br Altered responses to EGF stimulation are the
Altered responses to EGF stimulation are the potential results of network topology modulations (Koseska and Bastiaens, 2017;
Santos et al., 2007) that are induced by POI abundance changes. To verify this, we focused on two pairs of EGF-induced shape-switching relationships, KSR2 to p-MAPKAPK2 (MAPKAPK2 signaling is essential for tumor cell survival; Morandell et al., 2013) and TEC to p-ERK1/2 (a pair of non-monotonic rela-tionship). We performed additional perturbation experiments us-ing MEK inhibitor CI1040 and characterized signaling network
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variations over a large dynamic range of POI concentrations. We found that at the medium 1186-30-7 levels of KSR2, MAPKAPK2 phosphorylation was MAPK-ERK cascade-depen-dent and that it could be highly induced by EGF stimulation; high KRS2 expression levels contributed to MAPK-ERK-inde-pendent MAPKAPK2 signaling that had a weak response to EGF stimulation and was insensitive to MEK inhibition (Figures S1E–S1G). Increased TEC abundance led to non-monotonic ERK1/2 phosphorylation that was partially diminished by MEK inhibition (Figure S1H), indicating the presence of both MEK-dependent and MEK-independent pathways for the TEC over-expression-induced ERK activation. The MEK-dependent signaling was reduced at high TEC expression levels, potentially due to a negative regulatory mechanism that is only activated in the presence of high concentrations of TEC (Figures S1H–S1J). Here, by characterizing signaling network variations over a large dynamic range of POI concentration, our analysis revealed com-plex modulations of signaling network topology in a protein abundance-dependent manner.
Functional Classification of Kinases and Phosphatases Based on Signaling Network Modulations
To understand the regulatory and functional similarity of overex-pressed POIs, we indicated the sign for signaling relationships (according to their directionality) to the BP-R2 (Table S4; STAR Methods). Then, we applied the dimensional reduction algorithm t-distributed stochastic neighbor embedding (t-SNE) (van der Maaten and Hinton, 2008) to the matrix of all 60 measured signaling parameters (as signed-BP-R2 scores) over the 327 signaling network-influential kinases and phosphatases (Fig-ure 2A). As expected, homologous groups of kinases and phos-phatases showed nearly identical influences on signaling and overlapped with each other on the t-SNE plot (Figure 2A, green boxes). This demonstrates that our method sensitively, specif-ically, and reproducibly detected abundance-dependent signaling behaviors. All eight overexpressed SRC family mem-bers—SRC, YES1, BLK, LCK, LYN, HCK, FGR, and FRK—co-localized in the t-SNE analysis (Figure 2A, purple box), indicating that these kinases have similar abundance-dependent signaling effects, despite the previously revealed differential patterns of expression (Parsons and Parsons, 2004). Members of protein tyrosine phosphatase (PTPN1, PTPN2, and PTPN5) and dual-specificity phosphatase (DUSP3, DUSP4, DUSP6, DUSP7, DUSP10, and DUSP16) families were grouped together, suggest-ing similarities in regulating the measured cancer signaling network (Figure 2A, orange box). r> We then applied hierarchical clustering based on signed-BP-R2 scores of all of the measured phosphorylation sites (Figures S2A and S2B) to further analyze functional similarities among all of the kinases and phosphatases. This led to the identification of 10 major signaling response clusters (color coded on the t-SNE plot in Figure 2A). Correspondence analysis was per-
formed between these identified clusters and classes of kinases and phosphatases previously established based on catalytic domain sequences (Johannessen et al., 2010; Sacco et al., 2012b) (Figure S2C). In certain cases, proteins with partial sequence identity had similar influences on signaling. For example, all of the kinases in cluster 1 are receptor or non-recep-tor tyrosine kinases (Figure S2C). These kinases are early re-sponders to stimuli, as shown in the literature-based graph of canonical EGF receptor (EGFR) networks (Figure 2B). Clusters 5, 9, and 10 include non-receptor serine or threonine kinases and kinases classified in the group of ‘‘other’’ (i.e., kinases that do not fit into any of the major groups) (Figure S2C). Despite conserved catalytic domain sequences, kinases in clusters 5, 9, and 10 induced different cellular responses (Figure 2B). Clus-ter 7 proteins had negative relationships with the mediators of the MAPK-ERK pathway when cells were treated with EGF (Fig-ure 2B). Cluster 7 mostly consists of protein tyrosine phospha-tases, but also includes a few proteins from the classes of non-receptor serine or threonine kinase and lipid kinases (Fig-ure S2C). Comparing our identified clusters to the phylogenetic tree of the human kinome (Eid et al., 2017; Manning et al., 2002), we observed the enrichment of cluster 1 in the tyrosine kinase group (Figure S2D, orange arrow). In addition, PKC family members are enriched in cluster 5 (Figure S2C, brown arrow), and MAP3Ks are enriched in cluster 9 (Figure S2D, blue arrow). In summary, the human kinome- and phosphatome-wide over-expression analysis identified 10 clusters of kinases and phos-phatases, with distinct signaling patterns found for each cluster. These clusters partially matched the sequence-based classifica-tion and expanded the functional classification of the human ki-nases and phosphatases based on their abundance-dependent modulations to the cancer signaling network.