Skip to main content
SpringerLink
Log in

Metabolomic Profiling of Ovarian Carcinomas Using Mass Spectrometry

  • Protocol
  • First Online:
Ovarian Cancer

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1049))

Abstract

Most of the research on tumor cell metabolism has focused on glucose utilization. However, when glucose is limited, solid tumors are forced to catabolize alternative substrates such as fatty acids and amino acids as an energy source. Measuring these alternations in tumor cell metabolism enables us to track neoplastic changes in the tissue to lead towards a more reliable diagnostic outcome. Although a very small number of elements are used in biochemistry, the metabolome is structurally diverse for the production of simple compounds such as phosphate and amino acids as well as more structurally complex compounds such as nucleotides, oligosaccharides, and complex lipids. Characterization of the metabolome, therefore, requires analytical methods that can handle a wide range of molecular structures and physicochemical properties, including solubility, polarity, and molecular weight. A further factor for consideration in the selection of technology for metabolomics is the wide range of concentrations of biochemical typically present in biological systems. MS has established itself as the high-throughput, information-rich, industrially stable approach to assess both the composition of diverse sample types as well as changes to that composition following perturbation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Koutsaki M, Zaravinos A, Spandidos DA (2012) Modern trends into the epidemiology and screening of ovarian cancer. Genetic substrate of the sporadic form. Pathol Oncol Res 18(2):135–148

    Article  PubMed  Google Scholar 

  2. Cass I, Karlan BY (2010) Ovarian cancer symptoms speak out – but what are they really saying? J Natl Cancer Inst 102:211–212

    Article  PubMed  Google Scholar 

  3. National Cancer Institute Stat Fact Sheet. http://seer.cancer.gov/statfacts/html/ovary.html

  4. Nossov V, Amneus M, Su F, Lang J, Janco J et al (2008) The early detection of ovarian cancer: from traditional methods to proteomics. Can we really do better than serum CA-125? Am J Obstet Gynecol 199:215–223

    Article  PubMed  CAS  Google Scholar 

  5. Neesham D (2007) Ovarian cancer screening. Aust Fam Physician 36:126–128

    PubMed  Google Scholar 

  6. Cesario S (2010) Advances in the early detection of ovarian cancer: how to hear the whispers early. Nurs Womens Health 14:222–234

    Article  PubMed  Google Scholar 

  7. Bhosale P, Peungjesada S, Wei W, Levenback CF, Schmeler K et al (2010) Clinical utility of positron emission tomography/computed tomography in the evaluation of suspected recurrent ovarian cancer in the setting of normal CA-125 levels. Int J Gynecol Cancer 20:936–944

    Article  PubMed  Google Scholar 

  8. Lin B, White JT, Wu J, Lele S, Old LJ et al (2009) Deep depletion of abundant serum proteins reveals low-abundant proteins as potential biomarkers for human ovarian cancer. Proteomics Clin Appl 3:853–861

    Article  PubMed  CAS  Google Scholar 

  9. Zhen H, Yang S, Wu H, Wang S, Lv J et al (2010) LyGDI is a promising biomarker for ovarian cancer. Int J Gynecol Cancer 20:316–322

    Article  PubMed  Google Scholar 

  10. Kothandaraman N, Bajic VB, Brendan PN, Huak CY, Keow PB et al (2010) E2F5 status significantly improves malignancy diagnosis of epithelial ovarian cancer. BMC Cancer 10:64

    Article  PubMed  Google Scholar 

  11. Hellstrom I, Hellstrom K (2008) SMRP and HE4 as biomarkers for ovarian carcinoma when used alone and in combination with CA125 and/or each other. Adv Exp Med Biol 622:15–21

    Article  PubMed  CAS  Google Scholar 

  12. Lowe K, Shah C, Wallace E, Anderson G, Paley P et al (2008) Effects of personal characteristics of serum CA125, mesothelin, and HE4 levels in healthy postmenopausal women at high-risk for ovarian cancer. Cancer Epidemiol Biomarkers Prev 17:5480–5487

    Article  Google Scholar 

  13. Nolen B, Velikokhatnaya L, Marrangoni A, De Geest K, Lomakin A et al (2010) Serum biomarker panels for the discrimination of benign from malignant cases in patients with an adnexal mass. Gynecol Oncol 117:440–445

    Article  PubMed  CAS  Google Scholar 

  14. Zagouri F, Dimopoulos MA, Bournakis E, Papadimitriou CA (2010) Molecular markers in epithelial ovarian cancer: their role in prognosis and therapy. Eur J Gynaecol Oncol 31:268–277

    PubMed  CAS  Google Scholar 

  15. Warburg O (1956) On the origin of cancer cells. Science 123:309–314

    Article  PubMed  CAS  Google Scholar 

  16. Levine AJ, Puzio-Kuter AM (2010) The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science 330:1340–1344

    Article  PubMed  CAS  Google Scholar 

  17. Rous P, Robertson OH, Oliver J (1919) Experiments on the production of specific antisera for infections of unknown cause : I. Type experiments with known antigens-a bacterial hemotoxin (megatheriolysin), the pneumococcus, and poliomyelitic virus. J Exp Med 29:283–304

    Article  PubMed  CAS  Google Scholar 

  18. Cori CF, Cori GT (1925) The carbohydrate metabolism of tumors. J Biol Chem 65:397–405

    CAS  Google Scholar 

  19. Friis RR, Schwarz RT, Schmidt MF (1977) Phenotypes of Rous sarcoma virus-transformed fibroblasts: an argument for a multifunctional Src gene product. Med Microbiol Immunol 164:155–165

    Article  PubMed  CAS  Google Scholar 

  20. Elstrom RL, Bauer DE, Buzzai M, Karnauskas R, Harris MH et al (2004) Akt stimulates aerobic glycolysis in cancer cells. Cancer Res 64:3892–3899

    Article  PubMed  CAS  Google Scholar 

  21. Kim JW, Tchernyshyov I, Semenza GL, Dang CV (2006) HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab 3:177–185

    Article  PubMed  Google Scholar 

  22. Rimpi S, Nilsson JA (2007) Metabolic enzymes regulated by the Myc oncogene are possible targets for chemotherapy or chemoprevention. Biochem Soc Trans 35:305–310

    Article  PubMed  CAS  Google Scholar 

  23. Mazurek S, Boschek CB, Hugo F, Eigenbrodt E (2005) Pyruvate kinase type M2 and its role in tumor growth and spreading. Semin Cancer Biol 15:300–308

    Article  PubMed  CAS  Google Scholar 

  24. Mazurek S, Eigenbrodt E (2003) The tumor metabolome. Anticancer Res 23:1149–1154

    PubMed  CAS  Google Scholar 

  25. Vander Heiden MG, Locasale JW, Swanson KD, Sharfi H, Heffron GJ et al (2010) Evidence for an alternative glycolytic pathway in rapidly proliferating cells. Science 329:1492–1499

    Article  PubMed  CAS  Google Scholar 

  26. Spratlin JL, Serkova NJ, Eckhardt SG (2009) Clinical applications of metabolomics in oncology: a review. Clin Cancer Res 15:431–440

    Article  PubMed  CAS  Google Scholar 

  27. Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E (2009) Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem 81:6656–6667

    Article  PubMed  CAS  Google Scholar 

  28. Dehaven CD, Evans AM, Dai H, Lawton KA (2010) Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J Cheminform 2:9

    Article  PubMed  Google Scholar 

  29. Fong MY, McDunn J, Kakar SS (2011) Identification of metabolites in the normal ovary and their transformation in primary and metastatic ovarian cancer. PLoS One 6:e19963

    Article  PubMed  CAS  Google Scholar 

  30. Parman T, Bunin DI, Ng HH, McDunn JE, Wulff JE et al (2011) Toxicogenomics and metabolomics of pentamethylchromanol (PMCol)-induced hepatotoxicity. Toxicol Sci 124:487–501

    Article  PubMed  CAS  Google Scholar 

  31. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q et al (2009) Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457:910–914

    Article  PubMed  CAS  Google Scholar 

  32. Watson M, Roulston A, Belec L, Billot X, Marcellus R et al (2009) The small molecule GMX1778 is a potent inhibitor of NAD+ biosynthesis: strategy for enhanced therapy in nicotinic acid phosphoribosyltransferase 1-deficient tumors. Mol Cell Biol 29:5872–5888

    Article  PubMed  CAS  Google Scholar 

  33. Ganti S, Weiss RH (2011) Urine metabolomics for kidney cancer detection and biomarker discovery. Urol Oncol 29:551–557

    Article  PubMed  CAS  Google Scholar 

  34. Conrads TP, Anderson GA, Veenstra TD, Pasa-Tolic L, Smith RD (2000) Utility of accurate mass tags for proteome-wide protein identification. Anal Chem 72:3349–3354

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Physiology and Biophysics, University of Louisville, Louisville, KY, USA

    Miranda Y. Fong & Sham S. Kakar

  2. Metabolon, Inc., Durham, NC, USA

    Jonathan McDunn

Authors
  1. Miranda Y. Fong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan McDunn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sham S. Kakar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Group of Experimental Therapeutics, Federal State Institution N.N.Petrov Res Institute of Oncology, St. Petersburg, Russia

    Anastasia Malek

  2. Labs of Molecular Tumor Pathology & Func, Charité - Universitatsmedizin Berlin, Berlin, Germany

    Oleg Tchernitsa

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media, New York

About this protocol

Cite this protocol

Fong, M.Y., McDunn, J., Kakar, S.S. (2013). Metabolomic Profiling of Ovarian Carcinomas Using Mass Spectrometry. In: Malek, A., Tchernitsa, O. (eds) Ovarian Cancer. Methods in Molecular Biology, vol 1049. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-547-7_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-62703-547-7_18

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-546-0

  • Online ISBN: 978-1-62703-547-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation