Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through Cumulative Effects on Individual Targets

Full text of DOI:10.1016/j.chembiol.2019.06.003

Article
Divergent Polypharmacology-Driven Cellular
Activity of Structurally Similar Multi-Kinase
Inhibitors through Cumulative Effects on Individual
Targets
Graphical Abstract
Authors
Natalia J. Sumi, Claudia Ctortecka,
Qianqian Hu, ..., John M. Koomen,
Eric B. Haura, Uwe Rix
Correspondence
uwe.rix@moffitt.org
In Brief
Sumi et al. use a systems
polypharmacology approach involving
chemical proteomics,
phosphoproteomics, and RNA-seq to
elucidate the differential mechanism of
action of two multi-targeted drugs with
similar chemical structure. The
understanding of the polypharmacology
mechanism of foretinib allowed for the
rational development of a synergistic
drug combination.
Highlights
d
d
d
d
Small structural change in multi-kinase drugs can cause
strong cellular difference
Functional proteomics and transcriptomics provide systems
view of drug effects
Systems pharmacology can elucidate differential
mechanisms of related compounds
Understanding polypharmacology can lead to repurposing of
multi-targeted drugs
Sumi et al., 2019, Cell Chemical Biology 26, 1–13
September 19, 2019 ª 2019 Elsevier Ltd.
https://doi.org/10.1016/j.chembiol.2019.06.003

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Cell Chemical Biology
Article
Divergent Polypharmacology-Driven Cellular Activity
of Structurally Similar Multi-Kinase Inhibitors
through Cumulative Effects on Individual Targets
Natalia J. Sumi,1,2 Claudia Ctortecka,1 Qianqian Hu,1,2 Annamarie T. Bryant,1 Bin Fang,3 Lily L. Remsing Rix,1
Muhammad Ayaz,4 Fumi Kinose,5 Eric A. Welsh,6 Steven A. Eschrich,7,8 Harshani R. Lawrence,1,4,8 John M. Koomen,8,9
Eric B. Haura,5 and Uwe Rix1,8,10,
*
1Department of Drug Discovery, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
2Cancer Biology PhD Program, University of South Florida, Tampa, FL 33620, USA
3Proteomics and Metabolomics Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
4Chemical Biology Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
5Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
6Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
7Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
8Department of Oncologic Sciences, University of South Florida, Tampa, FL 33620, USA
9Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
10Lead Contact
*Correspondence: uwe.rix@moffitt.org
https://doi.org/10.1016/j.chembiol.2019.06.003
SUMMARY
2017). Although this can be accomplished in many cases with
drug combinations, it would be ideal to have a single drug with
Despite recent successes of precision and imm- defined pharmacokinetic and pharmacodynamic properties
capable of simultaneously targeting multiple disease-relevant
targets and pathways, a concept known as ‘‘polypharmacology’’
unotherapies there is a persisting need for novel tar-
geted or multi-targeted approaches in complex dis-
eases. Through a systems pharmacology approach,
including phenotypic screening, chemical and phos-
phoproteomics, and RNA-seq, we elucidated the
targets and mechanisms underlying the differential
anticancer activity of two structurally related multi-
kinase inhibitors, foretinib, and cabozantinib, in
(Anighoro et al., 2014; Hopkins, 2008; Knight et al., 2010). How-
ever, defining which combinations of targets confer vulnerability
is often difficult, particularly in the absence of a known disease
driver. Unbiased phenotypic screening using, for example,
RNAi or small-molecule inhibitors can provide new starting
points for identifying such vulnerabilities. Screening with small
molecules is a particularly attractive strategy as it immediately in-
fers the ‘‘druggability’’ of the underlying target(s). Moreover, if the
selected hit compound is clinical drug/candidate, this
lung cancer cells. Biochemical and cellular target
validation using probe molecules and RNAi revealed
a
a polypharmacology mechanism involving MEK1/2, approach entails the potential for fast and efficient repurposing.
In addition, as targeted drugs can have widely varying selectivity
profiles (Bantscheff et al., 2007; Davis et al., 2011; Knezevic
FER, and AURKB, which were each more potently in-
hibited by foretinib than cabozantinib. Based on this,
we developed a synergistic combination of foretinib
with barasertib, a more potent AURKB inhibitor, for
MYC-amplified small-cell lung cancer. This systems
pharmacology approach showed that small struc-
tural changes of drugs can cumulatively, through
multiple targets, result in pronounced anticancer
et al., 2016; Ong et al., 2009; Zhang et al., 2009), the observed
cellular activity can be the result of inhibition of the intended
(‘‘cognate’’) or of non-canonical targets (‘‘off-targets’’). These
on- or off-target effects can be readily distinguished by careful
library design with multiple compounds for each individual
cognate target (Ctortecka et al., 2018; Kuenzi et al., 2017).
Considering that polypharmacology per definitionem involves
multiple targets, it is important to elucidate off-target mecha-
nisms that translate into cellular activity, which can lead to iden-
activity differences and that detailed mechanistic
understanding of polypharmacology can enable
repurposing opportunities for cancers with unmet tification of new clinical opportunities (Kuenzi et al., 2017; Li
et al., 2010). This can be achieved by applying systems pharma-
medical need.
cology approaches involving, for instance, global proteomics
and transcriptomics or a combination thereof (Lamb et al.,
2006; Winter et al., 2012).
INTRODUCTION
We here explore these concepts in lung cancer, the leading
Many severe diseases, such as diabetes and cancer, are highly cause of cancer-related death in the United States (Siegel
complex and involve various deregulated pathways. An attrac- et al., 2018). Through unbiased viability-based drug screening
tive approach to developing new therapies is therefore based in a panel of non-small-cell lung cancer (NSCLC) cell lines, we
on targeting multiple disease-relevant pathways (Gentile et al., observed differential cellular activity of the multi-targeted clinical
Cell Chemical Biology 26, 1–13, September 19, 2019 ª 2019 Elsevier Ltd.
1

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Figure 1. Foretinib Displays Non-canonical
Anticancer Activity in NSCLC Cells
(A) Chemical structures of cabozantinib and
foretinib.
(B) Viability-based cellular drug screening after 72 h
of treatment with foretinib (FORE), cabozantinib
(CABO), motesanib (MOTE), and PF-04217903 (PF)
in a total of 22 NSCLC cells with different genetic
background. Displayed above the heatmap are the
IC50 or Ki values (in nM) for MET and VEGFR-2 in-
hibition. Increasing wedges indicate concentrations
of 0.5 and 2.5 mM. N/A: not applicable.
(C) Dose-response curves of FORE, CABO, PF, and
MOTE for inhibition of viability of H1155 and A427
cells after 72
h treatment and the respective
IC50 values (mM), n = 3. Data are represented as
mean SD.
(D) Representative clonogenic assay in A427 cells.
Cells were treated with the indicated doses of FORE
and CABO for 10 days. Data are representative of
three independent experiments.
these targets. We furthermore demon-
strate that an unbiased and integrated
systems pharmacology approach can
elucidate these complex mechanisms
and lead to new therapeutic opportunities.
RESULTS
Foretinib Displays Anticancer
Activity in NSCLC Cells Independent
of Its Cognate Targets
To identify new vulnerabilities in lung can-
cer, we performed a viability-based drug
screen in a panel of NSCLC cell lines with
kinase inhibitors cabozantinib (XL184, 1) and foretinib (XL880, 2) a customized library of 240 compounds, which included diverse
across multiple cell lines, with foretinib displaying markedly classes of targeted drugs. This screen revealed markedly
higher potency than cabozantinib. Foretinib and cabozantinib different potency of the two related multi-kinase inhibitors foreti-
show high structural similarity and similar potency for their nib and cabozantinib across various cell lines (Figures 1A and
cognate targets MET and VEGFR-2 (Qian et al., 2009; Yakes 1B). Foretinib and cabozantinib were developed as dual MET/
et al., 2011; You et al., 2011), suggesting that foretinib’s VEGFR-2 inhibitors and display similarly potent biochemical ac-
mechanism of action (MoA) in these cells involves one or more tivity of approximately 1 nM for both targets, as well as high
unrecognized off-targets. To identify these targets, we applied structural similarity based on a trisubstituted quinoline scaffold
an integrated systems pharmacology approach comprised of (Qian et al., 2009; Yakes et al., 2011). The only structural differ-
mass spectrometry (MS)-based chemical proteomics, global ences featured by foretinib are a fluorine substituent on the cen-
and tyrosine phosphoproteomics, as well as RNA sequencing tral phenyl ring and a terminal N-propyl-morpholino moiety
(RNA-seq)-based transcriptomics. This combined strategy shown by X-ray co-crystallization of MET and foretinib to extend
revealed a complex polypharmacology MoA for foretinib, which into the solvent space (PDB: 3LQ8) (Qian et al., 2009). Despite
involves simultaneous inhibition of MEK1/2, FER, and AURKB ki- these similarities, foretinib displayed much higher cellular activity
nases, and led to the rational design of a synergistic drug com- compared with cabozantinib in our screen, which was confirmed
bination with a more potent AURKB inhibitor in MYC-amplified in 14 NSCLC cell lines (Figures 1C and S1). PF-04217903 and
small-cell lung cancer (SCLC) cells, which depend on AURKB motesanib, other potent inhibitors of MET and VEGFR-2, respec-
and constitute a highly unmet medical need. Notably, compari- tively, like cabozantinib showed little to no effects suggesting
son of foretinib and cabozantinib also highlights that small foretinib’s activity in these cells to be independent of its cognate
changes in the chemical structures of closely related com- targets. Consistently, although foretinib and cabozantinib both
pounds can result in moderate differences in potency across inhibited MET autophosphorylation in H1648 cells (Figure S2A),
multiple individual targets. This in turn can lead to pronounced most NSCLC cell lines, including H1155 and A427 cells, did
differences in overall anticancer activity when amplified by differ- not display active MET signaling under basal conditions (Fig-
ential polypharmacology mechanisms that involve several of ure S2B). In addition, in contrast to MET inhibitor-sensitive
2
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Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Figure 2. Foretinib Induces Apoptosis in
NSCLC Cells
(A) Western blot for PARP1 and cleaved caspase-3
in H1155 (left) and A427 (right) cells. Cells were
treated with 0.5 and 1 mM of FORE and CABO for 24
h. Staurosporine (STAU) (50 nM) was used as pos-
itive control.
(B) Quantification of cleaved caspase-3/7 using
fluorescence microscopy after 24 and 48
h of
treatment with FORE and CABO in H1155 (left) and
A427 (right) cells, n = 3. Data are represented as
mean SD.
MET (Figures S4A and S4B), suggesting
i-foretinib and i-cabozantinib to be gener-
ally suitable probe molecules. Employing
these probes for chemical proteomics in
H1155 cells (Table S1), a total of 89 protein
kinases were detected with a minimum of 2
unique peptides, 41 of which had normal-
EBC1 cells (Figures S2C and S2D), inhibition of efflux pumps by ized spectrum abundance factor (NSAF) values >0.0006 for
verapamil did not sensitize these cells to cabozantinib, suggest- foretinib, a metric for relative protein abundance in the eluate
ing that cabozantinib’s lack of efficacy was not simply because (Zybailov et al., 2006). Foretinb and cabozantinib shared the ma-
of drug efflux (Figure S2E). Foretinib’s activity was also not corre- jority of their targets (Figure 4A). This was particularly the case for
lated with any specific oncogene driver (Figure 1B). However, more prominent kinase targets, while differences were mostly
foretinib, in contrast to cabozantinib, inhibited A427 cell growth discernible for kinases with low NSAF values, indicating either
also in a crystal violet clonogenic assay; Figure 1D). Finally, fore- low expression, binding affinity, or both. Consistently, compari-
tinib, but not cabozantinib, induced apoptosis in H1155 and son with the publicly available LINCS (Library of Integrated
A427 cells as demonstrated by immunoblot analysis of PARP1 Network-Based Cellular Signatures) dataset (ID: 20176) (Davis
and caspase-3 cleavage (Figure 2A), as well as live-cell imaging et al., 2011), which contains dissociation constants (KD) for mul-
of caspase-3/7 induction (Figure 2B). Taken together, the multi- tiple foretinib kinase targets (cabozantinib is not included in
kinase inhibitor foretinib, but not its close structural analog cabo- LINCS), showed weak or no (gray circles) KD values for foretinib
zantinib, displays anticancer activity across multiple NSCLC cell specific, but less prominent targets, such as PERK (E2AK3),
lines independently of its cognate targets.
STK33, and AMPKa1 (AAPK1) (Figure S4C). No individual kinase
showed strong preferential binding to foretinib over cabozantinib
and high NSAF value, and this was similar in A427 cells (Fig-
Systems Pharmacology Approach Reveals Differential
Targets and Pathways of Foretinib and Cabozantinib
ure S4D; Table S1). One exception was MAP4K5 (M4K5) (KD
=
To elucidate the MoA responsible for the differential cellular ac- 3.5 nM), which was also prominently enriched using activity-
tivity of foretinib and cabozantinib, we applied a systems phar- based protein profiling (Barglow and Cravatt, 2007; Patricelli
macology approach. We compared the target profiles of foretinib et al., 2011) of foretinib and cabozantinib in H1155 cells
and cabozantinib, their network-wide signaling effects and gene (Figure S4E; Table S1). However, small interfering RNA
expression changes upon drug treatment by a combination of (siRNA)-mediated silencing of MAP4K5 alone or in combination
chemical proteomics, phosphoproteomics, and RNA-seq (Fig- with cabozantinib did not reduce cell viability, indicating that its
ure 3). Data integration using gene set enrichment analysis inhibition is not responsible for the difference between foretinib
(GSEA) (Subramanian et al., 2005) nominated specific targets and cabozantinib activity (Figure S4F).
and pathways for functional validation.
In parallel, we performed quantitative phosphoproteomics in
Chemical proteomics, unbiased drug affinity chromatography, H1155 cells upon treatment with foretinib and cabozantinib.
followed by MS (Rix and Superti-Furga, 2009), was performed to Global phosphoproteomics was conducted using a 6-plex tan-
identify protein targets for both compounds. Based on the pub- dem mass tag isobaric labeling approach followed by basic pH
lished X-ray co-crystal structure of foretinib in complex with MET reversed-phase offline fractionation and global enrichment
(PDB: 3LQ8) (Qian et al., 2009), we synthesized immobilizable of phosphoserine/threonine/tyrosine (pSTY) peptides by
analogs of cabozantinib (i-cabozantinib, 10) and foretinib (i-fore- immobilized metal ion affinity chromatography. Tyrosine phos-
tinib, 18) by adding a linker to the solvent-exposed morpholino phoproteomics was performed by immunoprecipitation of phos-
moiety of foretinib and the equivalent position of cabozantinib, photyrosine (pY) peptides followed by label-free quantitative
for which no X-ray co-crystal structure has been reported (Fig- liquid chromatography tandem MS. Both datasets showed
ures S3A and S3B). Pull-downs with lysate from H1648 cells, even intensity distributions and good correlation between bio-
which express high MET levels, and in vitro MET kinase assays, logical replicates. In the global phosphoproteomics dataset,
indicated that both probes retained their ability to bind and inhibit 9,934 peptides were detected, of which 8,819 presented a
Cell Chemical Biology 26, 1–13, September 19, 2019
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Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Figure 3. Systems Pharmacology Approach to Interrogate the Drug-Modulated Proteome and Transcriptome
Scheme of the applied systems pharmacology approach including chemical proteomics, global, and tyrosine phosphoproteomics and RNA-seq performed in
parallel. FORE: foretinib, CABO: cabozantinib.
4
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Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Figure 4. Systems Pharmacology Reveals Differential Targets and Pathways of Foretinib and Cabozantinib
(A) Chemical proteomics comparison between foretinib (red) and cabozantinib (blue) kinase targets presenting NSAF > 0.0006 in H1155 cells (see also Fig-
ure S4C). Kinome phylogenetic tree adapted courtesy of Cell Signaling Technology (www.cellsignal.com).
(legend continued on next page)
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Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
posterior error probability (PEP) score <0.05 (Table S2). In the pY interrogation of targets, signaling pathways, and gene expres-
dataset, a total of 484 peptides were detected, 250 of which had sion profiles that are differentially modulated by foretinib and ca-
a PEP score <0.05 (Table S3). Comparing foretinib and cabozan- bozantinib nominated several targets as mechanistically relevant
tinib treatment showed multiple differential phosphorylation candidates.
changes, such as for palladin (PALLD), heterochromatin protein
1 (HP1A-CBX5), DNA topoisomerase 2-alpha (TOP2A), tyrosine- Foretinib Kills NSCLC Cells through a Polypharmacology
protein kinase FER, and d-catenin (CTNND1) (Figure 4B). In Mechanism Involving MEK1/2, FER, and AURKB
contrast, the majority of proteins did not show any appreciable We next asked if the selected targets were also inhibited in live
phosphorylation changes upon drug treatment or, as for cells by investigating changes in downstream signaling. Even
GSK3a/b, any changes different from those induced by though foretinib is a reasonably potent SRC, LYN, and YES
cabozantinib.
inhibitor, it unexpectedly did not decrease SRC autophosphor-
Finally, genome-wide gene expression changes in H1155 cells ylation at Y419 in H1155 cells (data not shown). However, fore-
upon 4 h of drug treatment with foretinib and cabozantinib were tinib, in contrast to cabozantinib, decreased phosphorylation of
determined by RNA-seq. All samples reached more than pERK at T202/Y204, which are direct downstream substrate
40,000,000 reads and average total alignment was 80%. A total sites of MEK1/2, in both H1155 and A427 cells (Figure 5A
of 15,165 genes were detected and 231 genes passed the spe- and S5A). Other MET and/or VEGFR inhibitors had no effect
cific selection criteria (see the STAR Methods, Data Analysis; on pERK in these cells. In addition, foretinib decreased phos-
(Table S4). Foretinib treatment resulted in a significantly different phorylation of histone H3 at S10 (Figure 5B), which is a bona
gene expression profile compared with DMSO and cabozantinib, fide downstream target of AURKB required for the binding of
which were much more similar to each other (Figure 4C).
HP1A to chromatin (Fischle et al., 2005; Hirota et al., 2005).
The different datasets were integrated using GSEA (Subrama- Considering that AURKB is a major regulator of the cell cycle
nian et al., 2005). Direct foretinib kinase targets with NSAF > and mitosis, we also investigated drug-induced cell-cycle
0.0006 were selected from the chemical proteomics dataset. In changes in H1155 and A427 cells (Figures 5C and S5B). This
addition, we selected signaling proteins that were differentially analysis showed that foretinib, but not cabozantinib, caused
regulated by foretinib compared with cabozantinib in the pY a pronounced accumulation of cells in G2 phase consistent
(p < 0.1 or rescue criteria as outlined in the STAR Methods, with inhibition of AURKB. Finally, because reliable antibodies
Data Analysis; 108 proteins) and global ( 2 average SD; 409 for the FER phosphorylation site of interest were lacking, we
proteins) phosphoprotemics datasets. Finally, the 231 genes performed pY immunoprecipitation and observed phosphoryla-
displaying differential expression when comparing foretinib tion changes of FER in response to treatment with foretinib and
treatment with DMSO and cabozantinib were selected from the the FER inhibitor GSK1838705A, but not cabozantinib or the
RNA-seq data. Subsequent combined GSEA analysis high- pan-SRC inhibitor saracatinib (Figure 5D). These analyses sug-
lighted multiple pathways/terms, such as ‘‘Kinase activity’’ and gest that foretinib inhibits MEK1/2, AURKB, and FER signaling
‘‘Binding’’ as expected (Table S5). In addition, several pathways in NSCLC cells.
representing biological processes, such as regulation of cell
To determine the contributions of the identified targets to fore-
contact, ‘‘cytoskeleton,’’ and the ‘‘cell cycle’’ were significantly tinib’s MoA, we applied a combined functional validation
enriched, suggesting that these participate in the MoA of foreti- approach using selective small-molecule probes and RNAi. The
nib (Figure 4D). To identify which direct foretinib targets were following tool compounds were utilized: GSK1838705A (FER in-
involved in modulating these pathways, we determined which ki- hibitor, KD = 200 nM) (Davis et al., 2011), barasertib (selective
nase targets were shared between the cytoskeleton and the cell- AURKB inhibitor, half maximal inhibitory concentration [IC50] =
cycle pathways (Figure 4E; Tables S6 and S7). This analysis 0.37 nM) (Mortlock et al., 2007), and trametinib (selective
nominated six kinases, AURKB, FER, MAP2K1 (MEK1), AURKA, MEK1/2 inhibitor, IC50 = 0.92/1.8 nM) (Yamaguchi et al., 2011).
SRC, and ATM, as potentially relevant foretinib targets. ATM dis- Whereas single-agent treatment with these probes showed no
played only a low NSAF and no associated KD in LINCS, and is (H1155, Figure 5E) or only moderate (A427, Figure S5C) effects,
likely an indirect foretinib-binding protein via a different direct pairwise and particularly triple combinations markedly
target. However, biochemical validation by in vitro kinase assays decreased cell viability. This suggests that foretinib displays a
with foretinib and cabozantinib confirmed MEK1, SRC, FER, and polypharmacology MoA involving all three kinases. Consistently,
AURKA/B to be functional foretinib targets (Figure 4F), which, combined inhibition of these three targets only minimally reduced
being more potently inhibited by foretinib than by cabozantinib, cell viability of foretinib-insensitive H661 cells (Figure S6A). To
were selected for further investigation. In summary, an unbiased verify that the effect of the combination with GSK1838705A in
(B) Global (pSTY) and phosphotyrosine (pY) proteomics data for H1155 cells after 3 h treatment with 1 mM of foretinib (red) or cabozantinib (blue).
(C) Heatmap representation of RNA-seq analysis in H1155 cells after 4 h of treatment with DMSO, cabozantinib (CABO), or foretinib (FORE) at 1 mM. Displayed are
the 231 genes that were differentially expressed.
(D) Pathway analysis using GSEA: top 40 pathways assigned with GSEA after combining the specific data. Colors classify pathways into clusters. Bar strength
correlates with the number of genes in each pathway.
(E) Venn diagram analysis of foretinib-binding kinases and the most predominant differentially regulated pathways between foretinib and cabozantinib.
Highlighted are foretinib-binding kinases that are shared by both pathways.
(F) Comparison of in vitro kinase assay IC50 values for foretinib (FORE) and cabozantinib (CABO) for select kinase targets. Displayed are the negative log IC50
values (in M).
6
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Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Figure 5. Foretinib Kills NSCLC Cells through
a Polypharmacology Mechanism Involving
Inhibition of MEK1/2, FER, and AURKB
(A) Western blot of pERK (T202/Y204) after 1 h
treatment of H1155 cells with 0.1, 0.5, and 1 mM
foretinib (FORE) or cabozantinib (CABO), 0.1 mM of
PF-04217903 (PF-METi), or 0.1 mM of motesanib
(MOTE-VEGFRi). Blots are representative of three
independent experiments.
(B) Western blot of pH3 (S10) after 6 h treatment of
H1155 cells with 0.05, 0.25, and 0.5 mM of foretinib
(FORE) and 0.05 mM of barasertib (AURKBi).
Blots are representative of three independent
experiments.
(C) Cell-cycle analysis of H1155 cells after 6 h of
treatment with foretinib (FORE) and cabozantinib
(CABO) at the indicated concentrations, n = 3. Data
are represented as mean + SD.
(D) Immunopreciptation of tyrosine phosphory-
lated FER in H1155 cells after 3 h treatment with
1 mM each of foretinib (FORE), saracatinib (SARA-
SRCi), GSK1838705A (GSK-FERi), cabozantinib
(CABO), or DMSO control. TCL, total cell lysate.
Blots are representative of three independent
experiments.
(E) H1155 cell viability upon treatment with
GSK1838705A (2.5 mM), barasertib (50 nM), and
trametinib (35 nM) alone or in the indicated combi-
nations. Cell viability was determined by CellTiter-
Glo assay after 72 h of treatment and normalized to
DMSO (first white bar), n = 3. Data are represented
as mean SD. ***p < 0.001 as determined using
Student’s t test.
(F) RNAi of FER and AURKB in combination with
trametinib (35 mM) in H1155 cells. Cell viability was
determined after 72 h of treatment with DMSO or trametinib by trypan blue-based cell counting, n = 3. Data are represented as mean SD. Blots are repre-
sentative of three independent experiments. *p < 0.05 and ***p < 0.001, respectively, as determined using Student’s t test.
foretinib-sensitive H1155 cells is primarily due to inhibition of FER Foretinib, but Not Cabozantinib, Potently Kills MYC-
and not its other potent targets, IGF1R/INSR (Sabbatini et al., Amplified SCLC Cells
2009), H1155 cells were treated with linsitinib (OSI-906), a selec- AURKB inhibition is known to confer strong sensitivity to SCLC
tive IGF1R/INSR inhibitor with no potency against FER (Mulvihill cells with MYC amplification (den Hollander et al., 2010; Li
et al., 2009). Indeed, combination of linsitinib with barasertib et al., 2016; Sos et al., 2012), which occurs in approximately
and trametinib did not show any additional effect on cell viability 5% of SCLC and is associated with poor survival (Iwakawa
over the combination of barasertib with trametinib (Figure S6B). et al., 2013; Kim et al., 2006). However, no targeted therapies
This indicates that inhibition of IGF1R/INSR is not responsible have been approved yet for SCLC and its treatment still relies
for the decrease in cell viability upon GSK1838795A treatment. on conventional chemotherapy, against which resistance rapidly
As FAK has also been reported to be a weak off-target of develops (Skarlos et al., 1994; Waqar and Morgensztern, 2017).
GSK1838705A, we confirmed that FAK autophosphorylation Based on our findings that foretinib is a moderately potent inhib-
was not affected by GSK1838705A (or foretinib) at the concentra- itor of AURKB, and that the polypharmacology mechanism of
tions used (Figure S6C). In addition, to evaluate if only inhibition of foretinib in NSCLC cells relies to large extent on AURKB inhibi-
AURKB or also AURKA is involved in the MoA of foretinib, H1155 tion, we hypothesized that MYC-amplified SCLC cells would
cells were treated with alisertib (MLN8237), a potent and selec- also be sensitive to foretinib, but less so to cabozantinib as a
tive AURKA inhibitor (Gorgun et al., 2010). Notably, compared weaker AURKB inhibitor. In addition, we hypothesized that
with the observed effects with AURKB inhibition, combining ali- MYC-amplified SCLC cells would be more sensitive to foretinib
sertib with FER and MEK1/2 inhibition resulted only in a minor than non-MYC-amplified SCLC cells. Foretinib indeed showed
decrease of viability (Figure S6D). Consistently, combined target- significantly more potent inhibition of viability of MYC-amplified
ing of AURKB, FER, and MEK1/2 using siRNA-mediated knock- cells than of SCLC cells without MYC amplification (Figures 6A
down of AURKB and FER and the specific small-molecule MEK and 6B). In fact, MYC-amplified SCLC cell lines were on average
inhibitor trametinib is more effective than each single treatment slightly more sensitive to foretinib than NSCLC cell lines. Similar
alone (Figures 5F and S5D). To summarize, foretinib’s anticancer to NSCLC cells, however, foretinib was markedly more potent
effects in NSCLC cells are the result of a polypharmacology than cabozantinib in SCLC cell lines, particularly in those with
mechanism involving MEK1/2, FER, and AURKB.
MYC amplification. In addition, the VEGFR and MET inhibitors
Cell Chemical Biology 26, 1–13, September 19, 2019
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Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Figure 6. Foretinib, but Not Cabozantinib, Potently Kills MYC-Amplified SCLC Cells
(A) Dose-response curves of foretinib (FORE), cabozantinib (CABO), PF-04217903 (PF), and motesanib (MOTE) for inhibition of viability of various SCLC cell lines
with indicated MYC amplification status after 72 h treatment and the respective IC50 values (mM), n = 2 (H209, N417, DMS114, H69, and DMS79), n = 3
(16HV, 86M1, H524, H82, and SW210.5). Data are represented as mean SD.
(legend continued on next page)
8
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Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
motesanib or PF-04217903, respectively, had no effect on SCLC fancy. In this study, we present a systems pharmacology
cells indicating that foretinib’s effect on cell viability is due to in- approach to elucidate the differential MoA of foretinib and cabo-
hibition of non-canonical foretinib targets, particularly AURKB. zantinib, two dual MET/VEGFR inhibitors with similar chemical
This was consistent with foretinib’s ability to inhibit phosphoryla- structures and target profiles. Despite their strong similarities,
tion of the AURKB downstream target H3 at S10 in 16HV SCLC we observed that foretinib is more potent than cabozantinib in
cells, as did the selective AURKB inhibitor barasertib (Figure 6C). NSCLC cell lines. Foretinib has been shown to bind to MET
Taken together, foretinib, in contrast to cabozantinib, potently in- within the ATP-binding site and an adjacent pocket causing
hibited the viability of MYC-amplified SCLC cells, but not SCLC the protein to adapt a ‘‘pseudo-inactive’’ form with the Asp-
cells without MYC amplification.
Phe-Gly (DFG) motif in the ‘‘out’’ conformation stabilized by a
stacking interaction of the DFG phenylalanine with foretinib’s flu-
orophenyl ring (Qian et al., 2009). Although it is generally ex-
pected that kinase inhibitors capable of inducing a DFG-out
Foretinib Synergizes with the Potent AURKB Inhibitor
Barasertib in MYC-Amplified SCLC Cells
Although foretinib is indeed a reasonably potent inhibitor of conformation are more selective (Liu and Gray, 2006), foretinib
AURKB, in vitro kinase assays, as well as immunoblotting for is known to be a multi-kinase inhibitor (Davis et al., 2011; Klaeger
H3 S10 phosphorylation (Figure 6C), indicated that barasertib, et al., 2017) consistent with our findings here. For cabozantinib,
which has been optimized for AURKB inhibition, was several several additional targets, such as ROS1 and RET have also
times more potent for this target. We therefore hypothesized been found (Davare et al., 2013; Yakes et al., 2011), but a simi-
that the combination of the potent and selective AURKB inhibitor larly comprehensive kinome-wide profiling has not been
barasertib with the polypharmacology-based compound foreti- described. The discrepancy in cellular activity in lung cancer be-
nib would result in increased cell death of MYC-amplified tween these two related compounds may therefore be explained
SCLC cells. Indeed, addition of foretinib showed a pronounced by differential target profiles and/or differential potency toward
shift of the barasertib dose-response curve in various MYC- shared targets rooted in dissimilar binding modes. The latter
amplified, but not non-MYC-amplified SCLC cells (Figures 7A was a particularly intriguing possibility considering the impor-
and S7A). Likewise, the addition of barasertib to foretinib dose- tance of the fluorophenyl moiety for foretinib binding, and that
response curves strongly enhanced foretinib efficacy in MYC- one of the major differences of cabozantinib is the lack of the
amplified SCLC cells (Figure S7B). Interestingly, this was also respective fluorine. Interestingly, a recent study reported an
apparent in H1155 cells, which like SCLC cells have neuroendo- induced dipole force, caused by a halogen-substituted phenyl
crine features, but not in A427 lung adenocarcinoma cells (Fig- moiety within a kinase inhibitor and involving an alanine directly
ures S7C and S7D). This increase in efficacy was statistically adjacent to the DFG motif, to lead to a pronounced outward shift
significant, and applying the Bliss model of independence further of the DFG motif in AURKA (Martin et al., 2012). Notably, AURKB
indicated pronounced synergy in multiple MYC-amplified SCLC and several other kinases, also more potently inhibited by foreti-
cell lines (Figure 7B). Finally, we investigated, whether the com- nib than cabozantinib (e.g., MAP4K5, SRC, YES, and LYN),
bination of foretinib with barasertib also increased the induction feature an alanine in this position, suggesting that a similar phe-
of apoptosis. Indeed, immunoblot analysis showed strong in- nomenon may contribute to the kinome-wide target differences
creases of PARP1 and caspase-3 cleavage upon combination between these two compounds. On the other hand, MET and
treatment (Figure 7C). Importantly, this increased effect was DDR2 also feature such an ADFG motif, but are similarly inhibited
only observed in MYC-amplified and not in non-MYC-amplified by both drugs. However, as AURKB makes a major contribution
SCLC cells (Figure S7E). It is interesting to note, although, that to foretinib’s MoA, this feature appears to be relevant for its anti-
the combination of foretinib with the BET inhibitor JQ1, which cancer activity in these cells.
has been shown to exhibit single-agent activity in MYC-amplified
Although AURKB was indeed an important foretinib target in
SCLC cells (Kaur et al., 2016), displayed minimal or no increase NSCLC cells, foretinib’s overall cellular activity was the result
in activity (data not shown). In summary, combination of the pol- of complex polypharmacology involving multiple targets, partic-
ypharmacology exhibiting multi-kinase inhibitor foretinib with the ularly MEK1/2 and FER in addition to AURKB. This is best illus-
more potent and selective AURKB inhibitor barasertib resulted in trated by the finding that single-target inhibition showed only
strong synergy in MYC-amplified, but not non-MYC-amplified little effect, whereas combined targeting was as effective as
SCLC cells.
foretinib treatment itself. Notably, the more pronounced effects
observed with siRNA-mediated gene silencing of AURKB
compared with pharmacological inhibition are consistent with
previous reports that describe more profound effects of AURKB
DISCUSSION
Recognizing that complex diseases require complex therapeutic knockdown due to loss of AURKB protein expression and subse-
solutions, the concept of polypharmacology has received signif- quent disruption of the chromosomal passenger complex
icant interest over the last decade. However, our understanding (Ditchfield et al., 2003; Weiss et al., 2007). Although the mecha-
of the optimal combination of targets for a disease and the nistic interplay between foretinib’s mechanistic targets here is
design rules for drugs that engage these targets is still in its in- largely unknown, we consistently observed that AURKB was
(B) IC50 comparison for inhibition of viability between MYC-amplified and non-MYC-amplified SCLC cells after 72 h treatment with foretinib. *p < 0.05 as
determined using Student’s t test. Horizontal bar indicates mean.
(C) Western blot of pH3 (S10) in 16HV cells after 6 h treatment with 0.05, 0.25, and 0.5 mM foretinib (FORE) and 0.05 mM barasertib (BARA). Blots are representative
of three independent experiments.
Cell Chemical Biology 26, 1–13, September 19, 2019
9

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Figure 7. Foretinib Synergizes with the Potent AURKB Inhibitor Barasertib in MYC-Amplified SCLC Cells
(A) Dose-response curves for inhibition of viability of MYC-amplified SCLC cells after 72 h treatment with barasertib (BARA) alone or in combination with 250 nM of
foretinib (FORE), n = 3. Data are represented as mean SD.
(B) Analysis of synergistic effects on cell viability upon combination of foretinib (FORE) and barasertib (BARA) at the indicated concentrations. Synergy was
determined using the Bliss model of independence. DBliss indicates the difference of observed inhibition of viability to the theoretically calculated additive effect.
***p < 0.001 as determined using Student’s t test, n = 3. Data are represented as mean SD.
(C) Western blot analysis for PARP1 and caspase-3 cleavage in MYC-amplified SCLC cells after 24 h of treatment with barasertib (concentrations) as in (B),
foretinib (250 nM) or their combination. Dashed lines indicate deletion of additional lanes. Blots are representative of three independent experiments.
downregulated upon FER knockdown in H1155 cells, which, 2012). Consistently, BI-847325, a dual MEK/AURKB inhibitor,
however was prevented by MEK inhibition with trametinib. As can overcome BRAF inhibitor resistance in BRAF-mutant mela-
this was not similarly observed in A427 cells it is unlikely that it noma cells by decreasing ERK and histone H3 phosphorylation
was the result of off-target silencing by siFER. Rather, it might (Phadke et al., 2015). Thus, there may be multiple mechanisms
indicate functional crosstalk and compensatory signaling be- underlying the crosstalk between foretinib’s main targets in
tween these targets that is inhibited by foretinib. The relevance these cells.
of foretinib’s simultaneous targeting of MEK and the mitotic ki-
Importantly, each of the three targets, which were mainly
nase AURKB is supported by reports that microtubule polymer- responsible for foretinib’s activity in NSCLC cells, was individu-
ization by MEK, independent of ERK, leads to defective mitotic ally slightly more potently inhibited by foretinib than by cabozan-
spindle formation and polyploid cells (Cao et al., 2010), although tinib. These moderate differences would not have been sufficient
MEK can also inhibit microtubule stabilization through ERK to explain such pronounced activity differences between the two
phosphorylation (Reszka et al., 1995). In addition, the BRAF/ drugs on the cellular level, if this effect had been the result of in-
MEK/ERK pathway transcriptionally regulates AURKB through hibition of only a single target. Rather, as all three kinases,
activation of the FOXM1 transcription factor (Bonet et al., AURKB, FER, and MEK1/2, play important roles in foretinib’s
10 Cell Chemical Biology 26, 1–13, September 19, 2019

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
polypharmacology MoA, these individual differences cumula- and potentially actionable targets that cooperate with each
tively resulted in markedly distinct cellular potency. This is partic- other within a disease network is challenging. Here, we pre-
ularly noteworthy as foretinib and cabozantinib are very close sent an unbiased systems pharmacology approach,
structural analogs. This illustrates how even minor modifications including phenotype-based drug screening, mass spec-
in the chemical structure of a multi-targeted drug, such as the trometry-based chemical and phosphoproteomics, and
addition of a halogen substituent, can have broad and unex- RNA-seq-based gene expression profiling, that is capable
pected consequences that, while potentially undesirable, may of differentiating the mechanisms of action of two multi-tar-
in some cases reveal new vulnerabilities, which can be har- geted drugs with highly similar chemical structures and
nessed for therapeutic purposes. Although medicinal chemists shared canonical targets, but markedly different cellular ef-
utilize this concept already regarding lead optimization for single ficacy in lung cancer cells. The underlying polypharmacol-
or a panel of related targets, a proteome-wide approach may ogy mechanism involved simultaneous targeting of at least
uncover new opportunities for drug design and development. three kinases, which were not mutated, but cooperatively
However, dissection of the underlying mechanisms can be play important roles for cancer cell survival. Importantly,
challenging and requires an unbiased, systems-wide strategy this analysis highlighted that small changes in the chemical
that integrates identification of differential drug targets with the structures of multi-targeted drugs can result in moderate
downstream signaling pathways modulated by these drugs. potency differences across multiple individual targets.
The systems approach described here, which integrated chem- Cumulatively, this effect can lead to pronounced differences
ical proteomics, phosphoproteomics, and RNA-seq data using in overall cellular activity of these drugs when amplified by
GSEA therefore was critical to reveal the specific targets respon- differential polypharmacology mechanisms that involve
sible for the biological effect of foretinib in lung cancer cells, several of these targets, indicating new opportunities for
although other systems pharmacology approaches may be drug design and development. Furthermore, the mecha-
equally suited.
nistic understanding gained from this study allowed for the
Although challenging, elucidation of the underlying MoA of a rational development of a synergistic drug combination in
polypharmacology drug is critical for the rational design of drug a recalcitrant cancer known to depend on the affected
combinations that enhance the overall cellular efficacy. This is signaling pathway. In summary, such a comprehensive
important as the involved targets are most likely, as is the case approach allows for the identification of drug targets and
for foretinib, targeted by chance and therefore not as potently in- polypharmacology mechanisms potentially resulting in the
hibited as could be achieved by medicinal chemistry efforts spe- repurposing of multi-targeted drugs and in the design of
cifically directed at these targets. Thus, to generate translational potent combination therapies for the treatment of complex
opportunities based on these mechanisms, it is necessary to diseases lacking directly actionable drivers.
either engage in such a challenging medicinal chemistry project
(Hopkins, 2008), or develop a mechanistically informed drug
combination with enhanced cellular efficacy, such as illustrated
STAR+METHODS
here on the example of MYC-amplified SCLC, a recalcitrant can-
cer known to exhibit some sensitivity to AURKB inhibition (den
Hollander et al., 2010; Li et al., 2016; Sos et al., 2012). Importantly,
Detailed methods are provided in the online version of this paper
and include the following:
the combination of foretinib, its polypharmacology MoA based in
part on moderate AURKB inhibition, with the highly potent
AURKB inhibitor barasertib, exhibited pronounced synergy and
potently killed MYC-amplified SCLC cells at physiologically
achievable concentrations. Interestingly, combination of foretinib
with the bromodomain inhibitor JQ1, which also displays single-
drug activity in these cells (Kaur et al., 2016), did not produce
similar synergy. This demonstrates that synergy is not always ob-
tained upon combination of two individually active compounds,
but requires a strong mechanistic foundation as shown here for
foretinib and barasertib and the enhanced targeting of the func-
tionally relevant target AURKB in these cells.
d KEY RESOURCES TABLE
d CONTACT FOR REAGENT AND RESOURCE SHARING
d EXPERIMENTAL MODEL AND SUBJECT DETAILS
B Cell Culture
d METHOD DETAILS
B Compounds
B Viability Assays
B Crystal Violet Assay
B Cell Cycle Analysis
B Quantification of Caspase 3/7 by Fluorescence Mi-
croscopy
B Cell Lysate Preparation for Immunoblotting and Immu-
noprecipitation
SIGNIFICANCE
B Immunoblotting
B Immunoprecipitation
Targeted drugs have become an important cornerstone in
modern medicine. However, many patients do not benefit
from these drugs due to, for instance, lack of actionable ge-
netic aberrations that function as disease drivers. In such
cases a feasible alternative may be based on network phar-
macology, which targets multiple disease-relevant proteins
at the same time, and can be achieved, for example, through
drug polypharmacology. However, the identification of novel
B RNA Interference
B Kinase assays
B Chemical Proteomics
B Activity-Based Protein Profiling (ABPP)
B RNA-Seq
B Chemical Synthesis
d QUANTIFICATION AND STATISTICAL ANALYSIS
d DATA AND CODE AVAILABILITY
Cell Chemical Biology 26, 1–13, September 19, 2019 11

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SUPPLEMENTAL INFORMATION
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A 110, 19519–19524.
Supplemental Information can be found online at https://doi.org/10.1016/j.
chembiol.2019.06.003.
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Hocker, M., Treiber, D.K., and Zarrinkar, P.P. (2011). Comprehensive analysis
of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046–1051.
ACKNOWLEDGMENTS
den Hollander, J., Rimpi, S., Doherty, J.R., Rudelius, M., Buck, A., Hoellein, A.,
Kremer, M., Graf, N., Scheerer, M., Hall, M.A., et al. (2010). Aurora kinases A
and B are up-regulated by Myc and are essential for maintenance of the ma-
lignant state. Blood 116, 1498–1505.
This work was supported by the NIH/NCI R01 CA181746 (to U.R.), the Moffitt
NIH/NCI SPORE in Lung Cancer P50 CA119997 (to E.B.H.), the NIH/NCI R50
CA211447 (to H.R.L.), Circle of Hope for Cancer Research (to N.J.S.), the Aus-
trian Marshall Plan Scholarships (to C.C.), and the H. Lee Moffitt Cancer Center
and Research Institute. We would like to thank Dr. Forest M. White, Dr. Nich-
olas J. Lawrence, Dr. Keiran S. Smalley, and Dr. W. Douglas Cress for their
constructive feedback. We further wish to acknowledge the Moffitt Lung Can-
cer Center of Excellence and the Moffitt Chemical Biology (Chemistry Unit),
Proteomics and Metabolomics, Molecular Genomics, Cancer Informatics,
Flow Cytometry, and Analytical Microscopy Core Facilities. Moffitt Core Facil-
ities are supported by the National Cancer Institute (award no. P30-CA076292)
as a Cancer Center Support Grant. The Proteomics and Metabolomics Core is
also supported by the Moffitt Foundation.
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AUTHOR CONTRIBUTIONS
Conceptualization, U.R. and N.J.S.; Investigation, N.J.S., C.C., A.T.B., B.F.,
L.L.R.R., Q.H., and U.R.; Formal Analysis, N.J.S., E.A.W., and U.R.; Writing –
Original Draft, N.J.S. and U.R.; Writing – Review & Editing, all authors; Funding
Acquisition, N.J.S., C.C., E.B.H., and U.R.; Software, N.J.S. and C.C., Visual-
ization, N.J.S., C.C., and U.R.; Resources, F.K., B.F., J.M.K., M.A., H.R.L.,
E.A.W., S.A.E., and U.R.; Supervision, S.A.E., H.R.L., J.M.K., E.B.H., and U.R.
Hirota, T., Lipp, J.J., Toh, B.H., and Peters, J.M. (2005). Histone H3 serine 10
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DECLARATION OF INTERESTS
Iwakawa, R., Takenaka, M., Kohno, T., Shimada, Y., Totoki, Y., Shibata, T.,
Tsuta, K., Nishikawa, R., Noguchi, M., Sato-Otsubo, A., et al. (2013).
Genome-wide identification of genes with amplification and/or fusion in small
cell lung cancer. Genes Chromosomes Cancer 52, 802–816.
The authors declare no competing interests.
Received: March 6, 2019
Revised: May 21, 2019
Accepted: June 13, 2019
Published: June 27, 2019
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Cell Chemical Biology 26, 1–13, September 19, 2019 13

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
STAR+METHODS
KEY RESOURCES TABLE
REAGENT or RESOURCE
Antibodies
SOURCE
IDENTIFIER
Mouse monoclonal anti-b-Actin
Sigma
Cat#A5441; RRID: AB_476744
Cat #3077; RRID: AB_2143884
Rabbit monoclonal anti-Phospho MET
(Tyr1234/1235) (D26)XP
Cell Signaling
Rabbit monoclonal anti-MET (D1C2)XP
Rabbit polyclonal anti-Phospho AKT(Ser473)
Rabbit polyclonal anti-Akt
Cell Signaling
Cell Signaling
Cat #8198;
RRID: AB_10858224
Cat #9271;
RRID: AB_329825
Cell Signaling
Cell Signaling
Cat #9272; RRID: AB_329827
Cat #9106; RRID: AB_331768
Mouse monoclocal anti-Phospho p44/42
MAPK (Erk1/2) (Thr202/Tyr204) (E10)
Rabbit polyclonal anti-MAP kinase
(ERK-1, ERK-2)
Sigma
Cat #M5670; RRID: AB_477216
Rabbit monoclonal anti-Phospho Histone
H3 (Ser10) (D2C8) XP
Cell Signaling
Cat #3377;
RRID: AB_1549592
Rabbit monoclonal Histone H3 (D1H2) XP
Cell Signaling
Cell Signaling
Cat #4499; RRID: AB_10544537
Cat #9661; RRID: AB_2341188
Rabbit polyclonal anti-Cleaved Caspase-3
(Asp175)
Rabbit polyclonal anti-PARP1
Cell Signaling
Cell Signaling
Cell Signaling
Cat #9542; RRID: AB_2160739
Cat#4268; RRID: AB_2278286
Cat#9411; RRID: AB_331228
Mouse monoclonal anti-FER (5D2)
Mouse monoclonal anti-Phospho Tyrosine
(P-Tyr-100)
Mouse monoclonal anti-AIM-1
BD Bioscience
Cell Signaling
Cat #611082; RRID: AB_2227708
Cat #8556; RRID: AB_10891442
Rabbit monoclonal anti-Phospho
FAK(Tyr397) (D20B1)
Rabbit monoclonal anti-FAK (D2R2E)
Mouse IgG, HRP-linked whole Ab
Rabbit IgG, HRP-linked whole Ab
Normal mouse IgG
Cell Signaling
GE Healthcare
GE Healthcare
Santa Cruz
Cat #13009
Cat# NA931; RRID: AB_772210
Cat# NA934; RRID: AB_2722659
Cat#sc-2025; RRID: AB_737182
Cat#8803
PTMScanꢀ Phospho-Tyrosine Rabbit
mAb (P-Tyr-1000)
Cell Signaling
Chemicals, Peptides, and Recombinant Proteins
DMSO used in proteomics
DMSO used in drug dilutions
DMSO used in cell culture
Foretinib
Acros Organics
Acros Organics
Sigma
Cat#167852500; CAS: 67-68-5
Cat#348445000; CAS: 67-68-5
D2438; CAS: 67-68-5
Chemietek
Cat#CT-FORE, CT-XL880; CAS: 849217-64-7
Cat#CT-XL184; CAS: 849217-68-1
Cat#CT-GSK212; CAS: 871700-17-3
Cat#S5793; CAS: 453562-69-1
Cat#S1094; CAS: 956905-27-4
Cat#S1147; CAS: 722544-51-6
Cat#S2703, CAS: 1116235-97-2
Cat#S2013; CAS: 869288-64-2
Cat#CT-O906; CAS: 867160-71-2
Cat#S1133; CAS: 1028486-01-2
N/A
XL184 (CABOZANTINIB) (FREE BASE)
GSK1120212 (TRAMETINIB)
Motesanib (AMG-706)
PF-04217903
Chemietek
Chemietek
Selleckchem
Selleckchem
Selleckchem
Selleckchem
Selleckchem
Chemietek
AZD1152-HQPA
GSK1838705A
PF-573228
OSI-906 (LINSITINIB)
Alisertib (MLN8237)
Selleckchem
This Paper
i-foretinib
i-cabozantinib
This Paper
N/A
(Continued on next page)
e1 Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Continued
REAGENT or RESOURCE
SOURCE
IDENTIFIER
Critical Commercial Assays
CellTiter-Gloꢀ Luminescent Cell Viability Assay
Crystal violet solution
Promega
G7573
Sigma
HT90132
4440
Caspase 3/7 reagent
Essen Bioscience
ThermoFisher Scientific
Thermo Scientificꢁ Pierceꢁ Coomassie
(Bradford) Protein Assay
Cat# PI23200
RNeasy Mini Kit
Quiagen
Cat#74104
IMAC enrichment Kit
PierceTM Enrichment Kit ATP Probe
Sigma
Cat#I1408
ThermoFisher Scientific
Cell Signaling
ThermoFisher Scientific
Nugen
Cat#88310
PTMScan Kit
https://media.cellsignal.com/pdf/8803.pdf
TMT Mass Tagging Kits and Reagent
Encore Complete RNA-Seq Library System
Cat#90064
N/A
KAPA library amplification kit for
Illumina platform
KAPABiosystem
Eurofin Kinase ProfilerTM platform
Eurofins Pharma Discovery
Services
https://www.eurofinsdiscoveryservices.com/
cms/cms-content/services/in-vitro-assays/
kinases/kinase-profiler/
Reaction Biology Kinase Hotspot platform
Deposited Data
Reaction Biology Corp
(Qian et al., 2009)
www.reactionbiology.com/webapps/site/
KinaseAssay.aspx
X-ray co-crystal structure of the MET kinase
domain bound to XL880 (foretinib)
PDB: 3LQ8
PXD012961
Chemical Proteomics in H1155 and A427
cells (Drug Pulldowns)
ProteomeXchange Consortium
via the PRIDE partner repository
(http://www.ebi.ac.uk/pride)
Activity-based protein profiling (ABPP)
ProteomeXchange Consortium
via the PRIDE partner repository
(http://www.ebi.ac.uk/pride)
PXD012965
PXD012963
PXD012962
GSE126850
Global phosphoproteomics (IMAC
Phosphoproteomics)
ProteomeXchange Consortium
via the PRIDE partner repository
(http://www.ebi.ac.uk/pride)
Tyrosine phosphoproteomics (Tyrosine
Phosphorylation)
ProteomeXchange Consortium
via the PRIDE partner repository
(http://www.ebi.ac.uk/pride)
RNA-Seq data
GEO database
Experimental Models: Cell Lines
H1155
Moffitt Lung Cancer Center of
Excellence Cell Line Core
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
A427
Moffitt Lung Cancer Center of
Excellence Cell Line Core
H3122
H2170
CALU3
H23
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
H1437
HOP62
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
(Continued on next page)
Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019 e2

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Continued
REAGENT or RESOURCE
A549
SOURCE
IDENTIFIER
N/A
Moffitt Lung Cancer Center of
Excellence Cell Line Core
H1299
HCC4006
H322
Moffitt Lung Cancer Center of
Excellence Cell Line Core
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
H292
Moffitt Lung Cancer Center of
Excellence Cell Line Core
H2342
H157
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
CALU6
H1395
H650
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
HCC2935
H1648
H661
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
H2122
H1944
H460
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
H358
Moffitt Lung Cancer Center of
Excellence Cell Line Core
16HV
Moffitt Lung Cancer Center of
Excellence Cell Line Core
86M1
Moffitt Lung Cancer Center of
Excellence Cell Line Core
H524
Moffitt Lung Cancer Center of
Excellence Cell Line Core
N417
Moffitt Lung Cancer Center of
Excellence Cell Line Core
H82
Moffitt Lung Cancer Center of
Excellence Cell Line Core
SW210.5
H69
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
DMS79
H209
Moffitt Lung Cancer Center of
Excellence Cell Line Core
Moffitt Lung Cancer Center of
Excellence Cell Line Core
DMS114
Moffitt Lung Cancer Center of
Excellence Cell Line Core
(Continued on next page)
e3 Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Continued
REAGENT or RESOURCE
Oligonucleotides
SOURCE
IDENTIFIER
ON -TARGET plus Non-target Pool
Dharmacon
Dharmacon
D-001810-10-20
SMARTPOOL: ON-TARGET plus Human
MAP4K5 (11183)
L-003589-00-0005
SMARTPOOL: ON-TARGET plus Human
AURKB (9212)
Dharmacon
Dharmacon
L-003326-00-0005
L-003129-00-0005
SMARTPOOL: ON-TARGET plus Human
FER (2241)
Software and Algorithms
Prism 7.03
GraphPad
https://www.graphpad.com/scientific-
software/prism/
R program
The R foundation
NIH.gov
https://cran.r-project.org/mirrors.html
https://imagej.nih.gov/ij/download.html
Image J - Version1.48
M5 Spectramax plate reader - SoftMaxPro
Software 6.2.1
Molecular Devices
https://www.moleculardevices.com/products/
microplate-readers/acquisition-and-analysis-
software/softmax-pro-software
ModFitLT V3.2.1
BD Biosciences
Essen Bioscience
LI-COR
http://www.bdbiosciences.com/ca/c/modfit-lt-
v33-for-pc/p/346033
Incucyte Zoom Software 2016B
https://www.essenbioscience.com/en/
products/software/
Odyssey FC Imaging System - ImageStudio
Lite Software 5.2.5
https://www.licor.com/bio/products/software/
image_studio/is_products_fc.html#fc
Mascot 2.4
Matrix Science
http://www.matrixscience.com/help/apr2012.html
http://coxdocs.org/doku.php?id=maxquant:start
MaxQuant 1.2.2.5 and 1.5.2.8
Max-Planck-Institute of
Biochemistry
Swiss-Prot
UniProt
https://www.uniprot.org/downloads#uniprotkblink
Scaffold 4.0
Proteome software
http://www.proteomesoftware.com/pdf/
scaffold_brochure.pdf?v=fa4ef332
Skyline 4.1
Htseq-count
DESeq2
MacCoss Lab Software
https://skyline.ms/project/home/software/
Skyline/begin.view
Stanford University and
EMBL Heidelberg
https://www.ncbi.nlm.nih.gov/pmc/articles/
PMC4287950/
Biocondutor
https://bioconductor.org/packages/release/
bioc/vignettes/DESeq2/inst/doc/DESeq2.html
https://www.ncbi.nlm.nih.gov/pubmed/?
term=25516281
https://www.ncbi.nlm.nih.gov/pubmed/?
term=20979621
Gene Set Enrichment Analysis (GSEA)
Broad Institute
http://software.broadinstitute.org/gsea/msigdb/
index.jsp https://www.ncbi.nlm.nih.gov/pmc/
articles/PMC1239896/
TopHat2
Other
Johns Hopkins University
https://www.ncbi.nlm.nih.gov/pubmed/23618408
LINCS (Library of Integrated Network-Based
Cellular Signatures) dataset
Harvard Medical School and
NIH LINCS Program
http://lincs.hms.harvard.edu/db/datasets/20176/
results?items_per_page=250&sort=&search=#
CONTACT FOR REAGENT AND RESOURCE SHARING
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Uwe Rix
(uwe.rix@moffitt.org).
Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019 e4

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Cell Culture
Cell lines were provided by the Moffitt Lung Cancer Center of Excellence Cell Line Core after testing negative for mycoplasma and
authentication by short tandem repeat (STR) analysis. Cells were cultivated in RPMI-1640 media with 10% FBS (complete RPMI) and
maintained in the incubator with 5% CO2 at 37ꢀC.
METHOD DETAILS
Compounds
Foretinib (GSK1363089, EXEL2880, XL880; Chemietek), cabozantinib (XL184, BMS-907351; Chemietek), motesanib free base
(AMG-706; Selleckchem), PF-04217903 (Selleckchem), barasertib (AZD1152-HQPA; Selleckchem), trametinib (GSK1120212;
Chemietek), GSK1838705A (Selleckchem), PF-573228 (Selleckchem), linsitinib (OSI-906; Chemietek) and alisertib (MLN-8237; Sell-
eckchem) were dissolved in DMSO (10 mM). Drug dilutions were made in DMSO as necessary.
Viability Assays
Cells were generally plated with a density of 1,000 cells per well in a black, clear bottom 384 well tissue culture plate (Fisher). A549
cells were plated with a density of 500 cells per well. After 24 hours, cells were drug treated and incubated for an additional 72 hours.
Inhibitors were diluted in complete RPMI media to the desired final concentration as described in each experiment. The viability of
cells was measured with CellTiter-Glo Luminescent Cell Viability Assay reagent (Promega). After addition of CellTiter-Glo reagent,
plates were shaken at 400 rpm for 2 minutes and placed in the dark for another 10 minutes. A M5 Spectramax plate reader (Molecular
Devices) using 500 ms integration was used to collect the data. Data were analyzed using GraphPad Prism and R. Values were
normalized to the DMSO control of each drug dilution.
Crystal Violet Assay
A427 cells were plated at a density of 3,000 cells per well in a 6-well plate. After 24 hours, cells were treated as indicated with com-
plete RPMI media and were grown for an additional 9 days. Cells were fixed with 1 ml of methanol and stained with 0.1% crystal violet
solution. Plates were imaged on a tabletop scanner. Crystal violet was extracted using methanol and quantified on a M5 Spectramax
plate reader (Molecular Devices) using a readout of 540 nm. Data were analyzed using GraphPad Prism.
Cell Cycle Analysis
Cells were plated at a density of 4.5 x 105 cells per well in a 6-well dish. After 24 hours, cells were treated as indicated with complete
RPMI media for 6 hours. Cells were harvested and incubated with Accutase (Innovative Cell Technologies) for 5 minutes to disrupt cell
aggregation. Cells were fixed with ice-cold 70% ethanol until use. Cells were stained with DAPI (4’,6-diamidino-2-phenylindole;
Sigma D9542)/0.1% Triton X-100/PBS solution at a final concentration of 1 mg/ml. Cells were analyzed using a FACSCanto II bench-
top analyzer (BD Biosciences) at 340 to 380 nm. Data was analyzed using ModFitLT V3.2.1 and Excel.
Quantification of Caspase 3/7 by Fluorescence Microscopy
Cell lines were plated at 1,000 cells per well in a black, clear bottom 384 well tissue culture plate (Corning). After 24 hours in the incu-
bator, cells were treated as indicated with complete RPMI media containing 5 mM of Incucyte Caspase 3/7 reagent (Essen Bioscience
#4440). Plates were placed in the IncuCyte and scanned every 2 hours for green fluorescence and in the phase contrast channel up to
a total of 72 hours. Data was analyzed using the IncuCyte Zoom software, which determined the phase and green object confluence
of each well. Green object confluence was normalized to the phase object confluence. Further analyses were performed using
GraphPad Prism.
Cell Lysate Preparation for Immunoblotting and Immunoprecipitation
Cells were harvested, pelleted by centrifugation and lysed with lysisbuffer (0.20% NP40, 50 mM Tris pH 7.5, 5% Glycerol,
1.5 mM MgCl2 and 100 mM NaCl) plus Roche cOmplete protease inhibitor cocktail and phosphatase inhibitor cocktail 2 (Sigma,
#P5726). Samples were centrifuged twice at 21,000 x g and 4ꢀC (10 min, 20 min) and the protein concentration determined using
a Bradford assay.
Immunoblotting
Total cell lysates were separated by SDS-PAGE and transferred to activated PVDF membranes using the TransBlot Turbo system
(BioRad). Membranes were blocked and probed with primary and secondary antibodies according to standard techniques. Images
were acquired using an Odyssey FC Imager (LI-COR) and quantified using Image J. Immunoblotting was performed with primary an-
tibodies against actin (Sigma, A5441), pMET(T1234/1235) (D26)XP (Cell Signaling, #3077), MET (D1C2)XP (Cell Signaling #8198),
p44/42MAPK(ERK1/2 - T202/204) (E10) (Cell Signaling, #9106), ERK1/2/MAP-Kinase (Sigma, #M5670), pY100 (Cell Signaling,
#9411), FER (5D2) (Cell Signaling, #4268), pHistone H3(S10) (D2C8)XP (Cell Signaling, #3377), Histone H3 (D1H2)XP (Cell Signaling,
#4499), cleaved caspase-3 (Asp175) (Cell Signaling, #9661), PARP1 (Cell Signaling, #9542), Aurora B/AIM-1 (BD Bioscience,
e5 Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
#611082), pFAK(T397) (D20B1) (Cell Signaling, #8556), FAK (D2R2E) (Cell Signaling, #130095), MAP4K5 (KHS [E-5]) (Santa Cruz, sc-
374070). Secondary antibodies were anti-rabbit and anti-mouse (GE Healthcare).
Immunoprecipitation
H1155 cells were plated at 4.5 x 106 cells per 100 mm dish/sample. After 24 hours, cells were treated as indicated for 3 hours. Lysates
were prepared using the lysis buffer described in the cell lysates preparation method. For each sample, 50 ml of Protein A/G beads
Plus – Agarose (Santa Cruz, #sc-2003) was incubated horizontally at 4ꢀC with 2 ml of pY100 antibody (Cell Signaling, #9411) or normal
mouse IgG (Santa Cruz, #sc-2025) for 4 hours. Beads were washed 3 times with 1 ml lysis buffer, with spins at 1,000 x g for 1 minute at
4ꢀC between washes. A small portion of total cell lysate (TCL-100 mg) of each sample was kept aside to test for the presence of the
protein of interest. The other 1 mg of protein of each sample was added to the coated beads and rotated at 4ꢀC overnight. Subse-
quently, beads were spun at 1,000 x g for 2 minutes at 4ꢀC and washed 4 times with 1 ml lysis buffer, again with spins at 1,000 x g for
1 minute at 4ꢀC between washes. The elution was performed by adding 20 ml of 4x Laemmli buffer and denaturation at 90ꢀC. Samples
were spun at 14,000 x g for 1 minute and analyzed by SDS-PAGE, in comparison to the TCL samples. Immunoblotting was performed
using the FER (5D2) (Cell Signaling, #4268) antibody.
RNA Interference
siRNA SMART pool (Dharmacon) stocks were re-suspended at 20 mM with 1x siRNA buffer (1:5 Thermo Scientific 5x siRNA Buffer
(B-002000-UB-100) with RNase-free water (B-002000-WB-100)) and shaken at 37ꢀC for 1 hour. siRNA stocks were diluted to the
desired final concentration in Opti-MEM media (Fisher 31985062), depending on the cell line (See table below), and incubated for
5 minutes. siRNA was incubated for additional 20 minutes in the respective well with the lipofectamine mix (OptiMEM media plus
lipofectamine mix) at 1:1 ratio. For the A427 cell line we used lipofectamine RNAiMax (Fisher 13-778-150) and for H1155 we used
lipofectamine 2000 (Fisher 11668019). A427 and H1155 cells were plated at 1.5 x 105 cell per well, left to attach for 24 hours and
treated with trametinib (35 nM) or DMSO. Cells were counted with trypan blue (Sigma) in triplicate after 72 hours of treatment.
Data was analyzed using Microsoft Excel and R. Leftover samples were used to confirm knockdown by western blotting. All the sam-
ples received the same concentration of siRNA; non-target siRNA (siNT) was used to balance the amount of siRNA in all samples to
match the concentration of the double knockdown sample.
siRNA Reagents (Dharmacon)
Final Concentration in Cells
ON-TARGET plus Non-target Pool (D-001810-10-20)
100 nM – H1155
40 nM – A427
ON-TARGET plus Human MAP4K5 (11183) – SMARTPOOL
(L-003589-00-0005)
20 nM – H1155
ON-TARGET plus Human AURKB (9212) – SMARTPOOL
(L-003326-00-0005)
80 nM – H1155
20 nM – A427
ON-TARGET plus Human FER (2241) – SMARTPOOL
(L-003129-00-0005)
20 nM – H1155
20 nM – A427
Kinase assays
In vitro kinase inhibition assays were performed for two doses of foretinib and cabozantinib (0.05 mM and 0.25 mM); and IC50 values
were determined using 10 mM ATP and a 3-fold drug dilution series. Assays were performed in duplicate using the Reaction Biology
Kinase Hotspot platform or Eurofins platform. The Reaction Biology Kinase Hotspot platform was used to determine the IC50 for the
Aurora kinases (A/B/C) and MAP4K5, as well the two dose inhibition assay for MAP4K5. The Eurofins platform was used to determine
the two dose inhibition for MET, CSK, cSRC, FAK, FER, JNK2a2, LYN and YES, Aurora kinase B, DDR2, IGF1R, IR and MEK1. IC50‘s
for cSRC, DDR2, FER, LYN, MEK1 and YES were also determined on the Eurofins Kinase Profilerꢁ platform.
Chemical Proteomics
Experiments were performed as previously described (Knezevic et al., 2016). Beads were incubated with 2.5 ml of i-foretinib or i-ca-
bozantinib. Pre-treatment was performed by incubating cell lysates with 20 mM of foretinib and cabozantinib. Experiments were
performed in duplicate (H1155) or triplicate (A427) using 5 mg protein per pulldown. SDS-PAGE, in-gel digestion with trypsin and
LC-MS/MS analyses were performed as described previously. The samples were analyzed on a QExactive Plus hybrid orbitrap
mass spectrometer (Thermo) coupled to a nanoflow liquid chromatograph (RSLC, Dionex). Data was searched with Mascot
(Matrix Science) against the human SwissProt 2015 (Dec.) and SwissProt 2016 (Jul.) database for H1155 and A427, respectively.
For both searches, two missed trypsin cleavages were allowed. Carbamidomethylation of cysteine and methionine oxidation
were selected as variable modifications. Results were imported to Scaffold 4.0 and data were analyzed setting the minimum number
of peptides to 2 and the protein and peptide threshold to 95% confidence.
Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019 e6

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
Activity-Based Protein Profiling (ABPP)
The ATP-probe-based ABPP was performed according to the manufacturer’s instructions of PierceTM Enrichment Kit with ATP Probe
(ThermoFisher Scientific, # 88310). H1155 cells were lysed using a 10 M urea/Pierce IP lysis buffer containing protease and phos-
phatase inhibitors (1:100). Samples were sonicated for 1 minute and then incubated for 10 minutes on ice. Samples were centrifuged
at 16,000 rpm at 4ꢀC for 10 minutes. Buffer exchange to remove endogenous ATP was performed using Zeba spin desalting columns
(Thermo Fisher Scientific), which were prepared following the manufacturer’s instructions, and protein concentration was determined
using Coomassie protein assay reagent (Thermo Fisher Scientific). Additional protease and phosphatase inhibitors (1:100) were
added to the lysates. Lysate was divided into 6 samples of 1 mg of protein (duplicates of DMSO, cabozantinib and foretinib treatment)
and 10 ul of 1 M MnCl2 were added to each sample. 20 mM of DMSO, foretinib or cabozantinib was added to the respective tubes and
rotated for 15 minutes at room temperature. Desthiobiotin-ATP probe was added to each sample at a final concentration of 5 mM and
incubated for 15 minutes at room temperature. Reduction and alkylation were performed as described by the manufacturer. Trypsin
digestion (20 mg/ml) was performed at 37ꢀC overnight. The 50% high capacity streptavidin agarose resin slurry (50 ul) was added to
each sample and rotated for 1 h at room temperature. After washes, peptide elution was performed by adding three times 100 ml
elution buffer (50% ACN, 0.1% trifluoroacetic acid (TFA) to each sample. Samples were frozen and lyophilized. Samples were resus-
pended in 20 ml of 2% ACN in 0.1% TFA solution containig Thermo Scientific Pierce Retention Time Calibration Mixture (PRTC) in
order to confirm correct operation of the LC-MS instrument.
A nanoflow ultra high performance liquid chromatograph (RSLC, Dionex) coupled to a nanoelectrospray benchtop Orbitrap mass
spectrometer (QExactive Plus, Thermo) was used to acquire MS/MS peptide sequences. The samples were loaded onto a pre-col-
˚
umn (2 cm x 100 mm ID packed with C18 PepMap100 reversed-phase resin, 5 mm particle size, 100 A pore size) and washed for 8 mi-
nutes with aqueous 2% ACN and 0.04% TFA. The captured peptides were eluted onto the analytical column, (C18 PepMap100, 75 mm
˚
ID x 50 cm, 2 mm particle size, 100 A pore size, Dionex). A 120-minute gradient was programmed as: 95% solvent A (2% ACN + 0.1%
FA) for 8 minutes, solvent B (90% ACN + 0.1% FA) from 5% to 38.5% in 90 minutes, then solvent B from 50% to 90% B in 7 minutes
and held at 90% for 5 minutes, followed by solvent B from 90% to 5% in 1 minute and re-equilibration for 10 minutes. The flow rate on
the analytical column was 300 nl/min. Sixteen tandem mass spectra were collected in a data-dependent manner following each sur-
vey scan. 60 second dynamic exclusion was used for previously sampled peptide peaks. Each sample was injected twice (technical
replicates).
Database searches and quantification were performed using MaxQuant 1.2.2.5. The fragment tolerance for MS/MS scans was set
to 20 ppm. Desthiobiotin was set as a variable modification. The false discovery rate (FDR) was set to 0.05.
Phosphoproteomics
For each of the six samples (foretinib, cabozantinib and DMSO – in duplicate), eleven 150 mm dishes were plated with 1.5 x 107
H1155 cells. After 24 hours, plates were treated with 1 mM of foretinib or cabozantinib for 3 hours; DMSO was used as control. Cells
were harvested, washed with phosphate-buffered saline (PBS) and pellets from each treatment were combined. Phosphoproteomic
samples were prepared following the PTMScan Kit Protocol (Cell Signaling) and as previously described (Ctortecka et al., 2018). Pep-
tide purification was performed using Sep-Pakꢀ C18 columns (Waters). Eluates were collected in the same tube and then aliquoted
for pY enrichment (20 mg per sample) or global phosphoproteomics (400 mg per sample). Samples were frozen at -80ꢀC overnight
and lyophilized for two days to ensure that there was no TFA left in the samples. Tyrosine phosphoproteomics was performed by
immunoprecipitation of phosphotyrosine peptides using the Antibody-beads [PTMScanꢀ Phospho-Tyrosine Rabbit mAb (P-Tyr-
1000) Kit, Cell Signaling #8803] followed by a label-free quantitative LC-MS/MS analysis.
Peptides for global phosphoproteomics (pSTY) were labeled using TMT 6-plex reagents accordingly to TMT Mass Tagging Kits
and Reagent Instruction (Thermo Scientific Mass Tagging Kit, #90064). Two channels were used for each type of sample (126
and 127 – DMSO1/2; 128 and 129 – foretinib1/2; 130 and 131 – cabozantinib1/2) and incubated for 1 hour at room temperature. Label
efficiency was confirmed by injecting approximately 1 mg of peptides for LC-MS/MS; all samples showed more than 99.4% labelling
efficiency by spectral counting. Reactions were quenched with 5% hydroxylamine for 15 minutes. Samples were combined and
lyophilized overnight. Samples were re-dissolved with 350 ml of 20 mM of ammonium formate (pH 10.0), vortexed for 10 minutes
and separated with basic pH reversed phase LC into 12 concatenated fractions using a Waters Xbridge C18 column (4.6 x
100 mm). Peptides were lyophilized and pSTY peptides were enriched using an IMAC enrichment kit (Sigma I1408). All samples
were evaporated using a speed vacuum centrifuge and resuspended in 2% ACN / 0.1% FA, which contained 50 fmol/ml of PRTC
(Thermo Scientific Pierce Retention Time Calibration Mixture, PIERCE) to confirm consistent performance of the analyses.
Data were acquired as described in ABPP methods. pY and IMAC data were queried using MaxQuant 1.2.2.5 and 1.5.2.8, respec-
tively. The fragment tolerance for MS/MS scans was set to 20 ppm. Fixed modifications were carbamidomethylation of cysteine; var-
iable modifications were set to oxidation of methionine, acetylation of the protein N-terminus and phosphorylation of serine, threonine
and tyrosine. The false discovery rate (FDR) was set to 0.05. The intensities of label-free phosphopeptides with PEP scores < 0.05
were manually validated.
RNA-Seq
H1155 cells were plated at a density of 0.5 x 106 cells in a 35 mm dish/treatment. After 24 hours, cells were treated with 1 mM of fore-
tinib or cabozantinib for 4 hours and then collected, pelleted and kept at -80ꢀC until all biological replicates were harvested. DMSO
treatment was used as control. RNA was extracted following manufacturer instructions (RNeasy Mini Kit – Qiagen, #74104), with the
addition of an extra DNase digestion step to increase the quality of the RNA. This digestion was performed following the method in
e7 Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
appendix E: DNase digestion of RNA before RNA cleanup from the RNase-Free DNase Set (Qiagen, #79254). To 87.5 ml of RNA sam-
ple was added 10 ml Buffer RDD and 2.5 ml DNase I stock solution (provided by the Moffitt’s Molecular Genomics Core). The sample
volumes were brought up to 100 ml with RNase-free water and incubated at room temperature for 10 minutes before proceeding with
the cleanup protocol of the RNeasy Mini Kit (Qiagen). Additionally, ethanol precipitation was performed after RNA extraction. The
volume of each sample was brought up to 200 ml and 1/10 volume (20 ml) of 5 M NaCl plus 2.5 volumes (500 ml) of absolute ethanol
were added prior to thorough vortex. Samples were centrifuged at 1,000 x g for 10 minutes; the supernatant was discarded, and
pellets were washed with 500 ml of freshly diluted 75% ethanol in RNase-free water. Samples were centrifuged again at 1,000 x g
for 10 minutes, supernatant was discarded, and samples were left to air-dry. Samples were resuspended in RNase-free water
and analyzed by Nanodrop to verify the 260/280 and 260/230 ratio > 1.8 and ꢁ2.0. Before cDNA synthesis, RNA quality and quantity
were verified by Agilent RNA Screen Tape System and Quant-iT Assays (Invitrogen). Quant-iT was used to quantify the RNA in the
samples and 100 ng per sample were used to start the cDNA synthesis. RNA-Seq library generation was performed following
the Encore Complete RNA-Seq Library System protocol (Nugen). The KAPA library amplification kit for Illumina platform
(KAPABiosystem) was used as described in the technical data sheet. Beads were resuspended in 20 ml of elution buffer. RNA-
Seq was performed on an Illumina NextSeq 500. All samples had a minimum of 40,000,000 reads. Paired-end reads were aligned
using TopHat2 (Kim et al., 2013) and HTSeq (Anders et al., 2015) was used to count reads that were mapped to the genes. Reads
were normalized using DESeq2 based on estimated library size. The average mapping rate was 80% across samples (range
75.5%-89.2%). Biological samples of each condition were run using 5 replicates. For each condition, the three replicates with the
fewest missing data were retained for the further analysis.
Chemical Synthesis
All reagents were purchased from commercial suppliers and used without further purification. 1H NMR spectra were recorded on an
Agilent-Varian Mercury 400 MHz spectrometer with CDCl3, CD3OD, or DMSO-d6 as the solvent. 13C NMR spectra were recorded at
100 MHz. All coupling constants are measured in Hertz, and the chemical shifts (dH and dC) are reported in parts per million. High-
resolution mass spectrometry was carried out on an Agilent 6210 LCꢂMS (ESI-TOF) system. Microwave reactions were performed in
Biotage Initiator 8 machines. HPLC analysis was performed using a JASCO HPLC system equipped with a PU-2089 Plus quaternary
gradient pump and a UV-2075 Plus UVꢂvis detector, using an Alltech Kromasil C-18 column (150 3 4.6 mm, 5 mm) and an Agilent
Eclipse XDB-C18 column (150 3 4.6 mm, 5 mm). The purities of the final compounds that underwent biological assessment
were >97% as measured by HPLC. Melting points were recorded on an Optimelt automated melting point system (Stanford Research
Systems). Thin-layer chromatography was performed using SiO2 60 F254 plates (Fisher), with observation under UV when necessary.
Anhydrous solvents (dimethylformamide, dichloromethane, methanol, and tetrahydrofuran) were used as purchased from Aldrich.
Burdick and Jackson HPLC-grade solvents (methanol and water) were purchased from VWR for HPLC and high-resolution mass
analysis. HPLC-grade TFA was purchased from Fisher. The SiO2 column purification of compounds were carried out using Biotage
automated Isolera purification system using a Biotage SiO2 prepacked columns.
i-Cabozantinib (10)
tert-Butyl (3-((4-chloro-6-methoxyquinolin-7-yl)oxy)propyl)carbamate (4). In a 50 mL round bottom flask, 4-chloro-6-methoxyqui-
nolin-7-ol (3) (1.17 g, 5.30 mmol) was dissolved in dry DMF (35.0 mL) and K2CO3 (7.32 g, 53 mmol) was added under argon atmo-
sphere. The mixture was stirred at RT for 15 minutes followed by the addition of tert-butyl (3-bromopropyl)carbamate (1.30 g,
5.30 mmol). After stirring the reaction at RT. for 16 hours, crude NMR showed ꢁ 60% conversion to the product. Added more
tert-butyl (3-bromopropyl)carbamate (0.5 equivalent, 650 mg, 2.65 mmol) and stirring was continued for an additional 8 hours.
The reaction was repeated using the same procedure employing 1.0 g (4.77 mmol) of 4-chloro-6-methoxyquinolin-7-ol. The com-
bined crude mixture from both reactions was filtered and the solid was washed with ꢁ 30 mL of EtOAc. The filtrate was washed
with water (2 x 50 mL) and concentrated. The crude solid was sonicated with methanol (3 mL) and solvent was decanted. The solid
was further rinsed with methanol (2 x 1.5 mL) and dried under vacuum to give the title compound as beige solid (2.92 g, 80%). Mp:
133-136ꢀC (dec.). 1H NMR (400 MHz, DMSO-d6): d 8.60 (d, J = 4.8 Hz, 1H), 7.57 (d, J = 4.8 Hz, 1H), 7.43 (s, 1H), 7.37 (s, 1H), 6.93 (t, J =
5.3 Hz, 1H, disappeared on D2O shake), 4.17 (t, J = 6.2 Hz, 2H), 3.97 (s, 3H), 3.13 (m, 2H), 1.92 (m, 2H), 1.38 (s, 9H); HPLC–MS (ESI+):
m/z 367.2 [100%, (M35Cl+H)+], 369.2 [40%, (M37Cl+H)+].
tert-Butyl (3-((6-methoxy-4-(4-nitrophenoxy)quinolin-7-yl)oxy)propyl)carbamate (5). The tert-Butyl (3-((4-chloro-6-methoxyquino-
lin-7-yl)oxy)propyl)carbamate (4) (2.46 g, 6.71 mmol) and p-nitrophenol (1.03 g, 7.38 mmol, 1.10 equiv.) were dissolved in a 20 mL
microwave vial and dry DIPEA (14.0 mL) was added. The microwave vial was capped and the reaction mixture was stirred at
130ꢀC (oil bath temp.) overnight. After completion of the reaction (monitored by HPLC-MS), the DIPEA was evaporated using a
V-10 system and the resulting thick brown oil was triturated using EtOAc/hexane to give the title compound as a white solid
(2.1 g, 67%). Mp: 212ꢀC (dec). 1H NMR (400 MHz, DMSO-d6): d 8.61 (d, J = 5.1 Hz, 1H), 8.34 (d, J = 9.2 Hz, 2H), 7.44 (d, J =
9.2 Hz, 2H), 7.43 (s, 1H), 7.37 (s, 1H), 6.94 (t, J = 6.2 Hz, 1H, peak intensity go down on D2O shake), 6.87 (d, J = 5.1 Hz, 1H), 4.17
(t, J = 6.4 Hz, 2H), 3.89 (s, 3H), 3.14 (m, 2H), 1.89-1.90 (m, 2H), 1.38 (s, 9H); HPLC–MS (ESI+): m/z 470.2 [100%, (M+H)+].
tert-Butyl (3-((4-(4-aminophenoxy)-6-methoxyquinolin-7-yl)oxy)propyl)carbamate (8). In a 50 mL round bottom flask, 10% Pd/C
(455.61 mg, 0.2 equiv.) was added to dry MeOH (50 mL) and argon gas was bubbled through the mixture for 10 minutes. The
tert-Butyl (3-((6-methoxy-4-(4-nitrophenoxy)quinolin-7-yl)oxy)propyl)carbamate (5) (2.01 g, 4.28 mmol) was added to the flask fol-
lowed by hydrazine monohydrate (1.26 mL, 25.69 mmol, 6 equiv.). After stirring for 2 h at rt, HPLC-MS showed complete conversion
of the starting material to the product. The reaction mixture was filtered through a short pad of celite and the filtrate was concentrated.
Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019 e8

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
The crude product was stirred in EtOAc/hexane (95:5,18 mL) at RT, filtered and dried under vacuum to give the title compound as a
white solid (1.61 g, 86%). Mp: 205ꢀC (dec). 1H NMR (400 MHz, DMSO-d6): d 8.42 (d, J = 5.2 Hz, 1H), 7.50 (s, 1H), 7.34 (s, 1H), 6.94 (brs,
partially overlapped, 1H, 40% reduced on D2O shake), 6.92 (d, J = 8.6 Hz, 2H), 6.66 (d, J = 8.6 Hz, 2H), 6.36 (d, J = 5.2 Hz, 1H), 5.17
(s, 2H), 4.15 (t, J = 6.2 Hz, 2H), 3.93 (s, 3H), 3.14 (m, 2H), 1.92 (m 2H), 1.38 (s, 9H); HPLC–MS (ESI+): m/z 440.2 [100%, (M+H)+].
1-((4-fluorophenyl)carbamoyl)cyclopropane-1-carboxylic acid (7). The Cyclopropane-1,1-dicarboxylic acid (6) (2 g, 15.37 mmol)
was dissolved in dry THF (20 mL) in a round bottom flask and the mixture was cooled to 0ꢀC (ice-water bath). Triethylamine
(1.12 mL, 15.37 mmol) was slowly added (ꢁover 2 min.) to the mixture and stirring was continued for 30 min. at the same temperature.
Thionyl chloride (1.12 mL, 15. 37 mmol) was added and stirring was continued for another 30 min. The p-fluoroaniline (1.48 mL,
15.37 mmol) was dissolved in THF (20 mL) and added to the reaction mixture via a dropping funnel (over 20 min.). The reaction
was stirred at 0ꢀC until the reaction went to completion (2 hours), diluted with EtOAc (ꢁ30 mL) and washed with brine (2 x 20 mL).
The organic layer was separated and dried (Na2SO4). The crude solid product obtained was triturated using EtOAc/hexane (1:3)
to give the title compound as an off-white solid (2.43 g, 67%). Mp: 166-172ꢀC reported 167-170ꢀC (Literature (Exelixis, WO2005/
30140, p=195). 1H NMR (400 MHz, DMSO-d6): d 13.09 (s, 1H, disappeared on D2O shake), 10.57 (s, 1H), 7.66 – 7.57 (m, 2H),
7.17 – 7.11 (m, 2H), 1.40 (s, 4H); 19F NMR (400 MHz, DMSO-d6): d -119.02 – -119.22 (m); HPLC–MS (ESI+): m/z 222.1 [100%,
(M-H)+], 246.1 [100%, (M+Na)+], 224.2 [80%, (M+H)+], 469.2 [35%, (2M+Na)+].
tert-butyl (3-((4-(4-(1-((4-fluorophenyl)carbamoyl)cyclopropane-1-carboxamido)phenoxy)-6-methoxyquinolin-7-yl)oxy)propyl)carba-
mate-bisTFA (9). The 1-((4-fluorophenyl)carbamoyl)cyclopropane-1-carboxylic acid (7) (60 mg, 0.26 mmol) was dissolved in dry
DMF (0.8 mL), cooled to 0ꢀC, followed by the addition of DIPEA (103.3 mL, 0.59 mmol, 2.2 equiv.), HOBt (79.91 mg, 0.59 mmol,
2.2 eq.) and EDCI ( 113.37 mg, 0.59 mmol, 2.2 eq.). After stirring the reaction mixture for 40 minutes, tert-Butyl (3-((4-(4-aminophe-
noxy)-6-methoxyquinolin-7-yl)oxy)propyl)carbamate (8) (118.15 mg, 0.26 mmol, 1 eq.) was added and the reaction was first warmed
to RT and then heated at 70ꢀC (oil bath) for 12 h. The crude reaction mixture was diluted with water (ꢁ3 mL) and extracted with EtOAc
(2 x 5 mL). The organic layer was dried (Na2SO4), evaporated and the crude product was purified using SiO2 chromatography (elu-
ents: MeOH:DCM , 5:95). The resulting material was further purified using preparative HPLC (Method: Gradient methanol-water with
0.1% TFA, 17 minutes run, flow rate 20 mL/min). The title compound was obtained (in the form of bis-TFA salt) as a thick oil (39 mg,
23%,) which partially solidified after drying under high-vacuum overnight. 1H NMR (400 MHz, DMSO-d6): d 10.32 (s, 1H, disappeared
on D2O shake), 10.02 (s, 1H, disappeared on D2O shake), 8.77 (d, J = 6.4 Hz, 1H), 7.85 (d, J = 9.0 Hz, 2H), 7.73 (s, 1H), 7.68 – 7.60
(m, 2H), 7.50 (s, 1H), 7.37 (d, J = 9.0 Hz, 2H), 7.21–7.12 (m, 2H), 6.97 (t, J = 6.0 Hz, 1H, disappeared on D2O shake), 6.79 (d, J =
6.4 Hz, 1H), 4.23 (t, J = 6.0 Hz, 2H), 4.03 (s, 3H), 3.18–3.11 (m, 2H), 2.04–1.92 (m, 2H), 1.55–1.44 (m, 4H), 1.38 (s, 9H); 19F NMR
(400 MHz, DMSO-d6): d -74.08 (s), -118.95 – -119.07 (m); HPLC–MS (ESI+): m/z 645.3 [100%, (M+H)+].
N-(4-((7-(3-aminopropoxy)-6-methoxyquinolin-4-yl)oxy)phenyl)-N-(4-fluorophenyl) cyclopropane-1,1-dicarboxamide.bisTFA (10). The
tert-butyl (3-((4-(4-(1-((4-fluorophenyl)carbamoyl)cyclopropane-1-carboxamido)phenoxy)-6-methoxyquinolin-7-yl)oxy)propyl)
carbamate-bisTFA (9) (18 mg, 0.027 mmol) was dissolved in dry DCM (0.8 mL) and TFA (0.2 mL) was added. The reaction mixture
was stirred at RT for 2 h at which point TLC showed complete consumption of the starting material. The solvent was evaporated
and the residual TFA was removed by evaporating the mixture with EtOAc (ꢁ 4 mL) and DCM (ꢁ 4 mL). Using a 4 mL vial, the crude
compound was partitioned between 0.5 mL of 1N NaHCO3 (aq.) and EtOAc (2 x 2 mL). The aqueous layer was re-extracted with DCM
(2 x 2 mL) and the combined organic layers were dried (Na2SO4), evaporated and lyophilized. The title compound was obtained as a
white solid (11.7 mg, 77%) Mp: 203ꢀC (dec). HPLC 99% [tR = 10.3 min, Gradient: MeOH-water (with 0.1% TFA) 40-95%, 1mL/min,
20 min]; 1H NMR (400 MHz, DMSO-d6) d 10.27 (s, 1H, disappeared on D2O shake), 10.04 (s, 1H, disappeared on D2O shake), 8.61
(s, 1H), 7.84 (brs, 2H, disappeared on D2O shake), 7.81 (d, J = 9.1 Hz, 2H), 7.64 (dd, J = 9.1, 5.1 Hz, 3H), 7.50 (s, 1H), 7.28 (d, J =
9.0 Hz, 2H), 7.18–7.13 (m, 2H), 6.60 (s, 1H), 4.29 (t, J = 5.9 Hz, 2H), 3.99 (s, 3H), 3.05-3.00 (m, 2H), 2.17–2.11 (m, 2H), 1.48 (d, J =
3.9 Hz, 4H); 19F NMR (400 MHz, DMSO-d6): d -73.64 (brs), -118.97– -119.09 (m); HPLC–MS (ESI+): m/z 545.3 [95%, (M+H)+],
273.2 [100%, (M+2H)2+]; LC-MS 545.2 [100%, (M+H)+]; HRMS (ESI+) m/z calculated for C30H29FN4O5 (M+H)+ 545.2194, found
545.2193.
i-Foretinib (18)
N-(3-fluoro-4-hydroxyphenyl)-N-(4-fluorophenyl)cyclopropane-1,1-dicarboxamide (11). The 1-((4-fluorophenyl)carbamoyl)cyclo-
propane-1-carboxylic acid (7), which was synthesized as described for the synthesis of i-cabozantinib, (100 mg, 0.45 mmol) was dis-
solved in dry THF (1 mL) and 1 drop of dry DMF was added at RT. The mixture was cooled to 0ꢀC and oxalyl chloride (38.4 mL,
0.45 mmol) was slowly added via a syringe. The mixture was stirred for 2 h at the same temperature and 3-fluoro-4-hydroxyaniline
(56.9 mg, 68.3 mmol, 1.20 equiv.) and pyridine (0.5 ml, 500 mmol) were added in THF (1 mL). The reaction was stirred at 0ꢀC for 1 h,
diluted with EtOAc (ꢁ5 mL) and the organic layer was washed with water (ꢁ5 mL). The organic layer was separated and stirred with
1M HCl (3 mL) for 30 minutes at rt. The organic layer was separated and the aqueous layer was extracted with EtOAc (5 mL). The
combined organics were dried (Na2SO4), evaporated and the solid obtained was triturated from EtOAc/hexane (1:3) to give the title
compound as a beige solid (72 mg, 48%). Mp: 187ꢀC (dec). 1H NMR (400 MHz, DMSO-d6): d 10.05 (s, 1H, disappeared on D2O shake),
9.90 (s, 1H, disappeared on D2O shake), 9.58 (s, 1H, disappeared on D2O shake), 7.61 (dd, J = 8.9, 5.1 Hz, 2H), 7.52 (dd, J = 13.5,
2.0 Hz, 1H), 7.14 (t, J = 8.9 Hz, 3H), 6.93–6.80 (m, 1H), 1.43 (brs, 4H); 19F NMR (376 MHz, DMSO-d6): d -119.02 – -119.22 (m), -135.34
(dd, J = 13.3, 10.2 Hz, 1H); HPLC–MS (ESI+): m/z 333.1 [90%, (M+H)+], 355.1 [100%, (M+Na) +], 687.2 [80%, (2M+Na)+].
tert-Butyl 4-(3-chloropropyl)piperazine-1-carboxylate (13). In a 5 mL microwave reaction vial, 1-boc-piperazine (12) (186 mg,
1 mmol) and 1-bromo-3-chloropropane (197.8 mL, 2 mmol) were dissolved in dry DMF (1 mL) and K2CO3 (276 mg, 2 mmol) was added
under argon. The reaction mixture was heated at 80ꢀC (oil bath) for 1 h at which point TLC showed complete consumption of the
e9 Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
starting material piperazine. The reaction mixture was filtered and concentrated to obtain a thick brown oil. The crude was rinsed with
hexane (5 mL x 4) to give the title compound as a light yellow oil (217 mg, 87%) which partially solidified overnight at 4ꢀC. Reaction
was repeated with 1-boc-piperazine (12) (1.86 g, 10 mmol) using the same procedure described here to obtain the title compound
(2.2 g, 84%). 1H NMR (400 MHz, CDCl3): d 3.60 (t, J = 6.5 Hz, 2H), 3.43 – 3.41 (t, J = 5.2 Hz, 4H), 2.48 (t, J = 7.0 Hz, 2H), 2.38 – 2.36
(J = 5.2 Hz, 4H), 1.94 (p, J = 6.8 Hz, 2H), 1.46 (s, 9H); HPLC–MS (ESI+): m/z 263.2 [100%, (M35Cl+H) +], 265.2 [40%, (M37Cl+H) +].
tert-Butyl 4-(3-((4-chloro-6-methoxyquinolin-7-yl)oxy)propyl)piperazine-1-carboxylate (14). The 4-chloro-6-methoxyquinolin-7-ol
(1.0 g, 4.77 mmol) was dissolved in dry DMF (10 mL) in a 20 ml microwave vial and K2CO3 (1.32 g, 9.54 mmol) was added at r.t.
The mixture was stirred under argon for 10 minutes followed by the addition of tert-Butyl 4-(3-chloropropyl)piperazine-1-carboxylate
(13) (1.5 g, 5.72 mmol). The reaction mixture was heated at 80ꢀC (oil bath) for 6 h at which point HPLC-MS showed complete con-
sumption of the starting material. The crude mixture was filtered and the solid was washed with EtOAc (ꢁ 5 mL). The organic filtrate
was combined and evaporated. The crude solid was dissolved in EtOAc (ꢁ100 mL) and the organic layer was washed with water
(50 mL x 2) and brine (50 mL) and dried (Na2SO4). After evaporation of the organic layer, the crude mixture was triturated from
EtOAc/hexane to give the title compound as light green solid (1.78 g, 86%). Mp: 116-119ꢀC. 1H NMR (400 MHz, MeOD): d 8.51
(d, J = 4.0 Hz, 1H), 7.49 (d, J = 4.0 Hz, 1H), 7.46 (s, 1H), 7.38 (s, 1H), 4.24 (t, J = 5.2 Hz, 2H), 4.01 (s, 3H), 3.45 (broad t, J =
3.6 Hz, 4H), 2.63 (t, J = 5.6 Hz, 3H), 2.48 (t, J = 4.4 Hz, 4H), 2.14 – 2.09 (m, 2H), 1.45 (s, 9H); HPLC–MS (ESI+): m/z 438.2 [40%,
(M37Cl+H) +], 436.2 [100%, (M35Cl+H) +].
tert-Butyl4-(3-((4-(2-fluoro-4-(1-((4-fluorophenyl)carbamoyl)cyclopropane-1-carboxamido) phenoxy)-6-methoxyquinolin-7-yl)oxy)
propyl)piperazine-1-carboxylate (15). The N-(3-fluoro-4-hydroxyphenyl)-N-(4-fluorophenyl)cyclopropane-1,1-dicarboxamide (11)
(381 mg, 1.15 mmol) and tert-Butyl 4-(3-((4-chloro-6-methoxyquinolin-7-yl)oxy)propyl)piperazine-1-carboxylate (14) (500 mg,
1.15 mmol) were dissolved in DIPEA (1.15 mL) in a 5 ml microwave vial and the reaction mixture was stirred at 130ꢀC (oil bath) for
3 h. The TLC showed ꢁ50% conversion of the starting material to the product. However, undesired side products also started to
form as indicated by TLC and HPLC-MS. At this stage, MeOH (1-2 mL) was added and the solvent mixture was evaporated using
Biotage V-10 system. The crude compound was purified using SiO2 column chromatography (eluting with EtOAc: Hexane, 0:10 to
10:0 followed by MeOH:DCM, 0:10 to 1.2:8.8). The title compound was obtained as a gray solid (207 mg, 25%). Notably, doing
the same reaction at elevated temperature or in a microwave reactor resulted in the formation of side products (indicated by
1
TLC). Mp: 178ꢀC (dec). H NMR (400 MHz, DMSO-d6): d 10.39 (s, 1H, disappeared on D2O shake), 10.01 (s, 1H, disappeared on
on D2O shake), 8.46 (d, J = 5.1 Hz, 1H), 7.90 (dd, J = 13.2, 2.0 Hz, 1H), 7.64 (dd, J = 9.0, 5.1 Hz, 2H), 7.52 (s, 2H), 7.43-7.38
(m, 2H), 7.15 (t, J = 9.0 Hz, 2H), 6.41 (d, J = 5.0 Hz, 1H), 4.19 (t, J = 6.0 Hz, 2H), 3.95 (s, 3H), 3.61 (brs, 2H), 3.17 – 3. 11 (m, 4H),
2.35 (brs, 4H), 1.99 – 1.96 (m, 2H), 1.48 (brs, 4H), 1.40 (s, 9H); 19F NMR (376 MHz, DMSO-d6) d -118.88 – -119.30 (m), -128.81
(dd, J = 13.3, 10.2 Hz); HPLC–MS (ESI+): m/z 732.3 [100%, (M+H) +].
N-(3-Fluoro-4-((6-methoxy-7-(3-(piperazin-1-yl)propoxy)quinolin-4-yl)oxy)phenyl)-N-(4-fluorophenyl)cyclopropane-1,1-dicarbox-
amide (16). tert-Butyl4-(3-((4-(2-fluoro-4-(1-((4-fluorophenyl)carbamoyl)cyclopropane-1-carboxamido) phenoxy)-6-methoxyqui-
nolin-7-yl)oxy)propyl)piperazine-1-carboxylate (15) (100 mg, 0.136 mmol) was dissolved in 20% TFA in DCM (1 mL) and stirred at
RT for 2 h. The TLC and HPLC-MS showed complete consumption of the starting material. The solvent was evaporated and residual
TFA was removed by evaporating the reaction mixture with DCM (2 mL x 4) and EtOAc (2 mL x 4). The crude product was rinsed with
DCM (2 mL) and hexane (2 mL) and dried under high vacuum to give the title compound (tris TFA salt) as a gummy oil which (partly
solidified after storage in a refrigerator overnight) (106 mg, 123%). 1H NMR (400 MHz, DMSO-d6): 10.47 (s, 1H, 50% disappeared on
D2O shake), 9.98 (s, 1H, 40% disappeared on D2O shake), 8.96 (s, 1H, disappeared on D2O shake), 8.67 (d, J = 5.4 Hz, 1H), 7.95
(dd, J = 13.2, 1.7 Hz, 1H), 7.67 (s, 1H), 7.64 (dd, J = 8.9, 5.1 Hz, 2H), 7.59 – 7.55 (m, 1H), 7.54 (s, 1H), 7.48 (t, J = 8.9 Hz, 1H), 7.16
(t, J = 8.9 Hz, 2H), 6.71 (brs, 1H), 4.29 (t, J = 5.8 Hz, 2H), 4.01 (s, 3H), 3.66 – 3.56 (m, 2H), 3.30 (brs, 4H), 3.15-3.12 (m, 2H), 2.18
(brs, 2H), 1.53 – 1.42 (m, 4H), 1.26 (t, J = 6.3 Hz, 4H); 19F NMR (376 MHz, DMSO-d6) d -73.99 (s), -118.91 – -119.01 (m), -128.69
(dd, J = 11.4, 8.3 Hz); HPLC–MS (ESI+): m/z 632.3 [100%, (M+H) +], 316.7 [100%, (M+2H)2+].
tert-Butyl(3-(4-(3-((4-(2-fluoro-4-(1-((4-fluorophenyl)carbamoyl)cyclopropane-1-carboxamido)phenoxy)-6-methoxyquinolin-7-yl)
oxy)propyl)piperazin-1-yl)propyl)carbamate (17). The N-(3-Fluoro-4-((6-methoxy-7-(3-(piperazin-1-yl)propoxy)quinolin-4-yl)oxy)
phenyl)-N-(4-fluorophenyl)cyclopropane-1,1-dicarboxamide (16) (104 mg, 0.13) was dissolved in dry DMF (0.2 mL) under inert con-
ditions, powdered and dry K2CO3 (55.79 mg, 0.40 mmol) was added to the mixture. After stirring at RT for 15 minutes, tert-butyl
(3-bromopropyl)carbamate (41.66 mg, 0.17 mmol, 1.3 equiv.) was added to the reaction mixture and heated at 80ꢀC for 12 h. The
HPLC-MS and TLC showed complete consumption of the starting material. The crude mixture was concentrated, MeOH (5 mL)
was added to the crude mixture before filtration. The filtrate was concentrated under reduced pressure and purified using Biotage
Isolera purification system (SiO2, eluting with MeOH:DCM, 0:10 to 1.5: 8.5). The title compound (in the form of bis TFA salt) was ob-
1
tained as a light yellow solid (67 mg, 63%). Mp: 188.2ꢀC (dec). H NMR (400 MHz, DMSO-d6): 10.39 (s, 1H, disappeared on D2O
shake), 10.01 (s, 1H, disappeared on D2O shake), 8.47 (d, J = 5.0 Hz, 1H), 7.90 (d, J = 13.4 Hz, 1H), 7.64 (dd, J = 8.9, 5.0 Hz, 2H),
7.53–7.49 (m, 2H), 7.43 – 7.39 (m, 2H), 7.16 (t, J = 8.8 Hz, 2H), 6.82 (brs, 1H, disappeared on D2O shake), 6.41 (d, J = 5.2 Hz, 1H),
4.19 (brt, J = 5.8 Hz, 2H), 3.95 (s, 3H), 3.108–3.03 (broad m, 9H), 2.95–2.90 (m, 3H), 2.29 – 2.23 (m, 4H), 2.02–1.96 (m, 2H), 1.47
(broad s, 4H), 1.37 (s, 9H); HPLC–MS (ESI+): m/z 789.4 [30%, (M+H)+], 395.2 [100%, (M+2H)2+].
N-(4-((7-(3-(4-(3-aminopropyl)piperazin-1-yl)propoxy)-6-methoxyquinolin-4-yl)oxy)-3-fluorophenyl)-N-(4-fluorophenyl)cyclopro-
pane-1,1-dicarboxamide.TrisTFA (18). The tert-Butyl(3-(4-(3-((4-(2-fluoro-4-(1-((4-fluorophenyl)carbamoyl)cyclopropane-1-car-
boxamido)phenoxy)-6-methoxyquinolin-7-yl)oxy)propyl)piperazin-1-yl)propyl)carbamate (17) (64 mg, 0.08 mmol) was dissolved in
dry DCM (2 mL) and TFA (0.2 mL) was added to the mixture. After stirring the reaction mixture at RT for 2 h, TLC showed complete
Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019 e10

Please cite this article in press as: Sumi et al., Divergent Polypharmacology-Driven Cellular Activity of Structurally Similar Multi-Kinase Inhibitors through
Cumulative Effects on Individual Targets, Cell Chemical Biology (2019), https://doi.org/10.1016/j.chembiol.2019.06.003
conversion of the starting material to the product. The solvent was evaporated and residual TFA was removed by evaporating the
product with DCM (2 mL x 4) and MeOH (2 mL x 4). The crude product was loaded on a short plug of celite and low polar impurities
were removed by eluting with DCM (10 mL). The product was eluted with MeOH:DCM (2:8), concentrated, and rinsed with DCM
(ꢁ2 mL x 3) and lyophilized for 24 h. The title compound (tris TFA salt) was obtained as a light yellow gummy solid (20 mg, 36%).
Mp: 227ꢀC (dec). HPLC: 98% [tR = 14.5 min, gradient MeOH-water (with 0.1% TFA), 5-95% over 20 min.]; 1H NMR (400 MHz,
CD3OD): d 8.73 (dd, J = 6.7, 2.1 Hz, 1H), 7.96 (dd, J = 12.7, 2.1 Hz, 1H), 7.87-7.85 (m, 1H), 7.59 – 7.54 (m, 3H), 7.51-7.47 (m, 2H),
7.07 (t, J =, 8.4 Hz, 2H), 6.99 (d, J = 6.0 Hz, 1H), 4.52 – 4.32 (m, 3H), 4.22-4.14 (m, 1H), 4.11 (s, 3H), 3.98 – 3.88 (m, 3H), 3.58 –
3.39 (m, 3H), 3.14 (t, J = 6.8 Hz, 2H), 3.06 (t, J = 7.2 Hz, 2H), 2.78 (t, J = 6.8 Hz, 1H), 2.72-2.65 (m, 1H), 2.47-2.42 (m, 1H), 2.38 –
2.27 (m, 1H), 2.01-1.89 (m, 1H), 1.66 (s, 4H); 19F NMR (376 MHz, DMSO-d6): d -73.91 (s), -118.85 – -119.10 (m), -128.62 – -128.91
(m); HPLC–MS (ESI+): m/z 689.3 [30%, (M+H) +], 345.3 [100%, (M+2H)2+]; LC-MS 689.3 [100%, (M+H)+], 345.2 [55%, (M+2H)2+];
HRMS (ESI+) m/z calculated for C37H42F2N6O2 (M+H)+ 689.3257, found 689.3255.
QUANTIFICATION AND STATISTICAL ANALYSIS
Most data were obtained from three different experiments and presented as the mean standard deviation (SD). Details of each
exact number of replicates are provided in the figure legends. Data were analyzed using GraphPad Prism and R. Statistical analyses
were performed using Student’s t-test and synergy was determined using the Bliss model of independence. All statistical details are
included in the figure legends. Additional details for each experiment type are described in the Method Details section of the
STAR Methods.
Based on the scatterplot densities, a log2 abundance of 7.5 was selected as cutoff for the analysis of RNA-Seq data to avoid abun-
dance noise. The selection criteria were: 1) either treatment group average must be > 7.5; 2) t-test < 0.01; 3)jfold changejR 1.5; 4)
Hellinger distance > 0.25. Genes that were significantly regulated in at least 1 of the 3 comparisons – fore vs DMSO + cabo, fore vs
DMSO or fore vs cabo – were selected for further analysis.
All MaxQuant data were first filtered for peptides with PEP score < 0.05. In addition, reverse and contaminant peptides and pep-
tides with no intensity were excluded. Data were then normalized using IRON (iterative rank-order normalization) (Welsh et al., 2013).
Our analyses further on mainly focused on the comparison between foretinib versus cabozantinib treatment.
In the ABPP analysis, first the technical duplicates were averaged, and then averaged between biological duplicate. Peptides with
jfold changej R 1.5 and p-value < 0.05 were kept for further analysis.
For the pY proteomics, after averaging between replicates, peptides with p-value < 0.1 were used for further analysis. In addition,
to overcome the missingness between duplicates in the dataset, we manually rescued peptides that 1) each treatment had 3 values
and the difference between them were less than 50%; 2) one treatment had 3 or 4 values and the other only 2, but the ratio between
the two values was less than 1.5 and the difference between treatment were less than 50%; 3) one treatment had 3 or 2 values and the
other treatment none. The cutoff for the global proteomics was +/- 2 average standard deviations.
Kinases specifically binding to foretinib over cabozantinib in chemical proteomics were prioritized using foretinib’s NSAF (normal-
ized spectrum abundance factor) > 0.0006 as additional criteria. We also compared the fold change between the two treatments,
taking into consideration the available Kd values from the HMS (Harvard Medical School) LINCS (Library of Integrated Network-
Based Cellular Signatures) dataset.
Integrated data analysis was performed by entering the gene names into the GSEA database (Broad Institute) and querying canon-
ical pathways and gene ontology (GO) gene sets, which included GO biological process, GO cellular component and GO molecular
function. Data were exported to Excel, analyzed and visualized in R.
DATA AND CODE AVAILABILITY
The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository
(http://www.ebi.ac.uk/pride) (Vizcaino et al., 2016). The accession number for the chemical proteomics data reported in this paper is
PRIDE: PXD012961. The accession number for the tyrosine phosphoproteomics data reported in this paper is PRIDE: PXD012962.
The accession number for the global phosphoproteomics data reported in this paper is PRIDE: PXD012963. The accession number
for the ABPP data reported in this paper is PRIDE: PXD012965. RNA-Seq data have been deposited in the GEO database. The acces-
sion number for the RNA-Seq data reported in this paper is GEO: GSE126850.
e11 Cell Chemical Biology 26, 1–13.e1–e11, September 19, 2019