br level of the GFP tagged POI with vemurafenib treatment
level of the GFP-tagged POI), with vemurafenib treatment compared to the DMSO-treated control (Figure 7C). This indi-cates that MEK1DD overexpression contributes to cell survival. Similarly, all nine examined kinases showed an enrichment of cell abundance in the fourth bin; in six cases, this enrichment was statistically significant (Figure 7C). These results reveal that in melanoma A375 cells, the overexpression of kinases capable of ligand-independent ERK activation reduces cellular dependency on signaling inputs from BRAFV600E.
Three of the tested kinases, MAP3K8, MOS, and SRC, were previously suggested to mediate MAPK-ERK inhibition resis-tance in A375 FG-4592 (Johannessen et al., 2013). In analyzing the cell viability data in the same study, we noted that MAP3K8 and MOS only induced resistance to RAF inhibition, whereas SRC overexpression caused resistance to both RAF and MEK individual inhibitions and the concurrent RAF-MEK inhibition (Figure S7E) (Johannessen et al., 2013). We hypothesize that the difference in drug responses are due to the MEK dependency of the POI overexpression-induced ERK activation (i.e., an over-expressed POI directly activating ERK will not be influenced by upstream RAF and/or MEK inhibition). To test this, A375 cells were transfected individually with vectors encoding ABL1,
fenib, MEK inhibitor CI1040, or the combination of both inhibitors for 3 h. As expected, cells overexpressing MEK1DD were not sensitive to vemurafenib, whereas treatment with CI1040 or the combination treatment completely blocked ERK activation (Fig-ures 7D and S7F). Similarly, cells that overexpressed MAP3K8, MAP3K2, or MOS were resistant to vemurafenib but sensitive to CI1040, suggesting that MEK activity is necessary for the MAPK-ERK reactivation induced by these three kinases (Fig-ure 7D, purple boxes). Other POIs, including ABL1, BLK, FES, NTRK2, SRC, and YES1, showed abundance-dependent ERK activation, even with the combination treatment of vemurafenib and CI1040, indicating that these proteins activate ERK in an MEK-independent manner (Figures 7D and S7F).
Analysis of the POI abundance-dependent p-MEK1/2 levels confirmed that MAP3K8, MAP3K2, and MOS induced MEK1/2 activity (Figures 7E and S7F). Kinases BLK, FES, SRC, and YES1 did not cause abundance-dependent MEK1/2 phosphory-lation in all conditions (Figure 7E), validating that the overex-pression of these kinases did not activate MEK (Figure S7F). Abundances of both ABL1 and NTRK2 showed positive correla-tions with p-MEK1/2 levels only when cells were treated simulta-neously with CI1040 and vemurafenib (Figure 7E). This suggests that ABL1 and NTRK2 activate both MEK and ERK. The addition of CI1040 blocked the MEK-ERK binding (Allen et al., 2003), lead-ing to the reduced ERK activity and the ERK-MEK negative feedback, whereas the POI-induced signal inputs on MEK were constant, resulting in the increased MEK phosphorylation levels (Figure S7F). The diverse MAPK-ERK reactivation mechanisms induced by these kinases are illustrated in Figure 7F. We note
(D and E) Overexpression-induced signaling relationships to p-ERK1/2 (D) and p-MEK1/2 (E) under the treatment conditions indicated by line colors.
(F) Illustrations of the diverse MAPK-ERK reactivation mechanisms induced by different assessed POIs and the targets of applied inhibitors, vemurafenib and CI1040.
that the data in our kinome and phosphatome screen with HEK293T cells were indicative of MEK dependency in MAPK-ERK reactivation, as the kinases MAP3K8, MOS, and MAP3K2 had high signaling relationship strength to p-MEK1/2 (Figure 7G). Our analysis predicted potential biomarkers of MAPK-ERK reac-
tivation and identified a key mechanism for drug resistance in melanoma cells carrying the BRAFV600E mutation. We further
classified MAPK-ERK reactivation mechanisms and revealed ki-nases that induce resistance to BRAF-MEK combined inhibition.
The data described here are unique for the broad coverage of the human kinome and phosphatome, the multiplexed measure-ment of cellular phosphorylation states and dynamics at sin-gle-cell resolution, and the wide continuous abundance range (over three orders of magnitude) over which proteins of interest were studied. Our analyses enabled protein abundance-deter-mined functional classification, signaling kinetics quantification, and the identification of potential biomarkers of drug resistance.
Protein abundance and mRNA expression levels of kinases and phosphatases have been quantified in normal and diseased tissues by multiple approaches (Petryszak et al., 2016; Uhlen et al., 2017; Wang et al., 2015). Our analysis, for the first time, characterized at kinome- and phosphatome-wide scope how these proteins differentially modulate signaling network behav-iors when expressed over a concentration gradient. In the over-expression effect-based functional classification, we assigned kinases and phosphatases into 10 clusters that partly agreed with the kinase and phosphatase catalytic specificities, indi-cating the dissimilar network alterations between signaling pro-tein overexpression and activation. Functions of these signaling proteins include non-catalytic roles such as allosteric regulation and scaffolding (Kung and Jura, 2016). We showed that our unique analysis is able to capture these non-catalytic effects (Figure S3B). This finding is also highly relevant in cancer thera-peutics in that targeting the catalytic function of a kinase may not affect the deregulated signaling caused by abundance changes. Future work with a kinome- and phosphatome-scale catalytically inactive mutant library would allow global characterization of signaling protein non-catalytic effects.