Phospho-Proteomic Analysis of Signaling Networks Governing

Cell Functions

Students / Postdocs

Neil KumarAlejandro Wolf-Yadlin

Hyung-Do KimDr. Yi Zhang Dr. Sampsa HautaniemiDr. Brian JoughinKristen Naegle

Collaborators

Prof. Forest White

Prof. Michael YaffeDr. Steve Wiley (PNNL)

NCI Integrative Cancer Biology ProgramAstraZeneca

genome mRNAexpression

proteinlevels

dynamicproteinoperatio

ns

environmental context(e.g., growth factors, cytokines,

extracellular matrix factors, mechanical forces, etc.)

cell / tissuephenotypicbehavior

Focus on Dynamic Protein Operations to understand how Cell Phenotypic Behaviors arise from Genome/Environment Convolution

WBs, FACS,mass spectrometryMulti-well kinase activity assays

Protein microarrays

Multi-Variate, Quantitative Protein-Centric Measurement --Protein Levels, States, Activities,

Locations, Interactions…

Fluorescencemicroscopy

*

‘cues’

‘signals’

‘response’(phenotype)

Question: Can We Understand How Cell Signaling Networks Integratively Process Information to Govern Phenotypic Responses?

[‘execution’-- transcription,metabolism, cytoskeleton]

Premise: Cell Behavior is Governed byMultivariate Network State

Thus, Seek Multivariate ‘Signal-Response’ Relationships-- which represent cellular

“information processing algorithms”

Example Problem in Cancer Biology: Dysregulation of ErbB System in Epithelial Cells

Yarden & Slikowski, Nat Rev Mol Cell Biol (2001)]

HER2: “Promiscuous Partner” in ErbB Family-- despite having no known ligand

Yarden & Slikowski, Nat Rev Mol Cell Biol (2001)]

HER2 Over-Expression in Breast Cancer

via gene amplification -- enhanced

tumor invasiveness

Anti-HER2 MAb Herceptin -- effective in only a portion of

HER2-overexpressing patients

Activation of HER2 by EGF/EGFR or HRG/HER3-- ligands may be autocrine in source

Yarden & Slikowski, Nat Rev Mol Cell Biol (2001)]

Parental(High EGFR)

24H(High EGFR & HER2)

EGFR:HER2:HER3:

200,000

20,00020,000

200,000

600,000

30,000

EGF: EGFR Binding -- EGFR/EGFR and EGFR/HER2 signaling

HRG: HER3 Binding -- HER3/HER2 signaling

Experimental Model System for Investigation of HER2 Overexpression Effects:

184A1 Human Mammary Epithelial Cells (HMECs)

Effects of HER2 Overexpression on HMECMigration and Proliferation

In response to EGF and HRG

HER2 Overexpression Enhances Migration But Not Proliferation

For Both EGF and HRG Treatment

Effects of HER2 Overexpression on HMECMigration and Proliferation

In response to EGF and HRG

EGF Stimulates Migration and Proliferation More Vigorously than HRG

Effects of HER2 Overexpression on HMECMigration and Proliferation

In response to EGF and HRG

Can We Understand How to Intervene in the ErbB Signaling Network to Abrogate the HER2-

ox Effect?

Effects of HER2 Overexpression on HMECMigration and Proliferation

In response to EGF and HRG-- thus, “context dependence” of HER2

overexpression effect

PI3K/Akt Pathway is Strongly Implicated inHER2-mediated Cell Migration

Yarden & Slikowski, Nat Rev Mol Cell Biol (2001)]

Can We Predict HER2-ox Cell MigrationEffect in terms of PI3K/Akt Activity?

*?

Yarden & Slikowski, Nat Rev Mol Cell Biol (2001)]

P-Akt (steady-state)

cell migration

1

0.5

1 2 3 4

Increased P-Akt Correlates with HER2-ox Enhancement of Migration

parentalcells --

s.f.

HER2-ox cells --

s.f.

HER2-ox cells + HRG

HER2-ox cells + EGF

cell migration

1

0.5

1 2 3 4

Inhibition of P-Akt Correlates with Diminished Migration for HRG Treatment

parentalcells --

s.f.

HER2-ox cells

-- s.f.

HER2-ox cells + HRG

HER2-ox cells + EGF

HER2-ox cells + HRG

+ LY

P-Akt (steady-state)

cell migration

1

0.5

1 2 3 4

BUT -- Inhibition of P-Akt Does NOT Correlate with Diminished Migration for EGF Treatment…

parentalcells --

s.f.

HER2-ox cells

-- s.f.

HER2-ox cells + HRG

HER2-ox cells + EGF

HER2-ox cells + HRG

+ LY

HER2-ox cells + EGF

+ LY

P-Akt (steady-state)

Hence, Must Turn to Multi-Variate

Signaling Network Model

for Effective Comprehension

Peptide mixture

N

R1

O

N

R2

OH

OH 2NH

R1

O

N

R2

OHCH3OH + CH3COCl

Modified peptides

Extracted proteins

TrizolTrypsin

Biological sample

IMACModified peptides

Modified phosphorylated

peptides

Reverse-phase

LC

MS

MS/MSMS/MSMS/MS

Full Scan MS

1 n

MASCOT or SEQUEST database search algorithm

Mass Spectrometry Phosphoproteomics

OCH3

GSHQISLDNPDYQQDFFPK

EGFR pY1148

200 400 600 800 1000 1200

m/z

Inte

nsity, co

un

ts

0

500

1000

y1

y2

y3

y4

y5 b9b8

b5

b4b3

10 min5 min

0 min

114 115 116 117m/z

30 min

2 x 107 cells (HMEC)

+ EGF (10 min)

Lyse, denature,

digest

pS

pS

pTpS

pYpY

pS

Anti-Phosphotyrosine peptide IP

pY pY

+ EGF (5 min)

114 115 116 117

Mix

+ EGF (0 min)

iTRAQ Label

+ EGF (30 min)

(Relative) Quantitative Signaling Network Measurements via iTRAQ Labeling

IMAC

GSHQISLDNPDYQQDFFPK

EGFR pY1148

200 400 600 800 1000 1200

m/z

Inte

nsity, co

un

ts

0

500

1000

y1

y2

y3

y4

y5 b9b8

b5

b4b3

10 min5 min

0 min

114 115 116 117m/z

30 min

2 x 107 cells (HMEC)

+ EGF (10 min)

Lyse, denature,

digest

pS

pS

pTpS

pYpY

pS

Anti-Phosphotyrosine peptide IP

pY pY

+ EGF (5 min)

114 115 116 117

Mix

+ EGF (0 min)

iTRAQ Label

+ EGF (30 min)

Signaling Network Activity: phospho-Y mass spec

IMAC

EGFR pY1148

Phosphorylated tyrosine ( )mapped on cell proliferation-associated proteins

pTyr-MS results - A (332 sites across 175 proteins)

Yarden & Slikowski, Nat Rev Mol Cell Biol (2001)

Phosphorylated tyrosine ( )mapped on cell migration-associated proteins

Zamir & Geiger, J Cell Sci (2001)

pTyr-MS results - B (332 sites across 175 proteins)

62 pY Sites on 45 Proteins across 4 Time-Pointsfor 6 Cell-Ligand Conditions

EGFR

EGFR

Y974

Y1148

Y1068

Y1173

Y704

S302/Y313PKCY313

Y204

ERK1 T202 /Y204

Y187

ERK2 T185 /Y187

GRB2

PLC-Y771

Y1253

Ca++

Ca++

Ca++

Ca++

IP3

SOS RAS

RAF

MEK

PKD AKT

CBL

Y455

Y552Y700

EPS15Y849

Y1045

Y659

GAB1Y259

Y406

Y1328

HER3

SHCY317

Y239

Y239/Y240

Y580

P110P85

Y607

Y464

Y467

HER2

Y783

Y705STAT3

SRCY418

Y1005

Y1248

Y877

Y1127/Y1139

EGFR

HER2

EGFEGF

EGF HRG

Fold Change:

Not registered x < 0.500.50 < x < 0.850.85 < x < 1.151.15 < x < 2.002.00 < x

0 5

10 30

Her2 Overexpression Effects on EGF-Induced Signaling - A

ERIN

CADH

-Actinin

Vinculin

Catenin-Y20

Y22FAK

Y576

Y317

PXN Y118

S84/Y88

Y88

- A c t i nF

Y96

Y213 Y217 Y221

Y228

Y296 Y291 Y280

Y334Catenin-

Y234Y249

Y387 Y327p130

SHCY317

Y239Y239/Y240

Y187

ERK2 T185 /Y187

RTK

Y19/Y22GEF

Y22

Caveolin

Y6/Y14

Y14

CRKL

Y132

Y251

Y207

Y221

GRIN

INTE

Y781 (1)

Y1189 (4)

Y1207 (4)

SRCY418

Y580

P110P85

Y607

Y464

Y467

GTP

Y1680

Ligand

Fold Change:

Not registered x < 0.500.50 < x < 0.850.85 < x < 1.151.15 < x < 2.002.00 < x

Her2 Overexpression Effects on EGF-Induced Signaling - B

EGFR

EGFR

Y974

Y1148

Y1068

Y1173

Y704

S302/Y313PKCY313

Y204

ERK1 T202 /Y204

Y187

ERK2 T185 /Y187

GRB2

PLC-Y771

Y1253

Ca++

Ca++

Ca++

Ca++

IP3

SOS RAS

RAF

MEK

PKD AKT

CBL

Y455

Y552Y700

EPS15Y849

Y1045

Y659

GAB1Y259

Y406

Y1328

HER3

SHCY317

Y239

Y239/Y240

Y580

P110P85

Y607

Y464

Y467

HER2

Y783

Y705STAT3

SRCY418

Y1005

Y1248

Y877

Y1127/Y1139

EGFR

HER2

EGFEGF

EGF HRG

Fold Change:

Not registered x < 0.500.50 < x < 0.850.85 < x < 1.151.15 < x < 2.002.00 < x

HRG vs EGF Signaling - A

ERIN

CADH

-Actinin

Vinculin

Catenin-Y20

Y22FAK

Y576

Y317

PXN Y118

S84/Y88

Y88

- A c t i nF

Y96

Y213 Y217 Y221

Y228

Y296 Y291Y280

Y334Catenin-

Y234Y249

Y387Y327

p130

SHCY317

Y239

Y239/Y240

Y187

ERK2 T185 /Y187

RTK

Y19/Y22GEF

Y22

Caveolin

Y6/Y14

Y14

CRKL

Y132

Y251

Y207

Y221

GRIN

INTE

Y781 (1)

Y1189 (4)

Y1207 (4)

SRCY418

Y580

P110P85

Y607

Y464

Y467

GTPY1680

Ligand

Fold Change:

Not registered x < 0.500.50 < x < 0.850.85 < x < 1.151.15 < x < 2.002.00 < x

HRG vs EGF Signaling - B

Can we Quantitatively Comprehend (and Predict) Phenotypic Response from Signals

across all Conditions (Cells, Stimuli, Drugs)

Signals

Computational Analysis -- Spectrum of Methods

differential equations

statistical mining

Bayesian networks

SPECIFIED ABSTRACTED

Markov chains

Boolean/fuzzy logic models

relationships

mechanisms

influences

(includingmolecularstructure-basedcomputation)

Appropriate approach depends on question and data

EGF

Proliferation or Migration

HRG

Signal#1

#1 #2

#3

#4#5

Principal Component / Partial Least-Square Regression

-- elucidates key signal combinations governing responses

PC3

PC2

PC1

2-PC PLSR Model Accounts for both Parental HMEC and HER2-overexpressing HMEC

Migration and Proliferation Behaviorfor All Ligand Treatments

X

EGF

HRGX HRG

EGF

Thus, although signaling network activity is altered by HER2-ox, the “information-processing algorithm”

relating signals to phenotypic behavior remains invariant

Translation to Targeted Phospho-Proteomic Assays --

a reduced model (9 phospho-sites on 6 proteins)recapitulates full model performance

… Including a priori Predictionof HER2-ox Effects

on Proliferation and Migrationunder all Treatment Conditions

Thus: the “information-processing algorithm”relating signals to phenotypic behavior of parental HMECs

remains invariant for relating signals to phenotypic behavior of HER2-ox HMECs

Reduced Model Offers

‘Network Gauge’ for

HER2-Mediated HMEC

Proliferation and Migration Behavior

-- “information-rich”

integrative signals

Can Our Approach Comprehend and Predict

Inhibitory Drug Effects?

X

X

XLY294002

gefitinib

PD98059

Train PLSR Signal-Response Model on 5 pY Sites Across 6 Cell-Ligand

Conditions for HMECs w/o Drugs

Predict Responses from Signals on 5 pY Sites Across 6 Cell-Ligand

Conditions for HMECs with Drugs

a priori Prediction :Effects of 3 Kinase Inhibitors

on HMEC Migration from 5 pY Signals

a priori Prediction :Effects of 3 Kinase Inhibitors

on HMEC Migration from 5 pY Signals

goodPI3K/Akt inhibitor

effectprediction(recall that uni-variate

prediction was unsuccessful)

a priori Prediction :Effects of 3 Kinase Inhibitors

on HMEC Migration from 5 pY Signals

goodMEK/Erk inhibitor

effectprediction

a priori Prediction :Effects of 3 Kinase Inhibitors

on HMEC Migration from 5 pY Signals

under-prediction of

EGFR inhibitor

effect

-- receptor level too far “upstream” for effective

signal integration?

Encouragement: premise that cell behavior is governed by multi-variate network state

may be useful forunderstanding drug effects

Computational Analysis -- Spectrum of Methods

differential equations

statistical mining

Bayesian networks

SPECIFIED ABSTRACTED

Markov chains

Boolean/fuzzy logic models

relationships

mechanisms

influences

(includingmolecularstructure-basedcomputation)

Appropriate approach depends on question and data

Fuzzy Logic Models --Elucidating Upstream/Downstream Signal-

Signal Influence Relationships

Strategy: Take Advantage of Peptide Sequence Information

Motif*Phosphorylated sequenceSubstrateKinase

X[D/E]Y[I/L/V]STAENAEYLRVAPQSEGFR

EEEEYFELVTGMFPRNYVTPVNRNGRB2

[-/R/A]--[-/I]Y[F/V/I/E][I/F][FLIV]VTQEQYELYCEMGSTFCBLEGFR

[D/E]YIGTAEPDYGALYEGRPLCG1

QLRNQGETPTTEVPACDC23

[S/T]PX[R/K]PQGQQPLSPQSGSPQSYN3CDK1

[S/T]PPQQGFFSSPSTSRTPEGFR

[P/L/I/M]X[L/I/D/E] SQENVKYSSSQPEPRTGCHK1

LSQEQMRHQSESQGVGLSDBRCA1ATM

[S/T]QGKKATQASQEYH2AX

* Motifs from http://hprd.org/

-- kinase substrate motifs, phosphopeptide-binding domain substrate motifs

Foreground: 199 phospho-sites studied by MS downstream of EGF treatment Background: 334876 tyrosine-centered sites from the

human proteome

-1, -2, -3 D/E: EGFR kinase products +3P: Abl, Crk, Fyn SH2 domain ligands

7-Time Point MRM EGFR Network Data[Wolf-Yadlin et al., PNAS USA (2007)]

Identifying more complicated motifs

• Test the significance of enrichment of every amino acid (and selected combinations of amino acids) at each position• Test the significance of enrichment of each pair of amino acids at each pair of positions• For each significantly enriched sequence motif, test the significance of submotifs

A greedy search allows us to look only at those nodes (of 3.2 x 1018) that are most likely to be statistically significant

Tactic #1: Integrating Motif Detection with Protein-Protein Interaction Networks

STRING DB of “interacting proteins”

Motif search tools

PhosphopeptideProtein

PYPGIDLsQVYELLE

LMTGDTYtAHAGAKF

RLMTGDTyTAHAGAK

KRNKPTVyGVSPNYDABL1

TLGRNTPyKTLEPVK

PPTVPNDyMTSPARL

SSTSSGGyRRTPSVTABI1

Phospho.ELM DB of phosphoproteinsand phosphopeptides

Do phosphopeptides in proteins adjacent to kinases and phosphopeptide binding domains in a protein-protein interaction network reveal motif specificity?

Tactic #2: Integrating Motif Detection with Dynamic Data

Motif search tools

PhosphopeptideProtein

PYPGIDLsQVYELLE

LMTGDTYtAHAGAKF

RLMTGDTyTAHAGAK

KRNKPTVyGVSPNYDABL1

TLGRNTPyKTLEPVK

PPTVPNDyMTSPARL

SSTSSGGyRRTPSVTABI1

Phospho.ELM DB of phosphoproteinsand phosphopeptides

Sequence motifs overrepresented in an MS dataset, or in a selected subset of an MS dataset, reflect the identity of kinase, phosphatases, and phosphopeptide binding domains active within the process/dynamics being probed by MS.

MS-derived phosphopeptide dynamics

More detailsZhang et al., Molecular & Cellular Proteomics 4: 1240 [2005]

Wolf-Yadlin et al., Molecular Systems Biology 2: e54 [2006]

Kumar et al., PLoS Computational Biology 3: e4 [2007]

Wolf-Yadlin et al., Proc. Natl. Acad. Sci. USA 104: 5860 [2007]

Kumar et al., Molecular Pharmacology (in press) [2008]

NCI Integrative Cancer Biology ProgramAstraZeneca