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Coupling an EML4-ALK–centric interactome with RNA interference identifies sensitizers to ALK inhibitors

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Science Signaling  18 Oct 2016:
Vol. 9, Issue 450, pp. rs12
DOI: 10.1126/scisignal.aaf5011

Networking ALK for combination therapies

Some lung cancers have high activity of the kinase ALK as the result of rearrangements between the genes EML4 and ALK. ALK inhibitors are effective in some patients, but resistance to single-agent therapy is common. Using phosphoproteomics and an RNA interference screen, Zhang et al. derived a signaling network mediated by ALK in EML4-ALK–rearranged lung cancer cell lines. From this network, they identified many candidates that could sensitize cells to ALK inhibition. Indeed, knocking down either of two of these two proteins, the scaffolding proteins FRS2 and CC2D1A, sensitized cell lines to the ALK inhibitors crizotinib and alectinib. Thus, a clinical strategy that inhibits FRS2 or CC2D1A might enhance the efficacy of ALK inhibitors in some patients.

Abstract

Patients with lung cancers harboring anaplastic lymphoma kinase (ALK) gene fusions benefit from treatment with ALK inhibitors, but acquired resistance inevitably arises. A better understanding of proximal ALK signaling mechanisms may identify sensitizers to ALK inhibitors that disrupt the balance between prosurvival and proapoptotic effector signals. Using affinity purification coupled with mass spectrometry in an ALK fusion lung cancer cell line (H3122), we generated an ALK signaling network and investigated signaling activity using tyrosine phosphoproteomics. We identified a network of 464 proteins composed of subnetworks with differential response to ALK inhibitors. A small hairpin RNA screen targeting 407 proteins in this network revealed 64 and 9 proteins that when knocked down sensitized cells to crizotinib and alectinib, respectively. Among these, knocking down fibroblast growth factor receptor substrate 2 (FRS2) or coiled-coil and C2 domain–containing protein 1A (CC2D1A), both scaffolding proteins, sensitized multiple ALK fusion cell lines to the ALK inhibitors crizotinib and alectinib. Collectively, our data set provides a resource that enhances our understanding of signaling and drug resistance networks consequent to ALK fusions and identifies potential targets to improve the efficacy of ALK inhibitors in patients.

INTRODUCTION

Mutations or gene rearrangements of key receptor tyrosine kinases (RTKs) confer oncogenic function by disrupting the balance between downstream prosurvival and proapoptotic signaling pathways (1). Direct analysis and modeling support the idea that oncogene inhibition by kinase inhibitors leads to a temporal imbalance in these signals, whereby proapoptotic signals outweigh prosurvival signals (2). For example, prosurvival signals from the kinases extracellular signal–regulated kinase (ERK) and AKT, regulated by the epidermal growth factor receptor (EGFR), degrade more quickly in response to EGFR-targeted tyrosine kinase inhibitors (TKIs) than proapoptotic signals from the mitogen-activated protein kinase (MAPK) p38, leading to cell death (1). Changes in downstream signaling that alter the decay rates of survival signals can alter the aggregate survival and death signaling, resulting in changes in tumor cell survival and, ultimately, tumor growth or regression (2). This model implies that the molecular network circuitry that lies between the oncogene and the distal prosurvival or proapoptotic signals could play an important role in affecting the temporal relationships and the ultimate cell decision in response to kinase inhibitors directed against a driver oncogene. This has potential clinical relevance in developing strategies to thwart residual disease in oncogene-driven cancers and eliminate “persister” cells that give rise to overt disease recurrence (35).

Downstream of RTKs is a complex network of kinases, phosphatases, adaptor proteins, and negative regulators that tune survival signals emanating from RTKs. A protein network centered on EGFR using literature knowledge identified subnetworks of proteins that influenced sensitivity to EGFR-targeting agents and led to rational combinations that enhanced responses to EGFR antagonists (6). Similarly, an experimentally generated protein network using mass spectrometry (MS)–based proteomics centered on mutant EGFR in lung cancer cells was shown to harbor subnetwork proteins that affect cell survival (7). Determining the functional relevance of each component in the balance of prosurvival and prodeath signals, as well as tuning responses to kinase inhibition, is complicated by the complexity of the network architecture and protein expression levels of each component. Simple signaling models along with mathematical modeling have demonstrated that combination effects of hitting two proteins can be non-obvious and is a manifestation of the topology or circuitry of the signaling network (8). The existence of feedback modules can further drive uncertainty as to the role of particular combination therapies. Counterintuitive results can be observed on the basis of which nodes are inhibited and how the nodes are organized in a network. For these reasons, focused experiments that assess removal of each node within a complex system may be necessary to fully understand their effects.

We hypothesized that an RTK-centered protein network could identify subnetwork proteins that affect responses to a kinase inhibitor directed against RTK. We hypothesized that a natural area to hunt for such subnetworks would be in the proximal signaling machinery used by RTK to transduce downstream signaling, by virtue of its ability to shape downstream imbalances between prosurvival and proapoptotic signals. To test this idea, we explored cells harboring a fusion of the gene encoding anaplastic lymphoma kinase (ALK) with that encoding echinoderm microtubule-associated protein-like 4 (EML4). This EML4-ALK rearrangement occurs in about 4 to 5% of lung cancer patients, and these patients derive some initial benefit from treatment with ALK TKIs (911). However, primary resistance and acquired resistance attenuate the curative potential of ALK TKIs and are thus major hurdles in ALK-directed therapy (12, 13). One resistance mechanism is the secondary ALK domain mutations, which in some cases can be overcome by newer-generation ALK TKIs that have activities against secondary mutations (12, 14, 15). A second resistance mechanism class involves bypass signaling mechanisms, such as activation of other RTKs, including EGFR and insulin-like growth factor 1 receptor (IGF-1R) (1618). Preclinical results suggest that cotargeting bypass targets, such as heat shock protein 90 (HSP90) (19), EGFR (18), or IGF-1R (16), can overcome ALK TKI resistance driven through these mechanisms.

To enrich these ALK TKI sensitizer proteins, we used (i) affinity purification combined with MS to identify ALK protein complexes in ALK-rearranged and ALK TKI–sensitive lung cancer cells and (ii) tyrosine phosphoproteomics to further identify both direct and indirect ALK signaling substrates. MS-based quantitative proteomics provides a powerful approach to characterizing the phosphotyrosine (pTyr) proteome, identifying targetable signaling, and decoding the composition of protein complexes. Integration of these two data sets enabled us to create an ALK signaling network or interactome from which to identify with RNA interference (RNAi) analysis key proteins regulating the balance between prosurvival and proapoptotic signals (Fig. 1). In the end, our findings revealed new insights into the EML4-ALK signaling network, the mechanisms of ALK TKIs, and the identities of a number of targets that sensitize ALK-rearranged lung cancer cells to ALK TKI.

Fig. 1 Schematic of the EML4-ALK–centric signaling network strategy.

Phosphoproteomics and tandem affinity precipitation approaches were applied to profile the global Tyr phosphoproteome, characterize the perturbation of the Tyr phosphoproteome by ALK TKIs, and uncover EML4-ALK protein complexes in EML4-ALK–driven non–small cell lung cancer (NSCLC) cells. All identified Tyr-phosphorylated proteins and physically interacting proteins were integrated into an EML4-ALK interactome, which informed a global EML4-ALK signaling network. A knowledge-based pathway analysis combined with TKI effects and an unbiased network-wide synthetic lethal screen were used to identify sensitizers to ALK TKI. GFP, green fluorescent protein.

RESULTS

Identification of proximal EML4-ALK protein complexes

To gain insight into the key protein interactions of EML4-ALK, we set out to dissect complexes of EML4-ALK and three known interacting partners and adaptor proteins [Src homology 2 domain–containing transforming protein 1 (SHC1), growth factor receptor–bound protein 2 (GRB2), and phosphoinositide 3-kinase regulatory subunit 2 (PIK3R2)], which form complexes with ALK fusion proteins in various cellular contexts (2023). A recent study highlighting the importance of ALK-driven MAPK signaling indirectly suggests an important role for adaptor proteins, such as GRB2 and SHC1, that couple RTKs to MAPK signaling (24). We first examined the effect of the loss of function of SHC1 or GRB2 on H3122 and STE1 cell viability using real-time analysis technology (Fig. 2A). Both H3122 and STE1 cells harbor an EML4-ALK fusion and are sensitive to ALK TKI. Our results show that small interfering RNA (siRNA)–mediated knockdown of ALK inhibits proliferation in both H3122 and STE1 cells, as expected. GRB2 knockdown resulted in the strongest suppression on cell viability compared to ALK knockdown in both cells. Knockdown of ALK, and to a lesser extent, GRB2, reduced the downstream phosphorylation of ERK (fig. S1). SHC1 knockdown had more modest effects compared with that of GRB2, but with stronger effects seen in STE1 compared with H3122 cells. These results indicate an important functional role for the EML4-ALK-SHC1-GRB2 complex in these ALK-rearranged lung cancer cells. Because ALK inhibitors decrease AKT phosphorylation, we also examined the effect of the loss of PIK3R2 on H3122 cell viability and found that knockdown of PIK3R2 reduced cell viability (Fig. 2A) (25). Next, we experimentally determined protein complexes of EML4-ALK and created a physical map (or “interactome”) of protein complexes. Currently reported ALK protein-protein interactions come from the fusion of nucleophosmin with ALK, full-length ALK, or EML4, but to our knowledge, no studies have performed experiments in EML4-ALK–rearranged lung cancer cells (20, 21). We genetically tagged four bait proteins (EM4-ALK, SHC1, GRB2, and PIK3R2) with streptavidin (Strep) and hemagglutinin (HA) sequences and then expressed them in H3122 cells using retroviruses. GFP was also tagged as the negative control. Two-step pulldown against two tags was subsequently used to isolate the protein complexes, which were then identified with MS (7). We identified 84, 96, 64, and 62 unique proteins from EML4-ALK, SHC1, GRB2, and PIK3R2 pulldowns, respectively, in H3122 cells. Results from the individual pulldowns were merged into a physical EML4-ALK bait-prey network containing 169 unique proteins (Fig. 2B). In addition to SHC1, GRB2, and PIK3R2, six other proteins [heat shock protein family D (Hsp60) member 1 (HSPD1), signal transducer and activator of transcription 3 (STAT3), Hsp90 alpha family class A member 1 (HSP90AA1), lymphoid-restricted membrane protein (LRMP), tubulin beta class I (TUBB), and ubiquitin A-52 residue ribosomal protein fusion product 1 (UBA52)] previously reported to interact with ALK were identified in our experiment and are common binding partners of ALK (21, 26, 27). We next determined how crizotinib altered the ALK complex. H3122 cells containing Strep-HA–tagged EML4-ALK were treated with and without crizotinib, and the EML4-ALK complexes were isolated with Strep pulldown and profiled with MS. We found that crizotinib dissociated both SHC1 and GRB2 from the ALK complex consistent with their role in mediating ALK signaling (Fig. 2C).

Fig. 2 Physical EML4-ALK protein-protein interactome.

(A) Functional association of selected bait proteins with EML4-ALK signaling. H3122 and STE1 cell viability (relative slope 1 per hour of index curve) was analyzed by real-time cellular analysis after treating with control siRNA or siRNA targeting ALK, SHC1, GRB2, and PIK3R2. The cell index was recorded every 15 min for over 120 hours. Data are means ± SD of three experiments. Knockdown of each protein was confirmed by Western blot. Blots are representative of three experiments. (B) Protein complex network constructed from tandem affinity precipitation for GFP and LC-MS/MS in H3122 cells expressing Strep-HA–tagged GFP–EML4-ALK, SHC1, GRB2, and PIK3R2 and visualized in Cytoscape v2.8.3. Bait proteins are indicated in yellow. Data represent two experiments, each with two biological replicates for each bait protein and each biological replicate run in a technical replicate. To remove nonspecific protein interactions, prey proteins identified from GFP pulldowns were subtracted from pulldown results from each bait. (C) Fold change in the abundance of EML4-ALK complex in response to crizotinib (3 hours) in H3122 cells, assessed with one-step Strep pulldown and profiled by LC-MS/MS. Changes in the abundance of binding partners of EML4-ALK were quantified by comparing peak area of their representative unmodified peptides with or without crizotinib treatment. Data represent two biological replicates for both dimethyl sulfoxide (DMSO) or crizotinib, and each biological replicate contained a technical replicate. A t test was used to compare EML4-ALK prey abundance between untreated and treated samples. (D) Kinase-phosphatase-adaptor subnetwork was extracted from the entire physical interactome. Red lines indicate interactions identified with the four bait proteins.

The physical EML4-ALK interactome is composed of different protein types such as kinases, phosphatases, and adaptors, which are critical elements for kinase signaling. We focused our attention on these core elements and extracted kinases, adaptors, and phosphatases from EML4-ALK physical network and created a smaller subnetwork using PhosphoSitePlus (28). Examination of this smaller subnetwork identified potential insertion points for other signaling proteins to modulate ALK-driven signaling events (Fig. 2D). We found EGFR bound to GRB2, suggesting how EGFR signaling can control this network and drive resistance to ALK TKI by facilitating EGFR-GRB2 signaling complexes. We found discoidin domain receptor tyrosine kinase 2 (DDR2), an RTK whose ligand is collagen, bound to both EML4-ALK and SHC1 baits. This may indicate a role for collagen signaling in affecting ALK signaling networks and ALK TKI sensitivity. Protein kinase C delta (PKC-δ), previously found to play a role in ALK TKI resistance, was found in complex with SHC1 (13). We also found insulin receptor substrate 2 (IRS2) bound to PIK3R2, suggesting an insertion point for IGF signaling that can drive ALK TKI resistance (16). Finally, we identified two phosphatases, protein tyrosine phosphatase nonreceptor type 12 (PTPN12) and inositol polyphosphate phosphatase like 1 (INPPL1), that could be involved in the dephosphorylation of ALK signaling substrates. PTPN12 has been reported to interact with and inhibit other kinases, including MAPK (29), protein tyrosine kinase 2 (PTK2) (29), ERB-B2 receptor tyrosine kinase 2 (ERBB2) (30), EGFR (30), hepatocyte growth factor receptor (MET) (31), activated CDC42 kinase 1 (ACK1) (31), and the Src family nonreceptor tyrosine kinase LCK (31). INPPL1 was reported to interact with MET (32), EGFR (33), and PIK3 (34). In summary, the physical interactome and visualization of smaller subnetworks identified known mediators of ALK TKI sensitivity, suggesting potential complexes that could be associated with ALK TKI resistance.

Characterizing basal EML4-ALK tyrosine phosphoproteome and crizotinib-induced perturbations

One limitation of this group of experiments is that tandem affinity purification (TAP) followed by MS may miss more distal changes in signaling, including ALK substrates and changes induced by ALK inhibition. To address this limitation, we characterized tyrosine phosphorylation in EML4-ALK–rearranged and ALK TKI–sensitive H3122 cells using an MS-based quantitative phosphoproteomic approach. Briefly, H3122 cells were exposed to crizotinib or DMSO for 3 hours, and then tyrosine-phosphorylated peptides were enriched with an antibody specifically recoginizing phosphorylated tyrosine (pY) residues and detected by liquid chromatography–tandem MS (LC-MS/MS) (35). We identified a total of 487 unique pTyr sites assigned to 310 unique phosphoproteins (table S1). Phosphorylation of 68 unique pTyr sites on 48 unique proteins decreased with crizotinib, whereas phosphorylation of 69 unique pTyr sites on 58 unique proteins increased with crizotinib (P < 0.05, |fold change| > 1.5) (Fig. 3A). These results are consistent with our studies in EGFR mutant lung cancer cells, where we also identified increased pTyr peptides following EGFR TKI (35). We identified several pTyr proteins previously linked to ALK signaling pathways in non–EML4-ALK cell contexts. These included receptor-type tyrosine protein phosphatase eta (PTPRJ) and PTPN6 (31, 36), which function as phosphatases and dephosphorylate ALK to decrease its activity. This group also included the p85 subunit of PI3K (37), STAT3 (38), P130CAS (39), fibroblast growth factor receptor substrate 2 (FRS2) (40), bifunctional purine biosynthesis protein PURH (PUR9) (41), and MAPK1 (21), which have been previously identified as ALK substrates. Our analysis found that phosphorylation of STAT3 (pTyr705; −3.76-fold), FRS2 (pTyr349; −7.58-fold), MAPK1 (pTyr187; −2.65-fold), and PTPN6 (pTyr 536; 2.34-fold) is reduced by crizotinib in H3122 cells (table S1), indicating that these proteins are common components of EML4-ALK signaling.

Fig. 3 Phosphoproteomics characterizes the response of H3122 cells to crizotinib.

(A) Fold change in the abundance of tyrosine-phosphorylated peptides in H3122 cells treated with crizotinib (1 μM, 3 hours), relative to those treated with DMSO, was quantified using label-free peak area. Data are representative of a single experiment with biological replicates for DMSO-, 100 nM crizotinib–, and 1000 nM crizotinib–treated samples with each run in a technical replicate. A two-sample t test was performed to compare differential phosphorylation for each tyrosine between the control and treatment group treated at 1000 nM. (B) Pathways in which crizotinib increased or decreased the Tyr phosphorylation of proteins found in (A) in H3122 cells. (C) Kinases and pTyr sites regulated by crizotinib in cells described in (A). (D) Kinase-substrate interactions among pTyr proteins identified in (A) were extracted from PhosphositePlus (www.phosphosite.org/homeAction.action), with the node size representing the number of substrates and the colors representing changes by crizotinib.

We next used pathway analysis tools to annotate signaling pathways regulated by crizotinib. We identified in total 119 signaling pathways overrepresented by the entire pTyr data set [false discovery rate (FDR) < 0.001, n > 5; table S2]. Next, we examined pathways enhanced or diminished by crizotinib treatment. As expected, many signaling pathways annotated to kinase signaling were inhibited by crizotinib, including signaling associated with RTK signaling as well as signaling from key adaptor proteins, such as IRS, GRB2, SHC, FRS2, and GRB2-associated binding protein 1 (GAB1) (Fig. 3B). Crizotinib treatment was associated with increased abundance of phosphopeptides associated with cell junction pathway signaling, including [catenin beta 1 (CTNNB1), cortactin (CTTN), catenin delta 1 (CTNND1)] and other proteins such as PXN, Rho-associated, coiled-coil containing protein kinase 2 (ROCK2), vav guanine nucleotide exchange factor 2 (VAV2), integrin subunit beta 1 (ITGB1), tight junction protein 1 (TJP1), and myeloid/lymphoid or mixed-lineage leukemia; translocated to, 4 (MLLT4).

Next, we examined alterations in the pTyr peptide abundance of kinases affected by crizotinib using our pTyr data set. Of 44 identified tyrosine-phosphorylated kinases, phosphorylation of 14 kinases was affected by crizotinib (Fig. 3C). These effects included reduced EML4-ALK, MAPK1, and MET phosphorylation. Altered changes in MET are expected because crizotinib has activity as an MET kinase inhibitor. To associate changes in kinase phosphorylation with changes in substrate phosphorylation, we extracted evidence on kinase-substrate pairs from PhosphositePlus (42). Kinases affected by crizotinib, including EML4-ALK, MET, cyclin-dependent kinase 1 (CDK1), PTK6, MAPK1, PKC-δ, EGFR, ROCK2, breakpoint cluster region protein (BCR), pyruvate kinase muscle (PKM), the tyrosine protein kinase FER (FPS/FES-related), and the tyrosine protein kinase LCK, have known pTyr protein substrates (Fig. 3D), suggesting that they have important roles in ALK signaling in these cells and hence are potential cotarget candidates for combination therapy. Adaptive resistance to kinase inhibitors, as evidenced by increased tyrosine-specific phosphorylation by TKI, can point to cotargeting strategies to thwart these adaptive changes (35). Our phosphoproteomics results show that phosphorylation of both EGFR and PKC-δ was enhanced by crizotinib. Cotargeting both ALK and EGFR has resulted in enhanced ALK TKI therapeutic efficacy (43), and studies in human samples have identified increased EGFR phosphorylation as a potential mechanism of ALK inhibitor resistance (18). Recently, PKC-δ signaling was shown to confer resistance to ALK TKI alone; cotargeting of PKC and ALK has been shown to synergistically eliminate ALK-rearranged lung cancer cells (13). PKC-δ directly binds and phosphorylates its known substrates, including phosphatases protein phosphatase 1 regulatory inhibitor subunit 14B (PPP1R14B) (44), ADAM metallopeptidase domain 9 (ADAM9) (45), the adaptor protein TJP1 (46), and kinases EGFR (47) and LCK (48), as well as CUB domain–containing protein 1(CDCP1) (49), HSP90 alpha family class B member 1 (HSP90AB1), and cytosolic phospholipase A2 (PLA2G4A) (50)]. We found that crizotinib increased the phosphorylation of both PKC-δ and its substrates, including EGFR, LCK, PTPN6, PPP1R14B, TJP1, CDCP1, PLA2G4A, ADAM9, and HSP90AB1 (fig. S2). These results suggest that ALK inhibition by crizotinib is associated with an adaptive change whereby PKC becomes activated and phosphorylates a set of substrate proteins. This may explain how gain of function of PKC signaling may drive resistance to ALK inhibitors (13).

The ALK interactome identifies kinase inhibitors that sensitize cells to ALK TKI

To fully understand EML4-ALK function and signaling, we constructed an EML4-ALK–centric protein-protein interactome network by integrating all known literature reporting protein-protein interactions between our experimentally identified pTyr proteins and our experimentally derived bait-prey interactions. This network, which contains a total of 464 proteins and 4443 interactions (data file S1), enabled us to track the signaling from EML4-ALK to downstream substrates and biological output involved in EML4-ALK–dependent signaling. We hypothesized that this interactome could provide a strategy to sensitize cells to ALK TKI and overcome resistance in some circumstances by selectively targeting proteins on central nodes/edges of the network (51). To make biological inferences from our EML4-ALK integrative network, we performed signaling pathway analysis on the entire network and identified a spectrum of overrepresented signaling pathways using Reactome Functional Interaction (Reactome FI) (P < 0.001, FDR < 0.001, n ≥ 6) (table S2) (http://apps.cytoscape.org/apps/reactomefiplugin ). Our results confirm known associations with PI3K (52), IGF-1R (16), MAPK (52), and EGFR (53) signaling pathways. Comparison of the entire network with functional protein groups reveals that the “kinome,” defined as all known human kinases, represents major signaling pathways of the entire network (54% overlap) and pTyr protein group (64% overlap) (fig. S3). We found that 77 and 89% of pathways represented by pTyr proteins increased and decreased by crizotinib are covered by the kinome (fig. S4).

To enrich key kinases and signaling pathways and to define their perturbations by crizotinib, we first generated three subnetworks through filtering pTyr peptides corresponding to kinases perturbed by crizotinib from the entire ALK interactome. In general, we wanted to identify protein interaction modules that were increased, decreased, or unaffected by crizotinib. Our goal was to cluster these three subnetworks to identify functional modules and predict cotargeting kinases and signaling pathways, which potentially sensitize cells to ALK TKI (Fig. 4A). We identified four protein modules, all found in our experimental data, that were decreased by crizotinib. We identified eight kinases (EML4-ALK, CDK1, MAPK7, PKM, PTK6, BCR, and MET) that were in clusters inhibited by crizotinib. We also identified a module corresponding to reduce STAT3 activity (38). We identified modules corresponding to histone deacetylase, proteasome, IRS, and fibroblast growth factor receptor (FGFR) as being negatively regulated by crizotinib (Fig. 4A).

Fig. 4 Generating EML4-ALK integrative network to identify ALK TKI sensitizers.

(A) Integrative EML4-ALK protein-protein network (data file S1) was generated by integrating (i) the physical EML4-ALK interactome identified in Fig. 2B and (ii) all pTyr protein results from H3122 cells. Clustering analysis was conducted to enrich functional protein modules on pTyr peptide abundance that was decreased, increased, or unaffected by crizotinib. (B) Cell viability of drug-sensitive cell lines (STE1, H3122, and H3122 EML4-ALKWT) and drug-resistant cell lines (H3122 TR2 and H3122 EML4-ALKL1196M) treated with ALK TKI (crizotinib or alectinib), ROCK inhibitor (ROCKi; Y27632), or combinations thereof (crizotinib or alectinib + Y27632). Data represent three to four data points for each drug concentration in a representative experiment for each cell line and drug combination. (C) Mean median inhibitory concentration (IC50) values from treatments in (B) were calculated with Prism6. No IC50 value was obtainable from H3122 TR2 cells (“—”) because the drug dosage graphs of the single-agent alectinib and its combination with Y27632 did not produce “S” curves.

In contrast, we identified four protein modules wherein their tyrosine phosphorylations were increased by crizotinib and five kinases (tyrosine protein kinase YES, EGFR, LCK, FER, and ROCK2) in complexes (Fig. 4A). On the basis of this observation of the ROCK cluster, we next tested the ability of a ROCK inhibitor, Y27632, to sensitize cells to ALK TKI. We tested parenteral and ALK TKI–sensitive H3122 and STE1 cells with both crizotinib and alectinib, as well as two ALK TKI–resistant lines, one by virtue of a gatekeeper L1196M mutation (H3122 EML4-ALKL1196M) and the other through a gain of EGFR signaling (H3122 TR2) (54). The L1196M mutation in ALK has been clinically identified as a major cause of acquired drug resistance, and cell lines harboring this mutant represent an ideal model in examining the ability of drugs to overcome ALK TKI resistance (12, 55). We generated an H3122 EML4-ALKL1196M cell line by cloning the gatekeeper mutation L1196M of EML4-ALK into parenteral H3122 cells, which results in resistance to crizotinib but sensitivity to alectinib (fig. S5A) (14). Exposure of cells to Y27632 had no effect on cell viability across any of the cell lines and neither did the combination of Y27632 with crizotinib; however, combining Y27632 with alectinib reduced the IC50 in multiple cell lines (H3122 and STE1), including H3122 EML4-ALKL1196M crizotinib-resistant cells (Fig. 4, B and C). We examined ROCK2 protein abundance in three ALK-rearranged cells (H3122, STE1, and H2228) and in H3122-derived resistant lines and found that ROCK2 was expressed in all tested cell lines (fig. S6A). We found no difference in ROCK2 abundance between the sensitive and resistant H3122 pair. Last, we also identified HSP90 grouped into another crizotinib-increased module. Our results confirmed the direct binding of HSP90 and cell division cycle 37 (CDC37) with ALK in complex (55, 56). Functionally, inhibiting HSP90 with AUY 922 or knocking down CDC37 decreased H3122 cell viability (fig. S6, B and C).

ALK interactome-wide synthetic lethal screening identifies ALK TKI sensitizers

Although pathway analysis is powerful in mining biologic insights from well-annotated proteins, many important network proteins are still not assigned into existing signaling pathways, and thus, their functional role in sensitizing to ALK TKI could be missed. To identify new ALK TKI sensitizers, we used a short hairpin–mediated RNA (shRNA) library screen to systemically examine the combination effects of loss of function for every node of the entire EML4-ALK integrative network with ALK inhibitors, including crizotinib and alectinib, on H3122 cell viability. Alectinib is a second-generation ALK TKI that can overcome EML4-ALK gatekeeper mutant resistance and hence was used to test the ability of loss of function of network components to sensitize resistance (14). A total of 2029 shRNA constructs targeting 407 proteins from the interactome were screened (table S3). We required five shRNA for each gene, and thus, 59 proteins were not included in this analysis. The 20, 50, and 80% inhibitory concentration (IC20, IC50, and IC80) doses of crizotinib and alectinib in H3122 cells were determined using Glo assays. Cells containing the shRNA library were treated with these doses, and the synergic effect of loss of function on each component of the EML4-ALK network with ALK TKIs was evaluated to identify ALK TKI sensitizers (Fig. 5A). The synergic effect of loss of function of each component of EML4-ALK network with ALK TKIs on H3122 cell viability was evaluated to identify ALK TKI sensitizers.

Fig. 5 shRNA library screen to identify the ALK TKI sensitizers.

(A) Schematic of the shRNA library screen for ALK TKI sensitizers (left), with a representative Venn diagram of shRNAs that sensitized cells to crizotinib, alectinib, or both (middle), and representative viability assays for one of these targets, CC2D1A (right). Data represent results from a single experiment, where five unique shRNA were targeted to each gene, and each drug/shRNA effect was assayed in five replicates. (B and C) Interactions among the proteins that sensitized cells to crizotinib (B) and alectinib (C) were extracted from the entire EML4-ALK interaction network. The protein types and pTyr change induced by crizotinib are highlighted.

Our results show the synergistic effect between loss of function of 64 proteins with crizotinib including 8 kinases, 12 adaptors, 7 enzymes, and 4 phosphatases (Fig. 5B and table S4). Of these 64 hits, 4 are known from previous literature to be related to ALK signaling [ALK phosphorylates and increase the activities of PTK2 (FAK1) (57), PIK3 catalytic subunit alpha isoform (PIK3CA) (37), bifunctional purine biosynthesis protein PURH (ATIC) (41), and LRMP (26, 58)] (Fig. 5B). In addition, another seven hits were in proteins whose pTyr was reduced by crizotinib in our data [GAB1, IRS1 (16), annexin A2 (ANXA2), eukaryotic translation elongation factor 1 alpha 1 (EEF1A1), sorting nexin family member 27 (SNX27), GRB2-associated and regulator of MAPK protein 1 (FAM59A), and plakophilin 4 (PKP4)] (Fig. 5B). These results suggest that cotargeting these proteins along the ALK axis can enhance sensitivity to crizotinib. The kinase COT, encoded by the MAP3K8 gene, was found in our analysis and also identified to drive resistance to ALK inhibitors in a gain-of-function screen (13). Conversely, we identified nine hits corresponding to proteins whose phosphorylation was increased by crizotinib, namely, coiled-coil and C2 domain containing 1A (CC2D1A), TJP1, heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1), Ser/Thr protein phosphatase 1 catalytic subunit beta isozyme (PPP1CB), paxillin (PXN), CTTN, target of MYB1-like 2 membrane trafficking protein (TOM1L2), transgelin 2 (TAGLN2), and programmed cell death 6 interacting protein (PDCD6IP) (Fig. 5B). Because they are not inhibited by crizotinib, these proteins may represent bypass signaling mechanisms and could also represent adaptive resistance changes induced by loss of negative feedback.

We found that the loss of function (through knockdown) of the nine proteins [protein tyrosine phosphatase receptor type K (PTPRK), amyloid beta (A4) precursor-like protein 2 (APLP2), peroxiredoxin 1 (PRDX1), CC2D1A, FRS2, CDC37, G protein–coupled receptor kinase interacting ArfGAP 1 (GIT1), myosin heavy chain 9 (MYH9), and PTPN11] sensitized H3122 cells to alectinib (Fig. 5C). We focused on FRS2, a kinase scaffold protein typically associated with FGF signaling. Previous studies found that ALK physically binds to and activates FRS2 (40). Our quantitative phosphoproteomics analysis found that crizotinib decreased phosphorylation of FRS2 (table S1). Together with the results from our shRNA screen, these results suggest that FRS2 is directly regulated by ALK in these cells and is necessary for ALK-driven survival. In addition, overexpression of another FRS family member, FRS3, was previously shown to drive resistance to ALK inhibitors (13). To validate the shRNA screen, we used two individual hairpins to deplete FRS2 and assessed the ability of FRS2 depletion to potentiate ALK TKI sensitivity. Our results verified that FRS2 depletion sensitizes H3122 cells to both crizotinib and alectinib (Fig. 6A). Decreased mRNA expression and protein abundance of FRS2 were verified by reverse transcription polymerase chain reaction (RT-PCR) and Western blotting, respectively (Fig. 6B). That depletion of FRS2 by shRNA enhanced the sensitivity of H3122 cells to crizotinib was also confirmed by real-time cell analysis (fig. S7). Combining FRS2 knockdown with alectinib decreased viability in crizotinib-resistant H3122 EML4-ALKL1196M cells (Fig. 6A). Next, we examined both FRS2 tyrosine phosphorylation and total protein abundance in multiple ALK fusion cell lines, including H3122 parent, H3122 TR2, H3122 EML4-ALKwild-type, H3122 EML4-ALKL1196M, H2228, and STE1 cells. We found that FRS2 was expressed in all the examined cell lines, with STE1 cells appearing to have the highest abundance of total and phosphorylated FRS2 (fig. S6A). We further evaluated the change of phosphorylation and total protein abundance of FRS2 in response to crizotinib treatment in a time course manner. We found that crizotinib reduced the abundance of tyrosine-phosphorylated FRS2 over the same time course as it did to the abundance of Tyr-phosphorylated ALK in H3122 cells (fig. S8).Together, our loss-of-function studies along with previously reported gain-of-function studies support that FRS2-mediated signaling is a key adaptor hub for ALK-rearranged lung cancer.

Fig. 6 Validating knockdown of FRS2 and CC2D1A as ALK TKI sensitizers.

(A) Cell viability in H3122 or H3122 EML4-ALKL1196M cell cultures transfected with two shRNAs targeting FRS2 and treated with crizotinib or alectinib. (B) Quantitative RT-PCR (left) and Western blotting (right) were done to assess FRS2 knockdown by shRNA in H3122 cells. (C) Cell viability in H3122 or H3122 EML4-ALKL1196M cell cultures transfected with shRNA targeting CC2D1A and treated with crizotinib or alectinib. (D) Quantitative RT-PCR (left) and Western blotting (right) were done to assess CC2D1A knockdown by shRNA in H3122 cells. RT-PCR data in (B) and (D) are means ± SD from a single experiment, each with triplicate data points. Western blots are representative of two experiments. (E) Immunohistochemistry staining for CC2D1A in a panel of ALK-positive human lung cancer tumor tissues. Shown are representative sections from seven patients.

Loss of CC2D1A sensitizes cells to ALK TKIs

Knockdown of five proteins (CC2D1A, GIT1, PTPRK, CDC37, and MYH9) sensitized H3122 cells to both crizotinib and alectinib. CC2D1A (also known as AKT kinase–interacting protein 1) is a receptor-selective scaffold protein that modulates diverse signaling pathways and determines the selectivity of receptor kinases (59, 60). We found that inhibition of ALK increased the phosphorylation of CC2D1A at pTyr 207 (table S1). The exact biological role of pTyr207 of CC2D1A remains unclear. Our loss-of-function screen indicated that knockdown of CC2D1A sensitized H3122 cells to both crizotinib and alectinib (Fig. 5). To verify these shRNA library screen results, we used two individual hairpins to knock down CC2D1A and assessed the ability of CC2D1A knockdown to alter ALK TKI sensitivity. Our results verified that shRNA-mediated depletion of CC2D1A sensitized H3122 cells to both crizotinib and alectinib (Fig. 6C). Decreased mRNA and protein abundance were verified by RT-PCR and Western blotting (Fig. 6D). Using a third shRNA against CC2D1A, we found that depletion of CC2D1A also sensitized H3122 EML4-ALKL1196M cells to alectinib (Fig. 6C). We next examined the expression of CC2D1A in seven distinct EML4-ALK–rearranged human lung cancer tissues using immunohistochemistry. We assessed both the staining intensity and percentage of tumors staining for CC2D1A and combined these into a singular measurement. Six of the seven EML4-ALK–positive tissues had strong and broad staining—and thus high abundance—of CC2D1A, whereas one tissue had weak abundance of CC2D1A (Fig. 6E). Although limited to a small number of samples, this suggests that CC2D1A might be coexpressed with EML4-ALK. Together, our results indicate that targeting CC2D1A may enhance ALK TKI efficacy in patients.

DISCUSSION

We set out to determine whether an experimentally derived signaling network or interactome centered on ALK in ALK-rearranged lung cancer cells could identify sensitizers to ALK TKI. We hypothesized that this interactome could be involved in modulating the balance of prosurvival and proapoptotic signals in aggregate and could identify targets of potential therapeutic value that may shift the survival/apoptosis signal balance. The interactome would be highly relevant to acquired drug resistance and could enable modeling or other hypotheses based on genetic or epigenetic alterations in interactome components. From a clinical point of view, targeting sensitizers could convert a limited partial response into a deeper partial response or even complete response and thus improve patient outcomes. Upfront targeting could be important in elimination of persister cells and help reduce minimal residual disease that can lead to overt drug resistance and tumor progression. A better understanding of key sensitizers could also be involved in predicting the degree of response to ALK TKI before onset of therapy by identifying which patients are likely to have deep and durable responses from those with weaker and more transient responses requiring initial upfront combination therapy. Future experiments using results generated here are necessary to answer these questions.

We devised a strategy combining tyrosine phosphoproteomics, tandem affinity precipitation, and interactome-wide RNAi screening to physically and functionally characterize an EML4-ALK signaling network or interactome. Our EML4-ALK interactome provides ALK biologists and clinicians with an important information source for further study. One advantage of the EML4-ALK interactome in this study is that it integrated both relatively static physical protein-protein interactions and transient pTyr events based on experimental observations and as such is not limited to previous literature-based interactions. Our data reveal that only a small portion (18.9%) of pathways enriched from the entire network are associated with ALK TKI (crizotinib) sensitivity, indicating that the majority (81.2%) of pathways are ALK TKI–independent and could be potential bypass signaling pathways of ALK TKIs. These pathways are possible cotargeting candidates for developing combined therapy with ALK TKIs. Furthermore, our results give mechanistic insights into EML4-ALK signaling and crizotinib action by linking the perturbation of the pTyr phosphoproteome to smaller-scale subnetworks. We have pinpointed 18 functional targets from clustering analysis of the ALK network, and some of these were experimentally shown to decrease H3122 EML4-ALKL1196M cell viability. For instance, crizotinib increased the phosphorylation of paxillin, which is a known substrate of the kinase SRC. Cotargeting ALK and SRC signaling has been shown to overcome ALK inhibitor resistance in patient-derived lung cancer cells (61). We recognize that a limitation of our study is the single time point used for the phosphoproteomic analysis; thus, dynamics and mechanisms of signaling changes remain unclear. For example, changes in tyrosine phosphorylation of paxillin may represent rebound changes after initial inhibition or truly represent immediate gain of tyrosine phosphorylation after ALK inhibition.

Our interactome-wide unbiased shRNA library synthetic lethal screen revealed novel protein sensitizers that could not be obtained from screening inhibitors and knowledge-based network analysis. For instance, there are only 28% total crizotinib sensitizers whose phosphorylation is regulated by crizotinib, whereas phosphorylation of the 72% crizotinib sensitizers identified from the shRNA library screen were not regulated by crizotinib. One advantage of using the interactome to direct the shRNA functional screen is that it increases the likelihood of identifying sensitizers through a focused, deep analysis of the shRNA barcodes. Notably, we observed that despite aiming for an IC20, IC50, and IC80 dose for each drug, there was significantly more cell death at a given dose in the alectinib-treated shRNA library cells than in the crizotinib-treated cells. Given that the robustness of synthetic lethality screens relies on the representation of each shRNA in multiple cells, a substantial decrease in cell number can bring about a bottleneck, reducing representation of many shRNAs below the threshold needed to achieve significance. This likely accounts for the discrepancy between the number of synergistic shRNAs between crizotinib and alectinib. We note here that this type of technical issue is much more likely to bias the experiment toward false-negative results and that the hits that were found for both drugs represent relevant biology. Thus, the failure for a gene that was a hit in the crizotinib screen to score as synthetic lethal in the alectinib screen should not indicate that this gene was a false positive in the crizotinib screen.

In addition to kinases, many adaptors, phosphatases, enzymes, and other types of proteins were recruited into various macromolecular signaling complexes, which could not be predicted by pathway analysis but could be important in altering the balance of prosurvival and prodeath effector signals downstream of EML4-ALK. FRS2 and CC2D1A are representative examples. Whereas other studies have found that ALK physically interacts with FRS2 in some contexts and increases its activity, our results show that FRS2 knockdown enhances the effects of both crizotinib and alectinib, thus demonstrating its role as a sensitizer to ALK TKI therapy (40). Although adaptor proteins such as FRS2 were traditionally felt to be poor candidates for therapeutic development, newer chemistry approaches, such as through phthalimide conjugation, may challenge this mindset (62). Our study also demonstrates CC2D1A as a sensitizer to ALK TKI therapy. Although CC2D1A is reportedly a scaffold of the PI3K-AKT signaling pathway, we observed no effect of CC2D1A knockdown on either Thr308 or Ser473 phosphorylation of AKT. Further studies are necessary to delineate the exact mechanisms of sensitization because CC2D1A may regulate the EGFR signaling pathway (59) or the nuclear factor κB cascade (63). Our analysis of tumor tissues from patients with EML4-ALK rearrangements shows a distribution of CC2D1A protein abundance, suggesting that this could be a modulator of tumor cell intrinsic sensitivity to ALK TKI. Further studies using larger numbers of ALK-rearranged lung cancers with known responses to ALK TKIs are needed to assess how the expression of FRS2 or CC2D1A may relate to the extent of clinical response. Last, we found that the knockdown of a number of phosphatases sensitized cells to ALK TKI, including PTPN12, PTPRK, PP1CB, and INPPL1. This result is interesting in light of previous studies implicating that phosphatase signaling affected the prosurvival and prodeath signal balance driven by oncogenes (1). Further experiments targeting phosphatases identified in our experiments are needed to see how they may disrupt the balance between prosurvival and prodeath signaling.

In conclusion, this proteome level and RNAi sensitization perspective provides a valuable resource for identifying other resistance mechanisms and cotargeting strategies for ALK-rearranged lung cancers. Further studies on biological mechanisms of other components within EML4-ALK interactome created in this study may identify more combinational therapeutic strategies to either improve the ALK-based therapy efficacy or overcome ALK TKI resistance. Biomarker approaches to discern key facets of the interactome may identify patients likely to derive more benefit from upfront combination therapy as opposed to single use of ALK inhibitors.

MATERIALS AND METHODS

Materials

Materials used included iodoacetamide, dithiothreitol, 1 M triethylammonium bicarbonate (TEAB), protease inhibitor cocktail, HA antibody–conjugated agarose, polybrene (Sigma-Aldrich); trypsin (Promega); formic acid (HCOOH) (Merck); StrepTactin Sepharose (IBA GmbH); d-biotin (Alfa Aesar); micro Bio-Spin chromatography columns (Bio-Rad); and Gateway LR Clonase II Enzyme Mix Kit, fetal bovine serum (FBS), and Lipofectamine 2000 (Invitrogen).

Cell lines and culture

Human NSCLC H3122 cell lines were provided by W. Pao (Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN) and confirmed to be free of Mycoplasma contamination. Cells were cultured in 10% FBS-RPMI (Invitrogen) medium at 37°C and 5% CO2 in a humidified environment, digested with Accutase (Innovative Cell Technologies Inc.), and separated into single cells by passing through BD Falcon cell strainer (Sigma-Aldrich). Phoenix human embryonic kidney (HEK) 293 cells used for producing lentivirus were obtained from the American Type Culture Collection (ATCC; Manassas, VA). Crizotinib (PF-02341066) and NVP-AUY922 were purchased from ChemieTek. Alectinib and Y27632 were purchased from Selleck Chemicals.

Stable crizotinib-resistant cell lines

Oncogenic fusion gene EML4-ALK variant 1 wild type or point mutant at L1196M was cloned into the retroviral pBabe-puro backbone (provided by J. Heuckmann, Universität zu Köln, Köln, Germany). The construction plasmids were cotransfected with VSV-G vector to Phoenix cells and cultured at 32°C for 48 hours to make the retroviral virus. Retroviral viruses carrying targeting genes were harvested, followed by infection of the H3122 cells with retroviral virus containing target genes. Positive cell clones were selected with puromycin (2 μg/ml) continuously for 2 weeks to remove cells. Resistance of stable cell lines to crizotinib was verified using CellTiter-Glo Luminescent (Promega) cell viability assay.

Tyrosine phosphoproteome extraction

Because only two original EML4-ALK–positive patient NSCLC cell lines were originally available [H3122 (variant1) and H2228 (variant 3a/b)], with the former demonstrating sensitivity to crizotinib and clear mechanism to create the acquired resistant cell model to ALK TKIs and the latter not demonstrating resistant mechanisms and with no resistant cell model available, we selected H3122 cells for our experiments. pTyr phosphoproteomes were purified, applying PhosphoScan kit (P-Tyr-100) (Cell Signaling Technology) according to the manufacturer’s recommendations. Briefly, cells (2 × 108) were lysed in urea buffer; extracted proteins (40 to 80 mg) were then reduced by dithiothreitol, alkylated by iodoacetamide, and then digested by trypsin. Peptide mixture was isolated from lysate using Sep-Pak C18 columns and then lyophilized. Phosphorylated peptides were dissolved in immunoaffinity buffer, enriched using immobilized antibody specific for pTyr, and eluted with acid. Sample volumes were then reduced to 20 μl by vacuum centrifugation (Speedvac) for LC-MS/MS analysis.

LC-MS/MS and peptide assignment

After in-gel digestion (for TAP experiment) or in-solution digestion (for phosphoproteomics experiment), peptides were analyzed using nanoflow LC (U3000, Dionex) coupled to an electrospray hybrid ion trap mass spectrometer (LTQ Orbitrap, Thermo Scientific). Samples were first loaded onto a precolumn (5 × 300–mm internal diameter packed with C18 reversed phase resin, 5 mm, 100 Å) and washed for 8 min with aqueous 2% acetonitrile and 0.04% trifluoroacetic acid. The trapped peptides were eluted onto the analytical column (C18; 75-μm internal diameter × 15 cm; Pepmap 100, Dionex). The 60-min gradient delivered at 300 nl/min was programmed as 95% solvent A (2% acetonitrile + 0.1% formic acid) for 8 min; solvent B (90% acetonitrile + 0.1% formic acid) was ramped from 5 to 50% over 35 min and then from 50 to 90% in 1 min and held at 90% for 5 min, followed by ramping down from 90 to 5% in 1 min and reequilibration for 10 min. After each survey scan, five MS/MS were collected in a data-dependent manner. The MS scans were performed in Orbitrap to obtain accurate peptide mass measurement, and the MS/MS scans were performed in linear ion trap using 60-s exclusion for previously sampled peptide peaks.

For peptide sequence assignment, Sequest and Mascot searches were performed against human entries in the UniProt database. Two missed tryptic cleavages were allowed, and the precursor mass tolerance was set to 1.08 daltons (to accommodate incorrect selection of the monoisotopic peak). MS/MS mass tolerance was 0.8 dalton. Dynamic modifications included carbamidomethylation (Cys), oxidation (Met), deamidation (Asn and Gln), and phosphorylation (Ser, Thr, and Tyr). To accurately identify phosphorylation sites, we integrated database search results from both Sequest and Mascot into Scaffold (www.proteomesoftware.com) and took multiple parameters (Scaffold peptide probability, XCorr, DeltaCn, Mascot ion score, and E value) into consideration. The following limits were used to establish data quality: 80% peptide probability, 10–parts per million fragment error, 40 Mascot score, and XCorr for 2+ > 2.5, XCorr for 3+ > 3, and DeltaCn > 0.1. Peptides could be identified by either database search alone as long as the quality metrics were exceeded. Finally, phosphorylation site assignment was manually validated from the raw data using published methods.

Quantification of tyrosine-phosphorylated peptides using extracted ion chromatograms

To quantify the perturbation of the pTyr proteome change response to ALK TKI treatment, the integrated peak areas for each pTyr peptide were calculated from extracted ion chromatograms using Quan Browser from Xcalibur 2.0. These values were restricted by mass/charge ratio (m/z; ±0.02) and retention time (60 s). Other parameters were the Genesis peak integration using smoothing point 9.0, a signal-to-noise (S/N) threshold of 0.5, and a peak detection minimum peak height (S/N) of 3.0. The masses and isotopic peak patterns of the target peptides were manually inspected to ensure proper sequence assignment and to verify peak quality. Peak area values of all precursors from all samples were merged into one spreadsheet using the PeakAreaSummary software, which was developed in-house (http://proteome.moffitt.org/proteomics/). PeakAreaSummary is an Excel add-in using Visual Basic for Applications. It first calculates the sum of peak areas for all the precursors in the same sample with the same m/z and retention time (using the same δ values as mentioned above) and then merges the peak area values from all the samples to get one value for any precursor.

Preprocessing to identify unique pY sites

After quantification, 628 pY sites were identified. An in-house algorithm, implemented to identify unique pY site, was used to remove redundant sites, merge peptides containing missed cleavages by using protein ID, peptide sequence, and phosphorylation site index (that is, amino acid residue number), and quantify peak areas. When it is only identifiable to the level of pairs of pYs, then the independent unit for analysis is the unique pY pair (instead of single site). Phosphopeptides produced with missed cleavages or fragments of the same phosphopeptides were merged. A total of 487 unique pTyr units (pYs or pY pairs) were identified.

Because of their detection and signal stability across samples, the CDC2-pY15 peptides were used for normalizing the peak areas across all 12 samples (six biological samples with technical duplicates; fig. S9). The normalized quantities across samples are shown in fig. S10.

Reproducibility between technical replicates for each pY was high and therefore included in the analyses. Averages of technical replicates from six biological samples were used in the analyses. Data were analyzed in log2 scale before parametric analyses and for the ease of interpretation. A two-sample t test was performed to compare differential phosphorylation for each tyrosine between the control and treatment group treated at 1000 nM. Because follow-up siRNA screening was planned, multiple comparisons were not adjusted. P value of less than 0.05 and 1.5-fold change were used to measure the change of pTyrs.

Strep-HA TAP

To dissect the EML4-ALK physical protein-protein interaction network, we isolated and identified components of protein complexes of EML4-ALK V1, SHC1, GRB2, and PIK3R2 coupling TAP with MS analysis. GFP was used as the control. Complementary DNA (cDNA) for EML4-ALK V1 was provided by W. Pao (Vanderbilt University, Nashville, TN). The design of PCR primers, the amplification, and the pENTR TOPO cloning of EML4-ALK V1, SHC1, GRB2, and PIK3R2 were performed as previously described (7). Briefly, EML4-ALK V1, SHC1, GRB2, P85B, and GFP were separately inserted into the pfMSCV-C-SH IRES GFP gateway vector from the pENTR TOPO vector using the Gateway LR Clonase II Enzyme Mix Kit. The retroviral expression clone was verified by DNA sequencing using an Applied Biosystems 3130xl Genetic analyzer (HITACHI) with data analyses performed using Lasergene software v7.2. Phoenix HEK293 cells were obtained from ATCC and grown in Dulbecco’s modified Eagle’s medium (DMEM) containing 10% FBS. On day 1, Phoenix cells (8 × 105) per well were seeded in six-well plates. On day 2, cells were transfected with 3 μg of VSV-G and 5 μg of retroviral plasmids using Lipofectamine 2000. Six hours after transfection, the supernatant was replaced with 2 ml of DMEM–20% FBS, and the cells were incubated in a 5% CO2 incubator at 32°C for 48 hours. The supernatant (viruses) was collected by centrifugation at 4°C and was either stored at −80°C or used immediately to infect the target cells. H3122 cells were maintained in RPMI 1640 medium supplemented with 10% FBS. For retroviral transduction, cells (2 × 105) per well were seeded in six-well plates. After overnight incubation, cells were infected with 800 μl of the virus supernatant plus Polybrene (6 μg/ml) for 24 hours and then supplemented with 4 ml of medium per well. Cells were grown continuously until cell sorting. One week after infection, GFP-positive cells were sorted using FACSVantage (BD Biosciences). GFP positivity and HA expression were assessed by flow cytometry and immunoblot, respectively, before expanding the cells to 10 × 15–cm dishes. At about 90% confluence, the EML4-ALK V1–, SHC1-, GRB2-, and PIK3R2-tagged cells were washed with ice-cold phosphate-buffered saline (PBS) containing 1 mM sodium orthovanadate and scraped with a cell lifter on ice. Dishes of each EML4-ALK–, SHC1-, GRB2-, PIKR2-, and GFP-expressing H3122 cell pellets (each consisting of 5 × 15–cm dishes) were collected in 15-ml conical tubes by centrifugation at 120g at 4°C for 5 min and lysed in TNN-HS buffer [50 mM Hepes (pH 8.0), 150 mM NaCl, 5 mM EDTA, 0.5% NP-40, 50 mM NaF, 1.5 mM Na3VO4, 1.0 mM phenylmethylsulfonyl fluoride, and protease inhibitor cocktail]. Insoluble material was removed by centrifugation at 14,000g for 15 min at 4°C. We transferred 200 μl of StrepTactin Sepharose (400 μl slurry per pulldown) to a 14-ml dust-free Falcon tube and washed twice with 1 ml of TNN-HS buffer. The lysates (extracted from 5 × 15–cm plates) were added to the washed StrepTactin Sepharose and rotated for 20 min at 4°C. The Sepharose beads and supernatant were transferred to a spin column and gravity-drained. The Sepharose was washed four times with 1 ml of TNN-HS buffer, and the bound proteins were eluted thrice with 300 μl of freshly prepared 2.5 mM d-biotin in TNN-HS buffer into a fresh dust-free 1.5-ml Eppendorf tube. One hundred microliters of HA antibody–conjugated agarose beads (200 μl of slurry per pulldown) was added to the biotin elute and rotated for 1 hour at 4°C. The samples were centrifuged at 200g for 1 min at 4°C, and the supernatant was removed. The HA antibody–conjugated beads were suspended in 1 ml of TNN-HS buffer, and the washed beads and buffer were loaded into in a fresh dust-free Bio-Spin column and gravity-drained. The HA antibody–conjugated agarose was sequentially washed three times with 1 ml of TNN-HS buffer and two times with 1 ml of TNN-HS buffer consisting of only Hepes, NaCl, and EDTA. Retained proteins were eluted from the column directly into a glass high-performance liquid chromatography vial using 500 μl of 100 mM HCOOH and immediately neutralized with 125 μl of 1 M TEAB. Two hundred microliters was removed for immunoblot analysis. The remaining samples were desalted by loading into SDS–polyacrylamide gel electrophoresis (SDS-PAGE) gels and running short-time electrophoresis; the bands were then sliced out from the gel and cut into small pieces. Regular in-gel digestion with trypsin was then performed, and the peptides were extracted from the gel pieces with 40% of acetonitrile in 0.1% trifluoroacetic acid solution. Resulting peptides were submitted to LC-MS/MS and database to identify the proteins.

To evaluate the effects of crizotinib on EML4-ALK complexes, we used a one-step Strep pulldown, instead of TAP of Strep and HA. The peak intensity of each protein was calculated by MaxQuant. A t test was used to determine changes induced by crizotinib as compared to DMSO control-treated samples.

Generating EML4-ALK integrative protein-protein network

To facilitate the application of various protein-protein interaction analysis software and databases, all identified proteins from phosphoproteomics and TAP experiments were first converted to uniform Gene name, UniProt name, and UniProt access number using g:Convert Gene ID Converter (64), UniProtKB, and PhosphoSitePlus (28). To maximize the known protein-protein interactions among identified tyrosine-phosphorylated proteins and EML4-ALK physical interactome proteins, four reciprocal tools including PSICQUIC (65), MetaCore, PhosphositePlus, and PhosphoPoint (66) were used to retrieve protein-protein interactions from existing databases. We first loaded all identified protein UniProtKB access numbers to PSICQUIC Universal Client Service (v0.31), a plug-in of Cytoscape (v2.8.3), allowing access to 31 registered protein-protein databases and more than 151 million binary interactions. The query mode was set as GET_BY_QUERY. We next loaded gene names of all identified proteins to MetaCore, another tool for protein-protein annotation that provides detailed reference and specific statement for each protein-protein interaction. The effect categories of MetaCore, including active, inactive, and unspecific, are especially meaningful for phosphoproteins. Analyzed networks and canonical pathways were selected as the network-building algorithm, the maximum number of nodes in a network was set at 50, and functional and binding interactions were used for network building. All mechanisms and effects were selected. All protein-protein interactions obtained from PSICQUIC and MetaCore and kinase-substrate interactions from PhosphoSitePlus and PhosphoPoint were merged using Cytoscape v2.8.3 to generate a union protein-protein interaction network. Contaminated nonexperimental nodes were removed. Because of different formats and protein-protein interaction names from different search engines, this network still contains some repeated interactions between protein-protein pairs. To refine the network to unique interactions for each protein-protein pair, we exported all interactions together with edge attribute to Excel file and manually checked each of the protein-protein interactions and removed all redundant interactions. To ensure that each protein-protein interaction was unique, we followed this policy: (i) if there are similar interactions between the same protein pair, the one more clearly stated and evidenced with literature was kept; (ii) if reverse effects (for example, active and inactive) were found between the sample protein pair, we kept both interactions; (iii) if there are kinase-substrate interaction and other types of interactions found between the same protein pair, only kinase-substrate interaction (KSI) was kept; and (iv) similar interaction statements were combined to make one interaction. The refined protein-protein interactions were merged with bait-prey interactions from TAP experiments in Cytoscape to generate the EML4-ALK integrated protein network.

Bioinformatics analysis to mine biological implications embedded in EML4-ALK integrative network

After constructing the EML4-ALK–integrated protein network, our next goal was to mine the biological implication of EML4-ALK network to cancer mechanism and discover potential new drug targets for therapy. To facilitate understanding of the function of proteins in the EML4-ALK–integrated network, we annotated the entire network proteins using PhosphoSitePlus and assistance from Cell Signaling Technology to 14 protein types: kinase, phosphatase, GAB, guanine nucleotide exchange factor, adaptor/scaffold, enzyme, chaperone, vesicle, motility polarity chemotaxis, transcription factor, ligase, HSPs, cytoskeletal proteins, and other.

To globally understand the functional hints embedded in the network, we simplified the network through creating a series of subnetworks according to different functional attributes including phosphorylation changes, drug response, and kinase proteins. Also, interactions among first-neighbor nodes for selected proteins were extracted to create the subnetwork; these subnetworks allow the network-wide understanding of the function of certain key proteins or protein group.

To identify key players from complicated networks, we performed cluster analysis on networks and subnetworks using Clust&See (67), a Cytoscape v2.8.3 plug-in. The graph clustering algorithm TFit (iterated transfer-fusion) was selected to cluster the networks due to its ability to gain accurate modules from multilevels. Modules containing less than five nodes were filtered out.

Pathway analysis was conducted on the network and on each subnetwork using Reactome FI. The FDR value cutoff was set as 0.001 to obtain significantly enriched pathways.

Network statistics analysis

Subnetworks in pairs according to different conditions were compared using the NetworkAnalyzer plug-in (68). All comparison analyses of different group lists were conducted and visualized with Venny 2.0.2 (http://bioinfogp.cnb.csic.es/tools/venny/index.html).

Cell viability analysis

CellTiter-Glo Luminescent (Promega) cell viability assay was used to examine the effects of TKI and RNAi alone and in combination with ALK TKI on cell viability of H3122 parent and ALK TKI–resistant cell lines following the manufacturer’s instructions. In general, cells were seeded to 96-well plates and grown in RPMI 1640 medium containing 10% FBS at 37°C. After 24 hours of cell attachment, the inhibitors targeting the selected component of EML4-ALK–integrated network were added in a series of concentrations. To determine the appropriate concentration scope for each drug, we started with all drugs from a maximum concentration of 10 μM and sequentially diluted them in a 1:5 ratio. According to screen results, we adjusted the maximum concentration and dilution ratio for some inhibitors to ensure that most data points were located within the “S” curve. After a 72-hour incubation, CellTiter-Glo was added, and the results were read using SpectraMax M5 (Molecular Device). The IC50 values for each inhibitor were calculated using GraphPad Prism 6 software.

Lethal screening using shRNA library

To further validate the functions of the EML4-ALK integrated network, we investigated the effect of loss of function of each of the whole network proteins on cell viability with or without ALK inhibitor treatment. We first built an shRNA sublibrary by selecting five shRNA clone IDs for each of the 407 genes from the Sigma-Aldrich website (www.sigmaaldrich.com/united-states.html) and the target sequence from the Broad Institute website (www.broadinstitute.org/rnai/public/clone/search). 293FT cells were transfected using TurboFect (ThermoScientific) with a library containing The RNAi Consortium (TRC) HIV–based (pLKO.1 and pLKO.2) lentiviral vectors, and viral medium was harvested as described previously (69). We plated106 H3122 wild-type cells in 10-cm cell culture dishes, allowing adherence for 24 hours. The cells were inoculated with shRNA viral medium for 72 hours, split according to confluency into experimental plates, and allowed to grow for another 24 hours. The cells were treated with a 1:10,000 dilution of DMSO or crizotinib or alectinib at IC20, IC50, or IC80 dose levels, in replicates of three plates per dose. After 72 hours, drug treatment media were replaced with fresh media, and the cells were allowed to grow for an additional 72 hours. Cells were removed using Accutase (Innovative Cell Technologies), pelleted, and stored at −20°C.

PCR and HiSeq

DNA was extracted from a cell pellet using a DNeasy Blood and Tissue Kit (Qiagen). DNA (500 ng) was added to 50 μl of PCR using the Phusion system (New England Biolabs) and custom indexing primers, and reactions were performed in duplicate for a total of 1000 ng per sample. The PCR sequence was as follows: 98°C for 1 min, 35 cycles at 98°C for 10 s, at 60°C for 20 s, and at 72°C for 30 s, followed by at 72°C for 5 min. We used 10 μl of each sample to check for appropriate bands on a 2% agarose gel. We pooled 5 μl of each sample into a single tube, concentrated using the Wizard Gel and PCR Clean-up System (Promega), and eluted in 50 μl. The eluent was then loaded on a Pippin Prep 2% gel (Sage Science) for targeted size selection. Pippin Prep parameters were set to elute DNA between 250 and 400 ng. The size-selected eluent was then run on an Illumina HiSeq 2500 with dual-indexing.

Search for significant gene hits

We used the Bioinformatics for Next Generation Sequencing (BiNGS!) algorithm for analyzing and interpreting functional genetic screen by deep sequencing data (70). In brief, a preprocessing step filtered out erroneous and low-quality reads. Filtered reads were mapped against the shRNA reference library (2 days after infection) using Bowtie (71). Output from this step is a P × N matrix, where P and N represent the shRNA counts and samples, respectively. shRNAs were also filtered where the median raw count in concentration 1 (for example, IC20) is greater than the maximum raw count in concentration 2 (for example, IC50) if the shRNA is enriched in concentration 1 and vice versa. We then used edgeR (72) to compute the differentially represented shRNAs. We then computed the FDR (Q value) for these shRNAs and carried out meta-analysis by combining Q values for all shRNAs representing the same gene using weighted Z-transformation approach (73). For each gene, we computed a weighted P value P(wZ), and we used this P(wZ) to sort the shRNA hits. We performed pairwise comparisons for each concentrations and grouped the genes into three classes using the following rules based on the P(wZ) obtained from each gene (similar to the classification rules in (74)]. Here, we considered genes, when deleted by shRNAs, that induced cell death in all dosages (IC20, IC50, and IC80) as candidate hits. The counts of gene hits were significantly lower from vehicle to IC20 and IC80 (E value < 2), maximum difference is vehicle to IC20 among all possible combinations, and no significant changes were noted from IC20 to IC50 and IC50 to IC80. This analysis approach is similar to our previously published research (75).

shRNA library screen results validation

To validate the results from shRNA library screening and examine their ability to sensitize ALK TKI–resistant cells, we constructed five hairpins of each CC2D1A and FRS2 into lentivirus in H239FT cells and then infected H3122, H3122 EML4-ALKL1196M, and STE1 cells. The infected cells were selected with puromycin (10 μg/ml) for more than 10 days. The shRNA-positive cells were seeded into 96-well plates, series-diluted crizotinib and alectinib were added, and Glo assay was performed to measure the combined effects of shRNA knockdown with ALK TKI on sensitive and resistant cell viability at 72 hours. At the same time, cells were seeded into 10-cm dishes and treated with DMSO, 1 μM crizotinib, and 1 μM alectinib for 3 hours. Cell pellets were collected for further signaling and mechanism analyses with both RT-PCR and Western blotting.

Western blot analysis

Cell lysates were separated by SDS-PAGE, and proteins were electrotransferred to nitrocellulose membranes. Regular enhanced chemiluminesence (ECL) Western blot was used in this study. Basically, members were blocked with 5% milk in tris-buffered saline (TBS) for 1 hour and incubated with primary antibodies recognizing target proteins at 4°C overnight, washed with TBS with Tween 20 (TTBS) three times for 5 min, incubated with ECL horseradish peroxidase–conjugated second antibodies at room temperature for 1 hour, washed with TTBS three times for 5 min, and added with super ECL, and then results were read. Another infrared-based Western blotting method was used in this study as well. Simply, membranes were blocked with Odyssey Blocking Buffer (LI-COR Biosciences) for 1 hour at room temperature and washed three times for 5 min with TTBS. Membranes were incubated with primary antibody in blocking buffer with gentle agitation overnight at 4°C and then washed three times for 5 min each with TTBS. Incubated membranes were fluorescently labeled with secondary antibody [1 μl of IRDye 680 goat mouse source antibody and 1 μl of IRDye 680 goat rabbit source antibody (1:10,000)] in blocking buffer, with gentle rotation for 1 hour at room temperature. Membranes were washed with TTBS three times for 5 min and rinsed with PBS to remove residual Tween 20. The Odyssey Infrared Imaging System quantified the 700- and 800-nm channel images, and Odyssey v3.0 software was used to calculate the intensities at 800 nm; these values were normalized to corresponding β-actin results (700 nm).

Quantitative RT-PCR

Cells containing scramble and target shRNA hairpins were washed with precold PBS. Cell pellets were then collected. RNA was extracted using the RNeasy Mini Kit (Qiagen). Purified RNA was reverse-transcribed to cDNA using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). TaqMan (Hs00183614_m1 FRS2 and Hs00214594_m1 CC2D1A FAM, Applied Biosystems) PCRs were conducted in Optical 96-well Reaction Plate with Barcode (code 128) on ABI 7900HT Fast Real-Time PCR (Applied Biosystems) instruments. 18S rRNA (VIC-MGB 4319413E1202056, Applied Biosystems) was used to normalize all samples. Gene expression was quantified by cycle threshold (CT) method, where expression was determined by comparing normalized CT of samples containing target gene shRNA hairpin to CT of samples containing control shRNA scramble hairpin.

Dynamic cell viability analysis using real-time cell assay

The xCELLigence real-time cell assay (RTCA) technology provides an accurate platform for noninvasive detection of cell viability (76). For cell viability analysis, effects of siRNA or drugs on cell viability were monitored by the dynamic, impedance-based xCELLigence System (Roche Applied Science) in a real-time, label-free manner. In general, H3122 cells were transfected with siRNA or treated with drugs as described previously in this paper and then seeded at a density of 8000 cells per well into E-Plate 16 (ACEA Biosciences Inc.) containing 100 μl of RPMI 1640 medium per well in 96-well plates supplemented with 10% FBS. The cell index was derived from measured cell-electrode impedance that correlates with cell viability. Nontargeted siRNA and DMSO were used as control of RNAi and drug treatment, respectively. Data were collected every 10 min, exported as an Excel file after analyses with RTCA software (v1.2), and plotted in Graphpad Prism 6.0 software. The slope per hour of cell index across all experimental times was used to monitor the cell viability.

Tumor microarray and immunohistochemistry analysis

A patient-derived tumor microarray (TMA) was created in the Moffitt Tissue Procurement Core Facility (Lung 6.2 TMA) under the auspices of an Institutional Review Board–approved protocol. No protected health information related to the tissue included in the TMA was revealed to the study team. Donor paraffin blocks were obtained through biopsy or surgical resection specimens. All tumors were tested as standard of care before ALK TKI therapy and use of the Vysis FISH probe. The Lung 6.2 TMA contained the following tissues: eight EML4-ALK–rearranged cases (seven with triplicate cores and one with a single core); six non–ALK-rearranged NSCLC cases (all six with triplicate cores); malignant tissue including breast, colon, diffuse large B cell lymphoma, glioma, hepatocellular carcinoma, osteosarcoma, ovarian carcinoma, prostate, and renal cell carcinoma (all with single cores); and normal tissue controls including placenta, spleen, stomach, and tonsil (all with single cores). All TMAs were sectioned at 5 μm, placed on charged slides, and baked for 60 min at 60°C.

Slides containing TMA and positive and negative controls were immunohistochemically stained using a Ventana Discovery XT automated system (Ventana Medical Systems) following the manufacturer’s instructions. Briefly, slides were deparaffinized with EZ Prep solution (Ventana) on an automated system. The rabbit primary antibody recognizing CC2D1A (HPA005436, Sigma) was diluted with Dako antibody diluent (Carpenteria) at a 1:200 ratio and incubated with TMA for 60 min. Slides were then incubated with Ventana OmniMap rabbit source secondary antibody for 8 min. The Ventana ChromoMap kit was used to detect the staining. Slides were also then counterstained with hematoxylin. Slides were then dehydrated and coverslipped as per normal laboratory protocol. Stained tissue microarrays were scanned using the Aperio ScanScope XT with a 20×/0.8–numerical aperture objective lens via Basler Tri-linear array detection. Images were viewed with ImageScope v12.1.0.5029 (Aperio), and snapshot images from select cores were extracted into .tif file format.

Stained TMA slides were scored as previously described (59) with slight modification. Staining intensity was graded as 0 (negative), 1 (weak), and 2 (strong); percentage of positive cells examined was scored as 0 (<5%), 1 (5 to 50%), and 2 (>50%). The two scores were multiplied, and the immunoreactive score (values from 0 to 4) was determined as follows: 0 as negative, values 1 to 2 as weakly positive, and 4 as strongly positive. The average immunoreactive score for EML4-ALK was calculated using Prism6 software.

SUPPLEMENTARY MATERIALS

www.sciencesignaling.org/cgi/content/full/9/450/rs12/DC1

Fig. S1. Effect of GRB2 knockdown on the phosphorylation of ERK in H3122 cells.

Fig. S2. PKC-δ subnetwork components that were increased in response to crizotinib.

Fig. S3. Comparison of the enrichment of signaling pathways in the entire EML4-ALK integrative network, the pTyr proteome subnetwork, and the kinome subnetwork.

Fig. S4. Alterations in kinome subnetworks affected by crizotinib.

Fig. S5. Sensitivities of ALK fusion cell lines to crizotinib.

Fig. S6. Abundance of total and phosphorylated FRS2 and total ROCK2, and the effect of CDC37 knockdown or HSP90 inhibition on H3122 cell viability.

Fig. S7. Knockdown of FRS2 sensitizes H3122 cells to crizotinib.

Fig. S8. Abundance of total and phosphorylated FRS2 in ALK fusion cell lines in response to crizotinib.

Fig. S9. Effect of crizotinib on CDC2 phosphorylation.

Fig. S10. Statistical normalization of the tyrosine phosphoproteome across all samples using CDC2 pTyr15.

Table S1. Quantitative analysis of the tyrosine phosphoproteome in H3122 cells in response to crizotinib.

Table S2. Signaling pathways enriched in the entire EML4-ALK integrative network and functional subnetworks.

Table S3. shRNA library covering the EML4-ALK integrative network.

Table S4. Synthetic lethal effect between loss of function of proteins and crizotinib and alectinib.

Data file S1. Integrative EML4-ALK signaling network.

REFERENCES AND NOTES

Acknowledgments: We thank W. Pao (Roche, Basel) for providing the H3122 cell lines, C. Lovly (Vanderbilt University) for providing the STE1 cell lines, J. Tanizaki (Dana-Farber Cancer Institute) for providing the H3122/TR2 cells, J. Heuckmann and R. Thomas (Universität zu Köln, Köln, Germany) for providing the plasmid constructs of EML4-ALK LL96M, and P. V. Hornbeck (Cell Signaling Technology) for his courtesy annotating the protein types for all experimentally identified proteins in this study using PhosphoSitePlus (www.phosphosite.org). We thank R. Hamilton (Moffitt Cancer Center) for editorial assistance. We thank F. Kinose (Moffitt Cancer Center) for help with cell culture. We also thank the Moffitt Analytic Microscopy Core Facility for scanning the TMA slides. Funding: The Moffitt Analytic Microscopy Core Facility is supported by the National Cancer Institute (NCI) as a Cancer Center Support Grant (P30-CA076292). This project was supported by a grant from the Moffitt Cancer Center SPORE in Lung Cancer (P50-CA119997), the Moffitt Lung Cancer Center of Excellence, the Colorado Lung Cancer SPORE in Lung Cancer (P50-CA058187), and the Research Project Grant (R01-CA157850) awarded by the NCI of NIH. The Moffitt Proteomics is supported, in part, by the NCI (P30-CA076292) as a Cancer Center Support Grant and the Moffitt Foundation. Author contributions: G.Z. and E.B.H. conceived the study. G.Z, H.S., J. Kim, A.I.R., Y.A.C., X.Z., L.S., R.Z.L., B.F., Y.B., J. Koomen, A.C.T., J.D., and E.B.H. contributed to the methodology. G.Z, H.S., J. Kim, A.I.R., Y.A.C., X.Z., L.S., Y.B., R.Z.L., and B.F. performed the data acquisition and analysis. G.Z. and E.B.H. wrote the manuscript, and G.Z., H.S., Y.A.C., B.F., J. Koomen, A.C.T., J.D., and E.B.H. revised the manuscript. J. Koomen, J.D., and E.B.H. provided the funding support through grants noted below. J. Koomen, J.D., and E.B.H. provided resources. J. Koomen, A.C.T., J.D., and E.B.H. supervised the study. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The MS data are deposited in the ProteomeXchange Consortium via PRIDE partner repository with the data set identifier PXD004935 and 10.6019/PXD004935.
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