Abstract
Background
Metabolism reprogramming plays a vital role in glioblastoma (GBM)
progression and recurrence by producing enough energy for highly
proliferating tumor cells. In addition, metabolic reprogramming is
crucial for tumor growth and immune‐escape mechanisms. Epidermal growth
factor receptor (EGFR) amplification and EGFR‐vIII mutation are often
detected in GBM cells, contributing to the malignant behavior. This
study aimed to investigate the functional role of the EGFR pathway on
fatty acid metabolism remodeling and energy generation.
Methods
Clinical GBM specimens were selected for single‐cell RNA sequencing and
untargeted metabolomics analysis. A metabolism‐associated RTK‐fatty
acid‐gene signature was constructed and verified. MK‐2206 and MK‐803
were utilized to block the RTK pathway and mevalonate pathway induced
abnormal metabolism. Energy metabolism in GBM with activated EGFR
pathway was monitored. The antitumor effect of Osimertinib and
Atorvastatin assisted by temozolomide (TMZ) was analyzed by an
intracranial tumor model in vivo.
Results
GBM with high EGFR expression had characteristics of lipid remodeling
and maintaining high cholesterol levels, supported by the single‐cell
RNA sequencing and metabolomics of clinical GBM samples. Inhibition of
the EGFR/AKT and mevalonate pathways could remodel energy metabolism by
repressing the tricarboxylic acid cycle and modulating ATP production.
Mechanistically, the EGFR/AKT pathway upregulated the expressions of
acyl‐CoA synthetase short‐chain family member 3 (ACSS3), acyl‐CoA
synthetase long‐chain family member 3 (ACSL3), and long‐chain fatty
acid elongation‐related gene ELOVL fatty acid elongase 2 (ELOVL2) in an
NF‐κB‐dependent manner. Moreover, inhibition of the mevalonate pathway
reduced the EGFR level on the cell membranes, thereby affecting the
signal transduction of the EGFR/AKT pathway. Therefore, targeting the
EGFR/AKT and mevalonate pathways enhanced the antitumor effect of TMZ
in GBM cells and animal models.
Conclusions
Our findings not only uncovered the mechanism of metabolic
reprogramming in EGFR‐activated GBM but also provided a combinatorial
therapeutic strategy for clinical GBM management.
Keywords: combinatorial therapeutic strategy, EGFR, energy metabolism,
glioblastoma
__________________________________________________________________
List of abbreviations
GBM
glioblastoma
CNS
central nervous system
RTKs
receptor tyrosine kinases
EGFR
epidermal growth factor receptor
ACSS3
acyl‐CoA synthetase short‐chain family member 3
ACSL3
acyl‐CoA synthetase long‐chain family member 3
ELOVL2
long‐chain fatty acid elongation‐related gene ELOVL fatty acid
elongase 2
GFAP
glial fibrillary acidic protein
CHI3L1
chitinase 3 like 1
TCA
citric acid cycle
OLIG2
oligodendrocyte transcription factor 2
SOX6
SRY‐box transcription factor 6
RFA
RTK‐fatty acid‐gene signature
PDGFA
platelet‐derived growth factor subunit A
DMEM
Dulbecco's modified Eagle's medium
siRNAs
small interfering RNAs
ChIP
chromatin immunoprecipitation
LC‒MS/MS
liquid chromatography‐tandem mass spectrometry
CNV
copy number variation
CGGA
Chinese glioma genome atlas
TCGA
The Cancer Genome Atlas Program
Rembrandt
repository of molecular brain neoplasia data
ROC
receiver operating characteristic
ANOVA
analyses of variance
IDH
isocitrate dehydrogenase
OCR
oxygen consumption rate
ECAR
extracellular acidification rate
PCs
phosphatidylcholines
LysoPCs
lysophosphatidylcholines
PE
phosphatidylethanolamine
PG
phosphatidyl glycerol
PI
phosphatidylinositol
PS
phosphatidylserine
VLCFA
very long‐chain fatty acid
HMG‐CoA
hydroxymethylglutaryl‐CoA
TMZ
temozolomide
BBB
blood‐brain‐barrier
ssGSEA
single‐sample gene set enrichment analysis
TKI
tyrosine kinase inhibitor
NSCLC
non‐small cell lung carcinoma
DMSO
dimethyl sulfoxide
OSI
Osimertinib
ATO
atorvastatin
HFD
high‐fat diet
1. BACKGROUND
Glioblastoma (GBM) is one of the most malignant primary neoplasms of
the central nervous system (CNS) in clinics. GBM is characterized by
high intra‐ and inter‐tumor heterogeneities, insensitivity to
conventional radiotherapy and chemotherapy, and frequent recurrence
[[58]1, [59]2, [60]3, [61]4]. In general, GBM patients exhibit rapid
progression and poor prognosis, with a median survival time of 16‐18
months, despite surgical resection combined with radio‐ and
temozolomide‐based chemotherapy since the first diagnosis [[62]5].
Therefore, there is an urgent need for better combinational therapeutic
strategies for GBM management.
Receptor tyrosine kinases (RTKs) are a family of cell membrane surface
proteins [[63]6] that participate in the regulation of important
cellular functions, including cell growth and protein synthesis,
through signal transduction [[64]7]. Hyperactivation of the
RTK/PI3K/AKT pathway is the typical molecular signature of GBM
pathomechanism [[65]8]. Epidermal growth factor receptor (EGFR)
amplification, overexpression, and pathogenic mutations are commonly
identified in GBM [[66]9]. Notably, the alternatively spliced oncogenic
EGFR‐vIII variant lacks exons 2‐7, resulting in the constitutive
tyrosine kinase activity without any ligand stimulations [[67]10]. GBM
with EGFR amplification and EGFR‐vIII mutation often exhibits severe
malignancies [[68]11]. Although the hyperactivated EGFR pathway plays
roles in lipogenesis and cholesterol uptake [[69]12, [70]13], whether
the EGFR pathway contributes to fatty acid metabolism and energy
generation remains unclear.
Reprogramming of cellular metabolism is another pathological hallmark
in tumorigenesis, supporting the high demand for energy of tumor cells
and promoting the establishment of an immunosuppressive
microenvironment [[71]14, [72]15]. Abnormal metabolisms of lipids,
amino acids, and nucleotides, as well as enhanced glycolysis, are
commonly observed in GBM cells [[73]16]. Interestingly, a high level of
cellular cholesterol plays a vital role in tumor cell survival and
disease progression [[74]17]. However, the actual effects of inhibiting
lipid metabolism and/or reducing cholesterol levels on the metabolic
remodeling of GBM cells need to be further characterized.
In this study, we explored the regulatory correlation between EGFR
expression and fatty acid metabolism‐associated genes for establishing
a signature in prognostic prediction, investigated the functional role
of EGFR/AKT and mevalonate pathway in energy metabolic reprogramming,
and developed a combinational therapeutic strategy against GBM.
2. MATERIALS AND METHODS
2.1. Clinical GBM sample acquisition
In total, 14 tumor tissue samples, namely TBD528‐T1, TBD528‐T2,
TBD629‐T1, TBD629‐T2, TBD629‐T3, TBD706‐T1, TBD706‐T2, TBD706‐T3,
TBD717‐T1, TBD717‐T2, TBD717‐T3, TBD717‐T4, TBD720‐T1, and TBD720‐T2,
from 5 patients who were diagnosed with the primary GBM based on their
head magnetic resonance imaging (MRI) examinations, were obtained by
multipoint sampling for single‐cell RNA sequencing. Another 66 GBM
samples were freshly harvested for RNA sequencing and untargeted
metabolomic analyses. Clinical information, MRI images, and tumor
specimens of typical GBM cases were collected from Beijing Tiantan
Hospital and Affiliated Hospital of Hebei University. Collection and
analysis of all clinical GBM samples were approved by the medical
ethics committee of Beijing Tiantan Hospital (Approval No. KY
2020‐093‐02) and Hebei University affiliated Hospital (Approval No.
HDFY‐LL‐2020‐017). Written informed consent was acquired from the
patients or immediate family members of the patients.
2.2. Single‐cell RNA sequencing and data processing
Fresh GBM samples were collected directly from the sampling room
immediately after the surgery and immersed in a tissue storage solution
(#130‐100‐008, Miltenyi, Biotec, Bergisch Gladbach, Germany). Samples
were mechanically and enzymatically dissociated using a tumor
dissociation kit (#130‐095‐929, Miltenyi), according to the
manufacturer's instructions. Briefly, GBM tissues were cut into small
pieces and digested into single‐cell suspensions. For single‐cell RNA
sequencing, barcode sequences were labeled on the single cells and
respective single‐cell libraries were constructed using the BD Rhapsody
system (San Jose, CA, USA). Libraries were finally sequenced on the
Illumina NovaSeq sequencing platform (San Diego, CA, USA).
Raw data were aligned to the human reference genome (GRCh38). A sparse
matrix containing gene expressions and barcode information was
constructed by UMI tools ([75]https://github.com/CGATOxford/UMI‐tools)
[[76]18], and imported into the Seurat R package [[77]19]. The
sequencing data were processed as previously described [[78]20].
Briefly, cells with a less than 200‐library size or a higher than 0.3%
mitochondrial transcript ratio were excluded. The filtered data were
analyzed for batch effect removal, dimensionality reduction, and
unsupervised clustering by the Seurat package in the default
parameters.
The human primary cell atlas (HPCA) was used to identify the cell types
described in our previous study [[79]20]. Briefly, the correlation
coefficients between the transcriptomes of each cell and the expression
profiles of each cell type‐specific genes in HPCA were analyzed by
using SingleR package. The CellMarker database
(ttp://xteam.xbio.top/CellMarker/) was employed to determine the cell
type based on their marker gene expressions [[80]21]. Marker genes,
such as CD3e for T‐cells, CD14 for macrophages, FCGR3B for
oligodendrocytes, MAG for neutrophils, CLDN5 for endothelial cells,
MYL9 for smooth muscle cells and fibroblast (SMC&Fibro), and PTPRZ1 for
tumor cells were visualized as violin plots using the ggplot2 R
package. Marker genes of each cell cluster were identified by the
“FindAllMarkers” function of the Seurat package and visualized as
violin plots.
The CNV for each sample was predicted by analyzing the expression
levels of selected gene markers located on each chromosome using the
InferCNV package. The CNV in tumor cells was determined by comparison
with nonmalignant cells, and visualized as a heatmap.
2.3. GBM public data collection and correlation analysis
Data from the Chinese Glioma Genome Atlas (CGGA), The Cancer Genome
Atlas (TCGA), and Repository for Molecular Brain Neoplasia Data
(Rembrandt) databases were obtained for analysis. Clinical information
and RNA sequencing data of glioma patients in the TCGA database were
obtained from the University of California Santa Cruz (UCSC) Xena
Functional Genomics Explorer ([81]https://xenabrowser.net/). RNA
sequencing data of CGGA, microarray data of Rembrandt, and
corresponding clinical data were downloaded from the CGGA website
([82]http://www.cgga.org.cn/). The RNA data processing was performed as
previously described [[83]20]. Briefly, count data were transformed to
fragments per kilobase per million reads (FPKM).
Correlation analysis between the expression levels of 2 genes was
measured by the ggcorrplot R package. The gene expression distributions
and respective correlation coefficients were visualized by the
Performance Analytics package. Survival curves were analyzed using the
Survival R package. All the analyses were performed with default
parameters.
2.4. GBM patient grouping and RTK‐fatty acid‐gene signature (RFA)
construction
The expression levels of glial fibrillary acidic protein (GFAP),
chitinase 3 like 1 (CHI3L1), oligodendrocyte transcription factor 2
(OLIG2), SRY‐box transcription factor 6 (SOX6), EGFR, and
platelet‐derived growth factor subunit A (PDGFA) in GBM patients from
the CGGA, TCGA, and Rembrandt databases were selected. For grouping,
patients were first separated into the G1/G2 versus G3 groups based on
the value of
[MATH: log2(FPKM<
/mi>GFAP+1)+log2(FPKM<
/mi>CHI3L1<
/mrow>+1)log2
(FPKM<
/mi>OLIG2+1)+log2(FPKM<
/mi>SOX6+1) :MATH]
. Next, the upper median value of
[MATH: [log2<
mo
stretchy="false">(FPKM<
/mi>EGFR+1)+log2(FPKM<
/mi>PDGFA+1)] :MATH]
was employed for the definition of G1 versus G2. For RFA signature
construction, The RFA score was calculated according to this formula:
[MATH: Σβi×Ei :MATH]
, where β[i] was the coefficient calculated by univariate COX
regression analysis of gene
[MATH: i :MATH]
, and E[i] refers to the expression of gene
[MATH: i :MATH]
. Gene
[MATH: i :MATH]
included GFAP, CHI3L1, EGFR, PDGFA, acyl‐CoA synthetase short chain
family member 3 (ACSS3), acyl‐CoA synthetase long chain family member 3
(ACSL3), and ELOVL fatty acid elongase 2 (ELOVL2) in the TCGA, CGGA,
and Rembrandt cohorts. Patients with RFA scores above the median were
considered high‐score individuals.
2.5. Correlation between RFA score and immune microenvironment of GBM
Single sample gene set enrichment analysis (ssGSEA) and CIBERSORT tools
were performed for evaluate the correlation between RFA scores and
infiltrated immune cells in the microenvironment of GBMs. Briefly, for
ssGSEA analysis, the immune metagene set was obtained [[84]22] and the
scores of each immune cells in GBM patients from the CGGA, TCGA, and
Rembrandt databases were analyzed by using GSVA v1.46.0 R package
([85]https://github.com/rcastelo/GSVA). The results were visualized as
heatmaps. For CIBERSORT analysis, the expression data of GBMs from the
CGGA, TCGA, and Rembrandt databases were uploaded to the online tool
([86]https://cibersortx.stanford.edu/) [[87]23] and LM22 containing 22
immune cell types was chosen as a signature matrix. The results were
downloaded and the correlation between RFA scores and macrophage M2 was
visualized as a scatter plot.
2.6. RNA sequencing analysis
Total RNA was isolated from fresh GBM samples using TRIzol reagent
(#15596‐026, Thermo Fisher, Waltham, MA, USA), according to the
manufacturer's protocol. Then rRNA was removed and RNA was fragmentated
for reverse transcription. The cDNA libraries were generated and
sequenced using the Illumina HiSeq4000 platform (San Diego, CA, USA).
The sequencing data were aligned to the human reference genome (hg19)
using the Hisat2 tool ([88]http://daehwankimlab.github.io/hisat2/main/)
[[89]24] and analyzed by the DESeq2 R package.
2.7. Cell culture, lentivirus, and chemicals
The human GBM cell line U‐87 MG was purchased from the American Type
Culture Collection (#HTB‐14), human GBM primary cell TBD0220 was
constructed by our laboratory, and mouse GBM cell line CT2A were
purchased from BLUEFBIO Co. Ltd. (#BFN60810497, Shanghai, China). All
the cells were maintained in our laboratory. Lentivirus vectors
encoding the EGFR‐vIII protein and luciferase reporter were constructed
by IBS Biotech. Co., Ltd (Shanghai, China). The U‐87 MG, and CT2A cells
were cultured in Dulbecco's modified Eagle medium (DMEM; #PM150210,
Procell Life Science&Technology Co.,Ltd., Wuhan, China) supplemented
with 10% fetal bovine serum (FBS; #HN‐FBS‐500, Shanghai Chuan Qiu
Biotechnology Co.,Ltd., Shanghai, China). TBD0220 cells were cultured
in DMEM/F12 (#PM150312, Procell Life Science&Technology Co.,Ltd.,
Wuhan, China) with 10% FBS. All cells were grown at 37°C in a cell
incubator with 5% CO[2]. U‐87 MG cells were transduced with EGFR‐vIII
lentiviral particles and screened with 2 μg/mL of puromycin (#P8230,
SolarBio Science & Technology Co., Ltd., Beijing, China) to generate
the U‐87 MG‐EGFR‐vIII cell line.
Temozolomide (TMZ; #HY‐17364), MK‐2206 (#HY‐10358), MK‐803 (#HY‐N0504),
Osimertinib (OSI) (#HY‐15772), and Atorvastatin (ATO) (#HY‐B0589)
chemicals were purchased from MedChemExpress (Shanghai, China). For
treating GBM cells, TMZ, MK‐2206, and MK‐803 were dissolved in DMSO
(#D8371, SolarBio Science & Technology Co., Ltd., Beijing, China) to
the storage concentrations of 100 mmol/L, 5 mmol/L, and 5 mmol/L,
respectively. For animal experiments, drugs were dissolved in DMSO and
diluted in the following solvents to the final concentrations of 10%
DMSO, 40% PEG300 (#HY‐Y0873, MedChemExpress), 5% Tween‐80 (#HY‐Y1891,
MedChemExpress), and 45% saline. Small interfering RNAs (siRNAs) were
synthesized by IBS Biotech. Co., Ltd. siRNA sequences are summarized in
Supplementary Table [90]S1.
2.8. Untargeted metabolomic analysis
TBD0220 cells were treated with DMSO, 5 μmol/L of MK‐2206, or 5 μmol/L
of MK‐803. After treatment, cells were washed once using prechilled PBS
and rapidly frozen in liquid nitrogen after the treatment. Then, cells
were scrape‐harvested using collection buffer (prechilled HPLC grade
methanol: Milli‐Q water = 4: 1, v/v) into 1.5 mL Eppendorf tubes. Fresh
GBM tissues were harvested after surgery and transferred into
collection buffer. Samples were dehydrated and redissolved in detection
buffer (prechilled HPLC grade methanol: Milli‐Q water = 1: 4, v/v) at
‐20°C condition after sonication. The metabolites were analyzed using
ultra‐performance liquid chromatography combined with a QE
high‐resolution mass spectrometer (BRE0032850, Thermo Fisher, Waltham,
MA, USA). Data were preprocessed under the criteria of RSD < 0.3 in the
QC sample, normalized using Progenesis QI software (v2.3, Nonlinear
Dynamics, Newcastle, UK) with the parameters of appropriate precursor
tolerance (5 ppm/10 ppm), product tolerance (10 ppm/20 ppm), and
production threshold (5%), and excluded by any peak with missing values
of > 50%. Different metabolites were identified using the OPLS‐DA
method with default parameters.
2.9. Conjoint analysis of RNA sequencing and metabolomics
RNA sequencing data and metabolomics data of 66 fresh GBM tissues were
integrated. The correlations between gene expression profile and
metabolites were analyzed.
2.10. Chromatin immunoprecipitation (ChIP)
TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII cells were cross‐linked by
formaldehyde (#F8775, Sigma Aldrich, St. Louis, MO, USA) at a final
concentration of 1% for 10min, followed by quenching of cross‐links
using glycine solution. Nuclear lysates were prepared and sonicated for
chromatin fragmentation. Adequate lysates containing 100 μg of total
protein per reaction were incubated with 5 μL p‐NF‐κB antibody (#3033S,
Cell Signaling Technology), and ChIP grade magnetic beads (#16‐663,
Millipore, Billerica, MA, USA) at 4°C overnight. The immunoprecipitated
and purified DNA samples were obtained using a QIAquick PCR
Purification kit (#28104, Qiagen, Duesseldorf, Germany) after reversing
the cross‐link, and measured by the quantitative real‐time polymerase
chain reaction (qPCR) method with specific primers. qPCR reactions were
performed using SYBR Green Mix (#Q711, Vazyme Biotech Co., Nanjing,
China) in a thermocycler instrument (ABI QuantStudio 3 Real‐time PCR
System). All primers were synthesized by GENEWIZ (Suzhou, China). The
ChIP primer sequences are summarized in Supplementary Table [91]S2.
2.11. Colony formation assay and cell growth assay
Colony formation and cell growth assays were performed as described
elsewhere [[92]25]. Briefly, for colony formation assay, a total of 500
cells were plated into 6‐well plates and treated with DMSO, 1 μmol/L of
MK‐2206, or 1 μmol/L of MK‐803 for 14 days. Then cells were prefixed by
4% paraformaldehyde for 10 min at room temperature and stained by
crystal violet staining solution (#C0121, Beyotime Biotechnology,
Shanghai, China) for 30 min at room temperature. After washing by
distilled water, the samples were captured using a bright‐field
microscope (#CX41, Olympus Corporation, Tokyo, Japan). For cell growth
assay, 5 × 10^3 cells per well were seeded into 96‐well plates and
treated with DMSO, 5 μmol/L of MK‐2206, or 5 μmol/L of MK‐803 for 5
days. Cell counting kit‐8 (#HY‐K0301, MedChemExpress, Shanghai, China)
was used to measure the proliferation of cells.
2.12. Seahorse XF Cell Mito Stress and Glycolysis Stress assays
TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII cells were used for the
Seahorse XF Cell Mito Stress assay. First, cells were seeded on the
Seahorse XF24 cell culture microplates (#102342‐100, Agilent
Technologies, Inc., Santa Clara, CA, USA) at a density of 1 × 10^4
cells per well. To measure the effects of inhibitors, DMSO, 5 μmol/L
MK‐2206, 5 μmol/L MK‐803, or 5 μmol/L OSI was added to the culture
medium. To detect the mechanistic effects of ACSS3, ACSL3, and ELOVL2,
respective siRNAs were transfected using Lipofectamine‐3000 reagent
(#L3000150, Thermo Fisher). After 24 h of transfection, the
mitochondrial stress and glycolytic function were measured. The
Seahorse XF Cell Mito Stress Test kit (#103015‐100, Agilent, Santa
Clara, CA, USA) and Seahorse XF Glycolysis Stress Test Kit
(#103020‐100, Agilent) were used following the manufacturer's protocol.
Briefly, a sensor cartridge (#102342‐100, Agilent Technologies, Inc.,
Santa Clara, CA, USA) was hydrated using Seahorse XF Calibrant at 37°C
in a non‐CO[2] incubator overnight. The medium were prepared by
supplementing Seahorse XF DMEM medium (#103575‐100, Agilent
Technologies, Inc., Santa Clara, CA, USA) with essential additives (1
mmol/L pyruvate, 2 mmol/L glutamine, and 10 mmol/L glucose for
MitoStress; 2 mmol/L glutamine for Glycolysis Stress) and warmed to
37°C. The compounds included in the kit were solubilized by the medium
and loaded into the ports on the sensor cartridge. After calibrating
the prepared sensor cartridge and placing the cell culture microplate
at 37°C in a non‐CO2 incubator for 45 min to 1 h, the oxygen
consumption rate (OCR) and extracellular acidification rate (ECAR) were
measured on a Seahorse XFe24 Analyzer (Agilent Technologies, Inc.,
Santa Clara, CA, USA).
2.13. ATP detection assay
ATP detection assays were performed using an ATP detection kit (#S0026,
Beyotime Biotechnology, Shanghai, China). Briefly, a total of 1 × 10^4
cells per well were seeded into 96‐well plates and treated with DMSO, 5
μmol/L MK‐2206, or 5 μmol/L MK‐803 for 24 h. Cells were lysed and
incubated with ATP detection buffer for 3‐5min. Then, relative
luminescence units were measured on a BioTek Gen5 Microplate Reader
(BioTek Instruments, Vermont, USA), and ATP concentrations were
calculated from the standard curve.
2.14. Protein preparation, western blotting, and antibodies
Total cell lysates were prepared from GBM cells using RIPA buffer (150
mmol/L NaCl, 1% Nonidet P‐40, 0.5% sodium deoxycholate, 0.1% SDS, 50
mmol/L Tris‐HCl pH = 7.4) supplied with Protease Inhibitor Cocktail
(#HY‐K0010, MedChemExpress), and PMSF (P0100, SolarBio Science &
Technology Co., Ltd., Beijing, China). Total protein samples were
analyzed by 8% or 10% sodium dodecyl‐sulfate polyacrylamide gel
electrophoresis (SDS‐PAGE). Antibodies against ACSS3 (1:500;
#16204‐1‐AP, Proteintech, Wuhan, China), ACSL3 (1:1000; #20710‐1‐AP,
Proteintech), ELOVL2 (1:1000; #ab176327, abcam, Cambridge, MA, USA),
p‐EGFR (1:1000; #3777S, Cell Signaling Technology), EGFR (1:1000;
#18986‐1‐AP, Proteintech), p‐AKT (1:1000; #4060S, Cell Signaling
Technology), AKT (1:1000; #9272S, Cell Signaling Technology), p‐NF‐κB
(1:1000; #3033S, Cell Signaling Technology), NF‐κB (1:1000; #8242S,
Cell Signaling Technology), Na‐K‐ATPase (1:1000; #3010S, Cell Signaling
Technology), CDK2 (1:1000; #AB40719, Absci, Vancouver, WA, USA), CDK4
(1:1000; #DF6102, Affinity Biosciences, Jiangsu, China), CDK6 (1:1000;
#ab124821, abcam), Cyclin D (1:1000; #AF0931, Affinity Biosciences),
p‐Rb (1:1000; #AF3103, Affinity Biosciences), Rb (1:500; #DF6840,
Affinity Biosciences), LKB1 (1:500; #YT2572, Immunoway, Plano, TX,
USA), p‐LKB1 (1:500; #YP0900, Immunoway), AMPKα1/2 (1:1000; #YT0216,
Immunoway), p‐AMPKα1/2 (1:500; #YP0575, Immunoway), β‐actin (1:5000;
#AF7018, Affinity Biosciences), β‐Tubulin (1:5000; #66240‐1‐Ig,
Proteintech), and GAPDH (1:5000; #60004‐1‐Ig, Proteintech) were used
for western blotting, according to the manufacturer's instructions.
Briefly, proteins separated in gel were transferred to polyvinylidene
fluoride (PVDF) membrane (#IPVH00010, Millipore, Billerica, MA, USA).
Then the membrane was incubated with blocking buffer [5% skim milk
[#8340, SolarBio Science & Technology Co., Ltd., Beijing, China] and
0.1% Tween‐20 [#T8220, SolarBio Science & Technology Co., Ltd.,
Beijing, China] in PBS) for 2 h at room temperature, followed by
incubated with the above specific antibodies at 4°C overnight. After
washed and incubated with HRP‐conjugated secondary antibodies
(1:10,000; #RS0001 and #RS0002, ImmunoWay Biotechnology Company, Plano,
TX, USA), immunoblots were performed using a FluoChem instrument
(ProteinSimple, San Jose, CA, USA).
2.15. Cell cycle analysis
Cell cycle status was detected using a Cell Cycle Assay Kit (#C543,
Dojindo, Kumamoto, Japan), according to the manufacturer's protocol.
Briefly, cells were digested by trypsin and fixed in 70% of ethanol
overnight at 4°C. The cells were then centrifuged to remove excess
ethanol and resuspended in a working solution with propidium iodide and
RNase at 37°C for 30 min and at 4°C for 30 min, respectively. Cell
cycle distribution was detected by BD FACSVerse instruments (BD
Biosciences, San Jose, CA, USA). Flow cytometry results were analyzed
by FlowJo v10.6.2 software (FlowJo, LLC, Ashland, OR, USA).
2.16. In vivo orthotopic xenograft mouse model and treatments
Female BALB/C nude mice aged 4 weeks were used to construct the GBM
orthotopic model. TBD0220 cells (1 × 10^5 cells in 3 μL PBS) infected
with luciferase lentiviruses were intracranially injected using the
guidance of a stereotactic instrument. TMZ (5 mg/kg), MK‐2206 (100
mg/kg), and MK‐803 (50 mg/kg) were administered by oral gavage every
day after 7 days of injections. Tumor growth was monitored by in vivo
Imaging System (IVIS; PerkinElmer Inc, Waltham, Massachusetts, USA)
spectrum on Day 7, Day 14, and Day 21. Kaplan‐Meier curves were used to
analyze survival. Four weeks after modeling, the brain tissues were
carefully harvested, fixed by formalin, embedded in paraffin, and used
for immunohistochemistry (IHC) analysis.
Female C57BL/6J mice aged 4 weeks were used to establish the
intracranial tumor model. A total of 2 × 10^4 CT2A cells in 3 μL PBS
were injected into mouse brains. After 7 days of tumor transplantation,
mice received daily oral gavage of TMZ (5 mg/kg), OSI (25 mg/kg),
and/or ATO (10 mg/kg). Bioluminescence imaging using IVIS was employed
on Days 3, 7, and 14 to detect tumor growth. On Day 21, the mice were
sacrificed and the tumors were carefully extracted for flow cytometry.
The BALB/C nude mice (#401) and C57BL/6J mice (#219) were obtained from
the Beijing Vital River Laboratory Animal Technology Co., Ltd.
(Beijing, China). All mice were housed in a specific pathogen free
(SPF) breeding barrier with individual ventilated cages. Tribromethyl
alcohol (#HY‐B1372, MedChemExpress, Shanghai, China) was used for
anesthetize mice. Mice should be sacrificed by cervical dislocation
after anesthesia in the following conditions: near death or immobile,
have significant weight loss, or unable to feed or drink. All animal
experimentations were approved by the Animal Ethical and Welfare
Committee of Hebei University (Approval No. IACUC‐2020XS001).
2.17. Intertumoral distributions of drugs in vivo
Drug distribution analysis was performed as previously described
[[93]26]. Briefly, GBM orthotopic models were constructed using TBD0220
cells in a total of 1 × 10^5 cells per mouse. After 7 days, MK‐2206,
MK‐803, OSI or ATO was administered by oral gavage. The brain tissues
were isolated and homogenized by a freezing grinder. The drug
concentrations were analyzed using a triple quadrupole liquid
chromatography‐tandem mass spectrometry (LC‐MS; Waters Corporation,
Milford, MA, USA). Briefly, chromatographic separation was performed on
a Waters BEH C18 column (2.1 × 100 mm, 1.7 μm). Mobile phase A
constitution contained 0.1% formic acid solution, and mobile phase B
was acetonitrile at a flow rate of 0.3 mL/min. The operation parameters
of MS were as follows: capillary voltage (3.0 kV), desolvation
temperature (500°C), cone gas flow (150 L/Hr), and desolvation gas flow
(800 L/Hr).
2.18. Hematoxylin and eosin (H&E) staining and IHC assay
Paraffin‐embedded tissues were stained with H&E, and IHC assays were
performed according to the protocol described in our previous study
[[94]25]. Briefly, tissues were cut into sections, followed by dewaxed,
hydrated, and performed for antigen retrieval. For H&E, hematoxylin
(#G1080, SolarBio Science & Technology Co., Ltd., Beijing, China) and
eosin (#G1100, SolarBio Science & Technology Co., Ltd., Beijing, China)
were sequentially stained. For IHC, after blocked by PBS containing 10%
FBS for 1 h at room temperature, samples were incubated with the
following antibodies at 4°C overnight: Ki67 (1:200; #ZM‐0167, ZSGB‐BIO,
Beijing, China), p‐AKT (1:200; #4060S, Cell Signaling Technology),
ACSS3 (1:100; #16204‐1‐AP, Proteintech), ACSL3 (1:100; #20710‐1‐AP,
Proteintech), ELOVL2 (1:200; #ab176327, abcam), MHC‐II (1:1000;
#68258S, Cell Signaling Technology), CD206 (1:400; #91992S, Cell
Signaling Technology), and CD8a (1:400; #98941S, Cell Signaling
Technology). Then samples were washed twice by PBS, incubated with
horse radish peroxidase‐conjugated secondary antibody, and colorized by
diaminobenzidine (DAB). The stained slices were mounted by neutral
resin and imaged using a bright‐field microscope (#CX41, Olympus
Corporation, Tokyo, Japan).
2.19. Detection of tumor infiltrated CD8^+ T‐cells and macrophages
Tumors were digested into single‐cell suspensions, and the red blood
cells were removed using red blood cell lysis buffer (#R1010, SolarBio
Science & Technology Co., Ltd., Beijing, China). The cells were
centrifuged and resuspended into MACS buffer (0.5% bovine serum
albumin, 2 mmol/L EDTA in PBS). The anti‐CD16/CD32 antibody (1:50;
#101319, BioLegend, San Diego, CA, USA) was utilized to block the
nonspecific binding sites. Tumor‐infiltrating cytotoxic T lymphocytes
were stained using anti‐CD45.2 (1:200; #109806, BioLegend), anti‐CD3
(1:40; #100236, BioLegend), and anti‐CD8a (1:20; #100734, BioLegend);
tumor‐infiltrating macrophages were stained by antibodies of
anti‐CD45.2 (1:200; #109806, BioLegend), anti‐F4/80 (1:20; #123110,
BioLegend), anti‐CD11b (1:80; #101226, BioLegend), anti‐CD11c (1:80;
#117318, BioLegend), and anti‐CD206 (1:40; #141708, BioLegend)
antibodies, according to the manufacturer's protocol. Briefly, the
cells were blocked for 10 min on ice, followed by stained with specific
antibodies for 30 min on ice away from the light. Then the samples were
detected on a BD FACSVerse instrument (BD Biosciences). Flow cytometry
results were analyzed by FlowJo v10.6.2 software.
2.20. Statistical analysis
All statistical analyses were performed using GraphPad Prism 8 software
and R language (v3.6.2). Independent‐sample Student's t‐test was used
for comparison between two experimental groups. One‐way and two‐way
analyses of variance (ANOVA) were used to compare at least three
experimental groups. The error bars in the figures represent the mean ±
standard deviation (SD). A P value of less than 0.05 was considered
statistically significant, defined as ^∗ P < 0.05, ^∗∗ P < 0.01, ^∗∗∗ P
< 0.001.
3. RESULTS
3.1. Single‐cell RNA sequencing depicts a unique transcriptional landscape of
GBM cells
High intra‐ and inter‐tumor heterogeneities of GBM cells, characterized
by diverse cell populations and cell cycle states, led to insensitivity
to conventional treatments and poor prognosis. To better understand the
complexity of the GBM microenvironment, a total of 14 tumor tissue
samples were collected by multipoint sampling from 5 GBM patients for
single‐cell RNA sequencing library construction using the BD Rhapsody
system. After a series of stringent cell quality control analyses, the
cells were annotated into 7 subtypes, of which the vast majority of
cells were tumorigenic, accounting for 69.22% of the cell population
(Figure [95]1A). The other cell types in that population were
macrophages (10.76%), oligodendrocytes (6.17%), neutrophils (5.60%), T
cells (4.32%), smooth muscle cells and fibroblasts (SMC&Fibro; 3.37%),
and endothelial cells (0.56%) (Figure [96]1A, Supplementary Figure
[97]S1A‐C). Copy number variation (CNV) is associated with
tumorigenesis, leading to increased genomic instability and abnormal
protein expression in tumor cells [[98]27, [99]28]. The combination of
chromosome 7 amplification and chromosome 10 deletion is the
pathological hallmark feature of GBM [[100]29]. Hence, we determined
the CNVs of tumor cells compared with nonmalignant cells using the
InferCNV R package [[101]30, [102]31], and the chromosomal changes were
observed in all samples but to different extents (Supplementary Figure
[103]S1D). Moreover, TBD717‐T1‐T4 and TBD528‐T1 samples displayed
chromosome 3 amplification. TBD717‐T1‐T4 samples also exhibited changes
in chromosome 12 amplification and chromosome 11 deletion. The
TBD629‐T3, TBD706‐T1, TBD706‐T2, TBD706‐T3, TBD720‐T1, and TBD720‐T2
samples indicated abnormal amplification of chromosome 20. Only the
TBD706‐T3 sample showed an amplification change for chromosome 19.
These results not only confirmed that all samples were GBM but also
reflected high intra‐ and inter‐tumor heterogeneities.
FIGURE 1.
FIGURE 1
[104]Open in a new tab
GBM patients with high levels of EGFR accompanied by ACSS3, ACSL3, and
ELOVL2 expressions show worse prognoses. (A) Cell types were annotated
and visualized as a UMAP plot in GBM single‐cell RNA sequencing data.
(B) Expressions of GFAP, CHI3L1, OLIG2, SOX6, EGFR, and PDGFA in
subcellular populations of tumor cells from GBM single‐cell data were
stained. (C) A total of 43,215 tumor cells were grouped into 4 cell
populations according to the expression features of key genes. (D)
Kaplan‐Meier curve was generated to evaluate the overall survival time
of GBM patients in distinct groups from the CGGA cohort, based on the
expressions of GFAP, CHI3L1, OLIG2, SOX6, EGFR, and PDGFA. G1 (red
line) represented patients with GFAP^high, CHI3L1^high, OLIG2^low,
SOX6^low, EGFR^high, and PDGFA^high; G2 (green line) represented
patients with GFAP^high, CHI3L1^high, OLIG2^low, SOX6^low, EGFR^low,
and PDGFA^low; and G3 (cyan line) represented patients with GFAP^low,
CHI3L1^low, OLIG2^high, SOX6^high. ^∗∗∗ P < 0.001 (Log‐rank test). (E)
Differentially expressed genes in CP1 were identified and visualized as
a volcano plot. The dotted lines represent 0.25 and ‐0.25 of log2‐fold
change. (F) The correlation analysis among the expressions of EGFR,
PDGFA, ACSS3, ACSL3, and ELOVL2 in the CGGA cohort. ^∗∗∗ P < 0.001
(Pearson). (G) Kaplan‐Meier curve analysis showed that patients with
high RFA scores in the CGGA cohort had a poor prognosis. ^∗∗∗ P <
0.0001 (Log‐rank test). Abbreviations: GBM, glioblastoma; UMAP, uniform
manifold approximation and projection; GFAP, glial fibrillary acidic
protein; CHI3L1, chitinase 3 like 1; OLIG2, oligodendrocyte
transcription factor 2; SOX6, SRY‐box transcription factor 6; EGFR,
epidermal growth factor receptor; PDGFA, platelet‐derived growth factor
subunit A; ACSS3, acyl‐CoA synthetase short‐chain family member 3;
ACSL3, acyl‐CoA synthetase long‐chain family member 3; ELOVL2,
long‐chain fatty acid elongation‐related gene ELOVL fatty acid elongase
2; CGGA, Chinese glioma genome atlas.
3.2. High levels of EGFR, ACSS3, ACSL3, and ELOVL2 confer a poor prognosis
for GBM patients
Next, we focused on the tumor subtypes, which accounted for the largest
proportion. We picked a tumor cell subset (a total of 43,215 cells) for
reanalysis and visualization using UMAP (Supplementary Figure
[105]S2A). By identifying the marker genes in clusters, we traced the
gene expression characteristics of different tumor cell types. Based on
the expression features of the astrocyte marker gene GFAP, mesenchymal
GBM marker gene CHI3L1, proneural GBM marker gene OLIG2 and SOX6
[[106]32], tumor cells could easily be distinguished. Furthermore, we
identified a mass of cells with GFAP and CHI3L1 positivity that showed
high expression levels of EGFR and platelet‐derived growth factor
subunit A (PDGFA). Therefore, according to the gene expression profiles
and distribution patterns of those genes, tumor cells were grouped into
4 different cell populations: CP1
(GFAP^+CHI3L1^+EGFR^+PDGFA^+OLIG2^−SOX6^−), CP2
(GFAP^+CHI3L1^+EGFR^−PDGFA^−OLIG2^−SOX6^−), CP3
(GFAP^−CHI3L1^−EGFR^−PDGFA^−OLIG2^+SOX6^+), and CP4
(GFAP^−CHI3L1^−EGFR^−PDGFA^−OLIG2^−SOX6^−) (Figure [107]1B‐C,
Supplementary Figure [108]S2B). Based on the expression profiles of
GFAP, CHI3L1, OLIG2, SOX6, EGFR, and PDGFA, we divided the glioma
patients from the CGGA [[109]33], TCGA, and Rembrandt cohorts into 3
groups: Group G1 presented high expression of GFAP and CHI3L1, along
with RTK pathway activation (high expression levels of EGFR and PDGFA
genes); Group G2 had a similar gene expression profile to G1, except
for the inactive RTK pathway (low levels of EGFR and PDGFA gene
activations); Group G3 was characterized by high expression levels of
OLIG2 and SOX6 (Supplementary Figure [110]S3). Kaplan‐Meier survival
analysis revealed that patients with G1 characteristics had the
shortest overall survival time, whereas patients with G3 features had
the longest lifetime and the patients with G2 features exhibited a
moderate survival time (Figure [111]1D, Supplementary Figure [112]S4).
Given that O‐6‐Methylguanine‐DNA Methyltransferase (MGMT) plays a vital
role in GBM chemoresistance via removing the TMZ‐induced
O6‐methylguanine (O6‐MeG) in DNA and has been recognized as an
important predictor of the responsiveness to TMZ [[113]34, [114]35], we
performed subgroup analysis of the G1, G2, and G3 groups based on the
expression level of MGMT and analyzed the overall survival curves. The
results in Supplementary Figure [115]S5A‐C showed that there was no
statistical difference between MGMT^low and MGMT^high subgroups in each
group. It is well‐known that the promoter methylation status is the
primary reason to determine the expression level of MGMT in GBM
[[116]36]. Therefore, we resorted each group into 2 subgroups based on
the promoter methylation status of MGMT (MGMTp) in TCGA and CGGA
cohorts (There is no MGMTp data in Rembrandt cohort), and performed the
Kaplan‐Meier analysis. The results in Supplementary Figure [117]S5D‐E
revealed that the overall survival time of MGMTp^methy and
MGMTp^unmethy in each group exihibited no statistical differences,
although G2 + MGMTp^unmethy subgroup had a shorter survival time
compared to G2 + MGMTp^methy subgroup in the TCGA cohort. Taken
together, these findings demonstrated that the above grouping strategy
contributes to prognostic prediction of glioma patients through an
MGMT‐independent mechanism.
By identifying differentially expressed genes of each population
(Figure [118]1E, Supplementary Figure [119]S6, Supplementary Table
[120]S3), we noticed that acyl‐CoA synthesis‐associated genes (ACSS3,
ACSL3, and ELOVL2) were enriched in the CP1 subgroup (Figure [121]1E,
Supplementary Table [122]S3), implying that RTK pathway activation in
GBM could be accompanied by vigorous fatty acid biosynthesis and
metabolism. The positive correlations among these genes were further
verified using glioma cohorts in the CGGA, TCGA, and Rembrandt
databases (Figure [123]1F, Supplementary Figure [124]S7, Supplementary
Table [125]S4‐S6). Because of the positive expression correlations and
the great prognostic values of the aforementioned genes, we attempted
to integrate their expression profiles to construct an RFA network for
GBM patients for clustering and prognosis prediction. As shown in
Supplementary Figure [126]S8A, a higher RFA score was significantly
associated with a higher tumor grade or malignancy. Kaplan‐Meier curve
showed a shortened overall survival time for patients with high RFA
scores (Figure [127]1G, Supplementary Figure [128]S8B). Through the
univariate and multivariate Cox analyses, we found that the efficacy of
the RFA score in prognosis prediction was similar to that of the
conventional evaluation criteria, such as the WHO classification,
isocitrate dehydrogenase (IDH) gene mutation status‐based
classification, or 1p/19q co‐deletion classification (Supplementary
Table [129]S7‐S9). In addition, the receiver operating characteristic
(ROC) curve for RFA scores that stratified prognosis exhibited higher
sensitivities (Supplementary Figure [130]S8C).
3.3. The EGFR/AKT pathway upregulates the expression of ACSS3, ACSL3, and
ELOVL2 by promoting the NF‐κB phosphorylation
Given the positive correlation between EGFR and fatty acid
metabolism‐associated genes (Figure [131]1F, Supplementary Figure
[132]S7), we hypothesized that the expression of ACSS3, ACSL3, and
ELOVL2 might be transcriptionally regulated by the EGFR/AKT pathway. To
prove this hypothesis, we employed the GBM cell lines U‐87 MG and
primary GBM cell TBD0220 with an endogenous EGFR‐vIII heterozygous
mutation to interrogate the expression patterns of ACSS3, ACSL3, and
ELOVL2 upon activation or inhibition of the EGFR/AKT pathway. As shown
in Figure [133]2A, the expression levels of ACSS3, ACSL3, and ELOVL2
were significantly increased after the EGF stimulation or EGFR‐vIII
mutant overexpression in U‐87 MG cells. Additionally, the AKT inhibitor
MK‐2206 also decreased the expression of these genes in TBD0220 cells.
Studies have shown that EGFR/AKT pathway‐driven NF‐κB signaling
activation plays an important role in the tumorigenesis and metastasis
of GBM [[134]37, [135]38, [136]39]. We found that NF‐κB could be
phosphorylated by the overexpressing EGFR‐vIII mutant and
dephosphorylated by MK‐2206, indicating the regulatory associations
among these genes (Figure [137]2B). The NF‐κB inhibitor JSH‐23
suppressed the expression of ACSS3, ACSL3, and ELOVL2 by decreasing the
levels of phosphorylated NF‐κB (p‐NF‐κB) in EGFR‐vIII‐overexpressing
U‐87 MG (U‐87 MG‐EGFR‐vIII), and TBD0220 cells (Figure [138]2C). ChIP
assays further demonstrated that the enrichment of p‐NF‐κB at these
gene promoters was significantly increased upon activation of the
EGFR/AKT pathway and decreased after MK‐2206 treatment
(Figure [139]2D‐F). Collectively, these results suggested that the
hyperactivated EGFR/AKT pathway can enhance the expression of the
ACSS3, ACSL3, and ELOVL2 genes via NF‐κB‐dependent transcriptional
activation.
FIGURE 2.
FIGURE 2
[140]Open in a new tab
EGFR/AKT pathway activation enhances the expression of ACSS3, ACSL3,
and ELOVL2 via NF‐κB activation. (A) TBD0220 cells were treated with 0,
0.1, 0.5, 1, 5, and 10 μmol/L MK‐2206 for 24 h. U‐87 MG cells were
stimulated with 0, 0.5, 5, and 50 ng/mL of EGF for 24 h, or
overexpressed with EGFR‐vIII mutant. The expression levels of EGFR,
p‐EGFR, AKT, p‐AKT, ACSS3, ACSL3, ELOVL2, and β‐actin were tested by
western blotting. Protein was normalized to their respective β‐actin
loading control and expressions were quantified by ImageJ software. (B)
Western blotting analysis of EGFR, p‐EGFR, AKT, p‐AKT, NF‐κB, p‐NF‐κB,
ACSS3, ACSL3, ELOVL2, and GAPDH expressions in TBD0220, U‐87 MG and
U‐87 MG‐EGFR‐vIII cells treated with DMSO or 5 μmol/L of MK‐2206 for 24
h. (C) Western blotting to check the expression levels of ACSS3, ACSL3,
ELOVL2, NF‐κB, p‐NF‐Κb, and β‐actin in TBD0220, U‐87 MG, and U‐87
MG‐EGFR‐vIII cells treated with DMSO or 100 μmol/L of JSH‐23 treatment.
Protein was normalized to their respective β‐actin loading control and
expressions were quantified by ImageJ software. (D‐F) TBD0220, and U‐87
MG‐EGFR‐vIII cells were treated with DMSO or 5 μmol/L MK‐2206 for 24 h.
ChIP analysis of the regulatory regions of p‐NF‐κB binding to the
promoters of ACSS3 (D), ACSL3 (E), and ELOVL2 (F). ^∗ P < 0.05, ^∗∗ P <
0.01, ^∗∗∗ P < 0.001 (independent‐sample Student's t‐test for TBD0220;
one‐way ANOVA for U‐87 MG). Abbreviations: EGFR, epidermal growth
factor receptor; p‐EGFR, phosphorylation of epidermal growth factor
receptor; EGF, epidermal growth factor; AKT, AKT serine/threonine
kinase 1; GBM, glioblastoma; ANOVA, analysis of variance; ACSS3,
acyl‐CoA synthetase short‐chain family member 3; ACSL3, acyl‐CoA
synthetase long‐chain family member 3; ELOVL2, long‐chain fatty acid
elongation‐related gene ELOVL fatty acid elongase 2.
3.4. Inhibition of the EGFR/AKT pathway represses ATP production, cell
proliferation, and cell cycle progression of GBM cells in vitro
To further dissect the mechanism of energy metabolism reprogramming
under the condition of EGFR/AKT pathway inhibition, we performed
Seahorse analysis to evaluate the metabolic change after stimulation
with MK‐2206 in TBD0220 and U‐87 MG cells. The results showed that the
oxygen consumption rate (OCR) was increased in U‐87 MG‐EGFR‐vIII cells
compared with that of WT cells but decreased in both TBD0220 and U‐87
MG‐EGFR‐vIII cells when treated with MK‐2206, when compared with the
control cell status (Figure [141]3A‐B). The changes in the basal
respiration, proton leak, and ATP production rates were in agreement
with OCRs for those cell lines (Figure [142]3A‐B). Subsequently, we
tried to investigate whether the EGFR/AKT pathway induced energy
metabolism remodeling via regulating ACSS3, ACSL3, and ELOVL2
expressions. Depleting ACSS3, ACSL3, and ELOVL2 expression respectively
or together decreased OCR, basal respiration, proton leak, and ATP
production rates in TBD0220 and U‐87 MG‐EGFR‐vIII cells, compared with
cells transfected siNC (Supplementary Figure [143]S9). Overexpression
of ACSS3, ACSL3, and ELOVL2 alleviated the inhibitory energy metabolism
and ATP production by MK‐2206 in TBD0220 and U‐87 MG‐EGFR‐vIII cells
(Supplementary Figure [144]S10). Moreover, the results of the ATP assay
demonstrated an increased ATP production in EGFR‐vIII‐overexpressing
U‐87 MG cells, but ATP production was significantly decreased upon
MK‐2206 treatment in TBD0220 and U‐87 MG‐EGFR‐vIII cells
(Figure [145]3C). Cell growth and colony formation assays showed that
EGFR/AKT pathway activation could improve the number of colonies and
the rate of GBM cell proliferation, while those effects were reversed
in MK‐2206 treated cells (Figure [146]3D‐E, Supplementary Figure
[147]S11). Supplementing ATP could partially rescue the proliferation
inhibition caused by MK‐2206 (Figure [148]3D‐E). The cell cycle
distributions of GBM cells under different treatment conditions were
monitored by flow cytometry analysis, revealed an increasing number of
G0/G1 cells transitioning into the S phase when the EGFR/AKT pathway
was hyperactivated, and an inverse effect was observed after
stimulation with MK‐2206 (Figure [149]3F, Supplementary Figure
[150]S12). The expressions of cell cycle‐associated proteins, such as
CDK2, CDK4, CDK6, and Cyclin D, as well as the phosphorylation of RB,
presented a significantly increased change in EGFR/AKT pathway
activation, which could be diminished by the MK‐2206 (Figure [151]3G).
FIGURE 3.
FIGURE 3
[152]Open in a new tab
EGFR/AKT pathway regulates mitochondrial respiration and proliferation
in GBM cells. (A‐B) TBD0220 (A), and U‐87 MG‐EGFR‐vIII cells (B) were
treated with DMSO or 5 μmol/L MK‐2206 for 24 h. The mitochondrial
functions were monitored by Seahorse XF Cell Mito Stress test. The OCR,
basal respiration, proton leak, and ATP production rates were measured
as illustrated. ^∗ P < 0.05, ^∗∗ P < 0.01, ^∗∗∗ P < 0.001
(independent‐sample Student's t‐test for TBD0220; one‐way ANOVA for
U‐87 MG). (C) ATP levels in TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII
cells were analyzed after 24 h of treatments with DMSO or 5 μmol/L of
MK‐2206. ^∗∗ P < 0.01, ^∗∗∗ P < 0.001 (independent‐sample Student's
t‐test for TBD0220; one‐way ANOVA for U‐87 MG). (D) The cell growth
assay for TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII lines treated with
DMSO, 5 μmol/L MK‐2206, or 5 μmol/L MK‐2206 plus 50 μmol/L ATP were
performed. ^∗∗∗ P < 0.001 (two‐way ANOVA). (E) The colony formation
assay of TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII lines treated with
DMSO or 1 μmol/L MK‐2206. (F) Cell cycle distributions were analyzed by
flow cytometry in TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII cells treated
with DMSO or 5 μmol/L MK‐2206. (G) Western blotting to show changes in
expressions of CDK2, CDK4, CDK6, Cyclin D, RB, p‐RB, and GAPDH in
TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII cells treated with DMSO or
MK‐2206. Abbreviations: EGFR, epidermal growth factor receptor; AKT,
AKT serine/threonine kinase 1; GBM, glioblastoma; ANOVA, analysis of
variance; DMSO, dimethyl sulfoxide; CDK2, cyclin‐dependent kinase 2;
CDK4, cyclin‐dependent kinase 4; CDK6, cyclin‐dependent kinase 6; OCR,
oxygen consumption rate.
In summary, these results suggested that the EGFR/AKT pathway may
contribute to the energy metabolism reprogramming, cell proliferation,
and cell cycle progression in GBM cells, which could be blocked by
treating these cells with the AKT inhibitor MK‐2206.
3.5. Hyperactivation of the EGFR/AKT pathway promotes energy metabolism by
elevating fatty acid metabolism and cholesterol levels in GBM
The RTK signaling pathway, especially the EGFR/PI3K/AKT axis, promotes
fatty acid biosynthesis and lipogenesis in GBM [[153]12]. Considering
the effect of transcriptional regulation of the EGFR/AKT pathway on the
gene expression profiles of ACSS3, ACSL3, and ELOVL2, we noticed that
EGFR/AKT pathway activation in GBM cells could induce the fatty acid
metabolism‐associated metabolic reprogramming. To investigate the
metabolic characteristics of GBM cells with hyperactivated EGFR/AKT
pathway, we obtained 66 fresh samples from GBM patients to interrogate
the metabolites and transcriptome features. The conjoint analysis
illustrated that most of the detected phosphatidylcholines (PCs) were
positively correlated with increased expressions of EGFR, ACSS3, ACSL3,
and ELOVL2, whereas lysophosphatidylcholines (LysoPCs) were negatively
related to the activation of these genes. Other metabolites, such as
phosphatidylethanolamine (PE), phosphatidyl glycerol (PG),
phosphatidylinositol (PI), and phosphatidylserine (PS), had no
significant changes in their levels in samples with differentially
expressed fatty acid metabolism‐associated genes (Figure [154]4A‐B).
Furthermore, we showed that the levels of medium and long‐chain fatty
acids increased proportionally with the upregulation of EGFR expression
in GBM samples (Figure [155]4C).
FIGURE 4.
FIGURE 4
[156]Open in a new tab
The hyperactivated EGFR/AKT pathway correlates with phospholipid
metabolism and accelerated cholesterol biosynthesis in GBM cells. (A) A
total of 66 GBM samples were analyzed using RNA sequencing and
untargeted metabolomics. The expressions of EGFR, ACSS3, ACSL3, ELOVL2
and corresponding metabolites such as LysoPC, PC, LysoPE, PE, PG, PI,
and PS were analyzed. (B) Untargeted metabolomic analysis of PC and
LysoPC correlated with the expressions of EGFR, ACSS3, ACSL3, and
ELOVL2 in GBM samples. (C) The long‐chain fatty acids and cholesterol
levels were positively correlated with EGFR, ACSS3, ACSL3, and ELOVL2
expressions in GBM samples. (D) The schematic diagram of fatty acid
beta‐oxidation and TCA cycle. (E‐J) The levels of crucial intermediates
in the TCA cycle, such as citrate (E), cis‐aconitate (F), isocitrate
(G), α‐ketoglutarate (H), ATP (I), and VLCFA behenic acid (J) were
downregulated in TBD0220 cells by 24 h of treatment with DMSO, 5 μmol/L
MK‐2206 or 5 μmol/L MK‐803. ^∗ P < 0.05, ^∗∗ P < 0.01, ^∗∗∗ P < 0.001
(One‐way ANOVA). Abbreviations: EGFR, epidermal growth factor receptor;
AKT, AKT serine/threonine kinase 1; GBM, glioblastoma; ANOVA, analysis
of variance; ACSS3, acyl‐CoA synthetase short‐chain family member 3;
ACSL3, acyl‐CoA synthetase long‐chain family member 3; ELOVL2,
long‐chain fatty acid elongation‐related gene ELOVL fatty acid elongase
2; TCA, citric acid cycle; VLCFA, very long‐chain fatty acid; LysoPC,
lysophosphatidylcholine; PC, phosphatidylcholine; PE,
phosphatidylethanolamine; PG, phosphatidyl glycerol; PI,
phosphatidylinositol; PS, phosphatidylserine.
It is well characterized that fatty acids can be catabolized by the
beta‐oxidation pathway to produce acetyl‐CoA, which is an important
component of the TCA cycle for ATP production [[157]40]
(Figure [158]4D). Hence, we applied a series of inhibitor assays to
explore the effects of restraining the EGFR/AKT pathway on energy
metabolism remodeling. Metabolite changes in TBD0220 cells were
monitored upon stimulation with MK‐2206. The results showed that
compared with the DMSO control, multiple intermediates in the TCA cycle
were decreased under MK‐2206 treatment (Figure [159]4E‐H), leading to
reduced ATP production (Figure [160]4I). The level of behenic acid
(C22: 0), a very long‐chain fatty acid (VLCFA), was decreased in
MK‐2206 treated GBM cells (Figure [161]4J). Metabolism pathway
enrichment analysis further revealed that differential metabolites were
enriched in pathways associated with energy metabolism, for example,
“central carbon metabolism in cancer”, “TCA cycle”, and
“glycerophospholipid metabolism” (Supplementary Figure [162]S13A),
indicating the effect of the EGFR/AKT pathway modulation on the energy
metabolism remodeling.
It was worth noting that elevated cholesterol levels were measured in
GBM samples with higher EGFR expression (Figure [163]4C). Considering
that catalytic conversion of acetyl‐CoA to hydroxymethylglutaryl‐CoA
(HMG‐CoA) is essential for both the mevalonate pathway and cholesterol
biosynthesis, we attempted to explore the metabolic change in TBD0220
cells with mevalonate pathway inhibition by MK‐803 treatment. LC‐MS
analysis showed that changes in the content of TCA cycle intermediates
as well as behenic acid levels after MK‐803 treatment were similar to
those after MK‐2206 stimulation (Figure [164]4E‐J). MK‐803‐induced
altered metabolites were enriched in the energy metabolism‐associated
pathways, such as “oxidative phosphorylation” and “central carbon
metabolism in cancer”, which were comparable to the changes upon
MK‐2206 treatment (Supplementary Figure [165]S13B).
Overall, these results suggested that EGFR/AKT pathway activation may
increase the content of various fatty acids and cholesterol levels,
conferring sthenic energy metabolism changes to GBM cells.
3.6. Blocking the mevalonate pathway reduces membrane‐localized EGFR levels,
affecting the EGFR/AKT signal transduction
Next, we attempted to elucidate the mechanistic involvement of the
mevalonate pathway in the metabolic remodeling of GBM cells. Given that
cholesterol acts as a regulator of cell proliferation and membrane
homeostasis [[166]41], we isolated the cytosolic contents and membrane
components of TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII cells upon MK‐803
treatment. Compared to the DMSO control, the population of either EGFR,
or EGFR‐vIII on the cell membrane was significantly decreased after
stimulating GBM cells with MK‐803, and cholesterol supplementation
rescued the membrane‐localized levels of EGFR or EGFR‐vIII
(Figure [167]5A). Then, we investigated the effect of MK‐803 on the
EGFR/AKT pathway by the selective ligand activation method. TBD0220,
U‐87 MG, and U‐87 MG‐EGFR‐vIII cells were stimulated with different
durations of EGF after MK‐803 treatment, which showed a significantly
weakened response to EGF and EGFR/AKT pathway transduction and a
rapidly decreasing AKT phosphorylation level after EGF stimulation
(Figure [168]5B). The expression of ACSS3, ACSL3, and ELOVL2 was
suppressed in GBM cells treated with MK‐803 treatment (Figure [169]5C).
FIGURE 5.
FIGURE 5
[170]Open in a new tab
MK‐803 inhibits EGFR/AKT pathway transduction via decreasing EGFR
levels on the cell membrane. (A) The components of cytosol and membrane
in TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII cells treated with DMSO,
MK‐803, or MK‐803 plus 50 μmol/L water‐soluble cholesterol were
fractionated. The distribution levels of EGFR, Na‐K‐ATPase, and
β‐Tubulin were measured by western blotting. (B) TBD0220, U‐87 MG, and
U‐87 MG‐EGFR‐vIII cells were stimulated by 50 ng/mL EGF at different
time points after 24 h pre‐treatment with DMSO or MK‐803. Western
blotting analysis was performed to detect the expression and
phosphorylation levels of EGFR, and AKT. GAPDH was used as the loading
control. (C) Western blotting analysis of ACSS3, ACSL3, ELOVL2, and
β‐Tubulin expressions in TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII cells
treated with DMSO or 5 μmol/L MK‐803 for 24h. Proteins were normalized
to their respective β‐Tubulin loading control and expressions were
quantified by ImageJ software. (D‐E) TBD0220 (D), and U‐87 MG‐EGFR‐vIII
cells (E) were treated with 5 μmol/L of MK‐803 for 24 h. The
mitochondrial functions in these cells were monitored by Seahorse XF
Cell Mito Stress assay. The OCR, basal respiration, proton leak, and
ATP production rates were measured as illustrated. ^∗∗ P < 0.01, ^∗∗∗ P
< 0.001 (independent‐sample Student's t‐test for TBD0220; one‐way ANOVA
for U‐87 MG). (F) ATP levels in TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII
cells were analyzed after 24 h of treatment with DMSO or 5 μmol/L
MK‐803. ^∗∗∗ P < 0.001 (independent‐sample Student's t‐test for
TBD0220; one‐way ANOVA for U‐87 MG). (G) The proliferation rates of
TBD0220, U‐87 MG, and U‐87 MG‐EGFR‐vIII cells were determined after
treatment with DMSO or 5 μmol/L MK‐803. ^∗∗∗ P < 0.001 (Two‐way ANOVA).
Abbreviations: EGFR, epidermal growth factor receptor; AKT, AKT
serine/threonine kinase 1; ANOVA, analysis of variance; ACSS3, acyl‐CoA
synthetase short‐chain family member 3; ACSL3, acyl‐CoA synthetase
long‐chain family member 3; ELOVL2, long‐chain fatty acid
elongation‐related gene ELOVL fatty acid elongase 2; DMSO, dimethyl
sulfoxide; OCR, oxygen consumption rate.
Subsequently, we measured the effect of MK‐803 on energy metabolism and
cell proliferation in GBM cells. MK‐803 treatment significantly
decreased the OCR in TBD0220 cells and antagonized the increase in OCR
by EGFR‐vIII in U‐87 MG cells (Figure [171]5D‐E). Basal respiration,
proton leak, and ATP production were significantly suppressed in GBM
cells treated with MK‐803 (Figure [172]5D‐E). Significantly decreased
intracellular ATP levels and inhibited cell proliferation rates were
also observed in both TBD0220, and U‐87 MG‐EGFR‐vIII cells after MK‐803
treatment (Figure [173]5F‐G).
Taken together, these results suggested that mevalonate pathway
inhibition by MK‐803 can reprogram energy metabolism and repress the
proliferation of GBM cells by blocking EGFR/AKT signal transduction and
the expression of fatty acid metabolism‐associated genes.
3.7. Targeting the EGFR/AKT and mevalonate pathways enhances the antitumor
effects of TMZ, contributing to the GBM growth suppression in vivo
TMZ is the first‐line chemotherapy drug for the clinical treatment of
malignant gliomas [[174]42]. We assessed the TMZ sensitization effects
on MK‐2206 and MK‐803 treatments in vivo. First, we evaluated the
ability of MK‐2206 and MK‐803 to cross the blood‐brain‐barrier (BBB).
TBD0220 cells were intracranially injected into nude mice to establish
the GBM orthotopic model. The brain tissues were isolated for drug
concentration measurement. As shown in Supplementary Table [175]S10 and
Supplementary Figure [176]S14‐S15, the tissue content of MK‐2206 was
573.03 ± 156.98 ng/g, significantly superior to TMZ (8.84 ± 3.23 ng/g),
while the concentration of MK‐803 (4.35 ± 0.74 ng/g) was approximately
half of that of TMZ. The above results indicated that MK‐2206 and
MK‐803 had good BBB permeability. Subsequently, we administered
chemotherapeutics to tumor‐bearing mice by oral gavage, according to
the experimental design shown in Figure [177]6A. Bioluminescence
imaging data indicated that compared with the sham controls, single
drug interruption of TMZ, MK‐2206, or MK‐803 could reduce the tumor
growth in xenograft mice, among which TMZ had the most effective
antitumor effect. Moreover, multidrug treatments exhibited a better
tumor inhibition effect than single‐drug therapy. The combination of
TMZ, MK‐2206, and MK‐803 showed the best therapeutic effect on
tumor‐bearing mice (Figure [178]6B‐C). Kaplan‐Meier survival curve
revealed that mice treated with a 3‐drug regimen had the best prognosis
(Figure [179]6D). H&E staining confirmed a significant decrease in the
tumor burden in the treatment group compared with that of
vehicle‐treated sham mice, which was consistent with the
bioluminescence results. The proliferative abilities of xenografts were
suppressed after treatment based on the Ki67 expression levels in IHC
assays. The expression of ACSS3, ACSL3, ELOVL2, and AKT phosphorylation
were inhibited in GBM mice treated with either MK‐2206, MK‐803, or
different drug combinations (Figure [180]6E, Supplementary Figure
[181]S16).
FIGURE 6.
FIGURE 6
[182]Open in a new tab
Targeting EGFR/AKT and mevalonate pathways suppresses GBM proliferation
and prolongs the survival time of tumor‐bearing mice. (A) The flow
diagram of nude mice xenograft model. (B) Representative brain
bioluminescence images of nude mice on Day 7, Day 14, and Day 21 after
implantation. (C) Tumor growth curves were quantitated and illustrated.
^∗ P < 0.05, ^∗∗ P < 0.01, ^∗∗∗ P < 0.001 (two‐way ANOVA). (D)
Kaplan‐Meier curves for different experimental and control groups. ^∗ P
< 0.05, ^∗∗ P < 0.01, ^∗∗∗ P < 0.001 (log‐rank test). (E)
Representative images of FFPE brain tissues for H&E and IHC staining of
Ki67, p‐AKT, ACSS3, ACSL3, and ELOVL2. Scale bar = 1 mm for H&E, 50 μm
for IHC. Abbreviations: EGFR, epidermal growth factor receptor; AKT,
AKT serine/threonine kinase 1; GBM, glioblastoma; ANOVA, analysis of
variance; FFPE, formalin‐fixed and paraffin‐embedded; ACSS3, acyl‐CoA
synthetase short‐chain family member 3; ACSL3, acyl‐CoA synthetase
long‐chain family member 3; ELOVL2, long‐chain fatty acid
elongation‐related gene ELOVL fatty acid elongase 2.
3.8. Inhibition of the EGFR/AKT and mevalonate pathways remodels the immune
microenvironment of GBM tumors
Given that enhanced fatty acid metabolism and cholesterol biosynthesis
support GBM growth and immunosuppressive microenvironment formation
[[183]43, [184]44, [185]45, [186]46], we explored the relationship
between RFA score and immune cell metabolism by parsing sequencing data
of glioma cohorts in the CGGA, TCGA, and Rembrandt databases. We found
that the RFA score was positively correlated with immunosuppressors
obtained from Wang et al. [[187]47] (Figure [188]7A, Supplementary
Figure [189]S17A, Supplementary Figure [190]S18A, Supplementary Table
[191]S11). We overlapped positively correlated genes with RFA from the
CGGA, TCGA, and Rembrandt cohorts and visualized the top 10 genes. The
results revealed that RFA scores were significantly correlated with
Caspase 4 (CASP4), E74 Like ETS Transcription Factor 4 (ELF4),
Leukocyte Associated Immunoglobulin Like Receptor 1 (LAIR1), LYN
Proto‐Oncogene, Src Family Tyrosine Kinase (LYN), Major
Histocompatibility Complex, Class I‐Related (MR1), Macrophage Scavenger
Receptor 1 (MSR1), Nuclear Factor Kappa B Subunit 1 (NFKB1),
Ras‐Related Protein Rab‐27A (RAB27A), Switch‐Associated Protein 70
(SWAP70), and Transforming Growth Factor Beta 1 (TGFB1) expressions
(Figure [192]7B, Supplementary Figure [193]S17B, Supplementary Figure
[194]S18B). By employing single‐sample gene set enrichment analysis
(ssGSEA), we observed that patients with high RFA scores had a
significantly strong correlation with immune cell lineages
(Figure [195]7C, Supplementary Figure [196]S17C, Supplementary Figure
[197]S18C). To further comprehensively investigate the RFA‐associated
immunological processes in GBM, we calculated the tumor purities of
glioma cohorts using the ESTIMATE R package, which indicated negative
associations between RFA scores and tumor purities, implying that GBM
with high RFA score might have higher intra‐tumor heterogeneity and
more infiltrated immune cells (Figure [198]7D, Supplementary Figure
[199]S17D, Supplementary Figure [200]S18D). The immune cell
compositions of tumors were analyzed by the CIBERSORT algorithm. A high
RFA score was correlated with increased M2 macrophage infiltration in
CGGA and Rembrandt cohorts, which represented immunosuppression
(Figure [201]7E, Supplementary Figure [202]S17E, Supplementary Figure
[203]S18E). Taken together, our results demonstrated that RFA scores
might provide valid evidence regarding the immune microenvironment
status of GBM tumors.
FIGURE 7.
FIGURE 7
[204]Open in a new tab
RFA score positively correlates with the immunosuppressive GBM
microenvironment. (A) Expressions of immunosuppressors were positively
correlated with respective RFA scores in glioma samples of the CGGA
cohort. The clinical information of gender, tumor grade, IDH mutation
status, 1p/19q co‐deletion status, and MGMT promoter methylation status
in GBM samples are displayed. (B) Correlations between RFA scores and
the expressions of CASP4, ELF4, LAIR1, LYN, MR1, MSR1, NFKB1, RAB27A,
SWAP70, and TGFB1 were analyzed in the CGGA cohort. (C) The ssGSEA for
correlation of RFA scores with corresponding immune cell lineages in
the CGGA cohort. (D) Tumor purity was negatively correlated with RFA
scores in GBM samples of the CGGA cohort. ^∗∗∗ P < 0.001 (Pearson) (E)
CIBERSORT analysis of immune cell compositions in GBM samples of the
CGGA cohort. The M2 macrophage levels were directly correlated with RFA
scores. ^∗∗∗ P < 0.001 (independent‐sample Student's t‐test).
Abbreviations: GBM, glioblastoma; RFA, RTK‐fatty acid‐gene signature;
CGGA, Chinese glioma genome atlas; MGMT, O6‐methylguanine DNA
methyltransferase; CASP4, caspase 4; ELF4, E74‐like factor 4, LAIR1,
leukocyte‐associated Ig‐like receptor 1; LYN, LYN Proto‐Oncogene, Src
family tyrosine kinase; MR1, major histocompatibility complex, class
I‐related; MSR1, macrophage scavenger receptor 1; NFKB1, nuclear factor
kappa B subunit 1; RAB27A, RAB27A, member RAS oncogene family; SWAP70,
switching B cell complex subunit; TGFB1, transforming growth factor
beta 1; ssGSEA, single‐sample gene set enrichment analysis.
To investigate the role of EGFR/AKT and mevalonate pathway inhibition
in GBM microenvironment modulation in vivo, we utilized a syngeneic
CT2A, a mouse‐derived GBM cell line with Pten deficiency [[205]48], to
establish an intracranial tumor model in immunocompetent C57BL/6J mice.
To apply the combinatorial therapeutic strategy to GBM therapy better
and faster, the AKT inhibitor MK‐2206 was changed to OSI, a
BBB‐permeable third‐generation EGFR tyrosine kinase inhibitor (TKI)
authorized for clinical non‐small cell lung carcinoma (NSCLC) treatment
[[206]49, [207]50], and MK‐803 was replaced by ATO, a commonly used
cholesterol‐lowering drug that inhibits the mevalonate pathway
[[208]51]. In vitro experiments demonstrated that OSI treatment reduced
the expressions of ACSS3, ACSL3, and ELOVL2 via EGFR/AKT pathway
blockade (Supplementary Figure [209]S19), and decreased energy
metabolism (Supplementary Figure [210]S20), resulting in GBM
proliferation inhibition and cell cycle arrest (Supplementary Figure
[211]S21), similar to MK‐2206. Intertumoral distribution assays
demonstrated a high level of OSI (1656.19 ± 61.32 ng/g) in the tumor
tissues and a comparable drug concentration of ATO (9.22 ± 0.22 ng/g)
to TMZ (Supplementary Table [212]S10, Supplementary Figures
[213]S22‐S23). The mice received different drug combinations via
intragastric administration after CT2A intracranial injection
(Figure [214]8A). Monitoring by bioluminescence imaging revealed that
tumor growth was significantly decreased in the TMZ, TMZ + OSI, TMZ +
ATO, and TMZ + OSI + ATO groups compared with the DMSO control group
(Figure [215]8B‐C). Furthermore, combined drug strategies had better
tumor inhibition effects than TMZ alone. Notably, the TMZ + OSI + ATO
regimen displayed the best antitumor outcome in this animal cohort
(Figure [216]8C). Considering that ATO is mainly used against
hyperlipidemia in the clinic, we developed a mouse model of high‐fat
diet (HFD)‐induced hyperlipidemia in addition to these drug treatments.
Compared to the chow diet as a control, the HFD showed a slightly
increased tumor growth in the TMZ therapy group, whereas there was no
significant difference in tumor growths between the HFD and chow diet
animals receiving the TMZ + OSI + ATO regimen, suggesting that the
effect of HFD could be suppressed by ATO treatment (Figure [217]8C). In
both TMZ + OSI + ATO and TMZ + OSI + ATO + HFD treated tumors, the
M1/M2 ratios of macrophages and tumor‐infiltrating cytotoxic T
lymphocytes were significantly increased (Figure [218]8D‐F,
Supplementary Figure [219]S24, Supplementary Figure [220]S25), implying
an enhanced antitumor immune response.
FIGURE 8.
FIGURE 8
[221]Open in a new tab
The combination therapeutic strategy of TMZ + OSI + ATO reduces tumor
proliferation and improves GBM microenvironment. (A) The flow diagram
of the C57BL/6J mice xenograft model is shown. (B) Representative brain
bioluminescence images of C57BL/6J mice on Day 3, Day 7, and Day 14
post‐implantation. (C) Quantitation of tumor growth curves. ^#not
significant, ^∗ P < 0.05, ^∗∗ P < 0.01, ^∗∗∗ P < 0.001 (two‐way ANOVA).
(D) IHC staining of MHC‐II, CD206, and CD8a in FFPE tumor tissue
sections. Scale bar = 100 μm. (E) Flow cytometry analysis to evaluate
the infiltration rate of cytotoxic T‐lymphocytes under different
combination treatments of TMZ, OSI, and ATO. ^∗ P < 0.05, ^∗∗ P < 0.01
(one‐way ANOVA). (F) Flow cytometry analysis to evaluate M1/M2 ratios
of macrophages under different combination treatments of TMZ, OSI, and
ATO. ^∗ P < 0.05 (one‐way ANOVA).
Abbreviations: GBM, glioblastoma; TMZ, temozolomide; OSI, Osimertinib;
ATO, Atorvastatin; MHC‐II, major histocompatibility complex, class II;
FFPE, formalin‐fixed paraffin‐embedded; ANOVA, analysis of variance.
To further verify the clinical effect of ATO on sensitizing GBM to TMZ,
we examined GBM patients who had undergone standard treatments and
follow‐ups in Beijing Tiantan Hospital and Affiliated Hospital of Hebei
University (Supplementary Figure [222]S26A). It is worth noting that
these patients routinely received ATO orally due to their history of
hyperlipidemia. GBM patients who took statins orally every day had
significantly prolonged OS and progression‐free survival (PFS) compared
to those without statins (Figure [223]9A‐B). As the typical case
(patient #1) showed, owing to the STUPP regimen and long‐term TMZ
treatment combined with ATO, PFS extended to 21 months, which was
significantly longer than the average of GBM patients without statins
(Figure [224]9B‐C). H&E and Ki67 staining identified that EGFR/AKT
signal activation represented by phosphorylation of EGFR and AKT was
still at a low level, although the EGFR level in primary GBM tissue was
significantly high. Moreover, these tumor tissue samples were weakly
positive for ACSS3, ACSL3, and ELOVL2, as well as mild signals for
CD8^+ T cells and M2 macrophages, presumably due to patient #1
receiving oral ATO daily before the first diagnosis of GBM
(Figure [225]9D). For comparison, the GBM tissue of patient #15, who
was a GBM patient without hyperlipidemia and enrolled in the “without
statins” group (Figure [226]9A‐B), was used for IHC‐staining. The
results showed that p‐EGFR and p‐AKT staining displayed a significantly
higher level compared to those of patient #1, indicating EGFR/AKT
pathway hyperactivation in the GBM tissue of patient #15. The signals
of ACSS3, ACSL3, and ELOVL2 were strongly positive in the tumor tissue
sample of patient #15, as well as more M2 macrophage infiltration and
few signals for CD8^+ T cells, compared to those of patient #1
(Supplementary Figure [227]S26B). In addition, transcriptomic analysis
of paired samples of primary and recurrent GBM showed that RFA scores
of recurrent samples were significantly increased, which supports the
importance of statins co‐therapy for PFS prolongation (Supplementary
Figure [228]S26C‐D).
FIGURE 9.
FIGURE 9
[229]Open in a new tab
TMZ synergizes ATO's antitumor potency in clinical GBM treatment. (A‐B)
Kaplan‐Meier curve of OS (A) and PFS (B) in GBM patients with statins
(n = 9) or without statins (n = 51). ^∗ P < 0.05, ^∗∗ P < 0.01
(log‐rank test). (C) Intracranial images of the typical GBM case from
diagnosis to postoperative follow‐ups by MRI and clinical examinations.
(D) Representative images of FFPE sections of primary tumor tissues
from patient #1 for H&E and IHC staining of Ki67, EGFR, p‐EGFR, p‐AKT,
CD34, ACSS3, ACSL3, ELOVL2, CD8, MHC‐II, and CD163. Scale bar = 20 μm.
Abbreviations: TMZ, temozolomide; ATO, Atorvastatin; GBM, glioblastoma;
MRI, magnetic resonance imaging; FFPE, formalin‐fixed
paraffin‐embedded; OS, overall survival; PFS, progression‐free
survival; EGFR, epidermal growth factor receptor; p‐EGFR,
phosphorylation of epidermal growth factor receptor; p‐AKT,
phosphorylation of AKT serine/threonine kinase 1; ACSS3, acyl‐CoA
synthetase short‐chain family member 3; ACSL3, acyl‐CoA synthetase
long‐chain family member 3; ELOVL2, long‐chain fatty acid
elongation‐related gene ELOVL fatty acid elongase 2; MHC‐II, major
histocompatibility complex, class II.
Taken together, these in vivo results provided strong evidence that
OSI‐mediated EGFR/AKT pathway inhibition could enhance the tumor
inhibitory effect of TMZ. Additionally, TMZ could synergize with the
effect of OSI + ATO combination therapy, benefitting GBM patients to
the greatest extent, especially for patients comorbid with
hyperlipidemia.
4. DISCUSSION
GBM is one of the most malignant primary tumors in clinics, featured by
high heterogeneity, resistance to conventional therapeutic strategies
and easy relapse. Aberrant EGFR amplification and mutation is a typical
feature of GBM, contributing to a more vicious phenotype. In this
study, we demonstrated augmented EGFR expression accompanied with fatty
acid metabolism‐associated genes ACSS3, ACSL3, and ELOVL2 in GBM. RTK
signaling pathway activated GBM had vigorous fatty acid metabolism and
high cholesterol levels. MK‐2206 and MK‐803 decreased tumor
proliferation via remodeling energy metabolism. Mechanically,
inhibition of AKT phosphorylation and cholesterol biosynthesis reduced
EGFR level on the cell membrane, affecting transduction of the EGFR/AKT
signaling pathway. Targeting EGFR/AKT and mevalonate pathway enhanced
the antitumor effect of TMZ.
Excessive RTK pathway activation caused by EGFR amplification and
mutation is commonly observed in 57.4% of GBM subjects [[230]9]. EGFR
is known to facilitate SREBP1 cleavage to promote fatty acid synthesis
[[231]12]. In this study, we found that EGFR was co‐expressed with
ACSS3, ACSL3, and ELOVL2 by single‐cell RNA sequencing, which was
further verified by bulk sequencing / microarray analysis of the CGGA,
TCGA, and Rembrandt databases. Furthermore, using dimensional reduction
to improve clustering and marker gene expression characteristics in
single‐cell transcriptomic analysis and verification in public
databases, we established a novel RFA signature with the companion
diagnostics that indicated that GBM patients with high RFA scores had
poor prognoses and short survival.
GBM tumor progression is always accompanied by metabolic abnormalities,
including increased glucose uptake and lipid metabolism. Remodeling
metabolic disorders may benefit GBM treatment. It has been demonstrated
that blocking glucose‐mediated SCAP glycosylation ameliorates
EGFR‐vIII‐driven GBM growth [[232]52]. Our previous research also
showed that targeting PTRF/cPLA2 axis‐mediated lipid metabolism
reprogramming could suppress tumor growth and remodel the immune
microenvironment in GBM by reversing abnormal LysoPC generation
[[233]53]. Importantly, we found that the EGFR pathway activation
promoted vigorous fatty acid metabolism and a higher level of
cholesterol, compared to the respective controls, through comprehensive
metabolomic and transcriptomic analyses of GBM samples. The
hyperactivated EGFR/AKT pathway upregulated ACSS3, ACSL3, and ELOVL2
expression levels via transcriptional regulation of NF‐κB
phosphorylation. Inhibiting the EGFR/AKT pathway, along with the
mevalonate pathway, suppressed energy generation and tumor growth in
vitro and enhanced the antitumor effect of TMZ in vivo. Inhibition of
the mevalonate pathway could reduce membrane‐localized protein levels,
including glucose transporters.
EGFR translocation on mitochondria is reported to regulate mitochondria
dynamics by interacting with MFN1 and disturbing MFN1 polymerization
[[234]54]. In gliomas, inhibition of EGFR translocation on mitochondria
by iPA promotes PUMA‐induced cell death [[235]55]. These findings
implied the correlation between mitochondria‐localized EGFR and the
balance of mitochondrial fission/fusion, high level of EGFR increased
the number of mitochondria in tumor cells. In current study, we
demonstrated that hyperactivation of the EGFR/AKT pathway contributed
to energy production. Blockade of EGFR/AKT pathway inhibits
mitochondrial function and reduces ATP production (Figure [236]3A‐C,
Supplementary Figures [237]S9‐S10) by decreasing the expression of
ACSS3, ACSL3, and ELOVL2. Metabolomic data revealed that multiple
intermediates in the TCA cycle and behenic acid were decreased under
MK‐2206 treatment (Figure [238]4D‐J). Collectively, we speculate that a
high level of EGFR/EGFR‐vIII in GBMs might not only promote
mitochondrial function by elevating the expression of ACSS3, ACSL3, and
ELOVL2 but also increase the number of mitochondria by promoting
mitochondrial fission, leading to vigorous energy production for tumor
growth. Besides, we uncovered that mevalonate pathway inhibition
suppressed EGFR/AKT pathway transduction by decreasing the level of
membrane‐localized EGFR in this study. Some studies have reported that
ATO augments the antitumor effect of TMZ by inhibiting the prenylated
Ras‐mediated signaling pathway [[239]56]. These studies suggest that
statins may be involved in multiple crucial regulators of the EGFR
signaling pathway. In addition, statins‐induced decrease of EGFR on the
cell membrane could be alleviated by supplement of cholesterol
(Figure [240]5A), indicating that cholesterol‐dependent membrane
dynamics plays a vital role in EGFR/AKT pathway transduction. Given
that mitochondrial membranes are composed of phospholipid bilayers,
proteins, and cholesterol, statins treatment may affect the
mitochondrial translocation of EGFR/EGFR‐vIII, leading to mitochondrial
dysfunction.
It is reported that cancer‐derived succinate promotes macrophage
polarization and malignant progression of tumors [[241]57]. Our results
confirmed that triple drugs treatment increased the rate of M1/M2
macrophages (Figure [242]8D and F), possibly because of the decrease of
succinate levels caused by TCA cycle inhibition resulting from the
blockade of the EGFR/AKT pathway. In addition, adenosine is reported as
an important regulator produced by tumor cells and immune cells in
tumor microenvironments that impair T cell‐mediated antitumor response
[[243]58, [244]59]. CD39 and CD73, two ectonucleotidases expressed on
the cell surface to catalyze extracellular ATP to adenosine, have been
recognized as novel immune checkpoints that are involved in antitumor
immune response [[245]60]. The in vivo results found that combination
treatment elevated the infiltration of CD8^+ T cells in tumor tissues
(Figure [246]8D‐E), which might be due to the decreased level of
adenosine caused by ATP production inhibition. These interesting
hypotheses deserve further exploration and verification.
The antitumor effects of RTK pathway inhibitors have been verified in
various cancers. For example, the first‐generation TKI Lapatinib was
approved by the U.S. Food and Drug Administration (FDA) to treat
advanced breast cancer [[247]61]. The first‐generation EGFR‐TKIs
Erlotinib, Gefitinib, and the second‐generation inhibitor Afatinib have
been approved by FDA to administrate NSCLC patients [[248]62, [249]63,
[250]64]. In view of the vital roles of EGFR in GBM malignancy and
metabolic remodeling, EGFR‐TKIs should have potential inhibitory
effects on tumor growth, but the therapeutic effects of the
first‐generation TKIs in clinical trials of GBM have not been
satisfactory [[251]65, [252]66]. This may be associated with the poor
permeability of these drugs to the CNS [[253]67]. A case report has
shown that the third‐generation EGFR‐TKI OSI has a highly
brain‐penetrating ability and a good antitumor effect against GBM
[[254]68]. Consistently, we found that OSI enhanced the therapeutic
effect of TMZ in combination therapy. Therefore, combined therapeutic
strategies involving TMZ auxiliary to OSI and ATO could obtain a better
treatment outcome.
Our in vitro and in vivo results demonstrated that statins could
effectively inhibit energy production in GBM cells, and the inhibitory
effect was more potent upon prolonging the treatment time. The
morphology of statin‐treated GBM cells exhibited stark changes (data
not shown), indicating that the mechanism of action of statins was not
only limited to modulating energy metabolism and the TCA cycle but also
to the regulation of cholesterol‐mediated cell membrane homeostasis.
Statin‐based treatments suppressed membrane‐localized EGFR levels and
EGFR/AKT pathway transduction in GBM cells. Cholesterol is essential
for tumor survival. Recent studies have found that the major portion of
cholesterol in GBM cells relies on exogenous uptake, which is different
from that in normal brain tissues [[255]13, [256]17]. Therefore, the
ATO treatment also blocked the production and secretion of cholesterol
by neurons and astrocytes, thereby inhibiting the uptake by glioma
cells and facilitating the antitumor effect of ATO.
In addition, we found that a phase II clinical trial ([257]NCT02029573)
of ATO in combination with radiotherapy and TMZ for GBM was completed
with negative results [[258]69]. The study showed that the combination
of ATO and conventional therapy could not prolong the PFS of GBM
patients but found that high levels of LDLs were associated with poor
prognosis. Our research has differences from the above. In this study,
we first identified a metabolism‐associated signature for GBM sorting
through single‐cell sequencing analysis. The signature contained EGFR
and lipid metabolism pathway‐associated genes. GBMs with a high
signature score had a better effect on the combinational therapeutic
strategy. In addition, 9 GBM patients (of 60 patients in total)
included in this study had hypercholesterolemia and a high signature
score (Figure [259]9, Supplementary Figure [260]S26). They had to take
statin drugs every day, even before the first diagnosis of GBM. We
speculate that this would lay the metabolic foundation and provide the
sensitization condition for the follow‐up radiotherapy and chemotherapy
(Figure [261]10).
FIGURE 10.
FIGURE 10
[262]Open in a new tab
Schematic illustration depicting the mechanism of hyperactivation of
EGFR/AKT and mevalonate pathway promoting energy metabolism, leading to
malignant progression of GBM. Combinational therapeutic strategy of
TMZ, TKI, and statins benefited GBM patients.
Abbreviations: EGFR, epidermal growth factor receptor; AKT, AKT
serine/threonine kinase 1; GBM, glioblastoma; TMZ, temozolomide; TKI,
tyrosine kinase inhibitor.
This study has certain limitations which need to be carefully reviewed.
We demonstrated that blocking the EGFR/AKT and mevalonate pathways
could remodel tumor metabolic disorder and significantly inhibit GBM
growth in both in vitro and in vivo settings. However, due to the
clinical usage limitation of TKI, we were unable to collect samples
from GBM patients receiving TMZ + OSI + ATO treatment at the same time.
Therefore, whether this combinatorial therapeutic strategy can benefit
clinical GBM patients, in general, needs further clinical
investigations. Moreover, the liver damage caused by statins should
also be carefully considered, especially for long‐term treatment plans.
5. CONCLUSIONS
In summary, our study uncovered a regulatory mechanism by which
EGFR/AKT pathway activation promotes energy metabolism by upregulating
ACSS3, ACSL3, and ELOVL2 expressions in GBM cells. Inhibitors against
the EGFR/AKT pathway and cholesterol synthesis could reverse disease
phenotypes by regulating the expressions of fatty acid
metabolism‐associated genes and decreasing the level of
membrane‐localized EGFR. Furthermore, by employing multi‐omics
approaches to comprehensively analyze and utilize glioma data from
multiple databases, we established a metabolism‐associated RFA
signature based on the degree of malignancy and metabolic
characteristics of gliomas, which could predict the prognosis of
patients. Furthermore, the combinatorial therapeutic strategy of TMZ,
auxiliary EGFR‐TKI, and statins can benefit GBM patients with high RFA
scores by significantly prolonging their survival time.
DECLARATIONS
AUTHOR CONTRIBUTIONS
Chunsheng Kang, Chuan Fang, and Tao Jiang conceptualized and designed
the study. Xiaoteng Cui, Jixing Zhao, Guanzhang Li, Chao Yang, Shixue
Yang, Qi Zhan, Junhu Zhou, Yunfei Wang, Menglin Xiao, Biao Hong, Kaikai
Yi, Fei Tong, Yanli Tan, and Qixue Wang conducted the experiments. Hu
Wang, Tao Jiang, and Chuan Fang provided support with managing GBM
clinical samples. Xiaoteng Cui, Jixing Zhao, and Guanzhang Li analyzed
the data, generated the figures, and prepared the manuscript. All
authors approved the final version of the manuscript.
CONFLICT OF INTERESTS STATEMENT
The authors declare that there is no conflict of interest in this
study.
FUNDING INFORMTAION
This work was supported by grants from the National Natural Science
Foundation of China (82002657, 82073322, 81761168038), the Hebei
Natural Science Foundation Precision Medicine Joint Project
(H2020201206), the Tianjin Key R&D Plan of Tianjin Science and
Technology Plan Project (20YFZCSY00360), Brain Tumor Precision
Diagnosis and Treatment and Translational Medicine Innovation Unit,
Chinese Academy of Medical Sciences (2019‐I2M‐5‐021), the Science and
Technology Project of Tianjin Municipal Health Commission
(TJWJ2021QN003), Key‐Area Research and Development Program of Guangdong
Province (2023B1111020008), and Multi‐input Project by Natural Science
Foundation of Tianjin Municipal Science and Technology Commission
(21JCQNJC01250).
ETHICS APPROVAL AND CONSENT TO PARTICIPATE
Collection and analysis of all clinical GBM samples were approved by
the medical ethics committee of Beijing Tiantan Hospital (Approval No.
KY 2020‐093‐02) and Hebei University affiliated Hospital (Approval No.
HDFY‐LL‐2020‐017). All participants had signed the informed consents.
All animal experimentations were approved by the Animal Ethical and
Welfare Committee (Approval No. IACUC‐2020XS001).
CONSENT FOR PUBLICATION
Not applicable.
Supporting information
Supporting information
[263]Click here for additional data file.^ (13.1MB, docx)
Supporting information
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