quantitative approaches to understanding signaling ...-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 py reduction w/...

Quantitative approaches to understanding signaling regulation of 3D cell migration

April 8, 2014

Metastasis is a multistep process

Nature Reviews | Cancer

Primary tumour Vascularization Detachment

Circulatingtumour cell

Adhesion toblood vessel wall Extravasation

Growth of secondary tumour

Intravasation

In the series of steps that comprise the metastatic process, cancer cells migrate or flow through vastly different microenvironments, including the stroma, the blood vessel endothelium, the vascular system and the tissue at a secondary site1,2 (FIG. 1). The ability to successfully negotiate each of these steps and advance towards the formation and growth of a secondary tumour is dependent, in part, on the physical interactions and mechanical forces between cancer cells and the microenvironment. For example, the physical interactions between a cell and the extracellular matrix — the collagen-rich scaffold on which it grows — have a key role in allowing cells to migrate from a tumour to nearby blood vessels. During intravasation and extravasation, cells must undergo large elastic deformations to penetrate endothelial cell–cell junctions. In the vascular system, the interplay between cell velocity and adhesion influences the binding of cancer cells to blood vessel walls and hence the location of sites where a secondary tumour can form and grow. A clearer understanding of the role of physical interactions and mechanical forces, and their interplay with biochemical changes, will provide new and important insights into the progression of cancer and may provide the basis for new therapeutic approaches.

Physical interactions in invasionFollowing the growth of a primary tumour, the combination of continued tumour pro-liferation, angiogenesis, accumulated genetic transformations and activation of complex signalling pathways trigger the metastatic

cascade (FIG. 2). In particular, the detachment of carcinoma cells from the epithelium and the subsequent invasion of the underlying stroma resembles, at both the cellular and molecular levels, the well-characterized epithelial-to-mesenchymal transition (EMT) in embryogenesis3. The role of EMT in cancer metastasis is being actively explored4,5. Critical to EMT is the loss of E-cadherin (an intercellular adhesion molecule) and cytokeratins, which leads to dramatic changes in the physical and mechanical properties of cells: specifically, reduced intercellular adhesion and a morphological change from cuboidal epithelial to mesenchymal6. One consequence of these changes is detachment from the primary tumour and the acquisition of a motile phenotype5. These cells also begin to express matrix metalloproteinases

(MMPs) on their surface, which promote the digestion of the laminin- and collagen IV-rich basement membrane7. After leaving the tumour microenvironment, motile tumour cells encounter the architecturally complex extracellular matrix (ECM), which is rich in collagen I and fibronectin8 (BOX 1). In the vicinity of a mammary tumour, the matrix is often stiffer than in normal tissue owing to enhanced collagen deposition9 and lysyl-oxidase-mediated crosslinking of the collagen fibres by tumour-associated fibroblasts10. Collagen crosslinking enhances integrin signalling as well as the bundling of individual fibres11. Such changes in the physicochemical properties of the matrix can enhance cell proliferation and invasion in a positive feedback loop9. Whether stiffening of the stromal matrix occurs in other solid tumours, besides mammary tumours, remains to be determined. However, despite recent technological advances (TABLE 1), remarkably little is known about the molecular and physical mechanisms that drive motile cancer cells away from primary tumour and into the stromal space, especially at the subcellular level.

Motility in three dimensions. Much of what we have learned about the physical and molecular mechanisms driving normal and cancer cell motility has come from in vitro studies using two-dimensional (2D) substrates12–14. However, the dimensionality of the system used to study cancer invasion can have a key role in dictating the mode of cell migration. This is not entirely surprising as the three-dimensional (3D) microenvironment of the ECM in vivo is characterized by many features, including the pore size and fibre orientation, features that are not found in conventional ECM-coated

O P I N I O N

The physics of cancer: the role of physical interactions and mechanical forces in metastasisDenis Wirtz, Konstantinos Konstantopoulos and Peter C. Searson

Abstract | Metastasis is a complex, multistep process responsible for >90% of cancer-related deaths. In addition to genetic and external environmental factors, the physical interactions of cancer cells with their microenvironment, as well as their modulation by mechanical forces, are key determinants of the metastatic process. We reconstruct the metastatic process and describe the importance of key physical and mechanical processes at each step of the cascade. The emerging insight into these physical interactions may help to solve some long-standing questions in disease progression and may lead to new approaches to developing cancer diagnostics and therapies.

Figure 1 | The metastatic process. In this complex process, cells detach from a primary, vascularized tumour, penetrate the surrounding tissue, enter nearby blood vessels (intravasation) and circulate in the vascular system. Some of these cells eventually adhere to blood vessel walls and are able to extrav-asate and migrate into the local tissue, where they can form a secondary tumour.

PERSPECT IVES

512 | JULY 2011 | VOLUME 11 www.nature.com/reviews/cancer

© 2011 Macmillan Publishers Limited. All rights reserved

Wirtz et al, Nat Rev Cancer, 2011

Invasive programs globally rewire cell signaling

EMT. Opposing this function, multiple pro-angiogenic

proteins were increased, notably angiopoietin-1 (ANGPT1),co-expression of thrombospondins together with the

ADAMTS1 protease [86] and VEGF-C.

Multiple receptor protein kinase networks were acti-vated in the mesenchymal state. Autocrine Gas6 stimula-

tion of Axl and Tyro3 activity were established from array

and RT-PCR RNA abundance measurements. The datasuggest inhibitors of Axl and Tyro3 activity may show

utility in limiting the survival of shed mesenchymal-like

NSCLC cells. A functional role of autocrine Axl-Gas6expression in lung [87] and breast cancer [88] progression

has been established. Autocrine activation of FGFR and

PDGFR signaling also was observed in both established(H1703) and metastable TGFb models, supporting previous

findings [25, 89]. This increase in kinase activity and

PDGFR autophosphorylation was associated withincreased PDGFR and PDGF RNA transcripts. In meta-

stable Snail and Zeb1 models, FGFR1 and PDGFR tran-

scripts were markedly increased, PDGF was modestlyincreased and FGF1 was unchanged or slightly decreased

(Supplementary Table S5). These data support the predic-

tion of kinase activation by autocrine co-expression ofreceptor–ligand transcripts. We asked what functional

EMT dependent acquisition of ITGα5β1/FN and TGFβ signaling, activation of MAPK1/Erk2 and PTK2/FAK, inactivation of GSK3

1 100 1 100

H358

Erlotinib (uM)

pErk

Erk

pAkt(473)

Akt

β-Actin

H358(TGF β)B

Glypican1/6FGFR1

FGF1/2

p

AXLTYRO3

ADAMTS1

CTGF

IKIP

TGFβR1/2BMPR2

TGFB1/2/3

LTBP2

FAM129BS628,S679,

S683

BRAF p

HDGF

RAS

FAM129A

p

pPTK2pRBM35A/B

GAS6

FGFBP1FGF1/2

ITGα5β1

FN

MARCKS

ANXA6

PEA15

PFTK1TGFBI

PXNS84/S85

T284/S288Y118

MAPK1Y187

SRCp

MST4

CAV1p

DUSP4/6G3BP1

G3BP2

ENG

CCDC80

HDGF

P2RY5

PTPN14

MAPK14p

PKN1GADD45

SGK1 GSK3InactiveS278

AKT1AKTIP

AKT3DDIT4

DIDO1

INHBB

THBS1/2

p

PDGFRA

PDGF A/B/C

STAT3

PTPN11

p

CAV1pINA p

PIK3R1

PIK3CB

p

pp

PLCG1p

ANGPT1

IL8IL18

IL7CCL26

CGB

SCG5

VEGFC

IL11

IL6CLCF1

IL6ST

IL6RCNTRF JAK2

CRLF1

CXCR7

SPARC

DISP2

GIT2

GIT2

ANXA6

MAP3K3

pSTAT3

ISL1

PTX3 YARS

Fig. 5 a Gain of new survivaland cytokine networks.Increased signaling throughIL-11/IL-6/gp130, TGFb/BMP2, Axl/Tyro3, integrina5b1, PDGFR and FGFR wereobserved. Downstreamactivation of BRAF/MAPK1and PTK2/paxillin signalingwere observed. Components ingreen were attenuated duringEMT while those in lightbrown/red were increased withEMT. b Activating MAPK1/Erkphosphorylation (T185/Y187),associated with TGFb-inducedEMT, was confirmed byimmunoblot

Clin Exp Metastasis

123

co-attenuated with the EMT-mediated reduction in Met,

Ron, EGFR and IGF1R kinase activation. These included

multiple SH2, cytoplasmic kinase and ubiquinylationadapters: pSHC, pGrb2, pGAB1, pPTK6 (Brk), pUBB,

pUBIQ and pNCKAP1. We asked whether attenuated Met

phosphorylation might be attributable to reduced HGF

binding to Met. While no change in HGF transcript abun-

dance was observed, ST14 (matriptase), an extracellularprotease which promotes proHGF activation, was markedly

reduced. SPINT2 and SPINT1, which augment ST14

EGFRY1110Y1172

EGFRERBB2ERBB3

p

METMST1R/RON

AREG

EREG

TGFA

SHC1GRB2GRB2 pp p

p

PTK6p

GAB1p

Plasma membrane

BTCIGF1R

p

NCKAP1p

UBBp

ANXA9

SHC1

UBIQp

PDCD6

PDCD6IP

p

p

SHC1p

p

SFN p

ITGα6β4

ST14HGF

SPINT1SPINT2

PRSS8

MUC1/4

GPR110

PTAFR

H19

LRIG3

HBEGF

NRG

GRB2/7p

CDC2T14/Y15

MCM2S139

CDC2CDCA7L

PCNA

SFN

CDKN2D

TMPRSS4

CCND1CCNB1

IGFBP2

IGFBP4

IGFBP7

STK39

STAP2

S100P

S100A2EZR

p

S100A14

S100A6

S100A4

TP53

S100A11

ARHGEF16

IL4R

MAP3K8

PA2G4p

PTPRK

PTPRG

macrophage

EGF family

CSF1

NKD2

DDIT4

DKK1

CTNNB1

LRP5

APOE

CTGF

TSC2

RTK tyrosine phosphorylation measured by elisa array, comparing ratios of mesenchymal-like (Mes) and epithelial (Epi) NSCLC lines: all changes p<0.01 except ErbB4 (ns) where error bars reflect the standard error of the mean

Pho

spho

tyro

sine

Con

tent Mesenchymal

Epithelial

25 Calu6

H1703

H292

H358

Calu6

H1703

H292

H358

Calu6

H1703

H292

H358

CB

L/CB

LBH

SP

D1

SH

C1/S

HC

2

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5

pY reduction w/ EGFR TKI

1 hr

4 hrs

24hrs

A

B C

Fig. 4 a Loss of autocrine RTK signaling networks through EGFR/ErbB2/ErbB3, Met/Ron and IGF1R signaling networks. AttenuatedIL4R, Muc1 and Muc4 and integrin a6b4 components also wereobserved. b Attenuated RTK tyrosine phosphorylation in H292 andH358, (epithelial phenotype) Calu6 and H1703 (mesenchymalphenotype) cell models were confirmed by quantitation of RTKarrays. c EGFR is functional and responds to exogenous ligand in

both epithelial and mesenchymal states. H292 and H358, (epithelial)Calu6 and H1703 (mesenchymal) cells were exposed to EGFR kinaseinhibitor (3 lM erlotinib, 2 h) or DMSO control prior to stimulationwith exogenous EGF (10 ng/ml, 10 min). The EGFR-dependentinhibition of substrates CBL and SHC were similar in epithelial andmesenchymal-like cells, relative for control HSPD1

Clin Exp Metastasis

123

Thomson et al, Clin Exp Metastasis, 2010

Nature Reviews | Cancer

Primary tumour Vascularization Detachment

Circulatingtumour cell

Adhesion toblood vessel wall Extravasation

Growth of secondary tumour

Intravasation

In the series of steps that comprise the metastatic process, cancer cells migrate or flow through vastly different microenvironments, including the stroma, the blood vessel endothelium, the vascular system and the tissue at a secondary site1,2 (FIG. 1). The ability to successfully negotiate each of these steps and advance towards the formation and growth of a secondary tumour is dependent, in part, on the physical interactions and mechanical forces between cancer cells and the microenvironment. For example, the physical interactions between a cell and the extracellular matrix — the collagen-rich scaffold on which it grows — have a key role in allowing cells to migrate from a tumour to nearby blood vessels. During intravasation and extravasation, cells must undergo large elastic deformations to penetrate endothelial cell–cell junctions. In the vascular system, the interplay between cell velocity and adhesion influences the binding of cancer cells to blood vessel walls and hence the location of sites where a secondary tumour can form and grow. A clearer understanding of the role of physical interactions and mechanical forces, and their interplay with biochemical changes, will provide new and important insights into the progression of cancer and may provide the basis for new therapeutic approaches.

Physical interactions in invasionFollowing the growth of a primary tumour, the combination of continued tumour pro-liferation, angiogenesis, accumulated genetic transformations and activation of complex signalling pathways trigger the metastatic

cascade (FIG. 2). In particular, the detachment of carcinoma cells from the epithelium and the subsequent invasion of the underlying stroma resembles, at both the cellular and molecular levels, the well-characterized epithelial-to-mesenchymal transition (EMT) in embryogenesis3. The role of EMT in cancer metastasis is being actively explored4,5. Critical to EMT is the loss of E-cadherin (an intercellular adhesion molecule) and cytokeratins, which leads to dramatic changes in the physical and mechanical properties of cells: specifically, reduced intercellular adhesion and a morphological change from cuboidal epithelial to mesenchymal6. One consequence of these changes is detachment from the primary tumour and the acquisition of a motile phenotype5. These cells also begin to express matrix metalloproteinases

(MMPs) on their surface, which promote the digestion of the laminin- and collagen IV-rich basement membrane7. After leaving the tumour microenvironment, motile tumour cells encounter the architecturally complex extracellular matrix (ECM), which is rich in collagen I and fibronectin8 (BOX 1). In the vicinity of a mammary tumour, the matrix is often stiffer than in normal tissue owing to enhanced collagen deposition9 and lysyl-oxidase-mediated crosslinking of the collagen fibres by tumour-associated fibroblasts10. Collagen crosslinking enhances integrin signalling as well as the bundling of individual fibres11. Such changes in the physicochemical properties of the matrix can enhance cell proliferation and invasion in a positive feedback loop9. Whether stiffening of the stromal matrix occurs in other solid tumours, besides mammary tumours, remains to be determined. However, despite recent technological advances (TABLE 1), remarkably little is known about the molecular and physical mechanisms that drive motile cancer cells away from primary tumour and into the stromal space, especially at the subcellular level.

Motility in three dimensions. Much of what we have learned about the physical and molecular mechanisms driving normal and cancer cell motility has come from in vitro studies using two-dimensional (2D) substrates12–14. However, the dimensionality of the system used to study cancer invasion can have a key role in dictating the mode of cell migration. This is not entirely surprising as the three-dimensional (3D) microenvironment of the ECM in vivo is characterized by many features, including the pore size and fibre orientation, features that are not found in conventional ECM-coated

O P I N I O N

The physics of cancer: the role of physical interactions and mechanical forces in metastasisDenis Wirtz, Konstantinos Konstantopoulos and Peter C. Searson

Abstract | Metastasis is a complex, multistep process responsible for >90% of cancer-related deaths. In addition to genetic and external environmental factors, the physical interactions of cancer cells with their microenvironment, as well as their modulation by mechanical forces, are key determinants of the metastatic process. We reconstruct the metastatic process and describe the importance of key physical and mechanical processes at each step of the cascade. The emerging insight into these physical interactions may help to solve some long-standing questions in disease progression and may lead to new approaches to developing cancer diagnostics and therapies.

Figure 1 | The metastatic process. In this complex process, cells detach from a primary, vascularized tumour, penetrate the surrounding tissue, enter nearby blood vessels (intravasation) and circulate in the vascular system. Some of these cells eventually adhere to blood vessel walls and are able to extrav-asate and migrate into the local tissue, where they can form a secondary tumour.

PERSPECT IVES

512 | JULY 2011 | VOLUME 11 www.nature.com/reviews/cancer

© 2011 Macmillan Publishers Limited. All rights reserved

Invasive programs globally rewire cell signaling

EMT. Opposing this function, multiple pro-angiogenic

proteins were increased, notably angiopoietin-1 (ANGPT1),co-expression of thrombospondins together with the

ADAMTS1 protease [86] and VEGF-C.

Multiple receptor protein kinase networks were acti-vated in the mesenchymal state. Autocrine Gas6 stimula-

tion of Axl and Tyro3 activity were established from array

and RT-PCR RNA abundance measurements. The datasuggest inhibitors of Axl and Tyro3 activity may show

utility in limiting the survival of shed mesenchymal-like

NSCLC cells. A functional role of autocrine Axl-Gas6expression in lung [87] and breast cancer [88] progression

has been established. Autocrine activation of FGFR and

PDGFR signaling also was observed in both established(H1703) and metastable TGFb models, supporting previous

findings [25, 89]. This increase in kinase activity and

PDGFR autophosphorylation was associated withincreased PDGFR and PDGF RNA transcripts. In meta-

stable Snail and Zeb1 models, FGFR1 and PDGFR tran-

scripts were markedly increased, PDGF was modestlyincreased and FGF1 was unchanged or slightly decreased

(Supplementary Table S5). These data support the predic-

tion of kinase activation by autocrine co-expression ofreceptor–ligand transcripts. We asked what functional

EMT dependent acquisition of ITGα5β1/FN and TGFβ signaling, activation of MAPK1/Erk2 and PTK2/FAK, inactivation of GSK3

1 100 1 100

H358

Erlotinib (uM)

pErk

Erk

pAkt(473)

Akt

β-Actin

H358(TGF β)B

Glypican1/6FGFR1

FGF1/2

p

AXLTYRO3

ADAMTS1

CTGF

IKIP

TGFβR1/2BMPR2

TGFB1/2/3

LTBP2

FAM129BS628,S679,

S683

BRAF p

HDGF

RAS

FAM129A

p

pPTK2pRBM35A/B

GAS6

FGFBP1FGF1/2

ITGα5β1

FN

MARCKS

ANXA6

PEA15

PFTK1TGFBI

PXNS84/S85

T284/S288Y118

MAPK1Y187

SRCp

MST4

CAV1p

DUSP4/6G3BP1

G3BP2

ENG

CCDC80

HDGF

P2RY5

PTPN14

MAPK14p

PKN1GADD45

SGK1 GSK3InactiveS278

AKT1AKTIP

AKT3DDIT4

DIDO1

INHBB

THBS1/2

p

PDGFRA

PDGF A/B/C

STAT3

PTPN11

p

CAV1pINA p

PIK3R1

PIK3CB

p

pp

PLCG1p

ANGPT1

IL8IL18

IL7CCL26

CGB

SCG5

VEGFC

IL11

IL6CLCF1

IL6ST

IL6RCNTRF JAK2

CRLF1

CXCR7

SPARC

DISP2

GIT2

GIT2

ANXA6

MAP3K3

pSTAT3

ISL1

PTX3 YARS

Fig. 5 a Gain of new survivaland cytokine networks.Increased signaling throughIL-11/IL-6/gp130, TGFb/BMP2, Axl/Tyro3, integrina5b1, PDGFR and FGFR wereobserved. Downstreamactivation of BRAF/MAPK1and PTK2/paxillin signalingwere observed. Components ingreen were attenuated duringEMT while those in lightbrown/red were increased withEMT. b Activating MAPK1/Erkphosphorylation (T185/Y187),associated with TGFb-inducedEMT, was confirmed byimmunoblot

Clin Exp Metastasis

123

co-attenuated with the EMT-mediated reduction in Met,

Ron, EGFR and IGF1R kinase activation. These included

multiple SH2, cytoplasmic kinase and ubiquinylationadapters: pSHC, pGrb2, pGAB1, pPTK6 (Brk), pUBB,

pUBIQ and pNCKAP1. We asked whether attenuated Met

phosphorylation might be attributable to reduced HGF

binding to Met. While no change in HGF transcript abun-

dance was observed, ST14 (matriptase), an extracellularprotease which promotes proHGF activation, was markedly

reduced. SPINT2 and SPINT1, which augment ST14

EGFRY1110Y1172

EGFRERBB2ERBB3

p

METMST1R/RON

AREG

EREG

TGFA

SHC1GRB2GRB2 pp p

p

PTK6p

GAB1p

Plasma membrane

BTCIGF1R

p

NCKAP1p

UBBp

ANXA9

SHC1

UBIQp

PDCD6

PDCD6IP

p

p

SHC1p

p

SFN p

ITGα6β4

ST14HGF

SPINT1SPINT2

PRSS8

MUC1/4

GPR110

PTAFR

H19

LRIG3

HBEGF

NRG

GRB2/7p

CDC2T14/Y15

MCM2S139

CDC2CDCA7L

PCNA

SFN

CDKN2D

TMPRSS4

CCND1CCNB1

IGFBP2

IGFBP4

IGFBP7

STK39

STAP2

S100P

S100A2EZR

p

S100A14

S100A6

S100A4

TP53

S100A11

ARHGEF16

IL4R

MAP3K8

PA2G4p

PTPRK

PTPRG

macrophage

EGF family

CSF1

NKD2

DDIT4

DKK1

CTNNB1

LRP5

APOE

CTGF

TSC2

RTK tyrosine phosphorylation measured by elisa array, comparing ratios of mesenchymal-like (Mes) and epithelial (Epi) NSCLC lines: all changes p<0.01 except ErbB4 (ns) where error bars reflect the standard error of the mean

Pho

spho

tyro

sine

Con

tent Mesenchymal

Epithelial

25 Calu6

H1703

H292

H358

Calu6

H1703

H292

H358

Calu6

H1703

H292

H358

CB

L/CB

LBH

SP

D1

SH

C1/S

HC

2

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5

pY reduction w/ EGFR TKI

1 hr

4 hrs

24hrs

A

B C

Fig. 4 a Loss of autocrine RTK signaling networks through EGFR/ErbB2/ErbB3, Met/Ron and IGF1R signaling networks. AttenuatedIL4R, Muc1 and Muc4 and integrin a6b4 components also wereobserved. b Attenuated RTK tyrosine phosphorylation in H292 andH358, (epithelial phenotype) Calu6 and H1703 (mesenchymalphenotype) cell models were confirmed by quantitation of RTKarrays. c EGFR is functional and responds to exogenous ligand in

both epithelial and mesenchymal states. H292 and H358, (epithelial)Calu6 and H1703 (mesenchymal) cells were exposed to EGFR kinaseinhibitor (3 lM erlotinib, 2 h) or DMSO control prior to stimulationwith exogenous EGF (10 ng/ml, 10 min). The EGFR-dependentinhibition of substrates CBL and SHC were similar in epithelial andmesenchymal-like cells, relative for control HSPD1

Clin Exp Metastasis

123

Targeting invasion-specific signaling may considerably improve survival

Thomson et al, Clin Exp Metastasis, 2010

Targeting AXL has minor effects on the primary tumor

Gjerdrum et al, PNAS, 2010

0 5 100

10

20

30ControlshAXL2

*

Time (weeks)

Tum

or d

iamet

er (m

m)

MDA-MB-231


0 5 100

10

20

30ControlshAXL2

*

Time (weeks)

Tum

or d

iamet

er (m

m)

Targeting AXL potently blocks metastasis

0 1 2 3 4 5 6 7

Lymph node

Lungs

Kidneys

Ovaries

Liver

Lymph node

Lungs

Kidneys

Ovaries

Liver

Lymph node

Lungs

Kidneys

Ovaries

Liver

Lymph node

Lungs

Kidneys

Ovaries

Liver

Metastases detected

BALB/c

shAXL

BALB/c

Control

MB-231

shAXL

MB231

Control

7 mice/group


0 5 100

10

20

30ControlshAXL2

*

Time (weeks)

Tum

or d

iamet

er (m

m)

Blocking metastasis is sufficient for improving survival

Structure of AXL/Gas6 complex

Gas6

AXL

EGF domains

Gla domain

Fn domains

SHBD domains

Ig domains

Sasaki et al, EMBO J, 2006

The systems approach to studying cell phenotype

Migration Growth

How does signal processing modulate a migration response?

Environment/Cues Cell Signaling Migration response

Quantitatively understanding 3D cell migration regulation

EGFR activation leads to AXL crosstalk

AXL with Gas6 did not elicit a substantial signaling response, and thesesignaling consequences were small compared with EGF-elicited, AXL-dependent signaling effects (fig. S3E). This observation is similar to resultsin other studies of Gas6-elicited signaling inMDA-MB-231 (44). Why dif-ferent cell lines display markedly distinct receptor activation patterns toGas6 remains a question for future studies (44–46).

To investigate the crosstalk between AXL and EGFR (as well as MET)signaling further, we next examined the ratio of fold activation (phosphoryl-ation) of various signaling proteins in the absence versus presence of AXL(Fig. 3A). The unstimulated conditions represented signaling networkactivity presumably arising from constitutive autocrine processes. Thisanalysis revealed more widespread AXL-dependent effects in EGF- orTGFa-stimulated cells compared with HGF-stimulated cells, with the largestdifference in activation observed for GSK3 (glycogen synthase kinase 3)and Akt. Further, the relative magnitude of effects across the phosphositesinvestigated was correlated between EGF- or TGFa-stimulated cells andunstimulated cells but was not correlated between HGF-stimulated andunstimulated cells (Fig. 3A, inset). These results suggest that AXL maymediate similar basal and EGFR-stimulated signaling pathways in TNBCcells, whereas HGF yields a distinct downstream AXL-mediated signature.

We then performed principal components analysis (PCA) to gain insightconcerning the network-level variation in signaling across these treatmentconditions (Fig. 3B). Principal component 1 (PC1)was found to correspondto EGF-induced signaling, and PC2 to HGF-elicited signaling, with TGFahaving an intermediate effect. Knockdown of AXL moved cells negativelyalong PC1 and reduced the magnitude of the effect of EGF stimulation. Ex-amination of the loading plot revealed separation between phosphosites

only mildly affected by knockdown [for example, phosphorylation ofSTAT3 (signal transducer and activator of transcription 3) and JNK(c-JunN-terminal kinase)] and those strongly affected (such as the phospho-rylation of Akt and GSK3), with the rest scattered at intermediate locations(Fig. 3C). Together, these data indicate that EGF and TGFa induce ErbB-mediated downstream signaling that is qualitatively similar to basalsignaling but is distinct from MET-mediated signaling, and this baseline-like signaling is disrupted by AXL knockdown. The difference betweenHGFand EGF, TGFa, and baseline signaling is likely a result of the absenceof signaling from EGFR, HER2, or AXL in the former case, because METis presumably transactivated also in the EGF- or TGFa-stimulated cases.An appealing interpretation is that autocrine EGFR ligand activity is consti-tutive and transactivates AXL.

AXL amplifies signaling in the EGFR-associated pathwaybut does not sensitize EGFR to its ligandBecause receptor activation can be quantitatively characterized in terms ofligand concentration–related sensitivity and maximal activation at satura-tion, we investigated how AXL influences the dose response of EGFR toEGF. We stimulated MDA-MB-231 cells with a range of concentrations ofEGFandmeasured the pan-phosphotyrosine on EGFR and the phosphoryl-ation of Akt (Fig. 4, A and B). Phosphorylation of Akt was chosen for mea-surement as a critical downstream signal that was strongly influenced byAXL knockdown, though not to imply that all transactivation-mediatedeffects are regulated throughAkt alone (Fig. 3C). Phosphorylation of EGFRwas unaffected by AXL knockdown except at very high (above saturating)EGF concentrations (Fig. 4A), likely as a result of altered trafficking or

0

6

12

18

EGFControl

pan

-pY

siCt

rl

siA

XL

0.0

0.5

1.0

Nor

mal

ized

AXL

FI

A B

C

pAkt

pTyk2

pSTAT3

pSrc

pIRS1

pHSP27

pcJun

pP38

pJNK

pGSK3

pERK

siAXLD

EGFR HER2 MET IGF1R0.00

0.25

0.50

0.75

1.00

1.25

siAXL surface

siCtrl surface

siAXL total

siCtrl total

Nor

mal

ized

rece

ptor

(a.u

.)

MET AXL

* *

*

Ctr

l

HG

F

EGF

Ctr

l

HG

F

EGF

z score–2 20

siCTRL

Fig. 2. EGF stimulation transactivates AXL andMET. (A) ELISA-based pan-phosphotyrosine (pan-pY) measurement of alternative receptors after EGFstimulation inMDA-MB-231 (*P<0.05, Student’s t test). (B) AXL knockdown,measured by ELISA. (C) Total and surface amounts of alternative receptorsin AXL-silenced MDA-MB-231 cells (*P < 0.05, Student’s t test). Data are

means ± SEM of three biological measurements. (D) Downstreamsignaling assessed by kinase phosphorylation in MDA-MB-231 cells5 min after stimulation with EGF, TGFa, or HGF in the presence orabsence (siAXL) of AXL. Each phosphosite was mean-centered andvariance-normalized.

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Migration Growth

AXL


Migration GrowthMigration Growth

AXL

AXL knockdown broadly influences cell signaling


AXL with Gas6 did not elicit a substantial signaling response, and thesesignaling consequences were small compared with EGF-elicited, AXL-dependent signaling effects (fig. S3E). This observation is similar to resultsin other studies of Gas6-elicited signaling inMDA-MB-231 (44). Why dif-ferent cell lines display markedly distinct receptor activation patterns toGas6 remains a question for future studies (44–46).

To investigate the crosstalk between AXL and EGFR (as well as MET)signaling further, we next examined the ratio of fold activation (phosphoryl-ation) of various signaling proteins in the absence versus presence of AXL(Fig. 3A). The unstimulated conditions represented signaling networkactivity presumably arising from constitutive autocrine processes. Thisanalysis revealed more widespread AXL-dependent effects in EGF- orTGFa-stimulated cells compared with HGF-stimulated cells, with the largestdifference in activation observed for GSK3 (glycogen synthase kinase 3)and Akt. Further, the relative magnitude of effects across the phosphositesinvestigated was correlated between EGF- or TGFa-stimulated cells andunstimulated cells but was not correlated between HGF-stimulated andunstimulated cells (Fig. 3A, inset). These results suggest that AXL maymediate similar basal and EGFR-stimulated signaling pathways in TNBCcells, whereas HGF yields a distinct downstream AXL-mediated signature.

We then performed principal components analysis (PCA) to gain insightconcerning the network-level variation in signaling across these treatmentconditions (Fig. 3B). Principal component 1 (PC1)was found to correspondto EGF-induced signaling, and PC2 to HGF-elicited signaling, with TGFahaving an intermediate effect. Knockdown of AXL moved cells negativelyalong PC1 and reduced the magnitude of the effect of EGF stimulation. Ex-amination of the loading plot revealed separation between phosphosites

only mildly affected by knockdown [for example, phosphorylation ofSTAT3 (signal transducer and activator of transcription 3) and JNK(c-JunN-terminal kinase)] and those strongly affected (such as the phospho-rylation of Akt and GSK3), with the rest scattered at intermediate locations(Fig. 3C). Together, these data indicate that EGF and TGFa induce ErbB-mediated downstream signaling that is qualitatively similar to basalsignaling but is distinct from MET-mediated signaling, and this baseline-like signaling is disrupted by AXL knockdown. The difference betweenHGFand EGF, TGFa, and baseline signaling is likely a result of the absenceof signaling from EGFR, HER2, or AXL in the former case, because METis presumably transactivated also in the EGF- or TGFa-stimulated cases.An appealing interpretation is that autocrine EGFR ligand activity is consti-tutive and transactivates AXL.

AXL amplifies signaling in the EGFR-associated pathwaybut does not sensitize EGFR to its ligandBecause receptor activation can be quantitatively characterized in terms ofligand concentration–related sensitivity and maximal activation at satura-tion, we investigated how AXL influences the dose response of EGFR toEGF. We stimulated MDA-MB-231 cells with a range of concentrations ofEGFandmeasured the pan-phosphotyrosine on EGFR and the phosphoryl-ation of Akt (Fig. 4, A and B). Phosphorylation of Akt was chosen for mea-surement as a critical downstream signal that was strongly influenced byAXL knockdown, though not to imply that all transactivation-mediatedeffects are regulated throughAkt alone (Fig. 3C). Phosphorylation of EGFRwas unaffected by AXL knockdown except at very high (above saturating)EGF concentrations (Fig. 4A), likely as a result of altered trafficking or

0

6

12

18

EGFControl

pan

-pY

siCt

rl

siA

XL

0.0

0.5

1.0

Nor

mal

ized

AX

L FI

A B

C

pAkt

pTyk2

pSTAT3

pSrc

pIRS1

pHSP27

pcJun

pP38

pJNK

pGSK3

pERK

siAXLD

EGFR HER2 MET IGF1R0.00

0.25

0.50

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1.25

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siCtrl surface

siAXL total

siCtrl total

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ized

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ptor

(a.u

.)

MET AXL

* *

*

Ctr

l

HG

F

EGF

Ctr

l

HG

F

EGF

z score–2 20

siCTRL

Fig. 2. EGF stimulation transactivates AXL andMET. (A) ELISA-based pan-phosphotyrosine (pan-pY) measurement of alternative receptors after EGFstimulation inMDA-MB-231 (*P<0.05, Student’s t test). (B) AXL knockdown,measured by ELISA. (C) Total and surface amounts of alternative receptorsin AXL-silenced MDA-MB-231 cells (*P < 0.05, Student’s t test). Data are

means ± SEM of three biological measurements. (D) Downstreamsignaling assessed by kinase phosphorylation in MDA-MB-231 cells5 min after stimulation with EGF, TGFa, or HGF in the presence orabsence (siAXL) of AXL. Each phosphosite was mean-centered andvariance-normalized.



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PCA deconvolves transactivation effect

cellular processes induced at such nonphysiological amounts of stimulation.Other receptor-proximal components, such as the adaptor protein SHC andthe CDC2 kinase, exhibited similar phosphorylation after stimulation at theEGF dose used in the signaling studies here (fig. S4). In contrast, AXLknockdown affected the phosphorylation of Akt in response to all dosesof EGF by a shift inmagnitude (“vertically”) rather than in sensitivity (“hor-izontally”) (Fig. 4B). To deconvolve these concomitant changes in the phos-phorylation of EGFR andAkt, we plotted the abundance of phosphorylatedAkt as a function of phosphorylated EGFR in cells treated with either con-trol siRNA or AXL siRNA (Fig. 4C). This revealed a uniform downwardshift across all stimulation amounts in the absence of AXL, indicating aconsistent fold change in the magnitude of signal transduction. Each curvecould bewell described to first approximation by a Hill function, with com-parable Kd (threshold of half-maximal activation) but markedly differentmaximal activation (Fig. 4D). To identify the level at which this regulationmay occur, we fit these data to alternative models of signal transductionfrom the receptor layer (see Materials and Methods). The data were bestexplained by a model in which basal and stimulated AXL activities exist,the latter in proportion to EGFRactivation and inwhich transduction of both

signals occurs through separately saturable processes (table S3). Thismodelis consistent with our biochemical observations (Fig. 2A). The effect ofbaseline activation ofAXLcan be observed from the plot of phosphorylatedAkt as a function of pan-phosphotyrosine EGFR, where at low EGFR ac-tivation in the presence ofAXL, the phosphorylation ofAktwas higher thana simpleHill regressionwould suggest (Fig. 4C). Biologically, this indicatesthat the components downstream of the receptor are saturated by maximalEGFR activation and that, at least with respect to phosphorylated Akt, thetransactivation of AXL increases the effective amount of RTK signaling andamplifies the signaling consequence of stimulation.

Multipathway signaling correctly predicts AXLknockdown inhibition of EGF-stimulated protrusionWe next asked how the broad effects on signaling that resulted from AXLknockdown might influence the migration behavior of cells. We elected touse acute membrane protrusion as a surrogate measurement of three-dimensional migratory capacity on the basis of our previous findings thatthis assay corresponds well to growth factor–stimulated invasive motilitywithin extracellular matrix (47). Protrusion measurements from wild-type

pSTAT3pJNK

pP38

pHSP27

pSrcpTyk2

pERK

pAktpGSK3

pIRS1pcJun

5

–4

5–5

EGF HGF

Component 1 (58%)

Com

pon

ent 2

(24%

)

–2

B CsiControl

siAXL

pGSK3 pAkt pcJun pERK pP38 pTyk2 pIRS1 pSrc pHSP27 pJNK pSTAT32–2

2–1

20

21

22

Rat

io o

f fol

dac

tivat

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with

siA

XL

A

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5

–4Component 1 (58%)

Com

pon

ent 2

(24%

)

0.0 1.0

* *

EGF

EGF

HGF

Vs. unstim. Effect corr.

Fig. 3. AXL knockdown attenuates downstream signaling in MDA-MB-231. (A) Ratios of fold activation after treatment with growth factorin AXL knockdown cells relative to wild-type cells: ([siAXL GF]/[siAXLUnstim]) ÷ ([siControl GF]/[siControl Unstim]). The unstimulated bar in-dicates the ratio of unstimulated abundance: [siAXL Unstim]/[siControl

Unstim]. Inset shows the Spearman correlation across all phosphositesbetween the unstimulated and stimulated ratios (*P < 0.05). (B) PCA scoreplot of signaling data after AXL knockdown. Line colors indicate stimulationconditions denoted in (A). (C) Loading plot of signaling data after AXLknockdown.




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Crosstalk quantitatively amplifies a qualitatively distinct set of pathways

MDA-MB-231 cells were used to train a family of partial least-squares re-gression models for how protrusion activity depends on multiple phospho-proteomic signals. Minimal models that use only three signals wereexamined to ascertain the most vital pathway predictors for growthfactor–induced motility. We identified models that fit the data by cross-validation (Q2 > 0.6) and found that these were enriched in inclusion ofGSK3, STAT3, and Akt as the key predictor signals (fig. S5A). Thesemodels involved similar weights for the predictor signals in both PCs,demonstrating consistency across the ensemble of top-fitting models intheir multipathway signaling-to-protrusion relationships (Fig. 5A).

This ensemble of models was then used to predict wild-typeMDA-MB-231 protrusion by cross-validation and to a priori predict protrusion mod-ulation by AXL knockdown (Fig. 5B). EGF-stimulated protrusion waspredicted to be the most substantially attenuated response after AXLknockdown, whereas HGF-stimulated protrusion was predicted to remainessentially unaffected. These predictions were indeed correct inMDA-MB-231 cells transfected with AXL siRNA: HGF-elicited protrusion was notsignificantly affected, whereas EGF-elicited protrusionwas significantly re-duced (Fig. 5C). Treatment with R428 confirmed that EGF-stimulated pro-trusion depended on AXL-mediated signaling in another TNBC line,MDA-MB-157, but that it did not in two other breast cancer cell lines,MCF7 and T47D, which lack AXL expression (Fig. 5D). The effect ofR428 phenocopied that of AXL siRNA treatment in terms of the protrusionresponse to EGF inMDA-MB-231 cells (Fig. 5, C andD). TGFa-stimulated

protrusion was also reduced in MDA-MB-231 cells by R428 treatment, al-though to a lesser degree, which was in accord with our model predictions(fig. S5B). These results indicate that along with amplification of EGFR-induced downstream signaling, the transactivation of AXL additionallyactivates a qualitatively distinct set of signals that are important for cellmigration in response to stimuli. Moreover, our three-pathway partial least-squares regression model successfully captured the integrated effects ofthese signals on this phenotypic response.

AXL is in proximity to ErbB and MET but not IGF1R or IRWe investigated whether the transactivation of AXL (and MET) by EGFRmight involve physicochemical proximity of these RTKs. Because of tech-nical limitations in capability for distinguishing receptor colocalization byother methods (fig. S6, A and B), we used a technique in which immuno-precipitation of cross-linked receptors from lysate was performed in amultiplexed fashion on barcoded fluorescent beads. The degree of AXLcross-linking with each of various other RTKs was quantified using anAXL antibody (Fig. 6A). Across multiple cell lines, we observed a signif-icant degree of AXL cross-linking with ErbB receptors, MET, and PDGFRbut not with insulin receptor (INSR) or IGF1R (Fig. 6B and fig. S6C). Theamount of AXL cross-linkingwas roughly proportional to the abundance ofthat particular RTK—with the exception of INSR and IGF1R, neither ofwhich garnered cross-linked AXL to a measureable extent (Fig. 6C). Weconfirmed cross-linking results with reciprocal immunoprecipitation assays

0 0.5 1 2 4 8 16 32 64 27 28 290

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pan

-pY *

*

****

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*

8 16 32 64 128 256 512 210 211 212256

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1024

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4096

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/pY

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/pY

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ax

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siCtrl siAXL siCtrl siAXL

pAkt

Fig. 4. AXL amplifies the EGFR signaling response. (A) ELISA of pan-pYEGFR in wild-type (siControl) or AXL-silenced (siAXL) MDA-MB-231 after5 min of treatment with varying EGF amounts. Data are means ± SEM.P < 0.05, Student’s t test. n = 3. (B) Phosphorylation of Akt in response to arange of EGF doses. Data are means ± SEM. P < 0.05, Student’s t test. n = 3.

(C) ELISA for the abundance of pan-pY on EGFR versus the phosphorylationof Akt in MDA-MB-231 cells. Lines show a Hill regression to each set of datawith SE of biological triplicate measurements. (D) Hill regression of each plotshows similar Kd values but significantly different maximal activation (F test).Error bars indicate SE of the fit.




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forE, the value ofEwas calculated with lsqnonlin by solving for the valuethat minimizes:

arg minE∈ ½0,1"

D1

IC50,1

!E

1 − E

" 1m1

þ D2

IC50,2

!E

1 − E

" 1m2

þ aD1D2

IC50,1IC50,2

!E

1 − E

" 12m1

þ 12m2

− 1

#########

#########

Fitting was performed with the nlinfit function within MatLab with ini-tial parameters identified by inspection of the single drug data.Optimizationwas unconstrained, but IC50 values were in all cases significantly positive,and all m values were significantly negative as verification of effectivefitting. Confidence intervals presentedwere calculated from the empiricallyderived Jacobianwith the nlparci function. Significancewas separately ver-ified by jackknife (60).

Partial least-squares regression and PCAReplicate measurements were averaged, and each signaling variable wasmean-centered and variance-normalized before further analysis. PCAwasperformed with singular value decomposition within the pca function. Thefirst two components explained 83% of the variance.

For reduced partial least-squares modeling, the output variable wasassembled from mean protrusion measurements for each growth factor,and the unstimulated condition set to 0. Model reduction was performedby training models with all possible combinations of three input variablesets. Three variables were chosen, as it was the smallest model size withsufficient well-trained models to ensure robust variable enrichment. Modelreduction with larger reducedmodels produced qualitatively similar results.Each individual reducedmodelwas then used concomitantly, and the resultswere shown by displaying the average and SE of loading values and pre-dictions. As a result of variation in baseline signaling, predictions forknockdown cells were taken to be the prediction for the knockdown andstimulated conditionminus the prediction of the knockdown and unstimu-lated condition.

Amplification modelingEach model was fit with the nlinfit function. To ensure robustness withrespect to initial parameter selection, fitting was performed 100 timeswith randomly selected initial parameters within the range of feasiblevalues. x is 0 with AXL knocked down and 1 with AXL present. [pEGFR]and [pAkt] are from measurements of pan-phosphotyrosine EGFR and pAktacross a dose range of EGF.Models were compared using the corrected anduncorrected Akaike information criterion denoted AICc and AIC, respec-tively (61).

To fit data to a model in which activation of AXL is in proportion toEGFR activation, and signaling integration is receptor-proximal, Eq. 1was used:

½pAkt" ¼ Bmax ð½pEGFR" þ a½pEGFR"xÞKD þ ð½pEGFR" þ a½pEGFR"xÞ

þ B0

For amodel inwhich amplification ofAkt activationwith respect to a setamount of EGFR activity, and AXL only affects this proportional relation-ship, Eq. 2 was used:

½pAkt" ¼ Bmax ð1þ axÞ½pEGFR"KD þ ½pEGFR"

þ B0

For a model in which no signaling effect from AXL exists, Eq. 3 wasused:

½pAkt" ¼ Bmax ½pEGFR"KD þ ½pEGFR"

þ B0

For a model in which some baseline activation of AXL is possible inaddition to proportional activation, and signaling integration is receptor-proximal, Eq. 4 was used:

½pAkt" ¼ Bmax ð½pEGFR" þ a½pEGFR"xþ xbÞKD þ ð½pEGFR" þ a½pEGFR"xþ xbÞ

þ B0

For a model in which Akt activated by AXL and activated by EGFR issummed, with proportional activation of AXL, Eq. 5 was used:

½pAkt" ¼Bmax,1½pEGFR"KD,1 þ ½pEGFR"

þxBmax,2½pEGFR"KD,2 þ ½pEGFR"

B0

For a model in which no signaling effect from AXL through EGFRpathway exists, but there is a baseline effect ofAXLpresent, Eq. 6was used:

½pAkt" ¼ Bmax½pEGFR"KD þ ½pEGFR"

þ xbþ B0

For a model with only baseline activation of AXL, and signalingintegration is receptor-proximal, Eq. 7 was used:

½pAkt" ¼ Bmax½pEGFR" þ axKD þ ½pEGFR" þ ax

þ B0

For a model with Akt activated by AXL and activated by EGFRsummed, with proportional and baseline activation of AXL, Eq. 8was used:

½pAkt" ¼Bmax,1½pEGFR"KD,1 þ ½pEGFR"

þxBmax,2ð½pEGFR" þ bÞKD,2 þ ð½pEGFR" þ bÞ

Total receptor quantificationTotal receptor amounts were measured with a bead-based ELISA (Novagen).For quantification of AXL and MER, established ELISA antibodies andstandards (R&D Systems) were used. The capture antibody was conjugatedto unconjugated beads (Bio-Rad) and used in a multiplexed fashion with theother targets. Linearity of the assay was validated during measurement bydilution series of both the lysates and standards.

Each cell linewas seeded sparsely, and the next daywas starved for 4 hoursand lysed. Receptor measurements were normalized to total protein content toprovide a receptormass fraction (that is, femtogramof receptor permilligramofcell lysate). Thismass fractionwas used in all subsequentmodeling. For recep-tor density calculations, a subconfluent plate of cellswas trypsinized, thenumberof cells was counted and lysed, and total protein was quantified. This providedtheconversion, for each cell line, frommilligramof lysate to number of cells.Combined with the known mass of each receptor, a value could then be con-verted tonumber of receptors per cell. Finally, receptor densitywas calculatedbyusing the surface area of a HeLa cell [1600 µm2, BNID 103718 (62)].

Assay selectionTodeterminewhichmethodsmight be suitable for studying such complexeson the cell surface, we developed a simple statistical model to describe the




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siCtrlsiAXL

AXL is required for EGF-elicited protrusion

in MDA-MB-231, in which we observed the association of AXL withEGFR but not with IGF1R (fig. S6D).

On the basis of these data, we sought a quantitative framework to under-stand the respective amounts of complexing observed between AXL andeachRTKacross different cell lines.According to fundamental stoichiomet-ric considerations, the amount of AXL observed in complex with a partic-ular RTK in a particular cell line should be approximately the product of theRTK abundance in that cell line, with proportionality described by coeffi-cients constituting (i) the cross-linking and protein loading efficiency and(ii) the antibody immunoprecipitation efficiencies and extent of colocaliza-tion.Withmeasurements of RTKabundance and the amount cross-linked toAXL, we determined the remaining parameters (see Materials andMethods) to provide away to account for differences in receptor expressionwhen interpreting cross-linking data (fig. S6D). With this quantitative for-mulation, we could then calculate whether the parameter characterizingAXL/RTK colocalization deviated significantly from 0 for each RTK(Fig. 6D). Significant deviation from 0 indicates colocalization. DespiteIGF1R and INSR being substantively abundant in various cell lines, thecalculated likelihood that they localized with AXL was not significant. Al-though this parameter includes the efficiency of immunoprecipitating

IGF1R or INSR, we verified that these two receptors were detected withsimilar efficiency both by direct ELISAof the same cell lysates and by quan-tification of a recombinant standard.We additionally confirmed cross-linkedimmunoprecipitation betweenAXLandEGFR to the exclusion of IGF1Rbyreciprocal immunoprecipitation in MDA-MB-231 (fig. S6E). Our quantita-tive analysis framework ruled out the possibility that merely low abundanceof IGF1R and INSR was a trivial explanation for the absence of significantcolocalization. We therefore conclude that AXL is colocalized with ErbB,MET, and PDGFR but not with IGF1R or INSR.

The amount of EGFR-AXL complex was much greater in MDA-MB-231 than in other cell lines, likely as a result of the differences in abundanceof EGFR (Fig. 6C).MCF7 cells transfectedwithAXLand treatedwith EGFshowed no synergistic response characteristic of receptor transactivation,consistent with the relatively little EGF-elicited signaling overall (fig.S7A). We therefore considered whether we could predict the importanceof AXL transactivation induced by activation of RTKs other than EGFR.MDA-MB-453 cells have large amounts of HER2 and HER3 in complexwithAXL, so our notionwould predict that AXL signalingmight contributeto a heregulin (HRG)–stimulated response in these cells. We learned bydirect test, using AXL transfection and HRG treatment, that this is in fact

siAXL (test) siCtrl (training)0

1

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ized

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ion

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onse

–1.2

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0.4

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pcJun

pIRS1

pSTAT3

pAkt

Principal component 1

Prin

cipa

l com

pone

nt 2

MB-231 MB-157 MCF7 T47D

***

***

n.s.

n.s.

EGF HGF

BX/Y loadingsA

C DNo inhibitor0.3 µM R428

–400

0

400

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Are

a ch

ange

[um

2 ]

siControlsiAXL

***

n.s.

EGF HGF

–400

0

400

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1200

Are

a ch

ange

[um

2 ]

Fig. 5. AXL signaling is required for EGF-elicited protrusion. (A) Mean load-ings of the reduced partial least-squares regression models. The red pointcorresponds to theprojectionof the phenotype. Error bars indicate the SE forthe family of reducedmodels. (B) Protrusion predictions from reducedpartialleast-squares regressionmodels for wild-type (by cross-validation) and AXLknockdown (by prediction) cells. Error bars indicate the SE of prediction

across the family of reduced models. (C) EGF-elicited protrusion responseof MDA-MB-231 cells upon AXL knockdown (***P < 0.001, Mann-Whitneytest; n= 13 to 25 from three independent experiments). (D) EGF-elicited pro-trusion responses with or without 0.3 mM R428 (***P < 0.001, Mann-Whitneytest; n = 17 to 35 from three independent experiments). MDA-MB-231 andMDA-MB-157 cells express AXL, whereas MCF7 and T47D cells do not.




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in MDA-MB-231, in which we observed the association of AXL withEGFR but not with IGF1R (fig. S6D).

On the basis of these data, we sought a quantitative framework to under-stand the respective amounts of complexing observed between AXL andeachRTKacross different cell lines.According to fundamental stoichiomet-ric considerations, the amount of AXL observed in complex with a partic-ular RTK in a particular cell line should be approximately the product of theRTK abundance in that cell line, with proportionality described by coeffi-cients constituting (i) the cross-linking and protein loading efficiency and(ii) the antibody immunoprecipitation efficiencies and extent of colocaliza-tion.Withmeasurements of RTKabundance and the amount cross-linked toAXL, we determined the remaining parameters (see Materials andMethods) to provide away to account for differences in receptor expressionwhen interpreting cross-linking data (fig. S6D). With this quantitative for-mulation, we could then calculate whether the parameter characterizingAXL/RTK colocalization deviated significantly from 0 for each RTK(Fig. 6D). Significant deviation from 0 indicates colocalization. DespiteIGF1R and INSR being substantively abundant in various cell lines, thecalculated likelihood that they localized with AXL was not significant. Al-though this parameter includes the efficiency of immunoprecipitating

IGF1R or INSR, we verified that these two receptors were detected withsimilar efficiency both by direct ELISAof the same cell lysates and by quan-tification of a recombinant standard.We additionally confirmed cross-linkedimmunoprecipitation betweenAXLandEGFR to the exclusion of IGF1Rbyreciprocal immunoprecipitation in MDA-MB-231 (fig. S6E). Our quantita-tive analysis framework ruled out the possibility that merely low abundanceof IGF1R and INSR was a trivial explanation for the absence of significantcolocalization. We therefore conclude that AXL is colocalized with ErbB,MET, and PDGFR but not with IGF1R or INSR.

The amount of EGFR-AXL complex was much greater in MDA-MB-231 than in other cell lines, likely as a result of the differences in abundanceof EGFR (Fig. 6C).MCF7 cells transfectedwithAXLand treatedwith EGFshowed no synergistic response characteristic of receptor transactivation,consistent with the relatively little EGF-elicited signaling overall (fig.S7A). We therefore considered whether we could predict the importanceof AXL transactivation induced by activation of RTKs other than EGFR.MDA-MB-453 cells have large amounts of HER2 and HER3 in complexwithAXL, so our notionwould predict that AXL signalingmight contributeto a heregulin (HRG)–stimulated response in these cells. We learned bydirect test, using AXL transfection and HRG treatment, that this is in fact

siAXL (test) siCtrl (training)0

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pcJun

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Principal component 1

Prin

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l com

pone

nt 2

MB-231 MB-157 MCF7 T47D

***

***

n.s.

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BX/Y loadingsA

C DNo inhibitor0.3 µM R428

–400

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ange

[um

2 ]

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–400

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ange

[um

2 ]

Fig. 5. AXL signaling is required for EGF-elicited protrusion. (A) Mean load-ings of the reduced partial least-squares regression models. The red pointcorresponds to theprojectionof the phenotype. Error bars indicate the SE forthe family of reducedmodels. (B) Protrusion predictions from reducedpartialleast-squares regressionmodels for wild-type (by cross-validation) and AXLknockdown (by prediction) cells. Error bars indicate the SE of prediction

across the family of reduced models. (C) EGF-elicited protrusion responseof MDA-MB-231 cells upon AXL knockdown (***P < 0.001, Mann-Whitneytest; n= 13 to 25 from three independent experiments). (D) EGF-elicited pro-trusion responses with or without 0.3 mM R428 (***P < 0.001, Mann-Whitneytest; n = 17 to 35 from three independent experiments). MDA-MB-231 andMDA-MB-157 cells express AXL, whereas MCF7 and T47D cells do not.




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How do TAM receptors process extracellular cues?

Environment/Cues Cell Signaling Migration response

Quantitatively understanding 3D cell migration regulation

Conundrum: AXL does not robustly respond to ligand stimulation

0 5 100

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Time (min)

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A172U87 BT549

03570

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pYTotal

pY/Total

Fold

Uns

timul

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ated

Fold

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timul

ated

Fold

Uns

timul

ated

Fold

Uns

timul

ated

Fold

Uns

timul

ated

Gas6 (nM)

0 0.156 0.625 2.5Gas6 (nM)

0 0.156 0.625 2.5Gas6 (nM)

0 0.156 0.625 2.5Gas6 (nM)

0 0.156 0.625 2.5Gas6 (nM)

0 0.156 0.625 2.5Gas6 (nM)

AXLTYRO3MER

0 100

1

4.5

Minutes

AXL

pY

d

Figure S1

3

MDA-MB-231

Attributes of a good sensor

0 1 2 3 4 0 1 2 3 4

Input Output

Large dynamic range


0 1 2 3 4 0 1 2 3 4

Input Output

Rapid response


0 1 2 3 4 0 1 2 3 4

Input Output

Memoryless

Many RTKs can be considered as “ligand concentration sensors”

100 ng/mL EGF/IGF1, 50 ng/mL HGF, hMLE-Twist1 Kim et al, Mol Cell Proteomics, 2011

Gas6 stimulation reveals complex response patterns

0 5 10

1.0

1.5

2.0

2.5

Minutes

AXL

pY

0 5 10

1.0

1.5

2.0

Minutes

BT549

A172

Gas6 (ng/mL) 100 4 20 0.8

AXL

pY

0 5 10

1.0

1.5

2.0

0 5 100.5

1.0

1.5

2.0

2.5

3.0

Minutes

U87

A549AX

L pY

AXL

pY

Minutes

AXL still poorly responsive to Gas6 at longer times

BT549

0.0

0.5

1.0

1.5

2.0

Fold

Uns

timul

ated

0 0.156 0.625 2.5Gas6 (nM)

A172

0

2

4

6

8

Fold

Uns

timul

ated

0 0.156 0.625 2.5Gas6 (nM)

pYTotal

pY/Total

A549

0

1

2

3

4

Fold

Uns

timul

ated

0 0.156 0.625 2.5Gas6 (nM)

U87

0

2

4

6

8

10

Fold

Uns

timul

ated

0 0.156 0.625 2.5Gas6 (nM)4 hrs stimulation

Structure of AXL/Gas6 complex

Gas6

AXL

EGF domains

Gla domain

Fn domains

SHBD domains

Ig domains

Sasaki et al, EMBO J, 2006

A differential equation model of receptor activation

[B](t

) ÷ [B

](t →

∞)

0

0.5

1

Time

Gas6

Ig1Ig2

A0 A1

A2A1,2

D1

D2

Rxn1

Rxn2Rxn4

Rxn3

Rxn6 Rxn5

TAM kinetic model allows mechanistic interpretation

AXL has a limited rapid response to stimulation

0 100

1

5.5

Minutes

AXL

pY

All possible outputs

Ptds Exposure is a Spatially Localized Process

Ruggiero et al, PNAS, 2012

10 µm

Exposed Ptds Membrane

5 µm

Perhaps robust receptor activation only occurs in response to local stimulation?

Cell debris

AXL expressing cell

Local stimulation shifts species abundance

A0 A1 A2 A1,2 D1 D2

10-2 10-1 100 101 102 103

Spatial inhomogeneity parameter-5

0

310

4 #/c

ell d

iffer

ence

vs.

hom

ogen

eous

stim

.

Gas6

Ig1Ig2

A0 A1

A2A1,2

D1

D2

Rxn1

Rxn2Rxn4

Rxn3

Rxn6 Rxn5

Local stimulation strongly promotes local AXL signaling

A0 A1 A2 A1,2 D1 D2

10-2 10-1 100 101 102 103


0

3

104 #

/cell

diff

eren

ce v

s.ho

mog

eneo

us s

tim.

AXL

pY D

ensit

yat

Gas

6 Pe

ak

10-2 10-1 100 101 102 103

Spatial inhomogeneity parameter

1400

0

D [L2/min] 0 0.1 1 10

Local stimulation results in greater overall AXL signaling

AXL

pY D

ensit

yat

Gas

6 Pe

ak

10-2 10-1 100 101 102 103


1400

0

D [L2/min] 0 0.1 1 10


Bulk

AXL

pY3.0

2.0

1.0

0.010-2 10-1 100 101 102 103

D [L2/min] 0 0.1 1 10

A0 A1 A2 A1,2 D1 D2

10-2 10-1 100 101 102 103


0

3

104 #

/cell

diff

eren

ce v

s.ho

mog

eneo

us s

tim.

MB231

0 10 20 300.0

0.5

1.0

1.5

2.0

2.5

Gas6, LipidGas6Gas6, 1/10 LipidLipid

Minutes

AXL

pY

A549

0 10 20 300.0

1.0

2.0

3.0

Minutes

AXL

pY

Gas6, LipidGas6Gas6, 1/10 LipidLipid

Local stimulation results in greater overall AXL signaling

20 ng/mL Gas6; 1X lipid: 100 μg/mL 5:3:2 w/w PE:PS:PC

(Adapted from Wikipedia)

Synergistic activation is specifically due to local stimulation

0 5 100

1

2

3 Gas6BSA

Pixels

pY S

tain

ing

Inte

nsity

Position of beads

17 um

Gas6-coated polystyrene beads

AXL is a sensor of ligand spatial heterogeneity

Low Gas6 High Gas6

Localized Gas6

Conclusion• AXL transactivation leads to selective pathway

amplification

• AXL-expressing cells rely on the receptor for robust migration response

• TAM receptors most robustly sense local ligand stimulation

• A quantitative model of AXL activation can be used to predict the consequence of novel interventions

Future directions

• Use the TAM signaling model to understand transactivation

• Model expansion to the full TAM family

• Rational design of improved TAM-targeted therapies

• Evaluation of TAM-mediated immune targeting

ThanksUROP/Summer Students

Jenny Kay Amalchi Castillo Ashley Powers Courtney Weldon Philip Smith Ceri Riley

CollaboratorsMiles Miller Shannon Hughes Hyung-Do Kim Michael Beste Joel Wagner

ThanksLauffenburger lab

Douglas Lauffenburger Hsinhwa Lee Miles Miller Shannon Hughes Brian Joughin Dave Clarke Hyung-Do Kim Elma Kurtagic Sarah Schrier Michael Beste Doug Jones Joel Wagner Allison Claas Butterstick Hughes

Griffith labShelly Peyton Edgar Sanchez Walker Inman

ThanksGertler lab

Frank Gertler Madeleine Oudin Guillaume Carmona Russel McConnell Michele Balsamo

Committee MembersForest White Alan Wells

FacilitiesEliza Vasile

HMS Orchestra Cluster KI Frontier Program

Thanks

Questions?

quantitative approaches to understanding signaling ...-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 py reduction w/...

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