Skip to main content

Applications of Metabolomics in Cancer Studies

  • Chapter
  • First Online:
Metabolomics: From Fundamentals to Clinical Applications

Part of the book series: Advances in Experimental Medicine and Biology ((PMISB,volume 965))

Abstract

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
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

Abbreviations

9-AA:

9-Aminoacridine

ALL:

Acute lymphoblastic leukaemia

ATP:

Adenosine triphosphate

CE:

Capillary electrophoresis

CLL:

Chronic lymphocytic leukaemia

ECOG:

Eastern Cooperative Oncology Group

ESI:

Electrospray ionisation

GC:

Gas chromatography

GC × GC:

Comprehensive two-dimensional gas chromatography

GPC:

Glycerophosphocholine

HIF:

Hypoxia inducible factor

HILIC:

Hydrophilic interaction chromatography

LC:

Liquid chromatography

MALDI:

Matrix-assisted laser desorption ionisation

MS:

Mass spectrometry

MS/MS:

Tandem mass spectrometry

NEDC:

N-(1-naphthyl)ethylenediamine dihydrochloride

NMR:

Nuclear magnetic resonance

NSCLC:

Non-small cell lung cancer

p53:

Cellular tumour antigen p53

PC:

Phosphocholine

PTC:

Papillary thyroid carcinoma

QqQ-MS:

Triple quadrupole mass spectrometry

TCA:

Tricarboxylic acid

tCho:

Total choline

TOF:

Time-of-flight

References

  1. Warburg O. Injuring of respiration the origin of cancer cells. Science. 1956;123(3191):309–14.

    Article  CAS  PubMed  Google Scholar 

  2. Diaz-Ruiz R, Uribe-Carvajal S, Devin A, Rigoulet M. Tumor cell energy metabolism and its common features with yeast metabolism. Biochim Biophys Acta. 2009;1796(2):252–65.

    CAS  PubMed  Google Scholar 

  3. Armitage EG, Barbas C. Metabolomics in cancer biomarker discovery: current trends and future perspectives. J Pharm Biomed Anal. 2014;87:1–11.

    Article  CAS  PubMed  Google Scholar 

  4. Boroughs LK, DeBerardinis RJ. Metabolic pathways promoting cancer cell survival and growth. Nat Cell Biol. 2015;17(4):351–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Dhakshinamoorthy S, Dinh N-T, Skolnick J, Styczynski MP. Metabolomics identifies the intersection of phosphoethanolamine with menaquinone-triggered apoptosis in an in vitro model of leukemia. Mol Bio Syst. 2015;11(9):2406–16.

    CAS  Google Scholar 

  6. Fiehn O, Putri SP, Saito K, Salek RM, Creek DJ. Metabolomics continues to expand: highlights from the 2015 metabolomics conference. Metabolomics. 2015;11(5):1036–40.

    Article  CAS  Google Scholar 

  7. Nicholson JK, Lindon JC, Holmes E. “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181–9.

    Article  CAS  PubMed  Google Scholar 

  8. Brennan L. NMR-based metabolomics: from sample preparation to applications in nutrition research. Prog Nucl Magn Reson Spectrosc. 2014;83:42–9.

    Article  CAS  PubMed  Google Scholar 

  9. Johnson SR, Lange BM. Open-access metabolomics databases for natural product research: present capabilities and future potential. Front Bioeng Biotechnol. 2015;3:22.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kumar D, Gupta A, Mandhani A, Sankhwar SN. NMR spectroscopy of filtered serum of prostate cancer: a new frontier in metabolomics. Prostate. 2016;76:1106–19.

    Article  CAS  PubMed  Google Scholar 

  11. Hu JD, Tang HQ, Zhang Q, Fan J, Hong J, Gu JZ, et al. Prediction of gastric cancer metastasis through urinary metabolomic investigation using GC/MS. World J Gastroenterol. 2011;17(6):727–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gil AM. NMR metabolomics of renal cancer: an overview NMR metabolomics of renal cancer: an overview. Bioanalysis. 2015;7(18):2361–74.

    Article  CAS  Google Scholar 

  13. Ye N, Liu C, Shi P. Metabolomics analysis of cervical cancer, cervical intraepithelial neoplasia and chronic cervicitis by 1H NMR spectroscopy. Eur J Gynaecol Oncol. 2015;36(2):174–80.

    CAS  PubMed  Google Scholar 

  14. Gupta A, Gupta S, Mahdi AA. 1H NMR-derived serum metabolomics of leukoplakia and squamous cell carcinoma. Clin Chim Acta. 2015;441:47–55.

    Article  CAS  PubMed  Google Scholar 

  15. Deja S, Porebska I, Kowal A, Zabek A, Barg W, Pawelczyk K, et al. Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease. J Pharm Biomed Anal. 2014;100:369–80.

    Article  CAS  PubMed  Google Scholar 

  16. Palmnas MSA, Vogel HJ. The future of NMR metabolomics in cancer therapy: towards personalizing treatment and developing targeted drugs? Metabolites. 2013;3(2):373–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bro R, Kamstrup-Nielsen MH, Engelsen SB, Savorani F, Rasmussen MA, Hansen L, et al. Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics. 2015;11(5):1376–80. Springer US.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Jiménez B, Mirnezami R, Kinross J, Cloarec O, Keun HC, Holmes E, et al. 1H HR-MAS NMR spectroscopy of tumor-induced local metabolic “field-effects” enables colorectal cancer staging and prognostication. J Proteome Res. 2013;12(2):959–68.

    Article  PubMed  CAS  Google Scholar 

  19. Lin Y, Ma C, Liu C, Wang Z, Yang J. NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer. Oncotarget. 2016. doi:10.18632/oncotarget.8762.

    Google Scholar 

  20. Tiziani S, Lopes V, Günther UL. Early stage diagnosis of oral cancer using 1H NMR-based metabolomics. Neoplasia. 2009;11(3):269–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Puchades-Carrasco L, Jantus-lewintre E, Pérez-Rambla C, García-García F, Lucas R, Calabuig S, et al. Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer. Oncotarget. 2016;7(11):12904–16.

    PubMed  PubMed Central  Google Scholar 

  22. Lefort N, Brown A, Lloyd V, Ouellette R, Touaibia M, Culf AS, et al. 1H NMR metabolomics analysis of the effect of dichloroacetate and allopurinol on breast cancers. J Pharm Biomed Anal. 2014;93:77–85.

    Article  CAS  PubMed  Google Scholar 

  23. Liu S, Wang W, Zhou X, Gu R, Ding Z. Dose responsive effects of cisplatin in L02 cells using NMR-based metabolomics. Environ Toxicol Pharmacol. 2014;37(1):150–7.

    Article  PubMed  CAS  Google Scholar 

  24. Wang H, Chen J, Feng Y, Zhou W, Zhang J, Yu Y, et al. 1H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells. Oncol Lett. 2015;9(6):2551–9.

    PubMed  PubMed Central  Google Scholar 

  25. Puchades-Carrasco L, Lecumberri R, Martínez-López J, Lahuerta JJ, Mateos MV, Prósper F, et al. Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving complete remission. Clin Cancer Res. 2013;19(17):4770–9.

    Article  CAS  PubMed  Google Scholar 

  26. Lei Z, Huhman DV, Sumner LW. Mass spectrometry strategies in metabolomics. J Biol Chem. 2011;286(29):25435–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Godzien J, Ciborowski M, Armitage EG, Jorge I, Camafeita E, Burillo E, et al. A single in-vial dual extraction strategy for the simultaneous lipidomics and proteomics analysis of HDL and LDL fractions. J Proteome Res. 2016;15(6):1762–75.

    Article  CAS  PubMed  Google Scholar 

  28. Calderón-Santiago M, Priego-Capote F, de Castro MDL. Enhancing detection coverage in untargeted metabolomics analysis by solid-phase extraction on-line coupled to LC-MS/MS. Electrophoresis. 2015;36(18):2179–87.

    Article  CAS  Google Scholar 

  29. Dettmer K, Aronov PA, Hammock BD. Mass Spectrom Rev. 2007;26(1):51–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Moco S, Vervoort J, Moco S, Bino RJ, De Vos RCH, Bino R. Metabolomics technologies and metabolite identification. Trends Anal Chem. 2007;26(9):855–66.

    Article  CAS  Google Scholar 

  31. Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R. Metabolomics for laboratory diagnostics. J Pharm Biomed Anal. 2015;113:108–20.

    Article  CAS  PubMed  Google Scholar 

  32. Li Y, Song X, Zhao X, Zou L, Xu G. Serum metabolic profiling study of lung cancer using ultra high performance liquid chromatography/quadrupole time-of-flight mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2014;966:147–53.

    Article  CAS  PubMed  Google Scholar 

  33. Xu X, Cheng S, Ding C, Lv Z, Chen D, Wu J, et al. Identification of bile biomarkers of biliary tract cancer through a liquid chromatography/mass spectrometry-based metabolomic method. Mol Med Rep. 2015;11(3):2191–8.

    CAS  PubMed  Google Scholar 

  34. Liang Q, Wang C, Li B. Metabolomic analysis using liquid chromatography/mass spectrometry for gastric cancer. Appl Biochem Biotechnol. 2015;176(8):2170–84.

    Article  CAS  PubMed  Google Scholar 

  35. Peng J, Chen YT, Chen CL, Li L. Development of a universal metabolome-standard method for long-term LC-MS metabolome profiling and its application for bladder cancer urine-metabolite- biomarker discovery. Anal Chem. 2014;86(13):6540–7.

    Article  CAS  PubMed  Google Scholar 

  36. Lin L, Huang Z, Gao Y, Chen Y, Hang W, Xing J, et al. LC-MS-based serum metabolic profiling for genitourinary cancer classification and cancer type-specific biomarker discovery. Proteomics. 2012;12(14):2238–46.

    Article  CAS  PubMed  Google Scholar 

  37. Kelly AD, Breitkopf SB, Yuan M, Goldsmith J, Spentzos D, Asara JM. Metabolomic profiling from formalin-fixed, paraffin-embedded tumor tissue using targeted LC/MS/MS: application in sarcoma. PLoS One. 2011;6(10):e25357.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bowers J, Hughes E, Skill N, Maluccio M, Raftery D. Detection of hepatocellular carcinoma in hepatitis C patients: biomarker discovery by LC-MS. J Chromatogr B Analyt Technol Biomed Life Sci. 2014;966:154–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Piszcz J, Lemancewicz D, Dudzik D, Ciborowski M. Differences and similarities between LC-MS derived serum fingerprints of patients with B-cell malignancies. Electrophoresis. 2013;34(19):2857–64.

    CAS  PubMed  Google Scholar 

  40. Lin L, Huang Z, Gao Y, Yan X, Xing J, Hang W. LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery. J Proteome Res. 2011;10(3):1396–405.

    Article  CAS  PubMed  Google Scholar 

  41. Bannur Z, Teh LK, Hennesy T, Rosli WRW, Mohamad N, Nasir A, et al. The differential metabolite profiles of acute lymphoblastic leukaemic patients treated with 6-mercaptopurine using untargeted metabolomics approach. Clin Biochem. 2014;47(6):427–31.

    Article  CAS  PubMed  Google Scholar 

  42. Huang G, Liu X, Jiao L, Xu C, Zhang Z, Wang L, et al. Metabolomic evaluation of the response to endocrine therapy in patients with prostate cancer. Eur J Pharmacol. 2014;729(1):132–7.

    Article  CAS  PubMed  Google Scholar 

  43. Dunn WB, Wilson ID, Nicholls AW, Broadhurst D. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis. 2012;4(18):2249–64.

    Article  CAS  PubMed  Google Scholar 

  44. Ranjbar MRN, Luo Y, DiPoto C, Varghese RS, Ferrarini A, Zhang C, et al. GC-MS based plasma metabolomics for identification of candidate biomarkers for hepatocellular carcinoma in Egyptian cohort. PLoS One. 2015;10(6):e0127299.

    Article  CAS  Google Scholar 

  45. Nakamizo S, Sasayama T, Shinohara M, Irino Y, Nishiumi S, Nishihara M, et al. GC/MS-based metabolomic analysis of cerebrospinal fluid (CSF) from glioma patients. J Neurooncol. 2013;113(1):65–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cameron SJS, Lewis KE, Beckmann M, Allison GG, Ghosal R, Lewis PD, et al. The metabolomic detection of lung cancer biomarkers in sputum. Lung Cancer. 2016;94:88–95.

    Article  PubMed  Google Scholar 

  47. Qiu Y, Cai G, Zhou B, Li D, Zhao A, Xie G, et al. A distinct metabolic signature of human colorectal cancer with prognostic potential. Clin Cancer Res. 2014;20(8):2136–46.

    Article  CAS  PubMed  Google Scholar 

  48. Pasikanti KK, Esuvaranathan K, Hong Y, Ho PC, Mahendran R, Raman Nee Mani L, et al. Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry. J Proteome Res. 2013;12(9):3865–73.

    Article  CAS  PubMed  Google Scholar 

  49. Budczies J, Denkert C, Müller BM, Brockmöller SF, Klauschen F, Györffy B, et al. Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue – a GC-TOFMS based metabolomics study. BMC Genomics. 2012;13:334.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yu L, Aa J, Xu J, Sun M, Qian S, Cheng L, et al. Metabolomic phenotype of gastric cancer and precancerous stages based on gas chromatography time-of-flight mass spectrometry. J Gastroenterol Hepatol. 2011;26(8):1290–7.

    Article  CAS  PubMed  Google Scholar 

  51. Wu H, Xue R, Lu C, Deng C, Liu T, Zeng H, et al. Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2009;877(27):3111–7.

    Article  CAS  PubMed  Google Scholar 

  52. Wojakowska A, Chekan M, Marczak Ł, Polanski K, Lange D, Pietrowska M, et al. Detection of metabolites discriminating subtypes of thyroid cancer: molecular profiling of FFPE samples using the GC/MS approach. Mol Cell Endocrinol. 2015;417:149–57.

    Article  CAS  PubMed  Google Scholar 

  53. Scalbert A, Brennan L, Fiehn O, Hankemeier T, Kristal BS, van Ommen B, et al. Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics. 2009;5(4):435–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zeng J, Yin P, Tan Y, Dong L, Hu C, Huang Q, et al. Metabolomics study of hepatocellular carcinoma: discovery and validation of serum potential biomarkers by using capillary electrophoresis-mass spectrometry. J Proteome Res. 2014;13(7):3420–31.

    Article  CAS  PubMed  Google Scholar 

  55. Chen JL, Fan J, Lu XJ. CE-MS based on moving reaction boundary method for urinary metabolomic analysis of gastric cancer patients. Electrophoresis. 2014;35(7):1032–9.

    Article  CAS  PubMed  Google Scholar 

  56. Kami K, Fujimori T, Sato H, Sato M, Yamamoto H, Ohashi Y, et al. Metabolomic profiling of lung and prostate tumor tissues by capillary electrophoresis time-of-flight mass spectrometry. Metabolomics. 2013;9(2):444–53.

    Article  CAS  PubMed  Google Scholar 

  57. Simó C, Ibáñez C, Gómez-Martínez Á, Ferragut JA, Cifuentes A. Is metabolomics reachable? Different purification strategies of human colon cancer cells provide different CE-MS metabolite profiles. Electrophoresis. 2011;32(13):1765–77.

    PubMed  Google Scholar 

  58. Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis: Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics. 2007;3(3):211–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lynn KS, Cheng ML, Chen YR, Hsu C, Chen A, Lih TM, et al. Metabolite identification for mass spectrometry-based metabolomics using multiple types of correlated ion information. Anal Chem. 2015;87(4):2143–51.

    Article  CAS  PubMed  Google Scholar 

  60. Berg M, Vanaerschot M, Jankevics A, Cuypers B, Breitling R, Dujardin J-C. LC-MS metabolomics from study design to data-analysis – using a versatile pathogen as a test case. Comput Struct Biotechnol J. 2013;4(5):e201301002.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Hao D, Sarfaraz MO, Farshidfar F, Bebb DG, Lee CY, Card CM, et al. Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment. Metabolomics. 2016;12(3):1–9.

    Article  CAS  Google Scholar 

  62. Fan Y, Zhou X, Xia T, Chen Z, Li J, Liu Q, et al. Human plasma metabolomics for identifying differential metabolites and predicting molecular subtypes of breast cancer. Oncotarget. 2016;7(9):9925–38.

    PubMed  PubMed Central  Google Scholar 

  63. Gao P, Zhou C, Zhao L, Zhang G, Zhang Y. Tissue amino acid profile could be used to differentiate advanced adenoma from colorectal cancer. J Pharm Biomed Anal. 2016;118:349–55.

    Article  CAS  PubMed  Google Scholar 

  64. Giskeødegård GF, Hansen AF, Bertilsson H, Gonzalez SV, Kristiansen KA, Bruheim P, et al. Metabolic markers in blood can separate prostate cancer from benign prostatic hyperplasia. Br J Cancer. 2015;113:1712–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Ellis DI, Dunn WB, Griffin JL, Allwood JW, Goodacre R. Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics. 2007;8(9):1243–66.

    Article  CAS  PubMed  Google Scholar 

  66. Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: a review. Clin Cancer Res. 2009;15(2):431–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Noto A, Cibecchini F, Fanos V, Mussap M. NGAL and metabolomics: the single biomarker to reveal the metabolome alterations in kidney injury. Biomed Res Int. 2013:612032, 6.

    Google Scholar 

  68. Zhang A, Sun H, Yan G, Wang P, Wang X. Mass spectrometry-based metabolomics: applications to biomarker and metabolic pathway research. Biomed Chromatogr. 2016;30(1):7–12.

    Article  PubMed  Google Scholar 

  69. Piszcz J, Armitage EG, Ferrarini A, Rupérez FJ, Kulczynska A, Bolkun L, et al. To treat or not to treat: metabolomics reveals biomarkers for treatment indication in chronic lymphocytic leukaemia patients. Oncotarget. 2016;7(16):22324–38.

    PubMed  PubMed Central  Google Scholar 

  70. Shang X, Zhong X, Tian X. Metabolomics of papillary thyroid carcinoma tissues: potential biomarkers for diagnosis and promising targets for therapy. Tumor Biol. 2016;37:11163–75.

    Article  CAS  Google Scholar 

  71. Wen CP, Zhang F, Liang D, Wen C, Gu J, Skinner H, et al. The ability of bilirubin in identifying smokers with higher risk of lung cancer: a large cohort study in conjunction with global metabolomic profiling. Clin Cancer Res. 2015;21(1):193–200.

    Article  CAS  PubMed  Google Scholar 

  72. Chen J, Zhang X, Cao R, Lu X, Zhao S, Fekete A, et al. Serum 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide discovered by metabolomics as potential diagnostic biomarker for epithelium ovarian cancer. J Proteome Res. 2011;10(5):2625–32.

    Article  CAS  PubMed  Google Scholar 

  73. Sanchez-Espiridion B, Liang D, Ajani JA, Liang S, Ye Y, Hildebrandt MAT, et al. Identification of serum markers of esophageal adenocarcinoma by global and targeted metabolic profiling. Clin Gastroenterol Hepatol. 2015;13(10):1730–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Brown DG, Rao S, Weir TL, O’Malia J, Bazan M, Brown RJ, et al. Metabolomics and metabolic pathway networks from human colorectal cancers, adjacent mucosa, and stool. Cancer Metab. 2016;4(1):11.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Crown SB, Antoniewicz MR. Parallel labeling experiments and metabolic flux analysis: past, present and future methodologies. Metab Eng. 2013;16:21–32.

    Article  CAS  PubMed  Google Scholar 

  76. Metallo CM, Walther JL, Stephanopoulos G. Evaluation of 13C isotopic tracers for metabolic flux analysis in mammalian cells. J Biotechnol. 2009;144(3):167–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Son J, Lyssiotis CA, Ying H, Wang X, Hua S, Ligorio M, et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Nature. 2013;496(7443):101–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Yang C, Ko B, Hensley CT, Jiang L, Wasti AT, Kim J, et al. Glutamine oxidation maintains the TCA cycle and cell survival during impaired mitochondrial pyruvate transport. Mol Cell. 2014;56(3):414–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Ye J, Mancuso A, Tong X, Ward PS, Fan J, Rabinowitz JD, et al. Pyruvate kinase M2 promotes de novo serine synthesis to sustain mTORC1 activity and cell proliferation. Proc Natl Acad Sci U S A. 2012;109(18):6904–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Caldwell RL, Caprioli RM. Tissue profiling by mass spectrometry: a review of methodology and applications. Mol Cell Proteomics. 2005;4(4):394–401.

    Article  CAS  PubMed  Google Scholar 

  81. Duncan MW, Nedelkov D, Walsh R, Hattan SJ. Applications of MALDI mass spectrometry in clinical chemistry. Clin Chem. 2016;62(1):134–43.

    Article  CAS  PubMed  Google Scholar 

  82. Aikawa H, Hayashi M, Ryu S, Yamashita M, Ohtsuka N, Nishidate M, et al. Visualizing spatial distribution of alectinib in murine brain using quantitative mass spectrometry imaging. Sci Rep. 2016;6:23749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Torok S, Vegvari A, Rezeli M, Fehniger TE, Tovari J, Paku S, et al. Localization of sunitinib, its metabolites and its target receptors in tumour-bearing mice: a MALDI-MS imaging study. Br J Pharmacol. 2015;172(4):1148–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Végvári Á, Shavkunov AS, Fehniger TE, Grabau D, Niméus E, Marko-Varga G. Localization of tamoxifen in human breast cancer tumors by MALDI mass spectrometry imaging. Clin Transl Med. 2016;5(1):10.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Reyzer ML, Hsieh Y, Ng K, Korfmacher WA, Caprioli RM. Direct analysis of drug candidates in tissue by matrix-assisted laser desorption/ionization mass spectrometry. J Mass Spectrom. 2003;38(10):1081–92.

    Article  CAS  PubMed  Google Scholar 

  86. Dekker TJA, Jones EA, Corver WE, van Zeijl RJM, Deelder AM, Tollenaar RAEM, et al. Towards imaging metabolic pathways in tissues. Anal Bioanal Chem. 2015;407(8):2167–76.

    Article  CAS  PubMed  Google Scholar 

  87. Kubo A, Ohmura M, Wakui M, Harada T, Kajihara S, Ogawa K, et al. Semi-quantitative analyses of metabolic systems of human colon cancer metastatic xenografts in livers of superimmunodeficient NOG mice. Anal Bioanal Chem. 2011;400(7):1895–904.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Van Hove ERA, Blackwell TR, Klinkert I, Eijkel GB, Heeren RMA, Glunde K. Multimodal mass spectrometric imaging of small molecules reveals distinct spatio-molecular signatures in differentially metastatic breast tumor models. Cancer Res. 2010;70(22):9012–21.

    Article  CAS  Google Scholar 

  89. Wang J, Qiu S, Chen S, Xiong C, Liu H, Wang J, et al. MALDI-TOF MS imaging of metabolites with a N-(1-naphthyl) ethylenediamine dihydrochloride matrix and its application to colorectal cancer liver metastasis. Anal Chem. 2015;87(1):422–30.

    Article  CAS  PubMed  Google Scholar 

  90. He J, Sinues PM-L, Hollmén M, Li X, Detmar M, Zenobi R. Fingerprinting breast cancer vs. normal mammary cells by mass spectrometric analysis of volatiles. Sci Rep. 2014;4:5196.

    CAS  PubMed  Google Scholar 

  91. Leichtle AB, Nuoffer JM, Ceglarek U, Kase J, Conrad T, Witzigmann H, et al. Serum amino acid profiles and their alterations in colorectal cancer. Metabolomics. 2012;8(4):643–53.

    Article  CAS  PubMed  Google Scholar 

  92. Gaul DA, Mezencev R, Long TQ, Jones CM, Benigno BB, Gray A, et al. Highly-accurate metabolomic detection of early-stage ovarian cancer. Sci Rep. 2015;5:16351.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Asiago VM, Alvarado LZ, Shanaiah N, Gowda GAN, Owusu-Sarfo K, Ballas RA, et al. Early detection of recurrent breast cancer using metabolite profiling. Cancer Res. 2010;70(21):8309–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Lodi A, Tiziani S, Khanim FL, Günther UL, Viant MR, Morgan GJ, et al. Proton NMR-based metabolite analyses of archived serial paired serum and urine samples from myeloma patients at different stages of disease activity identifies acetylcarnitine as a novel marker of active disease. PLoS One. 2013;8(2):e56422.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Tenori L, Oakman C, Morris PG, Gralka E, Turner N, Cappadona S, et al. Serum metabolomic profiles evaluated after surgery may identify patients with oestrogen receptor negative early breast cancer at increased risk of disease recurrence. Results from a retrospective study. Mol Oncol. 2015;9(1):128–39.

    Article  CAS  PubMed  Google Scholar 

  96. Alberice JV, Amaral AFS, Armitage EG, Lorente JA, Algaba F, Carrilho E, et al. Searching for urine biomarkers of bladder cancer recurrence using a liquid chromatography-mass spectrometry and capillary electrophoresis-mass spectrometry metabolomics approach. J Chromatogr A. 2013;1318:163–70.

    Article  CAS  PubMed  Google Scholar 

  97. Zhu J, Djukovic D, Deng L, Gu H, Himmati F, Abu Zaid M, et al. Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring. Anal Bioanal Chem. 2015;407:7857–63.

    Article  CAS  PubMed  Google Scholar 

  98. Armitage EG, Kotze HL, Allwood JW, Dunn WB, Goodacre R, Williams KJ. Metabolic profiling reveals potential metabolic markers associated with Hypoxia Inducible Factor-mediated signalling in hypoxic cancer cells. Sci Rep. 2015;5:15649.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov. 2016;9(5):307–22.

    Google Scholar 

  100. Lord SR, Patel N, Liu D, Fenwick J, Gleeson F, Buffa F, et al. Neoadjuvant window studies of metformin and biomarker development for drugs targeting cancer metabolism. J Natl Cancer Inst Monogr. 2015;2015(51):81–6.

    Article  PubMed  Google Scholar 

  101. Schuler KM, Rambally BS, DiFurio MJ, Sampey BP, Gehrig PA, Makowski L, et al. Antiproliferative and metabolic effects of metformin in a preoperative window clinical trial for endometrial cancer. Cancer Med. 2015;4(2):161–73.

    Article  CAS  PubMed  Google Scholar 

  102. He J, Wang K, Zheng N, Qiu Y, Xie G, Su M, et al. Metformin suppressed the proliferation of LoVo cells and induced a time-dependent metabolic and transcriptional alteration. Sci Rep. 2015;5:17423.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. van Asten JJA, Vettukattil R, Buckle T, Rottenberg S, van Leeuwen F, Bathen TF, et al. Increased levels of choline metabolites are an early marker of docetaxel treatment response in BRCA1-mutated mouse mammary tumors: an assessment by ex vivo proton magnetic resonance spectroscopy. J Transl Med. 2015;13:114.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. Poisson LM, Munkarah A, Madi H, Datta I, Hensley-Alford S, Tebbe C, et al. A metabolomic approach to identifying platinum resistance in ovarian cancer. J Ovarian Res. 2015;8(1):13.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Stäubert C, Bhuiyan H, Lindahl A, Broom OJ, Zhu Y, Islam S, et al. Rewired metabolism in drug-resistant leukemia cells: a metabolic switch hallmarked by reduced dependence on exogenous glutamine. J Biol Chem. 2015;290(13):8348–59.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Willis JCD, Lord GM. Immune biomarkers: the promises and pitfalls of personalized medicine. Nat Rev Immunol. 2015;15(5):323–9.

    Article  CAS  PubMed  Google Scholar 

  107. Wettersten HI, Hakimi AA, Morin D, Bianchi C, Johnstone ME, Donohoe DR, et al. Grade-dependent metabolic reprogramming in kidney cancer revealed by combined proteomics and metabolomics analysis. Cancer Res. 2015;75(12):2541–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Minton DR, Nanus DM. Kidney cancer: novel targets in altered tumour metabolism in kidney cancer. Nat Rev Urol. 2015;12(8):428–9.

    Article  PubMed  Google Scholar 

  109. Liesenfeld DB, Botma A, Habermann N, Toth R, Weigel C, Popanda O, et al. Aspirin reduces plasma concentrations of the oncometabolite 2-hydroxyglutarate: results of a randomized, double-blind, crossover trial. Cancer Epidemiol Biomarkers Prev. 2016;25(1):180–7.

    Article  CAS  PubMed  Google Scholar 

  110. Lovelace ES, Wagoner J, MacDonald J, Bammler T, Bruckner J, Brownell J, et al. Silymarin suppresses cellular inflammation by inducing reparative stress signaling. J Nat Prod. 2015;78(8):1990–2000.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Peng Z xiao, Wang Y, Gu X, Xue Y, Wu Q, Zhou J yi, et al. Metabolic transformation of breast cancer in a MCF-7 xenograft mouse model and inhibitory effect of volatile oil from Saussurea lappa Decne treatment. Metabolomics. Springer: US. 2015;11(3):636–56.

    Google Scholar 

  112. Barrajón-Catalán E, Taamalli A, Quirantes-Piné R, Roldan-Segura C, Arráez-Román D, Segura-Carretero A, et al. Differential metabolomic analysis of the potential antiproliferative mechanism of olive leaf extract on the JIMT-1 breast cancer cell line. J Pharm Biomed Anal. 2015;105:156–62.

    Article  PubMed  CAS  Google Scholar 

  113. Chen GQ, Tang CF, Shi XK, Lin CY, Fatima S, Pan XH, et al. Halofuginone inhibits colorectal cancer growth through suppression of Akt/mTORC1 signaling and glucose metabolism. Oncotarget. 2015;6(27):24148–62.

    Article  PubMed  PubMed Central  Google Scholar 

  114. Gao D, Wang Y, Xie W, Yang T, Jiang Y, Guo Y, et al. Metabolomics study on the antitumor effect of marine natural compound flexibilide in HCT-116 colon cancer cell line. J Chromatogr B Analyt Technol Biomed Life Sci. 2016;1014:17–23.

    Article  CAS  PubMed  Google Scholar 

  115. Yun J, Mullarky E, Lu C, Bosch KN, Kavalier A, Rivera K, et al. Vitamin C selectively kills KRAS and BRAF mutant colorectal cancer cells by targeting GAPDH. Science. 2015;350(6266):1391–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Li F, Yang XW, Krausz KW, Nichols RG, Xu W, Patterson AD, et al. Modulation of colon cancer by nutmeg. J Proteome Res. 2015;14(4):1937–46.

    Article  CAS  PubMed  Google Scholar 

  117. Trifonova O, Knight RA, Lisitsa A, Melino G, Antonov AV. Exploration of individuality in drug metabolism by high-throughput metabolomics: the fast line for personalized medicine. Drug Discov Today. 2016;21(1):103–10.

    Article  CAS  PubMed  Google Scholar 

  118. Schilsky RL. Personalized medicine in oncology: the future is now. Nat Rev. 2009;9:363–6.

    Google Scholar 

  119. Madlensky L, Natarajan L, Tchu S, Pu M, Mortimer J, Flatt SW, et al. Tamoxifen metabolite concentrations, CYP2D6 genotype, and breast cancer outcomes. Clin Pharmacol Ther. 2011;89(5):718–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Goldman JM, Melo JV. Chronic myeloid leukemia – advances in biology and new approaches to treatment. N Engl J Med. 2003;349:1451–64.

    Article  CAS  PubMed  Google Scholar 

  121. Navarrete A, Armitage EG, Musteanu M, García A, Mastrangelo A, Bujak R, et al. Metabolomic evaluation of Mitomycin C and rapamycin in a personalized treatment of pancreatic cancer. Pharmacol Res Perspect. 2014;2(6):e00067.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Bertini I, Cacciatore S, Jensen BV, Schou JV, Johansen JS, Kruhøffer M, et al. Metabolomic NMR fingerprinting to identify and predict survival of patients with metastatic colorectal cancer. Cancer Res. 2012;72(1):356–64.

    Article  CAS  PubMed  Google Scholar 

  123. Tenori L, Oakman C, Claudino WM, Bernini P, Cappadona S, Nepi S, et al. Exploration of serum metabolomic profiles and outcomes in women with metastatic breast cancer: a pilot study. Mol Oncol. 2012;6(4):437–44.

    Article  CAS  PubMed  Google Scholar 

  124. Li H, He J, Jia W. The influence of gut microbiota on drug metabolism and toxicity. Expert Opin Drug Metab Toxicol. 2016;12(1):31–40.

    Article  CAS  PubMed  Google Scholar 

  125. Vétizou M, Pitt JM, Daillère R, Lepage P, Waldschmitt N, Flament C, et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science. 2015;350(6264):1079–84.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

EGA acknowledges funding from the Spanish Ministry of Science and Technology (CTQ2014-55279-R). MC acknowledges funding from the Polish National Research Centre (2014/13/B/NZ5/01256).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emily Grace Armitage .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Armitage, E.G., Ciborowski, M. (2017). Applications of Metabolomics in Cancer Studies. In: Sussulini, A. (eds) Metabolomics: From Fundamentals to Clinical Applications. Advances in Experimental Medicine and Biology(), vol 965. Springer, Cham. https://doi.org/10.1007/978-3-319-47656-8_9

Download citation

Publish with us

Policies and ethics