Serum metabolomic profile as a means to distinguish stage of colorectal cancer

Omics technologies as powerful approaches to unravel colorectal cancer complexity and improve its management

Colorectal cancer (CRC) continues to rank among the deadliest and most prevalent cancers worldwide, necessitating an innovative and comprehensive approach that addresses this serious health challenge at various stages, from screening and diagnosis to treatment and prognosis. As CRC research progresses, the adoption of an omics-centered approach holds transformative potential to revolutionize the management of this disease. Advances in omics technologies encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics allow to unravel the oncogenic alterations at these levels, elucidating the intricacies and the heterogeneous nature of CRC. By providing a comprehensive molecular landscape of CRC, omics technologies enable the discovery of potential biomarkers for early non-invasive detection of CRC, definition of CRC subtypes, prediction of its staging, prognosis, and overall survival of CRC patients. They also allow the identification of potential therapeutic targets, prediction of drug response, tracking treatment efficacy, detection of residual disease and cancer relapse, and deciphering the mechanisms of drug resistance. Moreover, they allow the distinction of non-metastatic CRC patients from metastatic ones as well as the stratification of metastatic risk. Importantly, omics technologies open up new opportunities to establish molecular-based criteria to guide the selection of effective treatment paving the way for the personalization of therapy for CRC patients. This review consolidates current knowledge on the omics-based preclinical discoveries in CRC research emphasizing the significant potential of these technologies to improve CRC screening, diagnosis, and prognosis and promote the implementation of personalized medicine to ultimately reduce CRC prevalence and mortality.

NMR-based metabolomics combined with metabolic pathway analysis reveals metabolic heterogeneity of colorectal cancer tissue at different anatomical locations and stages

Colorectal cancer (CRC) still remains the leading cause of cancer death worldwide. This study aimed to profile the metabolic differences of colorectal cancer tissues (CCT) at different stages and sites, as compared with their distant noncancerous tissues (DNT), to investigate the temporal and spatial heterogeneities of metabolic characterization. Our NMR-based metabolomics fingerprinting revealed that many of the metabolite levels were significantly altered in CCT compared to DNT and esophageal cancer tissues, indicating deregulations of glucose metabolism, one-carbon metabolism, glutamine metabolism, amino acid metabolism, fatty acid metabolism, TCA cycle, choline metabolism, and so forth. A total of five biomarker metabolites, including glucose, glutamate, alanine, valine and histidine, were identified to distinguish between early and advanced stages of CCT. Metabolites that distinguish the different anatomical sites of CCT include glucose, glycerol, glutamine, inositol, succinate, and citrate. Those significant metabolic differences in CRC tissues at different pathological stages and sites suggested temporal and spatial heterogeneities of metabolic characterization in CCT, providing a metabolic foundation for further study on biofluid metabolism in CRC early detection.

Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction

Background

Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples. However, a multi-cancer screening strategy utilizing urine-based metabolomics has not yet been developed.

Methods

We enrolled 911 cancer patients with 548 lung cancer (LC), 177 with gastric cancer (GC), and 186 with colorectal cancer (CRC), alongside 563 individuals with non-cancerous benign diseases and 229 healthy controls (HC) and investigated the metabolic profiles of urine samples. Participants were randomly allocated to discovery and validation cohorts. The discovery cohort was used for identifying multi-cancer and tissue-specific signatures to build the cancer screening and tumor origin prediction models, while the validation cohort was employed for assessing the performance of these models.

Results

We identified and annotated a total of 360 metabolites from the urine samples. Using the LASSO regression algorithm, 18 metabolites were characterized as urinary metabolic biomarkers and exhibited excellent discriminative performance between cancer patients and HC with AUC of 0.96 in the validation cohort. In comparison with the performance of traditional tumor markers CEA, the screening model performed higher sensitivity across the cancer stages, with a particularly increase in sensitivity among early-stage cancer patients. Moreover, the screening model also exhibited in high classification of cancers from non-cancerous group, comprising with HC and benign disease participants. Furthermore, two non-overlapping metabolic panels were selected to differentiate LC from Non-LC and GC from CRC with the AUC values of 0.87 and 0.83 in validation cohorts, respectively. Additionally, the model accurately predicted the origin of three lethal cancers: lung, gastric, and colorectal, with an overall accuracy of 0.75. The AUC values for LC, GC, and CRC were 0.88, 0.88, and 0.80, respectively.

Discussion

Our study demonstrates the potential of urine-based metabolomics for multi-cancer early detection. The approach offers non-invasive cancer screening, promising widespread implementation in population-based programs for early detection and improved outcomes. Further validation and expansion are needed for broader clinical applicability.

Amino Acid Profiles in the Biological Fluids and Tumor Tissue of CRC Patients

Amino acids are the building blocks of proteins and essential players in pathways such as the citric acid and urea cycle, purine and pyrimidine biosynthesis, and redox cell signaling. Therefore, it is unsurprising that these molecules have a significant role in cancer metabolism and its metabolic plasticity. As one of the most prevalent malign diseases, colorectal cancer needs biomarkers for its early detection, prognostic, and prediction of response to therapy. However, the available biomarkers for this disease must be more powerful and present several drawbacks, such as high costs and complex laboratory procedures. Metabolomics has gathered substantial attention in the past two decades as a screening platform to study new metabolites, partly due to the development of techniques, such as mass spectrometry or liquid chromatography, which have become standard practice in diagnostic procedures for other diseases. Extensive metabolomic studies have been performed in colorectal cancer (CRC) patients in the past years, and several exciting results concerning amino acid metabolism have been found. This review aims to gather and present findings concerning alterations in the amino acid plasma pool of colorectal cancer patients.

Identifying metabolic features of colorectal cancer liability using Mendelian randomization

Background

Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival.

Methods

To investigate whether changes in circulating metabolites characterize the early stages of colorectal cancer (CRC) development, we examined the associations between a genetic risk score (GRS) associated with CRC liability (72 single-nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N = 6221). Linear regression models were applied to examine the associations between genetic liability to CRC and circulating metabolites measured in the same individuals at age 8 y, 16 y, 18 y, and 25 y.

Results

The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P < 0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N = 118,466, median age 58 y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk.

Conclusions

These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism and suggest that fatty acids may play an important role in CRC development.

Funding

This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/.

Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer

INTRODUCTION

Modern comprehensive instrumentations provide an unprecedented coverage of complex matrices in the form of high-dimensional, information rich data sets.

OBJECTIVES

In addition to the usual biomarker research that focuses on the detection of the studied condition, we aimed to define a proper strategy to conduct a correlation analysis on an untargeted colorectal cancer case study with a data set of 102 variables corresponding to metabolites obtained from serum samples analyzed with comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOF-MS). Indeed, the strength of association existing between the metabolites contains potentially valuable information about the molecular mechanisms involved and the underlying metabolic network associated to a global perturbation, at no additional analytical effort.

METHODS

Following Anscombe's quartet, we took particular attention to four main aspects. First, the presence of non-linear relationships through the comparison of parametric and non-parametric correlation coefficients: Pearson's r, Spearman's rho, Kendall's tau and Goodman-Kruskal's gamma. Second, the visual control of the detected associations through scatterplots and their associated regressions and angles. Third, the effect and handling of atypical samples and values. Fourth, the role of the precision of the data on the attribution of the ranks through the presence of ties.

RESULTS

Kendall's tau was found the method of choice for the data set at hand. Its application highlighted 17 correlations significantly altered in the active state of colorectal cancer (CRC) in comparison to matched healthy controls (HC), from which 10 were specific to this state in comparison to the remission one (R-CRC) investigated on distinct patients. 15 metabolites involved in the correlations of interest, on the 25 unique ones obtained, were annotated (Metabolomics Standards Initiative level 2).

CONCLUSIONS

The metabolites highlighted could be used to better understand the pathology. The systematic investigation of the methodological aspects that we expose allows to implement correlation analysis to various fields and many specific cases.

Gut Microbial and Associated Metabolite Markers for Colorectal Cancer Diagnosis

Globally, colorectal cancer (CRC) is the second most common cause of mortality worldwide. Considerable evidence indicates that dysbiosis of the gut microbial community and its metabolite secretions play a fundamental role in advanced adenoma (ADA) and CRC development and progression. This study is a systematic review that aims to assess the clinical association between gut microbial markers and/or gut and circulating metabolites with ADA and CRC. Five electronic databases were searched by four independent reviewers. Only controlled trials that compared ADA and/or CRC with healthy control (HC) using either untargeted (16s rRNA gene or whole genome sequencing) or targeted (gene-based real-time PCR) identification methods for gut microbiome profile, or untargeted or targeted metabolite profiling approaches from the gut or serum/plasma, were eligible. Three independent reviewers evaluated the quality of the studies using the Cochrane Handbook for Systematic Reviews of Interventions. Twenty-four studies were eligible. We identified strong evidence of two microbial markers Fusobacterium and Porphyromonas for ADA vs. CRC, and nine microbial markers Lachnospiraceae-Lachnoclostridium, Ruminococcaceae-Ruminococcus, Parvimonas spp., Parvimonas micra, Enterobacteriaceae, Fusobacterium spp., Bacteroides, Peptostreptococcus-Peptostreptococcus stomatis, Clostridia spp.-Clostridium hylemonae, Clostridium symbiosum, and Porphyromonas-Porphyromonas asaccharolytica for CRC vs. HC. The remaining metabolite marker evidence between the various groups, including ADA vs. HC, ADA vs. HC, and CRC vs. HC, was not of sufficient quality to support additional findings. The identified gut microbial markers can be used in a panel for diagnosing ADA and/or CRC. Further research in the metabolite markers area is needed to evaluate the possibility to use in diagnostic or prognostic markers for colorectal cancer.

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.

Plasma metabolomics, lipidomics and cytokinomics profiling predict disease recurrence in metastatic colorectal cancer patients undergoing liver resection

Purpose

In metastatic colorectal cancer (mCRC) patients (pts), treatment strategies integrating liver resection with induction chemotherapy offer better 5-year survival rates than chemotherapy alone. However, liver resection is a complex and costly procedure, and recurrence occurs in almost 2/3rds of pts, suggesting the need to identify those at higher risk. The aim of this work was to evaluate whether the integration of plasma metabolomics and lipidomics combined with the multiplex analysis of a large panel of plasma cytokines can be used to predict the risk of relapse and other patient outcomes after liver surgery, beyond or in combination with clinical morphovolumetric criteria.

Experimental design

Peripheral blood metabolomics and lipidomics were performed by 600 MHz NMR spectroscopy on plasma from 30 unresectable mCRC pts treated with bevacizumab plus oxaliplatin-based regimens within the Obelics trial (NCT01718873) and subdivided into responder (R) and non-R (NR) according to 1-year disease-free survival (DFS): ≥ 1-year (R, n = 12) and < 1-year (NR, n = 18). A large panel of cytokines, chemokines, and growth factors was evaluated on the same plasma using Luminex xMAP-based multiplex bead-based immunoassay technology. A multiple biomarkers model was built using a support vector machine (SVM) classifier.

Results

Sparse partial least squares discriminant analysis (sPLS-DA) and loading plots obtained by analyzing metabolomics profiles of samples collected at the time of response evaluation when resectability was established showed significantly different levels of metabolites between the two groups. Two metabolites, 3-hydroxybutyrate and histidine, significantly predicted DFS and overall survival. Lipidomics analysis confirmed clear differences between the R and NR pts, indicating a statistically significant increase in lipids (cholesterol, triglycerides and phospholipids) in NR pts, reflecting a nonspecific inflammatory response. Indeed, a significant increase in proinflammatory cytokines was demonstrated in NR pts plasma. Finally, a multiple biomarkers model based on the combination of presurgery plasma levels of 3-hydroxybutyrate, cholesterol, phospholipids, triglycerides and IL-6 was able to correctly classify patients by their DFS with good accuracy.

Conclusion

Overall, this exploratory study suggests the potential of these combined biomarker approaches to predict outcomes in mCRC patients who are candidates for liver metastasis resection after induction treatment for defining personalized management and treatment strategies.

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 &gt;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.

Metabolic-Dysregulation-Based iEESI-MS Reveals Potential Biomarkers Associated with Early-Stage and Progressive Colorectal Cancer

The application of rapid and accurate diagnostic methods can improve colorectal cancer (CRC) survival rates dramatically. Here, we used a non-targeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) to detect metabolite ions associated with the progression of CRC from 172 tissues (45 stage I/II CRC, 41 stage III/IV CRC, and 86 well-matched normal tissues). A support vector machine (SVM) model based on 10 differential metabolite ions for differentiating early-stage CRC from normal tissues was built with a good prediction accuracy of 92.6%. The biomarker panel consisting of lysophosphatidylcholine (LPC) (18:0) has good diagnostic potential in differentiating early-stage CRC from advanced-stage CRC. We showed that the down-regulation of LPC (18:0) in tumor tissues is associated with CRC progression and related to the regulation of the epidermal growth factor receptor. Pathway analysis showed that metabolic pathways in CRC are related to glycerophospholipid metabolism and purine metabolism. In conclusion, we built an SVM model with good performance to distinguish between early-stage CRC and normal groups based on iEESI-MS and found that LPC (18:0) is associated with the progression of CRC.

Integrated analysis of the faecal metagenome and serum metabolome reveals the role of gut microbiome-associated metabolites in the detection of colorectal cancer and adenoma

OBJECTIVE

To profile gut microbiome-associated metabolites in serum and investigate whether these metabolites could distinguish individuals with colorectal cancer (CRC) or adenoma from normal healthy individuals.

DESIGN

Integrated analysis of untargeted serum metabolomics by liquid chromatography-mass spectrometry and metagenome sequencing of paired faecal samples was applied to identify gut microbiome-associated metabolites with significantly altered abundance in patients with CRC and adenoma. The ability of these metabolites to discriminate between CRC and colorectal adenoma was tested by targeted metabolomic analysis. A model based on gut microbiome-associated metabolites was established and evaluated in an independent validation cohort.

RESULTS

In total, 885 serum metabolites were significantly altered in both CRC and adenoma, including eight gut microbiome-associated serum metabolites (GMSM panel) that were reproducibly detected by both targeted and untargeted metabolomics analysis and accurately discriminated CRC and adenoma from normal samples. A GMSM panel-based model to predict CRC and colorectal adenoma yielded an area under the curve (AUC) of 0.98 (95% CI 0.94 to 1.00) in the modelling cohort and an AUC of 0.92 (83.5% sensitivity, 84.9% specificity) in the validation cohort. The GMSM model was significantly superior to the clinical marker carcinoembryonic antigen among samples within the validation cohort (AUC 0.92 vs 0.72) and also showed promising diagnostic accuracy for adenomas (AUC=0.84) and early-stage CRC (AUC=0.93).

CONCLUSION

Gut microbiome reprogramming in patients with CRC is associated with alterations of the serum metabolome, and GMSMs have potential applications for CRC and adenoma detection.

1H-NMR Based Metabolomics Technology Identifies Potential Serum Biomarkers of Colorectal Cancer Lung Metastasis in a Mouse Model

Background

Lung metastasis is a common metastasis site of colorectal cancer which largely reduces the quality of life and survival rates of patients. The discovery of potential novel diagnostic biomarkers is very meaningful for the early diagnosis of colorectal cancer with lung metastasis.

Methods

In the present study, the metabonomic profiling of serum samples of lung metastasis mice was analyzed by ¹H-nuclear magnetic resonance (¹H-NMR). Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to elucidate the distinguishing metabolites between different groups, and all achieved excellent separations, which indicated that metastatic mice could be differentiated from control mice based on the metabolic profiles at serum levels. Furthermore, during lung metastasis of colorectal cancer, metabolic phenotypes changed significantly, and some of metabolites were identified.

Results

Among these metabolites, approximately 15 were closely associated with the lung metastasis process. Pathway enrichment analysis results showed deregulation of metabolic pathways participating in the process of lung metastasis, such as synthesis and degradation of ketone bodies pathway, amino acid metabolism pathway and pyruvate metabolism pathway.

Conclusion

The present study demonstrated the metabolic disturbances of serum samples of mice during the lung metastasis process of colorectal cancer and provides potential diagnostic biomarkers for the disease.

Metabolomic Pathway Activity with Genomic Single-Nucleotide Polymorphisms Associated with Colorectal Cancer Recurrence and 5-Year Overall Survival

PURPOSE

Metabolomic analysis in colorectal cancer (CRC) is an emerging research area with both prognostic and therapeutic targeting potential. We aimed to identify metabolomic pathway activity prognostic for CRC recurrence and overall survival and cross-reference such metabolomic data with prognostic genomic single-nucleotide polymorphisms (SNPs).

METHODS

A systematic search of PubMed, Embase and Cochrane Library was performed for studies reporting prognostic metabolomic pathway activity in CRC in keeping with PRISMA guidelines. The QUADOMICS tool was used to assess study quality. MetaboAnalyst software (version4.0) was used to map metabolites that were associated with recurrence and survival in CRC to recognise metabolic pathways and identify genomic SNPs associated with CRC prognosis, referencing the following databases: Human Metabolome Database (HMDB), the Small Molecule Pathway Database (SMPDB), PubChem and Kyoto Encyclopaedia of Genes and Genomes (KEGG) Pathway Database.

RESULTS

Nine studies met the inclusion criteria, reporting on 1117 patients. Increased metabolic activity in the urea cycle (p = 0.002, FDR = 0.198), ammonia recycling (p = 0.004, FDR = 0.359) and glycine and serine metabolism (p = 0.004, FDR = 0.374) was prognostic of CRC recurrence. Increased activity in aspartate metabolism (p < 0.001, FDR = 0.079) and ammonia recycling (p = 0.004, FDR = 0.345) was prognostic of survival. Eight resulting SNPs were prognostic for CRC recurrence (rs2194980, rs1392880, rs2567397, rs715, rs169712, rs2300701, rs313408, rs7018169) and three for survival (rs2194980, rs169712, rs12106698) of which two overlapped with recurrence (rs2194980, rs169712).

CONCLUSIONS

With a caveat on study heterogeneity, specific metabolites and metabolic pathway activity appear evident in the setting of poor prognostic colorectal cancers and such metabolic signatures are associated with specific genomic SNPs.

Metabolomic Comparison of Patients With Colorectal Cancer at Different Anticancer Treatment Stages

BACKGROUND

The difficulties of early diagnosis of colorectal cancer (CRC) result in a high mortality rate. The ability to predict the response of a patient to surgical resection or chemotherapy may be of great value for clinicians when planning CRC treatments. Metabolomics is an emerging tool for biomarker discovery in cancer research. Previous reports have indicated that the metabolic profile of individuals can be significantly altered between CRC patients and healthy controls. However, metabolic changes in CRC patients at different treatment stages have not been explored.

METHODS

To this end, we performed nuclear magnetic resonance (NMR)-based metabolomic analysis to determine metabolite aberrations in CRC patients before and after surgical resection or chemotherapy. In general, a total of 106 urine samples from four clinical groups, namely, healthy volunteers (n = 31), presurgery CRC patients (n = 25), postsurgery CRC patients (n = 25), and postchemotherapy CRC patients (n = 25), were collected and subjected to further analysis.

RESULTS

In the present study, we identified five candidate metabolites, namely, N-phenylacetylglycine, succinate, 4-hydroxyphenylacetate, acetate, and arabinose, in CRC patients compared with healthy individuals, three of which were reported for the first time. Furthermore, approximately ten metabolites were uniquely identified at each stage of CRC treatment, serving as good candidates for biomarker panel selection.

CONCLUSION

In summary, these potential metabolite candidates may provide promising early diagnostic and monitoring approaches for CRC patients at different anticancer treatment stages.

Changing Metabolic Patterns along the Colorectal Adenoma–Carcinoma Sequence

Colorectal cancer (CRC) is a major public health burden and one of the leading causes of cancer-related deaths worldwide. Screening programs facilitate early diagnosis and can help to reduce poor outcomes. Serum metabolomics can extract vital molecular information that may increase the sensitivity and specificity of colonoscopy in combination with histopathological examination. The present study identifies serum metabolite patterns of treatment-naïve patients, diagnosed with either advanced adenoma (AA) or CRC in colonoscopy screenings, in the framework of the SAKKOPI (Salzburg Colon Cancer Prevention Initiative) program. We used a targeted flow injection analysis and liquid chromatography-tandem mass spectrometry metabolomics approach (FIA- and LC-MS/MS) to characterise the serum metabolomes of an initial screening cohort and two validation cohorts (in total 66 CRC, 76 AA and 93 controls). The lipidome was significantly perturbed, with a proportion of lipid species being downregulated in CRC patients, as compared to AA and controls. The predominant alterations observed were in the levels of lyso-lipids, glycerophosphocholines and acylcarnitines, but additionally, variations in the quantity of hydroxylated sphingolipids could be detected. Changed amino acid metabolism was restricted mainly to metabolites of the arginine/dimethylarginine/NO synthase pathway. The identified metabolic divergences observed in CRC set the foundation for mechanistic studies to characterise biochemical pathways that become deregulated during progression through the adenoma to carcinoma sequence and highlight the key importance of lipid metabolites. Biomarkers related to these pathways could improve the sensitivity and specificity of diagnosis, as well as the monitoring of therapies.

Early detection of treatment futility in patients with metastatic colorectal cancer

PURPOSE

Chemotherapy options for treating CRC have rapidly expanded in recent years, and few have predictive biomarkers. Oncologists are challenged with evidence-based selection of treatments, and response is evaluated retrospectively based on serial imaging beginning after 2-3 months. As a result, cumulative toxicities may appear in patients who will not benefit. Early recognition of non-benefit would reduce cumulative toxicities. Our objective was to determine treatment-related changes in the circulating metabolome corresponding to treatment futility.

METHODS

Metabolomic studies were performed on serial plasma samples from patients with CRC in a randomized controlled trial of cetuximab vs. cetuximab + brivanib (N = 188). GC-MS quantified named 94 metabolites and concentrations were evaluated at baseline, Weeks 1, 4 and 12 after treatment initiation. In a discovery cohort (N = 68), a model distinguishing changes in metabolites associated with radiographic disease progression and response was generated using OPLS-DA. A cohort of 120 patients was used for validation of the model.

RESULTS

By one week after treatment, a stable model of 21 metabolites could distinguish between progression and partial response (R2Y = 0.859; Q2Y = 0.605; P = 5e-4). In the validation cohort, patients with the biomarker had a significantly shorter OS (P < 0.0001). In a separate cohort of patients with HCC on axitinib, appearance of the biomarker also signified a shorter PFS (1.7 months vs. 9.2 months, P = 0.001).

CONCLUSION

We have identified changes in the metabolome that appear within 1 week of starting treatment associated with treatment futility. The novel approach described is applicable to future efforts in developing a biomarker for early assessment of treatment efficacy.

Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse

Background: Colorectal cancer (CRC) is the fourth most commonly diagnosed and third most deadly cancer worldwide. Surgery is the main treatment option for early disease; however, a relevant proportion of CRC patients relapse. Here, variations among preoperative and postoperative serum metabolomic fingerprint of CRC patients were studied, and possible associations between metabolic variations and cancer relapse were explored. Methods: A total of 41 patients with stage I-III CRC, planned for radical resection, were enrolled. Serum samples, collected preoperatively (t0) and 4–6 weeks after surgery before the start of any treatment (t1), were analyzed via NMR spectroscopy. NMR data were analyzed using multivariate and univariate statistical approaches. Results: Serum metabolomic fingerprints show differential clustering between t0 and t1 (82–85% accuracy). Pyruvate, HDL-related parameters, acetone, and 3-hydroxybutyrate appear to be the major players in this discrimination. Eight out of the 41 CRC patients enrolled developed cancer relapse. Postoperative, relapsed patients show an increase of pyruvate and HDL-related parameters, and a decrease of Apo-A1 Apo-B100 ratio and VLDL-related parameters. Conclusions: Surgery significantly alters the metabolomic fingerprint of CRC patients. Some metabolic changes seem to be associated with the development of cancer relapse. These data, if validated in a larger cohort, open new possibilities for risk stratification in patients with early-stage CRC.

A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting

Simple Summary

Around 30–40% of patients with early stage colorectal cancer (eCRC) experience relapse after surgery. Current recommendations for adjuvant therapy are based on suboptimal risk-stratification tools. In elderly patients, risk of relapse assessment is particularly important to ultimately avoid unnecessary chemotherapy-related toxicity in this frailer population. Serum metabolomics via NMR spectroscopy may improve risk stratification by identifying patients with residual micrometastases after surgery and thus at higher risk of relapse. We evaluated the serum metabolomic fingerprints of 94 elderly patients with eCRC (65 relapse free and 29 relapsed), and of 75 elderly patients with metastatic disease. Metabolomics efficiently discriminated patients with relapse-free eCRC from those with metastatic disease, correctly predicting relapse in 69% of relapsed eCRC patients. The metabolomic score was strongly and independently associated with prognosis. Our data suggest metabolomics as a valid addition to standard tools to refine risk stratification for eCRC and warrant further investigation.

Abstract

Adjuvant treatment for patients with early stage colorectal cancer (eCRC) is currently based on suboptimal risk stratification, especially for elderly patients. Metabolomics may improve the identification of patients with residual micrometastases after surgery. In this retrospective study, we hypothesized that metabolomic fingerprinting could improve risk stratification in patients with eCRC. Serum samples obtained after surgery from 94 elderly patients with eCRC (65 relapse free and 29 relapsed, after 5-years median follow up), and from 75 elderly patients with metastatic colorectal cancer (mCRC) obtained before a new line of chemotherapy, were retrospectively analyzed via proton nuclear magnetic resonance spectroscopy. The prognostic role of metabolomics in patients with eCRC was assessed using Kaplan–Meier curves. PCA-CA-kNN could discriminate the metabolomic fingerprint of patients with relapse-free eCRC and mCRC (70.0% accuracy using NOESY spectra). This model was used to classify the samples of patients with relapsed eCRC: 69% of eCRC patients with relapse were predicted as metastatic. The metabolomic classification was strongly associated with prognosis (p-value 0.0005, HR 3.64), independently of tumor stage. In conclusion, metabolomics could be an innovative tool to refine risk stratification in elderly patients with eCRC. Based on these results, a prospective trial aimed at improving risk stratification by metabolomic fingerprinting (LIBIMET) is ongoing.

Rearrangements of Blood and Tissue Fatty Acid Profile in Colorectal Cancer - Molecular Mechanism and Diagnostic Potential

Colorectal cancer (CRC) is often diagnosed at an advanced stage due to the invasiveness of colonoscopy; thus, non-invasive CRC diagnostics are desirable. CRC is associated with lipid alterations. We aimed to verify whether fatty acid (FA) profiles in CRC patients may serve as a potential diagnostic tool for CRC diagnosis. FA profiles were assayed by GC-MS in cancer tissue, paired normal mucosa and serum from CRC patients and healthy controls. The levels of very long FAs – VLCFAs (26:0, 28:0 and 26:1) were the most highly increased FAs in cancer tissue compared to normal colon mucosa. Moreover, these FA were present in serum of CRC patients, they were absent in the serum of healthy subjects, or present in only trace amounts. To verify if cancer cells are the source of small amounts of these VLCFAs in the serum of patients we performed experiment in HT-29 CRC cells, which proved that CRC cells can produce and release VLCFAs into the blood. Most importantly, we defined a panel of FAs that may be assayed in a single analysis that definitely distinguishes CRC patients and healthy subjects, which was confirmed by PLS-DA and multivariate ROC analysis (AUC = 0.985). This study shows that selected FA panel may serve as a diagnostic marker for CRC.

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

Lipidomic Signatures for Colorectal Cancer Diagnosis and Progression Using UPLC-QTOF-ESI+MS

Metabolomics coupled with bioinformatics may identify relevant biomolecules such as putative biomarkers of specific metabolic pathways related to colorectal diagnosis, classification and prognosis. This study performed an integrated metabolomic profiling of blood serum from 25 colorectal cancer (CRC) cases previously classified (Stage I to IV) compared with 16 controls (disease-free, non-CRC patients), using high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI⁺ MS). More than 400 metabolites were separated and identified, then all data were processed by the advanced Metaboanalyst 5.0 online software, using multi- and univariate analysis, including specificity/sensitivity relationships (area under the curve (AUC) values), enrichment and pathway analysis, identifying the specific pathways affected by cancer progression in the different stages. Several sub-classes of lipids including phosphatidylglycerols (phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and PAs), fatty acids and sterol esters as well as ceramides confirmed the “lipogenic phenotype” specific to CRC development, namely the upregulated lipogenesis associated with tumor progression. Both multivariate and univariate bioinformatics confirmed the relevance of some putative lipid biomarkers to be responsible for the altered metabolic pathways in colorectal cancer.

Specificity of metabolic colorectal cancer biomarkers in serum through effect size

INTRODUCTION

Colorectal cancer is one of the most diagnosed cancers, leading to numerous deaths. In addition to existing screening methods, metabolic profiling could help both to diagnose and to understand the various states of the disease.

OBJECTIVES

Find specific candidate biomarkers (CB) in serum of patients with colorectal cancer (CRC), in comparison to the situation after remission (R-CRC), evaluated on distinct patients.

METHODS

All serum samples were analyzed using comprehensive two-dimensional gas chromatography (GC × GC) coupled to high resolution time of flight mass spectrometry (TOF-MS) through an optimized and validated untargeted analytical method regulated by a quality control (QC) system. First, we used a specific multi-approaches data (pre)processing workflow to highlight, annotate and assess the performances of the most altered metabolites between CRC patients (n = 18) and healthy control samples (HC, n = 19) specifically matched for age and gender, two of the most influential confounding factors. On the contrary, due to the difficulty to control for all clinical and demographic traits when sampling small cohorts, the samples from patients in remission (n = 17) were not matched. Because of the consequent risk of bias, the usual null hypothesis significance tests (NHST) could not be applied reliably. Therefore, we compared the R-CRC samples to another specifically matched group of healthy controls (R-HC, n = 17), and used this comparison to indirectly address the difference between patients with colorectal cancer and patients in remission through a measure called effect size (ES) whose methodological aspects were investigated.

RESULTS

24 candidate biomarkers were found significantly altered and able to discriminate the CRC and HC samples efficiently (Receiver Operating Characteristic (ROC) area under the curve (AUC) of 0.86, sensitivity and specificity of 0.72 and 0.78). 10 of those were found to have signals close to healthy levels in the R-CRC samples and were therefore specific to colorectal cancer. In the point-biserial case studied here, r-like (strength of association) and d-like (standardized mean difference) ES were directly convertible and only linear and rank-based ES were different. We therefore used and recommend Hedges' g, Spearman's rho and Kendall's tau, along with an unstandardized ES. The confidence intervals, that quantify the uncertainty of the measure, were well represented through scatterplots and distribution curves.

CONCLUSION

The candidate biomarkers found, along with their specificity, could help for the detection of colorectal cancer, the diagnosis of remission, and for the understanding of its pathophysiology, after proper validation on independent cohorts. The effect size, here applied on a MS global profiling data set, is an ideal complement to NHST and a useful tool to compare and combine distinct cohorts, within a study as well as between studies (meta-analysis).

Discovery and predictive modeling of urine microbiome, metabolite and cytokine biomarkers in hospitalized patients with community acquired pneumonia

Pneumonia is the leading cause of infectious related death costing 12 billion dollars annually in the United States alone. Despite improvements in clinical care, total mortality remains around 4%, with inpatient mortality reaching 5–10%. For unknown reasons, mortality risk remains high even after hospital discharge and there is a need to identify those patients most at risk. Also of importance, clinical symptoms alone do not distinguish viral from bacterial infection which may delay appropriate treatment and may contribute to short-term and long-term mortality. Biomarkers have the potential to provide point of care diagnosis, identify high-risk patients, and increase our understanding of the biology of disease. However, there have been mixed results on the diagnostic performance of many of the analytes tested to date. Urine represents a largely untapped source for biomarker discovery and is highly accessible. To test this hypothesis, we collected urine from hospitalized patients with community-acquired pneumonia (CAP) and performed a comprehensive screen for urinary tract microbiota signatures, metabolite, and cytokine profiles. CAP patients were diagnosed with influenza or bacterial (Streptococcus pneumoniae and Staphylococcus aureus) etiologies and compared with healthy volunteers. Microbiome signatures showed marked shifts in taxonomic levels in patients with bacterial etiology versus influenza and CAP versus normal. Predictive modeling of 291 microbial and metabolite values achieved a + 90% accuracy with LASSO in predicting specific pneumonia etiology. This study demonstrates that urine from patients hospitalized with pneumonia may serve as a reliable and accessible sample to evaluate biomarkers that may diagnose etiology and predict clinical outcomes.

Simultaneous Determination of 34 Amino Acids in Tumor Tissues from Colorectal Cancer Patients Based on the Targeted UHPLC-MS/MS Method

A targeted ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was established and validated for the simultaneous determination of 34 amino acids in tissue samples from colorectal cancer (CRC) patients. The chromatographic separation was achieved on an Agilent ZORBAX SB-C₁₈ column (3.0 × 150 mm, 5 μm) with a binary gradient elution system (A, 0.02% heptafluorobutyric acid and 0.2% formic acid in water, v/v; B, methanol). The run time was 10 min. The multiple reaction monitoring mode was chosen with an electrospray ionization source operating in the positive ionization mode for data acquisition. The linear correlation coefficients were >0.99 for all the analytes in their corresponding calibration ranges. The sample was pretreated based on tissue homogenate and protein precipitation with a 100 mg aliquot sample. The average recovery and matrix effect for 34 amino acids and 3 internal standards were 39.00%∼146.95% and 49.45%∼173.63%, respectively. The intra- and interday accuracy for all the analytes ranged from -13.52% to 14.21% (RSD ≤8.57%) and from -14.52% to 12.59% (RSD ≤10.31%), respectively. Deviations of stability under different conditions were within ±15% for all the analytes. This method was applied to simultaneous quantification of 34 amino acids in tissue samples from 94 CRC patients.

Plasma metabolites associated with colorectal cancer stage: Findings from an international consortium

Colorectal cancer is the second most common cause of cancer-related death globally, with marked differences in prognosis by disease stage at diagnosis. We studied circulating metabolites in relation to disease stage to improve the understanding of metabolic pathways related to colorectal cancer progression. We investigated plasma concentrations of 130 metabolites among 744 Stages I-IV colorectal cancer patients from ongoing cohort studies. Plasma samples, collected at diagnosis, were analyzed with liquid chromatography-mass spectrometry using the Biocrates AbsoluteIDQ™ p180 kit. We assessed associations between metabolite concentrations and stage using multinomial and multivariable logistic regression models. Analyses were adjusted for potential confounders as well as multiple testing using false discovery rate (FDR) correction. Patients presented with 23, 28, 39 and 10% of Stages I-IV disease, respectively. Concentrations of sphingomyelin C26:0 were lower in Stage III patients compared to Stage I patients (pFDR  < 0.05). Concentrations of sphingomyelin C18:0 and phosphatidylcholine (diacyl) C32:0 were statistically significantly higher, while citrulline, histidine, phosphatidylcholine (diacyl) C34:4, phosphatidylcholine (acyl-alkyl) C40:1 and lysophosphatidylcholines (acyl) C16:0 and C17:0 concentrations were lower in Stage IV compared to Stage I patients (pFDR  < 0.05). Our results suggest that metabolic pathways involving among others citrulline and histidine, implicated previously in colorectal cancer development, may also be linked to colorectal cancer progression.

Metabolomics profile in gastrointestinal cancers: Update and future perspectives

Despite recent progress in diagnosis and therapy, gastrointestinal (GI) cancers remain one of the most important causes of death with a poor prognosis due to late diagnosis. Serum tumor markers and detection of occult blood in the stool are the current tests used in the clinic of GI cancers; however, these tests are not useful as diagnostic screening since they have low specificity and low sensitivity. Considering that one of the hallmarks of cancer is dysregulated metabolism and metabolomics is an optimal approach to illustrate the metabolic mechanisms that belong to living systems, is now clear that this -omics could open a new way to study cancer. In the last years, nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for diseases' diagnosis nevertheless a few studies focus on the NMR capability to find new biomarkers for early diagnosis of GI cancers. For these reasons in this review, we will give an update on the status of NMR metabolomic studies for the diagnosis and development of GI cancers using biological fluids.

Metabolic Framework for the Improvement of Autism Spectrum Disorders by a Modified Ketogenic Diet: A Pilot Study

The ketogenic diet (KD) can improve the core features of autism spectrum disorders (ASD) in some children, but the effects on the overall metabolism remain unclear. This pilot study investigated the behavioral parameters in relation to blood metabolites and trace elements in a cohort of 10 typically developed controls (TC) and 17 children with ASD at baseline and following 3 months of treatment with a modified KD regimen. A nontargeted, multiplatform metabolomic approach was employed, including gas chromatography-mass spectrometry, ¹H nuclear magnetic resonance spectroscopy, and inductively coupled plasma-mass spectrometry. The associations among plasma metabolites, trace elements, and behavior scores were investigated. Employing a combination of metabolomic platforms, 118 named metabolites and 73 trace elements were assessed. Relative to TC, a combination of glutamate, galactonate, and glycerol discriminated ASD with 88% accuracy. ASD had higher concentrations of galactose intermediates, gut microbe-derived trimethylamine N-oxide and N-acetylserotonin, and lower concentrations of 3-hydroxybutyrate and selenium at baseline. Following 3 months of KD intervention, the levels of circulating ketones and acetylcarnitine were increased. KD restored lower selenium levels in ASD to that of controls, and correlation analysis identified a novel negative correlation between the changes in selenium and behavior scores. Based on the different behavior responses to KD, we found that high responders had greater concentrations of 3-hydroxybutyrate and ornithine, with lower galactose. These findings enhance our current understanding of the metabolic derangements present in ASD and may be of utility in predicting favorable responses to KD intervention.

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

1 H NMR-based metabolomics reveal overlapping discriminatory metabolites and metabolic pathway disturbances between colorectal tumor tissues and fecal samples

Previous studies have compared fecal metabolites from healthy and colorectal cancer (CRC) patients to predict the pro-CRC signatures. However, the systemic mechanistic link between feces and colonic tissues of CRC patients is still limited. The current study was a paralleled investigation of colonic tumor tissues and their normal adjacent tissues alongside patient-matched feces by using ¹ H nuclear magnetic resonance spectroscopy combined with pattern recognition to investigate how fecal metabolic phenotypes are linked to the changes in colorectal tumor profiles. A set of overlapping discriminatory metabolites across feces and tumor tissues of CRC were identified, including elevated levels of lactate, glutamate, alanine, succinate and reduced amounts of butyrate. These changes could indicate the networks for metabolic pathway perturbations in CRC potentially involved in the disruptions of glucose and glycolytic metabolism, TCA cycle, glutaminolysis, and short chain fatty acids metabolism. Furthermore, changes in fecal acetate were positively correlated with alterations of glucose and myo-inositol in colorectal tumor tissues, implying enhanced energy production for rapid cell proliferation. Compared to other fecal metabolites, acetate demonstrated the highest diagnostic performance for diagnosing CRC, with an AUC of 0.843 in the training set, and a good predictive ability in the validation set. Overall, these associations provide evidence of distinct metabolic signatures and metabolic pathway disturbances between the colonic tissues and feces within the same individual, and changes of fecal metabolic signature could reflect the CRC tissue microenvironment, highlighting the significance of the distinct fecal metabolic profiles as potential novel and noninvasive relevant indicators for CRC detection.

Global serum metabolomics profiling of colorectal cancer

Accurate diagnosis of colorectal cancer (CRC) relies on the use of invasive tools such as colonoscopy and sigmoidoscopy. Non-invasive tools are less sensitive in detecting the disease, particularly in the early stage. A number of researchers have used metabolomics analyses on serum/plasma samples of patients with CRC compared with normal healthy individuals in an effort to identify biomarkers for CRC. The aim of the present review is to compare reported serum metabolomics profiles of CRC and to identify common metabolites affected among these studies. A literature search was performed to include any experimental studies on global metabolomics profile of CRC using serum/plasma samples published up to March 2018. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was used to assess the quality of the studies reviewed. In total, nine studies were included. The studies used various analytical platforms and were performed on different populations. A pathway enrichment analysis was performed using the data from all the studies under review. The most affected pathways identified were protein biosynthesis, urea cycle, ammonia recycling, alanine metabolism, glutathione metabolism and citric acid cycle. The metabolomics analysis revealed levels of metabolites of glycolysis, tricarboxylic acid cycle, anaerobic respiration, protein, lipid and glutathione metabolism were significantly different between cancer and control samples. Although the majority of differentiating metabolites identified were different in the different studies, there were several metabolites that were common. These metabolites include pyruvic acid, glucose, lactic acid, malic acid, fumaric acid, 3-hydroxybutyric acid, tryptophan, phenylalanine, tyrosine, creatinine and ornithine. The consistent dysregulation of these metabolites among the different studies suggest the possibility of common diagnostic biomarkers for CRC.

The potential of nanoflow liquid chromatography-nano electrospray ionisation-mass spectrometry for global profiling the faecal metabolome

Faeces are comprised of a wide array of metabolites arising from the circulatory system as well as the human microbiome. A global metabolite analysis (metabolomics) of faecal extracts offers the potential to uncover new compounds which may be indicative of the onset of bowel diseases such as colorectal cancer (CRC). To date, faecal metabolomics is still in its infancy and the compounds of low abundance present in faecal extracts poorly characterised. In this study, extracts of faeces from healthy subjects were profiled using a sensitive nanoflow-nanospray LC-MS platform which resulted in highly repeatable peak retention times (<2% CV) and intensities (<15% CV). Analysis of the extracts revealed wide coverage of the faecal metabolome including detection of low abundant signalling compounds such as sex steroids and eicosanoids, alongside highly abundant pharmaceuticals and tetrapyrrole metabolites. A small pilot study investigating differences in metabolomics profiles of faecal samples obtained from 7 CRC, 25 adenomatous polyp and 26 healthy groups revealed that secondary bile acids, conjugated androgens, eicosanoids, phospholipids and an unidentified haem metabolite were potential classes of metabolites that discriminated between the CRC and control sample groups. However, much larger follow up studies are needed to confirm which components of the faecal metabolome are associated with actual CRC disease rather than dietary influences. This study reveals the potential of nanospray-nanoflow LC-MS profiling of faecal samples from large scale cohort studies for uncovering the role of the faecal metabolome in colorectal disease formation.

A strategy for early detection of response to chemotherapy drugs based on treatment-related changes in the metabolome

We describe a biomarker-based approach to delivering chemotherapy that entails monitoring treatment changes in the circulating metabolome that reflect efficacy. In-vitro, multiple tumor cell lines were exposed to numerous chemotherapeutics. Supernatants were collected at baseline and 72 hours post treatment. MTT assays were used to quantify growth inhibition. Clinical samples were derived from a phase II clinical trial of second-line axitinib in patients with advanced hepatocellular carcinoma. Sera were collected at baseline and 2–4 weeks after treatment initiation. Response to therapy was estimated by CT scan at 8 weeks. Samples were analyzed by gas chromatography-mass spectrometry to identify metabolomic changes associated with response. In vitro, we found drug-specific and generalizable patterns of change in the extracellular metabolome accompany growth inhibition. A cell death signature was also identified. This approach was also applied to clinical samples. While the in vitro signatures were detectable in vivo, a more robust signal was identified clinically that appeared within 4 weeks of administering drug that distinguished individuals with a treatment response. These changes were extinguished as tumor growth resumed. Serial monitoring of the metabolome during chemotherapy is a means to follow treatment efficacy and emergence of resistance, informing the oncologist whether to modify treatment.

Determination of volatile organic compounds in SW620 colorectal cancer cells and tumor-bearing mice

Early diagnosis and early treatment are important factors in reducing colorectal cancer (CRC) metastasis and mortality. Volatile organic compounds (VOCs) released by the human body have great potential for use in clinical diagnosis and therapeutic monitoring for CRC. The aim of our study was to identify VOCs with high specificity and high sensitivity for CRC and to provide a method for early diagnosis of CRC. Gas chromatography-mass spectrometry (GC-MS) was utilized to analyze metabolites in both the in vivo and in vitro experimental groups. In vivo, VOCs were analyzed in the blood of mice after cell inoculation and tumor resection. In vitro experiments were performed by comparing changes in VOCs in an HCoEpiC cell group, control group, SW620 cell group and Arsenic trioxide + SW620 group. We observed changes in VOCs in a series of CRC SW620 cells in vivo and in vitro. Among these changes, we found that the concentrations of 8 substances, including acetone, increased with tumor growth. Nine substances were found to be significantly elevated in the SW620 cancer cell group compared with the other groups. Only one substance was consumed by the tumor in both the in vivo and in vitro experiments. Our study showed that alkanes, lipids, alcohols, ketones, aldehyde, butylated hydroxytoluene (BHT) and hexamethylcyclotrisiloxane all existed at different levels in SW620 CRC cells compared to those in normal cells. We need more research to further confirm this hypothesis.

Non-invasive metastasis prognosis from plasma metabolites in stage II colorectal cancer patients: The DACHS study

Metastasis is the main cause of death from colorectal cancer (CRC). About 20% of stage II CRC patients develop metastasis during the course of disease. We performed metabolic profiling of plasma samples from non-metastasized and metachronously metastasized stage II CRC patients to assess the potential of plasma metabolites to serve as biomarkers for stratification of stage II CRC patients according to metastasis risk. We compared the metabolic profiles of plasma samples prospectively obtained prior to metastasis formation from non-metastasized vs. metachronously metastasized stage II CRC patients of the German population-based case-control multicenter DACHS study retrospectively. Plasma samples were analyzed from stage II CRC patients for whom follow-up data including the information on metachronous metastasis were available. To identify metabolites distinguishing non-metastasized from metachronously metastasized stage II CRC patients robust supervised classifications using decision trees and support vector machines were performed and verified by 10-fold cross-validation, by nested cross-validation and by traditional validation using training and test sets. We found that metabolic profiles distinguish non-metastasized from metachronously metastasized stage II CRC patients. Classification models from decision trees and support vector machines with 10-fold cross-validation gave average accuracy of 0.75 (sensitivity 0.79, specificity 0.7) and 0.82 (sensitivity 0.85, specificity 0.77), respectively, correctly predicting metachronous metastasis in stage II CRC patients. Taken together, plasma metabolic profiles distinguished non-metastasized and metachronously metastasized stage II CRC patients. The classification models consisting of few metabolites stratify non-invasively stage II CRC patients according to their risk for metachronous metastasis.

Metabolite Profiling of Clinical Cancer Biofluid Samples by NMR Spectroscopy

Metabolomics is a comprehensive characterization of the small polar molecules (metabolites) in different biological systems. One of the analytical platforms commonly used to study metabolic alterations in biofluid samples is proton nuclear magnetic resonance (¹H NMR) spectroscopy. NMR spectroscopy is very specific, quantitative, and highly reproducible. Moreover, sample preparation for NMR experiments is very simple and straightforward, and this gives NMR spectroscopy a distinct advantage over other metabolic profiling methods. It has already been shown that ¹H NMR-based profiling of biological fluids can be effective in differentiating benign from malignant lesions and in investigating the efficacy of specific cancer treatments. Therefore, ¹H NMR spectroscopy may become a promising tool for early noninvasive diagnosis and rapid assessment of treatment effects in cancer patients. Here, we describe a detailed protocol for ¹H NMR metabolite profiling in serum, plasma, and urine samples, including sample collection procedures, sample preparation for ¹H NMR experiments, spectral acquisition and processing, and quantitative profiling of ¹H NMR spectra. We also discuss several aspects of appropriate study design and some multivariate statistical methods that are commonly used to analyze metabolomics datasets.

Metabolomics Profiling on Different Stages of Colorectal Cancer: A Systematic Review

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Early diagnosis and accurate staging of the disease is vital to improve the prognosis. Metabolomics has been used to identify changes in metabolite profiles in the different stages of cancer in order to introduce new non-invasive molecular tools for staging. In this systematic review, we aim to identify the common metabolite changes in human biological samples and the dominant metabolic pathways associated with CRC progression. A broad systematic search was carried out from selected databases. Four reviewers screened and reviewed the titles, abstracts, and full-text articles according to the inclusion and exclusion criteria. Quality assessment was conducted on the eight articles which met the criteria. Data showed that the metabolites involved with redox status, energy metabolism and intermediates of amino acids, choline and nucleotides metabolism were the most affected during CRC progression. However, there were differences in the levels of individual metabolites detected between the studies, and this might be due to the study population, sample preparation, analytical platforms used and statistical tools. In conclusion, this systematic review highlights the changes in metabolites from early to late stages of CRC. Moreover, biomarkers for prognosis are important to reduce CRC-related mortality.

A Framework for Development of Useful Metabolomic Biomarkers and Their Effective Knowledge Translation

Despite the significant advantages of metabolomic biomarkers, no diagnostic tests based on metabolomics have been introduced to clinical use. There are many reasons for this, centered around substantial obstacles in developing clinically useful metabolomic biomarkers. Most significant is the need for interdisciplinary teams with expertise in metabolomics, analysis of complex clinical and metabolomic data, and clinical care. Importantly, the clinical need must precede biomarker discovery, and the experimental design for discovery and validation must reflect the purpose of the biomarker. Standard operating procedures for procuring and handling samples must be developed from the beginning, to ensure experimental integrity. Assay design is another challenge, as there is not much precedent informing this. Another obstacle is that it is not yet clear how to protect any intellectual property related to metabolomic biomarkers. Viewing a metabolomic biomarker as a natural phenomenon would inhibit patent protection and potentially stifle commercial interest. However, demonstrating that a metabolomic biomarker is actually a derivative of a natural phenomenon that requires innovation would enhance investment in this field. Finally, effective knowledge translation strategies must be implemented, which will require engagement with end users (clinicians and lab physicians), patient advocate groups, policy makers, and payer organizations. Addressing each of these issues comprises the framework for introducing a metabolomic biomarker to practice.

Probing the colorectal cancer proteome for biomarkers: Current status and perspectives

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Biomarkers that can facilitate better clinical management of CRC are in high demand to improve patient outcome and to reduce mortality. In this regard, proteomic analysis holds a promising prospect in the hunt of novel biomarkers for CRC and in understanding the mechanisms underlying tumorigenesis. This review aims to provide an overview of the current progress of proteomic research, focusing on discovery and validation of diagnostic biomarkers for CRC. We will summarize the contributions of proteomic strategies to recent discoveries of protein biomarkers for CRC and also briefly discuss the potential and challenges of different proteomic approaches in biomarker discovery and translational applications.

A quantitative multimodal metabolomic assay for colorectal cancer

Background

Early diagnosis of colorectal cancer (CRC) simplifies treatment and improves treatment outcomes. We previously described a diagnostic metabolomic biomarker derived from semi-quantitative gas chromatography-mass spectrometry. Our objective was to determine whether a quantitative assay of additional metabolomic features, including parts of the lipidome could enhance diagnostic power; and whether there was an advantage to deriving a combined diagnostic signature with a broader metabolomic representation.

Methods

The well-characterized Biocrates P150 kit was used to quantify 163 metabolites in patients with CRC (N = 62), adenoma (N = 31), and age- and gender-matched disease-free controls (N = 81). Metabolites included in the analysis included phosphatidylcholines, sphingomyelins, acylcarnitines, and amino acids. Using a training set of 32 CRC and 21 disease-free controls, a multivariate metabolomic orthogonal partial least squares (OPLS) classifier was developed. An independent set of 28 CRC and 20 matched healthy controls was used for validation. Features characterizing 31 colorectal adenomas from their healthy matched controls were also explored, and a multivariate OPLS classifier for colorectal adenoma could be proposed.

Results

The metabolomic profile that distinguished CRC from controls consisted of 48 metabolites (R²Y = 0.83, Q²Y = 0.75, CV-ANOVA p-value < 0.00001). In this quantitative assay, the coefficient of variance for each metabolite was <10%, and this dramatically enhanced the separation of these groups. Independent validation resulted in AUROC of 0.98 (95% CI, 0.93–1.00) and sensitivity and specificity of 93% and 95%. Similarly, we were able to distinguish adenoma from controls (R²Y = 0.30, Q²Y = 0.20, CV-ANOVA p-value = 0.01; internal AUROC = 0.82 (95% CI, 0.72–0.93)). When combined with the previously generated GC-MS signatures for CRC and adenoma, the candidate biomarker performance improved slightly.

Conclusion

The diagnostic power for metabolomic tests for colorectal neoplasia can be improved by utilizing a multimodal approach and combining metabolites from diverse chemical classes. In addition, quantification of metabolites enhances separation of disease-specific metabolomic profiles. Our future efforts will be focused on developing a quantitative assay for the metabolites comprising the optimal diagnostic biomarker.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3923-z) contains supplementary material, which is available to authorized users.

Colorectal Cancer Detection Using Targeted LC-MS Metabolic Profiling

Colorectal cancer (CRC) is one of the most commonly diagnosed malignancies and causes of cancer death. While the mortality rates from CRC have steadily declined, 50,000 individuals in the USA (and many times this number worldwide) still succumb to this illness every year. Early detection of CRC is the most critical need for improving 5-year survival and cure rates. Currently available CRC diagnostic techniques often miss early stage disease such that only 40% of newly diagnosed CRC patients are treated for local disease, Therefore, development of new screening methods that are highly sensitive, specific, noninvasive and easily accessible are critically desired for the early diagnosis and significant reduction in death rate from CRC. In this chapter we describe a targeted LC-MS based metabolic profiling approach used for the discovery of CRC metabolite biomarker candidates, based on highly reproducible hydrophilic interaction liquid chromatography coupled to triple-quadrupole mass spectrometry (HILIC-LC-QQQ-MS). A partial least squares-discriminant analysis (PLS-DA) model was able to differentiate CRC patients from both healthy controls and polyp patients, as well as to distinguish CRC patients based on the cancer stage.

NMR-based metabolomic techniques identify potential urinary biomarkers for early colorectal cancer detection

Better early detection methods are needed to improve the outcomes of patients with colorectal cancer (CRC). Proton nuclear magnetic resonance spectroscopy (¹H-NMR), a potential non-invasive early tumor detection method, was used to profile urine metabolites from 55 CRC patients and 40 healthy controls (HCs). Pattern recognition through orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to ¹H-NMR processed data. Model specificity was confirmed by comparison with esophageal cancers (EC, n=18). Unique metabolomic profiles distinguished all CRC stages from HC urine samples. A total of 16 potential biomarker metabolites were identified in stage I/II CRC, indicating amino acid metabolism, glycolysis, tricarboxylic acid (TCA) cycle, urea cycle, choline metabolism, and gut microflora metabolism pathway disruptions. Metabolite profiles from early stage CRC and EC patients were also clearly distinguishable, suggesting that upper and lower gastrointestinal cancers have different metabolomic profiles. Our study assessed important metabolomic variations in CRC patient urine samples, provided information complementary to that collected from other biofluid-based metabolomics analyses, and elucidated potential underlying metabolic mechanisms driving CRC. Our results support the utility of NMR-based urinary metabolomics fingerprinting in early diagnosis of CRC.

Critical review of reporting of the data analysis step in metabolomics

INTRODUCTION

We present the first study to critically appraise the quality of reporting of the data analysis step in metabolomics studies since the publication of minimum reporting guidelines in 2007.

OBJECTIVES

The aim of this study was to assess the standard of reporting of the data analysis step in metabolomics biomarker discovery studies and to investigate whether the level of detail supplied allows basic understanding of the steps employed and/or reuse of the protocol. For the purposes of this review we define the data analysis step to include the data pretreatment step and the actual data analysis step, which covers algorithm selection, univariate analysis and multivariate analysis.

METHOD

We reviewed the literature to identify metabolomic studies of biomarker discovery that were published between January 2008 and December 2014. Studies were examined for completeness in reporting the various steps of the data pretreatment phase and data analysis phase and also for clarity of the workflow of these sections.

RESULTS

We analysed 27 papers, published anytime in 2008 until the end of 2014 in the area or biomarker discovery in serum metabolomics. The results of this review showed that the data analysis step in metabolomics biomarker discovery studies is plagued by unclear and incomplete reporting. Major omissions and lack of logical flow render the data analysis' workflows in these studies impossible to follow and therefore replicate or even imitate.

CONCLUSIONS

While we await the holy grail of computational reproducibility in data analysis to become standard, we propose that, at a minimum, the data analysis section of metabolomics studies should be readable and interpretable without omissions such that a data analysis workflow diagram could be extrapolated from the study and therefore the data analysis protocol could be reused by the reader. That inconsistent and patchy reporting obfuscates reproducibility is a given. However even basic understanding and reuses of protocols are hampered by the low level of detail supplied in the data analysis sections of the studies that we reviewed.

Monitoring for Response to Antineoplastic Drugs: The Potential of a Metabolomic Approach

For most cancers, chemotherapeutic options are rapidly expanding, providing the oncologist with substantial choices. Therefore, there is a growing need to select the best systemic therapy, for any individual, that effectively halts tumor progression with minimal toxicity. Having the capability to predict benefit and to anticipate toxicity would be ideal, but remains elusive at this time. An alternative approach is an adaptive approach that involves close observation for treatment response and emergence of resistance. Currently, response to systemic therapy is estimated using radiographic tests. Unfortunately, radiographic estimates of response are imperfect and radiographic signs of response can be delayed. This is particularly problematic for targeted agents, as tumor shrinkage is often not apparent with these drugs. As a result, patients are exposed to prolonged courses of toxic drugs that may ultimately be found to be ineffective. A biomarker-based adaptive strategy that involves the serial analysis of the metabolome is attractive. The metabolome changes rapidly with changes in physiology. Changes in the circulating metabolome associated with various antineoplastic agents have been described, but further work will be required to understand what changes signify clinical benefit. We present an investigative approach for the discovery and validation of metabolomic response biomarkers, which consists of serial analysis of the metabolome and linkage of changes in the metabolome to measurable therapeutic benefit. Potential pitfalls in the development of metabolomic biomarkers of response and loss of response are reviewed.

1H-NMR-based metabolic profiling of a colorectal cancer CT-26 lung metastasis model in mice

Lung metastasis is an important cause for the low 5-year survival rate of colorectal cancer patients. Understanding the metabolic profile of lung metastasis of colorectal cancer is important for developing molecular diagnostic and therapeutic approaches. We carried out the metabonomic profiling of lung tissue samples on a mouse lung metastasis model of colorectal cancer using 1H-nuclear magnetic resonance (1H-NMR). The lung tissues of mice were collected at different intervals after marine colon cancer cell line CT-26 was intravenously injected into BALB/c mice. The distinguishing metabolites of lung tissue were investigated using 1H-NMR-based metabonomic assay, which is a highly sensitive and non-destructive method for biomarker identification. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied to analyze 1H-NMR profiling data to seek potential biomarkers. All of the 3 analyses achieved excellent separations between the normal and metastasis groups. A total of 42 metabolites were identified, ~12 of which were closely correlated with the process of metastasis from colon to lung. These altered metabolites indicated the disturbance of metabolism in metastatic tumors including glycolysis, TCA cycle, glutaminolysis, choline metabolism and serine biosynthesis. Our findings firstly identified the distinguishing metabolites in mouse colorectal cancer lung metastasis models, and indicated that the metabolite disturbance may be associated with the progression of lung metastasis from colon cancer. The altered metabolites may be potential biomarkers that provide a promising molecular approach for clinical diagnosis and mechanistic study of colorectal cancer with lung metastasis.

Racial disparity in metabolic regulation of cancer

Genetic mutations and metabolic reprogramming are two key hallmarks of cancer, required for proliferation, invasion, and metastasis of the disease. While genetic mutations, whether inherited or acquired, are critical for the initiation of tumor development, metabolic reprogramming is an effector mechanism imperative for adaptational transition during the progression of cancer. Recent findings in the literature emphasize the significance of molecular cross-talk between these two cellular processes in regulating signaling and differentiation of cancer cells. Genome-wide sequencing analyses of cancer genomes have highlighted the association of various genic mutations in predicting cancer risk and survival. Oncogenic mutational frequency is heterogeneously distributed among various cancer types in different populations, resulting in varying susceptibility to cancer risk. In this review, we explore and discuss the role of genetic mutations in metabolic enzymes and metabolic oncoregulators to stratify cancer risk in persons of different racial backgrounds.