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  • br modulations determined by kinase abundance In


    modulations determined by kinase abundance. In summary, these data show that protein overexpression effects can be cat-alytic or non-catalytic and suggest that activity-based modeling of signaling network deregulation for drug target discovery alone is likely insufficient.
    Signaling Relationships Detected in Kinome- and Phosphatome-wide Analysis
    The functions of many kinases and phosphatases analyzed in our screen are unknown or only poorly characterized. We hypothesized that our global analysis could lead to the identifica-tion of signaling relationships. To assess this, we performed a systematic comparison between all identified overexpression-induced signaling relationships and records in OmniPath, an integrated database of literature-curated signaling interaction information (Tureiā‚¬ et al., 2016). We first mapped all pairs of rela-tionships to the OmniPath signaling network and then computed the signed, directed paths for each pair of relationship (Krumsiek et al., 2011; Perfetto et al., 2016). The distance between an over-expressed protein and a measured FG4592 site is repre-sented by the length of the path (Figure 4A). For example, a distance of 0 indicates the relationship between the overex-pressed POI and its own phosphorylation levels. Of 14 pairs of signaling relationships with a known distance of 0, 12 had strong BP-R2 values with and without 10-min EGF stimulation (Fig-ure 4A), revealing that the phosphorylation level of a particular kinase is often determined by its own abundance, even in the absence of additional perturbation.
    We detected 208 (16%) strong relationships (BP-R2 > 0.13) with infinite distance (Figure 4A; Table S6), which is indicative of connections not described previously. In total, 93 overex- 
    pressed POIs contributed to these signaling relationships, which were enriched (in absolute count) in clusters 2, 3, and 4 and to a lesser extent in cluster 6 (Figure 4B). We did not detect any rela-tionships with infinite distance in clusters 9 or 10 (Figure 4B); POIs from these clusters participate in MAPK signal transduction (Figure 3A), which is well characterized. We also assessed the distribution of infinite paths for each kinase and phosphatase class and did not detect any enrichment (Figure S4A). There were 132 pairs of strong relationships between proteins with length of signed directed path >3 in OmniPath, suggesting potentially undiscovered direct or short-range connections (Figure 4A).
    Many potential signaling relationships were related to disease and to poorly characterized kinases (Figures 4C, 4D, and S4B). For instance, high levels of RIOK2 (highlighted in Figure 4C) have been recently shown to correlate with the poor prognosis of patients with non-small-cell lung cancer, but the underlying signaling mechanisms are unclear (Liu et al., 2016). We discov-ered that RIOK2 overexpression affected several phosphorylation sites, most strongly Thr172 on adenosine 50 monophosphate-activated protein kinase a (AMPKa), Ser257/Thr261 on MKK4/7, and Thr180/Tyr182 on p38 (Figure 4C), indicating the activation of the AMPK-p38 axis upon RIOK2 overexpression. The AMPK-p38 axis regulates cellular energy metabolism, contributing to cancer cell survival in nutrient-deficient conditions (Chaube et al., 2015; Zadra et al., 2015). In cancer proteome data from the Clinical Proteomic Tumor Analysis Consortium (Koboldt et al., 2012), we found that expression levels of RIOK2 were highly correlated with levels of AMPK subunits b and g and the AMPK activator LKB1 (STK11), confirming RIOK2 as a co-regulatory kinase in the AMPK signaling pathway (Figures S4C and S4D).
    Figure 5. Effects of EGF Stimulation on 39 Kinases and Phosphatases
    (A) Heatmap of signed-BP-R2 scores for measured signaling relationships over a 1-h EGF stimulation time course. Six identified groups of kinases and phos-phatases are labeled in color codes.
    (B) For one representative POI from each group, signaling relationships to all measured phosphorylation sites, as quantified by signed-BP-R2, are shown in the literature-guided canonical signaling network map.
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    To further illustrate the clinical relevance of overexpression-induced AMPK activation, we coupled our data of kinase and phosphatase overexpression effects with the proteome data of breast cancer and ovarian cancer patients and their prognosis in-formation (Mertins et al., 2016; Zhang et al., 2016) (Figures S4E and S4F). We found three kinases (CSNK1A1, NEK7, and TLK1) and a phosphatase (CDC25C) inducing AMPK activation when overexpressed and affecting patient outcomes; patients overex-pressing any of these kinases had significantly worse prognoses in comparison to the patients underexpressing the same kinase (Figures S4G and S4H). Our data suggest that kinase overexpres-sion-induced AMPK activation is related to the prognosis in can-cer patients and that AMPK is a potential therapeutic target for patients overexpressing proteins, such as RIOK2, CSNK1A1, CDC25C, NEK7, and TLK1.