Graphical abstract
graphic file with name fx1.jpg
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Highlights
* •
We developed a computational approach to predict synergistic
compound pairs
* •
The approach uses transcriptional data and pathway information for
scoring
* •
The predicted combinations with gemcitabine were validated in vitro
* •
Predicted combinations versus single agents had 2.82–5.18 times
higher synergies
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Molecular biology; Computational bioinformatics; Cancer systems biology
Introduction
Pancreatic cancer is one of the most aggressive human malignancies that
is commonly diagnosed only at an advanced stage ([34]Li et al., 2004;
[35]Stathis and Moore, 2010). Gemcitabine, a nucleoside analogue of
cytidine, is frequently used for treatment of pancreatic cancer, alone
and in combination with nab-paclitaxel, as a first line treatment in
patients with unresectable adenocarcinoma of the pancreas ([36]Vogl
et al., 2019). However, the efficacy of gemcitabine is low, with a
survival rate after 12 months of only 18% ([37]Burris et al., 1997;
[38]WasifSaif, 2006;[39]Sultana et al., 2007). Drug combinations have
been studied before for pancreatic ductal adenocarcinoma (the most
common type of pancreatic cancer) to sensitize the cells to the effect
of gemcitabine, increase efficacy of therapy and consequently improve
survival rate ([40]Jung et al., 2017; [41]Moore et al., 2007;
[42]Sultana et al., 2008; [43]Tu et al., 2017; [44]Yachida and
Iacobuzio-Donahue, 2013). Compound combinations may hence provide a
treatment option with increased efficacy and lower toxicity by
targeting several dysfunctional pathways at lower doses while also
potentially reducing the likelihood of drug resistance ([45]Mueller
et al., 2009).
Methodologically, there are various ways of measuring synergy
([46]Meyer et al., 2020; [47]Vlot et al., 2019) and the choice of
synergy metric directly influences the interpretation of the
combinatorial screen. With respect to the data side, large
combinatorial screening datasets have recently been published, such as
the Merck combinatorial screen ([48]O’Neil et al., 2016) with 22,737
experiments of 583 double combinations against 39 different cancer cell
lines and the NCI ALMANAC([49]Holbeck et al., 2017) with 5,000 pairs of
FDA-approved cancer drugs against a panel of 60 well-characterized
human tumor cell lines. Various combination screenings have been
integrated in resources such as DrugComb ([50]Zagidullin et al., 2019)
with 437,932 pairs. However, while cost and effort have been high for
generating such data, it is clear that even those currently largest
datasets cover drug chemical and cancer biological spaces only very
partially. Hence, for exploring both chemical and biological spaces
efficiently when exploring the potential of combination therapies, they
need to be at least complemented with computational approaches. These
approaches can be based on experimental screening data and features
from the ligand (chemical) side ([51]Preuer et al., 2017; [52]Zhang et
al., 2021), gene ([53]Jeon et al., 2018; [54]Kalantarmotamedi et al.,
2018; [55]Regan-Fendt et al., 2019), combination of gene expression and
chemical features ([56]Zhang et al., 2021), and pathways or biological
networks([57]Gu et al., 2015; [58]Li et al., 2018) as have been
reviewed in recent articles([59]Bulusu et al., 2015). However, one
limitation of machine learning based methods is that large scale data
of compound combination screens, of preferably even the same cancer
type, is required in the first place to be able to train a model on the
data.
Given that available combination screening data for pancreatic cancer
is limited, it would be very helpful in practice to be able to predict
compound combinations based on monotherapy data alone. Large scale gene
expression data of monotherapy of compounds on cancer cell lines is
available in databases such as Connectivity Map (CMap) ([60]Lamb
et al., 2006) and LINCS ([61]Subramanian et al., 2017), and this data
has been successfully used in the past for transcriptional drug
repositioning of single agents in several studies ([62]Jahchan et al.,
2013; [63]Landreville et al., 2012; [64]Wei et al., 2006). The
underlying hypothesis for matching single agent drug treatments to
diseases is that if the transcriptional responses of a compound is the
reverse of a disease gene expression profile that compound has a
therapeutic potential for treating that particular disease ([65]Lamb
et al., 2006). In other words, it is expected that compound treatments
that restore gene expression patterns of a disease to its norm will
also restore the physiological markers of the disease to the baseline
levels ([66]Wagner et al., 2015). Several methods have emerged for such
a type of analysis, most of which involve finding anticorrelation of
gene signatures of compounds and a disease of interest based on the
above principle ([67]Iorio et al., 2012; [68]KalantarMotamedi et al.,
2021; [69]Lamb, 2007; [70]Sirota et al., 2011).
Recent studies have attempted to hypothesize, based on transcriptional
data of single agents, which compounds are likely to be synergistic in
combination. Approaches can be categorized in methods that take into
account similarity of signatures of compound treatments based on gene
level ([71]Bansal et al., 2014; [72]Huang et al, 2014, [73]2019;
[74]Liu and Zhao, 2016; [75]Stathias et al., 2018), target level
([76]Regan-Fendt et al., 2019; [77]Yang et al., 2019) and pathway level
([78]Xu et al., 2018). This finding was also confirmed in DREAM
challenge of synergy prediction ([79]Hsu et al., 2016). Methods that
take into account high similarity of signature of compound signatures
have been successfully and more extensively validated than other
approaches. This included an earlier Dream challenge winner ([80]Bansal
et al., 2014), DrugComboRanker ([81]Huang et al., 2014; [82]Zheng
et al., 2021a) and SynergySeq ([83]Stathias et al., 2018) approach. In
some studies, apart from similarity of signatures, further
considerations have been taken into account. This includes identifying
dissimilarity of compound structures ([84]Liu and Zhao, 2016) and
maximal reversal of disease signature ([85]Huang et al., 2019;
[86]Stathias et al., 2018) as further important contributing factors in
finding more synergistic combinations. Many such approaches were
validated either retrospectively or prospectively successfully in
several cancers such as lung cancer ([87]Huang et al., 2014) and
glioblastoma ([88]Stathias et al., 2018). Target functional similarity
is also an appealing approach for synergy prediction. This can be
quantified by measuring similarity of protein targets on perturbed
pathways which is useful as it is independent of LINCS data. On the
other hand, SynGeNet ([89]Regan-Fendt et al., 2019) integrates gene
expression data, target information, and network pharmacology of drug
and disease for this purpose. It is based on scoring single agents
using Connectivity Map approach and integrating a network approach to
evaluate closeness of a drug’s known targets to important melanoma
targets. Moreover, pathway information is an additional important
resource for synergy prediction with limited studies focusing on that.
It has been suggested that inhibiting multiple modules of reactivated
disease signaling pathways is a promising strategy to identify drug
combinations that overcome resistance ([90]Xu et al., 2018).
The current study now proposes, and validates for pancreatic cancer
cells, a novel approach to identify potentially synergistic compound
combinations from monotherapy transcriptional data. First, we have used
pathway signatures of compounds (instead of gene signatures) because
pathway signatures are more robust and comparable across cell lines
([91]Wang et al., 2019). Second, we have introduced a novel hypothesis
about which types of pathway dysregulation potentially leads to
compound synergy. This has been achieved via a two-step process (see
[92]Figure 1). In the first step, we identified sets of pathways that
are dysregulated in the PANC-1 cell line compared with pancreatic
ductal epithelial cells. Then, we hypothesized that targeting the
dysregulated pathways of the disease efficiently would result in
identifying compounds with the desired disease-modulating effect on
PANC-1 cells ([93]Figure 1A). As gemcitabine (a current main therapy of
pancreatic cancer) was identified in the first step, we then were able
to elucidate the mechanistic action of gemcitabine on the
transcriptional level and identify important pathways of the PANC-1
disease signature that the gemcitabine instance in LINCS database
reverses (anticorrelated pathways; ACPs) and those pathways where it
does not reverse and have correlation of pathway signature with disease
signature (correlated pathways; CPs). We next hypothesized that the CPs
were the set of pathways that were contributing to gemcitabine
resistance in PANC-1, and hence found a matching second drug in the
database that would target preferably those pathways in the desired
manner (i.e., with anticorrelation to the disease signature). This gave
rise to the identification of two scores, termed Score1 and Score2,
related to the first and second instances of gemcitabine in the
database ([94]Figure 1B). Moreover, pathways that were in the CP
pathway set, and which were specifically important in PANC-1 compared
to other pancreatic cancer cell lines were subsequently identified,
giving rise to the Res-score (Resistance Score of the PANC-1 cell
line). Based on those three scores–Score1, Score2, and Res-score–and
some selected pathways, 30 selected candidate compounds were
experimentally validated in vitro as single agents and in combination
with gemcitabine, with methodological details and results as described
in the following section.
Figure 1.
[95]Figure 1
[96]Open in a new tab
Predicting compounds active as single agents and in combination with
gemcitabine against pancreatic cancer (PANC-1) cells
(A) Gene expression profiles of compounds in LINCS database treated on
different cell lines with different durations and pancreatic cancer
cells are used as input to the method and annotated with dysregulated
pathways. Next, correlation score of these pathway signatures is
calculated on those pathways that are most enriched in pancreatic
cancer cells. Finally, the compounds are rank ordered based on their
correlation scores.
(B) For predicting compound combinations, the single agent results were
used and the first instance of gemcitabine in the top rank-ordered
single agents was taken. Next, pathways were identified where
gemcitabine instances failed to reverse the disease signature. Then,
among top results, it was searched for another single agent that
reversed the pathways of the first and second instances of gemcitabine.
This gave rise to Score1 and Score2 and novel compounds were selected
for experimental validations.
Results
Prediction and experimental validation of single compounds against PANC-1
cells
Using the transcriptional drug repositioning approach described in
[97]STAR Methods we interrogated gene expression profiles of 20,413
compounds in LINCS ([98]Subramanian et al., 2017) which were applied to
77 different cell lines using the disease gene expression profiles from
the PANC-1 pancreatic cancer cell line ([99]Gysin et al., 2012),
compared with human pancreatic ductal epithelial cells. Among the
highest scoring compounds, two instances of gemcitabine were ranked
11^th and 291^st among 201,776 signatures in the LINCS database, which
serves as a retrospective validation of the approach (given gemcitabine
is used in the clinic against pancreatic cancer). For prospective
validation, candidate compounds were identified that were predicted to
have growth inhibition effect in the PANC-1 pancreatic cancer cell
line, which was subsequently validated experimentally in PANC-1 growth
inhibition assays. [100]Table 1 lists the criteria for the selection of
each compound along with their experimentally derived GI[50] and GI[90]
values. GI[90] values were included as PANC-1 is a highly resistant
cell line. Among the 29 compounds which have been predicted to inhibit
growth of PANC-1 cells, 18 (58%) showed GI[50] values below 10 μM (see
[101]Table 1). Among those, BMS-387032 (GI[50] = 114nM, GI[90] =
218nM), teniposide (GI[50] = 546nM, GI[90] = 4,371nM) and actinomycin D
(GI[50]<1nM, GI[90] = 4nM) were active in nanomolar concentrations and
low GI[90] values. (We do not propose all of these compounds as
potential therapies, but they were found to be true positives purely in
the context of the hypothesis we set out to validate.) For comparison
purposes, the clinically used pancreatic cancer drug, gemcitabine,
exhibited a GI[50] of 152nM, but no GI[90] value because it does not
reach 90% inhibition even at maximal tested concentrations in PANC-1
cells. Hence, the algorithm presented here was successful in
identifying active single agents in the first part of the validation
performed in this study.
Table 1.
Prediction and experimental results for selected single agents (‘S’)
and compound combinations (‘C’) according to the different synergy
hypotheses (Score1, Score2, Res-score and selected)
Compound Cell line Single/comb
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Score type
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Comb score
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Single score
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GI50 nM.
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GI90 nM
__________________________________________________________________
Loewe
__________________________________________________________________
Bliss
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ZIP
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ZIP pValue
__________________________________________________________________
HSA
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HAS pValue
__________________________________________________________________
Loewe
__________________________________________________________________
Loewe pValue
__________________________________________________________________
Bliss
__________________________________________________________________
Bliss pValue
__________________________________________________________________
Ic501 nM
__________________________________________________________________
Ic502 nM
__________________________________________________________________
Css
__________________________________________________________________
Prediction score GraphPad prism Combenefit SynergyFinderplus
Semagacestat PANC-1 C PC NA >10,000 >10,000 27.40 8.90 −3.37 0.24 3.69
0.53 3.00 0.62 −3.99 0.48 44.33 9.00 32.78
Gemcitabine PANC-1 S PC −0.45 152 >10,000 3.40 0.00
Scriptaid PANC-1 S&C Res −0.89 −0.33 3218 >10,000 33.30 10.40 −1.67
0.57 5.83 0.53 5.00 0.53 −2.92 0.67 10,000.00 28.47 61.35
Tacedinaline PANC-1 S&C Res −0.84 −0.39 >10,000 >10,000 26.50 8.00
−1.16 0.85 3.71 0.77 2.88 0.82 −1.96 0.84 5851.86 8.60 37.61
Salmeterol PANC-1 S&C Res −0.84 −0.33 4248 >10,000 14.20 2.10 −5.09
0.08 3.40 0.52 2.88 0.55 −5.49 0.28 5979.96 12.46 34.17
Triclosan PANC-1 C Res −1.00 −0.28 >10,000 >10,000 13.10 5.20 1.51 0.60
2.48 0.78 2.54 0.77 1.15 0.84 0.00 14.69 20.96
Entinostat PANC-1 S&C Res/Score1 −0.78 −0.45 11,007 16,626 51.50 26.70
11.17 0.10 22.01 0.12 22.17 0.12 11.13 0.19 20,000.00 13.63 73.02
Entinostat HPAF2 NA −6.91 0.16 −1.37 0.81 −1.84 0.77 −7.83 0.16
20,000.00 3.58 66.70
Entinostat K8484 NA −1.28 0.76 0.08 0.99 −1.92 0.78 −3.66 0.59 5921.93
1.78 71.90
Entinostat MIA PaCa2 NA 1.27 0.78 1.15 0.89 −1.17 0.86 −0.20 0.97
1000.00 4.13 66.08
Entinostat TB32048 NA −1.05 0.76 2.29 0.76 −1.09 0.81 −1.82 0.66
4734.84 3.97 77.38
Saracatinib PANC-1 S&C Score1 −0.66 −0.33 >10,000 >10,000 40.00 22.10
5.06 0.32 7.99 0.52 6.97 0.56 4.59 0.52 10,000.00 20.29 43.58
Thioridazine PANC-1 C Score1 −0.73 −0.21 9318 16,163 34.10 20.30 5.79
0.30 13.07 0.21 12.54 0.22 5.44 0.38 10,000.00 24.32 58.56
Loperamide PANC-1 S&C Score1 −0.71 −0.36 3200 >10,000 23.20 12.10 11.39
0.06 19.77 0.02 18.05 0.03 11.35 0.11 10,000.00 11.48 56.55
RS-17053 PANC-1 S&C Score1 −0.83 −0.39 3154 5275 1.60 0.50 −3.10 0.54
2.76 0.77 0.51 0.96 −4.22 0.55 4307.93 11.95 60.92
TW-37 PANC-1 C Score2 −0.81 −0.26 372 2376 15.10 1.20 −3.23 0.63 4.93
0.63 3.43 0.72 −3.77 0.65 733.39 12.03 61.11
Digoxin PANC-1 C Score2 −0.83 −0.21 25 66 6.70 5.30 −1.09 0.83 3.31
0.63 1.77 0.77 −1.57 0.82 30.38 18.88 49.13
Maprotiline PANC-1 S&C Selected CM −0.35 >10,000 >10,000 12.10 5.30
−2.27 0.65 2.62 0.71 2.39 0.74 −3.32 0.58 10,000.00 6.26 32.32
Racecadotril PANC-1 S&C Selected CM&FoM −0.36 >10,000 >10,000 31.30
24.00 5.28 0.23 6.93 0.31 6.51 0.38 5.47 0.25 202.42 2.34 29.39
Y-134 PANC-1 S&C Selected CM&SS −0.41 >10,000 >10,000 18.40 6.00 −1.42
0.77 5.19 0.60 3.70 0.71 −2.10 0.73 10,000.00 17.91 37.19
Dibenzazepine PANC-1 S&C Selected HS&FrM −0.45 8975 13,108 20.60 7.30
1.85 0.71 8.53 0.27 7.71 0.42 2.12 0.72 884.54 5.86 58.47
Palbociclib PANC-1 S&C Selected MK&AB&HS −0.45 6285 >10,000 7.00 2.90
−0.92 0.93 6.91 0.55 5.47 0.65 −1.17 0.93 10,000.00 9.87 48.63
Actinomycin D PANC-1 S −0.31 <1 4 18.70 1.30 −2.77 0.40 3.34 0.67 2.46
0.73 −3.84 0.56 1.26 20.55 60.58
L-168 PANC-1 S −0.28 >10,000 >10,000 17.20 5.00 −6.55 0.22 −0.49 0.95
−1.20 0.90 −8.12 0.12 10,000.00 18.69 42.31
Clofarabine PANC-1 S −0.40 >10,000 >10,000 15.40 8.40 −0.30 0.97 4.70
0.46 3.83 0.63 0.40 0.97 3472.36 5.37 33.33
BX-795 PANC-1 S −0.40 1619 9207 13.80 0.30 2.95 0.68 9.43 0.42 8.36
0.45 3.06 0.78 1258.68 24.86 48.93
Teniposide PANC-1 S −0.45 546 4371 11.60 1.80 −6.96 0.20 2.12 0.73 0.76
0.90 −7.10 0.28 314.06 14.53 56.14
Ciclopirox PANC-1 S −0.40 1002 1134 11.00 3.90 2.48 0.69 9.57 0.25 7.04
0.41 2.55 0.77 1292.55 28.12 63.24
Ursolic acid PANC-1 S −0.16 >10,000 >10,000 11.00 2.30 −10.80 0.01
−0.96 0.94 −1.83 0.87 −12.99 0.07 2.13 12.53 33.81
Phloretin PANC-1 S −0.40 >10,000 >10,000 8.10 8.10 0.75 0.81 1.16 0.83
1.09 0.82 0.75 0.88 10,000.00 6.90 24.92
BMS-387032 PANC-1 S −0.38 114 218 7.70 1.90 −7.61 0.28 −0.83 0.90 −1.29
0.86 −9.73 0.21 155.43 7.78 53.68
Serdemetan PANC-1 S −0.42 3094 >10,000 7.70 3.00 −0.38 0.94 8.96 0.17
5.85 0.37 −0.31 0.96 10,000.00 10.15 49.56
Leelamine PANC-1 S −0.31 7567 9462 7.20 3.50 −7.80 0.42 1.94 0.87 0.98
0.95 −10.75 0.20 10,000.00 14.10 52.10
STK525924 PANC-1 S −0.43 6684 11,519 7.20 1.40 12.81 0.06 7.57 0.42
6.71 0.45 14.45 0.09 7012.23 8.15 31.38
Medroxyprogesterone PANC-1 S −0.36 >10,000 >10,000 2.10 0.10 −5.39 0.25
2.56 0.67 2.84 0.68 −7.24 0.26 10,000.00 6.38 30.35
[102]Open in a new tab
Compounds that were selected to be active as single agents and not
based on any synergy scoring hypothesis were marked as ‘S’ and hence
did not have any synergy prediction score (Score Comb). However, these
compounds were tested in combination with gemcitabine experimentally
for comparison reason and synergy scores were calculated for them.
Compounds that were predicted to be active in combination were marked
as ‘C’. Single score is predicted score for the compound to be active
as single agent on PANC-1 cells and Comb Score is predicted score for
the compound to be active in combination with gemcitabine based on any
of the synergy scoring hypothesis identified in Score Type. GI[50] and
GI[90] values for each compound on PANC-1 cells were identified using
growth inhibition assays in vitro. Leowe and Bliss synergy scores were
calculated from experimental data of combination of the compound with
gemcitabine using Combenefit software. ZIP, HAS, Loewe and Bliss
synergy scores were calculated also using SynergyFinder Plus web tool.
pvalues for each synergy metric, IC50 and cell sensitivity score for
combination (CSS) is also provided based on SynergyFinder Plus tool.
Compound selection criteria is discussed in the Prospective
experimental validation of predicted synergistic compound combinations
section in the Results. Entinostat-gemcitabine combination which was
the highest synergistic pair was also tested on four other pancreatic
cancer cells (HPAF2, K8484, MIA PaCa2, TB32048).
Evaluation of synergy hypothesis on a pathway mechanistic level
We next selected compounds to be combined with gemcitabine which we
predicted to be synergistic against the PANC-1 cell line. Given
different instances of gemcitabine gave rise to somewhat different gene
expression profiles, the synergy scores were calculated separately for
the first instance (Score1), the second instance (Score2), and for the
resistance profile of the PANC-1 over other cell lines (Res-score).
The first instance of gemcitabine was ranked 11^th against the disease
query, which was derived using a concentration of 80nM applied on A375
cells for 6 h. The second instance of gemcitabine was ranked 291^st,
for a concentration of 37nM, which was applied on MCF7 cells for 24 h.
Given the large number of 201,776 profiles available, this represents
the current therapy being ranked in the top 0.15%. For the first
instance of gemcitabine and using Score1, we observed ([103]Figure 1B)
that it, as intended, reversed many enriched pathways (ACPs) in the
PANC-1 pancreatic cancer cell line, including (using NCBI
BioSystems([104]Geer et al., 2010) annotations) PLK1 signaling events,
Resolution of Sister Chromatid Cohesion, Kinesins, Cell Cycle,
Phosphorylation of Emi 1, and the Hedgehog Signaling Pathway
([105]Figure 2A; [106]Table S1). However, the transcriptomics signature
of the compound was showing an (undesired) correlation with the disease
in five other pathways (Correlated Pathways; CPs), namely Notch
signaling, Superpathway of steroid hormone biosynthesis,
Calcineurin-regulated NFAT-dependent transcription in lymphocytes,
Chromosome Maintenance, and Metabolism pathways ([107]Figure 2B). Among
these pathways, Notch signaling has been previously identified by
literature for its importance in gemcitabine resistance mechanisms,
consistent with our analysis ([108]Wang et al., 2009). Based on the
synergy hypothesis formulated above, these pathways were hypothesized
to be the CPs of the first instance of gemcitabine, which hence needed
to be reverted by a second compound to achieve synergy, and to
desensitize PANC-1 cells to gemcitabine treatment.
Figure 2.
[109]Figure 2
[110]Open in a new tab
Pathway signatures of compound combinations predicted to be
synergistic, compared to those of PANC-1 cells
(A) Pathways where the gemcitabine signature is anticorrelated with the
PANC-1 transcriptomic signature (Anticorrelated Pathways; ACPs). The
second compound should also anticorrelate with these pathways from the
disease signature side for having significant synergy score according
to the synergy hypothesis employed in this work. Compounds marked with
∗ are shown as they had retrospective validation and the rest of the
compounds were tested experimentally. Bliss and Loewe synergy scores
are shown where experimental data was generated in this work for
combination of the compound with gemcitabine in PANC-1 cells.
(B) Pathways where the gemcitabine signature for instance1 is
correlated with the PANC-1 cells signature (Correlated Pathways; CPs).
The second compound should anticorrelate with these pathways (and,
hence, counteract the undesired part of transcriptional dysregulation
introduced by the compound) for synergy to be observed. Numbers are
z-scores calculated after enrichment analysis.
(C) CPs of second instance of gemcitabine and how these are reversed by
other selected compounds.
(D) Gene expression of pancreatic cancer cells on set of CP pathways
for both instance 1 and 2 of gemcitabine are compared.
(E) A few pathways were selected, and compounds based on reversal of
each of these selected pathways were selected. (Color coding of
enrichment scores are consistent in all heatmap plots. Pathway
signatures are based on pathway enrichment scores calculated for
compounds in LINCS.)
(F) Pathways used for calculating Score1, Score2 and Res-score are
presented for five pancreatic cancer cell lines.
For the second instance of gemcitabine used for the calculation of
Score 2, we observed an undesired correlation of the following pathways
with the PANC-1 signature (CPs): FOXA1 transcription factor network,
MAPK targets/Nuclear events mediated by MAP kinases, TGF Beta Signaling
Pathway, and Signaling by Activin. Among these selected pathways, MAPK
has previously been identified to be related to gemcitabine resistance
mechanisms ([111]Figure 2C)([112]Fryer et al., 2011). Hence Score2 rank
orders compounds based on anticorrelation to the above subset of the
transcriptomic signature of disease, which was not yet sufficiently
attenuated by gemcitabine applied in isolation.
As for the calculation of the PANC-1 specific Res-score, we aimed to
identify compounds to be paired with gemcitabine that show synergy
specifically in the PANC-1 cell line, as it is known to be more
resistant to gemcitabine therapy than the BXPC3, Mia Paca-2, HPAFII and
HS766T cell lines ([113]Espey et al., 2011; [114]Fryer et al., 2011).
For this purpose, first, CPs of gemcitabine instances 1 and 2 were
identified, which represent part of the transcriptomic signature that
we deemed to be related to resistance (as above for the Score1 and
Score2 scores). Second, pathways that were specifically dysregulated in
the PANC-1 signature, compared to the other pancreatic cancer cell
lines BXPC3, Mia Paca-2, HPAFII, and HS766T, were selected
([115]Figure 2F), which were hence hypothesized to be of more relevance
for resistance of PANC-1 to treatment, compared with the other cell
lines (‘resistance pathway signature’). This pathway set included
(according to NCBI BioSystems) the Notch signaling pathway, the
Superpathway of steroid hormone biosynthesis, and MAPK targets/Nuclear
events mediated by MAP kinases. Five compounds were selected based on
reversal of these three pathways ([116]Figure 2D). Moreover, five
compounds were selected based on only targeting a few of pathways in
the pathway sets above ([117]Figure 2E) termed as selected score. All
shortlisted compounds based on each scoring system are listed in
[118]Table1. All pathways that contributed in each scoring system are
listed in [119]Table S2.
We can see that all three of our synergy hypotheses, according to
Score1, Score 2, and Res-score, give plausible mechanistic hypotheses
for the selection of compounds for pairing with gemcitabine in order to
achieve synergy, namely, by targeting pathways known to be involved in
resistance in this cell line.
Compound combination selection and retrospective validation
The highest scoring compounds to show synergy with gemcitabine,
according to the Score1, Score2, and Res-score as outlined above (and
in more detail in [120]STAR Methods) are listed in [121]Table S3, with
scores closest to −1 indicating highest predicted synergy. Compounds
that have a high negative Score1 with literature support for efficacy
in combination with gemcitabine (although this does not necessarily
represent synergy) include berbamine (Score1 = −0.88) and masitinib
(Score1 = −0.76). Berbamine is known to improve cytotoxicity of
gemcitabine in pancreatic cancer cell lines ([122]Jin and Wu, 2014),
while the tyrosine kinase inhibitor masitinib sensitizes
gemcitabine-refractory pancreatic cancer cell lines in vitro as well as
in phase2 clinical trials ([123]Humbert et al., 2010). On the other
hand, for Score2, gossypol (Score2 = −0.66) and menadione (Score2 =
−0.65) were exhibiting highly negative values and were also supported
by literature, since administration of gossypol combined with
gemcitabine has been shown previously to synergistically inhibit growth
of gemcitabine-resistant pancreatic cancer cells with high BCL-2
expression ([124]Wong et al., 2012).
Compounds with highly negative Res-score include triptolide
(Res-score = −0.98 and −0.96 for two distinct instances), panobinostat
(Res-score = −0.98), belinostat (Res-score = −0.98), fluvastatin
(Res-score = −0.95) and trichostatin-a (−0.96). Out of those compounds,
triptolide ([125]Wang and Lin, 2013) (in vitro) as well as
belinostat([126]Chien et al., 2014), fluvastatin([127]Bocci et al.,
2005) and trichostatin-a([128]Donadelli et al., 2007) (both in vitro
and in vivo) have previously exhibited a synergistic effect with
gemcitabine in pancreatic cancer cells. Belinostat and panobinostat
individually inhibited growth of 6 out of 14 pancreatic cancer cell
lines, including PANC-1, in previous work ([129]Chien et al., 2014).
Trichostatin-A and gemcitabine, on the other hand, synergistically
inhibited growth and induced apoptosis in four pancreatic cancer cell
lines and also reduced the tumor mass to 50% of its size in nude mice
xenografts ([130]Donadelli et al., 2007). Triptolide was found to
enhance apoptosis of gemcitabine on the PANC-1 and AsPC-1 pancreatic
cancer cell lines in vitro ([131]Wang and Lin, 2013). On the
mechanistic level, Belinostat, alone and in combination with
gemcitabine, also significantly decreased growth and increased
apoptosis of human pancreatic cancer tumors grown in immune deficient
mice ([132]Chien et al., 2014). Additionally, fluvastatin has been
shown to induce apoptosis in the MIAPaCa-2 pancreatic cancer cell line,
and to enhance the effect of gemcitabine synergistically ([133]Bocci
et al., 2005). Combined administration of fluvastatin with gemcitabine
on MIAPaCa-2 mouse xenografts has in a previous study almost completely
suppressed and significantly delayed relapse of tumor growth
([134]Bocci et al., 2005). Hence, we can see that there is significant
literature support for the different synergy scores evaluated here, in
particular for the Res-score, both on an empirical and a mechanistic
level.
Prospective experimental validation of predicted synergistic compound
combinations
16 compounds predicted to show synergy with gemcitabine (according to
Score1, Score2, and/or Res-score) were next selected for prospective
experimental validation as listed in [135]Table 1. An additional 13
compounds had been selected for their predicted activity as single
agents, but not in combination, and were screened also in combination
with gemcitabine as a baseline for comparison with the above scoring
system (which is a rather high baseline, since compounds were selected
for individual activity in the first instance already). We also used a
Gamma secretase/Notch pathway inhibitor (semagacestat) as a positive
control of synergy with gemcitabine, since Gamma-secretase inhibitors
have been shown previously to be synergistic with gemcitabine in
pancreatic cancer mouse models ([136]Cook et al., 2012).
Compounds that were selected using Res-score included salmeterol,
scriptaid, tacedinaline, triclosan, entinostat; Score1 led to selecting
entinostat, loperamide, RS-17053, saracatinib and thioridazine; and
Score2 shortlisted digoxin and TW-37. Five compounds were selected
based on a relatively small number of important pathways. These
included racecadotril, maprotiline, dibenzazepine, Y-134, and
palbociclib. Racecadotril was selected based on its effect on the
Chromosome Maintenance pathway (CM) and Folate Metabolism (FoM), while
maprotiline was selected based on reversal of CM. Dibenzazepine was
selected based on Hedgehog Signaling pathway (HS) and Fructose and
Mannose metabolism (FrM). Y-134 was selected based on reversal of CM
and Superpathway of steroid hormone biosynthesis (SS), while
palbociclib was selected due to reversal of MAPK targets/Nuclear events
mediated by MAP kinases (MK) pathway, its effect on the HS pathway, and
strengthening the effect of gemcitabine on the Aurora B signaling (AB)
pathway.
Hence, in total, 30 compounds (16 predicted to by synergistic with
gemcitabine, 13 predicted to be active as single agents, and a positive
control) were experimentally tested in combination with gemcitabine in
pancreatic cancer cells in vitro to evaluate our synergy hypothesis.
Drug combination screening
Among the 16 compounds predicted to have synergy with gemcitabine the
following showed higher synergy score using the Loewe model ([137]Vlot
et al., 2019) ([138]Table 1) with experimental data: entinostat
(SUM_SYN_WEIGHTED output from Combenefit of 51.5), saracatinib (40),
thioridazine (34.1), scriptaid (33.3), racecadotril (31.3),
tacedinaline (26.5), loperamide (23.2), and dibenzazepine (20.6). For
comparison, compounds that were predicted to be active as single agents
but not show synergy with gemcitabine, when tested in combination with
gemcitabine had an average synergy score of 10.15 (standard deviation
of ± 4.7), and the positive control (the Gamma secretase/Notch pathway
inhibitor, semagacestat, which is not active as a single agent itself)
obtained a synergy score of 27.4 in the Loewe model.
We next compared which of our synergy hypotheses has the highest
overall synergy to understand how gene expression data could be used
best to this end, the results of which are shown in [139]Table 2. To
evaluate our method to a background distribution, we have also
experimentally tested all compounds that were predicted to not be
synergistic (and active only as single agents) as a control. In this
relative comparison we found that compounds that scored highest with
Res-score were having on average 2.60 times higher synergy using the
Loewe synergy metric (pvalue = 0.04) and 3.32 times higher synergy
using the Bliss synergy metric (pvalue = 0.08) compared to compounds
that were predicted to be active only as single agents. Score1 was
leading to 2.82 and 5.18 times higher synergies in the Loewe and Bliss
synergy metrics on average, respectively (pvalues= 0.04 and 0.02),
compared to predicted single agent compounds. Compounds selected using
Score2 could not be evaluated using this method because the number of
selected compound combinations were limited and the resulting p values
were not significant. Loewe, Bliss, ZIP, and HAS synergy metrics
calculated using SynergyFinder Plus([140]Zheng et al., 2021b) tool were
also compared for combinations and single agents. It shows 4.4 times
higher synergy for Score1 and 2.59 times higher synergy for Res-score
using Loewe Synergy metric. 3.43 and 1.98 times higher synergy is
observed using HAS for Score1 and Res-Score respectively. Score-1 and
Res-score have hence both been validated with respect to their ability
to select synergistic compound combinations based on the data used in
this study.
Table 2.
Comparison of the different scoring systems used for selecting compound
combinations and their ability to identify synergistic compound pairs
Method
__________________________________________________________________
Lowe
__________________________________________________________________
Bliss
__________________________________________________________________
Loewe
__________________________________________________________________
Bliss
__________________________________________________________________
ZIP
__________________________________________________________________
HAS
__________________________________________________________________
Software
__________________________________________________________________
Combenefit
__________________________________________________________________
SynergyFinderplus
__________________________________________________________________
AVG TTEST DIV AVG TTEST DIV AVG TTEST DIV AVG TTEST AVG TTEST AVG TTEST
DIV
Res-score 27.72 0.04∗ 2.6 10.48 0.08 3.32 7.10 0.32 2.59 0.38 0.37 0.95
0.35 7.49 0.38 1.98
Score1 30.08 0.04∗ 2.82 16.34 0.02∗ 5.18 12.05 0.17 4.40 5.66 0.11 6.06
0.09 13.12 0.14 3.48
Score2 10.9 0.48 1.02 3.25 0.49 1.03 2.60 0.92 0.95 −2.67 0.90 −2.16
0.96 4.12 0.81 1.09
Selected 17.88 0.08 1.68 9.1 0.1 2.89 5.15 0.10 1.88 0.20 0.24 0.50
0.23 6.04 0.15 1.60
Single agents 10.67 3.15 2.74 −2.99 −2.27 3.77
[141]Open in a new tab
Synergy scores of the compound combinations to be synergistic, compared
with the compounds predicted to be active as single agents are
provided. AVG shows average synergy scores of all compounds selected in
each scoring system category. TTEST compares significance of scores of
predicted compounds in each scoring category versus predicted single
agents. DIV provides ratio of synergy scores of predicted compound
combinations versus single agents. Compounds that were predicted to be
synergistic using the Res-score were on average 2.60 times more
synergistic using the Lowe synergy metric (pvalue = 0.04), and 3.32
times more synergistic using the Bliss synergy metric (pvalue = 0.08).
Score1 was also leading to 2.82 and 5.18 times higher synergies in the
Lowe and Bliss synergy metrics using Combenefit software, respectively
(pvalues= 0.04 and 0.02). The evaluation of the Score2 selection was
non-conclusive, as only two combinations were selected, and the
resulting pvalue is not significant. Hence, Score1 and Res-score are
reliable scoring system for synergy prediction. Loewe, Bliss, ZIP, and
HAS synergy metrics calculated using SynergyFinder Plus tool were also
compared for combinations and single agents. It shows 4.4 times higher
synergy for Score1 and 2.59 times higher synergy for Res-score using
Loewe Synergy metric. 3.43 and1.98 times higher synergy is observed
using HAS for Score1 and Res-Score. The score ratios (DIV) is not
provided for Bliss and ZIP as they have negative values for single
agents which represents antagonism for single agents. Because the TTEST
for SynergyFinder Plus metrics does not show significant values, it is
better to rely on Combenefit scores in this case.
To evaluate synergy of experimentally tested compound pairs on PANC-1
cell line, dose-response matrices, and synergy metrics for top five
most synergistic compounds with gemcitabine, namely entinostat,
loperamide, thioridazine, saracatinib and scriptaid are provided in
[142]Figure 3 and next five most synergistic combinations, namely
palbociclib, racecadotril, STK525924, BX795, and semagacestat, are
provided in [143]Figure 4. [144]Figures 3 and [145]4 compares
dose-response, Bliss, HAS, Loewe, ZIP synergy metrics in 2D and Loewe
in 3D for top ten most synergistic compound combinations generated
using SynergyFinder Plus ([146]Zheng et al., 2021b) web tool.
Figure 3.
[147]Figure 3
[148]Open in a new tab
Cytotoxicity assay of the most synergistic combinations
(A–E) PANC-1 cells were treated with increasing doses of gemcitabine
(x-axis) versus predicted synergistic compounds (y-axis) in an 8×8
concentration checkerboard format for 24 h. Cell viability was
determined by measuring the total protein content using the
sulforhodamine B assays and percentage growth inhibition compared with
control was provided in the matrix in the right column. From left to
right dose-response, Bliss, HAS, Lowe, ZIP synergy metrics in 2D
heatmap format and Loewe synergy metric in 3D format were generated
using SynergyFinder web tool. Darker blue color represents high synergy
for each concentration of each compound in the combination. Synergy and
toxicity are presented for combination of gemcitabine with (A)
Entinostat, (B) Loperamide, (C) Thioridazine, (D)Saracatinib and
(E)Scriptaid. Data are represented as mean of three samples.
Figure 4.
[149]Figure 4
[150]Open in a new tab
Cytotoxicity assay of the most synergistic combinations
(A–E) From left to right dose-response, Bliss, HAS, Lowe, ZIP synergy
metrics in 2D heatmat format and Loewe synergy metric in 3D format for
gemcitabine combination with (A) Palbociclib, (B)Racecadotril,
(C)STK525924, (D) BX795 and (E)Semagacestat on PANC-1 cells are
visualised. Output is generated using SynergyFinder Plus webTool. Data
are represented as mean of three samples.
In all of these experiments, gemcitabine alone on its maximal doses
reached cytotoxicity of only 46%. As mentioned before semagacestat is a
gamma-secretase inhibitor and gamma-secretase inhibitors are known to
be synergistic with gemcitabine in pancreatic cancer cells ([151]Cook
et al., 2012) and hence this compound was chosen as a positive control.
This compound does not show any synergy with gemcitabine using Bliss
and ZIP models on PANC-1 cells ([152]Figure 4E) and just a moderate
synergy in Loewe and HAS metrics. Its maximal doses increases 46%
cytotoxicity of gemcitabine to only 47%. All other visualized compounds
show stronger synergy than semagacestat with gemcitabine in PANC-1
cells. Entinostat shows the highest synergy levels in PANC-1 cells.
However, synergy in entinostat-gemcitabine pair ([153]Figure 3A)
occures at high doses of 5000–7000nM which increases cytotoxicity of
gemcitabine from 45% to 78% and 86% in these doses and at a dose of
10,000nM to 89%. Loperamide ([154]Figure 3B) shows synergy in a wide
range of doses of this compound and gemcitabine with cytotoxicity
increasing from 32% to 71%. Synergy of thioridazine based on HSA metric
([155]Figure 3C) occures in doses of 100nM and 300nM and 100nM of
gemcitabine and cytotoxicity in these doses increases from 22% to 42%
and 52% respectively. Its maximal doses increases cytotoxicity to 81%.
Saracatinib ([156]Figure 3D) at low dose of 300nM and 100nM of
gemcitabine increases cytotoxicity of PANC-1 cells from 41% to 63% and
in maximal doses cytotoxicity reaches 71%. Scriptaid ([157]Figure 3E)
is mostly synergistic in high doses of 3000–10,000nM which increases
cytotoxicity of gemcitabine from 44% to 70% and 90% respectively.
Palbociclib ([158]Figure 4A) shows synergy with gemcitabine in a wide
range of doses from 300nM onwards with cytotoxicity increasing from 39%
(gemcitabine alone) to 70% in its maximal doses.
Racecadotril ([159]Figure 4B) shows moderate synergy in a wide range of
doses increasing cytotoxicity from 39% to 54%. Racecadotril is inactive
as a single agent with cytotoxicity of only 8% at dose of 10,000nM.
STK525924 ([160]Figure 4C) is mostly synergistic at dose of 3000nM and
30nM of gemcitabine, increasing cytotoxicity from 28% to 55%. STK525924
also as a single agent is quite inactive with cytotoxicity of only 5%
on its maximal dose of 10,000nM. In case of BX-795 ([161]Figure 4D),
synergy occurs mainly on its low doses of 100–300nM with cytotoxicity
increasing from 38% to 57% in doses as low as 100nM with 300nM of
gemcitabine. Its maximal cytotoxicity reaches 79%. 3D views of all
synergy metrics for the top 10 synergistic compounds are provided in
[162]Figures S1–S14 to complement visualization in [163]Figures 3 and
[164]4. [165]Figures S1–S5 particularly shows the most synergistic
compound combination gemcitabine-entinostat on PANC1, HPAFII, K8484,
MIA PaCa-2, and TB32048. It is shown that this combination is only
synergistic on PANC-1 cells.
To have a better overview of all tested compound combinations, synergy
score (Bliss and Loewe) and cell sensitivity of all compound pairs are
compared in [166]Figure 5. HSA and ZIP metrics versus cell sensitivity
are provided in [167]Figure S15. Bliss model ([168]Figure 5A) marks
combinations of STK525924, loperamide, entinostat on PANC-1, and
thioridazine as the highest synergistic compounds among which
entinostat, thioridazine, and loperamide show the highest combination
cell sensitivity. All entinostat-gemcitabine instances on all five
mentioned cell lines show the highest sensitivity but synergy occurs
only in PANC-1 cells and highest sensitivity occurs in the TB32048 cell
line. Loperamide, entinostat on PANC-1, and thioridazine are marked as
highest synergistic in Loewe ([169]Figure 5B), HSA ([170]Figure S15A),
and ZIP ([171]Figure S15B) models. Loewe ([172]Figure 5B) and HSA
([173]Figure S15A) models doe not mark STK525924 synergistic at all but
it is highly synergistic based on Bliss ([174]Figure 5A) and ZIP
([175]Figure S15B) models.
Figure 5.
[176]Figure 5
[177]Open in a new tab
Synergy score versus sensitivity score for all experimentally validated
compound combinations
(A and B) (A)Bliss and (B) Loewe synergy metrics versus sensitivity
score of experimentally tested compound combinations with gemcitabine
was visulalised using SynergyFinder Plus web application. All pairs are
tested on PANC-1 except entinostat-gemcitabine that is tested on five
pancreatic cancer cell lines, namely PANC1, HPAFII, K8484, MIA PaCa-2,
and TB32048, as indicated in the figure. Data are represented as mean
of three samples.
The combination of entinostat and gemcitabine ([178]Figure S16) shows
the highest synergy and cell growth inhibition at sub-GI[50]
concentrations in the PANC-1 cell line ([179]Figures 3A and [180]S16E),
but it did not show synergy in other human pancreatic cancer cell lines
(MIA PaCa-2 and HPAF-II, [181]Figures S16A and S16C) and mouse
pancreatic cancer cells (K8484 and TB32048, [182]Figures S16B and
S16D). This is in agreement with the selection criterion we used for
the Res-score because the aim of Res-score was to identify pathways
that are specific in PANC-1 (the most resistant cell line to
gemcitabine treatment) and to find synergistic combinations for this
pathway set.
Entinostat and gemcitabine act synergistically by inducing apoptosis
To understand the effects of entinostat combined with gemcitabine on
the growth inhibition of PANC-1 cells, the IncuCyte system was used to
obtain real time data on cell growth. It was found that the growth rate
was significantly reduced by the combination, compared to either single
agent ([183]Figure 6A). Furthermore, the long-term clonogenic assays
confirmed a greater inhibition in the combination than with either of
the single agents ([184]Figure 6B), and elevation of cleaved PARP,
cleaved caspase 3, and γH2AX on Western blots demonstrated the
induction of apoptosis by the combination ([185]Figure 6C). Hence, we
conclude that the combination of entinostat and gemcitabine acts
synergistically by inducing apoptosis in a more efficient manner than
either agent alone.
Figure 6.
[186]Figure 6
[187]Open in a new tab
IncuCyte time-lapse imaging, clonogenic assay, and Immunoblotting for
apoptosis
(A) Cell proliferation in PANC-1 cells treated as indicated at
synergistic concentrations (30nM Gemcitabine, 7μM Entinostat).
Normalized confluency change was also measured every 3 h over a period
of 84 h for each single agent and the combination. Confluency was
significantly reduced in the combination group after 72 h. It was found
that the growth rate was significantly reduced by the combination,
compared to either single agent, with clear presence of apoptotic
bodies. Data represents mean ± SD of 3 replicates, ∗ indicates p < 0.05
and ∗∗ indicates significance at p < 0.01 (based on the Kruskal–Wallis
non-parametric test). Scale bar (100μm)
(B) Clonogenic assays. It can be seen that the combination of
gemcitabine and entinostat showed higher capacity of cells to produce
progeny compared to single agent-treated groups. The number of
surviving cells drops significantly in the combination compared to
using each compound individually. Data are represented as mean ± SD,
n = 3. ∗p ≤ 0.05.
(C) PANC-1 cells were incubated with synergistic concentrations of 30nM
Gemcitabine (GEM) and 7μM Entinostat (E) and total proteins were
extracted after 24, 48, and 72 h for Western blotting. It can be seen
that cleaved PARP and cleaved caspase 3 were elevated by the drug
combination, indicating apoptosis at 48 and 72 h γH2AX, a marker of DNA
damage and (later) or apoptosis was elevated by gemcitabine by 24 h but
was enhanced by the combination. Protein expression of apoptotic
markers, cleaved-PARP and cleaved-caspase-3 are significantly increased
by the combination of gemcitabine and entinostat over time. The
increase in protein expression of ɣH2AX indicates that DNA damage along
with apoptosis is caused by this combination.
Entinostat in combination with gemcitabine causes increased
cytotoxicity via complementary mechanisms, where entinostat arrests
cells in the G1 phase and gemcitabine in the S phase.
To study cell proliferation dynamics and effects on cell division in
further detail, the FastFUCCI (Fluorescent Ubiquitination-Based Cell
Cycle Indicator) system([188]Koh et al., 2016) and live cell imaging
was used over a period of three days. In [189]Figure 7 it can be seen
that of the 80 DMSO-treated PANC-1 control cells 76 underwent one to
three cell division processes, resulting in 369 cells after three days
of observation. In the presence of gemcitabine, PANC-1 cells converted
from the G1 phase (indicated in red, visualizing Cdt1) to the S and G2
phases (indicated in green, visualizing geminin), with the total cell
number still increasing from 76 to 117, and eventually resulting in S
phase arrest in agreement with earlier observations ([190]Figure 7B;
arrested green fluorescent cells) ([191]Shi et al., 2001). On the other
hand, entinostat alone was found to arrest cells in the G1 phase after
at least one cell division. Only a few cells were non-viable with
entinostat treatment and 64% (65 out of 102 cells) entered mitosis. Six
of them failed to divide at mitosis but still proceeded into G1 phase
afterward. In contrast, the combination of gemcitabine and entinostat
dramatically increased cell death to over 83%. Since each drug
interfered at different times of the cell cycle, combination-treated
cells only in a few cases survived after a period of three days. In
this section, we were able to show that different complementary
mechanisms contribute to the observed compound synergy.
Figure 7.
[192]Figure 7
[193]Open in a new tab
Entinostat/Gemcitabine increase cellular cytotoxicity in PANC-1 cells
(A) In the control group, FastFUCCI PANC-1 cells underwent normal cell
division processes. In presence of gemcitabine, S phase arrest was
observed, while Entinostat blocked the G1 phase at a late time point.
Fifteen cells were killed with Entinostat treatment and 64% (65 out of
102 cells) seemed to enter mitosis, but some of them failed to split at
mitosis and still go back into G1. On the other hand,
Gemcitabine/Entinostat dramatically increased cell death given more
than 80% of cells died. Since each drug interfered at a different time
of cell cycle, combination-treated cells barely survived after three
days and synergy was observed. (Divisions on Day 0 was from Day 0–1,
Divisions on Day 3 was from Day 0–3.)
(B) Representative images of FastFUCCI PANC-1 cells treated for 72 h as
indicated. S/G2-M cells (green) from G1 cells (red) based on
fluorescently tagged forms of geminin and Cdt1, respectively. Scale
bar, 50μm.
Transcriptional level mechanism of action of synergistic compound pairs based
on LINCS data
In order to rationalize the synergy hypotheses used for selecting
compound combinations, we discuss the induced gene expression changes
and pathway signatures according to LINCS data in this section in more
detail for the most synergistic compounds paired with gemcitabine,
namely entinostat, thioridazine, and loperamide.
Entinostat was predicted to have a highly negative Res-score and
Score1, reflecting anticorrelation to the gemcitabine signature in the
Res-score pathway set (the part specific to PANC-1 cells) and Score1
(the part derived from the first gemcitabine instance); see
[194]Figure 2. In the Superpathway of steroid hormone biosynthesis
pathway HSD17B11 is upregulated by entinostat but downregulated by
gemcitabine. When looking into the underlying data at the individual
gene level, in the Chromosome maintenance pathway BRCA1, RFC5, LIG1,
POLE2, PCNA are downregulated by entinostat, while RFC2, PCNA, RPA2 and
LIG1 are upregulated by gemcitabine ([195]Table S4). As opposed to the
subsequent analyses, no literature evidence for those mechanistic
underpinnings of synergy could be found. Mechanistically, 64% of the
cells enter mitosis in the gemcitabine/entinostat combination
([196]Figure 5), compared to 15% in the combination of gemcitabine with
trichostatin-A([197]Gaulton et al., 2012). This is interesting to
observe, in particular, given it is known that entinostat inhibits
HDAC1 to a lesser extent (with an IC[50] value of 510nM) than
trichostatin A (IC[50] of 20nM) ([198]Gaulton et al., 2012), so based
purely on HDAC1 inhibition the opposite order would be expected. We
found that the entinostat transcriptional profile in LINCS reverses the
CPs (correlated pathways, where the compound does not have the intended
anticorrelation with the disease signature) of gemcitabine in the
chromosome maintenance pathway by downregulating BRCA1, RFC5, LIG1,
POLE2, and PCNA, while only PCNA and POLE2 are downregulated in the
gene expression signature of trichostatin-A ([199]Table S4). Hence, we
hypothesize that the synergistic effect of entinostat with gemcitabine
is not just due to HDAC inhibition and that taking systems data into
account when trying to decipher compound action provides additional
information over only looking at activity values against single
targets.
For loperamide, another drug synergistic with gemcitabine in the PANC-1
experiments, PDGFA has been found to be upregulated in the gemcitabine
signatures and downregulated in the loperamide signature. PDGFA is one
of the drivers of tumor growth, angiogenesis, and metastasis formation
in Pancreatic Ductal Adenocarcinoma (PDA) ([200]Sahraei et al., 2012),
and hence its downregulation plausibly contributes to the synergy
observed. GADD45A is equally upregulated by gemcitabine but
downregulated by loperamide. In this context, in p53 mutation positive
pancreatic cancer patients, GADD45A was upregulated in patients with
lower survival rate, also providing possible support for the
observations in this work ([201]Yamasawa et al., 2002).
Thioridazine downregulates RPA2, FOS, and INPP1, which are upregulated
in the gemcitabine gene signature. High levels of RPA2 expression have
been associated with adverse disease progression and it may also be a
therapeutic potential target for treating colon cancer itself
([202]Givalos et al., 2007), while FOS gene expression has been found
to be associated with progression of pancreatic cancer tumors([203]Guo
et al., 2015). INPP1 is highly expressed in aggressive human cancer
cells and primary high-grade human tumors([204]Benjamin et al., 2014).
Hence, thioridazine reverses the (undesired) CPs of gemcitabine on
RPA2, FOS, and INPP1, which have been previously shown to be related to
adverse patient treatment outcomes, also underpinning the rationale of
synergy of compound combinations validated in this work.
We next compared similarity of gene expression patterns on the
individual gene and pathway level. We found that on the gene level. Two
instances of gemcitabine are provided in the [205]Table S4. The
upregulated genes of the two instances have 6.4% Tanimoto similarity (6
shared genes in 94 total unique genes) and the downregulated genes have
13.6% Tanimoto similarity (12 shared genes in total 88 unique genes).
Hence, the gene signatures of both compounds are very different from
each other. However, pathway signatures of gemcitabine instances 1 and
2 have 82.9% correlation together. This shows that using pathway
signatures we get a more robust signal of specific compounds.
Hence, among the most synergistic compounds we have identified genes
that show anticorrelation between transcriptomic changes induced by
gemcitabine and the paired compound, providing a mechanistic rationale
for those observations that in many cases is also supported by clinical
evidence.
Discussion
In this work, we presented and validated a novel systematic approach to
predict the synergy of compound combinations based on transcriptional
data and pathway annotations. The synergy hypotheses used here were
based on the assumption that the transcriptional activity of a second
compound paired with the main therapy should be anticorrelated to the
disease signature not yet reverted by the main treatment. Thirty-one
compounds were shortlisted in total, among which 13 were predicted to
be active as single agents only. 16 compounds were predicted to show
synergy with gemcitabine while 12 were predicted to be active both as
single agents and in combination. For reference, we had one positive
synergistic control (semagacestat) and one single agent positive
control (gemcitabine).
Among the predicted combinations entinostat showed the highest synergy
(Loewe synergy of 51.5, Bliss synergy of 26.7) with gemcitabine, which
was higher than our positive control (Loewe synergy of 27.4, Bliss
synergy of 8.9). The entinostat-gemcitabine combination was previously
(but after the actual conductance of the current work) identified as a
synergistic combination in pancreatic cancer cells ([206]Ma et al.,
2017). Additionally, further novel synergistic pairs including
gemcitabine/thioridazine and gemcitabine/loperamide were identified.
While the combination of thioridazine with gemcitabine has been
patented before for non-small-cell lung carcinoma (NSCLC) ([207]Huang
et al., 2016), it is novel in pancreatic cancer as suggested from this
work. Thioridazine and its family member penfluridol has been shown to
cause cell death in pancreatic cancer cells via activation of protein
phosphatase 2 (PP2A) and to affect protein expression levels in cell
cycle regulation, apoptosis, and multiple kinase activities ([208]Chien
et al., 2015). Thioridazine inhibits cancer stem cells (CSC) of various
origins such as myeloid leukemia, glioblastoma, and lung, liver,
ovarian and breast cancers ([209]Chan et al., 2018). It has been
effective in vitro by inhibiting CSC spheroid formation and inducing
apoptosis and in vivo by reducing xenograft tumor volume in mice. The
plasma peak concentration (C[Max]) of thioridazine after a single oral
dose of 50 mg reaches 280 nM([210]Chigaev et al., 2015). We have shown
in this work that synergy between thioridazine and gemcitabine occurs
in a wide range of concentrations of both drugs, including at 100nM and
300nM of thioridazine. While after application of 300nM of gemcitabine
on PANC-1 cells 60% of the cancer cells were still surviving, addition
of thioridazine at a concentration of 300nM caused this to drop to 42%.
Hence, thioridazine at its safe dose increases cell death of PANC-1
cells induced by gemcitabine by 18% in absolute terms (or nearly a
third in relative terms), which given the PK considerations described
here may also translate into clinical relevance.
Another combination with gemcitabine suggested from the current work is
loperamide, which is an anti-diarrheal agent and targets the μ-opioid
receptors. Loperamide has been shown to enhance the cytotoxicity of
doxorubicin and reverse multi-drug resistance in breast cancer cells
([211]Zhou et al., 2012). It has also reversed multi drug resistance to
bortezomib in colon cancer cells ([212]Kim et al., 2019). Here, we have
shown that it increases cytotoxicity of gemcitabine to PANC-1 cells and
shows high synergy in a wide range of doses.
Overall, the computational approach presented here has successfully
predicted synergistic compound combinations for pancreatic cancer cells
using the transcriptional response data of single agents and gene
expression profiles of cancer cell lines. The method lends itself to
mechanistic interpretation and it is potentially applicable in other
cancer types and beyond.
Limitations of the study
Predicted combinations were validated for PANC-1 cells and only the
highest synergistic pair was validated on five pancreatic cancer cells
(PANC-1, MIA PaCa-2, HPAF-II, K8484, and TB32048). It was also shown
that this pair (Entinostat-gemcitabine) is selectively synergistic only
on PANC-1 cells as expected due to the type of scoring system chosen
(Res-score). As one limitation of the work, this selectivity on PANC-1
was experimentally proven only for the most synergistic combination and
not the rest of the pairs.
The computational approach is limited to the compound database used
here (LINCS) and cannot be extended to larger compound databases
without having transcriptional data of single agents. LINCS is also
limited to the 77 cell lines used for generating the data. The cell
lines represent the biological space used here for measuring compound
treatment effect which is not comprehensive.
STAR★Methods
Key resources table
REAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
__________________________________________________________________
cleaved PARP Cell Signaling Cell Signaling Technology Cat# 5625,
RRID:[213]AB_10699459
cleaved caspase 3 Abcam Abcam Cat# ab13847; RRID:[214]AB_443014
β-actin Abcam Abcam Cat# ab6276, RRID:[215]AB_2223210
γH2AX Millipore Millipore Cat# 05-636, RRID:[216]AB_309864
IRDye800CW- conjugated antibodies LI-COR
[217]http://www.licor.com/bio/products/reagents/irdye_secondary_antibod
ies/irdye_secondary_antibodies.jsp
IR680CW-conjugated antibodies LI-COR
[218]http://www.licor.com/bio/products/reagents/irdye_secondary_antibod
ies/irdye_secondary_antibodies.jsp
__________________________________________________________________
Chemicals, peptides, and recombinant proteins
__________________________________________________________________
maprotiline Sigma-Aldrich M9651
palbociclib Sigma-Aldrich PZ0383
tacedinaline Sigma-Aldrich C0621
digoxin Sigma-Aldrich D6003
medroxyprogesterone Sigma-Aldrich M6013
loperamide Sigma-Aldrich L4762
salmeterol Sigma-Aldrich PHR1947
triclosan Sigma-Aldrich PHR1338
paclitaxel Sigma-Aldrich T7402
phloretin Sigma-Aldrich P7912
teniposide Sigma-Aldrich SML0609
racecadotril Sigma-Aldrich SML0043
Y-134 Tocris 2676/10
RS-17053 Tocris 0985/10
L-168,049 Tocris 2311/10
actinomycin D Tocris 1229/10
BX-795 Selleckchem S1274
clofarabine Selleckchem S1218
serdematan Selleckchem S1172
BMS-387032 Selleckchem S1145
saracatinib Selleckchem S1006
TW-37 Selleckchem S1121
ursolic acid Selleckchem S2370
gemcitabine LKT G1745
ciclopirox LKT C3208
scriptaid Cayman 10572
entinostat Cayman 13284
NVP-TAE684 Biovision 1683
semagacestat Biovision 2430
BRD-A68061604 (STK525924) Vitas M Laboaratory STK525924
thioridazine MP Biomedicals 15689101
__________________________________________________________________
Experimental models: Cell lines
__________________________________________________________________
PANC-1, MIA PaCa-2 European Collection of Cell Cultures N/A
HPAF-II American Type Culture Collection N/A
K8484 and TB32048 David Tuveson’s lab at Cold Spring Harbor Laboratory
N/A
__________________________________________________________________
Software and algorithms
__________________________________________________________________
GraphPad Software Prism [219]www.graphpad.com
SynergyFinderPlus [220]Zheng et al., 2021b
[221]https://synergyfinderplus.org/
R R-Project [222]https://www.r-project.org
[223]Open in a new tab
Resource availability
Lead contact
Further information and requests for resources and reagents should be
directed to and will be fulfilled by the Lead Contact, Dr Yasaman
KalantarMotamedi([224]yk313@cantab.net)
Materials availability
All unique/stable reagents generated in this study are available from
the Lead Contact with a completed Materials Transfer Agreement.
Key resources including details of key reagents and cell lines used are
available in the Key Resources Table.
Experimental model and subject details
Cell culture and chemicals
Human pancreatic cancer cells (PANC-1, MIA PaCa-2 and HPAF-II) were
obtained from either the European Collection of Cell Cultures (PANC-1
and MIA PaCa-2) or the American Type Culture Collection (HPAF-II). They
were authenticated by the CRUK Cambridge Institute Biorepository core
facility, using either the Promega GenePrint10 system or the Promega
PowerPlex 16HS kit, and were grown in DMEM with 10% FBS (GIBCO, MA,
USA). Murine pancreatic cancer cells K8484 and TB32048 were established
from tumours in KRasG12D; p53R172H; Pdx1-Cre mice by members of David
Tuveson’s lab at Cold Spring Harbor Laboratory ([225]Hingorani et al.,
2005; [226]Olive et al., 2009) and were grown in DMEM with 5% FBS.
All cell lines were grown up to a maximum of 20 passages and for fewer
than 6 months following resuscitation. They were routinely verified to
be mycoplasma-free by the CRUK Cambridge Institute Biorepository core
facility using the Mycoprobe Mycoplasma Detection Kit (R&D Systems, MN,
USA). Maprotiline, palbociclib, tacedinaline, digoxin,
medroxyprogesterone, loperamide, salmeterol, triclosan, paclitaxel,
phloretin, teniposide and racecadotril were purchased from
Sigma-Aldrich; Y-134, RS-17053, L-168,049 and actinomycin D were
ordered from Tocris; BX-795, clofarabine, serdematan, BMS-387032,
saracatinib, TW-37 and ursolic acid were supplied by Selleckchem. In
addition to the above listed chemicals, gemcitabine and ciclopirox
(LKT), scriptaid and entinostat (Cayman), NVP-TAE684 and semagacestat
(Biovision), BRD-A68061604 (Vitas M Laboratory), thioridazine (MP
Biomedicals) were obtained, dissolved in DMSO in aliquots of 10-30mM,
kept at -20°C and used within 3 months. Final DMSO concentrations
(≤0.2%) were kept constant in all experiments.
Method details
Cytotoxicity assay and synergy calculation
Drug cytotoxicity in vitro was assessed by the means of Sulforhodamine
B colorimetric (SRB) assay ([227]Vichai and Kirtikara, 2006). Cells
were plated in a 96 well plate and dosed with a range of concentrations
of drugs (0.001 μM to 10 μM) and incubated for 72 h at 37°C. Cells were
then fixed (3% trichloroacetic acid, 90 minutes, 4°C), washed in water
and stained with a 0.057% SRB (Sigma-Aldrich, #230162-5G) solution in
acetic acid (w/v) for 30 minutes. The plates were washed (1% acetic
acid), and the protein-bound dye was dissolved in a 10 mM Tris base
solution (pH 10.5). Fluorescence was measured using the Tecan Infinite
M200 plate-reader (excitation 488 nm, emission 585 nm). 50% Growth
Inhibition (GI[50]) values of each drug were calculated by comparing
with solvent control.
For compound combination assays, cells were seeded for 24 hours in
96-well plates and then treated with a serial dilution of each agent
and gemcitabine in an 8 X 8 concentration format.
The effect of the combination was analyzed by Combenefit ([228]Di
Veroli et al., 2016). The Combenefit software generates a set of
synergy scores based on the Bliss ([229]1939) and Loewe models ([230]Di
Veroli et al., 2016). The choice of synergy scores significantly
influences interpretation of drug combination screens ([231]Meyer
et al., 2020; [232]Vlot et al., 2019). Similar synergy calculation
methodology is used in authors’ previous work ([233]Koh et al., 2015).
IncuCyte time-lapse imaging
Images of cells were acquired with the IncuCyte Live Cell Imaging
microscopy (Essen Bioscience, MI, USA) at every three hours under cell
culture conditions with 10X objective. Averaged cell confluence was
calculated from three random fields of view per well using the IncuCyte
in-built algorithm. Relative confluence values were obtained by
normalizing each value to the time zero value in each sample.
Clonogenic assay
Cells were plated 24 hours prior to treatment. After 48 hours of
treatment, equal numbers of viable cells from each sample were reseeded
in fresh medium and left to grow for a week or two depending on the
cell density. Cells were then fixed with 70% methanol and stained with
0.2% crystal violet (Sigma-Aldrich, MO, USA). Colonies were imaged and
quantified using the Gelcount (Oxford Optronix). Plating efficiency was
calculated from the ratio of the number of colonies to the number of
cells seeded. The number of colonies that arose after treatment was
expressed as surviving fraction. This was derived from the ratio of the
number of colonies formed after treatment to the number of cells seeded
multiplied by plating efficiency of the control ([234]Franken et al.,
2006).
Immunoblotting
For immunoblotting, whole-cell extracts were obtained by lysis in RIPA
buffer (50mM Tris pH8.0, 2mM EDTA, 150mM sodium chloride, 1% NP-40,
0.5% sodium deoxycholate, 0.1% SDS) and resolved using the SDS-PAGE gel
system (Life Technologies, MA, USA). Blots were analyzed using the
Odyssey Infrared Imaging System (LI-COR, NE, USA). Primary antibody
cleaved PARP (#5625S) was obtained from Cell Signaling (MA, USA),
cleaved caspase 3 (ab13847) and β-actin (ab6276) were purchased from
Abcam (Cambridge, UK) and primary antibody γH2AX from Millipore
(05-636). As secondary antibodies IRDye800CW- and IR680CW-conjugated
antibodies from LI-COR were used in immunoblotting.
Acquisition, processing and analysis of live-cell time-lapse sequences
PANC-1 FastFUCCI cells ([235]Koh et al., 2016) were kept in a
humidified chamber under cell culture conditions. Images were taken on
five fields of view per well, every seven minutes over 72 hours, using
the Zeiss Axio Observer system with 10X objective. An equalization of
intensities over time was then performed to each channel using the ZEN
software (Zeiss, Oberkochen, Germany).
Quantification and statistical analysis
Statistical analyses
Data from SRB assay were analyzed using the GraphPad Prism (Version 7)
([236]GraphPad Software, 2015) built-in tests or the Combenefit
([237]Di Veroli et al., 2016) software. An ordinary one-way ANOVA with
a Tukey’s multiple comparisons test was performed using GraphPad Prism
version 7 for Windows (GraphPad Software, CA, USA,
[238]www.graphpad.com). Data represents mean ± SD of 3 replicates, ∗
indicates p < 0.05 and ∗∗ indicates significance at p < 0.01 (based on
the Kruskal-Wallis non-parametric test).
Gene expression data retrieval and pathway signature calculation for
compounds
The compound dataset used in this project was retrieved from the LINCS
database ([239]Cheng and Li, 2016; [240]Subramanian et al., 2017)
(Phase I). LINCS at the time of this study contained gene expression
profiles of a set of 20,413 compounds applied to 77 different cell
lines including 59 cancer cell lines. In this work, the LINCS
Application Processing Interface ([241]Lincscloud.org, accessed 2015,
replaced by clue.io today) was used to retrieve gene signatures of all
compounds in the dataset, including the list of 50 most up- and
down-regulated landmark genes among significantly differentially
expressed genes in each cell line after each compound treatment
(without taking into account the expression level). Landmarks genes
were 978 genes profiled in L1000 platform that were sufficient to
recover 82% of the information in the full transcriptome
([242]Subramanian et al., 2017). In this work, gene expression of
different instances of the same compound on different cell lines were
not aggregated together and were treated separately.
As LINCS did not include any pancreatic cancer cell line, we used
pathways instead of genes to define effect of compounds on the cell.
From the NCBI BioSystems ([243]Geer et al., 2010) database (accessed in
2015) all human biological pathways and the name of genes that belonged
to those pathways were downloaded. This constituted 2,010 pathways with
annotated gene members in each pathway. In this work, for each compound
instance in LINCS, the number of genes up- and down-regulated in each
pathway were counted (separately for each direction). To normalize the
score for each compound, 20,000 random gene sets with the same length
as the compound signatures (50 genes) were generated to constitute a
background population. Next, z-scores were calculated for each pathway
‘p’ for each compound ‘c’ compared to the background population using
the following formula:
[MATH:
Scorec,p=Nc,p−μpσp :MATH]
where N[c,p] denotes number of shared genes in the compound c and
pathway ‘p’,
[MATH: μp :MATH]
and
[MATH: σp :MATH]
denote average and standard deviation of number of shared pathways with
pathway ‘p’ and the background population, respectively (random gene
sets).
Gene expression data retrieval and pathway signature calculation for
pancreatic cancer cell lines
We next needed to define the gene expression differences between
healthy and disease (here pancreatic cancer) states. For this purpose,
the gene expression profile of GEO dataset: [244]GSE45765, containing
the whole genome gene expression profile of normal human pancreatic
ductal epithelial cells specimen and pancreatic cancer cell lines
([245]Gysin et al., 2012) was imported using GenePattern ([246]Reich
et al., 2006) GEO Importer tool. Next, untreated cancer cell lines
(PANC-1 and BXPC3) were each compared with the normal human pancreatic
ductal epithelial cells specimen and the log[2] fold change was
calculated for each gene in each cell line (PANC-1 was used for synergy
prediction and testing only). The genes were sorted based on their
log[2] fold changes and the 50 most over- and underexpressed genes
constituted the disease signature for each pancreatic cancer cell line.
Next, the number of shared genes between the pancreatic cancer disease
signature and each of the pathways in Biosystems was counted and
enrichment scores were calculated for each pathway to generate a
pathway signature for each of the pancreatic cancer cell lines (per
direction, analogous to the compound pathway enrichment calculation).
The only difference was that for normalising pathway enrichment scores
for the disease signature the random gene sets were selected from genes
that were in the assay used for gene expression profiling of cancer
cells (in this case the HG-U133A_2 Affymetrix Human Genome U133A 2.0
Array).
This pathway enrichment analysis led to a pathway signature for each
compound in the LINCS database, and a pathway signature for each
pancreatic cancer cell line, based on the 50 most up- and downregulated
genes, per direction, which were annotated with NCBI Biosystems
pathways.
Compound-disease matching
Similar to the original ‘Connectivity Mapping’ approach ([247]Lamb
et al., 2006) we were interested in compounds whose pathway signature
was anticorrelated with the disease signature. To this end, the
pathways with highest normalised score in the disease were identified
for targeting by the compounds. Significantly up- or downregulated
pathways (with a p-value<0.01, equivalent to a Z-score cut-offs of
above 2.58 or under -2.58) were identified to this end. Next, the
Pearson correlation of the pathway signature of the compounds in LINCS
with the disease pathway signature was calculated, but only on the
subset of pathways that were found to be significantly dysregulated in
the diseases signature. Then, the compounds were rank ordered based on
their anticorrelation scores. This rank ordered list of compounds was
annotated with predicted protein targets and pathways to facilitate
selection of potentially active compounds in a more informed manner. In
this regard, a Naïve Bayes target prediction algorithm ([248]Koutsoukas
et al., 2013) was utilised to annotate ranked compounds with their
targets, based on bioactivity data from CHEMBL v.17 comprising 385,126
compound-protein pairs, 1,643 distinct proteins and 226,791 unique
compounds.
Acknowledgments