Abstract
Background
Despite great development in genome and proteome high-throughput
methods, treatment failure is a critical point in the management of
most solid cancers, including breast cancer (BC). Multiple alternative
mechanisms upon drug treatment are involved to offset therapeutic
effects, eventually causing drug resistance or treatment failure.
Methods
Here, we optimized a computational method to discover novel drug target
pathways in cancer subtypes using pathway cross-talk inhibition (PCI).
The in silico method is based on the detection and quantification of
the pathway cross-talk for distinct cancer subtypes. From a BC data set
of The Cancer Genome Atlas, we have identified different networks of
cross-talking pathways for different BC subtypes, validated using an
independent BC dataset from Gene Expression Omnibus. Then, we predicted
in silico the effects of new or approved drugs on different BC subtypes
by silencing individual or combined subtype-derived pathways with the
aim to find new potential drugs or more effective synergistic
combinations of drugs.
Results
Overall, we identified a set of new potential drug target pathways for
distinct BC subtypes on which therapeutic agents could synergically act
showing antitumour effects and impacting on cross-talk inhibition.
Conclusions
We believe that in silico methods based on PCI could offer valuable
approaches to identifying more tailored and effective treatments in
particular in heterogeneous cancer diseases.
Electronic supplementary material
The online version of this article (10.1186/s12967-018-1535-2) contains
supplementary material, which is available to authorized users.
Keywords: Monte Carlo cross-validation, Pathway cross-talk inhibition,
Breast cancer, Drugs, Classification, Subtypes
Background
Breast cancer (BC), the most invasive cancer in women worldwide, is a
heterogeneous disease, characterized by different subtypes that lead to
different clinical prognosis and responses to treatments [[29]1].
The advent of genome-wide technologies has made possible the generation
of new hypotheses about the role of genomics in the efficiency of drugs
developed for cancer and the event of adverse responses to cancer
therapy.
In this context, several studies examined the effects of drugs
considering protein network approaches [[30]2]. In particular, the
analysis of network models revealed that the partial inhibition of a
small number of proteins belonging to a network has a higher impact on
the disease than the complete inhibition of a single protein: indeed,
drugs that have only one target (single hit) often do not affect
complex system networks of proteins in an effective way [[31]2].
Moreover, experimental studies have shown that cancer cells are able to
resist to drug treatments by creating and establishing news
interactions in order to have an alternative signaling [[32]3]. In
network approaches, the effect of a drug treatment on some proteins,
represented by the nodes of a network, is amplified by the interactions
of these proteins with other proteins in the networks, being these
connections represented by edges [[33]2]. However, notwithstanding
useful to assess the drug effects on proteins, these approaches have
not still impacted in increasing the number of efficient drugs or in
suggesting new potential targets for cancer treatment.
Hence, two critical points for drug discovery tools are: (i) to inhibit
not single but multiple targets at the same time, and (ii) to prevent
the formation of new interactions that could lead to phenomena of
resistance or inefficacy of the drug.
Moreover, these two critical conditions can be further complicated by
the interactions between pathways (pathway cross-talk) that can modify
the effect of drugs.
For instance, in some cancer cells, rapamycin-like drugs inhibit mTORC1
complex but at the same time indirectly activate phosphatidylinositol
3-kinase (PI3-kinase) and AKT, mitigating the inhibitor effect of drugs
[[34]3]. Similarly, in triple negative BC the inhibition of AKT as a
consequence of a drug is indirectly damped by the activation of
receptor tyrosine kinases (RTKs), reducing the efficacy of the drug
[[35]4]. Moreover, pathway cross-talk, i.e. the one existing between
EGFR and HER2, is a possible evasive way for the cell to develop
resistance to HER family receptor inhibitors [[36]5]. These examples
illustrate how drugs targeting an individual protein or pathway may not
yield to the expected therapeutic effect due to activation of
alternative pathways that avoid the barrier inflicted by the cancer
drug.
Therefore, understanding how and where the pathway cross-talk can be
inhibited by drug treatments during a disease process ideally could
lead to more effective therapies, reducing the problem of drug
resistance.
Once a map of pathway cross-talk specific of a disease is known, a
potential solution hindering the formation of alternative signaling
pathways created in response to therapy could be the administration of
drugs able to act on both direct and indirect targets (generated by the
inhibition of direct targets).
However, on this potential solution, the current opinions are
conflicting. Indeed, multi-target drugs show lower affinity than
one-target-drugs [[37]2, [38]3]. One of the most promising approaches
is the use of synergistic drug combinations therapy able to act on both
direct and indirect targets [[39]2, [40]3]. The use of drug
combinations could overcome drug resistance issues associated with high
doses of single-hit drugs, and their best efficacy could lead to use a
lower drug concentrations reducing unwanted side-effect toxicity
[[41]2, [42]3].
Understanding the effect of individual or combined drugs is critical in
clinical studies. An important issue is the low reliability of the cell
lines to predict the efficacy of drugs since cell lines did not show to
be good models [[43]3]. Thus, computational methods are demanded to
deepen the potential role of a drug in a context of pathway cross-talk.
A recent work by Jaeger et al. [[44]6] has proposed a computational
method to simulate pathway cross-talk inhibition (PCI) given by
individual or combined drugs in BC. In that study the authors
considered the pathway cross-talk between two different pathways for
shared protein interactions. The authors developed their computational
algorithm (PCI index) considering all those KEGG pathways that contain
any of the primary targets of the Food and Drug Administration (FDA)
approved drugs in BC. No selections on pathways have been performed.
In our recent studies on BC [[45]7, [46]8], we generated a
computational approach to select a network of pathways specific for
distinct BC subtypes and quantified their cross-talk. In these works,
we focused on a network of pathways composed of interactions among ten
pathways; our aim was to study the role of miRNAs regulating pathway
interactions in distinct BC subtypes.
In the present study we propose a computational method based on the
work of Jaeger et al. [[47]6] and optimized by taking advantage from
results of our previous studies [[48]7, [49]8]. More precisely, here we
describe a procedure to build a network of pathways de-regulated for
different BC subtypes using gene expression data from The Cancer Genome
Atlas (TCGA) and a list of pathways obtained by Ingenuity Pathway
Analysis (IPA). We quantified pathway cross-talk with a dissimilarity
measure and we assessed potential drug target pathways through PCI. We
then applied PCI to quantify the effects of individual or combined
drugs in distinct BC subtypes on our network of pathways. Finally, we
speculated about the mode of action of FDA approved drugs and new
potential drug targets that could decrease the activity of
pathway-cross talk and therefore enhance clinical efficacy.
Methods
Datasets
We applied the computational approach on four BC subtypes with
different diagnostic classification and prognosis [[50]9]: “luminal A”
tumors expressing hormone receptors, with a favorable prognosis;
“luminal B” tumors expressing hormone receptors and high expression of
proliferation genes with a good prognosis although with an increased
risk of recurrence; “basal-like” tumors lacking the expression of
hormone receptors and HER2 but increased levels of cytokeratin
(myoepithelial) (CK 5/6 and CK 17), with shorter observed survival; and
“HER2” tumors overexpressing HER2, with the worse survival.
We considered gene expression data from tissue samples studied by
IlluminaHiSeq RNASeqV2 and derived from TCGA dataset: 233 BC luminal A
samples, 103 BC luminal B samples, 43 BC HER2-overexpressing samples,
74 BC basal samples and 113 normal samples (NS).
We tested the approach with respect to its performance in classifying
different subtypes by using an independent testing dataset from Gene
Expression Omnibus (GEO) database ([51]GSE58212): 121 luminal A, 69
luminal B, 36 basal and 32 HER2-overexpressing samples.
We validated the approach with respect to its performance in
identifying drug target pathways, by using the Matador database
[[52]10] that provides interactions between chemicals and proteins. The
association between drugs and BC was obtained using 13 drugs already
known and approved by FDA for BC [[53]11]: Tamoxifen, Raloxifene,
Torimefene, Anastrozole, Letrozole, Exemestane, Capecitabine,
Fluorouracil, Gemcitabine, Docetaxel, Vinblastine, Everolimus, and
Methotrexate.
The computational approach
The computational approach consists of seven steps (1. Differential
expression analysis; 2. Pathway enrichment; 3. Pathway cross-talk; 4.
Classification; 5. Pathway network; 6. Pathway cross-talk inhibition;
7. Drug target pathway network).
All the steps were applied to each of the four BC subtypes. The first
four steps were applied 50 times in order to obtain a solid network of
de-regulated cross-talking pathways for each BC subtype. More
specifically, in order to perform a bootstrapping, we implemented a
Monte Carlo cross-validation by randomly selecting some portions of the
dataset (60%) to build the training dataset of the classifier and the
rest of data (40%) as testing dataset. Step 1, 2, and 3 were applied on
the training data set. Step 4 was applied on both training and testing
dataset. To avoid problems of unbalanced classes, we randomly generated
the same number of samples for each class of BC subtypes and NS.
Then, for each BC subtype, we generated a network-based model of BC
subtype pathways (5. Pathway network), and we simulated in silico the
drug-induced inhibition of pathways (6. Pathway cross-talk inhibition)
focusing on the change of network efficiency.
At the end of the sixth steps, we found potential drug target pathways
for each BC subtype, which, if inhibited, can change significantly the
network efficiency. These drug targets could be considered for future
applications in drug discovery (7. Drug target pathway network).
Figure [54]1 summarizes the proposed computational approach.
Fig. 1.
Fig. 1
[55]Open in a new tab
Proposed approach for each subtype
We validated our network-based models of BC subtype pathways and BC
drug target pathways.
For the first validation task, we assessed the accuracy of using our
subtype pathways in classifying four different BC subtypes using the
GEO dataset, as data set independent from the TCGA dataset used for the
identification of the subtype-derived pathways.
For the second validation task, we evaluated the mechanism of action of
13 FDA approved drugs for BC on our BC subtype drug target pathway
network (DTPN). We compared the decrease of pathway cross-talk within
the DTCN followed by inhibition of drug target pathways.
Step 1: differential expression analysis
For each BC subtype, differential expression analysis (DEA) was applied
with respect to NS using TCGAbiolinks [[56]12]. In particular, we used
the edgeR package from Bioconductor [[57]13] to find differential
expressed genes (DEGs) between each BC subtype and NS. For each DEG we
calculated the log fold-change between the two conditions and corrected
p-values using Benjamini–Hochberg procedure for multiple testing
correction [[58]14]. We defined DEGs if the absolute value of log fold
change was > 1 and p value < 0.01. This step was applied 50 times on
the 50 training datasets producing every time a different list of DEGs.
Step 2: pathway enrichment analysis
For each BC subtype, we identified a group of pathways significantly
enriched with the list of subtype-derived DEGs. The original list of
pathways (589) was obtained from IPA. Pathway Enrichment Analysis (PEA)
was performed with a Fisher’s test between DEGs and genes within IPA
pathways [[59]15]. We defined pathways enriched with DEGs if p-value of
Fisher’s test was < 0.01. p-values were corrected using
Benjamini–Hochberg procedure for multiple-testing correction [[60]14].
As for the step 1, the step 2 was applied 50 times on the same training
dataset of the step 1.
Step 3: pathway cross-talk
Pathway cross-talk between pathways enriched with subtype-derived DEGs
was quantified using a discriminating score (DS) [[61]7, [62]8]. This
score was defined by comparing the mean of the gene expression levels
of each pair of pathways enriched with subtype-derived DEGs:
[MATH:
DS=Mx-My<
mi>Sx+Sy :MATH]
where
[MATH: Mx :MATH]
and
[MATH: Sx :MATH]
represent the mean and the standard deviation of the gene expression
levels in the pathway x, and
[MATH: My :MATH]
and
[MATH: Sy :MATH]
represent the mean and the standard deviation of the gene expression
levels in the pathway y.
From this step we created a matrix for each sample (BC subtypes and NS)
containing a DS value for each pair of pathways enriched with subtype
DEGs.
Step 4: classification
In order to select the best discriminating pairs of cross-talking
pathways, we implemented a Random Forest Classification using the
R-package [[63]16, [64]17] to classify each BC subtype versus NS using
the DS matrix obtained from step 3 as input of the classifier.
For each combination of pathways we estimated the Area Under Curve
(AUC) values by cross-validation method (k-fold cross-validation,
k = 10). We used the following parameters: mtry (number of variables
randomly sampled as candidates at each split) = sqrt(p), p being the
number of variables in the matrix of data; ntree (number of trees
grown) = 500.
The classification was performed on the training dataset 50 times for
each matrix obtained from the previous three steps. Every time we
obtained the top-10 pairs of subtype cross-talking pathways with the
best AUC value. Then we validated these pathways on the testing
dataset.
In conclusion, for each BC subtype and for all 50 bootstraps we
obtained 10 × 50 couples of pathways with the best AUC values validated
on the testing dataset.
Step 5: pathway network
For each BC subtype, by univocally selecting the pairs of cross-talking
pathways better discriminating each subtype, we then generated a
network-based model of BC subtype pathways, where the nodes of each
subtype network represent pathways and the links that connect nodes the
pathway cross-talks.
Step 6: pathways cross-talk inhibition
In a network-based model, the network efficiency is defined as the sum
of the reciprocals of the shortest direct path lengths between all
pairs of network elements.
If N is the number of network elements and d[i,j] is the shortest
direct path lengths of two elements i and j,
[MATH:
NE=∑i≠j1d(i,j)N(N-1)i,j∈N :MATH]
1
NE can range from 0 to 1, where 1 means that all nodes interact
directly with each other expressing the best efficiency of the network
[[65]2].
For each BC subtype, the network efficiency (NE) of the disease was
calculated starting from the subtype pathway network, the number of the
pathways (nodes) and the shortest direct path lengths between two
pathways (e.g. i, j in N).
The efficient drug-induced inhibition of a single pathway can be
modelled by the elimination of all direct interactions at the pathway.
The corresponding drug effect on the network can be measured by NE as
an index of network integrity reduction measuring the drug efficiency
[[66]2].
For each BC subtype, we simulated, in silico, the drug-induced
inhibition of pathways cross-talk by eliminating, one-by-one, all
direct cross-talks. We thus quantified the new NE value of the network,
that we called nNE. nNE is thus a function of k, being k the pathway or
the combination of the pairs of pathways inhibited in the network.
As effect of this operation, nNE was < NE or > NE, resulting in
increasing or decreasing the drug efficiency, respectively.
Step 7: drug target pathway network
For each BC subtype, we selected those cross-talking pathways from the
network, that, if inhibited, caused nNE < NE, thus building a potential
DTPN.
Furthermore, from the equation:
[MATH: PCI=100×1-nNENE
:MATH]
2
we quantified the percentage activity of the subtype pathway network
that is inhibited, defined as PCI [[67]6].
Figure [68]2 explains steps 6 and 7 of the proposed approach.
Fig. 2.
[69]Fig. 2
[70]Open in a new tab
Drug target pathway network. In de-regulated pathway network, the
activity of pathway interactions (network efficiency, (NE)) is
calculated. 1 and 4 Inhibition of an individual pathway and its
interactions. A new-NE (nNE) is calculated. 2 and 5 If nNE < NE the
inhibited pathway could be a potential drug target. 3 and 6 integration
of drug-pathway associations. 7 nNE is calculated inhibiting two
pathways
Validation of DTPN
From the independent GEO dataset [71]GSE58212 (121 luminal A BC, 69
luminal B BC, 36 basal BC and 32 HER2-overexpressing) we considered the
gene expression levels belonging to our BC subtype pathway networks. We
tested their ability to classify each subtype. We then investigated the
role of the top-10 genes that obtained the best classification
performance in the perspective of DTPN.
We validated DTPN by assessing if FDA-approved BC drugs have the target
pathways within DTPN, and evaluating how these drugs could reduce
effectively the NE of the DTPN.
For each BC subtype, we evaluated the association of 13 FDA-approved BC
drugs (from Matador database) and DTPN. For this purpose, we applied
PEA with a Fisher’s test between gene targets of the drug and genes
within IPA pathways. We defined pathways enriched with drug target
genes if p-value of Fisher’s test was < 0.01.
For each BC subtype, we measured the effects of the considered drugs,
when administered individually and in combination on the DTPN. We
quantified this effect by PCI.
Results
Luminal A
For the Luminal A we found a pathway network composed of 73 individual
pathways and 157 pathway cross-talks (Fig. [72]3a). AUC for this
pathway network was 0.93 in the TCGA training dataset and 0.88 in the
TCGA testing dataset, respectively (Fig. [73]3b).
Fig. 3.
[74]Fig. 3
[75]Open in a new tab
Luminal A BC. a Pathway network: nodes represent pathways (73) and
edges represent interactions between pathways (157); b Boxplot of AUC
values for training and testing dataset; c Trend of new network
efficiency (nNE) calculated after removal of each of the 73 individual
pathways; d nNE values calculated after removal of all combinations of
2628 couples of pathways. Red lines represent the efficiency of the
original network (NE)
The efficiency of the pathway network (NE) was 0.3218. nNE, calculated
after the drug-induced in silico inhibition of each single pathway
(among the 73) or each combination of pair of pathways (among the 2628)
is shown in Fig. [76]3c and d, respectively. The inhibition of 34/73
individual pathways reduces NE (red line) of the pathway network with
nNE values that ranges from 0.3043 to 0.3217 (Fig. [77]3c). The
inhibition of 1388 combinations (among 2628) reduces the NE (red line)
of the pathway network with values that range from 0.2927 to 0.3217
(Fig. [78]3d). Thus, the inhibition of couples of pathways of the cloud
seems to reduce the efficiency more than individual pathway.
Additional file [79]1 shows nNE values in the luminal A network after
inhibition of individual and pairs pathway(s). The top-10 pathways,
which, if inhibited, have resulted in a better efficiency reduction of
the network are: ‘LXR/RXR Activation’, ‘Extrinsic Prothrombin
Activation Pathway’, ‘Estrogen Receptor Signaling’, ‘Human Embryonic
Stem Cell Pluripotency’, ‘Ethanol Degradation IV’, ‘RAR Activation’,
‘Fatty Acid oxidation’, ‘Bladder Cancer Signaling’, ‘Factors Promoting
Cardiogenesis in Vertebrates’ and ‘Glioma Invasiveness Signaling’.
‘LXR/RXR Activation’ plays the most important role since its single
inhibition reduces the network efficiency of 5.5% (PCI). The inhibition
of both ‘LXR/RXR Activation Pathway’ and ‘Extrinsic and Prothrombin
Activation Pathway’ is the most effective reducing the efficiency of
the network of 9% (PCI). In line with the use of synergistic drug
combinations, drugs acting on both ‘LXR/RXR Activation Pathway’ and
‘Extrinsic and Prothrombin Activation Pathway’ could be more effective.
DTPN of Luminal A was built considering only the 34 pathways of the
network that if inhibited, reduced the NE (nNE < NE).
Starting from the 13 FDA approved drugs for BC (fluorouracil,
anastrozole, capecitabine, docetaxel, exemestane, fulvestrant,
gemcitabine, letrozole, methotrexate, raloxifene, tamoxifene,
toremifene, and vinblastine) we obtained 8 drugs that interact with the
DTPN of Luminal A (capecitabine, fulvestrant, gemcitabine,
methotrexate, raloxifene, tamoxifen, toremifene and vinblastine)
(Table [80]1).
Table 1.
Drug-pathway association in the DTPN of Luminal A subtype
Drug Pathway nNE and PCI (vs NE = 0.3218)
Capecitabine Eicosanoid 0.3194 (PCI 0.75%)
Fulvestrant Bladder cancer signaling (1); estrogen receptor (2);
regulation of cellular mechanics by calpain protease (3) (1) 0.3179
(PCI 1.18%)
(2) 0.3137 (PCI 2.50%)
(3) 0.3198 (PCI 0.59%)
(1–2) 0.3094 (PCI 3.84%)
(1–3) 0.3159 (PCI 1.81%)
(2–3) 0.3115 (PCI 3.18%)
(1–2–3) 0.3071 (PCI 4.56%)
Gemcitabine Regulation of cellular mechanics by calpain protease 0.3198
(PCI 0.59%)
Methotrexate FXR/RXR activation 0.3193 (PCI 0.75%)
Raloxifene Estrogen receptor (1); RAR activation (2) (1) 0.3137 (PCI
2.50%)
(2) 0.3170 (PCI 1.47%)
(1–2) 0.3098 (PCI 3.70%)
Tamoxifen Estrogen receptor (1); factors promoting cardiogenesis in
vertebrates (2); human embryonic stem cell pluripotency (3); RAR
activation (4) (1) 0.3137 (PCI 2.50%)
(2) 0.3180 (PCI 1.17%)
(3) 0.3157 (PCI 1.86%)
(4) 0.3170 (PCI 1.47%)
(1–2) 0.3092 (PCI 3.88%)
(1–3) 0.3071 (PCI 4.56%)
(1–4) 0.3098 (PCI 3.70%)
(2–3) 0.3098 (PCI 3.70%)
(2–4) 0.3129 (PCI 2.74%)
(3–4) 0.3103 (PCI 3.56%)
(1–2–3) 0.3003 (PCI 6.68%)
(2–3–4) 0.3025 (PCI 5.98%)
(3–4–1) 0.3051 (PCI 5.18%)
(1–2–3–4) 0.2941 (PCI 8.59%)
Toremifene FXR/RXR activation (1); pregnenolone biosynthesis (2) (1)
0.3193 (PCI 0.75%)
(2) 0.3192 (PCI 0.79%)
(1–2) 0.3165 (PCI 1.61%)
Vinblastine Axonal guidance signaling (1); FXR/RXR activation (2) (1)
0.3216 (PCI 0.05%)
(2) 0.3193 (PCI 0.75%)
(1–2) 0.3190 (PCI 0.86%)
[81]Open in a new tab
In particular, the inhibition of the ‘Estrogen Receptor Signaling’
reduces the NE of 2.50%. This inhibition could be obtained using three
FDA approved drug (fulvestrant, raloxifene and tamoxifen) that have a
significant number of target genes belonging to this pathway.
The ‘FXR/RXR Activation Pathway’ if inhibited, reduces the NE of 0.75%,
and it is target of three drugs (methotrexate, toremifene and
vinblastine). The ‘RAR Activation Pathway’, if inhibited, reduces the
NE of 1.5% and it is target of two drugs (raloxifene and tamoxifen).
The ‘Regulation of Cellular Mechanics by Calpain Protease’ pathway, if
inhibited, reduces the NE of 0.6% and is target of two drugs
(fulvestrant and gemcitabine).
Among the drug target pathways found in our DTPN, we found 5/34
pathways that were ranked as part of the top 10 pathways, playing the
major role in the reduction of NE. These pathways are ‘Estrogen
Receptor’ targeted by tamoxifen and raloxifene; ‘Human Embryonic Stem
Cell Pluripotency’ targeted by tamoxifen; ‘RAR Activation Pathway’
targeted by raloxifene and tamoxifen; ‘Bladder Cancer Signaling’
targeted by fulvestrant; ‘Factors Promoting Cardiogenesis in
Vertebrates’ targeted by tamoxifen. In particular, tamoxifen acting on
4 pathways reduces the NE of 8.59%, thus it is very effective.
FDA-approved drugs do not seem to act on ‘LXR/RXR Activation’,
‘Extrinsic Prothrombin Activation’; ‘Ethanol Degradation IV’, ‘Fatty
Acid oxidation’ and ‘Glioma Invasiveness Signaling’. In particular the
first two pathways, according to our findings, could be potential dug
targets for anticancer drug treatment.
Furthermore, considering FDA-approved drugs, our study confirms that
tamoxifen is the drug with the best efficacy on the pathway network
since it is able alone to inhibit four pathways (‘Estrogen receptor’,
‘Human Embryonic Stem Cell Pluripotency’, ‘Factors Promoting
Cardiogenesis in Vertebrates’ and ‘RAR activation’).
Luminal B
In luminal B we found a pathway network composed of 73 individual
pathways and 129 pathway cross-talks (Fig. [82]4a). In the TCGA
training dataset mean AUC was 0.98 and 0.96 in the TCGA testing dataset
(Fig. [83]4b).
Fig. 4.
[84]Fig. 4
[85]Open in a new tab
Luminal B. a Pathway network: nodes represent pathways (73) and edges
represent interactions between pathways (129); b Boxplot of AUC values
for training and testing dataset; c Trend of new network efficiency
(nNE) calculated after removal of individual pathways; d Trend of nNE
values calculated after removal of all combinations of couples of
pathways. Red lines represent the efficiency of the original network
(NE)
The efficiency of the pathway network was 0.3272. Figure [86]4c and d
show the trend of nNE in case of inhibition of 73 individual pathways
or each combination of pairs of pathways (among 2628), respectively.
The inhibition of 27/73 individual pathways reduces the NE of the
pathway network with values that range from 0.3123 to 0.3270
(Fig. [87]4c). The inhibition of 1199/2628 combinations of pairs of
pathways reduces the NE with values that range from 0.2935 to 0.32720
(Fig. [88]4d). As for luminal A, also in luminal B the inhibition of
pairs of pathways of the pathway network reduce the efficiency more
than individual pathway.
Additional file [89]2 shows nNE values in the luminal B after the
inhibition of individual and combined pathway(s). The top-10 pathways,
which if inhibited, lead to the best reduction of network efficiency
include: ‘Cell Cycle Control of Chromosomal Replication’, ‘P2Y
Purigenic Receptor Signaling Pathway’, ‘Growth Hormone Signaling’,
‘Epithelial Adherens Junction Signaling’, ‘Regulation of the
Epithelial-Mesenchymal Transition Pathway’, ‘Mitotic Roles of Polo-Like
Kinase’, ‘Tight Junction Signaling’, ‘Role of BRCA1 in DNA Damage
Response’, ‘Cellular Effects of Sildenafil (Viagra)’ and ‘Cell Cycle:
G2/M DNA Damage Checkpoint Regulation’. The inhibition of ‘Cell Cycle
Control of Chromosomal Replication’ plays an important role reducing
the efficiency of 4.5% (PCI). The inhibition of both ‘Growth Hormone
Signaling’ and ‘Regulation of the Epithelial-Mesenchymal Transition
Pathway’ plays the most important role reducing the efficiency of the
network of 10% (PCI).
DTPN of luminal B was built considering the 27 pathways that inhibited
reduced the NE. We obtained 4 BC drugs (among the 13 FDA approved)
(docetaxel, fulvestrant, raloxifene, and tamoxifen) that interact with
the DTPN of luminal B (Table [90]2).
Table 2.
Drug-pathway association in the DTPN of luminal B subtype
Drug Pathway nNE and PCI (vs NE = 0.3272)
Docetaxel Germ cell-sertoli cell junction signaling (1); epithelial
adherens junction signaling (2) (1) 0.3256 (PCI 0.46%)
(2) 0.3196 (PCI 2.30%)
(1–2) 0.3180 (PCI 2.80%)
Fulvestrant Estrogen-mediated S-phase entry 0.327093 (PCI 0.03%)
Raloxifene Estrogen-mediated S-phase entry 0.327093 (PCI 0.03%)
Tamoxifen Wnt/catenin signaling (1); regulation of the
epithelial-mesenchymal transition pathway (2); estrogen-mediated
S-phase entry (3) (1) 0.3254 (PCI 0.53%)
(2) 0.3201 (PCI 2.14%)
(3) 0.3270 (PCI 0.03%)
(1–2) 0.3182 (PCI 2.73%)
(1–3) 0.3251 (PCI 0.64%)
(3–2) 0.3196 (PCI 2.29%)
(1–2–3) 0.3174 (PCI 3.06%)
[91]Open in a new tab
In particular, the ‘Estrogen-mediated S-phase Entry Pathway’ is target
of three approved drugs (fulvestrant, raloxifene and tamoxifen). The
‘Epithelial Adherens Junction Signaling Pathway’, if inhibited, reduces
the NE of 2.3% and it is target of docetaxel. The ‘Germ Cell-Sertoli
Cell Junction Signaling Pathway’, if inhibited, reduces the NE of 0.4%
and it is target of docetaxel. The ‘Regulation of the
Epithelial-Mesenchymal Transition Pathway’, if inhibited, reduces the
NE of 2.1% and it is target of tamoxifen. The ‘Wnt/catenin Signaling
Pathway’, if inhibited, reduces the NE of 0.5% and it is target of
tamoxifen.
Among the drug target pathways found in luminal B DTPN, we found 2/27
pathways that were ranked as to be part of the top 5 pathways playing
the major role in the reduction of NE. These pathways are ‘Epithelial
Adherens Junction Signaling’ and ‘Regulation of the
Epithelial-Mesenchymal Transition Pathway’ targeted by docetaxel and
tamoxifen, respectively.
FDA-approved drugs do not seem to act on ‘Cell Cycle Control of
Chromosomal Replication’, ‘P2Y Purigenic Receptor Signaling Pathway’
and ‘Growth Hormone Signaling’; pathways that according our findings
were the best potential drug targets. In particular, tamoxifen acting
on three pathways reduces the NE of 3%.
Furthermore, considering FDA-approved drugs our study demonstrates that
docetaxel is the drug with the best action on pathway network since is
able to inhibit ‘Epithelial Adherens Junction Signaling’.
HER2-overexpressing BC
We found a pathway network in HER2 BC composed of 100 individual
pathways and 222 pathway cross-talks (Fig. [92]5a). Mean AUC was 0.98
and 0.91 in the TCGA training and testing dataset, respectively
(Fig. [93]5b).
Fig. 5.
[94]Fig. 5
[95]Open in a new tab
HER2-overexpressing BC (HER2). a Pathway network: nodes represent
pathways (100) and edges represent interactions between pathways (222);
b Boxplot of AUC values for training and testing dataset; c Trend of
new network efficiency (nNE) calculated after removal of individual
pathways; d nNE values calculated after removal of all combinations of
couples of pathways. Red lines represent the efficiency of the original
network (NE)
The efficiency of the pathway network in HER2 was 0.3492. Figure [96]5c
and d show nNE in case of inhibition of individual pathways or each
combination of pairs of pathways, respectively. The inhibition of
39/100 individual pathways reduces the NE of the pathway network with
values that range from 0.3309 to 0.3492 (Fig. [97]5c). The inhibition
of 2271/4950 combinations of pathways reduces the NE of the pathway
network with values that ranges from 0.3189 to 0.3492 (Fig. [98]5d). It
is confirmed that the inhibition of couples of pathways of the pathway
network reduces the efficiency more than individual pathway.
Additional file [99]3 shows nNE values after inhibition of individual
and combined pathway(s) in HER2. The top-10 pathways, which if
inhibited, have resulted in a better efficiency reduction of the
network include: ‘Role of BRCA1 in DNA Damage Response’, ‘NAD
biosynthesis II (from tryptophan)’, ‘Protein Kinase A Signaling’,
‘Ephrin Receptor Signaling’, ‘Growth Hormone Signaling’, ‘Cellular
Effects of Sildenafil (Viagra)’, ‘Human Embryonic Stem Cell
Pluripotency’, ‘Axonal Guidance Signaling’, ‘CXCR4 Signaling’ and
‘Acute Phase Response Signaling’. The inhibition of ‘Role of BRCA1 in
‘‘DNA Damage Response’ an important role reducing the efficiency of
5.2% (PCI). The inhibition of both ‘NAD biosynthesis II (from
tryptophan)’ and ‘Role of BRCA1 in DNA Damage Response’ plays a more
important role reducing the efficiency of the network of 8% (PCI).
DTPN was built considering 39 pathways whose inhibition reduces the
efficiency of the network. Starting from the 13 BC drugs, we obtained
five drugs (fluoracil, docetaxel, fulvestrant, methotrexate, and
tamoxifen) that interact with the HER 2 DTPN (Table [100]3).
Table 3.
Drug-pathway association in the DTPN of HER2 subtype
Drug Pathway nNE and PCI (vs NE = 0.3492)
Fluoracil Salvage pathways of pyrimidine deoxyribonucleotides 0.3482
(PCI 0.27%)
Docetaxel Epithelial adherens junction signaling 0.3485 (PCI 0.17%)
Fulvestrant Bladder cancer signaling 0.3473 (PCI 0.51%)
Methotrexate Salvage pathways of pyrimidine deoxyribonucleotides 0.3482
(PCI 0.27%)
Tamoxifen Human embryonic stem cell pluripotency (1); regulation of the
epithelial-mesenchymal transition (2) (1) 0.3464 (PCI 0.78%)
(2) 0.3490 (PCI 0.02%)
(1–2) 0.3462 (PCI 0.84%)
[101]Open in a new tab
In particular, the ‘Salvage Pathways of Pyrimidine
Deoxyribonucleotides’ is target of two FDA-approved BC drugs
(fluoracil, and methotrexate). The ‘Bladder Cancer Signaling Pathway’,
if inhibited, reduces the NE of 0.5% and it is target of fulvestrant.
The ‘Epithelial Adherens Junction Signaling Pathway’, if inhibited,
reduces the NE of 0.2%, and it is target of docetaxel. The ‘Human
Embryonic Stem Cell Pluripotency pathway’, if inhibited, reduces the NE
of 0.8% and it is target of tamoxifen. The ‘Regulation of the
Epithelial-Mesenchymal Transition Pathway’ if inhibited, reduces the NE
of 0.1% and it is target of tamoxifen. The ‘Regulation of the Salvage
Pathways of Pyrimidine Deoxyribonucleotides’, if inhibited, reduces the
NE of 0.2% and it is target of fluoracil, and methotrexate.
Among the drug target pathways found in our DTPN we found 1 pathway
that was ranked as to be part of the top 10 pathways playing the major
role in the reduction of NE (‘Human Embryonic Stem Cell Pluripotency’).
FDA-approved drugs do not seem to act on ‘Role of BRCA1 in DNA Damage
Response Pathway’ and ‘NAD biosynthesis II (from tryptophan)’, pathways
that, according to our findings, were the best potential drug targets.
Furthermore, considering FDA-approved drugs, our study confirms that
tamoxifen is the drug with the best action on pathway network since it
is able to inhibit ‘Human Embryonic Stem Cell Pluripotency’.
Basal BC
We found a pathway network in basal BC composed of 43 individual
pathways and 74 pathway cross-talks (Fig. [102]6a). In the TCGA
training dataset the mean AUC was 0.98 and 0.97 in the TCGA testing
dataset (Fig. [103]6b).
Fig. 6.
[104]Fig. 6
[105]Open in a new tab
Basal. a Pathway network: nodes represent pathways (43) and edges
represent interactions between pathways (74); b Boxplot of AUC values
for training and testing dataset; c Trend of new network efficiency
(nNE) calculated after removal of individual pathways; d Trend of nNE
values calculated after removal of all combinations of couples of
pathways. Red lines represent the efficiency of the original network
(NE)
The efficiency of the pathway network in basal BC was 0.3445. nNE in
case of the single inhibition of the 43 individual pathways or each
combination of pairs of pathways are shown in Fig. [106]6c and d,
respectively. The inhibition of 19/43 of individual pathways reduces
the NE of the pathway network with values that ranges from 0.2959 to
0.3443 (Fig. [107]6c). The inhibition of 416/903 combinations of
pathways reduces the NE of the pathway network with values that ranges
from 0.2148 to 0.3445 (Fig. [108]6d). The inhibition of couples of
pathways is confirmed to reduce the efficiency more than individual
pathway.
Additional file [109]4 shows nNE values after inhibition of individual
and combined pathway(s) in basal BC. The top-10 pathways which, if
inhibited, have resulted in a better efficiency reduction of the
network include: ‘EIF2 Signaling’, ‘Mismatch Repair in Eukaryotes’,
‘Aryl Hydrocarbon Receptor Signaling’, ‘Cell Cycle Control of
Chromosomal Replication’, ‘Cell Cycle: G2/M DNA Damage Checkpoint
Regulation’, ‘Role of BRCA1 in DNA Damage Response’, ‘Noradrenaline and
Adrenaline Degradation’, ‘Histamine Degradation’, ‘Tryptophan
Degradation X (Mammalian, via Tryptamine)’ and ‘Dopamine Degradation’.
The inhibition of ‘EIF2 Signaling’ plays a more important role reducing
the efficiency of 14% (PCI). The inhibition of both ‘Mismatch Repair in
Eukaryotes’ and ‘Cell Cycle:G2/M DNA Damage Checkpoint Regulation’
plays a more important role reducing the efficiency of the network of
37% (PCI).
DTPN of basal BC was built considering 19 pathways that reduced NE. We
obtained 6 drugs (fluoracil, capecitabine, fulvestrant, methotrexate,
raloxifene and tamoxifen) that interact with the DTPN in basal BC
(Table [110]4).
Table 4.
Drug-pathway association in the DTPN of basal BC subtype
Drug Pathway NE nNE and PCI (vs NE = 0.3445)
Fluorouracil Aryl hydrocarbon receptor signaling (1); salvage pathways
of pyrimidine deoxyribonucleotides (2) 0.3445 (1) 0.3241 (PCI 5.9%)
(2) 0.3425 (PCI 0.56%)
(1–2) 0.3208 (PCI 6.86%)
Capecitabine Triacylglycerol degradation 0.3445 0.3429 (PCI 0.46%)
Fulvestrant Aryl hydrocarbon receptor signaling 0.3445 0.3241 (PCI
5.9%)
Methotrexate Salvage pathways of pyrimidine deoxyribonucleotides 0.3445
0.3425 (PCI 0.56%)
Raloxifene Aryl hydrocarbon receptor signaling 0.3445 0.3241 (PCI 5.9%)
Tamoxifene Aryl hydrocarbon receptor signaling 0.3445 0.3241 (PCI 5.9%)
[111]Open in a new tab
In particular, the ‘Salvage Pathways of Pyrimidine
Deoxyribonucleotides’ is target of two FDA-approved drugs
(fluorouracil, and methotrexate) and ‘Aryl Hydrocarbon Receptor
Signaling’ of 4 FDA-approved drugs (fluorouracil, fulvestrant,
raloxifene, and tamoxifene). The ‘Aryl Hydrocarbon Receptor Signaling’,
if inhibited, reduces the NE of 5%. The ‘Salvage Pathways of Pyrimidine
Deoxyribonucleotides’, if inhibited, reduces the NE of 0.5%. The
‘Triacylglycerol Degradation Pathway’, if inhibited, reduces the NE of
0.4% and it is target of capecitabine.
Among drug target pathways found in our DTPN we found 1 pathway of the
top 5 pathways playing the major role in the reduction of NE (‘Aryl
Hydrocarbon Receptor Signaling’). In particular, fluorouracil acting on
2 pathways reduces the NE of 6.86%, thus it is very effective.
The considered FDA approved drugs for BC do not seem to act on ‘EIF2
Signaling’ and ‘Mismatch Repair in Eukaryotes’, pathways that,
according to our findings, could be interesting potential drug targets.
Validation of DTPN
To classify luminal A, we considered the expression levels of 2693
genes from the independent GEO dataset [112]GSE58212 and belonging to
our luminal A pathway network. We found PRC1 (AUC = 0.862), CCNB2
(AUC = 0.847), BIRC5 (AUC = 0.838), PTTG1 (AUC = 0.831), CCNA2
(AUC = 0.827), E2F1 (AUC = 0.826), KIF11 (AUC = 0.826), CDC25A
(AUC = 0.823), UBE2C (AUC = 0.82), and CDK1 (AUC = 0.817) as the top 10
genes obtaining the best classification performance. Among them, 7/10
genes play a crucial role in luminal A DTPC. PRC1, CCNB2, PTTG1, KIF11,
and CDC25A belong to ‘Mitotic Roles of Polo-Like Kinase’ pathway. The
inhibition of this pathway can reduce the NE of luminal A of 1% (PCI).
CCNA2 and CDK1 belong to ‘Regulation of Cellular Mechanics by Calpain
Protease’ pathway. Its inhibition can reduce the NE of luminal A of
0.6% (PCI).
In luminal B BC we considered the expression levels of 2158 genes from
the GEO dataset belonging to our luminal B pathway network. We found
CX3CL1 (AUC = 0.779), KIF11 (AUC = 0.777), PTTG1 (AUC = 0.766), PRC1
(AUC = 0.761), CCNA2 (AUC = 0.757), ACTG2 (AUC = 0.756), CCNB1
(AUC = 0.755), RND3 (AUC = 0.752), KIF23 (AUC = 0.751), and CLDN8
(AUC = 0.749) as the top 10 genes. Among them, 6/10 genes play a
crucial role in luminal B DTPN. KIF11, CCNB1, PTTG1, PRC1, and KIF23
belong to ‘Mitotic Roles of Polo-Like Kinase’ pathway, whose inhibition
can reduce the NE of luminal B of 2% (PCI). ACTG2 belongs to ‘Tight
Junction Signaling’ pathway. Its inhibition can reduce the NE of 1.7%
(PCI).
In HER2 BC we considered the expression levels of 1172 genes from the
GEO dataset belonging to 100 pathways of our HER2 pathway network. We
found ESR1 (AUC = 0.811), E2F1 (AUC = 0.78), RARG (AUC = 0.769), PNMT
(AUC = 0.758), PLCB3 (AUC = 0.757), ERBB2 (AUC = 0.741), MYL5
(AUC = 0.729), PPP1R10 (AUC = 0.729), BCL2 (AUC = 0.727), and WNT3
(AUC = 0.724), as the top 10 genes.
Among them 6/10 genes play a crucial role in DTPN in HER2. E2F1 belongs
to ‘Role of BRCA1 in DNA Damage Response’ and ‘Growth Hormone
Signaling’ pathways. These pathways are involved in HER2 DTPN since
their inhibition can reduce the NE of 5.3 and 1.1% (PCI), respectively.
PLCB3, ERBB2, MYL5, and WNT3 belong to ‘Axonal Guidance Signaling’
pathway, whose inhibition can reduce the NE of HER2 of 0.8% (PCI).
PPP1R10 belongs to ‘Protein Kinase A Signaling’. Its inhibition can
reduce the NE of 2.8% (PCI).
Furthermore, ESR1 is targeted by fulvestrant, toremifene, raloxifene,
and tamoxifen; ERBB2 is targeted by tamoxifen, and BCL2 is targeted by
fulvestrant, gemcitabine, docetaxel and tamoxifen.
In basal BC we considered expression levels of 1196 genes from the GEO
dataset belonging to 43 pathways of basal pathway network. We found
RHOB (AUC = 0.946), FBP1 (AUC = 0.942), RARA (AUC = 0.911), PPP1R14C
(AUC = 0.909), E2F3 (AUC = 0.905), F7 (AUC = 0.904), RND1
(AUC = 0.903), ESR1 (AUC = 0.902), CDC20 (AUC = 0.892), and CCNE1
(AUC = 0.892), as the top 10 genes.
RARA, ESR1, and CCNE1 belong to ‘Aryl Hydrocarbon Receptor Signaling’
pathway whose inhibition can reduce the NE of 6% (PCI). E2F3 belongs to
‘Role of BRCA1 in DNA Damage Response’ pathway. This pathway is
involved in basal DTPN since its inhibition can reduce the NE of 4%
(PCI).
DTPN pathways in BC subytpes
We have constructed a model of BC heterogeneity based on different
subtype pathway networks. Table [113]5 shows de-regulated pathways in
common in all subtypes, which can be considered as responsible for the
initial stages of BC, and those de-regulated pathways present only in
one subtype, specific of the behaviour of that subtype.
Table 5.
Pathways in common in all breast cancer subtypes and specific for each
subtype as derived from the proposed network approach
Common to all Luminal A Luminal B HER2 Basal
Assembly of RNA polymerase II complex Cell cycle regulation by BTG
family proteins Antioxidant action of vitamin C Actin cytoskeleton
signaling Aryl Hydrocarbon Receptor Signaling
Axonal guidance signaling Chemokine signaling Germ cell-sertoli cell
junction signaling Adrenergic signaling eNOS signaling
Coagulation system Chondroitin sulfate biosynthesis HMGB1 signaling
Breast cancer regulation by Stathmin1 Fatty acid oxidation I
Colorectal cancer metastasis signaling chondroitin sulfate biosynthesis
(late stages) IL-6 signaling cAMP-mediated signaling Gluconeogenesis I
EIF2 signaling Ephrin B signaling Linolenate biosynthesis II (animals)
Cardiac hypertrophy signaling Glycolysis I
Ethanol degradation II Granulocyte adhesion and diapedesis Antioxidant
action of vitamin C Caveolar-mediated endocytosis signaling IL-1
signaling
Ethanol Degradation IV Heparan sulfate biosynthesis Germ cell-sertoli
cell junction signaling Corticotropin Releasing Hormone Signaling
Mitochondrial Dysfunction
Extrinsic prothrombin activation pathway Heparan sulfate biosynthesis
(late stages) HMGB1_Signaling CREB signaling in neurons mTOR signaling
Fatty acid-oxidation LXR/RXR activation IL-6 signaling DNA
damage-induced 14–3–3 signaling Phenylalanine Degradation IV
(Mammalian, via Side Chain)
HIF1 signaling Pancreatic adenocarcinoma signaling Linolenate
biosynthesis II (animals) Endothelin-1 signaling Phototransduction
pathway
Histamine degradation Pregnenolone biosynthesis Gap junction signaling
PI3K/AKT signaling
Noradrenaline and adrenaline degradation Regulation of cellular
mechanics by calpain protease GDNF family ligand–receptor interactions
Oxidative ethanol degradation III Semaphorin signaling in neurons
Glycine betaine degradation
Putrescine degradation III Superoxide radicals degradation Glycogen
degradation II
Tryptophan degradation X (mammalian, via tryptamine) Induction of
apoptosis by HIV1
Leptin signaling in obesity
Macropinocytosis signaling
NAD biosynthesis II (from tryptophan)
Ovarian cancer signaling
Relaxin signaling
RhoGDI signaling
Role of IL-17A in psoriasis
Role of tissue factor in cancer
Sperm motility
Synaptic long term depression
tRNA splicing
Tryptophan degradation to 2-amino-3-carboxymuconate semialdehyde
TWEAK Signaling
[114]Open in a new tab
In italics pathways that are target of FDA-approved drugs for breast
cancer are indicated
We can observe in particular that: (i) ‘LXR/RXR Activation Pathway’,
that in our analyses emerged as a new potential drug target for luminal
A, is specific for this subtype; (ii) ‘NAD biosynthesis II (from
tryptophan)’ pathway, that according to our findings was found the best
potential drug target in HER2, is specific for HER2 subtype; and (iii)
‘Aryl Hydrocarbon Receptor Signaling’, that is targeted by 4
FDA-approved drugs (fluorouracil, fulvestrant, raloxifene, and
tamoxifene) is specific of basal subtype.
Discussion
The formation of new alternative signaling pathways upon drug treatment
is one of the main causes of inefficacy or development of drug
resistance in cancer. The study of alternative signalling through
pathway cross-talk can help drug-development strategies. Therefore, in
this work, we optimized a computational method to investigate the role
of pathways target of FDA approved drugs or new drugs that specifically
addresses this issue. Our in silico method is based on the
identification and quantification of the pathway cross-talk for
distinct cancer subtypes. The effects of drugs inhibiting individual or
combined pathways can be simulated and measured with the purpose to
find new potential drug-targets or synergistic combination drugs.
Most works that study drug-development approaches use network models.
Usually the most used methods to examine the effects of drugs involve
protein–protein interactions, metabolic control analysis or neural
network [[115]18–[116]21]. Nevertheless, most of these methods are
focused on the detection of drugs that have only one target (single
hit) and often are not able to reveal the pathway interactions in an
effective way [[117]2]. Furthermore, these methods show to depend on a
large number of parameters [[118]2]. Pathway cross-talk can be an ideal
framework to assess pathway interactions that could originate phenomena
of resistance or inefficacy of the drug. The investigation of
drug-induced modifications on the different levels of pathway
cross-talk can improve the knowledge of drug effects and potential drug
targets.
Although based by the computational method of Jaeger et al. [[119]6],
our application presents several differences: (1) the Jaeger’s work was
focused on cross-talk due to the presence of overlapping genes between
different pathways, we instead explore cross-talk as regulatory
interactions among distinct pathways; (2) in Jaeger’s work all KEGG
pathways were considered, we instead made a selection on pathways based
on their different activity in different cancer subtype vs normal
tissues. In line with the last scenario, the computational method of
Jaeger et al. is suitable to model static pathway cross-talks, while
our method could be used to represent dynamic disease progression.
Using Monte-Carlo cross validation and classification, we built
networks of cross-talking pathways able to classify with high
performance cancer subtypes versus NS. We inhibited pathway cross-talks
in such subtype-derived networks and identified a number of pathways
that could be potential targets of drugs by measuring drug ability in
reducing the network efficiency.
We applied our network-based model of cross-talking pathways to the BC
subtype gene expression level of an independent GEO dataset, to test
independently the ability of the pathway networks to classify BC
subtypes. We found that the top 10 genes achieving the best
classification performance belong to the expected cross-talking
pathways.
We then associated some drugs already approved by FDA for BC with the
networks of cross-talking pathways. The association was done
considering the number of known drug-target proteins mapped on pathways
and selecting potential synergistic combination of drugs. In general,
the inhibition of pairs of pathways of the pathway network reduces the
network efficiency more than the inhibition of individual pathways. The
considered FDA approved drugs for BC act on several pathways included
in the networks of cross-talking pathways. However, they do not act on
other pathways that, according to our findings, could be interesting
potential drug targets in BC.
In luminal A, we found that the individual inhibition of ‘LXR/RXR
Activation’ pathway reduces the efficiency of the network of 5.6%. LXRs
are nuclear receptors (NRs) involved in cholesterol, glucose, fatty
acid metabolism and inflammatory responses. NRs are a family of
transcription factors that bind to respond to lipophilic signaling
molecules (ligand) regulating downstream effectors. They also represent
one of the most important drug-development targets, since the designing
of synthetic compounds that mimic the functions of ligands can
selectively modulate the activity of NRs [[120]22]. Selective estrogen
receptor modulators (SERMs) and aromatase inhibitors (AI) are examples
of synthetic compounds, which block the production of estrogen
[[121]23]. LXRs ligands on prostate cancer reported an effect on cell
proliferation and cell cycle, acting on p27 and SKP2 [[122]24]. In BC
the effects of LXRs ligands seem to be slightly different, as there is
a decrease of SKP2 but not of p27 [[123]25]. However, there are
conflicting reports about the potential role of ‘LXR/RXR Activation’
pathway in BC [[124]25–[125]28]. In our diagnostic pathway cloud,
synergistic drug combinations acting on: (1) ‘LXR/RXR Activation’ and
‘Extrinsic and Prothrombin Activation’ Pathways; (2) ‘LXR/RXR
Activation’ and ‘Estrogen Receptor Signaling’; (3) ‘LXR/RXR Activation
pathway’ and ‘RAR Activation’ reduce of almost 9% the efficiency of the
network in luminal A. To date, synergistic drug combinations performing
on these pathways are not reported. Therefore, in luminal A we suggest
LXR/RXR Activation as drug-pathway target combined with a drug acting
on ‘Extrinsic and Prothrombin Activation’ Pathway, ‘Estrogen Receptor
Signaling’ or ‘RAR Activation’.
In luminal B subtype we found that the individual inhibition of ‘Cell
Cycle Control of Chromosomal Replication’ reduces the efficiency of the
network of 4.5% while, in our pathway network, synergistic drug
combinations acting on: (1) ‘Growth Hormone Signaling’ and ‘Regulation
of the Epithelial-Mesenchymal Transition’ Pathways; (2) ‘Mitotic Roles
of Polo-Like Kinase’ and ‘Cell Cycle Control of Chromosomal
Replication’; (3) ‘Cell Cycle Control of Chromosomal Replication’ and
‘Growth Hormone Signaling’ reduce of almost 10% the efficiency of the
network. To date, synergistic drug combinations targeting on these
pathways are not presented. Regarding FDA approved drugs, we confirm
docetaxel and tamoxifen as promising candidates for synergistic drug
combinations since they can modulate ‘Epithelial Adherens Junction
Signaling’ and ‘Regulation of the Epithelial-Mesenchymal Transition
Pathway’, respectively. Previous studies showed that a combination
therapy of these drugs in BC cell lines increases the
anti-proliferative effects of single agents [[126]29, [127]30].
In HER2 subtype we found that the inhibition of BRCA1 in ‘DNA Damage
Response’ reduces the efficiency of 5.2% while, in our pathway network,
synergistic drug combinations acting on ‘NAD biosynthesis II (from
tryptophan)’ and ‘Role of BRCA1 in DNA Damage Response’ reduce the
efficiency of 8%.
In basal subtype we found that the inhibition of EIF2 Signaling reduces
the efficiency of 8.5%. While, in our diagnostic pathway cloud,
synergistic drug combinations acting on ‘Mismatch Repair in Eukaryotes’
and ‘Cell Cycle:G2/M DNA Damage Checkpoint Regulation’ reduce the
efficiency of the network of 37%. Regarding FDA-approved drugs, we
suggest fulvestrant, or fluorouracil and/or capecitabine as promising
candidates for synergistic drug combinations since they can modulate
‘Epithelial Salvage Pathways of Pyrimidine Deoxyribonucleotides’, ‘Aryl
Hydrocarbon Receptor Signaling’ and ‘Triacylglycerol Degradation’.
Our approach presents some limitations. One limit is the dependence on
expression data analysis to identify affected pathway, mainly based on
the use of changes in gene expression that are largest in size or
level. However, some studies [[128]31] show that even small changes in
expression levels, which seems not to be of greatest functional
significance, can be relevant in terms of phenotypic difference. For
example a yeast cell containing a mutation in a gene that confers
temperature sensitivity, thus essential for survival at non-permissive
temperature, can indeed growth at the normal or permissive temperature.
The temperature-sensitive gene in this mutant cell usually has a point
mutation that leads to subtle changes in its protein production, but
has a high impact in the protein function and thus in the phenotype of
the cell. Thus, one possible risk of our approach is to lose
significant features from a biological point of view (e.g. significant
pathways), even if not from a statistical point of view. Furthermore,
in our work, as in common in many studies, there is also a dependency
in the data analysis workflow based on the used public datasets.
Computational methods that use biological information (as pathways)
from prior knowledge could have a bias towards pathways or genes that
are better known, since they are more present in the literature and
databases [[129]32–[130]34]. Moreover, different pathway databases can
give different list of genes of the same pathway leading to affect the
results of the computational method. On the other hand, public
experimental data could derive by different experimental conditions
and/or lack of standardizations of experimental designs. In addition,
the details of cell-specific information or clinical data of samples
are not always available [[131]32–[132]34]. However, we optimized our
computational method based on the reported databases by reducing the
bias on data from such databases performing a Monte Carlo
cross-validation.
As overall result, our approach have generated promising hypotheses for
identifying altered pathways as potential therapeutic targets, either
by using synergistic drug combinations and new/approved drugs. Despite
the number of drugs is increasing, the specific molecular mechanisms
underlying drug combinations with therapeutic effect remain often
unclear. We believe that the current efforts based on pathway
cross-talk drug strategies will provide key information that are
required to decipher molecular mechanism contributing to resistance or
inefficacy of drugs.
Conclusions
Overall, our computational method has identified a set of new potential
drug targets that have a large impact on cross-talk inhibition. We
consider that further experiments are required to enable the
translation of new drug target pathways into therapeutic strategies,
however our results show that methods focusing on the Monte Carlo
cross-validation and PCI could offer valuable approaches to discover
synergistic drugs.
Moreover, we focused our study on BC, but we suggest the application of
our approach also to other complex and heterogeneous diseases, in which
pathway cross-talk is likely to cooperate important functions.
Additional files
[133]12967_2018_1535_MOESM1_ESM.xlsx^ (68.9KB, xlsx)
Additional file 1. Shows the network efficiency after inhibition of
individual and combined pathway(s) in luminal A BC.
[134]12967_2018_1535_MOESM2_ESM.xlsx^ (68.2KB, xlsx)
Additional file 2. Shows the network efficiency after inhibition of
individual and combined pathway(s) in luminal B BC.
[135]12967_2018_1535_MOESM3_ESM.xlsx^ (131.8KB, xlsx)
Additional file 3. Shows the network efficiency after inhibition of
individual and combined pathway(s) in HER2 BC.
[136]12967_2018_1535_MOESM4_ESM.xlsx^ (29.3KB, xlsx)
Additional file 4. Shows the network efficiency after inhibition of
individual and combined pathway(s) in basal BC.
Authors’ contributions
CC made substantial contributions to conception and design of the work,
in data analysis and drafting the manuscript. GB made substantial
contributions to the interpretation of data. IC made substantial
contributions guiding and revising the work critically for important
intellectual content and has been involved in drafting the manuscript.
All authors read and approved the final manuscript.
Acknowledgements