Metabonomics of Human Colorectal Cancer: New ... - ACS Publications

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Metabonomics of Human Colorectal Cancer: New Approaches for Early Diagnosis and Biomarker Discovery Yan Ni,†,‡ Guoxiang Xie,†,‡ and Wei Jia*,†,‡ †

Center for Translational Medicine, and Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology & Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, China ‡ University of Hawaii Cancer Center, Honolulu, Hawaii 96813, United States ABSTRACT: Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC. KEYWORDS: Colorectal cancer, metabonomics, biomarker discovery, diagnosis, prognosis, translational medicine, mass spectrometry, NMR spectroscopy

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INTRODUCTION Colorectal cancer (CRC) is the third most common malignancy in the United States in both men and women and the second leading cause of death from cancer, with an estimated 136 830 new cases and 50 310 deaths in 2014.1 CRC could be effectively prevented through a screening process at the adenomatous polyp stage with sigmoidoscopy, barium enema, tomographic colonography, and colonoscopy,2−4 and it can be cured if diagnosed at an early stage.5 The 5 year survival rate of CRC can reach 90% when the tumor is detected at an early and localized stage, but the survival rate drops dramatically to 12% if the tumor has spread to distant organs.6 CRC is currently diagnosed by endoscopic and radiological imaging pre-operatively and confirmed with histopathological examination of biopsies or surgically removed specimens.7 However, the invasive and unpleasant nature of these diagnostic modalities often brings unwanted pain and discomfort to the patients, highlighting the demand for less or noninvasive tests with improved patient compliance for use in the clinic. The fecal occult blood test (FOBT) and the serum test of tumor markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) have been commonly used in the clinic, but the lack of sensitivity and specificity of these markers has significantly limited the clinical application of these markers in CRC diagnosis. Several tissue-specific tumor markers such as chromosomal deletions, loss of heterozygosity at chromosome 18q, increased protein level of p53, and © 2014 American Chemical Society

microsatellite instability are under investigation for CRC prognosis; however, none of these is yet ready for clinical use.8 Thus, it is urgent and of great importance to develop simple and noninvasive screening tools to facilitate the early detection and precise stage classification of CRC, ultimately ensuring that all patients receive the most appropriate treatment through timely monitoring of tumor progression, regression, and recurrence. Metabonomics,9,10 or metabolomics,11 has been applied in tumor biomarker discovery in recent years (e.g., lung, pancreatic, liver, prostate, and gynecologic cancers) to investigate its potential for use in biomarker discovery for cancer diagnosis, treatment, and prevention.12−15 Since 2008, metabonomics has been applied in human CRC biomarker discovery through measuring the global and temporal metabolic changes in biofluids and/or tissue samples (Figure 1A). The application of high-throughput analytical technology, e.g., nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled with chromatographic separation, enables the identification of a wide range of small-molecule metabolites involved in various biological pathways. Through the identification of key metabolic pathways, metabonomics allows a comprehensive investigation of biochemical mechanisms underlying CRC carcinogenesis. Several recent pubReceived: May 2, 2014 Published: August 8, 2014 3857

dx.doi.org/10.1021/pr500443c | J. Proteome Res. 2014, 13, 3857−3870

Journal of Proteome Research

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Figure 1. (A) Publications of CRC metabonomics over the last 6 years based on searching for “metabolomics OR metabonomics OR metaboli* profil* OR metaboli* signature” AND “colo* cancer OR colo* tumor” in the ISI Web-of-Science database. (B) Statistics of the number of CRC patients in different metabonomics studies. (C) Comparison of analytical platforms and biological samples reported in CRC metabonomics over the past 6 years. Note that together with nuclear magnetic resonance (NMR) spectroscopy, four separation techniques coupled to mass spectrometry (MS) have been applied in recent metabonomics studies, i.e., gas chromatography (GC), liquid chromatography (LC), capillary electrophoresis (CE), and Fourier transform ion cyclotron resonance (FTICR).

Figure 2. Workflow for CRC metabonomics studies (left) and a checklist for performing a high-quality metabonomics study (right).

nomics is the measurement of defined groups of chemically characterized and annotated metabolites,20 whereas untargeted metabonomics is the comprehensive analysis of all of the measurable analytes in a sample, including unknown chemicals. Both targeted and nontargeted metabonomics have been applied in biomarker discovery of CRC. A typical metabonomics study generally involves five steps (Figure 2): (1) study design, (2) sample collection and storage, (3) sample preparation and instrumental analysis, (4) data processing and analysis, and (5) metabolite marker identification and validation.

lications have reviewed the application of metabonomics and advances on analytical platforms for CRC biomarker discovery.16−18 This review will summarize the critical steps of clinical metabonomics studies applied in CRC early diagnosis and prognosis over the past 6 years and will discuss the challenges of and perspectives for future studies.

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METABONOMICS WORKFLOW FOR CRC BIOMARKER DISCOVERY Metabonomics can be divided into two distinct approaches, targeted and nontargeted metabonomics, each with their own inherent advantages and disadvantages.19 Targeted metabo3858



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Study Design

rectum and thus are potentially important as a type of noninvasive CRC biospecimen.25,26 Blood Samples. Plasma and serum are the most frequently used samples for metabonomics, as blood is a primary carrier of small molecules circulating throughout the body. The metabolite and protein compositions of plasma or serum can be altered at different pathological states, e.g., tissue lesions and organ dysfunction.27 Blood samples are often collected after overnight fasting except for studies focusing on metabolic responses to or effects on a specific treatment such as exercise and dietary supplementation. Serum samples are collected directly in tubes after the blood is allowed to clot and after centrifugation. Plasma samples are collected in tubes coated with an anticoagulant such as EDTA, citrate, and lithium heparin to avoid clotting.21 Lithium heparin is a preferred way of preparing plasma samples for metabonomics because citrate is an endogenous metabolite and EDTA can greatly affect mass spectrometry chromatograms.21 Both plasma and serum can be used for untargeted and targeted metabonomics studies, and neither is considered more useful than the other, although metabolic profile differences have been observed between plasma and serum samples collected from the same subjects.28 Urine Samples. Human urine collection is a simple procedure and can be done at home by participants or in the clinic with an appropriate container. However, since the metabolic profile changes with the day−night cycle and since food intake interferes with the profile,29 the time of collection is critical. Urine samples for metabolite profiling can be collected as “spot” samples over a period of time or within the 24 h period, and the first void morning urine samples are commonly recommended. Urine samples can be stored and transported in an insulated container with gel ice packs when collected at home. Stool/Fecal Samples. Stool samples are collected to study the role of metabolites from the human gut in the initiation, development, and progression of cancer. No special preparation is required for metabonomics study except that the general instructions for stool sample collection in the clinic should be followed. The fresh samples are collected in a clean container, not contaminated with urine, sealed in a plastic bag, and frozen immediately after delivery to the laboratory. Tissue Samples. Most types of tissue samples for metabonomics study are commonly collected from the leftover biopsy tissues or the tissues removed by surgery. The tissue samples are frozen in liquid nitrogen as rapidly as possible after collection to immediately quench the enzymatic metabolism, to minimize postmortem degradation, and to avoid sample contamination with the surrounding blood cells.21,24 Both tumor and adjacent non-tumor tissue samples are usually taken from the same patient by an experienced surgeon. Sample Storage. Blood and tissue samples are most likely to be collected in the clinic: fresh samples can be stored in a −80 °C freezer or liquid nitrogen immediately after collection. Urine and stool samples can be collected at home, so proper training and handling instructions are needed to ensure sample quality. All types of specimens can be transported from the hospital to the laboratory with dry ice and stored at −80 °C until analysis. Sample storage conditions (−20 or −80 °C), duration, and freeze−thaw cycles play important roles in metabolite analysis; therefore, splitting liquid samples or extracts into multiple aliquots is preferred in order to minimize the metabolite degradation from multiple freeze−thaw cycles.30

A successful metabonomics study starts with a reasonable design to ensure that the data is robust and statistically significant for further biological interpretation. Various factors should be taken into account to achieve a good research design, such as the sample size, the type of biospecimen to be collected for metabolic analysis, the subject’s personal information, sample preparation, and instrument analysis. Among them, it is important to obtain a complete health record of each participant, including general information from physical examination, personal health history, and diagnostic test results (Table 1). However, only 65% of metabonomics studies have Table 1. Example of Recommended Clinical Information To Be Obtained for Each Subject Recruited in a Human Metabonomics Study categories

record details

general information health history

Gender, age, race, weight, height, diet habit, smoking habit, physical activity, environment Medication history, nutrition supplementation, personal history, e.g., cancer-related diseases and having polyps in the colon or rectum, and family disease history Clinical biochemistry tests for both patients and healthy subjects; tumor marker test for patients (e.g., CEA and CA 19-9); histopathological report (e.g., cancer stage, metastasis, and recurrence)

diagnostic test

provided such information since 2008. Detailed clinical information on participating subjects is useful and helpful for the biological interpretation of the data obtained. In a casecontrol study for biomarker discovery of CRC diagnosis, it is preferable to recruit patients at different stages (e.g., TNM stages I−IV) without any medication together with age- and gender-matched healthy subjects without any inflammatory condition or gastrointestinal tract disorder. In practice, however, it is difficult to recruit patients in the early stage of CRC, often resulting in a small sample size of those CRC patients. For CRC prognosis studies, it is recommended to collect clinical samples from the same group of CRC patients at multiple time points, such as before and after treatment (chemotherapy, medication, or surgical operation), and the clinical information should be closely followed over multiple years. Sample Collection and Storage

Sample Collection. Most human metabonomics studies on CRC are performed with biofluids (e.g., serum, plasma, and urine) and tumor tissue samples and, less frequently, with fecal extracts (Figure 1B). Collecting multiple types of biospecimens at the same time is preferred because they provide complementary metabolic information, or cross-validation results.21 Blood and urine samples have traditionally been used because they reflect the global metabolic state of individuals and are readily accessible to researchers.22,23 Tumor tissues are preferable for mechanistic studies because they provide localized metabolic information.24 Drawbacks of using tumor tissues for metabonomics studies include sample heterogeneity, the limited amount of tissue available, the invasive sampling techniques required,21,24 and the need for a non-tumor tissue sample for control, which is often difficult to locate at a precise distance adjacent to the tumor. Stool or fecal extracts also provide useful information regarding the metabolic changes and gut microbiome status in both the colon and 3859



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Sample Preparation and Instrumental Analysis

appropriate platform largely depends on the specific group of metabolites to be analyzed. NMR spectroscopy provides a rapid, high-throughput, and reproducible means to measure metabolites with very few sample preparation steps.35 Notably, the research of intact human cancer tissues (including CRC) is becoming more popular with the application of HR-MAS as a powerful and nondestructive method.36,37 A very recent paper by Nicholson et al. applied HR-MAS for the rapid diagnosis and staging of CRC on intact tissue biopsies.38 GC−MS has been extensively used in metabonomics because of its advantages (high resolution and reproducibility) for detecting and separating a wide range of volatile and/or derivatized nonvolatile metabolites.34 In recent years, the advent of twodimensional GC−TOF−MS has comprehensively enhanced the metabolite coverage of conventional GC−MS. This platform has been successfully used for tissue-based global metabolic profiling of human CRC.39 The LC−MS technique is a complementary alternative to GC−MS analysis because it can measure nonvolatile polar compounds without complex sample derivatization. To this end, LC−MS simplifies the sample preparation steps and compound identification. Capillary electrophoresis mass spectrometry (CE−MS) is particularly suitable for the analysis of highly polar and ionic metabolites where CE separates ions based on their charge and size in a buffer solution.40 It has been used for urinary and tissue metabolite profiling of CRC patients.41,42 With the different advantages and limitations of NMR and MS techniques, multiple complementary analytical platforms are often utilized in order to maximize the spectrum of detectable metabolic changes associated with CRC.36,43,44 In addition, metabolite elucidation and target quantification of metabolites of interest can also be performed by multiple analytical platforms. For example, comprehensive metabolic profiles of CRC patients and controls were performed using Fourier transform ion cyclotron resonance mass spectrometry (FTICR−MS), and the chemical structures of detected markers were further annotated with LC−MS/MS and NMR technologies.45 CE−TOF−MS was used for the quantitative metabolome profiling of colon and stomach cancer, followed by LC−MS/MS for metabolite quantitation.41 In summary, a total of five different analytical platforms have been the most used in the field of CRC metabonomics, and they are NMR spectroscopy, GC−MS (GC−TOF−MS, GC/GC−TOF− MS), LC−MS (HPLC−MS, UPLC−QTOF−MS), CE−MS (CE−TOF−MS), and FTICR−MS (Figure 1 C). Both GC− MS and LC−MS have been reported in the metabolic profiling of serum and urine samples to a large extent, while their usage in tissue profiling is not as prevalent as NMR spectroscopy is.

Human metabolites consist of a great diversity of chemical classes, e.g., organic acids, amino acids, fatty acids, amines, sugars, sugar alcohols, steroids, nucleic acid bases, and many other miscellaneous substances that participate in various biological processes. Significant differences in their physical and chemical properties as well as their broad concentration ranges present great analytical challenges.31 Therefore, powerful analytical platforms with high resolution and high sensitivity are often required to identify and quantify these metabolites in a complex biological system, including NMR spectroscopy and MS coupled with chromatographic separation, e.g., gas chromatography mass spectrometry (GC−MS) and liquid chromatography mass spectrometry (LC−MS). Sample Preparation. Sample preparation methods differ according to specific sample type, metabolites of interest, and the instrument platform to be used. For GC−MS, urine samples are treated with urease followed by removal of particulates, because high-concentration urea is a major cause of instrument and assay performance deterioration.22 Since GC requires volatile and thermally stable analytes, biological samples with nonvolatile compounds must be derivatized before GC−MS analysis. However, it is straightforward to prepare urine samples for LC−MS applications. Urine samples are often diluted with water after being centrifuged or filtered to remove particulates.32 The first step for preparing serum and plasma samples for GC−MS and LC−MS analyses is to precipitate proteins from blood matrix through the addition of appropriate organic solvents.23 After centrifugation, the supernatant is dried or lyophilized, and it is then chemically derivatized for GC−MS application. The dried samples can be reconstituted in suitable solvents for LC−MS analysis. Tissue and stool samples can be lyophilized and processed in a similar fashion as that for blood samples. However, the most common procedure is to grind frozen samples to powder using a liquid nitrogen-cooled pestle and mortar followed by metabolite extraction from the ground powder. A bead-based homogenizer has been increasingly used to improve sample preparation efficiency, throughput, and reproducibility. Different extraction protocols for tissue and stool samples will lead to the observation of different fractions in the metabolite profile.21,24 Therefore, the specific metabolite extraction protocol used for tissue and stool samples is mainly driven by the research goals. In work using NMR spectroscopy analysis, urine samples or diluted urine samples are mixed with a proper volume of phosphate buffer (pH 7.4), whereas blood samples are diluted with 0.9% saline.21 Before high-resolution magic angle spinning (HR-MAS) analysis, each tissue sample needs to be flushed with deuterated water (D2O) to remove the residual blood, improve the homogeneity, suppress water, and add deuterium as the nucleus for the lock system.33 Generally, the tissue and stool extracts are resuspended in either D2O containing TSP as a chemical shift reference or in a mixture of chloroform-d (CDCl3) containing 0.03 v/v tetramethylsilane (TMS) and CD3OD, depending on the metabolite fractions of interest.21 The supernatant from biofluids or tissue/stool extracts is transferred to an NMR tube after centrifugation. Instrumental Analysis. Both NMR spectroscopy- and MSbased metabonomics have been increasingly applied in cancer biomarker discovery.13−15 Each analytical technique has advantages and disadvantages in terms of sensitivity, reproducibility, and accuracy,34,35 and the criterion to select an

Data Processing and Analysis

Along with the development of analytical techniques, advanced bioinformatics tools are required to process, analyze, and interpret large high-dimensional metabonomics data acquired from complex biological samples. In recent years, integrated software tools featuring different methods of data processing and analysis have been favored in metabonomics, such as XCMS online46 and Chenomx NMR Suite (Chenomx Inc., Alberta, Canada). Meanwhile, commercial software from instrument vendors such as MassLynx (Waters, MA, USA) and ChromaTOF (LECO, MI, USA) has also been developed to meet various expectations of the researchers in the field of metabonomics. Different analytical instruments generate different formats of original data files; however, the data processing 3860



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Table 2. List of Identified Metabolite Biomarkers for CRC Diagnosis Reported in More than One Metabonomics Study over the Last 6 Years seruma no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58

name 1-Deoxyglucose 2-Aminobutyratef 2-Hydroxybutyratef 3-Hydroxybutyratef 4-Hydroxyproline 5-Hydroxytryptophan 6-Phosphogluconate Acetate Acetyl-carnitine Alaninef Arachidonate Arginine Asparaginef Aspartatef Betaine Carnitine Cholesterol Choline Citrate Creatine Creatinine Cysteine Cystine D-Galactose Elaidate Fumaratef Glucose Glutamate Glutamine Glycerol Glycerol phosphate Glycine Hippurate Histidinef Hypoxanthine iso-Leucinef Kynurenate L-Glycine Lactate Leucine Tetracosanoate Linoleate Lysine Malate Mannose Margarate Methionine Myo-inositol Myristate Oleamide Oleatef Ornithine P-cresol Palmitate Phenol Phenylalaninef Phosphocholine Proline

GC 1 1 2 2 1

− + + + −

LC

ComMSe

urineb NMR

GC

CE

GC

LC

CE

NMR

GC

1−

1+

1+ 1+ 1−

1+ 1−

1− 1+

1+ 1−

1+ 1−

1−

1− 1+ 1+ 1+

NMR

1+

2+ 1+ 1−

stoold

1− 1+

1+ 1−

LC

tissuec

1+ 1− 1−

1+

1 1 1 2

+ +, 2 − + +

1−

1+ 1+ 1+ 1+

1+ 1+ 1+ 1+

1+

1+ 2 +, 1 − 4+ 1− 1−

1−

1−

1− 1−

1−

1− 1+

1+

1+

1+ 2− 1−

1+ 1− 1−

1+

2−

2 4 2 1 1

1+ 1+

1− 1+

− − + − +, 1 −

1+ 1− 1− 1−

3+ 1− 1−

1−

1− 1+

1 +,1 − 1−

1+

1+ 1+

1+ 1− 1+

1−

1 +,1 − 2+ 1−

1+ 1+

4 3 1 1

− + − −

1+ 1−

1+

1+

1+ 1+ 2+

1+

1− 1−

1−

1+ 1+ 1− 1+

1+ 1− 1+

1−

1−

1−

1− 1+ 1+

1−

1 4 2 1 1 2 4 3 3 4 1 2

+ + + + + + − + − + − +

1+ 1+

1+ 6 +, 1 − 1+

1+ 1+

1+ 1−

1+

1− 4−

1+ 1− 1+

1+

2 +, 1 −

1−

1−

1+ 1 +, 1 −

1− 1− 1−

2−

1− 1−

4 +, 1 − 1− 1+

5+ 1+ 5+

1− 3861

1+ 1+

1+ 1+

1+ 1+

1+



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Table 2. continued seruma no.

name

59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

Pyruvate Ribitol Scyllo inositol Serine Sphinganine Stearate Succinate Taurinef Threonine Threonate Trimethylamine N-oxide Tryptophan Tyrosinef Uracil Urea Uridine Valinef β-Alanine

GC

LC 1+

1−

ComMSe 1+ 1−

urineb NMR 1+1−

GC

LC

CE

LC

CE 1−

1−

1− 1+ 2+

stoold NMR

NMR

GC

1− 1 +, 2 −

1+

1+

1− 1−

2− 1−

1−

4 1 1 2

+ − + +

1+ 8+ 1+

1+

1− 1− 2− 1−

1+ 1−

1− 1− 1−

1+ 2+

1− 1− 1− 2−

GC

1−

1− 1+

2− 1−

tissuec

1−

1− 1−

1+

1− 1−

1+

2+ 2+ 2+

1+ 1+

1 +, 1 −

1+

1+

a

Refs 44, 60, 61, 68, 70, and 71. bRefs 43, 59, 72, and 73. cRefs 36, 37, 39, 41, 65, and 74−81. dRefs 82 and 83. eThe original study was performed on both GC−MS and LC−MS, but it did not indicate the specific platform used for each compound. The symbols + and − indicate the up- and downregulation of significant metabolites in CRC patients compared to that in healthy controls, respectively. The number before the + or − symbol indicates the number of publications. fThese metabolites have been reported as showing significant differences among CRC patients at different stages, e.g., early versus late stage.

Metabolite Marker Identification, Interpretation, and Validation

pipeline usually proceeds through a similar workflow including noise filtering, peak feature detection, spectral deconvolution, and chromatogram alignment.47 Detecting low-concentration metabolites and separating co-eluting compounds remain challenging for data processing; thus, robust and novel algorithms are in great need of further development.48 The obtained data matrix often contains hundreds to thousands of variables, and each variable represents a potential metabolite. At first, appropriate data pretreatment is needed before it can be subjected to statistical analysis. For example, normalization is a necessary step to remove systematic variations for urine samples between measurements. Data transformation and scaling reduces the bias of dominant metabolites with high concentrations, leading to a comparable scale for all of the detected metabolites. In practice, care should be exercised when processing data because the data pretreatment approaches and the order of execution may significantly affect statistical results, i.e., inappropriate scaling can inflate noise signals.49,50 After that, advanced multivariate statistical methods are often used together with univariate analysis, e.g., Student’s t test, to investigate relationships between different groups and to highlight differential metabolites that contribute to the relationship.49,51 Popular multivariate analysis methods used for metabonomics include principal component analysis (PCA) to examine natural clustering of samples and partial least-squares discriminant analysis (PLS-DA) to supervise the group difference (e.g., case-control).52,53 Multivariate statistical models require either proper cross-validation or independent validation data sets to avoid model overfitting.49 In recent metabonomics studies of human CRC, the receiver operating characteristic (ROC) curve and survival analysis have been increasingly used to evaluate the diagnostic and prognostic power of candidate metabolite biomarkers.

Metabolite annotation and identification, which is often conducted before statistical analysis in targeted metabonomics, is an essential step to translate original spectra into meaningful compounds and to uncover biologically relevant information. However, the confidence of metabolite identification varies depending on the analytical platform used, the robustness of analytical and computational methods applied, and the libraries/databases accessed. Thus, it is important to describe the process and confidence levels of metabolite identification in publications for assessment by the community.54 Currently, one of the most common methods for metabolite identification is to compare spectra from authentic standards. In addition, public and commercial libraries/databases are also used to putatively annotate metabolites or to characterize compound classes,55 such as HMDB,56 METLIN,57 and the NIST mass spectral database. For unknown compounds with high significance that are of great interest, it might be necessary to isolate them for full spectroscopic identification. The biological significance of differential metabolites will be further interpreted in terms of their participating metabolic pathways and biological functions using online databases and software tools,55 such as KEGG (http://www.kegg.jp), MetaCyc (http://metacyc.org), and Ingenuity Pathway Analysis (Ingenuity Systems, CA, USA). Candidate metabolite markers must be validated through multicenter and large-scale study, and only reproducible, specific, and sensitive markers will be recommended for use in clinical practice.58 Correlation analysis of metabolite markers or their relationship with markers obtained from other platforms, e.g., microbiomes, lipids, and proteins, can provide deeper mechanistic insights into diseases. 3862



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Table 3. List of Identified Metabolite Biomarkers for CRC Diagnosis Reported Once in the Last 6 Years seruma no.

name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59

1-4-Benzenedicarboxylate 1-Hexadecanol 1-Monooleoylglycerol 1-O-Heptadecylglycerol 11-Eicosenoate 11,14-Eicosadienoate 12a-Hydroxy-3-oxocholadienate 2-Hydroxyestradiol 2-Hydroxy-3-methylvalerate 2-Hydroxy-3-methylpentanoate 2-hydroxy-butyrate 2-Hydroxyhippurate 2-Oxobutanoate 2-Piperidinecarboxylate 3-Hydrobutyrate 3-Methy-histidine 3-Oxodecanoate 3,4,5-Trimethoxycinnamate 4-Aminohippurate 4-Hydroxybutyrate 4-Hydroxystyrene 5-Hydroxyindoleacetate 5-Hydroxytryptamine 5-Oxoproline Acetoacetate Adenine Adenosine monophosphate Adipate Allantoate Allisoleucine Allyl isothiocyanate Arabitol Azelate Behenate Benoate Benzaldehyde Benzeneacetate Butyrate Caprate Cerotate Chenodeoxycholate Cholesterol derivative Cholate cis-Aconitate Citrulline Creatinine enol Cystamine Decanoyl carnitine Deoxycholate Dihydrosphinganine Dopamine Elaidic carnitine Erythrotetrofuranose Formate Fructose-1,6P Fructose-6P Galactonate gamma-lactone Galactose Gamma Linolenate

GC

LC

Com-MSe

urineb NMR

GC

LC

tissuec CE

GC 1 1 1 1 1 1

LC

CE

stoold NMR

NMR

GC

1−

1+ 1−

− − + + + +

1− 1− 1+ 1− 1+ 1+ 1+ 1− 1− 1− 1+ 1− 1− 1+ 1− 1+ 1− 1+ 1+ 1+ 1+ 1− 1− 1+ 1+ 1− 1− 1+ 1− 1−

1− 1+ 1− 1+ 1− 1− 1− 1− 1+ 1− 1− 1− 1− 1+ 1− 1+ 1+ 1+ 1− 1− 1− 3863



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Table 3. continued seruma no.

name

60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118

Glucose-1P Glucuronate Glutamate Glutarate Glycerate Glycerol 1-(9-octadecenoate) Glycerol 1-stearate Glycerol-1-palmitate Glycerol-2-palmitate Glycerophosphocholine Glycolaldehyde Glycolaldehyde 3P Glycolate Heptadecanoate Histidinol Homoserine Homovanillate Hydroquinone Hydroxyacetate Hydroxyproline Hypoxathine Indoeacrylate Indole Indoleacetate Indoxyl Indoxyl sulfate Inisitol stereoisomer Inositol Inositol monophosphate Isobutyrate Isocitrate Isoglutamine Isovalerate Laurate LPC C20:4 LysoPC(14:0,16:1,20:0,18:1) Melissate (C30:0) Monooleoylglycerol Montanate (C28:0) N-Acdetyl-aspatate N-Acetyl-5-hydroxytryptamine N-Acetyl-L-lysine N-Acetyleglycine N-Methyl-hydantoin Nervonate Nicotinamide Octenedioate Oxalate P-hydroxybenzoate P-hydroxyphenylacetate Parabanate Parmitoylcarnitine Pelargonate Pemelate Pentacosylate (C25:0) Pentadecanoate Pentadecylate (C15:0) Pentothenate Phenylacetate

GC

LC

Com-MSe

urineb NMR

GC

LC

tissuec CE

GC

LC

CE

stoold NMR

NMR

GC

1+ 1− 1+ 1− 1− 1 1 1 1

+ + + + 1−

1+ 1+ 1− 1− 1+ 1+ 1− 1− 1− 1+ 1+ 1+ 1− 1− 1− 1− 1− 1− 1+ 1+ 1− 1+ 1+ 1+ 1+ 1− 1+ 1− 1+ 1+ 1− 1− 1− 1+ 1− 1− 1+ 1+ 1+ 1+ 1− 1+ 1− 1− 1+ 1+ 1− 1+ 1+

3864



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Table 3. continued seruma no. 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150

name Phenylacetylglutamine Phosphate Phosphocholine Phosphoethanolamine Phosphoethanolamine Phosphothreonine Picolinate Pipecolinate Polyamine Polyethylene glycol Proline betaine Propioate Propyl octadecanoate Putrescine Pyridoxal (V6) Sorbose Squalene Tetrahydrogestrinone Threitol Triglycerides Trihydroxycoprostanoate Ubiquinone Urate Ursodeoxycholate Valerate Xanthine Xanthurenate Xylose α-Aminoadipate β-Aspartylserine β-Hydroxybutyrate γ-Linolenate

GC

LC

Com-MSe

urineb NMR

GC

LC

tissuec CE

GC

LC

CE

stoold NMR

NMR

GC

1+ 1+ 1+ 1+ 1+ 1+ 1+ 1+ 1− 1+ 1+ 1+ 1+ 1− 1− 1− 1+ 1− 1− 1− 1− 1− 1− 1+ 1+ 1+ 1− 1− 1− 1+ 1+

a

Refs 44, 60, 61, 68, 70, and 71. bRefs 43, 59, 72, and 73. cRefs 36, 37, 39, 41, 65, and 74−81. dRefs 82 and 83. eThe original study was performed on both GC−MS and LC−MS, but it did not indicate the specific platform of each compound. The symbols + and − indicate up- and downregulation of significant metabolites in CRC patients compared to that in healthy controls, respectively. The number before the+ or − symbol indicates the number of publications.

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EARLY DETECTION AND DIAGNOSIS OF CRC Most of the symptoms associated with CRC often do not manifest until the late stages of the disease, so many patients are unfortunately diagnosed as advanced CRC at the time of initial diagnosis.6 Thus, one main goal of CRC metabonomics is to identify and develop novel biomarkers for early diagnosis of CRC in order to improve clinical treatment and survival outcomes. Table 2 lists a total of 76 metabolite biomarkers proposed for CRC diagnosis in the literature that have been reported in more than one metabonomics study over the last 6 years. For each metabolite marker, the up- or downregulation observed in CRC patients versus controls, the specific sample type, and the analytical platform used are also provided in Table 2. Our lab has performed a series of CRC metabonomics studies over the last 5 years.43,44,59,60 We have consistently observed distinct metabolic signatures of CRC in urine and serum samples from two independent cohorts (64 CRC patients versus 65 healthy controls, and 101 CRC patients versus 103 healthy controls, respectively) using both GC− TOF−MS and UPLC−QTOF−MS. The altered metabolic pathways that have been identified are glycolysis, tricarboxylic

acid (TCA) cycle, urea cycle, tryptophan, arginine, proline, pyrimidine, polyamine, amino acid and fatty acid metabolism, and gut microbial−host co-metabolism. Of great interest is a panel of metabolite markers in urine including citrate, hippurate, p-cresol, 2-aminobutyrate, myristate, putrescine, and kynurenate, which was able to discriminate CRC patients from their healthy controls with excellent AUC values of 0.993 and 0.998 for the training and test sets, respectively.43 Nishiumi et al.61 built a serum-based prediction model using 2-hydroxybutyrate, aspartic acid, kynurenine, and cystamine to discriminate CRC patients (n = 60) from their healthy controls (n = 60). The AUC, sensitivity, specificity, and accuracy were 0.9097, 85.0%, 85.0%, and 85.0%, respectively, in the training data set, and comparable results were also observed in the validation set. In the same study, however, the sensitivity, specificity, and accuracy of CEA were 35.0, 96.7, and 65.8%, respectively, and those of CA19-9 were 16.7, 100, and 58.3%, respectively. The mathematical model also demonstrated high sensitivity in detecting CRC at TNM stages 0−2 (82.8%), indicating the potential of these metabolite markers as a novel screening test for the early detection of CRC. 3865



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Ritchie et al.45 completed a large-scale (n = 222) metabonomics study on CRC using nontarget metabolic profiling of serum samples from three independent populations in the United Sates and Japan. The level of 28−36 carboncontaining, polyunsaturated long-chain fatty acids was reduced significantly in CRC patients compared to that in ethnically and geographically matched healthy controls. These consistent findings were further validated in two independent studies. The average AUC of significant long-chain fatty acids was 0.91 ± 0.04 across all of the samples from five independent cohorts. There are several interesting metabonomics studies on early detection of CRC; however, their results seemed to be too optimistic because of the limited number of recruited subjects and the lack of an independent validation sample set. For example, a targeted metabonomics study of 94 metabolites was performed in tumor samples obtained from only 16 colon and 12 stomach cancer patients to understand how cancer cells predominantly produce energy by glycolysis rather than oxidative phosphorylation via the TCA cycle.41 Differential fatty acid metabolism was observed in early stage CRC patients; however, only 8 healthy volunteers and 42 CRC patients were recruited in this study.62 Consistent findings of a number of metabolite markers can be observed from multiple independent studies regardless of the great variety of populations, sample types, and analytical and statistical techniques used. For example, the levels of phenylalanine, glutamate, proline, and taurine were found to be consistently increased in tissue samples of CRC patients from at least six independent studies. Glucose was found to be significantly reduced in tissue samples from nine metabonomics studies that were conducted with different types of analytical platforms, i.e., GC−MS (4), CE−MS (1), and NMR spectroscopy (4). However, there is also inconsistency in the metabolite profile changes among the studies. It is probable that more than half of metabonomics studies conducted and reported over the last 6 years have recruited a limited number of CRC patients, e.g., less than 40 (Figure 1B). Also, the complex background of patients and healthy controls (e.g., age, gender, diet, life style, and ethnicity) as well as various sample preparation and data analysis protocols may lead to the conflicting data in the regulation direction of metabolites. Thus, large-scale validation studies are needed before making clinical recommendations; however, only 15% of recent metabonomics studies on CRC have performed independent validation analysis. It is also important to examine how metabolites have been changed in different biospecimens, e.g., in the tissue, blood, and urine. For example, the level of lactate increased in blood, urine, and tissue samples, whereas the level of tyrosine increased in tissue samples but decreased in serum and urine samples (Table 2). The metabolism occurring in the tissue, blood, urine, and stool samples are interrelated; thus, comprehensive evaluation of metabolic variations in different biofluids and tissue samples provides a more broad metabolic profile of CRC. Besides the metabolite markers that have been identified more than once over the last 6 years, the significant metabolites that have been reported once can also be interesting and can provide useful information about the disturbed metabolic network associated with CRC (Table 3).

tumor spreading to lymph nodes or distant organs. However, histopathological evaluation seemed to be inefficacious for depicting the multidimensional factors of cancer prognosis due to the heterogeneity of the tumor. Thus, a new staging system that can integrate various clinical information, possibly including histopathological examination results, is urgently needed.63 Through measurement of time-related multiparametric metabolic responses,9 which is considered to be a realtime readout of the dynamic state of CRC pathophysiology,17 metabonomics has demonstrated its promising potential for use in CRC prognosis. Farshidfar et al.64 observed that the serum metabolite profile changed markedly with metastasis in three groups of CRC patients diagnosed with locoregional CRC (n = 42), liver-only metastasis (n = 45), and extrahepatic metastasis (n = 25). These three groups were distinguished by differential metabolites that are involved in the galactose, glutamine, and glutamate metabolic pathways, suggesting the alteration of liver metabolism during metastasis. This is the first CRC metabolomics study focusing on stage- and organ-specific changes in the serum metabolite profile. As the authors stated, early identification of metastases isolated to the liver may enable surgical resection, whereas more disseminated disease may be treated with palliative chemotherapy. The different patterns of circulating metabolites that they found along with the site of the disease may have clinical utility in enhancing staging accuracy and selecting patients for surgical or medical management.64 The direct measurement of global metabolites in tumor tissue samples has become more and more acceptable in the investigation of CRC over the last 6 years. Many researchers have used different analytical approaches to simultaneously measure metabolite levels in tumor tissues from surgical resection of the primary lesion and radical lymphadenectomy. Jimenez et al.65 used HR-MAS NMR spectroscopy to analyze metabolites in samples of intact tumor and its adjacent mucosa (10 cm from the tumor margin) obtained from 26 CRC patients. The authors found that tumor-adjacent mucosa can discriminate tumors according to T- and N-stage with higher predictive ability than that of tumor tissue itself. A similar work accomplished by Tessem et al.37 observed metabolic differences in normal colon mucosa between microsatellite instability (MSI-H) and microsatellite stable (MSS) colon tissues of 31 patients. In their study, MSI status was predicted with 80% accuracy (sensitivity and specificity of 79 and 82%, respectively) on a blinded validation set. Previous studies have shown that gene-specific promoter (hyper)methylation and separation of the MSI-H and MSS pathways is an early event in tumorgenesis of CRC. Thus, the observed metabolic difference related to MSI-H and MSS in normal mucosa may be used to improve the clinical diagnosis and characterization of CRC. Investigation of metabolic variations in CRC patients after surgical operation benefits the prognosis of clinical treatment. We performed a urinary metabonomics study on CRC patients (n = 60) before and after they received surgical operation,59 and we observed significantly downregulated gut flora metabolism due to the colon flush operation before surgery and decreased TCA intermediates, possibly indicating reduced energy metabolism, after surgery. Interestingly, four differential metabolites (succinate, phenylacetylglutamine, 2-hydroxyhippurate, and 5-hydroxytryptophan) in the patients after surgery showed recovery tendencies toward the normal concentration range present in the healthy controls (n = 63). Tissue samples

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STAGING AND PROGNOSIS OF CRC Cancer stage is a critical prognostic factor in clinical practice. The current CRC staging system largely relies on pathological assessment of the depth of tumor invasion and the extent of 3866



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fecal water, and tumor tissue are intimately correlated, although a clear understanding of the mechanism is still lacking. Correlation studies using different types of biospecimens from a group of subjects are necessary to pinpoint the key metabolic defects and to determine the molecular mechanisms underlying CRC carcinogenesis. Taken together, the challenging issues associated with metabonomics-based approaches are (1) the lack of a standardized procedures for sample collection, storage, handling, and preparation and for instrumental analysis and data analysis and (2) interindividual variability due to the highly transient and sensitive nature of metabolic flux and the complex interaction with the micro- and macroenvironment, including diet and the gut microbiota. In addition, it is impractical to use discovery metabonomic platforms for routine clinical testing because they are expensive, time-consuming, and require specialized personnel and operating procedures. A simplified metabonomic platform can be developed once CRC metabolite markers are clinically validated. In order to lessen the heterogeneity across studies and platforms, further efforts are necessary to establish standardized protocols to process and analyze samples and data, preferably with fully quantitative analytical protocols.69 A standardized and quantitative metabonomics workflow and reporting structure will benefit the entire community by facilitating data sharing and simplifying data integration. A checklist for performing a standard and high-quality metabonomics study has been summarized in Figure 2. Hopefully, the adoption of these standards will facilitate the integration of large amounts of metabonomics data and enhance the reproducibility and validity of identified candidate metabolite biomarkers. Furthermore, a top-down strategy using animal and cell models to test novel hypotheses generated from global metabonomic approaches can be taken to gain a systems understanding of the molecular mechanisms underlying colorectal carcinogenesis. Finally, clinically ready assays for candidate biomarkers, such as tandem MS, have to be developed so that metabonomics data is translatable to clinical applications. Translational cancer biomarker discovery is aimed at developing a simple noninvasive or less invasive method for the early detection and molecular classification of tumors to ensure that patients receive the most appropriate therapies while their disease progression, regression, and recurrence are closely monitored. Metabonomics is a powerful tool to identify promising candidate biomarkers with the potential to significantly augment current clinical diagnostic or screening tools, leading to a higher survival rate and better quality of life for cancer patients.

collected from four independent cohorts of 376 subjects were used to validate the prognostic markers of CRC, and a panel of 15 metabolite markers was consistently observed in the four independent cohorts, suggesting the tremendous potential of these metabolites for use in CRC prognosis.66 Ma et al. reported a similar metabonomics study of the metabolic differences in serum samples of 30 CRC patients and found decreased levels of L-valine, 5-oxo-L-proline, 1-deoxyglucose, Dturanose, D-maltose, arachidonic acid, and hexadecanoic acid and increased levels of L-tyrosine in those patients after surgical operation.67 NMR-based metabolic profiles identified by Bertini et al.68 were able to predict the overall survival (OS) rate of CRC patients. An OS predictor was obtained from serum metabolic fingerprinting of metastatic CRC patients with maximally divergent OS (n = 20), and the patients predicted to have short OS were found to have significantly reduced survival in the validation set (n = 108). This probably reflected the fact that metabolites are the end products of the ensemble of processes occurring in living organisms and can be regarded as the ultimate response of the organisms to disease-induced metabolic alterations, inflammatory processes, and dietary changes as a consequence of the pathologic status. More specifically, higher inflammatory response was observed in CRC patients compared to that in healthy controls and was even more evident in patients with short OS. Decreased serum concentration of polyunsaturated lipids was also observed in the patients with short OS.

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CONCLUSIONS AND FUTURE PERSPECTIVES Metabonomics is able to detect distinct metabolic changes in CRC patients through measuring the metabolites present in biological samples such as blood, urine, stool, and tumor tissue. To date, a number of candidate markers have been identified that have great clinical potential, but none of them has been translated into clinical diagnosis or prognosis of CRC for a number of reasons. First, the clinical protocols, including the inclusion criteria of CRC participants and sample collection, in most studies are not strictly controlled. Obtaining high-quality biospecimens will be the first step to ensure the quality of metabonomics data. Second, most of the studies lack sensitivity and specificity data for the biomarkers. Recent metabonomics studies of CRC have provided metabolite biomarkers (Tables 2 and 3) that can differentiate CRC from healthy controls. However, whether the identified metabolite biomarkers are specific to CRC, rather than other malignancies or inflammatory disease, is not clear. Future metabonomics studies need to evaluate the selectivity of metabolite biomarkers for CRC versus other malignancies. Third, the selection of sample types should be evaluated for different clinical applications. A wide range of samples including blood, urine, feces, and different tissue samples has been used in previous studies. However, little work has been done to examine different sample types and their suitability for different clinical applications. Fourth, most metabonomics studies have been focused on cross-sectional comparison using case-control samples collected at a specific time point. We believe that the early diagnostic and/or prognostic potential of metabolite biomarkers can be better evaluated in the high-quality, large-size longitudinal cohort studies where biospecimens and clinical information can be acquired at multiple time points from precancerous stages to cancer onset and then post-treatment stages in CRC patients. Fifth, previous findings suggest that metabolites in blood, urine,

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AUTHOR INFORMATION

Corresponding Author

*Phone: 808-564-5823; Fax: 808-586-2982; E-mail: wjia@cc. hawaii.edu. Notes

The authors declare no competing financial interest.

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