A machine-learned predictor of colonic polyps based on urinary metabolomics

Artificial Intelligence in Diagnostics: Enhancing Urine Test Accuracy Using a Mobile Phone-Based Reading System

Background

Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the effectiveness and accuracy of an AI-based program in automatically interpreting urine test strips using mobile phone cameras, an approach that may revolutionize point-of-care testing.

Methods

We developed novel urine test strips and an AI algorithm for image capture. Sample images from the Chungnam National University Sejong Hospital were collected to train a k-nearest neighbor classification algorithm to read the strips. A mobile application was developed for image capturing and processing. We assessed the accuracy, sensitivity, specificity, and ROC area under the curve for 10 parameters.

Results

In total, 2,612 urine test strip images were collected. The AI algorithm demonstrated 98.7% accuracy in detecting urinary nitrite and 97.3% accuracy in detecting urinary glucose. The sensitivity and specificity were high for most parameters. However, this system could not reliably determine the specific gravity. The optimal time for capturing the test strip results was 75 secs after dipping.

Conclusions

The AI-based program accurately interpreted urine test strips using smartphone cameras, offering an accessible and efficient method for urinalysis. This system can be used for immediate analysis and remote testing. Further research is warranted to refine test parameters such as specific gravity to enhance accuracy and reliability.

Potential of pre-diagnostic metabolomics for colorectal cancer risk assessment or early detection

This systematic review investigates the efficacy of metabolite biomarkers for risk assessment or early detection of colorectal cancer (CRC) and its precursors, focusing on pre-diagnostic biospecimens. Searches in PubMed, Web of Science, and SCOPUS through December 2023 identified relevant prospective studies. Relevant data were extracted, and the risk of bias was assessed with the QUADAS-2 tool. Among the 26 studies included, significant heterogeneity existed for case numbers, metabolite identification, and validation approaches. Thirteen studies evaluated individual metabolites, mainly lipids, while eleven studies derived metabolite panels, and two studies did both. Nine panels were internally validated, resulting in an area under the curve (AUC) ranging from 0.69 to 0.95 for CRC precursors and 0.72 to 1.0 for CRC. External validation was limited to one panel (AUC = 0.72). Metabolite panels and lipid-based biomarkers show promise for CRC risk assessment and early detection but require standardization and extensive validation for clinical use.

Artificial intelligence for the prevention and prediction of colorectal neoplasms

BACKGROUND

Colonoscopy is a useful as a cancer screening test. However, in countries with limited medical resources, there are restrictions on the widespread use of endoscopy. Non-invasive screening methods to determine whether a patient requires a colonoscopy are thus desired. Here, we investigated whether artificial intelligence (AI) can predict colorectal neoplasia.

METHODS

We used data from physical exams and blood analyses to determine the incidence of colorectal polyp. However, these features exhibit highly overlapping classes. The use of a kernel density estimator (KDE)-based transformation improved the separability of both classes.

RESULTS

Along with an adequate polyp size threshold, the optimal machine learning (ML) models' performance provided 0.37 and 0.39 Matthews correlation coefficient (MCC) for the datasets of men and women, respectively. The models exhibit a higher discrimination than fecal occult blood test with 0.047 and 0.074 MCC for men and women, respectively.

CONCLUSION

The ML model can be chosen according to the desired polyp size discrimination threshold, may suggest further colorectal screening, and possible adenoma size. The KDE feature transformation could serve to score each biomarker and background factors (health lifestyles) to suggest measures to be taken against colorectal adenoma growth. All the information that the AI model provides can lower the workload for healthcare providers and be implemented in health care systems with scarce resources. Furthermore, risk stratification may help us to optimize the efficiency of resources for screening colonoscopy.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Identification of urinary biomarkers of colorectal cancer: Towards the development of a colorectal screening test in limited resource settings

BACKGROUND

African colorectal cancer (CRC) rates are rising rapidly. A low-cost CRC screening approach is needed to identify CRC from non-CRC patients who should be sent for colonoscopy (a scarcity in Africa).

OBJECTIVE

To identify urinary metabolite biomarkers that, combined with easy-to-measure clinical variables, would identify patients that should be further screened for CRC by colonoscopy. Ideal metabolites would be water-soluble and easily translated into a sensitive, low-cost point-of-care (POC) test.

METHODS

Liquid-chromatography mass spectrometry (LC-MS/MS) was used to quantify 142 metabolites in spot urine samples from 514 Nigerian CRC patients and healthy controls. Metabolite concentration data and clinical characteristics were used to determine optimal sets of biomarkers for identifying CRC from non-CRC subjects.

RESULTS

Our statistical analysis identified N1, N12-diacetylspermine, hippurate, p-hydroxyhippurate, and glutamate as the best metabolites to discriminate CRC patients via POC screening. Logistic regression modeling using these metabolites plus clinical data achieved an area under the receiver-operator characteristic (AUCs) curves of 89.2% for the discovery set, and 89.7% for a separate validation set.

CONCLUSIONS

Effective urinary biomarkers for CRC screening do exist. These results could be transferred into a simple, POC urinary test for screening CRC patients in Africa.

Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer

Metabolomics is a fundamental approach to discovering novel biomarkers and their potential use for precision medicine. When applied for population screening, NMR-based metabolomics can become a powerful clinical tool in precision oncology. Urine tests can be more widely accepted due to their intrinsic non-invasiveness. Our review provides the first exhaustive evaluation of NMR metabolomics for the determination of colorectal cancer (CRC) in urine. A specific search in PubMed, Web of Science, and Scopus was performed, and 10 studies met the required criteria. There were no restrictions on the query for study type, leading to not only colorectal cancer samples versus control comparisons, but also prospective studies of surgical effects. With this review, all compounds in the included studies were merged into a database. In doing so, we identified up to 100 compounds in urine samples, and 11 were found in at least three articles. Results were analyzed in three groups: case (CRC and adenomas)/control, pre-/post-surgery, and combining both groups. When combining the case-control and the pre-/post-surgery groups, up to twelve compounds were found to be relevant. Seven down-regulated metabolites in CRC were identified, creatinine, 4-hydroxybenzoic acid, acetone, carnitine, d-glucose, hippuric acid, l-lysine, l-threonine, and pyruvic acid, and three up-regulated compounds in CRC were identified, acetic acid, phenylacetylglutamine, and urea. The pathways and enrichment analysis returned only two pathways significantly expressed: the pyruvate metabolism and the glycolysis/gluconeogenesis pathway. In both cases, only the pyruvic acid (down-regulated in urine of CRC patients, with cancer cell proliferation effect in the tissue) and acetic acid (up-regulated in urine of CRC patients, with chemoprotective effect) were present.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment

In recent years, the advantages of artificial intelligence (AI) in data processing and model analysis have emerged in the medical field, enabled by computer technology developments and the integration of multiple disciplines. The application of AI in the medical field has gradually deepened and broadened. Among them, the development of clinical medicine intelligent decision-making is the fastest. The advantage of clinical medicine intelligent decision-making is to make the diagnosis faster and more accurate on the basis of certain information. Urine detection technologies, such as urine proteomics, urine metabolomics, and urine RNomics, have developed rapidly with the advancements in omics and medical tests. Advances in urine testing have made it possible to obtain a wealth of information from easily accessible urine. However, it has always been a problem to extract effective information from this information and use it. AI technology provides the possibility to process and use the information in urine. AI, combined with urine detection, not only provides new possibilities for precise and individual diagnosis and disease treatment, but also helps promote non-invasive diagnosis and treatment. This article reviews the research and applications of AI combined with urine detection for disease diagnosis and treatment and discusses its existing problems and future development.

Comparing Metabolomics Profiles in Various Types of Liquid Biopsies among Screening Participants with and without Advanced Colorectal Neoplasms

Analysis of metabolomics has been suggested as a promising approach for early detection of colorectal cancer and advanced adenomas. We investigated and compared the metabolomics profile in blood, stool, and urine samples of screening colonoscopy participants and aimed to evaluate differences in metabolite concentrations between people with advanced colorectal neoplasms and those without neoplasms. Various types of bio-samples (plasma, feces, and urine) from 400 participants of screening colonoscopy were investigated using the MxP® Quant 500 kit (Biocrates, Innsbruck, Austria). We detected a broad range of metabolites in blood, stool, and urine samples (504, 331, and 131, respectively). Significant correlations were found between concentrations in blood and stool, blood and urine, and stool and urine for 93, 154, and 102 metabolites, of which 68 (73%), 126 (82%), and 39 (38%) were positive correlations. We found significant differences between participants with and without advanced colorectal neoplasms for concentrations of 123, 49, and 28 metabolites in blood, stool and urine samples, respectively. We detected mostly positive correlations between metabolite concentrations in blood samples and urine or stool samples, and mostly negative correlations between urine and stool samples. Differences between subjects with and without advanced colorectal neoplasms were found for metabolite concentrations in each of the three bio-fluids.

Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry

Colorectal cancer (CRC) has increasing global prevalence and poor prognostic outcomes, and the development of low- or less invasive screening tests is urgently required. Urine is an ideal biofluid that can be collected non-invasively and contains various metabolite biomarkers. To understand the metabolomic profiles of different stages of CRC, we conducted metabolomic profiling of urinary samples. Capillary electrophoresis-time-of-flight mass spectrometry was used to quantify hydrophilic metabolites in 247 subjects with stage 0 to IV CRC or polyps, and healthy controls. The 154 identified and quantified metabolites included metabolites of glycolysis, TCA cycle, amino acids, urea cycle, and polyamine pathways. The concentrations of these metabolites gradually increased with the stage, and samples of CRC stage IV especially showed a large difference compared to other stages. Polyps and CRC also showed different concentration patterns. We also assessed the differentiation ability of these metabolites. A multiple logistic regression model using three metabolites was developed with a randomly designated training dataset and validated using the remaining data to differentiate CRC and polys from healthy controls based on a panel of urinary metabolites. These data highlight the changes in metabolites from early to late stage of CRC and also the differences between CRC and polyps.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

Urinary Metabolomics to Identify a Unique Biomarker Panel for Detecting Colorectal Cancer: A Multicenter Study

BACKGROUND

Population-based screening programs are credited with earlier colorectal cancer diagnoses and treatment initiation, which reduce mortality rates and improve patient health outcomes. However, recommended screening methods are unsatisfactory as they are invasive, are resource intensive, suffer from low uptake, or have poor diagnostic performance. Our goal was to identify a urine metabolomic-based biomarker panel for the detection of colorectal cancer that has the potential for global population-based screening.

METHODS

Prospective urine samples were collected from study participants. Based upon colonoscopy and histopathology results, 342 participants (colorectal cancer, 171; healthy controls, 171) from two study sites (Canada, United States) were included in the analyses. Targeted liquid chromatography-mass spectrometry (LC-MS) was performed to quantify 140 highly valuable metabolites in each urine sample. Potential biomarkers for colorectal cancer were identified by comparing the metabolomic profiles from colorectal cancer versus controls. Multiple models were constructed leading to a good separation of colorectal cancer from controls.

RESULTS

A panel of 17 metabolites was identified as possible biomarkers for colorectal cancer. Using only two of the selected metabolites, namely diacetylspermine and kynurenine, a predictor for detecting colorectal cancer was developed with an AUC of 0.864, a specificity of 80.0%, and a sensitivity of 80.0%.

CONCLUSIONS

We present a potentially "universal" metabolomic biomarker panel for colorectal cancer independent of cohort clinical features based on a North American population. Further research is needed to confirm the utility of the profile in a prospective, population-based colorectal cancer screening trial.

IMPACT

A urinary metabolomic biomarker panel was identified for colorectal cancer with the potential of clinical application.

Current state and future direction of screening tool for colorectal cancer

As the second-most-common cause of cancer death, colorectal cancer (CRC) has been recognized as one of the biggest health concerns in advanced countries. The 5-year survival rate for patients with early-stage CRC is significantly better than that for patients with CRC detected at a late stage. The primary target for CRC screening and prevention is advanced neoplasia, which includes both CRC itself, as well as benign but histologically advanced adenomas that are at increased risk for progression to malignancy. Prevention of CRC through detection of advanced adenomas is important. It is, therefore, necessary to develop more efficient detection methods to enable earlier detection and therefore better prognosis. Although a number of CRC diagnostic methods are currently used for early detection, including stool-based tests, traditional colonoscopy, etc., they have not shown optimal results due to several limitations. Hence, development of more reliable screening methods is required in order to detect the disease at an early stage. New screening tools also need to be able to accurately diagnose CRC and advanced adenoma, help guide treatment, and predict the prognosis along with being relatively simple and non-invasive. As part of such efforts, many proposals for the early detection of colorectal neoplasms have been introduced. For example, metabolomics, referring to the scientific study of the metabolism of living organisms, has been shown to be a possible approach for discovering CRC-related biomarkers. In addition, a growing number of high-performance screening methodologies could facilitate biomarker identification. In the present, evidence-based review, the authors summarize the current state as recognized by the recent guideline recommendation from the American Cancer Society, US Preventive Services Task Force and the United States Multi-Society Task Force and discuss future direction of screening tools for colorectal cancer. Further, we highlight the most interesting publications on new screening tools, like molecular biomarkers and metabolomics, and discuss these in detail.

Metabolomics Biomarkers for Detection of Colorectal Neoplasms: A Systematic Review

BACKGROUND

Several approaches have been suggested to be useful in the early detection of colorectal neoplasms. Since metabolites are closely related to the phenotype and are available from different human bio-fluids, metabolomics are candidates for non-invasive early detection of colorectal neoplasms.

OBJECTIVES

We aimed to summarize current knowledge on performance characteristics of metabolomics biomarkers that are potentially applicable in a screening setting for the early detection of colorectal neoplasms.

DESIGN

We conducted a systematic literature search in PubMed and Web of Science and searched for biomarkers for the early detection of colorectal neoplasms in easy-to-collect human bio-fluids. Information on study design and performance characteristics for diagnostic accuracy was extracted.

RESULTS

Finally, we included 41 studies in our analysis investigating biomarkers in different bio-fluids (blood, urine, and feces). Although single metabolites mostly had limited ability to distinguish people with and without colorectal neoplasms, promising results were reported for metabolite panels, especially amino acid panels in blood samples, as well as nucleosides in urine samples in several studies. However, validation of the results is limited.

CONCLUSIONS

Panels of metabolites consisting of amino acids in blood and nucleosides in urinary samples might be useful biomarkers for early detection of advanced colorectal neoplasms. However, to make metabolomic biomarkers clinically applicable, future research in larger studies and external validation of the results is required.

Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls

Colorectal cancer (CRC) is one of the most daunting diseases due to its increasing worldwide prevalence, which requires imperative development of minimally or non-invasive screening tests. Urinary polyamines have been reported as potential markers to detect CRC, and an accurate pattern recognition to differentiate CRC with early stage cases from healthy controls are needed. Here, we utilized liquid chromatography triple quadrupole mass spectrometry to profile seven kinds of polyamines, such as spermine and spermidine with their acetylated forms. Urinary samples from 201 CRCs and 31 non-CRCs revealed the N₁,N₁₂-diacetylspermine showing the highest area under the receiver operating characteristic curve (AUC), 0.794 (the 95% confidence interval (CI): 0.704–0.885, p < 0.0001), to differentiate CRC from the benign and healthy controls. Overall, 59 samples were analyzed to evaluate the reproducibility of quantified concentrations, acquired by collecting three times on three days each from each healthy control. We confirmed the stability of the observed quantified values. A machine learning method using combinations of polyamines showed a higher AUC value of 0.961 (95% CI: 0.937–0.984, p < 0.0001). Computational validations confirmed the generalization ability of the models. Taken together, polyamines and a machine-learning method showed potential as a screening tool of CRC.

Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis

Introduction

Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications.

Objectives

To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking.

Methods

A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls.

Results

The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively.

Conclusion

These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.

Urine metabolomics as a predictor of patient tolerance and response to adjuvant chemotherapy in colorectal cancer

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.

Development and Validation of a High-Throughput Mass Spectrometry Based Urine Metabolomic Test for the Detection of Colonic Adenomatous Polyps

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%).

Clinical validation of a novel urine-based metabolomic test for the detection of colonic polyps on Chinese population

PURPOSE

Colorectal cancer is the fifth leading cause of cancer-related deaths in China. When detected early, with the removal of adenomatous polyps, precursors of colorectal cancer, it is preventable. The aim of this study was to evaluate a novel urine-based metabolomic diagnostic test for the detection of adenomatous polyps, PolypDx™, that was originally developed and validated using 1000 samples from Canadian Cohort, on Chinese population.

METHODS

Prospective urine samples were collected from 1000 participants undergoing colonoscopy examination, from March 2013 to July 2014 at Minhang District, Shanghai Centre for Disease Control and Prevention. One-dimensional nuclear magnetic resonance spectra of urine metabolites were analyzed to determine the concentrations of three key metabolites used in PolypDx™. The predicted results were then compared to the gold standard for colorectal cancer diagnostic, colonoscopy. Area under curve (AUC) was calculated specifically for the Chinese population and compared with the Canadian dataset. Sensitivity and specificity of this urine-based metabolomic diagnostic test were also compared with three commercially available fecal-based tests.

RESULTS

An AUC of 0.717 for PolypDx™ was calculated on Chinese dataset which is slightly lower than the AUC on the Canadian dataset. A sensitivity of 82.6% and a specificity of 42.4% were achieved on Chinese dataset.

CONCLUSIONS

Here, we validated a novel urine-based metabolomic diagnostic test for the detection of adenomatous polyps, PolypDx™, on Chinese population through a sample size of 1000 participants with a greater level of sensitivity than fecal-based tests.

Integrative analysis of the colorectal cancer proteome: potential clinical impact

INTRODUCTION

Colorectal cancer (CRC) is one of the common types of cancer that affects a significant proportion of the population and is a major contributor to cancer related mortality. The relatively poor survival rate of CRC could be improved through the identification of clinically useful biomarkers. Areas covered: This review highlights the need for biomarkers and discusses recent proteomics discoveries in the aspects of CRC clinical practice including diagnosis, prognosis, therapy, screening and molecular pathological epidemiology (MPE). Studies have been evaluated in relation to biomarker target, methodology, sample selection, limitations, and potential impact. Finally, the progress in proteomic approaches is briefly discussed and the main difficulties facing the translation of proteomics biomarkers into the clinical practice are highlighted. Expert commentary: The establishment of specific guidelines, best practice recommendations and the improvement in proteomic strategies will significantly improve the prospects for developing clinically useful biomarkers.

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.

Tissue amino acid profile could be used to differentiate advanced adenoma from colorectal cancer

Advanced adenomas are of higher risk to progress to colorectal cancer (CRC), the third leading cause of cancerous death worldwide. Endoscopy-based adenoma removal greatly contributes to arresting the progression of adenoma to CRC. Precise diagnosis, post-polypectomy surveillance and the follow-up clinical decisions predominantly depend on histopathologic inspection of the resected tissues. The common artificial histological inspection is not fully reliable and is only compatible with the en bloc removed tissues. An alternative measure ensuring more objective tissue malignance appraisal, which is applicable to various endoscopically acquired sample types are highly appreciated. In this study, we firstly employed capillary electrophoresis-mass spectrometry-based untargeted metabolomic technique to analyze CRC and corresponding paracancerous tissues to narrow the scope of malignancy-related metabolite changes. The primary results implied the most perturbated metabolites by CRC onset were amino acids. Subsequently, a targeted amino acid analysis by ultra-performance liquid chromatography-mass spectrometry indicated 9 amino acids were of different content between advanced adenoma and CRC tissues. Finally, regression analysis of the 9 differential amino acids exhibited that methionine, tyrosine, valine and isoleucine could be used to differentiate CRC from advanced adenomas with good sensitivity and specificity (p<0.001). Area under the receiver operating characteristic curve was 0.991. This study demonstrated the utility of metabolomic analysis in assisting malignance evaluation of colorectal neoplasia and the potential value of amino acids analysis in clinical pathology practice.

The promise of metabolic phenotyping in gastroenterology and hepatology

Disease risk and treatment response are determined, at the individual level, by a complex history of genetic and environmental interactions, including those with our endogenous microbiomes. Personalized health care requires a deep understanding of patient biology that can now be measured using a range of '-omics' technologies. Patient stratification involves the identification of genetic and/or phenotypic disease subclasses that require different therapeutic strategies. Stratified medicine approaches to disease diagnosis, prognosis and therapeutic response monitoring herald a new dimension in patient care. Here, we explore the potential value of metabolic profiling as applied to unmet clinical needs in gastroenterology and hepatology. We describe potential applications in a number of diseases, with emphasis on large-scale population studies as well as metabolic profiling on the individual level, using spectrometric and imaging technologies that will leverage the discovery of mechanistic information and deliver novel health care solutions to improve clinical pathway management.

Higher serum uric acid levels and advanced age are associated with an increased prevalence of colorectal polyps

The present study retrospectively analyzed the laboratory data of patients who had undergone a colonoscopy between April 2011 and March 2014, with the aim of assessing whether these variables could be used to predict the presence of colorectal polyps (CP). A total of 1,471 patients were enrolled (731 men, 68.5±10.8 years; 740 women, 66.7±10.8 years). One-way analysis of variance was performed to analyze the association between the presence of CP and a range of laboratory variables. Logistic regression analysis was performed to establish a regression equation to predict the presence of CP. Receiver-operator characteristics analysis was applied to investigate the performance of the regression equation. Patients with CP were older than those without CP (P<0.0001). Serum uric acid (UA) levels were higher in patients with CP, compared to those without CP (P=0.0007). To investigate the possibility that older age and higher UA levels could predict the presence of CP, logistic regression analysis was performed (P=0.0008). The regression equation was as follows: ln(p/1 - p) = 2.79015 - 0.01836 × age - 0.28542 × UA (mg/dl), where p indicates the presence of CP. Receiver-operator characteristic analysis showed the area under the curve to be 0.62092 and the threshold value of P was 0.4370. Sensitivity and specificity of the threshold value were 77.6 and 44.2%, respectively. Advanced age and higher serum UA levels were associated with the presence of CP. In conclusion, logistic regression analysis obtained a regression equation that predicted the presence of CP with a higher sensitivity, but poorer specificity, compared to fecal occult blood testing.

Exploring Metabolic Profile Differences between Colorectal Polyp Patients and Controls Using Seemingly Unrelated Regression

Despite the fact that colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world, the development of improved and robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC continues to be evasive. In particular, patients with colon polyps are at higher risk of developing colon cancer; however, noninvasive methods to identify these patients suffer from poor performance. In consideration of the challenges involved in identifying metabolite biomarkers in individuals with high risk for colon cancer, we have investigated NMR-based metabolite profiling in combination with numerous demographic parameters to investigate the ability of serum metabolites to differentiate polyp patients from healthy subjects. We also investigated the effect of disease risk on different groups of biologically related metabolites. A powerful statistical approach, seemingly unrelated regression (SUR), was used to model the correlated levels of metabolites in the same biological group. The metabolites were found to be significantly affected by demographic covariates such as gender, BMI, BMI(2), and smoking status. After accounting for the effects of the confounding factors, we then investigated potential of metabolites from serum to differentiate patients with polyps and age matched healthy controls. Our results showed that while only valine was slightly associated, individually, with polyp patients, a number of biologically related groups of metabolites were significantly associated with polyps. These results may explain some of the challenges and promise a novel avenue for future metabolite profiling methodologies.

The utility of Apc-mutant rats in modeling human colon cancer

Prior to the advent of genetic engineering in the mouse, the rat was the model of choice for investigating the etiology of cancer. Now, recent advances in the manipulation of the rat genome, combined with a growing recognition of the physiological differences between mice and rats, have reignited interest in the rat as a model of human cancer. Two recently developed rat models, the polyposis in the rat colon (Pirc) and Kyoto Apc Delta (KAD) strains, each carry mutations in the intestinal-cancer-associated adenomatous polyposis coli (Apc) gene. In contrast to mouse models carrying Apc mutations, in which cancers develop mainly in the small intestine rather than in the colon and there is no gender bias, these rat models exhibit colonic predisposition and gender-specific susceptibility, as seen in human colon cancer. The rat also provides other experimental resources as a model organism that are not provided by the mouse: the structure of its chromosomes facilitates the analysis of genomic events, the size of its colon permits longitudinal analysis of tumor growth, and the size of biological samples from the animal facilitates multiplexed molecular analyses of the tumor and its host. Thus, the underlying biology and experimental resources of these rat models provide important avenues for investigation. We anticipate that advances in disease modeling in the rat will synergize with resources that are being developed in the mouse to provide a deeper understanding of human colon cancer.

Colorectal cancer detection using targeted serum metabolic profiling

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.