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.

Metastasis is the major cause of cancer mortality. We aimed to find a metastasis-prone signature for early stage mismatch-repair proficient sporadic colorectal cancer (CRC) patients for better prognosis and informed use of adjuvant chemotherapy. The genome-wide expression profiles of 82 age-, ethnicity- and tissue-matched patients and healthy controls were analyzed using the Affymetrix U133 Plus 2 array. Metastasis-negative patients have 5 years or more of follow-up. A 10 x 10 two-level nested cross-validation design was used with several families of classification models to identify the optimal predictor for metastasis. The best classification model yielded a 54 gene-set (74 probe sets) with an estimated prediction accuracy of 71%. The specificity, sensitivity, negative and positive predictive values of the signature are 0.88, 0.58, 0.84 and 0.65, respectively, indicating that the gene-set can improve prognosis for early stage sporadic CRC patients. These 54 genes, including node molecules YWHAB, MAP3K5, LMNA, APP, GNAQ, F3, NFATC2, and TGM2, integrate multiple bio-functions in various compartments into an intricate molecular network, suggesting that cell-wide perturbations are involved in metastasis transformation. Further, querying the ;Connectivity Map' with a subset (70%) of these genes shows that Gly-His-Lys and securinine could reverse the differential expressions of these genes significantly, suggesting that they have combinatorial therapeutic effect on the metastasis-prone patients. These two perturbagens promote wound-healing, extracellular matrix remodeling and macrophage activation thus highlighting the importance of these pathways in metastasis suppression for early-stage CRC.

Primary colorectal lymphoma is a rare condition. It predominantly affects males between the sixth and seventh decade of life and most commonly occurs in the caecum. It often presents with abdominal pain and loss of weight and due to the nonspecific nature of these symptoms, patients frequently present late with advanced loco-regional disease. The histology is usually B cell and of intermediate grade. Therapy usually involves resection of the affected colon and regional lymphovascular structures, followed by adjuvant chemotherapy, with a reported 5-year survival of 27-55%.