A metabolomic approach to identifying platinum resistance in ovarian cancer
© Poisson et al.; licensee BioMed Central. 2015
Received: 18 December 2014
Accepted: 9 March 2015
Published: 26 March 2015
Acquisition of metabolic alterations has been shown to be essential for the unremitting growth of cancer, yet the relation of such alterations to chemosensitivity has not been investigated. In the present study our aim was to identify the metabolic alterations that are specifically associated with platinum resistance in ovarian cancer. A global metabolic analysis of the A2780 platinum-sensitive and its platinum-resistant derivative C200 ovarian cancer cell line was performed utilizing ultra-high performance liquid chromatography/mass spectroscopy and gas chromatography/mass spectroscopy. Per-metabolite comparisons were made between cell lines and an interpretive analysis was carried out using the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic library and the Ingenuity exogenous molecule library.
We observed 288 identified metabolites, of which 179 were found to be significantly different (t-test p < 0.05) between A2780 and C200 cells. Of these, 70 had increased and 109 had decreased levels in platinum resistant C200 cells. The top altered KEGG pathways based on number or impact of alterations involved the cysteine and methionine metabolism. An Ingenuity Pathway Analysis also revealed that the methionine degradation super-pathway and cysteine biosynthesis are the top two canonical pathways affected. The highest scoring network of altered metabolites was related to carbohydrate metabolism, energy production, and small molecule biochemistry. Compilation of KEGG analysis and the common network molecules revealed methionine and associated pathways of glutathione synthesis and polyamine biosynthesis to be most significantly altered.
Our findings disclose that the chemoresistant C200 ovarian cancer cells have distinct metabolic alterations that may contribute to its platinum resistance. This distinct metabolic profile of platinum resistance is a first step towards biomarker development for the detection of chemoresistant disease and metabolism-based drug targets specific for chemoresistant tumors.
KeywordsMetabolomics Ovarian cancer Platinum resistance A2780 C200 Methionine metabolism
Ovarian cancer is responsible for the highest mortality of all cancers of the female reproductive system. It accounts for approximately 3% of all cancers in women and is the fifth leading cause of cancer related death among women in the United States . Ovarian cancers are generally sensitive to chemotherapy and often initially respond well to standard primary treatment with surgery and first-line platinum and taxane-based chemotherapy. However, approximately 70% of the patients experience disease relapse within 2 years of the initial treatment. Of these, only a few benefit from subsequent therapies using a platinum and taxane combination. Patients with a short time to disease progression and with no benefit to further platinum-based therapy are classified as having platinum-resistant disease, whereas those with long-lasting response to primary treatment and/or response to second-line platinum-based therapy are said to have platinum-sensitive disease. Even though the presence or development of platinum resistance is a major obstacle in successful ovarian cancer treatment, platinum therapy is still the principal treatment for recurrent tumors .
The antitumor activity of platinum has been shown to be due to the formation of intra-strand DNA adducts, which are irreparable and will eventually lead to cell apoptosis . Additionally, cisplatin, a commonly used platinum-compound chemotherapy, is known to induce oxidative stress and endoplasmic reticulum stress, but the extent to which these pathways contribute to cell death is not yet established [4,5]. Platinum resistance has been attributed to reduced drug accumulation, improved drug efflux, drug inactivation, enhanced DNA repair ability and upregulation of anti-apoptotic or other survival genes [6,7]. While advancements have been made in understanding the molecular deregulation underlying chemoresistance, these have not translated into clinical applications to enhance the therapeutic outcome of platinum resistant tumors. Therefore, strategies addressing the identification of chemoresistant tumors that can be directly translated to clinic are required.
Metabolomics is a new discipline which evaluates diverse metabolite concentrations in biological specimens to gain insight into the ongoing metabolism. Metabolites are the end product of various metabolic pathways and may have application as biomarkers for cancer diagnosis, prognosis, and therapeutic evaluation . Apart from revealing diagnostic and prognostic biomarkers, this profile of cell functioning at the metabolite level will help obtain an elementary understanding of the process of carcinogenesis and chemoresistance that may provide opportunities for early diagnosis and treatment.
Recent metabolomic based studies in ovarian cancer have been applied to the screening of urine, plasma, and tumor tissue from ovarian cancer patients and control populations [9-14]. These studies have endeavored to discriminate between healthy and ovarian cancer patients [9-11], profile malignant and borderline ovarian tumors , and detect recurrent tumors [12,14]. All of these studies clearly demonstrate that metabolomic profiles in the urine, plasma, or tumor tissue can distinctly separate healthy women from those with benign or malignant ovarian tumors, indicating that the science of metabolomics can be successfully applied for ovarian cancer characterization and identification.
Since all chemotherapeutic drugs are metabolically processed, it can be extrapolated that metabolism plays a vital role in chemoresponse of the tumors. Cell death whether by apoptosis or necrosis requires energy from the cell and involves regulation by various metabolic enzymes. Targeting of metabolic enzymes from key metabolic pathways, like glycolysis , fatty acid synthesis , and glucose transport , have been shown to enhance the cytotoxicity of various chemotherapeutic agents and radiotherapy. Moreover, cisplatin treatment has recently been shown to induce intracellular metabolic changes . Thus, it can be postulated that chemoresistant tumor cells will have specific altered metabolism compared to chemosensitive tumor cells that could be detected by comparing their metabolites.
We designed our metabolomics-based study to identify metabolite variations that distinguish between platinum resistant and sensitive ovarian cancer cells. By using platinum sensitive A2780 and resistant C200 ovarian cancer cell lines, we are able to show that metabolite alterations can clearly separate the cells based on their platinum sensitivity. We identified significant metabolite variations in 6 different metabolic pathways participating in various signaling networks, with methionine metabolism and its associated metabolites being the centrally affected pathway.
A2780 and C200 cell lines were a kind gift from Dr. Thomas Hamilton, Fox Chase Cancer Center, PA. The cell lines were maintained in Roswell Park Memorial Institute media (HyClone-ThermoScientific; Waltham, MA) supplemented with 10% fetal bovine serum (BioAbChem; Ladson, SC) and insulin. For preparation of cells, cells were grown for 48 hours in insulin free media. Ten million cells were counted, washed with phosphate buffered saline and snap frozen.
Metabolomic profiling analysis was performed by Metabolon Inc. (Durham, NC) as previously described [19-22]. Briefly, sample preparation was conducted using an aqueous methanol extraction and the resulting extract were analyzed by ultra-performance liquid chromatography/mass spectroscopy (positive and negative modes) and gas chromatography/ mass spectroscopy. Raw data were extracted, peak-identified and quality control processed using Metabolon’s hardware and software. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities based on 3 criteria: retention index within a narrow retention index window of the proposed identification, nominal mass match to the library +/− 0.2 atomic mass units, and the mass spectroscopy/mass spectroscopy forward and reverse scores between the experimental data and authentic standards.
To control for sample concentration, each metabolite intensity value was standardized as a ratio against the Bradford protein measure for that sample. Missing values which indicate a limit of detection by the mass spectrometer were replaced with a small value (one half the study minimum) for analysis. The data were visualized by plotting the first and second components of a partial least squares discriminant analysis (PLS-DA) model. A z-score plot was generated by plotting each metabolite intensity of the resistant C200 cells relative to the mean and standard deviation of the sensitive (A2780) cells. A one-unit change indicated a one standard deviation change in intensity away from the A2780 mean. Each observation represents 1 point on this z-score plot. The observations were organized by metabolites (rows) within super-pathway and sub-pathway. Metabolite intensities were compared between lines by a two-sample t-test per metabolite allowing for unequal variance on a log2 scale. Significant metabolites (at p < 0.01) were selected for inclusion in the heatmap. Metabolites (rows) were ordered first by super-pathway and then by direction of change. The columns (samples) are ordered by hierarchical clustering using Pearson correlation and complete linkage. Overrepresentation of changed molecules within super-pathway and sub-pathway was tested using a Fisher’s exact test per grouping. Change determined from the per-metabolite t-tests (p < 0.05) was classified according to direction of change from control, i.e., up, down, and unchanged.
Pathway analysis was conducted in MetaboAnalyst (2.0; last accessed April 2013) using the Human Metabolon Database compound IDs to map to the 80 KEGG human reference pathways. The metabolites were ranked according to the t-test result. The Global Test for concerted change and the impact factor were calculated for these pathways. The impact factor used ‘betweenness’ centrality as the measure of impact, thus if altered, those molecules which act as hubs within a pathway contribute more strongly to the impact factor. Further functional analysis was conducted using the Ingenuity Pathway Analysis (IPA) core analysis of metabolites (QIAGEN, Redwood City, CA, USA; last accessed September 2014) is based on the Biocyc pathways and the proprietary Ingenuity knowledgebase. We used a p-value threshold of 0.05 and the set of all endogenous compounds as the reference group for the Fisher’s exact tests. The Human Metabolon Database number, the KEGG compound ID, or the PubChem ID numbers were used, with that order of preference, to map the metabolites to the IPA knowledgebase. Interactions from both experimentally validated and high confidence predictions were used. No restrictions were made on cell type or species.
Metabolomic profile of platinum sensitive A2780 and resistant C200 cells lines
Metabolic pathway analysis of altered profiles
Ingenuity pathway analysis (IPA)
IPA network analysis, using the proprietary Ingenuity knowledgebase, constructs networks of altered molecules which are not limited by canonical pathway boundaries. Eleven networks were constructed as reported in Additional file 3: Table S3. The top network uses 19 molecules and focuses on carbohydrate metabolism, energy production, and small molecule biochemistry (Figure 4B). Containing metabolites like glucose-6-phosphate (down in C200), pyruvate (high in C200) and citric acid (high in C200) from energy metabolism pathway and various lipids like oleic acid, malic acid, palmitate, linoleic acid, etc. (all low in C200); it has hubs at the signaling molecule extracellular-signal-related kinases1/2 (ERK) (Figure 4B). ERKs participate in the Ras-Raf-MEK-ERK signaling pathway and is reported to be up-regulated in almost all cancers . Network 2 uses 20 molecules and focuses on drug metabolism, molecular transport, and small molecule biochemistry. Most of the metabolites in this network are derived from the methionine-cysteine metabolism and related to amino acids that centralize on GSH (up in C200) and Akt as the signaling molecule (Additional file 4: Figure S1), that is a constituent of one of the most dysfunctional signal transduction pathways described in various cancers . Network 3 included 16 metabolites of pyrimidine metabolism to generate a linkage of nucleic acid metabolism and small molecule biochemistry (Additional file 5: Figure S2, Additional file 3: Table S3). The signaling hubs were calcium ions and epidermal growth factor receptor. Network 4 uses 15 molecules and focuses on free radical scavenging, lipid metabolism and small molecule biochemistry. Metabolites in this network are from methionine metabolism, phospholipids and nucleic acid metabolism, superoxide and GSH (Additional file 6: Figure S3). Network 5 with 12 metabolites was convened as carbohydrate metabolism, lipid metabolism and molecular transport. All metabolites except sn-glycerol3-phosphate were lower in C200 cells, including the central molecule, D-glucose. The lipids stearic acid and cholesterol were also lower in the C200 cells. The network revealed connections leading to pro-oncogenic signaling molecules like mammalian target of rapamycin complex 1 (mTORC1), c-Jun N-terminal kinases, VEGF and Peroxisome proliferator activator receptor (Additional file 7: Figure S4). Overall, the networks analysis suggests that altered metabolites of energy metabolism, methionine metabolism and lipids are linked with tumor promoting signaling molecules.
A central metabolite S-adenosylmethionine (SAM) from the methionine pathway, acts as a donor for methylation reactions involving methylation of histones, DNA, RNA and all general protein methylations [39,44], and also participates in biosynthesis of phosphatidylcholine, the major component of cell membranes, by forming the polar head group with choline . Methylation processes have been widely implicated in cancer progression, including ovarian [45,46]. Aberrant methylation has also been proposed as a contributing factor for acquisition of chemoresistance, especially in resistance against DNA-damaging platinum drugs [47,48]. Recently, a cisplatin-resistant cell line derived from the sensitive A2780 ovarian cancer cell lines were shown to preferentially select for DNA-hypermethylation and the obtained methylated gene signature was found to partially hold validation in a small subset of patient relapsed ovarian tumors . Thus it is possible that the chemoresistance in C200 ovarian cancer cells could be a result of increased methylation of selective genes, which could be reflected in its altered metabolism. SAM also provides methyl groups for biosynthesis of polyamines, a vital class of products involved in cell proliferation, and have been shown to be increased during malignancy . Polyamines include putrescine and its derivatives spermidine and spermine, which are synthesized from ornithine and shown to be required for proliferation [50-52]. On one hand, inhibition of polyamine synthesis has been shown to inhibit cancer cells, but on the other hand recent studies have reported decreased levels of spermine as a metabolic biomarker for cancer cells [50,53]. We observed an increased level of putrescine and decreased levels of spermine and spermidine (Figure 3G). This could either indicate a block after putrescine formation or increased utilization of these metabolites. Ornithine decarboxylase (ODC) is the first enzyme required for polyamine synthesis and is regulated by Myc oncogene [50,54]. Induction of spermidine/spermine N1-acetyltransferase 1(SAT1), the key enzyme regulating catabolism of polyamines has been reported to result in decreased spermine and spermidine and increased putrescine levels , similar to our observation in C200 cells (Figure 3G). ODC and SAT1 have been shown to be overexpressed in neoplastic prostate tissue . Platinum drugs have also been shown to regulate polyamine metabolism enzymes including SAT1 . Thus acquisition of platinum resistance in C200 cells could be related to perturbations in the enzymatic makeup of polyamine metabolism.
Our observations are similar to other studies where the altered methionine pathway has been advocated to play a role in ovarian and other cancers and its metabolites presented as biomarkers. Alteration of methionine pathway was suggested early in ovarian cancer , where accumulated homocysteine in ascites was an indicator of malignancy. A recent study has shown cisplatin treatment of embryonic mouse cells to induce significant changes in the methionine degradation pathway, including GSH and polyamine metabolism, along with other methionine associated pathways . Sarcosine (n-methylglycine), a product of the methionine degradation pathway was reported as a biomarker in urine of metastatic prostate cancer patients . Comparison of metabolites from early recurrence (within 2 years of surgery) and recurrence free (more than 5 years) prostate cancer patients found elevated products of methionine catabolism in serum, which included sarcosine, cysteine, cystathionine and homocysteine . Targeting of the methionine pathway is being actively investigated in cancer therapeutics. Targeting the epigenetic status by inhibiting methylation of various genes in tumors is one of the earliest and most pursued chemotherapeutic approaches [59-61]. SAM-mediated methylation and polyamine synthesis is being actively investigated in preclinical studies of various cancers [53,62]. Inhibition of GSH activity is also a major area under consideration for specific targeting of chemoresistance . Thus the methionine pathway metabolites appear to have the potential to act as biomarkers specifically for metastatic or recurrent tumors. Together, with the observation that most recurrent tumors are also chemoresistant and our findings, surfacing of the methionine pathway as an indicator of chemoresistance in ovarian cancer is significant. Thus, an in-depth analysis of the methionine metabolism encompassing and integrating the levels of input, intermediate and outcome metabolites flux, along with the expression and activation status of the enzymes catalyzing these reactions will provide a complete picture in understanding the functional significance of this pathway in platinum chemoresistance of ovarian cancer.
The altered metabolism of cancer cells has been well established. Our data shows that platinum sensitive and resistant ovarian cancer cells can be distinguished based on their metabolite profiles. This suggests that chemoresistance is associated with its own set of metabolic changes that can be exploited for biomarker and targeted therapeutic approaches. Based on our data, we propose the altered methionine pathway metabolites as the potential biomarkers for platinum resistance in ovarian cancer cells. Most of these metabolites are measurable in body fluids like urine or serum, which would make their translation into clinical practice easier, once they have been extensively validated in subsets of ovarian cancer patients. Although our study is limited by using an isolated in vitro cell line system, we observed alterations in metabolites similar to those reported to be associated with malignancy, specifically metastatic and recurrent tumors. While our data await further validation, a comprehensive analysis of the altered pathways may not only provide biomarkers of chemoresistance but can also provide clues of the biology underlying platinum resistance in ovarian cancer cells and offer plausible therapeutic targets to specifically target chemoresistance
Acetyl CoA carboxylase
Activation induced cytidine deaminase
DNA cytosine deaminase
Alanine amino transferase
Carbonic anhydrase VA
Cyclin dependent kinase inhibitor 1A
Cholinergic receptor, nicotin alpha 1
Carnitine palmitoyltransferase 1
Dipeptidyl peptidase 4
Dual oxidase 1
Epidermal growth factor receptor 1
Gamma –aminobutyric acid
High- density lipoprotein
11-beta-hydroxysteroid dehydrogenase type 1
Isocitrate dehydrogenase 1
Ingenuity Pathway Analysis
c-jun N-terminal kinase
Kyoto Encyclopedia of Genes and Genomes
Low density lipoprotein
Acyl CoA: monoacylglycerol acyltransferase
Mammalian target of rapamycin complex1
Mitochondrial tumor suppressor 1
Nicotinamide adenine dinucleotide, hydrogen ion
NADH-ubiquinone oxidoreductase iron-sulfur protein 1
Protein kinase C
Partial least squares discriminant analysis
Plasma membrane calcium ATPase
peroxisome proliferator-activated receptor alpha
Solute carrier family 22 member 6
Soluble phospholipase A2
Vascular endothelial growth factor
This study was supported by Henry Ford Health System internal funding and Ruth McVay funds to RR and Patterson Endowment funds to AM. We would also like to thank Ms. Stephanie Stebens for assistance with preparing the manuscript.
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