Integrated omics-based pathway analyses uncover CYP epoxygenase-associated networks as theranostic targets for metastatic triple negative breast cancer
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Current prognostic tools and targeted therapeutic approaches have limited value for metastatic triple negative breast cancer (TNBC). Building upon current knowledge, we hypothesized that epoxyeicosatrienoic acids (EETs) and related CYP450 epoxygenases may have differential roles in breast cancer signaling, and better understanding of which may uncover potential directions for molecular stratification and personalized therapy for TNBC patients.
We analyzed the oxylipin metabolome of paired tumors and adjacent normal mammary tissues from patients with pathologically confirmed breast cancer (N = 62). We used multivariate statistical analysis to identify important metabolite contributors and to determine the predictive power of tumor tissue metabolite clustering. In vitro functional assays using a panel of breast cancer cell lines were carried out to further confirm the crucial roles of endogenous and exogenous EETs in the metastasis transformation of TNBC cells. Deregulation of associated downstream signaling networks associated with EETs/CYPs was established using transcriptomics datasets from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Comparative TNBC proteomics using the same tissue specimens subjected to oxylipin metabolomics analysis was used as validation set.
Metabolite-by-metabolite comparison, tumor immunoreactivity, and gene expression analyses showed that CYP epoxygenases and arachidonic acid-epoxygenation products, EET metabolites, are strongly associated with TNBC metastasis. Notably, all the 4 EET isomers (5,6-, 8,9-, 11,12-, and 14,15-EET) was observed to profoundly drive the metastasis transformation of mesenchymal-like TNBC cells among the TNBC (basal- and mesenchymal-like), HER2-overexpressing and luminal breast cancer cell lines examined. Our pathway analysis revealed that, in hormone-positive breast cancer subtype, CYP epoxygenase overexpression is more related to immune cell-associated signaling, while EET-mediated Myc, Ras, MAPK, EGFR, HIF-1α, and NOD1/2 signaling are the molecular vulnerabilities of metastatic CYP epoxygenase-overexpressing TNBC tumors.
This study suggests that categorizing breast tumors according to their EET metabolite ratio classifiers and CYP epoxygenase profiles may be useful for prognostic and therapeutic assessment. Modulation of CYP epoxygenase and EET-mediated signaling networks may offer an effective approach for personalized treatment of breast cancer, and may be an effective intervention option for metastatic TNBC patients.
KeywordsCYP450 epoxygenase Epoxyeicosatrienoic acid Metastasis Oxylipin metabolome Triple negative breast cancer
Ductal carcinoma in situ
Epidermal Growth Factor Receptor
False discovery rate
Human epidermal growth factor receptor
Isobaric tags for relative and absolute quantitation
Kyoto Encyclopedia of Genes and Genome
Mitogen-activated protein kinase
Molecular Taxonomy of Breast Cancer International Consortium
Molecular Signatures Database
- NOD 1/2
Nucleotide-binding oligomerization domain-containing protein 1
Network Topology-based Analysis
Over Representation Analysis
Prediction Analysis of Microarray 50
Pathway Interaction Database
PLS Discriminant Analysis
Soluble epoxide hydrolase
The Cancer Genome Atlas
Triple negative breast cancer
Ultra-performance liquid chromatography- tandem mass spectrometry
WEB-based GEne SeT AnaLysis Toolkit
Molecular subtyping and immunohistopathological (IHC) examination of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor (HER2) status serve as frontline guides for clinical decision-making in breast cancer (BC) therapy [1, 2]. Current therapeutic approaches, however, are still ineffective for triple negative breast cancer (TNBC, ER−/PR−/HER2−) patients, which account for approximately 15% of all invasive mammary tumors [3, 4]. Currently, IHC analysis and gene expression profiling are the methods used for sub-classification of “TNBC” tumors, which may include basal-like 1, basal-like 2, immunomodulatory, mesenchymal, mesenchymal-stem-like, and luminal androgen receptor-positive clusters [5, 6, 7, 8]. However, this sub-classification scheme is ambiguous and still has limited translational applications [8, 9]. Although systemic chemotherapy may attenuate the aggressive nature of the disease, most TNBC patients have a disproportionally high tendency to develop rapid onset visceral and distal metastasis, recurrence, and decreased survival rate [6, 7, 8]. Better understanding of molecular signatures for TNBC is thus needed to identify prognostic markers for clinical outcome prediction and to develop suitable personalized medicine for TNBC patients.
In recent years, the development of analytical techniques for genomics, proteomics and metabolomics analyses has provided a more global insight into the molecular biology of TNBC . For example, gene expression profiling helps stratify molecular features of TNBC tumors [10, 11, 12], methylome sequencing data has highlighted the prognostic value of epigenetic changes  and global metabolomics and proteomics analyses correlate decreased citrate, increased sarcosine and 2-hydroxyglutarate levels, and differentiated long-chain fatty acid metabolism-related proteins with the different BC subtypes [14, 15]. However, information derived from individual omics data types may be constrained to the discovery of less inclusive molecular signatures with limited translational applications. Integrating biological information derived from cohorts of multiple level high resolution molecular analysis, e.g., The Cancer Genome Atlas (TCGA), with multi-omics, cross-platform data comparison may ultimately lead to refinement of existing diagnostic classification and therapeutic options for TNBC.
Recent lipidomics findings show that phospholipids and membrane-derived mediators are the main contributors to the aggressive and metastatic phenotype of ER or PR negative tumors . These lipid autocoids influence inter- and intracellular metabolic and inflammatory signaling, which are two important cancer hallmarks [17, 18]. Among these lipid products, eicosanoids, a group of metabolites derived from oxidative transformation of arachidonic acid (AA) by cyclooxygenase (COX), lipoxygenase (LOX) or cytochrome P450 (CYP) enzymes may have a major impact on malignant cell transformation and TNBC metastasis progression . While the cancer-associated eicosanoids derived from COX and LOX, e.g., prostaglandin E2 (PGE2) and hydroxyeicosatetraenoic acids (HETEs), are well-established, the roles of CYP-derived mediators are more enigmatic [20, 21]. Aside from the xenobiotic metabolizing CYPs primarily found in the liver, which are important in drug metabolism and resistance, the pro-metastatic, angiogenic and anti-apoptotic functions of extrahepatic CYPs (the CYP2J and CYP2C families) are of interest in cancer research [22, 23, 24, 25].
CYP2J/2C catalyzes conversion of AA to 4 regioisomeric epoxyeicosatrienoic acids, viz., 5,6-EET, 8,9-EET, 11,12-EET and 14,15-EET . Several studies utilizing in vitro and in vivo models have highlighted the relationship between specific EET (e.g., 14,15-EET) signaling and breast cancer progression [23, 26, 27, 28]. Expression of CYP2C8/9 and CYP2J2, and/or high level of 14,15-EET detected in patients were found to be positively correlated with aggressiveness of human BC , or that might be involved in breast cancer cell epithelial-mesenchymal transition and cisplatin resistance . The biological significance of these findings and their translational implications, however, are still elusive because information on the global and comprehensive effects of all EET metabolites, alone or in combination, on breast cancer metastasis have not been closely examined. We hypothesized that isomers of this endogenous EET metabolite class are key players in regulating the multi-layered network signaling in breast cancer. In particular, we are interested in exploring the roles of EETs in hormone-independent signaling processes in TNBC.
In this study, we examined the global oxylipin metabolite profiles of breast cancer tumors and show the importance of EET metabolites in relation to hormone receptor subtype, metastasis status and prospective clinical outcomes. We used information derived from integrative genomics, proteomics and metabolomics coupled with multi-source genetic, cellular and tissue analyses from public cohorts, i.e., The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data sets, or human BC cells or patient specimens. Our findings expose a new set of vulnerabilities and associated biomarkers within the TNBC lipid metabolite-protein-gene network and provide a clinically-relevant roadmap for the development of personalized intervention strategies for TNBC patients.
All chemicals and solvents were used as purchased. Oxylipin standards were purchased from Cayman Chemicals (Ann Arbor, MI). Antibodies used for western blotting and immunohistochemical staining were: FAK, p-FAK, Src, p-Src, EGFR, and p-EGFR obtained from Cell Signaling Technology (Beverly, MA); and CYP2B, CYP2C9/19, CYP2J, CYP3A, and flotillin-1 purchased from Santa Cruz (Santa Cruz, CA).
Human-derived TNBC (MBA-MB-231, Hs 578 T, MDA-MB-468, HCC1937 and BT-20), ER+/PR+/HER2- (MCF7, ZR75–1 and BT483), and HER2-overexpressing (SKBR3, MDA-MB-361 and BT474) cell lines and normal mammary epithelial cells (MCF10A) were all originally obtained from the American Type Culture Collection (Manassas, VA). Hs 578 T and MDA-MB-468 were gifts from Dr. Ruey-Hwa Chen of the Institute of Biological Chemistry, Academia Sinica, Taiwan. HCC1937, BT-20, ZR75-1, BT483, MDA-MB-361 and BT474 are obtained from Dr. Wen-Hua Lee, Genomics Research Center, Academia Sinica, Taiwan.
Functional experiments in human mammary cancer cell lines
MTT assay was performed to assess cell viability. Expression levels of proteins of interest were analyzed through western blotting. Transwell migration and invasion experiments were performed to measure the metastatic potential of cells. Migration experiment was carried out as follows: cells were placed into the upper chamber (5 × 104 cells per insert) with DMEM culture medium containing 0.1% FBS. Ten percent FBS was added to the lower well (6.5 mm diameter, 8 mm pore size; Costar, Cambridge, MA) as a chemoattractant. Cells were then treated with vehicle (DMSO, 0.5%) or inhibitors. After 24 h incubation, non-migrating cells were scraped from the upper surface of the membrane using a cotton swab. Cells remaining on the underside were fixed and stained with DAPI solution (1 mg/ml) and counted at 20x original magnification by inverted fluorescence microscopy. For invasion experiments, 8 mm filters were pre-coated with Matrigel (30 mg/filter) and incubated at 37 °C for 2 h, prior to following the protocol of migration assay. Three-dimensional culture models of normal and malignant mammary cells were established based on previously published protocols . A Zeiss LSM 780 plus Elyra confocal microscope was used to visualize and capture the stained 3D acini.
Gene knockdown using the shRNA technique
The shRNA clones were purchased from the National RNAi Core Facility Academia Sinica (Taiwan). shRNA sequences and target genes used are: CYP2C19: 5′-CGGCCCTGTGTTCACTCTGTATTTCTCGAGAAATACAGAGTGAACACAGGGTTTTTG-3′; CYP4A2: 5′-CCGGCTTGCTCTCCCAGGATCAATTCTCGAGAATTGATCCTGGGAGAGCAAGTTTTTG-3′; LacZ (control): 5′-CCGGCCGTCATAGCGATAACGAGTTCTCGAGAACTCGTTATCGCTATGACGGTTTTTG-3′. Gene and protein expression levels of target genes were analyzed by qRT-PCR and western blotting.
Real-time quantitative PCR
For real time PCR, total RNA was isolated using RNeasy Mini Kit (Quiagen). cDNA was generated by reverse transcription of RNA aliquots using the Takara PrimeScript RT Reagent Kit (Takara) according to the manufacturer’s instruction. The resulting cDNA was used for real-time PCR with SYBR® Premix Ex Taq™ Kit (Takara) in a StepOne Real-Time PCR Detection System (Life Technologies). All expression data were normalized to GADPH-encoding transcript levels. Primers used for real-time PCR are as follows: CYP2C19: (F:5′-CTTCTGTCCCGCCCTTCTATC-3′) (R:5′-GATAGTGAAATTTGGACCAGAGGA-3′); CYP2J2: (F:5′-GAAGGGCTTAGAGGAACGCA-3′) (R:5′- AGCGTTCTCCGAAGGTGATG-3′); sEH: (F:5′-TGCCCAGAGGACTTCTGAATG-3′) (R:5′-TTGGGGAGGCAGACTTTAGC-3′); GADPH: (F:5′-AGGGCTGCTTTTAACTCTGGT-3′) (R: 5′-CCCCACTTGATTTTGGAGGGA-3′).
Four data sets were used in this study: a data set from our own group, used for bioactive lipid mediator-targeted metabolomics and proteomics analysis; an mRNA microarray data set from TCGA breast cancer specimens and a gene expression data set from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (downloaded from cBioportal), used for pathway deregulation analysis. We selected systemically untreated patients from the following GEO data sets (GSE19615, GSE21653, GSE2603, GSE31519, GSE45255, GSE17907, GSE20271, GSE37946, GSE19615, GSE2603) from the online tool kmplotter  to generate survival plots correlated with expression of EET metabolizing enzymes. Our metabolomics cohort is composed of 62 paired tissue specimens derived from breast tumors and normal mammary tissues obtained during the same surgical procedure in patients. The specimens with mammary lesions were diagnosed at the National Defense Medical Center (NDMC), Taipei, Taiwan. This study was approved by the institutional review boards of the NDMC (IRB number: TSGHIRB-099-05-058). All participants signed an informed consent. Additionally, we downloaded and processed TCGA breast cancer mRNA expression data from 2007 tumors and relevant adjacent normal tissue controls from the TCGA data portal (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/?redirect=true) using the R package “TCGA-Assembler.” Normalized gene expression data from 1981 patients from the METABRIC cohort were also analyzed. Data from the TCGA and cBioportal were accessed and retrieved in November 2018.
Data set configurations for model training, validation, and testing
We used 53 of the specimens for metabolomics analysis as discovery and the remaining 9 as a validation data set. TCGA mRNA expression data (Agilent microarray) was used as a training set for pathway deregulation analysis while METABRIC was used for validation and testing. There was no sample overlap between the discovery/training and validation/test sets. Proteomics data derived from 8 TNBC samples used in the metabolomics study were used to test applicability of TNBC subtyping from PDS scores.
Collection, storage and handling of mammary tissue specimens
Tissue specimens from patients were flash frozen in liquid N2 and stored at -80 °C for long-term storage. Noncancerous mammary tissues were collected at least 5 cm away from the margins of tumors and were defined as normal breast tissues present without ductal carcinoma in situ (DCIS), atypical hyperplasia or benign breast disease by pathological confirmation. Normal tissue samples were selected to contain 80–100% normal ductal or peri-ductal tissue and only 0–20% adipose tissue, to ensure comparability with the tumor samples. Clinicopathological information for the tumors including treatment modalities or expression of hormone receptors and HER2 was obtained from the review of medical records and pathology reports. Tumors with ≥1% nuclear-stained cells by IHC were considered positive for estrogen receptor (ER) and progesterone receptor according to the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guidelines. HER2, ER and PR immunostaining were scored as 0, 1+, 2+, or 3+ according to ASCO/CAP guidelines.
Oxylipin metabolome analysis
Levels of bioactive lipid mediators in quiescent cells and in frozen tumor and normal mammary samples were determined using an optimized an ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) protocol with minor modifications . Only tissue samples containing ≤20% adipose tissue upon histopathological quality control were included in the metabolomics analysis. Liquid-liquid extraction on 100–200 mg tissue samples was conducted using chloroform:methanol (2:1), vortexed for 2 min, incubated for 30 min at 4 °C and centrifuged at 13000×g for 10 min. Lipid extracts were analyzed on UPLC-MS/MS system (ACQUITY UPLC, Waters) coupled with a TSQ Quantum Access Max (Thermo Fisher Scientific, USA) triple quadrupole mass spectrometer, operated in a negative MRM mode and fitted with an ACQUITY UPLC HSS BEH column (particle size 1.8 μm, 2.1 × 100 mm, Waters) at 400 μl/min flow rate using a 25 min gradient for analysis. Mobile phase A consisted of 0.1% NH4OH in water and mobile phase B consisted of 0.1% NH4OH in MeOH. The chromatogram acquisition, detection of mass spectral peaks and waveform processing were performed using ThermoXcalibur 2.1 SP1 software (Thermo Scientific). The calibration curve and quantification were performed using LCQuan 2.6.1 software (Thermo Scientific). The peak area of each quantified ion was calculated and normalized against the peak area of the corresponding internal standards. Coefficient of variation (CV), < 15% for five technical replicates and < 30% for each biological sample, respectively, was used to assess data inclusion.
Mass spectrometry (MS)-based quantitative proteomic analysis of clinical TNBC specimens
Total protein extracts from 8 clinical TNBC tumor and adjacent normal tissue specimens were obtained using dual lysis buffer system as previously reported . Protein extracts from combining both lysis buffers were precipitated by adding 6 volumes of cold acetone containing 10% trichloroacetic acid (TCA) at − 20 °C overnight, and then pelleted through centrifugation at 13,000×g for 15 min at 4 °C and air-dried. The protein pellet was dissolved with 8 M urea in 50 mM Tris buffer (pH 8.5), and the protein concentrations were measured by Pierce 660 nm protein assay (Thermo Scientific, Rockford, USA). The protein digestion, isobaric tags for relative and absolute quantification (iTRAQ) labeling, proteolytic peptide fractionation and LC-MS/MS analysis, and protein identification or quantification were carried out according to the method previously described. The 8 TNBC tumor and adjacent normal tissue specimens in this study were divided into two groups, TNBC-1 to 4 and TNBC-5 to 8, and each group was labeled with 8-plex iTRAQ reagent (AB SCIEX, Foster City, CA). Peptide and protein identification was performed using the Proteome Discoverer software (v.188.8.131.52., Thermo Fisher Scientific) with SEQUEST and MASCOT search algorithms (Matrix Science) against a Swiss-Prot human protein database of Human uniprot 148,986 entries. The parameters for database searches were set as follows: full trypsin digestion with 2 maximum missed cleavage sites, precursor mass tolerance of 10 ppm, fragment mass tolerance of 0.02 Da, dynamic modifications of oxidation at methionine (M) residues, and static modifications of carbamidomethylation at cysteine (C) residues, iTRAQ 8-plex at lysine residues and N-terminal proteolytic peptides. The identified peptides were validated using Percolator algorithm against the decoy database search which rescored peptide spectrum matches (PSM) by q-values and posterior error probabilities. All the peptides were filtered with the identified protein having a minimum of two unique peptides. For normalization of iTRAQ-labeled peptide ratios, Proteome Discoverer software (v.184.108.40.206., Thermo Fisher Scientific) contains the normalization factor to correct experimental bias. For quantitative analysis, the relative abundance of each protein present in two biological replicates was calculated based on the iTRAQ reporter ion ratios of 115/114 and 116/114 found at the peptide level.
IHC was performed using whole sections of formalin-fixed, paraffin-embedded tissue block (N-Histofine® Simple Stain AP, Nichirei Biosciences, Tokyo, Japan). Color developing was done using 3,3′-diaminobenzidine and slides were counterstained with hematoxylin. The primary antibody incubation step was omitted in the negative control. Images were taken using Zeiss Axioimager Z1 and processed using Carl Zeiss ZEN software 11. Automated scoring was conducted using IHC Profiler; an Image J plugin was used for quantitative analysis of immunoreactivity of tumor tissues against CYP2J2, CYP2C19, CYP3A4 and sEH antibodies. Percentile score of negative/weak positive, positive, and strongly positive DAB-stained cytoplasmic zones were calculated using a pixel-by-pixel scoring analysis along the whole image profile .
In silico association of related enzymes to receptor status and survival
Survival analysis for both the TCGA and METABRIC datasets was performed using the R package “survival.” Patient follow-up time was limited to 5 years, and only breast cancer-related deaths were counted. The probability of overall survival of systematically untreated patients based on CYP450 epoxygenase expressions for validation using independent cohorts was calculated using the Kaplan-Meier plotter database (http://kmplot.com/analysis/index.php?p=service&cancer=breast) accessed in January 2018. Affymetrix probe set IDs selected for the evaluation of CYP2B6, CYP2C18, EPHX3, CYP4A2, CYP2J and CYP3A4 were 219825_at, 215103_at, 220013_at, 206514_s_at, 205073_at, 205998_x_at. Hazard ratio with 95% confidence interval and log-rank P value were calculated and reported as displayed on the webpage. All other parameters were set to default.
Network association analysis
To correlate metabolomics data with protein and gene network relationships in TNBC, we first performed a single point statistical test using Wilcoxon signed-rank test on metabolites of interest and corresponding CYP enzymes (WSRT, pFDR < 0.05). Using cBioportal, we used gene enrichment analysis of TCGA and METABRIC datasets to identify top enriched and co-expressed genes. The list of CYP epoxygenase amplification-associated genes was generated by performing differential gene-expression analysis using a standard linear modeling procedure. Generated P values were corrected for multiple testing by controlling the false discovery rate (FDR) across genes using the Benjamini and Hochberg correction and by adopting the nested F correction across contrasts. Genes were grouped according to gene ontology (GO) molecular functions and biological processes to construct a comprehensive cancer-associated sub-network. Pathway and network enrichment analysis (with Bonferroni corrections) between metabolite, biosynthetic enzyme and related genes and biological processes were then performed using Metscape 3.1 App for Cytoscape 3.5.
Assembly of pathway associated gene sets and pathway deregulation analysis
Gene sets were imported from three pathway databases, KEGG, BioCarta and Reactome, downloaded from the MSigDB collections. The oncogenic signature and cancer hallmark signature gene set collections, also from MsigDB were included. A total of 186 KEGG, 217 BioCarta, 674 Reactome, 50 hallmark and 189 oncogenic signature pathways were analyzed. Gene identity was established according to their official gene symbols.
We used the R package “Pathifier” to perform pathway-based gene set analysis. The information derived from Pathifier was related to metabolite pathways and molecular functions, e.g., metastasis and proliferation, as well as characteristics of individual breast tumors, receptor subtype and status of CYP epoxygenase expression. This algorithm transforms gene-level information from curated data sets to pathway-level deregulation scores .
Functional enrichment analysis
WEB-based GEne SeT AnaLysis Toolkit  or STRING , a functional protein association tool were used to analyze and visualize protein networks. For the functional enrichment analysis, we relied on the overrepresentation enrichment analysis (ORA) and on the Network Topology-based Analysis functionalities. The P values for this analysis were adjusted with the Holm-Bonferroni method and the false discovery rates were reported. The gene list enrichment analysis was conducted according to all known human genes or against RNAseq data curated from TCGA–BRCA, applying a correction for multiple testing.
Data processing and statistical analysis of metabolomics data
Multivariate data analysis was carried out using SIMCA-P 11.0 software (Umetrics, SE). Metabolites included in the statistical analyses were those which were consistently detected in at least 80% of samples. All known artifact peaks, such as internal standards, column bleed, plasticizers, or reagent peaks, were excluded. Tissue type and receptor statuses were used as grouping variables for visualization of data. Fisher’s linear discriminant analysis (LDA) and linear support vector machines (SVM) were used to construct predictive signatures. The robustness of the classifier models were validated in a built-in iterative 7-fold leave-one-out approach. The resulting distributions of average prediction rates were visualized as tables. Univariate analyses and Euclidian hierarchical classification were carried out without replacement of missing data.
Data and code availability
Sample information and mRNA datasets for both the TCGA and METABRIC breast cancer specimens were retrieved from https://portal.gdc.cancer.gov/ and http://www.cbioportal.org/. Survival data for independent datasets were downloaded from http://kmplot.com/analysis. Codes used in this study were adopted from https://github.com/compgenome365/TCGA-Assembler-2 for TCGA Assembler, and https://bioconductor.org/packages/release/bioc/html/pathifier.html for Pathifier analysis. http://www.webgestalt.org/ was accessed as an online tool for the identification of subtype-specific pathways and over representation analysis (ORA) and network topology-based analysis (NTA A summary of publicly available information and websites used in this study is presented in Additional file 1: Table S1.
Oxylipin metabolome profiling differentiates normal and tumor tissues and stratifies BC subtypes based on receptor status
Clustering of tissue samples and signature metabolite contributors in the cross-validated score and loading plots were generated using supervised multivariate statistical analysis partial least squares-discriminant analysis (PLS-DA) (Fig. 1b). These results establish the association between deregulated oxylipin metabolism and mammary malignancy in paired tissue samples. We further showed that oxylipin metabolites differentiate breast cancer tumor subtypes (Fig. 1c). Remarkably, TNBC tumors were clustered separately from other groups in the score plot. As demonstrated in the loading and coefficients plots, tumor tissue classification is mainly contributed by the elevated 5,6-EET, 8,9-EET, 11,12-EET and 14,15-EET concentrations in TNBC tumors. This method correctly classified 57 out of 62 tumors based on receptor subtype, leading to a prediction accuracy of 91.9%.
The summation of oxylipin concentrations grouped by the specific catalytic enzyme class is presented in Fig. 1d. TNBC tumors are characterized by high levels of CYP epoxygenase metabolites, a unique and significant feature, compared to the other tumor subtypes. CYP hydroxylase and sEH metabolites are most significantly elevated in ER−/PR−/HER2+ tumors, while there is no statistical difference among the metabolites derived from the six catalytic enzymes in ER+/PR+/HER2− tumors and TPBC tumors. Concentration levels of CYP-derived hydroxyl and epoxy metabolites were significantly lower and sEH and 12/15-LOX metabolites were significantly higher in adjacent normal tissues than the tumor tissues.
EET metabolite levels corroborate with gene expression and immunoreactivity of CYP epoxygenases in TNBC tissues
To establish mRNA expression or tissue immunoreactivity as a proxy to metabolomics measurements, we measured the gene or protein expression levels of corresponding CYP epoxygenases in the tumor tissues. We performed qRT-PCR to compare the relative gene expressions of major EET-producing CYP epoxygenases (CYP2J2 and CYP2C19) [39, 40] and EET-metabolizing epoxide hydrolase (sEH) in the corresponding frozen tissue sections of the paired tissue samples subjected to oxylipin metabolomics analyses (Additional file 2: Figure S1). We observed a 2- to 10-fold increase in the gene expression of CYP2C19 and CYP2J2 in the triple negative mammary tumor tissues compared with the paired normal tissues. In the other subtypes, the mean difference in gene expression between paired tissue specimens was only between 1.5 and 3-fold. mRNA levels of sEH did not significantly differ between tumor and normal tissues in any of the BC specimens examined.
To obtain a more clinically relevant measure of protein expression levels, we randomly selected clinical paraffin-embedded tumor samples representative of all the breast cancer subtypes and subjected them to IHC analysis against CYP2C19/9, CYP2J2, CYP3A4 and sEH antibodies. We analyzed a total of 27 TNBC and 28 hormone receptor positive tissue specimens to determine whether the immunoreactivity can be generalized across a panel of tumor tissues collected within the same period. We assigned a three-tier scoring system for cytoplasmic stained cells in all the tissues using the automated IHC profiler plugin in ImageJ . This tool utilizes a pixel-by-pixel color intensity profiling algorithm which can divide the whole tissue image equally into three staining intensity zones (strong positive, positive, and weak positive/negative) based on the arbitrary color intensity relative to the unstained background and the darkest positive DAB-stained regions. Percentile distribution for each zone is calculated for each tissue image. The tumors are classified as strong positive, positive, or weak positive/negative if the percentile score for the corresponding zone is equal to or greater than 33%. Four representative IHC images for individual CYP2J2, CYP2C9/19 and sEH enzymes, with corresponding tumor subtype and tissue classification are presented in Fig. 2c. The average percentile scores per subtype are quantified in Fig. 2d. The quantified IHC data showed that the TNBC specimens may be classified as strongly positive against CYP2J2 (58%) and strong positive/positive against CYP2C9/19 (57% strong positive and 33% positive) antibodies, respectively, which are in good agreement with the high level of EETs and CYP expoxygenase/sEH gene overexpression detected in the same tumor specimen. All ER+/PR+ specimens have weak positive/negative scores (60%) for both CYP2C9/9 and CYP2J2. HER2+ tumor tissues are classified positive or weak positive/negative against CYP2J2 (33% positive and 45% weak positive/negative), and weak positive/negative against CYPC9/19 (40%). TPBC tumors are classified weak positive/negative against CYP2J2 (64%), and positive or weak positive/negative against CYPC9/19 (49% weak positive/negative and 35% positive). Interestingly, sEH scores for ER+/PR+, HER2+, and TPBC specimens are classified as positive (46%), positive (42%) and strong positive (33%), and positive (39%), respectively, while TNBC specimens are classified as 54% weak positive/negative. These data suggest that the IHC results of CYP epoxygenase and sEH may be useful as a tool for subtyping hormone positive vs. negative BC tumors. All tissue blocks, regardless of the receptor subtype, have 31–34% positive and 43–56% weak positive/negative immunoreactivity against CYP3A4 (Additional file 2: Figure S2).
To further probe the relationship between EET metabolites, and the gene and protein expression levels of CYP2C19 and CYP2J, either detected in frozen or paraffin-embedded tumor tissues, we assembled and compared the data from the tumor samples with corresponding metabolomics, gene expression and IHC scores. Four specimens for TNBC (a), 6 specimens for ER+/PR+/Her2– (b), 5 specimens for HER2-overexpressing (c), and 5 specimens for TPBC (d) were compared (Additional file 2: Figure S3). TNBC tumors indeed had higher CYP epoxygenase mRNA and protein expression levels than the hormone positive tumors, which were reflected in the parallel elevation of EET metabolite levels. These results demonstrate that gene expression or IHC analysis of both CYP epoxygenases may be used as surrogates to explore the roles of EET signaling in breast cancers, especially in TNBC.
EET metabolite levels are significantly elevated in human-derived mesenchymal-like TNBC cells
Depletion of endogenous CYP epoxygenases attenuates the metastatic phenotype of mesenchymal-like TNBC cells
Expression levels of several metastasis and stem-cell related proteins characteristically overexpressed in mesenchymal-like/basal B TNBC cells were further examined using Western blot analysis. As shown in Fig. 4d, phosphorylation of integrin regulated tyrosine kinases (EGFR, Src) and expression of a major metastatic membrane remodeling marker (Cav-1) were reduced in CYP2C19-depleted (shCYP2C19/9) MDA-MB-231 cells compared with the lacZ control. On the other hand, 17-ODYA-treated MDA-MB-231 cells had lower expression of the membrane stemness marker, CD44, two important membrane remodeling markers (Cav-1 and Rac1) and phosphorylation of integrin regulated tyrosine kinases (EGFR and FAK). Expression levels of these proteins were unaffected by CYP4A2-depletion and HET0016-treatment, while AUDA slightly increased expression of these protein markers. These results highlight that expression of CYP2C19/9 or inhibition of its activity is correlated with the expression of metastasis-related proteins in TNBC cells. Parallel immunoblotting results of basal A TNBC cell line (HCC1937) showed that expression levels of these proteins were not significantly affected in CYP2C19-depleted or CYP-inhibitor treated cells (Fig. 4e). Though it has been shown previously that 14,15-EET induced breast cancer cell EMT and drug resistance via activation of the integrin/FAK signaling axes , our findings pinpoint that CYP epoxygenase expression and all the 4 EET isomers are more relevant to the mesenchymal-like TNBC subtype. Targeting the expression or activity of CYP epoxygenases may be more effective to attenuate the metastasis burden for this TNBC subtype.
EETs selectively promote the metastatic phenotype in mesenchymal-like TNBC cells
We then investigated whether these effects may be generalized for all the TNBC cell lines by comparing the invasion and migration potential of cells treated with individual EET isomers at 2.5 nM, 10 nM of EETs adding 2.5 nM of each isomer, 14,15-EEZE (a putative EET receptor antagonist) at 100 nM, and cells treated with 10 nM of EETs and 100 nM 14,5-EEZE in combination (Fig. 5d). Cells cultured in media without exogenous EETs were used as a negative control. Of note, migration and invasion potential of HER2 overexpressing and luminal cell lines were not affected by any of the treatments. The metastatic potential of MDA-MB-468 and HCC1937 were not significantly affected by any of these treatments either, while addition of 10 nM EETs significantly induced migration and invasion in MDA-MB-231 and Hs578T cells. Addition of 100 nM 14,15-EEZE abolished the effect of pro-migratory and invasion-inductive effects of 10 nM EETs in both cell lines. These results recapitulate the potential of inhibiting either the enzymatic production of EETs or its receptor(s) as a means for attenuating downstream signaling pathways contributing to the aggressive nature TNBC subtypes with mesenchymal-like signatures.
CYP-epoxygenase expression correlates with decreased overall survival in TNBC patients
To further explore the wider clinical implications of EETs in TNBC pathology, we analyzed the correlation between mRNA expressions of EET metabolic enzymes and patient survival in silico. Kaplan–Meier analysis using data downloaded from kmplotter.com  showed a correlation between overexpression of major CYP epoxygenases (CYP2J2 and CYP2C19) and CYP hydroxylases (CYP2B6 and CYP4A2), CYP epoxygenase with preference for xenobiotic compounds (CYP3A4), epoxide hydrolase (EPHX3) and TNBC patient survival (Additional file 2: Figure S5). A strong association between decreased survival and overexpression of CYP2J2 (red line) (P = 0.0007) in TNBC patients was seen, while a weak or inverse association was observed for other receptor positive subtypes. Overexpression of CYP2C19 in TNBC patients showed a less pronounced correlation to survival rate (P = 0.1295). Interestingly, patients with CYP2C19-overexpressing ER+/PR+/HER2– or ER−/PR−/HER2+ tumors had better survival prospects (P = 0.0025 and 0.0224). An inverse relationship (P = 0.0123) was observed for epoxide hydrolase (EPHX3), an epoxide hydrolase isoform mainly responsible for the formation of DHETs from the substrate EETs, and survival in TNBC patients but not with the other subtypes. Expression levels of CYP2B6, CYP3A4, and CYP4A2, were not significantly associated with patient survival. These results indicate that expression level of CYP epoxygenase CY2J2 is the most relevant to TNBC patient survival.
Network association analysis highlights convergence between EET biosynthesis and expression of metastasis-related genes in TNBC
To elucidate whether EET metabolite levels, CYP epoxygenase overexpression or sEH downregulation are involved in regulating unique pathways associated with or independent from hormone-related signaling in the different breast cancer subtypes, we constructed a discovery set comprising of mRNA microarray profiles from 3 non-overlapping BC cohorts from the TCGA database [46, 47, 48]. Pre-processed data from the METABRIC was also downloaded from cBioportal and used as an independent validation set [48, 49]. For both data sets, we only included specimens with complete receptor subgroup information based on PAM50 (Prediction Analysis of Microarray 50, Prosigna) profiles, IHC-based hormone receptor signatures, and metastasis status. The numbers of specimens per BC subtype in the discovery and validation cohorts are summarized in Additional file 1: Table S4. In Fig. 6d, we show the population breakdown of the discovery set with CYP2J2 and CYP2C19 upregulation and corresponding EPHX3 downregulation. Notably, 50% of all metastatic TNBC and 27% of the overall patient cohort had such molecular features. This observation prompted us to analyze the gene enrichment associations for the TNBC specimens in the discovery set with mRNA expression z score of ≥2.0 for CYP2J2 and CYP2C19. A comparison of P values (P < 0.05) derived from student t-test and q values derived from Benjamini-Hochberg procedure false discovery rate (FDR < 0.01) were used to identify significantly enriched (overexpressed) genes based on gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Gene sets were grouped according to oncogenic signatures, cancer gene neighborhoods or gene profile modules associated with a variety of cancer processes as curated in the Broad Institute’s Molecular Signatures Database (MSigDB). Interesting relationships between the hallmarks of breast tumor growth and invasiveness were observed, highlighting that the genes related with activation of integrin and EMT signaling, e.g., Src, AKT, and cascades related with activation of transcription factors PPAR and MYC are concominantly upregulated in TNBC tissues with CYP epoxygenase upregulation (Additional file 3). The 10 most significantly enriched biological processes and associated unique genes are: metastasis (1233), adipocyte signaling (719), proliferation (480), transfer proteins (172), cytoskeleton remodeling (334), locomotion (214), fatty acid uptake and transport (222), adhesion (130), drug resistance (91) and redox signaling (104). The circle map of the biological network relationships between the 10 cancer relevant signaling and biological processes for the TNBC subpopulation is presented in Fig. 6e. Similar analyses were performed for the other subpopulations (TPBC, HER2-overexpressing, ER+/PR+/HER2–) in the discovery set with mRNA expression z score of ≥2.0 for CYP2J2 and CYP2C19 (Additional file 2: Figure S6 and Additional file 4). Relationships between the gene sets in these subtypes, however, cover a wider range of biological and molecular processes, e.g., nucleic acid signaling, developmental processes and membrane trafficking, compared with the TNBC subpopulation, which were more related with metastasis and fatty acid signaling and transport. Results from this set of analyses suggest that in TNBC, CYP epoxygenase upregulation and downstream EET metabolite production and signaling may drive metastasis-related processes, while this gene-metabolite signaling axis may be involved in facilitating other distinct processes in hormone signaling-dependent breast cancers.
Upregulation of CYP epoxygenase signaling is the strongest predictor of several cancer hallmarks in breast cancer subtypes
We then ranked the PDS according to the P values and FDR scores. The summary of significantly upregulated pathways for each breast cancer subtype, both for the discovery (TCGA) and validation data sets (METABRIC) are listed in Additional file 1: Tables S5-S7. We uncovered commonly upregulated pathways in tumors overexpressing CYP epoxygenases regardless of hormone receptor status. These pathways are related to metabolic signaling cascades including FA beta-oxidation, mitochondrial oxidative phosphorylation and activation of glycolysis. Canonical DNA and cell fate-related events including MAPK downstream activation, cell cycle checkpoints, RNA POL II/III transcription, maintenance of chromosome integrity, as well as NOTCH, hedgehog and c-Myc transcription factor signaling were also common features among all CYP overexpressing tumors. We think that these findings are related to the known roles of lipid mediators (EETs) in the activation of transcription factor families involved in cancer cell proliferation, as well as their roles as substrates in the oxidative phosphorylation signaling cascades.
Additional file 1: Table S5 lists the 50 significantly upregulated pathways in ER−/PR−/HER2+ tumor specimens (29 tumor specimens), with the most significant pathways related to HER2 and immune-related signaling. These include pathways involved in regulation of interferon-γ (IFNG), nuclear factor-κB (NF-κB), and inflammatory interleukins/cytokines including oncogenic IL1, IL2 and IL6, STAT3, STAT5A and AKT, which are known to facilitate downstream signaling and activation of oncogenic FoxO family of transcription factors, were also identified [37, 38, 50, 51]. Most intriguingly, pathways involving the immunoglobulin superfamily of transmembrane receptors, CD28 co-stimulation, and L1CAM signaling, not previously known to be associated with HER2 signaling or CYP epoxygenases, were upregulated. As shown in Additional file 1: Table S6, twenty significantly upregulated pathways related to DNA repair and cell fate, as well as nine mutation- and metabolism-related processes were observed for the clusters comprising of ER+/PR+/HER2– and TPBC specimens (76 luminal tumor specimens). Among these, transcription factors related to chromosome maintenance, cell cycle check points, and regulation of apoptosis, including MYC, JUN, CREBBP, NCOA1 and CREB1, were highlighted. Interestingly, 62 upregulated pathways observed in the cluster comprising of TNBC specimens (15 tumor specimens) were mostly related to known TNBC-related mutations and deregulated signaling, and metastasis and membrane remodeling signaling cascades, which were not previously known to be related to CYP or AA metabolism related signaling cellular events (Additional file 1: Table S7). Genes encoding important players in cell-cell communication and junction interactions, tumor vasculation, and cell surface interactions including platelet endothelial cell adhesion molecule-1 (PECAM), vascular endothelial growth factor (VEGF), and non-receptor protein-tyrosine kinases PTK2, Src and LCK were upregulated. Upregulation of epidermal growth factor receptor (EGFR) and cascades modulated by transcription factors PPARα, PPARγ, and NCOA1 were identified to be important nodes in these processes. Our pathway-level analysis revealed that CYP-epoxygenase overexpression and activation are related to facilitating different cancer-related networks unique for each breast cancer subtype and also highlights pathways which are known to be universal drivers in breast cancer progression, e.g., MAPK, NOTCH, hedgehog and c-Myc transcription factor signaling.
We next sought to examine the critical elements in the identified pathways, which may significantly impact the design of treatment strategies for tumors with CYP upregulation. To do this, we analyzed the identified subtype-specific pathways using Webgestalt [38, 51] and performed Over Representation Analysis (ORA) and Network Topology-based Analysis (NTA). These analyses account for systems-level dependencies and interactions between genes in the pathways based on random walk network propagation which may help reveal novel and cancer-type specific co-expression networks and modules . Additional file 2: Figure S8 shows the top ranking seed genes and the list of nodes related to processes upregulated in CYP epoxygenase overexpressing TNBC, HER2 and ER/PR/TPBC samples.
CYP epoxygenase overexpression, metastatic cascade activation and low survival characterize a distinct subset of TNBC patients
We applied the same approach to determine whether the activation of metastasis-related signaling pathways maybe explored as the Achilles heel of CYP-overexpressing TNBC tumors. We constructed a PDS heat map of the canonical pathways (N = 1330) and all the 200 TNBC samples from the discovery set. Notably, five distinct subsets were identified, with the 62 significantly upregulated pathways characterizing the subsets within this population (Fig. 7b). Eight TNBC-related signaling cascades (Myc, Ras, MAPK and EGFR activation, HIF-1α and NOD1/2 signaling, P53 downstream cascades and ceramide metabolism) were significantly upregulated in all of the 200 TNBC tumors in the TCGA dataset. Thirty-four among the 62 pathways were metastasis related and are mainly relevant to interaction with the extracellular matrix, membrane signaling and cytoskeleton rearrangement as well as endothelial cell interactions and degradation of adherence junctions. Twenty out of the 62 pathways were related to lipid metabolism and fatty acid transformations and showcase the lipidomic phenotype of TNBC Subtype I tumors. In contrast, TNBC Subset II shows an activation of pathways related to primary metabolome and nucleic acid metabolism signaling, which seems to fuel metastatic transformation. The majority of the TNBC Subsets III-V specimens had mutations and signaling pathways known to be dysregulated in TNBC. This subtyping scheme shows that metastasis transformation of specific TNBC subsets may be driven by different endogenous metabolite cascades. We speculate that the metastatic transformation of TNBC subsets (III-V) is fueled by oncogenic signaling previously known to be associated with TNBC tumors; e.g., c-Myc and EGFR, while these processes are driven by CYP epoxygenase-mediated signaling processes in TNBC Subset I. Notably, the metastatic subpopulation (TNBC Subset I) had lower survival rates compared with low CYP epoxygenase expressing TNBC tumors (Fig. 7c). This reiterates the survival pattern seen when comparing TNBC with receptor positive subtypes, thus demonstrating the consistency and validating the relationships between TNBC, epoxygenase expression and metastasis related signal activation. These results also suggest that targeting the vulnerabilities identified using ORA and NTA (Additional file 2: Figure S7) may be more effective in CYP-overexpressing TNBC subsets that may open the door for development of personalized therapies for this subpopulation of patients.
Convergence between EET biosynthesis and metastasis is validated using proteomics data of TNBC tumors
We then validated the conclusions drawn from pathway analysis of TCGA and METABRIC transcriptomics datasets using our own proteomics dataset. Eight paired TNBC tumor and adjacent normal tissue specimens, fragmented from the same tumor specimen used in the oxylipin metabolomics analysis were subjected to comparative proteomics using isobaric tags for relative and absolute quantitation (iTRAQ). All the peptides were filtered with a q-value threshold of 5% FDR, with the identified protein having a minimum of two unique peptides. For quantitative analysis, the relative abundance of each protein present in two biological replicates was calculated based on the iTRAQ reporter ion ratios of 115/114 and 116/114 found at the peptide level. Relative abundance of a total of approximately 3800 identified proteins is listed in Additional file 5. We annotated protein IDs to gene IDs using the Human Proteome database. We correlated the protein, gene expression levels and EET concentrations for the tissue samples and overlaid the expression values of each protein to their corresponding genes in the network. We compared the generated PDS with pathways identified from the TCGA and METABRIC datasets and observed that our proteomics data aligns well with the results derived from the large cohort-derived discovery and validation sets (Additional file 1: Table S7). We then searched for subnetworks whose expression across the patient population was highly discriminative of metastasis. Our results show that of the 8 specimens cross examined by proteomics analysis, stage IIB (N = 3) and IIIA tumors (N = 2) had higher protein expressions of CYP epoxygenase and metastasis-related genes (Fig. 7d). These results are consistent with the characteristics of the CYP overexpressing lipogenic and metastatic TNBC subtype identified via PDS analysis (Fig. 7b). Moreover, the concerted upregulation of FAK, AKT and downstream ceramide signaling are highly correlated with pathways associated with CYP epoxygenase overexpression in the discovery and validation cohorts (Additional file 1: Table S7 and Additional file 2: Figure S5). Taken together, integrative information from the tissue metabolome, proteome, and transcriptomics analyses show that EET and CYP epoxygenase expression may have broad clinical applications especially for predicting metastasis and survival, and for designing personalized therapy for TNBC patients.
In this study, we implemented a multi-omics, multiplatform approach using independent data sets derived from multiple cohorts and database resources, in-house collected patient samples, and a spectrum of BC cell lines to investigate specific metabolite-protein-gene pathways and signaling networks which are dysregulated or unique in different BC subtypes. The role of EETs as oncogenic metabolites specific to TNBC among the BC subtypes is highlighted by the elevated EET metabolite levels, CYP epoxygenase overexpression and sEH downregulation at the cellular level and in the TNBC tumor tissues. Of note, EET metabolite concentrations were also in good agreement with the gene and protein expression levels of CYP epoxygenases in TNBC tumor samples. Systematic cohort and bioinformatics analyses suggest that CYP epoxygenase overexpression is associated with specialized pathways dependent on BC hormone receptor status. Further, we observed that sEH protein expression is down-regulated in the TNBC tumor tissues, and thus suggest the potential role of sEH to be used as a tumor suppressor in hormone-independent BC types. We also demonstrated that this feature of TNBC tissues strongly correlates with the metastasis potential and survival outcomes as revealed by gene enrichment analyses of several publicly available transcriptomics datasets. In hormone receptor positive specimens, CYP overexpression did not have significant correlation with either the concentrations of EET metabolites in the tissues or expression of metastasis-related proteins. These results suggest that EETs may have important roles in the hormone-independent metastatic phenotype of TNBC, understanding of which may expose vulnerabilities that may be amenable to therapeutic intervention.
We used a two-tiered approach for pathway-based analysis of gene-network relationships to elucidate the biological significance of gene enrichments associated with CYP epoxygenase upregulation and to identify targetable nodes in the network. First, we used a PDS scoring strategy (Pathifier) to gauge the association between biological processes and CYP450 epoxygenase overexpression in the different breast cancer subtypes. Then we analyzed the subtype-specific pathways for systems level dependencies and pathway interactions [38, 39, 47]. In general, this approach is applicable for identification of co-expression networks and modules for populations which may be stratified according to specific molecular features. On the basis of PDS ranking, we discovered the pathways uniquely associated with HER2 overexpressing and TNBC tumor subtypes. For HER2-overexpressing tumors, these include pathways related to oncogenic signaling of interferon-γ (IFN-γ), IL1, IL2, and IL6, granulocyte-macrophage colony-stimulating factor (GM-CSF), and NF-κB. Intriguingly, pathways involving the immunoglobulin superfamily of transmembrane receptors, CD28 co-stimulation, and L1CAM signaling, not previously known to be associated with HER2 signaling or CYP epoxygenases, were upregulated. These results indicate that immune correspondence is important for HER2 positive tumors overexpressing CYP epoxygenases. It is possible that CYP epoxygenases are not expressed in HER2-overexpressing cells (as confirmed in the in vitro experiments), but are paracrine or autocrine mediators contributed by infiltrating leukocytes and other stromal cells. It would be interesting to investigate the expression of putative EET receptors and explore their roles in cancer-related signaling events in HER2 overexpressing tumors.
We have identified six TNBC-related signaling nodes, including Myc, Ras, MAPK, EGFR, HIF-1α and NOD1/2, which may be used as a gene signature for CYP epoxygenase overexpressing triple negative mammary tumors. Inhibitors or antagonists of their corresponding proteins may be considered as an approach for intervention of CYP epoxygenase overexpressing TNBC. Highly metastatic TNBC specimens may, on the other hand, be stratified according to whether they have a unique lipidomic phenotype or an upregulation of nucleic acid metabolism and signaling. Based on our network topology analyses for TNBC tissues with CYP epoxygenase upregulation, metastatic processes are highly correlated and driven by CYP epoxygenase-mediated signaling. Thus, categorizing TNBC tumors according to their EET/DHET ratio classifiers and CYP2C19 or CYP2J2 profiles, may be useful for prognostic and therapeutic assessment in TNBC. Our in vitro findings also highlight the metabolic differences between mesenchymal- and basal-like TNBC cells as well as HER2-overexpressing and luminal cell lines in terms of EET-mediated signaling and its contribution in mesenchymal-TNBC cell migration and invasion. Further probing the upstream or potential stromal regulators in TNBC may leverage the development of therapeutic agents.
Taken together, our results show that the EET signaling axis regulates unique molecular cascades which are dependent on the hormone receptor status of breast tumors. Our findings highlight the importance of EETs and their surrogate epoxygenase enzymes in the metastatic behavior of TNBC cells and tumors. Integrated pathway analysis show that metastatic TNBC overexpressing CYP-epoxgenases may be most responsive to therapies targeting Myc, Ras, MAPK, EGFR, HIF-1α and/or NOD1/2. Although further studies are warranted to delineate the mechanisms underlying the identified network connections, these observations may be useful for translational applications related to prediction of survival and metastatic outcomes in TNBC patients, as well as discovery of targetable vulnerabilities and associated biomarkers for future personalized medicine applications.
We thank Ms. Miranda Loney, Agricultural Biotechnology Research Center English Editor’s office, Academia Sinica, Taiwan, for English editorial assistance, the Metabolomics Core Facility of the Agricultural Biotechnology Research Center, and the Proteomics Core Laboratory at the Institute of Plant and Microbial Biology/Agricultural Biotechnology Research Center, Academia Sinica, Taiwan.
This work was supported by the Ministry of Science and Technology (MOST 105–3111-Y-001-036) and institutional grant funding from Academia Sinica, Taiwan.
Availability of data and materials
The materials used and the datasets generated during the current study are available from the corresponding author on reasonable request.
M.K.A., J.-Y.S., and, L.-F.S. conceived and designed experiments; M.K.A. and L.-F.S. developed the methods; M.K.A., J.-Y.S., G.-S.L., and J.-C.Y. were responsible for data acquisition; M.K.A., J.-Y.S., G.-S.L., Y.-J.L., and L.-F.S. performed data analysis and interpretation; M.K.A., Y.-J.L., C.-W.C., and H.-C.Y. performed statistical modelling and analysis; M.K.A., J.-Y.S., G.-S.L., J.-C.Y., J.-Y.L., C.-W.C., H.-C.Y., L.-F.S. wrote, reviewed and/or revised the manuscript; G.-S.L., C.-H.C., J.-C.Y., and L.-F.S. provided administrative, technical and material support; L.-F.S. supervised the study overall. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This study was approved by the institutional review boards of the NDMC (IRB number: TSGHIRB-099-05-058). All participants signed an informed consent.
Consent for publication
The authors declare that they have no competing interests.
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