Abstract
Breast cancer has become the most common cancer that leads to women’s
death. Breast cancer is a complex, highly heterogeneous disease
classified into various subtypes based on histological features, which
determines the therapeutic options. System identification of effective
drugs for each subtype remains challenging. In this work, we present a
computational network biology approach to screen precision drugs for
different breast cancer subtypes by considering the impact intensity of
candidate drugs on the pathway crosstalk mediated by miRNAs. Firstly,
we constructed and analyzed the subtype-specific risk pathway crosstalk
networks mediated by miRNAs. Then, we evaluated 36 Food and Drug
Administration (FDA)-approved anticancer drugs by quantifying their
effects on these subtype-specific pathway crosstalk networks and
combining with survival analysis. Finally, some first-line treatments
of breast cancer, such as Paclitaxel and Vincristine, were optimized
for each subtype. In particular, we performed precision screening of
subtype-specific therapeutic drugs and also confirmed some novel drugs
suitable for breast cancer treatment. For example, Sorafenib was
applicable for the basal subtype treatment, Irinotecan was optimum for
Her2 subtype treatment, Vemurafenib was suitable for the LumA subtype
treatment, and Vorinostat could apply to LumB subtype treatment. In
addition, the mechanism of these optimal therapeutic drugs in each
subtype of breast cancer was further dissected. In summary, our study
offers an effective way to screen precision drugs for various breast
cancer subtype treatments. We also dissected the mechanism of optimal
therapeutic drugs, which may provide novel insight into the precise
treatment of cancer and promote researches on the mechanisms of action
of drugs.
Keywords: breast cancer subtype, miRNA, pathway, crosstalk network,
precision drugs
1. Introduction
Breast cancer is the most common cancer type that leads to women’s
death, especially in China. The high heterogeneity of breast cancer
makes it a great challenge to adopt therapeutic options [[42]1],
because a heterogeneous group of diseases may exhibit distinct features
in terms of histological, prognostic, and clinical outcomes [[43]2]. At
present, breast cancer can mainly be classified into four primary
subtypes, including her2-enriched, luminal A, luminal B, and basal-like
[[44]3,[45]4], distinguished by the expression of some signature genes
such as the estrogen receptor (ER), progesterone receptor (PR), and
HER2. Different subtypes have distinct biological behaviors and
prognosis, and also exhibit various responses to drug therapy
[[46]5,[47]6]. Thus, further research on the biological heterogeneity
of each subtype of breast cancer will be an effective way to improve
the therapeutic efficacy and prognosis of breast cancer [[48]7].
The oncogenesis processes may result from the dysregulations of a
series of important biological pathways [[49]8]. Some studies have
shown that the pathway crosstalk exists extensively in the processes of
development and cell fate [[50]9,[51]10,[52]11]. Cancer cells have been
found to be able to establish alternative signaling pathways through
crosstalk to adapt to drug treatment. In addition, crosstalk can also
promote cancer therapy by inhibiting the main oncogenic pathways. The
inhibition of functional redundancy and pathway crosstalk that promotes
the survival of cancer cells can prevent the resistance in tumor
treatment [[53]12]. Therefore, it is essential to dissect the crosstalk
of dysfunctional pathways and further capture the key molecules that
mediate this functional crosstalk in breast cancer.
MicroRNAs are endogenous, non-coding RNA molecules that have been
widely regarded as important post-transcriptional regulators by damping
the expression level of their target genes. In recent years, studies
have indicated that miRNAs are important component elements of
biological pathways [[54]13]. They regulate the function of biological
pathways through target genes, and then work together with them to
disrupt the pathways of diseases. According to estimates, many
microRNAs play vital roles by regulating processes that are implicated
with the development of cancer [[55]14], such as proliferation,
apoptosis, cell cycle, angiogenesis, etc. Some studies suggest that the
crosstalk between miRNAs and the Wnt pathway may impact oncogenesis,
cancer metastasis, and even drug-resistance processes [[56]15].
Furthermore, miRNAs can also mediate the functional crosstalk of
pathways related with oncogenic processes by targeting their shared or
interacted genes, thus promoting the initiation and progression of
tumors.
In recent years, miRNAs have shown great promise to serve as a target
for drug therapy of cancer. More importantly, some studies have
nominated miRNA-based therapy as a promising strategy for the treatment
of breast cancer [[57]16]. Some evidence demonstrates that drugs could
modulate the expression of miRNAs in various diseases as well. For
example, an experiment has validated that simvastatin could lead to
cell death of breast cancer by up-regulating miR-140-5p [[58]17].
Triiodothyronine has been demonstrated to modulate miR-204 and thus
facilitate the proliferation process in breast cancer [[59]18].
Especially, Shenoda et al. have also demonstrated that miRNA could
mediate the expression of genes related with drug metabolism [[60]19].
Furthermore, Liu et al. have established a database SM2miR [[61]20],
which provides a comprehensive resource about the influences of drugs
on miRNA expression and offers unprecedented opportunities for
researchers on the screening and action mechanism of drugs for disease
treatment. In addition, our previous research also displays that miRNA
participates in the crosstalk among pathways that play important roles
in cancer development [[62]21], indicating that it might be more
effective for screening cancer treatment to evaluate the effects of
drugs on the miRNA-mediated crosstalk between pathways.
In order to match the best treatment for breast cancer, in the present
study, we firstly integrated the disease high-throughput molecular
profiles, miRNA regulation data, and pathway and drug data to construct
and analyze the miRNA-mediated pathway crosstalk network for various
breast cancer subtypes. Then, we derived a novel computational method
to screen precision drugs for different breast cancer subtypes by
quantifying the impact intensity of candidate drugs on the pathway
crosstalk mediated by miRNAs. Finally, survival analysis was combined
for further screening and optimization of the drugs for breast cancer
treatment ([63]Figure 1). In summary, our study proposes an effective
method to screen precision drugs for various breast cancer subtype
treatments. We also dissected the mechanism of optimal therapeutic
drugs, which may promote the shift from inexact medicine to precision
life science.
Figure 1.
[64]Figure 1
[65]Open in a new tab
The workflow of optimizing drugs for different subtypes of breast
cancer. (A) In this work, breast cancer was taken as the research
model. Firstly, we integrated related data resources, including
gene/miRNA expression profile of each breast cancer subtype and
matching patients’ survival information, miRNA-target relationship
data, PPI network and pathway data, and drug and drug target data. (B)
We identified the differential genes/miRNAs of each breast cancer
subtype, and then reconstructed KEGG pathway based on miRNA-target
interactions, which contained both genes and miRNAs. We also screened
the target genes and target miRNAs of Food and Drug Administration
(FDA)-approved anticancer drugs. (C) Identification of breast cancer
subtype-associated risk pathways based on the differential
genes/miRNAs, and calculated crosstalk for any two interrelated risk
pathways. Furthermore we constructed miRNA-mediated specific pathway
crosstalk networks in different subtypes of breast cancer,
respectively. (D) The effectiveness assessment of drugs on dysfunction
crosstalk network to screen candidate drugs, combined with survival
analysis to optimize drugs for each breast cancer subtype. (See Methods
section for details.)
2. Material and Methods
2.1. Sample Matched miRNA/Gene Expression Profiles and Clinical Data
The matched miRNA and gene expression data of breast cancer were
downloaded from TCGA (The Cancer Genome Atlas) database
([66]http://tcga-data.nci.nih.gov/), including 553 human breast cancer
samples and 87 normal samples. These breast cancer samples were divided
into four subtypes, including basal-like (n= 97), Her2 (n = 47),
luminal A (n = 291) and luminal B (n = 118) according to the guidelines
in Cirielloet et al. [[67]22]. All selected expression datasets were
log2-transformed, then standardized. Furthermore, clinical survival
data of these samples in each subtype were also obtained.
2.1.1. miRNA-Target Relationship Data
In this study, we collected experimentally verified miRNA-target
interactions data from four well-known data resources: miRTarBase
[[68]23], mir2Disease [[69]24], miRecords (V4.0) [[70]25], and TarBase
(V6.0) [[71]26]. MiRNA-target relationships in homo species were
extracted and combined together to obtain a more comprehensive dataset.
In total, 57,863 miRNA-target relationships involving 579 miRNAs and
14,652 target genes were collected and used for further analysis.
2.1.2. PPI Network and Pathway Data
The protein–protein interaction (PPI) network data used in this study
were integrated from two databases, HPRD (Human Protein Reference
Database) and STRING (Search Tool for the Retrieval of Interacting
Genes/Proteins) [[72]27,[73]28]. The interactions stored in HPRD were
mainly from experimental validation and text mining. For each recorded
entry in the STRING database, a weighted score was given to measure
their confidence of interaction by considering multiple factors. To
collect high-quality interaction data, we only extracted interactions
with a confidence score ≥900. Then, we combined interactions from the
HPRD and STRING databases. The pathway data used in this study for
functional analysis were obtained from the KEGG (Kyoto Encyclopedia of
Genes and Genomes) database [[74]29].
2.1.3. Drug and Drug Target Data
In this study, according to our research purpose, in order to improve
the practicability of our study, the candidate drugs need to satisfy
two requirements simultaneously. Firstly, existing gene targets and
regulatory effects on miRNA have to be confirmed. Secondly, the drugs
have to have been approved by US Food and Drug Administration (FDA,
[75]https://www.fda.gov/), which are prescribed for cancer treatment.
We extracted drugs and drug targets from DrugBank [[76]30] and SM2miR
[[77]20]. Finally, a total of 36 anticancer drugs were used in this
study. The complete information of the 36 anticancer drugs can be found
in [78]Supplementary Table S1, including drug ID and drug targets.
2.2. Reconstructed KEGG Pathway Graphs
The reconstructed KEGG (Kyoto Encyclopedia of Genes and Genomes)
pathway graphs contained both genes and miRNAs, replicating real
biological pathways. We firstly collected 220 KEGG pathway data and
converted them into undirected graphs with genes as nodes and their
interactions as edges by using our previously developed R package
“iSubpathway Miner” [[79]31]. Then, we reconstructed these pathways by
wiring miRNAs into these pathways through integrating miRNA-target
relations and pathway data. More details, if target genes of a specific
miRNA were over-represented within a pathway, the miRNA was wired into
the pathway by connecting with target genes within the pathway. The
hypergeometric test was used to evaluate the significance of
enrichment. The formulas is as follows:
[MATH:
P=1−∑t=0<
/mn>q−1ltn−lm−tnm
:MATH]
where
[MATH: n :MATH]
represents the number of background genes (all genome-wide genes),
[MATH: m :MATH]
is the number of genes involved in a given pathway,
[MATH: l :MATH]
is the number of target genes for a specific miRNA, and
[MATH: q :MATH]
is the number of miRNA target genes annotated in the given pathway.
2.3. Identification of Risk Genes and miRNAs Related to Breast Cancer
Subtypes
For each breast cancer subtype, we identified significant
differentially expressed genes/miRNAs by comparing the tumor with
normal samples in each subtype. The unpaired Student’s t-test and
fold-change methods were simultaneously used to evaluate differentially
expressed genes/miRNAs. Then, the significance p-values from the t-test
were calibrated by Benjamini-Hochberg multiple tests to obtain the
false discovery rate (FDR) values. Finally, we applied p < 0.01 and
[MATH: log2FC :MATH]
> 2 as thresholds to identify differentially expressed genes/miRNAs.
These significant differentially expressed genes/miRNAs were regarded
as breast cancer subtype-associated genes, which were also defined by
us as risk genes and miRNAs, respectively.
2.4. Mining Risk Pathways Associated with Breast Cancer Subtypes
In order to explore the roles of these risk genes and miRNAs in the
occurrence and development of breast cancer, we performed them to
conduct pathway enrichment analysis to dig out the pathways closely
related to breast cancer. We identified pathways with significant
enrichment results as risk pathways for each subtype based on risk
genes and miRNAs. The cumulative hypergeometric test was used to
calculate the significance of each pathway that enriched by risk genes
and miRNAs. The formula of the cumulative hypergeometric test is as
follows:
[MATH:
P=1−∑k=0<
/mn>mnkN−nM−kNM
:MATH]
where
[MATH: N :MATH]
represents the number of background genes (all genome-wide genes),
[MATH: M :MATH]
is the number of a given pathway’s genes and miRNAs that are annotated
in the
[MATH: N :MATH]
genes,
[MATH: n :MATH]
is total number of the risk genes and miRNAs of a given subtype of
breast cancer, and
[MATH: m :MATH]
is the number of risk genes and miRNAs in the given pathway.
2.5. Establishing the Risk Pathways’ Crosstalk of Breast Cancer
In each breast cancer subtype, we calculated the crosstalk of each pair
of risk pathways based on the correlation strength of genes and miRNAs
between them according to previous studies [[80]21]. The Pearson’s
product moment correlation coefficient and unpaired Student’s t-test
were performed to measure correlation strength for any two interrelated
pathways. As for genes and miRNAs presenting both in pathway
[MATH: i :MATH]
and
[MATH: j :MATH]
, we reckoned their correlation strength only if they interact with
other genes or miRNAs in the PPI network. Then, we used correlation
strength to construct and assess risk pathways’ crosstalk. The formula
of calculating correlation strength is as follows:
[MATH:
CSi,
j=FPi,Pj|Expi
,Expj=−2∗logeP
i+lo<
mi>gePj+<
/mo>logePi,j :MATH]
where
[MATH: i :MATH]
is the gene that is annotated in pathway
[MATH: a;j :MATH]
is the gene that is annotated in pathway
[MATH:
b; Expi :MATH]
and
[MATH:
Expj :MATH]
are the expression values of genes
[MATH: i :MATH]
and
[MATH: j :MATH]
in samples, respectively;
[MATH:
Pi
:MATH]
and
[MATH:
Pj
:MATH]
are the differential significance p-values of genes
[MATH: i :MATH]
and
[MATH: j :MATH]
calculated using the unpaired Student’s t-test, respectively; and P(
[MATH: i,j :MATH]
) is the significant p-value of expression correlation coefficient
between
[MATH: a :MATH]
and
[MATH: b :MATH]
genes/miRNAs based on the Pearson’s product moment correlation
coefficient.
The crosstalk of any pair of risk pathways was gained by adding up all
the correlation strengths between them, and crosstalk of risk pathways
[MATH: i :MATH]
and
[MATH: j :MATH]
was developed based on formula as follows:
[MATH:
Crosstalka,b=<
mstyle mathsize="140%"
displaystyle="true">∑anCS :MATH]
where n presents the number of all gene–gene, gene–miRNA, and
miRNA–miRNA interactions between any two pathways.
In order to strengthen the differences of risk pathways in different
subtypes, we constructed specific dysfunctional crosstalk networks
based on the specific crosstalk relationship in each subtype for
subsequent calculation and research, which means that when a pair of
crosstalk pathways only exist in a certain subtype, they will be
selected to construct the subtype crosstalk network.
2.6. Evaluating the Impacts of Drugs on Crosstalk
We integrated the drug information from the DrugBank and SM2miR
databases and screened them for Food and Drug Administration
(FDA)-approved anticancer drugs that contain both target genes and
target miRNAs, and a total of 36 anticancer drugs were screened.
Research has shown that the crosstalk among the signaling pathways
plays a key role in the occurrence and development of breast cancer.
Thus, evaluating the impact of drugs on pathway crosstalk based on the
expression of drug targets could help to optimize the treatment of
various subtypes of breast cancer. From this standpoint, in order to
assess the impacts of drug on dysfunction crosstalk network, for each
drug, we first removed its target genes and miRNAs from the specific
risk pathway crosstalk of a given subtype. Next, we recalculated the
crosstalk to quantify the destructive effects of drugs on different
subtypes. At the same time, a formula was designed and developed. The
destructive score (DS) of drug to crosstalk was gained using the
following formula:
[MATH:
DSd=
∑ik1−CrosstalkdCrosstalkk :MATH]
where
[MATH:
Crosstalkd
:MATH]
is the crosstalk after drug action, and
[MATH: k :MATH]
presents the number of all specific crosstalks in the subtype.
We determined the destructive score (DS) of all anticancer drugs to
specific crosstalk networks in each subtype to assess the impacts of
drugs on pathway crosstalk of the drugs. A higher DS score indicates
the greater effects of the drug on crosstalk between risk pathways. In
each subtype, we only screened anticancer drugs that could impact the
crosstalk between dysregulated pathways (DS score greater than zero) as
candidate drugs, and we ranked candidate drugs of each subtype by DS
score from high to low in various subtypes of breast cancer.
2.7. Survival Analysis
We performed survival analysis based on the targets of candidate drugs
that were implicated in the specific pathway crosstalk of each subtypes
of breast cancer to evaluate the effects for patient survival of
candidate drugs. For a given drug, we extracted its target genes and
miRNAs that target a specific crosstalk network as drug target
signatures. Each candidate drug target signature was performed for
survival analysis in patients of each subtype separately, and we used
the K-mean clustering method to stratify patients into shorter survival
time and longer survival time groups based on the level of these drug
target molecules’ expression. In this project, we used 100 as the
maximum number of iterations of k means algorithm, and randomly started
k means algorithm 20 times to return the best result. Then Kaplan–Meier
estimate method was used to evaluate the survival difference of these
two classified groups in each subtype, respectively. Finally, the
significance p-value of survival difference was estimated using the
log-rank test.
3. Results
3.1. Identifying Breast Cancer Subtype-Associated Risk Pathways
We identified the risk miRNAs and genes by comparing tumor samples in
each subtype with normal controls, respectively. The differentially
expressed genes and miRNAs were detected using t-test and fold-change
methods, and then multiple testing correction by the Benjamini–Hochberg
procedure was used. Genes/miRNAs with adjusted p-values < 0.01and
|log_2 FC| > 2 were identified as differential expression (risk
genes/miRNAs). In total, we obtained 4096 risk genes (2284 from
basal-like subtype, 2192 from her2-enriched subtype, 1831 from luminal
A subtype, and 2487 from luminal B subtype) and 223 risk miRNAs (148
from basal-like subtype, 72 from her2-enriched subtype, 76 from luminal
A subtype, and 116 from luminal B subtype). Unsupervised hierarchical
clustering analysis was performed to observe discrepancy of the
expression of risk genes and miRNAs between case samples and normal
samples, as shown in [81]Figure 2A. We also performed the degree of
overlap of risk genes and miRNAs between subtypes, displayed in
[82]Figure 2B. These results indicate that genes and miRNAs exhibit
widespread expression disorder in the various breast cancer subtypes.
Figure 2.
[83]Figure 2
[84]Open in a new tab
Global view of risk genes and miRNAs in each subtype of breast cancer.
(A) Heat maps show risk genes and miRNAs in four breast cancer
subtypes. Unsupervised hierarchical clustering analysis is used, which
divided genes and miRNAs into two clusters, the lower and higher
expression values are represented by green and the red colors,
respectively. (B) Venn plots of risk genes and miRNAs associated with
breast cancer subtypes separately. (C) Results of top 10 pathways with
significant enrichment result of each subtype. Note: Basal, basal-like
subtype; Her2, her2-enriched subtype; LumA, luminal A subtype; LumB,
luminal B subtype.
Breast cancer is affected by multiple factors and pathways. In order to
veritably and accurately reflect the changes of the pathways of breast
cancer, we used the methods that we developed previously to reconstruct
all biological pathways among KEGG, and miRNAs were added into the
signaling pathway to form a more abundant signaling pathway. To
discover the biological function of these risk genes and miRNAs, we
used pathway enrichment analysis to identify risk pathways in each
subtype. A pathway is identified as a risk pathway only if risk genes
and miRNAs are enriched in it under the significance level p < 0.05. In
total, there were 32 risk pathways in basal-like subtype, 29 risk
pathways in her2-enriched subtype, 21 risk pathways in luminal A
subtype, and 26 risk pathways in luminal B subtype. We show the top ten
pathways of each breast cancer subtype in [85]Figure 2C. We found that
some risk pathways such as the Chemokine signaling pathway,
ECM–receptor interaction, the PPAR signaling pathway, and Tyrosine
metabolism were simultaneously identified in different breast cancer
subtypes. Furthermore, we found some subtype-specific risk pathways in
each subtype of breast cancer. Amoebiasis, drug metabolism–other
enzymes, fatty acid metabolism, the p53 signaling pathway, and salivary
secretion were found in basal-like, cell adhesion molecules (CAMs) in
her2-enriched, histidine metabolism in Luminal A, and glycerolipid
metabolism and TGF-beta signaling pathway in Luminal B subtypes. These
subtype-specific risk pathways may be one of the reasons that resulted
in distinct molecular mechanisms and clinical outcomes of breast cancer
subtypes.
3.2. Constructing Risk Pathway Crosstalk Networks for Various Subtypes of
Breast Cancer
The occurrence of breast cancer is complex and there is crosstalk
between different functional biological pathways in the process of
cancer development. Thus, it is necessary to dissect the crosstalk of
dysfunctional pathways related to breast cancer. To elucidate the
molecular mechanism of various breast cancer subtypes, we analyzed the
crosstalk between dysfunctional pathways that are related to breast
cancer. In our study, the risk pathway crosstalk networks for each
breast cancer subtype were constructed. The quantification of crosstalk
was conducted by calculating both the correlation strength and the
dysfunction degree of genes and miRNAs in any two risk pathways of each
breast cancer subtype, and the expression correlation coefficient
between genes and miRNAs and the unpaired Student’s t-test of genes and
miRNAs were used for assessment of crosstalk.
Our results showed that there were crosstalks with significant
differences in the extent of crosstalk between risk pathways in each
subtype ([86]Figure 3). For example, ‘calcium signaling pathway’ and
‘focal adhesion’ have more crosstalk relationships with other pathways
in basal-like subtype. ‘Pathways in cancer’ and ‘focal adhesion’
crosstalk more with other pathways in her2-enriched subtype. In luminal
A subtype, ‘Jak−STAT signaling pathway’ has the greatest crosstalk with
‘cytokine−cytokine receptor interaction’. In luminal B subtype,
‘pathways in cancer’ and ‘cytokine−cytokine receptor interaction’
possess larger crosstalk values with other pathways.
Figure 3.
[87]Figure 3
[88]Open in a new tab
The crosstalk for each two interrelated risk pathways in breast cancer
subtypes. Heat maps of crosstalk between risk pathways for comparing
the heterogeneity of crosstalk across different subtypes of breast
cancer. The color of the box represents the crosstalk between the two
pathways, the lower and higher crosstalk are represented by blue and
the red colors, respectively.
Moreover, we found some subtype-specific crosstalk of pathways in
breast cancer. We extracted the specific crosstalk risk pathways of
each subtype and used them to construct the specific crosstalk network
of the risk pathway in four subtypes ([89]Figure 4). There are 197
specific crosstalk relationships in basal-like, 56 specific crosstalk
relationships in her2-enriched, 41 specific crosstalk relationships in
luminal A, and 74 specific crosstalk relationships in luminal B
subtypes. The above results indicate that these subtype-specific
crosstalks of risk pathways may be one of the molecular mechanisms that
lead to distinct clinical outcomes of breast cancer patients, which
will help us to understand the discrepancy between subtypes and points
a new way to optimize the treatment of breast cancer patients.
Figure 4.
[90]Figure 4
[91]Open in a new tab
The specific crosstalk network of each breast cancer subtype. The
yellow rectangle represents the pathways of the specific crosstalk
network. The thickness of edges represents the intensity of crosstalk
between pathways; the larger the crosstalk value, the thicker the edge.
3.3. Screening Candidate Therapeutic Drugs for Each Subtype of Breast Cancer
Based on DS Score
Previous experimental studies have demonstrated that cancer cells could
adapt signaling pathway circuits under drug treatment by establishing
alternative signaling routes through crosstalk [[92]32,[93]33]. Based
on this point of view, we developed an evaluation method to optimize
the therapeutic drugs for each subtype of breast cancer by assessing
the impact of drugs on crosstalk among risk pathways. The drug targets
of each drug were removed from risk pathways and we reconstructed
crosstalk networks targeted by drugs to evaluate the perturbance
effects of those drugs. Next, we recalculated the crosstalk to measure
the perturbance effects of drugs on different subtypes and optimize the
drug use for each subtype of breast cancer. We obtained 36 anticancer
drugs that target both genes and miRNAs, and the results of evaluation
of anticancer drugs are shown in [94]Table 1. We only screened
anticancer drugs of each subtype with a DS score greater than zero as
candidate drugs, and ranked candidate drugs of each subtype by DS score
from high to low. A higher DS score indicates the greater effects of
the drug on crosstalk between risk pathways. In total, there are 33
drugs in basal-like, 32 drugs in her2-enriched, 22 drugs in luminal A,
and 30 drugs in luminal B subtypes.
Table 1.
Screened candidate drugs for various subtypes of breast cancer based on
DS score.
DS Score Ranking Basal Her2 LumA LumB
1 5-Fluorouracil Arsenic trioxide Arsenic trioxide Arsenic trioxide
2 Arsenic trioxide Adriamycin 5-Fluorouracil Adriamycin
3 Tamoxifen 5-Fluorouracil Adriamycin 5-Fluorouracil
4 Trastuzumab Trastuzumab Trastuzumab Trastuzumab
5 Etoposide Paclitaxel Etoposide Etoposide
6 Cisplatin Temozolomide Tamoxifen Cisplatin
7 Paclitaxel Etoposide Vorinostat Topotecan
8 Vorinostat Gemcitabine Bicalutamide Irinotecan
9 Gemcitabine Everolimus Cisplatin Paclitaxel
10 Adriamycin Sunitinib Vemurafenib Tamoxifen
11 Temozolomide Tamoxifen Medroxyprogesterone acetate Vemurafenib
12 Cyclophosphamide Vorinostat Gemcitabine Gemcitabine
13 Bicalutamide Cisplatin Temozolomide Sunitinib
14 Sunitinib Sorafenib Everolimus Vorinostat
15 Vemurafenib Cyclophosphamide Sunitinib Temozolomide
16 Medroxyprogesterone acetate Goserelin Paclitaxel Everolimus
17 Everolimus Vemurafenib Oxaliplatin Lenalidomide
18 Vinblastine Bicalutamide Cyclophosphamide Cyclophosphamide
19 Lenalidomide Vinblastine Sorafenib Bicalutamide
20 Oxaliplatin Lenalidomide Irinotecan Goserelin
21 Sorafenib Imatinib mesylate Topotecan Rapamycin
22 Goserelin Bortezomib Lenalidomide Oxaliplatin
23 Irinotecan Oxaliplatin Vinblastine
24 Mitoxantrone Medroxyprogesterone acetate Sorafenib
25 Topotecan Melphalan Vincristine
26 Imatinib mesylate Gefitinib Medroxyprogesterone acetate
27 Vincristine Rapamycin Bortezomib
28 Gefitinib Vincristine Imatinib mesylate
29 Docetaxel Irinotecan Mitoxantrone
30 Bortezomib Topotecan Melphalan
31 Melphalan Mitoxantrone
32 Rapamycin Docetaxel
33 Epirubicin
[95]Open in a new tab
3.4. Dissecting the Effects of Candidate Therapeutic Drugs for Patient
Survival in Each Subtype of Breast Cancer
A drug could specifically interact with a target molecule to modulate a
physiological process and further impact the progression of a disease
[[96]34]. In order to further screen drugs for breast cancer patients,
we got the patients’ clinical survival information in each breast
cancer subtype. For each candidate therapeutic drug that was screened
based on DS score in different subtypes, we evaluated the drug target
signature’s influence on patient survival. Patients from each subtype
of breast cancer were divided into two groups (shorter survival time
group and longer survival time group) based on the expression of drug
target signatures. As shown in [97]Figure 5, we found that there were,
in total, six candidate therapeutic drugs screened based on DS score
(DS score greater than zero) that significantly correlated with overall
survival (OS) in the different subtypes of breast cancer patients.
Paclitaxel, Vincristine, and Sorafenib in basal-like, Irinotecan in
her2-enriched, Vemurafenib in luminal A, and Vorinostat in luminal B
subtypes. These six dugs not only impacted the crosstalk of risk
pathways, but they also had an effect on the patients’ survival in
their corresponding subtypes. This indicates that they may be more
suitable treatment candidates for the corresponding subtypes of breast
cancer. More details, according to drug target signatures of Paclitaxel
and Sorafenib in the basal-like subtype, these 97 patients were divided
into a shorter survival group (n = 5) and a longer survival group (n =
92), respectively. Vincristine drug target signatures divided 97
patients in the basal-like subtype into a shorter survival group (n =
63) and a longer survival group (n = 55). The 47 patients in the
her2-enriched subtype were separated into a shorter survival group (n =
10) and a longer survival group (n = 37) by Irinotecan drug target
signatures. Based on the drug target signatures of Vemurafenib in
luminal A subtype, the 287 patients (survival information was missing
in four patients) were stratified into a shorter survival group (n =
78) and a longer survival group (n = 209), and Vorinostat drug target
signatures stratified 118 luminal B subtype patients into a shorter
survival group (n = 63) and a longer survival group (n = 55). Here,
drugs’ signatures stratified the patients into two groups in a
statistically significant manner and their expression direction were
not considered.
Figure 5.
[98]Figure 5
[99]Open in a new tab
Kaplan-Meier survival curves of patients at shorter survival time group
or longer survival time group stratified by drug target signatures of
candidate drugs of each breast cancer subtype.
3.5. Dissecting the Mechanism of Candidate Drugs for Each Subtype
In our drugs’ optimization results, Paclitaxel, Sorafenib, and
Vincristine were found to have potential therapy effect in the
basal-like subtype of breast cancer. Consistent with clinical findings,
Paclitaxel and Vincristine were the optimal adjuvant therapy for
triple-negative breast cancer [[100]35,[101]36]. Sorafenib is a
multiple targeted agent which can inhibit tumor cell proliferation and
angiogenesis by inhibiting the activation of multiple different kinases
[[102]37], and our results indicate that Sorafenib plays a therapeutic
role in the basal-like subtype of breast cancer mainly through
affecting specific risk pathway crosstalk mediated by hsa-miR-30a,
hsa-miR-222, and hsa-miR-193a. Some studies have confirmed that
hsa-miR-30a, hsa-miR-222, and hsa-miR-193a play key roles in breast
cancer [[103]38,[104]39,[105]40]. Irinotecan, an antitumor enzyme
inhibitor mainly used for the treatment of colorectal cancer [[106]41],
is suitable for the her2-enriched subtype, which mediates the specific
crosstalk among the risk pathways of the her2-enriched subtype through
regulating hsa-miR-23a and hsa-miR-324. In accordance with the result
of WT Kuo and Eissa [[107]42,[108]43], hsa-miR-324 and hsa-miR-23a have
distinct biological functions in breast cancer. Vemurafenib has long
been approved for the treatment of metastatic melanoma with BRAF
mutation [[109]44], and our results showed that this drug had a
damaging effect on the specific crosstalk of risk pathway of the
luminal A subtype through action on hsa-miR-145. Just as some
researches have shown that miR-145 is a potential cancer biomarker and
serves as a novel target for cancer therapy, including breast cancer
[[110]45]. Vorinostat as an anticancer agent that inhibits histone
deacetylases, approved for cutaneous T-cell lymphoma [[111]46], and
plays a key role in the epigenetic regulation of gene expression.
Vorinostat could act on the specific risk pathways crosstalk of the
luminal B subtype via 14 miRNAs ([112]Figure 6), which have been found
to play important roles in the occurrence and development of breast
cancer, such as hsa-miR-155, hsa-miR-34a, hsa-miR-17, hsa-miR-22, and
hsa-miR-140 [[113]47,[114]48,[115]49,[116]50,[117]51].
Figure 6.
[118]Figure 6
[119]Open in a new tab
The mechanism of optimal therapeutic drugs in each subtype of breast
cancer. Sorafenib, Paclitaxel, and Vincristine were applicable for the
basal-like subtype treatment, Irinotecan was optimum for the
her2-enriched subtype treatment, Vemurafenib was suitable for the
luminal A subtype treatment, and Vorinostat was applied to the luminal
B subtype treatment.
4. Discussion
Breast cancer is a complex disease with high heterogeneity in terms of
the underlying molecular alterations, the cellular composition of
tumors, and even the clinical outcomes. Different subtypes exhibit
distinct biological behavior, prognosis, and usually different
responses to drug treatment [[120]52], yet identifing applicable drugs
for each subtype still largely remains limited. Therefore, it is
urgently needed to develop a systematic pipeline to identify
medications for different subtypes of breast cancer.
The occurrence and development of tumors is a complex process involving
many steps, links, and factors. It is mostly the action of a single
molecule (gene or miRNA) that leads to poor therapeutic effect among
many chemotherapeutic regimens [[121]53]. In recent years, many
researches have revealed that the occurrence of tumors is closely
related to the abnormality of biological pathways, and crosstalk of
abnormal pathways is one of the prime reasons for the poor outcomes of
tumor treatment [[122]54]. Studies have shown that regulatory molecules
such as non-coding RNA participate in the anomaly of biological
pathways through the regulation of genes, adding to the difficulty of
cancer treatment [[123]55]. In order to actually reflect the intricate
crosstalk of pathways, we have developed a new method based on
biological pathways—that is, reconstruction of biological pathways
which include both genes and miRNAs. We have also identified the
optimal drugs by quantifying the effect of candidate drugs on
miRNA-mediated crosstalk of pathways. We have successfully identified
the specific crosstalk of pathways in each subtype of breast cancer and
revealed their pathogenesis respectively by applying this method.
Moreover, we also screened applicable drugs for each subtype of breast
cancer. We successfully screened the most suitable drugs for each
subtype of breast cancer, including Paclitaxel and Vincristine, which
are breast cancer treatment drugs in clinical application. On the basis
of the original application, we accurately identified their
applications in each subtype, such that Paclitaxel and Vincristine were
best for basal-like, Irinotecan was suitable for her2-enriched, and
Vorinostat was the optimal drug for luminal B subtypes. We also
identified other anticancer drugs application in each subtypes of
breast cancer. The results show that our approach could help doctors to
further improve treatment strategies with the current menu of
chemotherapy options.
Currently, several methods have been proposed to optimize drugs for
human cancers. For example, Lamb et al. provided a computational method
to connect diseases and their potential therapeutic small molecules
based on gene expression profiles form disease and cultured human cells
treated with bioactive small molecules respectively [[124]56]. Gottlieb
et al. predicted novel drug indications based on multiple drug–drug and
disease–disease similarity measures [[125]57]. Furthermore, Malas et
al. prioritized drugs using the semantic information between drug and
disease concepts [[126]58]. Comparing with these methods, our study has
some unique features. First, we considered the role of non-coding RNAs
in our approach. Second, our study optimized anticancer drugs by
measuring their effects for mediating the crosstalk between risk
pathways, which was an important molecular mechanism in the initiation
and progression of human cancers. Finally, we optimized candidate drugs
for different breast cancer subtypes, which may further promote the
precise use of drugs for human cancer.
There are also several limitations in our study. First of all, drugs
targeting miRNAs for therapeutic purposes are limited, and there are
many drugs without miRNA targets. Secondly, miRNAs affected by the
drugs are required for further study. We believe that more and more
drugs that regulate miRNAs and drug-regulated miRNAs will be discovered
with the development of in-depth study on the interaction of drugs and
miRNAs, and our method can identify the optimal therapeutic agent for
complex diseases more accurately and comprehensively. In summary, the
results in this study highlight that dissecting subtype-specific risk
pathway crosstalk could provide novel insights into the underlying
molecular mechanisms and thus promote the drug discovery for various
breast cancer subtype. Moreover, we focused on breast cancer in this
study, but the method proposed here could also be applied to many other
complex diseases, as pathway crosstalk is widespread in biological
systems and the dysregulation of which play a critical role in the
occurrence of disease.
Acknowledgments