Abstract
Background
As an original traditional Chinese medicinal formula, Qin Huang formula
(QHF) is used as adjuvant therapy for treating lymphoma in our hospital
and has proven efficacy when combined with chemotherapy. However, the
underlying mechanisms of QHF have not been elucidated.
Methods
A network pharmacological-based analysis method was used to screen the
active components and predict the potential mechanisms of QHF in
treating B cell lymphoma. Then, a murine model was built to verify the
antitumor effect of QHF combined with Adriamycin (ADM) in vivo.
Finally, IHC, ELISA, ^18F-FDG PET-CT scan, and western blot were
processed to reveal the intriguing mechanism of QHF in treating B cell
lymphoma.
Results
The systemic pharmacological study revealed that QHF took effect
following a multiple-target and multiple-pathway pattern in the human
body. In vivo study showed that combination therapy with QHF and ADM
potently inhibited the growth of B cell lymphoma in a syngeneic murine
model, and significantly increased the proportion of tumor infiltrating
CD4+ and CD8+ T cells in the tumor microenvironment (TME). Furthermore,
the level of CXCL10 and IL-6 was significantly increased in the
combination group. Finally, the western blot exhibited that the level
of TLR2 and p38 MAPK increased in the combination therapy group.
Conclusion
QHF in combination of ADM enhances the antitumor effect of ADM via
modulating tumor immune microenvironment and can be a combination
therapeutic strategy for B cell lymphoma patients.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12906-022-03660-8.
Keywords: Qin Huang formula, B-cell lymphoma, Tumor microenvironment,
Tumor-infiltrating lymphocytes, Toll-like receptor, Systemic
pharmacology
Introduction
Non-Hodgkin lymphoma (NHL) is a malignancy that mainly infringes the
lymphoid system, with extranodal sites often affected as well. It has
arisen to the top 10 most common cancers in 2020 [[41]1]. Among all the
subtypes of NHLs, diffuse large B cell lymphoma (DLBCL) is the most
common subtype and accounts for 25-30% of diagnoses [[42]2]. Albeit
aggressive, most DLBCL is chemo-sensitive, and R-CHOP (rituximab,
cyclophosphamide, doxorubicin, vincristine, and prednisone) is the
standard regimen. However, due to resistance to R-CHOP, there are still
~ 30% of patients suffering from refractory or relapse (r/r) disease,
and their prognosis is rather poor [[43]3, [44]4]. Such treatment
plight has prompted the emergence of novel treatment options, such as
therapeutics targeting immune checkpoints, tumor microenvironment
(TME), signaling pathways, and cellular immunotherapy [[45]5]. As a
cellular immunotherapy, chimeric antigen receptor (CAR) T-cell therapy
has shown a promising future in the management of B cell lymphomas. Two
CAR T-cell products are now approved for the treatment of
relapsed/refractory large B-cell lymphoma [[46]6]. However, its acute
and late toxicities, as well as on-target off-tumor effect should be
addressed [[47]7]. PD-1/PD-L1 blockade, as an immune checkpoint
inhibitor (ICI), aims to protect cytotoxic T cells and restore their
function from PD-1/PD-L1 mediated immune evasion. However, its efficacy
in DLBCL remains unsatisfactory compared to classical Hodgkin Lymphoma
(cHL) [[48]8, [49]9]. Research accumulated that the pre-existing immune
landscape within the TME may influence the response to immunotherapies
[[50]10–[51]12] and R-CHOP [[52]13, [53]14]. Therefore, it is pivotal
to explore agents that can modulate TME. TME is an extremely intricate
environment that harbors diverse cells, structures, and matters, with
one component interacting with many others, forming complex regulatory
networks. Drugs that have multiple targets might be a promising
combination therapy to modulate TME, especially those that could elicit
both innate and adaptive immune responses against cancers.
Traditional Chinese medicine (TCM) has a long history in treating
various diseases and ailments. Formula, a prescription that contains
multiple drugs with different but related modes of action, is a
representative of combination therapy in the system of TCM [[54]15].
With the guidance of this philosophy, the Qin Huang formula (QHF), was
grouped and used as adjuvant therapy for treating lymphoma in our
hospital (Patent No.:2015103646392). Containing Scutellaria baicalensis
Georgi. (Lamiaceae) (SR), Astragalus membranaceus (Fisch) Bunge (AM),
Prunella vulgaris L. (Lamiaceae) (PV), and Curcuma Longa L.
(Zingiberaceae) (CL), the 4-herb formula conforms to the theology of
TCM in treating malignancies. Huang et al. [[55]16] found that
baicalin, an ingredient of SR, could down-regulate PI3K/Akt signaling
pathway and induce apoptosis in the Burkitt lymphoma cell line. Kumagai
et al. [[56]17] demonstrated that SR and its major components,
baicalein, and wogonin, could exert anti-proliferation and induce
apoptosis mediated by mitochondrial damage in lymphocytic leukemia,
Burkitt lymphoma, and myeloma cell lines. Studies [[57]18–[58]20] also
showed that PV and CL have a strong antiproliferation effect upon Raji,
a Burkitt lymphoma cell line, and other solid tumors. Meanwhile, it is
reported that AM and its extract could protect human epithelial and
endothelial cells from LPS-induced apoptosis [[59]21] and exert a
cardioprotective effect against Adriamycin-induced cardiotoxicity
[[60]22]. Thus, as a mixture of the four herbs, QHF was expected to
exert an antitumor effect in B cell lymphoma as well. And our previous
work [[61]23] found that QHF (also referred to as Qin Huang mixture)
has a modulating effect on the systemic immune function of lymphoma
patients who have completed R-CHOP chemotherapy. Besides, we also found
that when combined with R-CHOP, QHF can significantly lower the overall
side effect frequencies and improve the short-term effects of R-CHOP in
treating initial onset DLBCL patients [[62]24]. In addition, we
explored the effects of several major flavonoids of QHF in vitro and
found that wogonin, luteolin, kaempferol, quercetin, and silymarin can
inhibit the proliferation and induce apoptosis of the Raji, SU-DHL-4,
and A20 cell lines (data not published), which is consistent with other
studies [[63]25–[64]28]. Therefore, we hypothesized that QHF can
enhance the antitumor effects of chemotherapy by modulating immune
functions, especially the tumor immune microenvironment (TIME), of B
cell NHL. In this study, a systemic pharmacological analysis was
carried out to predict the targets and mechanisms of QHF in treating B
cell lymphoma. Then, the antitumor effect of QHF in combination with
Adriamycin (ADM) was evaluated on a murine model induced by the mouse B
cell lymphoma cell line A20. Moreover, the TME of the mouse B cell
lymphoma was further evaluated via IHC staining, ELISA, as well as
western blot to confirm our hypothesis. Our extensive study provides
experimental evidence regarding the antitumor effect of QHF + ADM
combination therapy and revealed its underlying mechanisms.
Methods
Screening the chemical constituents of QHF and their targets
The ingredients information of QHF was searched through four databases:
TCMSP [[65]29] ([66]https://www.tcmspw.com/tcmsp.php), DrugBank
[[67]30] ([68]https://go.drugbank.com/#), BATMAN-TCM [[69]31]
([70]http://bionet.ncpsb.org.cn/batman-tcm/), and ETCM [[71]32]^30
([72]http://www.tcmip.cn/ETCM/). These databases contain information on
chemical constituents and their targets of herbs. The screening
criteria were set as oral bioavailability (OB) ≥30% and drug-likeness
(DL) > 0.18 to collect the potential ingredients and their targets of
SR, AM, PV, and CL.
B cell lymphoma-associated targets were collected from the following
databases: Genecards [[73]33] ([74]https://www.genecards.org/), GEO
[[75]34] ([76]https://www.ncbi.nlm.nih.gov/geo/), DisGeNET [[77]35]
([78]https://www.disgenet.org/), and OMIM [[79]36]
([80]https://www.omim.org/). As of GEO analysis, the [81]GSE12453
dataset was utilized to draw the differentially expressed genes of
DLBCL compared with naive B cells. The results with P-adjusted value
< 0.05 and | log2(fold change) | > 1 were kept for further analysis.
Then R platform ggplot2 [[82]37] package was used to draw a volcano
plot. Finally, with the help of the R platform VennDiagram [[83]38]
package, the common targets of QHF and B cell lymphoma were analyzed.
The protein-protein interaction (PPI) network and drug-disease-target network
building
The common targets were imported to the STRING [[84]39] database
(version 11.0, [85]https://string-db.org) to construct the PPI network.
The gene expression was correlated with others by minimum required
interaction score thresholds of 0.9. Disconnected nodes in the network
were hidden. The nodes in the PPI network represent the protein
targets, while the edges stand for the interaction between two targets.
Then, topological parameters of nodes betweenness, closeness, degree,
network centrality, as well as local clustering coefficient were
analyzed via R in the network. Targets with node degrees over twice the
average were kept and ranked by node degree. Then the top 30 targets
were counted to be the core targets. Circlize [[86]40] package (version
0.4.13) of the R platform [[87]41] was used to construct an herb-target
chord plot to display the relationship between the herbs and the core
targets. And a drug-disease-target network was constructed with the
utilization of Cytoscape 3.6.0 software [[88]42] to show the
interactions of the compounds and the core targets.
Gene ontology (GO) functional and Kyoto encyclopedia of genes and genomes
(KEGG) pathway enrichment
GO and KEGG enrichment analysis was employed to further clarify the
biological interpretations and signaling pathways of the 30 core
targets we obtained in Part 2.2. ClusterProfiler [[89]43], an R
(version 4.1.0) package for GO and KEGG enrichment analysis tool, was
utilized to access the databases of GO and KEGG. Pathview [[90]44]
package of R platform was applied to visualize KEGG pathways. The genes
with adjusted P-value < 0.05 were retained.
The molecular docking processing
Although the relationship between the targets and compounds was
verified, the intensity of the interaction remained unclear. The
computer-assistant molecular docking technology was used in this study
to further investigate the ligation intensity. First, the SDF files of
the compound structures were downloaded from the PubChem database
([91]https://pubchem.ncbi.nlm.nih.gov/). Second, the protein data bank
(PDB) ([92]http://www.rcsb.org/) database was searched to obtain the
molecular structures of the target proteins. The ligand-receptor
compound structures were priorly chosen and modified by removing the
original ligands. The pocket of the combination was constructed in the
slot. Third, after adding polar hydrogen atoms, the AutoDock Vina
software [[93]45] (ver. 1.1.2, the Scripps Research Institute, U.S.)
was used to achieve the binding free energy of ligands and receptors.
Finally, the bonds within the ligand-receptor complexes were visualized
by Ligplot software [[94]46] (ver. 4.5.3, EBI, UK).
Materials and reagents
The herbal formula QHF is constituted of four medicinal herbs: SR
(20 g), AM (20 g), PV (15 g), and CL (10 g) at a rate of 4:4:3:2
(w/w/w/w). The herbs were obtained from Shanghai Wanshicheng
Pharmaceutical Co. Ltd. (Lot Number: SR 20210406-1; AM 20210515-1; PV
20210604-1; CL 20210526-1), and the frozen powder of QHF was prepared
by Shanghai Traditional Chinese Medicine Technology Co. Ltd. To obtain
the extract of QHF, the herbs were first soaked in water at tenfold
volume (v/w) overnight, then extract the decoction twice for 1 h each
time with a tenfold volume of water to herbs (v/w). After filtration,
the solution was evaporated under reduced pressure at 60 °C to a
relative density of 1.15, then the extract was frozen desiccated to
powder, and stored at − 20 °C for further use.
According to the pre-study of the effective dosage of QHF we conducted
on mice, we found QHF (6000 mg/kg) is more effective compared with QHF
(300 mg/kg) and QHF (3300 mg/kg), but tolerable as well (see Additional
file [95]1). Therefore, the regimen of the combination group (QHF
(6000 mg/kg) + ADM (5 mg/kg)) was used in this study.
Animals and treatments
Nineteen male Balb/c mice aged 6-8 weeks were purchased from the
Animals Care Centre at Shanghai Jiao Tong University. The animals were
given free access to food and water and acclimatized for 1 week under
standard conditions and 12 h light/dark cycle. All experimental
procedures were conducted under the requirements of the research ethics
Committee at Shanghai Jiao Tong University (Ethical reference no.
10687).
The mouse B cell lymphoma A20 cell line was kindly provided by the
Institute of Hematology, Shanghai Ruijin Hospital, and cultured in RPMI
1640 medium (Gibco, Gaithersburg, MD, USA) containing 10% fetal bovine
serum (FBS), 100 U/mL penicillin, and 100 μg/ mL streptomycin. The
cells were incubated at 37 °C with 5% CO[2]. Then the cells
(~ 1.0 × 10^7 cells/mouse) were transplanted subcutaneously into the
right axillary region of the mice. After 1 week or so, when the tumors
reached a size of 50 mm^3, the mice were randomly allocated into three
groups as follows:
* Control group (Control): intravenously injected with 0.1 mL PBS
three times on day 1, day 4, and day 8, respectively.
* Adriamycin group (ADM): received ADM (5 mg/kg) intravenously (i.v.)
three times on day 1, day 4, and day 8, respectively [[96]47].
* Combination therapy group (QHF + ADM): received 6000 mg/kg QHF
dissolved in ddH[2]O intragastrically, 0.1 mL per day; ADM was
administered same as ADM group.
The tumor diameters were measured routinely with a caliper. Tumor
volume was estimated as follows: length × width^2× 0.5. Besides, the
tumor growth inhibition rate (TGI, %) was calculated to evaluate the
anticancer effect of the two regimens.
[MATH: TGI%=1-tumorvolum
eoftreat
edgroup
tumorvolum
eofcontr
olgroup
*100% :MATH]
QHF was administered intragastrically (i.g.) every day throughout the
experiment. On day 12, the mice were sacrificed under euthanasia with
pentobarbital sodium. After dissection, the tumor of each mouse was
divided into two parts, one part was kept in 4% paraformaldehyde and
the other was freshly frozen in liquid nitrogen and then stored at
− 80 °C.
Immunohistochemistry (IHC) on formalin-fixed paraffin-embedded samples
Tumors were embedded in paraffin blocks and cut into tissue sections
4 μm thick. Then hematoxylin & eosin staining was processed to confirm
the presence of the tumors. Besides, paraffin sections were dewaxed
with xylene and rehydrated with alcohol at graded concentrations for
immunohistochemical staining. Endogenous peroxidase was inactivated
with 3% H[2]O[2] for 15 minutes. Then, the slides were blocked with 5%
goat serum for 20 min at 37 °C, followed by incubated with anti-CD4
(1:3000, Servicebio, China), anti-CD8 (1:1000, Servicebio, China),
anti-CD206 (1:500, Servicebio, China) and anti-iNOS (SP126, 1:100,
Abcam, UK) antibodies overnight at 4 °C. The next day, samples were
incubated with a secondary antibody (Servicebio, China) for 1 hour at
room temperature. After that, staining the samples with the
ready-to-use reagent DAB kit. Finally, the sections were observed with
a microscope.
The images of the tissue sections were obtained by the tissue slice
digital scanner. The Servicebio image analysis system was used to
automatically analyze and calculate the number of weak, medium, and
strong positive cells in the measurement area (negative for no
coloring, score 0 points; weak positive for light yellow, score 1
point; medium positive for brownish yellow, score 2 points; strong
positive for tan, score 3 points). The positive cell rate indicator was
used to assess the percentage of positive cells [[97]48].
[MATH: positivecellrate%=numberofposit
ivecells
numbe
roftotal
cells
*100%. :MATH]
Enzyme-linked immunosorbent assay (ELISA) on FFPE samples
The total protein was extracted from the freshly frozen tumor tissues
previously stored. First, 1 ml of RIPA buffer was prepared for every
0.1 g of tissue and protease and phosphatase inhibitors were added to a
final 1x concentration. The tissues were ground under 60 Hz for
4 cycles. Then, the homogenate was centrifuged at 12500 rpm at 4 °C for
15 minutes. After this, the supernatant was transferred to a clean tube
for ELISA. According to the manufacturer’s instructions, commercially
available ELISA kits (MultiSciences (LIANKE) Biotech Co., Ltd.,
Hangzhou, China) were applied for mouse protein expression detection of
CXCL10, IL-2, IL-17, IL-6, TGF-β, and IFN-γ levels.
Western blotting
Three freshly frozen samples of each group were also homogenized in
RIPA buffer supplemented with proteinase/phosphatase inhibitors. After
centrifuging, the protein concentration was determined with a BCA
protein assay kit (Beyotime, Shanghai, China). Twenty μg protein from
each sample was subjected to 12% SDS-PAGE and electrotransferred to
0.22uM PVDF membranes. Then, the membranes were subjected to blocking
buffer (Beyotime, Shanghai, China), followed by incubation overnight at
4 °C with primary antibodies (1:1000, Abcam, U.S.) against p38 MAPK, p-
ERK, TLR2, and β-actin. After that, the probed membranes were washed
with TBST three times, followed by incubation with secondary antibodies
(1:5000, Abcam, U.S) for 1 h at room temperature. The protein signals
were detected using a chemiluminescence ECL assay kit and imaged using
a Tanon 5200 imaging system. β-actin was served as the internal
reference. The expression levels of proteins were calculated by Image J
platform.
^18F-FDG PET-CT scan
One mouse of each group was picked out to receive the ^18F-FDG PET-CT
scanning on the first day (d1) and the last day (d8) of therapy,
respectively. The combination group underwent another ^18F-FDG PET-CT
scan on the 7th day after discontinuing therapy. PET/CT imaging was
performed on the Inveon MM Platform (Siemens Preclinical Solutions,
Knoxville, Tennessee, USA), which was equipped with a
computer-controlled bed and 8.5 cm transaxial and 5.7 cm axial fields
of view (FOV). After fasting overnight for 8 h and being anesthetized
under pentobarbital sodium, three mice received an intravenous infusion
of 100-200 uCi ^18F-FDG. The scanning process was performed with Inveon
Acquisition Workplace (IAW) 1.5.0.28. Before the PET scan, the CT X-ray
for attenuation correction was scanned for 10 minutes with a power of
80 Kv and 500 uA and an exposure time of 1100 ms. Then, 10-minute
static PET scans were acquired, followed by images reconstruction with
an OSEM3D (Three-Dimensional Ordered Subsets Expectation Maximum)
algorithm. The 3D regions of interest (ROIs) were drawn over the right
axillary region guided by CT, with the tracer uptake measured by the
Inveon Research Workplace (IRW) 3.0. Individual quantification of the
^18F-FDG uptake, as well as the max uptake values (SUV[max]) of the
mice, were determined.
Statistical analysis
The obtained data were expressed as mean ± standard errors. Statistical
analysis was performed on the platform of GraphPad Prism 9 (GraphPad
Software, San Diego, CA, USA). Comparisons among multiple groups were
performed by one-way ANOVA with Tukey’s multiple comparisons test.
Comparisons between two groups were assessed using Student’s unpaired
t-tests. A p-value < 0.05 was considered significant.
Results
Active components and hub genes of QHF in treating B cell lymphoma
A total of 271 active ingredients of QHF with OB ≥ 30% and DL > 0.18
were obtained. In the GEO database, the samples of [98]GSM312858-312869
(DLBCL), [99]GSM312870-312876 (naive B-cells), and
[100]GSM312877-312886 (memory B cells) from [101]GSE12453 were
selected. The volcano plot shows the relationship between the
significance and the foldchange of differentially expressed genes
(DEGs). They were highlighted by the red and green dots (Fig. [102]1A).
Filtered by p-adjusted value < 0.05, a total of 7141 transcriptionally
dysregulated genes of B-cell lymphoma were discovered. The Venn diagram
shows the overlapping of QHF (red color) and DLBCL (turquoise color)
related targets (Fig. [103]1B). Finally, we obtained a total of 541
common targets. To explore the interactions between the targets of QHF,
a PPI network was constructed with the STRING database. After importing
541 common targets into the database, a total of 539 nodes and 2807
edges were built with an average node degree of 10.4 (Fig. [104]1C). By
topology analysis of the interactions in the PPI network, we obtain a
bar plot displaying the core targets and their node degrees (Fig.
[105]1D). According to this figure, MAPK1/8/14, TP53, EP300, AKT1, APP,
STAT3, JUN, RELA, TNF, IL6, C3, HSP90AA1, AGT, NFKB1, RAC1, NR3C1,
EGFR, RXRA, ESR1, FOS, RHOA, APOB, RB1, SUMO1, FGA, NCOA1, PRKCD, and
ANXA1 were the top 30 targets with the most nodes degree. Among these
core targets, MAPK1/ERK2, MAPK8/JNK1, and MAPK14/p38 consist of the
MAPK signaling pathway, which regulates diverse biological functions,
including cell growth, differentiation, and survival. JUN is one of the
components of AP-1, which can be phosphorylated by MAPK8/JNK1 and
induce the expression of inflammatory cytokines, including IL-6 and
TNF-α. NFKB and AKT1 are the key molecules in the NF-κB signaling
pathway and PI3K-Akt signaling pathway, respectively. The above three
pathways are the downstream pathway of TLRs.
Fig. 1.
[106]Fig. 1
[107]Open in a new tab
Active ingredients and core targets screening. A. The volcano plot
displayed the significance and the fold change of differentially
expressed genes acquired from the GEO database; The red ones
represented up-regulated DEGs (log2(fold change) > 1), while the green
ones represented down-regulated DEGs (log2(fold change) < − 1). B. The
Venn diagram demonstrates the overlapping of network pharmacology-based
targets of QHF components (red color) and transcriptionally
dysregulated genes of B-cell lymphoma (turquoise color). C. The
Protein-Protein-Interaction (PPI) network of the core genes. D. The
core targets and their node degrees. E. The herb-target chord chart
displays the relationship of the most involved targets and herbs. F.
The drug-disease-target network indicated the relationship between the
drugs, compounds, and targets. The pink triangles of the left
peripheral circle represented the compounds of QHF, while the four
orange octagons represented the four herbs of QHF. The blue arrows of
the right peripheral circle represented the core targets of B cell
lymphoma
To further investigate the relationship between herbs, components, and
hub targets, the R platform was utilized to form a chord chart (Fig.
[108]1E). According to the plot, there were 64, 96, 72, and 13 edges in
AM, SR, PV, and CL, respectively. The herb which related to most
targets was SR (degree = 96). In addition, the relationship between
drugs and the disease was described by the drug-disease-target network
(Fig. [109]1F). In this network, there were 220 nodes and 625 edges.
Quercetin in AM and PV (degree = 20), kaempferol in AM and PV
(degree = 12), luteolin in PV (degree = 9), and wogonin in SR
(degree = 9) ranked the top four compounds that connected most quantity
of targets of DLBCL. In CL, stigmasterol relates to the most targets in
the network. Among these five active components, four belong to
flavonoids and only stigmasterol of CL belongs to sterol. The
structural formula and other important parameters of the major active
components of QHF were shown in Table [110]1.
Table 1.
The structural formula and other important parameters of the major
active components of QHF.
[111]graphic file with name 12906_2022_3660_Tab1_HTML.jpg
[112]Open in a new tab
^*Note: SR for Scutellaria baicalensis Georgi. (Lamiaceae); AM for
Astragalus membranaceus (Fisch) Bunge; PV for Prunella vulgaris L.
(Lamiaceae); CL for Curcuma Longa L. (Zingiberaceae).
Predicted mechanisms of QHF in treating B cell lymphoma
As mentioned above, the top 30 core targets (see Additional file
[113]2) were input for GO enrichment and KEGG pathway enrichment
analyses to show the biological activity and potential mechanisms of
the hub genes. The most enriched functions in the GO analysis are shown
in Fig. [114]2A. In detail, the top three terms in the GO biological
processes were DNA-binding transcription factor binding (GO:0140297),
RNA polymerase II-specific DNA-binding transcription factor binding
(GO:0061629), DNA-binding transcription activator activity, RNA
polymerase II-specific (GO:0033613).
Fig. 2.
[115]Fig. 2
[116]Open in a new tab
Predicted mechanisms of QHF in treating B cell lymphoma. A. The dot
plot of GO functional enrichment of QHF. B. The dot plot of KEGG
pathway enrichment analysis of QHF. C. The KEGG pathway diagram of
Toll-like receptor signaling pathway [[117]49–[118]51]. It shows that
QHF could target AKT, NF-κB, p38, ERK, and JNK to activate PI3K/Akt
signaling pathway, MAPK, and NF-κB signaling pathways. D.
Representative images of molecular docking show that the active
components of QHF bind to TLR2, ERK, JNK, and AKT primarily with
hydrogen bonds
KEGG pathways enrichment analysis was conducted to further explore the
possible functions of the targets. In this section (see Additional file
[119]3), Lipid and atherosclerosis (hsa05417), AGE-RAGE signaling
pathway in diabetic (hsa04933), Kaposi sarcoma-associated herpesvirus
infection (hsa05167), Hepatitis B (hsa05161), Fluid shear stress and
atherosclerosis (hsa05418), Pertussis (hsa05133), Yersinia infection
(hsa05135), Shigellosis (hsa05131), Chagas disease (hsa05142),
Toll-like receptor signaling pathway (hsa04620), C-type lectin receptor
signaling pathway (hsa04625), Th17 cell differentiation (hsa04659),
Pancreatic cancer (hsa05212), Neurotrophin signaling pathway
(hsa04722), Human cytomegalovirus infection (hsa05163), Coronavirus
disease-COVID-19 (hsa05171), IL-17 signaling pathway (hsa04657),
Endocrine resistance (hsa01522), Salmonella infection (hsa05132), T
cell receptor signaling pathway (hsa04660), TNF signaling pathway
(hsa04668) were significantly enriched (Fig. [120]2B). The KEGG pathway
diagram disclosed that a series of target genes constituted the
Toll-like receptor signaling pathway, including AKT, NFKB, ERK, JNK,
IL6, and p38 MAPK (Fig. [121]2C). Therefore, it is reckoned that QHF
exerts therapeutic effects through regulating immune-related functions.
Moreover, the molecular docking verified a strong affinity of active
components binding to targets in the predicted pathway (Fig. [122]2D).
The docking parameters of each target and binding free energies were
listed in Table [123]2. The results indicated that flavonoids of QHF,
including quercetin, luteolin, kaempferol, and wogonin, had a strong
affinity with core targets in terms of binding free energy.
Interestingly, ASTX029, the novel inhibitor of ERK [[124]52], binds to
ERK with a binding free energy of − 8.3, identical to that of
quercetin, suggesting a high affinity of quercetin binding with ERK.
The ligands and receptors were bound mainly by hydrogen bonds, which is
positively related to the binding affinity. For example, luteolin could
bind to TLR2, AKT, and JNK closely, with 6, 4, and 5 hydrogen bonds,
respectively, and quercetin bound to ERK with 6 hydrogen bonds. Apart
from the hydrogen bonds, the secondary effects were hydrophobic
interaction of amino. These results further revealed the affinity of
the active components of QHF binding to the transcriptionally
dysregulated genes of B-cell lymphoma, which is the foundation of drugs
to take effect.
Table 2.
The molecular docking parameters
Targets PDB ID Ligands Size of cube (X * Y * Z)
(nm^3) Center grid box (X, Y, Z) Binding free energy (kcal/mol)
TLR2 2Z80 Luteolin 100*100*100 −1.516 −8.3
−14.704
−14.946
ERK 7AUV Quercetin 96*80*124 0.759 −8.3
4.044
37.087
AKT 3O96 Luteolin 104*90*126 52.684 −9.9
76.365
34.530
JNK 2NO3 Luteolin 104*90*126 15.902 −8.8
58.495
0.023
[125]Open in a new tab
Antitumor effects of QHF in combination with ADM in vivo
In this study, we first investigated the in vivo effects of QHF and
Adriamycin (ADM) combination therapy on the growth of B cell lymphoma
in A20 tumor-bearing mice. The study scheme is depicted in
Fig. [126]3A. Mice bearing subcutaneous A20 tumors in QHF + ADM (n = 5)
and ADM (n = 6) groups were compared with mice in the control group
(n = 5). The tumor volume on day 12 was 744.07 mm^3 (QHF + ADM group),
1275.23 mm^3(ADM group), and 1829.89 mm^3 (control group), respectively
(Fig. [127]3B). Compared with the control group, the tumor volume of
the QHF + ADM group was significantly lowered (vs. control group,
p < 0.05). However, the ADM group showed no significant difference (vs.
control group, p > 0.05), but exhibited a declining trend (Fig.
[128]3C, D). There was a significant difference in TGI between
QHF + ADM group and ADM group on day 12 (p < 0.01) (Fig. [129]3E).
Fig. 3.
[130]Fig. 3
[131]Open in a new tab
QHF + ADM significantly inhibits the growth of A20 in the tumor-bearing
mice model. A. Scheme of the animal experiment protocol. B. Photographs
of all the tumor blocks collected from the Control group (n = 5), ADM
group (n = 6), and QHF + ADM group (n = 5). C. Tumor volume of the
tumor blocks collected from mice on day 12. It showed that QHF + ADM
group (n = 5) significantly lowered tumor volume when compared with the
control group (n = 5, p < 0.05). But there is no significance between
the ADM group (n = 6, p > 0.05) and the control group (n = 5,
p > 0.05). D. The tumor volume changes with time of the three groups.
The tumor growth of the QHF + ADM group (n = 5) was the slowest of the
three groups, followed by the ADM group (n = 5). E. Tumor growth
inhibition rate (TGI, %) of the treatment groups. It showed that
QHF + ADM (n = 5) could significantly inhibit tumor growth in an A20
murine model (vs. ADM (n = 5), p < 0.01). Data are presented as the
mean ± standard errors, *p < 0.05
QHF in combination with ADM increases tumor-infiltrating CD4+ and CD8+ T
cells
In light of the systematic pharmacology research we did previously, we
predicted that QHF probably exerts such tumor inhibition effect through
modulating immune functions by activating the Toll-like receptor (TLR)
signaling pathway. To verify the prediction, the tumors harvested were
used to explore the differences in TME among the three groups. Sixteen
tumor-bearing mice (three groups) were used, and their tumor blocks
were stained for tumor-associate macrophages (TAMs) and
tumor-infiltrating lymphocytes (TILs) via IHC. Chemokines and
cytokines, including CXCL10, IL-2, IL-17, IL-6, IFN-γ, and TGF-β were
also tested with ELISA. The IHC staining showed that there is no
significance among the three groups in the proportion of M1 macrophages
and M2 macrophages, but the combination group displayed a tendency to
decline in the proportion of M2 macrophages (see Additional file
[132]4). Interestingly, the proportions of CD4+ and CD8+ T-cells in the
tumor tissues were significantly higher in the combination therapy
group than in the ADM monotherapy group (Fig. [133]4A-D). As for the
CD4+ T-cell ratio, the QHF + ADM group showed a significant difference
when compared with the control group (p < 0.01) and the ADM monotherapy
group (p < 0.01) (Fig. [134]4A, B). Besides, there was a significant
increase of CD8+ T-cells in the combination group (vs. control group,
p < 0.05; vs. ADM group, p < 0.05) (Fig. [135]4C, D). The ELISA
revealed that mice in the combination group with QHF + ADM had higher
levels of CXCL10 (v.s. Control, p < 0.05) and IL-6 (v.s. Control,
p < 0.01). Meanwhile, the levels of TGF-β displayed a declining
tendency, although the difference was not significant (Fig. [136]4E).
Neither significant changes nor tendencies were observed for IL-2 and
IL-17 (data not shown, see Additional file [137]5). The PET-CT images
showed that compared with the pretreatment scans, the posttreatment
mice had a relatively higher SUV[max] value (Fig. [138]4F). As for the
mouse receiving ADM+ QHF, seven days after the therapy was
discontinued, the volume of tumor enormously increased but the SUV[max]
value lowered from 4.3 to 3, instead. These results suggested that
combination therapy could remarkably enhance the level of
tumor-infiltrating lymphocytes, especially the CD4+ and CD8+ T cells.
Fig. 4.
[139]Fig. 4
[140]Open in a new tab
QHF + ADM recruited CD4+ and CD8+ T cells in the A20 tumor sites. A.
Representative images of IHC staining for CD4+ T cell among the three
groups (200×). B. Bar plot of the positive cell rate of CD4+ T cells of
the control group (n = 3), ADM group (n = 3), and QHF + ADM group
(n = 3). C. Representative images of IHC staining for CD8+ T cells
among the three groups (200×). D. Bar plot of the positive cell rate of
CD8+ T cells of the control group (n = 3), ADM group (n = 3), and
QHF + ADM group (n = 3). E. Bar plot of TGF-β, IFN-γ, CXCL10, and IL-6
levels based on ELISA of the control group (n = 3), ADM group (n = 3),
and QHF + ADM group (n = 3).F. ^18F FDG PET-CT scan of the ADM group
(n = 1), and QHF + ADM group (n = 1). G. Expression of TLR2, p38 MAPK,
and β-actin by western blot. Data are presented as the mean ± standard
errors, *p < 0.05; **p < 0.01
QHF exerts an antitumor effect by targeting the toll-like receptor signaling
pathway
The protein levels of TLR2, p38 MAPK, and β-actin expression were
detected by western blot in the present study. As shown in Fig.
[141]4G, compared with the Control group (n = 3) and the ADM group
(n = 3), the combination group (n = 3) significantly increased the
expressions of TLR2 (v.s. Control group, p < 0.001; v.s. ADM group,
p < 0.0001) and p38 MAPK (v.s. Control group, p < 0.001) (see
Additional file [142]6).
Discussion
B cell non-Hodgkin lymphoma is a highly heterogeneous neoplasm, and its
most common subtype is diffuse large B cell lymphoma. Although it is
curable to most patients, there are still 30-40% recurrence or
refractory cases [[143]53]. And cancer immunotherapies are initially
used in these settings. The TME, especially the immune landscape within
it, is found to be crucial in the responses to the immunotherapies
[[144]54]. The TME of B-cell lymphoma mainly consists of T cells,
macrophages, natural killer (NK) cells, stromal cells, blood vessels,
and extracellular matrix [[145]55, [146]56]. A combination therapy
targeting TME has proven efficacy and displayed great potential for
novel immunotherapies. As a traditional formula, QHF consists of four
different herbs with various functions. In this study, we verified the
tumoricidal effect of QHF in combination with ADM using a murine model
and explored its underlying mechanisms with the guidance of systemic
pharmacology.
Systemic pharmacology is a comprehensive approach to clarify the active
compounds and drug targets, as well as to predict the mechanisms of
traditional Chinese medicinal formulas. Our work displayed that QHF has
541 significant targets in B cell lymphoma, among which MAPK1/ERK2 and
AKT1 are mostly enriched. Based on KEGG pathway enrichment analysis, we
discovered that the TLR signaling pathway, which plays a critical role
in activating both innate and adaptive immune responses, was one of the
major pathways that QHF might trigger. In addition, we filtered out
five active components of QHF, i.e., quercetin, luteolin, kaempferol,
wogonin, and stigmasterol, that might be the most promising ingredients
to represent QHF. The molecular docking technique also authenticated
the ligation of the five components and targets.
Our murine model built with an A20 cell line showed that compared with
ADM monotherapy, the QHF + ADM group could significantly inhibit the
tumor growth. Meanwhile, the tumor-infiltrating CD4+ and CD8+ T cells
of the QHF + ADM group were remarkably increased. Research demonstrated
that anti-CD20 mAb, such as rituximab, could achieve a long-lasting
“vaccinal effect” with the presence of both CD4+ and CD8+ T cells
[[147]57, [148]58], indicating the potential value of QHF as an add-on
therapy to the R-based therapy for lymphoma patients. In this study,
one mouse from each group was picked out to receive the ^18F-FDG PET-CT
scan before and after the treatment, respectively. Notably, although
the posttreatment tumor volume of the combination group is smaller than
the ADM group, the SUV[max] of the combination group is higher. What’s
more, after the treatment was discontinued for 1 week, the SUV[max] of
the combination group decreased to a level lower than that of
pretreatment. Evidence showed that SUV[max] is positively related to
the TILs and TAMs [[149]59, [150]60]. And a provoking study published
in Nature this year has proven that it is the myeloid cells, followed
by T cells, that uptake most FDG rather than tumor cells [[151]61].
Therefore, the dynamic changes of SUV[max] of the combination group
might indicate the level changes of infiltrating T cells. The ^18F-FDG
PET-CT scan provided an additional description of the increased
infiltrating T cells in the QHF + ADM group.
According to the immunophenotype of TME, tumors can be divided into
“hot” tumors and “cold” tumors. Hot tumors present with rich TILs,
whose initial immune response is obstructed by upregulation of immune
checkpoints or immunosuppressive cells. Anti-PD-1/PD-L1 antibodies
block tumor immune evasion by protecting CD8+ T cells from PD-1
mediated cell death [[152]62, [153]63]. However, such ICI has limited
efficacy in ‘cold’ tumors, where immune cells are less abundant or
completely absent in TME [[154]64]. Mullins et al. [[155]65] found that
TLR agonists tuned TME to inflamed immunophenotype by activating CD8+ T
cells, thus enhancing the antitumor effect of ICIs. This has
highlighted the promising value of TLR agonists. TLR is a big family
consisting of TLR2, TLR4, TLR7, TLR8, and other receptors. TLR
signaling pathway starts with TLR receptor and ligand recognition,
followed by TLR dimerization and a signaling cascade to produce
inflammatory molecules. In this study, we first predicted that the
active components of QHF bind to TLR2 with hydrogen bonds via molecular
docking methods. In theory, when the components of QHF bind to TLR2 and
activate the TLR2 signaling pathway, the constitutive MAPK or NF-κB
signaling pathway will be triggered to express various cytokines like
IL-6, TNF-α, and IFNs. It is also reported that activation of TLR2 can
reverse mouse Treg suppression, promote and generate efficient memory T
cells by increasing IFN-γ level, and enhance the cytotoxic activity of
CD8+ T cells [[156]66]. Besides, ADM can activate p38 to regulate the
expression of topoisomerase II, inducing apoptosis of lymphoma cells.
Therefore, QHF + ADM could enhance the antitumor effect via different
mechanisms theoretically. In the present study, the IL-6 level was
up-regulated in the QHF + ADM group, but the levels of TNF-α and IFN-γ
were not remarkably changed. As the IL-6 production regulation is
controlled through both transcriptional and posttranscriptional
mechanisms [[157]67], it remains elusive whether the increased IL-6 is
the result of TLR signaling pathway activation or not. But the increase
in CD4+ and CD8+ T cells in TME, as well as up-regulated CXCL10 and
TLR2 in the combination group, indicated QHF + ADM could modulate TME
by targeting TLR2.
As aforementioned, the immune cells within TME can interact with the
tumor cells directly or through chemokines and cytokines to impact the
behavior of the tumor and response rate [[158]68]. A variety of
chemokines are expressed by immune cells, including CCR7, CXCL9,
CXCL10, CCL2, and IFN-γ. Secreted by effector T cells, IFN-γ can induce
the expression of CXCL10, which in return attracts even more T cells
into the TME, thus forming a positive feedback loop [[159]69–[160]73].
Despite IFN-γ, TLR agonists also elicit cytokines and chemokines
expression in multiple cells [[161]65]. In a melanoma model, TLR2/6
agonists + IFN-γ can induce CXCL10 production, leading to more T cells
migrating into TME [[162]74]. In our study, the combination group
displayed upregulated levels of TLR2 and CXCL10, indicating that QHF
might target TLR signaling pathway to promote CXCL10 expression with
the presence of IFN-γ, and consequently increased T cell infiltration
and IFN-γ expression in return. Transforming growth receptor beta
(TGF-β), expressed by both tumor cells and tumor-associated cells,
directly inhibits T cell proliferation and activation while promoting
the survival of immunosuppressive Tregs. Therefore, in the context of
TME, upregulated TGF-β may lead to T cell dysfunction and limited
efficacy of cancer immunotherapies [[163]75]. Moreover, with the
presence of IL-6, TGF-β may also promote CD4+ T cell differentiation
along a Th17 pathway, which was predicted in the KEGG enrichment
analysis as mentioned before. Although IL-6 is associated with lymphoma
proliferation and poor prognosis, it is reported that IL-6 can prevent
apoptosis of T cells by upregulation of Bcl-2 and promote T cell
proliferation via the TCR pathway [[164]76]. Also, evidence shows that
increased IL-6 is associated with highly functional CAR T cells and
their expansion [[165]77]. Consistent with these findings, we observed
a downregulated level of TGF-β, as well as an upregulated level of IL-6
in the combination group, suggesting that QHF may aid to restore T cell
function and survival in the TME. Taken together, we speculated that
QHF took effect through modulating the TIME via targeting the TLR
signaling pathway.
Conclusion
QHF in combination with ADM improves the anti-lymphoma effect of ADM by
modulating TME. Besides, with a method of systematic pharmacology, the
active components of QHF were identified. This research established a
scientific method for analyzing chemical components and verifying
predicted mechanisms of Chinese herbal medicine.
Supplementary Information
[166]12906_2022_3660_MOESM1_ESM.pdf^ (74.3KB, pdf)
Additional file 1: Figure S1. Pre-study data of the effective dosage of
QHF. A.The image of the tumor blocks of the three groups.
B.Thebodyweight of the mice in three groups during the experiment. C.
The tumor blocks weight of the three groups. Compared with the LD group
(n=5), the tumor weight was significantly decreased in the HD group
(n=5, **p<0.01). Compared with the MD group (n=5), the tumor weight was
remarkably lowered in the HD group (n=5, *p<0.05)
[167]12906_2022_3660_MOESM2_ESM.xlsx^ (8.6KB, xlsx)
Additional file 2: Table S1. Total input gene list
[168]12906_2022_3660_MOESM3_ESM.xlsx^ (26.9KB, xlsx)
Additional file 3: Table S2. KEGG pathway enrichment results
[169]12906_2022_3660_MOESM4_ESM.pdf^ (379.4KB, pdf)
Additional file 4: Figure S2. IHC stainingsfor M1 and M2 macrophages.
A. Representative images of IHC staining for M1 macrophages among the
three groups (200×). B. Bar plot of the positive cell rate of M1
macrophages of the control group (n=3), ADM group (n=3), and QHF+ADM
group (n=3). C. Representative images of IHC staining for M2
macrophages among the three groups (200×). D. Bar plot of the positive
cell rate of M2 macrophages of the control group (n=3), ADM group
(n=3), and QHF+ADM group (n=3).Ns: p>0.05
[170]12906_2022_3660_MOESM5_ESM.pdf^ (11.2KB, pdf)
Additional file 5: Figure S3. ELISA results of IL-2 and IL-17. A.
Concentration of IL-2 of the control group (n=3), ADM group (n=3), and
ADM+QHF group (n=3). There is no significant difference between the
three groups (p>0.05). B. Concentration of IL-17 of the control group
(n=3), ADM group (n=3), and ADM+QHF group (n=3). There is no
significant difference between the three groups (p>0.05)
[171]12906_2022_3660_MOESM6_ESM.pdf^ (36.2KB, pdf)
Additional file 6: Figure S4. Image J results of the WB bands. A. Bar
plot of the expression of TLR2. Compared with the Control group (n=3)
and the ADM group (n=3), the ADM+QHF group (n=3) significantly
increased the expressions of TLR2 (v.s. Control group, ***p<0.001; v.s.
ADM group, ****p<0.0001). B. Bar plot of the expression of p38.Compared
with the Control group (n=3), the level of p38 MAPK was remarkably
increased in ADM group (n=3, ***p<0.001) and ADM+QHF group (n=3,
***p<0.001)
Acknowledgments