Alterations of the exo- and endometabolite profiles in breast cancer cell lines: A mass spectrometry-based metabolomics approach

Willmann, Lucas; Schlimpert, Manuel; Hirschfeld, Marc; Erbes, Thalia; Neubauer, Hans; Stickeler, Elmar; Kammerer, Bernd

doi:10.1016/j.aca.2016.04.047

Cited by 27 publications

(24 citation statements)

References 42 publications

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“…Methods for detaching adherent cells from culture dishes using trypsin have been shown to cause significant metabolite leakage [23,24,25]. Lastly, methods developed for targeted analyses are not suitable for untargeted analyses as they are optimized to extract metabolites from a specific metabolite class (e.g., only polar metabolites) [26,27,28]. …”

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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“…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%

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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“…25 Various types of chemometric algorithms have been reported for pattern recognition and classification of MS data, especially for discriminating between healthy control and cancer samples, or discriminating cancer subtypes. For instance, there are several papers reporting the use of partial least squares discriminant analysis (PLS-DA), 14,18,20 hierarchical cluster analysis (HCA), 14,27,28 principal component analysis (PCA), 14,29 support vector machines (SVM), 17,29 artificial neural networks (ANN), 28 principalcomponent analysis followed by linear discriminant analysis (PCA-LDA), 15 principal component directed partial least squares (PC-PLS), 30 and backward variable elimination partial least squares discriminant analysis (BVE-PLSDA). 31 Principal component analysis (PCA) is a method of exploratory analysis capable of reducing the original data into a few variables.…”

Section: Introductionmentioning

confidence: 99%

“…Other techniques such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) are often used in MS applications, including disease screening and diagnosis. 5 Some examples of MS applications includes toxicology screening and toxic drug quantification using quadrupole MS/MS; 10 identification of inborn errors in metabolism or genetic defects in newborns for prenatal screening programs using electrospray tandem MS; 11 detection of drug-induced hepatotoxicity using MS-based metabolomics; 12 and identification and quantification of bleomycin in serum and tumor tissue by high resolution LC-MS. 13 MS-based techniques have been largely employed for cancer identification, such as for breast cancer, 14 prostate cancer, 15,16 ovarian cancer, 17 lung cancer, 18 and pancreatic cancer; 19 as well as for identifying many biomarkers. 18,[20][21][22][23][24] One of the main fields using MS data is metabolomics, which aims to identify and quantify small molecules involved in metabolic reactions.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, there are several papers reporting the use of partial least squares discriminant analysis (PLS-DA), 14,18,20 hierarchical cluster analysis (HCA), 14,27,28 32 PCA reduces the data into a few principal components (PCs), where each one represents a piece of the original information. The first PC has the largest explained variance; therefore, they represent most of the information present in the original data.…”

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Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry

Morais

Lima

2017

J. Braz. Chem. Soc.

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Mass spectrometry (MS) is a powerful technique that can provide the biochemical signature of a wide range of biological materials such as cells and biofluids. However, MS data usually has a large range of variables which may lead to difficulties in discriminatory analysis and may require high computational cost. In this paper, principal component analysis with linear discriminant analysis (PCA-LDA) and quadratic discriminant analysis (PCA-QDA) were applied for discrimination between healthy control and cancer samples (ovarian and prostate cancer) based on MS data sets. In addition, an identification of prostate cancer subtypes was performed. The results obtained herein were very satisfactory, especially for PCA-QDA. Selectivity and specificity were found in a range of 90-100%, being equal or superior to support vector machines (SVM)-based algorithms. These techniques provided reliable identification of cancer samples which may lead to fast and less-invasive clinical procedures.Keywords: mass spectrometry, classification, ovarian cancer, prostate cancer, QDA IntroductionMass spectrometry (MS) is an analytical technique that is used for determining the chemical composition of a given sample, to quantify compounds, 1 and to help elucidate molecular structures. 2,3 This technique has been increasingly utilized in biomedical and clinical research, 4 since it can overcome many limitations of classical immunoassays 5,6 and supports the development of fast and less-invasive clinical procedures. [7][8][9] MS is usually coupled with chromatography such as liquid chromatography (LC-MS) and gas chromatography (GC-MS). Other techniques such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) are often used in MS applications, including disease screening and diagnosis.5 Some examples of MS applications includes toxicology screening and toxic drug quantification using quadrupole MS/MS; 10 identification of inborn errors in metabolism or genetic defects in newborns for prenatal screening programs using electrospray tandem MS; 11 detection of drug-induced hepatotoxicity using MS-based metabolomics; 12 and identification and quantification of bleomycin in serum and tumor tissue by high resolution LC-MS. 13 MS-based techniques have been largely employed for cancer identification, such as for breast cancer, 14 prostate cancer, 15,16 ovarian cancer, 17 lung cancer, 18 and pancreatic cancer; 19 as well as for identifying many biomarkers. 18,[20][21][22][23][24] One of the main fields using MS data is metabolomics, which aims to identify and quantify small molecules involved in metabolic reactions. 25 Metabolomics studies have been applied in several areas, especially cancer. 26These analyses are typically performed in either targeted or untargeted approaches. 25 The target approach aims to identify and quantify specific metabolites or metabolite class; whereas in the untargeted analysis a new hypothesis for further tests is generated by ...

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Alterations of the exo- and endometabolite profiles in breast cancer cell lines: A mass spectrometry-based metabolomics approach

Cited by 27 publications

References 42 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

Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry

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