Abstract
Simple Summary
The heterogeneity of complicated diseases like cancer negatively
affects patients’ responses to treatment. Finding homogeneous subgroups
of patients within the cancer population and finding the appropriate
treatment for each subgroup will improve patients’ survival. In this
study, we focus on triple-negative breast cancer (TNBC), where
approximately 80% of patients do not entirely respond to chemotherapy.
Our aim is to find subgroups of TNBC patients and identify drugs that
have the potential to tailor treatments for each group through drug
repositioning. After applying our method to TNBC, we found that
different targeted mechanisms were suggested for different groups of
patients. Our findings could help the research community to gain a
better understanding of different subgroups within the TNBC population
and can help the drugs to be repurposed with explainable results
regarding the targeted mechanism.
Abstract
Breast cancer (BC) is the leading cause of death among female patients
with cancer. Patients with triple-negative breast cancer (TNBC) have
the lowest survival rate. TNBC has substantial heterogeneity within the
BC population. This study utilized our novel patient stratification and
drug repositioning method to find subgroups of BC patients that share
common genetic profiles and that may respond similarly to the
recommended drugs. After further examination of the discovered patient
subgroups, we identified five homogeneous druggable TNBC subgroups. A
drug repositioning algorithm was then applied to find the drugs with a
high potential for each subgroup. Most of the top drugs for these
subgroups were chemotherapy used for various types of cancer, including
BC. After analyzing the biological mechanisms targeted by these drugs,
ferroptosis was the common cell death mechanism induced by the top
drugs in the subgroups with neoplasm subdivision and race as clinical
variables. In contrast, the antioxidative effect on cancer cells was
the common targeted mechanism in the subgroup of patients with an age
less than 50. Literature reviews were used to validate our findings,
which could provide invaluable insights to streamline the drug
repositioning process and could be further studied in a wet lab setting
and in clinical trials.
Keywords: subgrouping, patient stratification, triple negative breast
cancer, ferroptosis, antioxidant, drug repositioning, drug repurposing,
data mining, network analysis, explainable artificial intelligence
1. Introduction
Breast cancer (BC) is the leading cause of death among female patients
with cancer [[32]1,[33]2]. BC is a highly heterogeneous disease. BC has
intertumoral heterogeneity, where the tumor is heterogeneous among the
BC patients, and intratumor heterogeneity, where the tumor is
heterogeneous within the tumor of an individual patient [[34]3]. A
consequence of heterogeneity is that patients with breast cancer can
have different reactions to the same drug. BC is a clear example of the
need to have personalized medicine that is patient-centric rather than
disease-centric. Implementing precision medicine in a healthcare system
requires developing patient stratification methods to find homogeneous
subgroups of patients to tailor drugs to these subgroups. Developing de
novo drugs is time-consuming, taking between 10 and 15 years, and is
expensive, costing about $1.6 billion [[35]4,[36]5]. To overcome the
problems associated with developing drugs for a subpopulation of a
disease, drug repositioning (DR), the redirection of already approved
drugs to be used for additional diseases by finding new indications,
emerges as an alternative to support precision medicine implementation
[[37]6].
Different molecular features have been used to stratify patients and
reposition drugs. These molecular features range from using a single
gene to using heterogeneous molecular data types; for example, using
P53 to stratify patients based on their P53 status and using the RACK1
status to determine personalized treatment [[38]7,[39]8]. Moreover,
heterogeneous molecular data was used for cancer subtyping and the
repositioning of drugs after ranking genes based on the Gene Ontology
(GO) pathways analysis [[40]9]. The transcriptional response to drugs
was used to infer signaling interactions and to reposition drugs
[[41]10]. Drug repositioning was also accomplished by targeting
pathways that play a role in cancer proliferation and progression
[[42]11]. The oncogenic PI3K-dependent inhibitor was recommended as a
cancer therapy [[43]12]. Drugs were recommended for repositioning based
on the similarity between a particular cancer type therapy and a given
drug using a drug-pathway network [[44]13]. The association between
gene mutation and expression was also used to guide the drug discovery
process [[45]14]. A network analysis was used widely to reposition
drugs for different types of cancer. Studies have introduced many
heterogeneous network models to reposition drugs, including the
drugs–genes–diseases network [[46]15,[47]16], and
drug–targets–pathways–genes–diseases, where the association between
drugs and diseases was found based on multiple targeted genes and
pathways between drugs and diseases [[48]17]. The drug-disease
association was also inferred based on a set of heterogeneous networks
including drug–gene, disease–gene, protein–protein, and gene
co-expression networks to find drug-disease associations and to
reposition drugs [[49]18].
Several methods have been developed to understand the underlying
mechanism of BC and the recommend drugs. A gene expression analysis was
used to identify pathways involved in BC invasion and potential drug
targets [[50]19]. In the context of DR, drugs have been recommended for
their antiproliferation effect on BC patients [[51]20]. Additionally,
drugs that share side effects with BC therapies have been recommended
to be repositioned for BC [[52]21]. A pathway analysis was used with
single-cell data to recommend drugs for BC [[53]22]. A network analysis
also was used to reposition drugs for breast cancer. A tissue-specific
protein–protein interaction (PPI) network was used to evaluate and
reposition drugs over the drug–miRNA–diseases network [[54]23]. A
network propagation analysis was used to reposition drugs based on a
drug-pathway network, where pathways for each drug were identified by
an enrichment analysis of the genes regulated by each drug using the
Connectivity Map (CMAP) for the drug’s phenotypic profile [[55]24].
Moreover, computational methods with diverse molecular data types were
developed to identify therapeutic targets for BC [[56]25]. These
methods considered breast cancer in general without considering its
subtypes and did not address the heterogeneity of BC. Other methods
have been developed to stratify BC patients. This stratification was
done based on cell receptor statuses; for example, an estrogen receptor
positive (ER+) status and an estrogen receptor negative (ER−) status
[[57]26].
Based on the gene expression profile, four molecular subtypes have been
identified for breast cancer which are luminal A, luminal B, human
epidermal growth factor receptor 2 (HER2)-enriched, and basal-like
(triple negative) [[58]27]. Computational methods have been developed
to find therapeutic biomarkers and to reposition drugs for breast
cancer subtypes. The correlation between DNA copy number alterations
and gene expression was used to find biomarkers for each subtype
[[59]28]. Electronic health record (EHR) and differentially expressed
gene (DEG) data were used to reposition drugs for the four BC molecular
subtypes by finding drug pairs using drug–protein relations [[60]29].
mRNA was used to reposition drugs for these subtypes using gene
co-expression [[61]30]. Moreover, drugs were predicted based on the
suggested miRNA biomarkers for each subtype [[62]31]. Drugs were
recommended based on the number of hub genes each drug targeted within
the miRNA–protein–drug network [[63]32]. An integrated network of
relations between lncRNA, miRNA, and mRNA was used to recommend drugs
that reversed the lncRNA expression of each BC molecular subtype
[[64]33]. A pathway analysis was also used to reposition drugs for BC
subtypes [[65]34]. Biomarker predictions using cell line data were used
to stratify BC patients and suggested drugs for each subgroup [[66]35].
These methods have demonstrated the importance of stratifying BC
patients into homogeneous subgroups and repositioning drugs into these
subgroups. Still, they have considered only the genotypic
characteristics of BC patients without considering the significance of
phenotypic features. Other methods have taken phenotypic data into
consideration, but the majority of these methods include the phenotypic
features as the post-analysis of the stratification process, where
clustering methods have been used to cluster the genotypic profiles of
patients and then map the phenotypic traits onto each cluster in order
to assign patients to each subgroup [[67]36].
This study implements our data-driven approach to stratify BC patients
based on genotypic and phenotypic data. The genotypic data is mapped
onto a heterogeneous drug knowledge base to find druggable homogeneous
BC subgroups and to reposition drugs for each subgroup [[68]37]. We
have decided to focus on triple negative breast cancer (TNBC), as
defined by the lack of Estrogen Receptors (ERs) and progesterone
receptors (PRs) and by a HER2-negative status, due to its increasingly
recognized heterogeneity not only on the molecular level but also on
the pathologic and clinical levels [[69]38]. The lack of targets like
ER, PR, and HER2 in TNBC implies that chemotherapy remains the only
treatment of choice for patients with TNBC, which unfortunately fails
to achieve prolonged remission in the majority of cases. Indeed, on
recurrence, patients with TNBC have worse survival outcomes than
patients with ER-positive and/or HER2-positive BC subtypes
[[70]39,[71]40]. Other studies have demonstrated the importance of
finding homogeneous subpopulations within the TNBC population, but
these studies mainly focused on a set of genes or single nucleotide
polymorphisms (SNPs) to identify drug targets for TNBC [[72]41,[73]42].
Other methods had a broader range of data, such as gene expression
data, to identify key genes that could become drug targets [[74]43]. As
these methods represent a step toward understanding TNBC progression
and improving patient survival, targeting a heterogeneous disease
requires considering a wide variety of genotypic and phenotypic
factors. Therefore, we propose to use a data-driven approach in order
to dissect TNBC heterogeneity as much as possible and to discover
druggable molecular targets that may enhance the chemotherapy benefit
by blocking chemoresistance pathways and, thus, cancer recurrence in
TNBC patients.
2. Materials and Methods
Data from 980 breast cancer patients was obtained from The Cancer
Genome Atlas (TCGA). The dataset consisted of phenotypic data and
genotypic data. For the phenotypic variables, we used 19 clinical
variables ([75]Figure 1, Module 1 and [76]Supplementary Figure S1:
Table S1). The continuous phenotypic variables were converted into
categorical variables. The categorization of the clinical variables was
done based on medical guidelines [[77]44]. The genotypic variables were
the RNA-seq data of these patients.
Figure 1.
[78]Figure 1
[79]Open in a new tab
Flowchart of the data-driven drug repositioning process using
phenotypic and genotypic breast cancer data of TCGA, patient
stratification methods, and a knowledge base for recommendation of drug
repositioning.
A differential analysis was done between 94 normal and 980 tumor
samples. The normal and tumor data were downloaded from TCGA. Based on
the differential analysis done using EdgeR, we selected 1531 of the
most differentially expressed genes with a p-value less than 0.05, a
log2FC > +2 for upregulated genes, and a log2FC < −2 for the
downregulated genes. Then, the genotypic data were normalized using
log2 and categorized based on the z-score value for each gene in each
patient. These genes were categorized into upregulated (z-score > 1),
downregulated (z-score < 1), and normal (z-score between 1 and −1)
genes. The drug repositioning knowledge base (DR-KB) was represented as
a neo4j graph with a gene-centric schema, including relations between
genes, pathways, molecular function, cellular components, biological
processes, disease, and drugs ([80]Figure 1, Module 2). The relations
between these biomedical entities and the source databases of these
relations can be found in [81]Supplementary Figure S1: Table S2
[[82]45].
The method consisted of two parts. The first part was the
identification of the homogeneous subgroups within a heterogeneous
disease population. To find the homogeneity between patients in a
subgroup, genotypic pattern mining was performed for patients sharing
phenotypic features. The relations between gene patterns and biomedical
entities were extracted from the DR-KB after mapping the gene patterns
to the DR-KB graph. The second part of the method was to find suitable
drugs for each subgroup by developing and applying a drug repositioning
algorithm. This algorithm used a graph analysis to analyze each
subgroup’s network. The drugs in each network were then ranked
according to various factors, as discussed below.
2.1. Patient Stratification
To enable precision medicine, we stratified BC patients into
homogeneous subgroups. Each subgroup is defined by a set of clinical
variables and genotypic patterns common among the patients and
representing the underlying factors that are unique to a subgroup and
that differentiate it from the rest of the population. The patients in
a given subgroup should have a statistically significant difference
from the outer population based on gene expression patterns, defined as
the co-occurrence of genes which have certain regulation levels. For
example, if two genes are upregulated or downregulated in most patients
within a subgroup, these genes are considered a pattern in that
subgroup. In our data mining and network analysis method, gene–gene
interactions and enrichment analysis are used to contrast a subgroup
and the outer population instead of using only gene patterns. The
genotypic information is then integrated into a network. Neo4j is used
to do the network analysis on top of the contrast pattern mining that
is performed on the genotypic data.
We represent our subgroup stratification method as a three-level
framework. These levels are path expansion, floating subgroup
selection, and inclusion and exclusion. The subgrouping algorithm is an
extension of our exploratory data mining methods [[83]37,[84]46]. The
algorithm begins with a disease population. The patient stratification
process then divides the population into subgroups based on categorical
values of the clinical variables. To consider a subgroup as valid, the
subgroup should significantly differ from the outer population. The
algorithm adds clinical variables to the current subgroup to create a
more focused subgroup. The process starts with one categorical value
for a clinical variable which is expanded by adding more variables, as
long as there is a statistical significance of doing so and a better
contrast than the previous step. This process is similar to searching
among different paths to reach the local optimum solution. In our
method, the local optimum solution is a subgroup with the highest
contrast from the outer population. For example, if the algorithm finds
that patients with the TNBC subtype have the highest contrast from the
outer population, it will add another variable, such as invasive ductal
carcinoma (IDC), as a histological type. This creates a more focused
subgroup consisting of two variables, TNBC and IDC.
The algorithm checks different variables in parallel. We refer to this
process as checking different paths, where each path represents a
different set of variables that define a subgroup. The path expansion
processes expand the search for subgroups with a high contrast to the
outer population by considering the top
[MATH: X :MATH]
paths with high contrast scores.
To consider a subgroup valid so that the algorithm continues adding a
variable in that path, a contrast score is calculated. That score is
based on gene expression patterns in each subgroup and the interactions
between genes; the pathways, molecular functions, cellular components,
and biological processes to which each gene belongs; diseases; and
drugs that affect the gene expression patterns ([85]Figure 1, Module 2
and Equation (4)). The patterns of a subgroup should be frequent in
that subgroup. Support [[86]47] (Equation (1)) is used to determine if
a pattern is frequent based on a user-defined threshold. In addition to
a pattern being frequent in a subgroup, it must have a high contrast in
the given subgroup compared to the outer population. The growth rate
[[87]48] (Equation (2)) is used to evaluate the contrast of a pattern.
The support and growth rates are used as parameters to calculate the
gene expression pattern contrast score of a subgroup. We have developed
a J-value [[88]46] (Equation (3)) to calculate the contrast for all
patterns in a given subgroup.
[MATH:
Support(p,D<
mo>)=|
〈D,p〉||D|, :MATH]
(1)
where p is a pattern, D is the patients’ data in the dataset, ||
is the number of patients who have this pattern, and |D| is the total
number of patients in the dataset.
[MATH:
Growth (cp, SG1, SG2<
mo>)=Max{s1,s2}Min{s1,s2}, :MATH]
(2)
where cp is the contrast pattern, S[G][1] is the focus subgroup,
S[G][2]is the outer population, s[1] is the support of cp in the focus
subgroup, s[2] is the support of cp in the outer population.
[MATH:
J-value=T ∗ Jo
rg+M ∗ J¯avgT+M
, :MATH]
(3)
where
[MATH: T :MATH]
is a parameter related to the population size preference that depends
on the type of the disease under study and, whether it is a rare
disease or not, based on the concept of the Bayesian average
[[89]49,[90]50,[91]51].
Before doing path expansion, the algorithm randomly picks a set of
subgroups and calculates the average population size,
[MATH: M, :MATH]
of these subgroups.
[MATH:
Jorg
:MATH]
and
[MATH:
Javg
:MATH]
are calculated based on the initial J-value, which is
[MATH:
J2≤∑i≤J<
/mi>Growt<
mi>h(cp,
SG1,SG2) :MATH]
.
[MATH:
J-value
:MATH]
is a quantitative index used to evaluate the overall quality of a set
of contrasting patterns in the subgroup. In addition to
[MATH:
J-value
:MATH]
, a network analysis is used to calculate the network contrast score
(NCS) for each candidate subgroup (Equation (4)).
[MATH:
NCS=1−∑i=1<
/mn>nEi
,1∩Ei
,2Ei,1∪Ei
,2n, :MATH]
(4)
where E[i][,1] is biomedical entity, type i, in the focus group’s
network; and E[i][,2] is biomedical entity, type i, in the outer
population network. These biomedical entities are the genes and the
other biomedical entities that affect these genes like the disease, the
drug, and the biomedical entities to which these genes are part of,
like pathways, molecular function, cellular components, and biological
processes.
NCS is the score of contrast between the network of the subgroup of
interest and the outer population network. Mapping the gene patterns to
the DR-KB creates a heterogeneous network of different biomedical
entities. In this work, these biomedical entities include genes,
pathways, cellular functions, molecular components, biological
processes, diseases, and drugs. The subgroup contrast score (SPCscore)
[[92]37] (Equation (5)) is calculated for each subgroup.
[MATH:
SPCScore=J-value ∗ NC
S, :MATH]
(5)
The output subgroups are filtered based on a user-defined threshold for
the
[MATH:
SPCScore :MATH]
. A set of subgroups with high contrast is selected for further study.
This study focuses on five TNBC subgroups that have a statistically
significant contrast from the outer population. These subgroups are the
input for the next step in which a computational algorithm is used to
reposition drugs for each subgroup.
2.2. Drug Repositioning
A drug repositioning algorithm is used to identify candidate drugs for
each subgroup. As mentioned previously, a subgroup network consists of
heterogeneous entities represented as a neo4j graph, G = (V[G], E[G]).
In each network, there are a number of entities of each type, including
drugs. To find the drugs with the highest potential to treat the
patients in a subgroup, the drugs within each network are ranked
according to their relevance to the genotypic characteristics
([93]Figure 1, Module 3). We have developed a drug scoring function
that includes different factors to put the drug impacts within the
subgroup network (SGNW) into perspective, as well as their impacts in
general on human cells. We consider the number of abnormally expressed
genes in a subgroup, gene expression affected by each drug, and the
importance of each gene within the SGNW. For example, hub genes have
more significance than genes with few connections. Gene weight is based
on the percentage of entities of each biomedical type that connects to
each gene. The mean of gene frequency (MGF) is calculated for each gene
in the SGNW, as follows:
[MATH:
MGF(Genei)=∑k=1<
/mn>enE<
mrow>kiNEke, :MATH]
(6)
where
[MATH:
Genei :MATH]
is a gene in the SGNW for which frequency is calculated,
[MATH: e :MATH]
is the total number of entity types in
[MATH: S :MATH]
, and
[MATH:
Ek∈ S<
/mi> :MATH]
,
[MATH:
S=[Gene, Pathway, Biological process, <
/mo>Cellular component, Molecular function<
mo>] :MATH]
, and
[MATH:
nEki
:MATH]
are the total number of entities of type
[MATH: Ek
:MATH]
that are in direct interaction with
[MATH:
Genei :MATH]
.
[MATH:
NEk
:MATH]
is the total number of entities of type
[MATH: Ek
:MATH]
that exist in the SGNW. The
[MATH: MGF :MATH]
is calculated for all the genes in the SGNW.
For each drug, the algorithm calculates an initial weight, which is the
accumulative gene frequency (AGF), corresponding to the accumulative
weight of all the genes connected to each drug in the SGNW.
[MATH:
AGF(Drugj)=∑i=1<
/mn>mMGF
(Genei), :MATH]
(7)
where m is the total number of genes whose expression is altered by
[MATH:
Drugj :MATH]
within a given subgroup. The
[MATH: AGF :MATH]
for
[MATH:
Drugj :MATH]
is the overall summation of the
[MATH: MGF :MATH]
scores for each of these genes. This equation assigns a value that
represents the importance of the drugs in the context of their ability
to perturb gene expression. The weight represents the importance of a
gene in the subgroup network. The
[MATH: AGF :MATH]
value characterizes a drug’s importance as the average of the genes’
importance connected to that drug.
The other significant factor to consider is the patterns of each
subgroup. In our algorithm, we are not looking at the genes as
independent entities. In addition to taking the interaction of the
subgroup genes with other genes and other biomedical entities, we
consider the gene patterns formed within each subgroup. To address
this, we calculate the percentage of patterns targeted by each drug,
[MATH:
PA(Drugj)<
/mrow> :MATH]
, as follows:
[MATH:
PA(Drugj)<
mo>=NPjNP,
:MATH]
(8)
where
[MATH:
NPj
:MATH]
is the total number of gene expression patterns targeted by
[MATH:
Drugj :MATH]
in the subgroup of interest, and
[MATH: NP :MATH]
is the total number of gene expression patterns in this subgroup. Then,
the overlap score measure (OSM) is calculated for each drug in the
SGNW. The
[MATH: OSM :MATH]
of a drug is the summation of that drug’s importance on two levels. One
level is the connection between that drug and the genes in the
subgroups,
[MATH: AGF :MATH]
. The other level is the importance of the drug based on the patterns
targeted by that drug,
[MATH: PA :MATH]
:
[MATH:
OSM(Drugj)=PA(D
rugj)<
/mrow>+AGF(Drugj
). :MATH]
(9)
In addition to the accumulative gene weights and the percentages of
gene patterns impacted by each drug, the algorithm considers the ratio
of genes whose expression is affected by each drug. This is
accomplished by calculating gene percentages for each drug (GP) as
follows:
[MATH:
GP(Drugj)<
mo>=NGjNG, :MATH]
(10)
where
[MATH:
NGj
:MATH]
is the total number of genes whose expression is perturbed by
[MATH:
Drugj :MATH]
in the subgroup of interest, and
[MATH: NG :MATH]
is the total number of genes in this subgroup.
We also must ensure that the drugs with high a
[MATH: OSM :MATH]
are essential to a given subgroup. It is similar to the idea in the
information retrieval theory, where terms that appear in the majority
of documents have less importance, like stop words such as “the”, and
“is”, and so on. We were inspired by the idea of the inverse document
frequency (IDF) to calculate the drug score [[94]52]. The inverse drug
frequency (IDF) is calculated for each drug in each subgroup. This
helps to increase the score of drugs that are unique to a subgroup and
decrease the score of common drugs that could be related to cancer in
general, but not to that subgroup of interest in particular. The
biological reasoning behind using IDF as a scoring factor is to account
for the amount of perturbation a drug could cause in the human cells.
We want to select those drugs that specifically perturb several
subgroups’ gene patterns. The IDF is calculated as:
[MATH:
IDF(Drugj)=log10(NsnSj), :MATH]
(11)
where
[MATH: Ns :MATH]
is the total number of genes in the RD-KB and
[MATH:
nSj
:MATH]
is the number of genes targeted by
[MATH:
Drugj :MATH]
in that subgroup network.
Finally, the impact of each drug is evaluated within the SGNW to rank
the drugs based on different factors that determine each drug’s effect.
The final drug score (
[MATH:
DScore(Drugj) :MATH]
) is:
[MATH:
DScore(Drugj)=<
/mo>OSM(Dr
ugj) ∗ IDF
(Drugj)∗ GP(Drugj<
/mi>).
:MATH]
(12)
Each drug within the SGNW is assigned a
[MATH:
DScore :MATH]
for ranking purposes. The higher the score, the more potential the drug
has as a treatment for a given subgroup. Our method addresses the
importance of considering different factors in ranking drugs. The
ranking is not only based on the genes connected to the drugs and their
significance in the network, but also on the gene expression
perturbation a drug can cause in human cells and the number of gene
patterns affected by the drug within the subgroup’s network.
3. Results
Patients with triple-negative breast cancer (TNBC), who have negative
immunohistochemical staining for estrogen receptors (ER), progesterone
receptors (PR), and human epidermal growth factor receptor 2 (HER2)
[[95]53], have the lowest survival rate and highest mortality within BC
patients [[96]54,[97]55]. Patients with TNBC have a heterogeneous
response to therapy, in which approximately 80% of patients do not
completely respond to chemotherapy [[98]56]. The importance of finding
better treatments for TNBC has been demonstrated in many studies.
Multi-omics data and a network analysis was used to repurpose drugs for
TNBC [[99]13]. The network analysis was used to find modules active in
TNBC, but not in normal BC or other BC subtypes. Transcriptional and
interactome data were used to create a PPI network to identify highly
connected modules and hub genes as candidate drug targets for
repurposed therapies [[100]13]. Multi-target drugs were also considered
for repositioning to TNBC after creating a drug–target network with the
integration of pathway information and the identification of
protein–protein interactions [[101]57]. Our study offers new guidance
in considering heterogeneity within subgroups and the importance of
finding subpopulations within TNBC. Moreover, in addition to using the
direct relationship between drugs and genes in TNBC, our approach
leverages other factors, like the perturbation that the drug can cause
in the cell.
3.1. Subgroups and Drugs Analysis
Different criteria were used to filter and select subgroups. The
support of the subgroup patterns was greater than 70%, the growth was
greater than 1.5, and the confidence was 1. The resulting subgroups
were ranked based on their SPCscore. The subgroups with an SPCscore > 0
were retained for further filtering and analysis because an SPCscore =
0 means there is no significant contrast between a given subgroup and
the outer population. After filtering the subgroups based on their
contrast score and the biomedical importance to our research question,
the resulting subgroups were five TNBC subgroups with the patients’
neoplasm subdivision, race, and age as clinical variables. In this
work, after analyzing the top drugs for these subgroups, we found
ferroptosis as a common cell death mechanism. Ferroptosis is regulated
cell death (RCD). It is driven by lipid peroxidation and has an iron
dependency [[102]58]. In the subgroups below, we found more than one
drug within the top five drugs that plays a role in inducing
ferroptosis. This result suggests that focusing on ferroptosis in
treating TNBC may be a novel therapeutic route and may be advantageous
for these patients. We found the ferroptosis-inducing drugs were
suggested for the subgroups with race and neoplasm subdivision as
phenotypic variables. Still, there were no ferroptosis-inducing drugs
suggested for the younger subgroups with an age below 50. This finding
coincides with the previous finding of ferroptosis being negatively
correlated with age [[103]59]. The suggested subgroups are as follows:
1. Subgroup1 is for patients who are Black or African-American with no
history of a neoadjuvant treatment. This subgroup has 30 patients,
and it has 286 unique DEGs to this group ([104]Tables S3 and S4).
The drugs that can induce ferroptosis that were suggested for this
subgroup are Afatinib, Gefitinib, Bosutinib, Lapatinib, and
Fulvestrant.
1. Afatinib was found to be a ferroptosis inducer in TNBC by
targeting EGFR [[105]59]. Afatinib’s mechanism of action
includes binding to wild-type epidermal growth factor
receptors (EGFR) and irreversibly inhibiting the kinase
activity of all ErbB family members, which are well-recognized
as an oncogenic driver in epithelial cancers. Thus, Afatinib
can inhibit the proliferation of cancer cell lines [[106]60].
Analyzing the patient profiles in this subgroup showed that
EGFR was abnormally expressed in this subgroup. ErbB4 had an
abnormal expression as well.
2. Gefitinib is an epidermal growth factor receptor (EGFR) and a
tyrosine kinase inhibitor. It has antiproliferative and
anti-tumoral activity in BC [[107]61,[108]62]. Gefitinib
targets EGFR and is used to induce ferroptosis [[109]59]. The
proliferation of TNBC cells can be inhibited by targeting EGFR
kinase activity using gefitinib [[110]63]. Moreover, it can be
used as a combined therapy with multiple EGFR-TKIs, such as
lapatinib and erlotinib, to overcome the resistance to
EGFR-targeted therapy in TNBC. The inhibition of EGFR, in
combination with the dual inhibition of cdc7/CDK9, results in
reduced cell proliferation, accompanied by the induction of
apoptosis, the G2-M cell cycle arrest, the inhibition of DNA
replication, and the abrogation of CDK9-mediated
transcriptional elongation in TNBC cells [[111]63]. Using
gefitinib for this subgroup will inhibit EGFR and cdc7 that
was upregulated in this subgroup. Inhibiting them is necessary
to reduce cell proliferation. CDK1 and CDK5, which are
cyclin-dependent kinases (CDKs), were found to be upregulated
in this subgroup. These protein kinases control cancer
progression and inhibiting them prevents cancer proliferation
[[112]64].
3. Bosutinib promotes autophagy and research has shown that there
is crosstalk between autophagy and ferroptosis [[113]65].
BCR-Abl is the target for bosutinib, dasatinib, imatinib,
nilotinib, ponatinib, and regorafenib. Bosutinib is a dual
Src/Abl inhibitor [[114]66]. In this subgroup, Src was
upregulated; therefore, this drug will inhibit Src and reduce
cell proliferation. Src belongs to a different set of
mechanisms associated with cancer progression, like inducing a
metastatic phenotype, enhancing tumor growth, and enhancing
angiogenesis [[115]67].
4. Lapatinib is a tyrosine kinase inhibitor. This drug was used
as a combined therapy with siramesine, which is a lysosome
disrupting agent, to induce ferroptosis and reactive oxygen
species (ROS) in TNBC [[116]68]. Lapatinib is an inhibitor of
ErbB1 and ErbB2 and induces ferroptosis in BC cell lines by
altering iron regulation. It is used as a combined therapy to
induce ferroptosis in TNBC by inhibiting the iron transport
system, leading to an increase in ROS and cell death
[[117]69]. Ferroportin-1 (FPN) is an iron transport protein
responsible for the removal of iron from cells. Its expression
decreases after treatment with siramesine in combination with
lapatinib. The overexpression of FPN decreases ROS and cell
death, whereas the knockdown of FPN increases cell death after
siramesine and lapatinib treatment. This indicates a novel
induction of ferroptosis through altered iron regulation by
treating breast cancer cells with a lysosome disruptor and a
tyrosine kinase inhibitor [[118]68]. Lapatinib is a tyrosine
kinase inhibitor of the epidermal growth factor receptor
(EGFR) and ErbB2 (HER2) tyrosine kinases. Studies in vitro
showed that lapatinib inhibited the proliferation of ErbB2 and
EGFR, which are overexpressed in cancer cells
[[119]70,[120]71]. Siramesine and lapatinib gave the best
combination index, and this combination induced ferroptosis
through iron-mediated ROS and the downregulation of heme
oxygenase 1 (HO-1) levels [[121]72]. In TNBC, siramesine and
lapatinib increases Transferrin Receptor (TFRC) expression and
decreases ferroportin1 (FPN1) expression, thus elevating the
level of intracellular iron [[122]73].
2. Subgroup2: The patients in this subgroup are not Hispanic or Latino
and White; they have less than three positive lymph nodes, and
their histological type is an intraductal carcinoma. This subgroup
has 37 patients and 87 unique DEGs that are abnormal in this
subgroup, but not in the other subgroups ([123]Tables S3 and S4).
The drugs that can induce ferroptosis that are suggested for this
subgroup are fluvastatin, lovastatin, and gefitinib.
1. Fluvastatin targets HMG-CoA and can induce ferroptosis by
decreasing the expression of glutathione peroxidase 4 (GPX4).
This effect is time- and concentration-dependent
[[124]74,[125]75]. Treatment with this statin induces
ferroptosis by inhibiting GPX4 and the key products in the
mevalonate pathway, like 3-hydroxy-3-methyl-glutaryl-coenzyme
A reductase, and the depletion of CoQ10, that reduces the
levels of this key membrane antioxidant
[[126]74,[127]76,[128]77]. Fluvastatin can be used as a
combined therapy with RSL3, which is a direct inhibitor of
GPX4 [[129]74].
2. Lovastatin: Lovastatin is another statin drug that can inhibit
GPX4 and induce ferroptosis [[130]78]. Similar to fluvastatin,
the lovastatin target point is lipid synthesis, and it
represents a HMGCR/HMG-CoA reductase inhibitor
[[131]79,[132]80].
3. Gefitinib: As mentioned previously, it can be used to induce
ferroptosis in TNBC [[133]59].
After analyzing the genes that are affected by these drugs in this
subgroup, we found that fluvastatin, lovastatin, and gefitinib
downregulate CCT5, which was upregulated in this subgroup. CCT5
interacts with HMGCR, NFE2L2, and TP53. In the TP53 pathway, the
upregulation of GLS2, which is a transcription target of TP53, leads to
P53-dependent ferroptosis, and the inhibition of SC7A11 by TP53 can
also trigger ferroptosis. TP53 (tumor protein 53) represses SLC7A11 to
promote ferroptosis as a tumor suppression mechanism [[134]81] (SLC7A11
was upregulated in this group). Fluvastatin and lovastatin bind to
HMGCR and upregulate CDKN1A, which is a ferroptosis regulation gene
[[135]59].
* c.
Subgroup3: The patients in this subgroup are not Hispanic or
Latino. They have right-sided TNBC as their neoplasm subdivision,
they have less than three lymph nodes that are positive, and their
histological type is an intraductal carcinoma. This subgroup
contains 30 patients and has 118 unique DEGs that are abnormal in
this subgroup, but not in the other subgroups ([136]Tables S3 and
S4). The drugs that can induce ferroptosis that are suggested for
this subgroup are sunitinib and pazopanib.
1. Sunitinib: Sunitinib can induce ferroptosis by targeting the
von Hippel-Lindau (VHL), which increases sensitivity to
ferroptosis. Sunitinib interacted with 48 DEGs in this
subgroup, including CDO1 [[137]82]. The inhibition of CDO1
increases ROS and can induce ferroptosis [[138]83].
2. Pazopanib: Pazopanib belongs to the same category as
Sunitinib. Moreover, treating breast cancer cells with
Pazopanib was found to induce autophagic cell death [[139]84].
It is a necroptosis inhibitor [[140]85]. The mTOR pathway is a
regulator of iron metabolism. The VHL/HIF-α axis is the main
regulator target of iron metabolism. HIF-3α was downregulated
in this subgroup. Targeting the VHL gene pathway using drugs
like sunitinib, sorafenib, pazopanib, and axitinib causes the
VHL to be inactive. The inactivation of the VHL increases
sensitivity to ferroptosis. IRP1 can bind to the iron reaction
element of HIF-2α mRNA and inhibit its translation. Tempol, an
IRP1-activated drug, inhibits HIF-2α and HIF-1α protein
levels. PT2399 and PT2385 are inhibitors of HIF-2α [[141]86].
Pazopanib is a tyrosine kinase inhibitor (TKI) that belongs to
a class of drugs that targets PDGFR α/β and VEGFR activity
[[142]66]. PDGFR was found to be downregulated in this
subgroup. After analyzing the genes in this subgroup, we found
that sunitinib and pazopanib bind to FGFR2, which, in turn,
interacts with HSPB1 and STAT3. FGFR2 was upregulated in this
subgroup. Both of these genes play a role in ferroptosis
induction, as explained previously. Moreover, sunitinib binds
to PHKG2, which is important for the induction of ferroptosis
[[143]87,[144]88,[145]89].
* d.
Subgroup4: The patients in this subgroup are not Hispanic or
Latino; they have left side TNBC as their Neoplasm Subdivision.
They have less than three lymph nodes that are positive, and their
histological type is an intraductal carcinoma. This subgroup has 32
patients and has 103 unique DEGs that are abnormal in this
subgroup, but not in the other subgroups ([146]Tables S3 and S4).
The drugs that have the potential to induce ferroptosis in this
subgroup are dexamethasone and vorinostat.
1. Dexamethasone: High-dose dexamethasone disrupts the metabolism
of glutamate and cysteine, produces more ROS, and
downregulates GPX4 and system XC−, which are two key mediators
of ferroptosis [[147]90]. In this subgroup, ROS1 was
upregulated, GPX2–3 was downregulated, GPX5 was upregulated,
and GPX4 was in the normal range.
2. Vorinostat: Vorinostat is a histone deacetylase (HDAC)
inhibitor. In colon cancer cells, vorinostat can significantly
inhibit cell growth and can induce cell cycle arrest and
apoptosis [[148]91]. It may induce ferroptosis by glutamine
deprivation, resulting in the accumulation of ROS [[149]92].
In NSCLC, vorinostat, combined with erastin, can increase the
lipid peroxide levels and can inhibit HDAC to induce
ferroptosis [[150]93]. Moreover, when vorinostat is used as a
combined therapy with gefitinib or erlotinib, which are
EGFR-TKIs, it can promote oxidative stress-dependent apoptosis
by the suppression of the c-MYC-regulated NRF2 functions and
increase the levels of KEAP1 in NSCLC cells [[151]94].
The analysis of this subgroup’s genes and drugs showed that
dexamethasone bound to PTGS2, downregulated NFE2L2, and upregulated
HSPB1, STAT3, and CDKN1A. These kinds of regulations promote
ferroptosis. Dexamethasone downregulates GDF15 and NFKB2, and it
upregulates MEGF9. In this subgroup, GDF15 was upregulated, NFKB2 was
upregulated, and MEGF9 was downregulated. GDF15 interacts with
ferroptosis-related genes like ATG7, EGFR, FTH1, HSPB1, PHKG2, PTGS2,
AKR1C2, CDKN1A, DPP4, NFE2L2, and TP53. In this subgroup, AKR1C2 and
DPP4 were downregulated. NFKB2, which was upregulated in this subgroup,
interacted with EGFR, CDKN1A, and HSPA5. MEGF9, which was downregulated
in this subgroup, interacted with GCLM, HMOX1, PTGS2, AKR1C2, which
were downregulated in this subgroup; FDFT1 and NFE2L, which were
upregulated in this subgroup; and GCLM. The downregulation of FTH1 can
induce ferroptosis. IRP1 and IRP2, which were downregulated in this
subgroup, are iron regulatory proteins that can regulate iron
metabolism genes such as TFRC and FTH1 to maintain the stability of LIP
[[152]81]. In this subgroup, TFRC was upregulated while LIPC and LIPE
were downregulated. AKR1C2, which was downregulated in this subgroup,
is one of the FGRs. AKR1C1-3 (aldo-keto reductase family 1 member C1),
which wase downregulated in this subgroup, is involved in eliminating
end products of lipid peroxidation [[153]59]. Vorinostat upregulates
HMGCR, HMOX1, PHKG2, CDKN1A, and FDFT1.
* e.
Subgroup5: The patients in this subgroup are less than 50 years
old; their pathological stage is m0 with no history of neoadjuvant
treatment. This subgroup has 37 patients and has 216 unique DEGs
that are abnormal in this subgroup, but not in the other subgroups
([154]Tables S3 and S4). Opposite to what we observed in the other
four subgroups, most of the top drugs that are suggested to this
subgroup have an antioxidative effect on cancer cells. These drugs
are rifampicin, galantamine, cerulenin, and lipoic acid.
1. Rifampicin: Rifampicin was repurposed as an antiferroptosis
agent, and it functions as a lipid peroxyl radical scavenger
[[155]95,[156]96].
2. Galantamine: Galantamine has an antioxidant effect and
acethylcholinesterase and γ-secretase inhibitory activity
[[157]97].
3. Cerulenin: Cerulenin is an antifungal antibiotic that inhibits
fatty acid and steroid biosynthesis. Fatty acid synthase (FAS)
was observed to have a high expression in breast cancer cells
in comparison with the normal cells, and there is increasing
evidence that FAS plays a role in breast cancer development
[[158]98]. Moreover, fatty acid synthase (FAS) and ErbB2 have
been shown to promote breast cancer cell migration [[159]99].
The effect of cerulenin on breast cancer was tested in an in
vivo analysis. Due to cerulenin’s ability to inhibit fatty
acid synthase (FAS) that, in turn, decreases the expression of
ErbB1, 2, and 4, it may initiate an epithelial-to-mesenchymal
transition (EMT) as well as the migration and invasive ability
of cancer cells [[160]100].
4. Lipoic acid (LA): Lipoic acid is an antioxidant. It has an
anti-proliferative effect in breast cancer cells by reducing
breast cancer cell viability, cell cycle progression, and the
EMT. It downregulates furin, which, in turn, inhibits the
maturation of IGF-1R [[161]101,[162]102]. Combining lipoic
acid with radiation therapy was found to overcome the
resistance to radiation therapy and promote apoptosis
[[163]103].
3.2. Pathway Enrichment Analysis
We performed a pathways enrichment analysis in order to determine
common mechanisms underlying the effects of the top ten drugs for each
of the five subgroups ([164]Table S5). After removing the duplication
of drugs between subgroups, we identified 46 unique drugs in total for
the five subgroups. We performed an enrichment analysis for pathways
targeted by these drugs ([165]Figure 2). The statistical significance
of these pathways was calculated using Enrichr
[[166]104,[167]105,[168]106] with p < 0.05. The metabolism pathway was
targeted by 100% of the drugs. Metabolic reprogramming is considered a
hallmark of cancer that can be used for diagnosis, prognosis, and
treatment [[169]107]. Cancer cells show different metabolism patterns
as compared with normal cells, and targeting that difference is a
promising strategy for developing anticancer therapy [[170]108].
Studies have shown that targeting metabolic enzymes can reverse drug
resistance in cancer [[171]109,[172]110]. Targeting metabolism pathways
could be an important component in developing a comprehensive treatment
for breast cancer, considering the complexity of these pathways due to
their crosstalk with other signaling pathways [[173]111]. The
upregulation of some metabolic pathways was found to stimulate
metastasis in breast cancer [[174]112]. The importance of targeting
metabolism pathways also has been shown clearly in TNBC, where there is
a metabolic heterogeneity between patients of this cancer subtype. The
metabolism pathways were used to stratify TNBC patients into subgroups
based on the heterogeneity of the metabolism [[175]113,[176]114]. Our
findings coincide with what has been suggested in the literature and
points to the importance of developing drugs that target metabolism
pathways to tailor therapies for TNBC patients. The metabolic
heterogeneity of TNBC was targeted by our top drugs, those directed at
specific gene patterns contained within these subgroups. These patterns
belong to the metabolism, but they were unique to a given subgroup and
had high contrast with the outer population.
Figure 2.
[177]Figure 2
[178]Open in a new tab
Pathways enrichment analysis for the genes targeted by top ten drugs
for all the five subgroups of interest. The x-axis shows pathway names,
and the y-axis shows the percentage of drugs that target each of these
pathways.
The second pathway that was targeted by 93% of the top drugs was the
biological oxidation pathway. Increased oxidative stress plays a role
in carcinogenesis in breast cancer patients [[179]115]. In a study of
the immune microenvironment of breast cancer, oxidative stress in
combination with other biological phenotypes was associated with high
immune infiltration [[180]116]. Thus, targeting oxidation pathways
could be promising for developing immune therapies for breast cancer.
Studies have shown that the spread of BC metastatic cells and their
survival in circulation depends on different factors, including
antioxidant protection [[181]117]. The oxidative stress response is
used by breast cancer cells to adapt to their nutrient-poor environment
[[182]118]. Oxidative activity is one factor involved in the induction
of breast carcinogenesis [[183]119].
The metabolism and oxidation-related pathways can be observed more
frequently than others among the pathways targeted by our top drugs. In
analyzing the drugs, we focused on drugs that shared a common mechanism
within the top five drugs. Still, the pathway enrichment analysis
showed that the top ten drugs of all subgroups targeting metabolism and
oxidation-related processes inhibit cancer growth and development.
Moreover, we did an enrichment analysis for GO domains, cellular
components, biological processes, and molecular functions. The top
molecular functions that were highly enriched for the top ten drugs
were oxidoreductase activity, protein homodimerization activity, and
lipid binding. ([184]Supplementary Figure S1 and Table S6). The top
biological process enriched for the top drugs were the response to the
drug, the oxidation-reduction process, and the response to the
organonitrogen compound ([185]Supplementary Figure S2 and Table S6).
The top cellular components were the endoplasmic reticulum membrane,
nuclear outer membrane–endoplasmic reticulum membrane network, and
plasma membrane region ([186]Supplementary Figure S3 and Table S6).
3.3. Pharmacological Classes Analysis
We did a drug class enrichment analysis for the top ten drugs of the
five subgroups of interest to find the pharmacological classes to which
most of the drugs belong. Using the FDA Established Pharmacologic Class
(EPC), we found classes for 32 drugs. The top pharmacological class to
which most of these drugs belonged was the kinase inhibitor. Kinase
inhibitors have shown great potential as a TNBC regimen due to their
activity as antitumor and antiangiogenic therapies by targeting genes
and pathways that play a role in cancer development and proliferation,
like targeting EGFR, RON, MET, mTOR, BRAF, MEK, Src, and Bcr/Abl
[[187]120,[188]121,[189]122,[190]123,[191]124,[192]125]. Kinase
inhibitor drugs have been used in different breast cancer research and
clinical trials, especially for TNBC, and they are considered one of
the most successful targeted therapies for cancer [[193]126,[194]127].
They were used as a monotherapy and combined therapy for TNBC, and they
are considered a promising strategy to treat advanced TNBC [[195]128].
As a monotherapy, a tyrosine kinase inhibitor effectively inhibits the
proliferation and induced autophagy and apoptosis in TNBC cells
[[196]129]. A kinase inhibitor was found to have the ability to
preferentially suppress the growth and proliferation of TNBC cells in
comparison with non-TNBC cells [[197]130]. It was found to reduce the
proliferation of mesenchymal stem-like (MSL) cells and induce apoptosis
in TNBC cells as a combined therapy [[198]131,[199]132]. Moreover, a
combination of kinase inhibitors was suggested for TNBC, where
monotherapy has limited success [[200]133]. Kinase inhibitors were used
in combination with other inhibitors to overcome the resistance for
monotherapy, like using dasatinib in combination with Akt or mTOR
inhibitors to overcome dasatinib resistance and reduce the
proliferation in TNBC cells [[201]134]. Many kinase inhibitors were
effective as a TNBC therapy, and many others are a subject of ongoing
clinical trials [[202]135,[203]136].
4. Discussion
Breast cancer is a highly heterogeneous disease. Its heterogeneity
requires regimens to be tailored for homogeneous subgroups of patients
with common phenotypic and genotypic features that are unique to each
subgroup. This study applied our data-driven approach to stratify
breast cancer into subgroups using their phenotypic and genotypic data.
The method starts with the phenotypic features and partitions the
disease population based on the categorical values of the phenotypic
variables. This partitioning created different subgroups. For each
subgroup, the algorithm used the genotypic features to evaluate the
druggability potential, which refers to the necessity of treating a
given subgroup differently and tailoring drugs to that subgroup.
Since developing a de novo drug is expensive and time-consuming, we
developed a computational method to reposition drugs for each subgroup
based on the gene expression patterns and the relationship between
these genes and other biomedical entities like pathways and GO domains.
Each subgroup’s genotypic features are represented as a network and
processed using a graph database, neo4j. Each network consists of
heterogeneous biomedical entities. The core of each network is the
differentially expressed gene patterns in that subgroup. For each gene,
the network contains other genes that directly interact with that gene,
the pathways and GO domains to which that gene belongs, and the drugs
that affect the expression of the other genes in that network. The
opposite expression regulation between the drugs and the affected genes
is taken into account by retrieving the drugs from the knowledgebase
into the subgroup’s network using cMAP data. Each network has several
drugs, and to find the best drugs that can be suggested for a given
subgroup, we developed a method to rank the drugs within each network
using different factors. These factors include the percentage of
differentially expressed genes, the number of patterns targeted by each
drug, and the perturbation impact of a drug on human cells.
Breast cancer has three major subtypes, luminal A, luminal B, and
triple-negative breast cancer (TNBC). TNBC patients have the lowest
survival rates among all breast cancer patients. This motivated us to
focus our study on TNBC patients. We analyzed five subgroups composed
of combinations of the three population variables, which are age, race,
and neoplasm subdivision, as statistically significant phenotypic
variables. After analyzing the differentially expressed genes for these
subgroups, we found these subgroups had, on average, 162 unique
differentially expressed genes in addition to phenotypic-level
differences. This uniqueness does not only mean the gene is
differentially expressed in a given subgroup, but it also could mean
the gene has a differential expression pattern unique to a given
subgroup. It could be only upregulated in that subgroup while it is
downregulated in the other subgroups, and vice versa.
Analyzing the top five drugs for the subgroups of interest showed a
common mechanism shared by four subgroups, with race and neoplasm
subdivision as the significant phenotypic factors. Still, we found the
opposite mechanism was suggested for the subgroup with age as a
significant phenotypic factor. Inducing ferroptosis was the common
mechanism in the subgroups that were Black or African American, were
White, had right side breast cancer, or the subgroups with left side
breast cancer. In contrast, the antioxidant was the common targeted
mechanism in the subgroup with patients aged less than 50 years. This
finding coincides with what has been found previously [[204]59], where
age is negatively correlated with ferroptosis. The pathway enrichment
analysis also showed that the most targeted pathways by the top ten
drugs were pathways that have a relation to metabolism and oxidation.
The kinase inhibitor is considered one of the most targeted therapies
to treat breast cancer, especially TNBC. Since it is a
computational-based method, a literature review was used to validate
our findings. Still, these findings need more testing in a wet lab
setting to ensure it is valid to be suggested for healthcare
improvement. After validation in the wet lab, these results can
significantly improve TNBC survival and suggests drugs tailored for a
specific group of patients based on their phenotypic and genotypic
features.
The clinical relevance of our data regarding repurposing TKIs as
ferroptosis inducers is strengthened by recent findings based on the
functional analysis of breast cancer cell lines that demonstrate that
TNBCs are enriched in ferroptosis gene signatures and are vulnerable to
ferroptosis inducers, compared to other breast cancer subtypes, i.e.,
ER-positive and/or HER2-positive subtypes [[205]137]. Furthermore, age
is the most pertinent clinical characteristic that has been shown to
have a negative correlation with ferroptosis in certain tumors like BC,
i.e., in younger BC patients, ferroptosis is less clinically relevant
[[206]59] which coincides with our discovery of a completely different
class of repurposed medications that did not induce ferroptosis in our
younger (<50 years) cohort of BC patients. The potential benefit from
antioxidants in younger TNBC patients, as we have demonstrated in our
current bioinformatic analysis for patients younger than 50 years, is
further supported by biological data on the dependence on reactive
oxygen species (ROS) for the survival and malignant progression of TNBC
in younger patients. These high levels of ROS production that drive the
aggressiveness in TNBC result from oncogenic gene mutations, e.g.,
BRCA1 [[207]138], gene expression changes, e.g., BLT2 [[208]139], and
the attainment of stem-cell like properties [[209]140]. This high ROS
content induces multiple signaling which, in turn, leads to highly
proliferative, migratory, and drug-resistant phenotypes in TNBC and is
effectively attenuated by antioxidants, thereby reducing the growth and
metastasis of TNBC cells [[210]141]. The shorter survival observed in
the younger cohort or subgroup 5 when it was compared against all
remaining four subgroups “bundled” together ([211]Figure 3) is a
testament to the different ROS-dependent biology in subgroup 5, which
contributes to the cell survival, metastasis, chemoresistance, and
cancer relapse. Therefore, the additional use of antioxidants can be an
effective strategy for treating these younger TNBC breast cancer
patients in order to prevent chemoresistance through depleting ROS.
Figure 3.
[212]Figure 3
[213]Open in a new tab
Survival curves for subgroup 5 vs. other subgroups. The curve shows the
patients younger than 50 who have worse survival rates than the other 4
subgroups of interest have.
The limitation of this data-driven research is threefold: firstly, the
selection of thresholds for support and growth is dependent on the
availability of data and the research question to be answered.
Variations will affect the resulting number of patterns and the
subgroups. Secondly, while this paper is focused on TNBC using a
foundation developed for colorectal cancer [[214]37], a pan-cancer
study is needed to further test the robustness of the methods in all
major cancers. Thirdly, further studies are required in a “wet lab” in
order to validate our data and monitor the efficacy of repurposed drugs
suggested, like TKIs and antioxidants. For the use of these proposed
medications to be clinically relevant, further studies will need to
examine their potential interference with standard chemotherapy, the
timing of administration relative to standard chemotherapy, and the
appropriate treatment duration.
5. Conclusions
Breast cancer is a heterogeneous disease. Tailoring drugs for
homogeneous subgroups of patients is necessary to improve survival, but
developing de novo drugs is an expensive, time-consuming, and high-risk
process. Drug repositioning represents a promising direction to address
this problem by taking advantage of already FDA-approved drugs and
using them to treat additional diseases. We implemented our subgroup
stratification and drug repositioning method on breast cancer data. Our
analysis focused on TNBC subgroups because this subtype has the worst
survival rate among breast cancer patients. For this study, we analyzed
five TNBC subgroups. These subgroups have age, race, and neoplasm
subtype as significant phenotypic variables and had differentially
expressed genes unique to each subgroup. The analysis of these
subgroups and the suggested drugs showed that developing drugs that
target antioxidants and ferroptosis could be a promising direction to
tailor drugs for TNBC patients. The analysis of the top drugs suggested
for each subgroup showed that either these drugs are used to treat
cancer or are subject to ongoing research to be used for cancer. Most
of the recommended top ten drugs for all the subgroups belongs to the
kinase inhibitors class. This class of drugs is one of the
well-established target therapies for treating cancer. Many inhibitors
are subject to clinical trials as a regimen for breast cancer or TNBC.
Validating our findings in a wet lab will be a step toward improving
TNBC healthcare without developing de novo drugs, which will save
costs, time, and human lives.
Acknowledgments