MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status

Bathen, Tone Frost; Jensen, Line R.; Sitter, Beathe; Fjøsne, Hans E.; Halgunset, Jostein; Axelson, David E.; Gribbestad, Ingrid S.; Lundgren, Steinar

doi:10.1007/s10549-006-9400-z

Cited by 124 publications

(141 citation statements)

References 32 publications

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“…It has been shown that there is a metabolomic pattern of breast cancer, characterized mainly by increased lactate and glycine, and PC with respect to GPC [14]. Comparing the metabolomic profile with clinical parameters, it has been shown that Glycine, Taurine and PC are higher in tumors of higher degree and that the PC/GPC ratio is very increased in tumor tissue with respect to healthy [15].…”

Section: Breastmentioning

confidence: 99%

In Vivo Spectroscopy vis-à-vis Ex Vivo Spectroscopy: Pearls and Pitfalls: Imaging Review

Razak¹,

Gupta²,

Meena³

2017

Mol Biol

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Aims and objective: The objective of this communication is to bring the metabolomics by magnetic resonance (high resolution spectroscopy) to the clinical radiologist and compare the spectroscopy in vivo (NMR spectroscopy used in clinical) with the high resolution spectroscopy ex vivo (magic angle spectroscopy, HR -MORE). S and analyzing differences, advantages and limitations of each technique in different tissues. Review of the topic:In vivo spectroscopy that is usually performed in the clinic is a non-invasive technique that can provide important information for the diagnosis and monitoring of different lesions. The most well-known applications of in vivo spectroscopy today are those carried out by localized 1H spectroscopy, monovoxel or multivoxel (spectroscopic image), brain, prostate or breast. There are other modalities, such as the 31 P or 13ºC spectroscopy, but they are much less frequent and are only carried out in a few specialized centres. Without any doubt, the most widespread clinical use is confined to the central nervous system. Conclusion:The in vivo spectroscopy provides important metabolic information for the diagnosis of different pathologies, non-invasively, however, this technique has certain limitations, such as low spectral resolution, that is, the number of metabolites that can be identified/quantified; which results in low diagnostic specificity.Ex vivo spectroscopy, however, has a spectral resolution and sensitivity far superior to in vivo techniques, which allows detecting a large number of metabolites, and therefore greatly improves its diagnostic capacity. However, we must not forget that it is an invasive technique, since it is based on the analysis of pieces of tissue (biopsies, surgical pieces...) and therefore loses its usefulness in those lesions that are impossible to biopsy. In conclusion, each technique has its added value and its limitations and it is the correct use of them and the combination of them with other diagnostic parameters, where their clinical potential resides.

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Section: Breastmentioning

confidence: 99%

In Vivo Spectroscopy vis-à-vis Ex Vivo Spectroscopy: Pearls and Pitfalls: Imaging Review

Razak¹,

Gupta²,

Meena³

2017

Mol Biol

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“…In breast cancer patients without lymphatic spread, an important factor influencing further treatment decisions is breast cancer grade. Grade 2 and 3 invasive ductal carcinomas have been classified on the basis of the HR-MAS-determined metabolic profile (2). However, by comparing several multivariate techniques for this classification, large spreads in sensitivity and specificity are obtained, indicating low robustness of these results (2).…”

Section: Histopathologic Gradingmentioning

confidence: 99%

Magnetic Resonance Metabolomics of Intact Tissue: A Biotechnological Tool in Cancer Diagnostics and Treatment Evaluation

et al. 2010

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Personalized medicine is increasingly important in cancer treatment for its role in staging and its potential to improve stratification of patients. Different types of molecules, genes, proteins, and metabolites are being extensively explored as potential biomarkers. This review discusses the major findings and potential of tissue metabolites determined by high-resolution magic angle spinning magnetic resonance spectroscopy for cancer detection, characterization, and treatment monitoring. Cancer Res; 70(17); 6692-6. ©2010 AACR. The Concept of MetabolomicsThe metabolome is the complete set of small-molecule metabolites in an organism and the final downstream product of the preceding gene expression and protein activity. Disease and influences from environmental factors, such as diet and drugs, are important factors in shaping the dynamic composition of the metabolome. The purpose of using metabolomics is to monitor the metabolic state at a certain time point and thereby understand more complex biological interactions, or to define biomarkers related to specific conditions. Compared with the more well-established "omics" technologies (genomics, transcriptomics, and proteomics), metabolomics is a relatively new and emerging field. Metabolomics contributes to the diversity of technologies within systems biology, thus enabling a more holistic picture of the chosen biological system. Magnetic resonance spectroscopy (MRS) offers safe, nondestructive, and quantitative metabolite identification in an automated and high-throughput fashion. Its sensitivity is substantially lower than for mass spectrometry (microgram compared with picogram level), but MRS enables investigation of tissue samples by high-resolution magic angle spinning (HR-MAS) MRS with minimal sample preparation, keeping the sample intact after analysis (1). This review focuses on the use of HR-MAS in cancer detection, characterization, and treatment evaluation, in which several studies show that MR metabolomics has promise. Patients with identical clinical and morphologic cancer diagnosis may have very different outcomes. Moreover, the metabolic state in cancer compared with control tissue, or metabolic changes following response and/or resistance to therapy, usually involves multiple metabolites. Multivariate analysis, which handles interactions between multiple variables (metabolites), has, thus, become a commonly used strategy for analysis of large spectral data sets. Statistical methods, such as principal component analysis (PCA) and partial least squares regression (PLS), are then applied to matrices of spectral data for exploration and model building to establish classifiers that enable predictions and classifications related to the biological problem in question. Proper validation of the classifiers, preferably using separate data for training and testing, is of great importance. By this means, MR metabolomics can establish a more detailed tumor portrait by defining specific fingerprints reflecting diagnostic status or therapeutic response. O...

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“…Unsupervised principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) are the main methods used in this field (14). Previous studies have used PCA to investigate metabolic differentiation, as well as the description and recognition of the dynamic multivariate metabolism (14,15). Similarly, PLS-DA has been previously used for the analysis of metabolic changes (16).…”

Section: Introductionmentioning

confidence: 99%

1H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells

et al. 2015

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Abstract.A major obstacle of successful chemotherapy is the development of multidrug resistance (MDR) in the cancer cells, which is difficult to reverse. Metabolomic analysis, an emerging approach that has been increasingly applied in various fields, is able to reflect the unique chemical fingerprints of specific cellular processes in an organism. The assessment of such metabolite changes can be used to identify novel therapeutic biomarkers. In the present study, 1 H nuclear magnetic resonance (NMR) spectroscopy was used to analyze the extracellular metabolomic spectrum of the Tca8113 oral squamous carcinoma cell line, in which MDR was induced using the carboplatin (CBP) and pingyangmycin (PYM) chemotherapy drugs in vitro. The data were analyzed using the principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods. The results demonstrated that the extracellular metabolomic spectrum of metabolites such as glutamate, glycerophosphoethanol amine, α-Glucose and β-Glucose for the drug-induced Tca8113 cells was significantly different from the parental Tca8113 cell line. A number of biochemicals were also significantly different between the groups based on their NMR spectra, with drug-resistant cells presenting relatively higher levels of acetate and lower levels of lactate. In addition, a significantly higher peak was observed at δ 3.35 ppm in the spectrum of the PYM-induced Tca8113 cells. Therefore, 1 H NMR-based metabolomic analysis has a high potential for monitoring the formation of MDR during clinical tumor chemotherapy in the future.

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MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status

Cited by 124 publications

References 32 publications

In Vivo Spectroscopy vis-à-vis Ex Vivo Spectroscopy: Pearls and Pitfalls: Imaging Review

In Vivo Spectroscopy vis-à-vis Ex Vivo Spectroscopy: Pearls and Pitfalls: Imaging Review

Magnetic Resonance Metabolomics of Intact Tissue: A Biotechnological Tool in Cancer Diagnostics and Treatment Evaluation

1H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells

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