Applications of Metabolomics in Cancer Studies.

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.

A Single In-Vial Dual Extraction Strategy for the Simultaneous Lipidomics and Proteomics Analysis of HDL and LDL Fractions.

A single in-vial dual extraction (IVDE) procedure for the subsequent analysis of lipids and proteins in the high-density lipoprotein (HDL) and low-density lipoprotein (LDL) fractions derived from the same biological sample is presented. On the basis of methyl-tert-butyl ether (MTBE) extraction, IVDE leads to the formation of three phases: a protein pellet at the bottom, an aqueous phase with polar compounds, and an ether phase with lipophilic compounds. After sample extraction, performed within a high-performance liquid chromatography vial insert, the ether phase was directly injected for lipid fingerprinting, while the protein pellet, after evaporation of the remaining sample, was used for proteomics analysis. Human HDL and LDL isolates were used to test the suitability of the IVDE methodology for lipid and protein analysis from a single sample in terms of data quality and matching composition to that of HDL and LDL. Subsequently, HDL and LDL fractions isolated from ApoE-KO and wild-type mice were used to validate the capacity of IVDE for revealing changes in lipid and protein abundance. Results indicate that IVDE can be successfully used for the subsequent analysis of lipids and proteins with the advantages of time saving, simplicity, and reduced sample amount.

Missing value imputation strategies for metabolomics data.

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k-means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a "gray area" and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k-means nearest neighbor and the best approximation of positioning real zeros.

Influence of missing values substitutes on multivariate analysis of metabolomics data.

Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%-20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the most well known substitute for missing values is a mean imputation. In fact, some researchers consider this aspect of data analysis in their metabolomics pipeline as so routine that they do not even mention using this replacement approach. However, this may have a significant influence on the data analysis output(s) and might be highly sensitive to the distribution of samples between different classes. Therefore, in this study we have analysed different substitutes of missing values namely: zero, mean, median, k-nearest neighbours (kNN) and random forest (RF) imputation, in terms of their influence on unsupervised and supervised learning and, thus, their impact on the final output(s) in terms of biological interpretation. These comparisons have been demonstrated both visually and computationally (classification rate) to support our findings. The results show that the selection of the replacement methods to impute missing values may have a considerable effect on the classification accuracy, if performed incorrectly this may negatively influence the biomarkers selected for an early disease diagnosis or identification of cancer related metabolites. In the case of GC-MS metabolomics data studied here our findings recommend that RF should be favored as an imputation of missing value over the other tested methods. This approach displayed excellent results in terms of classification rate for both supervised methods namely: principal components-linear discriminant analysis (PC-LDA) (98.02%) and partial least squares-discriminant analysis (PLS-DA) (97.96%) outperforming other imputation methods.

Searching for urine biomarkers of bladder cancer recurrence using a liquid chromatography-mass spectrometry and capillary electrophoresis-mass spectrometry metabolomics approach.

The incidence and rate of recurrence of bladder cancer is high, particularly in developed countries, however current methods for diagnosis are limited to detecting high-grade tumours using often invasive methods. A panel of biomarkers to characterise tumours of different grades that could also distinguish between patients exhibiting the disease with first incidence or recurrence could be useful for bladder cancer diagnostics. In this study, potential metabolic biomarkers have been discovered through mass spectrometry based metabolomics of urine. Pre-treatment urine samples were collected from 48 patients diagnosed of urothelial bladder cancer. Patients were followed-up through the hospital pathological charts to identify whether and when the disease recurred or progressed. Subsequently, they were classified according to whether or not they suffered a tumour recurrence (recurrent or stable) as well as their risk group according to tumour grade and stage. Identified metabolites have been analysed in terms of disease characteristics (tumour stage and recurrence) and have provided an insight into bladder cancer progression. Using both liquid chromatography and capillary electrophoresis-mass spectrometry, a total of 27 metabolite features were highlighted as significantly different between patient groups. Some, for example histidine, phenylalanine, tyrosine and tryptophan have been previously linked with bladder cancer, however until now their connection with bladder cancer progression has not been previously reported. The candidate biomarkers revealed in this study could be useful in the clinic for diagnosis of bladder cancer and, through characterising the stage of the disease, could also be useful in prognostics.