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Integrated proximal proteomics reveals IRS2 as a determinant of cell survival in ALK-driven neuroblastoma

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Science Signaling  20 Nov 2018:
Vol. 11, Issue 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.


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.


Neuroblastoma (NB) is the most common extracranial childhood tumor. Tumors arise from the sympathetic nervous system and account for about 15% of pediatric cancer mortality (1). NB is characterized by clinical and biological heterogeneity causing non-uniform responses to treatment. The likelihood of cure varies widely according to age at diagnosis, disease stage, and tumor biology. Despite advances in treatment, high-risk NB continues to have a poor prognosis and a survival rate below 50% (1). Moreover, current therapeutic 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, resulting 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 (79) 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, 1216). These mechanisms render the ALK receptor constitutively active and, thus, tractable 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, 1720). Whereas TAE684 did not advance beyond preclinical use, the U.S. Food and Drug Administration 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 malignancies including NB ( 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 (2325). 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 cotargeted to ultimately postpone or overcome resistance.

Mass spectrometry (MS)–based quantitative phosphoproteomics is a powerful technology to unravel RTK signaling by providing an unbiased and comprehensive way of measuring signaling responses (26, 27). However, phospho-signaling networks are dynamic and highly complex because of the interplay between adaptor proteins, 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 phosphoproteome alone. Detailed mechanistic insight can be derived from integration of various proteomics datasets and aid in moving toward a comprehensive understanding of network connectivity (2830). We and others have specifically shown that integration of proximal signaling 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 identify immediate downstream amplifiers of oncogenic signaling drivers with pronounced impact on cell fates and, thus, previously unknown targetable nodes.

A few phosphoproteomics studies have successfully addressed constitutive ALK signaling through an inhibitor-based approach using crizotinib in NSCLC and NB cells (3032). 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 protein O3 (FoxO3) signaling axis.


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, B and C, and fig. S1, A and B). Immunoprecipitation of ALK revealed that all inhibitors markedly reduced the levels of phosphorylated 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, extracellular signal–regulated kinase (ERK) and Akt (also known as protein kinase B), displayed a time-dependent reduction in phosphorylation 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.

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 (8486). (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) Overview 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 experiments 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.

To investigate proximal ALK signaling in NB cells, we performed a large-scale MS-based quantitative proteomics analysis of four signaling layers: interactome, phosphotyrosine interactome, phosphoproteome, 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 chromatography–tandem MS (LC-MS/MS). We reasoned that combining the quantitative proteomics analysis for three ALK-targeting TKIs (TAE684, LDK378, and crizotinib) and analyzing the overlapping 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 measure 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 immunoprecipitates 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 phosphorylated 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 phosphotyrosines, assuming that secondary indirect binders would depend on ALK-driven phosphorylation for interaction. From this peptide pull-down analysis, we identified 20 proteins with phosphotyrosine 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 anti-phosphotyrosine immunoprecipitation followed by two sequential 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 phosphotyrosine 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 exceed 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 agreement 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 previously described (29). Among the regulated phosphorylated sites, 1733 showed an increased ratio (13%), whereas 1424 sites had decreased 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 downstream of aberrant ALK. The total 3157 regulated phosphorylation sites were derived from 1849 proteins, and compared to the measured NB1 cell proteome of 10,066 proteins, showing good reproducibility, the regulated phosphoproteome comprised 15% of the measured proteome (Fig. 1G and fig. S2E). These findings underscore the extent to which inhibitor treatment perturbs the cellular machinery of signaling proteins. Moreover, 41% of regulated proximal signaling adaptors also displayed regulation at the phosphorylation 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 measured 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 specificity independently of protein abundance. For each interactor, we mapped the results of the ALK phosphotyrosine interactome to include 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 confirmed (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 sequence (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 phosphotyrosine interactome revealed additional PTB, PI, and SH2 domain–containing interactors such as the phospholipase PLCγ 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 consistent with our hypothesis on the key role played by IRS2 in the control 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 clusters 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 phosphorylation 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 interacts with PI3K in NB1 cells through two tyrosine residues of IRS2 (Tyr675 and Tyr978) that reside in a classical PI3K PTB-binding motif (Fig. 2G and fig. S3, B and C) (39).

Fig. 2 Proximal signaling proteomics reveals drug-sensitive ALK adaptors.

(A) Four integrated heatmaps representing results from the ALK interactome, phosphotyrosine 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 phosphotyrosine 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 visualized 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. Significant associations are represented above the S curve. See also fig. S3 and data files S1 and S2.

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 regulation 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 detected proteins, only two proteins—the RTK epidermal growth factor receptor (EGFR) and focal adhesion kinase 1 (PTK2)—were identified, with a single site each being down-regulated by minimum two ALK inhibitors (data file S3). Knowing that our inhibitor-based approach may represent the combination of on-target and off-target effects, we conclude that the main reported off-targets did not confound the interpretation of our data.

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 overrepresentation 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 finding 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 overrepresented 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 adaptors 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 signaling pathways (Fig. 3D), in agreement with a previous study (31). In contrast, proteins with up-regulated sites were significantly associated 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, phosphatidylinositol, 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 protein ALKEML4-ALK, initial treatment with a MAPK inhibitor in combination 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 differentially responded to treatment with LDK378 and the ALK-targeting inhibitor lorlatinib in terms of cell viability (fig. S5, A and B), despite 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 viability (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 μM, 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 motifs 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 therapeutic 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 viability 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 treatment 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 phosphoproteomics 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 phosphorylation (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 treatment 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 incomplete ALK depletion or the inherent differences in the dynamics of ALK inhibition when comparing a 48-hour siRNA depletion experiment with a 30-min TKI treatment–based experiment. Moreover, 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 inhibition (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).

Fig. 4 ALK inhibition by lorlatinib 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 μM 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 intensities 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 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 independent 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 μM lorlatinib–treated cells to DMSO-treated cells, and ALK-depleted cells to siRNA control cells. See also fig. S7 and data file S5.

A search of the Cancer Cell Line Encyclopedia (CCLE) revealed that NB cell lines were in the top three cancer cell lines for high expression 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 downstream 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 reduction 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 important 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).

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.


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 determinant 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.

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 receptor signaling pathway” (Fig. 2D) are represented (light orange) together with proteins belonging to the KEGG pathways “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 phosphorylation 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.

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 homologous 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. Molecular 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 inhibiting tyrosine dephosphorylation of IRS proteins; thus, it is intriguing to speculate that aberrant ALK sustains constitutive signaling through a similar mechanism (55, 56). SH2B proteins interact specifically 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 molecular 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 a particular cell context and the greater abundance of IRS2 compared 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 proteins also have distinct biological functions. For example, IRS1 knockout 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, survival, 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 mammary 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 possible 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 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 patients 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 effects on viability observed when targeting MAPK and PI3K signaling alone, we reason that simultaneous inhibition of both signaling arms is required as shown effective in RAS-driven lung cancer (66). Whereas the MAPK and PI3K pathways are canonical to many RTKs, a shift in dependency from one RTK to another would allow a compensatory loop to reactivate the very same cascade and thus underscore plasticity within the signaling network rewiring. Related to this issue, we showed that ALK-inhibited NB1 cells retained the ability to boost downstream MAPK signaling in response to IGF-1. Therefore, a strategy cotargeting other RTKs (for example, IGF-1R in combination with ALK inhibition) 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 outcomes on ALK-targeted therapy. Nonetheless, 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 is important to consider whether more advanced experimental models are required to better represent the underlying molecular circuits to identify novel and effective combination therapies to overcome 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-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, additional factors may influence and modulate the functional survival response in the NB1 cells (68, 69). For instance, p53 is a direct transcriptional 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 investigation. However, data reported by Chen et al. (31) support regulation 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 efficacy 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.



TAE684, crizotinib (PF-02341066), LDK378 (ceritinib), lorlatinib, dasatinib, and KD025 were purchased from Selleck Chemicals (Selleckchem). LY294002 and U0126 were purchased from Cell Signaling 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 p110α 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-1Rβ (Tyr1131)/insulin receptor β (Tyr1146), rabbit anti–insulin receptor β (4B8), and rabbit anti–IGF-1Rβ (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 purchased 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 serum (Gibco) and penicillin (100 U/ml)–streptomycin (100 μg/ml) (Gibco). Cell lines were maintained at 37°C in a humidified atmosphere 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 μg/ml) (Gibco) for at least 2 weeks to ensure complete incorporation of amino acids. 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 containing 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 viability 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 independent biological experiments, each prepared in triplicate or quadruplicate for each concentration, were performed with reproducible 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 nonlinear curve fitting using GraphPad Prism software, and the experimentally 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 calculate interaction scores (ΔI) as previously described (71), and for positive ΔI 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 pretreatment. Cell lysates, protein concentration determination, and Western blotting were carried out as previously described (29).


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 analyzed by Western blotting.

RNAi and transfection

NB1, SH-SY5Y, CLBGA, and NBL-S cells were transfected using Lipofectamine RNAiMAX (Invitrogen) according to the manufacturer’s instructions, and all the assays were performed 48 to 72 hours after transfection. Double-stranded siRNA oligonucleotides targeting human IRS2 (sequence #1: 5′-UCAUCCCACCCUGUUUCCCUGAAUU-3′; sequence #2: 5′-GGUCCAUCUUCAGAGAAGAAAUCAA-3′; sequence #3: 5′-CAUGGGAGAGGGUUAUGCCUUUGAA-3′; sequence #4: 5′-CCAGCAGAUUGAUAGCUGUACGUAU-3′) and human ALK (sequence #1: 5′-GACAAGAUCCUGCAGAAUA-3′; sequence #2: 5′-GGAAGAGUCUGGCAGUUGA-3′) were purchased from GE Dharmacon. Cells were transfected 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 24 and 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 instructions. Luminescence was measured using an EnSpire 2300 Multilabel Reader (PerkinElmer Life Sciences). Three to four independent biological experiments, each prepared in triplicate or quadruplicate 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 immunoprecipitated from 8 mg of lysate in parallel for each SILAC condition, and the immunoprecipitated eluates were combined before 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 dithiothreitol (DTT) with the addition of 5 mM β-glycerophosphate, 5 mM sodium fluoride, 1 mM sodium orthovanadate, and 1 cOmplete inhibitor 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), including the nonphosphorylated counter peptide synthesized by Intavis 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 acetone. The acetone-precipitated proteins were solubilized in denaturation 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 antibodies (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 phosphopeptides 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, desalted 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 described (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 25 mM 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 mixture was concentrated on C18 Sep Pak Cartridges (Waters). Peptides were eluted with 40% ACN followed by 60% ACN. The combined eluate was reduced by SpeedVac, and the final peptide concentration was estimated by measuring absorbance at A280 on a NanoDrop (Thermo Fisher Scientific). Peptide (300 μg) 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 phosphopeptides 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 online nanoflow LC-MS/MS on a Q Exactive Plus (interactome and phosphoproteome), 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 or (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. Carbamidomethylation 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 modifications. The “maximum peptide mass” was set to 7500 Da, and the “modified peptide minimum score” and “modified maximum peptide 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 (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 independent 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 nonphosphorylated counterpart peptide (control) using the web tool 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 infinity P value cutoff of 3.125 (−log10). Statistical significance was concluded whenever S score > 1.

For the phosphoproteomics data, only peptides with a phosphorylation 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 phosphopeptides 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 distribution of ratios of identified and quantified nonphosphorylated peptides 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 quantifications were reported as a median of two replicates by iBAQ intensities (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 included 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 peptides. Linear sequence motifs for kinase substrates were annotated using Perseus version and analyzed for overrepresentation among the up-regulated phosphopeptides compared to the unregulated 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” function from the Bioconductor R package LIMMA (83). Subsequent data analysis was performed using Perseus version The quantile normalized ratios were further normalized by median subtraction in the rows, and the data were filtered for contaminants and reverse hits. Only peptides with a phosphorylation site localization probability of at least 0.75 (class 1; data file S5) were included in the bioinformatic analyses. Volcano plots were generated by plotting the –log10 transformed and FDR-adjusted P values (q-value threshold 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). Significance 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.


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.


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 ( 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.

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