For interactome data, ratios of identified and quantified interactors were normalized to the ratio of ALK to account for uneven efficiency during individual immunoprecipitations performed in parallel. Statistically significantly changing druggable ALK interactors were determined by significance B testing (P < 0.05) using Perseus version 1.3.9.10 (data file S1) (77).

For the phosphotyrosine interactome data, a minimum of three razor and unique peptides were required across eight conditions, and label-free quantitation intensities (79) were required for one of four phosphotyrosine-peptide conditions (DMSO, n = 2 independent biological replicates; LDK378, n = 2 independent biological replicates) with no restrictions for the nonphosphopeptide (data file S2). Quantitative interaction proteomics analysis was performed by t test–based comparison of protein intensities between each phosphotyrosine-containing peptide (bait) and the nonphosphorylated counterpart peptide (control) using the web tool pulldown.jensenlab.org. The NB1 cell line proteome was used to correct for protein abundance in the pull-down analysis. Data were analyzed with ratio cutoff of 2.0 (log2), P value cutoff of 3.0 (−log10), and infinity P value cutoff of 3.125 (−log10). Statistical significance was concluded whenever S score > 1.

For the phosphoproteomics data, only peptides with a phosphorylation site localization probability of at least 0.75 (class I; data file S3) were included in the bioinformatics analyses. To identify phosphorylation sites with statistically significantly regulated ratios, we compared the ratio distributions of all quantified phosphopeptides with all nonphosphorylated peptides, which we expect not to change and therefore specify our technical variance. To determine the level of regulation, cutoffs for up- and down-regulation were set to allow for an estimated 5% false-positive rate based on the distribution of ratios of identified and quantified nonphosphorylated peptides as described in (29). Regulated interactors and phosphorylation sites were considered common and representative of ALK signaling whenever deemed regulated by two of three inhibitors. Proteome data were filtered for common contaminants, and protein quantifications were reported as a median of two replicates by iBAQ intensities (table S4) (37). Analysis of GO term enrichment related to biological process (interactome) and KEGG pathway annotation enrichment (phosphoproteome) was performed using the DAVID bioinformatics resource (80). For the GO analysis, the used gene sets were derived from the pool down-regulated by inhibitors (two of three). For the KEGG analysis, gene sets derived from each pool of regulated phosphorylation sites (up- and down-regulated) for each inhibitor as well as the commonly regulated interactors (two of three inhibitors) were used. Statistical significance was concluded when P < 0.05 by Fisher’s exact test. The protein association network based on ALK interactome data was obtained using the STRING database (version 10) (81). All active interaction sources were included in the network, and a medium confidence score of more than 0.4 was required. To assess for sequence bias around the regulated phosphorylation sites, sequence motif logo plots (±6 amino acids adjacent to the identified phosphorylated sites) were generated and visualized using the iceLogo software (82) with default parameters (P < 0.01). The analysis was performed independently for the group of phosphorylation sites with up- and down-regulated SILAC ratios and compared with nonregulated site sequences, which was used as a common background. The nonregulated phosphorylation sites were defined as sites with ratios within less than 1 SD away from the mean of the distribution of identified nonphosphorylated peptides. Linear sequence motifs for kinase substrates were annotated using Perseus version 1.3.9.10 and analyzed for overrepresentation among the up-regulated phosphopeptides compared to the unregulated phosphopeptides using Fisher’s exact test. Motifs with P < 0.05 were considered statistically significant.

For the TMT 11-plex phosphoproteome, all measured peptide intensities were normalized using the “normalizeQuantiles” function from the Bioconductor R package LIMMA (83). Subsequent data analysis was performed using Perseus version 1.5.1.12. The quantile normalized ratios were further normalized by median subtraction in the rows, and the data were filtered for contaminants and reverse hits. Only peptides with a phosphorylation site localization probability of at least 0.75 (class 1; data file S5) were included in the bioinformatic analyses. Volcano plots were generated by plotting the –log10 transformed and FDR-adjusted P values (q-value threshold of 0.05) derived from a two-sided t test versus log2-transformed fold changes. Statistical significance was determined on the basis of a hyperbolic curve threshold of s0 = 0.1 using Perseus.

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