INKA , an integrative data analysis pipeline for phosphoproteomic inference of active kinases
2019; Springer Nature; Volume: 15; Issue: 4 Linguagem: Inglês
10.15252/msb.20188250
ISSN1744-4292
AutoresRobin Beekhof, Carolien van Alphen, Alex A. Henneman, Jaco C. Knol, Thang V. Pham, Frank Rolfs, Mariëtte Labots, Evan Henneberry, Tessa YS Le Large, Richard R. de Haas, Sander R. Piersma, Valentina Vurchio, Andrea Bertotti, Livio Trusolino, Henk M.W. Verheul, Connie R. Jiménez,
Tópico(s)Cancer-related Molecular Pathways
ResumoMethod12 April 2019Open Access Transparent process INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases Robin Beekhof Robin Beekhof orcid.org/0000-0002-9500-528X Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Carolien van Alphen Carolien van Alphen Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Alex A Henneman Alex A Henneman orcid.org/0000-0002-3746-4410 Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Jaco C Knol Jaco C Knol Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Thang V Pham Thang V Pham Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Frank Rolfs Frank Rolfs Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Mariette Labots Mariette Labots Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Evan Henneberry Evan Henneberry OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Tessa YS Le Large Tessa YS Le Large Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Richard R de Haas Richard R de Haas Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Sander R Piersma Sander R Piersma Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Valentina Vurchio Valentina Vurchio Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy Search for more papers by this author Andrea Bertotti Andrea Bertotti Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy Department of Oncology, University of Torino, Torino, Italy Search for more papers by this author Livio Trusolino Livio Trusolino Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy Department of Oncology, University of Torino, Torino, Italy Search for more papers by this author Henk MW Verheul Henk MW Verheul Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Connie R Jimenez Corresponding Author Connie R Jimenez [email protected] orcid.org/0000-0002-3103-4508 Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Robin Beekhof Robin Beekhof orcid.org/0000-0002-9500-528X Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Carolien van Alphen Carolien van Alphen Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Alex A Henneman Alex A Henneman orcid.org/0000-0002-3746-4410 Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Jaco C Knol Jaco C Knol Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Thang V Pham Thang V Pham Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Frank Rolfs Frank Rolfs Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Mariette Labots Mariette Labots Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Evan Henneberry Evan Henneberry OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Tessa YS Le Large Tessa YS Le Large Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Richard R de Haas Richard R de Haas Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Sander R Piersma Sander R Piersma Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Valentina Vurchio Valentina Vurchio Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy Search for more papers by this author Andrea Bertotti Andrea Bertotti Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy Department of Oncology, University of Torino, Torino, Italy Search for more papers by this author Livio Trusolino Livio Trusolino Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy Department of Oncology, University of Torino, Torino, Italy Search for more papers by this author Henk MW Verheul Henk MW Verheul Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Connie R Jimenez Corresponding Author Connie R Jimenez [email protected] orcid.org/0000-0002-3103-4508 Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Search for more papers by this author Author Information Robin Beekhof1,2,‡, Carolien Alphen1,2,‡, Alex A Henneman1,2,‡, Jaco C Knol1,2, Thang V Pham1,2, Frank Rolfs1,2, Mariette Labots1, Evan Henneberry2, Tessa YS Le Large1,2, Richard R Haas1,2, Sander R Piersma1,2, Valentina Vurchio3, Andrea Bertotti3,4, Livio Trusolino3,4, Henk MW Verheul1 and Connie R Jimenez *,1,2 1Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 2OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 3Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy 4Department of Oncology, University of Torino, Torino, Italy ‡These authors contributed equally to this work *Corresponding author. Tel: +31 20 444 2340; E-mail: [email protected] Molecular Systems Biology (2019)15:e8250https://doi.org/10.15252/msb.20188250 Correction(s) for this article INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases24 May 2019 §Correction added on 24 May 2019, after first online publication: the author affiliations have been corrected. PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase–substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/− drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value. Synopsis INKA (Integrative Inferred Kinase Activity) is an integrative data analysis approach ranking kinase activities in mass spectrometry-based phosphoproteome data derived from single samples. INKA reveals oncogenes, differential kinase activity and drug targets. INKA combines kinase-centric and substrate-centric information and enables ranking kinase activities and visualizing kinase-substrate networks in a single biological sample. INKA shows superior performance over its four components. INKA can be applied to both label-free count and intensity data and was modified to accommodate labeling data. INKA can be used both for single-sample and differential analysis and provides a versatile tool that can condense complex phosphoproteome data to actionable results. Introduction Cancer is associated with aberrant kinase activity (Hanahan & Weinberg, 2011), and among recurrently altered genes, approximately 75 encode kinases that may "drive" tumorigenesis and/or progression (Vogelstein et al, 2013). In the last decade, multiple kinase-targeted drugs, including small-molecule inhibitors and antibodies, have been approved for clinical use in cancer treatment (Knight et al, 2010). However, even when selected on the basis of extensive genomic knowledge, only a subpopulation of patients experiences clinical benefit (Valabrega et al, 2007; Flaherty et al, 2010; Huang et al, 2014), while invariably resistance also develops in responders. Resistance can not only result from mutations in the targeted kinase or downstream pathways, but also from alterations in more distal pathways (Al-Lazikani et al, 2012; Trusolino & Bertotti, 2012; Ramos & Bentires-Alj, 2015). This complexity calls for tailored therapy based on detailed knowledge of the individual tumor's biology, including a comprehensive profile of hyperactive kinases. MS-based phosphoproteomics enables global protein phosphorylation profiling of cells and tissues (Jimenez & Verheul, 2014; Casado et al, 2016), but to arrive at a prioritized list of actionable (combinations of) active kinases, a dedicated analysis pipeline is required as the data are massive and complex. Importantly, a prime prerequisite for personalized treatment requires that the analysis is based on a single sample. This is pivotal in a clinical setting, where one wishes to prioritize actionable kinases for treatment selection for individual patients. Different kinase ranking approaches have been described previously. Rikova et al (2007) sorted kinases on the basis of the sum of the spectral counts (an MS correlate of abundance) for all phosphopeptides attributed to a given kinase, and identified known and novel oncogenic kinases in lung cancer. This type of analysis can be performed in individual samples, but is limited by a focus on phosphorylation of the kinase itself, rather than the (usually extensive) set of its substrates. Instead, several substrate-centric approaches, focusing on phosphopeptides derived from kinase targets, also exist, including KSEA (Casado et al, 2013; Terfve et al, 2015; Wilkes et al, 2015), pCHIPS (Drake et al, 2016), and IKAP (Mischnik et al, 2016). The only single-sample implementation of substrate-centric kinase activity analysis is KARP and has been reported recently (Wilkes et al, 2017). Neither a kinase-centric nor a substrate-centric phosphorylation analysis may suffice by itself to optimally single out major activated (driver) kinase(s) of cancer cells. To achieve an optimized ranking of inferred kinase activities based on MS-derived phosphoproteomics data for single samples, we propose a multipronged, rather than a singular approach. In this study, we devised a phosphoproteomics analysis tool for prioritizing active kinases in single samples, called Integrative Inferred Kinase Activity (INKA) scoring. The INKA algorithm combines direct observations on phosphokinases (either all kinase-derived phosphopeptides or activation loop peptides specifically), with observations on phosphoproteins that are known or predicted substrates for the pertinent kinase. To demonstrate its utility, we analyzed (i) cancer cell lines with known driver kinases in a single-sample manner, (ii) cell lines in a differential setting (wild-type versus mutant, +/− drug), (iii) pre- and on-treatment tumor needle biopsies from cancer patients, (iv) cancer cell panels with available drug sensitivity data, encouraging further exploration of INKA's functional and clinical value, and (v) colorectal cancer patient-derived xenograft (PDX) samples with INKA-guided drug selection. INKA code is available through a web server at www.INKAscore.org (updating) and as a zip file (Code EV1, current version). Data are available under PXD006616, PXD008032, PXD012565, and PXD009995. Results INKA: integration of kinase-centric and substrate-centric evidence to infer kinase activity from single-sample phosphoproteomics data To infer kinase activity from phosphoproteomics data of single samples, we developed a multipronged data analysis approach. Figure 1 summarizes the data collection (Fig 1A) and analysis workflows (Fig 1B) of the current study. For in-house data generation, we utilized phosphotyrosine (pTyr)-based phosphoproteomics of cancer cell lines, patient-derived xenograft tumors, and tumor needle biopsies (Dataset EV2). Kinases covered by individual analysis approaches are detailed in Dataset EV3. Figure 1. Generic phosphoproteomics workflow and data analysis strategy Overview of an MS-based phosphoproteomics experiment. Proteins from a biological sample are digested with trypsin, and phosphopeptides are enriched for analysis by (orbitrap-based) LC-MS/MS. Phosphopeptides can be captured with various affinity resins; here, data were analyzed of phosphopeptides enriched with anti-phosphotyrosine antibodies and TiOx. Database-based phosphopeptide identification, and phosphosite localization and quantification are performed using a tool like MaxQuant. Scheme of INKA analysis for identification of active kinases in a single biological sample. Quantitative phosphodata for established kinases are taken as direct (kinase-centric) evidence, using either all phosphopeptides attributed to a given kinase ("kinome") or only those from the kinase activation loop segment ("activation loop"). Phosphosites are filtered for class I phosphosites (localization probability > 75%; Olsen et al, 2006), coupled to phosphopeptide spectral count data, and used for substrate-centric inference of kinases on the basis of kinase–substrate relationships that are either experimentally observed (provided by PhosphoSitePlus, "PSP") or predicted by an algorithm using sequence motif and protein–protein network information (NetworKIN, "NWK"). All evidence lines are integrated in a kinase-specific INKA score using the geometric mean of combined spectral count data ("C") for kinase-centric and substrate-centric modalities. Results are visualized in a scatter plot of INKA scores for kinases scoring ≥ 10% of the maximum ("INKA Plot"; horizontal shifts from the middle indicate evidence being more kinase-centric or more substrate-centric). For top 20 INKA-scoring kinases, a score bar graph ("INKA Ranking"), and a kinase–substrate relation network for pertinent kinases and their observed substrates ("INKA Network") are also produced. Download figure Download PowerPoint As a first component, phosphopeptides derived from established protein kinases (KinBase, http://kinase.com; Manning et al, 2002) are analyzed. Kinase hyperphosphorylation is commonly associated with increased kinase activity. This is the rationale for using the sum of spectral counts (the number of identified MS/MS spectra) for all phosphopeptides derived from a kinase as a proxy for its activity, and to rank kinases accordingly, as pioneered by Rikova et al (2007). Second, kinase activation loop phosphorylation is analyzed. Although all kinase-derived phosphopeptides are already used in the first analysis above, here only phosphorylation of a kinase domain essential for kinase catalytic activity is considered for scoring, effectively doubling its contribution to the INKA score as a weighing measure. Most kinases harbor an activation segment, residing between highly conserved Asp-Phe-Gly (DFG) and Ala-Pro-Glu (APE) motifs. Phosphorylation of residues in the activation loop counteracts the positive charge of a critical arginine in the catalytic loop, eliciting conformational changes and consequent kinase activation (Nolen et al, 2004). To identify phosphopeptides that are derived from a kinase activation segment, we use the Phomics toolbox (http://phomics.jensenlab.org; Munk et al, 2016). Subsequently, kinases are ranked after spectral count aggregation as described above. Third, as a substrate-centric complement to the kinase-centric analyses above, and similar to a key ingredient in KSEA analysis (Casado et al, 2013), one can backtrack phosphorylation of substrates to responsible kinases as an indirect way to monitor kinase activity. Therefore, experimentally established kinase–substrate relationships listed by PhosphoSitePlus (Hornbeck et al, 2015) are used to link substrate-associated spectral counts to specific kinases, followed by kinase ranking. Fourth, another substrate-centric analysis is included to complement the previous step. To date, databases logging experimental kinase–substrate relationships are far from complete, leaving a large proportion of phosphopeptides that cannot be mapped as a kinase substrate. Therefore, we apply the NetworKIN prediction algorithm (Linding et al, 2007; Horn et al, 2014) to observed phosphosites to generate a wider scope of kinase–substrate relationships. NetworKIN uses phosphorylation sequence motifs and protein–protein network (path length) information to predict and rank kinases that may be responsible for phosphorylation of specific substrate phosphosites. In our application, after applying score cutoffs to restrict the NetworKIN output to the most likely kinase–substrate pairs, kinases are ranked by the sum of all spectral counts associated with their predicted substrates. Finally, we devised a method to integrate the four analyses as described above and to provide a single metric that can pinpoint active kinases in single biological samples analyzed by phosphoproteomics (Fig 1B, Materials and Methods). Specifically, for a given kinase, associated values in either of the two kinase-centric analyses are summed, and the same is done for the two substrate-centric analyses. Subsequently, the geometric mean of both sums is taken as an integrated inferred kinase activity, or INKA score. A non-zero INKA score requires both kinase-centric and substrate-centric evidence to be present. Furthermore, a skew parameter is calculated (0 for exclusively kinase-centric, 1 for exclusively substrate-centric, and 0.5 for equal contribution; see Materials and Methods), indicating to which extent the INKA score is derived from kinase-centric or from substrate-centric evidence, respectively. For kinases that are missing from PhosphoSitePlus and cannot be inferred by NetworKIN prediction, a separate kinase-centric ranking is performed to include these MS-observed enzymes in the analysis. This group involves 172 out of 538 established protein kinases considered in our analyses (Appendix Fig S1). For kinases inferred through PhosphoSitePlus/NetworKIN but not observed by MS, the reciprocal analysis is not performed, as kinases display overlapping substrate specificities precluding unequivocal assignment of a substrate to a specific kinase. The results of an INKA analysis are visualized through different plots (Fig 1B). Individual analyses result in a bar graph with top 20 kinases. Integration by INKA scoring results in a scatter plot for all kinases with an INKA score of at least 10% of that of the top-scoring kinase (with the horizontal position indicating the skew toward kinase-centric or substrate-centric evidence). For the top 20 kinases (by INKA score), a ranked bar graph and a network of all inferred kinase–substrate connections are visualized as well (Fig 1B). The INKA analysis pipeline is available as a web service at http://www.inkascore.org, where the latest updated version is maintained and can be downloaded, while the current code is provided here as a zip file (Code EV1). INKA analysis of oncogene-driven cancer cell lines To assess performance of the INKA approach, pTyr IP-based phosphoproteomic data were generated and analyzed for four well-studied cell lines with known oncogenic driver kinases: K562 chronic myeloid leukemia (CML) cells (BCR-ABL fusion), SK-Mel-28 melanoma cells (mutant BRAF), HCC827-ER3 lung carcinoma cells (mutant EGFR), and H2228 lung carcinoma cells (EML4-ALK fusion). Figure 2 displays, per cell line, a row of bar graphs with the top 20 kinases for each of the four basic analyses (kinome, activation loop, PhosphoSitePlus, and NetworKIN) as well as the combined score analysis (INKA). Bars for known driver kinases are highlighted by coloring except for SK-Mel-28. For the latter cell line, driven by the serine/threonine kinase BRAF (not detected by pTyr-based phosphoproteomics), downstream driver targets in the MEK-ERK pathway (MAP2K1, MAP2K2, MAPK1, MAPK3) are highlighted (Fig 2B). The underlying data can be found in Dataset EV4. In general, drivers are among the top ranks of the four analysis arms albeit to somewhat different extents. Clearly, "kinome" analysis (Fig 2, first column of bar graphs) strongly suggests identification of hyperactive kinases, as was found previously (Rikova et al, 2007; Guo et al, 2008). However, the additional substrate-centric analyses provide more confidence that kinase phosphorylation correlates with target phosphorylation (i.e., kinase activity). This is reflected in top-ranking integrative INKA scores for all drivers (or a proxy in the special case of SK-Mel-28). Figure 3 shows scatter plots of INKA scores as a function of kinase-centric versus substrate-centric evidence contribution as well as inferred kinase–substrate relation networks for the top 20 kinases in the four cell lines. Larger versions of the networks can be found in the Appendix Figs S2–S5. Altogether, these results show that amplification-driven oncogenic kinases or constitutively active kinase(-fusions) rank high by INKA, in line with previous findings (Rikova et al, 2007; Guo et al, 2008). Figure 2. Ranking of top 20 kinases in four cell line use cases by each of four lines of evidence and integrative INKA scoring K562 chronic myelogenous leukemia cells with a BCR-ABL fusion. INKA score ranking indicates that ABL1/BCR-ABL (orange bars) exhibits principal kinase activity in this cell line, in line with a role as an oncogenic driver. SK-Mel-28 melanoma cells with mutant BRAF. In the "kinome" analysis, CDK1, CDK2, and CDK3 share a second place, based on phosphopeptides that cannot be unequivocally assigned to either of them. INKA scoring implicates MAPK3 as the number one activated kinase. As SK-Mel-28 is driven by BRAF, a serine/threonine kinase that is missed by pTyr-based phosphoproteomics, downstream targets in the MEK-ERK pathway are highlighted by blue coloring. Erlotinib-resistant HCC827-ER3 NSCLC cells with mutant EGFR. INKA scores reveal the driver EGFR (pink coloring) as second-highest ranking and MET as highest ranking kinase, respectively. H2228 NSCLC cells with an EML4-ALK fusion. The driver ALK (purple coloring) is ranked as a top 3 kinase by INKA score, slightly below PTK2 and SRC. Data information: For each cell line, bar graphs depict kinase ranking based on kinase-centric analyses (panel "Kinase phosphopeptides"), substrate-centric analyses (panel "Substrate phosphopeptides"), and combined scores (panel "INKA"). Bar segments represent the number and contribution of individual phosphopeptides (kinase-centric analyses) or phosphosites (substrate-centric analyses). Since substrate-centric inference attributes data from multiple, possibly numerous, substrate phosphosites to a single kinase, bar segments coalesce into a black stack in more extreme cases. P-values flanking INKA score bars were derived through a randomization procedure with 105 permutations of both peptide-spectral count links and kinase–substrate links. P-values in red are above a significance threshold of P < 0.05. Download figure Download PowerPoint Figure 3. INKA plots and kinase–substrate relation networks for four oncogene-driven cell lines K562 CML cells with a BCR-ABL fusion. ABL1 is the most activated kinase, with relatively equal contributions from both analysis arms. It is a highly connected, central node in the network. SK-Mel-28 melanoma cells with mutant BRAF. Downstream MEK-ERK pathway members are highlighted in lieu of BRAF which is missed by the current pTyr-based workflow. MAPK3 is the top activated kinase. The network includes two clusters with highly connected activated kinases, MAPK1/3 and SRC, respectively. Erlotinib-resistant HCC827-ER3 NSCLC cells with mutant EGFR. EGFR and MET are the most active, highly connected kinases. AXL, inactive in parental cells (see Appendix Fig S8), but associated with erlotinib resistance in this subline, can only be analyzed through the kinase-centric arm (pink bar highlighting). H2228 NSCLC cells with an EML4-ALK fusion. ALK is a high-ranking kinase with roughly equal evidence from both analysis arms. Multiple highly active and connected nodes imply relative insensitivity to ALK inhibition, in line with previous functional data. Larger networks are shown in Appendix Figs S2–S5. Data information: In INKA plots proper, the vertical position of kinases (drivers in red) is determined by their INKA score, whereas the horizontal position is determined by the (im)balance of evidence from kinase-centric and substrate-inferred arms of the analysis. Kinases not covered by PhosphoSitePlus (PSP) and NetworKIN (NWK) are visualized in a flanking bar graph. Download figure Download
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