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Metabolomics is ever evolving and rapidly progressing field. It involves studying set of metabolites inside a cell or body.

Metabolomics is ever evolving and rapidly progressing field. It involves studying set of metabolites inside a cell or body.

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Metabolomics

  1. 1. Content • Introduction • Methods • Applications • Challenges & problems • Future directions • Conclusion
  2. 2. Introduction Emerging Field of ‘Omics' Research • Unbiased global survey of all low molecular-weight molecules or metabolites in biofluid, cell, tissue, organ, or organism • Study of range of metabolites in cells or organs & ways they are altered in disease states and their changes over time as consequence of stimuli (including biological perturbation such as diet, disease or intervention) Any organic molecule detectable in body with MW < 1000 Dalton with concentration ≥ 1 µM Includes peptides, oligonucleotides, sugars, nucelosides, organic acids, ketones, aldehydes, amines, amino acids, lipids, steroids, alkaloids and drugs (xenobiotics) Includes human & microbial products Metabolome refers to complete set of small-molecule metabolites to be found within a biological sample, such as a single organism.
  3. 3. Introduction contd… • The name ‘metabolomics’ was coined in the late 1990s – The first paper using the word was by Oliver, S. G., Winson, M. K., Kell, D. B. & Baganz, F. (1998). Systematic functional analysis of the yeast genome.Trends Biotechnol.1998 Sep;16(9):373-8. • Study of metabolome, started decades ago with early applications in field of toxicology, inborn metabolic errors & nutrition • Original report to mention metabolomics approach in oncology dates back 25 years ago where authors claimed that cancer could be identified from nuclear magnetic resonance (NMR) spectra generated from blood samples* *Fossel et al. N Engl J Med 1986;315:1369–76
  4. 4. Genomics Proteomics Pharmacogenomics Transcriptomics Epigenomics Spliceomics Metabolomics THE ‘OMIC’ WORLDTHE ‘OMIC’ WORLD ‘OMICS’ REFERS TO LARGE SCALE ANALYSIS
  5. 5. Bioiformatics: Using techniques developed in fields of computational science & statistics Key element in data management & analysis of collected data sets GENOMICS TRANSCRIPTOMICS PROTEOMICS METABOLOMICS
  6. 6. Why Metabolomics ?.....!!!!! Since metabolome is closely tied to genotype of an organism, its physiology and its environment (what the organism eats or breathes), metabolomics offers a unique opportunity to look at genotype- phenotype as well as genotype- envirotype relationships
  7. 7. In Other Words…….. • Not all changes or abnormalities detected in genome or transcriptome may be causing abnormality or disease e.g. silent mutations • Similarly not all enzymes & protein products detected via proteomics are functional • Also they do not take into account environmental influences occurring at later stage • Can be used to monitor changes in genome or to measure effects of downregulation or upregulation of specific gene transcript • Metabolites are ultimate result of cellular pathways (taking into account changes in genome, trancriptome, proteome as well as metabolic influences) Direct correlation with abnormalities being caused
  8. 8. Some More Comparisons Genomics Transcriptomics Proteomics Metabolomics Target number 40,000 genes 150000 transcripts 1,000,000 proteins 2500 metabolites Specimen tissue, cells Tissue, cells Biofluids, tissue, cells Biofluids, tissue, cells Technique SNP arrays DNA arrays 2DE1 & MALDI2 -TOF MS3 NMR4 , GC-MS5 1:Two-dimensional gel electrophoresis 2:Matrix-assisted laser desorption/ionization 3:Time-of-flight mass spectrometry 4:Nuclear magnetic resonance 5:Gas chromatography–mass spectrometry
  9. 9. Trends Published papers Genomics Proteomics Metabolomics Genomics, Proteomics : 5 folds / 5yrs
  10. 10. Definitions • Metabolic profiling : – Quantitative study of a group of metabolites, known or unknown, within or associated with a particular metabolic pathway • Metabolic fingerprinting: – Measures a subset of the whole profile with little differentiation or quantitation of metabolites • Target isotope-based analysis: – Focuses on particular segment of metabolome by analysing only few selected metabolites comprising specific biochemical pathway
  11. 11. How does Metabolomics work? • ? Samples • ? Methods • ? Data collection • ? Determination of significance
  12. 12. Sample collection, treatment and processing Sample collection, treatment and processing Detection technique: • Nuclear Magnetic Resonance Spectroscopy (NMR) • Mass Spectrometry (MS) Detection technique: • Nuclear Magnetic Resonance Spectroscopy (NMR) • Mass Spectrometry (MS) Separation technique: •Gas Chromatography (GC) •High Performance Liquid Chromatography (HPLC) •Ultra Performance Liquid Chromatography (UPLC) •Capillary Electrophoresis (CE) Separation technique: •Gas Chromatography (GC) •High Performance Liquid Chromatography (HPLC) •Ultra Performance Liquid Chromatography (UPLC) •Capillary Electrophoresis (CE) Data analysis using multivariate analysis e.g. •Principle Component Analysis (PCA) •Partial Least-Squares (PLS) Method •Orthogonal PLS (OPLS) Data analysis using multivariate analysis e.g. •Principle Component Analysis (PCA) •Partial Least-Squares (PLS) Method •Orthogonal PLS (OPLS) Basic Workflow Validation followed by clinical application
  13. 13. Sample collection, treatment and processing Sample collection, treatment and processing Basic Workflow
  14. 14. Metabolomic Samples • Metabolomic assessment can be pursued both in vitro and in vivo using cells, fluids, or tissues • Biofluids are easiest to work with: – Serum – Plasma – Urine – Ascitic fluid/pleural fluid – Saliva – Bronchial washes – Prostatic secretions Maximum experience with serum and urine samples Maximum experience with serum and urine samples Currently, interest is evolving to use tissue samples directly Currently, interest is evolving to use tissue samples directly
  15. 15. Sample Collection & Handling • All biological samples collected for metabolic analysis require careful sample handling, special requirements for diet, physical activities, & other patient validation • Due to high susceptibility of metabolic pathways to exogenous environment, maintaining low temperature and consistent sample extraction is essential • For biofluids, standard sample volume: 0.1 to 0.5 mL • For NMR, minimal sample preparation is required (including direct analysis of intact tissue specimen)
  16. 16. Sample collection, treatment and processing Sample collection, treatment and processing Separation technique: •Gas Chromatography (GC) •High Performance Liquid Chromatography (HPLC) •Ultra Performance Liquid Chromatography (UPLC) •Capillary Electrophoresis (CE) Separation technique: •Gas Chromatography (GC) •High Performance Liquid Chromatography (HPLC) •Ultra Performance Liquid Chromatography (UPLC) •Capillary Electrophoresis (CE) Basic Workflow Both approaches involve an initial chromatographic stage in which metabolites are separated either in the gas or solution phase, resp.
  17. 17. Sample collection, treatment and processing Sample collection, treatment and processing Detection technique: • Nuclear Magnetic Resonance Spectroscopy (NMR) • Mass Spectrometry (MS) Detection technique: • Nuclear Magnetic Resonance Spectroscopy (NMR) • Mass Spectrometry (MS) Separation technique: •Gas Chromatography (GC) •Capillary Electrophoresis (CE) •High Performance Liquid Chromatography (HPLC) •Ultra Performance Liquid Chromatography (UPLC) Separation technique: •Gas Chromatography (GC) •Capillary Electrophoresis (CE) •High Performance Liquid Chromatography (HPLC) •Ultra Performance Liquid Chromatography (UPLC) Basic Workflow
  18. 18. Detection Techniques • Mass spectrometry (MS) • Nuclear magnetic resonance (NMR) spectroscopy • Others: • Ion-mobility spectrometry, • Electrochemical detection (coupled to HPLC) • Radiolabelling techniques (when combined with thin- layer chromatography) • MRSI (Magnetic resonance spectroscopic imaging) • PET scan Qualitative & quantitative assessment MS NMR
  19. 19. Nuclear Magnetic Resonance (NMR) Spectroscopy • Uses isotopes possessing property of magnetic spin • Isotopes usually used : 1 H and 13 C NMR spectroscopy, although 31 P NMR spectroscopy used to measure high-energy phosphate metabolites and phosphorylated lipid intermediates. • Relatively insensitive technique: Current detection limits are of order of 100 µM in a tissue extract or biofluid • Can be used in a non-invasive manner, making it possible to metabolically profile intact tissue or whole organ • Typical acquisition times: about 10 minutes • Highly reproducibleA variant of NMR called high resolution magic angle spinning NMR spectroscopy (HR-MAS) developed to improve spectral resolution in solids such as intact tissue samples It preserves tissue architecture so pathological evaluation is not compromised
  20. 20. Metabolites detected in cancer by NMR Leucine Acetate Lysine Taurine Isoleucine Glutamine Creatine Phosphoethanol-amine Valine Glutamate Phosphocreatine Myo-inositol Lactate Glutathione Free choline Scyllo-inositol β-hydroxybutyrate Succinate Phosphocholine Glycine α-ketoisovalerate Asparate Glycerophospho- choline Glycerol β-Glucose Fumarate Histidine NAD and NADH α-Glucose Tyrosine Phenylalanine Glycerophospho- ethanolamine Formate Dimethylamine Betaine Inosine Alanine Aspargine ADP and ATP Threonine UTP and UDP Inorganic phosphate Sugar Phosphates Cholesterols and esters Phosphatidyl- choline Phosphatidyl- ethanolamine Phosphatidyl- glycerol Plasmalogen Triacylglycerol
  21. 21. Gas Chromatography– & Liquid Chromatography–Mass Spectrometry (MS) • Both approaches involve an initial chromatographic stage followed by separation according to their mass to charge ratio • Current detection limits for MS-based approaches are of the order of 100 nM, allowing detection of large no. of metabolites. • However, not all metabolites can be ionized to an equal extent, potentially biasing the information produced. • Typical acquisition times of about 30 minutes
  22. 22. Comparison of NMR &MS MASS SPECTROMETRY – More sensitive for metabolite detection • Mass spectrometers can detect analytes routinely in femtomolar to attomolar range – Requires more tissue destruction – Difficulty in quantification NMR SPECTROSCOPY – Less sensitive for metabolite detection – Non-destructive, requires little sample handling & preparation: • Metabolites in liquid state (serum, urine and so on), • Intact tissues (e.g., tumors) or in vivo – Quantification easy: • Peak area of compound in NMR spectrum directly related to conc. of specific nuclei (e.g., 1 H, 13 C), making quantifi-cation of compounds in complex mixture very precise
  23. 23. Sample collection, treatment and processing Sample collection, treatment and processing Detection technique: • Nuclear Magnetic Resonance Spectroscopy (NMR) • Mass Spectrometry (MS) Detection technique: • Nuclear Magnetic Resonance Spectroscopy (NMR) • Mass Spectrometry (MS) Separation technique: •Gas Chromatography (GC) •Capillary Electrophoresis (CE) •High Performance Liquid Chromatography (HPLC) •Ultra Performance Liquid Chromatography (UPLC) Separation technique: •Gas Chromatography (GC) •Capillary Electrophoresis (CE) •High Performance Liquid Chromatography (HPLC) •Ultra Performance Liquid Chromatography (UPLC) Data analysis using multivariate analysis e.g. •Principle Component Analysis (PCA) •Partial Least-Squares (PLS) Method •Orthogonal PLS (OPLS) Data analysis using multivariate analysis e.g. •Principle Component Analysis (PCA) •Partial Least-Squares (PLS) Method •Orthogonal PLS (OPLS) Basic Workflow
  24. 24. DATA Analysis & Interpretation
  25. 25. DATA Analysis
  26. 26. • NMR/MS spectra from biofluids or tumor tissue contain hundreds of signals from endogenous metabolites: converted to spectral data sets, reduced to 100 to 500 spectral segments, & their respective signal intensities are directly entered into statistical programs • This first step of metabolomics analysis facilitates pattern recognition, or group clustering, such as normal versus cancer or responders versus nonresponders, • Multivariate statistics (e.g. Principle Component Analysis) designed for large data sets are then applied DATA Analysis
  27. 27. DATA Analysis
  28. 28. DATA Analysis
  29. 29. • Quantitation & association of putative biomarkers with respect to particular characteristic or outcome, such as tumor grade or response to therapy • Statistical approach represented by standard Student’s t test or ANOVA, depending on group number & size
  30. 30. APPLICATIONS
  31. 31. Applications • Increasingly being used in a variety of health applications including – Pharmacology & pre-clinical drug trials – Toxicology – Transplant monitoring – New-born screening – Clinical chemistry – Tool for functional genomics • However, a key limitation to metabolomics – ‘The human metabolome is not at all well characterized’
  32. 32. • On 23 January 2007, Human Metabolome Project, led by Dr. David Wishart of the University of Alberta, Canada, completed first draft of human metabolome, consisting of database of approximately 2500 metabolites • Project mandate: identify, quantify, catalogue & store all metabolites that can potentially be found in human tissues and biofluids at concentrations greater than one micromolar Wishart DS et al. "HMDB: the Human Metabolome Database". Nucleic Acids Research 35 : D521–6 Human Metabolome Project
  33. 33. Applications in the Field of Oncology • Goal of these omics-based studies is more effective, more specific, safer, more “personalized” medical care • Biomarker in cancer diagnosis, prognosis, & therapeutic response evaluation (including detection of residual tumor cells) • Screening tool • Detection of micrometastases • As both predictive & pharmacodynamic marker of drug effect including search for new drugs • In Nutrigenomics, to see effect of diet on cancer prevention as well as response to treatment • As translational research tool, can provide link between laboratory & clinic • Molecular analyses of cancers can reveal information about mechanisms of initiation, progression & provide foundation for clinical tests
  34. 34. 1. High glycolytic enzyme activities 2. The expression of the pyruvate kinase isoenzyme type M2(M2PK)) 3. High phosphometabolite levels 4. A high channelling of glucose carbons to synthetic processes 5. A high rate of pyrimidine and purine de novo synthesis 6. A high rate of fatty acid de novo synthesis 7. A low (ATP+GPT) : (CTP+UTP) ratio 8. Low AMP levels 9. A high glutaminolytic capacity 10. Release of immunosuppressive metabolites 11. A high methionine dependency Characterization of Tumor Metabolome Warburg effect 1) Mazurek S et al. Anticancer Res 2003;23:1149–54 M2-PK of particular interest as its inactive dimeric form is dominant in tumors & named tumor M2-PK (tM2-PK) 1 Quantification of this tumor M2-PK in plasma & stool allows early detection of tumors/ therapy
  35. 35. Diagnosis Carcinoma Prostate: Making a difference • Traditional biomarker: Prostate specific antigen (PSA) • Shortcomings: – Low specificity of PSA, – Inability to specify a cut-point below which cancer is unlikely – Non-trivial false negative rate for prostate biopsy – Over-diagnosis and over-treatment of relatively indolent tumors with low potential for morbidity or death if left untreated • Small percentage of cancers account for the mortality: those which are invasive and metastasize. What are the molecular markers and mediators for such cellular behaviors? • How can we tell apart the lethal cancers from the relatively innocuous cancers that look the same by histology and stage?
  36. 36. Carcinoma Prostate • Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Mehra R et al • Using a combination of LC & GC based MS, profiling of more than 1,126 metabolites across 262 clinical samples related to prostate cancer (42 tissues and 110 each of urine and plasma) • Sarcosine (can be detected non-invasively in urine): – Highly increased during prostate cancer progression to metastasis – Levels also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells – Knockdown of glycine-N-methyl transferase, the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion Sreekumar A et al. Nature 2009, 457:910-914
  37. 37. CONCLUSION : Sarcosine could be a potentially promising biomarker for early detection of prostate cancer as well as cut-off levels can be defined to mark biological aggressiveness of the disease Diagnosis: Carcinoma Prostate
  38. 38. Diagnostics of Prostate Cancer • Application of blood plasma metabolites fingerprinting for diagnosis of II stage of prostate cancer has been investigated • Area under the ROC-curve (0.994) suggests that the proposed approach is effective and can be used for clinical applications Lokhov, Archakov et al. Biomedical Chemistry. 2009 May-Jun;55(3):247-54. Sensitivity 95.0% Specificity 96.7% Accuracy 95.7% PSA-based diagnostics Sensitivity 35.0% Specificity 83.3% Accuracy 51.4% Metabolome-based diagnostics
  39. 39. Lung Cancer Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer. Mazzone PJ, Wang XF, Xu Y, Mekhail T, Beukemann MC, Na J, Kemling JW, Suslick KS, Sasidhar M •Pattern of exhaled breath volatile organic compounds represents metabolic biosignature with potential to identify & characterize lung cancer •Reported accuracy exceeding 80% in lung cancer detection, which is comparable to CT scan •Also colorimetric sensor array could identify subtype of lung cancer (small cell versus adenocarcinoma versus squamous cell) with accuracy approaching 90% •Combining breath biosignature with clinical risk factors may improve accuracy of signature Mazzone PJ et al. J Thorac Oncol. 2012 Jan;7(1):137-42.
  40. 40. Diagnosis: Breast Cancer • Several NMR studies analyzed breast biopsy sample identifying over 30 endogenous metabolites in breast tissue1,2 • Cancers reliably showed elevated phosphocholine, low glycer- ophosphocholine, & low glucose compared with benign tumors or healthy tissue • Also, when 91breast cancers & 48 adjacent normal tissue specimens examined after surgical resection using HR-MAS 1 H-NMR – Malignant phenotype could reliably be differentiated from normal tissue with sensitivity & specificity between 83% and 100% for tumor size, lymph node, and hormonal status, as well as histology1 • In vivo, when MRSI of breast is performed on patients before biopsy, precise differentiation of cancer and benign tissue possible based on choline detection, with a sensitivity of 100%3 • Importantly, biopsy could have been prevented 68% of the time if only performed on the choline-positive tissue 1.Bathen TF et al. Breast Cancer Res Treat 2007;104:181-9 2.Sitter B et al. Biomed 2006;19:30-40 3.Bartella L et al. Radiology 2007;245: 80-7
  41. 41. Diagnosis in Ovarian Cancer • Metabolomic differences between healthy women & ovarian cancer investigated. • 1 H-NMR spectroscopy done on serum from – 38 preoperative ovarian cancer patients, – 12 women with benign ovarian cysts, – 53 samples from healthy women • Separation rates were: – In premenopausal group: cancer vs normal/benign disease: 100 % – In postmenopausal group: cancer vs normal/benign diease: 97.4 % Odunsi K et al. Int J Cancer 2005;113:782-8.
  42. 42. Metabolic Biomarkers of Tumors
  43. 43. Metabolomics: Predictive Markers of Response to Therapy • Evolving innovative cancer drugs, many with cytostatic rather than cytotoxic mechanism of action, challenges our traditional way to asses tumor response based on volumetric changes as performed by standard imaging techniques • Compelling interest to develop new tools to monitor outcomes of therapeutic intervention • More specifically, non-invasive imaging techniques reporting on tissue function and metabolism such as PET scan, functional MRI studies & NMR spectroscopy hold great potential
  44. 44. Assessment of Response to Therapy • Use of metabolomics for assessment of treatment effect, as predictive measure of efficacy & as pharmacodynamic marker, has been shown in vitro for traditional chemotherapy as well as hormonal agents. • Goal is to define pretreatment metabolic profile based on which we can choose subgroup of patients who will benefit maximum from given therapy • Can be assessed both in vitro as well as in vivo • In vitro, use of 1 H-NMR on human glioma cell culture successfully predicted separation into drug-resistant & drug-sensitive groups before treatment with nitrosoureas El-Deredy et al. Cancer Res 1997;57:4196–9
  45. 45. Assessment of Response contd… • In vivo, 1 H-NMR,used to investigate metabolic changes associated with nitrosourea treatment of B16 melanoma in mice • During growth-inhibitory phase, significant accumulation of glucose, glutamine, aspartate, and serine-derived metabolites occurred • Growth recovery reflected activation of energy production systems and increased nucleotide synthesis thus characterising drug resistance Morvan D et al. Cancer Res 2007;67:2150–9
  46. 46. Application in Novel Therapeutics • Therapeutics in oncology now targeting aberrant pathways involved in growth, proliferation, and metastases • Biomarkers are being increasingly used in the early clinical development of such agents – To identify, validate, and optimize therapeutic targets and agents – To determine and confirm mechanism of drug action – As a pharmacodynamic end point – In predicting or monitoring responsiveness to treatment, toxicity, and resistance • Current examples of using metabolomics in developmental therapeutics are with tyrosine kinase inhibitors, proapoptotic agents, heat shock protein inhibitors and PIK3 inhibitors
  47. 47. Application in Therapeutics contd…. • Treatment with targeted therapies results in distinct metabolic profile between sensitive & resistant cells • Metabolic detection of imatinib resistance: – Decrease in mitochondrial glucose oxidation – Nonoxidative ribose synthesis from glucose – Highly elevated phosphocholine levels • These data indicate that NMR metabolomics may provide way for monitoring changes reflecting early resistance to novel targeted agents • Early metabolomic markers of resistance may dictate therapy adjustments that prevent overt phenotypic progression (clinical failure) Gottschalk S et al. Clin Cancer Res 2004;10:6661–8.
  48. 48. Detection of Chemotoxicity • Chemotherapy drugs capable to cause significant, irreversible, life threatening organ damage • Bothersome and distressing for patients and might affect the optimal delivery of treatment • Various studies have predicted the risk factors for drug induced organ damage • However, lack of biomarker to pick-up these changes in early phase causes potential morbidity and mortality • Metabolomics can more thoroughly address interplay between gene, drugs environment and thus increase our ability to predict individual variation in drug response phenotypes This approach has been coined pharmacometabolomics
  49. 49. As Biomarker for Chemotoxicity Metabolomic study of cisplatin-induced nephrotoxicity Portilla D,Li S, Nagothu KK, Megyesi J, Kaissling B, Schnackenberg L, Safirstein RL, Beger RD •Samples from mice treated with single injection of cisplatin were collected for 3 days and analyzed by 1H-NMR spectroscopy •Biochemical analysis of endogenous metabolites performed in serum, urine,& kidney tissue •Presence of glucose, amino acids, & trichloacetic acid cycle metabolites in urine after 48 h of cisplatin administration was demonstrated in mice subsequently developing renal failure •These metabolic alterations precede changes in serum creatinine •Study shows that cisplatin induces a unique NMR metabolic profile in urine of mice developing acute renal failure •Injury-induced metabolic profile may be used as a biomarker of cisplatin-induced nephrotoxicity Kidney Int 2006 Jun;69(12):2194-204
  50. 50. Problems & Challenges in Metabolomics •Metabolites have wide range of molecular weights & large variations in concentration •Metabolome is much more dynamic than proteome & genome, which makes metabolome more time sensitive •Loss of various metabolites during tissue extraction e.g. glutathione •Number of metabolites existing far smaller than the no. of transcripts Therefore, given metabolite pattern can reflect several genomic changes
  51. 51. Not All Metabolites can be Identified Carcinoma Pancreas •Tesiram et al. tried to determine NMR characteristics & metabolite profiles of serum samples from patients with pancreatic cancer compared with noncancerous control samples •Data showed that – Total choline (P = 0.03) – Taurine (P = 0.03) – Glucose plus triglycerides (P = 0.01) • Also detected were species that could not be individually identified and that were designated UCM (unresolved complex matter) •Levels of UCM were significantly higher in subjects with cancer, being almost double those of control samples Significantly higher in cancer versus control samples Tesiram et al. Pancreas. 2012 Apr;41(3):474-80.
  52. 52. Problems contd…. • Metabolic profiles are complex & highly susceptible to endogenous and exogenous factors (hormones, race, age, sex, rate of metabolism, diet, physical activities, xenobiotics) • Therefore samples collected for metabolic analysis require careful sample handling and information regarding diet, physical activities, and other patient validation • Marked heterogeneity across studies • Among distinct tumor types, profiles vary with respect to many metabolites, including alanine, citrate, glycine, lactate, nucleotides, and lipids, making it difficult to generalize findings across tumor groups
  53. 53. Future Directions • If pathognomonic metabolic profiles of various cancers/diseases can be identified & validated in various body fluids, metabolomics may save time, cost, & effort in obtaining definitive diagnosis in situations where no other test can provide answers • Future role as minimally invasive screening tool • Most of recent research into tumour metabolomics comes from NMR- based studies, studies aiming at using combination of NMR & MS so as to improve upon sensitivity, specificity & reproducibility • Improved sensitivity will also be possible using cryogenically cooled NMR probes, known as CRYOPROBES
  54. 54. Conclusion • Metabolomics is a novel discipline encompassing comprehensive metabolite evaluation, pattern recognition & statistical analyses • May provide ability to diagnose cancer in curative state, determine aggressiveness of cancer to help direct prognosis, therapy, & predict drug efficacy • Still in its infancy & has lagged behind other ‘omic’ sciences due to technical limitations, database challenges • It is a long path of discovery, confirmation, clinical trials, and approval to establish test validity and utility • Urgent need to establish spectral databases of metabolites, as well as cross- validation of NMR- or MS-obtained metabolites & correlation with other quantitative assays • Important to integrate it with other ‘omics’ technology so that the entire spectrum of the malignant phenotype can be characterized
  55. 55. THANK YOU
  56. 56. • Another interesting application of metabolomics is in area of heat shock protein 90 (Hsp90) inhibitors • Although their mechanism of action is not fully elucidated, current data suggest that this family of agents increase cellular destruction of client oncogenic proteins • In one study, colon cancer xenografts were treated with an Hsp90 inhibitor and extracts of these tumors were analyzed by 31 P-NMR, reflecting a significant increase in phosphocholine, valine and phosphoethanolamine levels, indicating altered phospholipid metabolism • These results, although preliminary, address that metabolic changes could be used as pharmacodynamic biomarkers of Hsp90 inhibitors, class of agents that do not seem to result in classic antitumor effects Application in Therapeutics contd…. Neckers L. Heat shock protein 90: the cancer chaperone. J Biosci 2007;32:517–30

Editor's Notes

  • Metabolomics is the solution to this problem. A comprehensive, systems biology conscious approach to understanding the Metabolome in its full scope. Metabolomics seeks to avoid reductionism and apply high throughput analysis methods on metabolic levels in the cell. It will revolutionize fields like metabolic engineering and increase our knowledge of biological function phenomenally.
  • Context dependent
    Metabolomics, one of the &amp;quot;omic&amp;quot; sciences in systems biology, is the global assessment and validation of endogenous small-molecule biochemicals (metabolites) within a biologic system.
    Perhaps the best description of this approach was offered by Steve Oliver of University of Cambridge, who used the term ‘metabolomics’ to describe “the complete set of metabolites/low molecular weight intermediates, which are context dependent, varying according to the physiology, developmental or pathological state of the cell, tissue, organ or organism”.
    Metabolomics, one of the &amp;quot;omic&amp;quot; sciences in systems biology, is the global assessment and validation of endogenous small-molecule biochemicals (metabolites) within a biologic system. Initially, putative quantitative metabolic biomarkers for cancer detection and/or assessment of efficacy of anticancer treatment are usually discovered in a preclinical setting (using animal and human cell cultures), followed by translational validation of these biomarkers in biofluid or tumor tissue. Based on the tumor origin, various biofluids, such as blood, urine, and expressed prostatic secretions, can be used for validating metabolic biomarkers noninvasively in cancer patients. Metabolite detection and quantification is usually carried out by nuclear magnetic resonance (NMR) spectroscopy, while mass spectrometry (MS) provides another highly sensitive metabolomics technology. Usually, sophisticated statistical analyses are carried out either on spectroscopic or on quantitative metabolic data sets to provide meaningful information about the metabolic makeup of the sample. Various metabolic biomarkers, related to glycolysis, mitochondrial citric cycle acid, choline and fatty acid metabolism, were recently reported to play important roles in cancer development and responsiveness to anticancer treatment using NMR-based metabolic profiling.Carefully designed and validated protocols for sample handling and sample extraction followed by appropriate NMR techniques and statistical analyses, which are required to establish quantitative (1)H-NMR-based metabolomics as a reliable analytical tool in the area of cancer biomarker discovery, are discussed in the present chapter.
    emerging field of metabolomics is based on the premise that the identification and measurement of metabolic products will enhance our understanding of physiology and disease
    Studies of tumour cell and tissue allow focused analysis on the tumour, whilst studies of biofluids have the appeal of concurrent assessment of tumour and host. 
  • The term metabolomics was first used in context of yeast in the late 90’s by mr.Oliver steve
    Stephen Oliver is a Professor in the Department of Biochemistry at the University of Cambridge
    Based on premise
    Identification and measurement of metabolic products will enhance our understanding of physiology and disease
    The first paper was titled, “Quantitative Analysis of Urine Vapor and Breath by Gas-Liquid Partition Chromatography”, by Robinson and Pauling in 1971.
    Terminology relating to metabolomics has been controversial.
    4
    The term “metabolome” was first used by Olivier et al. in 1998
    5
    to describe the set of metabolites synthesized by an organism, in
    a fashion analogous to that of the genome and proteome. This
    definition has been limited
    6
    to “the quantitative complement of
    all of the low molecular weight molecules present in cells in a
    particular physiological or developmental state”. Metabolomics
    was coined by Fiehn
    7
    and defined as a comprehensive analysis in
    which all metabolites of a biological system were identified and
    quantified
    Many of the bioanalytical methods used for metabolomics have been adapted (or in some cases simply adopted) from existing biochemical techniques.
    A sensitive and specific blood test for cancer has long been sought. The water-suppressed proton nuclear magnetic resonance (NMR) spectrum of plasma is dominated by the resonances of plasma lipoprotein lipids. We measured the mean line widths of the methyl and methylene resonances, which were found to be correlated with the presence or absence of malignant tumors. Values for the average line width were lower in patients with cancer. We analyzed plasma from 331 people (normal controls, patients with malignant and benign tumors, patients without tumors, and pregnant patients); NMR analysis and measurement of line widths were blinded to diagnosis or patient group. The mean line width for 44 normal controls (±SD) was 39.5±1.6 Hz. For 81 patients with untreated cancer, demonstrated by biopsy, the line width was 29.9±2.5 Hz. Patients with malignant tumors were reliably distinguished from normal controls by this method (P&amp;lt;0.0001), and differed from patients with diseases that did not involve tumors (line width, 36.1±2.6 Hz; P&amp;lt;0.0001). Patients with benign tumors (e.g., those of the breast, ovary, uterus, and colon) had line widths of 36.7±2.0 Hz and were different from those with malignant tumors (P&amp;lt;0.0001). However, pregnant patients and those with benign prostatic hyperplasia had line widths consistent with the presence of malignant tumors. The narrowing of lipoprotein-lipid resonances with cancer is consistent with the response of a host to tumor growth.
    We conclude that these preliminary results demonstrate that water-suppressed proton NMR spectroscopy is a potentially valuable approach to the detection of cancer and the monitoring of therapy. (N Engl J Med 1986; 315:1369–76.)
  • flux have a significant impact on metabolite concentra-
    tions10–12
    .This is because the control of the metabolic flux
    of a pathway is spread across all the enzymes present
    in the pathway, rather than being controlled by a rate-
    determining step. Furthermore, there is not necessarily a
    good quantitative relation between mRNA concentra-
    tions and enzyme function, but as metabolites are down-
    stream of both transcription and translation, they are
    potentially a better indicator of enzyme activity13
    .So,
    metabolomics offers a particularly sensitive method to
    monitor changes in a biological system, through observed
    changes in the metabolic network.
  • Important question is
  • For example influences occurring at level of proteomes wont be picked up by genome or transcriptome
    Metabolites are the ultimate result of cellular pathways (taking into account changes in genome, trancriptome, proteome as well as metabolic influences) hence more likely to
  • Is metabolomics the greatest “omics” of all? Certainly, it has
    been suggested that metabolomics may in fact provide the most
    “functional” information of the omics technologies.
    1
    This reflects
    the limitations associated with transcriptomics and proteomics;
    for example, changes in the transcriptome and proteome do not
    always result in altered biochemical phenotypes (the metabolome).
    1,2
    Furthermore, the metabolome represents the final “omic”
    level in a biological system, and metabolites represent functional
    entities, unlike messenger RNA molecules, which constitute the
    transcriptome.
    3 Metabolites thus have a clear function in the life
    of the biological system and are also contextual,
    3
    reflecting the
    surrounding environment. The metabolome can thus be thought
    of as a looking glass, which if looked through can show information concerning the physiological, developmental, and pathological
    status of a biological system
    for the detection and prevention of adulteration.
    Functional genomics, as the name implies, aims to decipher
    gene function by establishing a better understanding of the
    correlation between genes and the functional phenotype of an
    organism.
    28
    Since the metabolome of a system represents the
    amplification and integration of signals from other functional
    genomic levels (e.g., transcriptome and proteome),
    29
    metabolomics
    can be considered tool for functional genomics. Functional
    genomics represents a way to do “smarter” genomics, rather than
    simply gene mapping and sequencing, and motivation for this
    research endeavor arises because of the large proportion of open
    reading frames (typically 20-40%
    30
    ) in a fully sequenced organism
    that have no known function at the biochemical and phenotype
    levels. Such genes are referred to as “silent” or “orphan” genes.
    In the case of Saccharomyces cerevisiae, for example, around 6000
    protein encoding genes exist; however, there are less than 600
    low molecular weight intermediate metabolites (cited in ref 3)
    Determining gene function can be achieved through metabolite
    profiling of specific genetically altered organisms. These metabolite profiles may then be compared to that of a “control” organism
    to yield information about the metabolic consequence of the
    altered genome
    31
    and ultimately assign gene function. This
    approach was first used by Roessner et al.,
    Determining gene function can be achieved through metabolite
    profiling of specific genetically altered organisms. These metabolite profiles may then be compared to that of a “control” organism
    to yield information about the metabolic consequence of the
    altered genome
    31
    and ultimately assign gene function. This
    approach was first used by Roessner et al.,
    Systems biology uses an approach similar to that of functional
    genomics, but has significantly greater aims than the latter.
    Systems biology represents the ultimate challenge in that is aims
    to integrate genomics, transcriptomics, proteomics, and metabolomics
    32
    for a global understanding of biological systems. In
    essence, systems biology looks at the big picture to obtain a better
    understanding of how individual pathways or metabolic networks
    are related. Systems biology does not investigate individual genes,
    proteins, or metabolites one at a time, but rather investigates the
    behavior and relationships of all the elements in a particular
    biological system while it is functioning.
    33
    The general systems
    biology approach is a perturbation of the system (biologically,
    genetically, or chemically), followed by monitoring the impact of
    the perturbation at the genomic, proteomic, and metabolomic
    levels. These omic data can then be integrated and ultimately
    modeled computationally for a complete understanding of system
    functioning. The potential impact of systems biology is enormous,
    ranging from metabolite engineering
    1
    to reshaping medicine
    toward predictive, preventative, and personalized prevention of
    cellular dysfunction and disease
    One of the goals of systems biology is to define interacting cellular networks in the context
    of a disease phenotype, tissue-specific functions or reaction to specific stimulus or
    intervention. Systems biology as applied to cancer research encompasses the “omic”
    sciences of genomics, transcriptomics, proteomics, and metabolomics. Metabolomics
    (sometimes known as metabonomics) entails evaluation of the patterns and concentration of
    low molecular weight metabolites over broad classes of compounds in a tissue or organ.
    These metabolites are the small molecule intermediates and end products of the biochemical
    reactions in a cell, and are represented by compounds with mass typically in the range of
    80–1000 Daltons. Metabolomic studies range from targeted analysis of one or a small
    number of metabolites associated with a specific biological pathway to the unbiased
    profiling or fingerprinting of a large subset of metabolites associated with a specific
    phenotype or stimulus. Although complementary to genomics, transcriptomics and
    proteomics, metabolomics may have advantages for defining phenotypes because it is
    downstream of changes in genes and proteins, and thus may be a better indicator of distinct
    functional alterations in pathways affected by different pathological states. In this sense,
    metabolomic profiles represent the integration of genetic regulation, enzyme activity and
    metabolic reactions in a dynamic profile of the biological state of a tissue [8]. Furthermore,
    because the total complement of metabolites is likely to be considerably smaller than the
    number of genes, transcripts, or proteins, metabolomics may be able to more clearly
    characterize altered cellular networks and activity associated with disease states.
  • Metabolomics is lagging behind…still immaTURE OR INFACY…we can see the number of publication for genomics and proteomics has increased by five fold in last five years however metabolomics is only slowy catching up if atall. The reasons are
    Lack of familiarity about the subject
    Limited availabilty regarding tools and techniques which can be used
    Limited expertise
  • Most of the research today regarding metabolomics is based on characterizing metabolic profile
    Aims at finding unique metabolic characteristics for a cell
    Historical approaches to metabolite analysis include metabolite
    profiling, metabolite fingerprinting, and target analysis. Metabolite
    fingerprinting aims to rapidly classify numerous samples using
    multivariate statistics, typically without differentiation of individual
    metabolites or their quantitation. Target analysis is constrained
    exclusively to the qualitative and quantitative analysis of a
    particular metabolite or metabolites. As a result, only a very small
    fraction of the metabolome is focused upon, signals from all other
    components being ignored.
    13 Metabolite profiling involves the
    identification and quantitation by a particular analytical procedure
    of a predefined set of metabolites of known or unknown identity
    and belonging to a selected metabolic pathway.
    7,10
    By their nature,
    these approaches provide a restrictive noncomprehensive view
    of the metabolome. Nevertheless, metabolite profiling represents
    the oldest and most established approach and can be considered
    the precursor for metabolomics
    Metabolic Fingerprinting: A mass profile of the sample of interest is generated and then compared in a large sample population to screen for differences between the samples. ‘Metabolic fingerprinting’refers to measuring a subclass of metabolites to create a ‘bar code’ of metabolism
    In this approach, only a limited number of metabolites are quantified and used to distinguish between different samples, such as those of different disease or physiological states
    Metabolic profiling : has been proposed as a means of measuring the total complement of individual metabolites in a given biological sample
    Jeremy Nicholson to coin the word ‘metabonomics’.He
    defines metabonomics as “the quantitative measure-
    ment of the multivariate metabolic responses of multi-
    cellular systems to pathophysiological stimuli or
    genetic modification”27
    .In addition to the terms
    ‘Metabolic pro-
    filing’ has been proposed as a means of measuring the
    total complement of individual metabolites in a given
    biological sample, whereas ‘metabolic fingerprinting’
    refers to measuring a subclass of metabolites to create a
    ‘bar code’ of metabolism23,24
    .In this approach, only a
    limited number of metabolites are quantified and used
    to distinguish between different samples, such as those
    of different disease or physiological states
  • What r the samples where test can be performed..methods used….how is data collected…whether observed difference or abnormality is really significantand can we apply them in clinical field
  • Data analysis followed by validation and clinical application
  • Most experience to date is with serum and urine samples as a surrogate system for tumor biochemistry
    Interest is evolving for metabolomic
    studies directly using tumor tissue; however, such analyses require a more difficult and careful
    tissue preparation due to tissue heterogeneity. Surrounding stromal and epithelial cells can
    cause contamination of the resulting metabolic profile, thereby skewing results compared with
    that obtained from a pure tumor tissue sample. Microdissection techniques could enhance
    sample purity but also increase the required equipment and expertise.
  • For NMR, minimal sample preparation is required for urine and
    other low-molecular-weight metabolite-containing fluids, whereas blood, plasma, and serum
    require extraction (using acid, acetonitrile, or two-phase methanol/chloroform protocols) or
    NMR-weighted techniques to separate polar and lipophilic metabolites (see Table 1; refs. 23,
    24). Intact tissue specimens (e.g., biopsies, fine needle aspirates) can be analyzed using high-
    resolution magic angle spinning (HR-MAS). HR-MAS probes for solid state NMR, as well as
    cryoprobes and microprobes for liquid NMR, permit quantitative metabolic analysis on
    samples as small as 3 μL with improved signal-to-noise ratios and solvent suppression (5). MS
    analysis requires more labor-intensive and destructive tissue preparation than NMR, but has
    greater sensitivity for metabolite detection
    MS analysis requires more labor-intensive and destructive tissue preparation than NMR
  • Both approaches involve an initial chromatographic stage in which metabolites are separated either in the gas or solution phase, resp.
    Subsequently, metabolites are ionized and then separated according to their mass to charge ratio
    CE: Introduced in 1960s
    Higher separation efficiency than HPLC
    Wide range of metabolites than GC
    Charged analytes
  • Ion-mobility spectrometry, electrochemical detection (coupled to HPLC) and radiolabel (when combined with thin-layer chromatography)
    Magnetic resonance spectroscopic imaging (MRSI) measures metabolite concentrations in
    vivo, in an analogous fashion to the way conventional magnetic resonance imaging (MRI)
    measures water. Because the concentration of water and lipids in soft tissues such as the
    prostate is orders of magnitude greater than the concentration of metabolites, MRSI requires
    higher field strength than conventional MRI, and water and lipid suppression techniques to
    allow accurate resolution of metabolite spectra. Potential combined modality applications
    include combining MRSI and dynamic contrast enhanced MRI for enhanced visualization of
    suspicious prostate lesions or areas of recurrence, and overlaying MRSI images on
    transrectal ultrasound images for guiding prostate biopsy [13]. Current limitations to the use
    of MRSI include relatively high cost and limited availability of higher field strength (3 Tesla
    or higher) platforms needed for better spectral resolution. Most applications of MRSI in
    prostate cancer have focused on diagnostic imaging rather than metabolomic profiling of
    cellular networks so MRSI will not be further discussed in this article; for an excellent
    review see Sciarra et al. [14].
  • Certain isotopes possess the property of magnetic spin, causing their nuclei to behave in a similar manner to a tiny bar magnet. When they are placed in a magnetic field, the magnets either align or oppose the external magnet field. By applying a radiofrequency to the nuclei, one can cause the nuclei to flip into the other magnetic state and the differences in the populations between these two magnetic energy states can be detected as a radio wave as the system returns to equilibrium.
    A number of analytic platforms are used for metabolomic analyses; each has advantages and
    disadvantages and the choice of platform depends on the type of analytical problem to be
    evaluated. Most analyses employ forms of nuclear magnetic resonance (NMR) spectroscopy
    or mass spectrometry (MS). NMR spectroscopy exploits the specific magnetic spin or
    resonance frequency of the protons within atomic nuclei of specific molecules. When nuclei
    in a magnetic field are exposed to a radiofrequency pulse their protons temporarily move to
    a higher energy state, and then release a characteristic radiowave when they return to their
    normal energy state. For a mixture of metabolites in a biological sample the different
    patterns of energy release are represented as peaks in a chromatogram, and the area of the
    peaks is indicative of the relative concentration of each type of metabolite. NMR is used for
    liquids or tissue extracts. Advantages of NMR include its low cost, minimal sample
    preparation requirements, high reproducibility, ability to quantify metabolites, and
    identification of unknown metabolites. Proton or
    1H-NMR is the most common method and
    is used to detect hydrogen atoms in a molecule, but
    31P-NMR can also be used to measure
    phospholipid metabolism or high energy phosphates, and
    13C-NMR is used to measure
    carbon fluxes such as those involved in glucose metabolism [9,10]. A variant of NMR called high resolution magic angle spinning NMR spectroscopy (HR-MAS) was developed to
    improve spectral resolution in solids such as intact tissue samples. Because vibration of
    molecules in a solid state is restricted it is difficult to achieve adequate resolution of spectra
    with NMR. However, by spinning the sample at a precise “magic” angle to the induced
    magnetic field it is possible to resolve the spectra with high sensitivity. An advantage of
    HR-MAS is that it preserves the tissue architecture so pathological evaluation is not
    compromised, particularly if slower spinning speeds are used.
  • Both approaches involve an initial chromatographic stage in which metabolites are separated either in the gas or solution
    phase, respectively. Subsequently the metabolites are ionized and then separated according to their mass to charge ratio,
    which can be used to identify the metabolites.MS-based approaches are more sensitive than NMR spectroscopy, and so
    can potentially detect metabolites at a concentration two orders of magnitude below that of NMR.However, not all
    metabolites can be ionized (converted to a positively or negatively charged species suitable for mass spectrometry) to an
    equal extent, potentially biasing the information produced.This approach is the method of choice for plant
    metabolomics23,24
    where the challenge of profiling all the metabolites in a given tissue is even greater than that in
    mammals and yeast. In spite of the fact that plant genomes typically contain 20,000–50,000 genes, 50,000 metabolites
    have been identified in the plant kingdom with the number predicted to rise to about 200,000 (REF. 74),compared with
    30–600 metabolites identified in mammalian cells.The current detection limits for MS-based approaches are of the order
    of 100 nM, allowing the detection of about 1,000 metabolites,with typical acquisition times of about 30 minutes.
    Mass spectrometry (MS) requires an initial separation of metabolites by gas or liquid
    chromatography (GC, LC), followed by ionization of metabolites and resolution according
    to mass-to-charge ratio. The advantage of MS methods over NMR is much higher sensitivity
    and detection of metabolites at much lower concentrations, and it is more suitable for high
    throughput methods. However, these advantages come at the cost of more extensive sample
    preparation (particularly for GC-MS), and metabolite detection can be complicated by
    differences in ionization efficiency, stability, extraction efficiency, and fragmentation
    behavior. Derivatization is used to optimize these characteristics, but different reagents are
    used depending on the purpose of the derivatization and where in the GC-MS or LC-MS
    process it occurs, which can complicate comparisons across studies. Derivatization can also
    result in metabolite degradation. Other sources of variation include metabolite pK, polarity,
    processes of extraction and quenching, and type of instrument [8,12].
  • Mass spectroscopy (MS) based metabolomics techniques offer an excellent combination of sensitivity and selectiv ity. Mass spectroscopy includes a separation stage based on gas chromatography (GC–MS) or liquid chromatography (LC–MS). MS analysis requires more labor-intensive and destructive tissue preparation than NMR spectroscopy, but has greater sensitivity for metabolite detection [5,8,10,12].NMR (mostly 1H NMR) based metabolomics otherwise is non-destructive, requires little sample handling and preparation, is highly reproducible and allows tissue sample studies. Specifically, nuclear NMR (1H NMR) looks at a large spectrum of hydrogen-containing metabolites; the majority of them confined to the framework of organic compounds. However, when compared with GC–MS and LC–MS, NMR is a relatively insensitive method [5,8–10,12].
    Although cryogenically cooled probe technology, higher
    field­strength superconducting magnets [3] and minia tur­
    ized radiofrequency coils [4] have increased sensitivity,
    NMR spectroscopy is still orders of magnitude less
    sensitive than MS.
  • Data analysis and interpretation. The guiding principle of
    metabolomics is the global assessment of hundreds of
    endogenous metabolites in a biological sample simultaneously.
    Statistical analyses are then applied to provide meaningful
    information about the metabolic profile of the sample.
  • Because 1 H-NMR or MS spectra from biofluids or tumor
    tissue contain hundreds of signals from endogenous metabo-
    lites and are highly redundant, spectral data sets, reduced to
    100 to 500 spectral segments, and their respective signal
    intensities are directly entered into statistical programs (5, 21,
    29). This first step of metabolomics analysis facilitates pattern
    recognition, or group clustering, such as normal versus cancer
    or responders versus nonresponders, based on spectral pattern
    differences. The interpretation of scores reveals information
    about relationships between samples and illustrates trends,
    groupings, and/or outliers. In the last 5 years, due to the
    quantity and complexity of spectroscopic data from NMR and
    MS studies, the majority of metabolic profiling studies have
    used computer-aided statistical interpretation of the data. This
    improves the refining and distilling of complex raw data.
    Similar to gene array analyses, multivariate statistics have been
    designed for large data sets, with two major types of pattern
    recognition processes, unsupervised and supervised. Unsuper-
    vised data analysis, such as hierarchical cluster analysis and
    principal component analysis, measures the innate variation in
    data sets, whereas the supervised approach, including principal
    component regression and neural networks, uses prior infor-
    mation to generate the clusters of patterns (30). Although
    beyond the scope of this review, many other statistical
    approaches exist, including cluster analysis, linear discriminant
    analysis, Bayesian spectral decomposition, and several other
    chemometric methods (31).
  • Data analysis and interpretation. The guiding principle of
    metabolomics is the global assessment of hundreds of
    endogenous metabolites in a biological sample simultaneously.
    Statistical analyses are then applied to provide meaningful
    information about the metabolic profile of the sample.
  • Data analysis and interpretation. The guiding principle of
    metabolomics is the global assessment of hundreds of
    endogenous metabolites in a biological sample simultaneously.
    Statistical analyses are then applied to provide meaningful
    information about the metabolic profile of the sample.
  • Fig. 2. Three major steps of metabolomics analysis.The example is given for imatinib treatment in chronic myeloid leukemia cells using (1)
    1
    H-NMR spectra of cell extracts
    followed by principal component analysis for pattern recognition! (2) metabolite identification resulting in a biomarker! (3) metabolite quantification and validation.
    Adapted and reproduced with permission fromThomson Scientific and Serkova NJ, Spratlin JL, Eckhardt SG: NMR-based metabolomics:Translational application and
    treatment of cancer. Current Opinion in MolecularTherapeutics 2007; 9(6):572 ^ 85. Figure 4. F2007 Thomson Scientific.
  • Early detection and diagnosisof cancer when it is still in curable state
    Now characterization of tumor metabolome
    The aims of such tests include proper diagnosis, earlier diagnosis, prognosis/risk of metastases, response to specific therapies, and evidence of recurrence: “clinical utility”.
    Cancers are very heterogeneous in causation, progression, response to therapies, and risks of metastases and death
    , particularly because metabolic and molecular imaging technologies, such as positron emission tomography &amp; magnetic resonance spectroscopic imaging, enable the discrimination of metabolic markers noninvasively in vivo
  • Reference, human metabolome followed by characterisation of tumor metabolome
    The tumor metabolome is beginning to be characterized. Using standard metabolomic methods,
    tumors, in general, display elevated phospholipid levels [characterized by an elevation of total
    choline-containing compounds (tCho) and phosphocholine], increased glycolytic capacity,
    including increased utilization of glucose carbons to drive synthetic processes, high
    glutaminolytic function, and overexpression of the glycolytic isoenzyme, pyruvate kinase type
    M2 (M2-PK; refs. 12,33,34). M2-PK may be of particular interest as its inactive dimeric form
    is dominant in tumors and has been named tumor M2-PK. Interestingly, lipid metabolic profiles
    have been documented to be 83% accurate at discriminating between cancer patients and
    controls, using NMR-based metabolomics of blood samples (35). Importantly, in vivo, tCho
    determination via MRSI has detected breast, prostate, and brain tumors and correlates well
    with diagnosis via dynamic contrast enhanced-MRI (16,36-39).
    The quantification of the dimeric form of pyruvate kinase M2 (Tumor M2-PK) in plasma and stool allows early detection of tumors and therapy control.
  • Furthermore, the
    results of two randomized trials that demonstrated only modest mortality benefit associated
    with PSA screening have added to the controversy concerning the early detection paradigm
    for prostate cancer [3]
  • Metabolomic profiles were able to distinguish benign prostate, clinically localized prostate cancer and metastatic disease
    Sreekumar et al (Nature 2009) combined high-throughput liquid-and-gas-chromatography-based mass spectrometry to profile 1126 metabolites across 262 clinical samples related to prostate cancer (42 tissues; 110 urine, 110 plasma). Few differences in urine or plasma; 60 of 626 identified in prostate tumor tissue but not benign prostate. Six cpds showed increase from benign to PCA to metastatic PCA: sarcosine, uracil, kynurenine, glycerol-3-phosphate, leucine, and proline. Oncomine Concept Maps showed amino acid metabolism and methyltransferase activity increased.
    Sarcosine (an N-methyl derivative of the amino acid glycine)
  • Test additional metabolites for an expanded multiplex
    Evaluate clinical utility for different use scenarios:
    (a) diagnosis when PSA 4-10 ng/ml;
    (b) aggressivity/risk that tumor is metastatic
    Sarcosine (N-methylglycine) was much higher in metastatic tumors than localized, and nearly undetectable in benign prostate. Its levels were also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-methyl transferase attenuated prostate cancer invasion. Exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase, induced an invasive phenotype in benign prostate epithelial cells.
    Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway, binding to the promoter of GNMT.
    A test on urine sediment and supernatant is under development by Metabolon after licensing from the Chinnaiyan Lab at U of M
  • Based on premise that
    The sensor detects the unique pattern of volatile organic compounds, or the metabolic biosignature, present in exhaled breath. For the study, breath samples were drawn from 229 individuals, 92 with biopsy-proven, untreated lung cancer and 137 either at a risk for developing lung cancer or with indeterminate lung nodules.
  • Importantly, in vivo tCho determination via MRSI has detected breast, prostate &amp; brain tumors and correlates well with diagnosis via dynamic contrast enhanced-MRI
    Important in equivocal cases and to guide biopsy
    Cancer diagnosis. Pattern recognition technologies in all
    omics have been used for the diagnosis of several tumor types
    using a variety of experimental platforms. Perhaps the best Perhaps the best
    application of metabolomics thus far in cancer diagnostics is in
    breast cancer. Several NMR studies have analyzed breast biopsy
    samples and have identified over 30 endogenous metabolites in
    breast tissue. Breast cancers reliably showed elevated tCho
    levels (resulting from increased phosphocholine), low glycer-
    ophosphocholine, and low glucose compared with benign
    tumors or healthy tissue (17, 40–42). Furthermore, when 91
    breast cancers and 48 adjacent normal tissue specimens were
    examined after surgical resection using HR-MAS 1
    H-NMR
    metabolomics, a malignant phenotype could reliably be
    detected from normal tissue with sensitivity and specificity
    between 83% and 100% for tumor size, lymph node, and
    hormonal status, as well as histology (17). In vivo, when MRSI of the breast is performed on patients before biopsy, precise
    differentiation of cancer and benign tissue is possible based on
    choline detection, with a sensitivity of 100%. Importantly, a
    biopsy could have been prevented 68% of the time if only
    performed on the choline-positive tissue (refs. 36, 43; Fig. 3).
  • Metabolomic differences between healthy women and those with epithelial ovarian cancer have been investigated
    (15).
    1
    H-NMR spectroscopy was done on serum from 38
    preoperative epithelial ovarian cancer patients, 12 women with
    benign ovarian cysts, and 53 samples from healthy women.
    Serum metabolic profiles correctly separated women with
    cancer from normal premenopausal women and those with
    benign ovarian disease in 100% of cases; there was also a 97.4%
    separation rate for cancer patients versus normal postmeno-
    pausal women (Fig. 4). Interestingly, in another study, MS-
    based metabolic profiling of ovarian tumor tissue showed a
    statistically significant differentiation between invasive ovarian
    carcinomas and borderline tumors as reflected by differences in
    51 metabolites (P &amp;lt; 0.01; ref. 14). Importantly, the differences
    noted in these metabolites have previously been linked to
    prognosis in ovarian cancer and correspond to pathways
    responsible for regulation of pyrimidine metabolism (51).
  • Growth recovery reflected activation of energy production systems and increased nucleotide synthesis
    Using all of the accumulated metabolites and hence decreasing their concentration
  • Therapeutics in oncology is moving toward the use of drugs that specifically target aberrant
    pathways involved in growth, proliferation, and metastases. Biomarkers are being increasingly
    used in the early clinical development of such agents to identify, validate, and optimize
    therapeutic targets and agents; determine and confirm mechanism of drug action, as a
    pharmacodynamic end point; and in predicting or monitoring responsiveness to treatment,
    toxicity, and resistance (56). Current examples of using metabolomics in developmental
    therapeutics are with tyrosine kinase inhibitors, proapoptotic agents, and heat shock protein
    inhibitors (57-63).
  • One hypothesis explored was that treatment with targeted therapies, such as signal transduction
    inhibitors, would result in a distinct metabolic profile between sensitive and resistant cells.
    Imatinib, a tyrosine kinase inhibitor of the BCR-ABL oncogene, decreases cell proliferation
    and induces apoptosis in human chronic myeloid leukemia (64-66). Metabolically, imatinib
    interrupts the synthesis of macromolecules required for cell survival by deprivation of key
    substrates (58). Investigating glucose metabolism changes in imatinib-treated human leukemia
    BCR-ABL – positive cell lines with NMR showed decreased glucose uptake by inhibition of
    glycolysis, but unlike classic therapeutics, stimulated mitochondrial metabolism leading to cell
    differentiation (57). Imatinib also led to a significant decrease in phosphocholine in imatinib-
    sensitive cells that correlated with a decrease in cell proliferation rate (57). Metabolomic
    detection of imatinib resistance has also been reported; a decrease in mitochondrial glucose
    oxidation and a nonoxidative ribose synthesis from glucose, as well as highly elevated
    phosphocholine levels, was indicative of drug resistance and disease progression (58). These
    data indicate that NMR metabolomics may provide a method for monitoring changes in cellular
    metabolism that reflect early resistance to novel targeted agents. This could be particularly
    useful in hematologic malignancies where frequent tissue sampling is feasible and early
    metabolomic markers of resistance may dictate therapy adjustments that prevent overt
    phenotypic progression.
  • This approach has been coined pharmacometabolomics
  • (20 mg/kg body weight)
  • Chemical reactions in a biological system result in a number of intermediate molecules known as metabolites. Studying the nature of these metabolites can shed light on the functioning of the entire cellular system.
    The pursuit of this information has been variously described as metabolite profiling, metabolomics, and metabolomics. At this time, the use of these neologisms is still flexible, allowing for a great deal of overlap in their meaning, but in general, metabolite profiling is a study more likely to be found in the context of pharmaceutical research, whereas metabolomics is the domain of systems biologists and metabonomics more of an environmental or ecological pursuit.
    Mass spectrometry (MS) has emerged as the analytical method of choice for the study of metabolites, in large part because these differences are not as important in MS as in other techniques.
  • its potential application in early diagnosis (screening), cancer staging, drug discovery field, improving tumor characterization and not least, its potential impact in the field of monitoring response and toxicity to anticancer agents
  • Increased automation will allow the rapid generation of metabolomics databases to assist in patient screening
  • Should be used for identifying multivariate biomarkers, including fingerprints, profiles, or patterns characterizing state of cancer
    Metabolomics is a novel discipline encompassing comprehensive metabolite evaluation,
    pattern recognition, and statistical analyses. Biomarkers are widely used in clinical medicine
    for prognostic or predictive interpretation of disease status. Metabolomics should be used for
    identifying multivariate biomarkers, including fingerprints, profiles, or signatures, the patterns
    of which characterize a state of cancer. By using this technology, we might eventually be able
    to diagnose cancer earlier when it is still amenable to cure, determine aggressiveness of cancer
    to help direct prognosis and therapy, and predict drug efficacy. These signatures can be practical
    and accurate although they also require sophisticated analytic techniques (70,71).
  •  the Hsp90 inhibitor geldanamycin
  • ×