TAE684

Integrated proximal proteomics reveals IRS2 as a determinant of cell survival in ALK-driven neuroblastoma
Kristina B. Emdal1,2*, Anna-Kathrine Pedersen1*, Dorte B. Bekker-Jensen1, Alicia Lundby1,3, Shana Claeys4, Katleen De Preter4, Frank Speleman4, Chiara Francavilla1,5†‡, Jesper V. Olsen1†‡
Oncogenic anaplastic lymphoma kinase (ALK) is one of the few druggable targets in neuroblastoma, and therapy resistance to ALK-targeting tyrosine kinase inhibitors (TKIs) comprises an inevitable clinical challenge. Therefore, a better understanding of the oncogenic signaling network rewiring driven by ALK is necessary to improve and guide future therapies. Here, we performed quantitative mass spectrometry–based proteomics on neuroblastoma cells treated with one of three clinically relevant ALK TKIs (crizotinib, LDK378, or lorlatinib) or an experimentally used ALK TKI (TAE684) to unravel aberrant ALK signaling pathways. Our integrated proximal proteomics (IPP) strategy included multiple signaling layers, such as the ALK interactome, phosphotyrosine interactome, phosphoproteome, and proteome. We identified the signaling adaptor protein IRS2 (insulin receptor substrate 2) as a major ALK target and an ALK TKI–sensitive signaling node in neuroblastoma cells driven by oncogenic ALK. TKI treatment decreased the recruitment of IRS2 to ALK and reduced the tyrosine phosphorylation of IRS2. Furthermore, siRNA-mediated depletion of ALK or IRS2 decreased the phosphorylation of the survival-promoting kinase Akt and of a downstream target, the transcription factor FoxO3, and reduced the viability of three ALK-driven neuroblastoma cell lines. Collectively, our IPP analysis provides insight into the proximal architecture of oncogenic ALK signaling by revealing IRS2 as an adaptor protein that links ALK to neuroblastoma cell survival through the Akt-FoxO3 signaling axis.

Copyright © 2018 The Authors, some rights reserved; exclusive licensee
American Association for the Advancement of Science. No claim to original U.S. Government Works

INTRODUCTION
Neuroblastoma (NB) is the most common extracranial childhood tumor. Tumors arise from the sympathetic nervous system and ac- count for about 15% of pediatric cancer mortality (1). NB is charac- terized by clinical and biological heterogeneity causing non-uniform responses to treatment. The likelihood of cure varies widely accord- ing to age at diagnosis, disease stage, and tumor biology. Despite advances in treatment, high-risk NB continues to have a poor prog- nosis and a survival rate below 50% (1). Moreover, current thera- peutic strategies in oncology, which rely on targeting of oncogenic drivers, cannot be applied to NB because there are few recurrent somatic mutations associated with high-risk NBs (2). An improved understanding of how key oncogenic drivers support the disease will allow for the identification of sensitive nodes that are targetable, re- sulting in improved patient outcome and survival.
The most malignant tumors harbor amplification of the MYCN oncogene (around 20%), which is used as a biomarker for NB risk stratification (3, 4). Transgenic mouse and zebrafish models show

that MYCN overexpression serves as a tumor-initiating factor (5); however, cooperating genes accelerate NB tumor development and pathogenesis. These genes are clinically critical because MYCN remains a challenging target to directly inhibit (6). Among MYCN cooperating genes, mutated anaplastic lymphoma kinase (ALK) synergistically induces NB tumors in mouse and zebrafish models (7–9) and represents a target for precision therapy of high-risk NBs (2). ALK is a receptor tyrosine kinase (RTK) for which several ligands have been identified, including heparin and members of the FAM150 protein family (10, 11). ALK is the major predisposition gene for familial NB (12, 13), and oncogenic ALK signaling drives a substantial subset of sporadic NBs. Thus, up to 3% and 8 to 10% of these cases are supported by either ALK amplification (ALKAmp) or gain-of-function point mutations, respectively (2, 12–16). These mech- anisms render the ALK receptor constitutively active and, thus, tract- able for therapeutic intervention in NB.
Several highly potent and selective ALK-targeted tyrosine kinase inhibitors (TKIs)—TAE684, crizotinib, LDK378 (ceritinib), and lorlatinib—effectively block ALK-driven cell growth in NB cell lines and tumor models (8, 13, 15, 17–20). Whereas TAE684 did not ad-

1Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Fac- ulty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen, Denmark. 2Department of Biological Engineering and David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA. 3Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen, Denmark. 4Center for Medical Genetics Ghent, Cancer Research Institute Ghent, De Pintelaan 185, 9000 Ghent, Belgium. 5Division of Molecular and Cellular Functions, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK.
*These authors contributed equally to this work. †These authors contributed equally to this work.
‡Corresponding author. Email: [email protected] (C.F.); jesper. [email protected] (J.V.O.)
vance beyond preclinical use, the U.S. Food and Drug Adminis- tration approved crizotinib as a first-in-line drug for ALK-positive non–small cell lung cancer (NSCLC) and designated LDK378 and lorlatinib as breakthrough therapies (21, 22). These compounds are currently undergoing clinical evaluation for ALK-positive malignan- cies including NB (ClinicalTrials.gov: NCT01121588, NCT01742286, and NCT03107988) (23). Although crizotinib, LDK378, and lorlatinib may prove beneficial for high-risk NB, therapy resistance will most likely challenge any real clinical benefit, as has been evident for crizotinib in NSCLC and NB (23–25). Thus, the aim of our study was to increase knowledge about the TKI-sensitive nodes, because this may reveal ways to improve treatment efficacy by identifying

residual signaling proteins or unaffected nodes that could be cotar- geted to ultimately postpone or overcome resistance.
Mass spectrometry (MS)–based quantitative phosphoproteom- ics is a powerful technology to unravel RTK signaling by providing an unbiased and comprehensive way of measuring signaling re- sponses (26, 27). However, phospho-signaling networks are dynamic and highly complex because of the interplay between adaptor pro- teins, kinases, and phosphatases. Thus, oncogenic network rewiring involving proximal and distal nodes can be challenging to decipher from properties of individual signaling layers such as the phosphopro- teome alone. Detailed mechanistic insight can be derived from inte- gration of various proteomics datasets and aid in moving toward a comprehensive understanding of network connectivity (28–30). We and others have specifically shown that integration of proximal sig- naling information through analysis of the RTK interactome and phosphoproteome can identify protein subnetworks driving cell fate decision-making (29, 30). Consequently, understanding the role of proximal signaling adaptors can be beneficial in the quest to identi- fy immediate downstream amplifiers of oncogenic signaling drivers with pronounced impact on cell fates and, thus, previously un- known targetable nodes.
Afew phosphoproteomics studies have successfully addressed constitutive ALK signaling through an inhibitor-based approach using crizotinib in NSCLC and NB cells (30–32). However, for NB specifically, the depth of analysis regarding the number of quantified phosphotyrosine sites has been insufficient to capture many of the important regulatory sites (31). Here, we measured the responses of NB cells with ALKAmp to treatment with TAE684, crizotinib, LDK378, and lorlatinib by quantitative proteomics. Our integrated proximal proteomics (IPP) strategy included multiple signaling layers—the ALK interactome, phosphotyrosine interactome, phosphoproteome, and proteome—to provide a quantitative map of ALK TKI–sensitive signaling nodes in NB cells. Our IPP resource offered insight into the architecture of ALK proximal signaling by revealing a critical role for insulin receptor substrate 2 (IRS2) as the adaptor molecule linking ALK to NB cell survival through the Akt–forkhead box pro- tein O3 (FoxO3) signaling axis.

RESULTS
IPP refines aberrant ALK signaling networks in NB cells
To study the aberrant ALK signaling network in NB cells, we used the NB1 cell line that harbors amplification of full-length ALK and thus has high endogenous expression and activation of ALK (33). To modulate constitutive ALK activity, we treated NB1 cells for 48 hours with different concentrations of three ALK-targeting TKIs (TAE684, LDK378, and crizotinib), measured cell viability, and generated dose-response curves (Fig. 1A). Each of the three TKIs reduced NB1 cell viability in a dose-dependent manner, confirming ALK as an oncogenic driver of cell growth in this model. Thus, we considered the NB1 cell line to be a therapeutically relevant model for our subsequent proteomics analyses. We first determined the half-maximal inhibitory concentration (IC50) for each inhibitor and used concentrations within the 95% confidence interval (CI): 100 nM for TAE684, 250 nM for LDK378, and 500 nM for crizotinib (Fig. 1A). Then, we ensured that ALK signaling was effectively inhibited at these concentrations by performing time course analyses (Fig. 1,
Band C, and fig. S1, A and B). Immunoprecipitation of ALK re- vealed that all inhibitors markedly reduced the levels of phosphoryl-

ated ALK (Tyr1604) and abrogated the interaction with the ALK adaptor protein Shc (34), as soon as 15 min after treatment (Fig. 1B and fig. S1A). Furthermore, two ALK downstream targets, extracel- lular signal–regulated kinase (ERK) and Akt (also known as protein kinase B), displayed a time-dependent reduction in phosphoryla- tion as an indicator of their inhibition (Fig. 1C and fig. S1B). Because of the detection of residual phosphorylated ALK in LDK378- and crizotinib-treated cells up to 30 min after treatment (Fig. 1C and fig. S1B), we chose the 30-min time point for all subsequent analyses and used the inhibitors at their respective IC50 concentrations to ensure robust inhibition of ALK signaling.
To investigate proximal ALK signaling in NB cells, we performed a large-scale MS-based quantitative proteomics analysis of four sig- naling layers: interactome, phosphotyrosine interactome, phosphopro- teome, and proteome (Fig. 1D). To enable quantitative comparisons, we used a combination of stable isotope labeling by amino acids in cell culture (SILAC) (35) (for the interactome and phosphoproteome) and label-free approaches (for the phosphotyrosine interactome and proteome) (fig. S1C) combined with high-resolution liquid chro- matography–tandem MS (LC-MS/MS). We reasoned that combin- ing the quantitative proteomics analysis for three ALK-targeting TKIs (TAE684, LDK378, and crizotinib) and analyzing the overlap- ping effects on ALK signaling would help to control for off-targets and serve as a better readout for ALK signaling inhibition compared to the previously used single-treatment strategy (30, 31). Moreover, we performed three triple-SILAC experiments, allowing us to mea- sure the effect of each inhibitor in duplicate to obtain more robust insights into inhibitor-specific effects (fig. S1C). To focus on ALK proximal signaling, we performed MS analysis on ALK immuno- precipitates from lysates of inhibitor-treated NB1 cells to analyze the interactome. We identified and quantified 1467 proteins and obtained good reproducibility between the effects of each inhibitor across SILAC experiments (fig. S2, A and B, and data file S1). We identified 51 proteins whose association with ALK was significantly abrogated upon treatment with the three inhibitors (Fig. 1E and data file S1). In addition to known signaling interactors of full-length ALK such as Shc and FRS2 (34, 36), we identified several proteins not previously reported to associate with full-length ALK, including the tyrosine-protein phosphatase nonreceptor type 11 (PTPN11) and IRS2 (data file S1). To confirm the interaction between ALK and the identified interactors, we additionally performed an ALK phosphotyrosine interactome analysis (fig. S1C). Because the phos- phorylated tyrosine residues on ALK serve as docking sites for the immediate downstream mediators of ALK signaling, we generated phosphotyrosine-containing peptides derived from ALK and their corresponding nonphosphorylated peptides, incubated them with lysate from NB1 cells treated with DMSO or LDK378, and analyzed enriched proteins pulled down by these peptides by LC-MS/MS (fig. S1C). We reasoned that including lysates from ALK inhibitor– treated cells would allow us to identify direct binders of ALK phos- photyrosines, assuming that secondary indirect binders would depend on ALK-driven phosphorylation for interaction. From this peptide pull-down analysis, we identified 20 proteins with phos- photyrosine binding (PTB), phosphotyrosine interaction (PI), or Src-homology 2 (SH2) domains. The presence of these domains suggested that these proteins directly bound to phosphorylated ALK tyrosine-containing peptides (Fig. 1E and data file S2).
To analyze the phosphoproteome, pooled lysates from each triple- SILAC experiment were enriched for phosphorylated peptides by

A

100

Crizotinib LDK378

B

LDK378

C

DMSO

h
6
min
5 10

LDK378 250 nM

2

kDa

80

60

40
TAE684

pALK (Tyr1604)

10%

input
Neg.
h250 nM 1
ControlDMSO15 min30 min60 min ALK IP kDa
220
140
pALK (Tyr1604)

ALK
220
140

220
140

20

0
Conc. used 95% CI ALK
Crizotinib 0.50 M 0.36–1.05 M
LDK378 0.25 M 0.22–0.36 M
TAE684 0.10 M 0.07–0.20 M Shc
0.01 0.1 1 10
Inhibitor concentration ( M)
220
140
66
52
46
Plasma membrane full length pAkt
Intracellular pool ALK (Ser473) Truncated ALK (extracellular
cleavage of 220 kDa)
Akt
p66 Shc
p52 Shc pERK1/2 p46 Shc
(Thr202/Tyr204)
ERK1/2
60

60
44
42
44
42

GAPDH 36
D E

Constitutively active ALK

ALK interactome
51 drug-sensitive interactors
Phosphorylated sites identified and quantified
16,617 Phosphosites class I

P

P
P

P

HN

Cl
Cl
N
O
N
NH
2
N
Crizotinib
F

Cl
ALK phosphotyrosine interactome

20SH2/PTB/PI domain
13,327

Phosphosites by aa
pTyr: 678; pThr: 1451; pSer: 11,198

P
P

O
HN
N
N
NH
O
S
O
containing interactors

Phosphopeptides

P
P NH
LDK378 Druggable signaling

TAE684

NB1 cell proteome
10,066 proteins
12,550

Phosphoproteins 4637

NB1 cell line

Interactome

Phosphotyrosine interactome

Proteome
F

100

80

13,327 5.1 10.9

1733 1424
9.1 11.5
14.5 9.7
G
Regulated phosphoproteins n = 1849
374

1475

15%

Phosphoproteomics

60 8591

NB1 proteome

84.0 76.4 78.8 n = 10,066
40
Regulated phosphoproteins
20 n = 1849
41%

50

100 150
Time (min)

MaxQuant (FDR < 0.01) 0 All p-sites Up-regulated by inhibitor Down-regulated by inhibitor 1828 2130 ALK interactome LC-MS/MS quantification and identification pTyr pThr pSer n = 51 Fig. 1. Multilayered proteomics approach to study potentially druggable ALK signaling in NB cells. (A) Cell viability of NB1 NB cells in response to treatment (48 hours) with different concentrations of crizotinib, LDK378, and TAE684. Data are presented as means ± SEM of n = 3 to 6 independent experiments. (B and C) Lysates from NB1 cells treated with either dimethyl sulfoxide (DMSO) or LDK378 (250 nM) for different times and immunoprecipitated (IP) for ALK (B) or immunoblotted (B and C) as indicated (n = 3 independent experiments). p, phospho. Arrows indicate protein variants as previously described (84–86). (D) Schematic representation of the proteomics strategy using crizotinib, LDK378, and TAE684 to inhibit constitutive ALK signaling in NB1 cells. Drug-induced changes in the ALK interactome and phosphoproteome were measured after 30 min of inhibitor treatment including mapping of the ALK phosphotyrosine interactome and proteome analysis of untreated NB1 cells. (E) Over- view of results from ALK interactome (yellow; n = 2 independent experiments for each inhibitor; significance B test, P < 0.05), phosphotyrosine interactome [green; n = 4 pull-downs for each pY-peptide (bait) and non–p-peptide (control); t test for significance, P < 0.05, S score > 1], phosphoproteome analysis (blue; n = 2 independent ex- periments for each inhibitor), and proteome (pink; n = 2 independent experiments). aa, amino acids. (F) Number of total regulated phosphorylation sites by amino acid distribution [determined as previously described (28)]. (G) Overlap between the TKI-regulated phosphoproteome and the identified and quantified proteome (top) and adaptors with decreased ALK association (interactome) upon TKI treatment (bottom). See also figs. S1 and S2 and data files S1 to S4.

anti-phosphotyrosine immunoprecipitation followed by two se- quential rounds of TiO2 enrichment and analysis by LC-MS/MS (fig. S1C). We identified and quantified 16,617 phosphorylated sites, 13,327 of which were confidently localized to serine (84.0% of the total or 11,198 sites), threonine (10.9% or 1451 sites), or tyrosine (5.1% or 678 sites) residues in the peptide sequence (class I) within 4637 proteins (Fig. 1, E and F, and data file S3). Ultimately, this study expanded the coverage of identified and quantified phos- photyrosine sites in ALK-driven NB cells compared to a previous study (fig. S2, C and D) (31). Although Chen et al. analyzed three NB cell lines, the total number of phosphotyrosine sites did not ex- ceed 397, and there was limited overlap between cell lines (fig. S2C). However, despite differences in experimental setups regarding ALK TKIs, concentrations, and treatment time, there was a good agree- ment in the regulation of sites in ALK, IRS2, and ERK1/2 [mitogen- activated protein kinase 1/3 (MAPK1/3)] between our analyses and those of Chen et al. (fig. S2D). We deemed phosphorylation sites to be regulated if their ratios were higher or lower than the 2.5% most up- or down-regulated nonphosphorylated peptides, respectively; thus, cutoffs were individually determined for each inhibitor as pre- viously described (29). Among the regulated phosphorylated sites, 1733 showed an increased ratio (13%), whereas 1424 sites had de- creased ratios (10.7%) (Fig. 1F). Phosphorylated tyrosine residues were almost twofold enriched among the up- and down-regulated sites (from 5.1% to 9.1 and 11.5%, respectively) (Fig. 1F), supporting a greater role for tyrosine phosphorylation in the signaling down- stream of aberrant ALK. The total 3157 regulated phosphorylation sites were derived from 1849 proteins, and compared to the mea- sured NB1 cell proteome of 10,066 proteins, showing good repro- ducibility, the regulated phosphoproteome comprised 15% of the measured proteome (Fig. 1G and fig. S2E). These findings under- score the extent to which inhibitor treatment perturbs the cellular machinery of signaling proteins. Moreover, 41% of regulated prox- imal signaling adaptors also displayed regulation at the phosphoryl- ation level, suggesting a tight control of proximal functions in signaling transmission and control (Fig. 1G).
IPP reveals drug-sensitive ALK adaptors
To refine ALK proximal signaling across multiple signaling layers, we integrated the four proteomics datasets and focused on the 30 most regulated ALK adaptors from the interactome analysis (Fig. 2A). We ranked the interactors according to highest fold change in SILAC ratio upon inhibitor treatment, revealing several well- established subcomplexes such as the Shc, Gab1, and IRS complex. Many of the identified interactors displayed regulation involving several phosphorylated residues, including ALK (five Tyr residues, one Ser residue, and one Thr residue) and IRS2 (eight Tyr residues, four Ser residues, and one Thr residue) (Fig. 2A). Because of the large number of regulated phosphotyrosine sites for both ALK and IRS2, we proposed that IRS2 was a central node for ALK signaling transmission. Furthermore, when the protein abundance as mea- sured by iBAQ quantification in the NB1 proteome analysis (37) was taken into account (Fig. 2A), it was evident that the more prominently regulated interactors, or those with the highest fold changes in SILAC ratio upon inhibitor treatment, were of lower abundance in the proteome compared to ALK, whereas proteins ranked with lower SILAC ratios in the interactome generally were more abundant. These findings suggest that functional protein- protein interactions are achieved through a high degree of specific-

ity independently of protein abundance. For each interactor, we mapped the results of the ALK phosphotyrosine interactome to in- clude only significant interactions (Fig. 2A). Here, the direct binding of the majority (8 of 11) of PTB, PI, and SH2 domain–containing interactors to one or more ALK phosphotyrosine residues was con- firmed (for example, IRS2 binding to Tyr1096, Tyr1507, and Tyr1584). Moreover, a robust association (Fig. 2A; marked in red) for Shc1 and Shc3 binding to Tyr1507 in the Shc PTB domain consensus se- quence (NPTpY) (34, 36) was identified, confirming the validity of our approach. Other proteins with robust association included the regulatory subunits of phosphoinositide 3-kinase (PI3K), PIK3R1 and PIK3R2; the protein tyrosine phosphatase PTPN11; and the adaptor protein SH2B2 (Fig. 2A and fig. S3A). The phosphotyro- sine interactome revealed additional PTB, PI, and SH2 domain– containing interactors such as the phospholipase PLC and the transcription factors STAT1 and STAT3 (fig. S3A), but these were not affected by TKI treatment, and thus, we considered them to be TKI-insensitive interactors in our NB cell model. Interactors found in both the interactome and the phosphotyrosine interactome may directly associate with ALK (Fig. 2B). PI3K subunits bind to many sites as shown in other cellular contexts (38). This finding is consist- ent with our hypothesis on the key role played by IRS2 in the con- trol of ALK proximal signaling because IRS2 also contains multiple PI3K binding motifs (38). A functional network based on STRING of TKI-sensitive ALK interactors grouped these into two main clus- ters with functions related to RTK signaling and glycolysis (Fig. 2C). These findings were also confirmed by Gene Ontology (GO) term enrichment analysis for biological process (Fig. 2D). We validated the interaction of PI3K (p85 and p110), PTPN11, Grb2, IRS2, and SH2B1 with full-length ALK by immunoprecipitation analyses and showed that TKI treatment substantially abrogated their association to ALK (Fig. 2E). Moreover, we validated the decrease in phosphoryl- ation of IRS2 tyrosine residues and in the ALK/IRS2 interaction by the ALK TKIs by immunoprecipitating IRS2 (Fig. 2F). Because of the association between IRS2 and PI3K, we confirmed that IRS2 in- teracts with PI3K in NB1 cells through two tyrosine residues of IRS2 (Tyr675 and Tyr978) that reside in a classical PI3K PTB-binding mo- tif (Fig. 2G and fig. S3, B and C) (39).

The druggable ALK phosphoproteome identifies ALK-driven phosphorylation of FoxO3
To explore ALK signaling downstream of the constitutively active receptor, we searched for phosphorylated sites that had either up- or down-regulated ratios by at least two inhibitors and found 697 sites with down-regulated ratios and 634 sites with up-regulated ratios (Fig. 3A). Small-molecule inhibitors directed toward a common primary target can share potential off-targets. Thus, we examined the protein expression levels and the potential phosphorylation reg- ulation of the top 5 off-targets reported by Klaeger et al. (40) for LDK378 and crizotinib including those reported by the suppliers (fig. S4A). Ten of 24 potential off-targets were not detected in our deep proteome analysis of NB1 cells. Moreover, among the 14 de- tected proteins, only two proteins—the RTK epidermal growth factor receptor (EGFR) and focal adhesion kinase 1 (PTK2)—were identi- fied, with a single site each being down-regulated by minimum two ALK inhibitors (data file S3). Knowing that our inhibitor-based ap- proach may represent the combination of on-target and off-target effects, we conclude that the main reported off-targets did not con- found the interpretation of our data.

A ALK interactome Phosphoproteomics Proteome abundance Phosphotyrosine interactome B

Gene

1078
Tyr Tyr

1092
Tyr

1096
Tyr

1131
Tyr
ALK
1278 1282 1283
Tyr Tyr Tyr

1359
Tyr

1507
Tyr

1584
Tyr

1604

Function

Motif
ALK

ALK
SOS2 PIK3CB PIK3CA PIK3R2
SOS1
GRB2 PTPN11 PIK3R1
IRS2 CRABP1
SHC3
SHC1
GAB1
SH2B1
GAB2
FRS3
Receptor tyrosine kinase GEF
PI3K complex PI3K complex PI3K complex GEF
Signaling adaptor Phosphatase PI3K complex Signaling adaptor
Retinoic acid binder Signaling adaptor Signaling adaptor Signaling adaptor Signaling adaptor Signaling adaptor Signaling adaptor
Tyr1078 Tyr1092 Tyr1096 Tyr1131 Tyr1278 Tyr1282 Tyr1283 Tyr1359 Tyr1401 Tyr1507 Tyr1584 Tyr1586 Tyr1604
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P

TPI1
GPI
MSN
EZR
RLTPR
MDH2
PRDX2
PGAM1
SH2B2
Glycolytic enzyme Glycolytic enzyme Membrane linker Membrane linker Not known
TCA cycle enzyme Redox regulator Glycolytic enzyme Signaling adaptor
Phosphoproteomics

0 2 4
Median –log (SILAC ratio)
2

PTB/SH2/PI domain containing interactors

GAREML ALDOA RAD50
FRS2 PRKCSH
Grb2 adaptor Glycolytic enzyme DNA repair Signaling adaptor Calcium ion binder
SH2 domain
TKI sensitive PI3K PTPN11 SH2B2
TKI insensitive PLC STAT1 STAT3

0 1 2 3 4 5 6
Median
log (SILAC ratio)
2
0 2 4 6 8
Number of
down-regulated p sites
22 24 26 28 30 32 34
Median
log (iBAQ intensity)
2
0 2 4 6 8
–Log (P value)
10
S score > 1
PTB domain PI domain n/a
Shc1
Shc3
n/a

C D
Cluster 1: RTK signaling Transmembrane RTK signaling pathway 11/224

Cluster 2: Glycolysis
Insulin receptor signaling pathway
Intracellular signaling cascade Protein kinase cascade
Cellular response to hormone stimulus Glucose metabolic process Activation of MAPK activity
MAPKKK cascade Glycolysis
Cell surface receptor–linked signal transduction
6/37 15/1256 10/370
7/133
7/153
6/82
7/184
5/47 13/1856
0 2 4 6 8 10 12
–Log10(P value FDR corrected)

G 7 Peptide analyzed: PIK3R2

E

InputNeg.

control
kDa

InputNeg.

control
kDa
F

InputNeg.

control
DMSO TAE684CrizotinibLDK378kDa
6
5
4
IRS2 Tyr675: SSRSDDpYMPMSPA
PI3K complex
PIK3R1
PIK3CB PIK3CA

ALK

p110
PI3K
p85
PTPN11
220
140
110
85
72
ALK

IRS2

SH2B1
220
140
185

90
IRS2
pTyr
pALK (Tyr1604)
ALK
185
185
220
220
140
3
2
1
00

2 4 6 8 10

Grb2
25
ALK IP mouse
IRS2 IP
Log2(fold change iBAQ: phosphorylated/nonphosphorylated) Compared to nonphosphorylated control peptides

ALK IP rabbit
Compared to the scaled proteome measurements Proteins containing SH2, PI, SH2, Cbl-PTB,
Cbl-like–PTB, and IRS-type PTB domains

Fig. 2. Proximal signaling proteomics reveals drug-sensitive ALK adaptors. (A) Four integrated heatmaps representing results from the ALK interactome, phosphoty- rosine interactome, phosphoproteome, and proteome analysis. The list includes ALK and the 30 most TKI-sensitive (as assessed by decreased association) ALK adaptor proteins (shown by gene name) and displays the median log2 SILAC ratio (yellow) from the ALK interactome analysis, the number of phosphorylation sites (blue; phosphoproteome) with significantly decreased ratios in response to at least two inhibitors, the relative protein abundance by iBAQ value (pink; proteome), and the significantly associated with phosphotyrosine-specific binding (green; phosphotyrosine interactome; P < 0.05 and in red S score > 1). (B) Overview of site-specific phos- photyrosine interactions. Data are a graphical summary of fig. S3. TKI-sensitive and TKI-insensitive adaptors are highlighted. Only proteins containing SH2, PTB, and PI domains are included. Each square indicates a median SILAC ratio for the indicated ALK tyrosine residue. (C) Functional association network based on STRING and visual- ized by Cytoscape. ALK is gray, and IRS2 has a pink halo. (D) Significantly overrepresented GO terms for biological process among proteins listed in (A). (E and F) Lysates from NB1 cells treated with either DMSO, crizotinib, TAE684, or LDK378 for 30 min and immunoprecipitated for ALK (E) or IRS2 (F) and immunoblotted as indicated (n = 3 independent experiments). (G) Volcano plot showing the phosphotyrosine-specific interactors of the phosphorylated IRS2 Tyr675-containing peptide. SH2, PI, PTB, and Cbl-like PTB domain–containing proteins are indicated by gene name and a star. Log2 ratios of fold change of the median intensities of pull-downs (n = 4 independent experiments) of phosphorylated peptide (bait) versus nonphosphorylated peptide (control) (x axis) are plotted versus -log10 of the P values derived from a t test. Signifi- cant associations are represented above the S curve. See also fig. S3 and data files S1 and S2.

A

B

Phosphorylase kinase MAPKAPK1

TAE684

249
207
106
273

295

183
111
LDK378

n = 697
Akt
Src
PKA p70 ribosomal S6
JAK2
PKC
PAK2 Aurora-A

Crizotinib

Down-regulated p sites by inhibitors: 1424
ALK
Pim1
Src
Proline-directed kinase
Kinase substrate motif enrichment analysis Decreased in response to inhibitors Increased in response to inhibitors

0 2 4 6 8 10 12 14
–Log10(P value)
C

TAE684
387
143
314
LDK378

177
204 110

398

Crizotinib
n = 634

Up-regulated p sites by inhibitors: 1733

D
E

Thr
*
139
Thr
157
Ser
159
Ser
*
245
Ser
*
250
Thr
404

ErbB signaling pathway
Neurotrophin signaling pathway
ETV3

Insulin signaling pathway MAPK signaling pathway mTOR signaling pathway

Ser
*
1420
Ser
1420

Proteoglycans in cancer Regulation of actin cytoskeleton
Axon guidance Prolactin signaling pathway
Regulation of actin cytoskeleton
ZFHX3

FOXO3

Ser7 Ser12 Ser43 Ser
*
253
Ser

284
Ser

294
Ser
*
311
Ser
*
425

HTLV-I infection
Neurotrophin signaling pathway Oocyte meiosis
FoxO signaling pathway
KEGG pathway enrichment analysis Decreased in response to inhibitors Increased in response to inhibitors

ZBTB47

Thr
*
390

0
2
4
6
P value)
8
10
Ser21
Ser
*
131
Ser
327
Ser
*
489
Ser
*
526
Ser
*
531

F ERF

pFoxO3 (Ser253)
FoxO3

GAPDH

DMSOCrizotinibTAE684LDK378
kDa
97

97

36
*Down-regulated by inhibitors

by PKB/AKT1 and MAPKAPK5
by MAPK1

0 2 4
Median –log (SILAC ratio)
2

Fig. 3. Phosphoproteomics identifies ALK-driven phosphorylation of FoxO3. (A) Overlap in number of identified and quantified phosphorylation sites in NB1 cells treated with TAE684, LDK378, and crizotinib. (B) Kinase substrate motif enrichment analysis (Fisher’s exact test) including phosphorylation sites common to at least two of three inhibitors and displaying either decreased (697 sites) or increased (634 sites) ratio in response to inhibitors. (C) Sequence motif analysis by iceLogo of the ±6 amino acid residues flanking the regulated phosphorylation site (left: tyrosine specific; right: serine/threonine) compared to sites (tyrosine, serine, and threonine) that are not regulated. (D) KEGG pathway enrichment analysis (Fisher’s exact test) for proteins with sites displaying a decreased and increased ratio in response to TKI treatment. (E) Overview of phosphorylation regulation of transcription factors ranked according to their five most prominently decreased phosphorylation sites in response to ALK TKIs. Each square corresponds to ALK inhibitor–induced changes in phosphorylation sites as indicated. (F) Lysates from NB1 cells treated with either DMSO, crizotinib, TAE684, or LDK378 for 30 min and immunoblotted as indicated (n = 3 independent experiments). See also fig. S4. GAPDH, glyceraldehyde-3-phosphate dehydrogenase.

Kinase substrate motif enrichment analysis of these commonly regulated sites (by two of three inhibitors) revealed overrepresenta- tion of Src substrates among both the down- and up-regulated pools, whereas kinases such as phosphorylase kinase, MAPKAPK1, and Akt targeted the down-regulated phosphorylation sites (Fig. 3B). Whereas proline-directed kinase substrates were overrepresented among up-regulated sites, a greater diversity of kinase substrates was evident among down-regulated sites. For instance, substrate motifs of protein kinase A (PKA), Akt, and PKC were statistically significantly overrepresented among down-regulated sites. This find- ing was confirmed by sequence motif enrichment analysis, which showed overrepresentation of arginine (R) in the -3 and -5 position relative to the serine or threonine phosphorylation site (Fig. 3C), which is the canonical motif for PKA, Akt, and PKC (39). Moreover, phosphotyrosine sites with ALK kinase substrate motifs were over- represented in the down-regulated pool, supporting the validity of our TKI approach to unravel ALK signaling (Fig. 3B and fig. S4B). Among these, PTPN11, Shc, and IRS2 had the greatest decrease in SILAC ratios, confirming a dual regulation of ALK proximal adap- tors at the interactome and phosphorylation levels (fig. S4B). Last, the sequence motif of down-regulated phosphotyrosine sites revealed the presence of SH2 domain–binding motifs for PTPN11 (SHP2) [pY(I/V)X(I/V)] and PI3K (pYMXM and pYXXM), underscoring their involvement as effectors of ALK phosphotyrosine signaling (Fig. 3C). Consistently, down-regulated phosphotyrosine sites of IRS2 mainly contained the PI3K motifs (fig. S3C).
Analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment confirmed that proteins with down-regulated phosphorylation sites were shared among other canonical RTK sig- naling pathways (Fig. 3D), in agreement with a previous study (31). In contrast, proteins with up-regulated sites were significantly asso- ciated with pathways related to axon guidance, prolactin signaling, regulation of actin cytoskeleton, and signaling related to the FoxO transcription factors (Fig. 3D). The transcriptional activity of FoxO proteins is controlled by posttranslational modifications, including Akt-mediated phosphorylation at a critical inhibitory site (Ser253), which excludes FoxO from the nucleus, resulting in repression of transcriptional activity in NB cells (41). In support of ALK TKI– dependent inhibition of Akt (Fig. 1C and fig. S1B), FoxO3 ranked third among transcription factors with highly down-regulated phosphorylation sites including Ser253, which was also confirmed by Western blot analysis (Fig. 3, E and F). These findings indicate that aberrant ALK sustains downstream phosphorylation of FoxO3 at a critical inhibitory site.
Besides refining ALK signaling based on the shared targeted nodes by TAE684, crizotinib, and LDK378, our setup also allowed the study of effects for each inhibitor individually. All inhibitors affected not only common pathways (such as MAPK signaling) but also unique KEGG pathways (fig. S4C). Unique pathways included axon guidance, phos- phatidylinositol, and chemokine signaling among the down-regulated pool and regulation of actin cytoskeleton and RNA transport for the up-regulated pool (fig. S4C). Comparison of sequence motifs for down-regulated sites showed a similar overrepresentation of the SH2 binding motifs of PKA/PKB/PKC and PTPN11 for each inhibitor (39). In contrast, the sequence motifs for up-regulated sites were less biased toward known motifs, although crizotinib and TAE684 tended to up-regulate motifs with a glutamine (Q) in the +1 position relative to the serine or threonine phosphorylation site, the preferred motif for the DNA damage responsive kinases ATM (ataxia telangiectasia mutated)

and ATR (ataxia telangiectasia and Rad3-related), suggesting increased signaling because of DNA damage (fig. S4D) (39).
ALK-driven NB cells display differential responses to signaling inhibition compared to ALKEML4-ALK NSCLC cells
In NB, the aberrant activation of MAPK/ERK and Akt signaling pathways associates with poor outcomes and refractory disease (42, 43). These canonical RTK pathways also act downstream of oncogenic ALK. In lung cancer cells driven by the ALK fusion pro- tein ALKEML4-ALK, initial treatment with a MAPK inhibitor in com- bination with ALK inhibition has superior efficacy but does not benefit ALK-driven NBs (44, 45). Therefore, we characterized the downstream pathway dependencies in different ALK-driven NB cell lines (NB1: ALKAmp, SH-SY5Y: ALKF1174L, CLBGA: ALKR1275Q including NBL-S cells with wild-type ALK (ALKWT) and the ALK-), driven NSCLC cell line (H3122: ALKEML4-ALK). All cell lines differ- entially responded to treatment with LDK378 and the ALK-targeting inhibitor lorlatinib in terms of cell viability (fig. S5, A and B), de- spite downstream MAPK/ERK and Akt signaling inhibition, which served as surrogate markers for ALK inhibition in SH-SY5Y and CLBGA cells with low levels of phosphorylated ALK (fig. S5C). The ALKWT NBL-S cells were least responsive in terms of both cell via- bility (fig. S5, A and B) and downstream signaling (fig. S5C) with minimal inhibition of MAPK signaling. To examine the effect of downstream signaling inhibition, cells were treated with the MAPK kinase (MEK) inhibitor U0126 and the PI3K inhibitor LY294002. Despite an inhibitory effect on protein signaling at concentrations of 10 and 50 M, the effect on cell viability was minimal at these concentrations (fig. S5, A to C). Moreover, the combination of LDK378 with U0126 or LY294002 resulted in minor additional reductions (5 to 10%) in cell viability compared to LDK378 alone (fig. S5, D and E). The viability of SH-SY5Y cells was statistically significantly reduced upon combination treatment compared to LDK378 alone, whereas NB1, NBL-S, and H3122 cells only showed this effect for some concentrations of LDK378. Whereas the effects of LDK378 treatment were enhanced by additional inhibition of residual MAPK and PI3K/Akt signaling, the tested combination therapies only had modest effects. Therefore, we tested additional inhibitors in combination with LDK378 to target nodes of residual signaling to explain residual cell viability despite ALK inhibition. Given the presence of Src and proline-directed kinase substrate mo- tifs among the phosphorylation sites with increased SILAC ratios in response to ALK inhibition (Fig. 3B), we used dasatinib to target Src and the inhibitor KD025 to target Rho-associated kinase 2 (Rock2). The latter was targeted because of the identification of regulation of a proline-directed site (data file S3) and because Rock is a therapeu- tic target in NB (46). Dose-response cell viability assays in NB1, SH-SY5Y, CLBGA, NBL-S, and H3122 revealed sensitivity to both dasatinib and KD025 with the exception of minimal effect of KD025 in H3122 cells (fig. S6, A and B). However, the observed limited sensitivity to dasatinib was ascribed to measuring viabili- ty after only 48 hours of treatment. The combination of LDK378 and dasatinib showed a combinatorial effect in SH-SY5Y, CLBGA, and NBL-S cells, whereas combinatorial effects were minimal for NB1 and H3122 cells (fig. S6C). For the combination of LDK378 and KD025, SH-SY5Y cells showed an additive effect across a range of concentrations, whereas the other cell lines only showed such a response for one to three of the total of six tested concentrations (fig. S6D).

Constitutively active ALK regulates NB cell survival through the IRS2-PI3K-Akt-FoxO3 axis
Several lines of evidence from our IPP analysis pointed toward dual regulation of IRS2 and a key role for IRS2 as a proximal signaling adaptor that linked amplified full-length ALK to PI3K signaling in NB cells (Fig. 2, A and E to G). Furthermore, the ALK TKI treat- ment markedly reduced the phosphorylation of downstream Akt and FoxO3 (Figs. 1C and 3F and fig. S1B). To further establish a role for IRS2 downstream of ALK, we depleted NB1 cells for ALK using small interfering RNA (siRNA) and performed a quantitative phos- phoproteomics analysis using a tandem mass tag (TMT) 11-plex approach, allowing us to analyze the effects of ALK depletion and two different concentrations of lorlatinib in NB1 cells (fig. S7, A to C). We analyzed lorlatinib at both low and high dose because our IC50 determinations showed higher values (fig. S5A) than previously reported (19, 20), which we ascribe to our relatively shorter treatment time of 48 hours compared to 5 days. We identified and quantified 24,891 phosphorylated sites (pTyr: 430; pSer: 21,157; and pThr: 3304) (data file S5) and samples clustered according to treatments (Fig. 4A). Lorlatinib treatment or ALK depletion inhibited MAPK and Akt signaling, including inhibition of FoxO3 and IRS2 phos- phorylation (Fig. 4, B and C, and fig. S7D). Whereas ALK depletion by siRNA significantly down-regulated phosphorylation of serines and threonines on IRS2 (Fig. 4D and data file S5), lorlatinib treat- ment down-regulated phosphorylation of IRS2 Tyr576 and Tyr823. These IRS2 tyrosine sites were also identified upon ALK depletion (data file S5); however, the lack of statistically significant regulation (depletion compared to control) is most likely a consequence of in- complete ALK depletion or the inherent differences in the dynam- ics of ALK inhibition when comparing a 48-hour siRNA depletion experiment with a 30-min TKI treatment–based experiment. More- over, a minimal effect on the activation loop tyrosines on insulin- like growth factor-1 receptor (IGF-1R) (Tyr1161 and Tyr1165)/insulin receptor (INSR) (Tyr1185 and Tyr1189) phosphorylation seemed to rule out a potential compensatory cross-talk between ALK and IGF-1R as previously shown to exist for NSCLC upon chronic ALK inhi- bition (47). However, ALK inhibitor–treated NB1 cells respond to IGF-1 in terms of increased ERK phosphorylation, suggesting that IGF-1R retains its signaling capacity despite ALK inhibition and rules out its off-target inhibition (fig. S7E). In contrast, a response to insulin was not detected probably because of the relative lower expression levels of this receptor (about 20-fold lower compared to IGF-1R) in the NB1 cells (figs. S4A and S7F).
A search of the Cancer Cell Line Encyclopedia (CCLE) revealed that NB cell lines were in the top three cancer cell lines for high ex- pression of IRS2, ALK, and FOXO3 (fig. S8) (48). These findings prompted us to further examine the contribution of IRS2 to control PI3K-Akt-FoxO3 signaling downstream of ALK in NB1 cells as well as SH-SY5Y (ALKF1174L) and CLBGA (ALKR1275Q). Although the point-mutated cell lines had low ALK abundance, they displayed sensitivity to ALK inhibition in terms of cell viability and down- stream signaling including inhibition of FoxO3 phosphorylation (fig. S5, A to C). Moreover, depletion of IRS2 in these three NB cell lines was efficient and specific and resulted in the concomitant re- duction of phosphorylation of Akt and FoxO3, but minimal effect on phosphorylation of ERK (Fig. 5, A and B, and fig. S9A). Furthermore, we confirmed these findings in ALKWT NBL-S cells, suggesting that IRS2 served as an important link to PI3K-Akt-FoxO3 signaling in NB in general (fig. S9B). Whereas IRS2 has not previously been

shown to associate with aberrant ALK in NB cells, the closely related family member IRS1 interacts with the NPM-ALK fusion in anaplastic large-cell lymphoma cells and, upon overexpression, with full-length ALK in murine 32D murine myeloid cells (49, 50). The preference for IRS2 reported in our study may be cell context dependent and explained by a differential expression pattern. We found IRS2 to be 100- and 40-fold more abundant based on iBAQ values compared to IRS1 in NB1 and SH-SY5Y cells, respectively, which likely explains the apparent preference of ALK for IRS2 (fig. S9, C and D) (51). Because of the link of IRS2 to PI3K-Akt-FoxO3 and because Akt and FoxO3 have important roles in cell survival and apoptosis (52), we examined the effect of IRS2 depletion on cell viability and measured the activity of caspases, which serve as im- portant mediators of apoptosis. For all three ALK-driven cell lines, IRS2 depletion significantly reduced cell viability over a 48-hour time period compared to control cells (Fig. 5C). Furthermore, IRS2- depleted cells showed increased caspase-3/7 activity (Fig. 5D) and increased cleavage of caspase-3 was confirmed in NB1 and SH-SY5Y cells (and not detected in CLBGA and NBL-S), suggesting that IRS2 protects ALK-driven NB cells from apoptosis (Fig. 5E and fig. S9E).

DISCUSSION
In this study, we applied an IPP approach to characterize aberrant ALK signaling in NB cells on a systems-wide scale. Combining a pharmacological approach using three clinically relevant ALK TKIs with quantitative proteomics refined our understanding of ALK proximal and downstream signaling, expanded our knowledge on phosphotyrosine signaling, and led us to identify IRS2 as determi- nant of NB cell survival through the PI3K-Akt-FoxO3 axis (Fig. 6). The data represent a resource for aberrant ALK signaling in NB. They provide insights into the druggable ALK signaling network by revealing TKI-sensitive nodes and expanding our knowledge on how ALK-driven NB growth is supported by a complex network of adaptors to the activated receptor, phosphorylation of downstream proteins, and protein abundance.
We were intrigued to find that several components of the INSR signaling network were shared with ALK (Figs. 2D and 3D). At first, these findings may not seem surprising because ALK belongs to the INSR superfamily and has an intracellular kinase domain homolo- gous to that of INSR (53). However, because INSR and its closely related family member IGF-1R play crucial roles in breast, prostate, and thyroid cancers (54), our understanding of INSR and IGF-1R signaling may provide further insight into ALK signaling. Molecu- lar details about the ALK-IRS2 interaction and the contribution of SH2B proteins remain to be elucidated. SH2B proteins promote insulin signaling by both enhancing INSR catalytic activity and in- hibiting tyrosine dephosphorylation of IRS proteins; thus, it is in- triguing to speculate that aberrant ALK sustains constitutive signaling through a similar mechanism (55, 56). SH2B proteins interact spe- cifically with the phosphorylated tyrosine sites in the activation loop of the INSR and link through IRS proteins to PI3K signaling (57). However, further studies are required to establish the molecu- lar basis of these interactions in the context of aberrant ALK in NB cells.
IRS2 is an adaptor protein that has been well characterized in the context of the regulation of cellular glucose metabolism by INSR and IGF-1R signaling (58). In this study, we described a specific role for IRS2 in ALK-driven survival signaling and attributed this role to

Fig. 4. ALK inhibition by lor- latinib and ALK depletion by siRNA in NB1 cells reduces IRS2, Akt, FoxO3, and ERK phosphorylation. (A) Cluster dendrogram of the TMT 11- plex phosphoproteomics data showing the relation between analyzed samples: 10 nM lorlatinib–treated (lorlatinib low conc.) cells, 10 M lorlatinib– treated (lorlatinib high conc.) cells, and ALK-depleted cells to siRNA control and DMSO- treated cells (see also fig. S7C). Hierarchical clustering was performed in Perseus using quantile-based normalized in- tensities for identified and quantified phosphorylated peptides. (B and C) Volcano plots of -log10 transformed false discovery rate (FDR)–corrected P values versus log2(fold change) of median phosphorylation site intensities measured for NB1 cells upon low-dose lorlatinib (10 nM) (B) and siRNA depletion of ALK (C) as measured by TMT multiplexing analysis and MS. Fold change in (B) represents low-dose lorlatinib–treated NB1 cells (n = 2) compared to DMSO- treated cells (n = 3). Fold change

A

B

6

5.5

5

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

D

IRS2 IRS2 Tyr823 Tyr576
Lorlatinib high conc. (10 M) n = 1953
Lorlatinib low conc. IRS2
558
ALK
(10 nM) Thr344
524 AKT3
n = 1444 Tyr1078 Ser346
Tyr1092 Thr350 Ser474
210 251 Tyr1096 Thr363 620
Tyr1278 Ser365
FOXO3
Thr527 Tyr1283
Ser615 Ser253 90 Tyr1584
2,469 Ser619 Ser413 Tyr1604
siRNA ALK Ser620
n = 3430 Ser1174

MAPK3

MAPK1

IRS2

MAPK1
IRS2
IRS2Loading… IRS2
ALKIRS2
ALK
IRS2
FOXO3
IRS2
IRS2
IRS2 MAPK1
FOXO3
AKT3
AKT3
AKT3 MAPK3 AKT1 FOXO3
IRS2
ALK FOXO3IRS2
ALK
ALK
IRS2

in (C) represents ALK depletion by two different siRNA ALK- targeting sequences as well as their mix (n = 3) compared to control siRNA (siCTRL) cells (n = 3). Statistical analysis was performed for n = 2 to 3 inde- pendent experiments by two- sided t test, and significance was determined on the basis of an FDR of <0.05 and hyperbolic curve threshold of s0 = 0.1 using Perseus. (D) Overlap between significantly down-regulated phosphorylated sites for the conditions comparing 10 nM lorlatinib–treated (lorlatinib low conc.) cells to DMSO-treated cells, 10 M lorlatinib–treated cells to DMSO-treated cells, and ALK-depleted cells to siRNA control cells. See also fig. S7 and data file S5. C –2 –1 0 1 2 3 4 –2 –1.5 –1 –0.5 0 0.5 1 1.5 2 2.5 Log (fold change: siCTRL/siALK) 2 ALKFOXO3 IRS2 IRS2 IRS2 IRS2 IRS2 Loading... MAPK1 IRS2 ALK ALK IRS2 IRS2 ALK ALK IRS2 ALK ALK IRS2MAPK1 ALK MAPK3 IRS2 IRS2 ALK IRS2 ALK IRS2 IRS2 AKT3 ALK IRS2 ALK ALKIRS2 IRS2 IRS2 IRS2 IRS2 FOXO3 IRS2 IRS2 Low dose lorlatinib (10 nM) MAPK3 MAPK1 ALK siRNA depletion A B 1.4 NB1 NB1 SH-SY5Y CLBGA 1.2 siRNA: C #1 #2 Mix C #1 #2 Mix C #1 #2 Mix IRS2 ALK kDa 185 220 1.0 0.8 0.6 0.4 0.2 0.0 ** ** ** * ** P = 0.067 ** ** ** siCTRL siIRS2 #1 siIRS2 #2 siIRS2 mix pFoxO3 (Ser253) FoxO3 140 97 97 2.4 2.2 2.0 1.8 1.6 1.4 1.2 IRS2 pFoxO3/FoxO3 pAkt/Akt pERK/ERK SH-SY5Y siCTRL siIRS2 #1 siIRS2 #2 pAkt (Ser473) Akt 60 60 1.0 0.8 0.6 0.4 0.2 0.0 * ** * * siIRS2 mix pERK1/2 (Thr202/Tyr204) 44 42 2.0 1.8 IRS2 pFoxO3/FoxO3 pAkt/Akt pERK/ERK CLBGA ERK1/2 GAPDH 44 42 36 1.6 1.4 1.2 1.0 0.8 0.6 0.4 ** * ** * * * siCTRL siIRS2 #1 siIRS2 #2 siIRS2 mix C D 5.0 *** 0.2 0.0 4.5 IRS2 pFoxO3/FoxO3 pAkt/Akt pERK/ERK 1.0 ** 4.0 3.5 E NB1 siRNA: C #1 #2 Mix kDa SH-SY5Y siRNA: C #1 #2 Mix kDa 0.8 0.6 *** * 3.0 2.5 2.0 * GAPDH Cleaved caspase-3 36 17 14 GAPDH Cleaved caspase-3 36 17 14 0.4 1.5 * 5 25 ** 0.2 0.0 NB1 SH-SY5Y CLBGA siRNA control siRNA IRS2 mix 1.0 0.5 0.0 NB1 SH-SY5Y CLBGA siRNA control siRNA IRS2 mix 4 3 2 1 0 * NB1 ** 20 15 10 5 0 ** ** SH-SY5Y siCTRL siIRS2 #1 siIRS2 #2 siIRS2 mix Fig. 5. ALK regulates NB cell survival through the IRS2-FoxO3 axis. (A) Lysates from NB1, SH-SY5Y, and CLBGA cells depleted for IRS2 using siRNA and immunoblotted as indicated in (A) and quantified in (B). Blots are representative of n = 3 to 4 independent experiments. (C and D) Cell viability (C) and caspase activity normalized to cell viability (D) upon siRNA-mediated depletion of IRS2. (E) Immunoblots of cleaved caspase-3 and quantification for NB1 and SH-SY5Y cells upon IRS2 depletion. Blots are representative of n = 3 to 4 independent experiments. Data are represented relative to siRNA control (C or siCTRL) for each cell line, and values are presented as means ± SD of n = 3 to 4 (A, B, and E) or n = 4 to 5 (C and D) independent experiments. *P < 0.05, **P < 0.01, and ***P < 0.001 compared with siRNA control (one-sample t test on log-transformed fold changes relative to siRNA control). See also fig. S9. a particular cell context and the greater abundance of IRS2 com- pared to IRS1 in NB cells (fig. S9, C and D). However, despite a high level of homology and shared functions between IRS1 and IRS2, knockout and RNA interference (RNAi) studies show that IRS pro- teins also have distinct biological functions. For example, IRS1 knock- out mice display a generic growth defect, whereas IRS2 knockout mice show defective growth in only a few tissues such as the brain and pancreas (59, 60). Moreover, IRS2 promotes proliferation of neuronal precursors (59). Increasing evidence underscore the need to understand how tumor cell metabolism is regulated because the Warburg effect (61) enables cancer cells to fuel proliferation, sur- vival, and invasion, by undergoing metabolic reprogramming to specifically exploit aerobic glycolysis. In this context, nonredundant roles for IRS1 and IRS2 have emerged because although they can both activate PI3K, only IRS2 promotes aerobic glycolysis in mam- mary tumor cells (62). Moreover, IRS2, but not IRS1, can protect NB cells from apoptosis caused by high glucose levels (63). In our study, we identified in the ALK interactome a network of glycolytic enzymes including phosphoglycerate mutase 1 (PGAM1), fructose- bisphosphate aldolase A (ALDOA), glucose-6-phosphate isomerase (GPI), and triosephosphate isomerase 1 (TPI1) (64). Moreover, we also identified an ALK TKI–regulated phosphotyrosine site on TBC1D4, a Rab guanosine triphosphatase (GTPase)–activating protein that controls GLUT4 trafficking and thus glucose uptake in multiple cell types (65). Together, these findings support a role for IRS2 in specifically regulating glucose metabolism, and it is pos- sible that IRS2 plays a more substantial role at the intersection of ALK-driven cancer progression and metabolism than previously anticipated. Although many of the ALK inhibitors used in this study are in clinical trials for ALK-driven NBs, the parallel preclinical studies demonstrate their efficacy (8, 19, 21). However, long-term clinical Cell membrane ALK Whereas the MAPK and PI3K pathways are canonical to many RTKs, a shift in dependency from one RTK to another P Lipogenesis P P Ca2+ P P P P P P P P p85 p110 PI3K P P P P PLC- P P P P P P P P IRS2 P P P P P ALK interactome P P P P P P P P P P P P would allow a compensatory loop to re- activate the very same cascade and thus underscore plasticity within the signal- ing network rewiring. Related to this is- sue, we showed that ALK-inhibited NB1 cells retained the ability to boost down- stream MAPK signaling in response to IGF-1. Therefore, a strategy cotarget- ing other RTKs (for example, IGF-1R in combination with ALK inhibition) P P P Glycogenesis P PP P Translation initiation Cell growth P P P P Cell survival P P Nuclear membrane P P P P P P P P p38 kinases P P P Regulation of actin cytoskeleton/ migration P P P P P P P P should be tested, in addition, because combined IGF-1R– and ALK-targeted therapy has proven effective for NSCLC (47). Knowing whether cotargeting RTK bypass signaling can avoid or postpone development of therapy resistance may ultimately guide the development of strategies for NB patients with poor out- comes on ALK-targeted therapy. None- theless, given the resource richness of the IPP data provided here, additional avenues for hypothesis generation and exploring combinatorial treatments in more depth may emerge. Moreover, it Apoptosis Transcriptional activation Cell proliferation is important to consider whether more advanced experimental models are re- quired to better represent the underlying Identified in the interactome Identified in the phosphoproteome Dual identification (interactome and phosphoproteome) ALK tyrosine kinase inhibitor sensitive (down-regulation by 2 of 3) Not identified in phosphoproteome Fig. 6. The constitutive ALK signaling network in NB1 cells. Model summarizing main findings from our integrated proteomics approach to unravel oncogenic ALK signaling in NB1 cells linking IRS2 to survival signaling (highlighted in dark orange). ALK interactors belonging to the GO terms “transmembrane RTK signaling pathway” and “insulin recep- tor signaling pathway” (Fig. 2D) are represented (light orange) together with proteins belonging to the KEGG path- ways “ErbB signaling,” “neurotrophin signaling,” and “insulin signaling” from the down-regulated phosphoproteome (Fig. 3D). Transcription factors are represented on the basis of their relation to the KEGG pathways or their phospho- rylation regulation (Fig. 3E; more than two regulated phosphorylation sites). ALK TKI–sensitive nodes are highlighted by red boxes. Arrows indicate activation, T-bars indicate inhibition, and dotted arrows indicate translocation. molecular circuits to identify novel and effective combination therapies to over- come resistance. For instance, therapy- resistant NB cell lines developed to grow despite long-term ALK inhibition could serve this rationale. In NB cells from high-grade tumors, the physiological response to FoxO3 activation (such as FoxO3-induced cell death) is impaired and modulated by wild-type p53, which interferes with FoxO3-promoter recognition (67). Along these lines, we observed limited caspase- use of these compounds is anticipated to drive emergence of therapy resistance. Moreover, intrinsic resistance may challenge and limit clinical success given that only 1 of 11 crizotinib-treated NB pa- tients with ALK mutations showed complete response (23). Our study addresses the immediate adaptive response to ALK inhibition after short-term treatment. Despite our attempts to use the findings of this study to discover new effective combinatorial treatments, the results were disappointing. Inhibiting ALK in combination with MAPK, PI3K, Src, or Rock2 did not add major reductions to cell viability in the ALK-driven NB cell lines that we tested (figs. S5, D and E, and S6, C and D). Whereas these attempts served to target additional downstream signaling, other RTKs may sustain bypass signaling to drive growth and proliferation. Given the modest ef- fects on viability observed when targeting MAPK and PI3K signal- ing alone, we reason that simultaneous inhibition of both signaling arms is required as shown effective in RAS-driven lung cancer (66). mediated responses in NB1 cells upon IRS2-Akt-FoxO3 pathway inhibition compared to the point-mutated SH-SY5Y and CLBGA cells. Although all cell lines are reported to have wild-type p53, ad- ditional factors may influence and modulate the functional survival response in the NB1 cells (68, 69). For instance, p53 is a direct tran- scriptional target of MYCN in NB cells (70), which may ultimately interfere with the actions of activated FoxO3 and, thus, cause partial resistance to FoxO3-induced cell death. Ultimately, a more complex transcription factor rewiring in NB1 cells may explain the observed functional heterogeneity in terms of survival signaling. Moreover, whether ALK point-mutated cells rely more on IRS2 for oncogenic survival signaling downstream of other RTKs warrants further in- vestigation. However, data reported by Chen et al. (31) support reg- ulation by ALK given the reduced phosphorylation of IRS2 Tyr675 upon crizotinib treatment in the point-mutated SH-SY5Y (ALKF1174L) and NB1643 (ALKR1275Q) cell lines. Nevertheless, proximal signaling may be critically different between ALK amplified compared to ALK point-mutated NB cell lines. How and if this differential proximal dependency can be exploited therapeutically to impact clinical effi- cacy and resistance to ALK-targeted therapy remain to be determined. In conclusion, the present study shows how a focused IPP approach can broaden our understanding of proximal mediators of oncogenic ALK signaling. Using this integrative proteomics approach, we identified a key role for IRS2 in PI3K-Akt-FoxO3 survival signaling in NB cells. MATERIALS AND METHODS Reagents TAE684, crizotinib (PF-02341066), LDK378 (ceritinib), lorlatinib, dasatinib, and KD025 were purchased from Selleck Chemicals (Selleckchem). LY294002 and U0126 were purchased from Cell Sig- naling Technology. The stock solutions were prepared in DMSO and stored at -20°C. IGF-1 was purchased from PeproTech, and insulin was purchased from Sigma-Aldrich. The following antibodies were used: rabbit anti–phospho-ALK (Tyr1604), mouse anti-ALK (31F12), rabbit anti-ALK (C26G7), rabbit anti–phospho-Akt (Ser473) and anti-Akt, mouse anti–phospho-ERK1/2 (Thr202/Tyr204), rabbit anti-ERK1/2, rabbit anti–phospho-FoxO3a (Ser253) (D18H8) and anti- FoxO3a (D19A7), rabbit anti-PI3K p110 and anti-PI3K p85, rabbit anti-IRS1, rabbit anti-IRS2 (L1326), mouse anti-phosphotyrosine (P-Tyr-100), rabbit anti–cleaved caspase-3, rabbit anti–phospho– IGF-1R (Tyr1131)/insulin receptor  (Tyr1146), rabbit anti–insulin receptor  (4B8), and rabbit anti–IGF-1R (D23H3) (Cell Signaling Technology); rabbit anti-IRS2 for immunoprecipitation, mouse anti-GAPDH, anti-SHP2 [M163], and anti-Shc (Abcam); mouse anti-Grb2 (BD Biosciences, San Jose, CA); mouse anti-vinculin (Sigma-Aldrich); mouse anti-phosphotyrosine (4G10) (Millipore, Bedford, MA); and goat anti-rabbit horseradish peroxidase (HRP)–conjugated secondary antibody and goat anti-mouse HRP-conjugated secondary antibody (Jackson ImmunoResearch Laboratories). For phosphoproteomics, anti-phosphotyrosine immunoaffinity beads (P-Tyr-100 and P-Tyr-1000) were pur- chased from Cell Signaling Technology. Cell culture and SILAC labeling The human NB cell lines NB1, SH-SY5Y, CLBGA, and NBL-S and the human lung cancer cell line H3122 (provided by S. Mueller, Evotec, Munich, Germany) were cultured in RPMI 1640 including 2 mM l-glutamine (Gibco) supplemented with 10% fetal bovine se- rum (Gibco) and penicillin (100 U/ml)–streptomycin (100 g/ml) (Gibco). Cell lines were maintained at 37°C in a humidified atmo- sphere at 5% CO2. For SILAC-based quantitative MS, NB1 cells were labeled in SILAC RPMI (PAA Laboratories) supplemented with 10% dialyzed fetal bovine serum (Sigma-Aldrich), 2 mM l-glutamine (Gibco), and penicillin (100 U/ml)–streptomycin (100 g/ml) (Gibco) for at least 2 weeks to ensure complete incorporation of amino ac- ids. Three cell populations were obtained: one labeled with natural variants of the amino acids (light label; Lys0, Arg0) (Sigma-Aldrich), the second labeled with medium variants of amino acids {L-[2H4] Lys (+4) and L-[13C6]Arg (+6)} (Lys4, Arg6), and the third labeled with heavy variants of the amino acids {L-[13C6,15N2]Lys (+8) and L-[13C6,15N4]Arg (+10)} (Lys8, Arg10). Medium and heavy variants of amino acids were purchased from Cambridge Isotope Laboratories. Cell viability assay Cells were seeded in 96-well microplates 1 day before the start of treatment. At the onset of the experiment, growth medium contain- ing inhibitors was added to the cells with final concentrations as indicated in the figures. Control cells were treated with similar amount of vehicle as the treated cultures (0.1% DMSO). Cell viabil- ity was measured after 48 hours of treatment using the ATPlite 1step Luminescence Assay System (PerkinElmer Life Sciences) or CellTiter- Glo Luminescent Cell Viability Assay (Promega) according to the manufacturer’s instructions. Luminescence was measured using an EnSpire 2300 Multilabel Reader (PerkinElmer Life Sciences) or a Tecan infinite M200 PRO multimode microplate reader. Three in- dependent biological experiments, each prepared in triplicate or quadruplicate for each concentration, were performed with repro- ducible results. An IC50 value for each inhibitor was determined corresponding to the concentration giving 50% reduction in cell viability. The IC50 values were derived from a five-parameter non- linear curve fitting using GraphPad Prism software, and the experi- mentally used IC50 concentrations were within the 95% CI. The combination effect of LDK378 and dasatinib or KD025 (fig. S6, C and D) was determined using the Bliss independence model to cal- culate interaction scores (I) as previously described (71), and for positive I values, any two-drug combinations were considered synergistic. Cell stimulation, lysis, and Western blotting NB1, SH-SY5Y, CLBGA, NBL-S, and H3122 cells were cultured in complete medium and treated for the indicated time points with inhibitor (TAE684, LDK378, crizotinib, lorlatinib, LY294002, or U0126) as indicated, with their respective IC50 values reported. Control cells were treated with similar amount of DMSO as the treated cultures (0.1% DMSO). IGF-1 and insulin stimulations were performed using 100 ng/ml for 10 min after ALK inhibitor pretreat- ment. Cell lysates, protein concentration determination, and West- ern blotting were carried out as previously described (29). Immunoprecipitation Cell lysates for immunoprecipitation were prepared as described previously (29). Cell lysates (1 to 2 mg) were incubated overnight at 4°C with anti-ALK antibody (Cell Signaling Technology) or anti- IRS2 antibody (Abcam), with subsequent binding to protein G– Sepharose for 1 hour. After five washes with lysis buffer, the bound proteins were eluted by boiling in SDS sample buffer and resolved by SDS–polyacrylamide gel electrophoresis (SDS-PAGE) and ana- lyzed by Western blotting. RNAi and transfection NB1, SH-SY5Y, CLBGA, and NBL-S cells were transfected using Lipofectamine RNAiMAX (Invitrogen) according to the manufac- turer’s instructions, and all the assays were performed 48 to 72 hours after transfection. Double-stranded siRNA oligonucleotides target- ing human IRS2 (sequence #1: 5′-UCAUCCCACCCUGUUUC- CCUGAAUU-3′; sequence #2: 5′-GGUCCAUCUUCAGAGAAG AAAUCAA-3′; sequence #3: 5′-CAUGGGAGAGGGUUAUGCCU- UUGAA-3′; sequence #4: 5′-CCAGCAGAUUGAUAGCU- GUACGUAU-3′) and human ALK (sequence #1: 5′-GACAAGAUC- CUGCAGAAUA-3′; sequence #2: 5′-GGAAGAGUCUGGCAG UUGA-3′) were purchased from GE Dharmacon. Cells were trans- fected either individually or with a mixture of siRNA duplexes in equal amounts to a final concentration of 80 nM. As control, siRNA control duplex (siGENOME Non-Targeting siRNA pool #2) (GE Dharmacon) was used at a final concentration of 80 nM. Silencing of gene expression was monitored by Western blotting of cell lysates with an antibody against IRS2. Caspase-3/7 activity measurements Transfected cells were seeded in 96-well microplates. Activities of caspase-3 and caspase-7 together with cell viability were measured 24and 72 hours after transfection using the Caspase-Glo 3/7 Assay Kit (Promega) and ATPlite 1step Luminescence Assay System (PerkinElmer Life Sciences) according to the manufacturer’s in- structions. Luminescence was measured using an EnSpire 2300 Multilabel Reader (PerkinElmer Life Sciences). Three to four inde- pendent biological experiments, each prepared in triplicate or qua- druplicate for each transfection condition, were performed with reproducible results. Sample preparation for MS-based interactome analysis For each experiment, cells from three SILAC conditions (fig. S1C) were lysed in immunoprecipitation lysis buffer at 4°C. ALK was im- munoprecipitated from 8 mg of lysate in parallel for each SILAC condition, and the immunoprecipitated eluates were combined be- fore SDS-PAGE, Coomassie staining, and in-gel digestion essentially as described in (29). Sample preparation for MS-based phosphotyrosine interactome analysis Peptide pull-downs were performed on NB1 cell lysate [prepared using lysis buffer containing 50 mM tris (pH 8.5), 150 mM NaCl, 10 mM potassium chloride, 0.1% Triton X-100, and 0.5 mM dithio- threitol (DTT) with the addition of 5 mM -glycerophosphate, 5 mM sodium fluoride, 1 mM sodium orthovanadate, and 1 cOmplete in- hibitor cocktail tablet per 10 ml (Roche)], from DMSO- and LDK378- treated cells using biotinylated peptides; phosphotyrosine-containing peptides or the corresponding nonphosphorylated version (fig. S1C). Peptides included the following phosphotyrosines (±6 amino acids flanking the indicated phosphotyrosine residues) from ALK (Tyr1078, Tyr1092, Tyr1096, Tyr1131, Tyr1278, Tyr1283, Tyr1283, Tyr1359, Tyr1507 Tyr1584, and Tyr1604) and from IRS2 (Tyr675, Tyr742, and Tyr978), in-, cluding the nonphosphorylated counter peptide synthesized by In- tavis and coupled to Sepharose-streptavidin beads (GE Healthcare) in a buffer containing 50 mM tris (pH 8.5), 150 mM NaCl, and 0.1% NP-40. Pull-downs (1 mg of cell lysate per pull-down), elution, and on-bead trypsin digestion were carried out in 96-well multiscreen filter plates essentially as described by Eberl et al. (72). Sample preparation for MS-based phosphoproteome analysis For each experiment, cells from three SILAC conditions (fig. S1C) were lysed in immunoprecipitation lysis buffer at 4°C. Proteins were precipitated overnight at -20°C in fourfold excess of ice-cold ace- tone. The acetone-precipitated proteins were solubilized in dena- turation buffer [10 mM Hepes (pH 8.0), 6 M urea, and 2 M thiourea], and 5 mg of protein from each SILAC condition was mixed at a 1:1:1 ratio (total 15 mg per SILAC mix) and reduced with 1 mM DTT followed by alkylation with 5.5 mM chloroacetamide (CAA). Proteins were subjected to Lys-C digestion for 3 hours (Wako) and then, after a fourfold dilution using 50 mM tris (pH 8.0), digested with trypsin (Sigma-Aldrich) overnight at room temperature. The peptide mixtures were desalted and concentrated on C18 Sep Pak Cartridges (Waters) and eluted with 50% acetonitrile (ACN). Eluted peptide mixtures were dried almost to completeness in a SpeedVac and dissolved in Mops buffer [50 mM Mops (pH7.2), 10 mM sodium phosphate, and 50 mM NaCl] and subjected to anti-phosphotyrosine immunoprecipitation using a mixture of anti-phosphotyrosine anti- bodies (P-Tyr-100 and P-Tyr-1000; PTMScan kit, Cell Signaling Technology). Immunoaffinity beads were washed with increasing salt concentration (50 mM NaCl and 150 mM NaCl), and phospho- peptides were eluted with 0.1% trifluoroacetic acid (TFA) before loading onto a C18 StageTip. The unbound fraction from the anti- phosphotyrosine immunoprecipitation was acidified with TFA, de- salted using a C18 Sep Pak Cartridge (Waters), and eluted with 50% ACN and 0.1% TFA. Peptide mixtures were adjusted to 80% ACN and 6% TFA, and phosphopeptides were further enriched by two sequential rounds of titansphere chromatography as previously de- scribed (73). TMT labeling and phosphopeptide enrichment Cells were transfected with ALK-targeting siRNA or control siRNA for 48 hours and treated with two different concentrations of the inhibitor lorlatinib or DMSO (30-min treatment) to enable comparison between conditions (fig. S7). Cells were washed in phosphate-buffered saline (PBS) and lysed for 10 min at 99°C in 6 M guanidine-HCl, 100 mM tris (pH 8.5), 5 mM tris(2-carboxyethyl) phosphine (TCEP), and 10 mM CAA, and whole-cell extracts were sonicated. Cell lysates were digested by Lys-C (Wako) in an enzyme/ protein ratio of 1:100 (w/w) for 1 hour, followed by a dilution with 25mM tris buffer (pH 8.5), to 2 M guanidine-HCl and further digested overnight with trypsin (Sigma-Aldrich; 1:100, w/w). Protease activity was quenched by acidification with TFA, and the resulting peptide mix- ture was concentrated on C18 Sep Pak Cartridges (Waters). Peptides were eluted with 40% ACN followed by 60% ACN. The combined elu- ate was reduced by SpeedVac, and the final peptide concentration was estimated by measuring absorbance at A280 on a NanoDrop (Thermo Fisher Scientific). Peptide (300 g) from each sample was labeled with 1 of 11 different TMT reagents according to the manufacturer’s protocol (Thermo Fisher Scientific). After labeling, the samples were mixed and adjusted to 80% ACN and 6% TFA, and phosphopeptides were further enriched by two sequential rounds of titansphere chromatography as previously described (51). The eluted phospho- peptides were concentrated in a SpeedVac and fractionated with high-pH reversed-phase fractionation as described (74). Sample preparation for MS-based proteome analysis Cells were washed in PBS and lysed for 10 min at 99°C in 6 M guanidine-HCl, 100 mM tris (pH 8.5), 5 mM TCEP, and 10 mM CAA, and whole-cell extracts were sonicated. Cell lysates were digested by Lys-C (Wako) and trypsin (Sigma-Aldrich), and resulting peptides were processed by high-pH fractionation (14 fractions per biological replicate; two replicates in total) as described by Batth et al. (75). Liquid chromatography–tandem mass spectrometry Peptides from all samples were eluted from C18 StageTips using 40% ACN and 0.5% acetic acid. Peptides were analyzed using on- line nanoflow LC-MS/MS on a Q Exactive Plus (interactome and phos- phoproteome), a Q Exactive HF (phosphotyrosine interactome and proteome), or a Q Exactive HF-X (TMT 11-plex phosphoproteomics) mass spectrometer (Thermo Fisher Scientific), which was interfaced with an EASY-nLC system (Proxeon, Odense, Denmark) equipped with a nanoelectrospray ion source essentially as described (76). MS data analysis Raw MS files were analyzed by MaxQuant software version 1.5.3.33 or 1.6.0.17 (TMT 11-plex phosphoproteomics) using the Andromeda search engine (77, 78) by which the precursor MS signal intensities were determined and SILAC triplets were automatically quantified. Proteins were identified by searching the higher-energy collisional dissociation (HCD)–MS/MS peak lists against a target/decoy version of the human UniProt protein database (release 2015_03 or release April 2017 for TMT 11-plex phosphoproteomics) using default settings. TMT correction factors were edited for the TMT labels, and the reporter ion mass accuracy was set to 0.002 Da. Carbamido- methylation of cysteine was specified as fixed modification, and protein N-terminal acetylation, oxidation of methionine, pyro- glutamate formation from glutamine, and phosphorylation of serine, threonine, and tyrosine residues were considered as variable modi- fications. The “maximum peptide mass” was set to 7500 Da, and the “modified peptide minimum score” and “modified maximum pep- tide score” were set to 25. Everything else was set to default values. Bioinformatic analysis For interactome data, ratios of identified and quantified interactors were normalized to the ratio of ALK to account for uneven efficiency during individual immunoprecipitations performed in parallel. Statistically significantly changing druggable ALK interactors were determined by significance B testing (P < 0.05) using Perseus version 1.3.9.10 (data file S1) (77). For the phosphotyrosine interactome data, a minimum of three razor and unique peptides were required across eight conditions, and label-free quantitation intensities (79) were required for one of four phosphotyrosine-peptide conditions (DMSO, n = 2 indepen- dent biological replicates; LDK378, n = 2 independent biological replicates) with no restrictions for the nonphosphopeptide (data file S2). Quantitative interaction proteomics analysis was performed by t test–based comparison of protein intensities between each phosphotyrosine-containing peptide (bait) and the nonphosphoryl- ated counterpart peptide (control) using the web tool pulldown. jensenlab.org. The NB1 cell line proteome was used to correct for protein abundance in the pull-down analysis. Data were analyzed with ratio cutoff of 2.0 (log2), P value cutoff of 3.0 (-log10), and in- finity P value cutoff of 3.125 (-log10). Statistical significance was con- cluded whenever S score > 1.
For the phosphoproteomics data, only peptides with a phos- phorylation site localization probability of at least 0.75 (class I; data file S3) were included in the bioinformatics analyses. To identify phosphorylation sites with statistically significantly regulated ratios, we compared the ratio distributions of all quantified phosphopep- tides with all nonphosphorylated peptides, which we expect not to change and therefore specify our technical variance. To determine the level of regulation, cutoffs for up- and down-regulation were set to allow for an estimated 5% false-positive rate based on the distribu- tion of ratios of identified and quantified nonphosphorylated pep- tides as described in (29). Regulated interactors and phosphorylation sites were considered common and representative of ALK signaling whenever deemed regulated by two of three inhibitors. Proteome data were filtered for common contaminants, and protein quantifi-

cations were reported as a median of two replicates by iBAQ inten- sities (table S4) (37). Analysis of GO term enrichment related to biological process (interactome) and KEGG pathway annotation enrichment (phosphoproteome) was performed using the DAVID bioinformatics resource (80). For the GO analysis, the used gene sets were derived from the pool down-regulated by inhibitors (two of three). For the KEGG analysis, gene sets derived from each pool of regulated phosphorylation sites (up- and down-regulated) for each inhibitor as well as the commonly regulated interactors (two of three inhibitors) were used. Statistical significance was concluded when P < 0.05 by Fisher’s exact test. The protein association network based on ALK interactome data was obtained using the STRING database (version 10) (81). All active interaction sources were in- cluded in the network, and a medium confidence score of more than 0.4 was required. To assess for sequence bias around the regulated phosphorylation sites, sequence motif logo plots (±6 amino acids adjacent to the identified phosphorylated sites) were generated and visualized using the iceLogo software (82) with default parameters (P < 0.01). The analysis was performed independently for the group of phosphorylation sites with up- and down-regulated SILAC ratios and compared with nonregulated site sequences, which was used as a common background. The nonregulated phosphorylation sites were defined as sites with ratios within less than 1 SD away from the mean of the distribution of identified nonphosphorylated pep- tides. Linear sequence motifs for kinase substrates were annotated using Perseus version 1.3.9.10 and analyzed for overrepresentation among the up-regulated phosphopeptides compared to the un- regulated phosphopeptides using Fisher’s exact test. Motifs with P < 0.05 were considered statistically significant. For the TMT 11-plex phosphoproteome, all measured peptide intensities were normalized using the “normalizeQuantiles” func- tion from the Bioconductor R package LIMMA (83). Subsequent data analysis was performed using Perseus version 1.5.1.12. The quantile normalized ratios were further normalized by median sub- traction in the rows, and the data were filtered for contaminants and reverse hits. Only peptides with a phosphorylation site localiza- tion probability of at least 0.75 (class 1; data file S5) were included in the bioinformatic analyses. Volcano plots were generated by plot- ting the –log10 transformed and FDR-adjusted P values (q-value thresh- old of 0.05) derived from a two-sided t test versus log2-transformed fold changes. Statistical significance was determined on the basis of a hyperbolic curve threshold of s0 = 0.1 using Perseus. Statistical analysis Statistical analysis of MS data is described in the previous section. For experiments with effects measured as fold changes relative to a control within each experiment, fold changes were log2 transformed and statistical significance was determined on the basis of a one- sample t test asking if the mean (of minimum three independent experiments) was different from 0 (Fig. 5, B to E, and fig. S7B). Sig- nificance testing for data in fig. S5 (D and E) was performed by a two- sample t test with a Sidak-Bonferroni correction for multiple testing. A statistically significant difference was concluded when P < 0.05. SUPPLEMENTARY MATERIALS www.sciencesignaling.org/cgi/content/full/11/557/eaap9752/DC1 Fig. S1. Effect of crizotinib and TAE684 on NB1 cells and experimental workflow. Fig. S2. Quality control for ALK interactome, phosphoproteome, and proteome data. Fig. S3. Volcano plot representation of ALK phosphotyrosine interactome data. Fig. S4. Phosphoproteomics analysis of pathway regulation and sequence motif enrichment analysis. Fig. S5. Differential responses to LDK378, lorlatinib, U0126, and LY294002 in ALK mutant cell lines. Fig. S6. Effects of dasatinib and KD025 treatment alone and in combination with LDK378. Fig. S7. Analysis of signaling changes upon siRNA depletion of ALK or lorlatinib treatment in NB1 cells. Fig. S8. Gene expression data for ALK, IRS2, and FOXO3 in various cancer cell lines. Fig. S9. Effects of IRS2 depletion in NB cells. Data File S1. Summary of ALK interactome data from NB1 cells treated with crizotinib, TAE684, and LDK378 for 30 min. Data File S2. Summary of ALK and IRS2 phosphotyrosine interactome data. Data File S3. Summary of phosphoproteomics data from NB1 cells treated with crizotinib, TAE684, and LDK378 for 30 min.
Data File S4. Summary of NB1 cell line proteome data.
Data File S5. Summary of phosphoproteomics data by TMT 11-plex analysis upon siRNA depletion of ALK or ALK inhibition with low- and high-dose lorlatinib in NB1 cells.

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Acknowledgments: We thank members of the Proteomics Program at Novo Nordisk Foundation (NNF) Center for Protein Research (CPR) for valuable comments. We especially thank L. J. Jensen for valuable input on biostatistical analysis. Funding: Work at The Novo Nordisk Foundation Center for Protein Research (CPR) was funded in part by a generous donation from the Novo Nordisk Foundation (grant no. NNF14CC0001). The proteomics technology developments applied was part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 686547. We would like to thank the PRO-MS Danish National Mass Spectrometry Platform for Functional Proteomics and the CPR Mass Spectrometry Platform for instrument support and assistance. J.V.O. was supported by the Danish Cancer Society (R90-A5844 KBVU project grant) and the Lundbeck Foundation (R191-2015-703). C.F. was supported by a long-term EMBO fellowship (ALTF 746-2009) and the Wellcome Trust Sir Henry Dale fellowship 8107636/Z/15/Z. The work carried out in this study was supported by the European Union’s 7th Framework Programme (contract no. 259348-ASSET). K.B.E. was supported in part by ASSET, a Novo Nordisk STAR Fellowship, and the Lundbeck Foundation. Author
contributions: K.B.E., A.-K.P., and D.B.B.-J. performed the experiments supervised by C.F. K.B.E. performed all downstream MS data analysis under the supervision of J.V.O. A.L. provided the protocol for peptide pull-down. K.B.E., C.F., and J.V.O. designed the experiments, critically evaluated the results, and wrote the manuscript. S.C. performed initial preliminary
experiments to help guide the proteomics setup under K.D.P. and F.S. supervision. F.S. and K.D.P. edited the manuscript. All authors read and approved the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The raw MS data and associated tables have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifiers PXD006404 and PXD009477. All other data needed to evaluate the conclusions in the paper are present in the paper or the Supplementary Materials.

Submitted 18 September 2017 Accepted 17 September 2018 Published 20 November 2018 10.1126/scisignal.aap9752
Citation: K. B. Emdal, A.-K. Pedersen, D. B. Bekker-Jensen, A. Lundby, S. Claeys, K. De Preter, F. Speleman, C. Francavilla, J. V. Olsen, Integrated proximal proteomics reveals IRS2 as a determinant of cell survival in ALK-driven neuroblastoma. Sci. Signal. 11, eaap9752 (2018).

Integrated proximal proteomics reveals IRS2 as a determinant of cell survival in ALK-driven neuroblastoma
Kristina B. Emdal, Anna-Kathrine Pedersen, Dorte B. Bekker-Jensen, Alicia Lundby, Shana Claeys, Katleen De Preter, Frank Speleman, Chiara Francavilla and Jesper V. Olsen

Sci. Signal. 11 (557), eaap9752. DOI: 10.1126/scisignal.aap9752

Alternatives to ALK in neuroblastoma
Neuroblastoma is a common pediatric solid tumor that is often driven by oncogenic mutations or rearrangements
of the gene encoding the tyrosine kinase receptor ALK. In relapsed neuroblastoma, the frequency of ALK mutation is
increased, highlighting the importance of understanding ALK signaling in this cancer. Two papers identify alternative targets in ALK-driven neuroblastoma cells. By combining various proteomics analyses with protein-protein interaction
networks, Emdal et al. found that IRS2, an adaptor protein in the insulin receptor signaling pathway, linked ALK signaling
to neuroblastoma cell survival. Van den Eynden et al. integrated proteomics and gene expression analyses to identify
ETS family transcription factors and the MAPK phosphatase DUSP4 as targets of ALK signaling. These papers identify new targets that could be exploited to treat ALK-positive neuroblastoma.

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http://stke.sciencemag.org/content/sigtrans/11/557/eaar5680.full http://stke.sciencemag.org/content/sigtrans/11/531/eaaq1087.full http://stke.sciencemag.org/content/sigtrans/9/450/rs12.full http://stm.sciencemag.org/content/scitransmed/10/441/eaao4680.full

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