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

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  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
  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