Why Study Signaling Networks?
Define better therapeutic targets, or combinations
off therapeutic
h
i targets for
f cancer or other
h h
human
diseases
The Challenge: How do we get from genetic mutation to therapeutic targets?
Mutations in human glioblastoma tumor tissue, TCGA, Nature, 2008
Reprinted by permission from Macmillan Publishers Ltd: Nature. Source: The Cancer Genome Atlas Research Network.
"Comprehensive Genomic Characterization Defines Human Gliobastoma Genes and Core Pathways." Nature 455 (2008). © 2008.
Why Study Signaling Networks?
Disease Stratification and Personalized Medicine:
y g signaling
g
g networks can identify
y activated
Analyzing
pathways and thereby highlight therapeutic options
Courtesy of Elsevier, Inc., http://www.sciencedirect.com. Used with permission. For complete article, see Irish, Jonathan M., Randi Hovland,
Peter O Krutzik, et al. "Single Cell Profiling of Potentiated Phospho-Protein Networks in Cancer Cells" Cell 118, no. 2 (2004).
Why Study Signaling Networks?
Therapeutic Targeting and Efficacy – is the kinase gthe tar geted kinase? inhibitor actuallyy affecting
How are cells in a tumor responding to therapy?
Merrimack – Her3 antibody
AstraZeneca – Gefitinib (Iressa)
Why Study Signaling Networks?
Understanding mechanisms of resistance
Taniguchi et al, Nat. Reviews Mol. Cell Biol, 2006
Reprinted by permission from Macmillan Publishers Ltd: Nature Reviews Molecular Cell Biology. Source: Taniguchi, Cullen M., Brice Emanuelli,
and C. Ronald Kahn. "Critical Nodes in Signalling Pathways: Insights into Insulin Action." Nature Reviews Molecular Cell Biology 7 (2006). © 2006.
How to analyze signaling networks
Classical Approach: Western blots
Reverse-Phase Protein Microarray
Luminex/ELISA
Mass Spectrometry
Phospho-FACS
CS
Kinase Activity Assays
Classical Approach: Western blots – Target Selection?
Reprinted by permission from Macmillan Publishers Ltd: Nature Reviews Molecular Cell Biology. Source: Yarden, Yosef,
and Mark X. Sliwkowski. "Untangling the ErbB signalling network." Nature Reviews Molecular Cell Biology 2 (2001). © 2001.
Yarden and Slikowski, Nat. Reviews Mol. Cell Biol, 2001
Limitations of the Western blots for signaling analysis
Labor-intensive
Limited number of targets
Antibodies – not as specific as advertised
No discovery possibility
Quantitation sub-optimal
Micro-Western Arrays: Tackling the rate-limiting throughput of Western Blots
Reprinted by permission from Macmillan Publishers Ltd: Nature Methods. Source: Ciaccio, Mark F., et al. "Systems Analysis of EGF Receptor Signaling Dynamics with Microwestern Arrays." Nature Methods 7 (2010). © 2010
.
Ciaccio et al, Nat. Methods 2010
Micro-Western Arrays: Not the best looking westerns, but still functional
Reprinted by permission from Macmillan Publishers Ltd: Nature Methods. Source: Ciaccio, Mark F., et al. "Systems Analysis of EGF Receptor Signaling Dynamics with Microwestern Arrays." Nature Methods 7 (2010). © 2010
.
Ciaccio et al, Nat. Methods 2010
Reverse-phase microarrays: an alternate high-throughput
strategy for quantifying signaling networks
Reprinted by permission from Macmillan Publishers Ltd: Nature Methods. Source: Sevecka, Mark and Gavin MacBeath. "State-Based Discovery: A Multidimensional Screen for Small-Molecule Modulators of EGF Signaling." Nature Methods 3 (2006). © 2006.
Sevecka et al, Nat. Methods 2006
How accurate are reverse-phase microarrays?
Reprinted by permission from Macmillan Publishers Ltd: Nature Methods. Source: Sevecka, Mark and Gavin MacBeath. "State-Based Discovery: A Multidimensional Screen for Small-Molecule Modulators of EGF Signaling." Nature Methods 3 (2006). © 2006.
Reverse-phase arrays: lots of data on a single chip
Reprinted by permission from Macmillan Publishers Ltd: Nature Methods. Source: Sevecka, Mark and Gavin MacBeath. "State-Based Discovery: A Multidimensional Screen for Small-Molecule Modulators of EGF Signaling." Nature Methods 3 (2006). © 2006.
Sevecka et al, Nat. Methods 2006
Using reverse-phase arrays to understand signaling networks
Reprinted by permission from Macmillan Publishers Ltd: Nature Methods.
Source: Sevecka, Mark and Gavin MacBeath. "State-Based Discovery: A
Multidimensional Screen for Small-Molecule Modulators of EGF Signaling."
Nature Methods 3 (2006). © 2006.
Sevecka et al, Nat. Methods 2006
Reverse-phase microarrays work, but accurate quantification
is limited by non-specificity
non specificity of the antibody. How can we
overcome this limitation?
Luminex – improved specificity due to sandwich format,
improved throughput with ~FACS-based quantification
Ph
Phospho-specific
h
ifi mAb
Ab
pY
Anti-EGFR mAb
Phospho-specific mAb
pY
Anti-Erk2 mAb
Phospho-specific mAb
pY
Anti-SHC mAb
Up to 100 different
beads/targets per analysis
Sandwich-ELISA format
improves specificity
http://www.luminexcorp.com/
All of these techniques are limited to well-characterized nodes
in the signaling network, and limited by existing reagents.
How can we discover novel pathway/network components
while maintaining high coverage of the network?
Sample 1
Lysis
Sample 2
pS
Protein Proteolysis
Sample 3
pS
Desalt
pS
Sample 4
EGFR pY1148
Biological Samples:
cells, tumors, etc…
pS
GSHQISLDNPDpYQQDFFPK
pY
Sample 2
pY
Sample 3
pY
Sample 4
Intensity
y, counts
y2
Mix
500
y1
y3
b3
0
LC-MS/MS
LC MS/MS
analysis
Sample 1
Stable isotope
(iTRAQ) labeling
1000
Photo of QSTAR® XL Hybrid LC/MS/MS
System removed due to copyright restrictions.
pY
200
400
b4
b5
600
m//z
114 115 116 117
m/z
y4
y5
800
pS
b8 b9
1000
pS
pT
pS
1200
pY
IMAC
phosphopeptide
enrichment
pS
pY
pY
pY
pTyr-Peptide Immunoprecipitation
Why Study Signaling Networks?
Define better therapeutic targets, or combinations
off therapeutic
h
i targets ffor cancer or other
h h
human
diseases
The Challenge: How do we get from genetic mutation to therapeutic targets?
Mutations in human glioblastoma tumor tissue, TCGA, Nature, 2008
EGFRvIII Signaling Network Analysis
U87 Glioblastoma cell line
line
Kinase-dead
EGFR:
100,000
EGFRvIII: 2,000,000
Kinase
Ki
dead
Medium
High
Super High
100,000
1,500,000
100,000
2,000,000
100,000
3,000,000
Constitutive signaling
24 h
hours serum sttarvatition
w/ Frank Furnari, Web Cavenee
EGFRvIII signaling preferentially activates PI3K/Akt
Y974
M
Y1086
H
Y1148
V
I
I
I
V
I
I
I
Y845
SRC
Y1068
Y417
Y1173
SH
Fold Change:
IP3
STAT3
Ca++
Y1254
Y704
x < 1.00
1.00 < x < 1.50
1.50 < x < 2.00
2.00 < x < 3.00
3.00 < x
Ca++
PLC-γ
Ca++
Y318
Ca++
PKCδ
SHC
SOS
GRB2
RAS
RAF
Y605
Y689
GAB1
Y646
p85
p110
Y187
ERK2
T185 /Y187
MEK
Y204
Y524
ERK1
PKD
T202/Y204
AKT
Source: Huang, Paul H., et al. "Quantitative Analysis of EGFRvIII Cellular Signaling Networks Reveals a Combinatorial Therapeutic
Strategy for Glioblastoma." Proceedings of the National Academy of Sciences 104, no. 31 (2007). © 2007 National Academy of Sciences, USA.
Paul Huang
Self-similar phosphorylation profiles revealed by self organizing map
self-organizing
SOM clustering
(x value
)- 0
SOM clustering
( x value
)- 1
SOM clustering
(x value
)- 2
2.0
2
1.5
1 .5
1.0
1
0.5
0 .5
2.0
Fold Chan
nge
2
1.5
1 .5
1.0
1
0.5
0 .5
2.0
2
15
1.5
1 .5
Highly
g y
responsive
1.0
1
0.5
0 .5
DK
M
H
SH
DK
M
H
SH
DK
M
H
Source: Huang, Paul H., et al. "Quantitative Analysis of EGFRvIII Cellular Signaling Networks Reveals a Combinatorial Therapeutic Strategy for Glioblastoma." Proceedings of the National Academy of Sciences 104, no. 31 (2007). © 2007 National Academy of Sciences, USA.
SH
Paul Huang
The highly responsive cluster contains tyrosine phosphorylation of the c-Met
c-Met activation site
Source: Huang, Paul H., et al. "Quantitative Analysis of EGFRvIII Cellular Signaling Networks Reveals a Combinatorial Therapeutic Strategy for Glioblastoma." Proceedings of the National Academy of Sciences 104, no. 31 (2007). © 2007 National Academy of Sciences, USA.
Paul Huang
Combinatorial (c-Met and EGFR) inhibition decreases cell viability
Figure removed due to copyright restrictions. See Figure 1 (top) from Sattler,
Martin, et al. "A Novel Small Molecule Met Inhibitor Induces Apoptosis in Cells
Transformed by the Oncogenic TPR-MET Tyrosine Kinase." Cancer Research 63 (2003).
SU11274
Source: Huang, Paul H., et al. "Quantitative Analysis of EGFRvIII Cellular Signaling Networks Reveals a Combinatorial Therapeutic
Strategy for Glioblastoma." Proceedings of the National Academy of Sciences 104, no. 31 (2007). © 2007 National Academy of Sciences, USA.
Paul Huang
A second, more potent c-Met inhibitor displays similar behavior
Figure removed due to copyright restrictions. See Figure 1 from Christensen,
James G., et al. "A Selective Small Molecule Inhibitor of c-Met Kinase Inhibits
c-Met-Dependent Phenotypes in Vitro and Exhibits Cytoreductive Antitumor
Activity in Vivo." Cancer Research 63 (2003).
PHA665752
Source: Huang, Paul H., et al. "Quantitative Analysis of EGFRvIII Cellular Signaling Networks Reveals a Combinatorial Therapeutic Strategy for Glioblastoma." Proceedings of the National Academy of Sciences 104, no. 31 (2007). © 2007 National Academy of Sciences, USA.
Huang et al., Proc Natl Acad Sci U S A. (2007) 104:12867.
Independent validation of combinatorial targeting of
EGFR and c-Met
c Met in GBM
EGFR
c Met
c-Met
PDGFR
Figures from Science removed due to copyright restrictions. See Figures 2d and 3b from Stommel, Jayne M., et al. "Coactivation
of Receptor Tyrosine Kinases Affects the Response of Tumor Cells
to Targeted Therapies." Science 318 (2007).
Is there a technique that can determine which of these signals
are occurring in the same cell?
Phospho-FACS: Single cell signaling analysis
Courtesy of Elsevier, Inc., http://www.sciencedirect.com. Used with permission.
Krutzik et al., Clinical Immunology , 2004.
Personalized medicine by Phospho-FACS
Courtesy of Elsevier, Inc., http://www.sciencedirect.com. Used with permission. For complete article, see Irish, Jonathan M., Randi Hovland,
Peter O Krutzik, et al. "Single Cell Profiling of Potentiated Phospho-Protein Networks in Cancer Cells" Cell 118, no. 2 (2004).
Irish et al., Cell , 2004.
All of these techniques measure phosphorylation, but this
does not directly measure activity…
Janes, K. A. (2003)
Courtesy of American Society for Biochemistry and Molecular Biology. Used with permission.
Mol. Cell. Proteomics 2: 463-473
Why Study Signaling Networks?
Define better therapeutic targets
targets, or combinations
of therapeutic targets for cancer or other human
diseases
Disease Stratification and Personalized Medicine:
Analyzing signaling networks can identify activated
pathways and thereby highlight therapeutic options
Therapeutic Targeting and Efficacy – is the kinase
inhibitor actually affecting the targeted kinase?
How are cells in a tumor responding to therapy?
Understanding mechanisms of resistance
To clear up my mistake last Thursday:
Modafinil = Monoxidil
Provigil = Rogaine
MIT OpenCourseWare
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20.380J / 5.22J Biological Engineering Design
Spring 2010
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