bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
INKA, an integrative data analysis pipeline for phosphoproteomic
inference of active phosphokinases
Thang V. Pham1,2, Robin Beekhof1,2, Carolien van Alphen1,2, Jaco C. Knol1, Alex A.
Henneman1, Frank Rolfs1, Mariette Labots1, Evan Henneberry1, Tessa Y.S. Le Large1, Richard
R. de Haas1, Sander R. Piersma1, Henk M.W. Verheul1, Connie R. Jimenez1 *
1 OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam,
VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
2 These authors contributed equally to this work
* Correspondence should be addressed to C.R.J. ([email protected])
Running Title: Integrative tool ranks active kinases
Character Count: 50,356
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1 Abstract
2
3 Identifying (hyper)active kinases in cancer patient tumors is crucial to enable individualized
4 treatment with specific inhibitors. Conceptually, kinase activity can be gleaned from global
5 protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A
6 major challenge is to relate such profiles to specific kinases to identify (hyper)active kinases that
7 may fuel growth/progression of individual tumors. Approaches have hitherto focused on
8 phosphorylation of either kinases or their substrates. Here, we combine kinase-centric and
9 substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. INKA
10 utilizes label-free quantification of phosphopeptides derived from kinases, kinase activation
11 loops, kinase substrates deduced from prior experimental knowledge, and kinase substrates
12 predicted from sequence motifs, yielding a single score. This multipronged, stringent analysis
13 enables ranking of kinase activity and visualization of kinase-substrate relation networks in a
14 biological sample. As a proof of concept, INKA scoring of phosphoproteomic data for different
15 oncogene-driven cancer cell lines inferred top activity of implicated driver kinases, and relevant
16 quantitative changes upon perturbation. These analyses show the ability of INKA scoring to
17 identify (hyper)active kinases, with potential clinical significance.
18
19
20 Keywords
21 cancer / single-sample analysis / kinase-substrate phosphorylation network / drug selection /
22 computational tool
23
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24 Introduction
25
26 Cancer is associated with aberrant kinase activity (Hanahan & Weinberg, 2011), and among
27 recurrently altered genes some 75 encode kinases that may “drive” tumorigenesis and/or
28 progression (Vogelstein et al, 2013). In the last decade, multiple kinase-targeted drugs including
29 small-molecule inhibitors and antibodies have been approved for clinical use in cancer treatment
30 (Knight et al, 2010). However, even when selected on the basis of extensive genomic
31 knowledge, only a subpopulation of patients experiences clinical benefit (Huang et al, 2014;
32 Valabrega et al, 2007; Flaherty et al, 2010), while invariably resistance also develops in
33 responders. Resistance cannot only result from mutations in the targeted kinase or downstream
34 pathways, but also from alterations in more distal pathways (Al-Lazikani et al, 2012; Trusolino
35 & Bertotti, 2012; Ramos & Bentires-Alj, 2015). This complexity calls for tailored therapy based
36 on detailed knowledge of the individual tumor's biology, including a comprehensive profile of
37 (hyper)active kinases. MS-based phosphoproteomics enables global protein phosphorylation
38 profiling of cells and tissues (Jimenez & Verheul, 2014; Casado et al, 2016), but to arrive at a
39 prioritized list of actionable (combinations of) active kinases, a dedicated analysis pipeline is
40 required as the data are massive and complex. Importantly, a prime prerequisite for personalized
41 treatment requires that the analysis is based on a single sample.
42 Different kinase ranking approaches have been described previously. Rikova and colleagues
43 sorted kinases on the basis of the sum of the spectral counts (an MS correlate of abundance) for
44 all phosphopeptides attributed to a given kinase, and identified known and novel oncogenic
45 kinases in lung cancer (Rikova et al, 2007). This type of analysis can be performed in individual
46 samples, but is limited by a focus on phosphorylation of the kinase itself, rather than the (usually
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47 extensive) set of its substrates. Instead, several substrate-centric approaches also exist, including
48 KSEA (Casado et al, 2013; Terfve et al, 2015; Wilkes et al, 2015), pCHIPS (Drake et al, 2016),
49 and IKAP (Mischnik et al, 2016).
50 Neither a kinase-centric nor a substrate-centric phosphorylation analysis may suffice by itself to
51 optimally single out major activated (driver) kinase (s) of cancer cells. To achieve an optimized
52 ranking of inferred kinase activities based on MS-derived phosphoproteomics data for single
53 samples, we propose a multipronged, rather than a singular approach. In this study, we devised a
54 phosphoproteomics analysis tool for prioritizing active kinases in single samples, called
55 Integrative Inferred Kinase Activity (INKA) scoring. INKA combines direct observations on
56 phosphokinases (either all kinase-derived phosphopeptides or activation loop peptides
57 specifically), with observations on phosphoproteins that are known or predicted substrates for the
58 pertinent kinase. To demonstrate its utility, we analyzed cancer cell line 'cases' with known
59 driver kinases in a single-sample manner, such as would be required in a personalized medicine
60 setting and explored dynamic changes upon perturbation.
61
62 Results
63
64 INKA: integration of kinase-centric and substrate-centric evidence to infer kinase activity
65 from single-sample phosphoproteomics data
66 To infer kinase activity from phosphoproteomics data of single samples, we developed a
67 multipronged data analysis approach. Figure 1 summarizes the data collection (Fig 1A) and
68 analysis workflows (Fig 1B) of the current study. For in-house data generation, we utilized
69 phosphotyrosine (pTyr)-based phosphoproteomics of cancer cell lines and tumor needle biopsies
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70 (Supplementary Table 1). Kinases covered by individual analysis approaches are detailed in
71 Supplementary Table 2.
72 First, phosphopeptides derived from established protein kinases (KinBase, http://kinase.com)
73 (Manning et al, 2002) were analyzed. Kinase hyperphosphorylation is commonly associated with
74 increased kinase activity. This is the rationale for using the sum of spectral counts (the number of
75 identified MS/MS spectra) for all phosphopeptides derived from a kinase as a proxy for its
76 activity, and to rank kinases accordingly, as pioneered by Rikova et al. (Rikova et al, 2007)
77 Second, kinase activation loop phosphorylation was analyzed. Most kinases harbor an activation
78 segment, residing between highly conserved Asp-Phe-Gly and Ala-Pro-Glu motifs.
79 Phosphorylation of residues in the activation loop counteracts the positive charge of a critical
80 arginine in the catalytic loop, eliciting conformational changes and consequent kinase activation
81 (Nolen et al, 2004). To identify phosphopeptides that are derived from a kinase activation
82 segment, we used the Phomics toolbox (http://phomics.jensenlab.org) (Munk et al, 2016).
83 Subsequently, kinases were ranked after spectral count aggregation as described above.
84 Third, as a substrate-centric complement to the kinase-centric analyses above, and similar to a
85 key ingredient in KSEA analysis (Casado et al, 2013), one can backtrack phosphorylation of
86 substrates to responsible kinases as an indirect way to monitor kinase activity. Experimentally
87 established kinase-substrate relationships listed by PhosphoSitePlus (Hornbeck et al, 2015) were
88 used to link substrate-associated spectral counts to specific kinases, followed by kinase ranking.
89 Fourth, another substrate-centric analysis was included to complement the previous step. To
90 date, databases logging experimental kinase-substrate relationships are far from complete,
91 leaving a large proportion of phosphopeptides that cannot be mapped as a kinase substrate
92 through that avenue. Therefore, we applied the NetworKIN prediction algorithm (Linding et al,
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93 2007; Horn et al, 2014) to observed phosphosites to generate a wider scope of kinase-substrate
94 relationships. NetworKIN uses phosphorylation sequence motifs and protein-protein network
95 (path length) information to predict and rank kinases that may be responsible for phosphorylation
96 of specific substrate phosphosites. After applying score cutoffs to restrict the NetworKIN output
97 to the most likely kinase-substrate pairs, kinases were ranked by the sum of all spectral counts
98 associated with their predicted substrates.
99 Finally, we devised a method to integrate the four analyses as described above and to provide a
100 single metric for pinpointing active kinases in biological samples analyzed by
101 phosphoproteomics (Fig 1B, Materials and Methods). For a given kinase, associated values in
102 either of the two kinase-centric analyses are summed, and the same is done for the two substrate-
103 centric analyses. Subsequently, the geometric mean of both sums is taken as an integrated
104 inferred kinase activity, or INKA score. A non-zero INKA score requires both kinase-centric and
105 substrate-centric evidence to be present. Furthermore, a skew parameter is calculated (0 for
106 exclusively kinase-centric, 1 for exclusively substrate-centric, and 0.5 for equal contribution; see
107 Materials and Methods) indicating to which extent the INKA score is derived from kinase-
108 centric or from substrate-centric evidence. For kinases that are missing from PhosphoSitePlus
109 and can not be inferred by NetworKIN prediction as well, a separate kinase-centric ranking is
110 performed to include these MS-observed enzymes in the analysis. The latter involves 172 out of
111 538 established protein kinases considered in our analyses (Supplementary Fig 1 and
112 Supplementary Table 2). For kinases inferred through PhosphoSitePlus/NetworKIN but not
113 observed by MS, the reciprocal analysis is not performed as kinases display overlapping
114 substrate specificities precluding unequivocal assignment of a substrate to a specific kinase.
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115 Results are visualized through different plots (Fig 1B). Individual analyses each result in a bar
116 graph with top 20 kinases. Integration by INKA scoring results in a scatter plot for all kinases
117 with an INKA score that is at least 10% of the top scoring kinase (vertical: INKA score,
118 horizontal: skew towards kinase-centric or substrate-centric evidence). For the top 20 kinases (by
119 INKA score), a ranked bar graph and a network of all inferred kinase-substrate connections are
120 visualized as well (Fig 1B). This pipeline is available to users who can upload their data to
121 www.inkascore.org for INKA scoring.
122
123 INKA analysis of cancer cell line use cases
124 To assess performance of the INKA approach, we carried out pTyr-based phosphoproteomics
125 with four well-studied cell lines for which oncogenic driver kinases are known: K562 chronic
126 myeloid leukemia (CML) cells harboring a BCR-ABL fusion (Klein et al, 1976; Heisterkamp et
127 al), SK-Mel-28 melanoma cells carrying mutant BRAF (Carey et al, 1976; Davies et al, 2002),
128 HCC827-ER3 lung carcinoma cells with mutant EGFR (Girard et al, 2000; Zhang et al, 2012),
129 and H2228 lung carcinoma cells with an EML4-ALK fusion (Soda et al, 2007; Rikova et al,
130 2007). Figure 2 displays, on a single row per cell line, bar graphs with the top 20 kinases for each
131 of the four basic analyses (Kinome, Activation Loop, PhosphoSitePlus, and NetworKIN) as well
132 as combined score analysis (INKA). Bars for known driver kinases are highlighted by coloring
133 for each cell line except SK-Mel-28, which is driven by BRAF, a serine/threonine kinase that
134 cannot be detected by pTyr-based phosphoproteomics. In the latter case, downstream driver
135 targets in the MEK-ERK pathway (MAP2K1, MAP2K2, MAPK1, MAPK3) are highlighted (Fig
136 2B). Underlying data can be found in Supplementary Table 3.
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137 From the first column of bar graphs in Fig 2 it is evident that kinome analysis, based on all
138 phosphopeptides identified for a kinase, is a strong indicator for (hyper)active kinases, as was
139 found previously (Rikova et al, 2007; Guo et al, 2008). All tyrosine kinase drivers rank among
140 the top two. Activation loop analysis (Fig 2, second column) follows a similar trend, but suffers
141 from a focus on a small protein segment. With respect to substrate-centric analyses, both
142 PhosphoSitePlus and NetworKIN analyses (Fig 2, third and fourth columns, respectively) rank
143 driver kinases among the top 10. One of the two analyses often outperforms the other, but not in
144 a consistent pattern, underscoring our choice to use a combination of these approaches. Since
145 substrate-centric inference attributes data from multiple, possibly numerous, substrate
146 phosphosites to a single kinase, bar segments may coalesce into a black stack in more extreme
147 cases.
148 From the above it is clear that individual analysis modules harbor complementary layers of
149 information about phosphokinases and phosphosubstrates that can be integrated using INKA
150 scoring into a more balanced and accurate pattern of inferred kinase activity (Fig 2, last column).
151 Importantly, all driver (-related) kinases rank among the INKA top 3 for the tested cell lines
152 (with MAPK3 serving as a surrogate for the BRAF Ser/Thr kinase in SK-Mel-28).
153 In order to explore the statistical significance of the INKA score, and especially the impact of
154 kinase inference through substrate-centric PhosphoSitePlus and NetworKIN analyses, we
155 permuted both phosphopeptide-spectral count links (for a sample) and kinase-substrate links (for
156 all PhosphoSitePlus and NetworKIN data), followed by INKA score calculation. After 105
157 permutations, sample- and kinase-specific INKA score distributions were obtained and used to
158 derive p-values for each actual INKA score. Based on this calculation, almost all top 20 INKA
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159 scores were significant (Fig 2). Higher INKA scores clearly correlated with lower p-values
160 (Supplementary Fig 2), underscoring the relevance of the INKA score.
161
162 Linking INKA analysis to cell biology and treatment selection
163 To assess to what extent (hyper)active phosphokinases identified by the above INKA analysis
164 represent actionable drug targets, we investigated public cell line drug sensitivity data from the
165 'Genomics of Drug Sensitivity in Cancer' resource (Yang et al, 2013) (GDSC,
166 http://www.cancerrxgene.org, Supplementary Table 4).
167 K562. Clinically, treatment of BCR-ABL-positive CML patients with kinase inhibitors such as
168 nilotinib, imatinib, and dasatinib has shown great benefit (Kantarjian et al, 2010; Saglio et al,
169 2010; Larson et al, 2012), in line with addiction of tumor cells to the BCR-ABL fusion kinase.
170 BCR/ABL activity was inferred in all basic analyses of K562, causing ABL1 to stand out as a
171 prime candidate in the INKA analysis (Fig 2A, Fig 3A). The integrated analysis also inferred
172 phosphorylation of downstream signaling partners of ABL1 such as SRC-family kinases and
173 MAPK1/3 among all members of the kinase-substrate relation network for top 20 INKA kinases
174 in K562 (Fig 3A, enlarged in Supplementary Fig 3). As expected, GDSC data indicate K562
175 cells to be sensitive to various ABL inhibitors (Supplementary Table 4).
176 SK-Mel-28. A vast majority of patients with BRAF-mutant melanoma carry a mutation encoding
177 an activating V600E substitution in the BRAF kinase domain (Forbes et al, 2015; Lovly et al,
178 2012; Rubinstein et al, 2010). Treatment with small-molecule inhibitors of BRAF (Chapman et
179 al, 2011; Hauschild et al, 2012; Flaherty et al, 2012) and/or downstream MEKs (MAP2K1/2)
180 (Flaherty et al, 2012; Robert et al, 2015) often induces strong initial responses, but both single-
181 agent and combination treatments are commonly hampered by development of resistance. The
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182 BRAF Ser/Thr kinase cannot be directly observed with pTyr-based phosphoproteomics, but
183 INKA analysis of SK-Mel-28 revealed high- and medium-ranking activity of its downstream
184 pathway members MAPK3 and MAPK1, respectively, in addition to high activity of focal
185 adhesion kinase (PTK2) and SRC-family members (Fig 2B, Fig 3B). Of the latter, SRC is a
186 central node in the inferred kinase-substrate network of top 20 INKA-scoring kinases in SK-Mel-
187 28 (Fig 3B, Supplementary Fig 4). GDSC data indicate that inhibition of BRAF and downstream
188 MEK1/2 is successful in reducing SK-Mel-28 growth, while inhibition more downstream of
189 MAPK1/3 is less effective (Supplementary Table 4). Single-agent inhibition was not effective
190 for PTK2, and undocumented for SRC-family kinases FYN and YES1. Based on INKA data, one
191 could test 'combination therapy' with a PTK2 or SRC inhibitor and a BRAF or MEK1/2
192 inhibitor. Interestingly, a previous study showed the importance of SRC in BRAF inhibitor
193 resistance in BRAF-mutant melanoma cells and patient-derived tissues (Girotti et al, 2013).
194 HCC827-ER3. Non-small cell lung carcinoma (NSCLC)-derived HCC827 cells expressing
195 mutant EGFRE746-A750 are highly sensitive to EGFR inhibitors (GDSC, Supplementary Table 4).
196 HCC827-ER3 is a sub-line of HCC827 with in vivo acquired resistance to the EGFR inhibitor
197 erlotinib (Zhang et al, 2012), and cross-resistance to sunitinib (van der Mijn et al, 2016). INKA
198 analysis suggests that HCC827-ER3 still contains hyperactive EGFR, with its score ranking
199 second (Fig 2C, Fig 3C), indicating driver potential. Network visualization of inferred kinase-
200 substrate relations (Fig 3C, Supplementary Fig 5) shows that both EGFR and MET are highly
201 connected hyperactive nodes. Based on INKA scoring, acquired resistance of HCC827-ER3 to
202 erlotinib (Zhang et al, 2012; van der Mijn et al, 2016) could potentially be overcome by co-
203 targeting MET, which ranks first. However, high MET phosphorylation was already found in
204 parental, erlotinib-sensitive HCC827 cells (Zhang et al, 2012; van der Mijn et al, 2016)
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205 (Supplementary Table 3E, Supplementary Fig 6), that appear relatively insensitive to MET
206 inhibitors (Supplementary Table 4). Treating HCC827 with gefitinib, which inhibits EGFR but
207 not MET (Davis et al, 2011), not only reduces hyperphosphorylation but also overall levels of
208 MET (Guo et al, 2008). Thus, high INKA ranking of MET may be the result of EGFR-MET
209 crosstalk, rather than indicating driver activity. Interestingly, growth of HCC827-ER3 is
210 inhibited after co-targeting of EGFR and AXL (Guo et al, 2008; van der Mijn et al, 2016).
211 Unfortunately, AXL (hyper)activity cannot be pinpointed by INKA, as both experimental
212 (PhosphoSitePlus) and predicted (NetworKIN) kinase-substrate relationship data are lacking for
213 AXL. However, in a kinase-centric ranking devised for kinases that suffer from such a problem,
214 phospho-AXL ranks second with relatively high counts (Fig 2C, Fig 3C left panel) whereas it
215 was not identified in parental HCC827 cells (Supplementary Fig 6), in line with AXL being a
216 resistance hub in HCC827-ER3 (Guo et al, 2008; van der Mijn et al, 2016).
217 H2228. In NSCLC-derived H2228 cells, an EML4-ALK fusion underlies oncogenic ALK kinase
218 activity. Indeed, several ALK inhibitors have been approved for the treatment of ALK fusion-
219 positive NSCLC. ALK ranked third in INKA analysis (Fig 2D, Fig 3D), which may be due to
220 paucity of information for this kinase (as for AXL, see above) or indicate involvement of other
221 kinases in fueling cell growth. GDSC data indicate that effective inhibitors of H2228 growth are
222 rare, even when targeting ALK (alectinib: IC50 4.4 µM, Z-score -1.92; Supplementary Table 4).
223 The kinase-substrate network for top 20 INKA-scoring kinases in H2228 (Fig 3D,
224 Supplementary Fig 7) shows that in addition to ALK there are multiple hyperactive, highly
225 connected kinases (e.g., PTK2, SRC, EGFR) as candidates for combination treatment. Inhibitors
226 targeting these kinases individually do not have a significant effect on H2228 proliferation
227 (Supplementary Table 4). PTK2 and SRC are inhibited by several ALK inhibitors at IC50
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228 concentrations (Davis et al, 2011), ruling these kinases out as co-targets. Interestingly, EGFR, a
229 high-ranking kinase in INKA and a central node in the network, is implicated in reduced
230 sensitivity to ALK inhibition in H2228, while dual inhibition of ALK and EGFR results in highly
231 increased apoptosis (Voena et al, 2013). Furthermore, both simultaneous knockdown and
232 combined drug inhibition of these kinases inhibit H2228 cell proliferation and tumor xenograft
233 growth more potently than targeting either kinase alone (Li et al, 2016).
234
235 Testing the INKA approach with literature data
236 To further test our strategy for prioritizing targeting candidates, we also examined
237 phosphoproteome data from the literature (Guo et al, 2008; Bai et al, 2012) (Fig 4,
238 Supplementary Table 3). INKA analysis of data on EGFR-mutant NSCLC cell line H3255 (Guo
239 et al, 2008) uncovered major EGFR activity in these cells, with EGFR ranking first, followed by
240 MET (Fig 4A). In another study, the rhabdomyosarcoma-derived cell line A204 was associated
241 with PDGFR signaling (Bai et al, 2012), and INKA scoring of the underlying data accordingly
242 ranks PDGFR in second place (Fig 4B). In the same study, osteosarcoma-derived MNNG/HOS
243 cells were shown to be dependent on MET signaling and sensitive to MET inhibitors (Bai et al,
244 2012). In line with this, INKA analysis clearly pinpointed MET as the major driver candidate in
245 this cell line (Fig 4C).
246 Altogether, analysis of public datasets illustrates the capacity of INKA scoring to identify
247 kinases that are relevant oncogenic drivers in diverse cancer cell lines at baseline.
248
249 Testing the INKA approach in differential settings
250 To explore the discriminative power of INKA scoring, we analyzed pTyr-phosphoproteomic data
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251 from wild-type versus mutant cells, and from untreated versus drug-treated cells as genetic and
252 pharmacological dichotomies (Fig 5, Supplementary Table 3). First, we utilized raw data from
253 our laboratory (van der Mijn et al, 2014) to compare wild-type U87 glioblastoma cells with
254 isogenic U87-EGFRvIII cells overexpressing a constitutively active EGFR mutant. The EGFR
255 INKA score was significantly higher and dominating in U87-EGFRvIII relative to U87 (Fig 5A).
256 Some other kinases also exhibited higher-ranking INKA scores, including MET and EPHA2, for
257 which enhanced phosphorylation in U87-EGFRvIII was previously documented (Huang et al,
258 2007; Stommel et al, 2007), as well as SRC-family members. In a treatment setting, INKA
259 analysis clearly revealed a specific drug effect after targeting EGFR in U87-EGFRvIII, with the
260 high, first-rank INKA score for EGFR at baseline being halved after treatment with erlotinib (Fig
261 5B).
262 Second, in order to extend these findings to a clinical framework, INKA scoring was applied to
263 pTyr-phosphoproteomic data on patient tumor biopsies (Fig 5C,D). Biopsies were collected both
264 before and after two weeks of erlotinib treatment (standard dose, clinical trial NCT01636908;
265 Labots et al., submitted for publication). Importantly, the on-treatment biopsy from a patient
266 with advanced head and neck squamous cell carcinoma showed a reduced INKA score and rank
267 for EGFR as well as cell cycle-associated kinases (Fig 5C). Interestingly, in a pancreatic cancer
268 patient, no residual EGFR activity could be inferred by INKA in a tumor biopsy after erlotinib
269 treatment (Fig 5D).
270 Third, we assessed performance of INKA analysis using published phosphoproteome data
271 (Bensimon et al, 2010) of human G361 melanoma cells after induction of genotoxic stress with
272 neocarzinostatin, a radiomimetic that induces double-strand breaks (Fig 5E,F). As these data
273 were generated using an earlier-generation mass spectrometer that is less sensitive than current
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274 orbitrap-based instruments, count data did not work well, and MS intensity data were analyzed
275 instead. As expected in the context of DNA damage signaling, INKA scores for ATM and
276 PRKDC/DNA-PK exhibited a time-dependent increase after addition of neocarzinostatin (Fig
277 5E,F and Supplementary Fig 8). Moreover, the ATM INKA score was significantly reduced after
278 the addition of ATM inhibitor KU55933 (Fig 5E). INKA scoring suggests that the inhibitor
279 influences PRKDC as well (Fig 5F).
280 In summary, the above differential analyses of phosphoproteomes show that the INKA pipeline
281 can pinpoint target activation and inhibition after perturbation in both cell lines and clinical
282 samples.
283
284 Discussion
285
286 Present-day cancer treatment is increasingly shifting towards individualized therapy by specific
287 targeting of hyperactive kinases in patient tumors. In this context, with heterogeneity and
288 plasticity of kinase signaling in a specific tumor at a specific time, it is essential to have an
289 overview of (hyper)active kinases and prioritize ones that (help) drive malignancy to maximize
290 therapeutic success and minimize expensive failures and unnecessary burden for the patient.
291 Here, we present a novel pipeline, Integrative Inferred Kinase Activity (INKA) scoring, to
292 investigate phosphoproteomic data from a single sample and identify (hyper)active kinases as
293 candidates for (co-)targeting with kinase inhibitors. In a first demonstration of its application, we
294 have performed INKA analyses of established cancer cell lines with known oncogenic drivers.
295 We analyzed both data from tyrosine phosphoproteomics in our laboratory and similar data
296 described in the literature (five cell lines in each case). Furthermore, INKA could distinguish
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297 relevant differences between closely related mutant and wild type cells and reveal drug
298 perturbation effects in both tyrosine and global (TiO2) phosphoproteomics data. We also applied
299 INKA scoring to tumor needle biopsies of two patients before and after kinase inhibitor
300 treatment.
301 This study shows that data from label-free MS-based phosphoproteomics can be harnessed in a
302 multipronged analysis to infer and rank kinase activity in individual biological samples. In
303 kinase-centric analyses, phosphorylation of the kinase itself (either considering all sites or
304 focusing on the ones located in the activation loop) is used as a proxy for its activation. In
305 substrate-centric analyses, instead, substrate phosphorylation is used to deduce kinase activity
306 indirectly through kinase-substrate relationships (based on either experimental knowledge or
307 motif-based prediction). Here we developed INKA that combines kinase-centric and substrate-
308 centric evidence in a stringent meta-analysis, to yield an integrated metric for inferred kinase
309 activity. The results of INKA highlight kinases that are in line with known cancer biology and
310 show that INKA scoring clearly outperforms substrate-centric analyses alone, and also holds a
311 slight edge over phosphokinase ranking pioneered by Rikova et al12. In particular, kinases
312 driving tumor growth rank high in INKA scoring, illustrating the power of applying an
313 integrative analysis of in-depth phosphoproteomics data.
314 Meaningful substrate-centric inference of kinase activity is pivotal to INKA scoring. It depends
315 on the availability of comprehensive curated data on experimentally observed kinase-substrate
316 relationships or reliable predictions thereof. To date, this requirement has only been partly
317 fulfilled, and more substantial inferences could be made with further population of resources
318 (such as PhosphoSitePlus), especially when the latter also cover cancer-associated aberrations
319 such as fusion genes BCR-ABL and EML4-ALK (Medves & Demoulin, 2012; Lee et al, 2017).
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320 To overcome current limitations in information, our INKA pipeline provides a separate kinase-
321 centric ranking of kinases that are not yet covered by PhosphoSitePlus and NetworKIN, but do
322 show up as phosphoproteins in a sample. This reduces the chance of missing important kinases,
323 as illustrated by the case of AXL in the HCC827-ER3 cell line. Moreover, the INKA pipeline
324 generates network visualizations of all kinase-substrate relationships inferred for the top 20
325 kinases in an experiment. This provides a more instructive overview of phosphoproteomic
326 biology in a sample than mere scoring and ranking alone.
327 A next stage is to apply INKA to more advanced cancer models and, especially, clinical samples.
328 To analyze limited amounts of patient tumor tissue in a clinical practice setting, we have recently
329 downscaled tyrosine phosphoproteomics to clinical needle-biopsy levels56. Using this workflow,
330 we performed proof-of-concept analysis of patient samples from a clinical molecular profiling
331 study with kinase inhibitors (Labots et al., submitted), showing relevant effects.
332 In summary, INKA scoring can infer and rank kinase activity in a single biological sample, and
333 display differences between closely related yet genetically distinct cells and after drug
334 intervention. INKA analysis of phosphoproteomic data on tumor biopsies collected in kinase
335 inhibitor trials can pave the way for future clinical application. The ultimate goal would be
336 tailoring treatment selection for the individual patient.
337
338 Materials and Methods
339
340 Cell lines and culture
341 H2228 non-small cell lung cancer cells carry an EML4-ALK fusion gene, SK-Mel-28 melanoma
342 cells harbor a V600E BRAF mutation, and K562 chronic myelogenous leukemia cells carry a
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343 BCR-ABL1 fusion gene (philadelphia chromosome). HCC827 and HCC827-ER3 are non-small
344 cell lung cancer cell lines with a deletion of EGFR residues E746-A750, the latter cell line being
345 a subclone of HCC827 after selection for acquired resistance to erlotinib in a murine xenograft
346 setting (Zhang et al, 2012). HCC827, H2228, SK-Mel-28 and K562 were obtained from the
347 American Type Culture Collection (ATCC, Rockville, MD) and HCC827-ER3 was provided by
348 Prof. Balazs Halmos, Division of Hematology/Oncology, Columbia University Medical Center,
349 New York, USA.
350 H2228 and SK-Mel-28 were cultured in 175-cm2 flasks containing DMEM supplemented with
351 10% fetal bovine serum and 2mM L-glutamine. K562 was cultured as a suspension in RPMI
352 1640 containing 2 mM L-glutamine and supplemented with 10% fetal bovine serum. HCC827
353 and HCC827-ER3 were grown in 15-cm dishes with the latter medium. Cell lines tested negative
354 for mycoplasma.
355
356 Cell lysis and phosphoproteomics
357 Cells were grown to 70-80% confluency (adherent cell lines) or to a 1 x 106 cells/ml suspension
358 (K562), washed with phosphate-buffered saline (PBS), lysed at approximately 2-3 x 107 cells/ml
359 in 5 ml lysis buffer (9 M urea, 20 mM HEPES pH 8.0, 1 mM sodium orthovanadate, 2.5 mM
360 sodium pyrophosphate, 1mM ß-glycerophosphate), sonicated (3 cycles of 30 s), and extracts
361 were stored at -80 oC.
362 For phosphoproteomics, lysate aliquots equivalent to 10 mg total protein were used as described
363 before (van der Mijn et al, 2015). Proteins were reduced by incubation in 4.5 mM dithiothreitol
364 for 30 min at 55 oC, alkylated in 10 mM iodoacetamide for 15 min at room temperature in the
365 dark, and digested overnight at room temperature with 10 µg/ml trypsin in a fourfold increased
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366 volume containing 20 mM HEPES pH 8.0. After acidification (trifluoroacetic acid to 1% final
367 concentration), tryptic digests were desalted on Sep-Pak C18 cartridges (Waters
368 Chromatography, Etten-Leur, Netherlands) and lyophilized. Peptides were taken up in 700 µl
369 immunoprecipitation buffer (50 mM MOPS pH 7.2, 10 mM sodium phosphate, 50 mM NaCl)
370 and transferred at 4 oC to a microcentrifuge tube containing 40 µl of a 50% (v/v) slurry of
371 agarose beads harboring P-Tyr-1000 anti-phosphotyrosine monoclonal antibodies (Cell Signaling
372 Technologies, Danvers, USA) that had been washed and taken up in PBS. Following a 2-h
373 incubation at 4 oC on a rotator, beads were washed twice with cold PBS, and three times with
374 cold HPLC-grade water. Bound peptides were eluted with a total of 50 µl 0.15% trifluoroacetic
375 acid in two steps. After desalting with custom-made C18 stage tips, drying, and solubilization in
376 20 µl 4% acetonitrile/0.5% trifluoroacetic acid, samples were subjected to LC-MS/MS analysis.
377 For tumor biopsy phosphoproteomics, tumor needle biopsies from a patient with advanced head
378 and neck squamous cell carcinoma, obtained in an Institutional Review Board-approved
379 molecular profiling study before and after 2 weeks of treatment with erlotinib (NCT clinical
380 trials identifier 01636908; www.clinicaltrials.gov), were cut and processed as described
381 elsewehere (Labots et al, 2017). Peptide preparation from tumor biopsy lysates was performed
382 with 2.5 mg protein input for both pre- and on-treatment biopsies.
383
384 LC-MS/MS
385 Peptides were separated on an Ultimate 3000 nanoLC-MS/MS system (Dionex LC-Packings,
386 Amsterdam, The Netherlands) equipped with a 20-cm, 75-μm inner diameter fused silica
387 column, custom packed with 1.9-μm ReproSil-Pur C18-AQ silica beads (120-Å pore size; Dr.
388 Maisch, Ammerbuch-Entringen, Germany). After injection, peptides were trapped at 6 μl/min on
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389 a 10-mm, 100-μm inner diameter trap column packed with 5-μm ReproSil-Pur C18-AQ silica
390 beads (120-Å pore size) in buffer A (buffer A: 0.5% acetic acid, buffer B: 80% acetonitrile, 0.5%
391 acetic acid), and separated at 300 nl/min with a 10–40% buffer B gradient in 90 min (120 min
392 inject-to-inject). Eluting peptides were ionized at a potential of +2 kV and introduced into a Q
393 Exactive mass spectrometer (Thermo Fisher, Bremen, Germany). Intact masses were measured
394 in the orbitrap with a resolution of 70,000 (at m/z 200) using an automatic gain control (AGC)
395 target value of 3 × 106 charges. Peptides with the top 10 highest signals (charge states 2+ and
396 higher) were submitted to MS/MS in the higher-energy collision cell (4-Da isolation width, 25%
397 normalized collision energy). MS/MS spectra were acquired in the orbitrap with a resolution of
398 17,500 (at m/z 200) using an AGC target value of 2 × 105 charges and an underfill ratio of 0.1%.
399 Dynamic exclusion was applied with a repeat count of 1 and an exclusion time of 30 s.
400
401 Peptide identification and quantification
402 MS/MS spectra were searched against theoretical spectra based on a UniProt complete human
403 proteome FASTA file (release January 2014, no fragments; 42104 entries) using MaxQuant
404 1.4.1.2 software (Cox & Mann, 2008). Enzyme specificity was set to trypsin, and up to two
405 missed cleavages were allowed. Cysteine carboxamidomethylation (+57.021464 Da) was treated
406 as fixed modification and serine, threonine and tyrosine phosphorylation (+79.966330 Da),
407 methionine oxidation (+15.994915 Da) and N-terminal acetylation (+42.010565 Da) as variable
408 modifications. Peptide precursor ions were searched with a maximum mass deviation of 4.5 ppm,
409 and fragment ions with a maximum mass deviation of 20 ppm. Peptide and protein
410 identifications were filtered at a false discovery rate of 1% using a decoy database strategy. The
411 minimal peptide length was set at 7 amino acids, the minimum Andromeda score for modified
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412 peptides was 40, and the corresponding minimum delta score was 17. Proteins that could not be
413 differentiated based on MS/MS spectra alone were clustered into protein groups (default
414 MaxQuant settings). Peptide identifications were propagated across samples using the 'match
415 between runs' option. Phosphopeptide MS/MS spectral counts (Liu et al, 2004) were calculated
416 from the MaxQuant evidence file using R.
417
418 INKA analysis
419 The INKA analysis pipeline was implemented in R, utilizing custom tables with data extracted
420 from web resources including UniProt (UniProt Consortium, 2015) (for mapping attributes of
421 UniProt accessions; www.uniprot.org, mapping date 8 June 2016), PhosphoSitePlus (Hornbeck
422 et al, 2015) (for experimentally observed phosphorylation sites and kinase-substrate
423 relationships; www.phosphosite.org, Phosphorylation_site_dataset and
424 Kinase_Substrate_Dataset, versions of 3 July 2016), KinBase (Manning et al, 2002) (for
425 currently recognized protein kinases; kinase.com/web/current/kinbase, mapping date 20 July
426 2016), and HGNC (Yates et al, 2017) (for mapping to official gene symbols of the HUGO Gene
427 Nomenclature Committee; www.genenames.org). Furthermore, a tool for the identification of
428 kinase activation loop peptides (Munk et al, 2016), provided on the Phomics website
429 (phomics.jensenlab.org/ activation_loop_peptides), and a locally running version of the
430 NetworKIN algorithm (Horn et al, 2014) were used. For the latter NetworKIN engine,
431 NetworKIN version 3.0 code was used in combination with a UniProt human reference proteome
432 FASTA file derived from release 2014_01 filtered for "no fragments", and containing 21849
433 TrEMBL entries and 39703 Swiss-Prot entries.
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434 Data filtering and annotation. For phosphopeptide data, we used data from the MaxQuant
435 'modificationSpecificPeptides' table, combined with spectral count data calculated above.
436 Although INKA analysis can be performed with intensity-based quantification, we favor spectral
437 count-based quantification as it is less sensitive to peptides with outlier intensities and is more
438 robust for the analysis of aggregated data for multiple peptides, some of which may exhibit
439 dominantly high intensities. For our QExactive data, spectral counting outperformed intensity-
440 based quantification for INKA-based kinase ranking of known drivers (data not shown), yet for
441 the low level LTQ-FTMS data the intensity data worked better (Fig 5E,F; count data not shown).
442 Table rows with data linked to multiple UniProt gene symbols were deconvoluted into separate
443 rows with a single gene symbol. For phosphosite data, we used data from the MaxQuant
444 'Phospho (STY)Sites' table, filtering for so-called class I sites (localization probability > 0.75).
445 Table rows with data linked to multiple UniProt accessions, and those linked to multiple
446 phosphopeptides, were deconvoluted into separate rows. Data from the web resources mentioned
447 above were used to prioritize rows linking the same phosphosite to the same gene, only retaining
448 the row with the best annotated accession. Subsequently, the phosphosite data were merged with
449 pertinent phosphopeptide data in a single, non-redundant class-I phosphosite-phosphopeptide
450 table.
451 Plot data generation. (1) Kinome analysis: A table with data for kinome analysis was generated
452 from the deconvoluted phosphopeptide table obtained above by removing rows with different
453 UniProt gene symbols linking the same phosphopeptide to the same HGNC-mapped symbol, and
454 then filtering for rows with phosphopeptides derived from protein kinases (HGNC-mapped
455 symbol among those for established protein kinases catalogued in KinBase). (2) Activation Loop
456 analysis: the table generated for kinome analysis was further filtered for phosphopeptides that
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457 harbor activation loop sites as indicated by the Phomics web resource. (3) PhosphoSitePlus
458 (PSP) analysis: the non-redundant class-I phosphosite-phosphopeptide table was merged with a
459 table with data from PhosphoSitePlus detailing experimentally observed human kinase-substrate
460 relationships. (4) NetworKIN (NWK) analysis: based on phosphosite data in the non-redundant
461 class-I phosphosite-phosphopeptide table, kinase-substrate relationships were predicted (outside
462 R) using the NetworKIN algorithm in combination with the same FASTA file that was used for
463 peptide and protein identification. The results were imported in our R pipeline and restricted to
464 predictions associated with a NetworKIN score of at least 2 that, in addition, exceeded 90% of
465 the score of the top prediction for the same phosphosite. The non-redundant class-I phosphosite-
466 phosphopeptide table was then merged with the filtered NetworKIN results table.
467 From the four analysis tables, lists with plot data tables for individual samples were generated as
468 follows. For the kinase-centric kinome and activation loop analyses, per sample, kinase gene
469 symbols and associated spectral count values were extracted from all rows (corresponding to
470 phosphopeptides) in the respective tables, and spectral counts were multiplied by the number of
471 phosphorylations of the pertinent peptide so as to account linearly for all phosphorylation
472 activity impinging on the protein. For the substrate-centric PSP and NWK analyses, the same
473 was done with the corresponding analysis tables, but multiplied spectral counts were further
474 divided by the number of phosphosites inferred by MaxQuant in the 'Phospho (STY)Sites' table.
475 As the latter number may exceed the number of actual phosphomodifications of the
476 phosphopeptide, this makes sure the signal is not exaggerated.
477 Kinase bar graph plotting. To visualize kinase phosphorylation levels (kinase-centric analyses),
478 or substrate phosphorylation levels attributed to specific kinases (substrate-centric analyses), the
479 above plot data lists were used to produce, per sample, bar graphs for the top 20 kinases in each
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480 of the four analyses. Specifically, all signals (spectral counts) assigned to a specific kinase were
481 represented by stacked bar segments. For kinase-centric analyses, bar segments correspond to
482 individual phosphopeptides; for substrate-centric analyses, they represent individual
483 phosphosites (aggregated signal of one or more phosphopeptides).
484 INKA analysis and plotting. To infer kinase activity within a single sample, we integrated data
485 from the four analysis tables relevant to the sample (visualized separately in kinase bar graphs
486 above), and calculated, for each kinase, a single INKA score metric. First, from the different
487 analyses we aggregated cumulative signals (corresponding to bars above) assigned to a given
488 kinase, summing values from the kinome analysis and those from the activation loop analysis to
489 get a kinome-centric measure (퐶푘푖푛) according to equation (1), and summing values from the
490 PSP and NWK analyses to get a substrate-centric measure (퐶푠푢푏) according to equation (2):
491 퐶푘푖푛 = 퐶푘푖푛표푚푒 + 퐶푎푐푡푖푣푎푡푖표푛푙표표푝 (1)
492 퐶푠푢푏 = 퐶푃푆푃 + 퐶푁푊퐾 (2)
493 Subsequently, the geometric mean of these two measures was taken to obtain the final, integrated
494 INKA score via equation (3), and a 'skew' parameter was defined in equation (4) to indicate the
495 relative contribution of kinase-centric versus substrate-centric evidence to the INKA score:
496 푆푐표푟푒 = √퐶푘푖푛 ⋅ 퐶푠푢푏 (3)
2 퐶 497 푆푘푒푤 = 푎푟푐푡푎푛 ( 푠푢푏) (4) 휋 퐶푘푖푛
498 The skew parameter is a goniometric correlate of the ratio of substrate-centric and kinase-centric
499 evidence, with the corresponding angle normalized on [0, 휋] to give a value in [0,1]. The value is 2
500 0 when all evidence is kinase-centric, 1 when all evidence is substrate-centric, and 0.5 when
501 there is equal contribution from both sides. To produce an intuitive, visual representation of the
502 inferred (hyper)active kinases in a given experimental sample, for each kinase, the combination
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503 of the INKA score and skew parameter values were taken as vertical and horizontal coordinates,
504 respectively, to create a scatter plot.
505 Kinases for which there is no available data on experimentally observed substrates (PSP), nor
506 any reliable sequence motif with which to predict the kinase's substrates (NWK), will always get
507 a zero 퐶푠푢푏 score, and therefore a zero INKA score. Therefore, for these kinases we performed a
508 separate ranking solely based on the kinase-centric side of the INKA score, i.e, the sum of
509 signals in the kinome and activation loop analyses, 퐶푘푖푛. Flanking the INKA scatter plot, a bar
510 graph was plotted for the top 20 of such 'out-of-scope' kinases, only considering kinases
511 associated with a total of at least 2 spectral counts. As a more simplified visualization, INKA
512 scores for the top 20 kinases were also plotted in a bar graph for each sample.
513 Kinase-substrate relationship network plotting. For network visualization of inferred kinase-
514 substrate relations annotated with INKA scores, the 'network' R package (Butts, 2008) was used.
515 Per sample, relevant data were extracted from a merge of the PSP and NWK analysis tables
516 generated above. Edge lists were created with kinases and substrates as tail and head nodes,
517 respectively, of directed edges. Other data were stored as node and edge attributes in the network
518 object. Nodes were depicted as a hexagon for observed kinases (one or more phosphopeptides
519 derived from the kinase identified), as a pentagon for inferred kinases (no direct observation, but
520 linked to phosphorylation of one or more substrate phosphopeptides), or as a circle for non-
521 kinase substrates. INKA scores were used to define kinase node colors in a white to red gradient,
522 and kinases with at least one phosphorylated activation loop phosphosite were indicated by a
523 thicker node border. Edge widths were made to correlate with the associated substrate site
524 phosphorylation level, and edge colors to the analysis on which the kinase-substrate relationship
525 was based (PSP: coral, NWK: blue, both: green). Node coordinates were calculated using a
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526 Fruchterman-Reingold layout algorithm for force-directed graph drawing with empirically
527 chosen parameter settings (niter=100N, area=N1.8, repulse.rad=N1.5, ncell=N3; N: number of
528 nodes).
529 Statistical significance assessment. Random INKA scores for each kinase in each sample were
530 generated using a two-fold randomization procedure. First, all measured spectral count values
531 were permuted in a sample-wise fashion. Second, as the kinase responsible for phosphorylation
532 of a specific phosphosite, a kinase was randomly selected from the pool of all kinases that could
533 possibly be inferred. To this end, all kinase-substrate links in the PhosphoSitePlus data table and
534 in a proteome-wide NetworKIN analysis table (obtained for all phosphosites in all protein
535 sequences contained in the same FASTA file as used in the identification stages) were
536 randomized. The result of this procedure was then used as input for the calculation of INKA
537 scores in the same way as described above. Iterating this procedure 100,000 times gave sample-
538 and kinase-specific null distributions which enabled the calculation of p-values for each of the
539 original INKA scores associated with each sample.
540
541 Acknowledgements
542 543 SK-Mel-28 cells from ATCC were kindly provided by Dr. Elisa Giovannetti, AIRC/Start-Up
544 Unit, Pisa University, Italy. HCC827 ER3 cells were kindly provided by Prof. Balazs Halmos,
545 Division of Hematology/Oncology, Columbia University Medical Center, New York, USA. Raw
546 label-free phosphoproteomic data on human melanoma G361 cells after radiomimetic treatment
547 were kindly provided by Dr. A. Bensimon, ETH Zürich, Switzerland. Inge de Reus is
548 acknowledged for experimental assistance. Dr. Frank Koopman is thanked for critically reading
549 the manuscript. VitrOmics Healthcare Services (VHS), Cancer Center Amsterdam, and NWO-
25 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
550 Middelgroot (project number 91116017) are acknowledged for support of the mass spectrometry
551 infrastructure, and VHS also for support of RB.
552
553 Author Contributions
554
555 CRJ designed the experiments. TVP, RB, JCK, AH wrote the R script and performed the data
556 analysis. AH proposed the combination score and developed the web server. CvA and RdH
557 performed the experiments. CRJ, CvA and TLL gave input on the analysis. EH and SRP
558 optimized NetworKIN settings. SRP performed nanoLC-MS/MS, database searching and
559 prepared figures. HV and ML provided clinical expertise. CRJ, SRP, TVP, RB, CvA, JCK, AH,
560 FR, ML and HV wrote the paper.
561
562 Conflict Of Interest
563 The authors declare no conflicts of interest.
564
565 Data Availability
566 The mass spectrometry data from this publication have been deposited to the ProteomeXchange
567 Consortium via the PRIDE partner repository [www.ebi.ac.uk/pride/archive] and assigned the
568 identifier PXD006616.
569
570 Code Availability
571 Researchers can analyze their data by the INKA pipeline via www.inkascore.org.
572
26 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
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35 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
774 Figure Legends
775
776 Figure 1. Generic phosphoproteomics workflow and data analysis strategy.
777 A Overview of an MS-based phosphoproteomics experiment. Proteins from a biological
778 sample are digested with trypsin, and phosphopeptides are enriched for analysis by
779 (orbitrap-based) LC-MS/MS. Phosphopeptides can be captured with various affinity resins;
780 here, data were analyzed of phosphopeptides enriched with anti-phosphotyrosine antibodies
781 and TiOx. Database-based phosphopeptide identification, and phosphosite localization and
782 quantification is performed using a tool like MaxQuant.
783 B Scheme of INKA analysis for identification of active kinases in a single biological sample.
784 Quantitative phosphodata for established kinases are taken as direct (kinase-centric)
785 evidence, using either all phosphopeptides attributed to a given kinase ('Kinome'), or only
786 those from the kinase activation loop segment ('Activation Loop'). Phosphosites are filtered
787 for class I phosphosites (localization probability > 75%) (Olsen et al, 2006), coupled to
788 phosphopeptide spectral count data, and used for substrate-centric inference of kinases on
789 the basis of kinase-substrate relationships that are either experimentally observed (provided
790 by PhosphoSitePlus, 'PSP') or predicted by an algorithm using sequence motif and protein-
791 protein network information (NetworKIN, 'NWK'). All evidence lines are integrated in a
792 kinase-specific INKA score using the geometric mean of combined spectral count data ('C')
793 for kinase-centric and substrate-centric modalities. Results are visualized in a scatter plot of
794 INKA scores for kinases scoring ≥10% of the maximum ('INKA Plot '; horizontal shifts
795 from the middle indicate evidence being more kinase-centric or more substrate-centric). For
796 top 20 INKA-scoring kinases, a score bar graph ('INKA Ranking'), and a kinase-substrate
36 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
797 relation network for pertinent kinases and their observed substrates ('INKA Network') are
798 also produced.
799
800 Figure 2. Ranking of top 20 kinases in four cell line use cases by each of four lines of
801 evidence and integrative INKA scoring.
802 A K562 chronic myelogenous leukemia cells with a BCR-ABL fusion. INKA score ranking
803 indicates that ABL1/BCR-ABL (orange bars) exhibits principal kinase activity in this cell
804 line, in line with a role as an oncogenic driver.
805 B SK-Mel-28 melanoma cells with mutant BRAF. In the 'Kinome' analysis, CDK1, CDK2 and
806 CDK3 share a second place, based on phosphopeptides that cannot be unequivocally
807 assigned to either of them. INKA scoring implicates MAPK3 as the number one activated
808 kinase. As SK-Mel-28 is driven by BRAF, a serine/threonine kinase that is missed by pTyr-
809 based phosphoproteomics, downstream targets in the MEK-ERK pathway are highlighted by
810 blue coloring.
811 C Erlotinib-resistant HCC827-ER3 NSCLC cells with mutant EGFR. INKA scores reveal the
812 driver EGFR (pink coloring) as second-highest ranking and MET as highest ranking kinase,
813 respectively.
814 D H2228 NSCLC cells with an EML4-ALK fusion. The driver ALK (purple coloring) is ranked
815 as a top 3 kinase by INKA score, slightly below PTK2 and SRC.
816
817 Data information: For each cell line, bar graphs depict kinase ranking based on kinase-centric
818 analyses (panel "Kinase phosphopeptides"), substrate-centric analyses (panel "Substrate
819 phosphopeptides"), and combined scores (panel "INKA"). Bar segments represent the number
37 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
820 and contribution of individual phosphopeptides (kinase-centric analyses) or phosphosites
821 (substrate-centric analyses). P-values flanking INKA score bars were derived through a
822 randomization procedure with 105 permutations of both peptide-spectral count links and kinase-
823 substrate links.
824
825 Figure 3. INKA plots and kinase-substrate relation networks for four cell line use cases.
826 A K562 CML cells with a BCR-ABL fusion. ABL1 is the most activated kinase, with relatively
827 equal contributions from both analysis arms. It is a highly connected, central node in the
828 network.
829 B SK-Mel-28 melanoma cells with mutant BRAF. Downstream MEK-ERK pathway members
830 are highlighted in lieu of BRAF which is missed by the current pTyr-based workflow.
831 MAPK3 is the top activated kinase. The network includes two clusters with highly
832 connected activated kinases, MAPK1/3 and SRC, respectively.
833 C Erlotinib-resistant HCC827-ER3 NSCLC cells with mutant EGFR. EGFR and MET are the
834 most active, highly connected kinases. AXL, inactive in parental cells (see Supplementary
835 Fig 6), but associated with erlotinib resistance in this sub-line, can only be analyzed through
836 the kinase-centric arm (pink bar highlighting).
837 D H2228 NSCLC cells with an EML4-ALK fusion. ALK is a high-ranking kinase with roughly
838 equal evidence from both analysis arms. Multiple highly active and connected nodes imply
839 relative insensitivity to ALK inhibition, in line with previous functional data. Larger
840 networks are shown in Supplementary Figs 3-5 and Supplementary Fig 7.
841
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842 Data information: In INKA plots proper, the vertical position of kinases (drivers in red) is
843 determined by their INKA score, whereas the horizontal position is determined by the
844 (im)balance of evidence from kinase-centric and substrate-inferred arms of the analysis. Kinases
845 not covered by PhosphoSitePlus (PSP) and NetworKIN (NWK) are visualized in a flanking bar
846 graph.
847
848 Figure 4. INKA analysis of three cancer cell line cases from the literature.
849 A H3255 NSCLC cells with an EGFR mutation. INKA analysis shows that EGFR is a hyper-
850 activated kinase (top 2 in all branches), together with MET.
851 B A204 rhabdomyosarcoma cells with documented PDGFRA signaling. PDGFRA exhibits
852 variable ranking (top-intermediate) in individual analysis types for A204, but integrated
853 INKA analysis (right-most bar graph) infers it as a highly active, rank-2 kinase, after
854 EPHA1.
855 C MNNG/HOS osteosarcoma cells with documented MET signaling. MET consistently ranks
856 among the top 2 in all analysis arms, culminating in a first rank in the integrative INKA
857 analysis.
858
859 Data information: See the legend of Figure 2 for basic explanation.
860
861 Figure 5. INKA analysis in differential genetic and pharmacological settings.
862 A Effect of a monogenetic change in a cancer cell line use case. Comparison of U87
863 glioblastoma cells ("wild type") with isogenic U87-EGFRvIII cells overexpressing a
864 consitutively active EGFR variant ("mutant") grown under baseline conditions.
39 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
865 B Effect of drug treatment in a cancer cell line use case. Comparison of U87-EGFRvIII cells at
866 baseline with U87-EGFRvIII cells treated with 10 µM erlotinib for 2 h shows a clearly
867 reduced INKA score for EGFR.
868 C Effect of drug treatment in a patient with hypopharyngeal cancer. Tumor biopsies were
869 taken both before and after two weeks of erlotinib treatment.
870 D Same as panel C, but for a patient with pancreatic cancer.
871 E Time-dependent effect of radiomimetic treatment in a cancer cell line use case. MS
872 intensity-based INKA analysis of TiO2-captured phosphoproteomes from G361 melanoma
873 cells at different time points following treatment with the DNA damage-inducing drug
874 neocarzinostatin (NCS) in the absence or presence of ATM inhibitor KU55933 (ATM).
875 Plotted is the INKA score for ATM, exhibiting a time-dependent increase, which is not
876 observed with ATM blocking. Full INKA score bar graphs are shown in Supplementary Fig
877 8.
878 F Same as panel E, but plotting of the INKA score for PRKDC/DNA-PK, which exhibits
879 similar behavior. Raw data for panels A&B are from Van der Mijn et al. (van der Mijn et al,
880 2014). Raw data for panels E&F are from Bensimon et al. (Bensimon et al, 2010) and
881 averaged for replicate treatment conditions.
882
883 Supplementary Figure Legends
884
885 Supplementary Figure 1. Coverage of 538 unique protein kinases from KinBase by
886 resources providing kinase-substrate relationships.
40 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
887 The Venn diagram shows that 172 kinases in KinBase are missing from both PhosphoSitePlus
888 (PSP) and NetworKIN (NWK), 31 are only covered by NWK, 172 are only covered by PSP,
889 while 163 are covered by both NWK and PSP. Additionally, 14 proteins annotated by PSP are
890 not present in KinBase, but mostly involve small-molecule kinases or proteins with spurious
891 (electronic) annotation, with the exception of two fusion protein species with established protein
892 kinase activity (BCR-ABL and NPM-ALK).
893
894 Supplementary Figure 2. Correlation between kinase-specific INKA scores and estimated
895 p-values.
896 Log10-transformed INKA scores are plotted against (sign-switched) log10-transformed p-values
897 for each of four cell line use cases. Highly significant, positive Spearman rank correlation
898 coefficients for each of the cell lines demonstrate a monotonic trend: higher INKA scores are
899 associated with lower p-values. (a) K562 chronic myelogenous leukemia cells. (b) SK-Mel-28
900 melanoma cells. (c) HCC827-ER3 lung cancer cells. (d) H2228 lung cancer cells.
901
902 Supplementary Figure 3. Kinase-substrate relation network for top 20 INKA-scoring
903 kinases and their observed substrates in K-562 chronic myelogenous leukemia cells with a
904 BCR-ABL fusion. ABL1 is a highly connected and central node. Kinases downstream of BCR-
905 ABL signaling, such as SRC, are also active, albeit to a lower extent.
906
907 Supplementary Figure 4. Kinase-substrate relation network for top 20 INKA-scoring
908 kinases and their observed substrates in SK-Mel-28 melanoma cells with BRAFV600E.
41 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
909 Two clusters of activated kinases are observed, one containing BRAF targets MAPK1 and
910 MAPK3, and the other containing SRC as highly connected nodes.
911
912 Supplementary Figure 5. Kinase-substrate relation network for top 20 INKA-scoring
913 kinases and their observed substrates in HCC827-ER3 non-small cell lung carcinoma cells.
914 EGFR and MET are central and highly connected nodes. AXL, associated with erlotinib
915 resistance of HCC827-ER3 cells, is missed by INKA score-based analysis as no substrate-centric
916 data is available for this kinase.
917
918 Supplementary Figure 6. INKA plot and top 20 INKA-scoring kinase bar graph for
919 HCC827 cells with mutant EGFR.
920 In the INKA plot proper, the vertical position of kinases (driver in red) is determined by their
921 INKA score, whereas the horizontal position is determined by the (im)balance of evidence from
922 kinase-centric and substrate-inferred arms of the analysis. EGFR and MET are implicated as the
923 most active kinases. Kinases not covered by PhosphoSitePlus (PSP) and NetworKIN (NWK) are
924 visualized in a flanking bar graph on the left. Note absence from the bar graph of the AXL kinase
925 that is responsible for erlotinib resistance in the HCC827-ER3 sub-line (see Fig 3c).
926
927 Supplementary Figure 7. Kinase-substrate relation network for top 20 INKA-scoring
928 kinases and their observed substrates in H2228 non-small cell lung carcinoma cells with an
929 EML4-ALK fusion.
930 Multiple highly active and connected nodes are present in the network for H2228, implying
931 relative insensitivity to inhibition of ALK alone, in line with previous functional data. Dual
42 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
932 inhibition of the number-5 hyperactive node, EGFR, and ALK results in significant reduction of
933 proliferation (Voena et al, 2013).
934
935 Supplementary Figure 8. MS intensity-based INKA analysis of data published by
936 Bensimon et al. on TiO2-captured phosphoproteomes from G361 melanoma cells following
937 radiomimetic treatment.
938 A INKA score bar graphs for G361 at baseline.
939 B-E INKA score bar graphs for G361 after 10 min (B), 30 min (C), 120 min (D), or 360 min
940 (E) treatment with 200 ng/ml neocarzinostatin (NCS).
941 F,G INKA score bar graphs for G361 after 30 min (F) or 120 min (G) treatment with 200 ng/ml
942 neocarzinostatin in the presence of 10 µM KU55933 (ATM inhibitor, ATMi). DNA
943 damage-induced kinases ATM, ATR, and PRKDC/DNA-PK are highlighted in red.
944
945 Data information: raw data (Bensimon et al, 2010) were normalized and averaged for replicate
946 treatment conditions.
947
948 Supplementary Table Legends
949
950 Supplementary Table 1. Mass-spectrometric and annotation data for phosphopeptides that
951 were captured and identified in phosphotyrosine-based immunoprecipitation experiments
952 on cell lines and patient tumors.
953 Pair-wise tables "phosphopeptides..." and "phosphosites..." harbor information from selected
954 columns of the MaxQuant modificationSpecificPeptides.txt and Phospho (STY)Sites.txt export
43 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
955 files, respectively. The latter list phosphosites inferred by MaxQuant on the basis of mass-
956 spectrometric evidence. Raw intensities were median normalized, and MS/MS spectral counts
957 were obtained with the help of the MaxQuant evidence.txt export file.
958
959 Supplementary Table 2. Coverage of protein kinases by various resources used in this
960 study.
961 A 538 established protein kinases from KinBase (http://kinase.com/web/current/kinbase)
962 B 489 kinases for which activation loop segment annotation is available from Phomics
963 (http://phomics.jensenlab.org)
964 C 349 kinases linked to substrate phosphorylation in experimental studies, provided by
965 PhosphoSitePlus (http://www.phosphosite.org/staticDownloads.action)
966 D 194 kinases linked to substrate phosphorylation through prediction, provided by NetworKIN
967 (http://networkin.info)
968 E 172 protein kinases that are not covered by both PhosphoSitePlus and NetworKIN
969 F 14 entries from PhosphoSitePlus that are not covered by KinBase.
970
971 Supplementary Table 3. Quantitative result tables for INKA analyses.
972 A-O Detailed are analyses for (A) K562 chronic myeloid leukemia cells, (B) SK-Mel-28
973 melanoma cells, (C) HCC827-ER3 non-small cell lung carcinoma cells; sub-line of
974 HCC827, (D) H2228 non-small cell lung carcinoma cells. (E) HCC827 non-small cell
975 lung carcinoma cells; parental line of HCC827-ER3, (F) H3255 non-small cell lung
976 carcinoma cells (Guo et al, 2008), (G) A204 rhabdomyosarcoma cells (Bai et al, 2012),
977 (H) MNNG/HOS osteosarcoma cells (Bai et al, 2012), (I) U87 glioblastoma cells (van
44 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
978 der Mijn et al, 2014), (J) U87-EGFRvIII glioblastoma cells (van der Mijn et al, 2014),
979 (K) U87-EGFRvIII glioblastoma cells treated with erlotinib (van der Mijn et al, 2014),
980 (L) tumor tissue biopsy from a hypopharyngeal cancer patient (Pt23) before treatment,
981 (M) tumor tissue biopsy from a hypopharyngeal cancer patient (Pt23) after two weeks of
982 erlotinib treatment, (N) tumor tissue biopsy from a pancreatic cancer patient (Pt24) before
983 treatment, (O) tumor tissue biopsy from a pancreatic cancer patient (Pt24) after two
984 weeks of erlotinib treatment.
985
986 Data information: For each given kinase, metrics are given for individual analyses (Kinome,
987 ActLoop, PSP, and NWK), accompanied by kinase-centric and substrate-centric INKA score
988 components (Kin = Kinome + ActLoop, Sub = PSP + NWK), INKA scores (Score = √ (Kin ∗
989 Sub)), relative INKA scores (Rel.Score) and skew parameters indicating relative contribution
990 from kinase-centric versus substrate-centric evidence (Skew = arctan (Sub/Kin) ∗ 2/휋). Skew
991 ranges from 0 (kinase-centric evidence only) to 1 (substrate-centric evidence only), so that a
992 skew of 0.5 indicates equal contribution.
993
994 Supplementary Table 4. Drug sensitivity data for top 5 INKA-scoring kinases in four
995 cancer cell line use cases.
996 A K562 driven by BCR-ABL. The data show high sensitivity of the cell line only to inhibitors
997 of ABL.
998 B SK-Mel-28 driven by BRAF. Drug inhibition data are also listed for BRAF and ERK-
999 activating MEKs in addition to top INKA-scoring kinases. Among the latter, ERK1 and
45 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
1000 ERK2 exhibit limited sensitivity to inhibitors. Direct targeting of BRAF, or (upstream of
1001 ERK1/2) MEK1/2, is much more successful, in line with BRAF driver function.
1002 C HCC827 driven by EGFR. HCC827, the erlotinib-sensitive parental line of the erlotinib-
1003 resistant HCC827-ER3 sub-line, exhibits high sensitivity to EGFR inhibitors, but not MET
1004 inhibitors.
1005 D H2228 associated with EML4-ALK. The data show moderate sensitivity of H2228 to ALK
1006 inhibitors, but little efficacy of inhibitors targeting top INKA-scoring kinases.
1007
1008 Data information: data were derived from the Genomics of Drug Sensitivity in Cancer website
1009 (GDSC, http://www.cancerrxgene.org) and include cell line-specific IC50 values and Z-scores
1010 for relative sensitivity to a given drug compared to all other cell lines.
46 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
A Lysate Tryptic digest Desalt Dry p-peptide capture Desalt LC-MS/MS Ti or O O m/z database search B Phosphopeptide Phosphosite PSP NWK Activation Upstream Upstream Kinome Loop Kinase Kinase
BCR PRPF4B SRC MET ABL1 MAPK1 WEE1 ABL1 CDK1 ABL1 LYN LYN CDK3 MAPK14 ABL1 ABL2 CDK2 ABL2 FYN BTK PRPF4B MAPK3 HCK TTK ABL2 DYRK1B PKMYT1 KIT MAPK1 DYRK1A ERBB2 EGFR MAPK14 YES1 ABL2 NTRK1 LYN SRC RET MAP2K1 PEAK1 HIPK2 BCR ERBB2 MAPK3 HIPK1 JAK2 TYK2 BTK FYN MAP2K1 TEC
Top 20 kinases Top DYRK1B LYN LCK ITK DYRK1A LCK SYK FLT1 TNK2 HCK MET MAPK1 YES1 PTK2 ALK FGR FYN GSK3B MAPK3 YES1 HIPK1 GSK3A MAP2K4 INSR DDR1 DDR1 EGFR PDGFRB
0 50 100 150 0 10 20 30 40 50 60 0 50 100 150 200 250 0 50 100 150 200 Spectral counts Spectral counts Spectral counts Spectral counts
INtegrative INferred Kinase Activity score (INKA) Statistics
permutation = ( + ) × ( + ) and randomization
∑INKA Plot∑ ∑ INKA Ranking∑ INKA Network
ABL1 CBL FYB ABL2 ITK SH2D2A BCR PLCG1 1 ABL1 250 FYN LYN INPP5D FCGR2C DOK1 DOK2 SYK LCK CRK MAPK1 BTK BMX 0.9 225 BCR−ABL FYN FCGR2A TEC BCR 0.8 200 SRC NTRK2 WAS ABL2 TNK2 PEAK1 ABL1 BTK MAP2K2 PPP1CA ABI1 FGR 0.7 175 MAPK3 LYN HCK GAB2 HCK STAT5A EGFR JAK2 MAPK3 NCK1 0.6 150 YES1 PRKCD PTK2 PTTG1IP MPZL1 MAP2K1 NTRK1 LCK G6PD ABL2 Score MAPK1 CDK2 SHC1 0.5 125 JAK2 CDK1 SRC BCR KIT INPPL1 STAT5B
Relative Score Relative TEC GAB1 0.4 100 PDGFRB TNK2 RET TYK2 LYN CDK3 TTK HIPK2 TTK CTTN PFN1 PRKACA 0.3 75 MAP2K6 FYN SYK MAPK14 PIK3R3 PDGFRA MAPK1 MAPK14 CBLB CD46 SRC MAP3K6 PTPRA 0.2 BTK 50 PIK3R2 MAPK3 HCK HIPK2 MAP2K4
YES1 FGR MAP3K5 MAP2K3 YES1 0.1 25 ARHGAP35 JAK2 ACTN1 AURKC TNK2 SYKTECTTK ALK MAPK14 GSK3AGSK3BHIPK2 PTK2 FGRTYK2 Kinase−CentAKT3 AURKBEPHB4 ricFER CDK5AURKA Equal SubstrBMXAKT1ate−Cent AKT2 ric 0 50 100 150 200 250 AXL Contribution BLK INKA score Kinase phosphopeptides Substrate phosphopeptides INKA
Kinome Activation loop PhosphositePlus NetworKin Combined Score p-value BCR PRPF4B SRC MET ABL1 1.0e-05 ABL1 MAPK1 WEE1 ABL1 ABL2 1.0e-05 A CDK1 ABL1 LYN LYN BCR 2.0e-05 CDK3 MAPK14 ABL1 ABL2 LYN 1.0e-05 bioRxiv preprintCDK2 doi: https://doi.org/10.1101/259192; thisABL2 version posted February 2, 2018. The copyrightFYN holder for this preprint (which was notBTK MAPK1 5.0e-05 PRPF4B certified by peer review) is the author/funder.MAPK3 All rights reserved. No reuse allowedHCK without permission. TTK FYN 3.7e-04 ABL2 DYRK1B PKMYT1 KIT SRC 1.7e-04 MAPK1 DYRK1A ERBB2 EGFR BTK 5.3e-04 MAPK14 YES1 ABL2 NTRK1 MAPK3 2.2e-04 LYN SRC RET MAP2K1 HCK 2.2e-04 PEAK1 HIPK2 BCR ERBB2 YES1 1.7e-02 MAPK3 HIPK1 JAK2 TYK2 LCK 3.4e-03 BTK FYN MAP2K1 TEC JAK2 5.6e-03 DYRK1B LYN LCK ITK TEC 3.5e-02 DYRK1A LCK SYK FLT1 TNK2 9.9e-02 TNK2 HCK MET MAPK1 TTK 1.1e-02 YES1 PTK2 ALK FGR SYK 6.9e-02 FYN K562 GSK3B K562 MAPK3 K562 YES1 K562 MAPK14 K562 1.7e-01 HIPK1 Kinome GSK3A ActLoop MAP2K4 PSP INSR NWK HIPK2 6.8e-02 DDR1 DDR1 EGFR PDGFRB FGR INKA 3.2e-02
0 50 100 150 0 10 20 30 40 50 60 0 50 100 150 200 250 0 50 100 150 200 0 50 100 150 200 250
p-value PRPF4B PRPF4B SRC ABL1 MAPK3 2.2e-04 CDK3 MAPK1 WEE1 MAP2K1 SRC 4.0e-04 B CDK2 PTK2 RET LYN PTK2 4.3e-03 CDK1 MAPK3 JAK2 TTK FYN 9.9e-03 MAPK1 DYRK1B MAP2K1 EGFR YES1 1.0e-02 PTK2 DYRK1A PKMYT1 ERBB2 JAK2 4.5e-03 MAPK3 MAPK14 ERBB2 FGR LYN 8.7e-03 DYRK1B HIPK2 BMX KDR INSR 5.7e-03 DYRK1A HIPK1 MET MAP2K2 MAPK14 1.5e-02 TNK2 YES1 PTK2 YES1 MAPK1 1.3e-02 MAPK14 SRC MAPK3 INSR LCK 1.5e-02 PEAK1 LCK FGR MAP2K3 HIPK2 1.9e-02 YES1 GSK3B MAP2K2 FYN IGF1R 1.9e-02 FYN GSK3A MAP2K4 MAP2K6 CDK1 5.0e-02 SYK FYN ABL1 KIT GSK3A 2.9e-02 HIPK2 INSR MAP2K6 ITK GSK3B 2.9e-02 HIPK1 IGF1R MAP3K6 MAPK3 FGR 1.3e-02 AXL SK−Mel−28 MAPK9 SK−Mel−28 MAP3K5 SK−Mel−28 SRMS SK−Mel−28 CDK5 SK−Mel−28 1.7e-02 GSK3B Kinome DYRK4 ActLoop MAPK14 PSP MET NWK EPHA2 1.1e-01 GSK3A DYRK2 CDK5 MAP2K5 TYK2 INKA 4.3e-02
0 10 20 30 40 50 0 10 20 30 40 50 0 20 40 60 80 100 0 10 20 30 40 50 60 70 0 5 10 15 20 25 30 35
p-value MET MET SRC MET MET 1.0e-05 EGFR PTK2 MET EGFR EGFR 1.0e-05 C NRK GSK3B ABL1 ERBB2 EPHA2 1.0e-05 EPHA2 GSK3A MST1R PRKCE MST1R 4.1e-04 PTK2 EPHA2 EGFR FYN ERBB2 1.0e-05 PEAK1 EGFR BMX EPHA2 PTK2 2.4e-02 FRK MST1R FYN MAPK3 FYN 5.7e-04 CDK1 PRPF4B RET INSR SRC 4.0e-04 MINK1 EPHB4 LYN YES1 EPHB2 1.1e-02 ROR1 AXL EPHA2 ABL1 GSK3B 3.5e-04 TNK2 MAPK8 INSR EPHB2 EPHB4 8.5e-03 AXL MAPK10 PTK2 TYK2 GSK3A 4.4e-04 GSK3B HIPK2 WEE1 EPHB4 MAPK3 5.8e-04 GSK3A HIPK1 MAPK3 NTRK1 LYN 2.2e-03 CDK3 EPHB2 MAPK1 EPHB1 YES1 6.0e-03 CDK2 DYRK1B FGR ITK ABL1 1.7e-03 EPHB4 DYRK1A LCK ERBB4 CDK1 2.3e-02 EPHB2 HCC827_ER3 MAPK12 HCC827_ER3 CDK1 HCC827_ER3 MAP2K1 HCC827_ER3 MAPK1 HCC827_ER3 3.2e-03 PRKCD Kinome MAPK1 ActLoop ALK PSP EPHA4 NWK HIPK2 INKA 4.4e-03 LYN JAK1 MAP2K1 ADRBK1 FRK 1.4e-02
0 50 100 150 200 0 20 40 60 80 100 0 100 200 300 400 0 100 200 300 400 500 0 100 200 300 400 500
p-value PRPF4B PRPF4B SRC EGFR PTK2 1.6e-03 ALK PTK2 RET MET SRC 1.1e-04 D CDK1 MAPK14 WEE1 ERBB2 ALK 7.0e-04 CDK3 GSK3B MET INSR LYN 7.3e-04 CDK2 GSK3A PKMYT1 ABL1 EGFR 4.5e-04 PTK2 MAPK3 ERBB2 LYN MET 4.0e-04 MAPK14 MAPK1 ALK EPHA1 EPHA2 6.3e-03 LYN DYRK1B BMX MAP2K1 FYN 2.3e-03 GSK3B DYRK1A MAP2K1 YES1 ABL1 1.4e-03 GSK3A EPHA2 JAK2 TTK MAPK3 9.1e-04 EPHA2 YES1 PTK2 FGR YES1 3.4e-03 TNK2 SRC ABL1 FYN GSK3A 1.3e-03 MAPK3 LYN FGR KDR GSK3B 1.5e-03 MAPK1 LCK EGFR KIT MAPK14 5.6e-03 DYRK1B HIPK2 INSR EPHA2 INSR 2.1e-03 DYRK1A HIPK1 MAP2K4 ITK MAPK1 8.0e-03 YES1 HCK MAP3K6 TEC LCK 1.0e-02 FYN H2228 FYN H2228 MAP3K5 H2228 MAP2K6 H2228 ERBB2 H2228 3.8e-03 PEAK1 Kinome ABL2 ActLoop MAP2K6 PSP MAP2K7 NWK ABL2 3.8e-02 EGFR ABL1 MAP2K3 FLT1 HCK INKA 1.6e-02
0 10 20 30 40 50 0 10 20 30 40 50 0 50 100 150 200 0 50 100 150 0 10 20 30 40 50 Spectral counts Spectral counts Spectral counts Spectral counts INKA score CBL Not in PSP/NWK K562 FYB ITK PEAK1 SH2D2A 1 250 PLCG1 ABL1 FYN DDR1 INPP5D FCGR2C DOK1 DOK2
A SYK HIPK3 LCK CRK 0.9 225 BTK BMX CDK17 BCR−ABL FCGR2A TEC AXL BCR DYRK4 0.8 200 NTRK2 WAS ABL2 TNK2 PEAK1 ABL1 FAM20B MAP2K2 PPP1CA ABI1 FGR TRIM28 0.7 175 LYN HCK GAB2 STAT5A EGFR JAK2 MAPK3 NCK1 0.6 150 PRKCD PTK2 PTTG1IP
MPZL1 MAP2K1
Score NTRK1 ABL2 G6PD MAPK1 0.5 125 CDK2 SHC1 CDK1 BCR SRC KIT
Relative Score Relative INPPL1 STAT5B GAB1 0.4 100 PDGFRB RET LYN TYK2 CDK3 TTK HIPK2 CTTN PFN1 PRKACA 0.3 75 MAP2K6
Top-20 Kinases Top-20 FYN MAPK14 MAPK1 PIK3R3 PDGFRA SRC CBLB CD46 0.2 BTK 50 MAP3K6 PTPRA MAPK3 HCK PIK3R2 YES1 LCK MAP2K4 0.1 25 MAP3K5 MAP2K3 YES1 JAK2 ARHGAP35 0 5 10 15 AURKC TNK2 SYKTECTTK ALK ACTN1 MAPK14 GSK3AGSK3BHIPK2 PTK2 FGRTYK2 AKT3 AURKBEPHB4 FER CDK5AURKA BMXAKT1 AKT2 AXL BLK Not in PSP/NWK SK−Mel−28 MAP2K4 MAP3K6 TEC AXL 1 MAPK3 ● 35 MAP2K1 PEAK1 STAT3 JAK2 B MAPK1 DYRK4 SRC GSK3B 0.9 32 MAP2K6 SGK223 MAPK14 PTK2 CDK5 MAP2K3 CDK17 MAPK3 0.8 28 ABL1 HIPK3 PXN YES1
MAP2K2 WEE1 RET 0.7 25 PKMYT1 MAP3K5
VCL
SYK TTK 0.6 21 GSK3A FGR CDK1
Score CDK2 ERBB2 bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not TYK2 0.5 18 MET certified by peer review) is the author/funder. All rights reserved. No reuse allowed without FYNpermission. LYN PTK2
Relative Score Relative PRKACA MAPK14 YES1 LCK 0.4 JAK2 14 INSR BMX LYN PEAK1 BCAR1 Top-20 Kinases Top-20 MAPK1 MPZL1 IGF1R 0.3 11 SRC ● LCK ITK EGFR HIPK2 IGF1R FYN CDK5 INSR KDR CDK1 PTPRA 0.2 FGR 7 TNK2 GSK3A/B EPHA2 TYK2 PDGFRA EPHA2 0.1 4 HIPK2 ABL2 PDGFRB 0 2 4 6 SYK HCK CAV1 AURKA ALK AKT2 AKT3 ABL1 AKT1 AURKB BMX
MAP2K2 Not in PSP/NWK HCC827−ER3 IRS2 PDK1 MST1R ACP1 PAK4 CRKL JAK2 PEAK1 GSK3A DAG1 1 MET 530 PRKCE LRP6 KDR ITGB4 AXL STK11 GSK3B C RET ROR1 FGR EPHA2 ITK GIT1 PDGFRB BMX 0.9 477 BCAR1 SGK223 TYRO3 ARHGAP35 MAPK3 INPPL1 TYRO3 PTK2B PTPN11 ADRBK1 CTNND1 TNK2 GPRC5A 0.8 424 RASA1 MET MAPK1 DYRK4 ACTN1 EPN2 HGS EPS8 JUP CBLB PTK2 GAK MAP2K1 RIPK1 SGK223 ERRFI1 0.7 371 FYN PXN CAMK2A BMPR2 YES1 CTTN ITGB5 YBX1 CBL STAM2 SHC1 PKN2 VCL USP6NL GAB1 STAT3 ABI1 BMP2K 0.6 318 GAB2 EGFR EGFR APP OSMR HSP90AA1 PKM OCLN PAK1 FAM20B ERBB2 PAG1 EPB41
Score CAV1 ERBIN COPS5 YAP1 BCR 0.5 265 CD46 STAT1 ABL1 PLCG1 NOTCH1 TJP1 CDK4
Relative Score Relative DOCK7 HSP90AA2P PTPN6 RAB5A LYN TNS3 CRK STAM 0.4 212 SRC SPRY2 ERBB3 TTK G6PD PEAK1 CDK1 VIM FRK NCK1 ABI2 MPZL1 0.3 159 PFN1 ARHGAP5 CDK2 EMD TEC PRKCD RAC1 CFL1 RGS3 PTPRA CABLES1 Top-20 Kinases Top-20 KIT 0.2 106 PBK EPHA2 GJA1 LAT2 EFNB2 CCDC88A WEE1 ERBB2 SDCBP PTK2 EPHB4 MST1R PKMYT1 EPHB2 TFRC HIPK2 0.1 GSK3AGSK3B EPHB4EPHB2 FYNSRC 53 PTTG1IP MAPK3YES1LYNABL1 EFNB1 0 10 20 30 FRK CDK1 HIPK2MAPK1ERBB4 WASL ARAF CDK2 MINK1 ERBB3EPHB3EPHA4EPHB1TTKLCKTYK2INSR ALK AKT2 ALPK3MERTKTGFBR2JAK1MAPK10 MELKCDC42BPBMAPK14PDPK1 MAP4K4NEK1 EPHA3FERPAK2 IGF1RABL2 PRKCGPRKCACSNK1A1PTK2BHCK JAK2EPHA5ADRBK1 CDK5PRKCI PAK4FGR AKT1 ATR AURKA
Not in PSP/NWK ITGB1 H2228 ITK NCK1 CTNND1
PEAK1 CTNNA2 LCK 1 PTK2 50 TEC SGK223 SRC CDK2 CTNNA1 PEAK1 D DOCK4 NAMPT IRS2 AXL ABL2 ALK FYN 0.9 45 KIT HIPK3 PIK3R3 SGK223 PAG1 LYN HCK CDK17 GSK3A ABL1 MPZL1 PTPRA
0.8 40 LYN IRS1
EPHA2 PTTG1IP INSR MAP3K6 CAV1 EGFR CRK FGR 0.7 35 MAP2K6 BCAR1 PIK3R1 EPHA2 PXN PDGFRB INPP5D MET JAK2 MUC1 0.6 MAPK3 30 KDR GAB2 MAP2K3 CDK1 SDCBP FYN ABL1 SRC AP2B1 MAPK14 MAPK1 YES1 Score EGFR GAB1 GSK3A/B INPPL1 HIPK2 0.5 25 PTK2 MAPK3 MAP2K4 PDGFRA MAPK14 SHC1 Relative Score Relative INSR RET CD46 G6PD 0.4 20 MET GPRC5A MAP3K5 MAP2K1 TNK2 CTTN PRKACA BMX
Top-20 Kinases Top-20 LCK MAP2K2 0.3 MAPK1 ABL2 ERBB2 15 ERBB2 EML4 STAT3 CBL GSK3B YES1 HCK EPHA1 ITGB4 0.2 HIPK2 IGF1R JAK2 10 ARHGAP35 PTPRE PTPN11 FGR MST1R ALK TYK2 HSP90AA1 EPHA3 EPHB3 HSP90AA2P EPHB4 VCL 0.1 CDK5 5 MAP2K7 ACTN1 VASP TNK2 EPHA4 VIM 0 2 4 6 8 FGFR1 ERBB4 TNS3 AURKC TGFBR2 FRK SYK AKT3 AURKA AURKB AKT1 AKT2 Kinase−Centric Kinase−CentAXL ric Equal Substrate−CentBCR−ABL ric Counts Evidence Only Contribution Evidence Only Observed Inferred Non kinase Activation-loop Rel. INKA Score Substrate kinase kinase substrate phosphorylated NWK PSP Both
0 Color Key 1 Kinase bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Kinase phosphopeptides Substrate phosphopeptides INKA
EGFR PTK2 SRC ERBB2 EGFR PTK2 MET EGFR EGFR MET MET MAPK14 MET MET PTK2 EPHA2 GSK3B RET YES1 EPHA2 A MAPK14 GSK3A BMX FYN ERBB2 GSK3B PTK2B ABL1 EPHA2 SRC GSK3A PRPF4B MAP2K1 ABL1 GSK3A EPHB4 MAPK13 PTK2 MAP2K6 GSK3B EPHB3 EPHA2 GSK3B LYN FYN YES1 DYRK1B GSK3A ERBB4 YES1 PTK2B DYRK1A FYN MAP2K1 LCK PRKCD YES1 FGR KIT EPHB4 ERBB2 SRC MST1R FGR LYN EPHA4 LCK SYK PDGFRB EPHB3 DYRK1A FYN LCK KDR EPHA4 LYN EPHA1 EPHA2 TYK2 INSR FYN MAPK3 MAP3K5 NTRK1 ERBB4 CDK3 MAPK1 MAP2K6 ERBB3 ERBB3 H3255 H3255 H3255 H3255 H3255 MAPK3 CDK2 EPHB4 MAP2K4 EPHB4 INKA ranking CDK1 Kinome EGFR Activation Loop MAP2K3 PhosphoSitePlus LCK NetworKIN EPHA1
0 10 20 30 40 0 5 10 15 20 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80
FGFR1 PTK2 SRC EGFR EPHA2 PDGFRA DDR2 RET ERBB2 PDGFRA CDK1 FGFR1 BMX LYN FGFR1 EPHA2 GSK3A WEE1 EPHA4 FYN B PTK2 DDR2 EPHA2 PKMYT1 ITK FYN DYRK1A ERBB2 EPHA2 GSK3A PTK2 PRPF4B MAP2K1 PDGFRA ABL1 PEAK1 HIPK1 FGFR1 MET JAK2 GSK3A MAPK14 JAK2 ABL1 EPHB4 EPHB4 MAPK1 EPHA2 YES1 EGFR DYRK1A MAPK3 MET MAP2K1 MAPK3 PRPF4B FYN PDGFRA TEC EPHB3 TNK2 AXL GSK3B FYN TYK2 HIPK1 TYRO3 GSK3A NTRK1 EPHA7 MAPK14 EPHB4 FYN MAP2K6 TNK2 JAK2 EPHA7 ABL1 EPHB4 FER EPHB3 DDR1 PTK2 TYK2 EPHA3 MAPK1 JAK1 EGFR EPHB2 MELK A204 A204 A204 A204 A204 ABL2 AXL HIPK3 MAP2K2 EPHB6 INKA ranking ABL1 Kinome ABL1 Activation Loop FGR PhosphoSitePlus ABL2 NetworKIN ALK
0 10 20 30 40 0 5 10 15 20 25 0 20 40 60 80 120 0 10 20 30 40 50 0 10 20 30 40 50 60 70
MET PTK2 SRC EGFR MET PTK2 MET MET MET PTK2 PDGFRA GSK3A BMX ERBB2 FYN C CDK1 EPHA2 RET FYN EGFR GSK3A PTK2 FGR EPHA2 MAPK14 FYN ABL1 ABL1 ABL1 EPHA2 HIPK1 MST1R LYN PDGFRA PRKDC DYRK1A EGFR EPHA2 GSK3A MAPK14 AXL FGR PDGFRA FGFR1 HIPK1 PRPF4B WEE1 KDR EPHB4 EGFR HIPK3 PKMYT1 ITK TEC DYRK1A FYN FYN ERBB3 TYK2 EPHB4 FGFR1 ERBB2 LCK ABL2 ALK AXL DYRK2 MAP2K1 MAP2K1 PRPF4B INSR FLT1 AXL ABL1 PEAK1 GSK3B SRMS BMX HIPK3 MAPK1 GSK3A PDGFRB BTK TEC FGFR1 MNNG MNNG ALK MNNG YES1 MNNG CDK1 MNNG DYRK2 MAPK9 LYN ERBB4 DDR2 ABL1 Kinome JAK1 Activation Loop EPHA2 PhosphoSitePlus ABL2 NetworKIN DYRK1A INKA ranking
0 10 20 30 40 50 0 10 20 30 40 0 20 40 60 80 0 20 40 60 80 100 0 20 40 60 80 100 120 Spectral Counts Spectral Counts Spectral Counts Spectral Counts INKA Score bioRxiv preprint doi: https://doi.org/10.1101/259192; this version posted February 2, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
A U87 / U87-EGFRvIII B U87-EGFRvIII C Hypopharyngeal cancer patient 23 D Pancreatic cancer patient 24 E ATM SRC 7 30’ EGFR EGFR SRC 5x10 120’ MET EPHA2 YES1 MET 4x107 +ATMi SRC SRC PRKCD GSK3A 7 FYN FYN MAPK3 FRK 3x10
YES1 YES1 YES1 EPHA2 7 INKA score INKA 2x10 10’ GSK3A GSK3A FYN FYN 360’ 7 ABL1 ABL1 CDK2 EGFR 1x10 0’ 120’ 30’ MAPK3 MAPK3 EGFR LCK 0 LCK LCK LCK GSK3B GSK3B GSK3B GSK3A MAPK3 control ABL1 NCS +10miNCnS +30min NCS +30min EPHA2 EPHA2 PRKCD NCS +120miNCSn +360min NCS +120min MAPK1 MAPK1 ERBB2 FGR INSR INSR CDK1 LYN F PRKDC/DNA-PK CDK1 CDK1 GSK3B MET 5x107 CDK2 CDK2 LYN JAK2 120’ 4x107 +ATMi PTK2 PTK2 FGR PTK6 30’ JAK2 FRK 3x107 JAK2 CDK2 360’ FGR FGR JAK2 PTK2 7 10’ INKA score INKA 2x10 mutant Untreated ABL2 ABL2 ABL2 Untreated CDK1 Untreated wild type Erlotinib Erlotinib 7 MAPK14 MAPK14 INSR TYK2 Erlotinib 1x10 0’ 120’ 30’ 0 0 50 100 150 200 0 50 100 150 200 0 20 40 60 0 5 10 15 20 control
INKA score INKA score INKA score INKA score NCS +10miNCnS +30min NCS +30min NCS +120miNCSn +360min NCS +120min