Abstract
Network pharmacology has become a powerful means of understanding the
mechanisms underlying the action of Chinese herbs in cancer treatment.
This study aims to validate the preventive effects and molecular
mechanisms of a clinical prescription XIAOPI formula against breast
cancer. In vivo breast cancer xenograft data showed that XIAOPI delayed
breast cancer development and efficiently inhibited lung metastasis,
accompanied by prolonged survival benefits and decreased cancer stem
cell subpopulations. However, similar phenomenon were not observed in a
cell model. The herb-ingredient-target network analysis further
identified a total of 81 genes closely correlated with the breast
cancer chemoprevention effects of XIAOPI. Cytokine array analysis
further validated CXCL-1 as the key target of XIAOPI both in vitro and
in vivo. Evaluation of the mechanism demonstrated that CXCL-1
administration significantly abrogated the metastatic inhibition
effects of XIAOPI on breast cancer migration, invasion, stem cells
subpopulations, epithelial-mesenchymal transition(EMT), or mammosphere
formation abilities. Overall, our study provides experimental evidence
and molecular mechanisms that may facilitate the safe and effective use
of herbal medicine for the prevention of breast cancer growth or
metastasis, and may lead to CXCL-1-based therapeutic strategies for
mammary malignancies.
Introduction
Breast cancer is the most common malignancy in U.S. women, accounting
for nearly one in three cancers. In 2017, it was estimated that there
would be more than 2.8 million women with a history of breast cancer in
the U.S.^[42]1. New cases of breast cancer reached 255,180 and
mortality as high as 41,070^[43]2. Breast cancer is also one of the
most common diagnosed cancers among women in 140 of 184 countries
worldwide^[44]3. For example, breast cancer alone is expected to
account for 15% of all new cancers in women in China. It was estimated
that China had 272,400 new cases and 70,700 deaths of breast cancer in
2015^[45]1. From 2000 to 2015, the incidence of breast cancer in China
has increased steadily^[46]4. Finding a way to prevent breast cancer
and reduce its mortality has become an urgent issue worldwide.
Currently breast cancer prevention mainly includes three strategies:
lifestyle modification, chemoprevention, and prophylactic surgery.
Although alcohol consumption, obesity, and hormone replacement therapy
have been evaluated as independent risk factors for breast cancer,
there is no specific guideline for dietary or physical activity
intervention specifically to prevent breast cancer^[47]5. Because of
the lack of strong evidence and high-quality clinical trials, lifestyle
adjustments are only recommended to patients as a supplemental measure
in clinical settings. It is reasonable to encourage women to maintain
an ideal body weight by restricting fat intake and undertaking
30–45 minutes of vigorous physical exercise three to five times per
week^[48]6. Chemoprevention describes pharmacological intervention to
reverse, suppress, or inhibit carcinogenic transformation. Selective ER
modifiers (SERMs) and aromatase inhibitors (AIs) have been approved for
clinical prevention^[49]7. Tamoxifen reduces ER-positive breast cancer
risk by 62% but does not affect ER-negative breast cancer^[50]8.
Raloxifene, a second-generation SERM, was found to reduce the risk of
breast cancer by 44–76% in postmenopausal women^[51]9. Multiple
clinical trials have demonstrated that AIs were capable to reduce
breast cancer risk in postmenopausal women. For example, exemestane was
confirmed to reduce the risk of breast cancer by 65%, but these
protective effects were only observed in ER-positive cases^[52]10.
Although SERMS and AIs are recommended as chemoprevention agents in
clinical settings, their adverse events render their clinical use
problematic. Patients suffer incidence risks of endometrial cancer,
stroke, venous thromboembolism, musculoskeletal pain, osteoporosis, and
even bone fracture^[53]11. With regard to prophylactic surgery,
bilateral salpingoophorectomy and mastectomy are recommended to
high-risk patients, especially those who carry the BRCA1/2
mutation^[54]12. Numerous retrospective and cohort studies have
suggested that prophylactic mastectomy could reduce the risk of breast
cancer by more than 90%^[55]5. However, mastectomy does not completely
eliminate the risk of breast cancer because it is impossible to
eradicate all terminal duct-lobular units in many women. In addition,
8–64% of post-surgical women experience one or more complications such
as bleeding, infection, and skin flap necrosis^[56]13. Over 30% of
patients report feeling embarrassed about the appearance of their
breasts even after mammary reconstruction and difficulties in sexual
arousal^[57]14. It is still necessary to find novel and safe prevention
strategies.
Traditional Chinese Medicine (TCM) has a unique role in cancer
prevention. In TCM philosophy, cancer is caused by the disturbances in
endogenous physical condition and exogenous pathogenic factors. The
disharmony of the body-mind communication network may also trigger
cancer development^[58]15. For this reason, TCM doctors view cancer as
a systemic disease and focus on holistic enhancement of inner defenses
and restoration of normal balance for cancer therapy, which differs
from Western medicine, which focuses only on killing cancer cells. TCM
is appreciated in China’s rural areas and well-developed cities because
of its 5000-year-old history and well-established theoretical approach.
In a cross-sectional study conducted by the government of Taiwan, among
70,012 female breast cancer patients, 35.6% used TCM^[59]16. Several
formulas have been developed into commercial products and applied in
the tumor remission or stabilization stage. Examples of such treatments
include Kanglaite injection, compound Kushen injection, Elemene
emulsion injection, and Jinlong capsules, and they have shown sound
clinical efficacy in improving overall survival, reducing toxicity, and
preventing disease progression^[60]15,[61]17. A great deal of effort
has gone into understanding the therapeutic principles and molecular
targets of herbal medicine in cancer prevention and treatment. Many TCM
formulas or single active components have been reported to inhibit a
variety of processes in cancer cell growth, invasion, and metastasis by
modulating a wide range of molecular targets, including
cyclooxygenase-2 (COX-2), nuclear factor-κB (NF-κB), and nuclear factor
erythroid 2 -related factor 2 (Nrf2)-mediated antioxidant signaling
pathways^[62]18. However, the mechanisms underlying most formulas
remain unclear due to the complex composition of these medicines, which
means several targets may be involved in its action.
In order to determine the holistic way of TCM from a molecular to
system level, a variety of constructive technologies have been
developed over the past few decades. Representative technologies
include metabolomics, serum pharmacokinetics, proteomics, and genomic
arrays^[63]19. These methodologies tend to focus on component
identification, drug metabolism, and target screening. A more holistic
method is needed to analyze the correlations associated with the
herb-compound-target network. Recently, system biology has emerged as a
novel tool that can be integrated with pharmacology. System
pharmacology has made a significant contribution to investigation of
the holistic overview of TCM by pharmacokinetic evaluation (absorption,
distribution, and metabolism), target prediction, and network
analysis^[64]20. By using the method, it may be possible to shift the
single “one drug, one target” model to a more complex “drug-target
network interaction” strategy^[65]21. Meanwhile, system pharmacology
has greatly accelerated the drug target screening process. By combing
disease target database and molecular validation, it is becoming much
easier to explore the precise molecular target and mechanisms of TCM
formulas.
In the present study, we systematically exploited the breast cancer
prevention effects and mechanisms of a clinical effective prescription
XIAOPI formula. Firstly the breast cancer prevention effects of XIAOPI
formula was validated on MMTV-PyMT transgenic mice and in vitro cell
transformation model. Then the active ingredients in XIAOPI formula
were found via oral bioavailability and drug-likeness evaluation at a
molecular level. By utilizing the active ingredients as bait, we
predicted the potential targets and further constructed the drug-target
interactions at pharmacological level. We later identified CXCL-1 as
the key action gene of XIAOPI formula using cytokine chip analysis. We
further validated the importance of CXCL-1 in mediating the anti-cancer
effects of XIAOPI formula (Fig. [66]1). Our work highlights the role of
CXCL-1 as a key target in breast cancer development and in the action
of XIAOPI formula and also contributes to the exploration of the
mechanism underlying TCM and the promotion of its development in the
treatment of complex disease.
Figure 1.
[67]Figure 1
[68]Open in a new tab
Process overview.
Results
XIAOPI formula inhibits breast cancer tumorigenesis in vivo
To confirm the chemopreventive effects of XIAOPI formula, we firstly
used a MMTV-PyMT^+/− transgenic mouse model to assess the efficacy of
XIAOPI formula in vivo. The MMTV-PyMT^+/− transgenic mice developed
spontaneous and luminal-like breast cancer. In particular, mammary
hyperplasia can be detected in this model as early as 4 weeks of age,
and nearly 100% of mice develop breast cancer by 12–13 weeks,
accompanied by the appearance of pulmonary metastasis^[69]22. For this
reason, this model is usually used to evaluate the oncogenic mechanisms
underlying breast cancer or the chemopreventive effects of candidate
phytochemicals.
The cancer preventive activity of XIAOPI formula was assessed by
comparing the incidence of palpable lesions at three-day intervals in
the transgenic mice in each group. As shown in Fig. [70]2A, XIAOPI
treatment visibly limited the process of mammary oncogenesis. In
vehicle controls, tumors appeared as early as the 4^th week, while it
was delayed one week with XIAOPI intervention (Fig. [71]2B). Notably,
the tumor volume and number in XIAOPI group was significantly inhibited
compared with the vehicle group, further confirming the breast cancer
suppression effects of the formula (Fig. [72]2C and D). Meanwhile,
XIAOPI brought little influence on mice body weight (Fig. [73]2E).In
order to assess the precise morphological changes during mammary
oncogenic process between groups, mammary tissue from the 4^th to 8^th
week was collected from mice in each group and compared by carmine
staining analysis. Results showed that the length of ducts, the amount
of duct branches, and the number of terminal ends gradually increased
with age in wild-type mice. However, in MMTV-PyMT^+/− transgenic mice,
hyperplastic lesions could be observed as early as the 4^th week around
duct branches. No lesions were detected in the XIAOPI treatment group
until the 5^th week. Even at the 8^th week, the precancerous lesions
were also significantly smaller and fewer in number than in the vehicle
control group (Fig. [74]2F). These findings suggested that XIAOPI
formula could block the carcinogenic transformation of mammary tissue
efficiently.
Figure 2.
[75]Figure 2
[76]Open in a new tab
XIAOPI formula delays breast cancer onset in vivo. (A) Representative
mice and tumor in control and XIAOPI groups; XIAOPI formula
administration significantly inhibits (B) breast cancer onset time, (C)
tumor volume (mm^3) and (D) tumor number, but brought little influence
on (E) mice body weight; (F) Mammary whole mounting assay revealed that
MMTV-PyMT^+/− develops malignant lesions as early as the 4^th week,
while XIAOPI delays malignant transformation to the 5^th week.
XIAOPI formula inhibits breast cancer lung metastasis and CSCs in vivo
Because the MMTV-PyMT^+/− transgenic mouse model has a tendency to form
metastatic lesions in the lungs, it is rational to determine whether
XIAOPI formula can inhibit lung metastasis in breast cancer. As shown
in Fig. [77]3A, at the end of the experimental period, there were fewer
metastatic lung nodules in the XIAOPI group than in the vehicle group.
The survival curve of XIAOPI-treated mice was significantly longer than
in the vehicle control group, which showed serious death events after
the 12^th week (Fig. [78]3B). In order to determine whether the
decreased lung metastasis events was correlated to smaller numbers of
cancer stem cells (CSCs), ALDH assay was carried out to confirm the
number of CSCs in the metastatic lesions. As shown in Fig. [79]3C, as
many as 26.5 ± 2.3% CSCs in the vehicle group were positive for
ALDH1A3, as opposed to only 5.5 ± 1.2% in the XIAOPI treatment group.
Immunofluorescence assay further confirmed that ALDH1A3-postive CSCs
were significantly reduced in the XIAOPI-treated group. More
importantly, CSCs-related signaling β-catenin expression was also
inhibited after long-term XIAOPI administration, further demonstrating
that XIAOPI formula might inhibit breast cancer lung metastasis via
inhibiting CSCs (Fig. [80]3D).
Figure 3.
[81]Figure 3
[82]Open in a new tab
XIAOPI formula inhibits breast cancer lung metastasis in vivo. (A)
Representative picture of lung metastasis lesions in control and XIAOPI
groups; (B) XIAOPI formula prolongs MMTV-PyMT^+/− survival period
(P = 0.0056, values represented as the mean ± SD, n = 12); (C) XIAOPI
formula inhibits the CSCs population in the lung metastasis lesion
(*P < 0.001, values represented as the mean ± SD, n = 3); (D) XIAOPI
formula inhibits the number of ALDH^+ cells and β-catenin expression in
the lung section.
XIAOPI formula had little inhibitory effects on breast cancer cells in vitro
Based on the in vivo results, it is interesting and important to
explore whether XIAOPI had direct inhibitory effects on breast cancer
cells. Intriguingly, a cell proliferation assay showed that XIAOPI
formula had little inhibitory effect on MCF-7 and MDA-MB-231 breast
cancer cells within the range from 0 to 600 μg/ml. At higher
concentrations ranging from 600 to 1000 μg/ml, XIAOPI exhibited an
inhibitory effect on the proliferation of MDA-MB-231, but it had little
effect on MCF-7 cells. XIAOPI had little cytotoxic effect on MCF-10A, a
normal mammary epithelial cell line, demonstrating the safety of XIAOPI
treatment in vivo (Fig. [83]4A). In order to validate the potential
inhibition effects of XIAOPI formula on precancerous cells, the
oncogene RAS was transformed into MCF-10A cells. These results also
confirmed that XIAOPI had little influence on the viability of
RAS-transformed cells, indicating that the ability of XIAOPI to inhibit
breast cancer might not be attributed to the direct toxicity effects on
cancer cells (Fig. [84]4B). Since in vivo findings suggested that
XIAOPI formula could inhibit breast CSCs, it is necessary to explore
whether XIAOPI has similar effects in in vitro systems. Flow cytometry
assay indicated that XIAOPI formula had little influence on the
distribution of CD44 and CD24, the cell surface markers for breast
CSCs, indicating that the CSCs-inhibitory effects of XIAOPI formula
might be attributed to the tumor microenvironment (Fig. [85]4C).
Figure 4.
[86]Figure 4
[87]Open in a new tab
XIAOPI formula brought little influence on breast cancer cell
proliferation and CSCs population in vitro. (A) XIAOPi formula had
little proliferation inhibition effects on breast cancer cell line
MCF-7 and normal mammary epithelial cell line MCF-10A, but had an
obvious toxic effect on MDA-MB-231 on high concentrations from 800 to
1000 μg/ml (**P < 0.01, values represented as the mean ± SD, n = 3);
(B) XIAOPI formula had little inhibitory effects on the colony growth
of RAS-transformed MCF-10A cells; (C) XP formula had little influence
on the subpopulation size of CSCs in both MDA-MB-231 and MCF-7 cells.
Putative major ingredients and major targets of XIAOPI formula
Ingredient-target networks were established for all the 10 herbs. The
results indicated that the 10 herbs of XIAOPI yielded 105 components
and further resulted in 806 potential targets (Fig. [88]5). Regarding
each herb, there were 23 compounds in YYH targeting 246 genes, 6
compounds in RCR targeting 198 genes, 9 compounds in NZZ targeting 215
genes, 38 compounds in DS targeting 115 genes, 3 compounds in YJ
targeting 69 genes, 3 compounds in EZ targeting 32 genes, 8 compounds
in YMC targeting 223 genes, 6 compounds in HSW targeting 358 genes, 5
compounds in ML targeting 149 genes, and 4 compounds in BJ targeting
182 genes (Fig. [89]5 and Supplementary Table [90]1). The common
targets for at least 4 herbs are shown in Fig. [91]6A. Among these
genes, 2 were common targets of 9 herbs, 9 were common targets of 8
herbs, 23 were common targets of 7 herbs, 30 were common targets of 6
herbs, 65 were common targets of 5 herbs, and 69 were common targets of
4 herbs (Fig. [92]6B).
Figure 5.
[93]Figure 5
[94]Open in a new tab
The ingredient-target networks of 10 herbs in XIAOPI formula. The
diamond nodes represent ingredients, and the circular nodes represent
targets.
Figure 6.
[95]Figure 6
[96]Open in a new tab
(A) The herb-target networks of the 10 herbs. The diamond nodes
represent herbs, and the circular nodes represent targets. The targets
distributed in a circle represent they are acting by the same number of
herbs, which illustrated as “n”; (B) the histogram of “Summary of Herb
and Target Number”.
In order to identify the drug-targeting genes influencing breast cancer
progression, genes related to breast cancer were also extracted from
GENECARDS database and [97]GSE5764 microarray set. As shown in
Fig. [98]7A, there were 1383 breast cancer-associated genes (A,
relevance score >5) in the GENECARDS and 2609 validated genes in
[99]GSE5764 through GEO2R analysis tool (B, P < 0.05). Venn analysis
further confirmed that a total of 81 genes were overlapped between the
three groups (Supplementary Table [100]2). For “Gene Ontology (GO) and
pathway enrichment analysis”, a proper background gene set was needed
to assess the significance of the enrichment. In this study, two random
gene sets were picked from either a) all genes or b) intersecting set
between A and B as background genes. The purpose of the later is to
further investigate whether the enrichment is driven by the targets of
XIAOPI components. For GO term analysis (Fig. [101]7B), Biological
Process analysis of the 81 genes revealed that the enrichment on both
backgrounds were kept in consistent, except presynaptic process and
rhythmic process. Notably, Cellular Component analysis implied that the
extracellular matrix region/region part was most significantly
associated with XIAOPI action. Further Molecular Function analysis
indicated the terms existing in both background include binding,
catalytic activity, molecular function/transducer activity, nucleic
acid binding transcription factor activity, signal transducer activity,
structural molecular activity and transporter activity. For KEGG
enrichment analysis (Fig. [102]7C), the results demonstrated that the
interfered pathways of 81 genes co-existing in both backgrounds were
mainly responsible for supporting cancer growth and invasion like
HIF-1α signaling, focal adhesion signaling, PI3K-Akt signaling,
Toll-like receptor signaling, TNF signaling, FoxO signaling, etc. We
also performed a gene set ([103]GSE5764 microarray set) enrichment
analysis (GSEA) to reveal whether the 81 gene overlap were
significantly enriched. 49 of the 81 genes were significantly enriched
in the curated gene sets (C2), the enriched pathways were prostate
cancer, multi-cancer invasiveness, proliferation, invasive breast
cancer and etc. Among them, 34 genes were significantly enriched in
breast cancer-related pathways. Besides, 39 of the 81 genes were
significantly enriched in GO gene sets (C5), and the enriched pathways
were extracellular matrix, extracellular structure organization,
epidermis development, exocrine system development and etc. The
representative analysis extracellular structure organization was shown
in Fig. [104]7D (ES = 0.31937033, FDR < 0.01). ClueGo WikiPathway
analysis (P < 0.05) also indicated that multiple cancer-related
pathways were significantly involved in the mechanisms of XIAOPI
formula, such as focal adhesion, Estrogen signaling pathway, Toll-like
receptor signaling, angiogenesis signaling, inflammatory response
pathways, and AMPK signaling (Fig. [105]8).
Figure 7.
[106]Figure 7
[107]Open in a new tab
Gene Ontology (GO) and Pathway enrichment analysis of targets in XIAOPI
formula. (A) Combined with GENECARDS database A and [108]GSE7564
microarray B, it is revealed that a total of 81 genes were closely
correlated with anti-breast cancer activities of XIAOPI formula; (B) GO
terms analysis of the 81 genes containing 3 aspects including molecular
function, cellular component and biological process. Either a) all
genes background or b) intersecting genes set between A and B
background was selected to assess the significance of the 81 gene
enrichment, respectively (microenvironment-related functions are
labeled with *); (C) Pathway enrichment analysis of the 81 genes.
Either a) all genes background or b) intersecting genes set between A
and B background was selected to assess the significance of the 81 gene
enrichment, respectively; (D) The representative analysis extracellular
structure organization was shown in Fig. 7D (ES = 0.31937033,
FDR < 0.01) by GSEA analysis.
Figure 8.
[109]Figure 8
[110]Open in a new tab
Functional grouped network for the targets of XIAOPI formula by ClueGo
WikiPathway analysis (P < 0.05). Functional related groups partially
overlapped, only the label of the most significant term per group is
displayed.
The tumor microenvironment refers to the cellular environment where the
tumor survive, including surrounding blood vessels, immune cells,
fibroblasts, bone marrow-derived inflammatory cells, lymphocytes,
signaling molecules and the extracellular matrix^[111]23–[112]25.
Interestingly, the definition of TME coincidently met many aspects of
XIAOPI-affected signals predicted in above (* indicated in
Fig. [113]7B), we therefore postulated that XIAOPI formula might
prevent breast carcinogenesis via microenvironment modulation, which
would definitely need further experimental validation in the next part.
Identification of CXCL-1 as the critical target of XIAOPI through cytokine
array
Based on the results of network pharmacology analysis and the critical
role of tumor associated macrophages (TAMs) in cancer
microenvironment^[114]26, we sorted out TAMs from the solid tumor of
MMTV-PyMT^+/− mice using its markers CD11b and CD206 and then treating
TAMs with XIAOPI (Fig. [115]9A). We also collected the plasma from the
mice of the vehicle and XIAOPI group. The plasma and TAM supernatants
were then subjected to cytokine array analysis. Comparison of the
cytokine expression profiles of plasma between control and XIAOPI
treatment groups showed there to be 8 kinds of cytokines down-regulated
following XIAOPI administration including CCL11, G-CSF, IL-12p40/70,
IL-12/p70, CCL2, CXCL-2, sTMFR1 and TNF-α, while the expression of
TIMP-1, a cancer metastasis inhibition gene, was upregulated by XIAOPI.
Regarding TAMs supernatants, 6 kinds of cytokines were found
downregulated after XIAOPI administration, including GM-CSF, G-CSF,
CCL-2, CXCL-2, CXCL-1 and RANTES. By intersecting the cytokine change
profiles between in vivo and in vtiro, we found that a total of 4
cytokines (G-CSF, CCL2, CXCL-2, CXCL-1) share the similar changes
response to XIAOPI treatment (Fig. [116]9B). Immunofluorescence assay
further validated decreased intensity of TAMs in the tumors of XIAOPI
group, and qPCR demonstrated that all 4 of cytokines were downregulated
by XIAOPI treatment and CXCL-1 showed the most pronounced reduction
(Fig. [117]9C).
Figure 9.
[118]Figure 9
[119]Open in a new tab
XIAOPI formula targets TAMS/CXCL-1 signaling. (A) TAMs were sorted out
from tumors of MMTV-PyMT^+/− mice by flow cytometry; (B) Cytokine array
identified G-CSF, CCL2, CXCL-1/2 as common targets of XIAOPI formula
between in vitro and in vivo system; (C) Immunofluorescence assay found
that the TAMs density was significantly decreased following XIAOPI
administration, and qPCR assay further validated that CXCL-1 as the
most downregulated gene following XIAOPI administration (*P < 0.05,
**P < 0.01, values represented as the mean ± SD, n = 3).
XIAOPI formula inhibits breast cancer metastasis via downregulating
TAM-secreted CXCL-1
In order to validate the critical role of CXCL-1 in mediating the
anti-cancer activities of XIAOPI formula, we carried out in vitro
verification experiments using MDA-MB-231 cells. Wound healing and
transwell assay showed that the supernatants of TAMs could stimulate
cell migration and metastasis over vehicle-treated cells. However,
XIAOPI formula inhibited the TAM-induced cell migration in a
dose-dependent manner. Intriguingly, CXCL-1 administration was found to
aggravate the migration stimulating effect of TAMs. CXCL-1 abolished
the ability of XIAOPI formula to inhibit metastasis, indicating that
CXCL-1 is a critical mediator responsible for the anti-cancer
activities of the formula (Fig. [120]10A and B). Immunofluorescence
assay also demonstrated that TAM supernatants stimulated the EMT
process as evidenced by increased expression vimentin and decreased
level of E-cadherin. By contrast, XIAOPI formula was found to
efficiently inhibit EMT process induced by TAMs. What’s more important,
CXCL-1 was also found to promote the EMT transformation and relieve the
bioactivity of XIAOPI formula (Fig. [121]10C). These findings not only
indicated that the metastasis-promoting effects of CXCL-1 were closely
associated with EMT mechanism but that the anti-metastasis effects of
XIAOPI formula might be mediated through inhibition of the EMT process.
Because the EMT process is closely associated with the functions of
CSCs, we therefore further evaluated its influence on the functions of
CSCs. Flow cytometry analysis revealed that TAM supernatants could
increase the population of CSCs, and this effect was strengthened by
addition of CXCL-1. However, XIAOPI formula was also found to inhibit
the increase in the CSC population after TAM treatment. Similarly,
CXCL-1 was found to weaken the bioactivity of XIAOPI formula, which was
consistent with our findings given above (Fig. [122]10D). Mammosphere
assay also validated that CXCL-1 could assist TAM supernatants to
increase the size and number of mammosperes, while XIAOPI formula
inhibited their effects (Fig. [123]10E). All these findings
demonstrated the critical role of CXCL-1 in mediating the cancer
prevention activities of XIAOPI formula and highlighted the significant
effects of CXCL-1 in promoting breast cancer metastasis.
Figure 10.
[124]Figure 10
[125]Open in a new tab
XIAOPI formula inhibits breast cancer metastasis via CXCL-1. (A) Wound
healing assay indicated that XIAOPI formula could inhibit MDA-MB-231
migration dose-dependently, but CXCL-1 treatment inversely counteracted
against XIAOPI’s effects; (B) Transwell assay demonstrated that XIAOPI
formula could inhibit MDA-MB-231 cell invasion ability, and CXCL-1
administration restored the formula’s inhibition effects; (C)
Immunofluorescence assay validated that XIAOPI formula could activate
the EMT process, presenting as increased expression of E-cadherin and
decreased level of vimentin, but CXCL-1 overexpression reverse the EMT
process; (D) Flow cytometry assay also demonstrated that XIAOPI formula
could inhibit the CSCs subpopulation, but CXCL1 administration in the
TAMs supernatants counteracted with XIAOPI formula; (E) Mammosphere
formation assay indicated that the size and number of mammospheres were
both inhibited by XIAOPI administration, but re-increased following
CXCL-1 treatment (*P < 0.05, **P < 0.01, all values represented as the
mean ± SD, n = 3).
Discussion
Cancer is a heterogeneous disease. Its initiation is correlated not
only with oncogene activation and tumor suppressive gene inactivation,
but also closely associated with aberrant immune functions, stem cells
disorder and cytokine disregulation^[126]27. With regard to breast
cancer, BRCA1/2 mutations are reported to be mainly responsible for the
development of hereditary breast cancer, and corresponding gene
screening has become a routine tool for decreasing the risk ratio of
mammary malignancies. However, increasing amounts of evidence have
indicated that multiple factors also contribute to breast cancer
development, such as estrogen level, estrogen receptor status,
inflammation, macrophage activity, DNA repair ability, and
overactivation of mTOR survival signaling^[127]28,[128]29. The breast
cancer molecular phenotype also undergoes dynamic changes, such as a
transition of subtype from HER2-negative to -positive, increased cancer
stem cell population, and lymphocyte infiltration^[129]30. For this
reason, breast cancer therapy has been developed into a
multidisciplinary treatment including surgery, chemo-/radiotherapy,
endocrine therapy and targeted treatment. There is growing awareness
that single-target blocking strategies cannot eliminate cancer cells,
since there exists compensatory response in the signaling network. In
this way, a cocktail strategy or multi-targeting protocol has drawn
much more attention nowadays^[130]31. Coincidently, TCM is appreciated
for its multi-compounds, multi-targets and multi-pathways. TCM has
exhibited its mystic and profound therapeutic effects on some incurable
diseases including autoimmune disorders, chronic inflammation and
cancer.
In the present study, we demonstrated a clinical prescription XIAOPI
formula could efficiently prevent breast cancer progression in
MMTV-PyMT^+/− xenografts. However, it did not show a direct inhibition
effects on breast cancer cells or RAS-transformed mammary epithelial
cells, indicating that the anti-cancer activities of XIAOPI formula
might be attributed to systemic regulation, especially for the tumor
microenvironment. Because the XIAOPI formula consists of 10 herbs,
which might contain thousands of phytochemicals and molecular targets,
current techniques might be difficult to precisely explain its
underlying mechanisms. It is of great interest to elucidate the
molecular network of XIOPI formula using a systemic-based strategy.
Network pharmacology is recently emerging as a powerful tool for
understanding the complex action mechanisms of TCM formulas. Through
database searching and network analysis, a total of 105 active
compounds and 806 genes were finally identified in the XIAOPI formula.
Further gene crosstalk analysis identified 81 genes were closely
correlated to breast cancer development. Through Gene Ontology and
Pathway enrichment analysis, the 81 genes were validated to participate
in various biological processes during cancer initiation, such as
response to stimulus, immune system response, extracellular signals,
organelle, binding activity, as well as focal adhesion signaling, etc.
ClueGo WikiPathway analysis revealed that most signaling were belonged
to focal adhesion, Toll-like receptor signaling, angiogenesis signaling
and inflammatory response pathways, further indicating that XIAOPI
formula might act through tumor microenvironment. Cytokine array
further indicated that multiple cytokines were affected by
administration of XIAOPI formula both in vitro and in vivo. Among the
32 kinds of cytokines detected, CXCL-1 has been shown to be the
cytokine most profoundly affected by the formula. Other studies also
reported the application of bioinformatics analysis and network
pharmacology in breast cancer. Cong Fu et al. established a
literature-mined human signaling network by integrating data on
ubiquitin-mediated protein half-lives. Their study demonstrated that
the 26 S proteasome genes were significantly correlated with breast
cancer tumor progression and metastasis, and predicted clinical outcome
of breast cancer patients^[131]32. Zaman N et al. predicted breast
cancer subtype-specific drug targets based on signaling network
assessment of mutations and copy number variations^[132]33. McGee SR et
al. revealed a positive regulatory loop in the PIK3CA-mutated breast
cancer predicting survival outcome based on Cancer Hallmark Network
Framework^[133]34. Taken together, our work has great implications for
the development of breast cancer prognostic markers by integrating
bioinformatics and network pharmacology followed by experimental
validation.
CXCL-1 is a member of CXC the chemokine family, and it was firstly
reported to support melanoma growth by Richmond et al.^[134]35. Recent
progress also indicated the critical role of CXCL-1 in multiple
malignancies. CXCL-1 high expression in breast cancer cells could
attract CD11b^+Gr1^+ myeloid cells into the tumor and therefore
resulting in chemoresistance and metastasis^[135]36. CXCL-1
overexpression was also found to be significantly closely with gastric
cancer progression and poor survival. CXCL-1 depletion was capable of
inhibiting the migration and invasion ability of gastric cancer
cells^[136]37. Similar findings were also confirmed in bladder cancer.
CXCL-1 levels have been shown to be significantly higher in invasive
bladder cancer and to promote metastasis via regulating MMP-13
expression^[137]38. Notably, CXCL-1 was found to be secreted from tumor
stromal cells and affect cancer progression via a paracrine signaling.
CXCL-1 secreted from lymphatic endothelial cells could promote gastric
cancer migration, invasion and adhesion abilities via activating
integrin β1/FAK/AKT signaling^[138]39. CXCL-1 was also found to be a
cytokine secreted by tumor-associated macrophage, which recruits
myeloid-derived suppressor cells to form pre-metastatic niche and led
to liver metastasis from colorectal cancer^[139]40. The current study
demonstrated that after administration of XIAOPI formula, the density
of TAMs decreased significantly and the level of CXCL-1 was also
inhibited in both mouse plasma and cellular supernatants. When CXCL-1
cytokine was co-administrated with XIAOPI formula, the anti-metastatic
property of XIAOPI formula was blocked, indicating that CXCL-1 might be
principal gene involved in the network regulating the action of XIAOPI
formula.
Taken together, our study provides a candidate formula for preventing
breast cancer growth and metastasis, and highlights the role of CXCL-1
in mediating the bioactivities of XIAOPI formula. However, further
research is still needed to clarify the pathological significance of
CXCL-1 in promoting breast cancer metastasis and to identify the
potential CXCL-1 inhibitors from the formula via high-throughput
screening technique.
Materials and Methods
Preparation of XIAOPI Formula
XIAOPI formula consists of 10 herbs including Epimedium Brevicornum
(Chinese name Yin Yang Huo, YYH), Cistanche Deserticola (Chinese name
Rou Cong Rong, RCR), Leonurus Heterophyllus (Chinese name Yi Mu Cao,
YMC), Salvia Miltiorrhiza (Chinese name Dan Shen, DS), Curcuma
Aromatica (Chinese name Yu Jin, YJ), Rhizoma Curcumae (Chinese name E
Zhu, EZ), Ligustrum Lucidum (Chinese name Nv Zhen Zi, NZZ), Radix
Polygoni Multiflori Preparata (Chinese name He Shou Wu, HSW),
Crassostrea Gigas (Chinese name Mu Li, ML)and Carapax Trionycis
(Chinese name Bie Jia, BJ).The mixture was treated by ultrasound for 1
h followed by heating twice at 100 °C for 30 min each. The supernatant
was concentrated by rotary evaporation and stored overnight at −80 °C.
The freezing supernatant was then treated with a freeze dryer for 48 h
to find the raw aqueous extract powder. The production ratio was
14.6–15.2%. HPLC analysis of XIAOPI aqueous extract was conducted on an
Ultimate AQ-C18 column (250 mm × 4.6 mm, 5 μm). The mobile phase
consisted of (A) acetonitrile and (B) water using a gradient elution of
5–20% A at 0–40 min, 20–30% A at 40–50 min and 30–95% A at 50–80 min.
The solvent flowrate was 1.0 mL/min and the column temperature was
ambient. The results showed that the chemical fingerprints at 200 nm
were consistent across different batches (Supplementary Figure [140]1).
The powder was dissolved in phosphate buffer solution and passed
through 0.45 μm filter for later use.
Breast cancer xenografts and mammary whole mounting assay
All animal studies involving animal experiments were reviewed and
approved by the University of Hong Kong’s Committee for Ethical Review
of Research. All experiments were performed in accordance with the
guidelines and regulations issued by the Administration Office of
Laboratory Animals at Hong Kong. MMTV-PyMT^+/− transgenic mice, a
spontaneous breast cancer generation model, was used to confirm the
cancer prevention effects of XIAOPI formula. Animals were divided into
three groups including the wild type (WT) group, MMTV-PyMT^+/− group,
and MMTV-PyMT^+/− mice treated with XIAOPI group. XIAOPI formula was
administrated to MMTV-PyMT^+/− mice at 0.5 g/kg/d since the 4^th week
after birth by oral gavage. The number of tumors, time of tumor
appearance, tumor volume, and body weight were recorded at three-day
intervals. Tumor volume was calculated using the following formula:
volume (mm^[141]3) = width^2 × length/2. Tumor tissues were removed at
the end of the experiment and subjected to histological examination or
flow cytometry analysis. The lung metastasis nodules were calculated
and compared between groups. The mammary glands of PYMT mice were
excised, and whole-mounts stained with carmine alum were analyzed as
described previously^[142]41. In particular, the fourth abdominal
mammary gland was excised during necropsy, spread on glass slides for
10 min, and fixed in Carnoy’s fixative (6 parts 100% ethanol, 3 parts
chloroform, and 1 part glacial acetic acid) for 4 h. Subsequently, the
tissue was washed in 70% ethanol for 15 min, and the ethanol was
changed gradually to distilled water, with a final rinse in distilled
water for 5 min. Staining was carried out overnight in carmine alum.
The tissue was then dehydrated in graded alcohol solutions (70, 95, and
100% for 30 min each) and cleared in two changes of xylene (30 min
each), mounted, and cover slipped using Permount. Whole mounts were
recorded using a SPOT FLEX® color digital camera (Diagnostic
Instruments, Inc. Sterling Heights, MI, U.S.).
Cell culture
The human breast cancer cell lines MDA-MB-231 and MCF-7 were obtained
from the American Type Culture Collection. The cells were cultivated in
medium (L-15 for MDA-MB-231; 1640 for MCF-7) supplemented with 10% FBS
and 1% penicillin and streptomycin (Gibco Life Technologies, Lofer,
Austria) at 37 °C in a humidified incubator with or without 5% CO2. The
normal mammary epithelial cells MCF-10A were cultured in DMEM/F12
supplemented with 5% horse serum, 1% penicillin and streptomycin, 20
ng/ml recombinant human epidermal growth factor (EGF), 0.5 μg/ml
hydrocortisone, 100 ng/ml cholera toxin and 10 μg/ml insulin. The
sorted MDA-MB-231 cancer stem cells (CSCs) were subjected to in vitro
propagation in DMEM/F12 medium supplemented with 1% penicillin and
streptomycin, B27 (Invitrogen, Carlsbad, CA, U.S.), 20 ng/ml hEGF (BD
Bioscience, Bedford, MA, U.S.), 5 μg/ml insulin and 0.4% BSA for the
molecular mechanism study.
Flow cytometry analysis
Primary mouse mammary cells or breast cancer cell lines (MDA-MB-231 and
MCF-7) were washed with PBS and then harvested with trypsin. The
detached cells were washed with PBS containing 1% FBS (wash buffer),
and resuspended in the wash buffer (10^6 cells/100 μl). For ALDEFLUOR
assay, the experiment was performed using aldehyde dehydrogenase-based
cell detection kit (Stem Cell Technologies, Grenoble, France) as
described previously. Briefly, 2 × 10^5 cells were suspended in
Aldefluor® assay buffer containing ALDH substrate
(Bodipy-Aminoacetaldehyde) and incubated for 45 min at 37 °C. As a
reference control, the cells were suspended in buffer containing
Aldefluor® substrate in the presence of diethylaminobenzaldehyde
(DEAB), a specific ALDH1 enzyme inhibitor. The brightly fluorescent
ALDH1-expressing cells (ALDH1high) were detected by a 488 nm blue
laser. For the in vitro stem cell analysis/sorting, cells were
incubated with combinations of fluorescence-conjugated monoclonal
antibodies obtained from BD Biosciences (San Diego, CA, U.S.) against
human CD44-FITC and CD24-PE at 4 °C in the dark for 40 min and then
washed once with PBS. FITC- and PE-labeled isotype IgG1 served as the
negative control.
Immunofluorescence assay
Tumor samples obtained from in vivo studies were fixed in 4%
paraformaldehyde, dehydrated in 70% ethanol, paraffin embedded, and
sectioned (4 μm). For immunofluorescence analysis, paraffin-embedded
tumor sample sections were de-paraffinized in xylene twice for 10 min
each and rehydrated using a graded series of ethanol. The sections and
4% paraformaldehyde fixed cells were permeabilized with 0.2% triton
X-100. After blocking in 10% goat serum for 1 h, the slide was
incubated with primary antibodies including CD206, CD11b, ALDH1A3,
E-cadherin and vimentin (ABclonal, Cambridge, MA, U.S.) overnight at
4 °C, followed by secondary fluorescence-labeled antibodies (Santa
Cruz, CA, U.S.) for 2 h at room temperature. Finally, the samples were
incubated with DAPI for nuclear staining and the signal was detected
using a confocal microscope.
Herb-ingredient-target interaction analysis
The chemical ingredients were collected from TCM databases, including
the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database
([143]http://lsp.nwsuaf.edu.cn/tcmsp.php), the Traditional Chinese
Medicine Integrated Database (TCMID,
[144]http://www.megabionet.org/tcmid/) and the BATMAN-TCM
([145]http://bionet.ncpsb.org/batman-tcm/). The ingredients were
screened according to drug-likeness (DL) and oral bioavailability (OB)
values, and the ingredients were retained if DL ≥ 0.18 and OB ≥ 30, a
criterion suggested by TCMSP database^[146]42. Specifically, the
ingredients of ML were inorganic compounds with low DL values;
therefore they were not screened by these criteria. The
ingredient-target networks were constructed for these herbs using
Cytoscape software (Version 3.2.1).
Gene Ontology and Pathway enrichment analysis
The breast cancer-associated targets were collected from GeneCards
database ([147]http://www.genecards.org/). The dataset [148]GSE5764,
based on the platform of Affymetrix GeneChip^® Human Genome U133 Plus
2.0 Array, was retrieved from the National Center for Biotechnology
Information (NCBI) Gene Expression Omnibus (GEO) database
([149]http://www.ncbi.nlm.nih.gov/geo) and then analyzed by GEO2R
([150]http://www.ncbi.nlm.nih.gov/geo/geo2r/). The differentially
expressed genes (DEGs) were submitted to the Database for Annotation,
Visualization and Integrated Discovery (DAVID;
[151]http://david.abcc.ncifcrf.gov/). The significant enrichment
analysis of DEGs was assessed based on the gene ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG;
[152]http://www.genome.jp/kegg/kegg2.html). For Gene Ontology (GO)
analysis, it fell into 3 categories: molecular function, cellular
component and biological process. The basic unit of GO was GO-term.
Each GO-term belonged to a type of ontology. GO enrichment analysis
provided all GO terms that significantly enriched in DEGs comparing to
the genome background, and filtered the DEGs that corresponded to
biological functions. Firstly, all DEGs were mapped to GO terms in the
Gene Ontology database ([153]http://www.geneontology.org/), gene
numbers were calculated for every term, significantly enriched GO terms
in DEGs comparing to the genome background were defined by
hypergeometric test. The calculating formula of P-value was:
[MATH: P=1-∑i=0
m-1(M
i)(N-Mn-i)(Nn) :MATH]
Here N was the number of all genes with GO annotation; n was the number
of DEGs in N; M was the number of all genes that were annotated to the
certain GO terms; m was the number of DEGs in M. The calculated P-value
were gone through the false discovery rate (FDR) Correction, taking
FDR ≤ 0.05 as a threshold. GO terms meeting this condition were defined
as significantly enriched GO terms in DEGs. This analysis was able to
recognize the main biological functions that DEGs exercised. For KEGG
Pathway enrichment analysis, the calculating formula was the same as
that in GO analysis. For Gene Set Enrichment Analysis (GSEA) analysis,
3 major steps included calculation of an enrichment score (ES),
estimation of significance level of ES, and adjustment for multiple
hypothesis testing by FDR calculating based on GSEA java
software^[154]43.
Wound-healing and transwell migration assay
For wound healing assay, 2*10^5 cells were seeded on a 24-well plate.
When they grew to full confluence, a ‘wound’ was made in the middle of
a culture plate with a 10 μl pipette tip for MDA-MB-231. The
wound-healing process was recorded at 0 h, 12 h or 24 h after the
scratch under a 10 × objective microscope. The wound healing rate was
quantified as the distance of wound recovered versus that of the
original wound. With regard to transwell assay, transwell chambers (8
μm, Milipore) were used for cell invasion. The bottom chamber was
filled with culture medium containing 10% FBS. 1*10^5 cells were
suspended in serum-free medium and plated in the upper chamber with or
without drug treatment. After incubation for 24 h, the cells were
removed from the upper-chamber using a cotton swab. Cells penetrated
and attached to the bottom of the filter were fixed with 4%
formaldehyde in PBS, followed by 20 min staining of 0.5% crystal violet
and then subjected to imaging under a 20 × objective. Statistical
analysis of the number of invading cells was performed in three
independent experiments and results were averaged from five image
fields.
Cytokine array analysis
Mouse cytokine antibody array C2 kits were purchased from RayBiotech
(Norcross, GA, U.S.). The cytokine layout is listed in Supplementary
Figure [155]2. Briefly, cell supernatants from TAMs or mice plasma were
collected before and after BM treatment. After antibody array membranes
were blocked in 5% BSA for 30 min at room temperature, cell
supernatants were cultured with antibody arrays overnight at 4 °C and
washed for three times. Biotinylated antibody cocktail was then
incubated with the membranes for 2 h, followed by signal amplification
with HRP-streptavidin. Finally, the signals were detected by
chemiluminescence method with ECL kit purchased from GE Healthcare
(Buckinghamshire, U.K.).
Real-time PCR analysis
TRIzol reagent (Invitrogen, Carlsbad, CA, U.S.) was used to extract
total RNA, followed by reverse transcription reaction using the
first-strand cDNA synthesis kit (Roche, Mannheim, Germany). A SYBR
Green Kit (Roche, Mannheim, Germany) was used to perform real-time PCR
analysis on a Roche LightCycler 480 detector. PCR reaction conditions
were set as follows: 95 °C for 10 min followed by 40 cycles of 95 °C
for 10 s, 55 °C for 30 s, and 72 °C for 1 min. The target gene
expression was calculated using 2^−ΔΔCt and normalized to the
housekeeping gene. The primers’ sequences were as follows: G-CSF (F,
gctgctggagcagttgtg; R, gggatccccagagagtgg); CCL-2 (F,
catccacgtgttggctca; R, gatcatcttgctggtgaatgagt); CXCL-2 (F,
aaaatcatccaaaagatactgaacaa; R, ctttggttcttccgttgagg); CXCL-1 (F,
gactccagccacact ccaac; R, tgacagcgcagctcattg); GAPDH (F,
aagagggatgctgccctta; R, ttgtctacgggacga ggaaa).
Statistical analysis
Data analysis was performed with Statistical Product and Service
Solutions (SPSS) 19.0 software. The data are expressed as mean ± SD.
The student’s t-test was used to compare the statistically significant
difference between groups. The significance of multiple groups was
compared using one-way analysis of variance (ANOVA) followed by the
Dunnett’s post hoc test. A value of P < 0.05 was considered
significant.
Electronic supplementary material
[156]Supplementary files^ (546.4KB, pdf)
Acknowledgements