Metabolic profiles of cancer cells

Nature Reviews Cancer

Volumn 4, Issue 7, 2004, Pages 551-561

  Griffin, Julian L ^a   Shockcor, John P ^b  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b BRUKER BIOSPIN CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL CELL; ANIMAL TISSUE; AUTOMATION; CANCER CELL; CANCER DIAGNOSIS; CANCER GROWTH; CARCINOGENESIS; CELL METABOLISM; HUMAN; HUMAN CELL; MASS SPECTROMETRY; METABOLITE; MOUSE; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; PROTON NUCLEAR MAGNETIC RESONANCE; REVIEW; SYSTEMATIC REVIEW;

EID: 3042823722     PISSN: 1474175X     EISSN: None     Source Type: Journal    
DOI: 10.1038/nrc1390     Document Type: Review

References (88)

1. Chu, S. et al. The transcriptional program of sporulation in budding yeast. Science 282, 699-705 (1998).
2. Shalon, D., Smith, S. J. & Brown, P. O. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res. 6, 639-645 (1996).
3. Klose, J. et al. Genetic analysis of the mouse brain proteome. Nature Genet. 30, 385-393 (2002).
4. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531-537 (1999).
5. Moch, H. et al. High-throughput tissue microarray analysis to evaluate genes uncovered by cDNA microarray screening in renal cell carcinoma. Am. J. Pathol. 154, 981-986 (1999).
6. Celis, J. E. et al. Proteomics and immunohistochemistry define some of the steps involved in the squamous differentiation of the bladder transitional epithelium: a novel strategy for identifying metaplastic lesions. Cancer Res. 59, 3003-3009 (1999).
7. Seow, T. K. et al. Two-dimensional electrophoresis map of the human hepatocellular carcinoma cell line, HCC-M, and identification of the separated proteins by mass spectrometry. Electrophoresis 21, 1787-1813 (2000).
8. Voss, T., Ahorn, H., Haberl, P., Dohner, H. & Wilgenbus, K. Correlation of clinical data with proteomics profiles in 24 patients with B-cell chronic lymphocytic leukemia. Int. J. Cancer 91, 180-186 (2001).
9. Oliver, S. G. Functional genomics: lessons from yeast. Phil. Trans. R. Soc. Lond. B 357, 17-23 (2002).
10. Kell, D. B. & Westerhoff, H. V. Towards a rational approach to the optimization of flux in microbial biotransformations. Trends Biotechnol. 4, 137-142 (1986).
11. Fell, D. A. Understanding the Control of Metabolism (Portland Press, London, 1996).
12. Mendes, P., Kell, D. B. & Westerhoff, H. V. Why and when channeling can decrease pool size at constant net flux in a simple dynamic channel. Biochim. Biophys. Acta 1289, 175-186 (1996).
13. ter Kuile, B. H. & Westerhoff, H. V. Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway. FEBS Letts 500, 169-171 (2001).
14. Devaux, P. G., Horning, M. G. & Horning, E. C. Benyzl-oxime derivatives of steroids; a new metabolic profile procedure for human urinary steroids. Anal. Lett. 4, 151 (1971).
15. Horning, E. C. & Horning M. G. Human metabolic profiles obtained by GC and GC/MS. J. Chromatogr. Sci. 9, 129-140 (1971).
16. Fan, T. W. Metabolite profiling by one- and two-dimensional NMR analysis of complex mixtures. Prog. Nucl. Mag. Res. Spectrosc. 28, 161-219 (1996).
17. 1H NMR spectroscopy for three types of human brain and nervous system tumours. NMR Biomed. 8, 253-264 (1995).
18. Florian, C. L., Preece, N. E., Bhakoo, K. K., Williams, S. R. & Nobel, M. D. Cell type-specific fingerprinting of meningioma and meningeal cells by proton nuclear magnetic resonance spectroscopy. Cancer Res. 55, 420-427 (1995).
19. Williams, S. N., Anthony, M. L. & Brindle, K. M. Induction of apoptosis in two mammalian cell lines results in increased levels of fructose-1,6-phosphate and CDP-choline as determined by 31P MRS. Magn. Reson. Med. 40, 411-420 (1998).
20. 1H NMR diffusion spectroscopy pf BT4C rat glioma during thymidine kinase-mediated gene therapy in vivo: identification of apoptotic response. Cancer Res. 58, 3791-3799 (1998).
21. 1H nuclear magnetic resonance visible polyunsaturated fatty acids in BT4C gliomas undergoing ganciclovir-thymidine kinase gene therapy-induced programmed cell death. Cancer Res. 63, 3195-3201 (2003).
22. Raamsdonk, L. M. et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotechnol. 19, 45-50 (2001). This paper nicely illustrates how metabolomics can be used to phenotype yeast.
23. Fiehn, O. Combining genomics, metabolome analysis and biochemical modeling to understand metabolic networks. Comp. Funct. Genomics 2, 155-168 (2001).
24. Fiehn, O. Metabolomics - the link between genotypes and phenotypes. Plant Mol. Biol. 48,155-171 (2002).
25. Griffin, J. L., Sang, E., Evens, T., Davies, K. & Clarke, K. Metabolic profiles of dystrophin and utrophin expression in mouse models of Duchenne Muscular dystrophy. FEBS Letts. 530, 109-116 (2002).
26. Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nature Rev. Drug Discov. 1, 153-161 (2002). A thorough overview of the use of metabonomics in the field of toxicology and drug development written by some of the key researchers in this area.
27. Nicholson, J. K. & Wilson, I. Understanding 'global' systems biology: metabonomics and the continuum of metabolism. Nature Rev. Drug Discov. 2, 668-676 (2003).
28. Chung, Y. L., Stubbs, M., & Griffiths, J. R. Metabolic Profiling, its Role in Biomaker Discovery and Gene Function Analysis (eds Harrigan, G. C. & Goodacre, R.) 83-94 (Kluwer Academic Publishing, Dordrecht, 2003).
29. Tate, A. R. et al. Lipid metabolite peaks in pattern recognition analysis of tumour in vivo MR spectra. Anticancer Res, 16, 1575-1579 (1996).
30. 1H MRS spectra from brain tumours. NMR Biomed. 11, 177-191 (1998).
31. Cheng, L. L. et al. Enhanced resolution of proton NMR spectra of malignant lymph nodes using magic angle spinning. Magn. Reson. Med. 36, 653-658 (1996).
32. Chen, J.-H., Enloe, B. M., Fletcher, C. D., Cory, D. G. & Singer, S. Biochemical analysis using high-resolution magic angle spinning NMR spectroscopy distinguishes lipoma-like well-differentiated liposarcoma from normal fat. J. Am. Chem. Soc. 123, 9200-9201 (2001).
33. Millis, K. et al. Classification of human liposarcoma and lipoma using ex vivo proton NMR spectroscopy. Magn. Reson. Med. 41, 257-267 (1999).
34. 1H nuclear magnetic resonance analysis of intact prostatic hyperplastic and tumour tissues. Anal. Comm. 35, 113-115 (1998).
35. Griffiths, J. R. et al. Metabolic changes detected by in vivo magnetic resonance studies of HEPA-1 wild-type tumor deficient in hypoxia-inducible factor-1β (HIF-1β); evidence of an anabolic role for the HIF-1 pathway. Cancer Res. 62, 688-695 (2002). This study represents one of the first successes for the hypothesis-generating approach of metabolomics in understanding tumour metabolism and biochemistry.
36. Griffiths, J. R. & Stubbs, M. Opportunities for studying cancer by metabolomics: preliminary observations on tumors deficient in hypoxia-inducible factor 1. Adv. Enzyme Regul. 43, 67-76 (2003).
37. 2 tension. Proc. Natl Acad. Sci. USA 92, 5510-5514 (1995).
38. Maxwell, P. H. et al. Hypoxia-inducible factor 1 modulates gene expression in solid tumours and influences both angiogenesis and tumor growth. Proc. Natl Acad. Sci. USA 94, 8104-8109 (1997).
39. Mountford, C. E. & Wright, L. C. Organization of lipids in the plasma membranes of malignant and stimulated cells: a new model. Trends Biochem. Sci. 13, 172-177 (1988).
40. Cheng, L. L., Chang, I. W., Smith, B. L. & Gonzalez, R. G. Evaluating human breast ductal carcinomas with high-resolution magic-angle spinning proton magnetic resonance spectroscopy. J. Magn. Reson. 135, 194-202 (1998).
41. 1H NMR spectroscopy in vivo, ex vivo, and in vitro. J. Biol. Chem. 278, 45915-45923 (2003).
42. Sitter, B. et al. Cervical cancer tissue characterized by high-resolution magic angle spinning MR spectroscopy. MAGMA 16, 174-181 (2004).
43. 1H nuclear magnetic resonance spectra by the application of chemometric techniques. Magn. Reson. Med. 28, 214-236 (1992).
44. 1H magnetic resonance spectroscopic metabolite phenotypes. Neuroreport. 7, 1597-1600 (1996).
45. Tate, A. R. et al. Automated feature extraction for the classification of human in vivo 13C NMR spectra using statistical pattern recognition and wavelets. Magn. Reson. Med. 35, 834-840 (1996).
46. Preul, M. C., Caramanos, Z., Leblanc, R., Villemure, J. G. & Arnold, D. L. Using pattern analysis of in vivo proton MRS1 data to improve the diagnosis and surgical management of patients with brain tumors. NMR Biomed. 11, 192-200 (1998).
47. Hagberg, G. From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods. NMR Biomed. 11, 148-156 (1998).
48. Gerstle, R. J., Aylward, S. R., Kromhout-Schiro, S. & Mukherji, S. K. The role of neural networks in improving the accuracy of MR spectroscopy for the diagnosis of head and neck squamous cell carcinoma. Am. J. Neuroradiol. 21, 1133-1138 (2000).
49. 1H nuclear magnetic resonance spectra from human brain tumour biopsies. NMR Biomed. 11, 217-224 (1998).
50. Gribbestad, I. S., Sitter, B., Lundgren, S., Krane, J. & Axelson, D. Metabolite composition in breast tumors examined by proton nuclear magnetic resonance spectroscopy. Anticancer Res. 19, 1737-1746 (1999).
51. 1H brain tumor spectra: a multicenter study. Magn. Reson. Med. 49, 29-36 (2003).
52. 1H magnetic resonance spectroscopy. Magn. Reson. Med. 49, 223-232 (2003). An informative paper on the use of in vivo MRS as a tool for generating metabolic profiles of human brain tumours. In this study the authors distinguish meningiomas, grade II astrocytomas, anaplastic astrocytomas and glioblastomas using the relative ratios of lactate, alanine, saturated lipid, myo-inositol and choline.
53. Underwood, J. et al. A prototype decision support system for MR spectroscopy-assisted diagnosis of brain tumours. Madinfo. 10, 561-565 (2001)
54. El-Deredy, W. et al. Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks. Cancer Res. 57, 4196-4199 (1997).
55. Carmichael, P. L. Mechanisms of action of antiestrogens: relevance to clinical benefits and risks. Cancer Invest. 16, 604-611 (1998).
56. 1H NMR spectroscopy. Biochim. Biophys. Acta 1619, 151-158 (2003).
57. Chung, Y.-L. et al. The pharmacodynamic effect of 17-AAG on HT29 xenografts in mice monitored by magnetic resonance spectroscopy. Proc. Am. Assoc. Cancer Res. 43, 73 (2002).
58. Chung, Y.-L. et al. Magnetic resonance spectroscopic pharmacodynamic markers of Hsp90 inhibitor, 17-allylamino-17-demethoxygeldanamycin, in human colon cancer models. J. Natl Cancer Inst. 95, 1624-1633 (2003).
59. Chung, Y.-L. et al. The effects of CYC202 on tumors monitored by magnetic resonance spectroscopy. Proc. Am. Assoc. Cancer Res. 43, 336 (2002).
60. 31P-magnetic resonance spectroscopy study. Cancer Res. 61, 7536-7543 (2001).
61. Joshi, L. et al. Metabolomics of plant saponins: bioprospecting triterpene glycoside diversity with respect to mammalian cell targets. OMICS 6, 235-246 (2002).
62. 1H-NMR-based metabonomics. Nature Med. 8, 1439-1444 (2002).
63. Bathen, T. F., Engan, T. Krane, J. & Axelson, D. Analysis and classification of proton NMR spectra of lipoprotein fractions from healthy volunteers and patients with cancer or CHD. Anticancer Res. 20, 2393-2408 (2000).
64. Dwarakanath, S., Ferris, C. D., Pierre, J. W., Asplund, R. O. & Curtis, D. L. A neural network approach to the easily detection of cancer. Biomed. Sci. Instrum. 30, 230-243 (1994).
65. Diem, M., Boydston-White, S. & Chiriboga, L. Infrared spectroscopy of cells and tissues: shining lights onto a novel subject. Appl. Spectr. 53, A148-A161 (1999).
66. Schultz, C. P., Liu, K. Z., Johnston, J. B. & Mantsch, H. H. Prognosis of chronic lymphocytic leukemia from infrared spectra of lymphocytes. J. Mol. Struct. 408, 253-256 (1997).
67. Boustany, N. N. et al. Analysis of nucleotides and aromatic amino acids in normal and neoplastic colon mucosa by ultraviolet resonance Raman spectroscopy. Lab. Investigat. 79, 1201-1214 (1999).
68. Go, V. L., Butrum, R. R. & Wong, D. A. Diet, nutrition, and cancer prevention: the postgenomic era. J. Nutr. 133 (Suppl. 1), 3830-3836 (2003).
69. rd Exp. Nucl. Magn. Reson. Conf. 86 (2002).
70. Boros, L. G., Brackett, D. J. & Harrigan G. G. Metabolic biomarker and kinase drug target discovery in cancer using stable isotope-based dynamic metabolic profiling (SIDMAP). Curr. Cancer Drug Targets 3, 445-453 (2003).
71. Tzika, A. A. et al. Biochemical characterization of pediatric brain tumors by using in vivo and ex vivo magnetic resonance spectroscopy. J. Neurosurg. 96, 1023-1031 (2002).
72. 1 NMR spectra of the rat brain. J. Magn. Reson. 141, 104-120 (1999).
73. Lindon, J. C. et al. Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol. Appl. Pharmacol. 187, 137-146 (2003).
74. De Luca, V. & St. Pierre, B. The cell and developmental biology of alkaloid biosynthesis. Trends Plant Sci. 5, 168-173 (2000).
75. Lindon, J. C., Holmes, E. & Nicholson J. K. Pattern recognition methods and applications in biomedical magnetic resonance. Prog. Nuc. Magn. Reson. 39, 1-40 (2001).
76. Valafar, F. Pattern recognition techniques in microarray data analysis. Ann. NY Acad. Sci. 980, 41-64 (2002). An excellent and unbiased review of the current pattern-recognition techniques available to researchers. Although written from a DNA-microarray perspective, the information is still of relevance to those engaged in metabolomics.
77. Oliver, S. G., Winson, M. K., Kell, D. B. & Baganz, F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 16, 373-378 (1998).
78. Bochner, B. R., Gadzinski, P. & Panomitros, E. Phenotype microarrays for high-throughput phenotypic testing and assay of gene function. Genome Res. 11, 1246-1255 (2001).
79. Hanlon, E. B. et al. Prospects for in-vivo Raman spectroscopy. Phys. Med. Biol. 45, R1-R59 (2000).
80. Tweeddale, H., Notley-McRobb, L. & Ferenci, T. Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool ('metabolome') analysis. J. Bacteriol. 180, 5109-5116 (1998).
81. 31P-n. m. r. study. Biochem J. 291, 915-919 (1993).
82. 1H NMR spectra of a myeloma cell line correlates with the induced formation of cytoplasmic lipid droplets. Magn. Reson. Med. 29, 546-550 (1993).
83. Preul, M. C. et al. Accurate, noninvasive diagnosis of human brain tumors by using proton magnetic resonance spectroscopy. Nature Med. 2, 323-325 (1996).
84. 1H-NMR spectroscopy and self-organizing maps. NMR Biomed. 16, 1-11 (2003).
85. Anthony, M. L., Zhao, M. & Brindle, K. M. Inhibition of phosphatidylcholine biosynthesis following induction of apoptosis in HL-60 cells. J. Biol. Chem. 274, 19686-19692 (1999).
86. Singer, S., Millis, K., Souza, K. & Fletcher, C. Correlation of lipid content and composition with liposarcoma histology and grade. Ann. Surg. Oncol. 4, 557-563 (1997).
87. 1H magnetic resonance spectroscopy in squamous cell carcinoma of the head and neck region. Head Neck 24, 766-772 (2002).
88. 1H MRS markers of tumour growth in intrasplenic tumours and liver metastasis induced by injecton of HT-29 cells in nude mice spleen. NMR Biomed. 11, 93-106 (1998).