MicroArrays #1:
Technology & Gene Target ID
Timothy J. Triche, MD, PhD
Pathologist-in-Chief
Childrens Hospital Los Angeles
University of Southern California
Basic Forms of Array
Technology

Multiple Northerns/slot blots

Macroarrys (spotted cDNAs, filter substrate)

Microarrays (a la’ Pat Brown) (cDNA spots)

Microarrays (oligomers synthesized in situ)
(Affymetrix)

Microarrays (spotted oligomers, a la’
Wold/Caltech 50mer core)
Macro Array Technology

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Dozens or hundreds of
cDNAs spotted onto nylon
membranes in duplicate
arrays, in replicate
cDNAs from samples of
interest hybridized to
replicate membranes
Differential expression
inferred from spot density
Quantitation, hybridization
to homologous genes a
problem
Spotted cDNA Arrays

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Up to 10,000 genes
per chip
Full length cDNA
spots
Requires
competitive
hybridization
Based on Pat Brown
technology
Sequence validation
a problem
Oligomeric Spotted Arrays




Customizable like
spotted arrays
Performance
characteristics ~
Gene Chips
Gene
representation ~
Gene Chips, unlike
spotted arrays
Low cost
Illustration courtesy of B. Wold, Caltech
Reproducibility


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
Same sample
preparation
2 probe arrays
Comparative
expression
(r=1 +/- 2 SD)
Virtually no
false positives
Differential Gene Expression


T cells,
stimulated (y
axis) vs. control
(x axis)
All genes outside
inner R lines are
dysregulated (up
or down)
Rationale for Affymetrix Gene Chips


Pro:
• Current Standard for Array Technology
• Greatest versatility (re-sequencing,
expression, multiple species, directed arrays
in near future)
• Sequences by definition verified
• Allows user-constructed x-wise comparisons
• No need for competitive hybridizations
• Uses minimum amount of tissue/RNA
Con:
• No choice of individual genes on array
• Customized arrays prohibitively expensive
Gene Expression Analysis
B) Cut ~12 frozen sections
A) Cut pilot section of
OCT embedded frozen tissue C) Extract RNA (<5ug total RNA)
D) Synthesis of double-stranded cDNA
E) In-vitro transcription w/ biotinylated
nucleotides
F) Size confirmation of cRNA transcripts
tumor
non-tumor
dissection
of tumor
tissue when
possible
pure
tumor
G) Fragmentation of cRNA
500 bp
Affymetrix Core Facility
Micro Array Technology



> 20 24mers of
known expressed
gene sequence tiled
across chip, with
paired mismatches
Fluorerscent cmRNA
hybridized to chip
Fluorescence
intensity quantitated
and analyzed by
defined algorithms
Affymetrix Probe Arrays
Unlike cDNA arrays, a series of >20 24mers
are synthesized in situ by photolithography,
representing exonic sequences from 5’ to 3’
of the genes of interest:
Some Currently Available Chips





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HuFL (1,300 to 60,000 human genes)
P53 (detects over 400 known p53 mutations, in
codons 2-11)
HIV (HIV PRT sequencing, protease & RT,
codons 1-400, over 1,500 nucleotides)
CYP450 (cytochrome p450, 10 alleles of 2D6
gene & 2 alleles of 2C19 gene)
Mu 19, 11, & 6.5 (19,000 to 6,500 mouse
genes)
Rat Genome U34 (>24,000 rat genes & ESTs)
Yeast 61 (6,100 Saccharomyces ORFs)
Affymetrix SNP
Chips



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>1,500 intragenic polymorphisms
distributed over entire genome
Re-sequencing chip ~ p53, HIV
Number of SNPs to vastly
increase in near future
Polymorphisms likely to predict
disease susceptibility,
therapeutic responsiveness,
disease severity
Pathway Analysis:
Tumor
The P53 Paradigm
IGF-II
APC
PI.3Kgam
mapk3pk
rps6ka2
hek8
c-yes
FHIT
OB-cad2
wnt-1
ATM
plakoglobin
mdm2
intg/alpha
Bcl-2
erb-A
pax3
caveolin
waf1
gadd45
erb-B2
GLI
Kup70
25
20
RN
A
DNA
Gene
Mutation
15
10
Gene
Expressio
n
Expressio
n
Profiles
5
0
Fold change
Bax
Rb
Cell Cycle
(cancer)
p 53
Biologic Defect
Mutation Detection
p 21
mdm 2
Gadd 45
Apoptosis
Bcl-2
BRCA1
DNA Repair
MSH2
CdK
cyclin D1
Biologic
Pathways
P53: Mutation vs. Function
Neuroblastoma N-myc Southern
N-myc FISH
Shimada Classification System
Stroma-Rich
Distribution of
Immature Cells
Stroma-Poor
Unfavorable
Histology
Age > 5
Isolated
Clumped
Age
< 18 mos.
18-60 mos.
MKI > 200
MKI < 200
Nodular
Differentiating
Intermixed
WellDifferentiated
Undifferentiated
MKI > 100
MKI < 200
Neuroblastoma Survival
N-myc Status, Shimada classification, and Outcome
Neuroblastoma Prognosis
Comparison of MYCN Status, TRKA expression, & Survival
Myc-Mediated Cell Pathways
Amplified
N-myc DNA
Abundant
N-myc Protein
DNA
Myc:Max Instability
Heterodimers
Inhibit
differentiation
P53
Protein
Apoptosis
Promote
Proliferation
Biologic Interpretation
cdk2
Cdk 2
Cyclin A
Cyclin E
Cdk 2
+
RbE2F
p21
Cyclin A
PO4
Cyclin E
+
RB-PO4+E2F
cdk2
RB-PO4+E2F
G1
S
CyclinD
RB-PO4+E2F
+
G2
CyclinD
Cdc4 or 6
RbE2F
+
cdk2
Degradation
MDM2
PCNA
p16INK4
Cyclin B
p19
GADD 45
Induction:
RB-PO4+E2F
p21
Inhibition:
G1
cdk2
Cyclin B
p27KIP
Cdc4 or 6
M
p53
Apoptosis
BAX
BCL2
Pathway Analysis:
Tumor
The P53 Paradigm
IGF-II
APC
PI.3Kgam
mapk3pk
rps6ka2
hek8
c-yes
FHIT
OB-cad2
wnt-1
ATM
plakoglobin
mdm2
intg/alpha
Bcl-2
erb-A
pax3
caveolin
waf1
gadd45
erb-B2
GLI
Kup70
25
20
RN
A
DNA
Gene
Mutation
15
10
Gene
Expressio
n
Expressio
n
Profiles
5
0
Fold change
Bax
Rb
Cell Cycle
(cancer)
p 53
Biologic Defect
Mutation Detection
p 21
mdm 2
Gadd 45
Apoptosis
Bcl-2
BRCA1
DNA Repair
MSH2
CdK
cyclin D1
Biologic
Pathways
Colon Ca & Colon: clustering
Alon, et al: PNAS, June, 1999
Colon: Tumor vs. Normal
Alon, et al: PNAS, June, 1999
Tumor Profiling by Array Analysis
Khan, et al: Cancer Res, Nov. 1998
Ewing’s Sarcoma
Ewing’s Sarcoma Cytogenetics
Chr 11
chr 22
EWS
EWS
FLI-1
FLI-1
Normal
Der
Normal
Der
Fusion Genes in Childhood Sarcomas
Sarcoma

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PAX/FKHR fusion gene
found only in Alveolar
RMS
EWS/ets defines
Ewing’s/pPNETs,
including EOE in IRS
No fusion gene found in
Emb RMS
Other fusion genes define
other STSs
Fusion Gene
Alveolar RMS
PAX3,7/FKHR
Embryonal RMS
None (LOI,LOH)
Ewing’s
EWS/ets
Synoviosarcoma
SYT/SSX
Desmoplastic RCT
EWS/WT-1
Soft Part Melanoma EWS/ATF
Liposarcoma
TLS/CHOP
Ewing’s Interphase FISH
Alternate Splicing of EWS/Fli-1
Transcript
1
EWS
656
NH2
COOH
RNA BD
349
265
Type 3 Insert
Type 1 Insert
EWS /
Fli-1
NH2
COOH
ETS D
Type 2 Insert
Human 1
NH2
Fli-1
198 219
452
ETS
ETSDD
COOH
PCR of Chimeric Gene
Transcripts
Virtually all Ewing’s & pPNETs express a
variant of EWS/FLI-1 or an EWS/ets chimeric
gene
Type 3
Type 2
Type 1
Ewing’s Sarcoma:
Survival by Fusion Gene Status
Overall survival
1,0
p=0.034
,8
EWS-FLI1
type 1
( n=46)
,6
,4
EWS-FLI1
other types
(n=27)
,2
0,0
0
20
40
60
80
100
Follow-up (months)
From de Alava, et al:J Clin Oncol 1998
120
140
Induced EWS/FLI-1 Expression

3 clones
compared to
typical
Ewing’s cell
line

EWS/FLI-1
compared to
housekeeping
gene
(GAPDH)

Induced
levels ~
native tumor
levels
EWS/FLI-1 Induces Cell Cycle

E/F expression
induced, cells
harvested 24
hrs. later

Induced
compared to
uninduced

Majority of
uninduced cells
in G0/G1

Vast majority of
induced cells in
S/G2
Known EWS/FLI-1 Gene Targets
Numerous genes identfied by RDA, DD-PCR,
Subtraction
Libraries, etc., but no consistent pattern emerges –
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Denny et al:
May:
Sorensen:
Wu et al
Triche et al:
type 16 keratin, Integrin
Manic Fringe
Gastrin-Releasing Peptide
MAT1
E2F, Cyclin D
Ewing’s Gene Targets
E/F Induced Gene Expression

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Human cells + / - EWS/FLI1 expression were
analyzed
25
mRNA was hybridized to a
gene expression chip
15
Genes showing significant
variance were plotted
Three genes, including the
most affected, IGF-II, are
critical to development of
the dorsal somitic
mesoderm (=> RMS) and
neural crest (=> PNET)
20
10
5
0
-5
-10
-15
IGF-II/ex
APC
PI.3Kgam
mapk3pk
rpS6ka2
hek8
c-yes
FHIT
OB-cad-2
wnt-1
ATM
plakoglob
mdm2-a
intg/alph
Bcl-2-bet
erb-A
pax2
caveolinwaf1
gadd45
erb-B2 (he
GLI
Genes in Developing Somites

The Dorsal Somitic Mesoderm Gives Rise to both skeletal
muscle and neural crest derivatives
smo
wnt
ptc
shh
gli
PAX
IGF-II
?
IGF-R
#1 Problem in Array Interpretation:
•
•
•
•
Bioinformatics!
All array technologies generate
massive amounts of data
Individual gene data are rarely
informative
Marginally deviant genes and clusters
often the most informative
None of the above is generally
apparent from casual inspection
Bioinformatics Solutions

Affymetrix GeneChip (basic)

Spotfire (visualization)

GeneSpring (visualization, analytic)

MolPat (academic; cluster analysis)

GENECLUSTER (MIT)

SOMs (Self Organizing Maps)

Custom solutions (EM, etc.)
Needed Solutions:
Or, The Biologist’s Wish List
Neural net models of biologic pathways
 In silico representation of array data in
these models
 Linkage of in silico model systems to data
from biological systems (eg, transfectants,
knockouts, knockins, cre-lox mice)
 Predictive tools for additional gene finding
