Abstract

Background

   Neuroblastoma is an aggressive childhood malignancy of the sympathetic
   nervous system. Despite multi-modal therapy, survival of high-risk
   patients remains disappointingly low, underscoring the need for novel
   treatment strategies. The discovery of ALK activating mutations opened
   the way to precision treatment in a subset of these patients.
   Previously, we investigated the transcriptional effects of
   pharmacological ALK inhibition on neuroblastoma cell lines, six hours
   after TAE684 administration, resulting in the 77-gene ALK signature,
   which was shown to gradually decrease from 120 minutes after TAE684
   treatment, to gain deeper insight into the molecular effects of
   oncogenic ALK signaling.

Aim

   Here, we further dissected the transcriptional dynamic profiles of
   neuroblastoma cells upon TAE684 treatment in a detailed timeframe of
   ten minutes up to six hours after inhibition, in order to identify
   additional early targets for combination treatment.

Results

   We observed an unexpected initial upregulation of positively regulated
   MYCN target genes following subsequent downregulation of overall MYCN
   activity. In addition, we identified adrenomedullin (ADM), previously
   shown to be implicated in sunitinib resistance, as the earliest
   response gene upon ALK inhibition.

Conclusions

   We describe the early and late effects of ALK inhibitor TAE684
   treatment on the neuroblastoma transcriptome. The observed unexpected
   upregulation of ADM warrants further investigation in relation to
   putative ALK resistance in neuroblastoma patients currently undergoing
   ALK inhibitor treatment.

   Keywords: neuroblastoma, ALK, ALK inhibition, ALK signaling, dynamic

INTRODUCTION

   Neuroblastoma (NB) is a childhood cancer arising from malignant
   transformation of cells from the sympatho-adrenergic lineage of the
   developing neural crest [[36]1] and is characterized by a large
   clinical heterogeneity, including patients with tumours that
   spontaneously regress as well as patients with metastasis and
   refractory disease despite intensive therapy regimens [[37]2]. For
   these high-risk neuroblastoma cases, a better understanding of the
   genes and pathways that are involved in disease development and
   progression is currently fuelling the identification of new molecular
   targets for therapy [[38]3].

   Overall, given the low frequency of mutations in neuroblastoma, options
   for targeted therapy are relatively limited. However, the recurrent
   somatic mutations of the tyrosine kinase receptor ALK identified in
   8-10% of primary neuroblastoma tumours [[39]4–[40]8] and the emergence
   of ALK mutations at relapse mark ALK as an important novel drug target
   [[41]9, [42]10]. Several orally available small molecule ALK inhibitors
   have been developed and successfully applied in patients with other ALK
   mutant tumour entities, so-called ALKoma’s, most notably a subset of
   lung cancers [[43]11–[44]17]. Several of these small molecule ALK
   inhibitors have recently gone into phase 1 clinical trials for patients
   with refractory neuroblastoma, inflammatory myofibroblastic tumour,
   non-small-cell lung cancer (NSCLC) and anaplastic large-cell lymphoma
   (ALCL) [[45]18, [46]19].

   Despite the promising therapeutic potential of ALK inhibitors, an
   important drawback is the rapid occurrence of resistance due to escape
   mechanisms including secondary mutations or activation of other kinase
   signaling pathways [[47]17, [48]20–[49]30]. A better understanding of
   the downstream ALK signaling cascades can inspire the development of
   more rationally designed combinatorial treatment approaches. In a
   previous study, ALK downstream signaling was characterized by in depth
   transcriptome analysis of neuroblastoma cells treated for six hours
   with the ALK inhibitor TAE684, resulting in the 77-gene ALK signature
   and the identification of a negative MAPK feedback loop and of RET as
   ALK activated target gene [[50]31]. Moreover, we generated
   transcriptome data between 10 minutes and 6 hours after pharmacological
   ALK inhibition to show that our established 77-gene signature is
   gradually decreasing starting from 2 hours after the treatment. Here,
   we further explore this dynamic transcriptome data generated upon
   treatment with the ALK inhibitor TAE684 in order to look in more detail
   into the dynamics of early transcriptional responses following ALK
   inhibition to improve our understanding on downstream ALK signaling and
   identify novel targets for combination therapy. To this end, we
   performed differential gene expression analysis at different time
   points following TAE684 treatment of the neuroblastoma cell line CLB-GA
   with an activating ALK^R1275Q mutation.

RESULTS AND DISCUSSION

MAPK, PI[3]K, RET and MYC(N) signaling pathways are downregulated starting
from 1 to 2 hours after TAE684 treatment

   In order to gain more insights into the early dynamics of
   transcriptional changes after pharmacological inhibition of ALK
   signaling in neuroblastoma cells, the neuroblastoma cell line CLB-GA,
   which harbours an ALK^R1275Q mutation, was treated with the ALK
   inhibitor TAE684 and DMSO as control. While TAE684 did not go into
   clinical trials, TAE684 is a valid ALK-inhibitor as we previously
   showed that the transcriptional responses for this drug and the more
   clinically relevant ones, such as crizotinib, LDK378 and X396, are very
   similar [[51]31]. RNA expression profiling was performed on cells
   harvested 10 and 30 minutes, 1, 2, 4 and 6 hours after treatment
   ([52]Supplementary Figure 1).

   Our previously established 77-gene ALK signature [[53]31] significantly
   decreases from 2 hours after ALK inhibitor treatment, as shown earlier
   by Lambertz et al. [[54]31]. In this study, we evaluated the dynamic
   regulation of known ALK downstream pathways, i.e. MAPK, PI[3]K, RET and
   MYC/MYCN signaling pathways [[55]4–[56]7, [57]31–[58]34], by
   calculating signature activity scores for drugs specifically blocking
   these pathways. These drug signature scores summarize the
   transcriptional response of the components of a given signaling pathway
   upon pharmacological pathway inhibition. Our data confirm ALK
   regulation of these pathways and furthermore show that MAPK, PI[3]K and
   RET pathway activity is decreased as early as 1 to 2 hours after the
   start of TAE684 treatment, as the corresponding inhibitor scores become
   activated at those time points (Figure [59]1A–1C; [60]Supplementary
   Figures 2A–2C, 3A–3C). Furthermore, we observed that MYCN expression
   levels significantly drop 1 hour after the treatment (p = 0.0015)
   (Figure [61]1D; [62]Supplementary Figure 2D). As expected, MYC(N)
   activity scores [[63]35, [64]36] follow a delayed response at later
   time points (Figure [65]1E, 1F; [66]Supplementary Figures 2E, 2F, 3D,
   3E). These results are in line with the previously established
   regulatory role on MYCN transcription initiation for ALK, in addition
   to its effect on MYCN activity via phosphorylation [[67]32, [68]34,
   [69]37–[70]39].

Figure 1. Signature score analysis for MAPK, PI[3]K, RET and MYC(N) signaling
pathways, downregulated starting from 1 or 2 hours after TAE684 treatment.

   [71]Figure 1
   [72]Open in a new tab

   (A–E) MEK inhibitor (trametinib) (A), PI[3]K/mTOR inhibitor (BEZ-235)
   (B) and RET inhibitor (vandetanib) (C) signatures are upregulated from
   1 or 2 hours after treatment of the CLB-GA cell line with 320 nM
   TAE684, while MYCN mRNA expression levels (D) are downregulated from 1
   hour after treatment and the MYCN activity score from Valentijn et al.
   [[73]35] (E) and the MYC signature score from Fredlund et al. [[74]36]
   (F) from respectively 2 and 4 hours after pharmacological ALK blockade.
   Log2 transformed ratios of expression levels of TAE684 treated vs DMSO
   control samples are plotted.

   To identify other dynamically regulated pathways upon ALK inhibition,
   functional enrichment analysis was performed on a set of k-means
   clusters of genes with similar transcriptional responses over the
   timeframe. After k-means clustering with 9 centers, as defined by the
   elbow method, we combined clusters with similar expression patterns
   over time yielding a total of 6 different clusters (Figure [75]2A;
   [76]Supplementary Figures 4A, 4B, 5A). Moreover, 4 of these clusters,
   namely cluster A, B, D and E, are showing a clear dynamic pattern, with
   a change in expression starting at 120 or 240 minutes and increasing
   over time, while the other 2 are showing a more modest pattern over
   time with only a small change at the latest time point (Figure [77]2A,
   [78]Supplementary Figure 5A). Functional characterization of these 4
   clusters through evaluating the enrichment for gene ontology terms
   (MSigDB ‘c5 Gene Ontology (GO) Biological Process Ontology (BP) v6.0’),
   oncogenic signatures (MSigDB ‘c6 Oncogenic Signatures v5.0’) and
   pathways of the Reactome Database [[79]40, [80]41] confirmed that these
   clusters consist of MYC, MAPK or mTOR targets and are involved in
   regulation of the MAPK cascade, in keeping with the downregulation of
   the MYC(N), PI[3]K-AKT-mTOR and MAPK pathway (Figure [81]2B;
   [82]Supplementary Figure 5B, 5C). Moreover, cluster E is also enriched
   for genesets linked to epigenetic processes, regulating amongst others
   chromatin structure and gene expression (Figure [83]2B;
   [84]Supplementary Figures 5B, 5C).

Figure 2. Functional characterization of the genes in the clusters with the
MSigDB ‘c5 Gene Ontology (GO) Biological Process Ontology (BP) v6.0’.

   [85]Figure 2
   [86]Open in a new tab

   (A) The cluster plots represent the dynamic pattern of the expression
   of the genes belonging to 1 of the 6 clusters. The mean of the ratio of
   the expression in the TAE684 vs DMSO treated sample is plotted for each
   gene. The red lines show the average dynamic pattern of the expression
   of the genes belonging to these clusters, calculated by the average of
   the mean of the ratio of the expression in the TAE684 vs DMSO treated
   sample. (B) Plot showing the MSigDB ‘c5 Gene Ontology (GO) Biological
   Process Ontology (BP) v6.0’ genesets that are enriched in at least one
   of the four clusters, which are showing a clear dynamic pattern
   (clusters A, B, D, E). The size of each node corresponds to the number
   of genes overlapping between the cluster and the gene set and the
   colour represents the adjusted p-value of the enrichment test. Green
   stars indicate the genesets related to the MYC(N), KRAS-MAPK,
   PI[3]K/mTOR pathways.

   Taken together, this analysis provides insights into the dynamics of
   inactivation of ALK driven downstream pathways in ALK mutant
   neuroblastoma cells upon ALK inhibition and validates the applicability
   of the presented dataset to further scrutinize early temporal effects
   of ALK inhibition.

MYC(N) signaling is upregulated immediately following ALK inhibition

   To functionally scrutinize the transcriptional changes upon ALK
   inhibitor treatment, Gene Set Enrichment Analysis (GSEA [[87]42]) was
   performed using the ‘Hallmarks v5.0’ genesets from the Molecular
   Signatures Database (MSigDB)
   ([88]software.broadinstitute.org/gsea/msigdb) at the different time
   points. This analysis further validates that the signaling pathways
   KRAS (MAPK) and PI[3]K-mTOR are significantly repressed (Figure
   [89]3A). Remarkably, one of the MYC genesets (HALLMARK_MYC_TARGETS_1)
   is significantly enriched for genes upregulated 30 minutes after
   treatment and significantly enriched for genes downregulated 6 hours
   after pharmacological ALK blockade (Figure [90]3A, indicated in red).
   Moreover, we observed a significant increase in the Fredlund MYC
   signature [[91]36] 2 hours after ALK inhibition (p = 0.0038) (Figure
   [92]1F, [93]Supplementary Figure 2F). Therefore, we also performed GSEA
   on other MYC(N) activity signatures and confirmed for 4 other genesets
   an initial upregulation (at 10 or 30 minutes after ALK inhibitor
   treatment) followed by a downregulation (from 2 hours after ALK
   inhibitor treatment) (Figure [94]3B). The observation of this initial
   upregulation of MYCN activity scores is intriguing and needs further
   investigation.

Figure 3. Gene Set Enrichment Analysis (GSEA) with the hallmarks and the
MYC(N) signaling genesets shows that MYC(N) signaling is upregulated
immediately following ALK inhibition.

   [95]Figure 3
   [96]Open in a new tab

   (A and B) Heatmap showing the genesets of the ‘Hallmarks v5.0’ genesets
   from MSigDB (A) and an in house compiled gene set collection containing
   all MYC target genesets from this hallmark catalogue as well as
   publically available MYC(N) activity or target signatures [[97]35,
   [98]60–[99]66] (B), that are enriched among the genes upregulated after
   ALK inhibition (positively enriched) (red) or that are enriched among
   the genes downregulated after ALK inhibition (negatively enriched)
   (blue) according to GSEA (with FDR < 0.1) [[100]41]. Indicated in red
   are the genesets that are upregulated at earlier and downregulated at
   the later time points following ALK inhibition. Green stars indicate
   the genesets related to the MYC(N), KRAS-MAPK, PI[3]K/mTOR pathways.

Adrenomedullin (ADM) is the earliest upregulated gene upon pharmacological
ALK inhibition of NB cells

   In order to identify the set of early and late genes that significantly
   change transcriptionally, differential gene expression analysis
   comparing TAE684 treated cells with DMSO treated controls was performed
   at each time point. This analysis revealed that no significant
   transcriptional changes occur as early as 10 or 30 minutes after
   treatment (using adjusted p-value (False Discovery Rate (FDR)) < 0.05),
   while starting from 2 hours after treatment an increasing number of
   significantly, differentially expressed genes are identified
   ([101]Supplementary Table 1). Our observations are in line with these
   from Lai et al. [[102]43], who have shown that transcriptional events
   occur between 2 and 4 hours after inhibition of a receptor tyrosine
   kinase.

   Interestingly, one gene showed significant differential upregulation 1
   hour post-TAE684 treatment, i.e. adrenomedullin (ADM) (Figure [103]4A;
   [104]Supplementary Figure 6A). Interestingly, using second- and
   third-generation ALK inhibitors [[105]31], the upregulation of ADM upon
   ALK inhibition was confirmed in CLB-GA and Kelly ([106]Supplementary
   Figure 6B, 6C). Moreover, we could validate ADM upregulation 6 hours
   after treatment with TAE684 in more NB cell lines with an ALK
   amplification, an ALK^R1275Q or ALK^F1174L mutation (Figure [107]4B).
   Furthermore, the early upregulation of ADM was validated in the cell
   line NB-1 upon pharmacological ALK inhibition with TAE684 (Figure
   [108]4C), as the increase was already detectable 1 hour after the
   treatment. In addition, ADM expression levels are also significantly
   induced in the CLB-GA cell line, following PI[3]K/mTOR inhibitor
   BEZ-235, MEK antagonist trametinib and RET repressor vandetanib
   treatment [[109]31] ([110]Supplementary Figure 6D). Interestingly, ADM
   upregulation was also observed in the Anaplastic Large Cell Lymphoma
   (ALCL) cell line TS treated with three different ALK inhibitors for 6
   hours [[111]44] (Figure [112]4D).

Figure 4. Adrenomedullin (ADM) is the earliest upregulated gene upon
pharmacological ALK inhibition of NB cells.

   [113]Figure 4
   [114]Open in a new tab

   (A) ADM mRNA is significantly upregulated starting from 1 hour after
   TAE684 treatment of the CLB-GA cell line. Ratios of log2 transformed
   expression levels of TAE684 treated vs DMSO control samples are
   plotted. (B) ADM mRNA expression levels shown in several ALK wild type
   (NGP, SK-N-AS, IMR-32) and ALK mutant (NB-1, CLB-GA, LAN-5, UKF-NB3,
   SMS-KCNR, Kelly, SK-N-SH) cell lines treated for 6 hours with 0.32 μM
   TAE684 or DMSO. (C)ADM mRNA expression levels are upregulated in NB
   cell line NB-1 (ALK^amp) 1 hour and 6 hours after treatment with 0.32
   μM TAE684 compared to the DMSO control. (D) ADM expression levels are
   upregulated 6 hours upon ALK inhibition with three different ALK
   inhibitors in the ALCL cell line TS. (E) ADM mRNA expression levels are
   upregulated in 11 NB cell lines under hypoxic conditions. (F) ADM
   expression levels are higher in stage 4 primary neuroblastoma tumours
   versus neuroblastoma tumours of stage 1, 2, 3 and 4S. Boxplots
   represent mean ± 95% confidence interval. Statistical analyses:
   unpaired one-way ANOVA with Bonferroni correction (B. & C. & D.),
   paired t-test (E.) and unpaired t-test (F.). ^*P < 0.05, ^**P < 0.01,
   ^***P < 0.001.

Adrenomedullin is upregulated under hypoxic conditions in neuroblastoma

   As ADM is known to be induced under hypoxia [[115]45–[116]51], we
   investigated publically available transcriptome data from hypoxic
   versus normoxic neuroblastoma cells [[117]52] and confirmed ADM
   upregulation under hypoxic conditions in these cells (Figure [118]4E).
   Of further interest, ADM is higher expressed in more aggressive
   neuroblastoma tumours (stage 4 versus other stages) (Figure [119]4F).

   Furthermore, the observed ADM upregulation is intriguing and has a
   potentially important translational consequence. In addition to ADM
   activation upon hypoxia [[120]45–[121]51], ADM was also shown to act as
   growth factor, prevent apoptosis-mediated cell death, increase tumour
   cell motility and metastasis and induce angiogenesis in various cancer
   types [[122]47–[123]49, [124]51, [125]53–[126]59]. Of further
   importance, it has been reported that hypoxia, which can activate ADM
   [[127]45–[128]51], induces resistance to ALK inhibitors in non-small
   cell lung cancer [[129]60] and that ADM expression levels are increased
   in tyrosine kinase inhibitor sunitinib resistant renal cell carcinoma
   [[130]61]. In view of these observations, we evaluated whether
   combining an adrenomedullin receptor antagonist (ADM22-52) [[131]61]
   together with TAE684 would sensitize NB cells to ALK inhibition. No
   additional effects on decrease in cell viability were observed (data
   not shown). This observation could be due to the fact that elevated ADM
   levels have no effect on sensitivity for ALK inhibitors in
   neuroblastoma cells. Alternatively, given that the cell lines tested
   are very sensitive to the inhibitor with immediate strong and massive
   apoptotic effects, under these conditions elevated ADM levels may have
   no immediate effects on cell viability, while in a later stage in cells
   rendered resistant to ALK inhibition, combined exposure to both ALK
   inhibition and the ADM inhibitor might have effect.

MATERIALS AND METHODS

CLB-GA time series dataset

   The CLB-GA time series dataset has been described before [[132]31]. The
   neuroblastoma CLB-GA cell line (ALK^R1275Q) was cultured in RPMI-1640
   medium (Invitrogen), supplemented with fetal bovine serum (10%),
   kanamycin (100 μg/ml) penicillin/streptomycin (100 IU/ml), L-glutamine
   (2 mM) and HEPES (25 mM) (Life Technologies), and maintained at 37°C in
   a 5% C02-humidified environment. At different time points (10, 30
   minutes, 1, 2, 4 and 6 hours) after treatment with 0.32 μM of the small
   molecule ALK inhibitor NVP-TAE684 (hereafter TAE684) (SelleckChem,
   S1108) or DMSO (VWR) as solvent control, cells were harvested and RNA
   quality was analysed using Experion (Bio-Rad) prior to gene-expression
   profiling. Samples were labelled and hybridized to the Sureprint G3
   human GE 8x60K microarrays (Agilent Technologies), according to the
   manufacturer’s guidelines and starting from 200 ng RNA. The data was
   normalized with the vsn method in R version 3.3.3 (packages vsn and
   limma). The R package BioMart was used to annotate gene names to their
   corresponding probes. Probes detecting at least a two-fold higher
   expression than the negative control probes of the array in at least
   60% of the DMSO or TAE samples, were selected as background correction.
   Data can be accessed through ArrayExpress (accession number
   E-MTAB-3206) [[133]31].

Signature score analysis

   To establish MAPK, PI[3]K/mTOR and RET inhibitor signatures, we used
   published gene expression profiling data (ArrayExpress accession number
   E-MTAB-3206) [[134]31]. This dataset contains expression profiling data
   of the CLB-GA cell line 6 hours after treatment with MAPK inhibitor
   trametinib, the dual PI[3]K/mTOR repressor BEZ-235 or RET antagonist
   vandetanib and DMSO as control. Using the limma R-package, differential
   expression analysis was performed comparing the DMSO-control and the
   inhibitor treated samples. The established signatures consist of the
   differentially expressed genes with adjusted p-value (False Discovery
   Rate (FDR)) < 0.05. In addition, a published MYCN and a published MYC
   signature were used to establish the MYC(N) pathway activity [[135]35,
   [136]36]. Signatures score analysis was conducted using a rank-scoring
   algorithm as described previously [[137]31, [138]36]. For each time
   point and each duplicate separately, both the ratio between the TAE684
   treated sample and his corresponding control sample (Figure [139]1) as
   the absolute values ([140]Supplementary Figure 2) were plotted using
   GraphPad Prism 7.

Gene set enrichment analysis

   Gene set enrichment analysis (GSEA [[141]42]) was performed using the
   MSigDB ‘Hallmarks v5.0’ gene sets
   ([142]software.broadinstitute.org/gsea/msigdb) and an in house compiled
   gene set catalogue containing all MYC target genesets from this
   hallmark catalogue as well as publically available MYC(N) activity or
   target signatures [[143]35, [144]36, [145]62–[146]67]. The genesets,
   showing positively or negatively enrichment at minimum one time point
   and with a FDR < 0.1 are plotted in a heatmap. Data was plotted as the
   mean of the ratio of the TAE684 treated sample and his corresponding
   control sample at every time point.

Cluster analysis and pathway enrichment analysis in the clusters

   Using the Bayesian Information Criterion (BIC) and the elbow method, 9
   was defined as the optimal number of clusters in the dataset
   ([147]Supplementary Figure 4A, 4B). K-means clustering was performed on
   the expression level ratios of TAE684/DMSO samples for each time point
   in R (with k = 9). However, visual inspection of the expression
   patterns of the genes in the clusters showed that some clusters have a
   similar pattern over time (clusters 1 and 2, 4 and 7, 6 and 9)
   ([148]Supplementary Figure 4B) and were therefore merged, ending with 6
   final clusters ([149]Supplementary Figure 5A). For every cluster, the
   average response of the genes was plotted as a line (red line). The 4
   clusters that are showing a clear dynamic pattern over time, were
   screened for MSigDB ‘c5 Gene Ontology (GO) Biological Process Ontology
   (BP) v6.0’ ([150]software.broadinstitute.org/gsea/msigdb), MSigDB ‘c6
   Oncogenic Signatures v5.0’ and overrepresented Reactome pathways
   [[151]40, [152]41] using the R-packages clusterProfiler and Reactome
   Pathway Analysis [[153]68, [154]69].

Differential expression analysis

   At every time point, differential expression analysis was performed
   using the R package limma, comparing the DMSO-control and the
   TAE684-treated sample. The duplicate experiments, independently
   generated at different time points, were set as blocking factor.
   Significantly differentially expressed genes refer to those with an
   adjusted p-value (FDR) < 0.001. Results are shown in [155]Supplementary
   Table 2.

Analysis of ADM expression levels after ALK inhibition using qPCR

   ADM expression levels were measured in CLB-GA (ALK^R1275Q) and Kelly
   (ALK^F1174L) cells that were treated for 6 hours with DMSO as control
   solvent and 0.09 μM of ALK inhibitor, PF06463922 acetate
   (Sigma-Aldrich, PZ0271) (dissolved in sterile DMSO and diluted to the
   final concentration in culture medium) and in NB-1 (ALK^amp) cells that
   were treated for 1 and 6 hours with DMSO as control solvent and 0.32 μM
   of TAE684 (dissolved in sterile DMSO and diluted to the final
   concentration in culture medium).

   RNA extraction, cDNA synthesis and RT-qPCR of these samples was
   performed as described earlier [[156]31]. The Cq-values for ADM
   expression were normalized with data of at least two reference genes
   (TBP, YWHAZ, B2M, HPRT1 and SDHA) (primer sequences: [157]Supplementary
   Table 2) using qBasePlus software (Biogazelle).

Evaluation of ADM expression in public mRNA expression datasets

   ADM expression levels were evaluated in (1) CLB-GA cells treated with
   0.2 μM LDK378, 0.32 μM TAE684, 0.06 μM X396, 0.5 μM crizotinib, 0.05 μM
   trametinib (MEK-inhibitor), 0.5 μM BEZ-235 (dual PI[3]K/mTOR inhibitor)
   and 9.5 μM vandetanib for 6 hours (ArrayExpress accession number
   E-MTAB-3206) [[158]31], (2) 10 NB cell lines treated with 0.32 μM
   TAE684 for 6 hours (ArrayExpress accession number E-MTAB-3205)
   [[159]31], (3) the ALCL TS cell line treated with three different ALK
   inhibitors for 6 hours (GEO accession number [160]GSE6184) [[161]44],
   (4) 11 NB cell lines that were cultured under normoxic and hypoxic
   conditions (1% O[2] instead of 20%) for 18 hours (GEO accession number
   [162]GSE17714) [[163]52] and (5) a cohort of 283 neuroblastoma tumour
   samples (GEO accession number [164]GSE85047).

Statistical analyses

   Statistical significance was calculated with GraphPad Prism7 by
   unpaired one-way ANOVA with Bonferroni correction when comparing more
   than two unmatched groups, while (un)-paired t-test was chosen when
   comparing two groups.

CONCLUSIONS

   In summary, dynamic expression profiling following ALK inhibition of
   ALK mutated neuroblastoma cells revealed (1) unexpected early ADM
   upregulation with potential implications for design of more effective
   ALK targeted therapy, (2) an initial increase of MYC(N) activity
   immediately after ALK inhibition and (3) confirmed inhibition after 1
   to 2 hours of the ALK downstream MAPK, PI[3]K-AKT, RET and MYC(N)
   pathways.

SUPPLEMENTARY MATERIALS FIGURES AND TABLES

   [165]oncotarget-08-106820-s001.pdf^ (7.2MB, pdf)
   [166]oncotarget-08-106820-s002.xlsx^ (112KB, xlsx)

Acknowledgments