Principal component analysis for the comparison of metabolic profiles from human rectal cancer biopsies and colorectal xenografts using high-resolution magic angle spinning 1H magnetic resonance spectroscopy

A multimodal pipeline using NMR spectroscopy and MALDI-TOF mass spectrometry imaging from the same tissue sample

NMR spectroscopy and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) are both commonly used to detect large numbers of metabolites and lipids in metabolomic and lipidomic studies. We have demonstrated a new workflow, highlighting the benefits of both techniques to obtain metabolomic and lipidomic data, which has realized for the first time the combination of these two complementary and powerful technologies. NMR spectroscopy is frequently used to obtain quantitative metabolite information from cells and tissues. Lipid detection is also possible with NMR spectroscopy, with changes being visible across entire classes of molecules. Meanwhile, MALDI MSI provides relative measures of metabolite and lipid concentrations, mapping spatial information of many specific metabolite and lipid molecules across cells or tissues. We have used these two complementary techniques in combination to obtain metabolomic and lipidomic measurements from triple-negative human breast cancer cells and tumor xenograft models. We have emphasized critical experimental procedures that ensured the success of achieving NMR spectroscopy and MALDI MSI in a combined workflow from the same sample. Our data show that several phospholipid metabolite species were differentially distributed in viable and necrotic regions of breast tumor xenografts. This study emphasizes the power of combined NMR spectroscopy-MALDI imaging to advance metabolomic and lipidomic studies.

Assessment of Pediatric Cancer and Its Relationship to Environmental Contaminants: An Ecological Study in Idaho

The primary aim of this study was to determine the degree to which a multivariable principal component model based on several potentially carcinogenic metals and pesticides could explain the county-level pediatric cancer rates across Idaho. We contend that human exposure to environmental contaminants is one of the reasons for increased pediatric cancer incidence in the United States. Although several studies have been conducted to determine the relationship between environmental contaminants and carcinogenesis among children, research gaps exist in developing a meaningful association between them. For this study, pediatric cancer data was provided by the Cancer Data Registry of Idaho, concentrations of metals and metalloids in groundwater were collected from the Idaho Department of Water Resources, and pesticide use data were collected from the United States Geological Survey. Most environmental variables were significantly intercorrelated at an adjusted P-value <0.01 (97 out of 153 comparisons). Hence, a principal component analysis was employed to summarize those variables to a smaller number of components. An environmental burden index (EBI) was constructed using these principal components, which categorized the environmental burden profiles of counties into low, medium, and high. EBI was significantly associated with pediatric cancer incidence (P-value <0.05). The rate ratio of high EBI profile to low EBI profile for pediatric cancer incidence was estimated as 1.196, with lower and upper confidence intervals of 1.061 and 1.348, respectively. A model was also developed in the study using EBI to estimate the county-level pediatric cancer incidence in Idaho (Nash-Sutcliffe Efficiency = 0.97).

Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space

Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor microenvironment (TME). Better understanding of the TME could lead to novel diagnostic, prognostic, and therapeutic discoveries. One way to achieve this involves in vitro tumor models that recapitulate the in vivo TME composition and spatial arrangement. Here, we review the potential of harnessing in vitro tumor models and artificial intelligence to delineate the TME. This includes (i) identification of novel features, (ii) investigation of higher-order relationships, and (iii) analysis and interpretation of multiomics data in a (iv) holistic, objective, reproducible, and efficient manner, which surpasses previous methods of TME analysis. We also discuss limitations of this approach, namely inadequate datasets, indeterminate biological correlations, ethical concerns, and logistical constraints; finally, we speculate on future avenues of research that could overcome these limitations, ultimately translating to improved clinical outcomes.

Hormone-Independent Mouse Mammary Adenocarcinomas with Different Metastatic Potential Exhibit Different Metabolic Signatures

The metabolic characteristics of metastatic and non-metastatic breast carcinomas remain poorly studied. In this work, untargeted Nuclear Magnetic Resonance (NMR) metabolomics was used to compare two medroxyprogesterone acetate (MPA)-induced mammary carcinomas lines with different metastatic abilities. Different metabolic signatures distinguished the non-metastatic (59-2-HI) and the metastatic (C7-2-HI) lines, with glucose, amino acid metabolism, nucleotide metabolism and lipid metabolism as the major affected pathways. Non-metastatic tumours appeared to be characterised by: (a) reduced glycolysis and tricarboxylic acid cycle (TCA) activities, possibly resulting in slower NADH biosynthesis and reduced mitochondrial transport chain activity and ATP synthesis; (b) glutamate accumulation possibly related to reduced glutathione activity and reduced mTORC1 activity; and (c) a clear shift to lower phosphoscholine/glycerophosphocholine ratios and sphingomyelin levels. Within each tumour line, metabolic profiles also differed significantly between tumours (i.e., mice). Metastatic tumours exhibited marked inter-tumour changes in polar compounds, some suggesting different glycolytic capacities. Such tumours also showed larger intra-tumour variations in metabolites involved in nucleotide and cholesterol/fatty acid metabolism, in tandem with less changes in TCA and phospholipid metabolism, compared to non-metastatic tumours. This study shows the valuable contribution of untargeted NMR metabolomics to characterise tumour metabolism, thus opening enticing opportunities to find metabolic markers related to metastatic ability in endocrine breast cancer.

Magnetic resonance spectroscopy and its application in colorectal cancer diagnosis and screening: A narrative review

There are several slightly invasive methods to detect colorectal carcinoma (CRC) including colonoscopy and sigmoidoscopy; but there is no noninvasive, accurate screening test. It is recommended to initiate screening at the age of 50 for non-familial CRC. Laboratory tests are routinely suggested if internal observation and imaging are recommended for further evaluation. Spectroscopic-based imaging, such as magnetic resonance spectroscopy (MRS) is an interesting and promising tool with the potential to be an alternative to some minimally invasive procedures, such as biopsy. Accordingly, MRS might be a suitable substitution for invasive methods, such as colonoscopy. This article aimed to review the studies that have evaluated the MRS technique as a screening tool in CRC.

Metabolic models predict bacterial passengers in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a complex multifactorial disease. Increasing evidence suggests that the microbiome is involved in different stages of CRC initiation and progression. Beyond specific pro-oncogenic mechanisms found in pathogens, metagenomic studies indicate the existence of a microbiome signature, where particular bacterial taxa are enriched in the metagenomes of CRC patients. Here, we investigate to what extent the abundance of bacterial taxa in CRC metagenomes can be explained by the growth advantage resulting from the presence of specific CRC metabolites in the tumor microenvironment.

METHODS

We composed lists of metabolites and bacteria that are enriched on CRC samples by reviewing metabolomics experimental literature and integrating data from metagenomic case-control studies. We computationally evaluated the growth effect of CRC enriched metabolites on over 1500 genome-based metabolic models of human microbiome bacteria. We integrated the metabolomics data and the mechanistic models by using scores that quantify the response of bacterial biomass production to CRC-enriched metabolites and used these scores to rank bacteria as potential CRC passengers.

RESULTS

We found that metabolic networks of bacteria that are significantly enriched in CRC metagenomic samples either depend on metabolites that are more abundant in CRC samples or specifically benefit from these metabolites for biomass production. This suggests that metabolic alterations in the cancer environment are a major component shaping the CRC microbiome.

CONCLUSION

Here, we show with in sillico models that supplementing the intestinal environment with CRC metabolites specifically predicts the outgrowth of CRC-associated bacteria. We thus mechanistically explain why a range of CRC passenger bacteria are associated with CRC, enhancing our understanding of this disease. Our methods are applicable to other microbial communities, since it allows the systematic investigation of how shifts in the microbiome can be explained from changes in the metabolome.

Antitumor activity of RT2 peptide derived from crocodile leukocyte peptide on human colon cancer xenografts in nude mice

RT2, derived from the leukocyte peptide of Crocodylus siamensis, can kill human cervical cancer cells via apoptosis induction, but no evidence has shown in vivo. In this study, we investigated the antitumor effect of RT2 on human colon cancer xenografts in nude mice. Twenty-four mice were injected subcutaneously with human colon cancer HCT 116 cells. Eleven days after cancer cell implantation, the mice were treated with intratumoral injections of phosphate buffered saline (PBS) or RT2 (0.01, 0.1, and 1 mg/mouse) once every 2 days for a total of 5 times. The effect of a 10-day intratumoral injection of RT2 on body weight, biochemical, and hematological parameters in BALB/c mice showed no significant difference between the groups. Tumor volume showed a significant decrease only in the treatment group with RT2 (1 mg/mouse) at day 6 (P < .05), day 8 (P < .01), and day 10 (P < .01) after the first treatment. The protein expression levels of cleaved poly (ADP-ribose) polymerase (PARP), apoptosis-inducing factor (AIF), and the p53 tumor suppressor protein (p53) in xenograft tumors increased after treatment with RT2 (1 mg/mouse) compared to those in the PBS-injected group. Moreover, RT2 increased the expression of Endo G and Bcl-2 family proteins. Therefore, the peptide RT2 can inhibit tumor growth via the induction of apoptosis in an in vivo xenograft model.

Biochemical fingerprint of colorectal cancer cell lines using label-free live single-cell Raman spectroscopy

Label-free live single-cell Raman spectroscopy was used to obtain a chemical fingerprint of colorectal cancer cells including the classification of the SW480 and SW620 cell line model system, derived from primary and secondary tumour cells from the same patient. High-quality Raman spectra were acquired from hundreds of live cells, showing high reproducibility between experiments. Principal component analysis with linear discriminant analysis yielded the best cell classification, with an accuracy of 98.7 ± 0.3% (standard error) when compared with discrimination trees or support vector machines. SW480 showed higher content of the disordered secondary protein structure Amide III band, whereas SW620 showed larger α-helix and β-sheet band content. The SW620 cell line also displayed higher nucleic acid, phosphates, saccharide, and CH2 content. HL60, HT29, HCT116, SW620, and SW480 live single-cell spectra were classified using principal component analysis or linear discriminant analysis with an accuracy of 92.4 ± 0.4% (standard error), showing differences mainly in the β-sheet content, the cytochrome C bands, the CH-stretching regions, the lactate contributions, and the DNA content. The lipids contributions above 2,900 cm-1 and the lactate contributions at 1,785 cm-1 appeared to be dependent on the colorectal adenocarcinoma stage, the advanced stage cell lines showing lower lipid, and higher lactate content. The results demonstrate that these cell lines can be distinguished with high confidence, suggesting that Raman spectroscopy on live cells can distinguish between different disease stages, and could play an important role clinically as a diagnostic tool for cell phenotyping.

NMR metabolic fingerprints of murine melanocyte and melanoma cell lines: application to biomarker discovery

Melanoma is the most aggressive type of skin cancer and efforts to improve the diagnosis of this neoplasia are largely based on the use of cell lines. Metabolomics is currently undergoing great advancements towards its use to screening for disease biomarkers. Although NMR metabolomics includes both 1D and 2D methodologies, there is a lack of data in the literature regarding heteronuclear 2D NMR assignments of the metabolome from eukaryotic cell lines. The present study applied NMR-based metabolomics strategies to characterize aqueous and lipid extracts from murine melanocytes and melanoma cell lines with distinct tumorigenic potential, successfully obtaining fingerprints of the metabolites from the extracts of the cell lines by means of 2D NMR HSQC correlation maps. Relative amounts of the identified metabolites were compared between the 4 cell lines. Multivariate analysis of ¹H NMR data was able not only to differentiate the melanocyte cell line from the tumorigenic ones but also distinguish among the 3 tumorigenic cell lines. We also investigated the effects of mitogenic agents, and found that they can markedly influence the metabolome of the melanocyte cell line, resembling the pattern of most proliferative cell lines.

Tumor growth affects the metabonomic phenotypes of multiple mouse non-involved organs in an A549 lung cancer xenograft model

The effects of tumorigenesis and tumor growth on the non-involved organs remain poorly understood although many research efforts have already been made for understanding the metabolic phenotypes of various tumors. To better the situation, we systematically analyzed the metabolic phenotypes of multiple non-involved mouse organ tissues (heart, liver, spleen, lung and kidney) in an A549 lung cancer xenograft model at two different tumor-growth stages using the NMR-based metabonomics approaches. We found that tumor growth caused significant metabonomic changes in multiple non-involved organ tissues involving numerous metabolic pathways, including glycolysis, TCA cycle and metabolisms of amino acids, fatty acids, choline and nucleic acids. Amongst these, the common effects are enhanced glycolysis and nucleoside/nucleotide metabolisms. These findings provided essential biochemistry information about the effects of tumor growth on the non-involved organs.

High tumor glycine concentration is an adverse prognostic factor in locally advanced rectal cancer

BACKGROUND AND PURPOSE

Recognizing the link between altered tumor metabolism and disease aggressiveness, this study aimed to identify associations between tumor metabolic profiles and therapeutic outcome in locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

Pretreatment tumor metabolic profiles from 54 LARC patients receiving combined-modality neoadjuvant treatment and surgery were acquired by high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). Metabolite concentrations were correlated to TNM and the presence of disseminated tumor cells (DTC) at diagnosis, ypTN and tumor regression grade (TRG) following neoadjuvant treatment, and progression-free survival (PFS).

RESULTS

Pretreatment tumor metabolite concentrations showed no significant associations to TNM, DTC, ypTN or TRG. In univariate regression analysis, high concentrations of glycine, creatine and myo-inositol were significantly associated with poor PFS, with metastasis as main PFS event. In multivariate analysis, high glycine concentration remained most significantly associated with poor PFS (hazard ratio=4.4, 95% confidence interval=1.4-14.3, p=0.008).

CONCLUSIONS

High tumor glycine concentration was identified as adverse prognostic factor for PFS in LARC. In a patient population treated with curative intent but with metastatic disease as main PFS event further investigations of glycine as early predictor of metastatic progression and therapeutic target are warranted.

Prediction of survival for patients with advanced colorectal cancer using (1) H High-resolution magic angle spinning nuclear MR spectroscopy

PURPOSE

To evaluate whether the metabolic profiles of colorectal cancer specimens can be used for prediction of survival.

MATERIALS AND METHODS

The metabolic profiles of colorectal cancer tissues were determined using the high-resolution magic angle spinning (HR MAS) nuclear magnetic resonance (NMR) technique (16.4 T). HR MAS analysis was performed for 52 tissues taken from patients classified as survivors and nonsurvivors (30). Quantitative analysis was performed for each spectrum. Receiver operating characteristic (ROC) curves were used to evaluate the potential to predict patient survival over 5.5 years.

RESULTS

Analysis of (1) H NMR spectra led to the identification and quantitative analysis of 30 metabolites. A significant increase in the Tau/Gly and Tau/MI ratios were associated with long-term survival (P = 0.004 and P = 0.003, respectively). ROC analysis indicated that the Tau/MI ratio had the best predictive value for survival (sensitivity 64.7% and specificity 100%). Good predictive value of survival was found for Tau/Gly ratio (sensitivity 63.6% and specificity 96.3%). Moreover, the Glu/Gln metabolic ratio with a cutoff level of 1.74 was predictive of survival with a sensitivity of 83.3% and a specificity of 85.7%.

CONCLUSION

Our results indicate that HR MAS spectroscopy is potentially useful for survival prediction in advanced colorectal cancer.

Characterization of brivanib therapy response in hepatocellular carcinoma xenografts using ¹H HR-MAS spectroscopy and histopathology

Angiogenesis inhibition is an attractive therapeutic strategy in the management of solid tumors. Vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) are key factors in growth and neovascularization of hepatocellular carcinoma (HCC). Brivanib is a novel, orally available dual tyrosine kinase inhibitor that selectively targets the key angiogenesis receptors VEGF‑R2, FGF‑R1 and FGF‑R2. Recently, high‑resolution magic angle spinning magnetic resonance spectroscopy (HR‑MAS MRS) has provided the opportunity to investigate more detailed metabolic profiles from intact tissue specimens that are correlated with histopathology and is thus, a promising tool for monitoring changes induced by treatment. In the present study, 1H HR‑MAS MRS and immunohistochemistry were used to investigate the antitumor efficacy of brivanib in HCC xenograft models. Tumor growth was significantly suppressed in brivanib‑treated mice compared with the controls and treatment was associated with the inhibition of angiogenesis, increased apoptosis and inhibition of cell proliferation. Furthermore, HR‑MAS techniques showed altered metabolic profiles between the two groups. HR‑MAS spectra demonstrated a significant decrease in choline metabolite levels in the treated groups, concurrent with decreased cell proliferation and increased apoptosis. The results showed that 1H HR‑MAS MRS provides quantitative metabolite information that may be used to analyze the efficacy of brivanib treatment in Hep3B tumor xenografts. Thus, the HR‑MAS MRS technique may be a complementary method to support histopathological results and increase its potential for use in the clinic.

Global metabolite profiling of human colorectal cancer xenografts in mice using HPLC-MS/MS

Reversed-phase gradient LC-MS was used to perform untargeted metabonomic analysis on extracts of human colorectal cancer (CRC) cell lines (COLO 205, HT-29, HCT 116 and SW620) subcutaneously implanted into age-matched athymic nude male mice to study small molecule metabolic profiles and examine possible correlations with human cancer biopsies. Following high mass accuracy data analysis using MS and MS/MS, metabolites were identified by searching against major metabolite databases including METLIN, MASSBANK, The Human Metabolome Database, PubChem, Biospider, LipidMaps and KEGG. HT-29 and COLO 205 tumor xenografts showed a distribution of metabolites that differed from SW620 and HCT 116 xenografts (predominantly on the basis of relative differences in the amounts of amino acids and lipids detected). This finding is consistent with NMR-based analysis of human colorectal tissue, where the metabolite profiles of HT-29 tumors exhibit the greatest similarity to human rectal cancer tissue with respect to changes in the relative amounts of lipids and choline-containing compounds. As the metabolic signatures of cancer cells result from oncogene-directed metabolic reprogramming, the HT-29 xenografts in mice may prove to be a useful model to further study the tumor microenvironment and cancer biology.

Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery

In this study, the feasibility of high resolution magic angle spinning (HR MAS) magnetic resonance spectroscopy (MRS) of small tissue biopsies to distinguish between tumor and non-involved adjacent tissue was investigated. With the current methods, delineation of the tumor borders during breast cancer surgery is a challenging task for the surgeon, and a significant number of re-surgeries occur. We analyzed 328 tissue samples from 228 breast cancer patients using HR MAS MRS. Partial least squares discriminant analysis (PLS-DA) was applied to discriminate between tumor and non-involved adjacent tissue. Using proper double cross validation, high sensitivity and specificity of 91% and 93%, respectively was achieved. Analysis of the loading profiles from both principal component analysis (PCA) and PLS-DA showed the choline-containing metabolites as main biomarkers for tumor content, with phosphocholine being especially high in tumor tissue. Other indicative metabolites include glycine, taurine and glucose. We conclude that metabolic profiling by HR MAS MRS may be a potential method for on-line analysis of resection margins during breast cancer surgery to reduce the number of re-surgeries and risk of local recurrence.

Metabolomics of colorectal cancer: past and current analytical platforms

Metabolomics is coming of age as an important area of investigation which may help reveal answers to questions left unanswered or only partially understood from proteomic or genomic approaches. Increased knowledge of the relationship of genes and proteins to smaller biomolecules (metabolites) will advance our ability to diagnose, treat, and perhaps prevent cancer and other diseases that have eluded scientists for generations. Colorectal tumors are the second leading cause of cancer mortality in the USA, and the incidence is rising. Many patients present late, after the onset of symptoms, when the tumor has spread from the primary site. Once metastases have occurred, the prognosis is significantly worse. Understanding alterations in metabolic profiles that occur with tumor onset and progression could lead to better diagnostic tests as well as uncover new approaches to treat or even prevent colorectal cancer (CRC). In this review, we explore the various analytical technologies that have been applied in CRC metabolomics research and summarize all metabolites measured in CRC and integrate them into metabolic pathways. Early studies with nuclear magnetic resonance and gas-chromatographic mass spectrometry suggest that tumor cells are characterized by aerobic glycolysis, increased purine metabolism for DNA synthesis, and protein synthesis. Liquid chromatography, capillary electrophoresis, and ion mobility, each coupled with mass spectrometry, promise to advance the field and provide new insight into metabolic pathways used by cancer cells. Studies with improved technology are needed to identify better biomarkers and targets for treatment or prevention of CRC.

Autofluorescence spectroscopy for evaluating dysplasia in colorectal tissues

The aim of this study was to assess the applicability of autofluorescence (AF) spectroscopy as a method for the diagonosis of normal, benign and malignant of dysplasia in colorectal tissues experimentally. By improvement of optical design in laser pulse generator, wavelength-adjustable output was acquired and the optimal wavelength was defined as 380 nm. With 380-nm pulsed laser excitation, AF spectra of normal, benign and malignant colorectal tissues were recorded in the spectra region from 460-570 nm in vitro. The spectral analysis for discrimination among the different types of tissues was carried out using principal component analysis (PCA)-based Neural networks algorithm. The performance of analysis was pretty good with sensitivity, specificity and accuracy found to be 100%,90% and 96.7%, respectively. The AF spectroscopy may serve as an excellent tool for the evaluation of dysplasia in colorectal clinical diagnosis.

Metabolomics and detection of colorectal cancer in humans: a systematic review

Metabolomics represents one of the new omics sciences and capitalizes on the unique presence and concentration of small molecules in tissues and body fluids to construct a 'fingerprint' that can be unique to the individual and, within that individual, unique to environmental influences, including health and disease states. As such, metabolomics has the potential to serve an important role in diagnosis and management of human conditions. Colorectal cancer is a major public health concern. Current population-based screening methods are suboptimal and whether metabolomics could represent a new tool of screening is under investigation. The purpose of this systematic review is to summarize existing literature on metabolomics and colorectal cancer, in terms of diagnostic accuracies and distinguishing metabolites. Eight studies are included. A total of 12 metabolites (taurine, lactate, choline, inositol, glycine, phosphocholine, proline, phenylalanine, alanine, threonine, valine and leucine) were found to be more prevalent in colorectal cancer and glucose was found to be in higher proportion in control specimens using tissue metabolomics. Serum and urine metabolomics identified several other differential metabolites between controls and colorectal cancer patients. This article highlights the novelty of the field of metabolomics in colorectal oncology.

Development and validation of a liquid chromatography-mass spectrometry metabonomic platform in human plasma of liver failure caused by hepatitis B virus

This paper presents an liquid chromatography (LC)/mass spectrometry (MS)-based metabonomic platform that combined the discovery of differential metabolites through principal component analysis (PCA) with the verification by selective multiple reaction monitoring (MRM). These methods were applied to analyze plasma samples from liver disease patients and healthy donors. LC-MS raw data (about 1000 compounds), from the plasma of liver failure patients (n = 26) and healthy controls (n = 16), were analyzed through the PCA method and a pattern recognition profile that had significant difference between liver failure patients and healthy controls (P < 0.05) was established. The profile was verified in 165 clinical subjects. The specificity and sensitivity of this model in predicting liver failure were 94.3 and 100.0%, respectively. The differential ions with m/z of 414.5, 432.0, 520.5, and 775.0 were verified to be consistent with the results from PCA by MRM mode in 40 clinical samples, and were proved not to be caused by the medicines taken by patients through rat model experiments. The compound with m/z of 520.5 was identified to be 1-Linoleoylglycerophosphocholine or 1-Linoleoylphosphatidylcholine through exact mass measurements performed using Ion Trap-Time-of-Flight MS and METLIN Metabolite Database search. In all, it was the first time to integrate metabonomic study and MRM relative quantification of differential peaks in a large number of clinical samples. Thereafter, a rat model was used to exclude drug effects on the abundance of differential ion peaks. 1-Linoleoylglycerophosphocholine or 1-Linoleoylphosphatidylcholine, a potential biomarker, was identified. The LC/MS-based metabonomic platform could be a powerful tool for the metabonomic screening of plasma biomarkers.

Development and validation of a gas chromatography/mass spectrometry method for the metabolic profiling of human colon tissue

In this study, a gas chromatography/mass spectrometry (GC/MS) method was developed and validated for the metabolic profiling of human colon tissue. Each colon tissue sample (20 mg) was ultra-sonicated with 1 mL of a mixture of chloroform/methanol/water in the ratio of 20:50:20 (v/v/v), followed by centrifugation, collection of supernatant, drying, removal of moisture using anhydrous toluene and finally derivatization using N-methyl-N-trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS). A volume of 1 microL of the derivatized mixture was injected into the GC/MS system. A total of 53 endogenous metabolites were separated and identified in the GC/MS chromatogram, all of which were selected to evaluate the sample stability and precision of the method. Of the identified endogenous metabolites 19 belonging to diverse chemical classes and covering a wide range of the GC retention times (Rt) were selected to investigate the quantitative linearity of the method. The developed GC/MS method demonstrated good reproducibility with intra- and inter-day precision within relative standard deviation (RSD) of +/-15%. The metabolic profiles of the intact tissue were determined to be stable (100 +/- 15%) for up to 90 days at -80 degrees C. Satisfactory results were also obtained in the case of other stability-indicating studies such as freeze/thaw cycle stability, bench-top stability and autosampler stability. The developed method showed a good linear response for each of the 19 analytes tested (r(2) > 0.99). Our GC/MS metabolic profiling method was successfully applied to discriminate biopsied colorectal cancer (CRC) tissue from their matched normal tissue obtained from six CRC patients using orthogonal partial least-squares discriminant analysis [two latent variables, R(2)Y = 0.977 and Q(2) (cumulative) = 0.877].