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.

Objectives:Adenomatous polyps are precursors of colorectal cancer; their detection and removal is the goal of colon cancer screening programs. However, fecal-based methods identify patients with adenomatous polyps with low levels of sensitivity. The aim or this study was to develop a highly accurate, prototypic, proof-of-concept, spot urine-based diagnostic test using metabolomic technology to distinguish persons with adenomatous polyps from those without polyps.Methods:Prospective urine and stool samples were collected from 876 participants undergoing colonoscopy examination in a colon cancer screening program, from April 2008 to October 2009 at the University of Alberta. Colonoscopy reference standard identified 633 participants with no colonic polyps and 243 with colonic adenomatous polyps. One-dimensional nuclear magnetic resonance spectra of urine metabolites were analyzed to define a diagnostic metabolomic profile for colonic adenomas. A urine metabolomic diagnostic test for colonic adenomatous polyps was established using 67% of the samples (un-blinded training set) and validated using the other 33% of the samples (blinded testing set). The urine metabolomic diagnostic test's specificity and sensitivity were compared with those of fecal-based tests.Results:Using a two-component, orthogonal, partial least-squares model of the metabolomic profile, the un-blinded training set identified patients with colonic adenomatous polyps with 88.9% sensitivity and 50.2% specificity. Validation using the blinded testing set confirmed sensitivity and specificity values of 82.7% and 51.2%, respectively. Sensitivities of fecal-based tests to identify colonic adenomas ranged from 2.5 to 11.9%.Conclusions:We describe a proof-of-concept spot urine-based metabolomic diagnostic test that identifies patients with colonic adenomatous polyps with a greater level of sensitivity (83%) than fecal-based tests.

We report an automated diagnostic test that uses the NMR spectrum of a single spot urine sample to accurately distinguish patients who require a colonoscopy from those who do not. Moreover, our approach can be adjusted to tradeoff between sensitivity and specificity. We developed our system using a group of 988 patients (633 normal and 355 who required colonoscopy) who were all at average or above-average risk for developing colorectal cancer. We obtained a metabolic profile of each subject, based on the urine samples collected from these subjects, analyzed via 1H-NMR and quantified using targeted profiling. Each subject then underwent a colonoscopy, the gold standard to determine whether he/she actually had an adenomatous polyp, a precursor to colorectal cancer. The metabolic profiles, colonoscopy outcomes, and medical histories were then analysed using machine learning to create a classifier that could predict whether a future patient requires a colonoscopy. Our empirical studies show that this classifier has a sensitivity of 64% and a specificity of 65% and, unlike the current fecal tests, allows the administrators of the test to adjust the tradeoff between the two.

Background: Colorectal cancer is one of the leading causes of cancer deaths worldwide. The detection and removal of the precursors to colorectal cancer, adenomatous polyps, is the key for screening. The aim of this study was to develop a clinically scalable (high throughput, low cost, and high sensitivity) mass spectrometry (MS)-based urine metabolomic test for the detection of adenomatous polyps. Methods: Prospective urine and stool samples were collected from 685 participants enrolled in a colorectal cancer screening program to undergo colonoscopy examination. Statistical analysis was performed on 69 urine metabolites measured by one-dimensional nuclear magnetic resonance spectroscopy to identify key metabolites. A targeted MS assay was then developed to quantify the key metabolites in urine. A MS-based urine metabolomic diagnostic test for adenomatous polyps was established using 67% samples (un-blinded training set) and validated using the remaining 33% samples (blinded testing set). Results: The MS-based urine metabolomic test identifies patients with colonic adenomatous polyps with an AUC of 0.692, outperforming the NMR based predictor with an AUC of 0.670. Conclusion: Here we describe a clinically scalable MS-based urine metabolomic test that identifies patients with adenomatous polyps at a higher level of sensitivity (86%) over current fecal-based tests (<18%).

Colorectal cancer is the third leading cause of cancer-associated mortality in the western world. The ability to predict a patient's response to chemotherapy may be of great value for clinicians and patients when planning cancer treatment. The aim of the current study was to develop a urine metabolomics-based biomarker panel to predict adverse events and response to chemotherapy in patients with colorectal cancer. A retrospective chart review of patients diagnosed with stage III or IV colorectal cancer between 2008 and 2012 was performed. The exclusion criteria included chemotherapy for palliation and patients living outside of Alberta. Data was collected concerning the chemotherapy regimen, adverse events associated with chemotherapy, disease progression and recurrence and 5-year survival. Adverse events were subdivided as follows: Delays in treatment, dose reductions, hospitalizations and chemotherapy regime changes. Patients provided urine samples for analysis prior to any intervention. Nuclear magnetic resonance (NMR) spectra of urine samples were acquired. The 1H NMR spectrum of each urine sample was analyzed using Chenomx NMRSuite v7.0. Using machine learning, predictors were generated and evaluated using 10-fold cross-validation. Urine spectra were obtained for 62 patients. The best predictors resulted in area under the receiver operating characteristic curve values of: 0.542 for chemotherapy dose reduction, 0.612 for 5-year survival, 0.650 for cancer recurrence and 0.750 for treatment delay. Therefore, predictors were developed for response to and adverse events from chemotherapy for patients with colorectal cancer patients. The predictor for treatment delay has the most promise, and further studies will aid its refinement and improvement of its accuracy.