Pharmacometabonomic profiling as a predictor of toxicity in patients with inoperable colorectal cancer treated with capecitabine.

PURPOSE: Endogenous metabolic profiles have been shown to predict the fate and toxicity of drugs such as acetaminophen in healthy individuals. However, the clinical utility of metabonomics in oncology remains to be defined. We aimed to evaluate the effect of pretreatment serum metabolic profiles generated by (1)H NMR spectroscopy on toxicity in patients with inoperable colorectal cancer receiving single agent capecitabine. EXPERIMENTAL DESIGN: Serum was collected from 54 patients with a diagnosis of locally advanced or metastatic colorectal cancer prior to treatment with single agent capecitabine. (1)H NMR spectroscopy was used to generate metabolic profile data for each patient. Toxicities were graded according to National Cancer Institute Common Toxicity Criteria version 2.0. RESULTS: Higher levels of low-density lipoprotein-derived lipids, including polyunsaturated fatty acids and choline phospholipids predicted for higher grade toxicity over the treatment period. Statistical analyses revealed a "pharmacometabonomic" lipid profile that correlated with severity of toxicity. CONCLUSIONS: This study suggests that metabolic profiles can delineate subpopulations susceptible to adverse events and have a potential role in the assessment of treatment viability for cancer patients prior to commencing chemotherapy.

Detection of metabolic alterations in non-tumor gastrointestinal tissue of the Apc(Min/+) mouse by (1)H MAS NMR spectroscopy.

In this study, we have used metabolic profiling (metabolomics/metabonomics) via high resolution magic angle spinning (HRMAS) and solution state (1)H NMR spectroscopy to characterize small bowel and colon tissue from the Apc(Min/+) mouse model of early gastrointestinal (GI) tumorigenesis. Multivariate analysis indicated the presence of metabolic differences between the morphologically normal/non-tumor tissue from approximately 10 week-old Apc(Min/+) mice and their wild-type litter mates. The metabolic profile of isolated lamina propria and epithelial cells from the same groups could also be discriminated on the basis of genotype. Accounting for systematic variation in individual metabolite levels across different anatomical regions of the lower GI tract, the metabolic phenotype of Apc(Min/+) lamina propria tissue was defined by significant increases in the phosphocholine/glycerophosphocholine ratio (PC/GPC, +21%) and decreases in GPC (-25%) and the gut-microbial cometabolite dimethylamine (DMA, -40%) relative to wild type. In the whole tissue, elevated lactate (+15%) and myo-inositol (+19%) levels were detected. As the metabolic changes occurred in non-tumor tissue from animals of very low tumor burden (<2 polyps/animal), they are likely to represent the specific consequence of reduced Apc function and very early events in tumorigenesis. The observed increase in PC/GPC ratio has been previously reported with immortalisation and malignant transformation of cells and is consistent with the role of Apc as a tumor suppressor. Phospholipase A2, which hydrolyses phosphatidylcholine to Acyl-GPC, is a known modifier gene of the model phenotype (Mom1), and altered expression of choline phospholipid enzymes has been reported in gut tissue from Apc(Min/+) mice. These results indicate the presence of a metabolic phenotype associated with "field cancerization", highlighting potential biomarkers for monitoring disease progression, for early evaluation of response to chemoprevention, and for predicting the severity of the polyposis phenotype in the Apc(Min/+) model.

Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS).

Current clinical strategy for staging and prognostication of colorectal cancer (CRC) relies mainly upon the TNM or Duke system. This clinicopathological stage is a crude prognostic guide because it reflects in part the delay in diagnosis in the case of an advanced cancer and gives little insight into the biological characteristics of the tumor. We hypothesized that global metabolic profiling (metabonomics/metabolomics) of colon mucosae would define metabolic signatures that not only discriminate malignant from normal mucosae, but also could distinguish the anatomical and clinicopathological characteristics of CRC. We applied both high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) and gas chromatography mass spectrometry (GC/MS) to analyze metabolites in biopsied colorectal tumors and their matched normal mucosae obtained from 31 CRC patients. Orthogonal partial least-squares discriminant analysis (OPLS-DA) models generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from malignant samples (Q(2) > 0.50, Receiver Operator Characteristic (ROC) AUC >0.95, using 7-fold cross validation). A total of 31 marker metabolites were identified using the two analytical platforms. The majority of these metabolites were associated with expected metabolic perturbations in CRC including elevated tissue hypoxia, glycolysis, nucleotide biosynthesis, lipid metabolism, inflammation and steroid metabolism. OPLS-DA models showed that the metabolite profiles obtained via HR-MAS NMR could further differentiate colon from rectal cancers (Q(2)> 0.60, ROC AUC = 1.00, using 7-fold cross validation). These data suggest that metabolic profiling of CRC mucosae could provide new phenotypic biomarkers for CRC management.