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Using bioanalysis for cancer diagnosis and prognosis “In the field of cancer research, metabolic profiles of endogenous compounds, which basically differ in pathophysiological states, seem to be particularly useful.” Keywords: biomarkers n cancer diagnostics n metabolomic profile n nucleosides n urine samples
Among causes of deaths, cancer is a major one responsible for mortality of some 6 million humans each year. Simultaneously, approximately 10 million new cases of cancer diseases are diagnosed yearly [1]. There are no doubts then that cancer is one of the main targets in the search for new, reliable and efficient disease biomarkers that can be used in clinical practice to provide a real improvement of diagnosis and monitoring of therapy. Currently, due to exceptional development in instrumental and bioinformatics technologies, the most attractive approaches to biomarker discovery from the scientific point of view are based on -omics. Since the 1990s, genomics, transcriptomics, proteomics and more recently metabolomics, have become the ‘gold standards’ in advanced preclinical and clinical research. As a result, new genetic, expression, protein and metabolic biomarkers have been developed [1]. However, according to a GVK Bio Online Biomarker Database (GOBIOM) [2], from among ~8000 oncology biomarkers discovered with -omics technologies, only 120 have been approved by the US FDA for clinical practice [1]. However, what is most interesting for bioanalyticians is that metabolomics is characterized by the highest ‘success ratio’ – from 19 reported in the database biomarkers two (10%) were approved. It is the highest percentage among the reported -omics technologies for which the ‘success ratio’, expressed in percentage of approved biomarkers, ranged from 1.3 up to 1.6. In case of microRNA profiling there is no a single biomarker yet approved for clinical practice. Hence, the potential of bioanalysis in the metabolomics field appears to be promising. Nowadays, due to widespread access to highthroughput, ultrasensitive bioanalytical methods, metabolomics is the most dynamically developing branch of biomedical research. Of
special interest is medical metabolomics, which consists of the determination of qualitative and quantitative metabolite profiles of biological materials, instead of (often futile) searching for single biomarker compounds. Such metabolomic profiles can be obtained by bioinformatic (chemometric) processing of large sets of bioanalytical data. Among potential applications of metabolomics, special attention has been devoted to the medical diagnostics allowing detection of diseases at the stage of preliminary laboratory analysis. This approach is currently characterized by the search for biomarkers not as single, individual compounds but as whole-set groups or panels of metabolites whose qualitative and quantitative differences would reflect progressive pathophysiological changes [3]. Taking in mind the vast complexity of the pathophysiological and physiological processes it is not surprising that sets of different metabolites might represent in a more comprehensive way the changes in an organism related to the disease than a single selected biocompound would. The problem with the changing metabolite profiles is that one must operate in an abstract multi dimensional variable (bioanalyte) space, instead of the natural for the human mind 2D or 3D X, Y, Z variable data space. Therefore, determination and comparison of metabolite profiles became feasible only after bioinformatic tools for analytical data processing became available, allowing extraction of systematic information initially dispersed over large data matrices. In our group, the first biorelevant pharmacological information from large sets of chromatographic data was derived by multivariable analysis in 1991 [4]. In the field of cancer research, metabolic profiles of endogenous compounds, which basically differ in pathophysiological states, seem to be particularly useful. Obviously, analysis
10.4155/BIO.14.38 © 2014 Future Science Ltd
Bioanalysis (2014) 6(7), 907–909
Michał J Markuszewski Department of Biopharmaceutics & Pharmacodynamics, Medical University of Gdańsk, Poland
Roman Kaliszan Author for correspondence: Department of Biopharmaceutics & Pharmacodynamics, Medical University of Gdańsk, Poland [email protected]
ISSN 1757-6180
907
E ditorial |
Markuszewski & Kaliszan of metabolic profiles or metabolic fingerprints in biological materials can provide an objective indicator of the processes of carcinogenesis in the organism. For a long time, the elevated levels of some metabolites, such as nucleosides, have been observed in urogenital tract cancers, particularly in bladder cancer. Unfortunately, due to the structural diversity of bioanalytes with different physicochemical properties, there is no single analytical method to readily determine the whole human metabolome. However, there are several bioanalytical strategies that enable analysis of the complete metabolome.
“...due to structural diversity of bioanalytes with different physicochemical properties, there is no a single analytical method to readily determine the whole human metabolome.” Two of the most commonly used analytical tools, NMR and MS, coupled with separation techniques, such as HPLC, GC or CE, provide detailed information on metabolite structure, which is necessary for successful metabolite identification. NMR can detect a wide range of biochemical metabolites by chemical shift measurement. However, one of the main limitations of NMR is its poor sensitivity, which can be improved with the parallel use of MS. Bioanalytical data must normally be bioinformatically processed to extract systematic information from them, which can be related to the pathophysiological state of a patient in order to detect malignant disease and/or to monitor progress of the therapeutic process. One of a targeted group of metabolites are nucleosides and their modified derivatives. Nucleosides are constituents of RNA and during biodegradation processes they are normally transformed into uric acid, b-alanine and b-aminoisobutryic acid. In cancer, instead of the typical end products of RNA metabolism,
nucleosides also undergo methylation, resulting in modified nucleosides. It has been reported that modified nucleosides might be used as indicators of different types of urogenital cancer, such as urinary bladder cancer, prostate cancer and kidney cancer [5]. In our systematic research, an HPLC analysis of nucleosides profiles in urine from patients, combined with chemometric data processing, was carried out [6]. In addition, other analytical approaches, including CE, HPLC, and HPLC–MS, were applied to compare nucleoside metabolomic profiles in urine from patients and healthy subjects [7–9]. The latest approach revealed statistically significant (p
Using bioanalysis for cancer diagnosis and prognosis “In the field of cancer research, metabolic profiles of endogenous compounds, which basically differ in pathophysiological states, seem to be particularly useful.” Keywords: biomarkers n cancer diagnostics n metabolomic profile n nucleosides n urine samples
Among causes of deaths, cancer is a major one responsible for mortality of some 6 million humans each year. Simultaneously, approximately 10 million new cases of cancer diseases are diagnosed yearly [1]. There are no doubts then that cancer is one of the main targets in the search for new, reliable and efficient disease biomarkers that can be used in clinical practice to provide a real improvement of diagnosis and monitoring of therapy. Currently, due to exceptional development in instrumental and bioinformatics technologies, the most attractive approaches to biomarker discovery from the scientific point of view are based on -omics. Since the 1990s, genomics, transcriptomics, proteomics and more recently metabolomics, have become the ‘gold standards’ in advanced preclinical and clinical research. As a result, new genetic, expression, protein and metabolic biomarkers have been developed [1]. However, according to a GVK Bio Online Biomarker Database (GOBIOM) [2], from among ~8000 oncology biomarkers discovered with -omics technologies, only 120 have been approved by the US FDA for clinical practice [1]. However, what is most interesting for bioanalyticians is that metabolomics is characterized by the highest ‘success ratio’ – from 19 reported in the database biomarkers two (10%) were approved. It is the highest percentage among the reported -omics technologies for which the ‘success ratio’, expressed in percentage of approved biomarkers, ranged from 1.3 up to 1.6. In case of microRNA profiling there is no a single biomarker yet approved for clinical practice. Hence, the potential of bioanalysis in the metabolomics field appears to be promising. Nowadays, due to widespread access to highthroughput, ultrasensitive bioanalytical methods, metabolomics is the most dynamically developing branch of biomedical research. Of
special interest is medical metabolomics, which consists of the determination of qualitative and quantitative metabolite profiles of biological materials, instead of (often futile) searching for single biomarker compounds. Such metabolomic profiles can be obtained by bioinformatic (chemometric) processing of large sets of bioanalytical data. Among potential applications of metabolomics, special attention has been devoted to the medical diagnostics allowing detection of diseases at the stage of preliminary laboratory analysis. This approach is currently characterized by the search for biomarkers not as single, individual compounds but as whole-set groups or panels of metabolites whose qualitative and quantitative differences would reflect progressive pathophysiological changes [3]. Taking in mind the vast complexity of the pathophysiological and physiological processes it is not surprising that sets of different metabolites might represent in a more comprehensive way the changes in an organism related to the disease than a single selected biocompound would. The problem with the changing metabolite profiles is that one must operate in an abstract multi dimensional variable (bioanalyte) space, instead of the natural for the human mind 2D or 3D X, Y, Z variable data space. Therefore, determination and comparison of metabolite profiles became feasible only after bioinformatic tools for analytical data processing became available, allowing extraction of systematic information initially dispersed over large data matrices. In our group, the first biorelevant pharmacological information from large sets of chromatographic data was derived by multivariable analysis in 1991 [4]. In the field of cancer research, metabolic profiles of endogenous compounds, which basically differ in pathophysiological states, seem to be particularly useful. Obviously, analysis
10.4155/BIO.14.38 © 2014 Future Science Ltd
Bioanalysis (2014) 6(7), 907–909
Michał J Markuszewski Department of Biopharmaceutics & Pharmacodynamics, Medical University of Gdańsk, Poland
Roman Kaliszan Author for correspondence: Department of Biopharmaceutics & Pharmacodynamics, Medical University of Gdańsk, Poland [email protected]
ISSN 1757-6180
907
E ditorial |
Markuszewski & Kaliszan of metabolic profiles or metabolic fingerprints in biological materials can provide an objective indicator of the processes of carcinogenesis in the organism. For a long time, the elevated levels of some metabolites, such as nucleosides, have been observed in urogenital tract cancers, particularly in bladder cancer. Unfortunately, due to the structural diversity of bioanalytes with different physicochemical properties, there is no single analytical method to readily determine the whole human metabolome. However, there are several bioanalytical strategies that enable analysis of the complete metabolome.
“...due to structural diversity of bioanalytes with different physicochemical properties, there is no a single analytical method to readily determine the whole human metabolome.” Two of the most commonly used analytical tools, NMR and MS, coupled with separation techniques, such as HPLC, GC or CE, provide detailed information on metabolite structure, which is necessary for successful metabolite identification. NMR can detect a wide range of biochemical metabolites by chemical shift measurement. However, one of the main limitations of NMR is its poor sensitivity, which can be improved with the parallel use of MS. Bioanalytical data must normally be bioinformatically processed to extract systematic information from them, which can be related to the pathophysiological state of a patient in order to detect malignant disease and/or to monitor progress of the therapeutic process. One of a targeted group of metabolites are nucleosides and their modified derivatives. Nucleosides are constituents of RNA and during biodegradation processes they are normally transformed into uric acid, b-alanine and b-aminoisobutryic acid. In cancer, instead of the typical end products of RNA metabolism,
nucleosides also undergo methylation, resulting in modified nucleosides. It has been reported that modified nucleosides might be used as indicators of different types of urogenital cancer, such as urinary bladder cancer, prostate cancer and kidney cancer [5]. In our systematic research, an HPLC analysis of nucleosides profiles in urine from patients, combined with chemometric data processing, was carried out [6]. In addition, other analytical approaches, including CE, HPLC, and HPLC–MS, were applied to compare nucleoside metabolomic profiles in urine from patients and healthy subjects [7–9]. The latest approach revealed statistically significant (p
