2. 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.
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. Microarray Technology
Manufacture or Purchase Microarray
Hybridize
Detect
Data Analysis
5. Advantages of Microarrays
Small volume deposition (nL)
Minimal wasted reagents
Access many genes / proteins simultaneously
Can be automated
Potentially quantitative
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. 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. If Microarrays Are So Good Why
Didn’t 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
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. 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
11. 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
12. Principles of DNA Microarrays
(printing oligos by photolithography)
(Fodor et al. Science 1991;251:767-773)
13. Microarrays, such as Affymetrix’s
GeneChip, now include all 50,000
known human genes.
Science, 302:211, 10 October, 2003
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. 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
17. RNA extraction and labeling
to determine expression level
sample 1
RNA RNA sample 2
(tumor
cDNA cDNA (reference)
tissue)
cRNA cRNA
Cy3-UTP
Cy5-UTP green fluorescence
red fluorescence
sample of interest
reverse transcriptase, compared to
T7 RNA polymerase standard reference
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
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. 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. 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!
37. 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
38. 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
39. 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
40. “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
41. 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
43. 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 Microarray
50Kb x 1,000 = 50 Mb of With one microarray of 1.25 x 1.25
sequence. At a rate of 500 cm dimension, you can scan 50 Kb
bases per lane and 30 of sequence at once. You need
sequencing lanes, you can 1,000 microarrays to complete task.
This may be completed in a few
produce 15 Kb of sequence per days.
day. You need 10 years for the
project.
45. 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
46. 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.
48. 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
50. 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)
52. High-throughput proteomic analysis
Label all Proteins in Mixture
Haab et al. Genome Biology 2000;1:1-22
Protein array now commercially
available by BD Biosciences(2002)
53. 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
54. Competing High Throughput Protein Technologies
Bead-Based Technologies
Luminex-flow cytometry
Illumina-bead chips
Microfluidics
Zyomyx
Mass spectrometry
Ciphergen-protein chips
56. 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
57. 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
58. Differential Diagnosis of
Childhood Malignancies
Ewing Sarcoma: Yellow
Rhabdomyosarcoma: Red
Burkitt Lymphoma: Blue
Neuroblastoma: Green
Khan et al. Nature Medicine 2001;7:673-679
59. 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
60. 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
61. 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
62. 295 patients
Kaplan-Meier Survival Curves
metastases-free
survival
time (years) time (years)
63. 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+)
64. 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
65. 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 treatment
Science 2004;303:1754-5
66. 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
67. 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)
68. Gene Expression Analysis of Tumors
cDNA Microarray
Lakhani and Ashworth Nature Reviews Cancer 2001;1:151-157
70. 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)
Editor's Notes
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.