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.

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%).

BACKGROUND:Scientists have long been driven by the desire to describe, organize, classify, and compare objects using taxonomies and/or ontologies. In contrast to biology, geology, and many other scientific disciplines, the world of chemistry still lacks a standardized chemical ontology or taxonomy. Several attempts at chemical classification have been made; but they have mostly been limited to either manual, or semi-automated proof-of-principle applications. This is regrettable as comprehensive chemical classification and description tools could not only improve our understanding of chemistry but also improve the linkage between chemistry and many other fields. For instance, the chemical classification of a compound could help predict its metabolic fate in humans, its druggability or potential hazards associated with it, among others. However, the sheer number (tens of millions of compounds) and complexity of chemical structures is such that any manual classification effort would prove to be near impossible.RESULTS:We have developed a comprehensive, flexible, and computable, purely structure-based chemical taxonomy (ChemOnt), along with a computer program (ClassyFire) that uses only chemical structures and structural features to automatically assign all known chemical compounds to a taxonomy consisting of >4800 different categories. This new chemical taxonomy consists of up to 11 different levels (Kingdom, SuperClass, Class, SubClass, etc.) with each of the categories defined by unambiguous, computable structural rules. Furthermore each category is named using a consensus-based nomenclature and described (in English) based on the characteristic common structural properties of the compounds it contains. The ClassyFire webserver is freely accessible at http://classyfire.wishartlab.com/. Moreover, a Ruby API version is available at https://bitbucket.org/wishartlab/classyfire_api, which provides programmatic access to the ClassyFire server and database. ClassyFire has been used to annotate over 77 million compounds and has already been integrated into other software packages to automatically generate textual descriptions for, and/or infer biological properties of over 100,000 compounds. Additional examples and applications are provided in this paper.CONCLUSION:ClassyFire, in combination with ChemOnt (ClassyFire's comprehensive chemical taxonomy), now allows chemists and cheminformaticians to perform large-scale, rapid and automated chemical classification. Moreover, a freely accessible API allows easy access to more than 77 million "ClassyFire" classified compounds. The results can be used to help annotate well studied, as well as lesser-known compounds. In addition, these chemical classifications can be used as input for data integration, and many other cheminformatics-related tasks.

Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.

plotted along each chromosome.7. Mutations in the TGF-β (transforming growth factorβ) and CEA pathway members were observed in 5 out of 11 adenomas, overlapping with wnt/p53 mutations in 4 of adenomas: All 4 were TVAs.8. Further analyses of expression levels of CEA and TGF-β pathway members in 30 non dysplastic adenomas and normal colon tissues revealed a marked increase (over 8 fold) in CEA expression in 25% of adenoma samples which was linked to concomitant loss of TGF-β signaling.9. Functional studies revealed CEA association with the TGF-β Type I receptor and disruption of TGF-β tumor suppressor signaling with activation of STAT3.Conclusions: Small adenomas both TVAs and SSAs can resemble CRCs in genomic profiling and may reflect a high risk population.Disruption of the CEA/TGF-β pathway in early adenomas may reflect a new and early role for these pathways in CRC.The molecular signatures we characterize here support new approaches to biomarker driven targeting of CEA/TGF-β in high risk adenomas, potentially improving survival of CRCs.

In analysis of NMR spectra, well trained people are not highly variable (within themselves), but there is a potential for variability when different operators are used for analysis.One study has shown similar analytical results between three analysts examining four samples for nine metabolites [14].Another study used five people with multiple rounds of analysis of 18 spectra, to demonstrate good agreement between people for the most common metabolites [16].Furthermore, the experimental group (sampling method) was generally more important than variation between people [16].Despite the consistencies seen between people, it is still recommended that analysis be performed by the same person or group over a single study [16].However, for studies with a large number of samples, or long-term experiments, single operator analysis may not be feasible.In this study, metabolite spectra of urine samples acquired using NMR were assessed for consistency of metabolite quantification either over time (3 years) or between two groups of operators.The aim was to identify the metabolites that are reliably quantified to improve our standard operating protocols and experimental procedures.We first performed a subjective analysis of 70 metabolites, categorizing them based on their apparent consistency in metabolite concentration over time.We also looked at the difference between the

Colorectal CancerVol. 3, No. 3 EditorialUrine-based test for detection of colonic polyps: the coming of ageHaili Wang, Roman Eisner & Richard N FedorakHaili Wang* Author for correspondence: E-mail Address: haili@ualberta.ca University of Alberta, Edmonton, Alberta, Canada Metabolomic Technologies Inc., Edmonton, Alberta, Canada, Roman Eisner University of Alberta, Edmonton, Alberta, Canada Metabolomic Technologies Inc., Edmonton, Alberta, Canada & Richard N Fedorak University of Alberta, Edmonton, Alberta, Canada Metabolomic Technologies Inc., Edmonton, Alberta, CanadaPublished Online:31 Jul 2014https://doi.org/10.2217/crc.14.13AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleKeywords: adenomatous colonic polypscolon cancer screeningearly detection diagnostic testingfecal occult bloodmetabolomicsReferences1 Wang H, Tso VK, Slupsky CM et al. Metabolomics and detection of colorectal cancer in humans: a systematic review. Future Oncol. 6, 1395–1406 (2010).Link, CAS, Google Scholar2 Zhang A, Sun H, Yan G et al. Metabolomics in diagnosis and biomarker discovery of colorectal cancer. Cancer Lett. 345, 17–20 (2014).Crossref, Medline, CAS, Google Scholar3 Nishiumi S, Kobayashi T, Ikeda A et al. A novel serum metabolomics-based diagnostic approach for colorectal cancer. 7(7), PLoS ONE 7, e40459 (2012).Crossref, Medline, CAS, Google Scholar4 Leichtle AB, Nuoffer JM, Ceglarek U et al. Serum amino acid profiles and their alterations in colorectal cancer. Metabolomics 8, 643–653 (2012).Crossref, Medline, CAS, Google Scholar5 Farshidfar F, Weljie AM, Kopciuk K et al. Serum metabolomic profile as a means to distinguish stage of colorectal cancer. Genome Med. 4(5), 42 (2012).Crossref, Medline, CAS, Google Scholar6 Mal M, Koh PK, Cheah PY et al. Metabotyping of human colorectal cancer using two-dimensional gas chromatography mass spectrometry. Anal. Bioanal. Chem. 403, 483–493 (2012).Crossref, Medline, CAS, Google Scholar7 Ma Y, Zhang P, Wang F et al. An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer. Ann. Surg. 255, 720–730 (2012).Crossref, Medline, Google Scholar8 Cheng Y, Xie G, Chen T et al. Distinct urinary metabolic profile of human colorectal cancer. J. Proteome Res. 11, 1354–1363 (2012).Crossref, Medline, CAS, Google Scholar9 Qiu Y, Cai G, Su M et al. Urinary metabonomic study on colorectal cancer. J. Proteome Res. 9, 1627–1634 (2010).Crossref, Medline, CAS, Google Scholar10 Wang H, Tso V, Wong C et al. Development and validation of a highly sensitive urine-based test to identify patients with colonic adenomatous polyps. Clin. Transl. Gastroenterol. 5, e54 (2014).Crossref, Medline, Google Scholar11 Eisner R, Greiner R, Tso V et al. A machine-learned predictor of colonic polyps based on urinary metabolomics. Biomed. Res. Int. 2013, 303982 (2013).Crossref, Medline, Google Scholar12 Goodacre R, Broadhurst D, Smilde AK et al. Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3, 231–241 (2007).Crossref, CAS, Google Scholar13 Broadhurst DI, Kell DB. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2, 171–196 (2006).Crossref, CAS, Google ScholarFiguresReferencesRelatedDetails Vol. 3, No. 3 Follow us on social media for the latest updates Metrics Downloaded 37 times History Published online 31 July 2014 Published in print June 2014 Information© Future Medicine LtdKeywordsadenomatous colonic polypscolon cancer screeningearly detection diagnostic testingfecal occult bloodmetabolomicsFinancial & competing interests disclosureH Wang and R Fedorak are cofounders and shareholders in Metabolomic Technologies Inc. R Eisner is an employee of Metabolomic Technologies Inc. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download

Our use of the terms “higher” and “lower functional significance” or “value,” requires clarification. If, from one standpoint, we characterize certain forms of behavior as intrinsically valuable because of their significance for the nature of the organism, we might, from another standpoint, characterize other performances as most important because they resist the effect of injury. Without a doubt, the survival of “automatic” performances, in contrast to that which we have characterized as “higher,” more conscious, or more voluntary, is of special importance for the organism, inasmuch as they are those that ensure mere existence. In this sense, we would be justified in speaking of performances that have greater or less importance for survival. This is what is implied in the expression “the instinct of self-preservation.” If this means preservation only in the sense of continued survival, we may ask the questions: Does such an "instinct" exist in the normal organism? More specifically, can it be regarded as belonging to the highest level of functioning, or is not the appearance of such a "drive," as the predominant feature in an individual, itself a symptom of abnormality – a pathological phenomenon? As we shall see, the normal organism is characterized as a "Being" in a temporal succession of definite form. For the realization of this "Being," the existence, the "mere being alive," plays, of course, a prominent but by no means the essential role. Under extreme circumstances, it can be compatible with the "nature" of an organism to renounce life, that is, to give up its bodily existence, in order to save its most essential characteristics for example, a man's ethical convictions (see p. 256). Preservation of material existence becomes "essential" only after defect sets in, and possibly in certain emergencies. In the latter case, the body achieves the position of supreme importance, since all the other possibilities of self-realization are bound to it. Regarding the defective organism, the scale of performance values is likely to differ from that of the normal. In order to preclude any misunderstanding, we differentiate in the future between "functional significance" or value – by which we mean "essential to the nature of the organism" – and "survival importance," by which we mean "paramount in the preservation of its life." In the normal organism, the two usually go hand in hand inasmuch as here preservation also means preservation of the intrinsic nature so far as it is possible. In the pathologically changed organism, the preservation of existing potentialities, the survival importance, comes to the fore. At present we only wish to stress the importance of the principle of hierarchy indicated in the laws of disintegration, andwewill subsequently return to this question with special reference to the structure of the organism (cf. p. 372).

Top differentially expressed gene lists are often inconsistent between studies and it has been suggested that small sample sizes contribute to lack of reproducibility and poor prediction accuracy in discriminative models. We considered sex differences (69♂, 65 ♀) in 134 human skeletal muscle biopsies using DNA microarray. The full dataset and subsamples (n = 10 (5 ♂, 5 ♀) to n = 120 (60 ♂, 60 ♀)) thereof were used to assess the effect of sample size on the differential expression of single genes, gene rank order and prediction accuracy. Using our full dataset (n = 134), we identified 717 differentially expressed transcripts (p<0.0001) and we were able predict sex with ~90% accuracy, both within our dataset and on external datasets. Both p-values and rank order of top differentially expressed genes became more variable using smaller subsamples. For example, at n = 10 (5 ♂, 5 ♀), no gene was considered differentially expressed at p<0.0001 and prediction accuracy was ~50% (no better than chance). We found that sample size clearly affects microarray analysis results; small sample sizes result in unstable gene lists and poor prediction accuracy. We anticipate this will apply to other phenotypes, in addition to sex.

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 (1)H-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.

Polyphenols are a major class of bioactive phytochemicals whose consumption may play a role in the prevention of a number of chronic diseases such as cardiovascular diseases, type II diabetes and cancers. Phenol-Explorer, launched in 2009, is the only freely available web-based database on the content of polyphenols in food and their in vivo metabolism and pharmacokinetics. Here we report the third release of the database (Phenol-Explorer 3.0), which adds data on the effects of food processing on polyphenol contents in foods. Data on >100 foods, covering 161 polyphenols or groups of polyphenols before and after processing, were collected from 129 peer-reviewed publications and entered into new tables linked to the existing relational design. The effect of processing on polyphenol content is expressed in the form of retention factor coefficients, or the proportion of a given polyphenol retained after processing, adjusted for change in water content. The result is the first database on the effects of food processing on polyphenol content and, following the model initially defined for Phenol-Explorer, all data may be traced back to original sources. The new update will allow polyphenol scientists to more accurately estimate polyphenol exposure from dietary surveys.

Pharmacokinetic parameters have been collected and can be retrieved in both tabular and graphical form. The web interface has been enhanced and now allows the filtering of information according to various criteria. Phenol-Explorer 2.0, which will be periodically updated, should prove to be an even more useful and capable resource for polyphenol scientists because bioactivities and health effects of polyphenols are dependent on the nature and concentrations of metabolites reaching the target tissues. The Phenol-Explorer database is publicly available and can be found online at http://www.phenol-explorer.eu. URL: http://www.phenol-explorer.eu

Urine and plasma metabolites originate from endogenous metabolic pathways in different organs and exogenous sources (diet). Urine and plasma were obtained from advanced cancer patients and investigated to determine if variations in lean and fat mass, dietary intake, and energy metabolism relate to variation in metabolite profiles. Patients (n = 55) recorded their diets for 3 d and after an overnight fast they were evaluated by DXA and indirect calorimetry. Metabolites were measured by NMR and direct injection MS. Three algorithms were used [partial least squares discriminant-analysis, support vector machines (SVM), and least absolute shrinkage and selection operator] to relate patients' plasma/urine metabolic profile with their dietary/physiological assessments. Leave-one-out cross-validation and permutation testing were conducted to determine statistical validity. None of the algorithms, using 63 urine metabolites, could learn to predict variations in individual's resting energy expenditure, respiratory quotient, or their intake of total energy, fat, sugar, or carbohydrate. Urine metabolites predicted appendicular lean tissue (skeletal muscle) with excellent cross-validation accuracy (98% using SVM). Total lean tissue correlated highly with appendicular muscle (Pearson r = 0.98; P < 0.0001) and gave similar cross-validation accuracies. Fat mass was effectively predicted using the 63 urine metabolites or the 143 plasma metabolites, exclusively. In conclusion, in this population, lean and fat mass variation could be effectively predicted using urinary metabolites, suggesting a potential role for metabolomics in body composition research. Furthermore, variation in lean and fat mass potentially confounds metabolomic studies attempting to characterize diet or disease conditions. Future studies should account or correct for such variation.

The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40,000 (a 600% increase). This enormous expansion is a result of the inclusion of both 'detected' metabolites (those with measured concentrations or experimental confirmation of their existence) and 'expected' metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18 September 2012.).

Phenol-Explorer, launched in 2009, is the only comprehensive web-based database on the content in foods of polyphenols, a major class of food bioactives that receive considerable attention due to their role in the prevention of diseases. Polyphenols are rarely absorbed and excreted in their ingested forms, but extensively metabolized in the body, and until now, no database has allowed the recall of identities and concentrations of polyphenol metabolites in biofluids after the consumption of polyphenol-rich sources. Knowledge of these metabolites is essential in the planning of experiments whose aim is to elucidate the effects of polyphenols on health. Release 2.0 is the first major update of the database, allowing the rapid retrieval of data on the biotransformations and pharmacokinetics of dietary polyphenols. Data on 375 polyphenol metabolites identified in urine and plasma were collected from 236 peer-reviewed publications on polyphenol metabolism in humans and experimental animals and added to the database by means of an extended relational design. Pharmacokinetic parameters have been collected and can be retrieved in both tabular and graphical form. The web interface has been enhanced and now allows the filtering of information according to various criteria. Phenol-Explorer 2.0, which will be periodically updated, should prove to be an even more useful and capable resource for polyphenol scientists because bioactivities and health effects of polyphenols are dependent on the nature and concentrations of metabolites reaching the target tissues. The Phenol-Explorer database is publicly available and can be found online at http://www.phenol-explorer.eu. Database URL: http://www.phenol-explorer.eu.

The Human Metabolome Database (HMDB) (www. hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40 000 (a 600% increase). This enormous expansion is a result of the inclusion of both ‘detected’ metabolites (those with measured concentrations or experimental confirmation of their existence) and ‘expected’ metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.