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An Integrative Analysis of Tumor Proteomic and Phosphoproteomic Profiles to Examine the Relationships Between Kinase Activity and Phosphorylation*

Open AccessPublished:June 21, 2019DOI:https://doi.org/10.1074/mcp.RA119.001540
      Phosphorylation of proteins is a key way cells regulate function, both at the individual protein level and at the level of signaling pathways. Kinases are responsible for phosphorylation of substrates, generally on serine, threonine, or tyrosine residues. Though particular sequence patterns can be identified that dictate whether a residue will be phosphorylated by a specific kinase, these patterns are not highly predictive of phosphorylation. The availability of large scale proteomic and phosphoproteomic data sets generated using mass-spectrometry-based approaches provides an opportunity to study the important relationship between kinase activity, substrate specificity, and phosphorylation. In this study, we analyze relationships between protein abundance and phosphopeptide abundance across more than 150 tumor samples and show that phosphorylation at specific phosphosites is not well correlated with overall kinase abundance. However, individual kinases show a clear and statistically significant difference in correlation among known phosphosite targets for that kinase and randomly selected phosphosites. We further investigate relationships between phosphorylation of known activating or inhibitory sites on kinases and phosphorylation of their target phosphosites. Combined with motif-based analysis, this approach can predict novel kinase targets and show which subsets of a kinase's target repertoire are specifically active in one condition versus another.

      Graphical Abstract

      In cellular systems, function is largely carried out by proteins. Regulation of protein function is essential for appropriate cellular function, and dysfunction of regulation can lead to disease states such as cancer (
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      Deregulation of cell signaling in cancer.
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      Chapter 4: Protein interactions and disease.
      ). Though one level of protein regulation is through regulating the amount of protein present to accomplish the function, there are multiple other levels of functional regulation including localization, degradation, and post-translational modification (PTM)
      The abbreviations used are: PTM, post-translational modification; CPTAC, Clinical Proteomic Tumor Analysis Consortium; HGSC, high grade serous carcinoma; iMAC, immobilized metal ion affinity chromatography; iTRAQ, isobaric tag for relative and absolute quantitation; RPLC, reverse phase liquid chromatography; TCGA, The Cancer Genome Atlas.
      1The abbreviations used are: PTM, post-translational modification; CPTAC, Clinical Proteomic Tumor Analysis Consortium; HGSC, high grade serous carcinoma; iMAC, immobilized metal ion affinity chromatography; iTRAQ, isobaric tag for relative and absolute quantitation; RPLC, reverse phase liquid chromatography; TCGA, The Cancer Genome Atlas.
      (
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      ). There are many different forms of PTM utilized by cellular machinery, but phosphorylation is among the most prevalent and best understood (
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      ,
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      ). Phosphorylation can lead to structural changes affecting activity, changes in affinity for substrates or protein binding, degradation, or changes in localization (
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      ). Phosphorylation is employed in signaling cascades from pathways that link cell-surface receptors to transcription factors in the nucleus and regulate cell differentiation, growth, and migration, among others (
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      ), but these are limited in coverage (
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      ). These data sets have revealed a large number of sites on proteins that can be phosphorylated where there is no functional information about the kinase that is affecting this phosphorylation and/or the functional effect of the phosphorylation. Several recent studies have examined relationships between kinase activity and specific phosphorylation, for example, of phosphorylation of kinase substrates and kinase activity (
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      • Lawrence R.T.
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      ,
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      ), in the context of developing predictive methods for kinase specificity. However, there remains a great need to understand the complex relationships among kinase abundance, phosphorylation of activating sites and the activity of kinases. These relationships are important to the understanding of dysfunctional signaling pathways in cancer and to identify novel therapeutic treatments aimed at kinases and their downstream targets.
      The Cancer Genome Atlas (TCGA) recently characterized a large number of ovarian high-grade serous carcinoma (HGSC) tumors (
      • Cancer Genome Atlas Research Network
      Integrated genomic analyses of ovarian carcinoma.
      ) and breast cancer tumors (
      • Cancer Genome Atlas Research Network
      Comprehensive molecular portraits of human breast tumours.
      ). Previously we reported the first large-scale proteomic and phosphoproteomic characterizations of subsets of these tumors (
      • Zhang H.
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      Integrated proteogenomic characterization of human high-grade serous ovarian cancer.
      ,
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      ) by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) (
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      ). In our studies, we analyzed 69 HSGC and 83 breast cancer tumors using mass-spectrometry assisted proteomics to acquire quantitative measurements for more than 10,000 proteins and used phosphosite enrichment to identify and quantify the abundance of over 25,000 phosphorylated peptides mapping to phosphosites. Our previous analysis showed that tumors from short and long surviving patients were well-separated by phosphoproteomics when summarized at the pathway level but not as well by the protein or transcript abundance, indicating that phosphorylation levels are an effective measure of pathway activity.
      In the current study, we have leveraged the deep proteomic data sets generated by CPTAC to evaluate the relationship between protein abundance and the phosphorylation of cognate phosphosites. We investigate the ability of global proteome-wide correlation analysis of kinase protein expression measurements and phosphopeptide quantifications to pair phosphorylation sites with protein kinases. Integrated analyses of the proteome and phosphoproteome profiles is used to identify potential kinase-target phosphosite interactions in ovarian cancer. Our exploration of the association among protein abundance, phosphorylation and function indicate the complexity of such relationships in cancer.

      DISCUSSION

      Though kinase phosphorylation and signaling are crucial to the understanding of many biological processes, and mass-spectrometry techniques have advanced rapidly allowing the measurement of the abundance of tens of thousands of phosphosites from one sample, understanding of the basic relationships between kinase activity and phosphorylation remain unclear. We have analyzed deep proteomic and phosphoproteomic data from tumor samples for ovarian and breast tumors (
      • Zhang H.
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      • Wu C.
      • Moore R.J.
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      • Tabb D.L.
      • Fenyo D.
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      • Wang Y.
      • Rodriguez H.
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      • Cope L.
      • Pandey A.
      • Zhang B.
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      Integrated proteogenomic characterization of human high-grade serous ovarian cancer.
      ,
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      • Clauser K.R.
      • Wang P.
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      • Qiao J.W.
      • Cao S.
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      • Kawaler E.
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      • Gatza M.L.
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      • Wang J.
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      • Mesri M.
      • Rodriguez H.
      • Ding L.
      • Paulovich A.G.
      • Fenyo D.
      • Ellis M.J.
      • Carr S.A.
      Proteogenomics connects somatic mutations to signalling in breast cancer.
      ). In the current study, we show that phosphorylation levels are largely unrelated to the protein abundance of the cognate protein or the phosphorylation of other sites on the same protein, neither of which are surprising observations. Somewhat surprisingly we found that abundance of the kinase is largely uncorrelated with its activity, as assessed by phosphorylation of known substrates. However, we found that using a stringent threshold for this relationship was a reasonable approach for the identification of novel substrates for some kinases.
      Finally, we showed that phosphorylation of kinases on their activating or inhibiting sites did not seem to correlate well with their activity. In the case of GSK3B, the reported inhibitory site was positively correlated with activity, directly opposite of the expected relationship. This raises several possibilities. One possibility is that the original information about the site is incorrect. However, a number of publications have reported previously that this site is inhibitory under several conditions (
      • Ko H-W.
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      GSK3β inactivation promotes the oncogenic functions of EZH2 and enhances methylation of H3K27 in human breast cancers.
      ,
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      • Li P.
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      The cytoprotective effect of hyperoside against oxidative stress is mediated by the Nrf2-ARE signaling pathway through GSK-3beta inactivation.
      ). Another likely possibility is that the action of the kinase is highly context dependent, with different sets of substrates being targeted under different conditions. It's possible that the ovarian and breast cancer environments, overall, represent a set of conditions under which GSK3B acts differently. A third possibility is that the measurement of phosphorylation on specific sites by either mass spectrometry or RPPA is a population-based measurement, such that the effect of specific phosphorylation events is obscured by population differences.
      Previously, some studies have used phosphorylation levels that have been normalized to protein abundance (
      • Zhang H.
      • Liu T.
      • Zhang Z.
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      • Zhang B.
      • McDermott J.E.
      • Zhou J.Y.
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      • Chen L.
      • Ray D.
      • Sun S.
      • Yang F.
      • Chen L.
      • Wang J.
      • Shah P.
      • Cha S.W.
      • Aiyetan P.
      • Woo S.
      • Tian Y.
      • Gritsenko M.A.
      • Clauss T.R.
      • Choi C.
      • Monroe M.E.
      • Thomas S.
      • Nie S.
      • Wu C.
      • Moore R.J.
      • Yu K.H.
      • Tabb D.L.
      • Fenyo D.
      • Bafna V.
      • Wang Y.
      • Rodriguez H.
      • Boja E.S.
      • Hiltke T.
      • Rivers R.C.
      • Sokoll L.
      • Zhu H.
      • Shih I.M.
      • Cope L.
      • Pandey A.
      • Zhang B.
      • Snyder M.P.
      • Levine D.A.
      • Smith R.D.
      • Chan D.W.
      • Rodland K.D.
      Integrated proteogenomic characterization of human high-grade serous ovarian cancer.
      ) whereas others have used unnormalized phosphoproteomic data (
      • Mertins P.
      • Mani D.R.
      • Ruggles K.V.
      • Gillette M.A.
      • Clauser K.R.
      • Wang P.
      • Wang X.
      • Qiao J.W.
      • Cao S.
      • Petralia F.
      • Kawaler E.
      • Mundt F.
      • Krug K.
      • Tu Z.
      • Lei J.T.
      • Gatza M.L.
      • Wilkerson M.
      • Perou C.M.
      • Yellapantula V.
      • Huang K.L.
      • Lin C.
      • McLellan M.D.
      • Yan P.
      • Davies S.R.
      • Townsend R.R.
      • Skates S.J.
      • Wang J.
      • Zhang B.
      • Kinsinger C.R.
      • Mesri M.
      • Rodriguez H.
      • Ding L.
      • Paulovich A.G.
      • Fenyo D.
      • Ellis M.J.
      • Carr S.A.
      Proteogenomics connects somatic mutations to signalling in breast cancer.
      ,
      • Vasaikar S.
      • Huang C.
      • Wang X.
      • Petyuk V.A.
      • Savage S.R.
      • Wen B.
      • Dou Y.
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      • Arshad O.A.
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      • Mo Q.
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      • Rodriguez H.
      • Smith R.D.
      • Rodland K.D.
      • Liebler D.C.
      • Liu T.
      • Zhang B.
      Proteogenomic analysis of human colon cancer reveals new therapeutic opportunities.
      ), and often phosphoproteomic analyses are conducted without gathering corresponding global protein abundance data (
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      • Singer J.W.
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      Inhibition of interleukin-1 receptor-associated kinase-1 is a therapeutic strategy for acute myeloid leukemia subtypes.
      ,
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      Empirical inference of circuitry and plasticity in a kinase signaling network.
      ,
      • Beekhof R.
      • van Alphen C.
      • Henneman A.A.
      • Knol J.C.
      • Pham T.V.
      • Rolfs F.
      • Labots M.
      • Henneberry E.
      • Le Large T.Y.
      • de Haas R.R.
      • Piersma S.R.
      • Vurchio V.
      • Bertotti A.
      • Trusolino L.
      • Verheul H.M.
      • Jimenez C.R.
      INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases.
      ). There are differences of opinion about whether to normalize phosphopeptide abundance to the cognate protein abundance and each approach comes with advantages and caveats that must be considered in interpretation. Leaving the data unnormalized means that increases in phosphopeptide abundance (and thus measured phosphorylation of the associated sites) may also reflect changes in protein abundance. However, normalization may obscure information about kinase activity that is inherent in protein abundance.
      Previous studies have analyzed relationships within phosphoproteomic data sets to look at kinase activity. Ochoa et al., compiled a large set of phosphoproteomic data from different studies and used this to examine the relationship between cell treatment and kinase activation patterns by assessing overall phosphorylation of known kinase substrates (
      • Ochoa D.
      • Jonikas M.
      • Lawrence R.T.
      • El Debs B.
      • Selkrig J.
      • Typas A.
      • Villen J.
      • Santos S.D.
      • Beltrao P.
      An atlas of human kinase regulation.
      ). Additionally, they reported that the phosphorylation of one known activating site on AURKA was well-correlated with AURKA activity. However, our findings show that not all known activating or inhibitory sites on kinases behave in such a straightforward manner, with many sites seeming to display behavior indicative of more complicated regulatory processes. Similarly, Petsalaki et al., showed that known substrates of kinases were significantly enriched in groups of correlated phosphosites, showing that this approach could be used to identify candidate kinase-substrate relationships (
      • Petsalaki E.
      • Helbig A.O.
      • Gopal A.
      • Pasculescu A.
      • Roth F.P.
      • Pawson T.
      SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets.
      ). A study by Ayati et al., uses, in part, the same ovarian data set generated by our group to build a predictive method for identifying novel kinase substrates (
      • Ayati M.
      • Wiredja D.
      • Schlatzer D.
      • Maxwell S.
      • Li M.
      • Koyuturk M.
      • Chance M.R.
      CoPhosK: A method for comprehensive kinase substrate annotation using co-phosphorylation analysis.
      ). In this study, the authors show that phosphorylation sites known to be targets of a kinase are significantly more correlated with each other than are all phosphosites in the data set. This “co-phosphorylation” is significant, but the effect, like our results, is very small in terms of correlation. This result fits well with our results showing a modest, but significant, correlation between kinase abundance and substrate phosphorylation (see Fig. 4), given that it is likely that multiple phosphosites correlated with the same kinase level would also be correlated with each other.
      Given the highly heterogeneous nature of these samples, tumors representing different genetic backgrounds, environmental histories, and subtypes of ovarian and breast cancer, it is somewhat surprising that we uncovered any relationships at all. Many previous studies of such relationships have been focused on more highly controlled systems with homogenous genetic and environmental backgrounds and rigorously controlled experimental conditions. We recognize that a limitation of our findings is the heterogeneous nature of our data but emphasize that our findings represent a lower bound based on utilization of biologically relevant samples. Our findings are based on sampling the diverse cells in a tumor and will mask the dynamic nature of phosphorylation and signaling. However, our previous results have indicated that the state of phosphorylation in this snapshot of the distribution of dynamic states in tumors is more closely related to phenotype (overall survival) than the proteome, transcriptome, or genetic composition (
      • Zhang H.
      • Liu T.
      • Zhang Z.
      • Payne S.H.
      • Zhang B.
      • McDermott J.E.
      • Zhou J.Y.
      • Petyuk V.A.
      • Chen L.
      • Ray D.
      • Sun S.
      • Yang F.
      • Chen L.
      • Wang J.
      • Shah P.
      • Cha S.W.
      • Aiyetan P.
      • Woo S.
      • Tian Y.
      • Gritsenko M.A.
      • Clauss T.R.
      • Choi C.
      • Monroe M.E.
      • Thomas S.
      • Nie S.
      • Wu C.
      • Moore R.J.
      • Yu K.H.
      • Tabb D.L.
      • Fenyo D.
      • Bafna V.
      • Wang Y.
      • Rodriguez H.
      • Boja E.S.
      • Hiltke T.
      • Rivers R.C.
      • Sokoll L.
      • Zhu H.
      • Shih I.M.
      • Cope L.
      • Pandey A.
      • Zhang B.
      • Snyder M.P.
      • Levine D.A.
      • Smith R.D.
      • Chan D.W.
      • Rodland K.D.
      Integrated proteogenomic characterization of human high-grade serous ovarian cancer.
      ).

      Data Availability

      All raw primary MS data for the tumor samples analyzed in this study is publicly available from the CPTAC Data Coordinating Center (https://cptac-data-portal.georgetown.edu).

      Acknowledgments

      The proteomics work described herein was performed in the Environmental Molecular Sciences Laboratory, a U.S. Department of Energy (DOE) national scientific user facility located at the Pacific Northwest National Laboratory (PNNL) in Richland, Washington. PNNL is a multi-program national laboratory operated by Battelle Memorial Institute for the DOE under Contract DE-AC05-76RL01830.

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