Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data
Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data
Karen A. Ryall 2
Jimin Shin 2
Minjae Yoo 2
Trista K. Hinz 1
Jihye Kim 2
Jaewoo Kang 0
Lynn E. Heasley 1
Aik Choon Tan 0 2 3
0 Department of Computer Science and Engineering, Korea University , Seoul , Korea
1 Department of Craniofacial Biology, School of Dental Medicine, University of Colorado Anschutz Medical Campus , Aurora, CO , USA
2 Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine
3 Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus , Aurora, CO , USA
Motivation: Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret. Results: We developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055. Availability and implementation: KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/. Contact: Supplementary information: Supplementary data are available at Bioinformatics online.
Kinases play essential roles in cell survival, growth and proliferation
and are currently the largest protein class in clinical trials
(RaskAndersen et al., 2014). Kinases are frequently mutated in cancer and
acquire oncogenic properties to drive tumorgenesis. These cancer
cells are often ‘addicted’ to the mutated oncogenes (e.g. kinases).
Targeted cancer therapies have exploited this ‘oncogene addiction’
concept, and deployed small molecules that could inhibit these
oncogenic kinases (Sawyers, 2004). While kinases are predominantly
targeted for cancer therapy, they are also implicated in immunological,
neurological, metabolic and infectious diseases (Zhang et al., 2009).
Induction of cell death through inhibition of a specific essential
kinase creates selective pressure for cancer cells to develop resistance
mechanisms. Cancer cells often acquire resistance through
mutations that interfere with drug binding (Azam et al., 2008). Other
resistance mechanisms include target amplification, upregulation of
alternative kinase pathways, and intrinsic resistance of a subset of
cells in the larger population (Glickman and Sawyers, 2012; Sun
and Bernards, 2014). Combination of kinase inhibitors could limit
development of these resistance pathways and dramatically improve
cancer therapy (Al-Lazikani et al., 2012). In order for combination
therapy to be more widely adopted, new systems approaches are
needed to prioritize target combinations for experimental validation
(Ryall and Tan, 2015).
Before targeted kinase inhibitor therapies can be applied, kinase
dependency within a cancer cell needs to be established.
Highthroughput pharmacological screening is a powerful method for
determining kinase dependency, (Garnett et al., 2012; Barretina
et al., 2012). However, due to unexpected drug-kinase interactions
(polypharmacology), target deconvolution for drug screening data
remains a challenge in chemical systems biology. Moreover, highly
expressed kinases are not always effective molecular targets in
cancer (Wei et al., 2006). Unfortunately, the large number off-target
interactions of most kinase inhibitors can lead to misinterpretation
of drug screening results. For example, the commonly reported
targets of FDA-approved drug bosutinib are SRC and ABL; however,
bosutinib also inhibits another 40 kinases by more than 85%
inhibition at 500 nM (Anastassiadis et al., 2011). While this poses a
challenge for target deconvolution, it also provides a unique opportunity
to study the effects of a more comprehensive set of kinases as well as
combinations of kinases in a given screen. As quantitative kinase
inhibition data is becoming increasingly available (Davis et al., 2011;
Anastassiadis et al., 2011), it can be used to better identify critical
kinases following drug screens.
Here, we developed Kinase Addiction Ranker (KAR), an
algorithm that integrates high-throughput drug screening data,
comprehensive kinase inhibition data and gene expression profiles to
determine kinase dependency in cancer cells. This algorithm was
inspired by previous work using kinase inhibition profiles and drug
sensitivity data to predict kinase targets for leukemia patients
(Tyner et al., 2013). Using publicly available data, we demonstrated
the utility of KAR in ranking kinase targets for 21 lung cancer cell
lines and used statistical clustering to group cell lines by kinase
dependency. We experimentally validated KAR predictions for
nonsmall cell lung cancer cell line H1581. We also applied this approach
to previously published data from 151 leukemia patient samples.
2.1 Kinase addiction ranker (KAR) algorithm
We have developed KAR, a computational algorithm that integrates
high-throughput drug screening data, comprehensive quantitative
drug-kinase binding data, and transcriptomics data to predict kinase
dependence in cancer cells (Fig. 1). KAR generates lists of kinases
with high correlation with a phenotypic output such as cell
proliferation or survival. Kinases are scored based on the sensitivity of each
drug that inhibits the kinase. Since the ultimate goal of KAR is to
generate lists of kinase targets for therapeutic application, KAR first
filters low expressing kinases from subsequent analysis (Fig. 1). This
shortens computation time and ensures that each high scoring kinase
is expressed in the cell above a user-defined threshold.
After filtering kinases with low gene expression, the drugs used
in the screen are sorted into one of five bins based on drug sensitivity
(Fig. 1). Drugs meeting the highest sensitivity threshold (e.g.
IC50 1 lM) are placed into Bin 1. Kinase targets of drugs in Bin 1
receive the highest point value (20 points) by the algorithm. Bin 2
and 3 contain drugs with high (e.g. IC50 2 lM) and intermediate
(e.g. IC50 5 lM) sensitivity values. Kinase targets of drugs in these
bins receive fewer points than targets in Bin 1 (10 points and
5 points, respectively). Finally, Bin 4 and 5 contain drugs that do
not meet the threshold for sensitivity (e.g. IC50 5 lM). Targets of
Fig. 1. Kinase Addiction Ranker (KAR) algorithm overview. KAR integrates
drug sensitivity, kinase inhibition, and gene expression data to generate a
ranked list of kinase targets associated with drug sensitivity. Kinase targets
for each drug screened in a cancer sample are scored based on the sensitivity
of the drug
drugs in Bin 4 receive no points and targets of drugs in Bin 5 receive
negative 10 points by the algorithm. Four thresholds are used to
define the bins: Bin 1: IC50 < Threshold 1, Bin 2: Threshold
1 IC50 < Threshold 2, Bin 3: Threshold 2 IC50 < Threshold 3,
Bin 4: Threshold 3 IC50 Threshold 4, and Bin 5:
IC50 > Threshold 4. For our lung cancer cell line data we used IC50
thresholds of 1, 2, 5 and 10 lM to define the five bins and for the
leukemia patient data we used IC50 thresholds of 0.5, 1, 2.5 and
5 lM. Lower thresholds were used for the leukemia data since this
dataset had smaller ranges of IC50 values (max IC50 ¼ 10 lM). The
highest ranking kinases were consistent over a variety of different
threshold sets. See Supplementary Tables S1 and S2 for examples of
KAR ranking by varying thresholds for two different lung cancer
cell lines. The threshold for tiering drug sensitivity in KAR could be
tuned for different experiments as deemed appropriate by the user.
We used thresholds such that Bin 1 contained the top 15% most
sensitive drugs, Bin 2: top 15–20%, Bin 3: top 20–30%, Bin 4:
top 30–40% and Bin 5: bottom 60%. While we used IC50
measurements of sensitivity, the thresholds can easily be tuned to
accommodate other measures of sensitivity such as Ki (inhibition constant)
or percent of control measurements at a single concentration. Since
Ki measurements are less sensitive to assay type, they could be useful
for combining data from different experimental sources.
After binning the drugs, a score is calculated for each kinase in
the dataset. Each time the kinase is inhibited above the threshold,
the appropriate amount of points are added or subtracted based on
the bin of the drug that inhibited the kinase (Table 1). If the kinase
is not inhibited by the drug, no points are added or subtracted for
that drug. We sum over all drugs and kinases to get a final raw score
for each kinase. A kinase is considered inhibited based on previously
published competitive binding data of hundreds of kinases for each
drug. Data using different measurements of inhibition strength (e.g.
percent of control, Kd, IC50) were combined to ensure the most
comprehensive list of drugs was available for analysis. We defined a
kinase target as inhibited if its percent of control measurement at a
Kinase X inhibited?
Note: If a drug inhibits Kinase X, points are added based on the sensitivity
bin of the drug. For example, Drug 1 inhibits kinase X and is in sensitivity bin
1 (highest sensitivity), therefore 20 points are added to the score. Drugs that
do not inhibit Kinase X do not affect the score (Drugs 2, 5 and 6). The total
Raw score for Kinase X is calculated by summing over all the drugs in the
Raw KAR Score ¼ 20-10 þ 20 ¼ 30.
single drug concentration was less than 15% (>85% inhibition of
the target) or if its IC50 or Kd measurement was <1lM.
Similarly, KAR also calculates scores for each pair of kinases.
Here, points are only added or subtracted if two kinases are
inhibited above threshold by the same drug. Then the appropriate
number of points are given based on the bin of the drug that
inhibited the pair of targets. While points are calculated for every
possible combination of kinases in the dataset, certain pairs of targets
are unlikely to ever have a high score since the number of drugs that
inhibit a particular pair may be low or nonexistent. An example is
the pair FGFR1 and MTOR, which is only inhibited at the same
time by one compound in our dataset, AZD-7762.
KAR scales the raw scores such that the kinase or pair with the
highest raw score has a scaled score of 1. This allows for comparison
of KAR scores between samples. KAR also calculates a percent
effective score by computing the percentage of times the kinase is
inhibited and is included in one of the sensitivity bins receiving
positive points (sensitivity Bins 1–3). KAR also calculates chi-square
and Fisher’s exact test P-values for the kinases and combinations
using the contingency table for two variables: (i) kinase inhibited
(>85% inhibition or IC50/Kd < 1lM) and (ii) drug sensitivity
(sensitivity bins 1–3). The P-values compute if there is a significant
association between a kinase being inhibited and the inhibiting drug being
sensitive (sensitivity bins 1–3) and are not related to the KAR score.
We sort the table of kinase scores by P-value to ensure that kinases
that are not inhibited by as many compounds are not overlooked by
the algorithm since kinases that are inhibited by more compounds
have greater potential to receive higher scores. Each of these scores
together result in a more complete picture of the importance of a
given kinase or combination to the sample. The key kinases to test
in follow-up experiments will likely have significant P-values, high
scores, and high percent effective values. An example of all the
outputs of KAR for one of the cell lines we tested is given in
Supplementary Table S3.
2.2 Implementation of KAR
We implemented KAR in MATLAB (version 2015a) and Python
Scripting Language (version 2.7.8). We tested KAR in OSX Version
10. 9.5. KAR code is freely available for download at http://tanlab.
2.3 Drug sensitivity data
We obtained high-throughput pharmacological profiling data for 21
lung cancer cell lines from the Genomics of Drug Sensitivity in
Cancer (GDSC) database (Yang et al., 2012) (Supplementary
Table S4). Screening data from 151 leukemia patient samples was
obtained from a recent publication (Tyner et al., 2013)
(Supplementary Table S5).
2.4 Microarray gene expression data
We obtained microarray gene expression data for the 21 lung cancer
cell lines from the Cancer Cell Line Encyclopedia (GSE36133). Raw
CEL files for these cell lines were normalized using Robust
Multiarray Average (RMA) (Irizarry et al., 2003) approach in
Affymetrix Power Tools (APT).
2.5 Quantitative kinase inhibition data
For lung cancer cell lines study, we obtained comprehensive
quantitative kinase inhibition data for 49 kinase inhibitors used in the
GDSC database. References for publications and databases used to
acquire the kinase profiles are in Supplementary Table S6. For the
leukemia patient study, kinase inhibition data for 66 kinase
inhibitors were collected from published papers (Tyner et al., 2013).
References for publications and databases used to acquire the kinase
profiles are in Supplementary Table S7. Quantitative kinase binding
data was dichotomized as inhibited or not inhibited using thresholds
of IC50/Kd < 1lM or percent inhibition >85%. Databases such as
ChEMBL (Bento et al., 2014), PubChem (Wang et al., 2014) and
DSigDB (Yoo et al., 2015) are useful resources for finding published
quantitative kinase target information.
2.6 Cluster analysis
We performed hierarchical clustering of the data using the
MATLAB bioinformatics toolbox with Euclidean distance metric
and average linkage to generate the hierarchical tree. Data columns
were normalized so that the mean was 0 and the standard deviation
2.7 Cell proliferation assay
H1581 cells were plated at 100 cells per well in 96-well tissue
culture plates and treated with inhibitors at various doses. When the
DMSO-treated control wells became confluent (10 days) cell
numbers were assessed using a CYQUANT Direct Cell Proliferation
Assay (Invitrogen) according to the manufacturer’s instructions.
2.8 Quantifying combination effects
To quantify the combination effect of drugs used in this study, we
used the Bliss independence model, that predicts the combined
response C for two single compounds with effects A and B using the
following equation: C ¼ A B, where each effect is expressed as
fractional activity compared to control between 0 (maximal effect,
100% inhibition) and 1 (no effect, 0% inhibition). The combination
is synergistic if the %inhibition of the combination is greater than
the predicted C.
2.9 Immunoblot analysis
For immunoblot analysis cells were plated at 1.5 106 cells per
plate in 4–10 cm plates. Twenty-four hours later, cells were switched
to HITES media for 2 h and subsequently treated with either
DMSO, 100 nM ponatinib, 100 nM AZD8055, or the combination
of ponatinib þ AZD8055 for 2 h. Cells were collected in PBS,
centrifuged (3 min at 3000 rpm) and suspended in lysis buffer. Aliquots of
the cell lysates containing 60 mg of protein were submitted to
SDSPAGE and immunoblotted for phospho-mTOR (#5536), mTOR
(#2983), phospho-Akt S473 (#9271), Akt (#9272),
phosphop70S6K (#9234), p70S6K (#9202), phospho-S6 (#4857), S6
(#2317), phospho-ERK p-p44/42 MAPK (#9101); Cell Signaling
Technology and ERK1 (sc-93), ERK2 (SC-154), NaK-ATPase
a-subunit (sc-21712); Santa Cruz Biotechnology.
3.1 Determining kinase dependency in lung cancer
We initially applied KAR to a panel of 21 lung cancer cell lines using
drug sensitivity data from the GDSC database (Yang et al., 2012).
We obtained microarray gene expression data from the Cancer Cell
Line Encyclopedia (GSE36133). Raw CEL files for these cell lines
were normalized using RMA approach (Irizarry et al., 2003) in
Affymetrix Power Tools (APT). Genes with expression level lower
than seven (log2 signal from RMA) were deemed to be low
expressed and filtered out in this study. Drugs were included in our
analysis for each kinase inhibitor profiled in GDSC with published
kinase inhibition profiles. Sensitivity data for each cell line
contained data from between 21 and 49 kinase inhibitors with a median
of 30 inhibitors. The list of the kinase inhibitors used in this study is
available in Supplementary Table S4. Each pair of cell lines had at
least 20 overlapping kinase inhibitors screened in the dataset. The
top five ranking kinases and kinase pairs for each cell line are
provided in Supplementary Tables S8 and S9.
3.2 Cluster analysis of the kinase dependency in lung cancer cell lines
Hierarchical clustering of the scaled KAR scores (Fig. 2A) reveal
relationships in kinase dependence among the lung cancer cell lines
and kinases with similar scoring patterns. For example, MELK,
MAP4K4 and TAF1 group together and have high scores in the
same cell lines. Cell lines H1703, EPLC272H and H2009 group
together partially due to high scores in MELK, MAP4K4, TAF1 and
CAMKK2. A subset of kinases was selected for clustering by
identifying kinases with a significant association with drug sensitivity
(Fisher’s exact test, P < 0.05) in one of the 21 cell lines. MTOR was
most frequently significantly associated with drug sensitivity among
the lung cancer cell lines studied. This is relevant as MTOR is a key
kinase that regulates the survival pathway and has been previously
shown to be active in non-small cell cancer (Ekman et al., 2012;
Fumarola et al., 2014). Clustering results reveal that MTOR, EGFR
and ERBB2 are among the kinases with the most distinct scoring
patterns, making the scores of these kinase more unique identifiers
of the cell lines (Fig. 2A). This is interesting as EGFR is one of the
targets with FDA-approved drugs (e.g. gefitinib and erlotinib)
approved for non-small cell lung cancer.
In contrast, clustering based on drug sensitivity (Fig. 2B) instead
of kinase score resulted in different groupings of cell lines. For
example, H1623 and H2126 clustered together by drug sensitivity
partially due to shared sensitivity to EGFR inhibitors gefitinib and
Cell Line Top 5 kinases (Scaled score, P-value) ranked by KAR H1975 H1299
afatinib. However, when clustering based on kinase score (Fig. 2A),
we see more separation between these cell lines, with H1623 having
a higher EGFR score than H2126. Data from drugs with strong
offtarget effects on EGFR such as AZD-7762, bosutinib and ponatinib
decrease the EGFR score for H2126. The KAR score clustering also
highlights the high dependence of H2126 on MTOR and CDK9
compared to H1623. This is consistent with published data
suggesting that H2126 acquired resistance to EGFR inhibition via
activation of the AKT/MTOR pathway (Wu et al., 2013). This highlights
that clustering based on KAR could delineate the kinase dependency
in individual cell lines, which is not possible to distinguish based on
drug sensitivity data clusters.
Additionally, H292 grouped together with other cell lines
inhibited by bosutinib like H661 (Fig. 2B), but when we cluster based
on kinase score we more clearly see that H292 has a unique
sensitivity to FLT3 inhibition. These examples further illustrate how
incorporation of comprehensive kinase inhibition profiles for each drug
allows for better kinase target deconvolution.
KAR results also demonstrate that effective kinase targets cannot
be predicted based on gene expression alone. While some cell lines
have high kinase scores for kinases with high gene expression (e.g.
PC-14 - EGFR), many high expressing kinases have low associations
with drug sensitivity. For example, AURKA is one of the kinases
with the highest gene expression in cell line H1299, but has a scaled
score of 0.14 and a chi-square P-value of 0.784, indicating low
correlation with drug sensitivity. Another example is high MET
expression in H1975, but a scaled KAR score of 0.38 and a chi-square
P-value of 1.00.
3.3 Validation of KAR-predicted kinase dependency
To demonstrate that KAR could delineate kinase dependency in
individual cell lines, we validated KAR algorithm predictions for three
non-small cell lung cancer cell lines: H1975, H1299 and H1581
(Table 2) based on published literature and experimental results.
3.3.1 Validation of kinase dependency in H1975
H1975 is an EGFR double-mutant (L858R, T790M) cell line with
high EGFR gene expression. The first mutation (L858R) correlates
with sensitivity to EGFR kinase inhibitors (e.g. erlotinib and
gefitinib). In contrast, the second mutation (T790M) is the gatekeeper
mutation that generates resistance to the first-generation EGFR
inhibitors (erlotinib and gefitinib). While EGFR was the highest
scoring kinase by KAR, the association between EGFR and drug
sensitivity was not significant (chi-square P ¼ 0.08). This is due to
the T790M mutation that confers resistance to the EGFR drug
sensitivity for this cell line. EGFR, however, was present in significant
inhibition pairs with TNIK and GAK (chi-square P ¼ 0.01), which are
inhibited by the dual SRC/ABL inhibitor bosutinib. High scoring
single targets TNIK, MAP4K4, STK3, AAK1, GAK and EGFR are
also inhibited by bosutinib. These findings are supported by a
previous study demonstrating decreased proliferation in H1975 after
combining EGFR inhibitor gefitinib with bosutinib compared to
either drug alone (Kim et al., 2014).
3.3.2 Validation of kinase dependency in H1299
KAR results for H1299 showed high ranking for casein kinase 2
alpha (Table 2) and several high scoring combinations of kinases
containing casein kinase 2 (Supplementary Table S9). This target is
supported by a previous study showing that CX-4945, a selective
casein kinase 2 alpha inhibitor, induces dose-dependent decreases
in cell proliferation in H1299 and has an IC50 of 1.8 lM (Zhang
et al., 2013). More recently, CX-4945 was shown to
downregulate AKT/mTOR signaling pathway in H1299 and induces
apoptosis in this cell line (So et al., 2015). This supports that
H1299 is dependent on the CNSK2A1 and MTOR kinases as
predicted by KAR.
3.3.3 Validation of kinase dependency in H1581
The highest scoring kinases for FGFR1 amplified NSCLC cell line
H1581 were FGFR1, FGFR2, MKNK2 and MTOR (Table 2).
H1581 was the only lung cancer cell line analyzed with a significant
association between FGFR1 inhibition and drug sensitivity.
Previously, we have demonstrated that H1581 is a cell line that has
high FGFR1 gene and protein expressions, and this cell line is
sensitive to ponatinib, a FGFR1 inhibitor (Wynes et al., 2014).
Moreover, MTOR was identified as a biomarker of resistance to
targeted therapy in recent studies in breast cancer and
melanoma(Corcoran et al., 2013; Kelsey and Manning, 2013;
Elkabets et al., 2013). We experimentally tested this prediction by
combining FGFR1 (ponatinib) and MTOR (AZD8055) inhibitors in
this cell line. Experimental results show enhanced reduction in
proliferation with the combination and the combination is synergistic
by Bliss independence (Fig. 3A). Western blots confirm decreased
ERK1/2 activation with ponatinib and decreased mTOR, AKT,
p70S6K and S6 activation with AZD8055 (Fig. 3B-C). More
importantly, the combination of ponatinib and AZD8055 shows
inhibition of both ERK and MTOR pathways (Fig. 3B-C). This results is
consistent with a recent kinome-wide RNAi screens that identified
MTOR is synthetic lethal with FGFR1 in this cell line (Singleton
et al., 2015).
Taken together, we demonstrated that KAR-predicted kinase
dependency in these lung cancer cell lines could be validated by
experimental results and/or published literature. This supports the
utility of KAR in delineating kinase dependency by integrating
high-throughput drug screening data and in vitro kinase binding
Fig. 3. Experimental validation of KAR prediction of FGFR1 and mTOR
dependence for lung cancer cell line H1581. (A) 10 nm ponatinib (FGFR inhibitor)
and AZD8055 (mTOR inhibitor) were applied to H1581 cells and cell
proliferation was measured with the CYQUANT assay kit. The combination of
ponatinib and AZD8055 was synergistic by Bliss Independence (Additive
prediction ¼ 19.4%, Actual ¼ 6.7%). Bar graphs display mean percent of
control (POC) þ/ SEM (B) Western blots showing changes in signaling with
ponatinib, AZD8055, and the combo. Ponatinib decreases ERK1/2 activation
and AZD8055 decreases signaling downstream of mTOR. (C) Signaling
network diagram for pathways targeted by ponatinib and AZD8055 in H1581
3.4 Deciphering kinase dependency in leukemia patient samples
Next, we applied KAR to a dataset of 151 leukemia patient samples
screened with 66 kinase inhibitors (Tyner et al., 2013). The list of
the kinase inhibitors used in this study is available in Supplementary
Table S5. Since gene expression data was not available, no kinases
were filtered prior to scoring. As with the lung cancer data, we
applied hierarchical clustering to the scaled KAR scores for a subset
of the kinases for each patient sample to observe relationships in
scoring patterns (Fig. 4). We selected the 50 kinases with the highest
variance in score and the 10 kinases most commonly significantly
associated with drug sensitivity (chi-square P < 0.05). Even with
patients grouped by disease type, we see a large variance in kinase
dependence among the patients with no set of kinases uniquely
identifying a disease type. This variance further illustrates the need
for pharmacological screens to help plan targeted patient therapy.
FLT3, a kinase with mutations in up to 30% of acute myeloid
leukemia (AML) patients (Zarrinkar et al., 2009), had the highest
variance in score among the kinases analyzed. EPHA5, EPHA3 and
BTK were most commonly significantly associated with drug
sensitivity. These kinases had significant associations in 72, 58 and 54%
of the patient samples, respectively (Supplementary Table S10). Eph
receptors have been shown to affect cancer growth, migration and
invasion in vitro and in vivo (Pasquale, 2010). Consistently, a RNAi
screen identified EPHA5 sensitivity in a subset of the 30 patient
leukemia samples studied (Tyner et al., 2009). Interestingly, Eph
receptors use bidirectional signaling mechanisms to induce both tumor
promotion and suppression (Pasquale, 2010). The frequency of BTK
dependence is interesting given a phase IB/II clinical trial of BTK
inhibitor ibrutinib resulting in a high frequency of durable remissions
in patients with chronic lymphocytic leukemia (CLL) (Byrd et al.,
2013). The progression-free survival rate at 26 months was 75%.
This is consistent with our data showing 70% of CLL patient data
had significant association between BTK inhibition and drug
sensitivity (Supplementary Table S10).
We developed and validated KAR, a novel algorithm to improve
interpretation of high-throughput drug screens by incorporating
comprehensive drug-kinase binding profiles and transcriptomics data.
KAR takes advantage of drug polypharmacology to study a larger
variety of kinases as well as combinations of kinases. Two major
factors that could influence KAR data analysis are the (i) number of
effective drugs and (ii) diversity of drug targets. Influential kinases
cannot be rapidly identified without multiple inhibitors in your drug
set that target a given kinase. The KAR percent effective scores from
a preliminary screen can be used to identify kinases with potential
associations with drug sensitivity for further analysis with other
drugs. Moreover, many kinase pairs are uncommonly inhibited
together (e.g. FGFR1 and MTOR), and must be hypothesized based
on the single kinase scores. While our algorithm most directly
applies to studying kinase dependence, it could be easily modified to
study other targets if inhibition data for these targets is available.
Other approaches have been developed that use overlap in drug
kinase profiles to identify key targets. Gujral et al. used principal
component analysis to identify an optimal set of 32 kinase inhibitors
for profiling and then used elastic net regularization to identify key
kinases influencing cell migration(Gujral et al., 2014). Similar to
KAR, they also used gene expression data to filter kinases from
analysis. Tran et al. also applied elastic net regression to identify
important kinases for cancer cell lines following an in vitro screen
(Tran et al., 2014). Another algorithm based on set theory uses drug
screen data and kinase inhibitor profiles to predict the most
influential kinases and produce circuit diagrams illustrating if the kinase is
effective inhibited alone or if it needs to be inhibited with other
kinases (Berlow et al., 2013). Compared to these approaches, KAR
benefits from producing straightforward scores and P-values that
could be readily interpreted by scientists without computational
backgrounds. Moreover, the drug lists do not need preliminary
optimization, as chi-square and fisher’s exact test P-values take
differences in the number of inhibitors that target each kinases and the
total number of sensitive drugs into account.
We applied KAR to leukemia patient samples profiled with 66
kinase inhibitors (Tyner et al., 2013), demonstrating the
applicability of the tool for predicting patient therapy. Given resource
limitations when working with patient samples, it may not be possible to
screen patient biopsies with large numbers of compounds.
Therefore, future studies could benefit greatly from prior
optimization of the set of drugs used for profiling. One recent example of this
is Gujral et al.’s (2014) use of principal component analysis to
reduce the number of kinase inhibitors profiled to an optimal set of 32
from 178. Moreover, instead of using IC50 measurements of drug
sensitivity, which requires measurements at multiple concentrations,
cell viability measurements at single concentrations can be
KAR was inspired by a previous algorithm implemented using
Excel Visual Basic and macro code (Tyner et al., 2013). Compared
to the Tyner algorithm, we introduced tiering score values based on
drug sensitivity instead of target inhibition strength, percent
effective scores, optional incorporation of gene expression data, stronger
penalties for insensitive drugs, and calculation of P-values. We also
made the algorithm more accessible by providing MATLAB and
python functions. Calculation of P-values helps decrease potential for
false positives, as kinases targeted by more drugs have the potential
for higher raw scores. Incorporation of gene expression data helps
ensure that highly ranked kinases are translationally meaningful.
Moreover, we found that tiering kinase scores based on target
inhibition strength instead of drug sensitivity resulted in much lower
percent effective scores, indicating that weaker targets of the inhibitors
may not be accurate indicators of kinase dependency in the samples.
Moreover, a single threshold for kinase inhibition allows for easier
incorporation of kinase inhibition data from multiple platforms
with different inhibition measurement types (e.g. percent inhibition
compared to control, Kd, and IC50), allowing for more drugs to be
included in analysis. We believe that integrating high-throughput
drug screening data and in vitro kinome inhibition data as
demonstrated in this study could be a useful systems approach to identify
novel targets and kinase dependency in cancer cells.
In summary, KAR integrates drug sensitivity, comprehensive kinase
inhibition data and gene expression profiles to identify kinases
dependency in cancer cells. We applied KAR to published drug
screen data from lung cancer cell lines and leukemia patient samples.
Clustering analysis revealed lung cancer cell lines with similarities in
kinase dependence. We experimentally validated KAR predictions
of FGFR1 and MTOR dependence in lung cancer cell line H1581.
Our analysis revealed candidate kinases as potential targets in lung
cancer and leukemia for further pharmacological and biological
studies. We believe that the research reported in this study provides
a new research strategy to delineate kinase dependency in cancer
cells. This approach can be applied to other cancer cell lines and
patient tumor samples to discover effective kinase targets for
This work is partly supported by the National Institutes of Health under Ruth
L. Kirschstein National Research Service Award T32CA17468 (K.A.R.), the
National Institutes of Health P50CA058187, P30CA046934, Cancer League
of Colorado, and the David F. and Margaret T. Grohne Family Foundation.
Lynn Heasley is supported in part by a research grant from ARIAD
Al-Lazikani , B. et al. ( 2012 ) Combinatorial drug therapy for cancer in the postgenomic era . Nat. Biotechnol. , 30 , 679 - 692 .
Anastassiadis , T. et al. ( 2011 ) Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity . Nat. Biotechnol. , 29 , 1039 - 1045 .
Azam , M. et al. ( 2008 ) Activation of tyrosine kinases by mutation of the gatekeeper threonine . Nat. Struct. Mol. Biol ., 15 , 1109 - 1118 .
Barretina , J. et al. ( 2012 ) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity . Nature , 483 , 603 - 607 .
Berlow , N. et al. ( 2013 ) A new approach for prediction of tumor sensitivity to targeted drugs based on functional data . BMC Bioinformatics , 14 , 239 .
Bento , A.P. et al. ( 2014 ) The ChEMBL bioactivity database: an update . Nucl Acids Res ., 42 , D1083 - D1090 .
Byrd , J.C. et al. ( 2013 ) Targeting BTK with ibrutinib in relapsed chronic lymphocytic leukemia . N. Engl . J. Med., 369 , 32 - 42 .
Corcoran , R.B. et al. ( 2013 ) TORC1 suppression predicts responsiveness to RAF and MEK inhibition in BRAF-mutant melanoma . Sci. Transl. Med ., 5 , 196ra98 .
Davis , M.I. et al. ( 2011 ) Comprehensive analysis of kinase inhibitor selectivity . Nat. Biotechnol. , 29 , 1046 - 1051 .
Ekman , S. et al. ( 2012 ) The mTOR pathway in lung cancer and implications for therapy and biomarker analysis . J. Thoracic Oncol ., 7 , 947 - 953 .
Elkabets , M. et al. ( 2013 ) mTORC1 inhibition is required for sensitivity to PI3K p110a inhibitors in PIK3CA-mutant breast cancer . Sci. Transl. Med ., 5 , 196ra99 .
Fumarola , C. et al. ( 2014 ) Targeting PI3K/AKT/mTOR pathway in non small cell lung cancer . Biochem. Pharmacol. , 90 , 197 - 207 .
Garnett , M.J. et al. ( 2012 ) Systematic identification of genomic markers of drug sensitivity in cancer cells . Nature , 483 , 570 - 575 .
Glickman , M.S. and Sawyers , C.L. ( 2012 ) Converting cancer therapies into cures: lessons from infectious diseases . Cell , 148 , 1089 - 1098 .
Gujral , T.S. et al. ( 2014 ) Exploiting polypharmacology for drug target deconvolution . Proc. Natl Acad. Sci. USA , 111 , 5048 - 5053 .
Irizarry , R.A. et al. ( 2003 ) Exploration, normalization, and summaries of high density oligonucleotide array probe level data . Biostatistics , 4 , 249 - 264 .
Kelsey , I. and Manning , B.D. ( 2013 ) mTORC1 status dictates tumor response to targeted therapeutics . Sci. Signal. , 6 , pe31 .
Kim , J. et al. ( 2014 ) Bioinformatics-driven discovery of rational combination for over-coming EGFR-mutant lung cancer resistance to EGFR therapy . Bioinformatics , 30 , 2393 - 2398 .
Pasquale , E.B. ( 2010 ) Eph receptors and ephrins in cancer: bidirectional signalling and beyond . Nat. Rev. Cancer , 10 , 165 - 180 .
Rask-Andersen , M. et al. ( 2014 ) The druggable genome: evaluation of drug targets in clinical trials suggests major shifts in molecular class and indication . Annu. Rev. Pharmacol. Toxicol. , 54 , 9 - 26 .
Ryall , K.A. and Tan , A.C. ( 2015 ) Systems biology approaches for advancing the discovery of effective drug combinations . J. Cheminf. , 7 , 7 .
Sawyers , C. ( 2004 ) Targeted cancer therapy . Nature , 432 , 294 - 297 .
Singleton , K.R . et al. ( 2015 ) Kinome RNAi screens identify MTOR for synergistic targetting with FGFR1 in lung cancer and HNSCC cell lines . Cancer Res ., In press.
So , K.S . et al. ( 2015 ) AKT/mTOR down-regulation by CX-4945, a CK2 inhibitor, promotes apoptosis in chemorefractory non-small cell lung cancer cells . Anticancer Res ., 35 , 1537 - 1542 .
Sun , C. and Bernards , R. ( 2014 ) Feedback and redundancy in receptor tyrosine kinase signaling: relevance to cancer therapies . Trends Biochem. Sci. , 39 , 465 - 474 .
Tran , T.P. et al. ( 2014 ) Prediction of kinase inhibitor response using activity profiling, in vitro screening, and elastic net regression . BMC Syst. Biol ., 8 , 74 .
Tyner , J.W. et al. ( 2013 ) Kinase pathway dependence in primary human leukemias determined by rapid inhibitor screening . Cancer Res ., 73 , 285 - 296 .
Tyner , J.W. et al. ( 2009 ) RNAi screen for rapid therapeutic target identification in leukemia patients . Proc. Natl Acad. Sci. USA , 106 , 8695 - 8700 .
Wang , Y. et al. ( 2014 ) PubChem BioAssay : 2014 update. Nucleic Acids Res ., 42 , D1075 - D1082 .
Wei , G. et al. ( 2006 ) Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance . Cancer Cell , 10 , 331 - 342 .
Wu , K. et al. ( 2013 ) Gefitinib resistance resulted from STAT3-mediated Akt activation in lung cancer cells . Oncotarget , 4 , 2430 - 2438 .
Wynes , M.W. et al. ( 2014 ) FGFR1 mRNA and protein expression, not gene copy number, predict FGFR TKI sensitivity across all lung cancer histologies . Clin. Cancer Res ., 20 , 3299 - 3309 .
Yang , W. et al. ( 2012 ) Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells . Nucleic Acids Res ., 41 , D955 - D961 .
Yoo , M. et al. ( 2015 ) DSigDB: drug signatures database for gene set analysis . Bioinformatics , 31 , 3069 - 3071 .
Zarrinkar , P.P. et al. ( 2009 ) AC220 is a uniquely potent and selective inhibitor of FLT3 for the treatment of acute myeloid leukemia (AML) . Blood , 114 , 2984 - 2992 .
Zhang , J. et al. ( 2009 ) Targeting cancer with small molecule kinase inhibitors . Nat. Rev. Cancer , 9 , 28 - 39 .
Zhang , S. et al. ( 2013 ) Inhibition of CK2a down-regulates Notch1 signalling in lung cancer cells . J. Cell. Mol. Med ., 17 , 854 - 862 .