Metabolic profiling of breast cancer: Differences in central metabolism between subtypes of breast cancer cell lines

Willmann, Lucas; Schlimpert, Manuel; Halbach, Sebastian; Erbes, Thalia; Stickeler, Elmar; Kammerer, Bernd

doi:10.1016/j.jchromb.2015.07.021

Cited by 58 publications

(58 citation statements)

References 60 publications

(60 reference statements)

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“…Despite the widespread use of this cell line, a reliable and sensitive protocol for an untargeted extraction of metabolites has not been reported for this cell line. Current adherent cell extraction methods may not be ideally suited to our aims for several reasons: Methods that had been optimized for alternative analytical platforms were not suitable for untargeted LC-MS analysis [17,18,19,20,21,22,23]. Additionally, extraction methods for different mammalian cell lines may not be ideal for MDA-MB-231 cells due to the different metabolic profile and physical properties of each cell type [24].…”

Section: Introductionmentioning

confidence: 99%

Optimized Method for Untargeted Metabolomics Analysis of MDA-MB-231 Breast Cancer Cells

et al. 2016

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Cancer cells often have dysregulated metabolism, which is largely characterized by the Warburg effect—an increase in glycolytic activity at the expense of oxidative phosphorylation—and increased glutamine utilization. Modern metabolomics tools offer an efficient means to investigate metabolism in cancer cells. Currently, a number of protocols have been described for harvesting adherent cells for metabolomics analysis, but the techniques vary greatly and they lack specificity to particular cancer cell lines with diverse metabolic and structural features. Here we present an optimized method for untargeted metabolomics characterization of MDA-MB-231 triple negative breast cancer cells, which are commonly used to study metastatic breast cancer. We found that an approach that extracted all metabolites in a single step within the culture dish optimally detected both polar and non-polar metabolite classes with higher relative abundance than methods that involved removal of cells from the dish. We show that this method is highly suited to diverse applications, including the characterization of central metabolic flux by stable isotope labelling and differential analysis of cells subjected to specific pharmacological interventions.

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Section: Introductionmentioning

confidence: 99%

Optimized Method for Untargeted Metabolomics Analysis of MDA-MB-231 Breast Cancer Cells

et al. 2016

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“…A decrease of most amino acid levels has been reported in the blood plasma of breast cancer patients [28] and different breast cancer cell lines [29]. Assuming elevated energy consumption and consequent channeling of amino acids into the TCA cycle, Val, Tyr, Phe, Trp, Met, and Leu were decreased in breast cancer in both of the two studies.…”

Section: Resultsmentioning

confidence: 85%

“…1 mL 20 °C methanol/water ( v / v : 90/10) containing 1 μg/mL of phenyl β- d -glucopyranoside (Sigmal-Aldrich, Munich, Germany) as internal standard was transferred to the cell culture. Following the method of Willmann et al [29], direct methanolic extraction was applied. Petri dishes were immediately placed on ice and cells were detached with a cell lifter.…”

Section: Methodsmentioning

confidence: 99%

“…The alteration of metabolites could be a result of genetic, pathological, or environmental changes. As reported, many metabolite levels were altered in the blood plasma and urine of cancer patients or cancer cell lines [24,25,26,27,28,29,30,31]. Metabolic profiling can reveal the alteration of metabolite levels in cancer patients, and, consequently, novel biomarkers could be discovered [32].…”

Section: Introductionmentioning

confidence: 99%

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Metabolic Response to XD14 Treatment in Human Breast Cancer Cell Line MCF-7

Pan

Kather

Willmann

et al. 2016

IJMS

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XD14 is a 4-acyl pyrrole derivative, which was discovered by a high-throughput virtual screening experiment. XD14 inhibits bromodomain and extra-terminal domain (BET) proteins (BRD2, BRD3, BRD4 and BRDT) and consequently suppresses cell proliferation. In this study, metabolic profiling reveals the molecular effects in the human breast cancer cell line MCF-7 (Michigan Cancer Foundation-7) treated by XD14. A three-day time series experiment with two concentrations of XD14 was performed. Gas chromatography-mass spectrometry (GC-MS) was applied for untargeted profiling of treated and non-treated MCF-7 cells. The gained data sets were evaluated by several statistical methods: analysis of variance (ANOVA), clustering analysis, principle component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Cell proliferation was strongly inhibited by treatment with 50 µM XD14. Samples could be discriminated by time and XD14 concentration using PLS-DA. From the 117 identified metabolites, 67 were significantly altered after XD14 treatment. These metabolites include amino acids, fatty acids, Krebs cycle and glycolysis intermediates, as well as compounds of purine and pyrimidine metabolism. This massive intervention in energy metabolism and the lack of available nucleotides could explain the decreased proliferation rate of the cancer cells.

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“…Cancer metabolism requires energy derived from both anaerobic and aerobic glycolysis, which means lower substrate levels like glucose and glutamine being siphoned off to the tricarboxylic acid (TCA) cycle, and higher levels of bioenergic substrates, like lactate, which stimulates continuous growth (36). In a recent study, Willmann et al (39), used metabolomic profiling to sub-classify different breast cancer cell lines and differentiate not only between them, but also between the cancer cell lines and a non-cancer breast epithelial cell line as well. Research has shown that elevated glycine and lactate indicate increased metabolic changes due to rapid growth of cancer cells, and hence poorer prognosis (5).…”

Section: Discussionmentioning

confidence: 99%

Metabolomic profiling of breast tumors using ductal fluid

Canto

Marian

Varghese

et al. 2016

International Journal of Oncology

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Identification of new biomarkers for breast cancer remains critical in order to enhance early detection of the disease and improve its prognosis. Towards this end, we performed an untargeted metabolomic analysis of breast ductal fluid using an ultra-performance liquid chromatography coupled with a quadrupole time-of-light (UPLC-QTOF) mass spectrometer. We investigated the metabolomic profiles of breast tumors using ductal fluid samples collected by ductal lavage (DL). We studied fluid from both the affected breasts and the unaffected contralateral breasts (as controls) from 43 women with confirmed unilateral breast cancer. Using this approach, we identified 1560 ions in the positive mode and 538 ions in the negative mode after preprocessing of the UPLC-QTOF data. Paired t-tests applied on these data matrices identified 209 ions (positive and negative modes combined) with significant change in intensity level between affected and unaffected control breasts (adjusted P-values <0.05). Among these, 83 ions (39.7%) showed a fold change (FC) >1.2 and 66 ions (31.6%) were identified with putative compound names. The metabolites that we identified included endogenous metabolites such as amino acid derivatives (N-Acetyl-DL-tryptophan) or products of lipid metabolism such as N-linoleoyl taurine, trans-2-dodecenoylcarnitine, lysophosphatidylcholine LysoPC(18:2(9Z,12Z)), glycerophospholipids PG(18:0/0:0), and phosphatidylserine PS(20:4(5Z,8Z,11Z,14Z). Generalized LASSO regression further selected 21 metabolites when race, menopausal status, smoking, grade and TNM stage were adjusted for. A predictive conditional logistic regression model, using the LASSO selected 21 ions, provided diagnostic accuracy with the area under the curve of 0.956 (sensitivity/specificity of 0.907/0.884). This is the first study that shows the feasibility of conducting a comprehensive metabolomic profiling of breast tumors using breast ductal fluid to detect changes in the cellular microenvironment of the tumors and shows the potential for this approach to be used to improve detection of breast cancer.

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Metabolic profiling of breast cancer: Differences in central metabolism between subtypes of breast cancer cell lines

Cited by 58 publications

References 60 publications

Optimized Method for Untargeted Metabolomics Analysis of MDA-MB-231 Breast Cancer Cells

Optimized Method for Untargeted Metabolomics Analysis of MDA-MB-231 Breast Cancer Cells

Metabolic Response to XD14 Treatment in Human Breast Cancer Cell Line MCF-7

Metabolomic profiling of breast tumors using ductal fluid

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