Advertisement

Exploring Cancer Metabolism: Applications of Metabolomics and Metabolic Phenotyping in Cancer Research and Diagnostics

  • Gonçalo GraçaEmail author
  • Chung-Ho E. Lau
  • Luís G. GonçalvesEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1219)

Abstract

Altered metabolism is one of the key hallmarks of cancer. The development of sensitive, reproducible and robust bioanalytical tools such as Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry techniques offers numerous opportunities for cancer metabolism research, and provides additional and exciting avenues in cancer diagnosis, prognosis and for the development of more effective and personalized treatments. In this chapter, we introduce the current state of the art of metabolomics and metabolic phenotyping approaches in cancer research and clinical diagnostics.

Keywords

Metabolomics Cancer metabolism NMR spectroscopy Mass spectrometry Diagnostics Metabolic imaging Biofluids Tissues 

References

  1. Akita H, Ritchie SA, Takemasa I et al (2016) Serum metabolite profiling for the detection of pancreatic cancer: results of a large independent validation study. Pancreas 45:1418–1423PubMedCrossRefGoogle Scholar
  2. Amal H, Shi DY, Ionescu R et al (2015) Assessment of ovarian cancer conditions from exhaled breath. Int J Cancer 136:E614–E622.  https://doi.org/10.1002/ijc.29166CrossRefPubMedGoogle Scholar
  3. Amal H, Leja M, Funka K et al (2016) Detection of precancerous gastric lesions and gastric cancer through exhaled breath. Gut 65:400–407PubMedCrossRefGoogle Scholar
  4. Amberg A, Riefke B, Schlotterbeck G et al (2017) NMR and MS methods for metabolomics. Methods Mol Biol 1641:229–258.  https://doi.org/10.1007/978-1-4939-7172-5_13CrossRefPubMedGoogle Scholar
  5. An YJ, Cho HR, Kim TM et al (2015) An NMR metabolomics approach for the diagnosis of leptomeningeal carcinomatosis in lung adenocarcinoma cancer patients. Int J Cancer 136:162–171PubMedCrossRefGoogle Scholar
  6. Ang JE, Pandher R, Ang JC et al (2016) Plasma metabolomic changes following PI3K inhibition as pharmacodynamic biomarkers: preclinical discovery to phase I trial evaluation. Mol Cancer Ther 15:1412–1424PubMedPubMedCentralCrossRefGoogle Scholar
  7. Ang JE, Pal A, Asad YJ et al (2017) Modulation of plasma metabolite biomarkers of the MAPK pathway with MEK inhibitor RO4987655: pharmacodynamic and predictive potential in metastatic melanoma. Mol Cancer Ther 16:2315–2323PubMedPubMedCentralCrossRefGoogle Scholar
  8. Ardenkjaer-Larsen J-H, Boebinger GS, Comment A et al (2015) Facing and overcoming sensitivity challenges in biomolecular NMR spectroscopy. Angew Chem Int Ed Engl 54:9162–9185PubMedPubMedCentralCrossRefGoogle Scholar
  9. Asai Y, Itoi Y, Sugimoto M et al (2018) Elevated polyamines in saliva of pancreatic cancer. Cancers 10:E43.  https://doi.org/10.3390/cancers10020043CrossRefPubMedGoogle Scholar
  10. Bala L, Sharma A, Yellapa RK et al (2008) 1H NMR spectroscopy of ascitic fluid: discrimination between malignant and benign ascites and comparison of the results with conventional methods. NMR Biomed 21:606–614PubMedCrossRefGoogle Scholar
  11. Balog J, Sasi-Szabó L, Kinross J et al (2013) Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci Transl Med 5:194ra93.  https://doi.org/10.1126/scitranslmed.3005623CrossRefPubMedGoogle Scholar
  12. Balog J, Kumar S, Alexander J et al (2015) In vivo endoscopic tissue identification by rapid evaporative ionization mass spectrometry (REIMS). Angew Chem 54:11059–11062CrossRefGoogle Scholar
  13. Barash O, Zhang W, Halpern JM et al (2015) Differentiation between genetic mutations of breast cancer by breath volatolomics. Oncotarget 6:44864–44876.  https://doi.org/10.18632/oncotarget.6269CrossRefPubMedPubMedCentralGoogle Scholar
  14. Bathen TF, Geurts B, Sitter B et al (2013) Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery. PLoS One 8:e61578.  https://doi.org/10.1371/journal.pone.0061578CrossRefPubMedPubMedCentralGoogle Scholar
  15. Bharti SK, Wildes F, Hung C-F et al (2017) Metabolomic characterization of experimental ovarian cancer ascitic fluid. Metabolomics 113.  https://doi.org/10.1007/s11306-017-1254-3
  16. Bingol K (2018) Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR methods. High-Throughput 7:E9.  https://doi.org/10.3390/ht7020009CrossRefPubMedGoogle Scholar
  17. Bloch F, Hansen WW, Packard M (1946) The nuclear induction experiment. Phys Rev 70:474–485.  https://doi.org/10.1103/PhysRev.70.474CrossRefGoogle Scholar
  18. Bodzon-Kulakowska A, Suder P (2016) Imaging mass spectrometry: instrumentation, applications, and combination with other visualization techniques. Mass Spectrom Rev 35:147–169PubMedCrossRefGoogle Scholar
  19. Bouza M, Gonzalez-Soto J, Pereiro R et al (2017) Exhaled breath and oral cavity VOCs as potential biomarkers in oral cancer patients. J Breath Res 11:016015.  https://doi.org/10.1088/1752-7163/aa5e76CrossRefPubMedGoogle Scholar
  20. Bro R, Kamstrup-Nielsen MH, Engelsen SB et al (2015) Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics 11:1376–1380PubMedPubMedCentralCrossRefGoogle Scholar
  21. Broadhurst DI, Kell DB (2006) Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2:171–196CrossRefGoogle Scholar
  22. Cameron SJS, Lewis KE, Beckmann M et al (2016) The metabolomic detection of lung cancer biomarkers in sputum. Lung Cancer 94:88–95PubMedCrossRefGoogle Scholar
  23. Chan AW, Mercier P, Schiller D et al (2016) 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer. Br J Cancer 114:59–62PubMedCrossRefGoogle Scholar
  24. Chaumeil MM, Larson PEZ, Yoshihara HAI et al (2013) Non-invasive in vivo assessment of IDH1 mutational status in glioma. Nat Commun 4:2429PubMedPubMedCentralCrossRefGoogle Scholar
  25. Cheng XM, Liu XY, Liu X et al (2018) Metabolomics of non-muscle invasive bladder cancer: biomarkers for early detection of bladder cancer. Front Oncol 8:494.  https://doi.org/10.3389/fonc.2018.00494CrossRefPubMedPubMedCentralGoogle Scholar
  26. Choi C, Ganji SK, DeBerardinis RJ et al (2012) 2-hydroxyglutarate detection by magnetic resonance spectroscopy in subjects with IDH-mutated gliomas. Nat Med 18:624–629PubMedPubMedCentralCrossRefGoogle Scholar
  27. Cornel EB, Smits GA, Oosterhof GO et al (1993) Characterization of human prostate cancer, benign prostatic hyperplasia and normal prostate by in vitro 1H and 31P magnetic resonance spectroscopy. J Urol 150:2019–2024PubMedCrossRefGoogle Scholar
  28. Dalgliesh CE (1956) Two-dimensional paper chromatography of urinary indoles and related substances. Biochem J 64:481–485PubMedPubMedCentralCrossRefGoogle Scholar
  29. Dang L, White DW, Gross S et al (2009) Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462:739–744PubMedPubMedCentralCrossRefGoogle Scholar
  30. de Vogel S, Ulvik A, Meyer K et al (2014) Sarcosine and other metabolites along the choline oxidation pathway in relation to prostate cancer-a large nested case-control study within the JANUS cohort in Norway. Int J Cancer 134:197–206PubMedCrossRefGoogle Scholar
  31. Dinges SS, Hohm A, Vandergrift LA et al (2019) Cancer metabolomic markers in urine: evidence, techniques and recommendations. Nat Rev Urol 16:339–362PubMedCrossRefGoogle Scholar
  32. Dona AC, Jiménez B, Schäfer H et al (2014) Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem 86:9887–9894PubMedCrossRefGoogle Scholar
  33. Doskocz M, Marchewka Z, Jeż M et al (2015) Preliminary study on J-resolved NMR method usability for toxic kidney’s injury assessment. Adv Clin Exp Med 24:629–635PubMedCrossRefGoogle Scholar
  34. Dunphy MPS, Harding JJ, Venneti S (2018) In vivo PET assay of tumor glutamine flux and metabolism: in-human trial of 18F-(2S,4R)-4-fluoroglutamine. Radiology 287:667–675PubMedPubMedCentralCrossRefGoogle Scholar
  35. Emwas AH (2015) The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods Mol Biol 1277:161–193PubMedCrossRefGoogle Scholar
  36. Emwas AH, Roy R, McKay RT et al (2019) NMR spectroscopy for metabolomics research. Meta 9.  https://doi.org/10.3390/metabo9070123PubMedCentralCrossRefPubMedGoogle Scholar
  37. Fan TW-M, Lane AN (2016) Applications of NMR spectroscopy to systems biochemistry. Prog Nucl Magn Reson Spectrosc 92-93:18–53PubMedPubMedCentralCrossRefGoogle Scholar
  38. Fan TW-M, Lorkiewicz PK, Sellers K et al (2012) Stable isotope-resolved metabolomics and applications for drug development. Pharmacol Ther 133:366–391PubMedCrossRefGoogle Scholar
  39. Fu XA, Li MX, Knipp RJ et al (2014) Noninvasive detection of lung cancer using exhaled breath. Cancer Med 3:174–181PubMedCrossRefGoogle Scholar
  40. Fukutake N, Ueno M, Hiraoka N et al (2015) A novel multivariate index for pancreatic cancer detection based on the plasma free amino acid profile. PLoS One 10:e0132223.  https://doi.org/10.1371/journal.pone.0132223CrossRefPubMedPubMedCentralGoogle Scholar
  41. Ganti S, Taylor SL, Kim K et al (2012) Urinary acylcarnitines are altered in human kidney cancer. Int J Cancer 130:2791–2800PubMedCrossRefGoogle Scholar
  42. Garcia RA, Morales V, Martin S et al (2014) Volatile organic compounds analysis in breath air in healthy volunteers and patients suffering epidermoid laryngeal carcinomas. Chromatographia 77:501–509CrossRefGoogle Scholar
  43. Garrison RN, Kaelin LD, Galloway RH et al (1986) Malignant ascites. Clinical and experimental observations. Ann Surg 203:644–651PubMedPubMedCentralCrossRefGoogle Scholar
  44. Giskeodegard GF, Bertilsson H, Selnaes KM et al (2013) Spermine and citrate as metabolic biomarkers for assessing prostate cancer aggressiveness. PLoS One 8:e62375.  https://doi.org/10.1371/journal.pone.0062375CrossRefPubMedPubMedCentralGoogle Scholar
  45. Glish GL, Vachet RW (2003) The basics of mass spectrometry in the twenty-first century. Nat Rev Drug Dis 2:140–150CrossRefGoogle Scholar
  46. Goodwin RJA, Webborn PJH (2015) Future directions of imaging MS in pharmaceutical R&D. Bioanalysis 7(20):2667–2673Google Scholar
  47. Graça G, Desterro J, Sousa J et al (2017) Identification of putative biomarkers for leptomeningeal invasion in B-cell non-Hodgkin lymphoma by NMR metabolomics. Metabolomics 13:136CrossRefGoogle Scholar
  48. Graça G, Serrano-Contreras JI, Chekmeneva E (2019) Nuclear magnetic resonance spectroscopy: pulse sequences for chemical analysis. In: Worsfold P, Poole C, Townshend A, Miró M (eds) Encyclopedia of analytical science, vol 7, 3rd edn. Elsevier, Amsterdam, pp 354–365Google Scholar
  49. Gromski PS, Muhamadali H, Ellis DI et al (2015) A tutorial review: metabolomics and partial least squares-discriminant analysis - a marriage of convenience or a shotgun wedding. Anal Chim Acta 879:10–23PubMedCrossRefGoogle Scholar
  50. Guennec AL, Giraudeau P, Caldarelli S (2014) Evaluation of fast 2D NMR for metabolomics. Anal Chem 86:5946–5954PubMedCrossRefGoogle Scholar
  51. Hänel L, Kwiatkowski M, Heikaus L et al (2019) Mass spectrometry-based intraoperative tumor diagnostics. Future Sci OA 5:FSO373.  https://doi.org/10.4155/fsoa-2018-0087CrossRefPubMedPubMedCentralGoogle Scholar
  52. Hanna GB, Boshier PR, Markar SR et al (2019) Accuracy and methodological challenges of volatile organic compound-based exhaled breath tests for cancer diagnosis: a systematic review and meta-analysis. JAMA Oncol 5:e182815.  https://doi.org/10.1001/jamaoncol.2018.2815CrossRefPubMedGoogle Scholar
  53. Hao J, Astle W, De Iorio M et al (2012) BATMAN – an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics 28:2088–2090PubMedCrossRefGoogle Scholar
  54. Haukaas TH, Moestue SA, Vettukattil R et al (2016) Impact of freezing delay time on tissue samples for metabolomic studies. Front Oncol 6:17.  https://doi.org/10.3389/fonc.2016.00017CrossRefPubMedPubMedCentralGoogle Scholar
  55. Hilvo M, de Santiago I, Gopalacharyulu P et al (2016) Accumulated metabolites of hydroxybutyric acid serve as diagnostic and prognostic biomarkers of ovarian high-grade serous carcinomas. Cancer Res 76:796–804PubMedCrossRefGoogle Scholar
  56. Horská A, Barker PB (2010) Imaging of brain tumors: MR spectroscopy and metabolic imaging. Neuroimaging Clin N Am 20(3):293–310PubMedPubMedCentralCrossRefGoogle Scholar
  57. Ifa DR, Eberlin LS (2016) Ambient ionization mass spectrometry for cancer diagnosis and surgical margin evaluation. Clin Chem 62:111–123CrossRefGoogle Scholar
  58. Jin X, Yun SJ, Jeong P et al (2014) Diagnosis of bladder cancer and prediction of survival by urinary metabolomics. Oncotarget 5:1635–1645PubMedPubMedCentralGoogle Scholar
  59. Julià-Sapé M, Candiota AP, Arús C (2019) Cancer metabolism in a snapshot: MRS(I). NMR Biomed 11:e4054.  https://doi.org/10.1002/nbm.4054CrossRefGoogle Scholar
  60. Komoroski RA, Holder JC, Pappas AA et al (2011) 31P NMR of phospholipid metabolites in prostate cancer and benign prostatic hyperplasia. Magn Reson Med 65:911–913PubMedCrossRefGoogle Scholar
  61. Kumar S, Huang J, Abbassi-Ghadi N et al (2015) Mass spectrometric analysis of exhaled breath for the identification of volatile organic compound biomarkers in esophageal and gastric adenocarcinoma. Ann Surg 262:981–990PubMedCrossRefGoogle Scholar
  62. Le Gall G, Guttula K, Kellingray L et al (2018) Metabolite quantification of faecal extracts from colorectal cancer patients and healthy controls. Oncotarget 9:33278–33289PubMedPubMedCentralGoogle Scholar
  63. Li MX, Yang DK, Brock G et al (2015) Breath carbonyl compounds as biomarkers of lung cancer. Lung Cancer 90:92–97PubMedCrossRefGoogle Scholar
  64. Lin Y, Ma CC, Liu CK et al (2016) NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer. Oncotarget 7:29454–29464PubMedPubMedCentralGoogle Scholar
  65. Liu W, Bai XF, Liu YJ et al (2015) Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case. Sci Rep 5:13192PubMedPubMedCentralCrossRefGoogle Scholar
  66. Marchand J, Martineau E, Guitton Y et al (2017) Multidimensional NMR approaches towards highly resolved, sensitive and high-throughput quantitative metabolomics. Curr Opin Biotechnol 43:49–55PubMedCrossRefGoogle Scholar
  67. Marion D (2013) An introduction to biological NMR spectroscopy. Mol Cell Proteomics 12:3006–3025PubMedPubMedCentralCrossRefGoogle Scholar
  68. Mayers JR, Wu C, Clish CB et al (2014) Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat Med 20:1193–1198PubMedPubMedCentralCrossRefGoogle Scholar
  69. McDunn JE, Li Z, Adam KP et al (2013) Metabolomic signatures of aggressive prostate cancer. Prostate 73:1547–1560PubMedCrossRefGoogle Scholar
  70. Mirnezami R, Jiménez B, Li JV et al (2014) Rapid diagnosis and staging of colorectal cancer via high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy of intact tissue biopsies. Ann Surg 259:1138–1149PubMedCrossRefGoogle Scholar
  71. Molenaar RJ, Thota S, Nagata Y et al (2015) Clinical and biological implications of ancestral and non-ancestral IDH1 and IDH2 mutations in myeloid neoplasms. Leukemia 29:2134–2142PubMedPubMedCentralCrossRefGoogle Scholar
  72. Nagana Gowda GA, Raftery D (2017) Recent advances in NMR-based metabolomics. Anal Chem 89:490–510PubMedCrossRefGoogle Scholar
  73. Nicholson JK, O'Flynn MP, Sadler PJ et al (1984) Proton-nuclear-magnetic-resonance studies of serum, plasma and urine from fasting normal and diabetic subjects. Biochem J 217:365–375PubMedPubMedCentralCrossRefGoogle Scholar
  74. Pansuriya TC, van Eijk R, d'Adamo P et al (2011) Somatic mosaic IDH1 and IDH2 mutations are associated with enchondroma and spindle cell hemangioma in Ollier disease and Maffucci syndrome. Nat Genet 43:1256–1261PubMedPubMedCentralCrossRefGoogle Scholar
  75. Paul A, Kumar S, Raj A et al (2018) Alteration in lipid composition differentiates breast cancer tissues: a 1H HRMAS NMR metabolomic study. Metabolomics 14:119PubMedCrossRefGoogle Scholar
  76. Pavlova NN, Thompson CB (2016) The emerging hallmarks of cancer metabolism. Cell Metab 23:27–47.  https://doi.org/10.1016/j.cmet.2015.12.006CrossRefPubMedPubMedCentralGoogle Scholar
  77. Peng G, Hakim M, Broza YY et al (2010) Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br J Cancer 103:542–551PubMedPubMedCentralCrossRefGoogle Scholar
  78. Phelps DL, Balog J, Gildea LF et al (2018) The surgical intelligent knife distinguishes normal, borderline and malignant gynaecological tissues using rapid evaporative ionisation mass spectrometry (REIMS). Br J Cancer 118:1349–1358PubMedPubMedCentralCrossRefGoogle Scholar
  79. Porcari AM, Zhang J, Garza KY et al (2018) Multi-center study using desorption-electrospray-ionization-mass-spectrometry imaging for breast cancer diagnosis. Anal Chem 90:11324–11332PubMedCrossRefGoogle Scholar
  80. Psychogios N, Hau DD, Peng J et al (2011) The human serum metabolome. PLoS One 6:e16957.  https://doi.org/10.1371/journal.pone.0016957CrossRefPubMedPubMedCentralGoogle Scholar
  81. Purcell EM, Torrey HC, Pound RV (1946) Resonance absorption by nuclear magnetic moments in a solid. Phys Rev 69:37–38CrossRefGoogle Scholar
  82. Qin T, Liu H, Song Q et al (2010) The screening of volatile markers for hepatocellular carcinoma. Cancer Epidemiol Biomark Prev 19:2247–2253CrossRefGoogle Scholar
  83. Sakai A, Suzuki M, Kobayashi T et al (2016) Pancreatic cancer screening using a multiplatform human serum metabolomics system. Biomark Med 10:577–586PubMedCrossRefGoogle Scholar
  84. Sanderson SM, Locasale JW (2018) Revisiting the Warburg effect: some tumors hold their breath. Cell Metab 28:669–670PubMedPubMedCentralCrossRefGoogle Scholar
  85. Sangisetty SL, Miner TJ (2012) Malignant ascites: a review of prognostic factors, pathophysiology and therapeutic measures. World J Gastrointest Surg 4:87–95PubMedPubMedCentralCrossRefGoogle Scholar
  86. Shender VO, Pavlyukov MS, Ziganshin RH et al (2014) Proteome-metabolome profiling of ovarian cancer ascites reveals novel components involved in intercellular communication. Mol Cell Proteomics 13:3558–3571PubMedPubMedCentralCrossRefGoogle Scholar
  87. Slupsky CM, Steed H, Wells TH et al (2010) Urine metabolite analysis offers potential early diagnosis of ovarian and breast cancers. Clin Cancer Res 16:5835–5841PubMedCrossRefGoogle Scholar
  88. Sreekumar A, Poisson LM, Rajendiran TM et al (2009) Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457:910–914PubMedPubMedCentralCrossRefGoogle Scholar
  89. St John ER, Balog J, Mckenzie JS et al (2017) Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery. Breast Cancer Res 19:59.  https://doi.org/10.1186/s13058-017-0845-2CrossRefPubMedPubMedCentralGoogle Scholar
  90. Sugimoto M, Wong DT, Hirayama A et al (2010) Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics 6:78–95PubMedCrossRefGoogle Scholar
  91. Sun C, Lia T, Song X et al (2019) Spatially resolved metabolomics to discover tumor associated metabolic alterations. PNAS 116:52–57PubMedCrossRefGoogle Scholar
  92. Tanaka K, Isselbacher KJ (1967) The isolation and identification of N-isovalerylglycine from urine of patients with isovaleric acidemia. J Biol Chem 242:2966–2972PubMedGoogle Scholar
  93. Trygg J, Holmes E, Lundstedt T (2007) Chemometrics in metabonomics. J Proteome Res 6:469–479PubMedCrossRefGoogle Scholar
  94. Tsutsui H, Mochizuki T, Inoue K et al (2013) High-throughput LC-MS/MS based simultaneous determination of polyamines including N-acetylated forms in human saliva and the diagnostic approach to breast cancer patients. Anal Chem 85:11835–11842PubMedCrossRefGoogle Scholar
  95. Vettukattil R, Hetland TE, Flørenes VA et al (2013) Proton magnetic resonance metabolomic characterization of ovarian serous carcinoma effusions: chemotherapy-related effects and comparison with malignant mesothelioma and breast carcinoma. Hum Pathol 44:1859–1866PubMedCrossRefGoogle Scholar
  96. Wishart DS, Jewison T, Guo AC et al (2013) HMDB 3.0 — the human metabolome database in 2013. Nucleic Acids Res 41:D801–D807.  https://doi.org/10.1093/nar/gks1065CrossRefPubMedPubMedCentralGoogle Scholar
  97. Woo HM, Kim KM, Choi MH et al (2009) Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers. Clin Chim Acta 400:63–69PubMedCrossRefGoogle Scholar
  98. Yan H, Parsons DW, Jin GL et al (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:765–773PubMedPubMedCentralCrossRefGoogle Scholar
  99. Zhu A, Lee D, Shim H (2011) Metabolic PET imaging in cancer detection and therapy response. Semin Oncol 38:55–69PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Metabolism, Digestion and Reproduction, Faculty of MedicineImperial College LondonLondonUK
  2. 2.Proteomics of Non-Model Organisms Lab, ITQB Nova-Instituto de Tecnologia Química e Biológica António XavierUniversidade Nova de LisboaOeirasPortugal

Personalised recommendations