Special Issue "Cancer Metabolomics 2016"

A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (31 July 2016)

Special Issue Editors

Guest Editor
Prof. Dr. Claudiu T. Supuran

Neurofarba Department, Sezione di Scienze Farmaceutiche e Nutraceutiche, Università degli Studi di Firenze, Sesto Fiorentino (Florence) 50019, Italy
Website | E-Mail
Phone: +39-055-4573729/3005
Fax: +39-055-4573385
Interests: drug design; metalloenzymes; carbonic anhydrases; anticancer agents; antiinfectives; sulfonamides; coumarins
Guest Editor
Dr. Natalie Serkova

Associate Professor of Anesthesiology, Radiology and Pharmacology Director, Colorado Imaging and Metabolomics Core Anschutz Medical Center, University of Colorado Hospital, 12800 East 19th Ave, Aurora, CO 80045, USA
Website | E-Mail
Phone: 1 (303) 724 1086
Interests: Technological advances in NMR tissue metabolomics for cancer biomarkers; Technological advantages in MS biofluid metabolomics for cancer biomarkers; Metabolic Biomarkers for cancer detection; Metabolic Biomarkers for cancer treatment response; Non-invasive metabolic imaging (pre-clinical and clinical)

Special Issue Information

Dear Colleagues,

Metabolomics is an essential part of system biology, with the main focus on comprehensively analyzing endogenous small-molecules (metabolites) in cells, tissues, and body fluids. The analytical platforms comprise of NMR (nuclear magnetic resonance) and MS (mass spectrometry: LC-MS, GC-MS, ESI) technologies, followed by multivariate analyses on complex quantitative or semi-quantitative data sets (PCA, PLS-DA, neuronal networks). Since Warburg’s discovery of abnormal aerobic glycolysis in cancer cells, oncology logically represents a classic application area for basic and translational metabolomics efforts. Most recently, some individual metabolites have also became the major players in the truly clinical arena of cancer diagnostics: using in vivo metabolic imaging techniques, such as magnetic resonance spectroscopy (MRS) and positron emission tomography (PET), identified metabolic biomarkers are directly visualized in cancerous tissues without need of a biopsy. This Special Issue of Metabolites focuses on NMR- and MS-based preclinical and early clinical biomarker identification studies in various body fluids and tissue biopsies (aspirates); their translation into the “first-in-human” in vivo PET and MRS metabolic imaging will be discussed. The state-of-the-art “unconventional” technologies for metabolite identification and detection will also be briefly described. The main goals of this Special Issue are to provide all readers (independently of their technological level of expertise) with a comprehensive overview of cancer-related metabolic biomarkers and to summarize the most robust translational metabolomics protocols for future study designs.

Dr. Natalie Serkova
Prof. Dr. Claudiu T. Supuran
Guest Editors

Manuscript Submission Information

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Keywords

  • untargeted quantitative nmr metabolomics
  • highly sensitive untargeted ms metabolomics
  • targeted ms metabolomics approaches
  • cancer-related metabolic biomarkers
  • biomarker validation for clinical trials
  • in vivo metabolic imaging

Published Papers (6 papers)

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Research

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Open AccessArticle Distinguishing Benign from Malignant Pancreatic and Periampullary Lesions Using Combined Use of 1H-NMR Spectroscopy and Gas Chromatography–Mass Spectrometry
Metabolites 2017, 7(1), 3; doi:10.3390/metabo7010003
Received: 13 April 2016 / Revised: 9 December 2016 / Accepted: 8 January 2017 / Published: 13 January 2017
Cited by 1 | PDF Full-text (2062 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Previous work demonstrated that serum metabolomics can distinguish pancreatic cancer from benign disease. However, in the clinic, non-pancreatic periampullary cancers are difficult to distinguish from pancreatic cancer. Therefore, to test the clinical utility of this technology, we determined whether any pancreatic and periampullary
[...] Read more.
Previous work demonstrated that serum metabolomics can distinguish pancreatic cancer from benign disease. However, in the clinic, non-pancreatic periampullary cancers are difficult to distinguish from pancreatic cancer. Therefore, to test the clinical utility of this technology, we determined whether any pancreatic and periampullary adenocarcinoma could be distinguished from benign masses and biliary strictures. Sera from 157 patients with malignant and benign pancreatic and periampullary lesions were analyzed using proton nuclear magnetic resonance (1H-NMR) spectroscopy and gas chromatography–mass spectrometry (GC-MS). Multivariate projection modeling using SIMCA-P+ software in training datasets (n = 80) was used to generate the best models to differentiate disease states. Models were validated in test datasets (n = 77). The final 1H-NMR spectroscopy and GC-MS metabolomic profiles consisted of 14 and 18 compounds, with AUROC values of 0.74 (SE 0.06) and 0.62 (SE 0.08), respectively. The combination of 1H-NMR spectroscopy and GC-MS metabolites did not substantially improve this performance (AUROC 0.66, SE 0.08). In patients with adenocarcinoma, glutamate levels were consistently higher, while glutamine and alanine levels were consistently lower. Pancreatic and periampullary adenocarcinomas can be distinguished from benign lesions. To further enhance the discriminatory power of metabolomics in this setting, it will be important to identify the metabolomic changes that characterize each of the subclasses of this heterogeneous group of cancers. Full article
(This article belongs to the Special Issue Cancer Metabolomics 2016)
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Open AccessFeature PaperArticle Metabolic Effect of Estrogen Receptor Agonists on Breast Cancer Cells in the Presence or Absence of Carbonic Anhydrase Inhibitors
Metabolites 2016, 6(2), 16; doi:10.3390/metabo6020016
Received: 9 March 2016 / Revised: 27 April 2016 / Accepted: 18 May 2016 / Published: 26 May 2016
PDF Full-text (5579 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Metabolic shift is one of the major hallmarks of cancer development. Estrogen receptor (ER) activity has a profound effect on breast cancer cell growth through a number of metabolic changes driven by its effect on transcription of several enzymes, including carbonic anhydrases, Stearoyl-CoA
[...] Read more.
Metabolic shift is one of the major hallmarks of cancer development. Estrogen receptor (ER) activity has a profound effect on breast cancer cell growth through a number of metabolic changes driven by its effect on transcription of several enzymes, including carbonic anhydrases, Stearoyl-CoA desaturase-1, and oncogenes including HER2. Thus, estrogen receptor activators can be expected to lead to the modulation of cell metabolism in estrogen receptor positive cells. In this work we have investigated the effect of 17β-estradiol, an ER activator, and ferulic acid, a carbonic anhydrase inhibitor, as well as ER activator, in the absence and in the presence of the carbonic anhydrase inhibitor acetazolamide on the metabolism of MCF7 cells and MCF7 cells, stably transfected to express HER2 (MCF7HER2). Metabolic profiles were studied using 1D and 2D metabolomic Nuclear Magnetic Resonance (NMR) experiments, combined with the identification and quantification of metabolites, and the annotation of the results in the context of biochemical pathways. Overall changes in hydrophilic metabolites were largest following treatment of MCF7 and MC7HER2 cells with 17β-estradiol. However, the carbonic anhydrase inhibitor acetazolamide had the largest effect on the profile of lipophilic metabolites. Full article
(This article belongs to the Special Issue Cancer Metabolomics 2016)
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Open AccessArticle Metabolic Fingerprinting of Pseudomonas putida DOT-T1E Strains: Understanding the Influence of Divalent Cations in Adaptation Mechanisms Following Exposure to Toluene
Metabolites 2016, 6(2), 14; doi:10.3390/metabo6020014
Received: 16 February 2016 / Revised: 20 April 2016 / Accepted: 21 April 2016 / Published: 26 April 2016
PDF Full-text (1083 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Pseudomonas putida strains can adapt and overcome the activity of toxic organic solvents by the employment of several resistant mechanisms including efflux pumps and modification to lipopolysaccharides (LPS) in their membranes. Divalent cations such as magnesium and calcium play a crucial role in
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Pseudomonas putida strains can adapt and overcome the activity of toxic organic solvents by the employment of several resistant mechanisms including efflux pumps and modification to lipopolysaccharides (LPS) in their membranes. Divalent cations such as magnesium and calcium play a crucial role in the development of solvent tolerance in bacterial cells. Here, we have used Fourier transform infrared (FT-IR) spectroscopy directly on cells (metabolic fingerprinting) to monitor bacterial response to the absence and presence of toluene, along with the influence of divalent cations present in the growth media. Multivariate analysis of the data using principal component-discriminant function analysis (PC-DFA) showed trends in scores plots, illustrating phenotypic alterations related to the effect of Mg2+, Ca2+ and toluene on cultures. Inspection of PC-DFA loadings plots revealed that several IR spectral regions including lipids, proteins and polysaccharides contribute to the separation in PC-DFA space, thereby indicating large phenotypic response to toluene and these cations. Finally, the saturated fatty acid ratio from the FT-IR spectra showed that upon toluene exposure, the saturated fatty acid ratio was reduced, while it increased in the presence of divalent cations. This study clearly demonstrates that the combination of metabolic fingerprinting with appropriate chemometric analysis can result in practicable knowledge on the responses of important environmental bacteria to external stress from pollutants such as highly toxic organic solvents, and indicates that these changes are manifest in the bacterial cell membrane. Finally, we demonstrate that divalent cations improve solvent tolerance in P. putida DOT‑T1E strains. Full article
(This article belongs to the Special Issue Cancer Metabolomics 2016)
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Open AccessArticle Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information
Metabolites 2015, 5(3), 502-520; doi:10.3390/metabo5030502
Received: 9 June 2015 / Revised: 20 August 2015 / Accepted: 6 September 2015 / Published: 15 September 2015
Cited by 10 | PDF Full-text (696 KB) | HTML Full-text | XML Full-text
Abstract
Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However,
[...] Read more.
Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas. Thus, there is a need for biomarkers for a more reliable classification of glioma tumors as well as for prognosis. We analyzed relative metabolite concentrations in serum samples from 96 fasting glioma patients and 81 corresponding tumor samples with different diagnosis (glioblastoma, oligodendroglioma) and grade (World Health Organization (WHO) grade II, III and IV) using gas chromatography-time of flight mass spectrometry (GC-TOFMS). The acquired data was analyzed and evaluated by pattern recognition based on chemometric bioinformatics tools. We detected feature patterns in the metabolomics data in both tumor and serum that distinguished glioblastomas from oligodendrogliomas (ptumor = 2.46 × 10−8, pserum = 1.3 × 10−5) and oligodendroglioma grade II from oligodendroglioma grade III (ptumor = 0.01, pserum = 0.0008). Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (ptumor = 0.006, pserum = 0.004; AUROCCtumor = 0.846 (0.647–1.000), AUROCCserum = 0.958 (0.870–1.000)). Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (ptumor = 0.01, pserum = 0.001; AUROCCtumor = 1 (1.000–1.000), AUROCCserum = 1 (1.000–1.000)). In summary, we found different metabolic feature patterns in tumor tissue and serum for glioma diagnosis, grade and survival, which indicates that, following further verification, metabolomic profiling of glioma tissue as well as serum may be a valuable tool in the search for latent biomarkers for future characterization of malignant glioma. Full article
(This article belongs to the Special Issue Cancer Metabolomics 2016)

Review

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Open AccessReview Evaluation of Cancer Metabolomics Using ex vivo High Resolution Magic Angle Spinning (HRMAS) Magnetic Resonance Spectroscopy (MRS)
Metabolites 2016, 6(1), 11; doi:10.3390/metabo6010011
Received: 25 January 2016 / Revised: 15 March 2016 / Accepted: 17 March 2016 / Published: 22 March 2016
Cited by 4 | PDF Full-text (3043 KB) | HTML Full-text | XML Full-text
Abstract
According to World Health Organization (WHO) estimates, cancer is responsible for more deaths than all coronary heart disease or stroke worldwide, serving as a major public health threat around the world. High resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS) has demonstrated
[...] Read more.
According to World Health Organization (WHO) estimates, cancer is responsible for more deaths than all coronary heart disease or stroke worldwide, serving as a major public health threat around the world. High resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS) has demonstrated its usefulness in the identification of cancer metabolic markers with the potential to improve diagnosis and prognosis for the oncology clinic, due partially to its ability to preserve tissue architecture for subsequent histological and molecular pathology analysis. Capable of the quantification of individual metabolites, ratios of metabolites, and entire metabolomic profiles, HRMAS MRS is one of the major techniques now used in cancer metabolomic research. This article reviews and discusses literature reports of HRMAS MRS studies of cancer metabolomics published between 2010 and 2015 according to anatomical origins, including brain, breast, prostate, lung, gastrointestinal, and neuroendocrine cancers. These studies focused on improving diagnosis and understanding patient prognostication, monitoring treatment effects, as well as correlating with the use of in vivo MRS in cancer clinics. Full article
(This article belongs to the Special Issue Cancer Metabolomics 2016)
Open AccessReview Cancer Metabolomics and the Human Metabolome Database
Metabolites 2016, 6(1), 10; doi:10.3390/metabo6010010
Received: 1 December 2015 / Revised: 23 February 2016 / Accepted: 25 February 2016 / Published: 2 March 2016
Cited by 17 | PDF Full-text (3181 KB) | HTML Full-text | XML Full-text
Abstract
The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The
[...] Read more.
The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB). The HMDB is currently the world’s largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer. Full article
(This article belongs to the Special Issue Cancer Metabolomics 2016)
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