doi:10.1111/codi.12523
Special article
Metabolomics: a potential powerful ally in the fight against cancer
The dawn of a new ‘omic’ science: metabolomics Metabolomics is a newly emerging field of systems biology research and is focused on the high-throughput identification, characterization and quantification of small molecules (< 1500 Da) which compose the metabolome, the final products of cell metabolism [1]. The science began in the 1970s to 1980s [2] with early studies on urinary metabolites by gas chromatography–mass spectrometry (GC-MS), but the term metabolomics appeared in the scientific literature for the first time in 1998 [3], in a study on the genome of the yeast Saccharomyces cerevisiae by Oliver et al. Since then, 6446 metabolomic studies have been published including 1253 reviews, and 741 articles appeared in the first 6 months of 2013 confirming the great interest in research in this field. This young science aims to observe and comprehend the complex and still unexplored web of metabolic reactions and interactions of any organism from bacterium to eukaryotic cell and particularly the changes in response to homeostatic deregulation. The metabolome is dynamic by nature, reflecting the continuous interaction between biological organisms and external stimuli [4,5]. A broad range of different high-throughput technologies such as high-resolution magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), supported by the use of multivariate statistical analysis based on the use of neural networks, will now allow the study of different variables and their reciprocal behaviour simultaneously, to unravel the essence of the metabolome [6]. The Human Metabolome Database (HMDB) was created in 2007 by scientists working at the University of Alberta at Calgary, Canada. It included 2180 human metabolites, their compound description, their structure and chemical properties, their mass spectra and biofluid concentrations, and where possible even their metabolic pathways and association with human disease [7]. The third edition, HMDB 3.5, was published in 2013 and showed the number of metabolites to have increased to over 40 000 [8]. Metabolomics were initially applied to animal models to determine drug response and toxicity, and to specific metabolic diseases [9]. One of its most attractive aspects was the potential clinical applications, using easily obtained biological samples such as serum, urine, exhaled
breath and saliva to allow a rapid and comprehensive picture of the metabolic activity of a living organism to give information on its state of health [10].
Present and future applications of metabolomics in the clinical setting The first application of metabolomics to humans was made by Assfalg et al. [11] in 2008. They described the existence of individual metabolic phenotypes by analysing urine samples from 40 boys. Since then the idea of the existence of a specific individual metabolomic fingerprint led to work aiming to correlate metabolomic patterns derived from different biological fluids in human diseases. So far metabolomics has been applied to neurological [12], rheumatic [13] and cardiovascular diseases [14], type 2 diabetes mellitus [15], inflammatory bowel disease [16], adult and neonatal nephropathies [17] and infectious diseases [18,19]. The greatest effort has, however, been made in oncology, where interest in metabolomics has broadened its application to several aspects of cancer research, such as the identification of new biomarkers and of different cancer subtypes, postoperative follow-up and the development of personalized chemotherapy. The finding of potential molecular cancer biomarkers for screening and early diagnosis is one of the most promising fields in metabolomics even if the studies carried out so far are still at an early stage and require further validation. A disease state appears to be traced better through the multiple derangements of normal metabolic pathways, which would result in different products of modified metabolism rather than a single abnormal component. Due to their chemical features and low molecular weight, these metabolites are able to diffuse through biological membranes and can be found in trace amounts in biological samples [20]. This kind of complex analysis can be carried out thanks to highthroughput metabolomic technologies. Several studies have been published in the last few years aiming to identify biomarkers in different biological samples that will distinguish cancer patients from healthy subjects. These have focused on lung cancer [21–24], breast cancer [25,26], hepatocellular carcinoma [27,28], colorectal cancer [20,29], ovarian cancer [30], gastric cancer [31,32] and prostate cancer
Colorectal Disease ª 2013 The Association of Coloproctology of Great Britain and Ireland. 16, 235–238
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Metabolomics: a potential powerful ally in the fight against cancer
[33–35]. In some cancers, the metabolic profiles have been found to correlate with tumour stage [36], tumour size, lymph node status and hormonal status [37]. For example, high levels of citrate in prostate cancer give a better correlation with the Gleason score than does prostate-specific antigen [38]. This example shows the potential of metabolic profiling to support traditional diagnostic methods for accurate diagnosis and preoperative staging of disease. It could also be of value in real-time decision-making, for example during surgery [37] or in the search for residual cancer after surgery [39]. Metabolomics has been applied to the recognition of different molecular cancer subtypes, as has been demonstrated for lung cancer, with potential benefits in targeting biological therapies [40]. Another potential clinical application of metabolomics is in oncological follow-up, which would be specific for each type of cancer and may be a potentially important means for the early diagnosis of locoregional recurrence and metastatic disease. For example, metabolomics has been applied to the follow-up of patients undergoing curative resection of nonsmall cell lung cancer [41], showing a significant change in the metabolomic baseline pattern 3 years after surgery. A recent study has reported that a nanomaterial-based sensor-array can distinguish between the presurgical and postsurgical state in lung cancer, while GC-MS detected a reduction in five volatile compounds in exhaled breath [42]. Phenotypical assessment using metabolomics is more rapid than with proteomics or genomics, and it may transform the molecular targeting of cancer therapy through personalized pharmacological therapy. Metabolomics could be used to assess the response to traditional therapy as is well demonstrated in vitro in human glioma cell cultures [43]. It may allow the stratification of cancer patients into different subgroups responsive to specific targeted therapies on the basis of different patterns of molecular abnormality [40]. It could also find applications in monitoring clinical and biological responses to treatment, and may also help us to understand resistance to chemotherapy. The analysis of the volatile fraction of the metabolome which can be found in exhaled breath, the breathome, is attractive owing to its low invasiveness, good patient compliance and extensive potential use in the clinical field, both as a diagnostic and a research instrument [44]. Hippocrates would have been very proud to know that he predicted the role of the breath analysis in the diagnosis of human diseases when he first described foetor hepaticus as a sign of liver disease. Since its discovery, breathomics has been applied to in vitro studies by sampling cell cultures and used in vivo to analyse exhaled breath. It is well known that some diseases are character-
236
ized by the production of odiferous volatile compounds with a specific smell, such as the sweet acetonic breath of diabetic patients or the classic ‘maple syrup’ odour of the urine of patients affected by an inborn error of amino acid metabolism. With the development of new technologies able to identify and measure simultaneously a broad range of exhaled compounds [45], breath analysis could be seen as the technological evolution of human olfaction [46]. The potential application of the electronic nose, or e-nose, as a technology using different multisensory arrays (e.g. metal oxides, semiconductive polymers, surface acoustic waves) to analyse gaseous organic samples is very promising [47]. The particularity of this technology is that, in contrast to other analytical techniques, e-noses do not identify the individual components of the mixture but the mixture itself, based on changes in conductivity of the sensors induced by the different components. E-noses have been applied in fields very different from biology and medicine, such as in the food industry [48] and for environmental monitoring [49] and even in military applications. With the development of metabolomics, e-noses have begun to be a smart tool for diagnosis, thanks to their accuracy, portability and simplicity. They are also cheaper than GC-MS and owing to the rapidity of response, they permit on-line analysis. The early studies testing e-noses for the diagnosis of cancer and for screening have been very encouraging [50–52]. In conclusion, the development of metabolomics and its application to medicine is challenging traditional diagnostic and therapeutic approaches, opening new options for screening, monitoring and even therapy, particularly in cancer.
M. Di Lena, E. Travaglio and D. F. Altomare Department of Emergency and Organ Transplantation, University Aldo Moro of Bari, Bari, Italy E-mail: [email protected]
References 1 German JB, Hammock BD, Watkins SM. Metabolomics: building on a century of biochemistry to guide human health. Metabolomics 2005; 1: 3–9. 2 Gates SC, Sweeley CC. Quantitative metabolic profiling based on gas chromatography. Clin Chem 1978; 24: 1663–73. 3 Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol 1998; 16: 373–8. 4 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: 1181–9.
Colorectal Disease ª 2013 The Association of Coloproctology of Great Britain and Ireland. 16, 235–238
M. Di Lena et al.
5 Holmes E, Loo RL, Stamler J et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 2008; 453: 396–400. 6 Hollywood K, Brison DR, Goodacre R. Metabolomics: current technologies and future trends. Proteomics 2006; 6: 4716–23. 7 Wishart DS, Tzur D, Knox C et al. HMDB: the human metabolome database. Nucleic Acids Res 2007; 35: D521– 6. 8 Wishart DS, Jewison T, Guo AC et al. HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 2013; 41: D801–7. 9 Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: a review. Clin Cancer Res 2009; 15: 431–40. 10 Nicholson JK, Holmes E, Kinross JM, Ara W, Takats Z, Lindon JC. Metabolic phenotyping in clinical and surgical environments. Nature 2012; 491: 384–92. 11 Assfalg M, Bertini I, Colangiuli D et al. Evidence of different metabolic phenotypes in humans. Proc Natl Acad Sci USA 2008; 105: 1420–4. 12 Zhang AH, Sun H, Wang XJ. Recent advances in metabolomics in neurological disease, and future perspectives. Anal Bioanal Chem 2013; 405: 8143–50. 13 Priori R, Scrivo R, Brandt J et al. Metabolomics in rheumatic diseases: the potential of an emerging methodology for improved patient diagnosis, prognosis, and treatment efficacy. Autoimmun Rev 2013; 12: 1022–30. 14 Llorach R, Urpi-Sarda M, Tulipani S, Garcia-Aloy M, Monagas M, Andres-Lacueva C. Metabolomic fingerprint in patients at high risk of cardiovascular disease by cocoa intervention. Mol Nutr Food Res 2013; 57: 962–73. 15 Xu F, Tavintharan S, Sum CF, Woon K, Lim SC, Ong CN. Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. J Clin Endocrinol Metab 2013; 98: E1060–5. 16 Storr M, Vogel HJ, Schicho R. Metabolomics: is it useful for inflammatory bowel diseases? Curr Opin Gastroenterol 2013; 29:378–83. 17 Fanos V, Fanni C, Ottonello G, Noto A, Dessı A, Mussap M. Metabolomics in adult and pediatric nephrology. Molecules 2013; 18: 4844–57. 18 Cassol E, Misra V, Holman A, Kamat A, Morgello S, Gabuzda D. Plasma metabolomics identifies lipid abnormalities linked to markers of inflammation, microbial translocation, and hepatic function in HIV patients receiving protease inhibitors. BMC Infect Dis 2013; 13: 20319. 19 Sana TR, Gordon DB, Fischer SM et al. Global mass spectrometry based metabolomics profiling of erythrocytes infected with Plasmodium falciparum. PLoS One 2013; 8: e60840. 20 Altomare DF, Di Lena M, Porcelli F et al. Exhaled volatile organic compounds identify patients with colorectal cancer. Br J Surg 2013; 100: 144–50. 21 Phillips M, Altorki N, Austin JHM, Cameron RB, Cataneo RN. Prediction of lung cancer using volatile biomarkers in breath. Cancer Biomark 2007; 3: 95–109.
Metabolomics: a potential powerful ally in the fight against cancer
22 Mazzone PJ. Analysis of volatile organic compounds in the exhaled breath for the diagnosis of lung cancer. J Thorac Oncol 2008; 3: 774–80. 23 Phillips M, Altorki N, Austin JHM et al. Detection of lung cancer using weighted digital analysis of breath biomarkers. Clin Chim Acta 2008; 393: 76–84. 24 Poli D, Carbognani P, Corradi M et al. Exhaled volatile organic compounds in patients with non-small cell lung cancer: cross sectional and nested short-term follow-up study. Respir Res 2005; 10: 1–10. 25 Borgan E, Sitter B, Lingjærde OC et al. Merging transcriptomics and metabolomics – advances in breast cancer profiling. BMC Cancer 2010; 10: 628. 26 Phillips M, Cataneo RN, Saunders C, Hope P, Schmitt P, Wai J. Volatile biomarkers in the breath of women with breast cancer. J Breath Res 2010; 4: 026003. DOI:10. 1088/1752-7155/4/2/026003. 27 Qin T, Liu H, Song Q et al. The screening of volatile markers for hepatocellular carcinoma. Cancer Epidemiol Biomarkers Prev 2010; 19: 2247–53. 28 Wang B, Chen D, Chen Y et al. Metabonomic profiles discriminate hepatocellular carcinoma from liver cirrhosis by ultraperformance liquid chromatography-mass spectrometry. J Proteome Res 2012; 11: 1217–27. 29 Chen JL, Fan J, Yan LS et al. Urine metabolite profiling of human colorectal cancer by capillary electrophoresis mass spectrometry based on MRB. Gastroenterol Res Pract 2012; 2012: 125890. DOI:10.1155/2012/125890. 30 Denkert C, Budczies J, Kind T et al. Mass spectrometry based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Res 2006; 66: 10795–804. 31 Chen JL, Tang HQ, Hu JD, Fan J, Hong J, Gu JZ. Metabolomics of gastric cancer metastasis detected by gas chromatography and mass spectrometry. Metabolomics 2010; 16: 5874–80. 32 Xu ZQ, Broza YY, Ionsecu R et al. A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions. Br J Cancer 2013; 108: 941–50. 33 Roberts MJ, Schirra HJ, Lavin MF, Gardiner RA. Metabolomics: a novel approach to early and noninvasive prostate cancer detection. Korean J Urol 2011; 52: 79–89. 34 Trock B. Application of metabolomics to prostate cancer. Urol Oncol 2011; 29: 572–81. 35 Sreekumar A, Poisson LM, Rajendiran TM et al. Metabolomic profiles delineate potential role for Sarcosine in prostate cancer progression. Nature 2009; 457: 910–4. 36 Farshidfar F, Weljie AM, Kopciuk K et al. Serum metabolomic profile as a means to distinguish stage of colorectal cancer. Genome Med 2012; 4: 42. 37 Bathen TF, Jensen LR, Sitter B et al. MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status. Breast Cancer Res Treat 2007; 104: 181–9. 38 Klinea EE, Treata EG, Avernab TA, Davisa MS, Smitha AY, Sillerud LO. Citrate concentrations in human seminal fluid and expressed prostatic fluid determined via 1H
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nuclear magnetic resonance spectroscopy outperform prostate specific antigen in prostate cancer detection. J Urol 2006; 176: 2274–9. Balog J, Szaniszlo T, Schaefer KC et al. Identification of biological tissues by rapid evaporative ionization mass spectrometry. Anal Chem 2010; 82: 7343–50. Peled N, Barash O, Tisch U et al. Volatile fingerprints of cancer specific genetic mutations. Nanomedicine 2013; 9: 758–66. Poli D, Goldoni M, Caglieri A et al. Breath analysis in non small cell lung cancer patients after surgical tumour resection. Acta Biomed 2008; 79 (Suppl 1): 64–72. Broza YY, Kremer R, Tisch U et al. A nanomaterial-based breath test for short-term follow-up after lung tumor resection. Nanomedicine 2013; 9: 15–21. El-Deredy W, Ashmore SM, Branston NM, Darling JL, Williams SR, Thomas DGT. Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks advances in brief pretreatment prediction of the chemotherapeutic response of human glioma. Cancer Res 1997; 57: 4196–9. Miekisch W, Schubert JK, Noeldge-Schomburg GF. Diagnostic potential of breath analysis—focus on volatile organic compounds. Clin Chim Acta 2004; 347: 25–39.
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45 Pauling L, Robinson AB, Teranishit ROY, Cary P. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proc Natl Acad Sci USA 1971; 68: 2374–6. 46 Boots AW, van Berkel JJBN, Dallinga JW, Smolinska A, Wouters EF, van Schooten FJ. The versatile use of exhaled volatile organic compounds in human health and disease. J Breath Res 2012; 6: 027108. 47 Wilson AD, Baietto M. Applications and advances in electronic-nose technologies. Sensors 2009; 9: 5099–148. 48 Alam H, Saeed SH. Modern applications of electronic nose: a review. Int J Electr Comput Eng 2013; 1: 52–63. 49 Dentoni L, Capelli L, Sironi S, Rosso RD, Zanetti S. Development of an electronic nose for environmental odour monitoring. Sensors 2012; 12: 14363–81. 50 Barash O, Peled N, Tisch U, Bunn PA Jr, Hirsch FR, Haick H. Classification of lung cancer histology by gold nanoparticle sensors. Nanomedicine 2012; 8: 580–9. 51 Peng G, Tisch U, Adams O et al. Diagnosing lung cancer in exhaled breath using gold nanoparticles. Nat Nanotechnol 2009; 4: 669–73. 52 de Meji TG, Larbi IB, van der Schee MP et al. Electronic nose can discriminate colorectal carcinoma and advanced adenomas by fecal volatile biomarker analysis: proof of principle study. Int J Cancer 2014; 134: 1132–8.
Colorectal Disease ª 2013 The Association of Coloproctology of Great Britain and Ireland. 16, 235–238
Special article
Metabolomics: a potential powerful ally in the fight against cancer
The dawn of a new ‘omic’ science: metabolomics Metabolomics is a newly emerging field of systems biology research and is focused on the high-throughput identification, characterization and quantification of small molecules (< 1500 Da) which compose the metabolome, the final products of cell metabolism [1]. The science began in the 1970s to 1980s [2] with early studies on urinary metabolites by gas chromatography–mass spectrometry (GC-MS), but the term metabolomics appeared in the scientific literature for the first time in 1998 [3], in a study on the genome of the yeast Saccharomyces cerevisiae by Oliver et al. Since then, 6446 metabolomic studies have been published including 1253 reviews, and 741 articles appeared in the first 6 months of 2013 confirming the great interest in research in this field. This young science aims to observe and comprehend the complex and still unexplored web of metabolic reactions and interactions of any organism from bacterium to eukaryotic cell and particularly the changes in response to homeostatic deregulation. The metabolome is dynamic by nature, reflecting the continuous interaction between biological organisms and external stimuli [4,5]. A broad range of different high-throughput technologies such as high-resolution magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), supported by the use of multivariate statistical analysis based on the use of neural networks, will now allow the study of different variables and their reciprocal behaviour simultaneously, to unravel the essence of the metabolome [6]. The Human Metabolome Database (HMDB) was created in 2007 by scientists working at the University of Alberta at Calgary, Canada. It included 2180 human metabolites, their compound description, their structure and chemical properties, their mass spectra and biofluid concentrations, and where possible even their metabolic pathways and association with human disease [7]. The third edition, HMDB 3.5, was published in 2013 and showed the number of metabolites to have increased to over 40 000 [8]. Metabolomics were initially applied to animal models to determine drug response and toxicity, and to specific metabolic diseases [9]. One of its most attractive aspects was the potential clinical applications, using easily obtained biological samples such as serum, urine, exhaled
breath and saliva to allow a rapid and comprehensive picture of the metabolic activity of a living organism to give information on its state of health [10].
Present and future applications of metabolomics in the clinical setting The first application of metabolomics to humans was made by Assfalg et al. [11] in 2008. They described the existence of individual metabolic phenotypes by analysing urine samples from 40 boys. Since then the idea of the existence of a specific individual metabolomic fingerprint led to work aiming to correlate metabolomic patterns derived from different biological fluids in human diseases. So far metabolomics has been applied to neurological [12], rheumatic [13] and cardiovascular diseases [14], type 2 diabetes mellitus [15], inflammatory bowel disease [16], adult and neonatal nephropathies [17] and infectious diseases [18,19]. The greatest effort has, however, been made in oncology, where interest in metabolomics has broadened its application to several aspects of cancer research, such as the identification of new biomarkers and of different cancer subtypes, postoperative follow-up and the development of personalized chemotherapy. The finding of potential molecular cancer biomarkers for screening and early diagnosis is one of the most promising fields in metabolomics even if the studies carried out so far are still at an early stage and require further validation. A disease state appears to be traced better through the multiple derangements of normal metabolic pathways, which would result in different products of modified metabolism rather than a single abnormal component. Due to their chemical features and low molecular weight, these metabolites are able to diffuse through biological membranes and can be found in trace amounts in biological samples [20]. This kind of complex analysis can be carried out thanks to highthroughput metabolomic technologies. Several studies have been published in the last few years aiming to identify biomarkers in different biological samples that will distinguish cancer patients from healthy subjects. These have focused on lung cancer [21–24], breast cancer [25,26], hepatocellular carcinoma [27,28], colorectal cancer [20,29], ovarian cancer [30], gastric cancer [31,32] and prostate cancer
Colorectal Disease ª 2013 The Association of Coloproctology of Great Britain and Ireland. 16, 235–238
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M. Di Lena et al.
Metabolomics: a potential powerful ally in the fight against cancer
[33–35]. In some cancers, the metabolic profiles have been found to correlate with tumour stage [36], tumour size, lymph node status and hormonal status [37]. For example, high levels of citrate in prostate cancer give a better correlation with the Gleason score than does prostate-specific antigen [38]. This example shows the potential of metabolic profiling to support traditional diagnostic methods for accurate diagnosis and preoperative staging of disease. It could also be of value in real-time decision-making, for example during surgery [37] or in the search for residual cancer after surgery [39]. Metabolomics has been applied to the recognition of different molecular cancer subtypes, as has been demonstrated for lung cancer, with potential benefits in targeting biological therapies [40]. Another potential clinical application of metabolomics is in oncological follow-up, which would be specific for each type of cancer and may be a potentially important means for the early diagnosis of locoregional recurrence and metastatic disease. For example, metabolomics has been applied to the follow-up of patients undergoing curative resection of nonsmall cell lung cancer [41], showing a significant change in the metabolomic baseline pattern 3 years after surgery. A recent study has reported that a nanomaterial-based sensor-array can distinguish between the presurgical and postsurgical state in lung cancer, while GC-MS detected a reduction in five volatile compounds in exhaled breath [42]. Phenotypical assessment using metabolomics is more rapid than with proteomics or genomics, and it may transform the molecular targeting of cancer therapy through personalized pharmacological therapy. Metabolomics could be used to assess the response to traditional therapy as is well demonstrated in vitro in human glioma cell cultures [43]. It may allow the stratification of cancer patients into different subgroups responsive to specific targeted therapies on the basis of different patterns of molecular abnormality [40]. It could also find applications in monitoring clinical and biological responses to treatment, and may also help us to understand resistance to chemotherapy. The analysis of the volatile fraction of the metabolome which can be found in exhaled breath, the breathome, is attractive owing to its low invasiveness, good patient compliance and extensive potential use in the clinical field, both as a diagnostic and a research instrument [44]. Hippocrates would have been very proud to know that he predicted the role of the breath analysis in the diagnosis of human diseases when he first described foetor hepaticus as a sign of liver disease. Since its discovery, breathomics has been applied to in vitro studies by sampling cell cultures and used in vivo to analyse exhaled breath. It is well known that some diseases are character-
236
ized by the production of odiferous volatile compounds with a specific smell, such as the sweet acetonic breath of diabetic patients or the classic ‘maple syrup’ odour of the urine of patients affected by an inborn error of amino acid metabolism. With the development of new technologies able to identify and measure simultaneously a broad range of exhaled compounds [45], breath analysis could be seen as the technological evolution of human olfaction [46]. The potential application of the electronic nose, or e-nose, as a technology using different multisensory arrays (e.g. metal oxides, semiconductive polymers, surface acoustic waves) to analyse gaseous organic samples is very promising [47]. The particularity of this technology is that, in contrast to other analytical techniques, e-noses do not identify the individual components of the mixture but the mixture itself, based on changes in conductivity of the sensors induced by the different components. E-noses have been applied in fields very different from biology and medicine, such as in the food industry [48] and for environmental monitoring [49] and even in military applications. With the development of metabolomics, e-noses have begun to be a smart tool for diagnosis, thanks to their accuracy, portability and simplicity. They are also cheaper than GC-MS and owing to the rapidity of response, they permit on-line analysis. The early studies testing e-noses for the diagnosis of cancer and for screening have been very encouraging [50–52]. In conclusion, the development of metabolomics and its application to medicine is challenging traditional diagnostic and therapeutic approaches, opening new options for screening, monitoring and even therapy, particularly in cancer.
M. Di Lena, E. Travaglio and D. F. Altomare Department of Emergency and Organ Transplantation, University Aldo Moro of Bari, Bari, Italy E-mail: [email protected]
References 1 German JB, Hammock BD, Watkins SM. Metabolomics: building on a century of biochemistry to guide human health. Metabolomics 2005; 1: 3–9. 2 Gates SC, Sweeley CC. Quantitative metabolic profiling based on gas chromatography. Clin Chem 1978; 24: 1663–73. 3 Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol 1998; 16: 373–8. 4 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: 1181–9.
Colorectal Disease ª 2013 The Association of Coloproctology of Great Britain and Ireland. 16, 235–238
M. Di Lena et al.
5 Holmes E, Loo RL, Stamler J et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 2008; 453: 396–400. 6 Hollywood K, Brison DR, Goodacre R. Metabolomics: current technologies and future trends. Proteomics 2006; 6: 4716–23. 7 Wishart DS, Tzur D, Knox C et al. HMDB: the human metabolome database. Nucleic Acids Res 2007; 35: D521– 6. 8 Wishart DS, Jewison T, Guo AC et al. HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 2013; 41: D801–7. 9 Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: a review. Clin Cancer Res 2009; 15: 431–40. 10 Nicholson JK, Holmes E, Kinross JM, Ara W, Takats Z, Lindon JC. Metabolic phenotyping in clinical and surgical environments. Nature 2012; 491: 384–92. 11 Assfalg M, Bertini I, Colangiuli D et al. Evidence of different metabolic phenotypes in humans. Proc Natl Acad Sci USA 2008; 105: 1420–4. 12 Zhang AH, Sun H, Wang XJ. Recent advances in metabolomics in neurological disease, and future perspectives. Anal Bioanal Chem 2013; 405: 8143–50. 13 Priori R, Scrivo R, Brandt J et al. Metabolomics in rheumatic diseases: the potential of an emerging methodology for improved patient diagnosis, prognosis, and treatment efficacy. Autoimmun Rev 2013; 12: 1022–30. 14 Llorach R, Urpi-Sarda M, Tulipani S, Garcia-Aloy M, Monagas M, Andres-Lacueva C. Metabolomic fingerprint in patients at high risk of cardiovascular disease by cocoa intervention. Mol Nutr Food Res 2013; 57: 962–73. 15 Xu F, Tavintharan S, Sum CF, Woon K, Lim SC, Ong CN. Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. J Clin Endocrinol Metab 2013; 98: E1060–5. 16 Storr M, Vogel HJ, Schicho R. Metabolomics: is it useful for inflammatory bowel diseases? Curr Opin Gastroenterol 2013; 29:378–83. 17 Fanos V, Fanni C, Ottonello G, Noto A, Dessı A, Mussap M. Metabolomics in adult and pediatric nephrology. Molecules 2013; 18: 4844–57. 18 Cassol E, Misra V, Holman A, Kamat A, Morgello S, Gabuzda D. Plasma metabolomics identifies lipid abnormalities linked to markers of inflammation, microbial translocation, and hepatic function in HIV patients receiving protease inhibitors. BMC Infect Dis 2013; 13: 20319. 19 Sana TR, Gordon DB, Fischer SM et al. Global mass spectrometry based metabolomics profiling of erythrocytes infected with Plasmodium falciparum. PLoS One 2013; 8: e60840. 20 Altomare DF, Di Lena M, Porcelli F et al. Exhaled volatile organic compounds identify patients with colorectal cancer. Br J Surg 2013; 100: 144–50. 21 Phillips M, Altorki N, Austin JHM, Cameron RB, Cataneo RN. Prediction of lung cancer using volatile biomarkers in breath. Cancer Biomark 2007; 3: 95–109.
Metabolomics: a potential powerful ally in the fight against cancer
22 Mazzone PJ. Analysis of volatile organic compounds in the exhaled breath for the diagnosis of lung cancer. J Thorac Oncol 2008; 3: 774–80. 23 Phillips M, Altorki N, Austin JHM et al. Detection of lung cancer using weighted digital analysis of breath biomarkers. Clin Chim Acta 2008; 393: 76–84. 24 Poli D, Carbognani P, Corradi M et al. Exhaled volatile organic compounds in patients with non-small cell lung cancer: cross sectional and nested short-term follow-up study. Respir Res 2005; 10: 1–10. 25 Borgan E, Sitter B, Lingjærde OC et al. Merging transcriptomics and metabolomics – advances in breast cancer profiling. BMC Cancer 2010; 10: 628. 26 Phillips M, Cataneo RN, Saunders C, Hope P, Schmitt P, Wai J. Volatile biomarkers in the breath of women with breast cancer. J Breath Res 2010; 4: 026003. DOI:10. 1088/1752-7155/4/2/026003. 27 Qin T, Liu H, Song Q et al. The screening of volatile markers for hepatocellular carcinoma. Cancer Epidemiol Biomarkers Prev 2010; 19: 2247–53. 28 Wang B, Chen D, Chen Y et al. Metabonomic profiles discriminate hepatocellular carcinoma from liver cirrhosis by ultraperformance liquid chromatography-mass spectrometry. J Proteome Res 2012; 11: 1217–27. 29 Chen JL, Fan J, Yan LS et al. Urine metabolite profiling of human colorectal cancer by capillary electrophoresis mass spectrometry based on MRB. Gastroenterol Res Pract 2012; 2012: 125890. DOI:10.1155/2012/125890. 30 Denkert C, Budczies J, Kind T et al. Mass spectrometry based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Res 2006; 66: 10795–804. 31 Chen JL, Tang HQ, Hu JD, Fan J, Hong J, Gu JZ. Metabolomics of gastric cancer metastasis detected by gas chromatography and mass spectrometry. Metabolomics 2010; 16: 5874–80. 32 Xu ZQ, Broza YY, Ionsecu R et al. A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions. Br J Cancer 2013; 108: 941–50. 33 Roberts MJ, Schirra HJ, Lavin MF, Gardiner RA. Metabolomics: a novel approach to early and noninvasive prostate cancer detection. Korean J Urol 2011; 52: 79–89. 34 Trock B. Application of metabolomics to prostate cancer. Urol Oncol 2011; 29: 572–81. 35 Sreekumar A, Poisson LM, Rajendiran TM et al. Metabolomic profiles delineate potential role for Sarcosine in prostate cancer progression. Nature 2009; 457: 910–4. 36 Farshidfar F, Weljie AM, Kopciuk K et al. Serum metabolomic profile as a means to distinguish stage of colorectal cancer. Genome Med 2012; 4: 42. 37 Bathen TF, Jensen LR, Sitter B et al. MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status. Breast Cancer Res Treat 2007; 104: 181–9. 38 Klinea EE, Treata EG, Avernab TA, Davisa MS, Smitha AY, Sillerud LO. Citrate concentrations in human seminal fluid and expressed prostatic fluid determined via 1H
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Metabolomics: a potential powerful ally in the fight against cancer
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