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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%). [ABSTRACT FROM AUTHOR]

Cancer-associated muscle wasting is associated with reduction in functional status, in response to treatment and in life expectancy. Methods currently used to assess muscle loss involve diagnostic imaging techniques such as computed tomography (CT), which are costly, inconvenient, invasive, time consuming and have limited ability to detect early or slowly evolving wasting. We present a novel approach using single time-point urinary metabolite profiles to determine whether a patient is experiencing muscle wasting. We analyzed 93 random urine samples from patients with cancer using H-NMR. Using two successive CT images we assessed their lumbar skeletal muscle area (cm) to estimate the rate of muscle change (% loss or gain over time) for each patient. The average muscle change over time was −4.71%/100 days in the muscle-losing group and +3.91%/100 days in the comparator group. Bivariate statistics identified metabolites related with muscle loss, including constituents and metabolites of muscle (creatine, creatinine, 3-OH-isovalerate), amino acids (Leu, Ile, Val, Ala, Thr, Tyr, Gln, Ser) and intermediary metabolites. We also applied machine-learning techniques to identify patterns of urinary metabolites that identify which patients are likely to lose muscle mass. We evaluated the predictive performance of 8 machine-learning approaches using fivefold cross validation and permutation testing, and found that SVM provided the best generalization accuracy (82.2%). These results suggest that H-NMR analysis of a single random urine sample may be a fast, cheap, safe and inexpensive tool to screen and monitor muscle loss, and that useful classifiers for predicting related metabolic conditions are possible with the methodology presented. [ABSTRACT FROM AUTHOR]