NCI Integrative Cancer Biology Program Phospho-Proteomic Analysis of Signaling Networks Governing Cell Functions Students / Postdocs Neil Kumar Alejandro Wolf-Yadlin Hyung-Do Kim Dr. Yi Zhang Dr. Sampsa Hautaniemi Dr. Brian Joughin Kristen Naegle Collaborators Prof. Forest White Prof. Michael Yaffe Dr. Steve Wiley (PNNL) NCI Integrative Cancer Biology Program AstraZeneca

environmental context Focus on Dynamic Protein Operations to understand how Cell Phenotypic Behaviors arise from Genome/Environment Convolution cell / tissue phenotypic behavior environmental context (e.g., growth factors, cytokines, extracellular matrix factors, mechanical forces, etc.) dynamic protein operations mRNA expression genome protein levels

* Multi-Variate, Quantitative Protein-Centric Measurement -- Protein Levels, States, Activities, Locations, Interactions… WBs, FACS, mass spectrometry * Multi-well kinase activity assays Protein microarrays Fluorescence microscopy

‘response’ ‘cues’ (phenotype) ‘signals’ Question: Can We Understand How Cell Signaling Networks Integratively Process Information to Govern Phenotypic Responses? ‘response’ (phenotype) ‘cues’ ‘signals’ [‘execution’-- transcription, metabolism, cytoskeleton]

Premise: Cell Behavior is Governed by Multivariate 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)]

-- enhanced tumor invasiveness 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)]

184A1 Human Mammary Epithelial Cells (HMECs) Experimental Model System for Investigation of HER2 Overexpression Effects: 184A1 Human Mammary Epithelial Cells (HMECs) Parental (High EGFR) 24H (High EGFR & HER2) EGFR: HER2: HER3: 200,000 20,000 200,000 600,000 30,000 We have now extended the approach to 2 strain of the same cell line: WT or parental and 24H (overexpresses HER2) and two ligands EGF activates EGFR and HRG activates HER3 EGF: EGFR Binding -- EGFR/EGFR and EGFR/HER2 signaling HRG: HER3 Binding -- HER3/HER2 signaling

Effects of HER2 Overexpression on HMEC Migration and Proliferation In response to EGF and HRG

Effects of HER2 Overexpression on HMEC Migration 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 HMEC Migration and Proliferation In response to EGF and HRG EGF Stimulates Migration and Proliferation More Vigorously than HRG

Effects of HER2 Overexpression on HMEC Migration and Proliferation In response to EGF and HRG -- thus, “context dependence” of HER2 overexpression effect Can We Understand How to Intervene in the ErbB Signaling Network to Abrogate the HER2-ox Effect?

PI3K/Akt Pathway is Strongly Implicated in HER2-mediated Cell Migration Yarden & Slikowski, Nat Rev Mol Cell Biol (2001)]

*? Can We Predict HER2-ox Cell Migration Effect in terms of PI3K/Akt Activity? *? Yarden & Slikowski, Nat Rev Mol Cell Biol (2001)]

Increased P-Akt Correlates with HER2-ox Enhancement of Migration 1 HER2-ox cells + EGF cell migration HER2-ox cells -- s.f. HER2-ox cells + HRG 0.5 parental cells -- s.f. 1 2 3 4 P-Akt (steady-state)

Inhibition of P-Akt Correlates with Diminished Migration for HRG Treatment 1 HER2-ox cells + EGF cell migration HER2-ox cells + HRG HER2-ox cells -- s.f. 0.5 HER2-ox cells + HRG + LY parental cells -- s.f. 1 3 4 2 P-Akt (steady-state)

BUT -- Inhibition of P-Akt Does NOT Correlate with Diminished Migration for EGF Treatment… 1 HER2-ox cells + EGF HER2-ox cells + EGF + LY cell migration HER2-ox cells + HRG HER2-ox cells -- s.f. 0.5 HER2-ox cells + HRG + LY parental cells -- s.f. 1 3 4 2 P-Akt (steady-state)

Hence, Must Turn to Multi-Variate Signaling Network Model for Effective Comprehension

Mass Spectrometry Phosphoproteomics Trizol CH3OH + CH3COCl Trypsin N H 2N N N OH OCH3 O R2 O R2 Peptide mixture Modified peptides Biological sample Extracted proteins IMAC Modified peptides Full Scan MS Modified phosphorylated peptides Reverse-phase LC MS/MS MS/MS MS/MS 1 n MASCOT or SEQUEST database search algorithm MS

(Relative) Quantitative Signaling Network Measurements via iTRAQ Labeling EGFR pY1148 GSHQISLDNPDYQQDFFPK 2 x 107 cells (HMEC) 10 min 5 min 0 min 114 115 116 117 m/z 30 min 200 400 600 800 1000 1200 m/z Intensity, counts 500 y1 y2 y3 y4 y5 b9 b8 b5 b4 b3 + EGF (0 min) + EGF (30 min) + EGF (5 min) + EGF (10 min) Lyse, denature, digest iTRAQ Label 114 115 116 117 Mix pS pS IMAC pS pT pS pY pY pY pY Anti-Phosphotyrosine peptide IP

Signaling Network Activity: phospho-Y mass spec EGFR pY1148 GSHQISLDNPDYQQDFFPK 2 x 107 cells (HMEC) 10 min 5 min 0 min 114 115 116 117 m/z 30 min 200 400 600 800 1000 1200 m/z Intensity, counts 500 y1 y2 y3 y4 y5 b9 b8 b5 b4 b3 + EGF (0 min) + EGF (30 min) + EGF (5 min) + EGF (10 min) Lyse, denature, digest EGFR pY1148 iTRAQ Label 114 115 116 117 Mix pS pS IMAC pS pT pS pY pY pY pY Anti-Phosphotyrosine peptide IP

pTyr-MS results - A (332 sites across 175 proteins) Phosphorylated tyrosine ( )mapped on cell proliferation-associated proteins Yarden & Slikowski, Nat Rev Mol Cell Biol (2001)

pTyr-MS results - B (332 sites across 175 proteins) Phosphorylated tyrosine ( )mapped on cell migration-associated proteins Zamir & Geiger, J Cell Sci (2001)

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

Her2 Overexpression Effects on EGF-Induced Signaling - A 5 EGF EGF HRG E G F R E G F R H E R 2 E G F R H E R 3 H E R 2 10 30 Y974 Y877 Y1045 Y1068 Y1005 EPS15 Y849 Y1127/Y1139 Y1328 Y1248 Y1148 Y1173 Fold Change: Not registered x < 0.50 0.50 < x < 0.85 0.85 < x < 1.15 1.15 < x < 2.00 2.00 < x Y705 STAT3 Y704 CBL Y455 Y552 Y700 IP3 Ca++ Y771 SRC PLC- Y418 Y1253 Y783 Y313 SHC Y317 Y239 Y239/Y240 PKC S302/Y313 GRB2 SOS RAS RAF Y204 Y406 Y464 ERK1 T202 /Y204 Y259 GAB1 P85 MEK Y467 P110 Y187 Y607 Y659 Y580 ERK2 T185 /Y187 PKD AKT

Her2 Overexpression Effects on EGF-Induced Signaling - B Ligand G R I N T E Ca veo lin R T K Y781 (1) Y6/Y14 Y187 Y14 Y1189 (4) ERK2 T185 /Y187 Y1207 (4) Fold Change: Not registered x < 0.50 0.50 < x < 0.85 0.85 < x < 1.15 1.15 < x < 2.00 2.00 < x Y213 Y217 Y221 Y317 SHC Y96 Y239 Catenin- Y228 Y239/Y240 Y317 Y334 Y580 P110 P85 Y607 Y464 Y467 Y576 Y296 Y280 Y291 FAK Y22 S84/Y88 Y20 Catenin- Y88 PXN Y118 Y249 Y22 Y234 -Actinin GEF SRC Y418 p130 Y19/Y22 Y387 Y327 Y1680 Vinculin Y132 GTP Y207 CRKL Y251 Y221 - A c t i n F

HRG vs EGF Signaling - A EGF EGF EGF HRG E G F R E G F R H E R 2 E G F Y1328 H E R 3 H E R 2 Y974 Y877 Y1005 Y1045 Y1068 EPS15 Y849 Y1127/Y1139 Y1248 Y1148 Y1173 Fold Change: Not registered x < 0.50 0.50 < x < 0.85 0.85 < x < 1.15 1.15 < x < 2.00 2.00 < x Y704 CBL Y455 Y552 Y700 STAT3 IP3 Ca++ Y705 Y771 SRC PLC- Y418 Y1253 Y783 SHC Y317 Y239 Y239/Y240 Y313 PKC S302/Y313 GRB2 SOS RAS RAF Y204 Y659 GAB1 Y259 Y406 Y580 P110 P85 Y607 Y464 Y467 ERK1 T202 /Y204 MEK Y187 ERK2 T185 /Y187 PKD AKT

HRG vs EGF Signaling - B E R I N C A D H Ligand G R I N T E Ca veo lin K Y781 (1) Y6/Y14 Y187 Y14 Y1189 (4) ERK2 T185 /Y187 Y1207 (4) Fold Change: Not registered x < 0.50 0.50 < x < 0.85 0.85 < x < 1.15 1.15 < x < 2.00 2.00 < x Y213 Y217 Y221 Y317 SHC Y239 Y96 Catenin- Y228 Y239/Y240 Y317 Y334 Y464 Y576 Y296 Y280 Y291 P85 FAK Y22 Y467 P110 Y607 S84/Y88 Y20 Catenin- Y580 PXN Y118 Y88 Y249 Y22 Y234 -Actinin GEF SRC Y418 p130 Y19/Y22 Y387 Y327 Vinculin Y132 GTP Y207 CRKL Y1680 Y221 Y251 - A c t i n F

Can we Quantitatively Comprehend (and Predict) Phenotypic Response from Signals across all Conditions (Cells, Stimuli, Drugs) Signals

Computational Analysis -- Spectrum of Methods SPECIFIED ABSTRACTED differential equations Markov chains Bayesian networks mechanisms Boolean/fuzzy logic models statistical mining (including molecular structure-based computation) influences relationships Appropriate approach depends on question and data

Proliferation or Migration Principal Component / Partial Least-Square Regression -- elucidates key signal combinations governing responses #1 #2 PC3 Signal #1 PC1 #3 EGF #4 #5 HRG PC2 Proliferation or Migration

2-PC PLSR Model Accounts for both Parental HMEC and HER2-overexpressing HMEC Migration and Proliferation Behavior for All Ligand Treatments EGF EGF HRG X HRG X 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 Prediction of HER2-ox Effects on Proliferation and Migration under 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

HER2-Mediated HMEC Proliferation and Migration Behavior 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 gefitinib X LY294002 X 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

uni-variate prediction was unsuccessful) a priori Prediction : Effects of 3 Kinase Inhibitors on HMEC Migration from 5 pY Signals good PI3K/Akt inhibitor effect prediction (recall that uni-variate prediction was unsuccessful)

a priori Prediction : Effects of 3 Kinase Inhibitors on HMEC Migration from 5 pY Signals good MEK/Erk inhibitor effect prediction

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 for understanding drug effects

Computational Analysis -- Spectrum of Methods SPECIFIED ABSTRACTED differential equations Markov chains Bayesian networks mechanisms Boolean/fuzzy logic models statistical mining (including molecular structure-based computation) influences relationships Appropriate approach depends on question and data

Fuzzy Logic Models -- Elucidating Upstream/Downstream Signal-Signal Influence Relationships

Strategy: Take Advantage of Peptide Sequence Information -- kinase substrate motifs, phosphopeptide-binding domain substrate motifs Motif* Phosphorylated sequence Substrate Kinase X[D/E]Y[I/L/V] STAENAEYLRVAPQS EGFR EEEEYFELV TGMFPRNYVTPVNRN GRB2 [-/R/A]--[-/I]Y[F/V/I/E][I/F][FLIV]V TQEQYELYCEMGSTF CBL [D/E]Y IGTAEPDYGALYEGR PLCG1 QLRNQGETPTTEVPA CDC23 [S/T]PX[R/K] PQGQQPLSPQSGSPQ SYN3 CDK1 [S/T]P PQQGFFSSPSTSRTP [P/L/I/M]X[L/I/D/E] SQ ENVKYSSSQPEPRTG CHK1 LSQE QMRHQSESQGVGLSD BRCA1 ATM [S/T]Q GKKATQASQEY H2AX * Motifs from http://hprd.org/

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

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” Phosphopeptide Protein PYPGIDLsQVYELLE LMTGDTYtAHAGAKF RLMTGDTyTAHAGAK KRNKPTVyGVSPNYD ABL1 TLGRNTPyKTLEPVK PPTVPNDyMTSPARL SSTSSGGyRRTPSVT ABI1 Phospho.ELM DB of phosphoproteins and phosphopeptides Motif search tools 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 Phosphopeptide Protein PYPGIDLsQVYELLE LMTGDTYtAHAGAKF RLMTGDTyTAHAGAK KRNKPTVyGVSPNYD ABL1 TLGRNTPyKTLEPVK PPTVPNDyMTSPARL SSTSSGGyRRTPSVT ABI1 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. Phospho.ELM DB of phosphoproteins and phosphopeptides Motif search tools MS-derived phosphopeptide dynamics

NCI Integrative Cancer Biology Program More details Zhang 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 Program AstraZeneca