microarrays

Small volume deposition (nL)

Minimal wasted reagents

Access many genes / proteins simultaneously

Can be automated

Potentially quantitative
Limitations of Microarrays

Relatively new technology (10 years old)

Still has technical problems (background)
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Poor reproducibility between investigators

Still mostly manual procedure

Relatively expensive
Applications of Microarrays
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Gene expression patterns

Single nucleotide polymorphism (SNP) detection
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Sequence by hybridization / genotyping / mutation detection

Study protein expression (multianalyte assay)

Protein-protein interactions
Provides: Massive parallel information
If Microarrays Are So Good Why
Didn’t We Use Them Before??
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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
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
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
Microarray Fabrication
Two 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
Principles of DNA Microarrays
(printing oligos by photolithography)
(Fodor et al. Science 1991;251:767-773)
Microarrays, such as Affymetrix’s
GeneChip, now include all 50,000
known human genes.
Science, 302:211, 10 October, 2003
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 streptavidinfluor conjugates
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
Microarray Applications
Differential Gene Expression
sample 1
(tumor
tissue)
RNA extraction and labeling
to determine expression level
RNA
cDNA
cRNA
Cy5-UTP
red fluorescence
reverse transcriptase,
T7 RNA polymerase
cDNA
RNA
sample 2
(reference)
cRNA
Cy3-UTP
green fluorescence
sample of interest
compared to
standard reference
Reference tissue
cRNA (green)
Tumor tissue
cRNA (red)
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Human genes
on a microarray slide
Differential Gene Expression
(Budding vs Non-Budding Yeast)
Normal vs. Normal
Normal vs. Tumor
Lung Tumor: Up-Regulated
Lung Tumor: Down-Regulated
Lung Tumor: Up-Regulated
Signal transduction
Cytoskeleton
Proteases/Inhibitors
Kinases
Lung Tumor: Up-Regulated
Signal transduction
Cyclin
B1
Cytoskeleton
Cyclin-dependent
kinase
Tumor expressionrelated protein
Proteases/Inhibitors
Kinases
Lung Tumor: Down-Regulated
Signal transduction
Proteases/Inhibitors
Cytoskeleton
Kinases
Lung Tumor: Down-Regulated
Signal transduction
Cytoskeleton
Tumor necrosis
factor-related protein
Proteases/Inhibitors
Kinases
Genes Common to Many Tumors
(e.g.Kidney; Liver; Lung)
Up-regulated
Down-regulated
Microarray Applications
Whole Organism Biology
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 2000;405:827-836
Microarray Applications
Single Nucleotide Polymorphism (SNP) Analysis
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
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?
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!
Nature 2003 426: 789-796
Genotyping: SNP Microarray

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Immobilized allele-specific oligo probes
Hybridize with labeled PCR product
Assay multiple SNPs on a single array
TTAGCTAGTCTGGACATTAGCCATGCGGAT
GACCTGTAATCG
TTAGCTAGTCTGGACATTAGCCATGCGGAT
GACCTATAATCG
Many other methods
For SNP analysis have
been developed
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
Needs:250 ng of genomic DNA-1 PCR reaction
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
“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
SNP Detection by Mass Spectrometry

High throughput detection of SNPs can be
achieved by mass spectrometry
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SNP Center in Toronto (PMH) runs a
Sequenom Mass Spectrometry system
Microarray Applications
Sequencing by Hybridization
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
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.
Microarray
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.
Sequencing by Microarray Technology
GeneChip p53 Assay Reagents

p53 Primer Set:
PCR primer pairs of exons 2-11 optimized for a
single-tube multiplex reaction
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Fragment Reagent:
DNase 1 for DNA fragmentation
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Control Oligonucleotide F1:
Positive hybridization control
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p53 Reference DNA:
Human placental DNA
GeneChip p53 Assay
Performance Characteristics
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Bases of genomic DNA analyzed
1262 bp
Base calling accuracy for missense
mutations
> 99.9%
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.
Microarray Applications-Non Human - Chips
Avaliable Now (2004)

Pathogens (detection of Bird-Flu Virus strains)
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Smallpox (bioterrorism)
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Malaria (Plasmodium anopheles)
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Zebrafish/Xenopus laevis (model organisms)
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SARS Virus sequencing
Microarray Applications
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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
Microarray Applications
Protein Microarrays
Protein Microarrays
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Protein microarrays are lagging behind DNA microarrays
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Same idea but immobilized elements are proteins instead of
nucleic acids

Number of elements (proteins) on current protein microarrays
are limited (approx. 500)
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Antibodies for high density microarrays have limitations (crossreactivities)
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Aptamers or engineered antibodies/proteins may be viable
alternatives
(Aptamers:RNAs that bind proteins with high specificity and affinity)
Applications
Screening for:
 Small molecule
targets
 Post-translational
modifications
 Protein-protein
interactions
 Protein-DNA
interactions
 Enzyme assays
 Epitope mapping
High-throughput proteomic analysis
Label all Proteins in Mixture
Protein array now commercially
available by BD Biosciences(2002)
Haab et al. Genome Biology 2000;1:1-22
Cytokine Specific Microarray
(Microarray version of ELISA)
IL-1 
IL-6
IL-10
marker protein
cytokine
Detection system
BIOTINYLATED MAb
ANTIGEN
CAPTURE MAb
VEGF
MIX
Competing High Throughput Protein Technologies
Bead-Based Technologies
 Luminex-flow cytometry
 Illumina-bead chips
Microfluidics
 Zyomyx
Mass spectrometry
 Ciphergen-protein chips
Microarray Clinical Applications
Cancer Diagnostics
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 2000;406:747-752
Molecular Portraits of Cancer
Breast Cancer
Perou et al. Nature 2000;406:747-752
Green: Underexpression
Black: Equal expression
Red: Overexpression
Left Panel: Cell Lines
Right Panel: Breast Tumors
Figure Represents 1753 Genes
Differential Diagnosis of
Childhood Malignancies
Ewing Sarcoma: Yellow
Rhabdomyosarcoma: Red
Burkitt Lymphoma: Blue
Neuroblastoma: Green
Khan et al. Nature Medicine 2001;7:673-679
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
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: 1999-2009
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
Treatment Tailoring by Profiling
premenopausal, lymph node negative
Gene Expression profiling
60%
40%
Poor signature
~ 56 % metastases at 10 yrs
~ 50 % death at 10 yrs
Good signature
~ 13 % metastases at 10 yrs
~ 4 % death at 10 yrs
Adjuvant chemo- and
hormonal therapy
No adjuvant therapy
or hormonal therapy only
295 patients
survival
metastases-free
Kaplan-Meier Survival Curves
time (years)
time (years)
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
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Prognostic profile outperforms clinical parameters
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~30-40% reduction of unnecessary treatment and
avoidance of undertreatment (LN0 and LN+)
Therapeutic Implications
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Who to treat:

Prognostic profile as diagnostic tool
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Prognostic profile implemented in clinical trials
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improvement of accurate selection for adjuvant therapy
(less under- and over-treatment)
reduction in number of patients & costs (select only
patients that are at metastatic risk)
How to treat:
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Predictive profile for drug response
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selection of patients who benefit
Commercial Clashes

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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 treatment
Science 2004;303:1754-5
Commercial Clashes
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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
Tissue Microarrays

Printing on a slide tiny amounts of tissue
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Array many patients in one slide (e.g. 500)
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Process all at once (e.g. immunohistochemistry)
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Works with archival tissue (paraffin blocks)
Gene Expression Analysis of Tumors
cDNA Microarray
Lakhani and Ashworth Nature Reviews Cancer 2001;1:151-157
Tissue Microarray
Alizadeh et al. J Pathol 2001;195:41-52
Microarray Future: Conclusions
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Differential gene experssion studies will continue(robusness)
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Inexpensive, high-throughput, genome-wide scans for clinical applications
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Protein microarrays are now being deployed (may take over)
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Focus on biology and improved technology
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SNP analysis-Disease predisposition
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Pharmacogenomics
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Diagnostics-Multiparametric analysis
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Replacement of single-gene experiments(paradigm shift)