Tumour xenograft detection through quantitative analysis of the metabolic profile of urine in mice

LC–MS-Based Urine Metabolomics Analysis for the Diagnosis and Monitoring of Medulloblastoma

Medulloblastoma (MB) is the most common type of brain cancer in pediatric patients. Body fluid biomarkers will be helpful for clinical diagnosis and treatment. In this study, liquid chromatography–mass spectrometry (LC–MS)-based metabolomics was used to identify specific urine metabolites of MB in a cohort, including 118 healthy controls, 111 MB patients, 31 patients with malignant brain cancer, 51 patients with benign brain disease, 29 MB patients 1 week postsurgery and 80 MB patients 1 month postsurgery. The results showed an apparent separation for MB vs. healthy controls, MB vs. benign brain diseases, and MB vs. other malignant brain tumors, with AUCs values of 0.947/0.906, 0.900/0.873, and 0.842/0.885, respectively, in the discovery/validation group. Among all differentially identified metabolites, 4 metabolites (tetrahydrocortisone, cortolone, urothion and 20-oxo-leukotriene E4) were specific to MB. The analysis of these 4 metabolites in pre- and postoperative MB urine samples showed that their levels returned to a healthy state after the operation (especially after one month), showing the potential specificity of these metabolites for MB. Finally, the combination of two metabolites, tetrahydrocortisone and cortolone, showed diagnostic accuracy for distinguishing MB from non-MB, with an AUC value of 0.851. Our data showed that urine metabolomics might be used for MB diagnosis and monitoring.

Metabolomic profiling of neoplastic lesions in mice

Most cancers develop upon the accumulation of genetic alterations that provoke and sustain the transformed phenotype. Several metabolomic approaches now allow for the global assessment of intermediate metabolites, generating profound insights into the metabolic rewiring associated with malignant transformation. The metabolomic profiling of neoplastic lesions growing in mice, irrespective of their origin, can provide invaluable information on the mechanisms underlying oncogenesis, tumor progression, and response to therapy. Moreover, the metabolomic profiling of tumors growing in mice may result in the identification of novel diagnostic or prognostic biomarkers, which is of great clinical significance. Several methods can be applied to the metabolomic profiling of neoplastic lesions in mice, including mass spectrometry-based techniques (e.g., gas chromatography-, capillary electrophoresis-, or liquid chromatography-coupled mass spectrometry) as well as nuclear magnetic resonance. Here, we compare and discuss the advantages and disadvantages of all these techniques to provide a concise and reliable guide for readers interested in this active area of investigation.

Global metabolomics reveals urinary biomarkers of breast cancer in a mcf-7 xenograft mouse model

Global metabolomics analysis has the potential to uncover novel metabolic pathways that are differentially regulated during carcinogenesis, aiding in biomarker discovery for early diagnosis and remission monitoring. Metabolomics studies with human samples can be problematic due to high inter-individual variation; however xenografts of human cancers in mice offer a well-controlled model system. Urine was collected from a xenograft mouse model of MCF-7 breast cancer and analyzed by mass spectrometry-based metabolomics to identify metabolites associated with cancer progression. Over 10 weeks, 24 h urine was collected weekly from control mice, mice dosed with estradiol cypionate (1 mg/mL), mice inoculated with MCF-7 cells (1 × 107) and estradiol cypionate (1 mg/mL), and mice dosed with MCF-7 cells (1 × 107) only (n = 10/group). Mice that received both estradiol cypionate and MCF-7 cells developed tumors from four weeks after inoculation. Five urinary metabolites were identified that were associated with breast cancer; enterolactone glucuronide, coumaric acid sulfate, capric acid glucuronide, an unknown metabolite, and a novel mammalian metabolite, "taurosebacic acid". These metabolites revealed a correlation between tumor growth, fatty acid synthesis, and potential anti-proliferative effects of gut microbiota-metabolized food derivatives. These biomarkers may be of value for early diagnosis of cancer, monitoring of cancer therapeutics, and may also lead to future mechanistic studies.

Metabolomics reveals that tumor xenografts induce liver dysfunction

Metabolomics, based on ultraperformance liquid chromatography coupled with electrospray ionization quadrupole mass spectrometry, was used to explore metabolic signatures of tumor growth in mice. Urine samples were collected from control mice and mice injected with squamous cell carcinoma (SCCVII) tumor cells. When tumors reached ∼2 cm, all mice were killed and blood and liver samples collected. The urine metabolites hexanoylglycine, nicotinamide 1-oxide, and 11β,20α-dihydroxy-3-oxopregn-4-en-21-oic acid were elevated in tumor-bearing mice, as was asymmetric dimethylarginine, a biomarker for oxidative stress. Interestingly, SCCVII tumor growth resulted in hepatomegaly, reduced albumin/globulin ratios, and elevated serum triglycerides, suggesting liver dysfunction. Alterations in liver metabolites between SCCVII-tumor-bearing and control mice confirmed the presence of liver injury. Hepatic mRNA analysis indicated that inflammatory cytokines, tumor necrosis factor α, and transforming growth factor β were enhanced in SCCVII-tumor-bearing mice, and the expression of cytochromes P450 was decreased in tumor-bearing mice. Further, genes involved in fatty acid oxidation were decreased, suggesting impaired fatty acid oxidation in SCCVII-tumor-bearing mice. Additionally, activated phospholipid metabolism and a disrupted tricarboxylic acid cycle were observed in SCCVII-tumor-bearing mice. These data suggest that tumor growth imposes a global inflammatory response that results in liver dysfunction and underscore the use of metabolomics to temporally examine these changes and potentially use metabolite changes to monitor tumor treatment response.

A network-based feature selection approach to identify metabolic signatures in disease

The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.